From 67d716672af57dc4a11f254795c522226bf2a7e2 Mon Sep 17 00:00:00 2001 From: Zwart Date: Thu, 8 Feb 2024 15:22:46 -0800 Subject: [PATCH] removing all old summaries --- catalog/inventory/create_inventory_page.R | 3 +- .../Daily_Dissolved_oxygen/collection.json | 292 -------------- .../Daily_Water_temperature/collection.json | 362 ------------------ .../collection.json | 242 ------------ .../collection.json | 242 ------------ catalog/summaries/Beetles/collection.json | 177 --------- .../collection.json | 252 ------------ .../collection.json | 267 ------------- catalog/summaries/Phenology/collection.json | 177 --------- .../collection.json | 252 ------------ .../30min_latent_heat_flux/collection.json | 247 ------------ .../collection.json | 252 ------------ .../Daily_latent_heat_flux/collection.json | 247 ------------ catalog/summaries/Terrestrial/collection.json | 187 --------- .../collection.json | 237 ------------ catalog/summaries/Ticks/collection.json | 172 --------- .../GLEON_JRabaey_temp_physics.json | 224 ----------- .../models/model_items/GLEON_lm_lag_1day.json | 204 ---------- .../models/model_items/GLEON_physics.json | 196 ---------- .../models/model_items/USGSHABs1.json | 193 ---------- .../models/model_items/USUNEEDAILY.json | 191 --------- .../models/model_items/air2waterSat_2.json | 231 ----------- .../models/model_items/baseline_ensemble.json | 266 ------------- .../summaries/models/model_items/cb_f1.json | 231 ----------- .../models/model_items/cb_prophet.json | 311 --------------- .../models/model_items/climatology.json | 321 ---------------- .../summaries/models/model_items/fARIMA.json | 224 ----------- .../model_items/fARIMA_clim_ensemble.json | 218 ----------- .../models/model_items/fTSLM_lag.json | 224 ----------- .../models/model_items/flareGLM.json | 197 ---------- .../models/model_items/flareGLM_noDA.json | 197 ---------- .../models/model_items/flareGOTM.json | 196 ---------- .../models/model_items/flareGOTM_noDA.json | 196 ---------- .../models/model_items/flareSimstrat.json | 196 ---------- .../model_items/flareSimstrat_noDA.json | 195 ---------- .../models/model_items/flare_ler.json | 196 ---------- .../model_items/flare_ler_baselines.json | 192 ---------- .../summaries/models/model_items/lasso.json | 244 ------------ .../summaries/models/model_items/mean.json | 244 ------------ .../summaries/models/model_items/null.json | 231 ----------- .../models/model_items/persistenceRW.json | 306 --------------- .../model_items/procBlanchardMonod.json | 197 ---------- .../model_items/procBlanchardSteele.json | 197 ---------- .../models/model_items/procCTMIMonod.json | 197 ---------- .../models/model_items/procCTMISteele.json | 197 ---------- .../model_items/procEppleyNorbergMonod.json | 197 ---------- .../model_items/procEppleyNorbergSteele.json | 197 ---------- .../model_items/procHinshelwoodMonod.json | 197 ---------- .../model_items/procHinshelwoodSteele.json | 197 ---------- .../model_items/prophet_clim_ensemble.json | 232 ----------- .../summaries/models/model_items/randfor.json | 237 ------------ .../models/model_items/tg_arima.json | 334 ---------------- .../models/model_items/tg_auto_adam.json | 334 ---------------- .../models/model_items/tg_bag_mlp.json | 313 --------------- .../summaries/models/model_items/tg_ets.json | 334 ---------------- .../models/model_items/tg_humidity_lm.json | 334 ---------------- .../model_items/tg_humidity_lm_all_sites.json | 334 ---------------- .../models/model_items/tg_lasso.json | 320 ---------------- .../model_items/tg_lasso_all_sites.json | 320 ---------------- .../models/model_items/tg_precip_lm.json | 334 ---------------- .../model_items/tg_precip_lm_all_sites.json | 334 ---------------- .../models/model_items/tg_randfor.json | 334 ---------------- .../model_items/tg_randfor_all_sites.json | 306 --------------- .../models/model_items/tg_tbats.json | 334 ---------------- .../models/model_items/tg_temp_lm.json | 334 ---------------- .../model_items/tg_temp_lm_all_sites.json | 334 ---------------- 66 files changed, 2 insertions(+), 16208 deletions(-) delete mode 100644 catalog/scores/Aquatics/Daily_Dissolved_oxygen/collection.json delete mode 100644 catalog/scores/Aquatics/Daily_Water_temperature/collection.json delete mode 100644 catalog/summaries/Beetles/Weekly_beetle_community_abundance/collection.json delete mode 100644 catalog/summaries/Beetles/Weekly_beetle_community_richness/collection.json delete mode 100644 catalog/summaries/Beetles/collection.json delete mode 100644 catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/collection.json delete mode 100644 catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/collection.json delete mode 100644 catalog/summaries/Phenology/collection.json delete mode 100644 catalog/summaries/Terrestrial/30min_Net_ecosystem_exchange/collection.json delete mode 100644 catalog/summaries/Terrestrial/30min_latent_heat_flux/collection.json delete mode 100644 catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/collection.json delete mode 100644 catalog/summaries/Terrestrial/Daily_latent_heat_flux/collection.json delete mode 100644 catalog/summaries/Terrestrial/collection.json delete mode 100644 catalog/summaries/Ticks/Weekly_Amblyomma_americanum_population/collection.json delete mode 100644 catalog/summaries/Ticks/collection.json delete mode 100644 catalog/summaries/models/model_items/GLEON_JRabaey_temp_physics.json delete mode 100644 catalog/summaries/models/model_items/GLEON_lm_lag_1day.json delete mode 100644 catalog/summaries/models/model_items/GLEON_physics.json delete mode 100644 catalog/summaries/models/model_items/USGSHABs1.json delete mode 100644 catalog/summaries/models/model_items/USUNEEDAILY.json delete mode 100644 catalog/summaries/models/model_items/air2waterSat_2.json delete mode 100644 catalog/summaries/models/model_items/baseline_ensemble.json delete mode 100644 catalog/summaries/models/model_items/cb_f1.json delete mode 100644 catalog/summaries/models/model_items/cb_prophet.json delete mode 100644 catalog/summaries/models/model_items/climatology.json delete mode 100644 catalog/summaries/models/model_items/fARIMA.json delete mode 100644 catalog/summaries/models/model_items/fARIMA_clim_ensemble.json delete mode 100644 catalog/summaries/models/model_items/fTSLM_lag.json delete mode 100644 catalog/summaries/models/model_items/flareGLM.json delete mode 100644 catalog/summaries/models/model_items/flareGLM_noDA.json delete mode 100644 catalog/summaries/models/model_items/flareGOTM.json delete mode 100644 catalog/summaries/models/model_items/flareGOTM_noDA.json delete mode 100644 catalog/summaries/models/model_items/flareSimstrat.json delete mode 100644 catalog/summaries/models/model_items/flareSimstrat_noDA.json delete mode 100644 catalog/summaries/models/model_items/flare_ler.json delete mode 100644 catalog/summaries/models/model_items/flare_ler_baselines.json delete mode 100644 catalog/summaries/models/model_items/lasso.json delete mode 100644 catalog/summaries/models/model_items/mean.json delete mode 100644 catalog/summaries/models/model_items/null.json delete mode 100644 catalog/summaries/models/model_items/persistenceRW.json delete mode 100644 catalog/summaries/models/model_items/procBlanchardMonod.json delete mode 100644 catalog/summaries/models/model_items/procBlanchardSteele.json delete mode 100644 catalog/summaries/models/model_items/procCTMIMonod.json delete mode 100644 catalog/summaries/models/model_items/procCTMISteele.json delete mode 100644 catalog/summaries/models/model_items/procEppleyNorbergMonod.json delete mode 100644 catalog/summaries/models/model_items/procEppleyNorbergSteele.json delete mode 100644 catalog/summaries/models/model_items/procHinshelwoodMonod.json delete mode 100644 catalog/summaries/models/model_items/procHinshelwoodSteele.json delete mode 100644 catalog/summaries/models/model_items/prophet_clim_ensemble.json delete mode 100644 catalog/summaries/models/model_items/randfor.json delete mode 100644 catalog/summaries/models/model_items/tg_arima.json delete mode 100644 catalog/summaries/models/model_items/tg_auto_adam.json delete mode 100644 catalog/summaries/models/model_items/tg_bag_mlp.json delete mode 100644 catalog/summaries/models/model_items/tg_ets.json delete mode 100644 catalog/summaries/models/model_items/tg_humidity_lm.json delete mode 100644 catalog/summaries/models/model_items/tg_humidity_lm_all_sites.json delete mode 100644 catalog/summaries/models/model_items/tg_lasso.json delete mode 100644 catalog/summaries/models/model_items/tg_lasso_all_sites.json delete mode 100644 catalog/summaries/models/model_items/tg_precip_lm.json delete mode 100644 catalog/summaries/models/model_items/tg_precip_lm_all_sites.json delete mode 100644 catalog/summaries/models/model_items/tg_randfor.json delete mode 100644 catalog/summaries/models/model_items/tg_randfor_all_sites.json delete mode 100644 catalog/summaries/models/model_items/tg_tbats.json delete mode 100644 catalog/summaries/models/model_items/tg_temp_lm.json delete mode 100644 catalog/summaries/models/model_items/tg_temp_lm_all_sites.json diff --git a/catalog/inventory/create_inventory_page.R b/catalog/inventory/create_inventory_page.R index 654aac1097..5e1f461f2a 100644 --- a/catalog/inventory/create_inventory_page.R +++ b/catalog/inventory/create_inventory_page.R @@ -33,7 +33,8 @@ inventory_theme_df <- arrow::open_dataset(arrow::s3_bucket(config$inventory_buck inventory_data_df <- duckdbfs::open_dataset(glue::glue("s3://{config$inventory_bucket}/catalog/forecasts"), s3_endpoint = config$endpoint, anonymous=TRUE) |> - collect() + collect() |> + dplyr::filter(project_id == config$project_id) theme_models <- inventory_data_df |> distinct(model_id) diff --git a/catalog/scores/Aquatics/Daily_Dissolved_oxygen/collection.json b/catalog/scores/Aquatics/Daily_Dissolved_oxygen/collection.json deleted file mode 100644 index f522fa4259..0000000000 --- a/catalog/scores/Aquatics/Daily_Dissolved_oxygen/collection.json +++ /dev/null @@ -1,292 +0,0 @@ -{ - "id": "Daily_Dissolved_oxygen", - "description": "This page includes all models for the Daily_Dissolved_oxygen 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/GLEON_lm_lag_1day.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/air2waterSat_2.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/cb_f1.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/cb_prophet.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/climatology.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/null.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/persistenceRW.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_arima.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_auto_adam.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_bag_mlp.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_ets.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_tbats.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_humidity_lm.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_humidity_lm_all_sites.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_lasso.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_lasso_all_sites.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_precip_lm.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_precip_lm_all_sites.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_randfor.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_randfor_all_sites.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_temp_lm.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_temp_lm_all_sites.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/neon4cast-docs/", - "type": "text/html", - "title": "NEON Ecological Forecasting Challenge Documentation" - }, - { - "rel": "describedby", - "href": "https://projects.ecoforecast.org/neon4cast-docs/", - "title": "NEON Forecast Challenge Dashboard", - "type": "text/html" - } - ], - "title": "Daily_Dissolved_oxygen", - "extent": { - "spatial": { - "bbox": [ - [-149.6106, 18.1135, -66.7987, 68.6698] - ] - }, - "temporal": { - "interval": [ - [ - "2023-01-01T00:00:00Z", - "2023-11-30T00:00:00Z" - ] - ] - } - }, - "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." - } - ], - "assets": { - "data": { - "href": "\"s3://anonymous@bio230014-bucket01/challenges/scores/parquet/project_id=neon4cast/duration=P1D/variable=oxygen?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/scores/parquet/project_id=neon4cast/duration=P1D/variable=oxygen?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" - } - } -} diff --git a/catalog/scores/Aquatics/Daily_Water_temperature/collection.json b/catalog/scores/Aquatics/Daily_Water_temperature/collection.json deleted file mode 100644 index 7badca14de..0000000000 --- a/catalog/scores/Aquatics/Daily_Water_temperature/collection.json +++ /dev/null @@ -1,362 +0,0 @@ -{ - "id": "Daily_Water_temperature", - "description": "This page includes all models for the Daily_Water_temperature 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/cb_f1.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/cb_prophet.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/climatology.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/fARIMA.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/fARIMA_clim_ensemble.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/fTSLM_lag.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/GLEON_JRabaey_temp_physics.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/GLEON_lm_lag_1day.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/GLEON_physics.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/air2waterSat_2.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/baseline_ensemble.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_arima.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/flareGLM.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/flareGLM_noDA.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/flareGOTM.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/flareGOTM_noDA.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/flareSimstrat.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/flareSimstrat_noDA.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/flare_ler.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/flare_ler_baselines.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/null.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/persistenceRW.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_bag_mlp.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_ets.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_auto_adam.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_humidity_lm_all_sites.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_lasso.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_lasso_all_sites.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_precip_lm.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_precip_lm_all_sites.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_randfor.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_randfor_all_sites.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_tbats.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_humidity_lm.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_temp_lm.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_temp_lm_all_sites.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/neon4cast-docs/", - "type": "text/html", - "title": "NEON Ecological Forecasting Challenge Documentation" - }, - { - "rel": "describedby", - "href": "https://projects.ecoforecast.org/neon4cast-docs/", - "title": "NEON Forecast Challenge Dashboard", - "type": "text/html" - } - ], - "title": "Daily_Water_temperature", - "extent": { - "spatial": { - "bbox": [ - [-149.6106, 18.1135, -66.7987, 68.6698] - ] - }, - "temporal": { - "interval": [ - [ - "2023-01-01T00:00:00Z", - "2023-12-15T00:00:00Z" - ] - ] - } - }, - "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." - } - ], - "assets": { - "data": { - "href": "\"s3://anonymous@bio230014-bucket01/challenges/scores/parquet/project_id=neon4cast/duration=P1D/variable=temperature?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/scores/parquet/project_id=neon4cast/duration=P1D/variable=temperature?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" - } - } -} diff --git a/catalog/summaries/Beetles/Weekly_beetle_community_abundance/collection.json b/catalog/summaries/Beetles/Weekly_beetle_community_abundance/collection.json deleted file mode 100644 index dd94d54f40..0000000000 --- a/catalog/summaries/Beetles/Weekly_beetle_community_abundance/collection.json +++ /dev/null @@ -1,242 +0,0 @@ -{ - "id": "Weekly_beetle_community_abundance", - "description": "This page includes all models for the Weekly_beetle_community_abundance 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/mean.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_arima.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_auto_adam.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_ets.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_humidity_lm.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_lasso.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_precip_lm.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_randfor.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_tbats.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_temp_lm.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_randfor_all_sites.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_lasso_all_sites.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_humidity_lm_all_sites.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_temp_lm_all_sites.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_precip_lm_all_sites.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/neon4cast-docs/", - "type": "text/html", - "title": "NEON Ecological Forecasting Challenge Documentation" - }, - { - "rel": "describedby", - "href": "https://projects.ecoforecast.org/neon4cast-docs/", - "title": "NEON Forecast Challenge Dashboard", - "type": "text/html" - } - ], - "title": "Weekly_beetle_community_abundance", - "extent": { - "spatial": { - "bbox": [ - [-156.6194, 17.9696, -66.8687, 71.2824] - ] - }, - "temporal": { - "interval": [ - [ - "2023-01-02T00:00:00Z", - "2024-12-09T00:00:00Z" - ] - ] - } - }, - "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)" - }, - { - "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.”" - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "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 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=neon4cast/duration=P1W/variable=abundance?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=neon4cast/duration=P1W/variable=abundance?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" - } - } -} diff --git a/catalog/summaries/Beetles/Weekly_beetle_community_richness/collection.json b/catalog/summaries/Beetles/Weekly_beetle_community_richness/collection.json deleted file mode 100644 index d36812650d..0000000000 --- a/catalog/summaries/Beetles/Weekly_beetle_community_richness/collection.json +++ /dev/null @@ -1,242 +0,0 @@ -{ - "id": "Weekly_beetle_community_richness", - "description": "This page includes all models for the Weekly_beetle_community_richness 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/mean.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_arima.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_ets.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_precip_lm_all_sites.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_randfor_all_sites.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_tbats.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_randfor.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_humidity_lm_all_sites.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_lasso.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_precip_lm.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_temp_lm_all_sites.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_humidity_lm.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_temp_lm.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_lasso_all_sites.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_auto_adam.