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catalog/forecasts/aquatics/Daily_Chlorophyll_a/collection.json
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| Original file line number | Diff line number | Diff line change |
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| { | ||
| "id": "Daily_Chlorophyll_a", | ||
| "description": "This page includes all models for the Daily_Chlorophyll_a variable.", | ||
| "stac_version": "1.0.0", | ||
| "license": "CC0-1.0", | ||
| "stac_extensions": [ | ||
| "https://stac-extensions.github.io/scientific/v1.0.0/schema.json", | ||
| "https://stac-extensions.github.io/item-assets/v1.0.0/schema.json", | ||
| "https://stac-extensions.github.io/table/v1.2.0/schema.json" | ||
| ], | ||
| "type": "Collection", | ||
| "links": [ | ||
| { | ||
| "rel": "item", | ||
| "type": "application/json", | ||
| "href": "../../models/model_items/climatology.json" | ||
| }, | ||
| { | ||
| "rel": "item", | ||
| "type": "application/json", | ||
| "href": "../../models/model_items/persistenceRW.json" | ||
| }, | ||
| { | ||
| "rel": "parent", | ||
| "type": "application/json", | ||
| "href": "../collection.json" | ||
| }, | ||
| { | ||
| "rel": "root", | ||
| "type": "application/json", | ||
| "href": "../collection.json" | ||
| }, | ||
| { | ||
| "rel": "self", | ||
| "type": "application/json", | ||
| "href": "collection.json" | ||
| }, | ||
| { | ||
| "rel": "cite-as", | ||
| "href": "https://doi.org/10.1002/fee.2616" | ||
| }, | ||
| { | ||
| "rel": "about", | ||
| "href": "https://projects.ecoforecast.org/usgsrc4cast-docs/", | ||
| "type": "text/html", | ||
| "title": "EFI-USGS River Chlorophyll Forecasting Challenge Documentation" | ||
| }, | ||
| { | ||
| "rel": "describedby", | ||
| "href": "https://projects.ecoforecast.org/usgsrc4cast-docs/", | ||
| "title": "EFI-USGS River Chlorophyll Forecast Challenge Dashboard", | ||
| "type": "text/html" | ||
| } | ||
| ], | ||
| "title": "Daily_Chlorophyll_a", | ||
| "extent": { | ||
| "spatial": { | ||
| "bbox": [ | ||
| ["Inf", "Inf", "-Inf", "-Inf"] | ||
| ] | ||
| }, | ||
| "temporal": { | ||
| "interval": [ | ||
| [ | ||
| "2024-02-07T00:00:00Z", | ||
| "2024-03-17T00:00:00Z" | ||
| ] | ||
| ] | ||
| } | ||
| }, | ||
| "table:columns": [ | ||
| { | ||
| "name": "reference_datetime", | ||
| "type": "timestamp[us, tz=UTC]", | ||
| "description": "datetime that the forecast was initiated (horizon = 0)" | ||
| }, | ||
| { | ||
| "name": "datetime", | ||
| "type": "timestamp[us, tz=UTC]", | ||
| "description": "datetime of the forecasted value (ISO 8601)" | ||
| }, | ||
| { | ||
| "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)" | ||
| }, | ||
| { | ||
| "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.”" | ||
| }, | ||
| { | ||
| "name": "parameter", | ||
| "type": "string", | ||
| "description": "ensemble member or distribution parameter" | ||
| }, | ||
| { | ||
| "name": "prediction", | ||
| "type": "double", | ||
| "description": "predicted value for variable" | ||
| }, | ||
| { | ||
| "name": "pub_datetime", | ||
| "type": "timestamp[us, tz=UTC]", | ||
| "description": "datetime that forecast was submitted" | ||
| }, | ||
| { | ||
| "name": "project_id", | ||
| "type": "string", | ||
| "description": "unique identifier for the forecast project" | ||
| }, | ||
| { | ||
| "name": "duration", | ||
| "type": "string", | ||
| "description": "temporal duration of forecast (hourly, daily, 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": "reference_date", | ||
| "type": "string", | ||
| "description": "date that the forecast was initiated" | ||
| } | ||
| ], | ||
| "assets": { | ||
| "data": { | ||
| "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/project_id=usgsrc4cast/duration=P1D/variable=chla?endpoint_override=sdsc.osn.xsede.org", | ||
| "type": "application/x-parquet", | ||
| "title": "Database Access", | ||
| "roles": [ | ||
| "data" | ||
| ], | ||
| "description": "Use `arrow` for remote access to the database. This R code will return results for forecasts of the variable by the specific model .\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/project_id=usgsrc4cast/duration=P1D/variable=chla?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" | ||
| }, | ||
| "thumbnail": { | ||
| "href": "pending", | ||
| "type": "image/JPEG", | ||
| "roles": [ | ||
| "thumbnail" | ||
| ], | ||
| "title": "pending" | ||
| } | ||
| } | ||
| } |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,147 @@ | ||
| { | ||
| "id": "aquatics", | ||
