diff --git a/catalog/scores/models/model_items/.empty b/catalog/scores/models/model_items/.empty new file mode 100644 index 0000000000..e69de29bb2 diff --git a/catalog/scores/models/model_items/GLEON_JRabaey_temp_physics.json b/catalog/scores/models/model_items/GLEON_JRabaey_temp_physics.json deleted file mode 100644 index 5ab7f516e8..0000000000 --- a/catalog/scores/models/model_items/GLEON_JRabaey_temp_physics.json +++ /dev/null @@ -1,239 +0,0 @@ -{ - "stac_version": "1.0.0", - "stac_extensions": [ - "https://stac-extensions.github.io/table/v1.2.0/schema.json" - ], - "type": "Feature", - "id": "GLEON_JRabaey_temp_physics", - "bbox": [ - [ - -156.6194, - 71.2824, - -66.7987, - 71.2824 - ] - ], - "geometry": { - "type": "MultiPoint", - "coordinates": [ - [-102.4471, 39.7582], - [-82.0084, 29.676], - [-119.2575, 37.0597], - [-110.5871, 44.9501], - [-96.6242, 34.4442], - [-87.7982, 32.5415], - [-147.504, 65.1532], - [-105.5442, 40.035], - [-89.4737, 46.2097], - [-66.9868, 18.1135], - [-84.4374, 31.1854], - [-66.7987, 18.1741], - [-72.3295, 42.4719], - [-96.6038, 39.1051], - [-83.5038, 35.6904], - [-77.9832, 39.0956], - [-89.7048, 45.9983], - [-121.9338, 45.7908], - [-87.4077, 32.9604], - [-96.443, 38.9459], - [-122.1655, 44.2596], - [-149.143, 68.6698], - [-78.1473, 38.8943], - [-97.7823, 33.3785], - [-99.1139, 47.1591], - [-99.2531, 47.1298], - [-111.7979, 40.7839], - [-82.0177, 29.6878], - [-111.5081, 33.751], - [-119.0274, 36.9559], - [-88.1589, 31.8534], - [-149.6106, 68.6307], - [-84.2793, 35.9574], - [-105.9154, 39.8914] - ] - }, - "properties": { - "description": "\nmodel info: NA\n\nSites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU\n\nVariables: Daily Water_temperature", - "start_datetime": "2023-11-14", - "end_datetime": "2023-12-15", - "providers": [ - { - "url": "pending", - "name": "pending", - "roles": [ - "producer", - "processor", - "licensor" - ] - }, - { - "url": "https://www.ecoforecastprojectvt.org", - "name": "Ecoforecast Challenge", - "roles": [ - "host" - ] - } - ], - "license": "CC0-1.0", - "keywords": [ - "Forecasting", - "neon4cast", - "Daily Water_temperature" - ], - "table:columns": [ - { - "name": "reference_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that the forecast was initiated (horizon = 0)" - }, - { - "name": "site_id", - "type": "string", - "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model." - }, - { - "name": "datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime of the forecasted value (ISO 8601)" - }, - { - "name": "family", - "type": "string", - "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)." - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "name": "observation", - "type": "double", - "description": "observed value for variable" - }, - { - "name": "crps", - "type": "double", - "description": "crps forecast score" - }, - { - "name": "logs", - "type": "double", - "description": "logs forecast score" - }, - { - "name": "mean", - "type": "double", - "description": "mean forecast prediction" - }, - { - "name": "median", - "type": "double", - "description": "median forecast prediction" - }, - { - "name": "sd", - "type": "double", - "description": "standard deviation forecasts" - }, - { - "name": "quantile97.5", - "type": "double", - "description": "upper 97.5 percentile value of forecast" - }, - { - "name": "quantile02.5", - "type": "double", - "description": "upper 2.5 percentile value of forecast" - }, - { - "name": "quantile90", - "type": "double", - "description": "upper 90 percentile value of forecast" - }, - { - "name": "quantile10", - "type": "double", - "description": "upper 10 percentile value of forecast" - }, - { - "name": "project_id", - "type": "string", - "description": "unique project identifier" - }, - { - "name": "duration", - "type": "string", - "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention" - }, - { - "name": "variable", - "type": "string", - "description": "name of forecasted variable" - }, - { - "name": "model_id", - "type": "string", - "description": "unique model identifier" - }, - { - "name": "date", - "type": "string", - "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period." - } - ] - }, - "collection": "scores", - "links": [ - { - "rel": "collection", - "href": "../collection.json", - "type": "application/json", - "title": "GLEON_JRabaey_temp_physics" - }, - { - "rel": "root", - "href": "../../../catalog.json", - "type": "application/json", - "title": "Forecast Catalog" - }, - { - "rel": "parent", - "href": "../collection.json", - "type": "application/json", - "title": "GLEON_JRabaey_temp_physics" - }, - { - "rel": "self", - "href": "GLEON_JRabaey_temp_physics.json", - "type": "application/json", - "title": "Model Forecast" - }, - { - "rel": "item", - "href": "pending", - "type": "text/html", - "title": "Link for Model Code" - } - ], - "assets": { - "1": { - "type": "application/json", - "title": "Model Metadata", - "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/GLEON_JRabaey_temp_physics.json", - "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/GLEON_JRabaey_temp_physics.json\")\n\n" - }, - "2": { - "type": "text/html", - "title": "Link for Model Code", - "href": "pending", - "description": "The link to the model code provided by the model submission team" - }, - "3": { - "type": "application/x-parquet", - "title": "Database Access for Daily Water_temperature", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=GLEON_JRabaey_temp_physics?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=GLEON_JRabaey_temp_physics?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - } - } -} diff --git a/catalog/scores/models/model_items/GLEON_lm_lag_1day.json b/catalog/scores/models/model_items/GLEON_lm_lag_1day.json deleted file mode 100644 index 875dba6da6..0000000000 --- a/catalog/scores/models/model_items/GLEON_lm_lag_1day.json +++ /dev/null @@ -1,219 +0,0 @@ -{ - "stac_version": "1.0.0", - "stac_extensions": [ - "https://stac-extensions.github.io/table/v1.2.0/schema.json" - ], - "type": "Feature", - "id": "GLEON_lm_lag_1day", - "bbox": [ - [ - -156.6194, - 71.2824, - -66.7987, - 71.2824 - ] - ], - "geometry": { - "type": "MultiPoint", - "coordinates": [ - [-82.0084, 29.676], - [-89.4737, 46.2097], - [-89.7048, 45.9983], - [-99.1139, 47.1591], - [-99.2531, 47.1298], - [-82.0177, 29.6878], - [-149.6106, 68.6307] - ] - }, - "properties": { - "description": "\nmodel info: NA\n\nSites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK\n\nVariables: Daily Dissolved_oxygen, Daily Water_temperature", - "start_datetime": "2023-11-14", - "end_datetime": "2023-12-15", - "providers": [ - { - "url": "pending", - "name": "pending", - "roles": [ - "producer", - "processor", - "licensor" - ] - }, - { - "url": "https://www.ecoforecastprojectvt.org", - "name": "Ecoforecast Challenge", - "roles": [ - "host" - ] - } - ], - "license": "CC0-1.0", - "keywords": [ - "Forecasting", - "neon4cast", - "Daily Dissolved_oxygen", - "Daily Water_temperature" - ], - "table:columns": [ - { - "name": "reference_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that the forecast was initiated (horizon = 0)" - }, - { - "name": "site_id", - "type": "string", - "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model." - }, - { - "name": "datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime of the forecasted value (ISO 8601)" - }, - { - "name": "family", - "type": "string", - "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)." - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "name": "observation", - "type": "double", - "description": "observed value for variable" - }, - { - "name": "crps", - "type": "double", - "description": "crps forecast score" - }, - { - "name": "logs", - "type": "double", - "description": "logs forecast score" - }, - { - "name": "mean", - "type": "double", - "description": "mean forecast prediction" - }, - { - "name": "median", - "type": "double", - "description": "median forecast prediction" - }, - { - "name": "sd", - "type": "double", - "description": "standard deviation forecasts" - }, - { - "name": "quantile97.5", - "type": "double", - "description": "upper 97.5 percentile value of forecast" - }, - { - "name": "quantile02.5", - "type": "double", - "description": "upper 2.5 percentile value of forecast" - }, - { - "name": "quantile90", - "type": "double", - "description": "upper 90 percentile value of forecast" - }, - { - "name": "quantile10", - "type": "double", - "description": "upper 10 percentile value of forecast" - }, - { - "name": "project_id", - "type": "string", - "description": "unique project identifier" - }, - { - "name": "duration", - "type": "string", - "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention" - }, - { - "name": "variable", - "type": "string", - "description": "name of forecasted variable" - }, - { - "name": "model_id", - "type": "string", - "description": "unique model identifier" - }, - { - "name": "date", - "type": "string", - "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period." - } - ] - }, - "collection": "scores", - "links": [ - { - "rel": "collection", - "href": "../collection.json", - "type": "application/json", - "title": "GLEON_lm_lag_1day" - }, - { - "rel": "root", - "href": "../../../catalog.json", - "type": "application/json", - "title": "Forecast Catalog" - }, - { - "rel": "parent", - "href": "../collection.json", - "type": "application/json", - "title": "GLEON_lm_lag_1day" - }, - { - "rel": "self", - "href": "GLEON_lm_lag_1day.json", - "type": "application/json", - "title": "Model Forecast" - }, - { - "rel": "item", - "href": "pending", - "type": "text/html", - "title": "Link for Model Code" - } - ], - "assets": { - "1": { - "type": "application/json", - "title": "Model Metadata", - "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/GLEON_lm_lag_1day.json", - "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/GLEON_lm_lag_1day.json\")\n\n" - }, - "2": { - "type": "text/html", - "title": "Link for Model Code", - "href": "pending", - "description": "The link to the model code provided by the model submission team" - }, - "3": { - "type": "application/x-parquet", - "title": "Database Access for Daily Dissolved_oxygen", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=oxygen/model_id=GLEON_lm_lag_1day?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=oxygen/model_id=GLEON_lm_lag_1day?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "4": { - "type": "application/x-parquet", - "title": "Database Access for Daily Water_temperature", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=GLEON_lm_lag_1day?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=GLEON_lm_lag_1day?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - } - } -} diff --git a/catalog/scores/models/model_items/GLEON_physics.json b/catalog/scores/models/model_items/GLEON_physics.json deleted file mode 100644 index 7789ea4e4d..0000000000 --- a/catalog/scores/models/model_items/GLEON_physics.json +++ /dev/null @@ -1,211 +0,0 @@ -{ - "stac_version": "1.0.0", - "stac_extensions": [ - "https://stac-extensions.github.io/table/v1.2.0/schema.json" - ], - "type": "Feature", - "id": "GLEON_physics", - "bbox": [ - [ - -156.6194, - 71.2824, - -66.7987, - 71.2824 - ] - ], - "geometry": { - "type": "MultiPoint", - "coordinates": [ - [-82.0084, 29.676], - [-89.4737, 46.2097], - [-89.7048, 45.9983], - [-99.1139, 47.1591], - [-99.2531, 47.1298], - [-82.0177, 29.6878] - ] - }, - "properties": { - "description": "\nmodel info: NA\n\nSites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG\n\nVariables: Daily Water_temperature", - "start_datetime": "2023-11-14", - "end_datetime": "2023-12-15", - "providers": [ - { - "url": "pending", - "name": "pending", - "roles": [ - "producer", - "processor", - "licensor" - ] - }, - { - "url": "https://www.ecoforecastprojectvt.org", - "name": "Ecoforecast Challenge", - "roles": [ - "host" - ] - } - ], - "license": "CC0-1.0", - "keywords": [ - "Forecasting", - "neon4cast", - "Daily Water_temperature" - ], - "table:columns": [ - { - "name": "reference_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that the forecast was initiated (horizon = 0)" - }, - { - "name": "site_id", - "type": "string", - "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model." - }, - { - "name": "datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime of the forecasted value (ISO 8601)" - }, - { - "name": "family", - "type": "string", - "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)." - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "name": "observation", - "type": "double", - "description": "observed value for variable" - }, - { - "name": "crps", - "type": "double", - "description": "crps forecast score" - }, - { - "name": "logs", - "type": "double", - "description": "logs forecast score" - }, - { - "name": "mean", - "type": "double", - "description": "mean forecast prediction" - }, - { - "name": "median", - "type": "double", - "description": "median forecast prediction" - }, - { - "name": "sd", - "type": "double", - "description": "standard deviation forecasts" - }, - { - "name": "quantile97.5", - "type": "double", - "description": "upper 97.5 percentile value of forecast" - }, - { - "name": "quantile02.5", - "type": "double", - "description": "upper 2.5 percentile value of forecast" - }, - { - "name": "quantile90", - "type": "double", - "description": "upper 90 percentile value of forecast" - }, - { - "name": "quantile10", - "type": "double", - "description": "upper 10 percentile value of forecast" - }, - { - "name": "project_id", - "type": "string", - "description": "unique project identifier" - }, - { - "name": "duration", - "type": "string", - "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention" - }, - { - "name": "variable", - "type": "string", - "description": "name of forecasted variable" - }, - { - "name": "model_id", - "type": "string", - "description": "unique model identifier" - }, - { - "name": "date", - "type": "string", - "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period." - } - ] - }, - "collection": "scores", - "links": [ - { - "rel": "collection", - "href": "../collection.json", - "type": "application/json", - "title": "GLEON_physics" - }, - { - "rel": "root", - "href": "../../../catalog.json", - "type": "application/json", - "title": "Forecast Catalog" - }, - { - "rel": "parent", - "href": "../collection.json", - "type": "application/json", - "title": "GLEON_physics" - }, - { - "rel": "self", - "href": "GLEON_physics.json", - "type": "application/json", - "title": "Model Forecast" - }, - { - "rel": "item", - "href": "pending", - "type": "text/html", - "title": "Link for Model Code" - } - ], - "assets": { - "1": { - "type": "application/json", - "title": "Model Metadata", - "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/GLEON_physics.json", - "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/GLEON_physics.json\")\n\n" - }, - "2": { - "type": "text/html", - "title": "Link for Model Code", - "href": "pending", - "description": "The link to the model code provided by the model submission team" - }, - "3": { - "type": "application/x-parquet", - "title": "Database Access for Daily Water_temperature", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=GLEON_physics?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=GLEON_physics?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - } - } -} diff --git a/catalog/scores/models/model_items/TESTclimatology.json b/catalog/scores/models/model_items/TESTclimatology.json deleted file mode 100644 index e05cea5e88..0000000000 --- a/catalog/scores/models/model_items/TESTclimatology.json +++ /dev/null @@ -1,200 +0,0 @@ -{ - "stac_version": "1.0.0", - "stac_extensions": [ - "https://stac-extensions.github.io/table/v1.2.0/schema.json" - ], - "type": "Feature", - "id": "TESTclimatology", - "bbox": [ - [ - -156.6194, - 71.2824, - -66.7987, - 71.2824 - ] - ], - "geometry": { - "type": "MultiPoint", - "coordinates": [ - [37.3129, -79.8159], - [37.3032, -79.8372] - ] - }, - "properties": { - "description": [], - "start_datetime": "2023-09-22", - "end_datetime": "2023-10-27", - "providers": [ - { - "url": "pending", - "name": "pending", - "roles": [ - "producer", - "processor", - "licensor" - ] - }, - { - "url": "https://www.ecoforecastprojectvt.org", - "name": "Ecoforecast Challenge", - "roles": [ - "host" - ] - } - ], - "license": "CC0-1.0", - "keywords": [ - "Forecasting", - "vera4cast", - "Temp_C_mean" - ], - "table:columns": [ - { - "name": "reference_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that the forecast was initiated (horizon = 0)" - }, - { - "name": "site_id", - "type": "string", - "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model." - }, - { - "name": "datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime of the forecasted value (ISO 8601)" - }, - { - "name": "family", - "type": "string", - "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)." - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "name": "depth_m", - "type": "double", - "description": "depth (meters) in water column of prediction" - }, - { - "name": "observation", - "type": "double", - "description": "observed value for variable" - }, - { - "name": "crps", - "type": "double", - "description": "crps forecast score" - }, - { - "name": "logs", - "type": "double", - "description": "logs forecast score" - }, - { - "name": "mean", - "type": "double", - "description": "mean forecast prediction" - }, - { - "name": "median", - "type": "double", - "description": "median forecast prediction" - }, - { - "name": "sd", - "type": "double", - "description": "standard deviation forecasts" - }, - { - "name": "quantile97.5", - "type": "double", - "description": "upper 97.5 percentile value of forecast" - }, - { - "name": "quantile02.5", - "type": "double", - "description": "upper 2.5 percentile value of forecast" - }, - { - "name": "quantile90", - "type": "double", - "description": "upper 90 percentile value of forecast" - }, - { - "name": "quantile10", - "type": "double", - "description": "upper 10 percentile value of forecast" - }, - { - "name": "project_id", - "type": "string", - "description": "unique project identifier" - }, - { - "name": "duration", - "type": "string", - "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention" - }, - { - "name": "variable", - "type": "string", - "description": "name of forecasted variable" - }, - { - "name": "model_id", - "type": "string", - "description": "unique model identifier" - }, - { - "name": "date", - "type": "string", - "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period." - } - ] - }, - "collection": "scores", - "links": [ - { - "rel": "collection", - "href": "../collection.json", - "type": "application/json", - "title": "TESTclimatology" - }, - { - "rel": "root", - "href": "../../../catalog.json", - "type": "application/json", - "title": "Forecast Catalog" - }, - { - "rel": "parent", - "href": "../collection.json", - "type": "application/json", - "title": "TESTclimatology" - }, - { - "rel": "self", - "href": "TESTclimatology.json", - "type": "application/json", - "title": "Model Forecast" - } - ], - "assets": { - "1": { - "type": "application/json", - "title": "Model Metadata", - "href": "https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/TESTclimatology.json", - "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/TESTclimatology.json\")\n\n" - }, - "2": { - "type": "application/x-parquet", - "title": "Database Access for Temp_C_mean daily", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=Temp_C_mean/model_id=TESTclimatology?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=Temp_C_mean/model_id=TESTclimatology?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - } - } -} diff --git a/catalog/scores/models/model_items/USGSHABs1.json b/catalog/scores/models/model_items/USGSHABs1.json deleted file mode 100644 index 23de7335e0..0000000000 --- a/catalog/scores/models/model_items/USGSHABs1.json +++ /dev/null @@ -1,208 +0,0 @@ -{ - "stac_version": "1.0.0", - "stac_extensions": [ - "https://stac-extensions.github.io/table/v1.2.0/schema.json" - ], - "type": "Feature", - "id": "USGSHABs1", - "bbox": [ - [ - -156.6194, - 71.2824, - -66.7987, - 71.2824 - ] - ], - "geometry": { - "type": "MultiPoint", - "coordinates": [ - [-87.7982, 32.5415], - [-84.4374, 31.1854], - [-88.1589, 31.8534] - ] - }, - "properties": { - "description": "\nmodel info: NA\n\nSites: BLWA, FLNT, TOMB\n\nVariables: Daily Chlorophyll_a", - "start_datetime": "2023-11-12", - "end_datetime": "2023-11-30", - "providers": [ - { - "url": "pending", - "name": "pending", - "roles": [ - "producer", - "processor", - "licensor" - ] - }, - { - "url": "https://www.ecoforecastprojectvt.org", - "name": "Ecoforecast Challenge", - "roles": [ - "host" - ] - } - ], - "license": "CC0-1.0", - "keywords": [ - "Forecasting", - "neon4cast", - "Daily Chlorophyll_a" - ], - "table:columns": [ - { - "name": "reference_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that the forecast was initiated (horizon = 0)" - }, - { - "name": "site_id", - "type": "string", - "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model." - }, - { - "name": "datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime of the forecasted value (ISO 8601)" - }, - { - "name": "family", - "type": "string", - "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)." - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "name": "observation", - "type": "double", - "description": "observed value for variable" - }, - { - "name": "crps", - "type": "double", - "description": "crps forecast score" - }, - { - "name": "logs", - "type": "double", - "description": "logs forecast score" - }, - { - "name": "mean", - "type": "double", - "description": "mean forecast prediction" - }, - { - "name": "median", - "type": "double", - "description": "median forecast prediction" - }, - { - "name": "sd", - "type": "double", - "description": "standard deviation forecasts" - }, - { - "name": "quantile97.5", - "type": "double", - "description": "upper 97.5 percentile value of forecast" - }, - { - "name": "quantile02.5", - "type": "double", - "description": "upper 2.5 percentile value of forecast" - }, - { - "name": "quantile90", - "type": "double", - "description": "upper 90 percentile value of forecast" - }, - { - "name": "quantile10", - "type": "double", - "description": "upper 10 percentile value of forecast" - }, - { - "name": "project_id", - "type": "string", - "description": "unique project identifier" - }, - { - "name": "duration", - "type": "string", - "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention" - }, - { - "name": "variable", - "type": "string", - "description": "name of forecasted variable" - }, - { - "name": "model_id", - "type": "string", - "description": "unique model identifier" - }, - { - "name": "date", - "type": "string", - "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period." - } - ] - }, - "collection": "scores", - "links": [ - { - "rel": "collection", - "href": "../collection.json", - "type": "application/json", - "title": "USGSHABs1" - }, - { - "rel": "root", - "href": "../../../catalog.json", - "type": "application/json", - "title": "Forecast Catalog" - }, - { - "rel": "parent", - "href": "../collection.json", - "type": "application/json", - "title": "USGSHABs1" - }, - { - "rel": "self", - "href": "USGSHABs1.json", - "type": "application/json", - "title": "Model Forecast" - }, - { - "rel": "item", - "href": "pending", - "type": "text/html", - "title": "Link for Model Code" - } - ], - "assets": { - "1": { - "type": "application/json", - "title": "Model Metadata", - "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/USGSHABs1.json", - "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/USGSHABs1.json\")\n\n" - }, - "2": { - "type": "text/html", - "title": "Link for Model Code", - "href": "pending", - "description": "The link to the model code provided by the model submission team" - }, - "3": { - "type": "application/x-parquet", - "title": "Database Access for Daily Chlorophyll_a", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=USGSHABs1?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=USGSHABs1?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - } - } -} diff --git a/catalog/scores/models/model_items/air2waterSat_2.json b/catalog/scores/models/model_items/air2waterSat_2.json deleted file mode 100644 index 8a5ec9fbdd..0000000000 --- a/catalog/scores/models/model_items/air2waterSat_2.json +++ /dev/null @@ -1,246 +0,0 @@ -{ - "stac_version": "1.0.0", - "stac_extensions": [ - "https://stac-extensions.github.io/table/v1.2.0/schema.json" - ], - "type": "Feature", - "id": "air2waterSat_2", - "bbox": [ - [ - -156.6194, - 71.2824, - -66.7987, - 71.2824 - ] - ], - "geometry": { - "type": "MultiPoint", - "coordinates": [ - [-102.4471, 39.7582], - [-82.0084, 29.676], - [-119.2575, 37.0597], - [-110.5871, 44.9501], - [-96.6242, 34.4442], - [-87.7982, 32.5415], - [-147.504, 65.1532], - [-105.5442, 40.035], - [-89.4737, 46.2097], - [-66.9868, 18.1135], - [-84.4374, 31.1854], - [-66.7987, 18.1741], - [-72.3295, 42.4719], - [-96.6038, 39.1051], - [-83.5038, 35.6904], - [-77.9832, 39.0956], - [-89.7048, 45.9983], - [-121.9338, 45.7908], - [-87.4077, 32.9604], - [-96.443, 38.9459], - [-122.1655, 44.2596], - [-149.143, 68.6698], - [-78.1473, 38.8943], - [-97.7823, 33.3785], - [-99.1139, 47.1591], - [-99.2531, 47.1298], - [-111.7979, 40.7839], - [-82.0177, 29.6878], - [-111.5081, 33.751], - [-119.0274, 36.9559], - [-88.1589, 31.8534], - [-149.6106, 68.6307], - [-84.2793, 35.9574], - [-105.9154, 39.8914] - ] - }, - "properties": { - "description": "\nmodel info: NA\n\nSites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU\n\nVariables: Daily Dissolved_oxygen, Daily Water_temperature", - "start_datetime": "2023-11-14", - "end_datetime": "2023-12-15", - "providers": [ - { - "url": "pending", - "name": "pending", - "roles": [ - "producer", - "processor", - "licensor" - ] - }, - { - "url": "https://www.ecoforecastprojectvt.org", - "name": "Ecoforecast Challenge", - "roles": [ - "host" - ] - } - ], - "license": "CC0-1.0", - "keywords": [ - "Forecasting", - "neon4cast", - "Daily Dissolved_oxygen", - "Daily Water_temperature" - ], - "table:columns": [ - { - "name": "reference_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that the forecast was initiated (horizon = 0)" - }, - { - "name": "site_id", - "type": "string", - "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model." - }, - { - "name": "datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime of the forecasted value (ISO 8601)" - }, - { - "name": "family", - "type": "string", - "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)." - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "name": "observation", - "type": "double", - "description": "observed value for variable" - }, - { - "name": "crps", - "type": "double", - "description": "crps forecast score" - }, - { - "name": "logs", - "type": "double", - "description": "logs forecast score" - }, - { - "name": "mean", - "type": "double", - "description": "mean forecast prediction" - }, - { - "name": "median", - "type": "double", - "description": "median forecast prediction" - }, - { - "name": "sd", - "type": "double", - "description": "standard deviation forecasts" - }, - { - "name": "quantile97.5", - "type": "double", - "description": "upper 97.5 percentile value of forecast" - }, - { - "name": "quantile02.5", - "type": "double", - "description": "upper 2.5 percentile value of forecast" - }, - { - "name": "quantile90", - "type": "double", - "description": "upper 90 percentile value of forecast" - }, - { - "name": "quantile10", - "type": "double", - "description": "upper 10 percentile value of forecast" - }, - { - "name": "project_id", - "type": "string", - "description": "unique project identifier" - }, - { - "name": "duration", - "type": "string", - "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention" - }, - { - "name": "variable", - "type": "string", - "description": "name of forecasted variable" - }, - { - "name": "model_id", - "type": "string", - "description": "unique model identifier" - }, - { - "name": "date", - "type": "string", - "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period." - } - ] - }, - "collection": "scores", - "links": [ - { - "rel": "collection", - "href": "../collection.json", - "type": "application/json", - "title": "air2waterSat_2" - }, - { - "rel": "root", - "href": "../../../catalog.json", - "type": "application/json", - "title": "Forecast Catalog" - }, - { - "rel": "parent", - "href": "../collection.json", - "type": "application/json", - "title": "air2waterSat_2" - }, - { - "rel": "self", - "href": "air2waterSat_2.json", - "type": "application/json", - "title": "Model Forecast" - }, - { - "rel": "item", - "href": "pending", - "type": "text/html", - "title": "Link for Model Code" - } - ], - "assets": { - "1": { - "type": "application/json", - "title": "Model Metadata", - "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/air2waterSat_2.json", - "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/air2waterSat_2.json\")\n\n" - }, - "2": { - "type": "text/html", - "title": "Link for Model Code", - "href": "pending", - "description": "The link to the model code provided by the model submission team" - }, - "3": { - "type": "application/x-parquet", - "title": "Database Access for Daily Dissolved_oxygen", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=oxygen/model_id=air2waterSat_2?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=oxygen/model_id=air2waterSat_2?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "4": { - "type": "application/x-parquet", - "title": "Database Access for Daily Water_temperature", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=air2waterSat_2?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=air2waterSat_2?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - } - } -} diff --git a/catalog/scores/models/model_items/baseline_ensemble.json b/catalog/scores/models/model_items/baseline_ensemble.json deleted file mode 100644 index 4866fbf032..0000000000 --- a/catalog/scores/models/model_items/baseline_ensemble.json +++ /dev/null @@ -1,281 +0,0 @@ -{ - "stac_version": "1.0.0", - "stac_extensions": [ - "https://stac-extensions.github.io/table/v1.2.0/schema.json" - ], - "type": "Feature", - "id": "baseline_ensemble", - "bbox": [ - [ - -156.6194, - 71.2824, - -66.7987, - 71.2824 - ] - ], - "geometry": { - "type": "MultiPoint", - "coordinates": [ - [-122.3303, 45.7624], - [-71.2874, 44.0639], - [-78.0418, 39.0337], - [-97.57, 33.4012], - [-104.7456, 40.8155], - [-99.1066, 47.1617], - [-87.8039, 32.5417], - [-81.4362, 28.1251], - [-83.5019, 35.689], - [-66.8687, 17.9696], - [-72.1727, 42.5369], - [-84.4686, 31.1948], - [-106.8425, 32.5907], - [-96.6129, 39.1104], - [-96.5631, 39.1008], - [-67.0769, 18.0213], - [-88.1612, 31.8539], - [-80.5248, 37.3783], - [-109.3883, 38.2483], - [-105.5824, 40.0543], - [-100.9154, 46.7697], - [-99.0588, 35.4106], - [-112.4524, 40.1776], - [-84.2826, 35.9641], - [-81.9934, 29.6893], - [-155.3173, 19.5531], - [-105.546, 40.2759], - [-78.1395, 38.8929], - [-76.56, 38.8901], - [-119.7323, 37.1088], - [-119.2622, 37.0334], - [-110.8355, 31.9107], - [-89.5864, 45.5089], - [-103.0293, 40.4619], - [-87.3933, 32.9505], - [-119.006, 37.0058], - [-89.5857, 45.4937], - [-95.1921, 39.0404], - [-89.5373, 46.2339], - [-99.2413, 47.1282], - [-121.9519, 45.8205], - [-110.5391, 44.9535], - [-102.4471, 39.7582], - [-82.0084, 29.676], - [-119.2575, 37.0597], - [-110.5871, 44.9501], - [-96.6242, 34.4442], - [-87.7982, 32.5415], - [-105.5442, 40.035], - [-66.9868, 18.1135], - [-84.4374, 31.1854], - [-66.7987, 18.1741], - [-72.3295, 42.4719], - [-96.6038, 39.1051], - [-83.5038, 35.6904], - [-77.9832, 39.0956], - [-121.9338, 45.7908], - [-87.4077, 32.9604], - [-96.443, 38.9459], - [-122.1655, 44.2596], - [-78.1473, 38.8943], - [-97.7823, 33.3785], - [-111.7979, 40.7839], - [-82.0177, 29.6878], - [-111.5081, 33.751], - [-119.0274, 36.9559], - [-88.1589, 31.8534], - [-84.2793, 35.9574], - [-105.9154, 39.8914] - ] - }, - "properties": { - "description": "\nmodel info: NA\n\nSites: ABBY, BART, BLAN, CLBJ, CPER, DCFS, DELA, DSNY, GRSM, GUAN, HARV, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TREE, UKFS, UNDE, WOOD, WREF, YELL, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, COMO, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, MART, MAYF, MCDI, MCRA, POSE, PRIN, REDB, SUGG, SYCA, TECR, TOMB, WALK, WLOU\n\nVariables: Daily Red_chromatic_coordinate, Daily Water_temperature", - "start_datetime": "2023-11-14", - "end_datetime": "2023-12-15", - "providers": [ - { - "url": "pending", - "name": "pending", - "roles": [ - "producer", - "processor", - "licensor" - ] - }, - { - "url": "https://www.ecoforecastprojectvt.org", - "name": "Ecoforecast Challenge", - "roles": [ - "host" - ] - } - ], - "license": "CC0-1.0", - "keywords": [ - "Forecasting", - "neon4cast", - "Daily Red_chromatic_coordinate", - "Daily Water_temperature" - ], - "table:columns": [ - { - "name": "reference_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that the forecast was initiated (horizon = 0)" - }, - { - "name": "site_id", - "type": "string", - "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model." - }, - { - "name": "datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime of the forecasted value (ISO 8601)" - }, - { - "name": "family", - "type": "string", - "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)." - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "name": "observation", - "type": "double", - "description": "observed value for variable" - }, - { - "name": "crps", - "type": "double", - "description": "crps forecast score" - }, - { - "name": "logs", - "type": "double", - "description": "logs forecast score" - }, - { - "name": "mean", - "type": "double", - "description": "mean forecast prediction" - }, - { - "name": "median", - "type": "double", - "description": "median forecast prediction" - }, - { - "name": "sd", - "type": "double", - "description": "standard deviation forecasts" - }, - { - "name": "quantile97.5", - "type": "double", - "description": "upper 97.5 percentile value of forecast" - }, - { - "name": "quantile02.5", - "type": "double", - "description": "upper 2.5 percentile value of forecast" - }, - { - "name": "quantile90", - "type": "double", - "description": "upper 90 percentile value of forecast" - }, - { - "name": "quantile10", - "type": "double", - "description": "upper 10 percentile value of forecast" - }, - { - "name": "project_id", - "type": "string", - "description": "unique project identifier" - }, - { - "name": "duration", - "type": "string", - "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention" - }, - { - "name": "variable", - "type": "string", - "description": "name of forecasted variable" - }, - { - "name": "model_id", - "type": "string", - "description": "unique model identifier" - }, - { - "name": "date", - "type": "string", - "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period." - } - ] - }, - "collection": "scores", - "links": [ - { - "rel": "collection", - "href": "../collection.json", - "type": "application/json", - "title": "baseline_ensemble" - }, - { - "rel": "root", - "href": "../../../catalog.json", - "type": "application/json", - "title": "Forecast Catalog" - }, - { - "rel": "parent", - "href": "../collection.json", - "type": "application/json", - "title": "baseline_ensemble" - }, - { - "rel": "self", - "href": "baseline_ensemble.json", - "type": "application/json", - "title": "Model Forecast" - }, - { - "rel": "item", - "href": "pending", - "type": "text/html", - "title": "Link for Model Code" - } - ], - "assets": { - "1": { - "type": "application/json", - "title": "Model Metadata", - "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/baseline_ensemble.json", - "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/baseline_ensemble.json\")\n\n" - }, - "2": { - "type": "text/html", - "title": "Link for Model Code", - "href": "pending", - "description": "The link to the model code provided by the model submission team" - }, - "3": { - "type": "application/x-parquet", - "title": "Database Access for Daily Red_chromatic_coordinate", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=rcc_90/model_id=baseline_ensemble?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=rcc_90/model_id=baseline_ensemble?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "4": { - "type": "application/x-parquet", - "title": "Database Access for Daily Water_temperature", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=baseline_ensemble?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=baseline_ensemble?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - } - } -} diff --git a/catalog/scores/models/model_items/cb_f1.json b/catalog/scores/models/model_items/cb_f1.json deleted file mode 100644 index b433c61875..0000000000 --- a/catalog/scores/models/model_items/cb_f1.json +++ /dev/null @@ -1,246 +0,0 @@ -{ - "stac_version": "1.0.0", - "stac_extensions": [ - "https://stac-extensions.github.io/table/v1.2.0/schema.json" - ], - "type": "Feature", - "id": "cb_f1", - "bbox": [ - [ - -156.6194, - 71.2824, - -66.7987, - 71.2824 - ] - ], - "geometry": { - "type": "MultiPoint", - "coordinates": [ - [-102.4471, 39.7582], - [-82.0084, 29.676], - [-119.2575, 37.0597], - [-110.5871, 44.9501], - [-96.6242, 34.4442], - [-87.7982, 32.5415], - [-147.504, 65.1532], - [-105.5442, 40.035], - [-89.4737, 46.2097], - [-66.9868, 18.1135], - [-84.4374, 31.1854], - [-66.7987, 18.1741], - [-72.3295, 42.4719], - [-96.6038, 39.1051], - [-83.5038, 35.6904], - [-77.9832, 39.0956], - [-89.7048, 45.9983], - [-121.9338, 45.7908], - [-87.4077, 32.9604], - [-96.443, 38.9459], - [-122.1655, 44.2596], - [-149.143, 68.6698], - [-78.1473, 38.8943], - [-97.7823, 33.3785], - [-99.1139, 47.1591], - [-99.2531, 47.1298], - [-111.7979, 40.7839], - [-82.0177, 29.6878], - [-111.5081, 33.751], - [-119.0274, 36.9559], - [-88.1589, 31.8534], - [-149.6106, 68.6307], - [-84.2793, 35.9574], - [-105.9154, 39.8914] - ] - }, - "properties": { - "description": [], - "start_datetime": "2023-10-11", - "end_datetime": "2023-12-15", - "providers": [ - { - "url": "pending", - "name": "pending", - "roles": [ - "producer", - "processor", - "licensor" - ] - }, - { - "url": "https://www.ecoforecastprojectvt.org", - "name": "Ecoforecast Challenge", - "roles": [ - "host" - ] - } - ], - "license": "CC0-1.0", - "keywords": [ - "Forecasting", - "neon4cast", - "Daily Dissolved_oxygen", - "Daily Water_temperature" - ], - "table:columns": [ - { - "name": "reference_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that the forecast was initiated (horizon = 0)" - }, - { - "name": "site_id", - "type": "string", - "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model." - }, - { - "name": "datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime of the forecasted value (ISO 8601)" - }, - { - "name": "family", - "type": "string", - "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)." - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "name": "observation", - "type": "double", - "description": "observed value for variable" - }, - { - "name": "crps", - "type": "double", - "description": "crps forecast score" - }, - { - "name": "logs", - "type": "double", - "description": "logs forecast score" - }, - { - "name": "mean", - "type": "double", - "description": "mean forecast prediction" - }, - { - "name": "median", - "type": "double", - "description": "median forecast prediction" - }, - { - "name": "sd", - "type": "double", - "description": "standard deviation forecasts" - }, - { - "name": "quantile97.5", - "type": "double", - "description": "upper 97.5 percentile value of forecast" - }, - { - "name": "quantile02.5", - "type": "double", - "description": "upper 2.5 percentile value of forecast" - }, - { - "name": "quantile90", - "type": "double", - "description": "upper 90 percentile value of forecast" - }, - { - "name": "quantile10", - "type": "double", - "description": "upper 10 percentile value of forecast" - }, - { - "name": "project_id", - "type": "string", - "description": "unique project identifier" - }, - { - "name": "duration", - "type": "string", - "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention" - }, - { - "name": "variable", - "type": "string", - "description": "name of forecasted variable" - }, - { - "name": "model_id", - "type": "string", - "description": "unique model identifier" - }, - { - "name": "date", - "type": "string", - "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period." - } - ] - }, - "collection": "scores", - "links": [ - { - "rel": "collection", - "href": "../collection.json", - "type": "application/json", - "title": "cb_f1" - }, - { - "rel": "root", - "href": "../../../catalog.json", - "type": "application/json", - "title": "Forecast Catalog" - }, - { - "rel": "parent", - "href": "../collection.json", - "type": "application/json", - "title": "cb_f1" - }, - { - "rel": "self", - "href": "cb_f1.json", - "type": "application/json", - "title": "Model Forecast" - }, - { - "rel": "item", - "href": "pending", - "type": "text/html", - "title": "Link for Model Code" - } - ], - "assets": { - "1": { - "type": "application/json", - "title": "Model Metadata", - "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/cb_f1.json", - "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/cb_f1.json\")\n\n" - }, - "2": { - "type": "text/html", - "title": "Link for Model Code", - "href": "pending", - "description": "The link to the model code provided by the model submission team" - }, - "3": { - "type": "application/x-parquet", - "title": "Database Access for Daily Dissolved_oxygen", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=oxygen/model_id=cb_f1?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=oxygen/model_id=cb_f1?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "4": { - "type": "application/x-parquet", - "title": "Database Access for Daily Water_temperature", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=cb_f1?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=cb_f1?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - } - } -} diff --git a/catalog/scores/models/model_items/cb_prophet.json b/catalog/scores/models/model_items/cb_prophet.json deleted file mode 100644 index 87e55271a8..0000000000 --- a/catalog/scores/models/model_items/cb_prophet.json +++ /dev/null @@ -1,326 +0,0 @@ -{ - "stac_version": "1.0.0", - "stac_extensions": [ - "https://stac-extensions.github.io/table/v1.2.0/schema.json" - ], - "type": "Feature", - "id": "cb_prophet", - "bbox": [ - [ - -156.6194, - 71.2824, - -66.7987, - 71.2824 - ] - ], - "geometry": { - "type": "MultiPoint", - "coordinates": [ - [-82.0084, 29.676], - [-87.7982, 32.5415], - [-89.4737, 46.2097], - [-84.4374, 31.1854], - [-89.7048, 45.9983], - [-99.1139, 47.1591], - [-99.2531, 47.1298], - [-82.0177, 29.6878], - [-88.1589, 31.8534], - [-122.3303, 45.7624], - [-156.6194, 71.2824], - [-71.2874, 44.0639], - [-78.0418, 39.0337], - [-147.5026, 65.154], - [-97.57, 33.4012], - [-104.7456, 40.8155], - [-99.1066, 47.1617], - [-145.7514, 63.8811], - [-87.8039, 32.5417], - [-81.4362, 28.1251], - [-83.5019, 35.689], - [-66.8687, 17.9696], - [-72.1727, 42.5369], - [-149.2133, 63.8758], - [-84.4686, 31.1948], - [-106.8425, 32.5907], - [-96.6129, 39.1104], - [-96.5631, 39.1008], - [-67.0769, 18.0213], - [-88.1612, 31.8539], - [-80.5248, 37.3783], - [-109.3883, 38.2483], - [-105.5824, 40.0543], - [-100.9154, 46.7697], - [-99.0588, 35.4106], - [-112.4524, 40.1776], - [-84.2826, 35.9641], - [-81.9934, 29.6893], - [-155.3173, 19.5531], - [-105.546, 40.2759], - [-78.1395, 38.8929], - [-76.56, 38.8901], - [-119.7323, 37.1088], - [-119.2622, 37.0334], - [-110.8355, 31.9107], - [-89.5864, 45.5089], - [-103.0293, 40.4619], - [-87.3933, 32.9505], - [-119.006, 37.0058], - [-149.3705, 68.6611], - [-89.5857, 45.4937], - [-95.1921, 39.0404], - [-89.5373, 46.2339], - [-99.2413, 47.1282], - [-121.9519, 45.8205], - [-110.5391, 44.9535], - [-102.4471, 39.7582], - [-119.2575, 37.0597], - [-110.5871, 44.9501], - [-96.6242, 34.4442], - [-147.504, 65.1532], - [-105.5442, 40.035], - [-66.9868, 18.1135], - [-66.7987, 18.1741], - [-72.3295, 42.4719], - [-96.6038, 39.1051], - [-83.5038, 35.6904], - [-77.9832, 39.0956], - [-121.9338, 45.7908], - [-87.4077, 32.9604], - [-96.443, 38.9459], - [-122.1655, 44.2596], - [-78.1473, 38.8943], - [-97.7823, 33.3785], - [-111.7979, 40.7839], - [-111.5081, 33.751], - [-119.0274, 36.9559], - [-84.2793, 35.9574], - [-105.9154, 39.8914] - ] - }, - "properties": { - "description": "\nmodel info: NA\n\nSites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ARIK, BIGC, BLDE, BLUE, CARI, COMO, CUPE, GUIL, HOPB, KING, LECO, LEWI, MART, MAYF, MCDI, MCRA, POSE, PRIN, REDB, SYCA, TECR, WALK, WLOU\n\nVariables: Daily Chlorophyll_a, Daily Green_chromatic_coordinate, Daily latent_heat_flux, Daily Net_ecosystem_exchange, Daily Dissolved_oxygen, Daily Red_chromatic_coordinate, Daily Water_temperature", - "start_datetime": "2023-11-14", - "end_datetime": "2023-12-15", - "providers": [ - { - "url": "pending", - "name": "pending", - "roles": [ - "producer", - "processor", - "licensor" - ] - }, - { - "url": "https://www.ecoforecastprojectvt.org", - "name": "Ecoforecast Challenge", - "roles": [ - "host" - ] - } - ], - "license": "CC0-1.0", - "keywords": [ - "Forecasting", - "neon4cast", - "Daily Chlorophyll_a", - "Daily Green_chromatic_coordinate", - "Daily latent_heat_flux", - "Daily Net_ecosystem_exchange", - "Daily Dissolved_oxygen", - "Daily Red_chromatic_coordinate", - "Daily Water_temperature" - ], - "table:columns": [ - { - "name": "reference_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that the forecast was initiated (horizon = 0)" - }, - { - "name": "site_id", - "type": "string", - "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model." - }, - { - "name": "datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime of the forecasted value (ISO 8601)" - }, - { - "name": "family", - "type": "string", - "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)." - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "name": "observation", - "type": "double", - "description": "observed value for variable" - }, - { - "name": "crps", - "type": "double", - "description": "crps forecast score" - }, - { - "name": "logs", - "type": "double", - "description": "logs forecast score" - }, - { - "name": "mean", - "type": "double", - "description": "mean forecast prediction" - }, - { - "name": "median", - "type": "double", - "description": "median forecast prediction" - }, - { - "name": "sd", - "type": "double", - "description": "standard deviation forecasts" - }, - { - "name": "quantile97.5", - "type": "double", - "description": "upper 97.5 percentile value of forecast" - }, - { - "name": "quantile02.5", - "type": "double", - "description": "upper 2.5 percentile value of forecast" - }, - { - "name": "quantile90", - "type": "double", - "description": "upper 90 percentile value of forecast" - }, - { - "name": "quantile10", - "type": "double", - "description": "upper 10 percentile value of forecast" - }, - { - "name": "project_id", - "type": "string", - "description": "unique project identifier" - }, - { - "name": "duration", - "type": "string", - "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention" - }, - { - "name": "variable", - "type": "string", - "description": "name of forecasted variable" - }, - { - "name": "model_id", - "type": "string", - "description": "unique model identifier" - }, - { - "name": "date", - "type": "string", - "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period." - } - ] - }, - "collection": "scores", - "links": [ - { - "rel": "collection", - "href": "../collection.json", - "type": "application/json", - "title": "cb_prophet" - }, - { - "rel": "root", - "href": "../../../catalog.json", - "type": "application/json", - "title": "Forecast Catalog" - }, - { - "rel": "parent", - "href": "../collection.json", - "type": "application/json", - "title": "cb_prophet" - }, - { - "rel": "self", - "href": "cb_prophet.json", - "type": "application/json", - "title": "Model Forecast" - }, - { - "rel": "item", - "href": "pending", - "type": "text/html", - "title": "Link for Model Code" - } - ], - "assets": { - "1": { - "type": "application/json", - "title": "Model Metadata", - "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/cb_prophet.json", - "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/cb_prophet.json\")\n\n" - }, - "2": { - "type": "text/html", - "title": "Link for Model Code", - "href": "pending", - "description": "The link to the model code provided by the model submission team" - }, - "3": { - "type": "application/x-parquet", - "title": "Database Access for Daily Chlorophyll_a", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=cb_prophet?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=cb_prophet?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "4": { - "type": "application/x-parquet", - "title": "Database Access for Daily Green_chromatic_coordinate", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=gcc_90/model_id=cb_prophet?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=gcc_90/model_id=cb_prophet?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "5": { - "type": "application/x-parquet", - "title": "Database Access for Daily latent_heat_flux", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=le/model_id=cb_prophet?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=le/model_id=cb_prophet?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "6": { - "type": "application/x-parquet", - "title": "Database Access for Daily Net_ecosystem_exchange", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=nee/model_id=cb_prophet?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=nee/model_id=cb_prophet?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "7": { - "type": "application/x-parquet", - "title": "Database Access for Daily Dissolved_oxygen", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=oxygen/model_id=cb_prophet?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=oxygen/model_id=cb_prophet?