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/neon4cast-docs/", - "type": "text/html", - "title": "NEON Ecological Forecasting Challenge Documentation" - }, - { - "rel": "describedby", - "href": "https://projects.ecoforecast.org/neon4cast-docs/", - "title": "NEON Forecast Challenge Dashboard", - "type": "text/html" - } - ], - "title": "Weekly_beetle_community_richness", - "extent": { - "spatial": { - "bbox": [ - [-156.6194, 17.9696, -66.8687, 71.2824] - ] - }, - "temporal": { - "interval": [ - [ - "2023-01-02T00:00:00Z", - "2024-12-09T00:00:00Z" - ] - ] - } - }, - "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)" - }, - { - "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.”" - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "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 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=neon4cast/duration=P1W/variable=richness?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=neon4cast/duration=P1W/variable=richness?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" - } - } -} diff --git a/catalog/summaries/Beetles/collection.json b/catalog/summaries/Beetles/collection.json deleted file mode 100644 index 77945c89aa..0000000000 --- a/catalog/summaries/Beetles/collection.json +++ /dev/null @@ -1,177 +0,0 @@ -{ - "id": "Beetles", - "description": "This page includes variables for the Beetles 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": "Weekly_beetle_community_abundance/collection.json" - }, - { - "rel": "child", - "type": "application/json", - "href": "Weekly_beetle_community_richness/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/neon4cast-docs/", - "type": "text/html", - "title": "NEON Ecological Forecasting Challenge Documentation" - }, - { - "rel": "describedby", - "href": "https://projects.ecoforecast.org/neon4cast-docs/", - "title": "NEON Forecast Challenge Dashboard", - "type": "text/html" - } - ], - "title": "Beetles", - "extent": { - "spatial": { - "bbox": [ - [-156.6194, 17.9696, -66.8687, 71.2824] - ] - }, - "temporal": { - "interval": [ - [ - "2023-01-01T00:00:00Z", - "2024-12-09T00:00:00Z" - ] - ] - } - }, - "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)" - }, - { - "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.”" - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "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 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/vera4cast/forecasts/summaries/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/vera4cast/forecasts/summaries/parquet/?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |>\n dplyr::filter(variable %in% c(\"abundance\", \"richness\")) |>\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://www.neonscience.org/sites/default/files/styles/max_width_1170px/public/image-content-images/Beetles_pinned.jpg", - "type": "image/JPEG", - "roles": [ - "thumbnail" - ], - "title": "Beetle Image" - } - } -} diff --git a/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/collection.json b/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/collection.json deleted file mode 100644 index f35929fe7c..0000000000 --- a/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/collection.json +++ /dev/null @@ -1,252 +0,0 @@ -{ - "id": "Daily_Green_chromatic_coordinate", - "description": "This page includes all models for the Daily_Green_chromatic_coordinate 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/cb_prophet.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/climatology.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/persistenceRW.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_arima.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_auto_adam.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_bag_mlp.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_ets.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_humidity_lm.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_humidity_lm_all_sites.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_lasso.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_lasso_all_sites.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_precip_lm.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_tbats.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_temp_lm.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_temp_lm_all_sites.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_precip_lm_all_sites.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_randfor.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/neon4cast-docs/", - "type": "text/html", - "title": "NEON Ecological Forecasting Challenge Documentation" - }, - { - "rel": "describedby", - "href": "https://projects.ecoforecast.org/neon4cast-docs/", - "title": "NEON Forecast Challenge Dashboard", - "type": "text/html" - } - ], - "title": "Daily_Green_chromatic_coordinate", - "extent": { - "spatial": { - "bbox": [ - [-156.6194, 17.9696, -66.8687, 71.2824] - ] - }, - "temporal": { - "interval": [ - [ - "2023-01-01T00:00:00Z", - "2024-01-21T00:00:00Z" - ] - ] - } - }, - "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)" - }, - { - "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.”" - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "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 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=neon4cast/duration=P1D/variable=gcc_90?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=neon4cast/duration=P1D/variable=gcc_90?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" - } - } -} diff --git a/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/collection.json b/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/collection.json deleted file mode 100644 index 069c93992a..0000000000 --- a/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/collection.json +++ /dev/null @@ -1,267 +0,0 @@ -{ - "id": "Daily_Red_chromatic_coordinate", - "description": "This page includes all models for the Daily_Red_chromatic_coordinate 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/tg_tbats.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/cb_prophet.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/climatology.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/persistenceRW.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/prophet_clim_ensemble.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_arima.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_bag_mlp.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_ets.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_temp_lm.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_temp_lm_all_sites.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/baseline_ensemble.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_humidity_lm.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_humidity_lm_all_sites.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_lasso.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_lasso_all_sites.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_precip_lm.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_precip_lm_all_sites.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_randfor.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_randfor_all_sites.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_auto_adam.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/neon4cast-docs/", - "type": "text/html", - "title": "NEON Ecological Forecasting Challenge Documentation" - }, - { - "rel": "describedby", - "href": "https://projects.ecoforecast.org/neon4cast-docs/", - "title": "NEON Forecast Challenge Dashboard", - "type": "text/html" - } - ], - "title": "Daily_Red_chromatic_coordinate", - "extent": { - "spatial": { - "bbox": [ - [-156.6194, 17.9696, -66.8687, 71.2824] - ] - }, - "temporal": { - "interval": [ - [ - "2023-01-01T00:00:00Z", - "2024-01-21T00:00:00Z" - ] - ] - } - }, - "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)" - }, - { - "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.”" - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "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 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=neon4cast/duration=P1D/variable=rcc_90?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=neon4cast/duration=P1D/variable=rcc_90?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" - } - } -} diff --git a/catalog/summaries/Phenology/collection.json b/catalog/summaries/Phenology/collection.json deleted file mode 100644 index ccfb429056..0000000000 --- a/catalog/summaries/Phenology/collection.json +++ /dev/null @@ -1,177 +0,0 @@ -{ - "id": "Phenology", - "description": "This page includes variables for the Phenology 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_Green_chromatic_coordinate/collection.json" - }, - { - "rel": "child", - "type": "application/json", - "href": "Daily_Red_chromatic_coordinate/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/neon4cast-docs/", - "type": "text/html", - "title": "NEON Ecological Forecasting Challenge Documentation" - }, - { - "rel": "describedby", - "href": "https://projects.ecoforecast.org/neon4cast-docs/", - "title": "NEON Forecast Challenge Dashboard", - "type": "text/html" - } - ], - "title": "Phenology", - "extent": { - "spatial": { - "bbox": [ - [-156.6194, 17.9696, -66.8687, 71.2824] - ] - }, - "temporal": { - "interval": [ - [ - "2023-01-01T00:00:00Z", - "2024-12-09T00:00:00Z" - ] - ] - } - }, - "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)" - }, - { - "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.”" - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "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 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/vera4cast/forecasts/summaries/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/vera4cast/forecasts/summaries/parquet/?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |>\n dplyr::filter(variable %in% c(\"gcc_90\", \"rcc_90\")) |>\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://www.neonscience.org/sites/default/files/styles/max_1300x1300/public/image-content-images/_BFP8455.jpg", - "type": "image/JPEG", - "roles": [ - "thumbnail" - ], - "title": "Phenology Image" - } - } -} diff --git a/catalog/summaries/Terrestrial/30min_Net_ecosystem_exchange/collection.json b/catalog/summaries/Terrestrial/30min_Net_ecosystem_exchange/collection.json deleted file mode 100644 index 9ba8b5ba9f..0000000000 --- a/catalog/summaries/Terrestrial/30min_Net_ecosystem_exchange/collection.json +++ /dev/null @@ -1,252 +0,0 @@ -{ - "id": "30min_Net_ecosystem_exchange", - "description": "This page includes all models for the 30min_Net_ecosystem_exchange 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/cb_prophet.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/climatology.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/lasso.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/persistenceRW.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_arima.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_precip_lm.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_precip_lm_all_sites.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_randfor.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_tbats.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_auto_adam.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_bag_mlp.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_ets.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_temp_lm.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_temp_lm_all_sites.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_humidity_lm.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_humidity_lm_all_sites.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/USUNEEDAILY.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/neon4cast-docs/", - "type": "text/html", - "title": "NEON Ecological Forecasting Challenge Documentation" - }, - { - "rel": "describedby", - "href": "https://projects.ecoforecast.org/neon4cast-docs/", - "title": "NEON Forecast Challenge Dashboard", - "type": "text/html" - } - ], - "title": "30min_Net_ecosystem_exchange", - "extent": { - "spatial": { - "bbox": [ - [-156.6194, 17.9696, -66.8687, 71.2824] - ] - }, - "temporal": { - "interval": [ - [ - "2023-01-01T00:00:00Z", - "2024-01-22T00:00:00Z" - ] - ] - } - }, - "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)" - }, - { - "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.”" - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "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 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=neon4cast/duration=P1D/variable=nee?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=neon4cast/duration=P1D/variable=nee?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" - } - } -} diff --git a/catalog/summaries/Terrestrial/30min_latent_heat_flux/collection.json b/catalog/summaries/Terrestrial/30min_latent_heat_flux/collection.json deleted file mode 100644 index 6ffb3148b3..0000000000 --- a/catalog/summaries/Terrestrial/30min_latent_heat_flux/collection.json +++ /dev/null @@ -1,247 +0,0 @@ -{ - "id": "30min_latent_heat_flux", - "description": "This page includes all models for the 30min_latent_heat_flux 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/tg_precip_lm_all_sites.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_randfor.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_tbats.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_temp_lm.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_temp_lm_all_sites.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/cb_prophet.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/climatology.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/lasso.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/randfor.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_arima.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_ets.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_humidity_lm_all_sites.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_precip_lm.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_auto_adam.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_bag_mlp.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_humidity_lm.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/neon4cast-docs/", - "type": "text/html", - "title": "NEON Ecological Forecasting Challenge Documentation" - }, - { - "rel": "describedby", - "href": "https://projects.ecoforecast.org/neon4cast-docs/", - "title": "NEON Forecast Challenge Dashboard", - "type": "text/html" - } - ], - "title": "30min_latent_heat_flux", - "extent": { - "spatial": { - "bbox": [ - [-156.6194, 17.9696, -66.8687, 71.2824] - ] - }, - "temporal": { - "interval": [ - [ - "2023-01-01T00:00:00Z", - "2024-01-22T00:00:00Z" - ] - ] - } - }, - "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)" - }, - { - "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.”" - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "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 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=neon4cast/duration=P1D/variable=le?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=neon4cast/duration=P1D/variable=le?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" - } - } -} diff --git a/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/collection.json b/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/collection.json deleted file mode 100644 index 69e42aefe7..0000000000 --- a/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/collection.json +++ /dev/null @@ -1,252 +0,0 @@ -{ - "id": "Daily_Net_ecosystem_exchange", - "description": "This page includes all models for the Daily_Net_ecosystem_exchange 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/cb_prophet.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/climatology.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/lasso.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/persistenceRW.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_arima.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_precip_lm.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_precip_lm_all_sites.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_randfor.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_tbats.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_auto_adam.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_bag_mlp.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_ets.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_temp_lm.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_temp_lm_all_sites.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_humidity_lm.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_humidity_lm_all_sites.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/USUNEEDAILY.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/neon4cast-docs/", - "type": "text/html", - "title": "NEON Ecological Forecasting Challenge Documentation" - }, - { - "rel": "describedby", - "href": "https://projects.ecoforecast.org/neon4cast-docs/", - "title": "NEON Forecast Challenge Dashboard", - "type": "text/html" - } - ], - "title": "Daily_Net_ecosystem_exchange", - "extent": { - "spatial": { - "bbox": [ - [-156.6194, 17.9696, -66.8687, 71.2824] - ] - }, - "temporal": { - "interval": [ - [ - "2023-01-01T00:00:00Z", - "2024-01-22T00:00:00Z" - ] - ] - } - }, - "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)" - }, - { - "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.”" - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "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 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=neon4cast/duration=P1D/variable=nee?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=neon4cast/duration=P1D/variable=nee?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" - } - } -} diff --git a/catalog/summaries/Terrestrial/Daily_latent_heat_flux/collection.json b/catalog/summaries/Terrestrial/Daily_latent_heat_flux/collection.json deleted file mode 100644 index 6ee353c7a1..0000000000 --- a/catalog/summaries/Terrestrial/Daily_latent_heat_flux/collection.json +++ /dev/null @@ -1,247 +0,0 @@ -{ - "id": "Daily_latent_heat_flux", - "description": "This page includes all models for the Daily_latent_heat_flux 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/tg_precip_lm_all_sites.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_randfor.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_tbats.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_temp_lm.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_temp_lm_all_sites.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/cb_prophet.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/climatology.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/lasso.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/randfor.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_arima.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_ets.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_humidity_lm_all_sites.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_precip_lm.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_auto_adam.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_bag_mlp.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_humidity_lm.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/neon4cast-docs/", - "type": "text/html", - "title": "NEON Ecological Forecasting Challenge Documentation" - }, - { - "rel": "describedby", - "href": "https://projects.ecoforecast.org/neon4cast-docs/", - "title": "NEON Forecast Challenge Dashboard", - "type": "text/html" - } - ], - "title": "Daily_latent_heat_flux", - "extent": { - "spatial": { - "bbox": [ - [-156.6194, 17.9696, -66.8687, 71.2824] - ] - }, - "temporal": { - "interval": [ - [ - "2023-01-01T00:00:00Z", - "2024-01-22T00:00:00Z" - ] - ] - } - }, - "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)" - }, - { - "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.”" - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "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 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=neon4cast/duration=P1D/variable=le?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=neon4cast/duration=P1D/variable=le?