| "description": "This page includes variables for the aquatics group.", | ||
| "stac_version": "1.0.0", | ||
| "license": "CC0-1.0", | ||
| "stac_extensions": [ | ||
| "https://stac-extensions.github.io/scientific/v1.0.0/schema.json", | ||
| "https://stac-extensions.github.io/item-assets/v1.0.0/schema.json", | ||
| "https://stac-extensions.github.io/table/v1.2.0/schema.json" | ||
| ], | ||
| "type": "Collection", | ||
| "links": [ | ||
| { | ||
| "rel": "child", | ||
| "type": "application/json", | ||
| "href": "Daily_Chlorophyll_a/collection.json" | ||
| }, | ||
| { | ||
| "rel": "parent", | ||
| "type": "application/json", | ||
| "href": "../collection.json" | ||
| }, | ||
| { | ||
| "rel": "root", | ||
| "type": "application/json", | ||
| "href": "../collection.json" | ||
| }, | ||
| { | ||
| "rel": "self", | ||
| "type": "application/json", | ||
| "href": "collection.json" | ||
| }, | ||
| { | ||
| "rel": "cite-as", | ||
| "href": "https://doi.org/10.1002/fee.2616" | ||
| }, | ||
| { | ||
| "rel": "about", | ||
| "href": "https://projects.ecoforecast.org/usgsrc4cast-docs/", | ||
| "type": "text/html", | ||
| "title": "EFI-USGS River Chlorophyll Forecasting Challenge Documentation" | ||
| }, | ||
| { | ||
| "rel": "describedby", | ||
| "href": "https://projects.ecoforecast.org/usgsrc4cast-docs/", | ||
| "title": "EFI-USGS River Chlorophyll Forecast Challenge Dashboard", | ||
| "type": "text/html" | ||
| } | ||
| ], | ||
| "title": "aquatics", | ||
| "extent": { | ||
| "spatial": { | ||
| "bbox": [ | ||
| ["Inf", "Inf", "-Inf", "-Inf"] | ||
| ] | ||
| }, | ||
| "temporal": { | ||
| "interval": [ | ||
| [ | ||
| "2024-02-07T00:00:00Z", | ||
| "2024-03-17T00:00:00Z" | ||
| ] | ||
| ] | ||
| } | ||
| }, | ||
| "table:columns": [ | ||
| { | ||
| "name": "reference_datetime", | ||
| "type": "timestamp[us, tz=UTC]", | ||
| "description": "datetime that the forecast was initiated (horizon = 0)" | ||
| }, | ||
| { | ||
| "name": "datetime", | ||
| "type": "timestamp[us, tz=UTC]", | ||
| "description": "datetime of the forecasted value (ISO 8601)" | ||
| }, | ||
| { | ||
| "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)" | ||
| }, | ||
| { | ||
| "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.”" | ||
| }, | ||
| { | ||
| "name": "parameter", | ||
| "type": "string", | ||
| "description": "ensemble member or distribution parameter" | ||
| }, | ||
| { | ||
| "name": "prediction", | ||
| "type": "double", | ||
| "description": "predicted value for variable" | ||
| }, | ||
| { | ||
| "name": "pub_datetime", | ||
| "type": "timestamp[us, tz=UTC]", | ||
| "description": "datetime that forecast was submitted" | ||
| }, | ||
| { | ||
| "name": "project_id", | ||
| "type": "string", | ||
| "description": "unique identifier for the forecast project" | ||
| }, | ||
| { | ||
| "name": "duration", | ||
| "type": "string", | ||
| "description": "temporal duration of forecast (hourly, daily, 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": "reference_date", | ||
| "type": "string", | ||
| "description": "date that the forecast was initiated" | ||
| } | ||
| ], | ||
| "assets": { | ||
| "data": { | ||
| "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/?endpoint_override=sdsc.osn.xsede.org", | ||
| "type": "application/x-parquet", | ||
| "title": "Database Access", | ||
| "roles": [ | ||
| "data" | ||
| ], | ||
| "description": "Use `arrow` for remote access to the database. This R code will return results for the NEON Ecological Forecasting Aquatics theme.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |>\n dplyr::filter(variable %in% c(\"chla\")) |>\n 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" | ||
| }, | ||
| "thumbnail": { | ||
| "href": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/thumbnails/image/Back-b.jpg", | ||
| "type": "image/JPEG", | ||
| "roles": [ | ||
| "thumbnail" | ||
| ], | ||
| "title": "USGS Streamgage" | ||
| } | ||
| } | ||
| } |
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|---|---|---|
|
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| "interval": [ | ||
| [ | ||
| "2024-02-07T00:00:00Z", | ||
| "2024-03-14T00:00:00Z" | ||
| "2024-03-17T00:00:00Z" | ||
| ] | ||
| ] | ||
| } | ||
|
|
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|
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| "interval": [ | ||
| [ | ||
| "2024-02-07T00:00:00Z", | ||
| "2024-03-14T00:00:00Z" | ||
| "2024-03-17T00:00:00Z" | ||
| ] | ||
| ] | ||
| } | ||
|
|
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