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "8": { - "type": "application/x-parquet", - "title": "Database Access for Daily Red_chromatic_coordinate", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=rcc_90/model_id=cb_prophet?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=rcc_90/model_id=cb_prophet?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "9": { - "type": "application/x-parquet", - "title": "Database Access for Daily Water_temperature", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=cb_prophet?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=cb_prophet?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - } - } -} diff --git a/catalog/scores/models/model_items/cfs.json b/catalog/scores/models/model_items/cfs.json deleted file mode 100644 index 96f7d7a0ca..0000000000 --- a/catalog/scores/models/model_items/cfs.json +++ /dev/null @@ -1,223 +0,0 @@ -{ - "stac_version": "1.0.0", - "stac_extensions": [ - "https://stac-extensions.github.io/table/v1.2.0/schema.json" - ], - "type": "Feature", - "id": "cfs", - "bbox": [ - [ - -156.6194, - 71.2824, - -66.7987, - 71.2824 - ] - ], - "geometry": { - "type": "MultiPoint", - "coordinates": [ - [37.3032, -79.8372] - ] - }, - "properties": { - "description": "\nmodel info: NOAA Climate Forecasting System forecasts as downloaded from open-meteo.com. Submitted forecasts only include the first ~6 months because there are 4 ensemble members available (only 1 is available from 6 - 9 months).\n\nSites: fcre\n\nVariables: AirTemp_C_mean, RH_percent_mean, Rain_mm_sum, WindSpeed_ms_mean, ShortwaveRadiationUp_Wm2_mean", - "start_datetime": "2023-10-13", - "end_datetime": "2024-02-01", - "providers": [ - { - "url": "pending", - "name": "pending", - "roles": [ - "producer", - "processor", - "licensor" - ] - }, - { - "url": "https://www.ecoforecastprojectvt.org", - "name": "Ecoforecast Challenge", - "roles": [ - "host" - ] - } - ], - "license": "CC0-1.0", - "keywords": [ - "Forecasting", - "vera4cast", - "AirTemp_C_mean, RH_percent_mean, Rain_mm_sum, WindSpeed_ms_mean, ShortwaveRadiationUp_Wm2_mean" - ], - "table:columns": [ - { - "name": "reference_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that the forecast was initiated (horizon = 0)" - }, - { - "name": "site_id", - "type": "string", - "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model." - }, - { - "name": "datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime of the forecasted value (ISO 8601)" - }, - { - "name": "family", - "type": "string", - "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)." - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "name": "depth_m", - "type": "double", - "description": "depth (meters) in water column of prediction" - }, - { - "name": "observation", - "type": "double", - "description": "observed value for variable" - }, - { - "name": "crps", - "type": "double", - "description": "crps forecast score" - }, - { - "name": "logs", - "type": "double", - "description": "logs forecast score" - }, - { - "name": "mean", - "type": "double", - "description": "mean forecast prediction" - }, - { - "name": "median", - "type": "double", - "description": "median forecast prediction" - }, - { - "name": "sd", - "type": "double", - "description": "standard deviation forecasts" - }, - { - "name": "quantile97.5", - "type": "double", - "description": "upper 97.5 percentile value of forecast" - }, - { - "name": "quantile02.5", - "type": "double", - "description": "upper 2.5 percentile value of forecast" - }, - { - "name": "quantile90", - "type": "double", - "description": "upper 90 percentile value of forecast" - }, - { - "name": "quantile10", - "type": "double", - "description": "upper 10 percentile value of forecast" - }, - { - "name": "project_id", - "type": "string", - "description": "unique project identifier" - }, - { - "name": "duration", - "type": "string", - "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention" - }, - { - "name": "variable", - "type": "string", - "description": "name of forecasted variable" - }, - { - "name": "model_id", - "type": "string", - "description": "unique model identifier" - }, - { - "name": "date", - "type": "string", - "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period." - } - ] - }, - "collection": "scores", - "links": [ - { - "rel": "collection", - "href": "../collection.json", - "type": "application/json", - "title": "cfs" - }, - { - "rel": "root", - "href": "../../../catalog.json", - "type": "application/json", - "title": "Forecast Catalog" - }, - { - "rel": "parent", - "href": "../collection.json", - "type": "application/json", - "title": "cfs" - }, - { - "rel": "self", - "href": "cfs.json", - "type": "application/json", - "title": "Model Forecast" - } - ], - "assets": { - "1": { - "type": "application/json", - "title": "Model Metadata", - "href": "https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/cfs.json", - "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/cfs.json\")\n\n" - }, - "2": { - "type": "application/x-parquet", - "title": "Database Access for AirTemp_C_mean daily", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=AirTemp_C_mean/model_id=cfs?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=AirTemp_C_mean/model_id=cfs?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "3": { - "type": "application/x-parquet", - "title": "Database Access for RH_percent_mean daily", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=RH_percent_mean/model_id=cfs?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=RH_percent_mean/model_id=cfs?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "4": { - "type": "application/x-parquet", - "title": "Database Access for Rain_mm_sum daily", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=Rain_mm_sum/model_id=cfs?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=Rain_mm_sum/model_id=cfs?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "5": { - "type": "application/x-parquet", - "title": "Database Access for WindSpeed_ms_mean daily", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=WindSpeed_ms_mean/model_id=cfs?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=WindSpeed_ms_mean/model_id=cfs?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "6": { - "type": "application/x-parquet", - "title": "Database Access for ShortwaveRadiationUp_Wm2_mean daily", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=ShortwaveRadiationUp_Wm2_mean/model_id=cfs?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=ShortwaveRadiationUp_Wm2_mean/model_id=cfs?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - } - } -} diff --git a/catalog/scores/models/model_items/climatology.json b/catalog/scores/models/model_items/climatology.json deleted file mode 100644 index 490b1f7c08..0000000000 --- a/catalog/scores/models/model_items/climatology.json +++ /dev/null @@ -1,335 +0,0 @@ -{ - "stac_version": "1.0.0", - "stac_extensions": [ - "https://stac-extensions.github.io/table/v1.2.0/schema.json" - ], - "type": "Feature", - "id": "climatology", - "bbox": [ - [ - -156.6194, - 71.2824, - -66.7987, - 71.2824 - ] - ], - "geometry": { - "type": "MultiPoint", - "coordinates": [ - [-82.0084, 29.676], - [-87.7982, 32.5415], - [-84.4374, 31.1854], - [-82.0177, 29.6878], - [-88.1589, 31.8534], - [-122.3303, 45.7624], - [-71.2874, 44.0639], - [-78.0418, 39.0337], - [-97.57, 33.4012], - [-104.7456, 40.8155], - [-99.1066, 47.1617], - [-87.8039, 32.5417], - [-81.4362, 28.1251], - [-83.5019, 35.689], - [-66.8687, 17.9696], - [-72.1727, 42.5369], - [-84.4686, 31.1948], - [-106.8425, 32.5907], - [-96.6129, 39.1104], - [-96.5631, 39.1008], - [-67.0769, 18.0213], - [-88.1612, 31.8539], - [-80.5248, 37.3783], - [-109.3883, 38.2483], - [-105.5824, 40.0543], - [-100.9154, 46.7697], - [-99.0588, 35.4106], - [-112.4524, 40.1776], - [-84.2826, 35.9641], - [-81.9934, 29.6893], - [-155.3173, 19.5531], - [-105.546, 40.2759], - [-78.1395, 38.8929], - [-76.56, 38.8901], - [-119.7323, 37.1088], - [-119.2622, 37.0334], - [-110.8355, 31.9107], - [-89.5864, 45.5089], - [-103.0293, 40.4619], - [-87.3933, 32.9505], - [-119.006, 37.0058], - [-89.5857, 45.4937], - [-95.1921, 39.0404], - [-89.5373, 46.2339], - [-99.2413, 47.1282], - [-121.9519, 45.8205], - [-110.5391, 44.9535], - [-156.6194, 71.2824], - [-147.5026, 65.154], - [-145.7514, 63.8811], - [-149.2133, 63.8758], - [-149.3705, 68.6611], - [-102.4471, 39.7582], - [-119.2575, 37.0597], - [-110.5871, 44.9501], - [-96.6242, 34.4442], - [-105.5442, 40.035], - [-66.9868, 18.1135], - [-66.7987, 18.1741], - [-72.3295, 42.4719], - [-96.6038, 39.1051], - [-83.5038, 35.6904], - [-77.9832, 39.0956], - [-121.9338, 45.7908], - [-87.4077, 32.9604], - [-96.443, 38.9459], - [-122.1655, 44.2596], - [-78.1473, 38.8943], - [-97.7823, 33.3785], - [-111.7979, 40.7839], - [-111.5081, 33.751], - [-119.0274, 36.9559], - [-84.2793, 35.9574], - [-105.9154, 39.8914] - ] - }, - "properties": { - "description": ["\nmodel info: Historical DOY mean and sd. Assumes normal distribution\n\n\nSites: BARC, BLWA, FLNT, SUGG, TOMB, ABBY, BART, BLAN, CLBJ, CPER, DCFS, DELA, DSNY, GRSM, GUAN, HARV, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TREE, UKFS, UNDE, WOOD, WREF, YELL, BARR, BONA, DEJU, HEAL, TOOL, ARIK, BIGC, BLDE, BLUE, COMO, CUPE, GUIL, HOPB, KING, LECO, LEWI, MART, MAYF, MCDI, MCRA, POSE, PRIN, REDB, SYCA, TECR, WALK, WLOU\n\nVariables: Daily Chlorophyll_a, Daily Green_chromatic_coordinate, Daily latent_heat_flux, 30min latent_heat_flux, Daily Net_ecosystem_exchange, 30min Net_ecosystem_exchange, Daily Dissolved_oxygen, Daily Red_chromatic_coordinate, Daily Water_temperature", "\nmodel info: NA\n\nSites: BARC, BLWA, FLNT, SUGG, TOMB, ABBY, BART, BLAN, CLBJ, CPER, DCFS, DELA, DSNY, GRSM, GUAN, HARV, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TREE, UKFS, UNDE, WOOD, WREF, YELL, BARR, BONA, DEJU, HEAL, TOOL, ARIK, BIGC, BLDE, BLUE, COMO, CUPE, GUIL, HOPB, KING, LECO, LEWI, MART, MAYF, MCDI, MCRA, POSE, PRIN, REDB, SYCA, TECR, WALK, WLOU\n\nVariables: Daily Chlorophyll_a, Daily Green_chromatic_coordinate, Daily latent_heat_flux, 30min latent_heat_flux, Daily Net_ecosystem_exchange, 30min Net_ecosystem_exchange, Daily Dissolved_oxygen, Daily Red_chromatic_coordinate, Daily Water_temperature"], - "start_datetime": "2023-11-14", - "end_datetime": "2023-12-15", - "providers": [ - { - "url": "pending", - "name": "pending", - "roles": [ - "producer", - "processor", - "licensor" - ] - }, - { - "url": "https://www.ecoforecastprojectvt.org", - "name": "Ecoforecast Challenge", - "roles": [ - "host" - ] - } - ], - "license": "CC0-1.0", - "keywords": [ - "Forecasting", - "neon4cast", - "Daily Chlorophyll_a", - "Daily Green_chromatic_coordinate", - "Daily latent_heat_flux", - "30min latent_heat_flux", - "Daily Net_ecosystem_exchange", - "30min Net_ecosystem_exchange", - "Daily Dissolved_oxygen", - "Daily Red_chromatic_coordinate", - "Daily Water_temperature" - ], - "table:columns": [ - { - "name": "reference_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that the forecast was initiated (horizon = 0)" - }, - { - "name": "site_id", - "type": "string", - "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model." - }, - { - "name": "datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime of the forecasted value (ISO 8601)" - }, - { - "name": "family", - "type": "string", - "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)." - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "name": "observation", - "type": "double", - "description": "observed value for variable" - }, - { - "name": "crps", - "type": "double", - "description": "crps forecast score" - }, - { - "name": "logs", - "type": "double", - "description": "logs forecast score" - }, - { - "name": "mean", - "type": "double", - "description": "mean forecast prediction" - }, - { - "name": "median", - "type": "double", - "description": "median forecast prediction" - }, - { - "name": "sd", - "type": "double", - "description": "standard deviation forecasts" - }, - { - "name": "quantile97.5", - "type": "double", - "description": "upper 97.5 percentile value of forecast" - }, - { - "name": "quantile02.5", - "type": "double", - "description": "upper 2.5 percentile value of forecast" - }, - { - "name": "quantile90", - "type": "double", - "description": "upper 90 percentile value of forecast" - }, - { - "name": "quantile10", - "type": "double", - "description": "upper 10 percentile value of forecast" - }, - { - "name": "project_id", - "type": "string", - "description": "unique project identifier" - }, - { - "name": "duration", - "type": "string", - "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention" - }, - { - "name": "variable", - "type": "string", - "description": "name of forecasted variable" - }, - { - "name": "model_id", - "type": "string", - "description": "unique model identifier" - }, - { - "name": "date", - "type": "string", - "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period." - } - ] - }, - "collection": "scores", - "links": [ - { - "rel": "collection", - "href": "../collection.json", - "type": "application/json", - "title": "climatology" - }, - { - "rel": "root", - "href": "../../../catalog.json", - "type": "application/json", - "title": "Forecast Catalog" - }, - { - "rel": "parent", - "href": "../collection.json", - "type": "application/json", - "title": "climatology" - }, - { - "rel": "self", - "href": "climatology.json", - "type": "application/json", - "title": "Model Forecast" - }, - { - "rel": "item", - "href": "pending", - "type": "text/html", - "title": "Link for Model Code" - } - ], - "assets": { - "1": { - "type": "application/json", - "title": "Model Metadata", - "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/climatology.json", - "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/climatology.json\")\n\n" - }, - "2": { - "type": "text/html", - "title": "Link for Model Code", - "href": "pending", - "description": "The link to the model code provided by the model submission team" - }, - "3": { - "type": "application/x-parquet", - "title": "Database Access for Daily Chlorophyll_a", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=climatology?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=climatology?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "4": { - "type": "application/x-parquet", - "title": "Database Access for Daily Green_chromatic_coordinate", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=gcc_90/model_id=climatology?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=gcc_90/model_id=climatology?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "5": { - "type": "application/x-parquet", - "title": "Database Access for Daily latent_heat_flux", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=le/model_id=climatology?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=le/model_id=climatology?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "6": { - "type": "application/x-parquet", - "title": "Database Access for 30min latent_heat_flux", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=PT30M/variable=le/model_id=climatology?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=PT30M/variable=le/model_id=climatology?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "7": { - "type": "application/x-parquet", - "title": "Database Access for Daily Net_ecosystem_exchange", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=nee/model_id=climatology?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=nee/model_id=climatology?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "8": { - "type": "application/x-parquet", - "title": "Database Access for 30min Net_ecosystem_exchange", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=PT30M/variable=nee/model_id=climatology?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=PT30M/variable=nee/model_id=climatology?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "9": { - "type": "application/x-parquet", - "title": "Database Access for Daily Dissolved_oxygen", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=oxygen/model_id=climatology?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=oxygen/model_id=climatology?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "10": { - "type": "application/x-parquet", - "title": "Database Access for Daily Red_chromatic_coordinate", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=rcc_90/model_id=climatology?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=rcc_90/model_id=climatology?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "11": { - "type": "application/x-parquet", - "title": "Database Access for Daily Water_temperature", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=climatology?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=climatology?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - } - } -} diff --git a/catalog/scores/models/model_items/climatology2.json b/catalog/scores/models/model_items/climatology2.json deleted file mode 100644 index e2cd083bee..0000000000 --- a/catalog/scores/models/model_items/climatology2.json +++ /dev/null @@ -1,206 +0,0 @@ -{ - "stac_version": "1.0.0", - "stac_extensions": [ - "https://stac-extensions.github.io/table/v1.2.0/schema.json" - ], - "type": "Feature", - "id": "climatology2", - "bbox": [ - [ - -156.6194, - 71.2824, - -66.7987, - 71.2824 - ] - ], - "geometry": { - "type": "MultiPoint", - "coordinates": [ - [37.3129, -79.8159], - [37.3032, -79.8372] - ] - }, - "properties": { - "description": "\nmodel info: Same is the other climatology\n\nSites: bvre, fcre\n\nVariables: Chla_ugL_mean, Temp_C_mean", - "start_datetime": "2023-10-07", - "end_datetime": "2023-11-10", - "providers": [ - { - "url": "pending", - "name": "pending", - "roles": [ - "producer", - "processor", - "licensor" - ] - }, - { - "url": "https://www.ecoforecastprojectvt.org", - "name": "Ecoforecast Challenge", - "roles": [ - "host" - ] - } - ], - "license": "CC0-1.0", - "keywords": [ - "Forecasting", - "vera4cast", - "Chla_ugL_mean, Temp_C_mean" - ], - "table:columns": [ - { - "name": "reference_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that the forecast was initiated (horizon = 0)" - }, - { - "name": "site_id", - "type": "string", - "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model." - }, - { - "name": "datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime of the forecasted value (ISO 8601)" - }, - { - "name": "family", - "type": "string", - "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)." - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "name": "depth_m", - "type": "double", - "description": "depth (meters) in water column of prediction" - }, - { - "name": "observation", - "type": "double", - "description": "observed value for variable" - }, - { - "name": "crps", - "type": "double", - "description": "crps forecast score" - }, - { - "name": "logs", - "type": "double", - "description": "logs forecast score" - }, - { - "name": "mean", - "type": "double", - "description": "mean forecast prediction" - }, - { - "name": "median", - "type": "double", - "description": "median forecast prediction" - }, - { - "name": "sd", - "type": "double", - "description": "standard deviation forecasts" - }, - { - "name": "quantile97.5", - "type": "double", - "description": "upper 97.5 percentile value of forecast" - }, - { - "name": "quantile02.5", - "type": "double", - "description": "upper 2.5 percentile value of forecast" - }, - { - "name": "quantile90", - "type": "double", - "description": "upper 90 percentile value of forecast" - }, - { - "name": "quantile10", - "type": "double", - "description": "upper 10 percentile value of forecast" - }, - { - "name": "project_id", - "type": "string", - "description": "unique project identifier" - }, - { - "name": "duration", - "type": "string", - "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention" - }, - { - "name": "variable", - "type": "string", - "description": "name of forecasted variable" - }, - { - "name": "model_id", - "type": "string", - "description": "unique model identifier" - }, - { - "name": "date", - "type": "string", - "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period." - } - ] - }, - "collection": "scores", - "links": [ - { - "rel": "collection", - "href": "../collection.json", - "type": "application/json", - "title": "climatology2" - }, - { - "rel": "root", - "href": "../../../catalog.json", - "type": "application/json", - "title": "Forecast Catalog" - }, - { - "rel": "parent", - "href": "../collection.json", - "type": "application/json", - "title": "climatology2" - }, - { - "rel": "self", - "href": "climatology2.json", - "type": "application/json", - "title": "Model Forecast" - } - ], - "assets": { - "1": { - "type": "application/json", - "title": "Model Metadata", - "href": "https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/climatology2.json", - "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/climatology2.json\")\n\n" - }, - "2": { - "type": "application/x-parquet", - "title": "Database Access for Chla_ugL_mean daily", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=Chla_ugL_mean/model_id=climatology2?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=Chla_ugL_mean/model_id=climatology2?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "3": { - "type": "application/x-parquet", - "title": "Database Access for Temp_C_mean daily", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=Temp_C_mean/model_id=climatology2?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=Temp_C_mean/model_id=climatology2?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - } - } -} diff --git a/catalog/scores/models/model_items/ecmwf_ifs04.json b/catalog/scores/models/model_items/ecmwf_ifs04.json deleted file mode 100644 index 0416919ca0..0000000000 --- a/catalog/scores/models/model_items/ecmwf_ifs04.json +++ /dev/null @@ -1,223 +0,0 @@ -{ - "stac_version": "1.0.0", - "stac_extensions": [ - "https://stac-extensions.github.io/table/v1.2.0/schema.json" - ], - "type": "Feature", - "id": "ecmwf_ifs04", - "bbox": [ - [ - -156.6194, - 71.2824, - -66.7987, - 71.2824 - ] - ], - "geometry": { - "type": "MultiPoint", - "coordinates": [ - [37.3032, -79.8372] - ] - }, - "properties": { - "description": "\nmodel info: ECMWF IFS Ensemble weather model downloaded from open-meteo.com. Since ECMWF IFS Ensemble does output solar radiation on open-meteo.com, the solar radiation from GFS seamless is used.\n\nSites: fcre\n\nVariables: AirTemp_C_mean, RH_percent_mean, Rain_mm_sum, BP_kPa_mean, WindSpeed_ms_mean", - "start_datetime": "2023-10-14", - "end_datetime": "2023-11-08", - "providers": [ - { - "url": "pending", - "name": "pending", - "roles": [ - "producer", - "processor", - "licensor" - ] - }, - { - "url": "https://www.ecoforecastprojectvt.org", - "name": "Ecoforecast Challenge", - "roles": [ - "host" - ] - } - ], - "license": "CC0-1.0", - "keywords": [ - "Forecasting", - "vera4cast", - "AirTemp_C_mean, RH_percent_mean, Rain_mm_sum, BP_kPa_mean, WindSpeed_ms_mean" - ], - "table:columns": [ - { - "name": "reference_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that the forecast was initiated (horizon = 0)" - }, - { - "name": "site_id", - "type": "string", - "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model." - }, - { - "name": "datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime of the forecasted value (ISO 8601)" - }, - { - "name": "family", - "type": "string", - "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)." - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "name": "depth_m", - "type": "double", - "description": "depth (meters) in water column of prediction" - }, - { - "name": "observation", - "type": "double", - "description": "observed value for variable" - }, - { - "name": "crps", - "type": "double", - "description": "crps forecast score" - }, - { - "name": "logs", - "type": "double", - "description": "logs forecast score" - }, - { - "name": "mean", - "type": "double", - "description": "mean forecast prediction" - }, - { - "name": "median", - "type": "double", - "description": "median forecast prediction" - }, - { - "name": "sd", - "type": "double", - "description": "standard deviation forecasts" - }, - { - "name": "quantile97.5", - "type": "double", - "description": "upper 97.5 percentile value of forecast" - }, - { - "name": "quantile02.5", - "type": "double", - "description": "upper 2.5 percentile value of forecast" - }, - { - "name": "quantile90", - "type": "double", - "description": "upper 90 percentile value of forecast" - }, - { - "name": "quantile10", - "type": "double", - "description": "upper 10 percentile value of forecast" - }, - { - "name": "project_id", - "type": "string", - "description": "unique project identifier" - }, - { - "name": "duration", - "type": "string", - "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention" - }, - { - "name": "variable", - "type": "string", - "description": "name of forecasted variable" - }, - { - "name": "model_id", - "type": "string", - "description": "unique model identifier" - }, - { - "name": "date", - "type": "string", - "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period." - } - ] - }, - "collection": "scores", - "links": [ - { - "rel": "collection", - "href": "../collection.json", - "type": "application/json", - "title": "ecmwf_ifs04" - }, - { - "rel": "root", - "href": "../../../catalog.json", - "type": "application/json", - "title": "Forecast Catalog" - }, - { - "rel": "parent", - "href": "../collection.json", - "type": "application/json", - "title": "ecmwf_ifs04" - }, - { - "rel": "self", - "href": "ecmwf_ifs04.json", - "type": "application/json", - "title": "Model Forecast" - } - ], - "assets": { - "1": { - "type": "application/json", - "title": "Model Metadata", - "href": "https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/ecmwf_ifs04.json", - "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/ecmwf_ifs04.json\")\n\n" - }, - "2": { - "type": "application/x-parquet", - "title": "Database Access for AirTemp_C_mean daily", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=AirTemp_C_mean/model_id=ecmwf_ifs04?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=AirTemp_C_mean/model_id=ecmwf_ifs04?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "3": { - "type": "application/x-parquet", - "title": "Database Access for RH_percent_mean daily", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=RH_percent_mean/model_id=ecmwf_ifs04?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=RH_percent_mean/model_id=ecmwf_ifs04?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "4": { - "type": "application/x-parquet", - "title": "Database Access for Rain_mm_sum daily", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=Rain_mm_sum/model_id=ecmwf_ifs04?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=Rain_mm_sum/model_id=ecmwf_ifs04?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "5": { - "type": "application/x-parquet", - "title": "Database Access for BP_kPa_mean daily", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=BP_kPa_mean/model_id=ecmwf_ifs04?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=BP_kPa_mean/model_id=ecmwf_ifs04?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "6": { - "type": "application/x-parquet", - "title": "Database Access for WindSpeed_ms_mean daily", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=WindSpeed_ms_mean/model_id=ecmwf_ifs04?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=WindSpeed_ms_mean/model_id=ecmwf_ifs04?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - } - } -} diff --git a/catalog/scores/models/model_items/fARIMA.json b/catalog/scores/models/model_items/fARIMA.json deleted file mode 100644 index c3996fc398..0000000000 --- a/catalog/scores/models/model_items/fARIMA.json +++ /dev/null @@ -1,239 +0,0 @@ -{ - "stac_version": "1.0.0", - "stac_extensions": [ - "https://stac-extensions.github.io/table/v1.2.0/schema.json" - ], - "type": "Feature", - "id": "fARIMA", - "bbox": [ - [ - -156.6194, - 71.2824, - -66.7987, - 71.2824 - ] - ], - "geometry": { - "type": "MultiPoint", - "coordinates": [ - [-102.4471, 39.7582], - [-82.0084, 29.676], - [-119.2575, 37.0597], - [-110.5871, 44.9501], - [-96.6242, 34.4442], - [-87.7982, 32.5415], - [-147.504, 65.1532], - [-105.5442, 40.035], - [-89.4737, 46.2097], - [-66.9868, 18.1135], - [-84.4374, 31.1854], - [-66.7987, 18.1741], - [-72.3295, 42.4719], - [-96.6038, 39.1051], - [-83.5038, 35.6904], - [-77.9832, 39.0956], - [-89.7048, 45.9983], - [-121.9338, 45.7908], - [-87.4077, 32.9604], - [-96.443, 38.9459], - [-122.1655, 44.2596], - [-149.143, 68.6698], - [-78.1473, 38.8943], - [-97.7823, 33.3785], - [-99.1139, 47.1591], - [-99.2531, 47.1298], - [-111.7979, 40.7839], - [-82.0177, 29.6878], - [-111.5081, 33.751], - [-119.0274, 36.9559], - [-88.1589, 31.8534], - [-149.6106, 68.6307], - [-84.2793, 35.9574], - [-105.9154, 39.8914] - ] - }, - "properties": { - "description": "\nmodel info: NA\n\nSites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU\n\nVariables: Daily Water_temperature", - "start_datetime": "2023-11-10", - "end_datetime": "2023-12-15", - "providers": [ - { - "url": "pending", - "name": "pending", - "roles": [ - "producer", - "processor", - "licensor" - ] - }, - { - "url": "https://www.ecoforecastprojectvt.org", - "name": "Ecoforecast Challenge", - "roles": [ - "host" - ] - } - ], - "license": "CC0-1.0", - "keywords": [ - "Forecasting", - "neon4cast", - "Daily Water_temperature" - ], - "table:columns": [ - { - "name": "reference_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that the forecast was initiated (horizon = 0)" - }, - { - "name": "site_id", - "type": "string", - "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model." - }, - { - "name": "datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime of the forecasted value (ISO 8601)" - }, - { - "name": "family", - "type": "string", - "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)." - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "name": "observation", - "type": "double", - "description": "observed value for variable" - }, - { - "name": "crps", - "type": "double", - "description": "crps forecast score" - }, - { - "name": "logs", - "type": "double", - "description": "logs forecast score" - }, - { - "name": "mean", - "type": "double", - "description": "mean forecast prediction" - }, - { - "name": "median", - "type": "double", - "description": "median forecast prediction" - }, - { - "name": "sd", - "type": "double", - "description": "standard deviation forecasts" - }, - { - "name": "quantile97.5", - "type": "double", - "description": "upper 97.5 percentile value of forecast" - }, - { - "name": "quantile02.5", - "type": "double", - "description": "upper 2.5 percentile value of forecast" - }, - { - "name": "quantile90", - "type": "double", - "description": "upper 90 percentile value of forecast" - }, - { - "name": "quantile10", - "type": "double", - "description": "upper 10 percentile value of forecast" - }, - { - "name": "project_id", - "type": "string", - "description": "unique project identifier" - }, - { - "name": "duration", - "type": "string", - "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention" - }, - { - "name": "variable", - "type": "string", - "description": "name of forecasted variable" - }, - { - "name": "model_id", - "type": "string", - "description": "unique model identifier" - }, - { - "name": "date", - "type": "string", - "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period." - } - ] - }, - "collection": "scores", - "links": [ - { - "rel": "collection", - "href": "../collection.json", - "type": "application/json", - "title": "fARIMA" - }, - { - "rel": "root", - "href": "../../../catalog.json", - "type": "application/json", - "title": "Forecast Catalog" - }, - { - "rel": "parent", - "href": "../collection.json", - "type": "application/json", - "title": "fARIMA" - }, - { - "rel": "self", - "href": "fARIMA.json", - "type": "application/json", - "title": "Model Forecast" - }, - { - "rel": "item", - "href": "pending", - "type": "text/html", - "title": "Link for Model Code" - } - ], - "assets": { - "1": { - "type": "application/json", - "title": "Model Metadata", - "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/fARIMA.json", - "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/fARIMA.json\")\n\n" - }, - "2": { - "type": "text/html", - "title": "Link for Model Code", - "href": "pending", - "description": "The link to the model code provided by the model submission team" - }, - "3": { - "type": "application/x-parquet", - "title": "Database Access for Daily Water_temperature", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=fARIMA?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=fARIMA?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - } - } -} diff --git a/catalog/scores/models/model_items/fARIMA_clim_ensemble.json b/catalog/scores/models/model_items/fARIMA_clim_ensemble.json deleted file mode 100644 index 6afb2c8b29..0000000000 --- a/catalog/scores/models/model_items/fARIMA_clim_ensemble.json +++ /dev/null @@ -1,233 +0,0 @@ -{ - "stac_version": "1.0.0", - "stac_extensions": [ - "https://stac-extensions.github.io/table/v1.2.0/schema.json" - ], - "type": "Feature", - "id": "fARIMA_clim_ensemble", - "bbox": [ - [ - -156.6194, - 71.2824, - -66.7987, - 71.2824 - ] - ], - "geometry": { - "type": "MultiPoint", - "coordinates": [ - [-102.4471, 39.7582], - [-82.0084, 29.676], - [-110.5871, 44.9501], - [-96.6242, 34.4442], - [-87.7982, 32.5415], - [-105.5442, 40.035], - [-89.4737, 46.2097], - [-66.9868, 18.1135], - [-84.4374, 31.1854], - [-66.7987, 18.1741], - [-72.3295, 42.4719], - [-96.6038, 39.1051], - [-83.5038, 35.6904], - [-77.9832, 39.0956], - [-121.9338, 45.7908], - [-87.4077, 32.9604], - [-96.443, 38.9459], - [-78.1473, 38.8943], - [-97.7823, 33.3785], - [-111.7979, 40.7839], - [-82.0177, 29.6878], - [-111.5081, 33.751], - [-84.2793, 35.9574], - [-105.9154, 39.8914], - [-122.1655, 44.2596], - [-88.1589, 31.8534], - [-119.2575, 37.0597], - [-119.0274, 36.9559] - ] - }, - "properties": { - "description": "\nmodel info: NA\n\nSites: ARIK, BARC, BLDE, BLUE, BLWA, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, MART, MAYF, MCDI, POSE, PRIN, REDB, SUGG, SYCA, WALK, WLOU, MCRA, TOMB, BIGC, TECR\n\nVariables: Daily Water_temperature", - "start_datetime": "2023-11-10", - "end_datetime": "2023-12-15", - "providers": [ - { - "url": "pending", - "name": "pending", - "roles": [ - "producer", - "processor", - "licensor" - ] - }, - { - "url": "https://www.ecoforecastprojectvt.org", - "name": "Ecoforecast Challenge", - "roles": [ - "host" - ] - } - ], - "license": "CC0-1.0", - "keywords": [ - "Forecasting", - "neon4cast", - "Daily Water_temperature" - ], - "table:columns": [ - { - "name": "reference_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that the forecast was initiated (horizon = 0)" - }, - { - "name": "site_id", - "type": "string", - "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model." - }, - { - "name": "datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime of the forecasted value (ISO 8601)" - }, - { - "name": "family", - "type": "string", - "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)." - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "name": "observation", - "type": "double", - "description": "observed value for variable" - }, - { - "name": "crps", - "type": "double", - "description": "crps forecast score" - }, - { - "name": "logs", - "type": "double", - "description": "logs forecast score" - }, - { - "name": "mean", - "type": "double", - "description": "mean forecast prediction" - }, - { - "name": "median", - "type": "double", - "description": "median forecast prediction" - }, - { - "name": "sd", - "type": "double", - "description": "standard deviation forecasts" - }, - { - "name": "quantile97.5", - "type": "double", - "description": "upper 97.5 percentile value of forecast" - }, - { - "name": "quantile02.5", - "type": "double", - "description": "upper 2.5 percentile value of forecast" - }, - { - "name": "quantile90", - "type": "double", - "description": "upper 90 percentile value of forecast" - }, - { - "name": "quantile10", - "type": "double", - "description": "upper 10 percentile value of forecast" - }, - { - "name": "project_id", - "type": "string", - "description": "unique project identifier" - }, - { - "name": "duration", - "type": "string", - "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention" - }, - { - "name": "variable", - "type": "string", - "description": "name of forecasted variable" - }, - { - "name": "model_id", - "type": "string", - "description": "unique model identifier" - }, - { - "name": "date", - "type": "string", - "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period." - } - ] - }, - "collection": "scores", - "links": [ - { - "rel": "collection", - "href": "../collection.json", - "type": "application/json", - "title": "fARIMA_clim_ensemble" - }, - { - "rel": "root", - "href": "../../../catalog.json", - "type": "application/json", - "title": "Forecast Catalog" - }, - { - "rel": "parent", - "href": "../collection.json", - "type": "application/json", - "title": "fARIMA_clim_ensemble" - }, - { - "rel": "self", - "href": "fARIMA_clim_ensemble.json", - "type": "application/json", - "title": "Model Forecast" - }, - { - "rel": "item", - "href": "pending", - "type": "text/html", - "title": "Link for Model Code" - } - ], - "assets": { - "1": { - "type": "application/json", - "title": "Model Metadata", - "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/fARIMA_clim_ensemble.json", - "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/fARIMA_clim_ensemble.json\")\n\n" - }, - "2": { - "type": "text/html", - "title": "Link for Model Code", - "href": "pending", - "description": "The link to the model code provided by the model submission team" - }, - "3": { - "type": "application/x-parquet", - "title": "Database Access for Daily Water_temperature", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=fARIMA_clim_ensemble?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=fARIMA_clim_ensemble?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - } - } -} diff --git a/catalog/scores/models/model_items/fTSLM_lag.json b/catalog/scores/models/model_items/fTSLM_lag.json deleted file mode 100644 index 134cf7038b..0000000000 --- a/catalog/scores/models/model_items/fTSLM_lag.json +++ /dev/null @@ -1,239 +0,0 @@ -{ - "stac_version": "1.0.0", - "stac_extensions": [ - "https://stac-extensions.github.io/table/v1.2.0/schema.json" - ], - "type": "Feature", - "id": "fTSLM_lag", - "bbox": [ - [ - -156.6194, - 71.2824, - -66.7987, - 71.2824 - ] - ], - "geometry": { - "type": "MultiPoint", - "coordinates": [ - [-102.4471, 39.7582], - [-82.0084, 29.676], - [-119.2575, 37.0597], - [-110.5871, 44.9501], - [-96.6242, 34.4442], - [-87.7982, 32.5415], - [-147.504, 65.1532], - [-105.5442, 40.035], - [-89.4737, 46.2097], - [-66.9868, 18.1135], - [-84.4374, 31.1854], - [-66.7987, 18.1741], - [-72.3295, 42.4719], - [-96.6038, 39.1051], - [-83.5038, 35.6904], - [-77.9832, 39.0956], - [-89.7048, 45.9983], - [-121.9338, 45.7908], - [-87.4077, 32.9604], - [-96.443, 38.9459], - [-122.1655, 44.2596], - [-149.143, 68.6698], - [-78.1473, 38.8943], - [-97.7823, 33.3785], - [-99.1139, 47.1591], - [-99.2531, 47.1298], - [-111.7979, 40.7839], - [-82.0177, 29.6878], - [-111.5081, 33.751], - [-119.0274, 36.9559], - [-88.1589, 31.8534], - [-149.6106, 68.6307], - [-84.2793, 35.9574], - [-105.9154, 39.8914] - ] - }, - "properties": { - "description": "\nmodel info: NA\n\nSites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU\n\nVariables: Daily Water_temperature", - "start_datetime": "2023-11-10", - "end_datetime": "2023-12-15", - "providers": [ - { - "url": "pending", - "name": "pending", - "roles": [ - "producer", - "processor", - "licensor" - ] - }, - { - "url": "https://www.ecoforecastprojectvt.org", - "name": "Ecoforecast Challenge", - "roles": [ - "host" - ] - } - ], - "license": "CC0-1.0", - "keywords": [ - "Forecasting", - "neon4cast", - "Daily Water_temperature" - ], - "table:columns": [ - { - "name": "reference_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that the forecast was initiated (horizon = 0)" - }, - { - "name": "site_id", - "type": "string", - "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model." - }, - { - "name": "datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime of the forecasted value (ISO 8601)" - }, - { - "name": "family", - "type": "string", - "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)." - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "name": "observation", - "type": "double", - "description": "observed value for variable" - }, - { - "name": "crps", - "type": "double", - "description": "crps forecast score" - }, - { - "name": "logs", - "type": "double", - "description": "logs forecast score" - }, - { - "name": "mean", - "type": "double", - "description": "mean forecast prediction" - }, - { - "name": "median", - "type": "double", - "description": "median forecast prediction" - }, - { - "name": "sd", - "type": "double", - "description": "standard deviation forecasts" - }, - { - "name": "quantile97.5", - "type": "double", - "description": "upper 97.5 percentile value of forecast" - }, - { - "name": "quantile02.5", - "type": "double", - "description": "upper 2.5 percentile value of forecast" - }, - { - "name": "quantile90", - "type": "double", - "description": "upper 90 percentile value of forecast" - }, - { - "name": "quantile10", - "type": "double", - "description": "upper 10 percentile value of forecast" - }, - { - "name": "project_id", - "type": "string", - "description": "unique project identifier" - }, - { - "name": "duration", - "type": "string", - "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention" - }, - { - "name": "variable", - "type": "string", - "description": "name of forecasted variable" - }, - { - "name": "model_id", - "type": "string", - "description": "unique model identifier" - }, - { - "name": "date", - "type": "string", - "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period." - } - ] - }, - "collection": "scores", - "links": [ - { - "rel": "collection", - "href": "../collection.json", - "type": "application/json", - "title": "fTSLM_lag" - }, - { - "rel": "root", - "href": "../../../catalog.json", - "type": "application/json", - "title": "Forecast Catalog" - }, - { - "rel": "parent", - "href": "../collection.json", - "type": "application/json", - "title": "fTSLM_lag" - }, - { - "rel": "self", - "href": "fTSLM_lag.json", - "type": "application/json", - "title": "Model Forecast" - }, - { - "rel": "item", - "href": "pending", - "type": "text/html", - "title": "Link for Model Code" - } - ], - "assets": { - "1": { - "type": "application/json", - "title": "Model Metadata", - "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/fTSLM_lag.json", - "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/fTSLM_lag.json\")\n\n" - }, - "2": { - "type": "text/html", - "title": "Link for Model Code", - "href": "pending", - "description": "The link to the model code provided by the model submission team" - }, - "3": { - "type": "application/x-parquet", - "title": "Database Access for Daily Water_temperature", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=fTSLM_lag?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=fTSLM_lag?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - } - } -} diff --git a/catalog/scores/models/model_items/fableARIMA.json b/catalog/scores/models/model_items/fableARIMA.json deleted file mode 100644 index 66c6cd32d1..0000000000 --- a/catalog/scores/models/model_items/fableARIMA.json +++ /dev/null @@ -1,206 +0,0 @@ -{ - "stac_version": "1.0.0", - "stac_extensions": [ - "https://stac-extensions.github.io/table/v1.2.0/schema.json" - ], - "type": "Feature", - "id": "fableARIMA", - "bbox": [ - [ - -156.6194, - 71.2824, - -66.7987, - 71.2824 - ] - ], - "geometry": { - "type": "MultiPoint", - "coordinates": [ - [37.3032, -79.8372], - [37.3129, -79.8159] - ] - }, - "properties": { - "description": "\nmodel info: ARIMA fit using the ARIMA() function in the fable R package\n\nSites: fcre, bvre\n\nVariables: Chla_ugL_mean, Bloom_binary_mean", - "start_datetime": "2023-10-12", - "end_datetime": "2023-12-04", - "providers": [ - { - "url": "pending", - "name": "pending", - "roles": [ - "producer", - "processor", - "licensor" - ] - }, - { - "url": "https://www.ecoforecastprojectvt.org", - "name": "Ecoforecast Challenge", - "roles": [ - "host" - ] - } - ], - "license": "CC0-1.0", - "keywords": [ - "Forecasting", - "vera4cast", - "Chla_ugL_mean, Bloom_binary_mean" - ], - "table:columns": [ - { - "name": "reference_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that the forecast was initiated (horizon = 0)" - }, - { - "name": "site_id", - "type": "string", - "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model." - }, - { - "name": "datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime of the forecasted value (ISO 8601)" - }, - { - "name": "family", - "type": "string", - "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)." - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "name": "depth_m", - "type": "double", - "description": "depth (meters) in water column of prediction" - }, - { - "name": "observation", - "type": "double", - "description": "observed value for variable" - }, - { - "name": "crps", - "type": "double", - "description": "crps forecast score" - }, - { - "name": "logs", - "type": "double", - "description": "logs forecast score" - }, - { - "name": "mean", - "type": "double", - "description": "mean forecast prediction" - }, - { - "name": "median", - "type": "double", - "description": "median forecast prediction" - }, - { - "name": "sd", - "type": "double", - "description": "standard deviation forecasts" - }, - { - "name": "quantile97.5", - "type": "double", - "description": "upper 97.5 percentile value of forecast" - }, - { - "name": "quantile02.5", - "type": "double", - "description": "upper 2.5 percentile value of forecast" - }, - { - "name": "quantile90", - "type": "double", - "description": "upper 90 percentile value of forecast" - }, - { - "name": "quantile10", - "type": "double", - "description": "upper 10 percentile value of forecast" - }, - { - "name": "project_id", - "type": "string", - "description": "unique project identifier" - }, - { - "name": "duration", - "type": "string", - "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention" - }, - { - "name": "variable", - "type": "string", - "description": "name of forecasted variable" - }, - { - "name": "model_id", - "type": "string", - "description": "unique model identifier" - }, - { - "name": "date", - "type": "string", - "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period." - } - ] - }, - "collection": "scores", - "links": [ - { - "rel": "collection", - "href": "../collection.json", - "type": "application/json", - "title": "fableARIMA" - }, - { - "rel": "root", - "href": "../../../catalog.json", - "type": "application/json", - "title": "Forecast Catalog" - }, - { - "rel": "parent", - "href": "../collection.json", - "type": "application/json", - "title": "fableARIMA" - }, - { - "rel": "self", - "href": "fableARIMA.json", - "type": "application/json", - "title": "Model Forecast" - } - ], - "assets": { - "1": { - "type": "application/json", - "title": "Model Metadata", - "href": "https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/fableARIMA.json", - "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/fableARIMA.json\")\n\n" - }, - "2": { - "type": "application/x-parquet", - "title": "Database Access for Chla_ugL_mean daily", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=Chla_ugL_mean/model_id=fableARIMA?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=Chla_ugL_mean/model_id=fableARIMA?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "3": { - "type": "application/x-parquet", - "title": "Database Access for Bloom_binary_mean daily", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=Bloom_binary_mean/model_id=fableARIMA?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=Bloom_binary_mean/model_id=fableARIMA?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - } - } -} diff --git a/catalog/scores/models/model_items/fableETS.json b/catalog/scores/models/model_items/fableETS.json deleted file mode 100644 index 80fd415961..0000000000 --- a/catalog/scores/models/model_items/fableETS.json +++ /dev/null @@ -1,206 +0,0 @@ -{ - "stac_version": "1.0.0", - "stac_extensions": [ - "https://stac-extensions.github.io/table/v1.2.0/schema.json" - ], - "type": "Feature", - "id": "fableETS", - "bbox": [ - [ - -156.6194, - 71.2824, - -66.7987, - 71.2824 - ] - ], - "geometry": { - "type": "MultiPoint", - "coordinates": [ - [37.3129, -79.8159], - [37.3032, -79.8372] - ] - }, - "properties": { - "description": "\nmodel info: fable package exponential smoothing model fable::ETS()\n\nSites: bvre, fcre\n\nVariables: Chla_ugL_mean, Bloom_binary_mean", - "start_datetime": "2023-10-12", - "end_datetime": "2023-12-04", - "providers": [ - { - "url": "pending", - "name": "pending", - "roles": [ - "producer", - "processor", - "licensor" - ] - }, - { - "url": "https://www.ecoforecastprojectvt.org", - "name": "Ecoforecast Challenge", - "roles": [ - "host" - ] - } - ], - "license": "CC0-1.0", - "keywords": [ - "Forecasting", - "vera4cast", - "Chla_ugL_mean, Bloom_binary_mean" - ], - "table:columns": [ - { - "name": "reference_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that the forecast was initiated (horizon = 0)" - }, - { - "name": "site_id", - "type": "string", - "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model." - }, - { - "name": "datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime of the forecasted value (ISO 8601)" - }, - { - "name": "family", - "type": "string", - "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)." - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "name": "depth_m", - "type": "double", - "description": "depth (meters) in water column of prediction" - }, - { - "name": "observation", - "type": "double", - "description": "observed value for variable" - }, - { - "name": "crps", - "type": "double", - "description": "crps forecast score" - }, - { - "name": "logs", - "type": "double", - "description": "logs forecast score" - }, - { - "name": "mean", - "type": "double", - "description": "mean forecast prediction" - }, - { - "name": "median", - "type": "double", - "description": "median forecast prediction" - }, - { - "name": "sd", - "type": "double", - "description": "standard deviation forecasts" - }, - { - "name": "quantile97.5", - "type": "double", - "description": "upper 97.5 percentile value of forecast" - }, - { - "name": "quantile02.5", - "type": "double", - "description": "upper 2.5 percentile value of forecast" - }, - { - "name": "quantile90", - "type": "double", - "description": "upper 90 percentile value of forecast" - }, - { - "name": "quantile10", - "type": "double", - "description": "upper 10 percentile value of forecast" - }, - { - "name": "project_id", - "type": "string", - "description": "unique project identifier" - }, - { - "name": "duration", - "type": "string", - "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention" - }, - { - "name": "variable", - "type": "string", - "description": "name of forecasted variable" - }, - { - "name": "model_id", - "type": "string", - "description": "unique model identifier" - }, - { - "name": "date", - "type": "string", - "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period." - } - ] - }, - "collection": "scores", - "links": [ - { - "rel": "collection", - "href": "../collection.json", - "type": "application/json", - "title": "fableETS" - }, - { - "rel": "root", - "href": "../../../catalog.json", - "type": "application/json", - "title": "Forecast Catalog" - }, - { - "rel": "parent", - "href": "../collection.json", - "type": "application/json", - "title": "fableETS" - }, - { - "rel": "self", - "href": "fableETS.json", - "type": "application/json", - "title": "Model Forecast" - } - ], - "assets": { - "1": { - "type": "application/json", - "title": "Model Metadata", - "href": "https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/fableETS.json", - "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/fableETS.json\")\n\n" - }, - "2": { - "type": "application/x-parquet", - "title": "Database Access for Chla_ugL_mean daily", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=Chla_ugL_mean/model_id=fableETS?