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" - } - } -} diff --git a/catalog/summaries/Terrestrial/collection.json b/catalog/summaries/Terrestrial/collection.json deleted file mode 100644 index 5966bac21c..0000000000 --- a/catalog/summaries/Terrestrial/collection.json +++ /dev/null @@ -1,187 +0,0 @@ -{ - "id": "Terrestrial", - "description": "This page includes variables for the Terrestrial 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_Net_ecosystem_exchange/collection.json" - }, - { - "rel": "child", - "type": "application/json", - "href": "Daily_latent_heat_flux/collection.json" - }, - { - "rel": "child", - "type": "application/json", - "href": "30min_Net_ecosystem_exchange/collection.json" - }, - { - "rel": "child", - "type": "application/json", - "href": "30min_latent_heat_flux/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/neon4cast-docs/", - "type": "text/html", - "title": "NEON Ecological Forecasting Challenge Documentation" - }, - { - "rel": "describedby", - "href": "https://projects.ecoforecast.org/neon4cast-docs/", - "title": "NEON Forecast Challenge Dashboard", - "type": "text/html" - } - ], - "title": "Terrestrial", - "extent": { - "spatial": { - "bbox": [ - [-156.6194, 17.9696, -66.8687, 71.2824] - ] - }, - "temporal": { - "interval": [ - [ - "2023-01-01T00:00:00Z", - "2024-12-09T00:00:00Z" - ] - ] - } - }, - "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)" - }, - { - "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.”" - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "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 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/vera4cast/forecasts/summaries/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/vera4cast/forecasts/summaries/parquet/?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |>\n dplyr::filter(variable %in% c(\"nee\", \"le\")) |>\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://projects.ecoforecast.org/neon4cast-catalog/img/BONA_Twr.jpg", - "type": "image/JPEG", - "roles": [ - "thumbnail" - ], - "title": "NEON Field Tower" - } - } -} diff --git a/catalog/summaries/Ticks/Weekly_Amblyomma_americanum_population/collection.json b/catalog/summaries/Ticks/Weekly_Amblyomma_americanum_population/collection.json deleted file mode 100644 index fdae2fd069..0000000000 --- a/catalog/summaries/Ticks/Weekly_Amblyomma_americanum_population/collection.json +++ /dev/null @@ -1,237 +0,0 @@ -{ - "id": "Weekly_Amblyomma_americanum_population", - "description": "This page includes all models for the Weekly_Amblyomma_americanum_population 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/tg_tbats.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_arima.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_ets.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_humidity_lm_all_sites.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_precip_lm.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_randfor.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_temp_lm.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_randfor_all_sites.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_temp_lm_all_sites.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_humidity_lm.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_lasso.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_lasso_all_sites.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_precip_lm_all_sites.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "../../models/model_items/tg_auto_adam.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/neon4cast-docs/", - "type": "text/html", - "title": "NEON Ecological Forecasting Challenge Documentation" - }, - { - "rel": "describedby", - "href": "https://projects.ecoforecast.org/neon4cast-docs/", - "title": "NEON Forecast Challenge Dashboard", - "type": "text/html" - } - ], - "title": "Weekly_Amblyomma_americanum_population", - "extent": { - "spatial": { - "bbox": [ - [-96.5631, 29.6893, -76.56, 39.1008] - ] - }, - "temporal": { - "interval": [ - [ - "2023-01-02T00:00:00Z", - "2024-12-09T00:00:00Z" - ] - ] - } - }, - "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)" - }, - { - "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.”" - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "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 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=neon4cast/duration=P1W/variable=amblyomma_americanum?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=neon4cast/duration=P1W/variable=amblyomma_americanum?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" - } - } -} diff --git a/catalog/summaries/Ticks/collection.json b/catalog/summaries/Ticks/collection.json deleted file mode 100644 index a9d0f4d635..0000000000 --- a/catalog/summaries/Ticks/collection.json +++ /dev/null @@ -1,172 +0,0 @@ -{ - "id": "Ticks", - "description": "This page includes variables for the Ticks 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": "Weekly_Amblyomma_americanum_population/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/neon4cast-docs/", - "type": "text/html", - "title": "NEON Ecological Forecasting Challenge Documentation" - }, - { - "rel": "describedby", - "href": "https://projects.ecoforecast.org/neon4cast-docs/", - "title": "NEON Forecast Challenge Dashboard", - "type": "text/html" - } - ], - "title": "Ticks", - "extent": { - "spatial": { - "bbox": [ - [-96.5631, 29.6893, -76.56, 39.1008] - ] - }, - "temporal": { - "interval": [ - [ - "2023-01-01T00:00:00Z", - "2024-12-09T00:00:00Z" - ] - ] - } - }, - "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)" - }, - { - "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.”" - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "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 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/vera4cast/forecasts/summaries/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/vera4cast/forecasts/summaries/parquet/?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |>\n dplyr::filter(variable %in% c(\"amblyomma_americanum\")) |>\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://raw.githubusercontent.com/eco4cast/neon4cast-ci/main/catalog/thumbnail_plots/tick_drag.jpg", - "type": "image/JPEG", - "roles": [ - "thumbnail" - ], - "title": "NEON Ticks" - } - } -} diff --git a/catalog/summaries/models/model_items/GLEON_JRabaey_temp_physics.json b/catalog/summaries/models/model_items/GLEON_JRabaey_temp_physics.json deleted file mode 100644 index 6f3aeda12a..0000000000 --- a/catalog/summaries/models/model_items/GLEON_JRabaey_temp_physics.json +++ /dev/null @@ -1,224 +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": [ - [-105.9154, 39.8914], - [-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] - ] - }, - "properties": { - "description": "\nmodel info: NA\n\nSites: WLOU, 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\n\nVariables: Daily Water_temperature", - "start_datetime": "2023-11-14", - "end_datetime": "2024-01-18", - "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)" - }, - { - "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.”" - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "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 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" - } - ] - }, - "collection": "forecasts", - "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": null, - "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": null, - "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/forecasts/summaries/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/forecasts/summaries/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/summaries/models/model_items/GLEON_lm_lag_1day.json b/catalog/summaries/models/model_items/GLEON_lm_lag_1day.json deleted file mode 100644 index 8442f9f9c0..0000000000 --- a/catalog/summaries/models/model_items/GLEON_lm_lag_1day.json +++ /dev/null @@ -1,204 +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": "2024-01-19", - "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)" - }, - { - "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.”" - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "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 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" - } - ] - }, - "collection": "forecasts", - "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": null, - "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": null, - "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/forecasts/summaries/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/forecasts/summaries/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/forecasts/summaries/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/forecasts/summaries/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/summaries/models/model_items/GLEON_physics.json b/catalog/summaries/models/model_items/GLEON_physics.json deleted file mode 100644 index 76d11169d5..0000000000 --- a/catalog/summaries/models/model_items/GLEON_physics.json +++ /dev/null @@ -1,196 +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": "2023-12-22", - "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 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)" - }, - { - "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.”" - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "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 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" - } - ] - }, - "collection": "forecasts", - "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": null, - "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": null, - "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/forecasts/summaries/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/forecasts/summaries/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/summaries/models/model_items/USGSHABs1.json b/catalog/summaries/models/model_items/USGSHABs1.json deleted file mode 100644 index 0ad47c9be8..0000000000 --- a/catalog/summaries/models/model_items/USGSHABs1.json +++ /dev/null @@ -1,193 +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], - [-88.1589, 31.8534], - [-84.4374, 31.1854] - ] - }, - "properties": { - "description": "\nmodel info: NA\n\nSites: BLWA, TOMB, FLNT\n\nVariables: Daily Chlorophyll_a", - "start_datetime": "2023-11-12", - "end_datetime": "2024-01-19", - "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)" - }, - { - "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.”" - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "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 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" - } - ] - }, - "collection": "forecasts", - "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": null, - "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": null, - "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/forecasts/summaries/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/forecasts/summaries/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/summaries/models/model_items/USUNEEDAILY.json b/catalog/summaries/models/model_items/USUNEEDAILY.json deleted file mode 100644 index 7693f94820..0000000000 --- a/catalog/summaries/models/model_items/USUNEEDAILY.json +++ /dev/null @@ -1,191 +0,0 @@ -{ - "stac_version": "1.0.0", - "stac_extensions": [ - "https://stac-extensions.github.io/table/v1.2.0/schema.json" - ], - "type": "Feature", - "id": "USUNEEDAILY", - "bbox": [ - [ - -156.6194, - 71.2824, - -66.7987, - 71.2824 - ] - ], - "geometry": { - "type": "MultiPoint", - "coordinates": [ - [-155.3173, 19.5531] - ] - }, - "properties": { - "description": "\nmodel info: \"Home brew ARIMA.\" We didn't use a formal time series framework because of all the missing values in both our response variable and the weather covariates. So we used a GAM to fit a seasonal component based on day of year, and we included NEE the previous day as as an AR 1 term. We did some model selection, using cross validation, to identify temperature and relative humidity as weather covariates.\n\nSites: PUUM\n\nVariables: Daily Net_ecosystem_exchange", - "start_datetime": "2023-12-12", - "end_datetime": "2024-01-16", - "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" - ], - "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)" - }, - { - "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.”" - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "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 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" - } - ] - }, - "collection": "forecasts", - "links": [ - { - "rel": "collection", - "href": "../collection.json", - "type": "application/json", - "title": "USUNEEDAILY" - }, - { - "rel": "root", - "href": "../../../catalog.json", - "type": "application/json", - "title": "Forecast Catalog" - }, - { - "rel": "parent", - "href": "../collection.json", - "type": "application/json", - "title": "USUNEEDAILY" - }, - { - "rel": "self", - "href": "USUNEEDAILY.json", - "type": "application/json", - "title": "Model Forecast" - }, - { - "rel": "item", - "href": "https://drive.google.com/file/d/10uvb3HWR0nHOHrBSQTTPc9vZnYgFgbVa/view?usp=sharing", - "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/USUNEEDAILY.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/USUNEEDAILY.json\")\n\n" - }, - "2": { - "type": "text/html", - "title": "Link for Model Code", - "href": "https://drive.google.com/file/d/10uvb3HWR0nHOHrBSQTTPc9vZnYgFgbVa/view?usp=sharing", - "description": "The link to the model code provided by the model submission team" - }, - "3": { - "type": "application/x-parquet", - "title": "Database Access for Daily Net_ecosystem_exchange", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=nee/model_id=USUNEEDAILY?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/forecasts/summaries/parquet/duration=P1D/variable=nee/model_id=USUNEEDAILY?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/summaries/models/model_items/air2waterSat_2.json b/catalog/summaries/models/model_items/air2waterSat_2.json deleted file mode 100644 index 061ac8098d..0000000000 --- a/catalog/summaries/models/model_items/air2waterSat_2.json +++ /dev/null @@ -1,231 +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": [ - [-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], - [-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], - [-149.6106, 68.6307], - [-84.2793, 35.9574], - [-105.9154, 39.8914] - ] - }, - "properties": { - "description": "\nmodel info: NA\n\nSites: LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, TOOK, WALK, WLOU\n\nVariables: Daily Dissolved_oxygen, Daily Water_temperature", - "start_datetime": "2023-11-14", - "end_datetime": "2024-01-20", - "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 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)" - }, - { - "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.”" - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "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 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" - } - ] - }, - "collection": "forecasts", - "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": null, - "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": null, - "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/forecasts/summaries/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/forecasts/summaries/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/forecasts/summaries/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/forecasts/summaries/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/summaries/models/model_items/baseline_ensemble.json b/catalog/summaries/models/model_items/baseline_ensemble.json deleted file mode 100644 index d034b4133b..0000000000 --- a/catalog/summaries/models/model_items/baseline_ensemble.json +++ /dev/null @@ -1,266 +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": [ - [-87.7982, 32.5415], - [-105.5442, 40.035], - [-66.9868, 18.1135], - [-84.4374, 31.1854], - [-66.7987, 18.1741], - [-72.3295, 42.4719], - [-82.0177, 29.6878], - [-111.5081, 33.751], - [-119.0274, 36.9559], - [-88.1589, 31.8534], - [-84.2793, 35.9574], - [-105.9154, 39.8914], - [-96.443, 38.9459], - [-122.1655, 44.2596], - [-78.1473, 38.8943], - [-97.7823, 33.3785], - [-111.7979, 40.7839], - [-102.4471, 39.7582], - [-82.0084, 29.676], - [-119.2575, 37.0597], - [-110.5871, 44.9501], - [-96.6242, 34.4442], - [-96.6038, 39.1051], - [-83.5038, 35.6904], - [-77.9832, 39.0956], - [-121.9338, 45.7908], - [-87.4077, 32.9604], - [-88.1612, 31.8539], - [-80.5248, 37.3783], - [-109.3883, 38.2483], - [-105.5824, 40.0543], - [-100.9154, 46.7697], - [-99.0588, 35.4106], - [-72.1727, 42.5369], - [-84.4686, 31.1948], - [-106.8425, 32.5907], - [-96.6129, 39.1104], - [-96.5631, 39.1008], - [-67.0769, 18.0213], - [-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], - [-78.1395, 38.8929], - [-76.56, 38.8901], - [-119.7323, 37.1088], - [-119.2622, 37.0334], - [-110.8355, 31.9107], - [-112.4524, 40.1776], - [-84.2826, 35.9641], - [-81.9934, 29.6893], - [-155.3173, 19.5531], - [-105.546, 40.2759], - [-99.1066, 47.1617], - [-87.8039, 32.5417], - [-81.4362, 28.1251], - [-83.5019, 35.689], - [-66.8687, 17.9696], - [-122.3303, 45.7624], - [-71.2874, 44.0639], - [-78.0418, 39.0337], - [-97.57, 33.4012], - [-104.7456, 40.8155] - ] - }, - "properties": { - "description": "\nmodel info: NA\n\nSites: BLWA, COMO, CUPE, FLNT, GUIL, HOPB, SUGG, SYCA, TECR, TOMB, WALK, WLOU, MCDI, MCRA, POSE, PRIN, REDB, ARIK, BARC, BIGC, BLDE, BLUE, KING, LECO, LEWI, MART, MAYF, LENO, MLBS, MOAB, NIWO, NOGP, OAES, HARV, JERC, JORN, KONA, KONZ, LAJA, STEI, STER, TALL, TEAK, TREE, UKFS, UNDE, WOOD, WREF, YELL, SCBI, SERC, SJER, SOAP, SRER, ONAQ, ORNL, OSBS, PUUM, RMNP, DCFS, DELA, DSNY, GRSM, GUAN, ABBY, BART, BLAN, CLBJ, CPER\n\nVariables: Daily Water_temperature, Daily Red_chromatic_coordinate", - "start_datetime": "2023-11-14", - "end_datetime": "2024-01-15", - "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", - "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)" - }, - { - "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.”" - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "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 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" - } - ] - }, - "collection": "forecasts", - "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": null, - "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": null, - "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/forecasts/summaries/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/forecasts/summaries/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" - }, - "4": { - "type": "application/x-parquet", - "title": "Database Access for Daily Red_chromatic_coordinate", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/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/forecasts/summaries/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" - } - } -} diff --git a/catalog/summaries/models/model_items/cb_f1.json b/catalog/summaries/models/model_items/cb_f1.json deleted file mode 100644 index c702738d65..0000000000 --- a/catalog/summaries/models/model_items/cb_f1.json +++ /dev/null @@ -1,231 +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": [ - [-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], - [-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] - ] - }, - "properties": { - "description": [], - "start_datetime": "2023-10-11", - "end_datetime": "2024-11-10", - "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", - "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)" - }, - { - "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.”" - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "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 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" - } - ] - }, - "collection": "forecasts", - "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": [], - "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": [], - "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/forecasts/summaries/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/forecasts/summaries/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/forecasts/summaries/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/forecasts/summaries/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/summaries/models/model_items/cb_prophet.json b/catalog/summaries/models/model_items/cb_prophet.json deleted file mode 100644 index e26e7e4809..0000000000 --- a/catalog/summaries/models/model_items/cb_prophet.json +++ /dev/null @@ -1,311 +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], - [-89.5373, 46.2339], - [-103.0293, 40.4619], - [-89.5857, 45.4937], - [-95.1921, 39.0404], - [-119.2622, 37.0334], - [-89.5864, 45.5089], - [-87.3933, 32.9505], - [-149.3705, 68.6611], - [-99.2413, 47.1282], - [-110.8355, 31.9107], - [-121.9519, 45.8205], - [-119.006, 37.0058], - [-110.5391, 44.9535], - [-102.4471, 39.7582], - [-119.2575, 37.0597], - [-110.5871, 44.9501], - [-96.6242, 34.4442], - [-147.504, 65.1532], - [-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], - [-105.5442, 40.035], - [-66.9868, 18.1135] - ] - }, - "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, UNDE, STER, TREE, UKFS, SOAP, STEI, TALL, TOOL, WOOD, SRER, WREF, TEAK, YELL, ARIK, BIGC, BLDE, BLUE, CARI, GUIL, HOPB, KING, LECO, LEWI, MART, MAYF, MCDI, MCRA, POSE, PRIN, REDB, SYCA, TECR, WALK, WLOU, COMO, CUPE\n\nVariables: Daily Chlorophyll_a, Daily Green_chromatic_coordinate, Daily Net_ecosystem_exchange, Daily latent_heat_flux, Daily Red_chromatic_coordinate, Daily Dissolved_oxygen, Daily Water_temperature", - "start_datetime": "2023-11-14", - "end_datetime": "2024-01-19", - "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 latent_heat_flux", - "Daily Red_chromatic_coordinate", - "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)" - }, - { - "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.”" - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "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 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" - } - ] - }, - "collection": "forecasts", - "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": null, - "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": null, - "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/forecasts/summaries/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/forecasts/summaries/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/forecasts/summaries/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/forecasts/summaries/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 Net_ecosystem_exchange", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/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/forecasts/summaries/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" - }, - "6": { - "type": "application/x-parquet", - "title": "Database Access for Daily latent_heat_flux", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/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/forecasts/summaries/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" - }, - "7": { - "type": "application/x-parquet", - "title": "Database Access for Daily Red_chromatic_coordinate", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/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/forecasts/summaries/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" - }, - "8": { - "type": "application/x-parquet", - "title": "Database Access for Daily Dissolved_oxygen", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/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/forecasts/summaries/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" - }, - "9": { - "type": "application/x-parquet", - "title": "Database Access for Daily Water_temperature", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/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/forecasts/summaries/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/summaries/models/model_items/climatology.json b/catalog/summaries/models/model_items/climatology.json deleted file mode 100644 index a78983c976..0000000000 --- a/catalog/summaries/models/model_items/climatology.