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  "@id": "urn:neon4cast-stac:745c0ba93c",
  "name": "climatology",
  "description": "All summaries for the Daily_Green_chromatic_coordinate variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd.  Assumes normal distribution.\n                                    The model predicts this variable at the following sites: 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, BONA, DEJU, HEAL, BARR, TOOL.\n                                    Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)",
  "datePublished": "2023-11-15",
  "keywords": [
    "Summaries",
    "neon4cast",
    "Phenology",
    "climatology",
    "Green_chromatic_coordinate",
    "gcc_90",
    "Daily",
    "P1D",
    "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",
    "BONA",
    "DEJU",
    "HEAL",
    "BARR",
    "TOOL"
  ],
  "citation": {
    "@type": "CreativeWork",
    "@id": "https://doi.org/10.1002/ecs2.4686",
    "url": "https://esajournals.onlinelibrary.wiley.com/doi/full/10.1002/ecs2.4686",
    "name": "A community convention for ecological forecasting: Output files and metadata version 1.0",
    "description": "This paper summarizes the open community conventions developed by the Ecological Forecasting Initiative (EFI) for the common formatting and archiving of ecological forecasts and the metadata associated with these forecasts. ",
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      "url": "https://doi.org/10.1002/ecs2.4686"
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  "variableMeasured": [
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      "@type": "PropertyValue",
      "name": "reference_datetime",
      "description": "datetime that the forecast was initiated (horizon = 0)"
    },
    {
      "@type": "PropertyValue",
      "name": "site_id",
      "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)"
    },
    {
      "@type": "PropertyValue",
      "name": "datetime",
      "description": "datetime of the forecasted value (ISO 8601)"
    },
    {
      "@type": "PropertyValue",
      "name": "family",
      "description": "For ensembles: \u201censemble.\u201d Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The \u201csample\u201d distribution is synonymous with \u201censemble.\u201dFor summary statistics: \u201csummary.\u201d"
    },
    {
      "@type": "PropertyValue",
      "name": "pub_datetime",
      "description": "datetime that forecast was submitted"
    },
    {
      "@type": "PropertyValue",
      "name": "mean",
      "description": "mean forecast prediction"
    },
    {
      "@type": "PropertyValue",
      "name": "median",
      "description": "median forecast prediction"
    },
    {
      "@type": "PropertyValue",
      "name": "sd",
      "description": "standard deviation forecasts"
    },
    {
      "@type": "PropertyValue",
      "name": "quantile97.5",
      "description": "upper 97.5 percentile value of forecast"
    },
    {
      "@type": "PropertyValue",
      "name": "quantile02.5",
      "description": "upper 2.5 percentile value of forecast"
    },
    {
      "@type": "PropertyValue",
      "name": "quantile90",
      "description": "upper 90 percentile value of forecast"
    },
    {
      "@type": "PropertyValue",
      "name": "quantile10",
      "description": "upper 10 percentile value of forecast"
    },
    {
      "@type": "PropertyValue",
      "name": "project_id",
      "description": "unique identifier for the forecast project"
    },
    {
      "@type": "PropertyValue",
      "name": "duration",
      "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
    },
    {
      "@type": "PropertyValue",
      "name": "variable",
      "description": "name of forecasted variable"
    },
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      "@type": "PropertyValue",
      "name": "model_id",
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    }
  ],
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      "contentUrl": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/climatology.json",
      "encodingFormat": "application/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",
      "name": "Model Metadata"
    },
    {
      "@type": "DataDownload",
      "contentUrl": "https://github.com/eco4cast/neon4cast-ci/blob/main/baseline_models/models/aquatics_persistenceRW.R",
      "encodingFormat": "text/html",
      "description": "The link to the model code provided by the model submission team",
      "name": "Link for Model Code"
    },
    {
      "@type": "DataDownload",
      "contentUrl": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=gcc_90/model_id=climatology?endpoint_override=sdsc.osn.xsede.org",
      "encodingFormat": "application/x-parquet",
      "description": "Use `R` or `Python` code for remote access to the database. This code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/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### Python\n\n```# Use code below\n\n\nimport ibis\n\n       con = ibis.duckdbf.connect()\n\n\n       con.raw_sql(f'''\n\n       CREATE OR REPLACE SECRET secret (\n\n       TYPE S3,\n\n       ENDPOINT 'sdsc.osn.xsede.org',\n\n       URL_STYLE 'path'\n\n\n       );/n\n       '''\n\n\n       path = \"s3:///project_id=neon4cast/duration=P1D/variable=gcc_90/model_id=climatology\"\ncon.read_parquet(path + \"/**\")",
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