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=Chla_ugL_mean/model_id=fableETS?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "3": { - "type": "application/x-parquet", - "title": "Database Access for Bloom_binary_mean daily", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=Bloom_binary_mean/model_id=fableETS?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=Bloom_binary_mean/model_id=fableETS?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - } - } -} diff --git a/catalog/scores/models/model_items/fableNNETAR.json b/catalog/scores/models/model_items/fableNNETAR.json deleted file mode 100644 index a419febdf6..0000000000 --- a/catalog/scores/models/model_items/fableNNETAR.json +++ /dev/null @@ -1,206 +0,0 @@ -{ - "stac_version": "1.0.0", - "stac_extensions": [ - "https://stac-extensions.github.io/table/v1.2.0/schema.json" - ], - "type": "Feature", - "id": "fableNNETAR", - "bbox": [ - [ - -156.6194, - 71.2824, - -66.7987, - 71.2824 - ] - ], - "geometry": { - "type": "MultiPoint", - "coordinates": [ - [37.3129, -79.8159], - [37.3032, -79.8372] - ] - }, - "properties": { - "description": "\nmodel info: autoregressive neural net fit using the NNETAR() function in the fable R package\n\nSites: bvre, fcre\n\nVariables: Bloom_binary_mean, Chla_ugL_mean", - "start_datetime": "2023-10-12", - "end_datetime": "2023-12-04", - "providers": [ - { - "url": "pending", - "name": "pending", - "roles": [ - "producer", - "processor", - "licensor" - ] - }, - { - "url": "https://www.ecoforecastprojectvt.org", - "name": "Ecoforecast Challenge", - "roles": [ - "host" - ] - } - ], - "license": "CC0-1.0", - "keywords": [ - "Forecasting", - "vera4cast", - "Bloom_binary_mean, Chla_ugL_mean" - ], - "table:columns": [ - { - "name": "reference_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that the forecast was initiated (horizon = 0)" - }, - { - "name": "site_id", - "type": "string", - "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model." - }, - { - "name": "datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime of the forecasted value (ISO 8601)" - }, - { - "name": "family", - "type": "string", - "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)." - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "name": "depth_m", - "type": "double", - "description": "depth (meters) in water column of prediction" - }, - { - "name": "observation", - "type": "double", - "description": "observed value for variable" - }, - { - "name": "crps", - "type": "double", - "description": "crps forecast score" - }, - { - "name": "logs", - "type": "double", - "description": "logs forecast score" - }, - { - "name": "mean", - "type": "double", - "description": "mean forecast prediction" - }, - { - "name": "median", - "type": "double", - "description": "median forecast prediction" - }, - { - "name": "sd", - "type": "double", - "description": "standard deviation forecasts" - }, - { - "name": "quantile97.5", - "type": "double", - "description": "upper 97.5 percentile value of forecast" - }, - { - "name": "quantile02.5", - "type": "double", - "description": "upper 2.5 percentile value of forecast" - }, - { - "name": "quantile90", - "type": "double", - "description": "upper 90 percentile value of forecast" - }, - { - "name": "quantile10", - "type": "double", - "description": "upper 10 percentile value of forecast" - }, - { - "name": "project_id", - "type": "string", - "description": "unique project identifier" - }, - { - "name": "duration", - "type": "string", - "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention" - }, - { - "name": "variable", - "type": "string", - "description": "name of forecasted variable" - }, - { - "name": "model_id", - "type": "string", - "description": "unique model identifier" - }, - { - "name": "date", - "type": "string", - "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period." - } - ] - }, - "collection": "scores", - "links": [ - { - "rel": "collection", - "href": "../collection.json", - "type": "application/json", - "title": "fableNNETAR" - }, - { - "rel": "root", - "href": "../../../catalog.json", - "type": "application/json", - "title": "Forecast Catalog" - }, - { - "rel": "parent", - "href": "../collection.json", - "type": "application/json", - "title": "fableNNETAR" - }, - { - "rel": "self", - "href": "fableNNETAR.json", - "type": "application/json", - "title": "Model Forecast" - } - ], - "assets": { - "1": { - "type": "application/json", - "title": "Model Metadata", - "href": "https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/fableNNETAR.json", - "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/fableNNETAR.json\")\n\n" - }, - "2": { - "type": "application/x-parquet", - "title": "Database Access for Bloom_binary_mean daily", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=Bloom_binary_mean/model_id=fableNNETAR?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=Bloom_binary_mean/model_id=fableNNETAR?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "3": { - "type": "application/x-parquet", - "title": "Database Access for Chla_ugL_mean daily", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=Chla_ugL_mean/model_id=fableNNETAR?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=Chla_ugL_mean/model_id=fableNNETAR?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - } - } -} diff --git a/catalog/scores/models/model_items/flareGLM.json b/catalog/scores/models/model_items/flareGLM.json deleted file mode 100644 index 1bd2d79a12..0000000000 --- a/catalog/scores/models/model_items/flareGLM.json +++ /dev/null @@ -1,212 +0,0 @@ -{ - "stac_version": "1.0.0", - "stac_extensions": [ - "https://stac-extensions.github.io/table/v1.2.0/schema.json" - ], - "type": "Feature", - "id": "flareGLM", - "bbox": [ - [ - -156.6194, - 71.2824, - -66.7987, - 71.2824 - ] - ], - "geometry": { - "type": "MultiPoint", - "coordinates": [ - [-82.0084, 29.676], - [-89.4737, 46.2097], - [-89.7048, 45.9983], - [-99.1139, 47.1591], - [-99.2531, 47.1298], - [-82.0177, 29.6878], - [-149.6106, 68.6307] - ] - }, - "properties": { - "description": "\nmodel info: NA\n\nSites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK\n\nVariables: Daily Water_temperature", - "start_datetime": "2023-11-15", - "end_datetime": "2023-12-15", - "providers": [ - { - "url": "pending", - "name": "pending", - "roles": [ - "producer", - "processor", - "licensor" - ] - }, - { - "url": "https://www.ecoforecastprojectvt.org", - "name": "Ecoforecast Challenge", - "roles": [ - "host" - ] - } - ], - "license": "CC0-1.0", - "keywords": [ - "Forecasting", - "neon4cast", - "Daily Water_temperature" - ], - "table:columns": [ - { - "name": "reference_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that the forecast was initiated (horizon = 0)" - }, - { - "name": "site_id", - "type": "string", - "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model." - }, - { - "name": "datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime of the forecasted value (ISO 8601)" - }, - { - "name": "family", - "type": "string", - "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)." - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "name": "observation", - "type": "double", - "description": "observed value for variable" - }, - { - "name": "crps", - "type": "double", - "description": "crps forecast score" - }, - { - "name": "logs", - "type": "double", - "description": "logs forecast score" - }, - { - "name": "mean", - "type": "double", - "description": "mean forecast prediction" - }, - { - "name": "median", - "type": "double", - "description": "median forecast prediction" - }, - { - "name": "sd", - "type": "double", - "description": "standard deviation forecasts" - }, - { - "name": "quantile97.5", - "type": "double", - "description": "upper 97.5 percentile value of forecast" - }, - { - "name": "quantile02.5", - "type": "double", - "description": "upper 2.5 percentile value of forecast" - }, - { - "name": "quantile90", - "type": "double", - "description": "upper 90 percentile value of forecast" - }, - { - "name": "quantile10", - "type": "double", - "description": "upper 10 percentile value of forecast" - }, - { - "name": "project_id", - "type": "string", - "description": "unique project identifier" - }, - { - "name": "duration", - "type": "string", - "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention" - }, - { - "name": "variable", - "type": "string", - "description": "name of forecasted variable" - }, - { - "name": "model_id", - "type": "string", - "description": "unique model identifier" - }, - { - "name": "date", - "type": "string", - "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period." - } - ] - }, - "collection": "scores", - "links": [ - { - "rel": "collection", - "href": "../collection.json", - "type": "application/json", - "title": "flareGLM" - }, - { - "rel": "root", - "href": "../../../catalog.json", - "type": "application/json", - "title": "Forecast Catalog" - }, - { - "rel": "parent", - "href": "../collection.json", - "type": "application/json", - "title": "flareGLM" - }, - { - "rel": "self", - "href": "flareGLM.json", - "type": "application/json", - "title": "Model Forecast" - }, - { - "rel": "item", - "href": "pending", - "type": "text/html", - "title": "Link for Model Code" - } - ], - "assets": { - "1": { - "type": "application/json", - "title": "Model Metadata", - "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/flareGLM.json", - "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/flareGLM.json\")\n\n" - }, - "2": { - "type": "text/html", - "title": "Link for Model Code", - "href": "pending", - "description": "The link to the model code provided by the model submission team" - }, - "3": { - "type": "application/x-parquet", - "title": "Database Access for Daily Water_temperature", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=flareGLM?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=flareGLM?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - } - } -} diff --git a/catalog/scores/models/model_items/flareGLM_noDA.json b/catalog/scores/models/model_items/flareGLM_noDA.json deleted file mode 100644 index 5e785d737e..0000000000 --- a/catalog/scores/models/model_items/flareGLM_noDA.json +++ /dev/null @@ -1,212 +0,0 @@ -{ - "stac_version": "1.0.0", - "stac_extensions": [ - "https://stac-extensions.github.io/table/v1.2.0/schema.json" - ], - "type": "Feature", - "id": "flareGLM_noDA", - "bbox": [ - [ - -156.6194, - 71.2824, - -66.7987, - 71.2824 - ] - ], - "geometry": { - "type": "MultiPoint", - "coordinates": [ - [-82.0084, 29.676], - [-89.4737, 46.2097], - [-89.7048, 45.9983], - [-99.1139, 47.1591], - [-99.2531, 47.1298], - [-82.0177, 29.6878], - [-149.6106, 68.6307] - ] - }, - "properties": { - "description": "\nmodel info: NA\n\nSites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK\n\nVariables: Daily Water_temperature", - "start_datetime": "2023-11-15", - "end_datetime": "2023-12-15", - "providers": [ - { - "url": "pending", - "name": "pending", - "roles": [ - "producer", - "processor", - "licensor" - ] - }, - { - "url": "https://www.ecoforecastprojectvt.org", - "name": "Ecoforecast Challenge", - "roles": [ - "host" - ] - } - ], - "license": "CC0-1.0", - "keywords": [ - "Forecasting", - "neon4cast", - "Daily Water_temperature" - ], - "table:columns": [ - { - "name": "reference_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that the forecast was initiated (horizon = 0)" - }, - { - "name": "site_id", - "type": "string", - "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model." - }, - { - "name": "datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime of the forecasted value (ISO 8601)" - }, - { - "name": "family", - "type": "string", - "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)." - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "name": "observation", - "type": "double", - "description": "observed value for variable" - }, - { - "name": "crps", - "type": "double", - "description": "crps forecast score" - }, - { - "name": "logs", - "type": "double", - "description": "logs forecast score" - }, - { - "name": "mean", - "type": "double", - "description": "mean forecast prediction" - }, - { - "name": "median", - "type": "double", - "description": "median forecast prediction" - }, - { - "name": "sd", - "type": "double", - "description": "standard deviation forecasts" - }, - { - "name": "quantile97.5", - "type": "double", - "description": "upper 97.5 percentile value of forecast" - }, - { - "name": "quantile02.5", - "type": "double", - "description": "upper 2.5 percentile value of forecast" - }, - { - "name": "quantile90", - "type": "double", - "description": "upper 90 percentile value of forecast" - }, - { - "name": "quantile10", - "type": "double", - "description": "upper 10 percentile value of forecast" - }, - { - "name": "project_id", - "type": "string", - "description": "unique project identifier" - }, - { - "name": "duration", - "type": "string", - "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention" - }, - { - "name": "variable", - "type": "string", - "description": "name of forecasted variable" - }, - { - "name": "model_id", - "type": "string", - "description": "unique model identifier" - }, - { - "name": "date", - "type": "string", - "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period." - } - ] - }, - "collection": "scores", - "links": [ - { - "rel": "collection", - "href": "../collection.json", - "type": "application/json", - "title": "flareGLM_noDA" - }, - { - "rel": "root", - "href": "../../../catalog.json", - "type": "application/json", - "title": "Forecast Catalog" - }, - { - "rel": "parent", - "href": "../collection.json", - "type": "application/json", - "title": "flareGLM_noDA" - }, - { - "rel": "self", - "href": "flareGLM_noDA.json", - "type": "application/json", - "title": "Model Forecast" - }, - { - "rel": "item", - "href": "pending", - "type": "text/html", - "title": "Link for Model Code" - } - ], - "assets": { - "1": { - "type": "application/json", - "title": "Model Metadata", - "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/flareGLM_noDA.json", - "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/flareGLM_noDA.json\")\n\n" - }, - "2": { - "type": "text/html", - "title": "Link for Model Code", - "href": "pending", - "description": "The link to the model code provided by the model submission team" - }, - "3": { - "type": "application/x-parquet", - "title": "Database Access for Daily Water_temperature", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=flareGLM_noDA?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=flareGLM_noDA?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - } - } -} diff --git a/catalog/scores/models/model_items/flareGOTM.json b/catalog/scores/models/model_items/flareGOTM.json deleted file mode 100644 index c7c18a1b4b..0000000000 --- a/catalog/scores/models/model_items/flareGOTM.json +++ /dev/null @@ -1,211 +0,0 @@ -{ - "stac_version": "1.0.0", - "stac_extensions": [ - "https://stac-extensions.github.io/table/v1.2.0/schema.json" - ], - "type": "Feature", - "id": "flareGOTM", - "bbox": [ - [ - -156.6194, - 71.2824, - -66.7987, - 71.2824 - ] - ], - "geometry": { - "type": "MultiPoint", - "coordinates": [ - [-82.0084, 29.676], - [-89.4737, 46.2097], - [-89.7048, 45.9983], - [-99.1139, 47.1591], - [-99.2531, 47.1298], - [-82.0177, 29.6878] - ] - }, - "properties": { - "description": "\nmodel info: NA\n\nSites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG\n\nVariables: Daily Water_temperature", - "start_datetime": "2023-11-15", - "end_datetime": "2023-12-15", - "providers": [ - { - "url": "pending", - "name": "pending", - "roles": [ - "producer", - "processor", - "licensor" - ] - }, - { - "url": "https://www.ecoforecastprojectvt.org", - "name": "Ecoforecast Challenge", - "roles": [ - "host" - ] - } - ], - "license": "CC0-1.0", - "keywords": [ - "Forecasting", - "neon4cast", - "Daily Water_temperature" - ], - "table:columns": [ - { - "name": "reference_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that the forecast was initiated (horizon = 0)" - }, - { - "name": "site_id", - "type": "string", - "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model." - }, - { - "name": "datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime of the forecasted value (ISO 8601)" - }, - { - "name": "family", - "type": "string", - "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)." - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "name": "observation", - "type": "double", - "description": "observed value for variable" - }, - { - "name": "crps", - "type": "double", - "description": "crps forecast score" - }, - { - "name": "logs", - "type": "double", - "description": "logs forecast score" - }, - { - "name": "mean", - "type": "double", - "description": "mean forecast prediction" - }, - { - "name": "median", - "type": "double", - "description": "median forecast prediction" - }, - { - "name": "sd", - "type": "double", - "description": "standard deviation forecasts" - }, - { - "name": "quantile97.5", - "type": "double", - "description": "upper 97.5 percentile value of forecast" - }, - { - "name": "quantile02.5", - "type": "double", - "description": "upper 2.5 percentile value of forecast" - }, - { - "name": "quantile90", - "type": "double", - "description": "upper 90 percentile value of forecast" - }, - { - "name": "quantile10", - "type": "double", - "description": "upper 10 percentile value of forecast" - }, - { - "name": "project_id", - "type": "string", - "description": "unique project identifier" - }, - { - "name": "duration", - "type": "string", - "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention" - }, - { - "name": "variable", - "type": "string", - "description": "name of forecasted variable" - }, - { - "name": "model_id", - "type": "string", - "description": "unique model identifier" - }, - { - "name": "date", - "type": "string", - "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period." - } - ] - }, - "collection": "scores", - "links": [ - { - "rel": "collection", - "href": "../collection.json", - "type": "application/json", - "title": "flareGOTM" - }, - { - "rel": "root", - "href": "../../../catalog.json", - "type": "application/json", - "title": "Forecast Catalog" - }, - { - "rel": "parent", - "href": "../collection.json", - "type": "application/json", - "title": "flareGOTM" - }, - { - "rel": "self", - "href": "flareGOTM.json", - "type": "application/json", - "title": "Model Forecast" - }, - { - "rel": "item", - "href": "pending", - "type": "text/html", - "title": "Link for Model Code" - } - ], - "assets": { - "1": { - "type": "application/json", - "title": "Model Metadata", - "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/flareGOTM.json", - "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/flareGOTM.json\")\n\n" - }, - "2": { - "type": "text/html", - "title": "Link for Model Code", - "href": "pending", - "description": "The link to the model code provided by the model submission team" - }, - "3": { - "type": "application/x-parquet", - "title": "Database Access for Daily Water_temperature", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=flareGOTM?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=flareGOTM?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - } - } -} diff --git a/catalog/scores/models/model_items/flareGOTM_noDA.json b/catalog/scores/models/model_items/flareGOTM_noDA.json deleted file mode 100644 index d991c1fb86..0000000000 --- a/catalog/scores/models/model_items/flareGOTM_noDA.json +++ /dev/null @@ -1,211 +0,0 @@ -{ - "stac_version": "1.0.0", - "stac_extensions": [ - "https://stac-extensions.github.io/table/v1.2.0/schema.json" - ], - "type": "Feature", - "id": "flareGOTM_noDA", - "bbox": [ - [ - -156.6194, - 71.2824, - -66.7987, - 71.2824 - ] - ], - "geometry": { - "type": "MultiPoint", - "coordinates": [ - [-82.0084, 29.676], - [-89.4737, 46.2097], - [-89.7048, 45.9983], - [-99.1139, 47.1591], - [-99.2531, 47.1298], - [-82.0177, 29.6878] - ] - }, - "properties": { - "description": "\nmodel info: NA\n\nSites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG\n\nVariables: Daily Water_temperature", - "start_datetime": "2023-11-15", - "end_datetime": "2023-12-15", - "providers": [ - { - "url": "pending", - "name": "pending", - "roles": [ - "producer", - "processor", - "licensor" - ] - }, - { - "url": "https://www.ecoforecastprojectvt.org", - "name": "Ecoforecast Challenge", - "roles": [ - "host" - ] - } - ], - "license": "CC0-1.0", - "keywords": [ - "Forecasting", - "neon4cast", - "Daily Water_temperature" - ], - "table:columns": [ - { - "name": "reference_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that the forecast was initiated (horizon = 0)" - }, - { - "name": "site_id", - "type": "string", - "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model." - }, - { - "name": "datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime of the forecasted value (ISO 8601)" - }, - { - "name": "family", - "type": "string", - "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)." - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "name": "observation", - "type": "double", - "description": "observed value for variable" - }, - { - "name": "crps", - "type": "double", - "description": "crps forecast score" - }, - { - "name": "logs", - "type": "double", - "description": "logs forecast score" - }, - { - "name": "mean", - "type": "double", - "description": "mean forecast prediction" - }, - { - "name": "median", - "type": "double", - "description": "median forecast prediction" - }, - { - "name": "sd", - "type": "double", - "description": "standard deviation forecasts" - }, - { - "name": "quantile97.5", - "type": "double", - "description": "upper 97.5 percentile value of forecast" - }, - { - "name": "quantile02.5", - "type": "double", - "description": "upper 2.5 percentile value of forecast" - }, - { - "name": "quantile90", - "type": "double", - "description": "upper 90 percentile value of forecast" - }, - { - "name": "quantile10", - "type": "double", - "description": "upper 10 percentile value of forecast" - }, - { - "name": "project_id", - "type": "string", - "description": "unique project identifier" - }, - { - "name": "duration", - "type": "string", - "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention" - }, - { - "name": "variable", - "type": "string", - "description": "name of forecasted variable" - }, - { - "name": "model_id", - "type": "string", - "description": "unique model identifier" - }, - { - "name": "date", - "type": "string", - "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period." - } - ] - }, - "collection": "scores", - "links": [ - { - "rel": "collection", - "href": "../collection.json", - "type": "application/json", - "title": "flareGOTM_noDA" - }, - { - "rel": "root", - "href": "../../../catalog.json", - "type": "application/json", - "title": "Forecast Catalog" - }, - { - "rel": "parent", - "href": "../collection.json", - "type": "application/json", - "title": "flareGOTM_noDA" - }, - { - "rel": "self", - "href": "flareGOTM_noDA.json", - "type": "application/json", - "title": "Model Forecast" - }, - { - "rel": "item", - "href": "pending", - "type": "text/html", - "title": "Link for Model Code" - } - ], - "assets": { - "1": { - "type": "application/json", - "title": "Model Metadata", - "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/flareGOTM_noDA.json", - "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/flareGOTM_noDA.json\")\n\n" - }, - "2": { - "type": "text/html", - "title": "Link for Model Code", - "href": "pending", - "description": "The link to the model code provided by the model submission team" - }, - "3": { - "type": "application/x-parquet", - "title": "Database Access for Daily Water_temperature", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=flareGOTM_noDA?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=flareGOTM_noDA?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - } - } -} diff --git a/catalog/scores/models/model_items/flareSimstrat.json b/catalog/scores/models/model_items/flareSimstrat.json deleted file mode 100644 index f742d38533..0000000000 --- a/catalog/scores/models/model_items/flareSimstrat.json +++ /dev/null @@ -1,211 +0,0 @@ -{ - "stac_version": "1.0.0", - "stac_extensions": [ - "https://stac-extensions.github.io/table/v1.2.0/schema.json" - ], - "type": "Feature", - "id": "flareSimstrat", - "bbox": [ - [ - -156.6194, - 71.2824, - -66.7987, - 71.2824 - ] - ], - "geometry": { - "type": "MultiPoint", - "coordinates": [ - [-82.0084, 29.676], - [-89.4737, 46.2097], - [-89.7048, 45.9983], - [-99.1139, 47.1591], - [-99.2531, 47.1298], - [-82.0177, 29.6878] - ] - }, - "properties": { - "description": "\nmodel info: NA\n\nSites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG\n\nVariables: Daily Water_temperature", - "start_datetime": "2023-11-15", - "end_datetime": "2023-12-15", - "providers": [ - { - "url": "pending", - "name": "pending", - "roles": [ - "producer", - "processor", - "licensor" - ] - }, - { - "url": "https://www.ecoforecastprojectvt.org", - "name": "Ecoforecast Challenge", - "roles": [ - "host" - ] - } - ], - "license": "CC0-1.0", - "keywords": [ - "Forecasting", - "neon4cast", - "Daily Water_temperature" - ], - "table:columns": [ - { - "name": "reference_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that the forecast was initiated (horizon = 0)" - }, - { - "name": "site_id", - "type": "string", - "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model." - }, - { - "name": "datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime of the forecasted value (ISO 8601)" - }, - { - "name": "family", - "type": "string", - "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)." - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "name": "observation", - "type": "double", - "description": "observed value for variable" - }, - { - "name": "crps", - "type": "double", - "description": "crps forecast score" - }, - { - "name": "logs", - "type": "double", - "description": "logs forecast score" - }, - { - "name": "mean", - "type": "double", - "description": "mean forecast prediction" - }, - { - "name": "median", - "type": "double", - "description": "median forecast prediction" - }, - { - "name": "sd", - "type": "double", - "description": "standard deviation forecasts" - }, - { - "name": "quantile97.5", - "type": "double", - "description": "upper 97.5 percentile value of forecast" - }, - { - "name": "quantile02.5", - "type": "double", - "description": "upper 2.5 percentile value of forecast" - }, - { - "name": "quantile90", - "type": "double", - "description": "upper 90 percentile value of forecast" - }, - { - "name": "quantile10", - "type": "double", - "description": "upper 10 percentile value of forecast" - }, - { - "name": "project_id", - "type": "string", - "description": "unique project identifier" - }, - { - "name": "duration", - "type": "string", - "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention" - }, - { - "name": "variable", - "type": "string", - "description": "name of forecasted variable" - }, - { - "name": "model_id", - "type": "string", - "description": "unique model identifier" - }, - { - "name": "date", - "type": "string", - "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period." - } - ] - }, - "collection": "scores", - "links": [ - { - "rel": "collection", - "href": "../collection.json", - "type": "application/json", - "title": "flareSimstrat" - }, - { - "rel": "root", - "href": "../../../catalog.json", - "type": "application/json", - "title": "Forecast Catalog" - }, - { - "rel": "parent", - "href": "../collection.json", - "type": "application/json", - "title": "flareSimstrat" - }, - { - "rel": "self", - "href": "flareSimstrat.json", - "type": "application/json", - "title": "Model Forecast" - }, - { - "rel": "item", - "href": "pending", - "type": "text/html", - "title": "Link for Model Code" - } - ], - "assets": { - "1": { - "type": "application/json", - "title": "Model Metadata", - "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/flareSimstrat.json", - "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/flareSimstrat.json\")\n\n" - }, - "2": { - "type": "text/html", - "title": "Link for Model Code", - "href": "pending", - "description": "The link to the model code provided by the model submission team" - }, - "3": { - "type": "application/x-parquet", - "title": "Database Access for Daily Water_temperature", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=flareSimstrat?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=flareSimstrat?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - } - } -} diff --git a/catalog/scores/models/model_items/flareSimstrat_noDA.json b/catalog/scores/models/model_items/flareSimstrat_noDA.json deleted file mode 100644 index 90e3694f2d..0000000000 --- a/catalog/scores/models/model_items/flareSimstrat_noDA.json +++ /dev/null @@ -1,210 +0,0 @@ -{ - "stac_version": "1.0.0", - "stac_extensions": [ - "https://stac-extensions.github.io/table/v1.2.0/schema.json" - ], - "type": "Feature", - "id": "flareSimstrat_noDA", - "bbox": [ - [ - -156.6194, - 71.2824, - -66.7987, - 71.2824 - ] - ], - "geometry": { - "type": "MultiPoint", - "coordinates": [ - [-82.0084, 29.676], - [-89.4737, 46.2097], - [-99.1139, 47.1591], - [-99.2531, 47.1298], - [-82.0177, 29.6878] - ] - }, - "properties": { - "description": "\nmodel info: NA\n\nSites: BARC, CRAM, PRLA, PRPO, SUGG\n\nVariables: Daily Water_temperature", - "start_datetime": "2023-11-15", - "end_datetime": "2023-12-15", - "providers": [ - { - "url": "pending", - "name": "pending", - "roles": [ - "producer", - "processor", - "licensor" - ] - }, - { - "url": "https://www.ecoforecastprojectvt.org", - "name": "Ecoforecast Challenge", - "roles": [ - "host" - ] - } - ], - "license": "CC0-1.0", - "keywords": [ - "Forecasting", - "neon4cast", - "Daily Water_temperature" - ], - "table:columns": [ - { - "name": "reference_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that the forecast was initiated (horizon = 0)" - }, - { - "name": "site_id", - "type": "string", - "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model." - }, - { - "name": "datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime of the forecasted value (ISO 8601)" - }, - { - "name": "family", - "type": "string", - "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)." - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "name": "observation", - "type": "double", - "description": "observed value for variable" - }, - { - "name": "crps", - "type": "double", - "description": "crps forecast score" - }, - { - "name": "logs", - "type": "double", - "description": "logs forecast score" - }, - { - "name": "mean", - "type": "double", - "description": "mean forecast prediction" - }, - { - "name": "median", - "type": "double", - "description": "median forecast prediction" - }, - { - "name": "sd", - "type": "double", - "description": "standard deviation forecasts" - }, - { - "name": "quantile97.5", - "type": "double", - "description": "upper 97.5 percentile value of forecast" - }, - { - "name": "quantile02.5", - "type": "double", - "description": "upper 2.5 percentile value of forecast" - }, - { - "name": "quantile90", - "type": "double", - "description": "upper 90 percentile value of forecast" - }, - { - "name": "quantile10", - "type": "double", - "description": "upper 10 percentile value of forecast" - }, - { - "name": "project_id", - "type": "string", - "description": "unique project identifier" - }, - { - "name": "duration", - "type": "string", - "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention" - }, - { - "name": "variable", - "type": "string", - "description": "name of forecasted variable" - }, - { - "name": "model_id", - "type": "string", - "description": "unique model identifier" - }, - { - "name": "date", - "type": "string", - "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period." - } - ] - }, - "collection": "scores", - "links": [ - { - "rel": "collection", - "href": "../collection.json", - "type": "application/json", - "title": "flareSimstrat_noDA" - }, - { - "rel": "root", - "href": "../../../catalog.json", - "type": "application/json", - "title": "Forecast Catalog" - }, - { - "rel": "parent", - "href": "../collection.json", - "type": "application/json", - "title": "flareSimstrat_noDA" - }, - { - "rel": "self", - "href": "flareSimstrat_noDA.json", - "type": "application/json", - "title": "Model Forecast" - }, - { - "rel": "item", - "href": "pending", - "type": "text/html", - "title": "Link for Model Code" - } - ], - "assets": { - "1": { - "type": "application/json", - "title": "Model Metadata", - "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/flareSimstrat_noDA.json", - "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/flareSimstrat_noDA.json\")\n\n" - }, - "2": { - "type": "text/html", - "title": "Link for Model Code", - "href": "pending", - "description": "The link to the model code provided by the model submission team" - }, - "3": { - "type": "application/x-parquet", - "title": "Database Access for Daily Water_temperature", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=flareSimstrat_noDA?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=flareSimstrat_noDA?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - } - } -} diff --git a/catalog/scores/models/model_items/flare_ler.json b/catalog/scores/models/model_items/flare_ler.json deleted file mode 100644 index c0c09632a0..0000000000 --- a/catalog/scores/models/model_items/flare_ler.json +++ /dev/null @@ -1,211 +0,0 @@ -{ - "stac_version": "1.0.0", - "stac_extensions": [ - "https://stac-extensions.github.io/table/v1.2.0/schema.json" - ], - "type": "Feature", - "id": "flare_ler", - "bbox": [ - [ - -156.6194, - 71.2824, - -66.7987, - 71.2824 - ] - ], - "geometry": { - "type": "MultiPoint", - "coordinates": [ - [-82.0084, 29.676], - [-89.4737, 46.2097], - [-89.7048, 45.9983], - [-99.1139, 47.1591], - [-99.2531, 47.1298], - [-82.0177, 29.6878] - ] - }, - "properties": { - "description": "\nmodel info: NA\n\nSites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG\n\nVariables: Daily Water_temperature", - "start_datetime": "2023-11-14", - "end_datetime": "2023-12-15", - "providers": [ - { - "url": "pending", - "name": "pending", - "roles": [ - "producer", - "processor", - "licensor" - ] - }, - { - "url": "https://www.ecoforecastprojectvt.org", - "name": "Ecoforecast Challenge", - "roles": [ - "host" - ] - } - ], - "license": "CC0-1.0", - "keywords": [ - "Forecasting", - "neon4cast", - "Daily Water_temperature" - ], - "table:columns": [ - { - "name": "reference_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that the forecast was initiated (horizon = 0)" - }, - { - "name": "site_id", - "type": "string", - "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model." - }, - { - "name": "datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime of the forecasted value (ISO 8601)" - }, - { - "name": "family", - "type": "string", - "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)." - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "name": "observation", - "type": "double", - "description": "observed value for variable" - }, - { - "name": "crps", - "type": "double", - "description": "crps forecast score" - }, - { - "name": "logs", - "type": "double", - "description": "logs forecast score" - }, - { - "name": "mean", - "type": "double", - "description": "mean forecast prediction" - }, - { - "name": "median", - "type": "double", - "description": "median forecast prediction" - }, - { - "name": "sd", - "type": "double", - "description": "standard deviation forecasts" - }, - { - "name": "quantile97.5", - "type": "double", - "description": "upper 97.5 percentile value of forecast" - }, - { - "name": "quantile02.5", - "type": "double", - "description": "upper 2.5 percentile value of forecast" - }, - { - "name": "quantile90", - "type": "double", - "description": "upper 90 percentile value of forecast" - }, - { - "name": "quantile10", - "type": "double", - "description": "upper 10 percentile value of forecast" - }, - { - "name": "project_id", - "type": "string", - "description": "unique project identifier" - }, - { - "name": "duration", - "type": "string", - "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention" - }, - { - "name": "variable", - "type": "string", - "description": "name of forecasted variable" - }, - { - "name": "model_id", - "type": "string", - "description": "unique model identifier" - }, - { - "name": "date", - "type": "string", - "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period." - } - ] - }, - "collection": "scores", - "links": [ - { - "rel": "collection", - "href": "../collection.json", - "type": "application/json", - "title": "flare_ler" - }, - { - "rel": "root", - "href": "../../../catalog.json", - "type": "application/json", - "title": "Forecast Catalog" - }, - { - "rel": "parent", - "href": "../collection.json", - "type": "application/json", - "title": "flare_ler" - }, - { - "rel": "self", - "href": "flare_ler.json", - "type": "application/json", - "title": "Model Forecast" - }, - { - "rel": "item", - "href": "pending", - "type": "text/html", - "title": "Link for Model Code" - } - ], - "assets": { - "1": { - "type": "application/json", - "title": "Model Metadata", - "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/flare_ler.json", - "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/flare_ler.json\")\n\n" - }, - "2": { - "type": "text/html", - "title": "Link for Model Code", - "href": "pending", - "description": "The link to the model code provided by the model submission team" - }, - "3": { - "type": "application/x-parquet", - "title": "Database Access for Daily Water_temperature", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=flare_ler?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=flare_ler?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - } - } -} diff --git a/catalog/scores/models/model_items/flare_ler_baselines.json b/catalog/scores/models/model_items/flare_ler_baselines.json deleted file mode 100644 index e7098681c6..0000000000 --- a/catalog/scores/models/model_items/flare_ler_baselines.json +++ /dev/null @@ -1,207 +0,0 @@ -{ - "stac_version": "1.0.0", - "stac_extensions": [ - "https://stac-extensions.github.io/table/v1.2.0/schema.json" - ], - "type": "Feature", - "id": "flare_ler_baselines", - "bbox": [ - [ - -156.6194, - 71.2824, - -66.7987, - 71.2824 - ] - ], - "geometry": { - "type": "MultiPoint", - "coordinates": [ - [-82.0084, 29.676], - [-82.0177, 29.6878] - ] - }, - "properties": { - "description": "\nmodel info: NA\n\nSites: BARC, SUGG\n\nVariables: Daily Water_temperature", - "start_datetime": "2023-11-14", - "end_datetime": "2023-12-15", - "providers": [ - { - "url": "pending", - "name": "pending", - "roles": [ - "producer", - "processor", - "licensor" - ] - }, - { - "url": "https://www.ecoforecastprojectvt.org", - "name": "Ecoforecast Challenge", - "roles": [ - "host" - ] - } - ], - "license": "CC0-1.0", - "keywords": [ - "Forecasting", - "neon4cast", - "Daily Water_temperature" - ], - "table:columns": [ - { - "name": "reference_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that the forecast was initiated (horizon = 0)" - }, - { - "name": "site_id", - "type": "string", - "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model." - }, - { - "name": "datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime of the forecasted value (ISO 8601)" - }, - { - "name": "family", - "type": "string", - "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)." - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "name": "observation", - "type": "double", - "description": "observed value for variable" - }, - { - "name": "crps", - "type": "double", - "description": "crps forecast score" - }, - { - "name": "logs", - "type": "double", - "description": "logs forecast score" - }, - { - "name": "mean", - "type": "double", - "description": "mean forecast prediction" - }, - { - "name": "median", - "type": "double", - "description": "median forecast prediction" - }, - { - "name": "sd", - "type": "double", - "description": "standard deviation forecasts" - }, - { - "name": "quantile97.5", - "type": "double", - "description": "upper 97.5 percentile value of forecast" - }, - { - "name": "quantile02.5", - "type": "double", - "description": "upper 2.5 percentile value of forecast" - }, - { - "name": "quantile90", - "type": "double", - "description": "upper 90 percentile value of forecast" - }, - { - "name": "quantile10", - "type": "double", - "description": "upper 10 percentile value of forecast" - }, - { - "name": "project_id", - "type": "string", - "description": "unique project identifier" - }, - { - "name": "duration", - "type": "string", - "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention" - }, - { - "name": "variable", - "type": "string", - "description": "name of forecasted variable" - }, - { - "name": "model_id", - "type": "string", - "description": "unique model identifier" - }, - { - "name": "date", - "type": "string", - "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period." - } - ] - }, - "collection": "scores", - "links": [ - { - "rel": "collection", - "href": "../collection.json", - "type": "application/json", - "title": "flare_ler_baselines" - }, - { - "rel": "root", - "href": "../../../catalog.json", - "type": "application/json", - "title": "Forecast Catalog" - }, - { - "rel": "parent", - "href": "../collection.json", - "type": "application/json", - "title": "flare_ler_baselines" - }, - { - "rel": "self", - "href": "flare_ler_baselines.json", - "type": "application/json", - "title": "Model Forecast" - }, - { - "rel": "item", - "href": "pending", - "type": "text/html", - "title": "Link for Model Code" - } - ], - "assets": { - "1": { - "type": "application/json", - "title": "Model Metadata", - "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/flare_ler_baselines.json", - "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/flare_ler_baselines.json\")\n\n" - }, - "2": { - "type": "text/html", - "title": "Link for Model Code", - "href": "pending", - "description": "The link to the model code provided by the model submission team" - }, - "3": { - "type": "application/x-parquet", - "title": "Database Access for Daily Water_temperature", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=flare_ler_baselines?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=flare_ler_baselines?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - } - } -} diff --git a/catalog/scores/models/model_items/gem_global.json b/catalog/scores/models/model_items/gem_global.json deleted file mode 100644 index b457687e34..0000000000 --- a/catalog/scores/models/model_items/gem_global.json +++ /dev/null @@ -1,229 +0,0 @@ -{ - "stac_version": "1.0.0", - "stac_extensions": [ - "https://stac-extensions.github.io/table/v1.2.0/schema.json" - ], - "type": "Feature", - "id": "gem_global", - "bbox": [ - [ - -156.6194, - 71.2824, - -66.7987, - 71.2824 - ] - ], - "geometry": { - "type": "MultiPoint", - "coordinates": [ - [37.3032, -79.8372] - ] - }, - "properties": { - "description": "\nmodel info: Candian GEM Global Ensemble model downloaded from open-meteo.com\n\nSites: fcre\n\nVariables: Rain_mm_sum, ShortwaveRadiationUp_Wm2_mean, BP_kPa_mean, WindSpeed_ms_mean, AirTemp_C_mean, RH_percent_mean", - "start_datetime": "2023-10-14", - "end_datetime": "2023-11-30", - "providers": [ - { - "url": "pending", - "name": "pending", - "roles": [ - "producer", - "processor", - "licensor" - ] - }, - { - "url": "https://www.ecoforecastprojectvt.org", - "name": "Ecoforecast Challenge", - "roles": [ - "host" - ] - } - ], - "license": "CC0-1.0", - "keywords": [ - "Forecasting", - "vera4cast", - "Rain_mm_sum, ShortwaveRadiationUp_Wm2_mean, BP_kPa_mean, WindSpeed_ms_mean, AirTemp_C_mean, RH_percent_mean" - ], - "table:columns": [ - { - "name": "reference_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that the forecast was initiated (horizon = 0)" - }, - { - "name": "site_id", - "type": "string", - "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model." - }, - { - "name": "datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime of the forecasted value (ISO 8601)" - }, - { - "name": "family", - "type": "string", - "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)." - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "name": "depth_m", - "type": "double", - "description": "depth (meters) in water column of prediction" - }, - { - "name": "observation", - "type": "double", - "description": "observed value for variable" - }, - { - "name": "crps", - "type": "double", - "description": "crps forecast score" - }, - { - "name": "logs", - "type": "double", - "description": "logs forecast score" - }, - { - "name": "mean", - "type": "double", - "description": "mean forecast prediction" - }, - { - "name": "median", - "type": "double", - "description": "median forecast prediction" - }, - { - "name": "sd", - "type": "double", - "description": "standard deviation forecasts" - }, - { - "name": "quantile97.5", - "type": "double", - "description": "upper 97.5 percentile value of forecast" - }, - { - "name": "quantile02.5", - "type": "double", - "description": "upper 2.5 percentile value of forecast" - }, - { - "name": "quantile90", - "type": "double", - "description": "upper 90 percentile value of forecast" - }, - { - "name": "quantile10", - "type": "double", - "description": "upper 10 percentile value of forecast" - }, - { - "name": "project_id", - "type": "string", - "description": "unique project identifier" - }, - { - "name": "duration", - "type": "string", - "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention" - }, - { - "name": "variable", - "type": "string", - "description": "name of forecasted variable" - }, - { - "name": "model_id", - "type": "string", - "description": "unique model identifier" - }, - { - "name": "date", - "type": "string", - "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period." - } - ] - }, - "collection": "scores", - "links": [ - { - "rel": "collection", - "href": "../collection.json", - "type": "application/json", - "title": "gem_global" - }, - { - "rel": "root", - "href": "../../../catalog.json", - "type": "application/json", - "title": "Forecast Catalog" - }, - { - "rel": "parent", - "href": "../collection.json", - "type": "application/json", - "title": "gem_global" - }, - { - "rel": "self", - "href": "gem_global.json", - "type": "application/json", - "title": "Model Forecast" - } - ], - "assets": { - "1": { - "type": "application/json", - "title": "Model Metadata", - "href": "https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/gem_global.json", - "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/gem_global.json\")\n\n" - }, - "2": { - "type": "application/x-parquet", - "title": "Database Access for Rain_mm_sum daily", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=Rain_mm_sum/model_id=gem_global?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=Rain_mm_sum/model_id=gem_global?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "3": { - "type": "application/x-parquet", - "title": "Database Access for ShortwaveRadiationUp_Wm2_mean daily", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=ShortwaveRadiationUp_Wm2_mean/model_id=gem_global?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=ShortwaveRadiationUp_Wm2_mean/model_id=gem_global?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "4": { - "type": "application/x-parquet", - "title": "Database Access for BP_kPa_mean daily", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=BP_kPa_mean/model_id=gem_global?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=BP_kPa_mean/model_id=gem_global?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "5": { - "type": "application/x-parquet", - "title": "Database Access for WindSpeed_ms_mean daily", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=WindSpeed_ms_mean/model_id=gem_global?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=WindSpeed_ms_mean/model_id=gem_global?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "6": { - "type": "application/x-parquet", - "title": "Database Access for AirTemp_C_mean daily", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=AirTemp_C_mean/model_id=gem_global?