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": "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], - [-147.504, 65.1532] - ] - }, - "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, CARI\n\nVariables: Daily Chlorophyll_a, Daily Green_chromatic_coordinate, Daily Net_ecosystem_exchange, 30min Net_ecosystem_exchange, Daily latent_heat_flux, 30min latent_heat_flux, Daily Red_chromatic_coordinate, Daily Dissolved_oxygen, 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, CARI\n\nVariables: Daily Chlorophyll_a, Daily Green_chromatic_coordinate, Daily Net_ecosystem_exchange, 30min Net_ecosystem_exchange, Daily latent_heat_flux, 30min latent_heat_flux, Daily Red_chromatic_coordinate, Daily Dissolved_oxygen, Daily Water_temperature"], - "start_datetime": "2023-11-14", - "end_datetime": "2024-01-22", - "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 Chlorophyll_a", - "Daily Green_chromatic_coordinate", - "Daily Net_ecosystem_exchange", - "30min Net_ecosystem_exchange", - "Daily latent_heat_flux", - "30min latent_heat_flux", - "Daily Red_chromatic_coordinate", - "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)" - }, - { - "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.”" - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "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 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" - } - ] - }, - "collection": "forecasts", - "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": ["https://github.com/eco4cast/neon4cast-ci/blob/main/baseline_models/models/aquatics_climatology.R", null], - "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": ["https://github.com/eco4cast/neon4cast-ci/blob/main/baseline_models/models/aquatics_climatology.R", null], - "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/forecasts/summaries/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/forecasts/summaries/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/forecasts/summaries/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/forecasts/summaries/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 Net_ecosystem_exchange", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/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/forecasts/summaries/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" - }, - "6": { - "type": "application/x-parquet", - "title": "Database Access for 30min Net_ecosystem_exchange", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/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/forecasts/summaries/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" - }, - "7": { - "type": "application/x-parquet", - "title": "Database Access for Daily latent_heat_flux", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/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/forecasts/summaries/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" - }, - "8": { - "type": "application/x-parquet", - "title": "Database Access for 30min latent_heat_flux", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/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/forecasts/summaries/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" - }, - "9": { - "type": "application/x-parquet", - "title": "Database Access for Daily Red_chromatic_coordinate", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/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/forecasts/summaries/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" - }, - "10": { - "type": "application/x-parquet", - "title": "Database Access for Daily Dissolved_oxygen", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/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/forecasts/summaries/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" - }, - "11": { - "type": "application/x-parquet", - "title": "Database Access for Daily Water_temperature", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/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/forecasts/summaries/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/summaries/models/model_items/fARIMA.json b/catalog/summaries/models/model_items/fARIMA.json deleted file mode 100644 index f1236dc927..0000000000 --- a/catalog/summaries/models/model_items/fARIMA.json +++ /dev/null @@ -1,224 +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": "2024-01-19", - "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)" - }, - { - "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.”" - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "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 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" - } - ] - }, - "collection": "forecasts", - "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": null, - "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": null, - "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/forecasts/summaries/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/forecasts/summaries/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/summaries/models/model_items/fARIMA_clim_ensemble.json b/catalog/summaries/models/model_items/fARIMA_clim_ensemble.json deleted file mode 100644 index b3d324f298..0000000000 --- a/catalog/summaries/models/model_items/fARIMA_clim_ensemble.json +++ /dev/null @@ -1,218 +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": [ - [-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], - [-77.9832, 39.0956], - [-121.9338, 45.7908], - [-87.4077, 32.9604], - [-122.1655, 44.2596], - [-83.5038, 35.6904], - [-102.4471, 39.7582], - [-82.0084, 29.676], - [-96.6242, 34.4442], - [-87.7982, 32.5415], - [-66.9868, 18.1135], - [-66.7987, 18.1741], - [-72.3295, 42.4719], - [-110.5871, 44.9501], - [-105.5442, 40.035], - [-119.2575, 37.0597], - [-89.4737, 46.2097], - [-84.4374, 31.1854], - [-96.6038, 39.1051], - [-88.1589, 31.8534], - [-119.0274, 36.9559] - ] - }, - "properties": { - "description": "\nmodel info: NA\n\nSites: MCDI, POSE, PRIN, REDB, SUGG, SYCA, WALK, WLOU, LEWI, MART, MAYF, MCRA, LECO, ARIK, BARC, BLUE, BLWA, CUPE, GUIL, HOPB, BLDE, COMO, BIGC, CRAM, FLNT, KING, TOMB, TECR\n\nVariables: Daily Water_temperature", - "start_datetime": "2023-11-10", - "end_datetime": "2024-01-15", - "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)" - }, - { - "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.”" - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "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 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" - } - ] - }, - "collection": "forecasts", - "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": null, - "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": null, - "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/forecasts/summaries/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/forecasts/summaries/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/summaries/models/model_items/fTSLM_lag.json b/catalog/summaries/models/model_items/fTSLM_lag.json deleted file mode 100644 index fd572e1b0a..0000000000 --- a/catalog/summaries/models/model_items/fTSLM_lag.json +++ /dev/null @@ -1,224 +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": [ - [-88.1589, 31.8534], - [-149.6106, 68.6307], - [-84.2793, 35.9574], - [-105.9154, 39.8914], - [-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] - ] - }, - "properties": { - "description": "\nmodel info: NA\n\nSites: TOMB, TOOK, WALK, WLOU, 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\n\nVariables: Daily Water_temperature", - "start_datetime": "2023-11-10", - "end_datetime": "2024-01-19", - "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)" - }, - { - "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.”" - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "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 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" - } - ] - }, - "collection": "forecasts", - "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": null, - "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": null, - "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/forecasts/summaries/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/forecasts/summaries/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/summaries/models/model_items/flareGLM.json b/catalog/summaries/models/model_items/flareGLM.json deleted file mode 100644 index 0612168d6a..0000000000 --- a/catalog/summaries/models/model_items/flareGLM.json +++ /dev/null @@ -1,197 +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": "2024-01-16", - "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)" - }, - { - "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.”" - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "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 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" - } - ] - }, - "collection": "forecasts", - "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": null, - "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": null, - "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/forecasts/summaries/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/forecasts/summaries/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/summaries/models/model_items/flareGLM_noDA.json b/catalog/summaries/models/model_items/flareGLM_noDA.json deleted file mode 100644 index ff4e2b8114..0000000000 --- a/catalog/summaries/models/model_items/flareGLM_noDA.json +++ /dev/null @@ -1,197 +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": [ - [-99.2531, 47.1298], - [-82.0177, 29.6878], - [-149.6106, 68.6307], - [-82.0084, 29.676], - [-89.4737, 46.2097], - [-89.7048, 45.9983], - [-99.1139, 47.1591] - ] - }, - "properties": { - "description": "\nmodel info: NA\n\nSites: PRPO, SUGG, TOOK, BARC, CRAM, LIRO, PRLA\n\nVariables: Daily Water_temperature", - "start_datetime": "2023-11-15", - "end_datetime": "2024-01-16", - "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)" - }, - { - "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.”" - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "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 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" - } - ] - }, - "collection": "forecasts", - "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": null, - "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": null, - "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/forecasts/summaries/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/forecasts/summaries/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/summaries/models/model_items/flareGOTM.json b/catalog/summaries/models/model_items/flareGOTM.json deleted file mode 100644 index 3bd889a4da..0000000000 --- a/catalog/summaries/models/model_items/flareGOTM.json +++ /dev/null @@ -1,196 +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": [ - [-99.2531, 47.1298], - [-82.0177, 29.6878], - [-82.0084, 29.676], - [-89.4737, 46.2097], - [-89.7048, 45.9983], - [-99.1139, 47.1591] - ] - }, - "properties": { - "description": "\nmodel info: NA\n\nSites: PRPO, SUGG, BARC, CRAM, LIRO, PRLA\n\nVariables: Daily Water_temperature", - "start_datetime": "2023-11-15", - "end_datetime": "2024-01-16", - "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)" - }, - { - "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.”" - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "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 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" - } - ] - }, - "collection": "forecasts", - "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": null, - "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": null, - "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/forecasts/summaries/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/forecasts/summaries/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/summaries/models/model_items/flareGOTM_noDA.json b/catalog/summaries/models/model_items/flareGOTM_noDA.json deleted file mode 100644 index 954d0a7712..0000000000 --- a/catalog/summaries/models/model_items/flareGOTM_noDA.json +++ /dev/null @@ -1,196 +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": [ - [-99.2531, 47.1298], - [-82.0177, 29.6878], - [-82.0084, 29.676], - [-89.4737, 46.2097], - [-89.7048, 45.9983], - [-99.1139, 47.1591] - ] - }, - "properties": { - "description": "\nmodel info: NA\n\nSites: PRPO, SUGG, BARC, CRAM, LIRO, PRLA\n\nVariables: Daily Water_temperature", - "start_datetime": "2023-11-15", - "end_datetime": "2024-01-16", - "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)" - }, - { - "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.”" - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "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 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" - } - ] - }, - "collection": "forecasts", - "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": null, - "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": null, - "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/forecasts/summaries/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/forecasts/summaries/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/summaries/models/model_items/flareSimstrat.json b/catalog/summaries/models/model_items/flareSimstrat.json deleted file mode 100644 index b8df68cd5d..0000000000 --- a/catalog/summaries/models/model_items/flareSimstrat.json +++ /dev/null @@ -1,196 +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": [ - [-99.2531, 47.1298], - [-82.0177, 29.6878], - [-89.4737, 46.2097], - [-89.7048, 45.9983], - [-99.1139, 47.1591], - [-82.0084, 29.676] - ] - }, - "properties": { - "description": "\nmodel info: NA\n\nSites: PRPO, SUGG, CRAM, LIRO, PRLA, BARC\n\nVariables: Daily Water_temperature", - "start_datetime": "2023-11-15", - "end_datetime": "2024-01-16", - "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)" - }, - { - "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.”" - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "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 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" - } - ] - }, - "collection": "forecasts", - "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": null, - "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": null, - "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/forecasts/summaries/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/forecasts/summaries/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/summaries/models/model_items/flareSimstrat_noDA.json b/catalog/summaries/models/model_items/flareSimstrat_noDA.json deleted file mode 100644 index 66a151a06e..0000000000 --- a/catalog/summaries/models/model_items/flareSimstrat_noDA.json +++ /dev/null @@ -1,195 +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": "2024-01-16", - "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)" - }, - { - "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.”" - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "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 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" - } - ] - }, - "collection": "forecasts", - "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": null, - "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": null, - "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/forecasts/summaries/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/forecasts/summaries/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/summaries/models/model_items/flare_ler.json b/catalog/summaries/models/model_items/flare_ler.json deleted file mode 100644 index 3aee1887eb..0000000000 --- a/catalog/summaries/models/model_items/flare_ler.json +++ /dev/null @@ -1,196 +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": "2024-01-15", - "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)" - }, - { - "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.”" - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "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 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" - } - ] - }, - "collection": "forecasts", - "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": null, - "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": null, - "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/forecasts/summaries/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/forecasts/summaries/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/summaries/models/model_items/flare_ler_baselines.json b/catalog/summaries/models/model_items/flare_ler_baselines.json deleted file mode 100644 index 7e50f07f7e..0000000000 --- a/catalog/summaries/models/model_items/flare_ler_baselines.json +++ /dev/null @@ -1,192 +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": "2024-01-15", - "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)" - }, - { - "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.”" - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "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 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" - } - ] - }, - "collection": "forecasts", - "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": null, - "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": null, - "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/forecasts/summaries/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/forecasts/summaries/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/summaries/models/model_items/lasso.json b/catalog/summaries/models/model_items/lasso.json deleted file mode 100644 index a7874ff4ea..0000000000 --- a/catalog/summaries/models/model_items/lasso.json +++ /dev/null @@ -1,244 +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": [ - [-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], - [-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] - ] - }, - "properties": { - "description": [], - "start_datetime": "2023-11-14", - "end_datetime": "2024-01-19", - "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" - ], - "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)" - }, - { - "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.”" - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "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 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" - } - ] - }, - "collection": "forecasts", - "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": [], - "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": [], - "description": "The link to the model code provided by the model submission team" - }, - "3": { - "type": "application/x-parquet", - "title": "Database Access for Daily Net_ecosystem_exchange", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/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/forecasts/summaries/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" - }, - "4": { - "type": "application/x-parquet", - "title": "Database Access for Daily latent_heat_flux", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/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/forecasts/summaries/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" - } - } -} diff --git a/catalog/summaries/models/model_items/mean.json b/catalog/summaries/models/model_items/mean.json deleted file mode 100644 index ebd982ed14..0000000000 --- a/catalog/summaries/models/model_items/mean.json +++ /dev/null @@ -1,244 +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": [ - [-149.2133, 63.8758], - [-84.4686, 31.1948], - [-104.7456, 40.8155], - [-99.1066, 47.1617], - [-112.4524, 40.1776], - [-84.2826, 35.9641], - [-89.5864, 45.5089], - [-103.0293, 40.4619], - [-156.6194, 71.2824], - [-71.2874, 44.0639], - [-96.5631, 39.1008], - [-67.0769, 18.0213], - [-88.1612, 31.8539], - [-147.5026, 65.154], - [-97.57, 33.4012], - [-106.8425, 32.5907], - [-96.6129, 39.1104], - [-149.3705, 68.6611], - [-89.5857, 45.4937], - [-95.1921, 39.0404], - [-78.0418, 39.0337], - [-109.3883, 38.2483], - [-105.5824, 40.0543], - [-81.9934, 29.6893], - [-119.2622, 37.0334], - [-110.8355, 31.9107], - [-80.5248, 37.3783], - [-100.9154, 46.7697], - [-122.3303, 45.7624], - [-81.4362, 28.1251], - [-83.5019, 35.689], - [-66.8687, 17.9696], - [-121.9519, 45.8205], - [-145.7514, 63.8811], - [-76.56, 38.8901], - [-119.7323, 37.1088], - [-87.8039, 32.5417], - [-78.1395, 38.8929], - [-110.5391, 44.9535], - [-89.5373, 46.2339], - [-99.2413, 47.1282], - [-155.3173, 19.5531], - [-105.546, 40.2759], - [-87.3933, 32.9505], - [-119.006, 37.0058], - [-72.1727, 42.5369], - [-99.0588, 35.4106] - ] - }, - "properties": { - "description": [], - "start_datetime": "2023-11-20", - "end_datetime": "2024-12-09", - "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", - "Weekly beetle_community_richness", - "Weekly beetle_community_abundance" - ], - "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)" - }, - { - "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.”" - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "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 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" - } - ] - }, - "collection": "forecasts", - "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": [], - "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": [], - "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_richness", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/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/forecasts/summaries/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" - }, - "4": { - "type": "application/x-parquet", - "title": "Database Access for Weekly beetle_community_abundance", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/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/forecasts/summaries/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" - } - } -} diff --git a/catalog/summaries/models/model_items/null.json b/catalog/summaries/models/model_items/null.json deleted file mode 100644 index b621f9fc09..0000000000 --- a/catalog/summaries/models/model_items/null.json +++ /dev/null @@ -1,231 +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": [ - [-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], - [-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] - ] - }, - "properties": { - "description": [], - "start_datetime": "2023-10-10", - "end_datetime": "2024-10-08", - "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", - "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)" - }, - { - "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.”" - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "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 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" - } - ] - }, - "collection": "forecasts", - "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": [], - "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": [], - "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/forecasts/summaries/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/forecasts/summaries/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/forecasts/summaries/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/forecasts/summaries/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/summaries/models/model_items/persistenceRW.json b/catalog/summaries/models/model_items/persistenceRW.json deleted file mode 100644 index 2cdb782767..0000000000 --- a/catalog/summaries/models/model_items/persistenceRW.json +++ /dev/null @@ -1,306 +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": [ - [-149.6106, 68.6307], - [-82.0084, 29.676], - [-87.7982, 32.5415], - [-89.4737, 46.2097], - [-84.4374, 31.1854], - [-89.7048, 45.9983], - [-119.7323, 37.1088], - [-119.2622, 37.0334], - [-110.8355, 31.9107], - [-89.5864, 45.5089], - [-103.0293, 40.4619], - [-87.3933, 32.9505], - [-67.0769, 18.0213], - [-88.1612, 31.8539], - [-80.5248, 37.3783], - [-109.3883, 38.2483], - [-105.5824, 40.0543], - [-100.9154, 46.7697], - [-149.2133, 63.8758], - [-84.4686, 31.1948], - [-106.8425, 32.5907], - [-96.6129, 39.1104], - [-96.5631, 39.1008], - [-81.9934, 29.6893], - [-155.3173, 19.5531], - [-105.546, 40.2759], - [-78.1395, 38.8929], - [-76.56, 38.8901], - [-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], - [-99.0588, 35.4106], - [-112.4524, 40.1776], - [-84.2826, 35.9641], - [-89.5373, 46.2339], - [-99.2413, 47.1282], - [-121.9519, 45.8205], - [-110.5391, 44.9535], - [-119.006, 37.0058], - [-149.3705, 68.6611], - [-89.5857, 45.4937], - [-95.1921, 39.0404], - [-81.4362, 28.1251], - [-83.5019, 35.689], - [-66.8687, 17.9696], - [-72.1727, 42.5369], - [-119.0274, 36.9559], - [-88.1589, 31.8534], - [-84.2793, 35.9574], - [-105.9154, 39.8914], - [-111.5081, 33.751], - [-87.4077, 32.9604], - [-96.443, 38.9459], - [-122.1655, 44.2596], - [-149.143, 68.6698], - [-78.1473, 38.8943], - [-97.7823, 33.3785], - [-83.5038, 35.6904], - [-77.9832, 39.0956], - [-121.9338, 45.7908], - [-99.1139, 47.1591], - [-99.2531, 47.1298], - [-111.7979, 40.7839], - [-82.0177, 29.6878], - [-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], - [-96.6038, 39.1051], - [-66.7987, 18.1741], - [-72.3295, 42.4719] - ] - }, - "properties": { - "description": ["\nmodel info: Random walk from the fable package with ensembles used to represent uncertainty\n\nSites: TOOK, BARC, BLWA, CRAM, FLNT, LIRO, SJER, SOAP, SRER, STEI, STER, TALL, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, HEAL, JERC, JORN, KONA, KONZ, OSBS, PUUM, RMNP, SCBI, SERC, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, OAES, ONAQ, ORNL, UNDE, WOOD, WREF, YELL, TEAK, TOOL, TREE, UKFS, DSNY, GRSM, GUAN, HARV, TECR, TOMB, WALK, WLOU, SYCA, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, LECO, LEWI, MART, PRLA, PRPO, REDB, SUGG, ARIK, BIGC, BLDE, BLUE, CARI, COMO, CUPE, KING, GUIL, HOPB\n\nVariables: Daily Chlorophyll_a, Daily Green_chromatic_coordinate, Daily Net_ecosystem_exchange, Daily Red_chromatic_coordinate, Daily Dissolved_oxygen, Daily Water_temperature", "\nmodel info: NA\n\nSites: TOOK, BARC, BLWA, CRAM, FLNT, LIRO, SJER, SOAP, SRER, STEI, STER, TALL, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, HEAL, JERC, JORN, KONA, KONZ, OSBS, PUUM, RMNP, SCBI, SERC, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, OAES, ONAQ, ORNL, UNDE, WOOD, WREF, YELL, TEAK, TOOL, TREE, UKFS, DSNY, GRSM, GUAN, HARV, TECR, TOMB, WALK, WLOU, SYCA, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, LECO, LEWI, MART, PRLA, PRPO, REDB, SUGG, ARIK, BIGC, BLDE, BLUE, CARI, COMO, CUPE, KING, GUIL, HOPB\n\nVariables: Daily Chlorophyll_a, Daily Green_chromatic_coordinate, Daily Net_ecosystem_exchange, Daily Red_chromatic_coordinate, Daily Dissolved_oxygen, Daily Water_temperature"], - "start_datetime": "2023-11-15", - "end_datetime": "2024-01-20", - "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 Chlorophyll_a", - "Daily Green_chromatic_coordinate", - "Daily Net_ecosystem_exchange", - "Daily Red_chromatic_coordinate", - "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)" - }, - { - "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.”" - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "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 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" - } - ] - }, - "collection": "forecasts", - "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": ["https://github.com/eco4cast/neon4cast-ci/blob/main/baseline_models/models/aquatics_persistenceRW.R", null], - "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": ["https://github.com/eco4cast/neon4cast-ci/blob/main/baseline_models/models/aquatics_persistenceRW.R", null], - "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/forecasts/summaries/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/forecasts/summaries/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/forecasts/summaries/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/forecasts/summaries/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/forecasts/summaries/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/forecasts/summaries/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 Red_chromatic_coordinate", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/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/forecasts/summaries/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" - }, - "7": { - "type": "application/x-parquet", - "title": "Database Access for Daily Dissolved_oxygen", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/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/forecasts/summaries/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" - }, - "8": { - "type": "application/x-parquet", - "title": "Database Access for Daily Water_temperature", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/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/forecasts/summaries/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/summaries/models/model_items/procBlanchardMonod.json b/catalog/summaries/models/model_items/procBlanchardMonod.json deleted file mode 100644 index 239e53fb01..0000000000 --- a/catalog/summaries/models/model_items/procBlanchardMonod.json +++ /dev/null @@ -1,197 +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": "2024-01-15", - "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" - ], - "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)" - }, - { - "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.”" - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "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 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" - } - ] - }, - "collection": "forecasts", - "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": null, - "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": null, - "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/forecasts/summaries/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/forecasts/summaries/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/summaries/models/model_items/procBlanchardSteele.json b/catalog/summaries/models/model_items/procBlanchardSteele.json deleted file mode 100644 index 3b9587ea91..0000000000 --- a/catalog/summaries/models/model_items/procBlanchardSteele.json +++ /dev/null @@ -1,197 +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": "2024-01-15", - "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" - ], - "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)" - }, - { - "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.”" - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "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 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" - } - ] - }, - "collection": "forecasts", - "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": [], - "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": [], - "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/forecasts/summaries/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/forecasts/summaries/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/summaries/models/model_items/procCTMIMonod.json b/catalog/summaries/models/model_items/procCTMIMonod.json deleted file mode 100644 index b2574d9dbd..0000000000 --- a/catalog/summaries/models/model_items/procCTMIMonod.json +++ /dev/null @@ -1,197 +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": "2024-01-15", - "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" - ], - "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)" - }, - { - "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.”" - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "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 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" - } - ] - }, - "collection": "forecasts", - "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": null, - "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": null, - "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/forecasts/summaries/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/forecasts/summaries/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/summaries/models/model_items/procCTMISteele.json b/catalog/summaries/models/model_items/procCTMISteele.json deleted file mode 100644 index 9f3db2ba9f..0000000000 --- a/catalog/summaries/models/model_items/procCTMISteele.json +++ /dev/null @@ -1,197 +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": "2024-01-15", - "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" - ], - "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)" - }, - { - "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.”" - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "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 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" - } - ] - }, - "collection": "forecasts", - "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": [], - "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": [], - "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/forecasts/summaries/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/forecasts/summaries/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/summaries/models/model_items/procEppleyNorbergMonod.json b/catalog/summaries/models/model_items/procEppleyNorbergMonod.json deleted file mode 100644 index 7720c4a437..0000000000 --- a/catalog/summaries/models/model_items/procEppleyNorbergMonod.json +++ /dev/null @@ -1,197 +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": "2024-01-15", - "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" - ], - "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)" - }, - { - "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.”" - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "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 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" - } - ] - }, - "collection": "forecasts", - "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": null, - "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": null, - "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/forecasts/summaries/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/forecasts/summaries/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/summaries/models/model_items/procEppleyNorbergSteele.json b/catalog/summaries/models/model_items/procEppleyNorbergSteele.json deleted file mode 100644 index 36e0aa7e47..0000000000 --- a/catalog/summaries/models/model_items/procEppleyNorbergSteele.json +++ /dev/null @@ -1,197 +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": "2024-01-15", - "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" - ], - "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)" - }, - { - "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.”" - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "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 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" - } - ] - }, - "collection": "forecasts", - "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": null, - "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": null, - "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/forecasts/summaries/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/forecasts/summaries/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/summaries/models/model_items/procHinshelwoodMonod.json b/catalog/summaries/models/model_items/procHinshelwoodMonod.json deleted file mode 100644 index 3ad54ebe2b..0000000000 --- a/catalog/summaries/models/model_items/procHinshelwoodMonod.json +++ /dev/null @@ -1,197 +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": "2024-01-15", - "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" - ], - "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)" - }, - { - "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.”" - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "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 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" - } - ] - }, - "collection": "forecasts", - "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": null, - "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": null, - "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/forecasts/summaries/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/forecasts/summaries/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/summaries/models/model_items/procHinshelwoodSteele.json b/catalog/summaries/models/model_items/procHinshelwoodSteele.json deleted file mode 100644 index 1b5fb4ba2b..0000000000 --- a/catalog/summaries/models/model_items/procHinshelwoodSteele.json +++ /dev/null @@ -1,197 +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": "2024-01-15", - "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" - ], - "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)" - }, - { - "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.”" - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "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 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" - } - ] - }, - "collection": "forecasts", - "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": null, - "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": null, - "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/forecasts/summaries/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/forecasts/summaries/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/summaries/models/model_items/prophet_clim_ensemble.json b/catalog/summaries/models/model_items/prophet_clim_ensemble.json deleted file mode 100644 index 0f37d3d3ff..0000000000 --- a/catalog/summaries/models/model_items/prophet_clim_ensemble.json +++ /dev/null @@ -1,232 +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": [ - [-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], - [-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] - ] - }, - "properties": { - "description": [], - "start_datetime": "2023-11-13", - "end_datetime": "2024-01-14", - "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)" - }, - { - "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.”" - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "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 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" - } - ] - }, - "collection": "forecasts", - "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": [], - "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": [], - "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/forecasts/summaries/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/forecasts/summaries/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/summaries/models/model_items/randfor.json b/catalog/summaries/models/model_items/randfor.json deleted file mode 100644 index 594a96f67f..0000000000 --- a/catalog/summaries/models/model_items/randfor.json +++ /dev/null @@ -1,237 +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": [ - [-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], - [-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] - ] - }, - "properties": { - "description": [], - "start_datetime": "2023-11-14", - "end_datetime": "2024-01-19", - "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)" - }, - { - "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.”" - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "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 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" - } - ] - }, - "collection": "forecasts", - "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": [], - "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": [], - "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/forecasts/summaries/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/forecasts/summaries/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/summaries/models/model_items/tg_arima.json b/catalog/summaries/models/model_items/tg_arima.json deleted file mode 100644 index 277b58fc2b..0000000000 --- a/catalog/summaries/models/model_items/tg_arima.json +++ /dev/null @@ -1,334 +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], - [-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 Net_ecosystem_exchange, Daily latent_heat_flux, Daily Red_chromatic_coordinate, Daily Dissolved_oxygen, Daily Water_temperature, Weekly beetle_community_richness, Weekly beetle_community_abundance, Weekly Amblyomma_americanum_population", - "start_datetime": "2023-01-01", - "end_datetime": "2024-12-09", - "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 Net_ecosystem_exchange", - "Daily latent_heat_flux", - "Daily Red_chromatic_coordinate", - "Daily Dissolved_oxygen", - "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)" - }, - { - "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.”" - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "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 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" - } - ] - }, - "collection": "forecasts", - "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": null, - "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": null, - "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/forecasts/summaries/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/forecasts/summaries/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/forecasts/summaries/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/forecasts/summaries/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 Net_ecosystem_exchange", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/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/forecasts/summaries/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" - }, - "6": { - "type": "application/x-parquet", - "title": "Database Access for Daily latent_heat_flux", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/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/forecasts/summaries/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" - }, - "7": { - "type": "application/x-parquet", - "title": "Database Access for Daily Red_chromatic_coordinate", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/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/forecasts/summaries/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" - }, - "8": { - "type": "application/x-parquet", - "title": "Database Access for Daily Dissolved_oxygen", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/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/forecasts/summaries/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" - }, - "9": { - "type": "application/x-parquet", - "title": "Database Access for Daily Water_temperature", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/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/forecasts/summaries/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/forecasts/summaries/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/forecasts/summaries/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/forecasts/summaries/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/forecasts/summaries/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/forecasts/summaries/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/forecasts/summaries/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/summaries/models/model_items/tg_auto_adam.json b/catalog/summaries/models/model_items/tg_auto_adam.json deleted file mode 100644 index 8779503df1..0000000000 --- a/catalog/summaries/models/model_items/tg_auto_adam.json +++ /dev/null @@ -1,334 +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": [ - [-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], - [-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": "2023-01-01", - "end_datetime": "2023-12-26", - "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 Green_chromatic_coordinate", - "Daily Net_ecosystem_exchange", - "Daily latent_heat_flux", - "Weekly beetle_community_abundance", - "Daily Dissolved_oxygen", - "Daily Red_chromatic_coordinate", - "Weekly beetle_community_richness", - "Daily Chlorophyll_a", - "Weekly Amblyomma_americanum_population", - "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)" - }, - { - "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.”" - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "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 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" - } - ] - }, - "collection": "forecasts", - "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": [], - "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": [], - "description": "The link to the model code provided by the model submission team" - }, - "3": { - "type": "application/x-parquet", - "title": "Database Access for Daily Green_chromatic_coordinate", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/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/forecasts/summaries/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" - }, - "4": { - "type": "application/x-parquet", - "title": "Database Access for Daily Net_ecosystem_exchange", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/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/forecasts/summaries/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" - }, - "5": { - "type": "application/x-parquet", - "title": "Database Access for Daily latent_heat_flux", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/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/forecasts/summaries/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 Weekly beetle_community_abundance", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/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/forecasts/summaries/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" - }, - "7": { - "type": "application/x-parquet", - "title": "Database Access for Daily Dissolved_oxygen", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/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/forecasts/summaries/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/forecasts/summaries/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/forecasts/summaries/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 Weekly beetle_community_richness", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/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/forecasts/summaries/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" - }, - "10": { - "type": "application/x-parquet", - "title": "Database Access for Daily Chlorophyll_a", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/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/forecasts/summaries/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" - }, - "11": { - "type": "application/x-parquet", - "title": "Database Access for Weekly Amblyomma_americanum_population", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/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/forecasts/summaries/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" - }, - "12": { - "type": "application/x-parquet", - "title": "Database Access for Daily Water_temperature", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/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/forecasts/summaries/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" - } - } -} diff --git a/catalog/summaries/models/model_items/tg_bag_mlp.json b/catalog/summaries/models/model_items/tg_bag_mlp.json deleted file mode 100644 index 58a7a94c50..0000000000 --- a/catalog/summaries/models/model_items/tg_bag_mlp.json +++ /dev/null @@ -1,313 +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], - [-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], - [-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], - [-84.2793, 35.9574], - [-105.9154, 39.8914], - [-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] - ] - }, - "properties": { - "description": [], - "start_datetime": "2023-11-14", - "end_datetime": "2024-01-19", - "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 latent_heat_flux", - "Daily Red_chromatic_coordinate", - "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)" - }, - { - "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.”" - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "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 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" - } - ] - }, - "collection": "forecasts", - "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": [], - "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": [], - "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/forecasts/summaries/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/forecasts/summaries/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/forecasts/summaries/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/forecasts/summaries/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 Net_ecosystem_exchange", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/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/forecasts/summaries/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" - }, - "6": { - "type": "application/x-parquet", - "title": "Database Access for Daily latent_heat_flux", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/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/forecasts/summaries/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" - }, - "7": { - "type": "application/x-parquet", - "title": "Database Access for Daily Red_chromatic_coordinate", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/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/forecasts/summaries/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" - }, - "8": { - "type": "application/x-parquet", - "title": "Database Access for Daily Water_temperature", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/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/forecasts/summaries/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" - }, - "9": { - "type": "application/x-parquet", - "title": "Database Access for Daily Dissolved_oxygen", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/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/forecasts/summaries/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" - } - } -} diff --git a/catalog/summaries/models/model_items/tg_ets.json b/catalog/summaries/models/model_items/tg_ets.json deleted file mode 100644 index 9bd16aab3a..0000000000 --- a/catalog/summaries/models/model_items/tg_ets.json +++ /dev/null @@ -1,334 +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], - [-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 Net_ecosystem_exchange, Daily latent_heat_flux, 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": "2024-12-09", - "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 Net_ecosystem_exchange", - "Daily latent_heat_flux", - "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)" - }, - { - "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.”" - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "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 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" - } - ] - }, - "collection": "forecasts", - "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": null, - "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": null, - "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/forecasts/summaries/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/forecasts/summaries/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/forecasts/summaries/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/forecasts/summaries/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 Net_ecosystem_exchange", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/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/forecasts/summaries/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" - }, - "6": { - "type": "application/x-parquet", - "title": "Database Access for Daily latent_heat_flux", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/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/forecasts/summaries/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" - }, - "7": { - "type": "application/x-parquet", - "title": "Database Access for Daily Dissolved_oxygen", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/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/forecasts/summaries/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/forecasts/summaries/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/forecasts/summaries/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/forecasts/summaries/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/forecasts/summaries/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/forecasts/summaries/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/forecasts/summaries/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/forecasts/summaries/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/forecasts/summaries/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/forecasts/summaries/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/forecasts/summaries/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/summaries/models/model_items/tg_humidity_lm.json b/catalog/summaries/models/model_items/tg_humidity_lm.json deleted file mode 100644 index c53ae1fa36..0000000000 --- a/catalog/summaries/models/model_items/tg_humidity_lm.json +++ /dev/null @@ -1,334 +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], - [-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], - [-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], - [-111.5081, 33.751], - [-119.0274, 36.9559], - [-84.2793, 35.9574], - [-105.9154, 39.8914], - [-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] - ] - }, - "properties": { - "description": "\nmodel info: NA\n\nSites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK, 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, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SYCA, TECR, WALK, WLOU, ARIK, BIGC, BLDE, BLUE, CARI, COMO, CUPE, GUIL, HOPB, KING, LECO, LEWI, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, REDB\n\nVariables: Daily Chlorophyll_a, Daily Green_chromatic_coordinate, Daily Net_ecosystem_exchange, Daily Dissolved_oxygen, Daily Water_temperature, Weekly beetle_community_abundance, Daily Red_chromatic_coordinate, Daily latent_heat_flux, Weekly Amblyomma_americanum_population, Weekly beetle_community_richness", - "start_datetime": "2023-11-14", - "end_datetime": "2024-01-20", - "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 Chlorophyll_a", - "Daily Green_chromatic_coordinate", - "Daily Net_ecosystem_exchange", - "Daily Dissolved_oxygen", - "Daily Water_temperature", - "Weekly beetle_community_abundance", - "Daily Red_chromatic_coordinate", - "Daily latent_heat_flux", - "Weekly Amblyomma_americanum_population", - "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)" - }, - { - "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.”" - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "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 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" - } - ] - }, - "collection": "forecasts", - "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": null, - "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": null, - "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/forecasts/summaries/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/forecasts/summaries/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/forecasts/summaries/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/forecasts/summaries/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 Net_ecosystem_exchange", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/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/forecasts/summaries/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" - }, - "6": { - "type": "application/x-parquet", - "title": "Database Access for Daily Dissolved_oxygen", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/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/forecasts/summaries/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 Water_temperature", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/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/forecasts/summaries/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" - }, - "8": { - "type": "application/x-parquet", - "title": "Database Access for Weekly beetle_community_abundance", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=abundance/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/forecasts/summaries/parquet/duration=P1W/variable=abundance/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 Red_chromatic_coordinate", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/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/forecasts/summaries/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" - }, - "10": { - "type": "application/x-parquet", - "title": "Database Access for Daily latent_heat_flux", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/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/forecasts/summaries/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" - }, - "11": { - "type": "application/x-parquet", - "title": "Database Access for Weekly Amblyomma_americanum_population", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=amblyomma_americanum/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/forecasts/summaries/parquet/duration=P1W/variable=amblyomma_americanum/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" - }, - "12": { - "type": "application/x-parquet", - "title": "Database Access for Weekly beetle_community_richness", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=richness/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/forecasts/summaries/parquet/duration=P1W/variable=richness/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/summaries/models/model_items/tg_humidity_lm_all_sites.json b/catalog/summaries/models/model_items/tg_humidity_lm_all_sites.json deleted file mode 100644 index 1c3bc6ba83..0000000000 --- a/catalog/summaries/models/model_items/tg_humidity_lm_all_sites.json +++ /dev/null @@ -1,334 +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": [ - [-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], - [-88.1589, 31.8534], - [-149.6106, 68.6307], - [-84.2793, 35.9574], - [-105.9154, 39.8914], - [-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] - ] - }, - "properties": { - "description": "\nmodel info: NA\n\nSites: 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, TOMB, TOOK, WALK, WLOU, 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\n\nVariables: Daily Green_chromatic_coordinate, Daily latent_heat_flux, Daily Net_ecosystem_exchange, Daily Dissolved_oxygen, Weekly Amblyomma_americanum_population, Daily Water_temperature, Daily Red_chromatic_coordinate, Daily Chlorophyll_a, Weekly beetle_community_richness, Weekly beetle_community_abundance", - "start_datetime": "2023-11-14", - "end_datetime": "2024-01-20", - "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 Green_chromatic_coordinate", - "Daily latent_heat_flux", - "Daily Net_ecosystem_exchange", - "Daily Dissolved_oxygen", - "Weekly Amblyomma_americanum_population", - "Daily Water_temperature", - "Daily Red_chromatic_coordinate", - "Daily Chlorophyll_a", - "Weekly beetle_community_richness", - "Weekly beetle_community_abundance" - ], - "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)" - }, - { - "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.”" - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "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 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" - } - ] - }, - "collection": "forecasts", - "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": null, - "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": null, - "description": "The link to the model code provided by the model submission team" - }, - "3": { - "type": "application/x-parquet", - "title": "Database Access for Daily Green_chromatic_coordinate", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/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/forecasts/summaries/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" - }, - "4": { - "type": "application/x-parquet", - "title": "Database Access for Daily latent_heat_flux", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/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/forecasts/summaries/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" - }, - "5": { - "type": "application/x-parquet", - "title": "Database Access for Daily Net_ecosystem_exchange", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/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/forecasts/summaries/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" - }, - "6": { - "type": "application/x-parquet", - "title": "Database Access for Daily Dissolved_oxygen", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/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/forecasts/summaries/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" - }, - "7": { - "type": "application/x-parquet", - "title": "Database Access for Weekly Amblyomma_americanum_population", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=amblyomma_americanum/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/forecasts/summaries/parquet/duration=P1W/variable=amblyomma_americanum/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 Water_temperature", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/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/forecasts/summaries/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" - }, - "9": { - "type": "application/x-parquet", - "title": "Database Access for Daily Red_chromatic_coordinate", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/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/forecasts/summaries/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" - }, - "10": { - "type": "application/x-parquet", - "title": "Database Access for Daily Chlorophyll_a", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/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/forecasts/summaries/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" - }, - "11": { - "type": "application/x-parquet", - "title": "Database Access for Weekly beetle_community_richness", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=richness/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/forecasts/summaries/parquet/duration=P1W/variable=richness/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" - }, - "12": { - "type": "application/x-parquet", - "title": "Database Access for Weekly beetle_community_abundance", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=abundance/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/forecasts/summaries/parquet/duration=P1W/variable=abundance/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/summaries/models/model_items/tg_lasso.json b/catalog/summaries/models/model_items/tg_lasso.json deleted file mode 100644 index d8853f5fca..0000000000 --- a/catalog/summaries/models/model_items/tg_lasso.json +++ /dev/null @@ -1,320 +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, Weekly beetle_community_abundance, Daily Water_temperature, Daily Red_chromatic_coordinate, Weekly beetle_community_richness, Weekly Amblyomma_americanum_population", - "start_datetime": "2023-11-14", - "end_datetime": "2024-01-20", - "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 Chlorophyll_a", - "Daily Green_chromatic_coordinate", - "Daily Dissolved_oxygen", - "Weekly beetle_community_abundance", - "Daily Water_temperature", - "Daily Red_chromatic_coordinate", - "Weekly beetle_community_richness", - "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)" - }, - { - "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.”" - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "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 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" - } - ] - }, - "collection": "forecasts", - "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": null, - "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": null, - "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/forecasts/summaries/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/forecasts/summaries/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/forecasts/summaries/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/forecasts/summaries/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/forecasts/summaries/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/forecasts/summaries/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 Weekly beetle_community_abundance", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=abundance/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/forecasts/summaries/parquet/duration=P1W/variable=abundance/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/forecasts/summaries/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/forecasts/summaries/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" - }, - "8": { - "type": "application/x-parquet", - "title": "Database Access for Daily Red_chromatic_coordinate", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/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/forecasts/summaries/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" - }, - "9": { - "type": "application/x-parquet", - "title": "Database Access for Weekly beetle_community_richness", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=richness/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/forecasts/summaries/parquet/duration=P1W/variable=richness/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" - }, - "10": { - "type": "application/x-parquet", - "title": "Database Access for Weekly Amblyomma_americanum_population", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=amblyomma_americanum/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/forecasts/summaries/parquet/duration=P1W/variable=amblyomma_americanum/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/summaries/models/model_items/tg_lasso_all_sites.json b/catalog/summaries/models/model_items/tg_lasso_all_sites.json deleted file mode 100644 index a9ad7ef134..0000000000 --- a/catalog/summaries/models/model_items/tg_lasso_all_sites.json +++ /dev/null @@ -1,320 +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], - [-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], - [-110.5871, 44.9501], - [-96.6242, 34.4442], - [-147.504, 65.1532], - [-105.5442, 40.035] - ] - }, - "properties": { - "description": [], - "start_datetime": "2023-11-14", - "end_datetime": "2024-01-19", - "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 Water_temperature", - "Daily Red_chromatic_coordinate", - "Weekly beetle_community_abundance", - "Weekly Amblyomma_americanum_population", - "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)" - }, - { - "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.”" - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "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 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" - } - ] - }, - "collection": "forecasts", - "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": [], - "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": [], - "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/forecasts/summaries/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/forecasts/summaries/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/forecasts/summaries/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/forecasts/summaries/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/forecasts/summaries/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/forecasts/summaries/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 Water_temperature", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/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/forecasts/summaries/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" - }, - "7": { - "type": "application/x-parquet", - "title": "Database Access for Daily Red_chromatic_coordinate", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/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/forecasts/summaries/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" - }, - "8": { - "type": "application/x-parquet", - "title": "Database Access for Weekly beetle_community_abundance", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=abundance/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/forecasts/summaries/parquet/duration=P1W/variable=abundance/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" - }, - "9": { - "type": "application/x-parquet", - "title": "Database Access for Weekly Amblyomma_americanum_population", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=amblyomma_americanum/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/forecasts/summaries/parquet/duration=P1W/variable=amblyomma_americanum/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" - }, - "10": { - "type": "application/x-parquet", - "title": "Database Access for Weekly beetle_community_richness", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=richness/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/forecasts/summaries/parquet/duration=P1W/variable=richness/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/summaries/models/model_items/tg_precip_lm.json b/catalog/summaries/models/model_items/tg_precip_lm.json deleted file mode 100644 index c822847dab..0000000000 --- a/catalog/summaries/models/model_items/tg_precip_lm.json +++ /dev/null @@ -1,334 +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], - [-111.5081, 33.751], - [-119.0274, 36.9559], - [-84.2793, 35.9574], - [-105.9154, 39.8914], - [-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] - ] - }, - "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, SYCA, TECR, WALK, WLOU, ARIK, BIGC, BLDE, BLUE, CARI, COMO, CUPE, GUIL, HOPB, KING, LECO, LEWI, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, REDB\n\nVariables: Daily Chlorophyll_a, Daily Green_chromatic_coordinate, Daily Net_ecosystem_exchange, Daily latent_heat_flux, Weekly beetle_community_abundance, Weekly Amblyomma_americanum_population, Daily Dissolved_oxygen, Daily Water_temperature, Daily Red_chromatic_coordinate, Weekly beetle_community_richness", - "start_datetime": "2023-11-14", - "end_datetime": "2024-01-20", - "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 Chlorophyll_a", - "Daily Green_chromatic_coordinate", - "Daily Net_ecosystem_exchange", - "Daily latent_heat_flux", - "Weekly beetle_community_abundance", - "Weekly Amblyomma_americanum_population", - "Daily Dissolved_oxygen", - "Daily Water_temperature", - "Daily Red_chromatic_coordinate", - "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)" - }, - { - "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.”" - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "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 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" - } - ] - }, - "collection": "forecasts", - "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": null, - "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": null, - "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/forecasts/summaries/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/forecasts/summaries/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/forecasts/summaries/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/forecasts/summaries/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 Net_ecosystem_exchange", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/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/forecasts/summaries/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" - }, - "6": { - "type": "application/x-parquet", - "title": "Database Access for Daily latent_heat_flux", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/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/forecasts/summaries/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" - }, - "7": { - "type": "application/x-parquet", - "title": "Database Access for Weekly beetle_community_abundance", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=abundance/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/forecasts/summaries/parquet/duration=P1W/variable=abundance/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 Weekly Amblyomma_americanum_population", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=amblyomma_americanum/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/forecasts/summaries/parquet/duration=P1W/variable=amblyomma_americanum/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 Dissolved_oxygen", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/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/forecasts/summaries/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" - }, - "10": { - "type": "application/x-parquet", - "title": "Database Access for Daily Water_temperature", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/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/forecasts/summaries/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" - }, - "11": { - "type": "application/x-parquet", - "title": "Database Access for Daily Red_chromatic_coordinate", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/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/forecasts/summaries/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" - }, - "12": { - "type": "application/x-parquet", - "title": "Database Access for Weekly beetle_community_richness", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=richness/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/forecasts/summaries/parquet/duration=P1W/variable=richness/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/summaries/models/model_items/tg_precip_lm_all_sites.json b/catalog/summaries/models/model_items/tg_precip_lm_all_sites.json deleted file mode 100644 index 6ed2c42417..0000000000 --- a/catalog/summaries/models/model_items/tg_precip_lm_all_sites.json +++ /dev/null @@ -1,334 +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], - [-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], - [-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], - [-84.2793, 35.9574], - [-105.9154, 39.8914], - [-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] - ] - }, - "properties": { - "description": "\nmodel info: NA\n\nSites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK, NIWO, 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, WALK, WLOU, ARIK, BIGC, BLDE, BLUE, CARI, COMO, CUPE, GUIL, HOPB, KING, LECO, LEWI, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, REDB, SYCA, TECR\n\nVariables: Daily Chlorophyll_a, Daily latent_heat_flux, Daily Net_ecosystem_exchange, Daily Green_chromatic_coordinate, Weekly beetle_community_richness, Daily Dissolved_oxygen, Daily Water_temperature, Daily Red_chromatic_coordinate, Weekly Amblyomma_americanum_population, Weekly beetle_community_abundance", - "start_datetime": "2023-11-14", - "end_datetime": "2024-01-20", - "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 Chlorophyll_a", - "Daily latent_heat_flux", - "Daily Net_ecosystem_exchange", - "Daily Green_chromatic_coordinate", - "Weekly beetle_community_richness", - "Daily Dissolved_oxygen", - "Daily Water_temperature", - "Daily Red_chromatic_coordinate", - "Weekly Amblyomma_americanum_population", - "Weekly beetle_community_abundance" - ], - "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)" - }, - { - "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.”" - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "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 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" - } - ] - }, - "collection": "forecasts", - "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": null, - "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": null, - "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/forecasts/summaries/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/forecasts/summaries/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 latent_heat_flux", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/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/forecasts/summaries/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" - }, - "5": { - "type": "application/x-parquet", - "title": "Database Access for Daily Net_ecosystem_exchange", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/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/forecasts/summaries/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" - }, - "6": { - "type": "application/x-parquet", - "title": "Database Access for Daily Green_chromatic_coordinate", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/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/forecasts/summaries/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" - }, - "7": { - "type": "application/x-parquet", - "title": "Database Access for Weekly beetle_community_richness", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=richness/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/forecasts/summaries/parquet/duration=P1W/variable=richness/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 Dissolved_oxygen", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/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/forecasts/summaries/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" - }, - "9": { - "type": "application/x-parquet", - "title": "Database Access for Daily Water_temperature", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/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/forecasts/summaries/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" - }, - "10": { - "type": "application/x-parquet", - "title": "Database Access for Daily Red_chromatic_coordinate", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/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/forecasts/summaries/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" - }, - "11": { - "type": "application/x-parquet", - "title": "Database Access for Weekly Amblyomma_americanum_population", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=amblyomma_americanum/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/forecasts/summaries/parquet/duration=P1W/variable=amblyomma_americanum/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" - }, - "12": { - "type": "application/x-parquet", - "title": "Database Access for Weekly beetle_community_abundance", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=abundance/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/forecasts/summaries/parquet/duration=P1W/variable=abundance/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/summaries/models/model_items/tg_randfor.json b/catalog/summaries/models/model_items/tg_randfor.json deleted file mode 100644 index 239dffdc43..0000000000 --- a/catalog/summaries/models/model_items/tg_randfor.json +++ /dev/null @@ -1,334 +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 latent_heat_flux, Daily Net_ecosystem_exchange, Daily Green_chromatic_coordinate, Weekly beetle_community_abundance, Weekly Amblyomma_americanum_population, Daily Dissolved_oxygen, Daily Water_temperature, Daily Red_chromatic_coordinate, Weekly beetle_community_richness", - "start_datetime": "2023-11-14", - "end_datetime": "2024-01-19", - "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 latent_heat_flux", - "Daily Net_ecosystem_exchange", - "Daily Green_chromatic_coordinate", - "Weekly beetle_community_abundance", - "Weekly Amblyomma_americanum_population", - "Daily Dissolved_oxygen", - "Daily Water_temperature", - "Daily Red_chromatic_coordinate", - "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)" - }, - { - "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.”" - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "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 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" - } - ] - }, - "collection": "forecasts", - "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": null, - "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": null, - "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/forecasts/summaries/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/forecasts/summaries/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 latent_heat_flux", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/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/forecasts/summaries/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" - }, - "5": { - "type": "application/x-parquet", - "title": "Database Access for Daily Net_ecosystem_exchange", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/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/forecasts/summaries/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" - }, - "6": { - "type": "application/x-parquet", - "title": "Database Access for Daily Green_chromatic_coordinate", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/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/forecasts/summaries/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" - }, - "7": { - "type": "application/x-parquet", - "title": "Database Access for Weekly beetle_community_abundance", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=abundance/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/forecasts/summaries/parquet/duration=P1W/variable=abundance/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 Weekly Amblyomma_americanum_population", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=amblyomma_americanum/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/forecasts/summaries/parquet/duration=P1W/variable=amblyomma_americanum/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 Dissolved_oxygen", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/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/forecasts/summaries/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" - }, - "10": { - "type": "application/x-parquet", - "title": "Database Access for Daily Water_temperature", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/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/forecasts/summaries/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" - }, - "11": { - "type": "application/x-parquet", - "title": "Database Access for Daily Red_chromatic_coordinate", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/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/forecasts/summaries/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" - }, - "12": { - "type": "application/x-parquet", - "title": "Database Access for Weekly beetle_community_richness", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=richness/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/forecasts/summaries/parquet/duration=P1W/variable=richness/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/summaries/models/model_items/tg_randfor_all_sites.json b/catalog/summaries/models/model_items/tg_randfor_all_sites.json deleted file mode 100644 index 574958e44a..0000000000 --- a/catalog/summaries/models/model_items/tg_randfor_all_sites.json +++ /dev/null @@ -1,306 +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": [ - [-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], - [-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], - [-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] - ] - }, - "properties": { - "description": [], - "start_datetime": "2023-11-14", - "end_datetime": "2024-01-19", - "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_richness", - "Daily Dissolved_oxygen", - "Daily Water_temperature", - "Daily Red_chromatic_coordinate", - "Weekly Amblyomma_americanum_population", - "Weekly beetle_community_abundance" - ], - "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)" - }, - { - "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.”" - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "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 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" - } - ] - }, - "collection": "forecasts", - "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": [], - "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": [], - "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_richness", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=richness/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/forecasts/summaries/parquet/duration=P1W/variable=richness/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 Dissolved_oxygen", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/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/forecasts/summaries/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" - }, - "5": { - "type": "application/x-parquet", - "title": "Database Access for Daily Water_temperature", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/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/forecasts/summaries/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" - }, - "6": { - "type": "application/x-parquet", - "title": "Database Access for Daily Red_chromatic_coordinate", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/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/forecasts/summaries/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" - }, - "7": { - "type": "application/x-parquet", - "title": "Database Access for Weekly Amblyomma_americanum_population", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=amblyomma_americanum/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/forecasts/summaries/parquet/duration=P1W/variable=amblyomma_americanum/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" - }, - "8": { - "type": "application/x-parquet", - "title": "Database Access for Weekly beetle_community_abundance", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=abundance/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/forecasts/summaries/parquet/duration=P1W/variable=abundance/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/summaries/models/model_items/tg_tbats.json b/catalog/summaries/models/model_items/tg_tbats.json deleted file mode 100644 index b70b8cd056..0000000000 --- a/catalog/summaries/models/model_items/tg_tbats.json +++ /dev/null @@ -1,334 +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], - [-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], - [-110.5871, 44.9501], - [-96.6242, 34.4442] - ] - }, - "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, CARI, COMO, CUPE, GUIL, HOPB, KING, LECO, LEWI, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, REDB, SYCA, TECR, WALK, WLOU, ARIK, BIGC, BLDE, BLUE\n\nVariables: Daily Chlorophyll_a, Daily latent_heat_flux, Daily Net_ecosystem_exchange, Daily Red_chromatic_coordinate, Daily Green_chromatic_coordinate, Weekly Amblyomma_americanum_population, Weekly beetle_community_richness, Daily Water_temperature, Weekly beetle_community_abundance, Daily Dissolved_oxygen", - "start_datetime": "2023-01-01", - "end_datetime": "2024-12-09", - "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 latent_heat_flux", - "Daily Net_ecosystem_exchange", - "Daily Red_chromatic_coordinate", - "Daily Green_chromatic_coordinate", - "Weekly Amblyomma_americanum_population", - "Weekly beetle_community_richness", - "Daily Water_temperature", - "Weekly beetle_community_abundance", - "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)" - }, - { - "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.”" - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "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 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" - } - ] - }, - "collection": "forecasts", - "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": null, - "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": null, - "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/forecasts/summaries/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/forecasts/summaries/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 latent_heat_flux", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/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/forecasts/summaries/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" - }, - "5": { - "type": "application/x-parquet", - "title": "Database Access for Daily Net_ecosystem_exchange", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/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/forecasts/summaries/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" - }, - "6": { - "type": "application/x-parquet", - "title": "Database Access for Daily Red_chromatic_coordinate", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/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/forecasts/summaries/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" - }, - "7": { - "type": "application/x-parquet", - "title": "Database Access for Daily Green_chromatic_coordinate", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/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/forecasts/summaries/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" - }, - "8": { - "type": "application/x-parquet", - "title": "Database Access for Weekly Amblyomma_americanum_population", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/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/forecasts/summaries/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" - }, - "9": { - "type": "application/x-parquet", - "title": "Database Access for Weekly beetle_community_richness", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/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/forecasts/summaries/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" - }, - "10": { - "type": "application/x-parquet", - "title": "Database Access for Daily Water_temperature", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/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/forecasts/summaries/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" - }, - "11": { - "type": "application/x-parquet", - "title": "Database Access for Weekly beetle_community_abundance", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/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/forecasts/summaries/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 Daily Dissolved_oxygen", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/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/forecasts/summaries/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" - } - } -} diff --git a/catalog/summaries/models/model_items/tg_temp_lm.json b/catalog/summaries/models/model_items/tg_temp_lm.json deleted file mode 100644 index 0e283bbda7..0000000000 --- a/catalog/summaries/models/model_items/tg_temp_lm.json +++ /dev/null @@ -1,334 +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": [ - [-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], - [-149.6106, 68.6307], - [-84.2793, 35.9574], - [-105.9154, 39.8914], - [-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] - ] - }, - "properties": { - "description": "\nmodel info: NA\n\nSites: 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, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, TOOK, WALK, WLOU, 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\n\nVariables: Daily latent_heat_flux, Daily Green_chromatic_coordinate, Daily Net_ecosystem_exchange, Daily Red_chromatic_coordinate, Daily Water_temperature, Daily Dissolved_oxygen, Daily Chlorophyll_a, Weekly beetle_community_abundance, Weekly Amblyomma_americanum_population, Weekly beetle_community_richness", - "start_datetime": "2023-11-14", - "end_datetime": "2024-01-20", - "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 latent_heat_flux", - "Daily Green_chromatic_coordinate", - "Daily Net_ecosystem_exchange", - "Daily Red_chromatic_coordinate", - "Daily Water_temperature", - "Daily Dissolved_oxygen", - "Daily Chlorophyll_a", - "Weekly beetle_community_abundance", - "Weekly Amblyomma_americanum_population", - "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)" - }, - { - "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.”" - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "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 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" - } - ] - }, - "collection": "forecasts", - "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": null, - "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": null, - "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/forecasts/summaries/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/forecasts/summaries/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" - }, - "4": { - "type": "application/x-parquet", - "title": "Database Access for Daily Green_chromatic_coordinate", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/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/forecasts/summaries/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 Net_ecosystem_exchange", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/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/forecasts/summaries/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" - }, - "6": { - "type": "application/x-parquet", - "title": "Database Access for Daily Red_chromatic_coordinate", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/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/forecasts/summaries/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" - }, - "7": { - "type": "application/x-parquet", - "title": "Database Access for Daily Water_temperature", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/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/forecasts/summaries/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" - }, - "8": { - "type": "application/x-parquet", - "title": "Database Access for Daily Dissolved_oxygen", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/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/forecasts/summaries/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" - }, - "9": { - "type": "application/x-parquet", - "title": "Database Access for Daily Chlorophyll_a", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/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/forecasts/summaries/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" - }, - "10": { - "type": "application/x-parquet", - "title": "Database Access for Weekly beetle_community_abundance", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=abundance/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/forecasts/summaries/parquet/duration=P1W/variable=abundance/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" - }, - "11": { - "type": "application/x-parquet", - "title": "Database Access for Weekly Amblyomma_americanum_population", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=amblyomma_americanum/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/forecasts/summaries/parquet/duration=P1W/variable=amblyomma_americanum/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" - }, - "12": { - "type": "application/x-parquet", - "title": "Database Access for Weekly beetle_community_richness", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=richness/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/forecasts/summaries/parquet/duration=P1W/variable=richness/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/summaries/models/model_items/tg_temp_lm_all_sites.json b/catalog/summaries/models/model_items/tg_temp_lm_all_sites.json deleted file mode 100644 index ff65c4d293..0000000000 --- a/catalog/summaries/models/model_items/tg_temp_lm_all_sites.json +++ /dev/null @@ -1,334 +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], - [-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], - [-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] - ] - }, - "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, MCRA, OKSR, POSE, PRIN, REDB, SYCA, TECR, WALK, WLOU, ARIK, BIGC, BLDE, BLUE, CARI, COMO, CUPE, GUIL, HOPB, KING, LECO, LEWI, MART, MAYF, MCDI\n\nVariables: Daily Chlorophyll_a, Daily latent_heat_flux, Daily Green_chromatic_coordinate, Daily Net_ecosystem_exchange, Daily Red_chromatic_coordinate, Daily Water_temperature, Daily Dissolved_oxygen, Weekly Amblyomma_americanum_population, Weekly beetle_community_richness, Weekly beetle_community_abundance", - "start_datetime": "2023-11-14", - "end_datetime": "2024-01-20", - "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 Chlorophyll_a", - "Daily latent_heat_flux", - "Daily Green_chromatic_coordinate", - "Daily Net_ecosystem_exchange", - "Daily Red_chromatic_coordinate", - "Daily Water_temperature", - "Daily Dissolved_oxygen", - "Weekly Amblyomma_americanum_population", - "Weekly beetle_community_richness", - "Weekly beetle_community_abundance" - ], - "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)" - }, - { - "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.”" - }, - { - "name": "pub_datetime", - "type": "timestamp[us, tz=UTC]", - "description": "datetime that forecast was submitted" - }, - { - "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 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" - } - ] - }, - "collection": "forecasts", - "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": null, - "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": null, - "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/forecasts/summaries/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/forecasts/summaries/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 latent_heat_flux", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/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/forecasts/summaries/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" - }, - "5": { - "type": "application/x-parquet", - "title": "Database Access for Daily Green_chromatic_coordinate", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/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/forecasts/summaries/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" - }, - "6": { - "type": "application/x-parquet", - "title": "Database Access for Daily Net_ecosystem_exchange", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/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/forecasts/summaries/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 Red_chromatic_coordinate", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/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/forecasts/summaries/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" - }, - "8": { - "type": "application/x-parquet", - "title": "Database Access for Daily Water_temperature", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/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/forecasts/summaries/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" - }, - "9": { - "type": "application/x-parquet", - "title": "Database Access for Daily Dissolved_oxygen", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/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/forecasts/summaries/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" - }, - "10": { - "type": "application/x-parquet", - "title": "Database Access for Weekly Amblyomma_americanum_population", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=amblyomma_americanum/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/forecasts/summaries/parquet/duration=P1W/variable=amblyomma_americanum/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" - }, - "11": { - "type": "application/x-parquet", - "title": "Database Access for Weekly beetle_community_richness", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=richness/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/forecasts/summaries/parquet/duration=P1W/variable=richness/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" - }, - "12": { - "type": "application/x-parquet", - "title": "Database Access for Weekly beetle_community_abundance", - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=abundance/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/forecasts/summaries/parquet/duration=P1W/variable=abundance/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" - } - } -}