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# Conflicts:
#	catalog/forecasts/Terrestrial/30min_latent_heat_flux/collection.json
#	catalog/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/collection.json
#	catalog/forecasts/collection.json
#	catalog/forecasts/models/collection.json
#	catalog/forecasts/models/model_items/persistenceRW.json
#	catalog/forecasts/models/model_items/tg_arima.json
#	catalog/forecasts/models/model_items/tg_bag_mlp.json
#	catalog/forecasts/models/model_items/tg_ets.json
#	catalog/forecasts/models/model_items/tg_humidity_lm.json
#	catalog/forecasts/models/model_items/tg_humidity_lm_all_sites.json
#	catalog/forecasts/models/model_items/tg_lasso.json
#	catalog/forecasts/models/model_items/tg_precip_lm.json
#	catalog/forecasts/models/model_items/tg_precip_lm_all_sites.json
#	catalog/forecasts/models/model_items/tg_randfor.json
#	catalog/forecasts/models/model_items/tg_tbats.json
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addelany committed Jan 2, 2024
2 parents e685d23 + 0297e76 commit d45212c
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"interval": [
[
"2023-01-01T00:00:00Z",
"2024-01-24T00:00:00Z"
"2024-02-05T00:00:00Z"
]
]
}
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"type": "application/json",
"href": "../../models/model_items/tg_bag_mlp.json"
},
{
"rel": "item",
"type": "application/json",
"href": "../../models/model_items/neon4cast_example.json"
},
{
"rel": "parent",
"type": "application/json",
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"interval": [
[
"2023-01-01T00:00:00Z",
"2024-11-13T00:00:00Z"
"2024-11-25T00:00:00Z"
]
]
}
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"type": "application/json",
"href": "../../models/model_items/tg_auto_adam.json"
},
{
"rel": "item",
"type": "application/json",
"href": "../../models/model_items/precip_mod.json"
},
{
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"type": "application/json",
"href": "../../models/model_items/neon4cast_example.json"
},
{
"rel": "parent",
"type": "application/json",
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"interval": [
[
"2023-01-01T00:00:00Z",
"2024-11-13T00:00:00Z"
"2024-11-25T00:00:00Z"
]
]
}
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2 changes: 1 addition & 1 deletion catalog/forecasts/Aquatics/collection.json
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"interval": [
[
"2023-01-01T00:00:00Z",
"2024-12-09T00:00:00Z"
"2024-12-23T00:00:00Z"
]
]
}
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"interval": [
[
"2023-01-02T00:00:00Z",
"2024-12-09T00:00:00Z"
"2024-12-23T00:00:00Z"
]
]
}
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"interval": [
[
"2023-01-02T00:00:00Z",
"2024-12-09T00:00:00Z"
"2024-12-23T00:00:00Z"
]
]
}
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2 changes: 1 addition & 1 deletion catalog/forecasts/Beetles/collection.json
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"interval": [
[
"2023-01-01T00:00:00Z",
"2024-12-09T00:00:00Z"
"2024-12-23T00:00:00Z"
]
]
}
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{
"rel": "item",
"type": "application/json",
"href": "../../models/model_items/cb_prophet.json"
"href": "../../models/model_items/tg_bag_mlp.json"
},
{
"rel": "item",
"type": "application/json",
"href": "../../models/model_items/climatology.json"
"href": "../../models/model_items/tg_ets.json"
},
{
"rel": "item",
"type": "application/json",
"href": "../../models/model_items/persistenceRW.json"
"href": "../../models/model_items/tg_humidity_lm.json"
},
{
"rel": "item",
"type": "application/json",
"href": "../../models/model_items/tg_arima.json"
"href": "../../models/model_items/tg_humidity_lm_all_sites.json"
},
{
"rel": "item",
"type": "application/json",
"href": "../../models/model_items/tg_auto_adam.json"
"href": "../../models/model_items/tg_lasso.json"
},
{
"rel": "item",
"type": "application/json",
"href": "../../models/model_items/tg_bag_mlp.json"
"href": "../../models/model_items/tg_lasso_all_sites.json"
},
{
"rel": "item",
"type": "application/json",
"href": "../../models/model_items/tg_ets.json"
"href": "../../models/model_items/tg_precip_lm.json"
},
{
"rel": "item",
"type": "application/json",
"href": "../../models/model_items/tg_humidity_lm.json"
"href": "../../models/model_items/persistenceRW.json"
},
{
"rel": "item",
"type": "application/json",
"href": "../../models/model_items/tg_humidity_lm_all_sites.json"
"href": "../../models/model_items/tg_arima.json"
},
{
"rel": "item",
"type": "application/json",
"href": "../../models/model_items/tg_lasso.json"
"href": "../../models/model_items/tg_auto_adam.json"
},
{
"rel": "item",
"type": "application/json",
"href": "../../models/model_items/tg_lasso_all_sites.json"
"href": "../../models/model_items/cb_prophet.json"
},
{
"rel": "item",
"type": "application/json",
"href": "../../models/model_items/tg_precip_lm.json"
"href": "../../models/model_items/climatology.json"
},
{
"rel": "item",
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"type": "application/json",
"href": "../../models/model_items/tg_randfor.json"
},
{
"rel": "item",
"type": "application/json",
"href": "../../models/model_items/PEG.json"
},
{
"rel": "parent",
"type": "application/json",
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"interval": [
[
"2023-01-01T00:00:00Z",
"2024-01-24T00:00:00Z"
"2024-02-05T00:00:00Z"
]
]
}
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"type": "application/json",
"href": "../../models/model_items/tg_auto_adam.json"
},
{
"rel": "item",
"type": "application/json",
"href": "../../models/model_items/PEG.json"
},
{
"rel": "parent",
"type": "application/json",
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"interval": [
[
"2023-01-01T00:00:00Z",
"2024-01-24T00:00:00Z"
"2024-02-05T00:00:00Z"
]
]
}
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"interval": [
[
"2023-01-01T00:00:00Z",
"2024-12-09T00:00:00Z"
"2024-12-23T00:00:00Z"
]
]
}
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147 changes: 147 additions & 0 deletions catalog/forecasts/Terrestrial/30min_latent_heat_flux/collection.json
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{
"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/climatology.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": [
[
"2023-11-14T00:00:00Z",
"2024-01-09T00:00:00Z"
]
]
}
},
"table:columns": [
{
"name": "reference_datetime",
"type": "timestamp[us, tz=UTC]",
"description": "datetime that the forecast was initiated (horizon = 0)"
},
{
"name": "datetime",
"type": "timestamp[us, tz=UTC]",
"description": "datetime of the forecasted value (ISO 8601)"
},
{
"name": "site_id",
"type": "string",
"description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
},
{
"name": "family",
"type": "string",
"description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
},
{
"name": "parameter",
"type": "string",
"description": "ensemble member or distribution parameter"
},
{
"name": "prediction",
"type": "double",
"description": "predicted value for variable"
},
{
"name": "pub_datetime",
"type": "timestamp[us, tz=UTC]",
"description": "datetime that forecast was submitted"
},
{
"name": "project_id",
"type": "string",
"description": "unique identifier for the forecast project"
},
{
"name": "duration",
"type": "string",
"description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
},
{
"name": "variable",
"type": "string",
"description": "name of forecasted variable"
},
{
"name": "model_id",
"type": "string",
"description": "unique model identifier"
},
{
"name": "reference_date",
"type": "string",
"description": "date that the forecast was initiated"
}
],
"assets": {
"data": {
"href": "\"s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/project_id=neon4cast/duration=PT30M/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=PT30M/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"
}
}
}
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