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=AirTemp_C_mean/model_id=gem_global?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "7": { - "type": "application/x-parquet", - "title": "Database Access for RH_percent_mean daily", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=RH_percent_mean/model_id=gem_global?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=RH_percent_mean/model_id=gem_global?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - } - } -} diff --git a/catalog/scores/models/model_items/gfs_seamless.json b/catalog/scores/models/model_items/gfs_seamless.json deleted file mode 100644 index 7f7aeb974c..0000000000 --- a/catalog/scores/models/model_items/gfs_seamless.json +++ /dev/null @@ -1,229 +0,0 @@ -{ - "stac_version": "1.0.0", - "stac_extensions": [ - "https://stac-extensions.github.io/table/v1.2.0/schema.json" - ], - "type": "Feature", - "id": "gfs_seamless", - "bbox": [ - [ - -156.6194, - 71.2824, - -66.7987, - 71.2824 - ] - ], - "geometry": { - "type": "MultiPoint", - "coordinates": [ - [37.3032, -79.8372] - ] - }, - "properties": { - "description": "\nmodel info: NOAA Global Ensemble Forecasting Model downloaded using the https://open-meteo.com. The seamless model combines the 0.25 and 0.5 degree resolution products to get a full 35-day ahead forecast\n\nSites: fcre\n\nVariables: RH_percent_mean, Rain_mm_sum, ShortwaveRadiationUp_Wm2_mean, BP_kPa_mean, AirTemp_C_mean, WindSpeed_ms_mean", - "start_datetime": "2023-10-13", - "end_datetime": "2023-12-03", - "providers": [ - { - "url": "pending", - "name": "pending", - "roles": [ - "producer", - "processor", - "licensor" - ] - }, - { - "url": "https://www.ecoforecastprojectvt.org", - "name": "Ecoforecast Challenge", - "roles": [ - "host" - ] - } - ], - "license": "CC0-1.0", - "keywords": [ - "Forecasting", - "vera4cast", - "RH_percent_mean, Rain_mm_sum, ShortwaveRadiationUp_Wm2_mean, BP_kPa_mean, AirTemp_C_mean, WindSpeed_ms_mean" - ], - "table:columns": [ - { - "name": "reference_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that the forecast was initiated (horizon = 0)" - }, - { - "name": "site_id", - "type": "string", - "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model." - }, - { - "name": "datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime of the forecasted value (ISO 8601)" - }, - { - "name": "family", - "type": "string", - "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)." - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "name": "depth_m", - "type": "double", - "description": "depth (meters) in water column of prediction" - }, - { - "name": "observation", - "type": "double", - "description": "observed value for variable" - }, - { - "name": "crps", - "type": "double", - "description": "crps forecast score" - }, - { - "name": "logs", - "type": "double", - "description": "logs forecast score" - }, - { - "name": "mean", - "type": "double", - "description": "mean forecast prediction" - }, - { - "name": "median", - "type": "double", - "description": "median forecast prediction" - }, - { - "name": "sd", - "type": "double", - "description": "standard deviation forecasts" - }, - { - "name": "quantile97.5", - "type": "double", - "description": "upper 97.5 percentile value of forecast" - }, - { - "name": "quantile02.5", - "type": "double", - "description": "upper 2.5 percentile value of forecast" - }, - { - "name": "quantile90", - "type": "double", - "description": "upper 90 percentile value of forecast" - }, - { - "name": "quantile10", - "type": "double", - "description": "upper 10 percentile value of forecast" - }, - { - "name": "project_id", - "type": "string", - "description": "unique project identifier" - }, - { - "name": "duration", - "type": "string", - "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention" - }, - { - "name": "variable", - "type": "string", - "description": "name of forecasted variable" - }, - { - "name": "model_id", - "type": "string", - "description": "unique model identifier" - }, - { - "name": "date", - "type": "string", - "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period." - } - ] - }, - "collection": "scores", - "links": [ - { - "rel": "collection", - "href": "../collection.json", - "type": "application/json", - "title": "gfs_seamless" - }, - { - "rel": "root", - "href": "../../../catalog.json", - "type": "application/json", - "title": "Forecast Catalog" - }, - { - "rel": "parent", - "href": "../collection.json", - "type": "application/json", - "title": "gfs_seamless" - }, - { - "rel": "self", - "href": "gfs_seamless.json", - "type": "application/json", - "title": "Model Forecast" - } - ], - "assets": { - "1": { - "type": "application/json", - "title": "Model Metadata", - "href": "https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/gfs_seamless.json", - "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/gfs_seamless.json\")\n\n" - }, - "2": { - "type": "application/x-parquet", - "title": "Database Access for RH_percent_mean daily", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=RH_percent_mean/model_id=gfs_seamless?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=RH_percent_mean/model_id=gfs_seamless?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "3": { - "type": "application/x-parquet", - "title": "Database Access for Rain_mm_sum daily", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=Rain_mm_sum/model_id=gfs_seamless?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=Rain_mm_sum/model_id=gfs_seamless?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "4": { - "type": "application/x-parquet", - "title": "Database Access for ShortwaveRadiationUp_Wm2_mean daily", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=ShortwaveRadiationUp_Wm2_mean/model_id=gfs_seamless?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=ShortwaveRadiationUp_Wm2_mean/model_id=gfs_seamless?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "5": { - "type": "application/x-parquet", - "title": "Database Access for BP_kPa_mean daily", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=BP_kPa_mean/model_id=gfs_seamless?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=BP_kPa_mean/model_id=gfs_seamless?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "6": { - "type": "application/x-parquet", - "title": "Database Access for AirTemp_C_mean daily", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=AirTemp_C_mean/model_id=gfs_seamless?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=AirTemp_C_mean/model_id=gfs_seamless?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "7": { - "type": "application/x-parquet", - "title": "Database Access for WindSpeed_ms_mean daily", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=WindSpeed_ms_mean/model_id=gfs_seamless?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=WindSpeed_ms_mean/model_id=gfs_seamless?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - } - } -} diff --git a/catalog/scores/models/model_items/glm_aed_v1.json b/catalog/scores/models/model_items/glm_aed_v1.json deleted file mode 100644 index 701ecb4cc8..0000000000 --- a/catalog/scores/models/model_items/glm_aed_v1.json +++ /dev/null @@ -1,247 +0,0 @@ -{ - "stac_version": "1.0.0", - "stac_extensions": [ - "https://stac-extensions.github.io/table/v1.2.0/schema.json" - ], - "type": "Feature", - "id": "glm_aed_v1", - "bbox": [ - [ - -156.6194, - 71.2824, - -66.7987, - 71.2824 - ] - ], - "geometry": { - "type": "MultiPoint", - "coordinates": [ - [37.3032, -79.8372] - ] - }, - "properties": { - "description": "\nmodel info: GLM-AED with Ensemble Kalman Filter as implemented in FLARE. This version used DA to update model states but not model parameters.\n\nSites: fcre\n\nVariables: DIC_mgL_sample, DO_mgL_mean, NH4_ugL_sample, SRP_ugL_sample, Temp_C_mean, fDOM_QSU_mean, Chla_ugL_mean, Secchi_m_sample, Bloom_binary_mean", - "start_datetime": "2023-10-14", - "end_datetime": "2023-12-01", - "providers": [ - { - "url": "pending", - "name": "pending", - "roles": [ - "producer", - "processor", - "licensor" - ] - }, - { - "url": "https://www.ecoforecastprojectvt.org", - "name": "Ecoforecast Challenge", - "roles": [ - "host" - ] - } - ], - "license": "CC0-1.0", - "keywords": [ - "Forecasting", - "vera4cast", - "DIC_mgL_sample, DO_mgL_mean, NH4_ugL_sample, SRP_ugL_sample, Temp_C_mean, fDOM_QSU_mean, Chla_ugL_mean, Secchi_m_sample, Bloom_binary_mean" - ], - "table:columns": [ - { - "name": "reference_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that the forecast was initiated (horizon = 0)" - }, - { - "name": "site_id", - "type": "string", - "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model." - }, - { - "name": "datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime of the forecasted value (ISO 8601)" - }, - { - "name": "family", - "type": "string", - "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)." - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "name": "depth_m", - "type": "double", - "description": "depth (meters) in water column of prediction" - }, - { - "name": "observation", - "type": "double", - "description": "observed value for variable" - }, - { - "name": "crps", - "type": "double", - "description": "crps forecast score" - }, - { - "name": "logs", - "type": "double", - "description": "logs forecast score" - }, - { - "name": "mean", - "type": "double", - "description": "mean forecast prediction" - }, - { - "name": "median", - "type": "double", - "description": "median forecast prediction" - }, - { - "name": "sd", - "type": "double", - "description": "standard deviation forecasts" - }, - { - "name": "quantile97.5", - "type": "double", - "description": "upper 97.5 percentile value of forecast" - }, - { - "name": "quantile02.5", - "type": "double", - "description": "upper 2.5 percentile value of forecast" - }, - { - "name": "quantile90", - "type": "double", - "description": "upper 90 percentile value of forecast" - }, - { - "name": "quantile10", - "type": "double", - "description": "upper 10 percentile value of forecast" - }, - { - "name": "project_id", - "type": "string", - "description": "unique project identifier" - }, - { - "name": "duration", - "type": "string", - "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention" - }, - { - "name": "variable", - "type": "string", - "description": "name of forecasted variable" - }, - { - "name": "model_id", - "type": "string", - "description": "unique model identifier" - }, - { - "name": "date", - "type": "string", - "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period." - } - ] - }, - "collection": "scores", - "links": [ - { - "rel": "collection", - "href": "../collection.json", - "type": "application/json", - "title": "glm_aed_v1" - }, - { - "rel": "root", - "href": "../../../catalog.json", - "type": "application/json", - "title": "Forecast Catalog" - }, - { - "rel": "parent", - "href": "../collection.json", - "type": "application/json", - "title": "glm_aed_v1" - }, - { - "rel": "self", - "href": "glm_aed_v1.json", - "type": "application/json", - "title": "Model Forecast" - } - ], - "assets": { - "1": { - "type": "application/json", - "title": "Model Metadata", - "href": "https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/glm_aed_v1.json", - "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/glm_aed_v1.json\")\n\n" - }, - "2": { - "type": "application/x-parquet", - "title": "Database Access for DIC_mgL_sample daily", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=DIC_mgL_sample/model_id=glm_aed_v1?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=DIC_mgL_sample/model_id=glm_aed_v1?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "3": { - "type": "application/x-parquet", - "title": "Database Access for DO_mgL_mean daily", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=DO_mgL_mean/model_id=glm_aed_v1?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=DO_mgL_mean/model_id=glm_aed_v1?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "4": { - "type": "application/x-parquet", - "title": "Database Access for NH4_ugL_sample daily", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=NH4_ugL_sample/model_id=glm_aed_v1?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=NH4_ugL_sample/model_id=glm_aed_v1?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "5": { - "type": "application/x-parquet", - "title": "Database Access for SRP_ugL_sample daily", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=SRP_ugL_sample/model_id=glm_aed_v1?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=SRP_ugL_sample/model_id=glm_aed_v1?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "6": { - "type": "application/x-parquet", - "title": "Database Access for Temp_C_mean daily", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=Temp_C_mean/model_id=glm_aed_v1?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=Temp_C_mean/model_id=glm_aed_v1?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "7": { - "type": "application/x-parquet", - "title": "Database Access for fDOM_QSU_mean daily", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=fDOM_QSU_mean/model_id=glm_aed_v1?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=fDOM_QSU_mean/model_id=glm_aed_v1?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "8": { - "type": "application/x-parquet", - "title": "Database Access for Chla_ugL_mean daily", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=Chla_ugL_mean/model_id=glm_aed_v1?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=Chla_ugL_mean/model_id=glm_aed_v1?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "9": { - "type": "application/x-parquet", - "title": "Database Access for Secchi_m_sample daily", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=Secchi_m_sample/model_id=glm_aed_v1?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=Secchi_m_sample/model_id=glm_aed_v1?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "10": { - "type": "application/x-parquet", - "title": "Database Access for Bloom_binary_mean daily", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=Bloom_binary_mean/model_id=glm_aed_v1?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=Bloom_binary_mean/model_id=glm_aed_v1?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - } - } -} diff --git a/catalog/scores/models/model_items/icon_seamless.json b/catalog/scores/models/model_items/icon_seamless.json deleted file mode 100644 index f0d17090c5..0000000000 --- a/catalog/scores/models/model_items/icon_seamless.json +++ /dev/null @@ -1,229 +0,0 @@ -{ - "stac_version": "1.0.0", - "stac_extensions": [ - "https://stac-extensions.github.io/table/v1.2.0/schema.json" - ], - "type": "Feature", - "id": "icon_seamless", - "bbox": [ - [ - -156.6194, - 71.2824, - -66.7987, - 71.2824 - ] - ], - "geometry": { - "type": "MultiPoint", - "coordinates": [ - [37.3032, -79.8372] - ] - }, - "properties": { - "description": "\nmodel info: The DWD Icon EPS Seamless model downloaded from open-meteo.com\n\nSites: fcre\n\nVariables: BP_kPa_mean, RH_percent_mean, Rain_mm_sum, AirTemp_C_mean, WindSpeed_ms_mean, ShortwaveRadiationUp_Wm2_mean", - "start_datetime": "2023-10-14", - "end_datetime": "2023-11-05", - "providers": [ - { - "url": "pending", - "name": "pending", - "roles": [ - "producer", - "processor", - "licensor" - ] - }, - { - "url": "https://www.ecoforecastprojectvt.org", - "name": "Ecoforecast Challenge", - "roles": [ - "host" - ] - } - ], - "license": "CC0-1.0", - "keywords": [ - "Forecasting", - "vera4cast", - "BP_kPa_mean, RH_percent_mean, Rain_mm_sum, AirTemp_C_mean, WindSpeed_ms_mean, ShortwaveRadiationUp_Wm2_mean" - ], - "table:columns": [ - { - "name": "reference_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that the forecast was initiated (horizon = 0)" - }, - { - "name": "site_id", - "type": "string", - "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model." - }, - { - "name": "datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime of the forecasted value (ISO 8601)" - }, - { - "name": "family", - "type": "string", - "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)." - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "name": "depth_m", - "type": "double", - "description": "depth (meters) in water column of prediction" - }, - { - "name": "observation", - "type": "double", - "description": "observed value for variable" - }, - { - "name": "crps", - "type": "double", - "description": "crps forecast score" - }, - { - "name": "logs", - "type": "double", - "description": "logs forecast score" - }, - { - "name": "mean", - "type": "double", - "description": "mean forecast prediction" - }, - { - "name": "median", - "type": "double", - "description": "median forecast prediction" - }, - { - "name": "sd", - "type": "double", - "description": "standard deviation forecasts" - }, - { - "name": "quantile97.5", - "type": "double", - "description": "upper 97.5 percentile value of forecast" - }, - { - "name": "quantile02.5", - "type": "double", - "description": "upper 2.5 percentile value of forecast" - }, - { - "name": "quantile90", - "type": "double", - "description": "upper 90 percentile value of forecast" - }, - { - "name": "quantile10", - "type": "double", - "description": "upper 10 percentile value of forecast" - }, - { - "name": "project_id", - "type": "string", - "description": "unique project identifier" - }, - { - "name": "duration", - "type": "string", - "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention" - }, - { - "name": "variable", - "type": "string", - "description": "name of forecasted variable" - }, - { - "name": "model_id", - "type": "string", - "description": "unique model identifier" - }, - { - "name": "date", - "type": "string", - "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period." - } - ] - }, - "collection": "scores", - "links": [ - { - "rel": "collection", - "href": "../collection.json", - "type": "application/json", - "title": "icon_seamless" - }, - { - "rel": "root", - "href": "../../../catalog.json", - "type": "application/json", - "title": "Forecast Catalog" - }, - { - "rel": "parent", - "href": "../collection.json", - "type": "application/json", - "title": "icon_seamless" - }, - { - "rel": "self", - "href": "icon_seamless.json", - "type": "application/json", - "title": "Model Forecast" - } - ], - "assets": { - "1": { - "type": "application/json", - "title": "Model Metadata", - "href": "https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/icon_seamless.json", - "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/icon_seamless.json\")\n\n" - }, - "2": { - "type": "application/x-parquet", - "title": "Database Access for BP_kPa_mean daily", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=BP_kPa_mean/model_id=icon_seamless?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=BP_kPa_mean/model_id=icon_seamless?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "3": { - "type": "application/x-parquet", - "title": "Database Access for RH_percent_mean daily", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=RH_percent_mean/model_id=icon_seamless?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=RH_percent_mean/model_id=icon_seamless?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "4": { - "type": "application/x-parquet", - "title": "Database Access for Rain_mm_sum daily", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=Rain_mm_sum/model_id=icon_seamless?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=Rain_mm_sum/model_id=icon_seamless?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "5": { - "type": "application/x-parquet", - "title": "Database Access for AirTemp_C_mean daily", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=AirTemp_C_mean/model_id=icon_seamless?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=AirTemp_C_mean/model_id=icon_seamless?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "6": { - "type": "application/x-parquet", - "title": "Database Access for WindSpeed_ms_mean daily", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=WindSpeed_ms_mean/model_id=icon_seamless?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=WindSpeed_ms_mean/model_id=icon_seamless?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "7": { - "type": "application/x-parquet", - "title": "Database Access for ShortwaveRadiationUp_Wm2_mean daily", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=ShortwaveRadiationUp_Wm2_mean/model_id=icon_seamless?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=ShortwaveRadiationUp_Wm2_mean/model_id=icon_seamless?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - } - } -} diff --git a/catalog/scores/models/model_items/inflow_gefsClimAED.json b/catalog/scores/models/model_items/inflow_gefsClimAED.json deleted file mode 100644 index 261c1883b0..0000000000 --- a/catalog/scores/models/model_items/inflow_gefsClimAED.json +++ /dev/null @@ -1,259 +0,0 @@ -{ - "stac_version": "1.0.0", - "stac_extensions": [ - "https://stac-extensions.github.io/table/v1.2.0/schema.json" - ], - "type": "Feature", - "id": "inflow_gefsClimAED", - "bbox": [ - [ - -156.6194, - 71.2824, - -66.7987, - 71.2824 - ] - ], - "geometry": { - "type": "MultiPoint", - "coordinates": [ - [37.3078, -79.8357] - ] - }, - "properties": { - "description": "\nmodel info: flow is forecasted as using a linear relationship between historical flow, month, and 5-day sum of precipitation. Temperature is forecasted using a linear relationship between historical water temperature, month, and 5-day mean air temperature. NOAA GEFS is then used to get the future values of 5-day sum precipitation and mean temperature. Nutrients are forecasting using the DOY climatology. The DOY climatology was developed using a linear interpolation of the historical samples.\n\nSites: tubr\n\nVariables: DIC_mgL_sample, DRSI_mgL_sample, Flow_cms_mean, Temp_C_mean, CH4_umolL_sample, DOC_mgL_sample, TN_ugL_sample, NH4_ugL_sample, TP_ugL_sample, NO3NO2_ugL_sample, SRP_ugL_sample", - "start_datetime": "2023-10-13", - "end_datetime": "2023-12-03", - "providers": [ - { - "url": "pending", - "name": "pending", - "roles": [ - "producer", - "processor", - "licensor" - ] - }, - { - "url": "https://www.ecoforecastprojectvt.org", - "name": "Ecoforecast Challenge", - "roles": [ - "host" - ] - } - ], - "license": "CC0-1.0", - "keywords": [ - "Forecasting", - "vera4cast", - "DIC_mgL_sample, DRSI_mgL_sample, Flow_cms_mean, Temp_C_mean, CH4_umolL_sample, DOC_mgL_sample, TN_ugL_sample, NH4_ugL_sample, TP_ugL_sample, NO3NO2_ugL_sample, SRP_ugL_sample" - ], - "table:columns": [ - { - "name": "reference_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that the forecast was initiated (horizon = 0)" - }, - { - "name": "site_id", - "type": "string", - "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model." - }, - { - "name": "datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime of the forecasted value (ISO 8601)" - }, - { - "name": "family", - "type": "string", - "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)." - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "name": "depth_m", - "type": "double", - "description": "depth (meters) in water column of prediction" - }, - { - "name": "observation", - "type": "double", - "description": "observed value for variable" - }, - { - "name": "crps", - "type": "double", - "description": "crps forecast score" - }, - { - "name": "logs", - "type": "double", - "description": "logs forecast score" - }, - { - "name": "mean", - "type": "double", - "description": "mean forecast prediction" - }, - { - "name": "median", - "type": "double", - "description": "median forecast prediction" - }, - { - "name": "sd", - "type": "double", - "description": "standard deviation forecasts" - }, - { - "name": "quantile97.5", - "type": "double", - "description": "upper 97.5 percentile value of forecast" - }, - { - "name": "quantile02.5", - "type": "double", - "description": "upper 2.5 percentile value of forecast" - }, - { - "name": "quantile90", - "type": "double", - "description": "upper 90 percentile value of forecast" - }, - { - "name": "quantile10", - "type": "double", - "description": "upper 10 percentile value of forecast" - }, - { - "name": "project_id", - "type": "string", - "description": "unique project identifier" - }, - { - "name": "duration", - "type": "string", - "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention" - }, - { - "name": "variable", - "type": "string", - "description": "name of forecasted variable" - }, - { - "name": "model_id", - "type": "string", - "description": "unique model identifier" - }, - { - "name": "date", - "type": "string", - "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period." - } - ] - }, - "collection": "scores", - "links": [ - { - "rel": "collection", - "href": "../collection.json", - "type": "application/json", - "title": "inflow_gefsClimAED" - }, - { - "rel": "root", - "href": "../../../catalog.json", - "type": "application/json", - "title": "Forecast Catalog" - }, - { - "rel": "parent", - "href": "../collection.json", - "type": "application/json", - "title": "inflow_gefsClimAED" - }, - { - "rel": "self", - "href": "inflow_gefsClimAED.json", - "type": "application/json", - "title": "Model Forecast" - } - ], - "assets": { - "1": { - "type": "application/json", - "title": "Model Metadata", - "href": "https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/inflow_gefsClimAED.json", - "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/inflow_gefsClimAED.json\")\n\n" - }, - "2": { - "type": "application/x-parquet", - "title": "Database Access for DIC_mgL_sample daily", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=DIC_mgL_sample/model_id=inflow_gefsClimAED?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=DIC_mgL_sample/model_id=inflow_gefsClimAED?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "3": { - "type": "application/x-parquet", - "title": "Database Access for DRSI_mgL_sample daily", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=DRSI_mgL_sample/model_id=inflow_gefsClimAED?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=DRSI_mgL_sample/model_id=inflow_gefsClimAED?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "4": { - "type": "application/x-parquet", - "title": "Database Access for Flow_cms_mean daily", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=Flow_cms_mean/model_id=inflow_gefsClimAED?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=Flow_cms_mean/model_id=inflow_gefsClimAED?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "5": { - "type": "application/x-parquet", - "title": "Database Access for Temp_C_mean daily", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=Temp_C_mean/model_id=inflow_gefsClimAED?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=Temp_C_mean/model_id=inflow_gefsClimAED?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "6": { - "type": "application/x-parquet", - "title": "Database Access for CH4_umolL_sample daily", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=CH4_umolL_sample/model_id=inflow_gefsClimAED?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=CH4_umolL_sample/model_id=inflow_gefsClimAED?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "7": { - "type": "application/x-parquet", - "title": "Database Access for DOC_mgL_sample daily", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=DOC_mgL_sample/model_id=inflow_gefsClimAED?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=DOC_mgL_sample/model_id=inflow_gefsClimAED?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "8": { - "type": "application/x-parquet", - "title": "Database Access for TN_ugL_sample daily", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=TN_ugL_sample/model_id=inflow_gefsClimAED?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=TN_ugL_sample/model_id=inflow_gefsClimAED?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "9": { - "type": "application/x-parquet", - "title": "Database Access for NH4_ugL_sample daily", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=NH4_ugL_sample/model_id=inflow_gefsClimAED?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=NH4_ugL_sample/model_id=inflow_gefsClimAED?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "10": { - "type": "application/x-parquet", - "title": "Database Access for TP_ugL_sample daily", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=TP_ugL_sample/model_id=inflow_gefsClimAED?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=TP_ugL_sample/model_id=inflow_gefsClimAED?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "11": { - "type": "application/x-parquet", - "title": "Database Access for NO3NO2_ugL_sample daily", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=NO3NO2_ugL_sample/model_id=inflow_gefsClimAED?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=NO3NO2_ugL_sample/model_id=inflow_gefsClimAED?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "12": { - "type": "application/x-parquet", - "title": "Database Access for SRP_ugL_sample daily", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=SRP_ugL_sample/model_id=inflow_gefsClimAED?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=SRP_ugL_sample/model_id=inflow_gefsClimAED?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - } - } -} diff --git a/catalog/scores/models/model_items/lasso.json b/catalog/scores/models/model_items/lasso.json deleted file mode 100644 index 55273cd790..0000000000 --- a/catalog/scores/models/model_items/lasso.json +++ /dev/null @@ -1,259 +0,0 @@ -{ - "stac_version": "1.0.0", - "stac_extensions": [ - "https://stac-extensions.github.io/table/v1.2.0/schema.json" - ], - "type": "Feature", - "id": "lasso", - "bbox": [ - [ - -156.6194, - 71.2824, - -66.7987, - 71.2824 - ] - ], - "geometry": { - "type": "MultiPoint", - "coordinates": [ - [-122.3303, 45.7624], - [-156.6194, 71.2824], - [-71.2874, 44.0639], - [-78.0418, 39.0337], - [-147.5026, 65.154], - [-97.57, 33.4012], - [-104.7456, 40.8155], - [-99.1066, 47.1617], - [-145.7514, 63.8811], - [-87.8039, 32.5417], - [-81.4362, 28.1251], - [-83.5019, 35.689], - [-66.8687, 17.9696], - [-72.1727, 42.5369], - [-149.2133, 63.8758], - [-84.4686, 31.1948], - [-106.8425, 32.5907], - [-96.6129, 39.1104], - [-96.5631, 39.1008], - [-67.0769, 18.0213], - [-88.1612, 31.8539], - [-80.5248, 37.3783], - [-109.3883, 38.2483], - [-105.5824, 40.0543], - [-100.9154, 46.7697], - [-99.0588, 35.4106], - [-112.4524, 40.1776], - [-84.2826, 35.9641], - [-81.9934, 29.6893], - [-155.3173, 19.5531], - [-105.546, 40.2759], - [-78.1395, 38.8929], - [-76.56, 38.8901], - [-119.7323, 37.1088], - [-119.2622, 37.0334], - [-110.8355, 31.9107], - [-89.5864, 45.5089], - [-103.0293, 40.4619], - [-87.3933, 32.9505], - [-119.006, 37.0058], - [-149.3705, 68.6611], - [-89.5857, 45.4937], - [-95.1921, 39.0404], - [-89.5373, 46.2339], - [-99.2413, 47.1282], - [-121.9519, 45.8205], - [-110.5391, 44.9535] - ] - }, - "properties": { - "description": [], - "start_datetime": "2023-11-14", - "end_datetime": "2023-12-09", - "providers": [ - { - "url": "pending", - "name": "pending", - "roles": [ - "producer", - "processor", - "licensor" - ] - }, - { - "url": "https://www.ecoforecastprojectvt.org", - "name": "Ecoforecast Challenge", - "roles": [ - "host" - ] - } - ], - "license": "CC0-1.0", - "keywords": [ - "Forecasting", - "neon4cast", - "Daily latent_heat_flux", - "Daily Net_ecosystem_exchange" - ], - "table:columns": [ - { - "name": "reference_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that the forecast was initiated (horizon = 0)" - }, - { - "name": "site_id", - "type": "string", - "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model." - }, - { - "name": "datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime of the forecasted value (ISO 8601)" - }, - { - "name": "family", - "type": "string", - "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)." - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "name": "observation", - "type": "double", - "description": "observed value for variable" - }, - { - "name": "crps", - "type": "double", - "description": "crps forecast score" - }, - { - "name": "logs", - "type": "double", - "description": "logs forecast score" - }, - { - "name": "mean", - "type": "double", - "description": "mean forecast prediction" - }, - { - "name": "median", - "type": "double", - "description": "median forecast prediction" - }, - { - "name": "sd", - "type": "double", - "description": "standard deviation forecasts" - }, - { - "name": "quantile97.5", - "type": "double", - "description": "upper 97.5 percentile value of forecast" - }, - { - "name": "quantile02.5", - "type": "double", - "description": "upper 2.5 percentile value of forecast" - }, - { - "name": "quantile90", - "type": "double", - "description": "upper 90 percentile value of forecast" - }, - { - "name": "quantile10", - "type": "double", - "description": "upper 10 percentile value of forecast" - }, - { - "name": "project_id", - "type": "string", - "description": "unique project identifier" - }, - { - "name": "duration", - "type": "string", - "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention" - }, - { - "name": "variable", - "type": "string", - "description": "name of forecasted variable" - }, - { - "name": "model_id", - "type": "string", - "description": "unique model identifier" - }, - { - "name": "date", - "type": "string", - "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period." - } - ] - }, - "collection": "scores", - "links": [ - { - "rel": "collection", - "href": "../collection.json", - "type": "application/json", - "title": "lasso" - }, - { - "rel": "root", - "href": "../../../catalog.json", - "type": "application/json", - "title": "Forecast Catalog" - }, - { - "rel": "parent", - "href": "../collection.json", - "type": "application/json", - "title": "lasso" - }, - { - "rel": "self", - "href": "lasso.json", - "type": "application/json", - "title": "Model Forecast" - }, - { - "rel": "item", - "href": "pending", - "type": "text/html", - "title": "Link for Model Code" - } - ], - "assets": { - "1": { - "type": "application/json", - "title": "Model Metadata", - "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/lasso.json", - "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/lasso.json\")\n\n" - }, - "2": { - "type": "text/html", - "title": "Link for Model Code", - "href": "pending", - "description": "The link to the model code provided by the model submission team" - }, - "3": { - "type": "application/x-parquet", - "title": "Database Access for Daily latent_heat_flux", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=le/model_id=lasso?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=le/model_id=lasso?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "4": { - "type": "application/x-parquet", - "title": "Database Access for Daily Net_ecosystem_exchange", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=nee/model_id=lasso?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=nee/model_id=lasso?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - } - } -} diff --git a/catalog/scores/models/model_items/mean.json b/catalog/scores/models/model_items/mean.json deleted file mode 100644 index 827bb4291c..0000000000 --- a/catalog/scores/models/model_items/mean.json +++ /dev/null @@ -1,259 +0,0 @@ -{ - "stac_version": "1.0.0", - "stac_extensions": [ - "https://stac-extensions.github.io/table/v1.2.0/schema.json" - ], - "type": "Feature", - "id": "mean", - "bbox": [ - [ - -156.6194, - 71.2824, - -66.7987, - 71.2824 - ] - ], - "geometry": { - "type": "MultiPoint", - "coordinates": [ - [-122.3303, 45.7624], - [-156.6194, 71.2824], - [-96.6129, 39.1104], - [-96.5631, 39.1008], - [-67.0769, 18.0213], - [-84.2826, 35.9641], - [-145.7514, 63.8811], - [-149.3705, 68.6611], - [-87.8039, 32.5417], - [-81.4362, 28.1251], - [-83.5019, 35.689], - [-99.2413, 47.1282], - [-121.9519, 45.8205], - [-110.5391, 44.9535], - [-71.2874, 44.0639], - [-80.5248, 37.3783], - [-109.3883, 38.2483], - [-105.5824, 40.0543], - [-155.3173, 19.5531], - [-105.546, 40.2759], - [-84.4686, 31.1948], - [-106.8425, 32.5907], - [-89.5857, 45.4937], - [-95.1921, 39.0404], - [-100.9154, 46.7697], - [-81.9934, 29.6893], - [-66.8687, 17.9696], - [-72.1727, 42.5369], - [-88.1612, 31.8539], - [-76.56, 38.8901], - [-119.7323, 37.1088], - [-87.3933, 32.9505], - [-119.006, 37.0058], - [-99.1066, 47.1617], - [-104.7456, 40.8155], - [-99.0588, 35.4106], - [-112.4524, 40.1776], - [-89.5864, 45.5089], - [-89.5373, 46.2339], - [-78.0418, 39.0337], - [-119.2622, 37.0334], - [-149.2133, 63.8758], - [-78.1395, 38.8929], - [-103.0293, 40.4619], - [-97.57, 33.4012], - [-147.5026, 65.154], - [-110.8355, 31.9107] - ] - }, - "properties": { - "description": [], - "start_datetime": "2023-11-20", - "end_datetime": "2024-11-25", - "providers": [ - { - "url": "pending", - "name": "pending", - "roles": [ - "producer", - "processor", - "licensor" - ] - }, - { - "url": "https://www.ecoforecastprojectvt.org", - "name": "Ecoforecast Challenge", - "roles": [ - "host" - ] - } - ], - "license": "CC0-1.0", - "keywords": [ - "Forecasting", - "neon4cast", - "Weekly beetle_community_abundance", - "Weekly beetle_community_richness" - ], - "table:columns": [ - { - "name": "reference_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that the forecast was initiated (horizon = 0)" - }, - { - "name": "site_id", - "type": "string", - "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model." - }, - { - "name": "datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime of the forecasted value (ISO 8601)" - }, - { - "name": "family", - "type": "string", - "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)." - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "name": "observation", - "type": "double", - "description": "observed value for variable" - }, - { - "name": "crps", - "type": "double", - "description": "crps forecast score" - }, - { - "name": "logs", - "type": "double", - "description": "logs forecast score" - }, - { - "name": "mean", - "type": "double", - "description": "mean forecast prediction" - }, - { - "name": "median", - "type": "double", - "description": "median forecast prediction" - }, - { - "name": "sd", - "type": "double", - "description": "standard deviation forecasts" - }, - { - "name": "quantile97.5", - "type": "double", - "description": "upper 97.5 percentile value of forecast" - }, - { - "name": "quantile02.5", - "type": "double", - "description": "upper 2.5 percentile value of forecast" - }, - { - "name": "quantile90", - "type": "double", - "description": "upper 90 percentile value of forecast" - }, - { - "name": "quantile10", - "type": "double", - "description": "upper 10 percentile value of forecast" - }, - { - "name": "project_id", - "type": "string", - "description": "unique project identifier" - }, - { - "name": "duration", - "type": "string", - "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention" - }, - { - "name": "variable", - "type": "string", - "description": "name of forecasted variable" - }, - { - "name": "model_id", - "type": "string", - "description": "unique model identifier" - }, - { - "name": "date", - "type": "string", - "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period." - } - ] - }, - "collection": "scores", - "links": [ - { - "rel": "collection", - "href": "../collection.json", - "type": "application/json", - "title": "mean" - }, - { - "rel": "root", - "href": "../../../catalog.json", - "type": "application/json", - "title": "Forecast Catalog" - }, - { - "rel": "parent", - "href": "../collection.json", - "type": "application/json", - "title": "mean" - }, - { - "rel": "self", - "href": "mean.json", - "type": "application/json", - "title": "Model Forecast" - }, - { - "rel": "item", - "href": "pending", - "type": "text/html", - "title": "Link for Model Code" - } - ], - "assets": { - "1": { - "type": "application/json", - "title": "Model Metadata", - "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/mean.json", - "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/mean.json\")\n\n" - }, - "2": { - "type": "text/html", - "title": "Link for Model Code", - "href": "pending", - "description": "The link to the model code provided by the model submission team" - }, - "3": { - "type": "application/x-parquet", - "title": "Database Access for Weekly beetle_community_abundance", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1W/variable=abundance/model_id=mean?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1W/variable=abundance/model_id=mean?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "4": { - "type": "application/x-parquet", - "title": "Database Access for Weekly beetle_community_richness", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1W/variable=richness/model_id=mean?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1W/variable=richness/model_id=mean?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - } - } -} diff --git a/catalog/scores/models/model_items/null.json b/catalog/scores/models/model_items/null.json deleted file mode 100644 index 20ff4bbed9..0000000000 --- a/catalog/scores/models/model_items/null.json +++ /dev/null @@ -1,246 +0,0 @@ -{ - "stac_version": "1.0.0", - "stac_extensions": [ - "https://stac-extensions.github.io/table/v1.2.0/schema.json" - ], - "type": "Feature", - "id": "null", - "bbox": [ - [ - -156.6194, - 71.2824, - -66.7987, - 71.2824 - ] - ], - "geometry": { - "type": "MultiPoint", - "coordinates": [ - [-102.4471, 39.7582], - [-82.0084, 29.676], - [-119.2575, 37.0597], - [-110.5871, 44.9501], - [-96.6242, 34.4442], - [-87.7982, 32.5415], - [-147.504, 65.1532], - [-105.5442, 40.035], - [-89.4737, 46.2097], - [-66.9868, 18.1135], - [-84.4374, 31.1854], - [-66.7987, 18.1741], - [-72.3295, 42.4719], - [-96.6038, 39.1051], - [-83.5038, 35.6904], - [-77.9832, 39.0956], - [-89.7048, 45.9983], - [-121.9338, 45.7908], - [-87.4077, 32.9604], - [-96.443, 38.9459], - [-122.1655, 44.2596], - [-149.143, 68.6698], - [-78.1473, 38.8943], - [-97.7823, 33.3785], - [-99.1139, 47.1591], - [-99.2531, 47.1298], - [-111.7979, 40.7839], - [-82.0177, 29.6878], - [-111.5081, 33.751], - [-119.0274, 36.9559], - [-88.1589, 31.8534], - [-149.6106, 68.6307], - [-84.2793, 35.9574], - [-105.9154, 39.8914] - ] - }, - "properties": { - "description": [], - "start_datetime": "2023-10-10", - "end_datetime": "2023-12-15", - "providers": [ - { - "url": "pending", - "name": "pending", - "roles": [ - "producer", - "processor", - "licensor" - ] - }, - { - "url": "https://www.ecoforecastprojectvt.org", - "name": "Ecoforecast Challenge", - "roles": [ - "host" - ] - } - ], - "license": "CC0-1.0", - "keywords": [ - "Forecasting", - "neon4cast", - "Daily Dissolved_oxygen", - "Daily Water_temperature" - ], - "table:columns": [ - { - "name": "reference_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that the forecast was initiated (horizon = 0)" - }, - { - "name": "site_id", - "type": "string", - "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model." - }, - { - "name": "datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime of the forecasted value (ISO 8601)" - }, - { - "name": "family", - "type": "string", - "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)." - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "name": "observation", - "type": "double", - "description": "observed value for variable" - }, - { - "name": "crps", - "type": "double", - "description": "crps forecast score" - }, - { - "name": "logs", - "type": "double", - "description": "logs forecast score" - }, - { - "name": "mean", - "type": "double", - "description": "mean forecast prediction" - }, - { - "name": "median", - "type": "double", - "description": "median forecast prediction" - }, - { - "name": "sd", - "type": "double", - "description": "standard deviation forecasts" - }, - { - "name": "quantile97.5", - "type": "double", - "description": "upper 97.5 percentile value of forecast" - }, - { - "name": "quantile02.5", - "type": "double", - "description": "upper 2.5 percentile value of forecast" - }, - { - "name": "quantile90", - "type": "double", - "description": "upper 90 percentile value of forecast" - }, - { - "name": "quantile10", - "type": "double", - "description": "upper 10 percentile value of forecast" - }, - { - "name": "project_id", - "type": "string", - "description": "unique project identifier" - }, - { - "name": "duration", - "type": "string", - "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention" - }, - { - "name": "variable", - "type": "string", - "description": "name of forecasted variable" - }, - { - "name": "model_id", - "type": "string", - "description": "unique model identifier" - }, - { - "name": "date", - "type": "string", - "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period." - } - ] - }, - "collection": "scores", - "links": [ - { - "rel": "collection", - "href": "../collection.json", - "type": "application/json", - "title": "null" - }, - { - "rel": "root", - "href": "../../../catalog.json", - "type": "application/json", - "title": "Forecast Catalog" - }, - { - "rel": "parent", - "href": "../collection.json", - "type": "application/json", - "title": "null" - }, - { - "rel": "self", - "href": "null.json", - "type": "application/json", - "title": "Model Forecast" - }, - { - "rel": "item", - "href": "pending", - "type": "text/html", - "title": "Link for Model Code" - } - ], - "assets": { - "1": { - "type": "application/json", - "title": "Model Metadata", - "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/null.json", - "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/null.json\")\n\n" - }, - "2": { - "type": "text/html", - "title": "Link for Model Code", - "href": "pending", - "description": "The link to the model code provided by the model submission team" - }, - "3": { - "type": "application/x-parquet", - "title": "Database Access for Daily Dissolved_oxygen", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=oxygen/model_id=null?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=oxygen/model_id=null?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "4": { - "type": "application/x-parquet", - "title": "Database Access for Daily Water_temperature", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=null?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=null?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - } - } -} diff --git a/catalog/scores/models/model_items/persistenceFO.json b/catalog/scores/models/model_items/persistenceFO.json deleted file mode 100644 index 647f85d83f..0000000000 --- a/catalog/scores/models/model_items/persistenceFO.json +++ /dev/null @@ -1,199 +0,0 @@ -{ - "stac_version": "1.0.0", - "stac_extensions": [ - "https://stac-extensions.github.io/table/v1.2.0/schema.json" - ], - "type": "Feature", - "id": "persistenceFO", - "bbox": [ - [ - -156.6194, - 71.2824, - -66.7987, - 71.2824 - ] - ], - "geometry": { - "type": "MultiPoint", - "coordinates": [ - [37.3129, -79.8159] - ] - }, - "properties": { - "description": "\nmodel info: another persistence forecast\n\nSites: bvre\n\nVariables: Temp_C_mean", - "start_datetime": "2023-09-27", - "end_datetime": "2023-10-30", - "providers": [ - { - "url": "pending", - "name": "pending", - "roles": [ - "producer", - "processor", - "licensor" - ] - }, - { - "url": "https://www.ecoforecastprojectvt.org", - "name": "Ecoforecast Challenge", - "roles": [ - "host" - ] - } - ], - "license": "CC0-1.0", - "keywords": [ - "Forecasting", - "vera4cast", - "Temp_C_mean" - ], - "table:columns": [ - { - "name": "reference_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that the forecast was initiated (horizon = 0)" - }, - { - "name": "site_id", - "type": "string", - "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model." - }, - { - "name": "datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime of the forecasted value (ISO 8601)" - }, - { - "name": "family", - "type": "string", - "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)." - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "name": "depth_m", - "type": "double", - "description": "depth (meters) in water column of prediction" - }, - { - "name": "observation", - "type": "double", - "description": "observed value for variable" - }, - { - "name": "crps", - "type": "double", - "description": "crps forecast score" - }, - { - "name": "logs", - "type": "double", - "description": "logs forecast score" - }, - { - "name": "mean", - "type": "double", - "description": "mean forecast prediction" - }, - { - "name": "median", - "type": "double", - "description": "median forecast prediction" - }, - { - "name": "sd", - "type": "double", - "description": "standard deviation forecasts" - }, - { - "name": "quantile97.5", - "type": "double", - "description": "upper 97.5 percentile value of forecast" - }, - { - "name": "quantile02.5", - "type": "double", - "description": "upper 2.5 percentile value of forecast" - }, - { - "name": "quantile90", - "type": "double", - "description": "upper 90 percentile value of forecast" - }, - { - "name": "quantile10", - "type": "double", - "description": "upper 10 percentile value of forecast" - }, - { - "name": "project_id", - "type": "string", - "description": "unique project identifier" - }, - { - "name": "duration", - "type": "string", - "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention" - }, - { - "name": "variable", - "type": "string", - "description": "name of forecasted variable" - }, - { - "name": "model_id", - "type": "string", - "description": "unique model identifier" - }, - { - "name": "date", - "type": "string", - "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period." - } - ] - }, - "collection": "scores", - "links": [ - { - "rel": "collection", - "href": "../collection.json", - "type": "application/json", - "title": "persistenceFO" - }, - { - "rel": "root", - "href": "../../../catalog.json", - "type": "application/json", - "title": "Forecast Catalog" - }, - { - "rel": "parent", - "href": "../collection.json", - "type": "application/json", - "title": "persistenceFO" - }, - { - "rel": "self", - "href": "persistenceFO.json", - "type": "application/json", - "title": "Model Forecast" - } - ], - "assets": { - "1": { - "type": "application/json", - "title": "Model Metadata", - "href": "https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/persistenceFO.json", - "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/persistenceFO.json\")\n\n" - }, - "2": { - "type": "application/x-parquet", - "title": "Database Access for Temp_C_mean daily", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=Temp_C_mean/model_id=persistenceFO?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=Temp_C_mean/model_id=persistenceFO?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - } - } -} diff --git a/catalog/scores/models/model_items/persistenceRW.json b/catalog/scores/models/model_items/persistenceRW.json deleted file mode 100644 index 538cc95c46..0000000000 --- a/catalog/scores/models/model_items/persistenceRW.json +++ /dev/null @@ -1,321 +0,0 @@ -{ - "stac_version": "1.0.0", - "stac_extensions": [ - "https://stac-extensions.github.io/table/v1.2.0/schema.json" - ], - "type": "Feature", - "id": "persistenceRW", - "bbox": [ - [ - -156.6194, - 71.2824, - -66.7987, - 71.2824 - ] - ], - "geometry": { - "type": "MultiPoint", - "coordinates": [ - [-82.0084, 29.676], - [-87.7982, 32.5415], - [-89.4737, 46.2097], - [-84.4374, 31.1854], - [-89.7048, 45.9983], - [-99.1139, 47.1591], - [-99.2531, 47.1298], - [-82.0177, 29.6878], - [-88.1589, 31.8534], - [-149.6106, 68.6307], - [-122.3303, 45.7624], - [-156.6194, 71.2824], - [-71.2874, 44.0639], - [-78.0418, 39.0337], - [-147.5026, 65.154], - [-97.57, 33.4012], - [-104.7456, 40.8155], - [-99.1066, 47.1617], - [-145.7514, 63.8811], - [-87.8039, 32.5417], - [-81.4362, 28.1251], - [-83.5019, 35.689], - [-66.8687, 17.9696], - [-72.1727, 42.5369], - [-149.2133, 63.8758], - [-84.4686, 31.1948], - [-106.8425, 32.5907], - [-96.6129, 39.1104], - [-96.5631, 39.1008], - [-67.0769, 18.0213], - [-88.1612, 31.8539], - [-80.5248, 37.3783], - [-109.3883, 38.2483], - [-105.5824, 40.0543], - [-100.9154, 46.7697], - [-99.0588, 35.4106], - [-112.4524, 40.1776], - [-84.2826, 35.9641], - [-81.9934, 29.6893], - [-155.3173, 19.5531], - [-105.546, 40.2759], - [-78.1395, 38.8929], - [-76.56, 38.8901], - [-119.7323, 37.1088], - [-119.2622, 37.0334], - [-110.8355, 31.9107], - [-89.5864, 45.5089], - [-103.0293, 40.4619], - [-87.3933, 32.9505], - [-119.006, 37.0058], - [-149.3705, 68.6611], - [-89.5857, 45.4937], - [-95.1921, 39.0404], - [-89.5373, 46.2339], - [-99.2413, 47.1282], - [-121.9519, 45.8205], - [-110.5391, 44.9535], - [-102.4471, 39.7582], - [-119.2575, 37.0597], - [-110.5871, 44.9501], - [-96.6242, 34.4442], - [-147.504, 65.1532], - [-105.5442, 40.035], - [-66.9868, 18.1135], - [-66.7987, 18.1741], - [-72.3295, 42.4719], - [-96.6038, 39.1051], - [-83.5038, 35.6904], - [-77.9832, 39.0956], - [-121.9338, 45.7908], - [-87.4077, 32.9604], - [-96.443, 38.9459], - [-122.1655, 44.2596], - [-149.143, 68.6698], - [-78.1473, 38.8943], - [-97.7823, 33.3785], - [-111.7979, 40.7839], - [-111.5081, 33.751], - [-119.0274, 36.9559], - [-84.2793, 35.9574], - [-105.9154, 39.8914] - ] - }, - "properties": { - "description": ["\nmodel info: Random walk from the fable package with ensembles used to represent uncertainty\n\nSites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ARIK, BIGC, BLDE, BLUE, CARI, COMO, CUPE, GUIL, HOPB, KING, LECO, LEWI, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, REDB, SYCA, TECR, WALK, WLOU\n\nVariables: Daily Chlorophyll_a, Daily Green_chromatic_coordinate, Daily Net_ecosystem_exchange, Daily Dissolved_oxygen, Daily Red_chromatic_coordinate, Daily Water_temperature", "\nmodel info: NA\n\nSites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ARIK, BIGC, BLDE, BLUE, CARI, COMO, CUPE, GUIL, HOPB, KING, LECO, LEWI, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, REDB, SYCA, TECR, WALK, WLOU\n\nVariables: Daily Chlorophyll_a, Daily Green_chromatic_coordinate, Daily Net_ecosystem_exchange, Daily Dissolved_oxygen, Daily Red_chromatic_coordinate, Daily Water_temperature"], - "start_datetime": "2023-11-15", - "end_datetime": "2023-12-15", - "providers": [ - { - "url": "pending", - "name": "pending", - "roles": [ - "producer", - "processor", - "licensor" - ] - }, - { - "url": "https://www.ecoforecastprojectvt.org", - "name": "Ecoforecast Challenge", - "roles": [ - "host" - ] - } - ], - "license": "CC0-1.0", - "keywords": [ - "Forecasting", - "neon4cast", - "Daily Chlorophyll_a", - "Daily Green_chromatic_coordinate", - "Daily Net_ecosystem_exchange", - "Daily Dissolved_oxygen", - "Daily Red_chromatic_coordinate", - "Daily Water_temperature" - ], - "table:columns": [ - { - "name": "reference_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that the forecast was initiated (horizon = 0)" - }, - { - "name": "site_id", - "type": "string", - "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model." - }, - { - "name": "datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime of the forecasted value (ISO 8601)" - }, - { - "name": "family", - "type": "string", - "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)." - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "name": "observation", - "type": "double", - "description": "observed value for variable" - }, - { - "name": "crps", - "type": "double", - "description": "crps forecast score" - }, - { - "name": "logs", - "type": "double", - "description": "logs forecast score" - }, - { - "name": "mean", - "type": "double", - "description": "mean forecast prediction" - }, - { - "name": "median", - "type": "double", - "description": "median forecast prediction" - }, - { - "name": "sd", - "type": "double", - "description": "standard deviation forecasts" - }, - { - "name": "quantile97.5", - "type": "double", - "description": "upper 97.5 percentile value of forecast" - }, - { - "name": "quantile02.5", - "type": "double", - "description": "upper 2.5 percentile value of forecast" - }, - { - "name": "quantile90", - "type": "double", - "description": "upper 90 percentile value of forecast" - }, - { - "name": "quantile10", - "type": "double", - "description": "upper 10 percentile value of forecast" - }, - { - "name": "project_id", - "type": "string", - "description": "unique project identifier" - }, - { - "name": "duration", - "type": "string", - "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention" - }, - { - "name": "variable", - "type": "string", - "description": "name of forecasted variable" - }, - { - "name": "model_id", - "type": "string", - "description": "unique model identifier" - }, - { - "name": "date", - "type": "string", - "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period." - } - ] - }, - "collection": "scores", - "links": [ - { - "rel": "collection", - "href": "../collection.json", - "type": "application/json", - "title": "persistenceRW" - }, - { - "rel": "root", - "href": "../../../catalog.json", - "type": "application/json", - "title": "Forecast Catalog" - }, - { - "rel": "parent", - "href": "../collection.json", - "type": "application/json", - "title": "persistenceRW" - }, - { - "rel": "self", - "href": "persistenceRW.json", - "type": "application/json", - "title": "Model Forecast" - }, - { - "rel": "item", - "href": "pending", - "type": "text/html", - "title": "Link for Model Code" - } - ], - "assets": { - "1": { - "type": "application/json", - "title": "Model Metadata", - "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/persistenceRW.json", - "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/persistenceRW.json\")\n\n" - }, - "2": { - "type": "text/html", - "title": "Link for Model Code", - "href": "pending", - "description": "The link to the model code provided by the model submission team" - }, - "3": { - "type": "application/x-parquet", - "title": "Database Access for Daily Chlorophyll_a", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=persistenceRW?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=persistenceRW?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "4": { - "type": "application/x-parquet", - "title": "Database Access for Daily Green_chromatic_coordinate", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=gcc_90/model_id=persistenceRW?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=gcc_90/model_id=persistenceRW?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "5": { - "type": "application/x-parquet", - "title": "Database Access for Daily Net_ecosystem_exchange", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=nee/model_id=persistenceRW?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=nee/model_id=persistenceRW?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "6": { - "type": "application/x-parquet", - "title": "Database Access for Daily Dissolved_oxygen", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=oxygen/model_id=persistenceRW?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=oxygen/model_id=persistenceRW?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "7": { - "type": "application/x-parquet", - "title": "Database Access for Daily Red_chromatic_coordinate", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=rcc_90/model_id=persistenceRW?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=rcc_90/model_id=persistenceRW?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "8": { - "type": "application/x-parquet", - "title": "Database Access for Daily Water_temperature", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=persistenceRW?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=persistenceRW?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - } - } -} diff --git a/catalog/scores/models/model_items/procBlanchardMonod.json b/catalog/scores/models/model_items/procBlanchardMonod.json deleted file mode 100644 index 944c10a2eb..0000000000 --- a/catalog/scores/models/model_items/procBlanchardMonod.json +++ /dev/null @@ -1,212 +0,0 @@ -{ - "stac_version": "1.0.0", - "stac_extensions": [ - "https://stac-extensions.github.io/table/v1.2.0/schema.json" - ], - "type": "Feature", - "id": "procBlanchardMonod", - "bbox": [ - [ - -156.6194, - 71.2824, - -66.7987, - 71.2824 - ] - ], - "geometry": { - "type": "MultiPoint", - "coordinates": [ - [-82.0084, 29.676], - [-89.4737, 46.2097], - [-89.7048, 45.9983], - [-99.1139, 47.1591], - [-99.2531, 47.1298], - [-82.0177, 29.6878], - [-149.6106, 68.6307] - ] - }, - "properties": { - "description": "\nmodel info: NA\n\nSites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK\n\nVariables: Daily Chlorophyll_a", - "start_datetime": "2023-11-13", - "end_datetime": "2023-11-30", - "providers": [ - { - "url": "pending", - "name": "pending", - "roles": [ - "producer", - "processor", - "licensor" - ] - }, - { - "url": "https://www.ecoforecastprojectvt.org", - "name": "Ecoforecast Challenge", - "roles": [ - "host" - ] - } - ], - "license": "CC0-1.0", - "keywords": [ - "Forecasting", - "neon4cast", - "Daily Chlorophyll_a" - ], - "table:columns": [ - { - "name": "reference_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that the forecast was initiated (horizon = 0)" - }, - { - "name": "site_id", - "type": "string", - "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model." - }, - { - "name": "datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime of the forecasted value (ISO 8601)" - }, - { - "name": "family", - "type": "string", - "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)." - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "name": "observation", - "type": "double", - "description": "observed value for variable" - }, - { - "name": "crps", - "type": "double", - "description": "crps forecast score" - }, - { - "name": "logs", - "type": "double", - "description": "logs forecast score" - }, - { - "name": "mean", - "type": "double", - "description": "mean forecast prediction" - }, - { - "name": "median", - "type": "double", - "description": "median forecast prediction" - }, - { - "name": "sd", - "type": "double", - "description": "standard deviation forecasts" - }, - { - "name": "quantile97.5", - "type": "double", - "description": "upper 97.5 percentile value of forecast" - }, - { - "name": "quantile02.5", - "type": "double", - "description": "upper 2.5 percentile value of forecast" - }, - { - "name": "quantile90", - "type": "double", - "description": "upper 90 percentile value of forecast" - }, - { - "name": "quantile10", - "type": "double", - "description": "upper 10 percentile value of forecast" - }, - { - "name": "project_id", - "type": "string", - "description": "unique project identifier" - }, - { - "name": "duration", - "type": "string", - "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention" - }, - { - "name": "variable", - "type": "string", - "description": "name of forecasted variable" - }, - { - "name": "model_id", - "type": "string", - "description": "unique model identifier" - }, - { - "name": "date", - "type": "string", - "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period." - } - ] - }, - "collection": "scores", - "links": [ - { - "rel": "collection", - "href": "../collection.json", - "type": "application/json", - "title": "procBlanchardMonod" - }, - { - "rel": "root", - "href": "../../../catalog.json", - "type": "application/json", - "title": "Forecast Catalog" - }, - { - "rel": "parent", - "href": "../collection.json", - "type": "application/json", - "title": "procBlanchardMonod" - }, - { - "rel": "self", - "href": "procBlanchardMonod.json", - "type": "application/json", - "title": "Model Forecast" - }, - { - "rel": "item", - "href": "pending", - "type": "text/html", - "title": "Link for Model Code" - } - ], - "assets": { - "1": { - "type": "application/json", - "title": "Model Metadata", - "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/procBlanchardMonod.json", - "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/procBlanchardMonod.json\")\n\n" - }, - "2": { - "type": "text/html", - "title": "Link for Model Code", - "href": "pending", - "description": "The link to the model code provided by the model submission team" - }, - "3": { - "type": "application/x-parquet", - "title": "Database Access for Daily Chlorophyll_a", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=procBlanchardMonod?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=procBlanchardMonod?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - } - } -} diff --git a/catalog/scores/models/model_items/procBlanchardSteele.json b/catalog/scores/models/model_items/procBlanchardSteele.json deleted file mode 100644 index 11e7434e2e..0000000000 --- a/catalog/scores/models/model_items/procBlanchardSteele.json +++ /dev/null @@ -1,212 +0,0 @@ -{ - "stac_version": "1.0.0", - "stac_extensions": [ - "https://stac-extensions.github.io/table/v1.2.0/schema.json" - ], - "type": "Feature", - "id": "procBlanchardSteele", - "bbox": [ - [ - -156.6194, - 71.2824, - -66.7987, - 71.2824 - ] - ], - "geometry": { - "type": "MultiPoint", - "coordinates": [ - [-82.0084, 29.676], - [-89.4737, 46.2097], - [-89.7048, 45.9983], - [-99.1139, 47.1591], - [-99.2531, 47.1298], - [-82.0177, 29.6878], - [-149.6106, 68.6307] - ] - }, - "properties": { - "description": [], - "start_datetime": "2023-11-13", - "end_datetime": "2023-11-30", - "providers": [ - { - "url": "pending", - "name": "pending", - "roles": [ - "producer", - "processor", - "licensor" - ] - }, - { - "url": "https://www.ecoforecastprojectvt.org", - "name": "Ecoforecast Challenge", - "roles": [ - "host" - ] - } - ], - "license": "CC0-1.0", - "keywords": [ - "Forecasting", - "neon4cast", - "Daily Chlorophyll_a" - ], - "table:columns": [ - { - "name": "reference_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that the forecast was initiated (horizon = 0)" - }, - { - "name": "site_id", - "type": "string", - "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model." - }, - { - "name": "datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime of the forecasted value (ISO 8601)" - }, - { - "name": "family", - "type": "string", - "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)." - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "name": "observation", - "type": "double", - "description": "observed value for variable" - }, - { - "name": "crps", - "type": "double", - "description": "crps forecast score" - }, - { - "name": "logs", - "type": "double", - "description": "logs forecast score" - }, - { - "name": "mean", - "type": "double", - "description": "mean forecast prediction" - }, - { - "name": "median", - "type": "double", - "description": "median forecast prediction" - }, - { - "name": "sd", - "type": "double", - "description": "standard deviation forecasts" - }, - { - "name": "quantile97.5", - "type": "double", - "description": "upper 97.5 percentile value of forecast" - }, - { - "name": "quantile02.5", - "type": "double", - "description": "upper 2.5 percentile value of forecast" - }, - { - "name": "quantile90", - "type": "double", - "description": "upper 90 percentile value of forecast" - }, - { - "name": "quantile10", - "type": "double", - "description": "upper 10 percentile value of forecast" - }, - { - "name": "project_id", - "type": "string", - "description": "unique project identifier" - }, - { - "name": "duration", - "type": "string", - "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention" - }, - { - "name": "variable", - "type": "string", - "description": "name of forecasted variable" - }, - { - "name": "model_id", - "type": "string", - "description": "unique model identifier" - }, - { - "name": "date", - "type": "string", - "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period." - } - ] - }, - "collection": "scores", - "links": [ - { - "rel": "collection", - "href": "../collection.json", - "type": "application/json", - "title": "procBlanchardSteele" - }, - { - "rel": "root", - "href": "../../../catalog.json", - "type": "application/json", - "title": "Forecast Catalog" - }, - { - "rel": "parent", - "href": "../collection.json", - "type": "application/json", - "title": "procBlanchardSteele" - }, - { - "rel": "self", - "href": "procBlanchardSteele.json", - "type": "application/json", - "title": "Model Forecast" - }, - { - "rel": "item", - "href": "pending", - "type": "text/html", - "title": "Link for Model Code" - } - ], - "assets": { - "1": { - "type": "application/json", - "title": "Model Metadata", - "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/procBlanchardSteele.json", - "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/procBlanchardSteele.json\")\n\n" - }, - "2": { - "type": "text/html", - "title": "Link for Model Code", - "href": "pending", - "description": "The link to the model code provided by the model submission team" - }, - "3": { - "type": "application/x-parquet", - "title": "Database Access for Daily Chlorophyll_a", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=procBlanchardSteele?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=procBlanchardSteele?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - } - } -} diff --git a/catalog/scores/models/model_items/procCTMIMonod.json b/catalog/scores/models/model_items/procCTMIMonod.json deleted file mode 100644 index 2e2dc4042e..0000000000 --- a/catalog/scores/models/model_items/procCTMIMonod.json +++ /dev/null @@ -1,212 +0,0 @@ -{ - "stac_version": "1.0.0", - "stac_extensions": [ - "https://stac-extensions.github.io/table/v1.2.0/schema.json" - ], - "type": "Feature", - "id": "procCTMIMonod", - "bbox": [ - [ - -156.6194, - 71.2824, - -66.7987, - 71.2824 - ] - ], - "geometry": { - "type": "MultiPoint", - "coordinates": [ - [-82.0084, 29.676], - [-89.4737, 46.2097], - [-89.7048, 45.9983], - [-99.1139, 47.1591], - [-99.2531, 47.1298], - [-82.0177, 29.6878], - [-149.6106, 68.6307] - ] - }, - "properties": { - "description": "\nmodel info: NA\n\nSites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK\n\nVariables: Daily Chlorophyll_a", - "start_datetime": "2023-11-13", - "end_datetime": "2023-11-30", - "providers": [ - { - "url": "pending", - "name": "pending", - "roles": [ - "producer", - "processor", - "licensor" - ] - }, - { - "url": "https://www.ecoforecastprojectvt.org", - "name": "Ecoforecast Challenge", - "roles": [ - "host" - ] - } - ], - "license": "CC0-1.0", - "keywords": [ - "Forecasting", - "neon4cast", - "Daily Chlorophyll_a" - ], - "table:columns": [ - { - "name": "reference_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that the forecast was initiated (horizon = 0)" - }, - { - "name": "site_id", - "type": "string", - "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model." - }, - { - "name": "datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime of the forecasted value (ISO 8601)" - }, - { - "name": "family", - "type": "string", - "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)." - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "name": "observation", - "type": "double", - "description": "observed value for variable" - }, - { - "name": "crps", - "type": "double", - "description": "crps forecast score" - }, - { - "name": "logs", - "type": "double", - "description": "logs forecast score" - }, - { - "name": "mean", - "type": "double", - "description": "mean forecast prediction" - }, - { - "name": "median", - "type": "double", - "description": "median forecast prediction" - }, - { - "name": "sd", - "type": "double", - "description": "standard deviation forecasts" - }, - { - "name": "quantile97.5", - "type": "double", - "description": "upper 97.5 percentile value of forecast" - }, - { - "name": "quantile02.5", - "type": "double", - "description": "upper 2.5 percentile value of forecast" - }, - { - "name": "quantile90", - "type": "double", - "description": "upper 90 percentile value of forecast" - }, - { - "name": "quantile10", - "type": "double", - "description": "upper 10 percentile value of forecast" - }, - { - "name": "project_id", - "type": "string", - "description": "unique project identifier" - }, - { - "name": "duration", - "type": "string", - "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention" - }, - { - "name": "variable", - "type": "string", - "description": "name of forecasted variable" - }, - { - "name": "model_id", - "type": "string", - "description": "unique model identifier" - }, - { - "name": "date", - "type": "string", - "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period." - } - ] - }, - "collection": "scores", - "links": [ - { - "rel": "collection", - "href": "../collection.json", - "type": "application/json", - "title": "procCTMIMonod" - }, - { - "rel": "root", - "href": "../../../catalog.json", - "type": "application/json", - "title": "Forecast Catalog" - }, - { - "rel": "parent", - "href": "../collection.json", - "type": "application/json", - "title": "procCTMIMonod" - }, - { - "rel": "self", - "href": "procCTMIMonod.json", - "type": "application/json", - "title": "Model Forecast" - }, - { - "rel": "item", - "href": "pending", - "type": "text/html", - "title": "Link for Model Code" - } - ], - "assets": { - "1": { - "type": "application/json", - "title": "Model Metadata", - "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/procCTMIMonod.json", - "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/procCTMIMonod.json\")\n\n" - }, - "2": { - "type": "text/html", - "title": "Link for Model Code", - "href": "pending", - "description": "The link to the model code provided by the model submission team" - }, - "3": { - "type": "application/x-parquet", - "title": "Database Access for Daily Chlorophyll_a", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=procCTMIMonod?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=procCTMIMonod?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - } - } -} diff --git a/catalog/scores/models/model_items/procCTMISteele.json b/catalog/scores/models/model_items/procCTMISteele.json deleted file mode 100644 index eaa30dd53f..0000000000 --- a/catalog/scores/models/model_items/procCTMISteele.json +++ /dev/null @@ -1,212 +0,0 @@ -{ - "stac_version": "1.0.0", - "stac_extensions": [ - "https://stac-extensions.github.io/table/v1.2.0/schema.json" - ], - "type": "Feature", - "id": "procCTMISteele", - "bbox": [ - [ - -156.6194, - 71.2824, - -66.7987, - 71.2824 - ] - ], - "geometry": { - "type": "MultiPoint", - "coordinates": [ - [-82.0084, 29.676], - [-89.4737, 46.2097], - [-89.7048, 45.9983], - [-99.1139, 47.1591], - [-99.2531, 47.1298], - [-82.0177, 29.6878], - [-149.6106, 68.6307] - ] - }, - "properties": { - "description": [], - "start_datetime": "2023-11-13", - "end_datetime": "2023-11-30", - "providers": [ - { - "url": "pending", - "name": "pending", - "roles": [ - "producer", - "processor", - "licensor" - ] - }, - { - "url": "https://www.ecoforecastprojectvt.org", - "name": "Ecoforecast Challenge", - "roles": [ - "host" - ] - } - ], - "license": "CC0-1.0", - "keywords": [ - "Forecasting", - "neon4cast", - "Daily Chlorophyll_a" - ], - "table:columns": [ - { - "name": "reference_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that the forecast was initiated (horizon = 0)" - }, - { - "name": "site_id", - "type": "string", - "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model." - }, - { - "name": "datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime of the forecasted value (ISO 8601)" - }, - { - "name": "family", - "type": "string", - "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)." - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "name": "observation", - "type": "double", - "description": "observed value for variable" - }, - { - "name": "crps", - "type": "double", - "description": "crps forecast score" - }, - { - "name": "logs", - "type": "double", - "description": "logs forecast score" - }, - { - "name": "mean", - "type": "double", - "description": "mean forecast prediction" - }, - { - "name": "median", - "type": "double", - "description": "median forecast prediction" - }, - { - "name": "sd", - "type": "double", - "description": "standard deviation forecasts" - }, - { - "name": "quantile97.5", - "type": "double", - "description": "upper 97.5 percentile value of forecast" - }, - { - "name": "quantile02.5", - "type": "double", - "description": "upper 2.5 percentile value of forecast" - }, - { - "name": "quantile90", - "type": "double", - "description": "upper 90 percentile value of forecast" - }, - { - "name": "quantile10", - "type": "double", - "description": "upper 10 percentile value of forecast" - }, - { - "name": "project_id", - "type": "string", - "description": "unique project identifier" - }, - { - "name": "duration", - "type": "string", - "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention" - }, - { - "name": "variable", - "type": "string", - "description": "name of forecasted variable" - }, - { - "name": "model_id", - "type": "string", - "description": "unique model identifier" - }, - { - "name": "date", - "type": "string", - "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period." - } - ] - }, - "collection": "scores", - "links": [ - { - "rel": "collection", - "href": "../collection.json", - "type": "application/json", - "title": "procCTMISteele" - }, - { - "rel": "root", - "href": "../../../catalog.json", - "type": "application/json", - "title": "Forecast Catalog" - }, - { - "rel": "parent", - "href": "../collection.json", - "type": "application/json", - "title": "procCTMISteele" - }, - { - "rel": "self", - "href": "procCTMISteele.json", - "type": "application/json", - "title": "Model Forecast" - }, - { - "rel": "item", - "href": "pending", - "type": "text/html", - "title": "Link for Model Code" - } - ], - "assets": { - "1": { - "type": "application/json", - "title": "Model Metadata", - "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/procCTMISteele.json", - "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/procCTMISteele.json\")\n\n" - }, - "2": { - "type": "text/html", - "title": "Link for Model Code", - "href": "pending", - "description": "The link to the model code provided by the model submission team" - }, - "3": { - "type": "application/x-parquet", - "title": "Database Access for Daily Chlorophyll_a", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=procCTMISteele?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=procCTMISteele?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - } - } -} diff --git a/catalog/scores/models/model_items/procEppleyNorbergMonod.json b/catalog/scores/models/model_items/procEppleyNorbergMonod.json deleted file mode 100644 index 4a39706c31..0000000000 --- a/catalog/scores/models/model_items/procEppleyNorbergMonod.json +++ /dev/null @@ -1,212 +0,0 @@ -{ - "stac_version": "1.0.0", - "stac_extensions": [ - "https://stac-extensions.github.io/table/v1.2.0/schema.json" - ], - "type": "Feature", - "id": "procEppleyNorbergMonod", - "bbox": [ - [ - -156.6194, - 71.2824, - -66.7987, - 71.2824 - ] - ], - "geometry": { - "type": "MultiPoint", - "coordinates": [ - [-82.0084, 29.676], - [-89.4737, 46.2097], - [-89.7048, 45.9983], - [-99.1139, 47.1591], - [-99.2531, 47.1298], - [-82.0177, 29.6878], - [-149.6106, 68.6307] - ] - }, - "properties": { - "description": "\nmodel info: NA\n\nSites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK\n\nVariables: Daily Chlorophyll_a", - "start_datetime": "2023-11-13", - "end_datetime": "2023-11-30", - "providers": [ - { - "url": "pending", - "name": "pending", - "roles": [ - "producer", - "processor", - "licensor" - ] - }, - { - "url": "https://www.ecoforecastprojectvt.org", - "name": "Ecoforecast Challenge", - "roles": [ - "host" - ] - } - ], - "license": "CC0-1.0", - "keywords": [ - "Forecasting", - "neon4cast", - "Daily Chlorophyll_a" - ], - "table:columns": [ - { - "name": "reference_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that the forecast was initiated (horizon = 0)" - }, - { - "name": "site_id", - "type": "string", - "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model." - }, - { - "name": "datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime of the forecasted value (ISO 8601)" - }, - { - "name": "family", - "type": "string", - "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)." - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "name": "observation", - "type": "double", - "description": "observed value for variable" - }, - { - "name": "crps", - "type": "double", - "description": "crps forecast score" - }, - { - "name": "logs", - "type": "double", - "description": "logs forecast score" - }, - { - "name": "mean", - "type": "double", - "description": "mean forecast prediction" - }, - { - "name": "median", - "type": "double", - "description": "median forecast prediction" - }, - { - "name": "sd", - "type": "double", - "description": "standard deviation forecasts" - }, - { - "name": "quantile97.5", - "type": "double", - "description": "upper 97.5 percentile value of forecast" - }, - { - "name": "quantile02.5", - "type": "double", - "description": "upper 2.5 percentile value of forecast" - }, - { - "name": "quantile90", - "type": "double", - "description": "upper 90 percentile value of forecast" - }, - { - "name": "quantile10", - "type": "double", - "description": "upper 10 percentile value of forecast" - }, - { - "name": "project_id", - "type": "string", - "description": "unique project identifier" - }, - { - "name": "duration", - "type": "string", - "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention" - }, - { - "name": "variable", - "type": "string", - "description": "name of forecasted variable" - }, - { - "name": "model_id", - "type": "string", - "description": "unique model identifier" - }, - { - "name": "date", - "type": "string", - "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period." - } - ] - }, - "collection": "scores", - "links": [ - { - "rel": "collection", - "href": "../collection.json", - "type": "application/json", - "title": "procEppleyNorbergMonod" - }, - { - "rel": "root", - "href": "../../../catalog.json", - "type": "application/json", - "title": "Forecast Catalog" - }, - { - "rel": "parent", - "href": "../collection.json", - "type": "application/json", - "title": "procEppleyNorbergMonod" - }, - { - "rel": "self", - "href": "procEppleyNorbergMonod.json", - "type": "application/json", - "title": "Model Forecast" - }, - { - "rel": "item", - "href": "pending", - "type": "text/html", - "title": "Link for Model Code" - } - ], - "assets": { - "1": { - "type": "application/json", - "title": "Model Metadata", - "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/procEppleyNorbergMonod.json", - "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/procEppleyNorbergMonod.json\")\n\n" - }, - "2": { - "type": "text/html", - "title": "Link for Model Code", - "href": "pending", - "description": "The link to the model code provided by the model submission team" - }, - "3": { - "type": "application/x-parquet", - "title": "Database Access for Daily Chlorophyll_a", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=procEppleyNorbergMonod?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=procEppleyNorbergMonod?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - } - } -} diff --git a/catalog/scores/models/model_items/procEppleyNorbergSteele.json b/catalog/scores/models/model_items/procEppleyNorbergSteele.json deleted file mode 100644 index 2f7dfb0469..0000000000 --- a/catalog/scores/models/model_items/procEppleyNorbergSteele.json +++ /dev/null @@ -1,212 +0,0 @@ -{ - "stac_version": "1.0.0", - "stac_extensions": [ - "https://stac-extensions.github.io/table/v1.2.0/schema.json" - ], - "type": "Feature", - "id": "procEppleyNorbergSteele", - "bbox": [ - [ - -156.6194, - 71.2824, - -66.7987, - 71.2824 - ] - ], - "geometry": { - "type": "MultiPoint", - "coordinates": [ - [-82.0084, 29.676], - [-89.4737, 46.2097], - [-89.7048, 45.9983], - [-99.1139, 47.1591], - [-99.2531, 47.1298], - [-82.0177, 29.6878], - [-149.6106, 68.6307] - ] - }, - "properties": { - "description": "\nmodel info: NA\n\nSites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK\n\nVariables: Daily Chlorophyll_a", - "start_datetime": "2023-11-13", - "end_datetime": "2023-11-30", - "providers": [ - { - "url": "pending", - "name": "pending", - "roles": [ - "producer", - "processor", - "licensor" - ] - }, - { - "url": "https://www.ecoforecastprojectvt.org", - "name": "Ecoforecast Challenge", - "roles": [ - "host" - ] - } - ], - "license": "CC0-1.0", - "keywords": [ - "Forecasting", - "neon4cast", - "Daily Chlorophyll_a" - ], - "table:columns": [ - { - "name": "reference_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that the forecast was initiated (horizon = 0)" - }, - { - "name": "site_id", - "type": "string", - "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model." - }, - { - "name": "datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime of the forecasted value (ISO 8601)" - }, - { - "name": "family", - "type": "string", - "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)." - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "name": "observation", - "type": "double", - "description": "observed value for variable" - }, - { - "name": "crps", - "type": "double", - "description": "crps forecast score" - }, - { - "name": "logs", - "type": "double", - "description": "logs forecast score" - }, - { - "name": "mean", - "type": "double", - "description": "mean forecast prediction" - }, - { - "name": "median", - "type": "double", - "description": "median forecast prediction" - }, - { - "name": "sd", - "type": "double", - "description": "standard deviation forecasts" - }, - { - "name": "quantile97.5", - "type": "double", - "description": "upper 97.5 percentile value of forecast" - }, - { - "name": "quantile02.5", - "type": "double", - "description": "upper 2.5 percentile value of forecast" - }, - { - "name": "quantile90", - "type": "double", - "description": "upper 90 percentile value of forecast" - }, - { - "name": "quantile10", - "type": "double", - "description": "upper 10 percentile value of forecast" - }, - { - "name": "project_id", - "type": "string", - "description": "unique project identifier" - }, - { - "name": "duration", - "type": "string", - "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention" - }, - { - "name": "variable", - "type": "string", - "description": "name of forecasted variable" - }, - { - "name": "model_id", - "type": "string", - "description": "unique model identifier" - }, - { - "name": "date", - "type": "string", - "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period." - } - ] - }, - "collection": "scores", - "links": [ - { - "rel": "collection", - "href": "../collection.json", - "type": "application/json", - "title": "procEppleyNorbergSteele" - }, - { - "rel": "root", - "href": "../../../catalog.json", - "type": "application/json", - "title": "Forecast Catalog" - }, - { - "rel": "parent", - "href": "../collection.json", - "type": "application/json", - "title": "procEppleyNorbergSteele" - }, - { - "rel": "self", - "href": "procEppleyNorbergSteele.json", - "type": "application/json", - "title": "Model Forecast" - }, - { - "rel": "item", - "href": "pending", - "type": "text/html", - "title": "Link for Model Code" - } - ], - "assets": { - "1": { - "type": "application/json", - "title": "Model Metadata", - "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/procEppleyNorbergSteele.json", - "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/procEppleyNorbergSteele.json\")\n\n" - }, - "2": { - "type": "text/html", - "title": "Link for Model Code", - "href": "pending", - "description": "The link to the model code provided by the model submission team" - }, - "3": { - "type": "application/x-parquet", - "title": "Database Access for Daily Chlorophyll_a", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=procEppleyNorbergSteele?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=procEppleyNorbergSteele?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - } - } -} diff --git a/catalog/scores/models/model_items/procHinshelwoodMonod.json b/catalog/scores/models/model_items/procHinshelwoodMonod.json deleted file mode 100644 index 57b7b47959..0000000000 --- a/catalog/scores/models/model_items/procHinshelwoodMonod.json +++ /dev/null @@ -1,212 +0,0 @@ -{ - "stac_version": "1.0.0", - "stac_extensions": [ - "https://stac-extensions.github.io/table/v1.2.0/schema.json" - ], - "type": "Feature", - "id": "procHinshelwoodMonod", - "bbox": [ - [ - -156.6194, - 71.2824, - -66.7987, - 71.2824 - ] - ], - "geometry": { - "type": "MultiPoint", - "coordinates": [ - [-82.0084, 29.676], - [-89.4737, 46.2097], - [-89.7048, 45.9983], - [-99.1139, 47.1591], - [-99.2531, 47.1298], - [-82.0177, 29.6878], - [-149.6106, 68.6307] - ] - }, - "properties": { - "description": "\nmodel info: NA\n\nSites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK\n\nVariables: Daily Chlorophyll_a", - "start_datetime": "2023-11-13", - "end_datetime": "2023-11-30", - "providers": [ - { - "url": "pending", - "name": "pending", - "roles": [ - "producer", - "processor", - "licensor" - ] - }, - { - "url": "https://www.ecoforecastprojectvt.org", - "name": "Ecoforecast Challenge", - "roles": [ - "host" - ] - } - ], - "license": "CC0-1.0", - "keywords": [ - "Forecasting", - "neon4cast", - "Daily Chlorophyll_a" - ], - "table:columns": [ - { - "name": "reference_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that the forecast was initiated (horizon = 0)" - }, - { - "name": "site_id", - "type": "string", - "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model." - }, - { - "name": "datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime of the forecasted value (ISO 8601)" - }, - { - "name": "family", - "type": "string", - "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)." - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "name": "observation", - "type": "double", - "description": "observed value for variable" - }, - { - "name": "crps", - "type": "double", - "description": "crps forecast score" - }, - { - "name": "logs", - "type": "double", - "description": "logs forecast score" - }, - { - "name": "mean", - "type": "double", - "description": "mean forecast prediction" - }, - { - "name": "median", - "type": "double", - "description": "median forecast prediction" - }, - { - "name": "sd", - "type": "double", - "description": "standard deviation forecasts" - }, - { - "name": "quantile97.5", - "type": "double", - "description": "upper 97.5 percentile value of forecast" - }, - { - "name": "quantile02.5", - "type": "double", - "description": "upper 2.5 percentile value of forecast" - }, - { - "name": "quantile90", - "type": "double", - "description": "upper 90 percentile value of forecast" - }, - { - "name": "quantile10", - "type": "double", - "description": "upper 10 percentile value of forecast" - }, - { - "name": "project_id", - "type": "string", - "description": "unique project identifier" - }, - { - "name": "duration", - "type": "string", - "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention" - }, - { - "name": "variable", - "type": "string", - "description": "name of forecasted variable" - }, - { - "name": "model_id", - "type": "string", - "description": "unique model identifier" - }, - { - "name": "date", - "type": "string", - "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period." - } - ] - }, - "collection": "scores", - "links": [ - { - "rel": "collection", - "href": "../collection.json", - "type": "application/json", - "title": "procHinshelwoodMonod" - }, - { - "rel": "root", - "href": "../../../catalog.json", - "type": "application/json", - "title": "Forecast Catalog" - }, - { - "rel": "parent", - "href": "../collection.json", - "type": "application/json", - "title": "procHinshelwoodMonod" - }, - { - "rel": "self", - "href": "procHinshelwoodMonod.json", - "type": "application/json", - "title": "Model Forecast" - }, - { - "rel": "item", - "href": "pending", - "type": "text/html", - "title": "Link for Model Code" - } - ], - "assets": { - "1": { - "type": "application/json", - "title": "Model Metadata", - "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/procHinshelwoodMonod.json", - "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/procHinshelwoodMonod.json\")\n\n" - }, - "2": { - "type": "text/html", - "title": "Link for Model Code", - "href": "pending", - "description": "The link to the model code provided by the model submission team" - }, - "3": { - "type": "application/x-parquet", - "title": "Database Access for Daily Chlorophyll_a", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=procHinshelwoodMonod?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=procHinshelwoodMonod?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - } - } -} diff --git a/catalog/scores/models/model_items/procHinshelwoodSteele.json b/catalog/scores/models/model_items/procHinshelwoodSteele.json deleted file mode 100644 index 326bf89827..0000000000 --- a/catalog/scores/models/model_items/procHinshelwoodSteele.json +++ /dev/null @@ -1,212 +0,0 @@ -{ - "stac_version": "1.0.0", - "stac_extensions": [ - "https://stac-extensions.github.io/table/v1.2.0/schema.json" - ], - "type": "Feature", - "id": "procHinshelwoodSteele", - "bbox": [ - [ - -156.6194, - 71.2824, - -66.7987, - 71.2824 - ] - ], - "geometry": { - "type": "MultiPoint", - "coordinates": [ - [-82.0084, 29.676], - [-89.4737, 46.2097], - [-89.7048, 45.9983], - [-99.1139, 47.1591], - [-99.2531, 47.1298], - [-82.0177, 29.6878], - [-149.6106, 68.6307] - ] - }, - "properties": { - "description": "\nmodel info: NA\n\nSites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK\n\nVariables: Daily Chlorophyll_a", - "start_datetime": "2023-11-13", - "end_datetime": "2023-11-30", - "providers": [ - { - "url": "pending", - "name": "pending", - "roles": [ - "producer", - "processor", - "licensor" - ] - }, - { - "url": "https://www.ecoforecastprojectvt.org", - "name": "Ecoforecast Challenge", - "roles": [ - "host" - ] - } - ], - "license": "CC0-1.0", - "keywords": [ - "Forecasting", - "neon4cast", - "Daily Chlorophyll_a" - ], - "table:columns": [ - { - "name": "reference_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that the forecast was initiated (horizon = 0)" - }, - { - "name": "site_id", - "type": "string", - "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model." - }, - { - "name": "datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime of the forecasted value (ISO 8601)" - }, - { - "name": "family", - "type": "string", - "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)." - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "name": "observation", - "type": "double", - "description": "observed value for variable" - }, - { - "name": "crps", - "type": "double", - "description": "crps forecast score" - }, - { - "name": "logs", - "type": "double", - "description": "logs forecast score" - }, - { - "name": "mean", - "type": "double", - "description": "mean forecast prediction" - }, - { - "name": "median", - "type": "double", - "description": "median forecast prediction" - }, - { - "name": "sd", - "type": "double", - "description": "standard deviation forecasts" - }, - { - "name": "quantile97.5", - "type": "double", - "description": "upper 97.5 percentile value of forecast" - }, - { - "name": "quantile02.5", - "type": "double", - "description": "upper 2.5 percentile value of forecast" - }, - { - "name": "quantile90", - "type": "double", - "description": "upper 90 percentile value of forecast" - }, - { - "name": "quantile10", - "type": "double", - "description": "upper 10 percentile value of forecast" - }, - { - "name": "project_id", - "type": "string", - "description": "unique project identifier" - }, - { - "name": "duration", - "type": "string", - "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention" - }, - { - "name": "variable", - "type": "string", - "description": "name of forecasted variable" - }, - { - "name": "model_id", - "type": "string", - "description": "unique model identifier" - }, - { - "name": "date", - "type": "string", - "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period." - } - ] - }, - "collection": "scores", - "links": [ - { - "rel": "collection", - "href": "../collection.json", - "type": "application/json", - "title": "procHinshelwoodSteele" - }, - { - "rel": "root", - "href": "../../../catalog.json", - "type": "application/json", - "title": "Forecast Catalog" - }, - { - "rel": "parent", - "href": "../collection.json", - "type": "application/json", - "title": "procHinshelwoodSteele" - }, - { - "rel": "self", - "href": "procHinshelwoodSteele.json", - "type": "application/json", - "title": "Model Forecast" - }, - { - "rel": "item", - "href": "pending", - "type": "text/html", - "title": "Link for Model Code" - } - ], - "assets": { - "1": { - "type": "application/json", - "title": "Model Metadata", - "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/procHinshelwoodSteele.json", - "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/procHinshelwoodSteele.json\")\n\n" - }, - "2": { - "type": "text/html", - "title": "Link for Model Code", - "href": "pending", - "description": "The link to the model code provided by the model submission team" - }, - "3": { - "type": "application/x-parquet", - "title": "Database Access for Daily Chlorophyll_a", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=procHinshelwoodSteele?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=procHinshelwoodSteele?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - } - } -} diff --git a/catalog/scores/models/model_items/prophet_clim_ensemble.json b/catalog/scores/models/model_items/prophet_clim_ensemble.json deleted file mode 100644 index cd633bf883..0000000000 --- a/catalog/scores/models/model_items/prophet_clim_ensemble.json +++ /dev/null @@ -1,247 +0,0 @@ -{ - "stac_version": "1.0.0", - "stac_extensions": [ - "https://stac-extensions.github.io/table/v1.2.0/schema.json" - ], - "type": "Feature", - "id": "prophet_clim_ensemble", - "bbox": [ - [ - -156.6194, - 71.2824, - -66.7987, - 71.2824 - ] - ], - "geometry": { - "type": "MultiPoint", - "coordinates": [ - [-122.3303, 45.7624], - [-71.2874, 44.0639], - [-78.0418, 39.0337], - [-97.57, 33.4012], - [-104.7456, 40.8155], - [-99.1066, 47.1617], - [-87.8039, 32.5417], - [-81.4362, 28.1251], - [-83.5019, 35.689], - [-66.8687, 17.9696], - [-72.1727, 42.5369], - [-84.4686, 31.1948], - [-106.8425, 32.5907], - [-96.6129, 39.1104], - [-96.5631, 39.1008], - [-67.0769, 18.0213], - [-88.1612, 31.8539], - [-80.5248, 37.3783], - [-109.3883, 38.2483], - [-105.5824, 40.0543], - [-100.9154, 46.7697], - [-99.0588, 35.4106], - [-112.4524, 40.1776], - [-84.2826, 35.9641], - [-81.9934, 29.6893], - [-155.3173, 19.5531], - [-105.546, 40.2759], - [-78.1395, 38.8929], - [-76.56, 38.8901], - [-119.7323, 37.1088], - [-119.2622, 37.0334], - [-110.8355, 31.9107], - [-89.5864, 45.5089], - [-103.0293, 40.4619], - [-87.3933, 32.9505], - [-119.006, 37.0058], - [-89.5857, 45.4937], - [-95.1921, 39.0404], - [-89.5373, 46.2339], - [-99.2413, 47.1282], - [-121.9519, 45.8205], - [-110.5391, 44.9535] - ] - }, - "properties": { - "description": [], - "start_datetime": "2023-11-13", - "end_datetime": "2023-12-15", - "providers": [ - { - "url": "pending", - "name": "pending", - "roles": [ - "producer", - "processor", - "licensor" - ] - }, - { - "url": "https://www.ecoforecastprojectvt.org", - "name": "Ecoforecast Challenge", - "roles": [ - "host" - ] - } - ], - "license": "CC0-1.0", - "keywords": [ - "Forecasting", - "neon4cast", - "Daily Red_chromatic_coordinate" - ], - "table:columns": [ - { - "name": "reference_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that the forecast was initiated (horizon = 0)" - }, - { - "name": "site_id", - "type": "string", - "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model." - }, - { - "name": "datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime of the forecasted value (ISO 8601)" - }, - { - "name": "family", - "type": "string", - "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)." - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "name": "observation", - "type": "double", - "description": "observed value for variable" - }, - { - "name": "crps", - "type": "double", - "description": "crps forecast score" - }, - { - "name": "logs", - "type": "double", - "description": "logs forecast score" - }, - { - "name": "mean", - "type": "double", - "description": "mean forecast prediction" - }, - { - "name": "median", - "type": "double", - "description": "median forecast prediction" - }, - { - "name": "sd", - "type": "double", - "description": "standard deviation forecasts" - }, - { - "name": "quantile97.5", - "type": "double", - "description": "upper 97.5 percentile value of forecast" - }, - { - "name": "quantile02.5", - "type": "double", - "description": "upper 2.5 percentile value of forecast" - }, - { - "name": "quantile90", - "type": "double", - "description": "upper 90 percentile value of forecast" - }, - { - "name": "quantile10", - "type": "double", - "description": "upper 10 percentile value of forecast" - }, - { - "name": "project_id", - "type": "string", - "description": "unique project identifier" - }, - { - "name": "duration", - "type": "string", - "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention" - }, - { - "name": "variable", - "type": "string", - "description": "name of forecasted variable" - }, - { - "name": "model_id", - "type": "string", - "description": "unique model identifier" - }, - { - "name": "date", - "type": "string", - "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period." - } - ] - }, - "collection": "scores", - "links": [ - { - "rel": "collection", - "href": "../collection.json", - "type": "application/json", - "title": "prophet_clim_ensemble" - }, - { - "rel": "root", - "href": "../../../catalog.json", - "type": "application/json", - "title": "Forecast Catalog" - }, - { - "rel": "parent", - "href": "../collection.json", - "type": "application/json", - "title": "prophet_clim_ensemble" - }, - { - "rel": "self", - "href": "prophet_clim_ensemble.json", - "type": "application/json", - "title": "Model Forecast" - }, - { - "rel": "item", - "href": "pending", - "type": "text/html", - "title": "Link for Model Code" - } - ], - "assets": { - "1": { - "type": "application/json", - "title": "Model Metadata", - "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/prophet_clim_ensemble.json", - "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/prophet_clim_ensemble.json\")\n\n" - }, - "2": { - "type": "text/html", - "title": "Link for Model Code", - "href": "pending", - "description": "The link to the model code provided by the model submission team" - }, - "3": { - "type": "application/x-parquet", - "title": "Database Access for Daily Red_chromatic_coordinate", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=rcc_90/model_id=prophet_clim_ensemble?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=rcc_90/model_id=prophet_clim_ensemble?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - } - } -} diff --git a/catalog/scores/models/model_items/randfor.json b/catalog/scores/models/model_items/randfor.json deleted file mode 100644 index 1ac2a13ab6..0000000000 --- a/catalog/scores/models/model_items/randfor.json +++ /dev/null @@ -1,252 +0,0 @@ -{ - "stac_version": "1.0.0", - "stac_extensions": [ - "https://stac-extensions.github.io/table/v1.2.0/schema.json" - ], - "type": "Feature", - "id": "randfor", - "bbox": [ - [ - -156.6194, - 71.2824, - -66.7987, - 71.2824 - ] - ], - "geometry": { - "type": "MultiPoint", - "coordinates": [ - [-122.3303, 45.7624], - [-156.6194, 71.2824], - [-71.2874, 44.0639], - [-78.0418, 39.0337], - [-147.5026, 65.154], - [-97.57, 33.4012], - [-104.7456, 40.8155], - [-99.1066, 47.1617], - [-145.7514, 63.8811], - [-87.8039, 32.5417], - [-81.4362, 28.1251], - [-83.5019, 35.689], - [-66.8687, 17.9696], - [-72.1727, 42.5369], - [-149.2133, 63.8758], - [-84.4686, 31.1948], - [-106.8425, 32.5907], - [-96.6129, 39.1104], - [-96.5631, 39.1008], - [-67.0769, 18.0213], - [-88.1612, 31.8539], - [-80.5248, 37.3783], - [-109.3883, 38.2483], - [-105.5824, 40.0543], - [-100.9154, 46.7697], - [-99.0588, 35.4106], - [-112.4524, 40.1776], - [-84.2826, 35.9641], - [-81.9934, 29.6893], - [-155.3173, 19.5531], - [-105.546, 40.2759], - [-78.1395, 38.8929], - [-76.56, 38.8901], - [-119.7323, 37.1088], - [-119.2622, 37.0334], - [-110.8355, 31.9107], - [-89.5864, 45.5089], - [-103.0293, 40.4619], - [-87.3933, 32.9505], - [-119.006, 37.0058], - [-149.3705, 68.6611], - [-89.5857, 45.4937], - [-95.1921, 39.0404], - [-89.5373, 46.2339], - [-99.2413, 47.1282], - [-121.9519, 45.8205], - [-110.5391, 44.9535] - ] - }, - "properties": { - "description": [], - "start_datetime": "2023-11-14", - "end_datetime": "2023-12-09", - "providers": [ - { - "url": "pending", - "name": "pending", - "roles": [ - "producer", - "processor", - "licensor" - ] - }, - { - "url": "https://www.ecoforecastprojectvt.org", - "name": "Ecoforecast Challenge", - "roles": [ - "host" - ] - } - ], - "license": "CC0-1.0", - "keywords": [ - "Forecasting", - "neon4cast", - "Daily latent_heat_flux" - ], - "table:columns": [ - { - "name": "reference_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that the forecast was initiated (horizon = 0)" - }, - { - "name": "site_id", - "type": "string", - "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model." - }, - { - "name": "datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime of the forecasted value (ISO 8601)" - }, - { - "name": "family", - "type": "string", - "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)." - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "name": "observation", - "type": "double", - "description": "observed value for variable" - }, - { - "name": "crps", - "type": "double", - "description": "crps forecast score" - }, - { - "name": "logs", - "type": "double", - "description": "logs forecast score" - }, - { - "name": "mean", - "type": "double", - "description": "mean forecast prediction" - }, - { - "name": "median", - "type": "double", - "description": "median forecast prediction" - }, - { - "name": "sd", - "type": "double", - "description": "standard deviation forecasts" - }, - { - "name": "quantile97.5", - "type": "double", - "description": "upper 97.5 percentile value of forecast" - }, - { - "name": "quantile02.5", - "type": "double", - "description": "upper 2.5 percentile value of forecast" - }, - { - "name": "quantile90", - "type": "double", - "description": "upper 90 percentile value of forecast" - }, - { - "name": "quantile10", - "type": "double", - "description": "upper 10 percentile value of forecast" - }, - { - "name": "project_id", - "type": "string", - "description": "unique project identifier" - }, - { - "name": "duration", - "type": "string", - "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention" - }, - { - "name": "variable", - "type": "string", - "description": "name of forecasted variable" - }, - { - "name": "model_id", - "type": "string", - "description": "unique model identifier" - }, - { - "name": "date", - "type": "string", - "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period." - } - ] - }, - "collection": "scores", - "links": [ - { - "rel": "collection", - "href": "../collection.json", - "type": "application/json", - "title": "randfor" - }, - { - "rel": "root", - "href": "../../../catalog.json", - "type": "application/json", - "title": "Forecast Catalog" - }, - { - "rel": "parent", - "href": "../collection.json", - "type": "application/json", - "title": "randfor" - }, - { - "rel": "self", - "href": "randfor.json", - "type": "application/json", - "title": "Model Forecast" - }, - { - "rel": "item", - "href": "pending", - "type": "text/html", - "title": "Link for Model Code" - } - ], - "assets": { - "1": { - "type": "application/json", - "title": "Model Metadata", - "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/randfor.json", - "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/randfor.json\")\n\n" - }, - "2": { - "type": "text/html", - "title": "Link for Model Code", - "href": "pending", - "description": "The link to the model code provided by the model submission team" - }, - "3": { - "type": "application/x-parquet", - "title": "Database Access for Daily latent_heat_flux", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=le/model_id=randfor?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=le/model_id=randfor?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - } - } -} diff --git a/catalog/scores/models/model_items/tg_arima.json b/catalog/scores/models/model_items/tg_arima.json deleted file mode 100644 index a26531f63d..0000000000 --- a/catalog/scores/models/model_items/tg_arima.json +++ /dev/null @@ -1,349 +0,0 @@ -{ - "stac_version": "1.0.0", - "stac_extensions": [ - "https://stac-extensions.github.io/table/v1.2.0/schema.json" - ], - "type": "Feature", - "id": "tg_arima", - "bbox": [ - [ - -156.6194, - 71.2824, - -66.7987, - 71.2824 - ] - ], - "geometry": { - "type": "MultiPoint", - "coordinates": [ - [-82.0084, 29.676], - [-87.7982, 32.5415], - [-89.4737, 46.2097], - [-84.4374, 31.1854], - [-89.7048, 45.9983], - [-99.1139, 47.1591], - [-99.2531, 47.1298], - [-82.0177, 29.6878], - [-88.1589, 31.8534], - [-149.6106, 68.6307], - [-99.2413, 47.1282], - [-121.9519, 45.8205], - [-110.5391, 44.9535], - [-122.3303, 45.7624], - [-156.6194, 71.2824], - [-71.2874, 44.0639], - [-78.0418, 39.0337], - [-147.5026, 65.154], - [-97.57, 33.4012], - [-104.7456, 40.8155], - [-99.1066, 47.1617], - [-145.7514, 63.8811], - [-87.8039, 32.5417], - [-81.4362, 28.1251], - [-83.5019, 35.689], - [-66.8687, 17.9696], - [-72.1727, 42.5369], - [-149.2133, 63.8758], - [-84.4686, 31.1948], - [-106.8425, 32.5907], - [-96.6129, 39.1104], - [-96.5631, 39.1008], - [-67.0769, 18.0213], - [-88.1612, 31.8539], - [-80.5248, 37.3783], - [-109.3883, 38.2483], - [-105.5824, 40.0543], - [-100.9154, 46.7697], - [-99.0588, 35.4106], - [-112.4524, 40.1776], - [-84.2826, 35.9641], - [-81.9934, 29.6893], - [-155.3173, 19.5531], - [-105.546, 40.2759], - [-78.1395, 38.8929], - [-76.56, 38.8901], - [-119.7323, 37.1088], - [-119.2622, 37.0334], - [-110.8355, 31.9107], - [-89.5864, 45.5089], - [-103.0293, 40.4619], - [-87.3933, 32.9505], - [-119.006, 37.0058], - [-149.3705, 68.6611], - [-89.5857, 45.4937], - [-95.1921, 39.0404], - [-89.5373, 46.2339], - [-102.4471, 39.7582], - [-119.2575, 37.0597], - [-110.5871, 44.9501], - [-96.6242, 34.4442], - [-147.504, 65.1532], - [-105.5442, 40.035], - [-66.9868, 18.1135], - [-66.7987, 18.1741], - [-72.3295, 42.4719], - [-96.6038, 39.1051], - [-83.5038, 35.6904], - [-77.9832, 39.0956], - [-121.9338, 45.7908], - [-87.4077, 32.9604], - [-96.443, 38.9459], - [-122.1655, 44.2596], - [-149.143, 68.6698], - [-78.1473, 38.8943], - [-97.7823, 33.3785], - [-111.7979, 40.7839], - [-111.5081, 33.751], - [-119.0274, 36.9559], - [-84.2793, 35.9574], - [-105.9154, 39.8914] - ] - }, - "properties": { - "description": "\nmodel info: NA\n\nSites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, ARIK, BIGC, BLDE, BLUE, CARI, COMO, CUPE, GUIL, HOPB, KING, LECO, LEWI, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, REDB, SYCA, TECR, WALK, WLOU\n\nVariables: Daily Chlorophyll_a, Daily Green_chromatic_coordinate, Daily latent_heat_flux, Daily Net_ecosystem_exchange, Daily Dissolved_oxygen, Daily Red_chromatic_coordinate, Daily Water_temperature, Weekly beetle_community_richness, Weekly beetle_community_abundance, Weekly Amblyomma_americanum_population", - "start_datetime": "2023-01-01", - "end_datetime": "2023-12-15", - "providers": [ - { - "url": "pending", - "name": "pending", - "roles": [ - "producer", - "processor", - "licensor" - ] - }, - { - "url": "https://www.ecoforecastprojectvt.org", - "name": "Ecoforecast Challenge", - "roles": [ - "host" - ] - } - ], - "license": "CC0-1.0", - "keywords": [ - "Forecasting", - "neon4cast", - "Daily Chlorophyll_a", - "Daily Green_chromatic_coordinate", - "Daily latent_heat_flux", - "Daily Net_ecosystem_exchange", - "Daily Dissolved_oxygen", - "Daily Red_chromatic_coordinate", - "Daily Water_temperature", - "Weekly beetle_community_richness", - "Weekly beetle_community_abundance", - "Weekly Amblyomma_americanum_population" - ], - "table:columns": [ - { - "name": "reference_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that the forecast was initiated (horizon = 0)" - }, - { - "name": "site_id", - "type": "string", - "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model." - }, - { - "name": "datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime of the forecasted value (ISO 8601)" - }, - { - "name": "family", - "type": "string", - "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)." - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "name": "observation", - "type": "double", - "description": "observed value for variable" - }, - { - "name": "crps", - "type": "double", - "description": "crps forecast score" - }, - { - "name": "logs", - "type": "double", - "description": "logs forecast score" - }, - { - "name": "mean", - "type": "double", - "description": "mean forecast prediction" - }, - { - "name": "median", - "type": "double", - "description": "median forecast prediction" - }, - { - "name": "sd", - "type": "double", - "description": "standard deviation forecasts" - }, - { - "name": "quantile97.5", - "type": "double", - "description": "upper 97.5 percentile value of forecast" - }, - { - "name": "quantile02.5", - "type": "double", - "description": "upper 2.5 percentile value of forecast" - }, - { - "name": "quantile90", - "type": "double", - "description": "upper 90 percentile value of forecast" - }, - { - "name": "quantile10", - "type": "double", - "description": "upper 10 percentile value of forecast" - }, - { - "name": "project_id", - "type": "string", - "description": "unique project identifier" - }, - { - "name": "duration", - "type": "string", - "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention" - }, - { - "name": "variable", - "type": "string", - "description": "name of forecasted variable" - }, - { - "name": "model_id", - "type": "string", - "description": "unique model identifier" - }, - { - "name": "date", - "type": "string", - "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period." - } - ] - }, - "collection": "scores", - "links": [ - { - "rel": "collection", - "href": "../collection.json", - "type": "application/json", - "title": "tg_arima" - }, - { - "rel": "root", - "href": "../../../catalog.json", - "type": "application/json", - "title": "Forecast Catalog" - }, - { - "rel": "parent", - "href": "../collection.json", - "type": "application/json", - "title": "tg_arima" - }, - { - "rel": "self", - "href": "tg_arima.json", - "type": "application/json", - "title": "Model Forecast" - }, - { - "rel": "item", - "href": "pending", - "type": "text/html", - "title": "Link for Model Code" - } - ], - "assets": { - "1": { - "type": "application/json", - "title": "Model Metadata", - "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_arima.json", - "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_arima.json\")\n\n" - }, - "2": { - "type": "text/html", - "title": "Link for Model Code", - "href": "pending", - "description": "The link to the model code provided by the model submission team" - }, - "3": { - "type": "application/x-parquet", - "title": "Database Access for Daily Chlorophyll_a", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "4": { - "type": "application/x-parquet", - "title": "Database Access for Daily Green_chromatic_coordinate", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=gcc_90/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=gcc_90/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "5": { - "type": "application/x-parquet", - "title": "Database Access for Daily latent_heat_flux", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=le/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=le/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "6": { - "type": "application/x-parquet", - "title": "Database Access for Daily Net_ecosystem_exchange", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=nee/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=nee/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "7": { - "type": "application/x-parquet", - "title": "Database Access for Daily Dissolved_oxygen", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=oxygen/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=oxygen/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "8": { - "type": "application/x-parquet", - "title": "Database Access for Daily Red_chromatic_coordinate", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=rcc_90/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=rcc_90/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "9": { - "type": "application/x-parquet", - "title": "Database Access for Daily Water_temperature", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "10": { - "type": "application/x-parquet", - "title": "Database Access for Weekly beetle_community_richness", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1W/variable=richness/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1W/variable=richness/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "11": { - "type": "application/x-parquet", - "title": "Database Access for Weekly beetle_community_abundance", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1W/variable=abundance/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1W/variable=abundance/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "12": { - "type": "application/x-parquet", - "title": "Database Access for Weekly Amblyomma_americanum_population", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1W/variable=amblyomma_americanum/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1W/variable=amblyomma_americanum/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - } - } -} diff --git a/catalog/scores/models/model_items/tg_auto_adam.json b/catalog/scores/models/model_items/tg_auto_adam.json deleted file mode 100644 index 692ffe66f9..0000000000 --- a/catalog/scores/models/model_items/tg_auto_adam.json +++ /dev/null @@ -1,349 +0,0 @@ -{ - "stac_version": "1.0.0", - "stac_extensions": [ - "https://stac-extensions.github.io/table/v1.2.0/schema.json" - ], - "type": "Feature", - "id": "tg_auto_adam", - "bbox": [ - [ - -156.6194, - 71.2824, - -66.7987, - 71.2824 - ] - ], - "geometry": { - "type": "MultiPoint", - "coordinates": [ - [-82.0084, 29.676], - [-87.7982, 32.5415], - [-89.4737, 46.2097], - [-84.4374, 31.1854], - [-89.7048, 45.9983], - [-99.1139, 47.1591], - [-99.2531, 47.1298], - [-82.0177, 29.6878], - [-88.1589, 31.8534], - [-149.6106, 68.6307], - [-122.3303, 45.7624], - [-156.6194, 71.2824], - [-71.2874, 44.0639], - [-78.0418, 39.0337], - [-147.5026, 65.154], - [-97.57, 33.4012], - [-104.7456, 40.8155], - [-99.1066, 47.1617], - [-145.7514, 63.8811], - [-87.8039, 32.5417], - [-81.4362, 28.1251], - [-83.5019, 35.689], - [-66.8687, 17.9696], - [-72.1727, 42.5369], - [-149.2133, 63.8758], - [-84.4686, 31.1948], - [-106.8425, 32.5907], - [-96.6129, 39.1104], - [-96.5631, 39.1008], - [-67.0769, 18.0213], - [-88.1612, 31.8539], - [-80.5248, 37.3783], - [-109.3883, 38.2483], - [-105.5824, 40.0543], - [-100.9154, 46.7697], - [-99.0588, 35.4106], - [-112.4524, 40.1776], - [-84.2826, 35.9641], - [-81.9934, 29.6893], - [-155.3173, 19.5531], - [-105.546, 40.2759], - [-78.1395, 38.8929], - [-76.56, 38.8901], - [-119.7323, 37.1088], - [-119.2622, 37.0334], - [-110.8355, 31.9107], - [-89.5864, 45.5089], - [-103.0293, 40.4619], - [-87.3933, 32.9505], - [-119.006, 37.0058], - [-149.3705, 68.6611], - [-89.5857, 45.4937], - [-95.1921, 39.0404], - [-89.5373, 46.2339], - [-99.2413, 47.1282], - [-121.9519, 45.8205], - [-110.5391, 44.9535], - [-102.4471, 39.7582], - [-119.2575, 37.0597], - [-110.5871, 44.9501], - [-96.6242, 34.4442], - [-147.504, 65.1532], - [-105.5442, 40.035], - [-66.9868, 18.1135], - [-66.7987, 18.1741], - [-72.3295, 42.4719], - [-96.6038, 39.1051], - [-83.5038, 35.6904], - [-77.9832, 39.0956], - [-121.9338, 45.7908], - [-87.4077, 32.9604], - [-96.443, 38.9459], - [-122.1655, 44.2596], - [-149.143, 68.6698], - [-78.1473, 38.8943], - [-97.7823, 33.3785], - [-111.7979, 40.7839], - [-111.5081, 33.751], - [-119.0274, 36.9559], - [-84.2793, 35.9574], - [-105.9154, 39.8914] - ] - }, - "properties": { - "description": [], - "start_datetime": "2023-01-01", - "end_datetime": "2023-12-09", - "providers": [ - { - "url": "pending", - "name": "pending", - "roles": [ - "producer", - "processor", - "licensor" - ] - }, - { - "url": "https://www.ecoforecastprojectvt.org", - "name": "Ecoforecast Challenge", - "roles": [ - "host" - ] - } - ], - "license": "CC0-1.0", - "keywords": [ - "Forecasting", - "neon4cast", - "Daily Chlorophyll_a", - "Daily Green_chromatic_coordinate", - "Daily latent_heat_flux", - "Daily Net_ecosystem_exchange", - "Daily Dissolved_oxygen", - "Daily Red_chromatic_coordinate", - "Daily Water_temperature", - "Weekly beetle_community_richness", - "Weekly beetle_community_abundance", - "Weekly Amblyomma_americanum_population" - ], - "table:columns": [ - { - "name": "reference_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that the forecast was initiated (horizon = 0)" - }, - { - "name": "site_id", - "type": "string", - "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model." - }, - { - "name": "datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime of the forecasted value (ISO 8601)" - }, - { - "name": "family", - "type": "string", - "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)." - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "name": "observation", - "type": "double", - "description": "observed value for variable" - }, - { - "name": "crps", - "type": "double", - "description": "crps forecast score" - }, - { - "name": "logs", - "type": "double", - "description": "logs forecast score" - }, - { - "name": "mean", - "type": "double", - "description": "mean forecast prediction" - }, - { - "name": "median", - "type": "double", - "description": "median forecast prediction" - }, - { - "name": "sd", - "type": "double", - "description": "standard deviation forecasts" - }, - { - "name": "quantile97.5", - "type": "double", - "description": "upper 97.5 percentile value of forecast" - }, - { - "name": "quantile02.5", - "type": "double", - "description": "upper 2.5 percentile value of forecast" - }, - { - "name": "quantile90", - "type": "double", - "description": "upper 90 percentile value of forecast" - }, - { - "name": "quantile10", - "type": "double", - "description": "upper 10 percentile value of forecast" - }, - { - "name": "project_id", - "type": "string", - "description": "unique project identifier" - }, - { - "name": "duration", - "type": "string", - "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention" - }, - { - "name": "variable", - "type": "string", - "description": "name of forecasted variable" - }, - { - "name": "model_id", - "type": "string", - "description": "unique model identifier" - }, - { - "name": "date", - "type": "string", - "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period." - } - ] - }, - "collection": "scores", - "links": [ - { - "rel": "collection", - "href": "../collection.json", - "type": "application/json", - "title": "tg_auto_adam" - }, - { - "rel": "root", - "href": "../../../catalog.json", - "type": "application/json", - "title": "Forecast Catalog" - }, - { - "rel": "parent", - "href": "../collection.json", - "type": "application/json", - "title": "tg_auto_adam" - }, - { - "rel": "self", - "href": "tg_auto_adam.json", - "type": "application/json", - "title": "Model Forecast" - }, - { - "rel": "item", - "href": "pending", - "type": "text/html", - "title": "Link for Model Code" - } - ], - "assets": { - "1": { - "type": "application/json", - "title": "Model Metadata", - "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_auto_adam.json", - "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_auto_adam.json\")\n\n" - }, - "2": { - "type": "text/html", - "title": "Link for Model Code", - "href": "pending", - "description": "The link to the model code provided by the model submission team" - }, - "3": { - "type": "application/x-parquet", - "title": "Database Access for Daily Chlorophyll_a", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=tg_auto_adam?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=tg_auto_adam?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "4": { - "type": "application/x-parquet", - "title": "Database Access for Daily Green_chromatic_coordinate", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=gcc_90/model_id=tg_auto_adam?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=gcc_90/model_id=tg_auto_adam?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "5": { - "type": "application/x-parquet", - "title": "Database Access for Daily latent_heat_flux", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=le/model_id=tg_auto_adam?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=le/model_id=tg_auto_adam?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "6": { - "type": "application/x-parquet", - "title": "Database Access for Daily Net_ecosystem_exchange", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=nee/model_id=tg_auto_adam?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=nee/model_id=tg_auto_adam?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "7": { - "type": "application/x-parquet", - "title": "Database Access for Daily Dissolved_oxygen", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=oxygen/model_id=tg_auto_adam?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=oxygen/model_id=tg_auto_adam?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "8": { - "type": "application/x-parquet", - "title": "Database Access for Daily Red_chromatic_coordinate", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=rcc_90/model_id=tg_auto_adam?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=rcc_90/model_id=tg_auto_adam?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "9": { - "type": "application/x-parquet", - "title": "Database Access for Daily Water_temperature", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=tg_auto_adam?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=tg_auto_adam?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "10": { - "type": "application/x-parquet", - "title": "Database Access for Weekly beetle_community_richness", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1W/variable=richness/model_id=tg_auto_adam?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1W/variable=richness/model_id=tg_auto_adam?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "11": { - "type": "application/x-parquet", - "title": "Database Access for Weekly beetle_community_abundance", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1W/variable=abundance/model_id=tg_auto_adam?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1W/variable=abundance/model_id=tg_auto_adam?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "12": { - "type": "application/x-parquet", - "title": "Database Access for Weekly Amblyomma_americanum_population", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1W/variable=amblyomma_americanum/model_id=tg_auto_adam?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1W/variable=amblyomma_americanum/model_id=tg_auto_adam?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - } - } -} diff --git a/catalog/scores/models/model_items/tg_bag_mlp.json b/catalog/scores/models/model_items/tg_bag_mlp.json deleted file mode 100644 index cb4611979c..0000000000 --- a/catalog/scores/models/model_items/tg_bag_mlp.json +++ /dev/null @@ -1,328 +0,0 @@ -{ - "stac_version": "1.0.0", - "stac_extensions": [ - "https://stac-extensions.github.io/table/v1.2.0/schema.json" - ], - "type": "Feature", - "id": "tg_bag_mlp", - "bbox": [ - [ - -156.6194, - 71.2824, - -66.7987, - 71.2824 - ] - ], - "geometry": { - "type": "MultiPoint", - "coordinates": [ - [-82.0084, 29.676], - [-87.7982, 32.5415], - [-89.4737, 46.2097], - [-84.4374, 31.1854], - [-89.7048, 45.9983], - [-99.1139, 47.1591], - [-99.2531, 47.1298], - [-82.0177, 29.6878], - [-88.1589, 31.8534], - [-149.6106, 68.6307], - [-84.4686, 31.1948], - [-106.8425, 32.5907], - [-96.6129, 39.1104], - [-96.5631, 39.1008], - [-67.0769, 18.0213], - [-88.1612, 31.8539], - [-80.5248, 37.3783], - [-109.3883, 38.2483], - [-105.5824, 40.0543], - [-100.9154, 46.7697], - [-99.0588, 35.4106], - [-112.4524, 40.1776], - [-84.2826, 35.9641], - [-81.9934, 29.6893], - [-155.3173, 19.5531], - [-105.546, 40.2759], - [-78.1395, 38.8929], - [-76.56, 38.8901], - [-119.7323, 37.1088], - [-119.2622, 37.0334], - [-110.8355, 31.9107], - [-89.5864, 45.5089], - [-103.0293, 40.4619], - [-87.3933, 32.9505], - [-119.006, 37.0058], - [-149.3705, 68.6611], - [-89.5857, 45.4937], - [-95.1921, 39.0404], - [-89.5373, 46.2339], - [-99.2413, 47.1282], - [-121.9519, 45.8205], - [-110.5391, 44.9535], - [-122.3303, 45.7624], - [-156.6194, 71.2824], - [-71.2874, 44.0639], - [-78.0418, 39.0337], - [-147.5026, 65.154], - [-97.57, 33.4012], - [-104.7456, 40.8155], - [-99.1066, 47.1617], - [-145.7514, 63.8811], - [-87.8039, 32.5417], - [-81.4362, 28.1251], - [-83.5019, 35.689], - [-66.8687, 17.9696], - [-72.1727, 42.5369], - [-149.2133, 63.8758], - [-102.4471, 39.7582], - [-119.2575, 37.0597], - [-110.5871, 44.9501], - [-96.6242, 34.4442], - [-147.504, 65.1532], - [-105.5442, 40.035], - [-66.9868, 18.1135], - [-66.7987, 18.1741], - [-72.3295, 42.4719], - [-96.6038, 39.1051], - [-83.5038, 35.6904], - [-77.9832, 39.0956], - [-121.9338, 45.7908], - [-87.4077, 32.9604], - [-96.443, 38.9459], - [-122.1655, 44.2596], - [-149.143, 68.6698], - [-78.1473, 38.8943], - [-97.7823, 33.3785], - [-111.7979, 40.7839], - [-111.5081, 33.751], - [-119.0274, 36.9559], - [-84.2793, 35.9574], - [-105.9154, 39.8914] - ] - }, - "properties": { - "description": [], - "start_datetime": "2023-11-14", - "end_datetime": "2023-12-15", - "providers": [ - { - "url": "pending", - "name": "pending", - "roles": [ - "producer", - "processor", - "licensor" - ] - }, - { - "url": "https://www.ecoforecastprojectvt.org", - "name": "Ecoforecast Challenge", - "roles": [ - "host" - ] - } - ], - "license": "CC0-1.0", - "keywords": [ - "Forecasting", - "neon4cast", - "Daily Chlorophyll_a", - "Daily Green_chromatic_coordinate", - "Daily latent_heat_flux", - "Daily Net_ecosystem_exchange", - "Daily Dissolved_oxygen", - "Daily Red_chromatic_coordinate", - "Daily Water_temperature" - ], - "table:columns": [ - { - "name": "reference_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that the forecast was initiated (horizon = 0)" - }, - { - "name": "site_id", - "type": "string", - "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model." - }, - { - "name": "datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime of the forecasted value (ISO 8601)" - }, - { - "name": "family", - "type": "string", - "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)." - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "name": "observation", - "type": "double", - "description": "observed value for variable" - }, - { - "name": "crps", - "type": "double", - "description": "crps forecast score" - }, - { - "name": "logs", - "type": "double", - "description": "logs forecast score" - }, - { - "name": "mean", - "type": "double", - "description": "mean forecast prediction" - }, - { - "name": "median", - "type": "double", - "description": "median forecast prediction" - }, - { - "name": "sd", - "type": "double", - "description": "standard deviation forecasts" - }, - { - "name": "quantile97.5", - "type": "double", - "description": "upper 97.5 percentile value of forecast" - }, - { - "name": "quantile02.5", - "type": "double", - "description": "upper 2.5 percentile value of forecast" - }, - { - "name": "quantile90", - "type": "double", - "description": "upper 90 percentile value of forecast" - }, - { - "name": "quantile10", - "type": "double", - "description": "upper 10 percentile value of forecast" - }, - { - "name": "project_id", - "type": "string", - "description": "unique project identifier" - }, - { - "name": "duration", - "type": "string", - "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention" - }, - { - "name": "variable", - "type": "string", - "description": "name of forecasted variable" - }, - { - "name": "model_id", - "type": "string", - "description": "unique model identifier" - }, - { - "name": "date", - "type": "string", - "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period." - } - ] - }, - "collection": "scores", - "links": [ - { - "rel": "collection", - "href": "../collection.json", - "type": "application/json", - "title": "tg_bag_mlp" - }, - { - "rel": "root", - "href": "../../../catalog.json", - "type": "application/json", - "title": "Forecast Catalog" - }, - { - "rel": "parent", - "href": "../collection.json", - "type": "application/json", - "title": "tg_bag_mlp" - }, - { - "rel": "self", - "href": "tg_bag_mlp.json", - "type": "application/json", - "title": "Model Forecast" - }, - { - "rel": "item", - "href": "pending", - "type": "text/html", - "title": "Link for Model Code" - } - ], - "assets": { - "1": { - "type": "application/json", - "title": "Model Metadata", - "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_bag_mlp.json", - "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_bag_mlp.json\")\n\n" - }, - "2": { - "type": "text/html", - "title": "Link for Model Code", - "href": "pending", - "description": "The link to the model code provided by the model submission team" - }, - "3": { - "type": "application/x-parquet", - "title": "Database Access for Daily Chlorophyll_a", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=tg_bag_mlp?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=tg_bag_mlp?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "4": { - "type": "application/x-parquet", - "title": "Database Access for Daily Green_chromatic_coordinate", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=gcc_90/model_id=tg_bag_mlp?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=gcc_90/model_id=tg_bag_mlp?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "5": { - "type": "application/x-parquet", - "title": "Database Access for Daily latent_heat_flux", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=le/model_id=tg_bag_mlp?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=le/model_id=tg_bag_mlp?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "6": { - "type": "application/x-parquet", - "title": "Database Access for Daily Net_ecosystem_exchange", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=nee/model_id=tg_bag_mlp?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=nee/model_id=tg_bag_mlp?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "7": { - "type": "application/x-parquet", - "title": "Database Access for Daily Dissolved_oxygen", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=oxygen/model_id=tg_bag_mlp?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=oxygen/model_id=tg_bag_mlp?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "8": { - "type": "application/x-parquet", - "title": "Database Access for Daily Red_chromatic_coordinate", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=rcc_90/model_id=tg_bag_mlp?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=rcc_90/model_id=tg_bag_mlp?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "9": { - "type": "application/x-parquet", - "title": "Database Access for Daily Water_temperature", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=tg_bag_mlp?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=tg_bag_mlp?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - } - } -} diff --git a/catalog/scores/models/model_items/tg_ets.json b/catalog/scores/models/model_items/tg_ets.json deleted file mode 100644 index bdc0cd6286..0000000000 --- a/catalog/scores/models/model_items/tg_ets.json +++ /dev/null @@ -1,349 +0,0 @@ -{ - "stac_version": "1.0.0", - "stac_extensions": [ - "https://stac-extensions.github.io/table/v1.2.0/schema.json" - ], - "type": "Feature", - "id": "tg_ets", - "bbox": [ - [ - -156.6194, - 71.2824, - -66.7987, - 71.2824 - ] - ], - "geometry": { - "type": "MultiPoint", - "coordinates": [ - [-82.0084, 29.676], - [-87.7982, 32.5415], - [-89.4737, 46.2097], - [-84.4374, 31.1854], - [-89.7048, 45.9983], - [-99.1139, 47.1591], - [-99.2531, 47.1298], - [-82.0177, 29.6878], - [-88.1589, 31.8534], - [-149.6106, 68.6307], - [-100.9154, 46.7697], - [-99.0588, 35.4106], - [-112.4524, 40.1776], - [-84.2826, 35.9641], - [-81.9934, 29.6893], - [-155.3173, 19.5531], - [-105.546, 40.2759], - [-78.1395, 38.8929], - [-76.56, 38.8901], - [-119.7323, 37.1088], - [-119.2622, 37.0334], - [-110.8355, 31.9107], - [-89.5864, 45.5089], - [-103.0293, 40.4619], - [-87.3933, 32.9505], - [-119.006, 37.0058], - [-149.3705, 68.6611], - [-89.5857, 45.4937], - [-95.1921, 39.0404], - [-89.5373, 46.2339], - [-99.2413, 47.1282], - [-121.9519, 45.8205], - [-110.5391, 44.9535], - [-122.3303, 45.7624], - [-156.6194, 71.2824], - [-71.2874, 44.0639], - [-78.0418, 39.0337], - [-147.5026, 65.154], - [-97.57, 33.4012], - [-104.7456, 40.8155], - [-99.1066, 47.1617], - [-145.7514, 63.8811], - [-87.8039, 32.5417], - [-81.4362, 28.1251], - [-83.5019, 35.689], - [-66.8687, 17.9696], - [-72.1727, 42.5369], - [-149.2133, 63.8758], - [-84.4686, 31.1948], - [-106.8425, 32.5907], - [-96.6129, 39.1104], - [-96.5631, 39.1008], - [-67.0769, 18.0213], - [-88.1612, 31.8539], - [-80.5248, 37.3783], - [-109.3883, 38.2483], - [-105.5824, 40.0543], - [-102.4471, 39.7582], - [-119.2575, 37.0597], - [-110.5871, 44.9501], - [-96.6242, 34.4442], - [-147.504, 65.1532], - [-105.5442, 40.035], - [-66.9868, 18.1135], - [-66.7987, 18.1741], - [-72.3295, 42.4719], - [-96.6038, 39.1051], - [-83.5038, 35.6904], - [-77.9832, 39.0956], - [-121.9338, 45.7908], - [-87.4077, 32.9604], - [-96.443, 38.9459], - [-122.1655, 44.2596], - [-149.143, 68.6698], - [-78.1473, 38.8943], - [-97.7823, 33.3785], - [-111.7979, 40.7839], - [-111.5081, 33.751], - [-119.0274, 36.9559], - [-84.2793, 35.9574], - [-105.9154, 39.8914] - ] - }, - "properties": { - "description": "\nmodel info: NA\n\nSites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, ARIK, BIGC, BLDE, BLUE, CARI, COMO, CUPE, GUIL, HOPB, KING, LECO, LEWI, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, REDB, SYCA, TECR, WALK, WLOU\n\nVariables: Daily Chlorophyll_a, Daily Green_chromatic_coordinate, Daily latent_heat_flux, Daily Net_ecosystem_exchange, Daily Dissolved_oxygen, Daily Red_chromatic_coordinate, Daily Water_temperature, Weekly beetle_community_richness, Weekly beetle_community_abundance, Weekly Amblyomma_americanum_population", - "start_datetime": "2023-01-01", - "end_datetime": "2023-12-15", - "providers": [ - { - "url": "pending", - "name": "pending", - "roles": [ - "producer", - "processor", - "licensor" - ] - }, - { - "url": "https://www.ecoforecastprojectvt.org", - "name": "Ecoforecast Challenge", - "roles": [ - "host" - ] - } - ], - "license": "CC0-1.0", - "keywords": [ - "Forecasting", - "neon4cast", - "Daily Chlorophyll_a", - "Daily Green_chromatic_coordinate", - "Daily latent_heat_flux", - "Daily Net_ecosystem_exchange", - "Daily Dissolved_oxygen", - "Daily Red_chromatic_coordinate", - "Daily Water_temperature", - "Weekly beetle_community_richness", - "Weekly beetle_community_abundance", - "Weekly Amblyomma_americanum_population" - ], - "table:columns": [ - { - "name": "reference_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that the forecast was initiated (horizon = 0)" - }, - { - "name": "site_id", - "type": "string", - "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model." - }, - { - "name": "datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime of the forecasted value (ISO 8601)" - }, - { - "name": "family", - "type": "string", - "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)." - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "name": "observation", - "type": "double", - "description": "observed value for variable" - }, - { - "name": "crps", - "type": "double", - "description": "crps forecast score" - }, - { - "name": "logs", - "type": "double", - "description": "logs forecast score" - }, - { - "name": "mean", - "type": "double", - "description": "mean forecast prediction" - }, - { - "name": "median", - "type": "double", - "description": "median forecast prediction" - }, - { - "name": "sd", - "type": "double", - "description": "standard deviation forecasts" - }, - { - "name": "quantile97.5", - "type": "double", - "description": "upper 97.5 percentile value of forecast" - }, - { - "name": "quantile02.5", - "type": "double", - "description": "upper 2.5 percentile value of forecast" - }, - { - "name": "quantile90", - "type": "double", - "description": "upper 90 percentile value of forecast" - }, - { - "name": "quantile10", - "type": "double", - "description": "upper 10 percentile value of forecast" - }, - { - "name": "project_id", - "type": "string", - "description": "unique project identifier" - }, - { - "name": "duration", - "type": "string", - "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention" - }, - { - "name": "variable", - "type": "string", - "description": "name of forecasted variable" - }, - { - "name": "model_id", - "type": "string", - "description": "unique model identifier" - }, - { - "name": "date", - "type": "string", - "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period." - } - ] - }, - "collection": "scores", - "links": [ - { - "rel": "collection", - "href": "../collection.json", - "type": "application/json", - "title": "tg_ets" - }, - { - "rel": "root", - "href": "../../../catalog.json", - "type": "application/json", - "title": "Forecast Catalog" - }, - { - "rel": "parent", - "href": "../collection.json", - "type": "application/json", - "title": "tg_ets" - }, - { - "rel": "self", - "href": "tg_ets.json", - "type": "application/json", - "title": "Model Forecast" - }, - { - "rel": "item", - "href": "pending", - "type": "text/html", - "title": "Link for Model Code" - } - ], - "assets": { - "1": { - "type": "application/json", - "title": "Model Metadata", - "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_ets.json", - "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_ets.json\")\n\n" - }, - "2": { - "type": "text/html", - "title": "Link for Model Code", - "href": "pending", - "description": "The link to the model code provided by the model submission team" - }, - "3": { - "type": "application/x-parquet", - "title": "Database Access for Daily Chlorophyll_a", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "4": { - "type": "application/x-parquet", - "title": "Database Access for Daily Green_chromatic_coordinate", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=gcc_90/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=gcc_90/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "5": { - "type": "application/x-parquet", - "title": "Database Access for Daily latent_heat_flux", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=le/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=le/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "6": { - "type": "application/x-parquet", - "title": "Database Access for Daily Net_ecosystem_exchange", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=nee/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=nee/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "7": { - "type": "application/x-parquet", - "title": "Database Access for Daily Dissolved_oxygen", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=oxygen/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=oxygen/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "8": { - "type": "application/x-parquet", - "title": "Database Access for Daily Red_chromatic_coordinate", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=rcc_90/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=rcc_90/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "9": { - "type": "application/x-parquet", - "title": "Database Access for Daily Water_temperature", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "10": { - "type": "application/x-parquet", - "title": "Database Access for Weekly beetle_community_richness", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1W/variable=richness/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1W/variable=richness/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "11": { - "type": "application/x-parquet", - "title": "Database Access for Weekly beetle_community_abundance", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1W/variable=abundance/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1W/variable=abundance/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "12": { - "type": "application/x-parquet", - "title": "Database Access for Weekly Amblyomma_americanum_population", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1W/variable=amblyomma_americanum/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1W/variable=amblyomma_americanum/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - } - } -} diff --git a/catalog/scores/models/model_items/tg_humidity_lm.json b/catalog/scores/models/model_items/tg_humidity_lm.json deleted file mode 100644 index e41779f37a..0000000000 --- a/catalog/scores/models/model_items/tg_humidity_lm.json +++ /dev/null @@ -1,328 +0,0 @@ -{ - "stac_version": "1.0.0", - "stac_extensions": [ - "https://stac-extensions.github.io/table/v1.2.0/schema.json" - ], - "type": "Feature", - "id": "tg_humidity_lm", - "bbox": [ - [ - -156.6194, - 71.2824, - -66.7987, - 71.2824 - ] - ], - "geometry": { - "type": "MultiPoint", - "coordinates": [ - [-82.0084, 29.676], - [-87.7982, 32.5415], - [-89.4737, 46.2097], - [-84.4374, 31.1854], - [-89.7048, 45.9983], - [-99.1139, 47.1591], - [-99.2531, 47.1298], - [-82.0177, 29.6878], - [-88.1589, 31.8534], - [-149.6106, 68.6307], - [-122.3303, 45.7624], - [-156.6194, 71.2824], - [-71.2874, 44.0639], - [-78.0418, 39.0337], - [-147.5026, 65.154], - [-97.57, 33.4012], - [-104.7456, 40.8155], - [-99.1066, 47.1617], - [-145.7514, 63.8811], - [-87.8039, 32.5417], - [-81.4362, 28.1251], - [-83.5019, 35.689], - [-66.8687, 17.9696], - [-72.1727, 42.5369], - [-149.2133, 63.8758], - [-84.4686, 31.1948], - [-106.8425, 32.5907], - [-96.6129, 39.1104], - [-96.5631, 39.1008], - [-67.0769, 18.0213], - [-88.1612, 31.8539], - [-80.5248, 37.3783], - [-109.3883, 38.2483], - [-105.5824, 40.0543], - [-100.9154, 46.7697], - [-99.0588, 35.4106], - [-112.4524, 40.1776], - [-84.2826, 35.9641], - [-81.9934, 29.6893], - [-155.3173, 19.5531], - [-105.546, 40.2759], - [-78.1395, 38.8929], - [-76.56, 38.8901], - [-119.7323, 37.1088], - [-119.2622, 37.0334], - [-110.8355, 31.9107], - [-89.5864, 45.5089], - [-103.0293, 40.4619], - [-87.3933, 32.9505], - [-119.006, 37.0058], - [-149.3705, 68.6611], - [-89.5857, 45.4937], - [-95.1921, 39.0404], - [-89.5373, 46.2339], - [-99.2413, 47.1282], - [-121.9519, 45.8205], - [-110.5391, 44.9535], - [-102.4471, 39.7582], - [-119.2575, 37.0597], - [-110.5871, 44.9501], - [-96.6242, 34.4442], - [-147.504, 65.1532], - [-105.5442, 40.035], - [-66.9868, 18.1135], - [-66.7987, 18.1741], - [-72.3295, 42.4719], - [-96.6038, 39.1051], - [-83.5038, 35.6904], - [-77.9832, 39.0956], - [-121.9338, 45.7908], - [-87.4077, 32.9604], - [-96.443, 38.9459], - [-122.1655, 44.2596], - [-149.143, 68.6698], - [-78.1473, 38.8943], - [-97.7823, 33.3785], - [-111.7979, 40.7839], - [-111.5081, 33.751], - [-119.0274, 36.9559], - [-84.2793, 35.9574], - [-105.9154, 39.8914] - ] - }, - "properties": { - "description": "\nmodel info: NA\n\nSites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ARIK, BIGC, BLDE, BLUE, CARI, COMO, CUPE, GUIL, HOPB, KING, LECO, LEWI, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, REDB, SYCA, TECR, WALK, WLOU\n\nVariables: Daily Chlorophyll_a, Daily Green_chromatic_coordinate, Daily latent_heat_flux, Daily Dissolved_oxygen, Daily Red_chromatic_coordinate, Daily Water_temperature, Daily Net_ecosystem_exchange", - "start_datetime": "2023-11-14", - "end_datetime": "2023-12-15", - "providers": [ - { - "url": "pending", - "name": "pending", - "roles": [ - "producer", - "processor", - "licensor" - ] - }, - { - "url": "https://www.ecoforecastprojectvt.org", - "name": "Ecoforecast Challenge", - "roles": [ - "host" - ] - } - ], - "license": "CC0-1.0", - "keywords": [ - "Forecasting", - "neon4cast", - "Daily Chlorophyll_a", - "Daily Green_chromatic_coordinate", - "Daily latent_heat_flux", - "Daily Dissolved_oxygen", - "Daily Red_chromatic_coordinate", - "Daily Water_temperature", - "Daily Net_ecosystem_exchange" - ], - "table:columns": [ - { - "name": "reference_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that the forecast was initiated (horizon = 0)" - }, - { - "name": "site_id", - "type": "string", - "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model." - }, - { - "name": "datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime of the forecasted value (ISO 8601)" - }, - { - "name": "family", - "type": "string", - "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)." - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "name": "observation", - "type": "double", - "description": "observed value for variable" - }, - { - "name": "crps", - "type": "double", - "description": "crps forecast score" - }, - { - "name": "logs", - "type": "double", - "description": "logs forecast score" - }, - { - "name": "mean", - "type": "double", - "description": "mean forecast prediction" - }, - { - "name": "median", - "type": "double", - "description": "median forecast prediction" - }, - { - "name": "sd", - "type": "double", - "description": "standard deviation forecasts" - }, - { - "name": "quantile97.5", - "type": "double", - "description": "upper 97.5 percentile value of forecast" - }, - { - "name": "quantile02.5", - "type": "double", - "description": "upper 2.5 percentile value of forecast" - }, - { - "name": "quantile90", - "type": "double", - "description": "upper 90 percentile value of forecast" - }, - { - "name": "quantile10", - "type": "double", - "description": "upper 10 percentile value of forecast" - }, - { - "name": "project_id", - "type": "string", - "description": "unique project identifier" - }, - { - "name": "duration", - "type": "string", - "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention" - }, - { - "name": "variable", - "type": "string", - "description": "name of forecasted variable" - }, - { - "name": "model_id", - "type": "string", - "description": "unique model identifier" - }, - { - "name": "date", - "type": "string", - "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period." - } - ] - }, - "collection": "scores", - "links": [ - { - "rel": "collection", - "href": "../collection.json", - "type": "application/json", - "title": "tg_humidity_lm" - }, - { - "rel": "root", - "href": "../../../catalog.json", - "type": "application/json", - "title": "Forecast Catalog" - }, - { - "rel": "parent", - "href": "../collection.json", - "type": "application/json", - "title": "tg_humidity_lm" - }, - { - "rel": "self", - "href": "tg_humidity_lm.json", - "type": "application/json", - "title": "Model Forecast" - }, - { - "rel": "item", - "href": "pending", - "type": "text/html", - "title": "Link for Model Code" - } - ], - "assets": { - "1": { - "type": "application/json", - "title": "Model Metadata", - "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_humidity_lm.json", - "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_humidity_lm.json\")\n\n" - }, - "2": { - "type": "text/html", - "title": "Link for Model Code", - "href": "pending", - "description": "The link to the model code provided by the model submission team" - }, - "3": { - "type": "application/x-parquet", - "title": "Database Access for Daily Chlorophyll_a", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=tg_humidity_lm?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=tg_humidity_lm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "4": { - "type": "application/x-parquet", - "title": "Database Access for Daily Green_chromatic_coordinate", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=gcc_90/model_id=tg_humidity_lm?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=gcc_90/model_id=tg_humidity_lm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "5": { - "type": "application/x-parquet", - "title": "Database Access for Daily latent_heat_flux", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=le/model_id=tg_humidity_lm?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=le/model_id=tg_humidity_lm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "6": { - "type": "application/x-parquet", - "title": "Database Access for Daily Dissolved_oxygen", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=oxygen/model_id=tg_humidity_lm?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=oxygen/model_id=tg_humidity_lm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "7": { - "type": "application/x-parquet", - "title": "Database Access for Daily Red_chromatic_coordinate", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=rcc_90/model_id=tg_humidity_lm?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=rcc_90/model_id=tg_humidity_lm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "8": { - "type": "application/x-parquet", - "title": "Database Access for Daily Water_temperature", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=tg_humidity_lm?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=tg_humidity_lm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "9": { - "type": "application/x-parquet", - "title": "Database Access for Daily Net_ecosystem_exchange", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=nee/model_id=tg_humidity_lm?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=nee/model_id=tg_humidity_lm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - } - } -} diff --git a/catalog/scores/models/model_items/tg_humidity_lm_all_sites.json b/catalog/scores/models/model_items/tg_humidity_lm_all_sites.json deleted file mode 100644 index e0a6237b49..0000000000 --- a/catalog/scores/models/model_items/tg_humidity_lm_all_sites.json +++ /dev/null @@ -1,328 +0,0 @@ -{ - "stac_version": "1.0.0", - "stac_extensions": [ - "https://stac-extensions.github.io/table/v1.2.0/schema.json" - ], - "type": "Feature", - "id": "tg_humidity_lm_all_sites", - "bbox": [ - [ - -156.6194, - 71.2824, - -66.7987, - 71.2824 - ] - ], - "geometry": { - "type": "MultiPoint", - "coordinates": [ - [-82.0084, 29.676], - [-87.7982, 32.5415], - [-89.4737, 46.2097], - [-84.4374, 31.1854], - [-89.7048, 45.9983], - [-99.1139, 47.1591], - [-99.2531, 47.1298], - [-82.0177, 29.6878], - [-88.1589, 31.8534], - [-149.6106, 68.6307], - [-122.3303, 45.7624], - [-156.6194, 71.2824], - [-71.2874, 44.0639], - [-78.0418, 39.0337], - [-147.5026, 65.154], - [-97.57, 33.4012], - [-104.7456, 40.8155], - [-99.1066, 47.1617], - [-145.7514, 63.8811], - [-87.8039, 32.5417], - [-81.4362, 28.1251], - [-83.5019, 35.689], - [-66.8687, 17.9696], - [-72.1727, 42.5369], - [-149.2133, 63.8758], - [-84.4686, 31.1948], - [-106.8425, 32.5907], - [-96.6129, 39.1104], - [-96.5631, 39.1008], - [-67.0769, 18.0213], - [-88.1612, 31.8539], - [-80.5248, 37.3783], - [-109.3883, 38.2483], - [-105.5824, 40.0543], - [-100.9154, 46.7697], - [-99.0588, 35.4106], - [-112.4524, 40.1776], - [-84.2826, 35.9641], - [-81.9934, 29.6893], - [-155.3173, 19.5531], - [-105.546, 40.2759], - [-78.1395, 38.8929], - [-76.56, 38.8901], - [-119.7323, 37.1088], - [-119.2622, 37.0334], - [-110.8355, 31.9107], - [-89.5864, 45.5089], - [-103.0293, 40.4619], - [-87.3933, 32.9505], - [-119.006, 37.0058], - [-149.3705, 68.6611], - [-89.5857, 45.4937], - [-95.1921, 39.0404], - [-89.5373, 46.2339], - [-99.2413, 47.1282], - [-121.9519, 45.8205], - [-110.5391, 44.9535], - [-102.4471, 39.7582], - [-119.2575, 37.0597], - [-110.5871, 44.9501], - [-96.6242, 34.4442], - [-147.504, 65.1532], - [-105.5442, 40.035], - [-66.9868, 18.1135], - [-66.7987, 18.1741], - [-72.3295, 42.4719], - [-96.6038, 39.1051], - [-83.5038, 35.6904], - [-77.9832, 39.0956], - [-121.9338, 45.7908], - [-87.4077, 32.9604], - [-96.443, 38.9459], - [-122.1655, 44.2596], - [-149.143, 68.6698], - [-78.1473, 38.8943], - [-97.7823, 33.3785], - [-111.7979, 40.7839], - [-111.5081, 33.751], - [-119.0274, 36.9559], - [-84.2793, 35.9574], - [-105.9154, 39.8914] - ] - }, - "properties": { - "description": "\nmodel info: NA\n\nSites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ARIK, BIGC, BLDE, BLUE, CARI, COMO, CUPE, GUIL, HOPB, KING, LECO, LEWI, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, REDB, SYCA, TECR, WALK, WLOU\n\nVariables: Daily Chlorophyll_a, Daily Green_chromatic_coordinate, Daily latent_heat_flux, Daily Net_ecosystem_exchange, Daily Dissolved_oxygen, Daily Red_chromatic_coordinate, Daily Water_temperature", - "start_datetime": "2023-11-14", - "end_datetime": "2023-12-15", - "providers": [ - { - "url": "pending", - "name": "pending", - "roles": [ - "producer", - "processor", - "licensor" - ] - }, - { - "url": "https://www.ecoforecastprojectvt.org", - "name": "Ecoforecast Challenge", - "roles": [ - "host" - ] - } - ], - "license": "CC0-1.0", - "keywords": [ - "Forecasting", - "neon4cast", - "Daily Chlorophyll_a", - "Daily Green_chromatic_coordinate", - "Daily latent_heat_flux", - "Daily Net_ecosystem_exchange", - "Daily Dissolved_oxygen", - "Daily Red_chromatic_coordinate", - "Daily Water_temperature" - ], - "table:columns": [ - { - "name": "reference_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that the forecast was initiated (horizon = 0)" - }, - { - "name": "site_id", - "type": "string", - "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model." - }, - { - "name": "datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime of the forecasted value (ISO 8601)" - }, - { - "name": "family", - "type": "string", - "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)." - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "name": "observation", - "type": "double", - "description": "observed value for variable" - }, - { - "name": "crps", - "type": "double", - "description": "crps forecast score" - }, - { - "name": "logs", - "type": "double", - "description": "logs forecast score" - }, - { - "name": "mean", - "type": "double", - "description": "mean forecast prediction" - }, - { - "name": "median", - "type": "double", - "description": "median forecast prediction" - }, - { - "name": "sd", - "type": "double", - "description": "standard deviation forecasts" - }, - { - "name": "quantile97.5", - "type": "double", - "description": "upper 97.5 percentile value of forecast" - }, - { - "name": "quantile02.5", - "type": "double", - "description": "upper 2.5 percentile value of forecast" - }, - { - "name": "quantile90", - "type": "double", - "description": "upper 90 percentile value of forecast" - }, - { - "name": "quantile10", - "type": "double", - "description": "upper 10 percentile value of forecast" - }, - { - "name": "project_id", - "type": "string", - "description": "unique project identifier" - }, - { - "name": "duration", - "type": "string", - "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention" - }, - { - "name": "variable", - "type": "string", - "description": "name of forecasted variable" - }, - { - "name": "model_id", - "type": "string", - "description": "unique model identifier" - }, - { - "name": "date", - "type": "string", - "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period." - } - ] - }, - "collection": "scores", - "links": [ - { - "rel": "collection", - "href": "../collection.json", - "type": "application/json", - "title": "tg_humidity_lm_all_sites" - }, - { - "rel": "root", - "href": "../../../catalog.json", - "type": "application/json", - "title": "Forecast Catalog" - }, - { - "rel": "parent", - "href": "../collection.json", - "type": "application/json", - "title": "tg_humidity_lm_all_sites" - }, - { - "rel": "self", - "href": "tg_humidity_lm_all_sites.json", - "type": "application/json", - "title": "Model Forecast" - }, - { - "rel": "item", - "href": "pending", - "type": "text/html", - "title": "Link for Model Code" - } - ], - "assets": { - "1": { - "type": "application/json", - "title": "Model Metadata", - "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_humidity_lm_all_sites.json", - "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_humidity_lm_all_sites.json\")\n\n" - }, - "2": { - "type": "text/html", - "title": "Link for Model Code", - "href": "pending", - "description": "The link to the model code provided by the model submission team" - }, - "3": { - "type": "application/x-parquet", - "title": "Database Access for Daily Chlorophyll_a", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=tg_humidity_lm_all_sites?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=tg_humidity_lm_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "4": { - "type": "application/x-parquet", - "title": "Database Access for Daily Green_chromatic_coordinate", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=gcc_90/model_id=tg_humidity_lm_all_sites?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=gcc_90/model_id=tg_humidity_lm_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "5": { - "type": "application/x-parquet", - "title": "Database Access for Daily latent_heat_flux", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=le/model_id=tg_humidity_lm_all_sites?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=le/model_id=tg_humidity_lm_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "6": { - "type": "application/x-parquet", - "title": "Database Access for Daily Net_ecosystem_exchange", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=nee/model_id=tg_humidity_lm_all_sites?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=nee/model_id=tg_humidity_lm_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "7": { - "type": "application/x-parquet", - "title": "Database Access for Daily Dissolved_oxygen", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=oxygen/model_id=tg_humidity_lm_all_sites?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=oxygen/model_id=tg_humidity_lm_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "8": { - "type": "application/x-parquet", - "title": "Database Access for Daily Red_chromatic_coordinate", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=rcc_90/model_id=tg_humidity_lm_all_sites?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=rcc_90/model_id=tg_humidity_lm_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "9": { - "type": "application/x-parquet", - "title": "Database Access for Daily Water_temperature", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=tg_humidity_lm_all_sites?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=tg_humidity_lm_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - } - } -} diff --git a/catalog/scores/models/model_items/tg_lasso.json b/catalog/scores/models/model_items/tg_lasso.json deleted file mode 100644 index 3ad707e59f..0000000000 --- a/catalog/scores/models/model_items/tg_lasso.json +++ /dev/null @@ -1,314 +0,0 @@ -{ - "stac_version": "1.0.0", - "stac_extensions": [ - "https://stac-extensions.github.io/table/v1.2.0/schema.json" - ], - "type": "Feature", - "id": "tg_lasso", - "bbox": [ - [ - -156.6194, - 71.2824, - -66.7987, - 71.2824 - ] - ], - "geometry": { - "type": "MultiPoint", - "coordinates": [ - [-82.0084, 29.676], - [-87.7982, 32.5415], - [-89.4737, 46.2097], - [-84.4374, 31.1854], - [-89.7048, 45.9983], - [-99.1139, 47.1591], - [-99.2531, 47.1298], - [-82.0177, 29.6878], - [-88.1589, 31.8534], - [-149.6106, 68.6307], - [-122.3303, 45.7624], - [-156.6194, 71.2824], - [-71.2874, 44.0639], - [-78.0418, 39.0337], - [-147.5026, 65.154], - [-97.57, 33.4012], - [-104.7456, 40.8155], - [-99.1066, 47.1617], - [-145.7514, 63.8811], - [-87.8039, 32.5417], - [-81.4362, 28.1251], - [-83.5019, 35.689], - [-66.8687, 17.9696], - [-72.1727, 42.5369], - [-149.2133, 63.8758], - [-84.4686, 31.1948], - [-106.8425, 32.5907], - [-96.6129, 39.1104], - [-96.5631, 39.1008], - [-67.0769, 18.0213], - [-88.1612, 31.8539], - [-80.5248, 37.3783], - [-109.3883, 38.2483], - [-105.5824, 40.0543], - [-100.9154, 46.7697], - [-99.0588, 35.4106], - [-112.4524, 40.1776], - [-84.2826, 35.9641], - [-81.9934, 29.6893], - [-155.3173, 19.5531], - [-105.546, 40.2759], - [-78.1395, 38.8929], - [-76.56, 38.8901], - [-119.7323, 37.1088], - [-119.2622, 37.0334], - [-110.8355, 31.9107], - [-89.5864, 45.5089], - [-103.0293, 40.4619], - [-87.3933, 32.9505], - [-119.006, 37.0058], - [-149.3705, 68.6611], - [-89.5857, 45.4937], - [-95.1921, 39.0404], - [-89.5373, 46.2339], - [-99.2413, 47.1282], - [-121.9519, 45.8205], - [-110.5391, 44.9535], - [-102.4471, 39.7582], - [-119.2575, 37.0597], - [-110.5871, 44.9501], - [-96.6242, 34.4442], - [-147.504, 65.1532], - [-105.5442, 40.035], - [-66.9868, 18.1135], - [-66.7987, 18.1741], - [-72.3295, 42.4719], - [-96.6038, 39.1051], - [-83.5038, 35.6904], - [-77.9832, 39.0956], - [-121.9338, 45.7908], - [-87.4077, 32.9604], - [-96.443, 38.9459], - [-122.1655, 44.2596], - [-149.143, 68.6698], - [-78.1473, 38.8943], - [-97.7823, 33.3785], - [-111.7979, 40.7839], - [-111.5081, 33.751], - [-119.0274, 36.9559], - [-84.2793, 35.9574], - [-105.9154, 39.8914] - ] - }, - "properties": { - "description": "\nmodel info: NA\n\nSites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ARIK, BIGC, BLDE, BLUE, CARI, COMO, CUPE, GUIL, HOPB, KING, LECO, LEWI, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, REDB, SYCA, TECR, WALK, WLOU\n\nVariables: Daily Chlorophyll_a, Daily Green_chromatic_coordinate, Daily Dissolved_oxygen, Daily Red_chromatic_coordinate, Daily Water_temperature", - "start_datetime": "2023-11-14", - "end_datetime": "2023-12-15", - "providers": [ - { - "url": "pending", - "name": "pending", - "roles": [ - "producer", - "processor", - "licensor" - ] - }, - { - "url": "https://www.ecoforecastprojectvt.org", - "name": "Ecoforecast Challenge", - "roles": [ - "host" - ] - } - ], - "license": "CC0-1.0", - "keywords": [ - "Forecasting", - "neon4cast", - "Daily Chlorophyll_a", - "Daily Green_chromatic_coordinate", - "Daily Dissolved_oxygen", - "Daily Red_chromatic_coordinate", - "Daily Water_temperature" - ], - "table:columns": [ - { - "name": "reference_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that the forecast was initiated (horizon = 0)" - }, - { - "name": "site_id", - "type": "string", - "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model." - }, - { - "name": "datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime of the forecasted value (ISO 8601)" - }, - { - "name": "family", - "type": "string", - "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)." - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "name": "observation", - "type": "double", - "description": "observed value for variable" - }, - { - "name": "crps", - "type": "double", - "description": "crps forecast score" - }, - { - "name": "logs", - "type": "double", - "description": "logs forecast score" - }, - { - "name": "mean", - "type": "double", - "description": "mean forecast prediction" - }, - { - "name": "median", - "type": "double", - "description": "median forecast prediction" - }, - { - "name": "sd", - "type": "double", - "description": "standard deviation forecasts" - }, - { - "name": "quantile97.5", - "type": "double", - "description": "upper 97.5 percentile value of forecast" - }, - { - "name": "quantile02.5", - "type": "double", - "description": "upper 2.5 percentile value of forecast" - }, - { - "name": "quantile90", - "type": "double", - "description": "upper 90 percentile value of forecast" - }, - { - "name": "quantile10", - "type": "double", - "description": "upper 10 percentile value of forecast" - }, - { - "name": "project_id", - "type": "string", - "description": "unique project identifier" - }, - { - "name": "duration", - "type": "string", - "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention" - }, - { - "name": "variable", - "type": "string", - "description": "name of forecasted variable" - }, - { - "name": "model_id", - "type": "string", - "description": "unique model identifier" - }, - { - "name": "date", - "type": "string", - "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period." - } - ] - }, - "collection": "scores", - "links": [ - { - "rel": "collection", - "href": "../collection.json", - "type": "application/json", - "title": "tg_lasso" - }, - { - "rel": "root", - "href": "../../../catalog.json", - "type": "application/json", - "title": "Forecast Catalog" - }, - { - "rel": "parent", - "href": "../collection.json", - "type": "application/json", - "title": "tg_lasso" - }, - { - "rel": "self", - "href": "tg_lasso.json", - "type": "application/json", - "title": "Model Forecast" - }, - { - "rel": "item", - "href": "pending", - "type": "text/html", - "title": "Link for Model Code" - } - ], - "assets": { - "1": { - "type": "application/json", - "title": "Model Metadata", - "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_lasso.json", - "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_lasso.json\")\n\n" - }, - "2": { - "type": "text/html", - "title": "Link for Model Code", - "href": "pending", - "description": "The link to the model code provided by the model submission team" - }, - "3": { - "type": "application/x-parquet", - "title": "Database Access for Daily Chlorophyll_a", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=tg_lasso?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=tg_lasso?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "4": { - "type": "application/x-parquet", - "title": "Database Access for Daily Green_chromatic_coordinate", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=gcc_90/model_id=tg_lasso?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=gcc_90/model_id=tg_lasso?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "5": { - "type": "application/x-parquet", - "title": "Database Access for Daily Dissolved_oxygen", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=oxygen/model_id=tg_lasso?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=oxygen/model_id=tg_lasso?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "6": { - "type": "application/x-parquet", - "title": "Database Access for Daily Red_chromatic_coordinate", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=rcc_90/model_id=tg_lasso?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=rcc_90/model_id=tg_lasso?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "7": { - "type": "application/x-parquet", - "title": "Database Access for Daily Water_temperature", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=tg_lasso?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=tg_lasso?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - } - } -} diff --git a/catalog/scores/models/model_items/tg_lasso_all_sites.json b/catalog/scores/models/model_items/tg_lasso_all_sites.json deleted file mode 100644 index 2c6301163f..0000000000 --- a/catalog/scores/models/model_items/tg_lasso_all_sites.json +++ /dev/null @@ -1,314 +0,0 @@ -{ - "stac_version": "1.0.0", - "stac_extensions": [ - "https://stac-extensions.github.io/table/v1.2.0/schema.json" - ], - "type": "Feature", - "id": "tg_lasso_all_sites", - "bbox": [ - [ - -156.6194, - 71.2824, - -66.7987, - 71.2824 - ] - ], - "geometry": { - "type": "MultiPoint", - "coordinates": [ - [-82.0084, 29.676], - [-87.7982, 32.5415], - [-89.4737, 46.2097], - [-84.4374, 31.1854], - [-89.7048, 45.9983], - [-99.1139, 47.1591], - [-99.2531, 47.1298], - [-82.0177, 29.6878], - [-88.1589, 31.8534], - [-149.6106, 68.6307], - [-122.3303, 45.7624], - [-156.6194, 71.2824], - [-71.2874, 44.0639], - [-78.0418, 39.0337], - [-147.5026, 65.154], - [-97.57, 33.4012], - [-104.7456, 40.8155], - [-99.1066, 47.1617], - [-145.7514, 63.8811], - [-87.8039, 32.5417], - [-81.4362, 28.1251], - [-83.5019, 35.689], - [-66.8687, 17.9696], - [-72.1727, 42.5369], - [-149.2133, 63.8758], - [-84.4686, 31.1948], - [-106.8425, 32.5907], - [-96.6129, 39.1104], - [-96.5631, 39.1008], - [-67.0769, 18.0213], - [-88.1612, 31.8539], - [-80.5248, 37.3783], - [-109.3883, 38.2483], - [-105.5824, 40.0543], - [-100.9154, 46.7697], - [-99.0588, 35.4106], - [-112.4524, 40.1776], - [-84.2826, 35.9641], - [-81.9934, 29.6893], - [-155.3173, 19.5531], - [-105.546, 40.2759], - [-78.1395, 38.8929], - [-76.56, 38.8901], - [-119.7323, 37.1088], - [-119.2622, 37.0334], - [-110.8355, 31.9107], - [-89.5864, 45.5089], - [-103.0293, 40.4619], - [-87.3933, 32.9505], - [-119.006, 37.0058], - [-149.3705, 68.6611], - [-89.5857, 45.4937], - [-95.1921, 39.0404], - [-89.5373, 46.2339], - [-99.2413, 47.1282], - [-121.9519, 45.8205], - [-110.5391, 44.9535], - [-102.4471, 39.7582], - [-119.2575, 37.0597], - [-110.5871, 44.9501], - [-96.6242, 34.4442], - [-147.504, 65.1532], - [-105.5442, 40.035], - [-66.9868, 18.1135], - [-66.7987, 18.1741], - [-72.3295, 42.4719], - [-96.6038, 39.1051], - [-83.5038, 35.6904], - [-77.9832, 39.0956], - [-121.9338, 45.7908], - [-87.4077, 32.9604], - [-96.443, 38.9459], - [-122.1655, 44.2596], - [-149.143, 68.6698], - [-78.1473, 38.8943], - [-97.7823, 33.3785], - [-111.7979, 40.7839], - [-111.5081, 33.751], - [-119.0274, 36.9559], - [-84.2793, 35.9574], - [-105.9154, 39.8914] - ] - }, - "properties": { - "description": [], - "start_datetime": "2023-11-14", - "end_datetime": "2023-12-15", - "providers": [ - { - "url": "pending", - "name": "pending", - "roles": [ - "producer", - "processor", - "licensor" - ] - }, - { - "url": "https://www.ecoforecastprojectvt.org", - "name": "Ecoforecast Challenge", - "roles": [ - "host" - ] - } - ], - "license": "CC0-1.0", - "keywords": [ - "Forecasting", - "neon4cast", - "Daily Chlorophyll_a", - "Daily Green_chromatic_coordinate", - "Daily Dissolved_oxygen", - "Daily Red_chromatic_coordinate", - "Daily Water_temperature" - ], - "table:columns": [ - { - "name": "reference_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that the forecast was initiated (horizon = 0)" - }, - { - "name": "site_id", - "type": "string", - "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model." - }, - { - "name": "datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime of the forecasted value (ISO 8601)" - }, - { - "name": "family", - "type": "string", - "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)." - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "name": "observation", - "type": "double", - "description": "observed value for variable" - }, - { - "name": "crps", - "type": "double", - "description": "crps forecast score" - }, - { - "name": "logs", - "type": "double", - "description": "logs forecast score" - }, - { - "name": "mean", - "type": "double", - "description": "mean forecast prediction" - }, - { - "name": "median", - "type": "double", - "description": "median forecast prediction" - }, - { - "name": "sd", - "type": "double", - "description": "standard deviation forecasts" - }, - { - "name": "quantile97.5", - "type": "double", - "description": "upper 97.5 percentile value of forecast" - }, - { - "name": "quantile02.5", - "type": "double", - "description": "upper 2.5 percentile value of forecast" - }, - { - "name": "quantile90", - "type": "double", - "description": "upper 90 percentile value of forecast" - }, - { - "name": "quantile10", - "type": "double", - "description": "upper 10 percentile value of forecast" - }, - { - "name": "project_id", - "type": "string", - "description": "unique project identifier" - }, - { - "name": "duration", - "type": "string", - "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention" - }, - { - "name": "variable", - "type": "string", - "description": "name of forecasted variable" - }, - { - "name": "model_id", - "type": "string", - "description": "unique model identifier" - }, - { - "name": "date", - "type": "string", - "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period." - } - ] - }, - "collection": "scores", - "links": [ - { - "rel": "collection", - "href": "../collection.json", - "type": "application/json", - "title": "tg_lasso_all_sites" - }, - { - "rel": "root", - "href": "../../../catalog.json", - "type": "application/json", - "title": "Forecast Catalog" - }, - { - "rel": "parent", - "href": "../collection.json", - "type": "application/json", - "title": "tg_lasso_all_sites" - }, - { - "rel": "self", - "href": "tg_lasso_all_sites.json", - "type": "application/json", - "title": "Model Forecast" - }, - { - "rel": "item", - "href": "pending", - "type": "text/html", - "title": "Link for Model Code" - } - ], - "assets": { - "1": { - "type": "application/json", - "title": "Model Metadata", - "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_lasso_all_sites.json", - "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_lasso_all_sites.json\")\n\n" - }, - "2": { - "type": "text/html", - "title": "Link for Model Code", - "href": "pending", - "description": "The link to the model code provided by the model submission team" - }, - "3": { - "type": "application/x-parquet", - "title": "Database Access for Daily Chlorophyll_a", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=tg_lasso_all_sites?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=tg_lasso_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "4": { - "type": "application/x-parquet", - "title": "Database Access for Daily Green_chromatic_coordinate", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=gcc_90/model_id=tg_lasso_all_sites?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=gcc_90/model_id=tg_lasso_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "5": { - "type": "application/x-parquet", - "title": "Database Access for Daily Dissolved_oxygen", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=oxygen/model_id=tg_lasso_all_sites?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=oxygen/model_id=tg_lasso_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "6": { - "type": "application/x-parquet", - "title": "Database Access for Daily Red_chromatic_coordinate", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=rcc_90/model_id=tg_lasso_all_sites?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=rcc_90/model_id=tg_lasso_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "7": { - "type": "application/x-parquet", - "title": "Database Access for Daily Water_temperature", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=tg_lasso_all_sites?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=tg_lasso_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - } - } -} diff --git a/catalog/scores/models/model_items/tg_precip_lm.json b/catalog/scores/models/model_items/tg_precip_lm.json deleted file mode 100644 index dc4604826a..0000000000 --- a/catalog/scores/models/model_items/tg_precip_lm.json +++ /dev/null @@ -1,328 +0,0 @@ -{ - "stac_version": "1.0.0", - "stac_extensions": [ - "https://stac-extensions.github.io/table/v1.2.0/schema.json" - ], - "type": "Feature", - "id": "tg_precip_lm", - "bbox": [ - [ - -156.6194, - 71.2824, - -66.7987, - 71.2824 - ] - ], - "geometry": { - "type": "MultiPoint", - "coordinates": [ - [-82.0084, 29.676], - [-87.7982, 32.5415], - [-89.4737, 46.2097], - [-84.4374, 31.1854], - [-89.7048, 45.9983], - [-99.1139, 47.1591], - [-99.2531, 47.1298], - [-82.0177, 29.6878], - [-88.1589, 31.8534], - [-149.6106, 68.6307], - [-122.3303, 45.7624], - [-156.6194, 71.2824], - [-71.2874, 44.0639], - [-78.0418, 39.0337], - [-147.5026, 65.154], - [-97.57, 33.4012], - [-104.7456, 40.8155], - [-99.1066, 47.1617], - [-145.7514, 63.8811], - [-87.8039, 32.5417], - [-81.4362, 28.1251], - [-83.5019, 35.689], - [-66.8687, 17.9696], - [-72.1727, 42.5369], - [-149.2133, 63.8758], - [-84.4686, 31.1948], - [-106.8425, 32.5907], - [-96.6129, 39.1104], - [-96.5631, 39.1008], - [-67.0769, 18.0213], - [-88.1612, 31.8539], - [-80.5248, 37.3783], - [-109.3883, 38.2483], - [-105.5824, 40.0543], - [-100.9154, 46.7697], - [-99.0588, 35.4106], - [-112.4524, 40.1776], - [-84.2826, 35.9641], - [-81.9934, 29.6893], - [-155.3173, 19.5531], - [-105.546, 40.2759], - [-78.1395, 38.8929], - [-76.56, 38.8901], - [-119.7323, 37.1088], - [-119.2622, 37.0334], - [-110.8355, 31.9107], - [-89.5864, 45.5089], - [-103.0293, 40.4619], - [-87.3933, 32.9505], - [-119.006, 37.0058], - [-149.3705, 68.6611], - [-89.5857, 45.4937], - [-95.1921, 39.0404], - [-89.5373, 46.2339], - [-99.2413, 47.1282], - [-121.9519, 45.8205], - [-110.5391, 44.9535], - [-102.4471, 39.7582], - [-119.2575, 37.0597], - [-110.5871, 44.9501], - [-96.6242, 34.4442], - [-147.504, 65.1532], - [-105.5442, 40.035], - [-66.9868, 18.1135], - [-66.7987, 18.1741], - [-72.3295, 42.4719], - [-96.6038, 39.1051], - [-83.5038, 35.6904], - [-77.9832, 39.0956], - [-121.9338, 45.7908], - [-87.4077, 32.9604], - [-96.443, 38.9459], - [-122.1655, 44.2596], - [-149.143, 68.6698], - [-78.1473, 38.8943], - [-97.7823, 33.3785], - [-111.7979, 40.7839], - [-111.5081, 33.751], - [-119.0274, 36.9559], - [-84.2793, 35.9574], - [-105.9154, 39.8914] - ] - }, - "properties": { - "description": "\nmodel info: NA\n\nSites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ARIK, BIGC, BLDE, BLUE, CARI, COMO, CUPE, GUIL, HOPB, KING, LECO, LEWI, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, REDB, SYCA, TECR, WALK, WLOU\n\nVariables: Daily Chlorophyll_a, Daily Green_chromatic_coordinate, Daily latent_heat_flux, Daily Net_ecosystem_exchange, Daily Dissolved_oxygen, Daily Red_chromatic_coordinate, Daily Water_temperature", - "start_datetime": "2023-11-14", - "end_datetime": "2023-12-15", - "providers": [ - { - "url": "pending", - "name": "pending", - "roles": [ - "producer", - "processor", - "licensor" - ] - }, - { - "url": "https://www.ecoforecastprojectvt.org", - "name": "Ecoforecast Challenge", - "roles": [ - "host" - ] - } - ], - "license": "CC0-1.0", - "keywords": [ - "Forecasting", - "neon4cast", - "Daily Chlorophyll_a", - "Daily Green_chromatic_coordinate", - "Daily latent_heat_flux", - "Daily Net_ecosystem_exchange", - "Daily Dissolved_oxygen", - "Daily Red_chromatic_coordinate", - "Daily Water_temperature" - ], - "table:columns": [ - { - "name": "reference_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that the forecast was initiated (horizon = 0)" - }, - { - "name": "site_id", - "type": "string", - "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model." - }, - { - "name": "datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime of the forecasted value (ISO 8601)" - }, - { - "name": "family", - "type": "string", - "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)." - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "name": "observation", - "type": "double", - "description": "observed value for variable" - }, - { - "name": "crps", - "type": "double", - "description": "crps forecast score" - }, - { - "name": "logs", - "type": "double", - "description": "logs forecast score" - }, - { - "name": "mean", - "type": "double", - "description": "mean forecast prediction" - }, - { - "name": "median", - "type": "double", - "description": "median forecast prediction" - }, - { - "name": "sd", - "type": "double", - "description": "standard deviation forecasts" - }, - { - "name": "quantile97.5", - "type": "double", - "description": "upper 97.5 percentile value of forecast" - }, - { - "name": "quantile02.5", - "type": "double", - "description": "upper 2.5 percentile value of forecast" - }, - { - "name": "quantile90", - "type": "double", - "description": "upper 90 percentile value of forecast" - }, - { - "name": "quantile10", - "type": "double", - "description": "upper 10 percentile value of forecast" - }, - { - "name": "project_id", - "type": "string", - "description": "unique project identifier" - }, - { - "name": "duration", - "type": "string", - "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention" - }, - { - "name": "variable", - "type": "string", - "description": "name of forecasted variable" - }, - { - "name": "model_id", - "type": "string", - "description": "unique model identifier" - }, - { - "name": "date", - "type": "string", - "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period." - } - ] - }, - "collection": "scores", - "links": [ - { - "rel": "collection", - "href": "../collection.json", - "type": "application/json", - "title": "tg_precip_lm" - }, - { - "rel": "root", - "href": "../../../catalog.json", - "type": "application/json", - "title": "Forecast Catalog" - }, - { - "rel": "parent", - "href": "../collection.json", - "type": "application/json", - "title": "tg_precip_lm" - }, - { - "rel": "self", - "href": "tg_precip_lm.json", - "type": "application/json", - "title": "Model Forecast" - }, - { - "rel": "item", - "href": "pending", - "type": "text/html", - "title": "Link for Model Code" - } - ], - "assets": { - "1": { - "type": "application/json", - "title": "Model Metadata", - "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_precip_lm.json", - "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_precip_lm.json\")\n\n" - }, - "2": { - "type": "text/html", - "title": "Link for Model Code", - "href": "pending", - "description": "The link to the model code provided by the model submission team" - }, - "3": { - "type": "application/x-parquet", - "title": "Database Access for Daily Chlorophyll_a", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=tg_precip_lm?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=tg_precip_lm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "4": { - "type": "application/x-parquet", - "title": "Database Access for Daily Green_chromatic_coordinate", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=gcc_90/model_id=tg_precip_lm?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=gcc_90/model_id=tg_precip_lm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "5": { - "type": "application/x-parquet", - "title": "Database Access for Daily latent_heat_flux", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=le/model_id=tg_precip_lm?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=le/model_id=tg_precip_lm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "6": { - "type": "application/x-parquet", - "title": "Database Access for Daily Net_ecosystem_exchange", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=nee/model_id=tg_precip_lm?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=nee/model_id=tg_precip_lm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "7": { - "type": "application/x-parquet", - "title": "Database Access for Daily Dissolved_oxygen", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=oxygen/model_id=tg_precip_lm?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=oxygen/model_id=tg_precip_lm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "8": { - "type": "application/x-parquet", - "title": "Database Access for Daily Red_chromatic_coordinate", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=rcc_90/model_id=tg_precip_lm?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=rcc_90/model_id=tg_precip_lm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "9": { - "type": "application/x-parquet", - "title": "Database Access for Daily Water_temperature", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=tg_precip_lm?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=tg_precip_lm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - } - } -} diff --git a/catalog/scores/models/model_items/tg_precip_lm_all_sites.json b/catalog/scores/models/model_items/tg_precip_lm_all_sites.json deleted file mode 100644 index 7e2cb02d67..0000000000 --- a/catalog/scores/models/model_items/tg_precip_lm_all_sites.json +++ /dev/null @@ -1,328 +0,0 @@ -{ - "stac_version": "1.0.0", - "stac_extensions": [ - "https://stac-extensions.github.io/table/v1.2.0/schema.json" - ], - "type": "Feature", - "id": "tg_precip_lm_all_sites", - "bbox": [ - [ - -156.6194, - 71.2824, - -66.7987, - 71.2824 - ] - ], - "geometry": { - "type": "MultiPoint", - "coordinates": [ - [-82.0084, 29.676], - [-87.7982, 32.5415], - [-89.4737, 46.2097], - [-84.4374, 31.1854], - [-89.7048, 45.9983], - [-99.1139, 47.1591], - [-99.2531, 47.1298], - [-82.0177, 29.6878], - [-88.1589, 31.8534], - [-149.6106, 68.6307], - [-122.3303, 45.7624], - [-156.6194, 71.2824], - [-71.2874, 44.0639], - [-78.0418, 39.0337], - [-147.5026, 65.154], - [-97.57, 33.4012], - [-104.7456, 40.8155], - [-99.1066, 47.1617], - [-145.7514, 63.8811], - [-87.8039, 32.5417], - [-81.4362, 28.1251], - [-83.5019, 35.689], - [-66.8687, 17.9696], - [-72.1727, 42.5369], - [-149.2133, 63.8758], - [-84.4686, 31.1948], - [-106.8425, 32.5907], - [-96.6129, 39.1104], - [-96.5631, 39.1008], - [-67.0769, 18.0213], - [-88.1612, 31.8539], - [-80.5248, 37.3783], - [-109.3883, 38.2483], - [-105.5824, 40.0543], - [-100.9154, 46.7697], - [-99.0588, 35.4106], - [-112.4524, 40.1776], - [-84.2826, 35.9641], - [-81.9934, 29.6893], - [-155.3173, 19.5531], - [-105.546, 40.2759], - [-78.1395, 38.8929], - [-76.56, 38.8901], - [-119.7323, 37.1088], - [-119.2622, 37.0334], - [-110.8355, 31.9107], - [-89.5864, 45.5089], - [-103.0293, 40.4619], - [-87.3933, 32.9505], - [-119.006, 37.0058], - [-149.3705, 68.6611], - [-89.5857, 45.4937], - [-95.1921, 39.0404], - [-89.5373, 46.2339], - [-99.2413, 47.1282], - [-121.9519, 45.8205], - [-110.5391, 44.9535], - [-102.4471, 39.7582], - [-119.2575, 37.0597], - [-110.5871, 44.9501], - [-96.6242, 34.4442], - [-147.504, 65.1532], - [-105.5442, 40.035], - [-66.9868, 18.1135], - [-66.7987, 18.1741], - [-72.3295, 42.4719], - [-96.6038, 39.1051], - [-83.5038, 35.6904], - [-77.9832, 39.0956], - [-121.9338, 45.7908], - [-87.4077, 32.9604], - [-96.443, 38.9459], - [-122.1655, 44.2596], - [-149.143, 68.6698], - [-78.1473, 38.8943], - [-97.7823, 33.3785], - [-111.7979, 40.7839], - [-111.5081, 33.751], - [-119.0274, 36.9559], - [-84.2793, 35.9574], - [-105.9154, 39.8914] - ] - }, - "properties": { - "description": "\nmodel info: NA\n\nSites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ARIK, BIGC, BLDE, BLUE, CARI, COMO, CUPE, GUIL, HOPB, KING, LECO, LEWI, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, REDB, SYCA, TECR, WALK, WLOU\n\nVariables: Daily Chlorophyll_a, Daily Green_chromatic_coordinate, Daily latent_heat_flux, Daily Net_ecosystem_exchange, Daily Dissolved_oxygen, Daily Red_chromatic_coordinate, Daily Water_temperature", - "start_datetime": "2023-11-14", - "end_datetime": "2023-12-15", - "providers": [ - { - "url": "pending", - "name": "pending", - "roles": [ - "producer", - "processor", - "licensor" - ] - }, - { - "url": "https://www.ecoforecastprojectvt.org", - "name": "Ecoforecast Challenge", - "roles": [ - "host" - ] - } - ], - "license": "CC0-1.0", - "keywords": [ - "Forecasting", - "neon4cast", - "Daily Chlorophyll_a", - "Daily Green_chromatic_coordinate", - "Daily latent_heat_flux", - "Daily Net_ecosystem_exchange", - "Daily Dissolved_oxygen", - "Daily Red_chromatic_coordinate", - "Daily Water_temperature" - ], - "table:columns": [ - { - "name": "reference_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that the forecast was initiated (horizon = 0)" - }, - { - "name": "site_id", - "type": "string", - "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model." - }, - { - "name": "datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime of the forecasted value (ISO 8601)" - }, - { - "name": "family", - "type": "string", - "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)." - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "name": "observation", - "type": "double", - "description": "observed value for variable" - }, - { - "name": "crps", - "type": "double", - "description": "crps forecast score" - }, - { - "name": "logs", - "type": "double", - "description": "logs forecast score" - }, - { - "name": "mean", - "type": "double", - "description": "mean forecast prediction" - }, - { - "name": "median", - "type": "double", - "description": "median forecast prediction" - }, - { - "name": "sd", - "type": "double", - "description": "standard deviation forecasts" - }, - { - "name": "quantile97.5", - "type": "double", - "description": "upper 97.5 percentile value of forecast" - }, - { - "name": "quantile02.5", - "type": "double", - "description": "upper 2.5 percentile value of forecast" - }, - { - "name": "quantile90", - "type": "double", - "description": "upper 90 percentile value of forecast" - }, - { - "name": "quantile10", - "type": "double", - "description": "upper 10 percentile value of forecast" - }, - { - "name": "project_id", - "type": "string", - "description": "unique project identifier" - }, - { - "name": "duration", - "type": "string", - "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention" - }, - { - "name": "variable", - "type": "string", - "description": "name of forecasted variable" - }, - { - "name": "model_id", - "type": "string", - "description": "unique model identifier" - }, - { - "name": "date", - "type": "string", - "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period." - } - ] - }, - "collection": "scores", - "links": [ - { - "rel": "collection", - "href": "../collection.json", - "type": "application/json", - "title": "tg_precip_lm_all_sites" - }, - { - "rel": "root", - "href": "../../../catalog.json", - "type": "application/json", - "title": "Forecast Catalog" - }, - { - "rel": "parent", - "href": "../collection.json", - "type": "application/json", - "title": "tg_precip_lm_all_sites" - }, - { - "rel": "self", - "href": "tg_precip_lm_all_sites.json", - "type": "application/json", - "title": "Model Forecast" - }, - { - "rel": "item", - "href": "pending", - "type": "text/html", - "title": "Link for Model Code" - } - ], - "assets": { - "1": { - "type": "application/json", - "title": "Model Metadata", - "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_precip_lm_all_sites.json", - "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_precip_lm_all_sites.json\")\n\n" - }, - "2": { - "type": "text/html", - "title": "Link for Model Code", - "href": "pending", - "description": "The link to the model code provided by the model submission team" - }, - "3": { - "type": "application/x-parquet", - "title": "Database Access for Daily Chlorophyll_a", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=tg_precip_lm_all_sites?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=tg_precip_lm_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "4": { - "type": "application/x-parquet", - "title": "Database Access for Daily Green_chromatic_coordinate", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=gcc_90/model_id=tg_precip_lm_all_sites?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=gcc_90/model_id=tg_precip_lm_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "5": { - "type": "application/x-parquet", - "title": "Database Access for Daily latent_heat_flux", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=le/model_id=tg_precip_lm_all_sites?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=le/model_id=tg_precip_lm_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "6": { - "type": "application/x-parquet", - "title": "Database Access for Daily Net_ecosystem_exchange", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=nee/model_id=tg_precip_lm_all_sites?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=nee/model_id=tg_precip_lm_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "7": { - "type": "application/x-parquet", - "title": "Database Access for Daily Dissolved_oxygen", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=oxygen/model_id=tg_precip_lm_all_sites?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=oxygen/model_id=tg_precip_lm_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "8": { - "type": "application/x-parquet", - "title": "Database Access for Daily Red_chromatic_coordinate", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=rcc_90/model_id=tg_precip_lm_all_sites?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=rcc_90/model_id=tg_precip_lm_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "9": { - "type": "application/x-parquet", - "title": "Database Access for Daily Water_temperature", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=tg_precip_lm_all_sites?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=tg_precip_lm_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - } - } -} diff --git a/catalog/scores/models/model_items/tg_randfor.json b/catalog/scores/models/model_items/tg_randfor.json deleted file mode 100644 index ca6f5fd056..0000000000 --- a/catalog/scores/models/model_items/tg_randfor.json +++ /dev/null @@ -1,328 +0,0 @@ -{ - "stac_version": "1.0.0", - "stac_extensions": [ - "https://stac-extensions.github.io/table/v1.2.0/schema.json" - ], - "type": "Feature", - "id": "tg_randfor", - "bbox": [ - [ - -156.6194, - 71.2824, - -66.7987, - 71.2824 - ] - ], - "geometry": { - "type": "MultiPoint", - "coordinates": [ - [-82.0084, 29.676], - [-87.7982, 32.5415], - [-89.4737, 46.2097], - [-84.4374, 31.1854], - [-89.7048, 45.9983], - [-99.1139, 47.1591], - [-99.2531, 47.1298], - [-82.0177, 29.6878], - [-88.1589, 31.8534], - [-149.6106, 68.6307], - [-122.3303, 45.7624], - [-156.6194, 71.2824], - [-71.2874, 44.0639], - [-78.0418, 39.0337], - [-147.5026, 65.154], - [-97.57, 33.4012], - [-104.7456, 40.8155], - [-99.1066, 47.1617], - [-145.7514, 63.8811], - [-87.8039, 32.5417], - [-81.4362, 28.1251], - [-83.5019, 35.689], - [-66.8687, 17.9696], - [-72.1727, 42.5369], - [-149.2133, 63.8758], - [-84.4686, 31.1948], - [-106.8425, 32.5907], - [-96.6129, 39.1104], - [-96.5631, 39.1008], - [-67.0769, 18.0213], - [-88.1612, 31.8539], - [-80.5248, 37.3783], - [-109.3883, 38.2483], - [-105.5824, 40.0543], - [-100.9154, 46.7697], - [-99.0588, 35.4106], - [-112.4524, 40.1776], - [-84.2826, 35.9641], - [-81.9934, 29.6893], - [-155.3173, 19.5531], - [-105.546, 40.2759], - [-78.1395, 38.8929], - [-76.56, 38.8901], - [-119.7323, 37.1088], - [-119.2622, 37.0334], - [-110.8355, 31.9107], - [-89.5864, 45.5089], - [-103.0293, 40.4619], - [-87.3933, 32.9505], - [-119.006, 37.0058], - [-149.3705, 68.6611], - [-89.5857, 45.4937], - [-95.1921, 39.0404], - [-89.5373, 46.2339], - [-99.2413, 47.1282], - [-121.9519, 45.8205], - [-110.5391, 44.9535], - [-102.4471, 39.7582], - [-119.2575, 37.0597], - [-110.5871, 44.9501], - [-96.6242, 34.4442], - [-147.504, 65.1532], - [-105.5442, 40.035], - [-66.9868, 18.1135], - [-66.7987, 18.1741], - [-72.3295, 42.4719], - [-96.6038, 39.1051], - [-83.5038, 35.6904], - [-77.9832, 39.0956], - [-121.9338, 45.7908], - [-87.4077, 32.9604], - [-96.443, 38.9459], - [-122.1655, 44.2596], - [-149.143, 68.6698], - [-78.1473, 38.8943], - [-97.7823, 33.3785], - [-111.7979, 40.7839], - [-111.5081, 33.751], - [-119.0274, 36.9559], - [-84.2793, 35.9574], - [-105.9154, 39.8914] - ] - }, - "properties": { - "description": "\nmodel info: NA\n\nSites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ARIK, BIGC, BLDE, BLUE, CARI, COMO, CUPE, GUIL, HOPB, KING, LECO, LEWI, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, REDB, SYCA, TECR, WALK, WLOU\n\nVariables: Daily Chlorophyll_a, Daily Green_chromatic_coordinate, Daily latent_heat_flux, Daily Net_ecosystem_exchange, Daily Dissolved_oxygen, Daily Red_chromatic_coordinate, Daily Water_temperature", - "start_datetime": "2023-11-14", - "end_datetime": "2023-12-15", - "providers": [ - { - "url": "pending", - "name": "pending", - "roles": [ - "producer", - "processor", - "licensor" - ] - }, - { - "url": "https://www.ecoforecastprojectvt.org", - "name": "Ecoforecast Challenge", - "roles": [ - "host" - ] - } - ], - "license": "CC0-1.0", - "keywords": [ - "Forecasting", - "neon4cast", - "Daily Chlorophyll_a", - "Daily Green_chromatic_coordinate", - "Daily latent_heat_flux", - "Daily Net_ecosystem_exchange", - "Daily Dissolved_oxygen", - "Daily Red_chromatic_coordinate", - "Daily Water_temperature" - ], - "table:columns": [ - { - "name": "reference_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that the forecast was initiated (horizon = 0)" - }, - { - "name": "site_id", - "type": "string", - "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model." - }, - { - "name": "datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime of the forecasted value (ISO 8601)" - }, - { - "name": "family", - "type": "string", - "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)." - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "name": "observation", - "type": "double", - "description": "observed value for variable" - }, - { - "name": "crps", - "type": "double", - "description": "crps forecast score" - }, - { - "name": "logs", - "type": "double", - "description": "logs forecast score" - }, - { - "name": "mean", - "type": "double", - "description": "mean forecast prediction" - }, - { - "name": "median", - "type": "double", - "description": "median forecast prediction" - }, - { - "name": "sd", - "type": "double", - "description": "standard deviation forecasts" - }, - { - "name": "quantile97.5", - "type": "double", - "description": "upper 97.5 percentile value of forecast" - }, - { - "name": "quantile02.5", - "type": "double", - "description": "upper 2.5 percentile value of forecast" - }, - { - "name": "quantile90", - "type": "double", - "description": "upper 90 percentile value of forecast" - }, - { - "name": "quantile10", - "type": "double", - "description": "upper 10 percentile value of forecast" - }, - { - "name": "project_id", - "type": "string", - "description": "unique project identifier" - }, - { - "name": "duration", - "type": "string", - "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention" - }, - { - "name": "variable", - "type": "string", - "description": "name of forecasted variable" - }, - { - "name": "model_id", - "type": "string", - "description": "unique model identifier" - }, - { - "name": "date", - "type": "string", - "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period." - } - ] - }, - "collection": "scores", - "links": [ - { - "rel": "collection", - "href": "../collection.json", - "type": "application/json", - "title": "tg_randfor" - }, - { - "rel": "root", - "href": "../../../catalog.json", - "type": "application/json", - "title": "Forecast Catalog" - }, - { - "rel": "parent", - "href": "../collection.json", - "type": "application/json", - "title": "tg_randfor" - }, - { - "rel": "self", - "href": "tg_randfor.json", - "type": "application/json", - "title": "Model Forecast" - }, - { - "rel": "item", - "href": "pending", - "type": "text/html", - "title": "Link for Model Code" - } - ], - "assets": { - "1": { - "type": "application/json", - "title": "Model Metadata", - "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_randfor.json", - "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_randfor.json\")\n\n" - }, - "2": { - "type": "text/html", - "title": "Link for Model Code", - "href": "pending", - "description": "The link to the model code provided by the model submission team" - }, - "3": { - "type": "application/x-parquet", - "title": "Database Access for Daily Chlorophyll_a", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=tg_randfor?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=tg_randfor?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "4": { - "type": "application/x-parquet", - "title": "Database Access for Daily Green_chromatic_coordinate", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=gcc_90/model_id=tg_randfor?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=gcc_90/model_id=tg_randfor?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "5": { - "type": "application/x-parquet", - "title": "Database Access for Daily latent_heat_flux", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=le/model_id=tg_randfor?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=le/model_id=tg_randfor?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "6": { - "type": "application/x-parquet", - "title": "Database Access for Daily Net_ecosystem_exchange", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=nee/model_id=tg_randfor?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=nee/model_id=tg_randfor?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "7": { - "type": "application/x-parquet", - "title": "Database Access for Daily Dissolved_oxygen", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=oxygen/model_id=tg_randfor?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=oxygen/model_id=tg_randfor?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "8": { - "type": "application/x-parquet", - "title": "Database Access for Daily Red_chromatic_coordinate", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=rcc_90/model_id=tg_randfor?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=rcc_90/model_id=tg_randfor?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "9": { - "type": "application/x-parquet", - "title": "Database Access for Daily Water_temperature", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=tg_randfor?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=tg_randfor?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - } - } -} diff --git a/catalog/scores/models/model_items/tg_randfor_all_sites.json b/catalog/scores/models/model_items/tg_randfor_all_sites.json deleted file mode 100644 index 001fb3878f..0000000000 --- a/catalog/scores/models/model_items/tg_randfor_all_sites.json +++ /dev/null @@ -1,300 +0,0 @@ -{ - "stac_version": "1.0.0", - "stac_extensions": [ - "https://stac-extensions.github.io/table/v1.2.0/schema.json" - ], - "type": "Feature", - "id": "tg_randfor_all_sites", - "bbox": [ - [ - -156.6194, - 71.2824, - -66.7987, - 71.2824 - ] - ], - "geometry": { - "type": "MultiPoint", - "coordinates": [ - [-102.4471, 39.7582], - [-82.0084, 29.676], - [-119.2575, 37.0597], - [-110.5871, 44.9501], - [-96.6242, 34.4442], - [-87.7982, 32.5415], - [-147.504, 65.1532], - [-105.5442, 40.035], - [-89.4737, 46.2097], - [-66.9868, 18.1135], - [-84.4374, 31.1854], - [-66.7987, 18.1741], - [-72.3295, 42.4719], - [-96.6038, 39.1051], - [-83.5038, 35.6904], - [-77.9832, 39.0956], - [-89.7048, 45.9983], - [-121.9338, 45.7908], - [-87.4077, 32.9604], - [-96.443, 38.9459], - [-122.1655, 44.2596], - [-149.143, 68.6698], - [-78.1473, 38.8943], - [-97.7823, 33.3785], - [-99.1139, 47.1591], - [-99.2531, 47.1298], - [-111.7979, 40.7839], - [-82.0177, 29.6878], - [-111.5081, 33.751], - [-119.0274, 36.9559], - [-88.1589, 31.8534], - [-149.6106, 68.6307], - [-84.2793, 35.9574], - [-105.9154, 39.8914], - [-122.3303, 45.7624], - [-156.6194, 71.2824], - [-71.2874, 44.0639], - [-78.0418, 39.0337], - [-147.5026, 65.154], - [-97.57, 33.4012], - [-104.7456, 40.8155], - [-99.1066, 47.1617], - [-145.7514, 63.8811], - [-87.8039, 32.5417], - [-81.4362, 28.1251], - [-83.5019, 35.689], - [-66.8687, 17.9696], - [-72.1727, 42.5369], - [-149.2133, 63.8758], - [-84.4686, 31.1948], - [-106.8425, 32.5907], - [-96.6129, 39.1104], - [-96.5631, 39.1008], - [-67.0769, 18.0213], - [-88.1612, 31.8539], - [-80.5248, 37.3783], - [-109.3883, 38.2483], - [-105.5824, 40.0543], - [-100.9154, 46.7697], - [-99.0588, 35.4106], - [-112.4524, 40.1776], - [-84.2826, 35.9641], - [-81.9934, 29.6893], - [-155.3173, 19.5531], - [-105.546, 40.2759], - [-78.1395, 38.8929], - [-76.56, 38.8901], - [-119.7323, 37.1088], - [-119.2622, 37.0334], - [-110.8355, 31.9107], - [-89.5864, 45.5089], - [-103.0293, 40.4619], - [-87.3933, 32.9505], - [-119.006, 37.0058], - [-149.3705, 68.6611], - [-89.5857, 45.4937], - [-95.1921, 39.0404], - [-89.5373, 46.2339], - [-99.2413, 47.1282], - [-121.9519, 45.8205], - [-110.5391, 44.9535] - ] - }, - "properties": { - "description": [], - "start_datetime": "2023-11-14", - "end_datetime": "2023-12-15", - "providers": [ - { - "url": "pending", - "name": "pending", - "roles": [ - "producer", - "processor", - "licensor" - ] - }, - { - "url": "https://www.ecoforecastprojectvt.org", - "name": "Ecoforecast Challenge", - "roles": [ - "host" - ] - } - ], - "license": "CC0-1.0", - "keywords": [ - "Forecasting", - "neon4cast", - "Daily Dissolved_oxygen", - "Daily Red_chromatic_coordinate", - "Daily Water_temperature" - ], - "table:columns": [ - { - "name": "reference_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that the forecast was initiated (horizon = 0)" - }, - { - "name": "site_id", - "type": "string", - "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model." - }, - { - "name": "datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime of the forecasted value (ISO 8601)" - }, - { - "name": "family", - "type": "string", - "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)." - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "name": "observation", - "type": "double", - "description": "observed value for variable" - }, - { - "name": "crps", - "type": "double", - "description": "crps forecast score" - }, - { - "name": "logs", - "type": "double", - "description": "logs forecast score" - }, - { - "name": "mean", - "type": "double", - "description": "mean forecast prediction" - }, - { - "name": "median", - "type": "double", - "description": "median forecast prediction" - }, - { - "name": "sd", - "type": "double", - "description": "standard deviation forecasts" - }, - { - "name": "quantile97.5", - "type": "double", - "description": "upper 97.5 percentile value of forecast" - }, - { - "name": "quantile02.5", - "type": "double", - "description": "upper 2.5 percentile value of forecast" - }, - { - "name": "quantile90", - "type": "double", - "description": "upper 90 percentile value of forecast" - }, - { - "name": "quantile10", - "type": "double", - "description": "upper 10 percentile value of forecast" - }, - { - "name": "project_id", - "type": "string", - "description": "unique project identifier" - }, - { - "name": "duration", - "type": "string", - "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention" - }, - { - "name": "variable", - "type": "string", - "description": "name of forecasted variable" - }, - { - "name": "model_id", - "type": "string", - "description": "unique model identifier" - }, - { - "name": "date", - "type": "string", - "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period." - } - ] - }, - "collection": "scores", - "links": [ - { - "rel": "collection", - "href": "../collection.json", - "type": "application/json", - "title": "tg_randfor_all_sites" - }, - { - "rel": "root", - "href": "../../../catalog.json", - "type": "application/json", - "title": "Forecast Catalog" - }, - { - "rel": "parent", - "href": "../collection.json", - "type": "application/json", - "title": "tg_randfor_all_sites" - }, - { - "rel": "self", - "href": "tg_randfor_all_sites.json", - "type": "application/json", - "title": "Model Forecast" - }, - { - "rel": "item", - "href": "pending", - "type": "text/html", - "title": "Link for Model Code" - } - ], - "assets": { - "1": { - "type": "application/json", - "title": "Model Metadata", - "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_randfor_all_sites.json", - "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_randfor_all_sites.json\")\n\n" - }, - "2": { - "type": "text/html", - "title": "Link for Model Code", - "href": "pending", - "description": "The link to the model code provided by the model submission team" - }, - "3": { - "type": "application/x-parquet", - "title": "Database Access for Daily Dissolved_oxygen", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=oxygen/model_id=tg_randfor_all_sites?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=oxygen/model_id=tg_randfor_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "4": { - "type": "application/x-parquet", - "title": "Database Access for Daily Red_chromatic_coordinate", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=rcc_90/model_id=tg_randfor_all_sites?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=rcc_90/model_id=tg_randfor_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "5": { - "type": "application/x-parquet", - "title": "Database Access for Daily Water_temperature", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=tg_randfor_all_sites?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=tg_randfor_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - } - } -} diff --git a/catalog/scores/models/model_items/tg_tbats.json b/catalog/scores/models/model_items/tg_tbats.json deleted file mode 100644 index d17b34f5fa..0000000000 --- a/catalog/scores/models/model_items/tg_tbats.json +++ /dev/null @@ -1,349 +0,0 @@ -{ - "stac_version": "1.0.0", - "stac_extensions": [ - "https://stac-extensions.github.io/table/v1.2.0/schema.json" - ], - "type": "Feature", - "id": "tg_tbats", - "bbox": [ - [ - -156.6194, - 71.2824, - -66.7987, - 71.2824 - ] - ], - "geometry": { - "type": "MultiPoint", - "coordinates": [ - [-82.0084, 29.676], - [-87.7982, 32.5415], - [-89.4737, 46.2097], - [-84.4374, 31.1854], - [-89.7048, 45.9983], - [-99.1139, 47.1591], - [-99.2531, 47.1298], - [-82.0177, 29.6878], - [-88.1589, 31.8534], - [-149.6106, 68.6307], - [-122.3303, 45.7624], - [-156.6194, 71.2824], - [-71.2874, 44.0639], - [-78.0418, 39.0337], - [-147.5026, 65.154], - [-97.57, 33.4012], - [-104.7456, 40.8155], - [-99.1066, 47.1617], - [-145.7514, 63.8811], - [-87.8039, 32.5417], - [-81.4362, 28.1251], - [-83.5019, 35.689], - [-66.8687, 17.9696], - [-72.1727, 42.5369], - [-149.2133, 63.8758], - [-84.4686, 31.1948], - [-106.8425, 32.5907], - [-96.6129, 39.1104], - [-96.5631, 39.1008], - [-67.0769, 18.0213], - [-88.1612, 31.8539], - [-80.5248, 37.3783], - [-109.3883, 38.2483], - [-105.5824, 40.0543], - [-100.9154, 46.7697], - [-99.0588, 35.4106], - [-112.4524, 40.1776], - [-84.2826, 35.9641], - [-81.9934, 29.6893], - [-155.3173, 19.5531], - [-105.546, 40.2759], - [-78.1395, 38.8929], - [-76.56, 38.8901], - [-119.7323, 37.1088], - [-119.2622, 37.0334], - [-110.8355, 31.9107], - [-89.5864, 45.5089], - [-103.0293, 40.4619], - [-87.3933, 32.9505], - [-119.006, 37.0058], - [-149.3705, 68.6611], - [-89.5857, 45.4937], - [-95.1921, 39.0404], - [-89.5373, 46.2339], - [-99.2413, 47.1282], - [-121.9519, 45.8205], - [-110.5391, 44.9535], - [-110.5871, 44.9501], - [-96.6242, 34.4442], - [-147.504, 65.1532], - [-105.5442, 40.035], - [-66.9868, 18.1135], - [-66.7987, 18.1741], - [-72.3295, 42.4719], - [-96.6038, 39.1051], - [-83.5038, 35.6904], - [-77.9832, 39.0956], - [-121.9338, 45.7908], - [-87.4077, 32.9604], - [-96.443, 38.9459], - [-122.1655, 44.2596], - [-149.143, 68.6698], - [-78.1473, 38.8943], - [-97.7823, 33.3785], - [-111.7979, 40.7839], - [-111.5081, 33.751], - [-119.0274, 36.9559], - [-84.2793, 35.9574], - [-105.9154, 39.8914], - [-102.4471, 39.7582], - [-119.2575, 37.0597] - ] - }, - "properties": { - "description": "\nmodel info: NA\n\nSites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, BLDE, BLUE, CARI, COMO, CUPE, GUIL, HOPB, KING, LECO, LEWI, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, REDB, SYCA, TECR, WALK, WLOU, ARIK, BIGC\n\nVariables: Daily Chlorophyll_a, Daily Green_chromatic_coordinate, Daily latent_heat_flux, Daily Net_ecosystem_exchange, Daily Dissolved_oxygen, Daily Red_chromatic_coordinate, Daily Water_temperature, Weekly beetle_community_richness, Weekly beetle_community_abundance, Weekly Amblyomma_americanum_population", - "start_datetime": "2023-01-01", - "end_datetime": "2023-12-15", - "providers": [ - { - "url": "pending", - "name": "pending", - "roles": [ - "producer", - "processor", - "licensor" - ] - }, - { - "url": "https://www.ecoforecastprojectvt.org", - "name": "Ecoforecast Challenge", - "roles": [ - "host" - ] - } - ], - "license": "CC0-1.0", - "keywords": [ - "Forecasting", - "neon4cast", - "Daily Chlorophyll_a", - "Daily Green_chromatic_coordinate", - "Daily latent_heat_flux", - "Daily Net_ecosystem_exchange", - "Daily Dissolved_oxygen", - "Daily Red_chromatic_coordinate", - "Daily Water_temperature", - "Weekly beetle_community_richness", - "Weekly beetle_community_abundance", - "Weekly Amblyomma_americanum_population" - ], - "table:columns": [ - { - "name": "reference_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that the forecast was initiated (horizon = 0)" - }, - { - "name": "site_id", - "type": "string", - "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model." - }, - { - "name": "datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime of the forecasted value (ISO 8601)" - }, - { - "name": "family", - "type": "string", - "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)." - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "name": "observation", - "type": "double", - "description": "observed value for variable" - }, - { - "name": "crps", - "type": "double", - "description": "crps forecast score" - }, - { - "name": "logs", - "type": "double", - "description": "logs forecast score" - }, - { - "name": "mean", - "type": "double", - "description": "mean forecast prediction" - }, - { - "name": "median", - "type": "double", - "description": "median forecast prediction" - }, - { - "name": "sd", - "type": "double", - "description": "standard deviation forecasts" - }, - { - "name": "quantile97.5", - "type": "double", - "description": "upper 97.5 percentile value of forecast" - }, - { - "name": "quantile02.5", - "type": "double", - "description": "upper 2.5 percentile value of forecast" - }, - { - "name": "quantile90", - "type": "double", - "description": "upper 90 percentile value of forecast" - }, - { - "name": "quantile10", - "type": "double", - "description": "upper 10 percentile value of forecast" - }, - { - "name": "project_id", - "type": "string", - "description": "unique project identifier" - }, - { - "name": "duration", - "type": "string", - "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention" - }, - { - "name": "variable", - "type": "string", - "description": "name of forecasted variable" - }, - { - "name": "model_id", - "type": "string", - "description": "unique model identifier" - }, - { - "name": "date", - "type": "string", - "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period." - } - ] - }, - "collection": "scores", - "links": [ - { - "rel": "collection", - "href": "../collection.json", - "type": "application/json", - "title": "tg_tbats" - }, - { - "rel": "root", - "href": "../../../catalog.json", - "type": "application/json", - "title": "Forecast Catalog" - }, - { - "rel": "parent", - "href": "../collection.json", - "type": "application/json", - "title": "tg_tbats" - }, - { - "rel": "self", - "href": "tg_tbats.json", - "type": "application/json", - "title": "Model Forecast" - }, - { - "rel": "item", - "href": "pending", - "type": "text/html", - "title": "Link for Model Code" - } - ], - "assets": { - "1": { - "type": "application/json", - "title": "Model Metadata", - "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_tbats.json", - "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_tbats.json\")\n\n" - }, - "2": { - "type": "text/html", - "title": "Link for Model Code", - "href": "pending", - "description": "The link to the model code provided by the model submission team" - }, - "3": { - "type": "application/x-parquet", - "title": "Database Access for Daily Chlorophyll_a", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "4": { - "type": "application/x-parquet", - "title": "Database Access for Daily Green_chromatic_coordinate", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=gcc_90/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=gcc_90/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "5": { - "type": "application/x-parquet", - "title": "Database Access for Daily latent_heat_flux", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=le/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=le/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "6": { - "type": "application/x-parquet", - "title": "Database Access for Daily Net_ecosystem_exchange", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=nee/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=nee/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "7": { - "type": "application/x-parquet", - "title": "Database Access for Daily Dissolved_oxygen", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=oxygen/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=oxygen/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "8": { - "type": "application/x-parquet", - "title": "Database Access for Daily Red_chromatic_coordinate", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=rcc_90/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=rcc_90/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "9": { - "type": "application/x-parquet", - "title": "Database Access for Daily Water_temperature", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "10": { - "type": "application/x-parquet", - "title": "Database Access for Weekly beetle_community_richness", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1W/variable=richness/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1W/variable=richness/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "11": { - "type": "application/x-parquet", - "title": "Database Access for Weekly beetle_community_abundance", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1W/variable=abundance/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1W/variable=abundance/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "12": { - "type": "application/x-parquet", - "title": "Database Access for Weekly Amblyomma_americanum_population", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1W/variable=amblyomma_americanum/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1W/variable=amblyomma_americanum/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - } - } -} diff --git a/catalog/scores/models/model_items/tg_temp_lm.json b/catalog/scores/models/model_items/tg_temp_lm.json deleted file mode 100644 index e226e24146..0000000000 --- a/catalog/scores/models/model_items/tg_temp_lm.json +++ /dev/null @@ -1,328 +0,0 @@ -{ - "stac_version": "1.0.0", - "stac_extensions": [ - "https://stac-extensions.github.io/table/v1.2.0/schema.json" - ], - "type": "Feature", - "id": "tg_temp_lm", - "bbox": [ - [ - -156.6194, - 71.2824, - -66.7987, - 71.2824 - ] - ], - "geometry": { - "type": "MultiPoint", - "coordinates": [ - [-82.0084, 29.676], - [-87.7982, 32.5415], - [-89.4737, 46.2097], - [-84.4374, 31.1854], - [-89.7048, 45.9983], - [-99.1139, 47.1591], - [-99.2531, 47.1298], - [-82.0177, 29.6878], - [-88.1589, 31.8534], - [-149.6106, 68.6307], - [-122.3303, 45.7624], - [-156.6194, 71.2824], - [-71.2874, 44.0639], - [-78.0418, 39.0337], - [-147.5026, 65.154], - [-97.57, 33.4012], - [-104.7456, 40.8155], - [-99.1066, 47.1617], - [-145.7514, 63.8811], - [-87.8039, 32.5417], - [-81.4362, 28.1251], - [-83.5019, 35.689], - [-66.8687, 17.9696], - [-72.1727, 42.5369], - [-149.2133, 63.8758], - [-84.4686, 31.1948], - [-106.8425, 32.5907], - [-96.6129, 39.1104], - [-96.5631, 39.1008], - [-67.0769, 18.0213], - [-88.1612, 31.8539], - [-80.5248, 37.3783], - [-109.3883, 38.2483], - [-105.5824, 40.0543], - [-100.9154, 46.7697], - [-99.0588, 35.4106], - [-112.4524, 40.1776], - [-84.2826, 35.9641], - [-81.9934, 29.6893], - [-155.3173, 19.5531], - [-105.546, 40.2759], - [-78.1395, 38.8929], - [-76.56, 38.8901], - [-119.7323, 37.1088], - [-119.2622, 37.0334], - [-110.8355, 31.9107], - [-89.5864, 45.5089], - [-103.0293, 40.4619], - [-87.3933, 32.9505], - [-119.006, 37.0058], - [-149.3705, 68.6611], - [-89.5857, 45.4937], - [-95.1921, 39.0404], - [-89.5373, 46.2339], - [-99.2413, 47.1282], - [-121.9519, 45.8205], - [-110.5391, 44.9535], - [-102.4471, 39.7582], - [-119.2575, 37.0597], - [-110.5871, 44.9501], - [-96.6242, 34.4442], - [-147.504, 65.1532], - [-105.5442, 40.035], - [-66.9868, 18.1135], - [-66.7987, 18.1741], - [-72.3295, 42.4719], - [-96.6038, 39.1051], - [-83.5038, 35.6904], - [-77.9832, 39.0956], - [-121.9338, 45.7908], - [-87.4077, 32.9604], - [-96.443, 38.9459], - [-122.1655, 44.2596], - [-149.143, 68.6698], - [-78.1473, 38.8943], - [-97.7823, 33.3785], - [-111.7979, 40.7839], - [-111.5081, 33.751], - [-119.0274, 36.9559], - [-84.2793, 35.9574], - [-105.9154, 39.8914] - ] - }, - "properties": { - "description": "\nmodel info: NA\n\nSites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ARIK, BIGC, BLDE, BLUE, CARI, COMO, CUPE, GUIL, HOPB, KING, LECO, LEWI, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, REDB, SYCA, TECR, WALK, WLOU\n\nVariables: Daily Chlorophyll_a, Daily Green_chromatic_coordinate, Daily latent_heat_flux, Daily Net_ecosystem_exchange, Daily Dissolved_oxygen, Daily Red_chromatic_coordinate, Daily Water_temperature", - "start_datetime": "2023-11-14", - "end_datetime": "2023-12-15", - "providers": [ - { - "url": "pending", - "name": "pending", - "roles": [ - "producer", - "processor", - "licensor" - ] - }, - { - "url": "https://www.ecoforecastprojectvt.org", - "name": "Ecoforecast Challenge", - "roles": [ - "host" - ] - } - ], - "license": "CC0-1.0", - "keywords": [ - "Forecasting", - "neon4cast", - "Daily Chlorophyll_a", - "Daily Green_chromatic_coordinate", - "Daily latent_heat_flux", - "Daily Net_ecosystem_exchange", - "Daily Dissolved_oxygen", - "Daily Red_chromatic_coordinate", - "Daily Water_temperature" - ], - "table:columns": [ - { - "name": "reference_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that the forecast was initiated (horizon = 0)" - }, - { - "name": "site_id", - "type": "string", - "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model." - }, - { - "name": "datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime of the forecasted value (ISO 8601)" - }, - { - "name": "family", - "type": "string", - "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)." - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "name": "observation", - "type": "double", - "description": "observed value for variable" - }, - { - "name": "crps", - "type": "double", - "description": "crps forecast score" - }, - { - "name": "logs", - "type": "double", - "description": "logs forecast score" - }, - { - "name": "mean", - "type": "double", - "description": "mean forecast prediction" - }, - { - "name": "median", - "type": "double", - "description": "median forecast prediction" - }, - { - "name": "sd", - "type": "double", - "description": "standard deviation forecasts" - }, - { - "name": "quantile97.5", - "type": "double", - "description": "upper 97.5 percentile value of forecast" - }, - { - "name": "quantile02.5", - "type": "double", - "description": "upper 2.5 percentile value of forecast" - }, - { - "name": "quantile90", - "type": "double", - "description": "upper 90 percentile value of forecast" - }, - { - "name": "quantile10", - "type": "double", - "description": "upper 10 percentile value of forecast" - }, - { - "name": "project_id", - "type": "string", - "description": "unique project identifier" - }, - { - "name": "duration", - "type": "string", - "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention" - }, - { - "name": "variable", - "type": "string", - "description": "name of forecasted variable" - }, - { - "name": "model_id", - "type": "string", - "description": "unique model identifier" - }, - { - "name": "date", - "type": "string", - "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period." - } - ] - }, - "collection": "scores", - "links": [ - { - "rel": "collection", - "href": "../collection.json", - "type": "application/json", - "title": "tg_temp_lm" - }, - { - "rel": "root", - "href": "../../../catalog.json", - "type": "application/json", - "title": "Forecast Catalog" - }, - { - "rel": "parent", - "href": "../collection.json", - "type": "application/json", - "title": "tg_temp_lm" - }, - { - "rel": "self", - "href": "tg_temp_lm.json", - "type": "application/json", - "title": "Model Forecast" - }, - { - "rel": "item", - "href": "pending", - "type": "text/html", - "title": "Link for Model Code" - } - ], - "assets": { - "1": { - "type": "application/json", - "title": "Model Metadata", - "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_temp_lm.json", - "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_temp_lm.json\")\n\n" - }, - "2": { - "type": "text/html", - "title": "Link for Model Code", - "href": "pending", - "description": "The link to the model code provided by the model submission team" - }, - "3": { - "type": "application/x-parquet", - "title": "Database Access for Daily Chlorophyll_a", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=tg_temp_lm?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=tg_temp_lm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "4": { - "type": "application/x-parquet", - "title": "Database Access for Daily Green_chromatic_coordinate", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=gcc_90/model_id=tg_temp_lm?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=gcc_90/model_id=tg_temp_lm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "5": { - "type": "application/x-parquet", - "title": "Database Access for Daily latent_heat_flux", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=le/model_id=tg_temp_lm?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=le/model_id=tg_temp_lm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "6": { - "type": "application/x-parquet", - "title": "Database Access for Daily Net_ecosystem_exchange", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=nee/model_id=tg_temp_lm?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=nee/model_id=tg_temp_lm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "7": { - "type": "application/x-parquet", - "title": "Database Access for Daily Dissolved_oxygen", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=oxygen/model_id=tg_temp_lm?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=oxygen/model_id=tg_temp_lm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "8": { - "type": "application/x-parquet", - "title": "Database Access for Daily Red_chromatic_coordinate", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=rcc_90/model_id=tg_temp_lm?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=rcc_90/model_id=tg_temp_lm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "9": { - "type": "application/x-parquet", - "title": "Database Access for Daily Water_temperature", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=tg_temp_lm?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=tg_temp_lm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - } - } -} diff --git a/catalog/scores/models/model_items/tg_temp_lm_all_sites.json b/catalog/scores/models/model_items/tg_temp_lm_all_sites.json deleted file mode 100644 index ab2682ebcc..0000000000 --- a/catalog/scores/models/model_items/tg_temp_lm_all_sites.json +++ /dev/null @@ -1,328 +0,0 @@ -{ - "stac_version": "1.0.0", - "stac_extensions": [ - "https://stac-extensions.github.io/table/v1.2.0/schema.json" - ], - "type": "Feature", - "id": "tg_temp_lm_all_sites", - "bbox": [ - [ - -156.6194, - 71.2824, - -66.7987, - 71.2824 - ] - ], - "geometry": { - "type": "MultiPoint", - "coordinates": [ - [-82.0084, 29.676], - [-87.7982, 32.5415], - [-89.4737, 46.2097], - [-84.4374, 31.1854], - [-89.7048, 45.9983], - [-99.1139, 47.1591], - [-99.2531, 47.1298], - [-82.0177, 29.6878], - [-88.1589, 31.8534], - [-149.6106, 68.6307], - [-122.3303, 45.7624], - [-156.6194, 71.2824], - [-71.2874, 44.0639], - [-78.0418, 39.0337], - [-147.5026, 65.154], - [-97.57, 33.4012], - [-104.7456, 40.8155], - [-99.1066, 47.1617], - [-145.7514, 63.8811], - [-87.8039, 32.5417], - [-81.4362, 28.1251], - [-83.5019, 35.689], - [-66.8687, 17.9696], - [-72.1727, 42.5369], - [-149.2133, 63.8758], - [-84.4686, 31.1948], - [-106.8425, 32.5907], - [-96.6129, 39.1104], - [-96.5631, 39.1008], - [-67.0769, 18.0213], - [-88.1612, 31.8539], - [-80.5248, 37.3783], - [-109.3883, 38.2483], - [-105.5824, 40.0543], - [-100.9154, 46.7697], - [-99.0588, 35.4106], - [-112.4524, 40.1776], - [-84.2826, 35.9641], - [-81.9934, 29.6893], - [-155.3173, 19.5531], - [-105.546, 40.2759], - [-78.1395, 38.8929], - [-76.56, 38.8901], - [-119.7323, 37.1088], - [-119.2622, 37.0334], - [-110.8355, 31.9107], - [-89.5864, 45.5089], - [-103.0293, 40.4619], - [-87.3933, 32.9505], - [-119.006, 37.0058], - [-149.3705, 68.6611], - [-89.5857, 45.4937], - [-95.1921, 39.0404], - [-89.5373, 46.2339], - [-99.2413, 47.1282], - [-121.9519, 45.8205], - [-110.5391, 44.9535], - [-102.4471, 39.7582], - [-119.2575, 37.0597], - [-110.5871, 44.9501], - [-96.6242, 34.4442], - [-147.504, 65.1532], - [-105.5442, 40.035], - [-66.9868, 18.1135], - [-66.7987, 18.1741], - [-72.3295, 42.4719], - [-96.6038, 39.1051], - [-83.5038, 35.6904], - [-77.9832, 39.0956], - [-121.9338, 45.7908], - [-87.4077, 32.9604], - [-96.443, 38.9459], - [-122.1655, 44.2596], - [-149.143, 68.6698], - [-78.1473, 38.8943], - [-97.7823, 33.3785], - [-111.7979, 40.7839], - [-111.5081, 33.751], - [-119.0274, 36.9559], - [-84.2793, 35.9574], - [-105.9154, 39.8914] - ] - }, - "properties": { - "description": "\nmodel info: NA\n\nSites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ARIK, BIGC, BLDE, BLUE, CARI, COMO, CUPE, GUIL, HOPB, KING, LECO, LEWI, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, REDB, SYCA, TECR, WALK, WLOU\n\nVariables: Daily Chlorophyll_a, Daily Green_chromatic_coordinate, Daily latent_heat_flux, Daily Net_ecosystem_exchange, Daily Dissolved_oxygen, Daily Red_chromatic_coordinate, Daily Water_temperature", - "start_datetime": "2023-11-14", - "end_datetime": "2023-12-15", - "providers": [ - { - "url": "pending", - "name": "pending", - "roles": [ - "producer", - "processor", - "licensor" - ] - }, - { - "url": "https://www.ecoforecastprojectvt.org", - "name": "Ecoforecast Challenge", - "roles": [ - "host" - ] - } - ], - "license": "CC0-1.0", - "keywords": [ - "Forecasting", - "neon4cast", - "Daily Chlorophyll_a", - "Daily Green_chromatic_coordinate", - "Daily latent_heat_flux", - "Daily Net_ecosystem_exchange", - "Daily Dissolved_oxygen", - "Daily Red_chromatic_coordinate", - "Daily Water_temperature" - ], - "table:columns": [ - { - "name": "reference_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that the forecast was initiated (horizon = 0)" - }, - { - "name": "site_id", - "type": "string", - "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model." - }, - { - "name": "datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime of the forecasted value (ISO 8601)" - }, - { - "name": "family", - "type": "string", - "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)." - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "name": "observation", - "type": "double", - "description": "observed value for variable" - }, - { - "name": "crps", - "type": "double", - "description": "crps forecast score" - }, - { - "name": "logs", - "type": "double", - "description": "logs forecast score" - }, - { - "name": "mean", - "type": "double", - "description": "mean forecast prediction" - }, - { - "name": "median", - "type": "double", - "description": "median forecast prediction" - }, - { - "name": "sd", - "type": "double", - "description": "standard deviation forecasts" - }, - { - "name": "quantile97.5", - "type": "double", - "description": "upper 97.5 percentile value of forecast" - }, - { - "name": "quantile02.5", - "type": "double", - "description": "upper 2.5 percentile value of forecast" - }, - { - "name": "quantile90", - "type": "double", - "description": "upper 90 percentile value of forecast" - }, - { - "name": "quantile10", - "type": "double", - "description": "upper 10 percentile value of forecast" - }, - { - "name": "project_id", - "type": "string", - "description": "unique project identifier" - }, - { - "name": "duration", - "type": "string", - "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention" - }, - { - "name": "variable", - "type": "string", - "description": "name of forecasted variable" - }, - { - "name": "model_id", - "type": "string", - "description": "unique model identifier" - }, - { - "name": "date", - "type": "string", - "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period." - } - ] - }, - "collection": "scores", - "links": [ - { - "rel": "collection", - "href": "../collection.json", - "type": "application/json", - "title": "tg_temp_lm_all_sites" - }, - { - "rel": "root", - "href": "../../../catalog.json", - "type": "application/json", - "title": "Forecast Catalog" - }, - { - "rel": "parent", - "href": "../collection.json", - "type": "application/json", - "title": "tg_temp_lm_all_sites" - }, - { - "rel": "self", - "href": "tg_temp_lm_all_sites.json", - "type": "application/json", - "title": "Model Forecast" - }, - { - "rel": "item", - "href": "pending", - "type": "text/html", - "title": "Link for Model Code" - } - ], - "assets": { - "1": { - "type": "application/json", - "title": "Model Metadata", - "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_temp_lm_all_sites.json", - "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_temp_lm_all_sites.json\")\n\n" - }, - "2": { - "type": "text/html", - "title": "Link for Model Code", - "href": "pending", - "description": "The link to the model code provided by the model submission team" - }, - "3": { - "type": "application/x-parquet", - "title": "Database Access for Daily Chlorophyll_a", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=tg_temp_lm_all_sites?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=tg_temp_lm_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "4": { - "type": "application/x-parquet", - "title": "Database Access for Daily Green_chromatic_coordinate", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=gcc_90/model_id=tg_temp_lm_all_sites?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=gcc_90/model_id=tg_temp_lm_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "5": { - "type": "application/x-parquet", - "title": "Database Access for Daily latent_heat_flux", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=le/model_id=tg_temp_lm_all_sites?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=le/model_id=tg_temp_lm_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "6": { - "type": "application/x-parquet", - "title": "Database Access for Daily Net_ecosystem_exchange", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=nee/model_id=tg_temp_lm_all_sites?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=nee/model_id=tg_temp_lm_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "7": { - "type": "application/x-parquet", - "title": "Database Access for Daily Dissolved_oxygen", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=oxygen/model_id=tg_temp_lm_all_sites?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=oxygen/model_id=tg_temp_lm_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "8": { - "type": "application/x-parquet", - "title": "Database Access for Daily Red_chromatic_coordinate", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=rcc_90/model_id=tg_temp_lm_all_sites?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=rcc_90/model_id=tg_temp_lm_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "9": { - "type": "application/x-parquet", - "title": "Database Access for Daily Water_temperature", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=tg_temp_lm_all_sites?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=tg_temp_lm_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - } - } -} diff --git a/catalog/scores/models/model_items/tslm.json b/catalog/scores/models/model_items/tslm.json deleted file mode 100644 index b28605cd65..0000000000 --- a/catalog/scores/models/model_items/tslm.json +++ /dev/null @@ -1,234 +0,0 @@ -{ - "stac_version": "1.0.0", - "stac_extensions": [ - "https://stac-extensions.github.io/table/v1.2.0/schema.json" - ], - "type": "Feature", - "id": "tslm", - "bbox": [ - [ - -156.6194, - 71.2824, - -66.7987, - 71.2824 - ] - ], - "geometry": { - "type": "MultiPoint", - "coordinates": [ - [-102.4471, 39.7582], - [-82.0084, 29.676], - [-119.2575, 37.0597], - [-110.5871, 44.9501], - [-96.6242, 34.4442], - [-87.7982, 32.5415], - [-147.504, 65.1532], - [-105.5442, 40.035], - [-89.4737, 46.2097], - [-66.9868, 18.1135], - [-84.4374, 31.1854], - [-66.7987, 18.1741], - [-72.3295, 42.4719], - [-96.6038, 39.1051], - [-83.5038, 35.6904], - [-77.9832, 39.0956], - [-89.7048, 45.9983], - [-121.9338, 45.7908], - [-87.4077, 32.9604], - [-96.443, 38.9459], - [-122.1655, 44.2596], - [-149.143, 68.6698], - [-78.1473, 38.8943], - [-97.7823, 33.3785], - [-99.1139, 47.1591], - [-99.2531, 47.1298], - [-111.7979, 40.7839], - [-82.0177, 29.6878], - [-111.5081, 33.751], - [-119.0274, 36.9559], - [-88.1589, 31.8534], - [-149.6106, 68.6307], - [-84.2793, 35.9574], - [-105.9154, 39.8914] - ] - }, - "properties": { - "description": [], - "start_datetime": "2023-09-15", - "end_datetime": "2023-10-20", - "providers": [ - { - "url": "pending", - "name": "pending", - "roles": [ - "producer", - "processor", - "licensor" - ] - }, - { - "url": "https://www.ecoforecastprojectvt.org", - "name": "Ecoforecast Challenge", - "roles": [ - "host" - ] - } - ], - "license": "CC0-1.0", - "keywords": [ - "Forecasting", - "neon4cast", - "Daily Water_temperature", - "Daily Dissolved_oxygen" - ], - "table:columns": [ - { - "name": "reference_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that the forecast was initiated (horizon = 0)" - }, - { - "name": "site_id", - "type": "string", - "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model." - }, - { - "name": "datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime of the forecasted value (ISO 8601)" - }, - { - "name": "family", - "type": "string", - "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)." - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "name": "observation", - "type": "double", - "description": "observed value for variable" - }, - { - "name": "crps", - "type": "double", - "description": "crps forecast score" - }, - { - "name": "logs", - "type": "double", - "description": "logs forecast score" - }, - { - "name": "mean", - "type": "double", - "description": "mean forecast prediction" - }, - { - "name": "median", - "type": "double", - "description": "median forecast prediction" - }, - { - "name": "sd", - "type": "double", - "description": "standard deviation forecasts" - }, - { - "name": "quantile97.5", - "type": "double", - "description": "upper 97.5 percentile value of forecast" - }, - { - "name": "quantile02.5", - "type": "double", - "description": "upper 2.5 percentile value of forecast" - }, - { - "name": "quantile90", - "type": "double", - "description": "upper 90 percentile value of forecast" - }, - { - "name": "quantile10", - "type": "double", - "description": "upper 10 percentile value of forecast" - }, - { - "name": "project_id", - "type": "string", - "description": "unique project identifier" - }, - { - "name": "duration", - "type": "string", - "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention" - }, - { - "name": "variable", - "type": "string", - "description": "name of forecasted variable" - }, - { - "name": "model_id", - "type": "string", - "description": "unique model identifier" - }, - { - "name": "date", - "type": "string", - "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period." - } - ] - }, - "collection": "scores", - "links": [ - { - "rel": "collection", - "href": "../collection.json", - "type": "application/json", - "title": "tslm" - }, - { - "rel": "root", - "href": "../../../catalog.json", - "type": "application/json", - "title": "Forecast Catalog" - }, - { - "rel": "parent", - "href": "../collection.json", - "type": "application/json", - "title": "tslm" - }, - { - "rel": "self", - "href": "tslm.json", - "type": "application/json", - "title": "Model Forecast" - } - ], - "assets": { - "1": { - "type": "application/json", - "title": "Model Metadata", - "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tslm.json", - "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tslm.json\")\n\n" - }, - "2": { - "type": "application/x-parquet", - "title": "Database Access for Daily Water_temperature", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=tslm?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=tslm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "3": { - "type": "application/x-parquet", - "title": "Database Access for Daily Dissolved_oxygen", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=oxygen/model_id=tslm?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=oxygen/model_id=tslm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - } - } -} diff --git a/catalog/scores/models/model_items/xgboost_parallel.json b/catalog/scores/models/model_items/xgboost_parallel.json deleted file mode 100644 index 4eb7bbc7fd..0000000000 --- a/catalog/scores/models/model_items/xgboost_parallel.json +++ /dev/null @@ -1,291 +0,0 @@ -{ - "stac_version": "1.0.0", - "stac_extensions": [ - "https://stac-extensions.github.io/table/v1.2.0/schema.json" - ], - "type": "Feature", - "id": "xgboost_parallel", - "bbox": [ - [ - -156.6194, - 71.2824, - -66.7987, - 71.2824 - ] - ], - "geometry": { - "type": "MultiPoint", - "coordinates": [ - [-105.546, 40.2759], - [-78.1395, 38.8929], - [-76.56, 38.8901], - [-119.7323, 37.1088], - [-119.2622, 37.0334], - [-110.8355, 31.9107], - [-89.5864, 45.5089], - [-103.0293, 40.4619], - [-87.3933, 32.9505], - [-119.006, 37.0058], - [-149.3705, 68.6611], - [-89.5857, 45.4937], - [-95.1921, 39.0404], - [-89.5373, 46.2339], - [-99.2413, 47.1282], - [-121.9519, 45.8205], - [-84.4686, 31.1948], - [-106.8425, 32.5907], - [-96.6129, 39.1104], - [-96.5631, 39.1008], - [-67.0769, 18.0213], - [-88.1612, 31.8539], - [-80.5248, 37.3783], - [-109.3883, 38.2483], - [-105.5824, 40.0543], - [-100.9154, 46.7697], - [-99.0588, 35.4106], - [-112.4524, 40.1776], - [-84.2826, 35.9641], - [-81.9934, 29.6893], - [-155.3173, 19.5531], - [-82.0084, 29.676], - [-87.7982, 32.5415], - [-89.4737, 46.2097], - [-84.4374, 31.1854], - [-89.7048, 45.9983], - [-99.1139, 47.1591], - [-99.2531, 47.1298], - [-82.0177, 29.6878], - [-88.1589, 31.8534], - [-110.5391, 44.9535], - [-122.3303, 45.7624], - [-156.6194, 71.2824], - [-71.2874, 44.0639], - [-78.0418, 39.0337], - [-147.5026, 65.154], - [-97.57, 33.4012], - [-104.7456, 40.8155], - [-99.1066, 47.1617], - [-145.7514, 63.8811], - [-87.8039, 32.5417], - [-81.4362, 28.1251], - [-83.5019, 35.689], - [-66.8687, 17.9696], - [-72.1727, 42.5369], - [-149.2133, 63.8758] - ] - }, - "properties": { - "description": [], - "start_datetime": "2023-10-19", - "end_datetime": "2023-11-22", - "providers": [ - { - "url": "pending", - "name": "pending", - "roles": [ - "producer", - "processor", - "licensor" - ] - }, - { - "url": "https://www.ecoforecastprojectvt.org", - "name": "Ecoforecast Challenge", - "roles": [ - "host" - ] - } - ], - "license": "CC0-1.0", - "keywords": [ - "Forecasting", - "neon4cast", - "Daily Net_ecosystem_exchange", - "Daily latent_heat_flux", - "Daily Dissolved_oxygen", - "Daily Green_chromatic_coordinate", - "Daily Water_temperature", - "Daily Red_chromatic_coordinate", - "Daily Chlorophyll_a" - ], - "table:columns": [ - { - "name": "reference_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that the forecast was initiated (horizon = 0)" - }, - { - "name": "site_id", - "type": "string", - "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model." - }, - { - "name": "datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime of the forecasted value (ISO 8601)" - }, - { - "name": "family", - "type": "string", - "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)." - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "name": "observation", - "type": "double", - "description": "observed value for variable" - }, - { - "name": "crps", - "type": "double", - "description": "crps forecast score" - }, - { - "name": "logs", - "type": "double", - "description": "logs forecast score" - }, - { - "name": "mean", - "type": "double", - "description": "mean forecast prediction" - }, - { - "name": "median", - "type": "double", - "description": "median forecast prediction" - }, - { - "name": "sd", - "type": "double", - "description": "standard deviation forecasts" - }, - { - "name": "quantile97.5", - "type": "double", - "description": "upper 97.5 percentile value of forecast" - }, - { - "name": "quantile02.5", - "type": "double", - "description": "upper 2.5 percentile value of forecast" - }, - { - "name": "quantile90", - "type": "double", - "description": "upper 90 percentile value of forecast" - }, - { - "name": "quantile10", - "type": "double", - "description": "upper 10 percentile value of forecast" - }, - { - "name": "project_id", - "type": "string", - "description": "unique project identifier" - }, - { - "name": "duration", - "type": "string", - "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention" - }, - { - "name": "variable", - "type": "string", - "description": "name of forecasted variable" - }, - { - "name": "model_id", - "type": "string", - "description": "unique model identifier" - }, - { - "name": "date", - "type": "string", - "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period." - } - ] - }, - "collection": "scores", - "links": [ - { - "rel": "collection", - "href": "../collection.json", - "type": "application/json", - "title": "xgboost_parallel" - }, - { - "rel": "root", - "href": "../../../catalog.json", - "type": "application/json", - "title": "Forecast Catalog" - }, - { - "rel": "parent", - "href": "../collection.json", - "type": "application/json", - "title": "xgboost_parallel" - }, - { - "rel": "self", - "href": "xgboost_parallel.json", - "type": "application/json", - "title": "Model Forecast" - } - ], - "assets": { - "1": { - "type": "application/json", - "title": "Model Metadata", - "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/xgboost_parallel.json", - "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/xgboost_parallel.json\")\n\n" - }, - "2": { - "type": "application/x-parquet", - "title": "Database Access for Daily Net_ecosystem_exchange", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=nee/model_id=xgboost_parallel?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=nee/model_id=xgboost_parallel?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "3": { - "type": "application/x-parquet", - "title": "Database Access for Daily latent_heat_flux", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=le/model_id=xgboost_parallel?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=le/model_id=xgboost_parallel?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "4": { - "type": "application/x-parquet", - "title": "Database Access for Daily Dissolved_oxygen", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=oxygen/model_id=xgboost_parallel?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=oxygen/model_id=xgboost_parallel?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "5": { - "type": "application/x-parquet", - "title": "Database Access for Daily Green_chromatic_coordinate", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=gcc_90/model_id=xgboost_parallel?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=gcc_90/model_id=xgboost_parallel?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "6": { - "type": "application/x-parquet", - "title": "Database Access for Daily Water_temperature", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=xgboost_parallel?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=xgboost_parallel?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "7": { - "type": "application/x-parquet", - "title": "Database Access for Daily Red_chromatic_coordinate", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=rcc_90/model_id=xgboost_parallel?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=rcc_90/model_id=xgboost_parallel?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "8": { - "type": "application/x-parquet", - "title": "Database Access for Daily Chlorophyll_a", - "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=xgboost_parallel?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=xgboost_parallel?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - } - } -}