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{#sec-spatiotemporal-asset-catalog}\n\nThe catalog of submitted forecasts and the evaluation of the forecasts (\"scores\") is available through the SpatioTemporal Asset Catalogs browser (below). A full page version can be found [here](https://radiantearth.github.io/stac-browser/#/external/raw.githubusercontent.com/LTREB-reservoirs/vera4cast/main/catalog/catalog.json) \n\n

\n\n\n```{=html}\n\n```\n", - "supporting": [], - "filters": [ - "rmarkdown/pagebreak.lua" - ], - "includes": {}, - "engineDependencies": {}, - "preserve": {}, - "postProcess": true - } -} \ No newline at end of file diff --git a/dashboard/.quarto/_freeze/index/execute-results/html.json b/dashboard/.quarto/_freeze/index/execute-results/html.json deleted file mode 100644 index 5fc4bd1a29..0000000000 --- a/dashboard/.quarto/_freeze/index/execute-results/html.json +++ /dev/null @@ -1,14 +0,0 @@ -{ - "hash": "89b9f8f72c09ee9917f2d234975646a9", - "result": { - "markdown": "---\ntitle: \"Forecasting Challenge\"\n---\n\n\nWelcome to the NEON Forecasting Challenge, hosted by the Ecologial Forecasting Initiative Research Coordination Network\n\nWe invite you to submit forecasts!\n\n## Why a forecasting challenge? \n\nWe are using forecasts to compare the predictability of different ecosystem variables, in different ecosystem conditions to identify the fundamental predictability of freshwater ecosystems.\n\n
\n
\n\n\n\n::: {.cell}\n\n:::\n\n
\n
\n\n## What sites are being forecasted?\n\n\n::: {.cell}\n\n:::\n\n
\n
\nThis material is based upon work supported by the National Science Foundation under Grant DEB-1926388. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.\n
\n
\nPage last updated on 2023-11-06\n", - "supporting": [], - "filters": [ - "rmarkdown/pagebreak.lua" - ], - "includes": {}, - "engineDependencies": {}, - "preserve": {}, - "postProcess": true - } -} \ No newline at end of file diff --git a/dashboard/.quarto/_freeze/instructions/execute-results/html.json b/dashboard/.quarto/_freeze/instructions/execute-results/html.json deleted file mode 100644 index 63f2d6910a..0000000000 --- a/dashboard/.quarto/_freeze/instructions/execute-results/html.json +++ /dev/null @@ -1,14 +0,0 @@ -{ - "hash": "19c07fac2c4fd42450b25dcf49c9d179", - "result": { - "markdown": "---\ntitle: \"How to forecast\"\nnumber-sections: true\neditor: \n markdown: \n wrap: sentence\n---\n\n\n## tl;dr: How to submit a forecast\n\nWe provide an overview of the steps for submitting with the details below:\n\n1) Register at . You are not required to register if your forecast submission uses the word \"example\" in your model_id\". Any forecasts with \"example\" in the model_id will not be used in analyses.\n2) Generate forecast with required columns.\n3) Write the forecast to a file that follows the standardized naming format.\n4) Submit forecast using a provided function.\n5) Watch forecast be evaluated as new data is collected. \n\n## Generating a forecast\n\n### Any approach is allowed\n\nYou can use any approach to make a prediction about the future conditions at any of the sites and variables\n\n### Must include uncertainty\n\nForecasts require you to make a access of confidence in the prediction of the future. You can represent your confidence (i.e., uncertainty in forecast) using a distribution or numerically using an ensemble (or sample) of predictions. \n\n### Any model drivers/covariates/features are welcome\n\nYou can use any data as inputs. This includes all the targets data available to date. All sensor-based target data is available in near-real time (1 day latency). You may want to use the updated target data to re-train a model or use in data assimilation.\n\nAs this is a genuine forecasting challenge, you will need forecasted drivers if your model uses drivers as inputs. If you are interested in using forecasted meteorology, we have developed an R package ([https://github.com/FLARE-forecast/RopenMeteo](https://github.com/FLARE-forecast/RopenMeteo)) that can be used to access ensemble weather forecasts or seasonal forecasts for the sites. The R package is a wrapper around the open-meteo.com API. The README in the R package repo provides instructions for use.\n\n### Forecasts can be for a range of horizons\n\nForecasts can be submitted at the daily time step 1 day to 10 years ahead\n\n### Forecasts can be submitted everyday\n\nSince forecasts can be submitted everyday, automation is important. We provide an example GitHub repository that can be used to automate your forecast with GitHub Actions. It also includes the use of a custom Docker Container [eco4cast/rocker-neon4cast:latest](https://github.com/LTREB-reservoirs/vera4cast/blob/main/Dockerfile) that has many of the packages and functions needed to generate and submit forecasts. \n\n## Forecast file format\n\nThe file is a csv format with the following columns:\n\n- `project_id`: use `neon4cast`\n\n- `model_id`: the short name of the model defined as the model_id in the file name (see below) and in your registration.\n The model_id should have no spaces.\n model_id should reflect a method to forecast one or a set of target variables and must be unique to the vera4cast Challenge\n\n- `datetime`: forecast timestamp.\n Format `%Y-%m-%d %H:%M:%S` with UTC as the time zone.\n Forecasts submitted with a `%Y-%m-%d` format will be converted to a full datetime assuming UTC mid-night.\n\n- `reference_datetime`: The start of the forecast; this should be 0 times steps in the future.\n There should only be one value of `reference_datetime` in the file.\n Format is `%Y-%m-%d %H:%M:%S` with UTC as the time zone.\n Forecasts submitted with a `%Y-%m-%d` format will be converted to a full datetime assuming UTC mid-night.\n\n- `duration`: the time-step of the forecast.\n Use the value of `P1D` for a daily forecast, `P1W` for a weekly forecast, and `PT30M` for 30 minute forecast.\n This value should match the duration of the target variable that you are forecasting.\n Formatted as [ISO 8601 duration](https://en.wikipedia.org/wiki/ISO_8601#Durations)\n\n- `site_id`: code for site (`bvre`, `fcre`, or `tubr`)\n\n- `family` name of the probability distribution that is described by the parameter values in the parameter column (see list below for accepted distribution).\n An ensemble forecast as a family of `ensemble`.\n See note below about family\n\n- `parameter` the parameters for the distribution (see note below about parameter column) or the number of the ensemble member.\n For example the parameters for normal are `mu` and `sigma`.\n\n- `variable`: standardized variable name\n\n- `prediction`: forecasted value for the parameter in the parameter column\n\n## Representing uncertainity \n\nUncertainty is represented through the family - parameter columns\n\n#### Parameteric forecast\n\nFor a parametric forecast with the normal distribution, the `family` column uses the word `normal` to designate a normal distribution and the parameter column must have values of `mu` and `sigma` for each forecasted variable, site_id, depth and time combination.\n\nParameteric forecasts for binary variables should use `bernoulli` as the distribution.\n\nThe following names and parameterization of the distribution are supported (family: parameters):\n\n- `lognormal`: `mu`, `sigma`\n- `normal`: `mu`,`sigma`\n- `bernoulli`: `prob`\n- `beta`: `shape1`, `shape2`\n- `uniform`: `min`, `max`\n- `gamma`: `shape`, `rate`\n- `logistic`: `location`, `scale`\n- `exponential`: `rate`\n- `poisson`: `lambda`\n\nIf you are submitting a forecast that is not in the supported list we recommend using the ensemble format and sampling from your distribution to generate a set of ensemble members that represents your distribution. \n\n#### Ensemble (or sample) forecast\n\nEnsemble (or sample) forecasts use the `family` value of `ensemble` and the `parameter` values are the ensemble index. \n\nWhen forecasts using the ensemble family are scored, we assume that the ensemble is a set of equally likely realizations that are sampled from a distribution is comparable to that of the observations (i.e., no broad adjustments are required to make the ensemble more consistent with observations). This is referred to as a \"perfect ensemble\" in Bröcker and Smith (2007). Ensemble (or sample) forecasts are transformed to a probability distribution function (e.g., dressed) using the default methods in the `scoringRules` R package (empirical version of the quantile decomposition for the `crps` and kernel density estimation using a Gaussian kernel for the `logs`). Kernel density estimation uses the default bandwidth produced by the `bw.nrd` function in the R `stats` package.\n\n### Supported distributions\n\nThe following names and parameterization of the distribution are supported (family: parameters):\n\n- `lognormal`: `mu`, `sigma`\n- `normal`: `mu`,`sigma`\n- `bernoulli`: `prob`\n- `beta`: `shape1`, `shape2`\n- `uniform`: `min`, `max`\n- `gamma`: `shape`, `rate`\n- `logistic`: `location`, `scale`\n- `exponential`: `rate`\n- `poisson`: `lambda`\n\n### Example forecasts\n\nHere is an example of a forecast that uses a normal distribution:\n\n\n::: {.cell}\n\n```{.r .cell-code}\ndf <- readr::read_csv(\"https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/forecasts/raw/T20231001231345_daily-2023-10-01-climatology.csv.gz\", show_col_types = FALSE)\ndplyr::glimpse(df)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\nRows: 288\nColumns: 11\n$ model_id \"climatology\", \"climatology\", \"climatology\", \"clima…\n$ datetime 2023-10-02, 2023-10-02, 2023-10-02, 2023-10-02, 20…\n$ reference_datetime 2023-10-01, 2023-10-01, 2023-10-01, 2023-10-01, 20…\n$ site_id \"bvre\", \"bvre\", \"bvre\", \"bvre\", \"bvre\", \"bvre\", \"bv…\n$ variable \"Chla_ugL_mean\", \"Chla_ugL_mean\", \"Temp_C_mean\", \"T…\n$ family \"normal\", \"normal\", \"normal\", \"normal\", \"normal\", \"…\n$ parameter \"mu\", \"sigma\", \"mu\", \"sigma\", \"mu\", \"sigma\", \"mu\", …\n$ prediction 10.041987, 2.587292, 18.331126, 2.531732, 9.541139,…\n$ depth_m 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1…\n$ project_id \"vera4cast\", \"vera4cast\", \"vera4cast\", \"vera4cast\",…\n$ duration \"P1D\", \"P1D\", \"P1D\", \"P1D\", \"P1D\", \"P1D\", \"P1D\", \"P…\n```\n:::\n:::\n\n\nFor an ensemble (or sample) forecast, the `family` column uses the word `ensemble` to designate that it is a ensemble forecast and the parameter column is the ensemble member number (`1`, `2`, `3` ...)\n\n\n::: {.cell}\n\n```{.r .cell-code}\ndf <- readr::read_csv(\"https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/forecasts/raw/T20231001231348_daily-2023-10-01-persistenceRW.csv.gz\", show_col_types = FALSE)\ndplyr::glimpse(df)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\nRows: 28,800\nColumns: 11\n$ model_id \"persistenceRW\", \"persistenceRW\", \"persistenceRW\", …\n$ datetime 2023-10-02, 2023-10-03, 2023-10-04, 2023-10-05, 20…\n$ reference_datetime 2023-10-01, 2023-10-01, 2023-10-01, 2023-10-01, 20…\n$ site_id \"bvre\", \"bvre\", \"bvre\", \"bvre\", \"bvre\", \"bvre\", \"bv…\n$ family \"ensemble\", \"ensemble\", \"ensemble\", \"ensemble\", \"en…\n$ parameter 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …\n$ variable \"Chla_ugL_mean\", \"Chla_ugL_mean\", \"Chla_ugL_mean\", …\n$ prediction 10.59539, 13.04463, 13.86989, 13.81281, 14.09787, 1…\n$ depth_m 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1…\n$ project_id \"vera4cast\", \"vera4cast\", \"vera4cast\", \"vera4cast\",…\n$ duration \"P1D\", \"P1D\", \"P1D\", \"P1D\", \"P1D\", \"P1D\", \"P1D\", \"P…\n```\n:::\n:::\n\n\n## Submission process\n\nIndividual forecast files can be uploaded any time.\n\nTeams will submit their forecast csv files through an R function.\n\nThe function is available using the following code\n\n\n::: {.cell}\n\n```{.r .cell-code}\nremotes::install_github(\"eco4cast/neon4cast\")\n```\n:::\n\n\nThe submit function is\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(neon4cast)\nneon4cast::submit(forecast_file = \"your_file.csv\")\n```\n:::\n\n\nIf you will be submitting multiple forecasts using the same model_id, use the following\n\n\n::: {.cell}\n\n```{.r .cell-code}\nneon4cast::submit(forecast_file = \"your_file.csv\",\n first_submission = FALSE)\n```\n:::\n\n\n## Post submission\n\n### Processing\n\nAfter submission, our servers will process uploaded files by converting to a parquet format on our public s3 storage. A `pub_datetime` column will be added that denotes when a forecast was submitted.\n\n### Evaluation\n\nAll forecasts are scored daily using new data until the full horizon of the forecast has been scored. Forecasts are scored using the `crps` and `logs` functions in the [`scoringRules`](https://cran.r-project.org/web/packages/scoringRules/index.html) R package. \n\n### Comparsion\n\nForecast performance can be compared to the performance of baseline models. We are automatically submitting the following baseline models:\n\n- `climatology`: the normal distribution (mean and standard deviation) of that day-of-year in the historical observations\n- `persistenceRW`: a random walk model that assumes no change in the mean behavior. The random walk is initialized using the most resent observation.\n\nOur [forecast performance](performance.qmd#performance) page includes evaluations of all submitted models.\n\n### Catalog\n\nInformation for accessing the forecasts and scores can be found on our [forecast catalog](catalog.qmd#sec-spatiotemporal-asset-catalog) page. \n", - "supporting": [], - "filters": [ - "rmarkdown/pagebreak.lua" - ], - "includes": {}, - "engineDependencies": {}, - "preserve": {}, - "postProcess": true - } -} \ No newline at end of file diff --git a/dashboard/.quarto/_freeze/performance/execute-results/html.json b/dashboard/.quarto/_freeze/performance/execute-results/html.json deleted file mode 100644 index c254d77676..0000000000 --- a/dashboard/.quarto/_freeze/performance/execute-results/html.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "hash": "b195b6b78fedfc67974a62c0ad79c36a", - "result": { - "markdown": "---\ntitle: \"Forecast performance\"\n---\n\n\n\n::: {.cell}\n\n:::\n\n\nThis page visualizes the forecasts and forecast performance for the focal target variables.\n\n## Most recent forecasts {#sec-performance}\n\n\n::: {.cell}\n\n:::\n\n\nForecasts submitted on 2023-11-05\n\n::: panel-tabset\n\n### Terrestrial: Net Ecosystem Exchange\n\n\n::: {.cell}\n::: {.cell-output-display}\n```{=html}\n
\n\n```\n:::\n:::\n\n\n### Terrestrial: Latent Heat Flux\n\n\n::: {.cell}\n::: {.cell-output-display}\n```{=html}\n
\n\n```\n:::\n:::\n\n### Phenology: Greeness\n\n\n::: {.cell}\n::: {.cell-output .cell-output-stdout}\n```\nNULL\n```\n:::\n:::\n\n\n\n### Phenology: Redness\n\n\n::: {.cell}\n::: {.cell-output-display}\n```{=html}\n
\n\n```\n:::\n:::\n\n\n### Aquatics: water temperature\n\n\n::: {.cell}\n::: {.cell-output-display}\n```{=html}\n
\n\n```\n:::\n:::\n\n\n### Aquatics: dissolved oxygen\n\n\n::: {.cell}\n::: {.cell-output .cell-output-stdout}\n```\nNULL\n```\n:::\n:::\n\n\n### Aquatics: chrophyll-a\n\n\n::: {.cell}\n::: {.cell-output-display}\n```{=html}\n
\n\n```\n:::\n:::\n\n\n### Beetle community richness\n\n\n::: {.cell}\n::: {.cell-output .cell-output-stdout}\n```\nNULL\n```\n:::\n:::\n\n\n### Beetle community abundance\n\n\n::: {.cell}\n::: {.cell-output .cell-output-stdout}\n```\nNULL\n```\n:::\n:::\n\n\n\n### Ticks: Amblyomma americanum\n\n\n::: {.cell}\n::: {.cell-output .cell-output-stdout}\n```\nNULL\n```\n:::\n:::\n\n\n:::\n\n## Forecast analysis\n\n\n::: {.cell}\n\n:::\n\n\nBelow are forecasts submitted 30 days ago and include the observations used to evaluate them. Mouse over to see the team id, scroll to zoom.\n\n::: panel-tabset\n### Terrestrial: Net Ecosystem Exchange\n\n\n::: {.cell}\n::: {.cell-output-display}\n```{=html}\n
\n\n```\n:::\n:::\n\n\n### Terrestrial: Latent Heat Flux\n\n\n::: {.cell}\n::: {.cell-output-display}\n```{=html}\n
\n\n```\n:::\n:::\n\n### Phenology: Greeness\n\n\n::: {.cell}\n::: {.cell-output-display}\n```{=html}\n
\n\n```\n:::\n:::\n\n\n\n### Phenology: Redness\n\n\n::: {.cell}\n::: {.cell-output-display}\n```{=html}\n
\n\n```\n:::\n:::\n\n\n### Aquatics: water temperature\n\n\n::: {.cell}\n::: {.cell-output-display}\n```{=html}\n
\n\n```\n:::\n:::\n\n\n### Aquatics: disolved oxygen\n\n\n::: {.cell}\n::: {.cell-output-display}\n```{=html}\n
\n\n```\n:::\n:::\n\n\n### Aquatics: chrophyll-a\n\n\n::: {.cell}\n::: {.cell-output-display}\n```{=html}\n
\n\n```\n:::\n:::\n\n\n### Beetle community richness\n\n\n::: {.cell}\n::: {.cell-output-display}\n```{=html}\n
\n\n```\n:::\n:::\n\n\n### Beetle community abundance\n\n\n::: {.cell}\n::: {.cell-output-display}\n```{=html}\n
\n\n```\n:::\n:::\n\n\n\n### Ticks: Amblyomma americanum\n\n\n::: {.cell}\n::: {.cell-output-display}\n```{=html}\n
\n\n```\n:::\n:::\n\n\n\n:::\n\n## Aggregated scores\n\nAverage skill scores of each model across all sites.\\\n\nScores are shown by reference date and forecast horizon (in days).\\\n\nScores are averaged across all submissions of the model with a given horizon or a given `reference_datetime` using submissions made since 2023-10-07.\\\n\n::: panel-tabset\n\n### Terrestrial: Net Ecosystem Exchange\n\n\n::: {.cell}\n::: {.cell-output-display}\n```{=html}\n
\n\n```\n:::\n:::\n\n\n### Terrestrial: Latent Heat Flux\n\n\n::: {.cell}\n::: {.cell-output-display}\n```{=html}\n
\n\n```\n:::\n:::\n\n### Phenology: Greeness\n\n\n::: {.cell}\n::: {.cell-output-display}\n```{=html}\n
\n\n```\n:::\n:::\n\n\n\n### Phenology: Redness\n\n\n::: {.cell}\n::: {.cell-output-display}\n```{=html}\n
\n\n```\n:::\n:::\n\n\n### Aquatics: water temperature\n\n\n::: {.cell}\n::: {.cell-output-display}\n```{=html}\n
\n\n```\n:::\n:::\n\n\n### Aquatics: dissolved oxygen\n\n\n::: {.cell}\n::: {.cell-output-display}\n```{=html}\n
\n\n```\n:::\n:::\n\n\n### Aquatics: chrophyll-a\n\n\n::: {.cell}\n::: {.cell-output-display}\n```{=html}\n
\n\n```\n:::\n:::\n\n\n### Beetle community richness\n\n\n::: {.cell}\n::: {.cell-output .cell-output-stdout}\n```\nNULL\n```\n:::\n:::\n\n\n### Beetle community abundance\n\n\n::: {.cell}\n::: {.cell-output .cell-output-stdout}\n```\nNULL\n```\n:::\n:::\n\n\n\n### Ticks: Amblyomma americanum\n\n\n::: {.cell}\n::: {.cell-output .cell-output-stdout}\n```\nNULL\n```\n:::\n:::\n\n\n:::\n", - "supporting": [], - "filters": [ - "rmarkdown/pagebreak.lua" - ], - "includes": { - "include-in-header": [ - "\n\n\n\n\n\n\n\n" - ] - }, - "engineDependencies": {}, - "preserve": {}, - "postProcess": true - } -} \ No newline at end of file diff --git a/dashboard/.quarto/_freeze/site_libs/clipboard/clipboard.min.js b/dashboard/.quarto/_freeze/site_libs/clipboard/clipboard.min.js deleted file mode 100644 index 1103f811ed..0000000000 --- a/dashboard/.quarto/_freeze/site_libs/clipboard/clipboard.min.js +++ /dev/null @@ -1,7 +0,0 @@ -/*! - * clipboard.js v2.0.11 - * https://clipboardjs.com/ - * - * 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Forecast catalog

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What types of models are submitting forecasts?

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Catalog of forecast submissions and evaluations

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The catalog of submitted forecasts and the evaluation of the forecasts (“scores”) is available through the SpatioTemporal Asset Catalogs browser (below). A full page version can be found here

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CC-BY

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zek%T`o@TM;6&Tov82u^@Vju`&G7MuRRjCEecg~RV8zc;lIHaY~vWLeIi3G!?8?(`c~}cbSLE)C(^Riv{Vz#v=9gz zx`9?krqH?t5+OPHit+f>4Zs150d)RGMot4}h@@HBl)Dz|l4_O4L3<>ERh051$;drv zG|8s8`NI$$#z&|XJ^WxQ5CZ@(Ii^{EquY>oZU;G0n!AT7xf;AuxpRP^5(lPg&a(2# zl0TS2vty7wIIPbz%vX>Th5=4bT7A^QTT#C~2w`|# z3{q-tmGF6b~a1U+&{S&bcWyuRihH&P}Dr2R$n%9YL~tsMQ;gIL2itpiaaa?V+%Ru|Wf&lJn zV}CD}R2&1I^^EBAJEB@TJNu~2Mm&$1eQTS$Y{+qe^r%|piBQ=jcQnA;n4DEYpCFPy F|JkQ - - - - - - - - -NEON Ecological Forecast Challenge - Forecasting Challenge - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

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Forecasting Challenge

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Welcome to the NEON Forecasting Challenge, hosted by the Ecologial Forecasting Initiative Research Coordination Network

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We invite you to submit forecasts!

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Why a forecasting challenge?

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We are using forecasts to compare the predictability of different ecosystem variables, in different ecosystem conditions to identify the fundamental predictability of freshwater ecosystems.

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What sites are being forecasted?

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This material is based upon work supported by the National Science Foundation under Grant DEB-1926388. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

Page last updated on 2023-11-06

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How to forecast

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1 tl;dr: How to submit a forecast

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We provide an overview of the steps for submitting with the details below:

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  1. Register at https://forms.gle/kg2Vkpho9BoMXSy57. You are not required to register if your forecast submission uses the word “example” in your model_id”. Any forecasts with “example” in the model_id will not be used in analyses.
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  2. Generate forecast with required columns.
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  3. Write the forecast to a file that follows the standardized naming format.
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2 Generating a forecast

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2.1 Any approach is allowed

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You can use any approach to make a prediction about the future conditions at any of the sites and variables

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2.2 Must include uncertainty

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Forecasts require you to make a access of confidence in the prediction of the future. You can represent your confidence (i.e., uncertainty in forecast) using a distribution or numerically using an ensemble (or sample) of predictions.

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2.3 Any model drivers/covariates/features are welcome

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You can use any data as inputs. This includes all the targets data available to date. All sensor-based target data is available in near-real time (1 day latency). You may want to use the updated target data to re-train a model or use in data assimilation.

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As this is a genuine forecasting challenge, you will need forecasted drivers if your model uses drivers as inputs. If you are interested in using forecasted meteorology, we have developed an R package (https://github.com/FLARE-forecast/RopenMeteo) that can be used to access ensemble weather forecasts or seasonal forecasts for the sites. The R package is a wrapper around the open-meteo.com API. The README in the R package repo provides instructions for use.

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2.4 Forecasts can be for a range of horizons

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Forecasts can be submitted at the daily time step 1 day to 10 years ahead

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2.5 Forecasts can be submitted everyday

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Since forecasts can be submitted everyday, automation is important. We provide an example GitHub repository that can be used to automate your forecast with GitHub Actions. It also includes the use of a custom Docker Container eco4cast/rocker-neon4cast:latest that has many of the packages and functions needed to generate and submit forecasts.

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3 Forecast file format

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The file is a csv format with the following columns:

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  • project_id: use neon4cast

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  • model_id: the short name of the model defined as the model_id in the file name (see below) and in your registration. The model_id should have no spaces. model_id should reflect a method to forecast one or a set of target variables and must be unique to the vera4cast Challenge

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  • datetime: forecast timestamp. Format %Y-%m-%d %H:%M:%S with UTC as the time zone. Forecasts submitted with a %Y-%m-%d format will be converted to a full datetime assuming UTC mid-night.

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  • reference_datetime: The start of the forecast; this should be 0 times steps in the future. There should only be one value of reference_datetime in the file. Format is %Y-%m-%d %H:%M:%S with UTC as the time zone. Forecasts submitted with a %Y-%m-%d format will be converted to a full datetime assuming UTC mid-night.

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  • duration: the time-step of the forecast. Use the value of P1D for a daily forecast, P1W for a weekly forecast, and PT30M for 30 minute forecast. This value should match the duration of the target variable that you are forecasting. Formatted as ISO 8601 duration

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  • site_id: code for site (bvre, fcre, or tubr)

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  • family name of the probability distribution that is described by the parameter values in the parameter column (see list below for accepted distribution). An ensemble forecast as a family of ensemble. See note below about family

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  • parameter the parameters for the distribution (see note below about parameter column) or the number of the ensemble member. For example the parameters for normal are mu and sigma.

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  • prediction: forecasted value for the parameter in the parameter column

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4 Representing uncertainity

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Uncertainty is represented through the family - parameter columns

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4.0.1 Parameteric forecast

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For a parametric forecast with the normal distribution, the family column uses the word normal to designate a normal distribution and the parameter column must have values of mu and sigma for each forecasted variable, site_id, depth and time combination.

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Parameteric forecasts for binary variables should use bernoulli as the distribution.

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The following names and parameterization of the distribution are supported (family: parameters):

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If you are submitting a forecast that is not in the supported list we recommend using the ensemble format and sampling from your distribution to generate a set of ensemble members that represents your distribution.

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4.0.2 Ensemble (or sample) forecast

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Ensemble (or sample) forecasts use the family value of ensemble and the parameter values are the ensemble index.

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When forecasts using the ensemble family are scored, we assume that the ensemble is a set of equally likely realizations that are sampled from a distribution is comparable to that of the observations (i.e., no broad adjustments are required to make the ensemble more consistent with observations). This is referred to as a “perfect ensemble” in Bröcker and Smith (2007). Ensemble (or sample) forecasts are transformed to a probability distribution function (e.g., dressed) using the default methods in the scoringRules R package (empirical version of the quantile decomposition for the crps and kernel density estimation using a Gaussian kernel for the logs). Kernel density estimation uses the default bandwidth produced by the bw.nrd function in the R stats package.

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4.1 Supported distributions

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The following names and parameterization of the distribution are supported (family: parameters):

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Here is an example of a forecast that uses a normal distribution:

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Rows: 288
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-$ model_id           <chr> "climatology", "climatology", "climatology", "clima…
-$ datetime           <date> 2023-10-02, 2023-10-02, 2023-10-02, 2023-10-02, 20…
-$ reference_datetime <date> 2023-10-01, 2023-10-01, 2023-10-01, 2023-10-01, 20…
-$ site_id            <chr> "bvre", "bvre", "bvre", "bvre", "bvre", "bvre", "bv…
-$ variable           <chr> "Chla_ugL_mean", "Chla_ugL_mean", "Temp_C_mean", "T…
-$ family             <chr> "normal", "normal", "normal", "normal", "normal", "…
-$ parameter          <chr> "mu", "sigma", "mu", "sigma", "mu", "sigma", "mu", …
-$ prediction         <dbl> 10.041987, 2.587292, 18.331126, 2.531732, 9.541139,…
-$ depth_m            <dbl> 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1…
-$ project_id         <chr> "vera4cast", "vera4cast", "vera4cast", "vera4cast",…
-$ duration           <chr> "P1D", "P1D", "P1D", "P1D", "P1D", "P1D", "P1D", "P…
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For an ensemble (or sample) forecast, the family column uses the word ensemble to designate that it is a ensemble forecast and the parameter column is the ensemble member number (1, 2, 3 …)

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df <- readr::read_csv("https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/forecasts/raw/T20231001231348_daily-2023-10-01-persistenceRW.csv.gz", show_col_types = FALSE)
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Rows: 28,800
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-$ model_id           <chr> "persistenceRW", "persistenceRW", "persistenceRW", …
-$ datetime           <dttm> 2023-10-02, 2023-10-03, 2023-10-04, 2023-10-05, 20…
-$ reference_datetime <dttm> 2023-10-01, 2023-10-01, 2023-10-01, 2023-10-01, 20…
-$ site_id            <chr> "bvre", "bvre", "bvre", "bvre", "bvre", "bvre", "bv…
-$ family             <chr> "ensemble", "ensemble", "ensemble", "ensemble", "en…
-$ parameter          <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
-$ variable           <chr> "Chla_ugL_mean", "Chla_ugL_mean", "Chla_ugL_mean", …
-$ prediction         <dbl> 10.59539, 13.04463, 13.86989, 13.81281, 14.09787, 1…
-$ depth_m            <dbl> 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1…
-$ project_id         <chr> "vera4cast", "vera4cast", "vera4cast", "vera4cast",…
-$ duration           <chr> "P1D", "P1D", "P1D", "P1D", "P1D", "P1D", "P1D", "P…
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5 Submission process

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Individual forecast files can be uploaded any time.

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Teams will submit their forecast csv files through an R function.

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The function is available using the following code

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remotes::install_github("eco4cast/neon4cast")
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The submit function is

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6 Post submission

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6.1 Processing

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After submission, our servers will process uploaded files by converting to a parquet format on our public s3 storage. A pub_datetime column will be added that denotes when a forecast was submitted.

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6.2 Evaluation

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All forecasts are scored daily using new data until the full horizon of the forecast has been scored. Forecasts are scored using the crps and logs functions in the scoringRules R package.

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6.3 Comparsion

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Forecast performance can be compared to the performance of baseline models. We are automatically submitting the following baseline models:

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6.4 Catalog

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Information for accessing the forecasts and scores can be found on our forecast catalog page.

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Below are forecasts submitted 30 days ago and include the observations used to evaluate them. Mouse over to see the team id, scroll to zoom.

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- - - - - \ No newline at end of file diff --git a/dashboard/docs/search.json b/dashboard/docs/search.json deleted file mode 100644 index 63c0478806..0000000000 --- a/dashboard/docs/search.json +++ /dev/null @@ -1,135 +0,0 @@ -[ - { - "objectID": "catalog.html#sec-spatiotemporal-asset-catalog", - "href": "catalog.html#sec-spatiotemporal-asset-catalog", - "title": "Forecast catalog", - "section": "Catalog of forecast submissions and evaluations", - "text": "Catalog of forecast submissions and evaluations\nThe catalog of submitted forecasts and the evaluation of the forecasts (“scores”) is available through the SpatioTemporal Asset Catalogs browser (below). A full page version can be found here" - }, - { - "objectID": "index.html", - "href": "index.html", - "title": "Forecasting Challenge", - "section": "", - "text": "Welcome to the NEON Forecasting Challenge, hosted by the Ecologial Forecasting Initiative Research Coordination Network\nWe invite you to submit forecasts!" - }, - { - "objectID": "index.html#why-a-forecasting-challenge", - "href": "index.html#why-a-forecasting-challenge", - "title": "Forecasting Challenge", - "section": "Why a forecasting challenge?", - "text": "Why a forecasting challenge?\nWe are using forecasts to compare the predictability of different ecosystem variables, in different ecosystem conditions to identify the fundamental predictability of freshwater ecosystems." - }, - { - "objectID": "index.html#what-sites-are-being-forecasted", - "href": "index.html#what-sites-are-being-forecasted", - "title": "Forecasting Challenge", - "section": "What sites are being forecasted?", - "text": "What sites are being forecasted?\n This material is based upon work supported by the National Science Foundation under Grant DEB-1926388. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. Page last updated on 2023-11-06" - }, - { - "objectID": "targets.html", - "href": "targets.html", - "title": "What to forecast", - "section": "", - "text": "library(tidyverse)" - }, - { - "objectID": "targets.html#targets-files", - "href": "targets.html#targets-files", - "title": "What to forecast", - "section": "Targets files", - "text": "Targets files\n\nTerrestrial fluxes\n\nurl <- \"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/targets/project_id=neon4cast/duration=P1D/terrestrial_daily-targets.csv.gz\"\n\n\nterrestrial_targets <- read_csv(url, show_col_types = FALSE)\n\n\nglimpse(terrestrial_targets)\n\nRows: 111,244\nColumns: 6\n$ project_id <chr> \"neon4cast\", \"neon4cast\", \"neon4cast\", \"neon4cast\", \"neon4…\n$ site_id <chr> \"BART\", \"BART\", \"BART\", \"BART\", \"BART\", \"BART\", \"BART\", \"B…\n$ datetime <dttm> 2017-02-02, 2017-02-02, 2017-02-03, 2017-02-03, 2017-02-0…\n$ duration <chr> \"P1D\", \"P1D\", \"P1D\", \"P1D\", \"P1D\", \"P1D\", \"P1D\", \"P1D\", \"P…\n$ variable <chr> \"le\", \"nee\", \"le\", \"nee\", \"le\", \"nee\", \"le\", \"nee\", \"le\", …\n$ observation <dbl> 2.7087720, 0.5801368, 8.4720864, 0.6873984, 6.6410325, 0.6…\n\n\n\n\n\n\n\nvariable\nduration\nDescription\n\n\n\n\nle\nP1D\ndaily mean latent heat flux (W/m2)\n\n\nnee\nP1D\ndaily mean Net ecosystem exchange (gC/m2/day)\n\n\n\n\n\n\nterrestrial_targets |> \n filter(site_id %in% c(\"HARV\", \"OSBS\")) |> \n ggplot(aes(x = datetime, y = observation)) +\n geom_point() +\n facet_grid(variable~site_id, scales = \"free_y\") +\n theme_bw()\n\n\n\n\n\n\nAquatics\n\nurl <- \"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/targets/project_id=neon4cast/duration=P1D/aquatics-targets.csv.gz\"\n\n\naquatics_targets <- read_csv(url, show_col_types = FALSE)\n\n\nglimpse(aquatics_targets)\n\nRows: 58,082\nColumns: 6\n$ project_id <chr> \"neon4cast\", \"neon4cast\", \"neon4cast\", \"neon4cast\", \"neon4…\n$ site_id <chr> \"ARIK\", \"ARIK\", \"ARIK\", \"ARIK\", \"ARIK\", \"ARIK\", \"ARIK\", \"A…\n$ datetime <dttm> 2016-08-17, 2016-08-18, 2016-08-19, 2016-08-20, 2016-08-2…\n$ duration <chr> \"P1D\", \"P1D\", \"P1D\", \"P1D\", \"P1D\", \"P1D\", \"P1D\", \"P1D\", \"P…\n$ variable <chr> \"temperature\", \"temperature\", \"temperature\", \"temperature\"…\n$ observation <dbl> 24.55817, 20.60144, 20.14269, 17.72215, 17.73065, 19.86279…\n\n\n\n\n\n\n\n\n\n\n\n\nvariable\nduration\nDescription\n\n\n\n\ntemperature\nP1D\nSurface Mean Daily Water Temperature (Celsius)\n\n\nchla\nP1D\ndaily mean Chlorophyll-a (ug/L)\n\n\noxygen\nP1D\nSurface Mean Daily Dissolved Oxygen Concentration (mgL)\n\n\n\n\n\n\naquatics_targets |> \n filter(site_id %in% c(\"BARC\", \"CRAM\")) |> \n ggplot(aes(x = datetime, y = observation)) +\n geom_point() +\n facet_grid(variable~site_id, scales = \"free_y\") +\n theme_bw()\n\nWarning: Removed 219 rows containing missing values (`geom_point()`).\n\n\n\n\n\n\n\nPhenology\n\nurl <- \"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/targets/project_id=neon4cast/duration=P1D/phenology-targets.csv.gz\"\n\n\nphenology_targets <- read_csv(url, show_col_types = FALSE)\n\n\nglimpse(phenology_targets)\n\nRows: 266,678\nColumns: 6\n$ project_id <chr> \"neon4cast\", \"neon4cast\", \"neon4cast\", \"neon4cast\", \"neon4…\n$ site_id <chr> \"ABBY\", \"ABBY\", \"ABBY\", \"ABBY\", \"ABBY\", \"ABBY\", \"ABBY\", \"A…\n$ datetime <dttm> 2016-01-29, 2016-01-30, 2016-01-31, 2016-02-01, 2016-02-0…\n$ duration <chr> \"P1D\", \"P1D\", \"P1D\", \"P1D\", \"P1D\", \"P1D\", \"P1D\", \"P1D\", \"P…\n$ variable <chr> \"gcc_90\", \"gcc_90\", \"gcc_90\", \"gcc_90\", \"gcc_90\", \"gcc_90\"…\n$ observation <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…\n\n\n\n\n\n\n\nvariable\nduration\nDescription\n\n\n\n\ngcc_90\nP1D\nNA\n\n\nrcc_90\nP1D\nNA\n\n\n\n\n\n\nphenology_targets |> \n filter(site_id %in% c(\"HARV\", \"OSBS\")) |> \n ggplot(aes(x = datetime, y = observation)) +\n geom_point() +\n facet_grid(variable~site_id, scales = \"free_y\") +\n theme_bw()\n\nWarning: Removed 1672 rows containing missing values (`geom_point()`).\n\n\n\n\n\n\n\nBeetle communities\n\nurl <- \"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/targets/project_id=neon4cast/duration=P1W/beetles-targets.csv.gz\"\n\n\nbeetles_targets <- read_csv(url, show_col_types = FALSE)\n\n\nglimpse(beetles_targets)\n\nRows: 5,280\nColumns: 6\n$ project_id <chr> \"neon4cast\", \"neon4cast\", \"neon4cast\", \"neon4cast\", \"neon4…\n$ site_id <chr> \"ABBY\", \"ABBY\", \"ABBY\", \"ABBY\", \"ABBY\", \"ABBY\", \"ABBY\", \"A…\n$ datetime <dttm> 2016-09-12, 2016-09-12, 2016-09-26, 2016-09-26, 2017-05-0…\n$ duration <chr> \"P1W\", \"P1W\", \"P1W\", \"P1W\", \"P1W\", \"P1W\", \"P1W\", \"P1W\", \"P…\n$ variable <chr> \"abundance\", \"richness\", \"abundance\", \"richness\", \"abundan…\n$ observation <dbl> 0.3599440, 14.0000000, 0.8171691, 13.0000000, 0.2012987, 1…\n\n\n\n\n\n\n\n\n\n\n\n\nvariable\nduration\nDescription\n\n\n\n\nabundance\nP1W\nTotal number of carabid individuals per trap-night, estimated each week of the year at each NEON site\n\n\nrichness\nP1W\nTotal number of unique ‘species’ in a sampling bout for each NEON site each week.\n\n\n\n\n\n\nbeetles_targets |> \n filter(site_id %in% c(\"HARV\", \"OSBS\")) |> \n ggplot(aes(x = datetime, y = observation)) +\n geom_point() +\n facet_grid(variable~site_id, scales = \"free_y\") +\n theme_bw()\n\n\n\n\n\n\nTick populations\n\nurl <- \"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/targets/project_id=neon4cast/duration=P1W/ticks-targets.csv.gz\"\n\n\nticks_targets <- read_csv(url, show_col_types = FALSE)\n\n\nglimpse(ticks_targets)\n\nRows: 622\nColumns: 6\n$ project_id <chr> \"neon4cast\", \"neon4cast\", \"neon4cast\", \"neon4cast\", \"neon4…\n$ site_id <chr> \"BLAN\", \"BLAN\", \"BLAN\", \"BLAN\", \"BLAN\", \"BLAN\", \"BLAN\", \"B…\n$ datetime <dttm> 2015-04-20, 2015-05-11, 2015-06-01, 2015-06-08, 2015-06-2…\n$ duration <chr> \"P1W\", \"P1W\", \"P1W\", \"P1W\", \"P1W\", \"P1W\", \"P1W\", \"P1W\", \"P…\n$ variable <chr> \"amblyomma_americanum\", \"amblyomma_americanum\", \"amblyomma…\n$ observation <dbl> 0.000000, 9.815951, 10.000000, 19.393939, 3.137255, 3.6613…\n\n\n\n\n\n\n\nvariable\nduration\nDescription\n\n\n\n\n\n\n\n\nticks_targets |> \n filter(site_id %in% c(\"BLAN\", \"OSBS\")) |> \n ggplot(aes(x = datetime, y = observation)) +\n geom_point() +\n facet_grid(variable~site_id, scales = \"free_y\") +\n theme_bw()" - }, - { - "objectID": "targets.html#sites", - "href": "targets.html#sites", - "title": "What to forecast", - "section": "Sites", - "text": "Sites\n\nsite_list <- read_csv(\"../neon4cast_field_site_metadata.csv\", show_col_types = FALSE) |> \n rename(site_id = field_site_id) |> \n select(site_id, field_site_name, terrestrial, aquatics, phenology, ticks, beetles) \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nsite_id\nfield_site_name\nterrestrial\naquatics\nphenology\nticks\nbeetles\n\n\n\n\nABBY\nAbby Road NEON\n1\n0\n1\n0\n1\n\n\nARIK\nArikaree River NEON\n0\n1\n0\n0\n0\n\n\nBARC\nLake Barco NEON\n0\n1\n0\n0\n0\n\n\nBARR\nUtqiaġvik NEON\n1\n0\n1\n0\n1\n\n\nBART\nBartlett Experimental Forest NEON\n1\n0\n1\n0\n1\n\n\nBIGC\nUpper Big Creek NEON\n0\n1\n0\n0\n0\n\n\nBLAN\nBlandy Experimental Farm NEON\n1\n0\n1\n1\n1\n\n\nBLDE\nBlacktail Deer Creek NEON\n0\n1\n0\n0\n0\n\n\nBLUE\nBlue River NEON\n0\n1\n0\n0\n0\n\n\nBLWA\nBlack Warrior River NEON\n0\n1\n0\n0\n0\n\n\nBONA\nCaribou-Poker Creeks Research Watershed NEON\n1\n0\n1\n0\n1\n\n\nCARI\nCaribou Creek NEON\n0\n1\n0\n0\n0\n\n\nCLBJ\nLyndon B. Johnson National Grassland NEON\n1\n0\n1\n0\n1\n\n\nCOMO\nComo Creek NEON\n0\n1\n0\n0\n0\n\n\nCPER\nCentral Plains Experimental Range NEON\n1\n0\n1\n0\n1\n\n\nCRAM\nCrampton Lake NEON\n0\n1\n0\n0\n0\n\n\nCUPE\nRio Cupeyes NEON\n0\n1\n0\n0\n0\n\n\nDCFS\nDakota Coteau Field Site NEON\n1\n0\n1\n0\n1\n\n\nDEJU\nDelta Junction NEON\n1\n0\n1\n0\n1\n\n\nDELA\nDead Lake NEON\n1\n0\n1\n0\n1\n\n\nDSNY\nDisney Wilderness Preserve NEON\n1\n0\n1\n0\n1\n\n\nFLNT\nFlint River NEON\n0\n1\n0\n0\n0\n\n\nGRSM\nGreat Smoky Mountains National Park NEON\n1\n0\n1\n0\n1\n\n\nGUAN\nGuanica Forest NEON\n1\n0\n1\n0\n1\n\n\nGUIL\nRio Guilarte NEON\n0\n1\n0\n0\n0\n\n\nHARV\nHarvard Forest & Quabbin Watershed NEON\n1\n0\n1\n0\n1\n\n\nHEAL\nHealy NEON\n1\n0\n1\n0\n1\n\n\nHOPB\nLower Hop Brook NEON\n0\n1\n0\n0\n0\n\n\nJERC\nThe Jones Center At Ichauway NEON\n1\n0\n1\n0\n1\n\n\nJORN\nJornada Experimental Range NEON\n1\n0\n1\n0\n1\n\n\nKING\nKings Creek NEON\n0\n1\n0\n0\n0\n\n\nKONA\nKonza Prairie Agroecosystem NEON\n1\n0\n1\n0\n1\n\n\nKONZ\nKonza Prairie Biological Station NEON\n1\n0\n1\n1\n1\n\n\nLAJA\nLajas Experimental Station NEON\n1\n0\n1\n0\n1\n\n\nLECO\nLeConte Creek NEON\n0\n1\n0\n0\n0\n\n\nLENO\nLenoir Landing NEON\n1\n0\n1\n1\n1\n\n\nLEWI\nLewis Run NEON\n0\n1\n0\n0\n0\n\n\nLIRO\nLittle Rock Lake NEON\n0\n1\n0\n0\n0\n\n\nMART\nMartha Creek NEON\n0\n1\n0\n0\n0\n\n\nMAYF\nMayfield Creek NEON\n0\n1\n0\n0\n0\n\n\nMCDI\nMcDiffett Creek NEON\n0\n1\n0\n0\n0\n\n\nMCRA\nMcRae Creek NEON\n0\n1\n0\n0\n0\n\n\nMLBS\nMountain Lake Biological Station NEON\n1\n0\n1\n0\n1\n\n\nMOAB\nMoab NEON\n1\n0\n1\n0\n1\n\n\nNIWO\nNiwot Ridge NEON\n1\n0\n1\n0\n1\n\n\nNOGP\nNorthern Great Plains Research Laboratory NEON\n1\n0\n1\n0\n1\n\n\nOAES\nMarvin Klemme Range Research Station NEON\n1\n0\n1\n0\n1\n\n\nOKSR\nOksrukuyik Creek NEON\n0\n1\n0\n0\n0\n\n\nONAQ\nOnaqui NEON\n1\n0\n1\n0\n1\n\n\nORNL\nOak Ridge NEON\n1\n0\n1\n1\n1\n\n\nOSBS\nOrdway-Swisher Biological Station NEON\n1\n0\n1\n1\n1\n\n\nPOSE\nPosey Creek NEON\n0\n1\n0\n0\n0\n\n\nPRIN\nPringle Creek NEON\n0\n1\n0\n0\n0\n\n\nPRLA\nPrairie Lake NEON\n0\n1\n0\n0\n0\n\n\nPRPO\nPrairie Pothole NEON\n0\n1\n0\n0\n0\n\n\nPUUM\nPu’u Maka’ala Natural Area Reserve NEON\n1\n0\n1\n0\n1\n\n\nREDB\nRed Butte Creek NEON\n0\n1\n0\n0\n0\n\n\nRMNP\nRocky Mountains NEON\n1\n0\n1\n0\n1\n\n\nSCBI\nSmithsonian Conservation Biology Institute NEON\n1\n0\n1\n1\n1\n\n\nSERC\nSmithsonian Environmental Research Center NEON\n1\n0\n1\n1\n1\n\n\nSJER\nSan Joaquin Experimental Range NEON\n1\n0\n1\n0\n1\n\n\nSOAP\nSoaproot Saddle NEON\n1\n0\n1\n0\n1\n\n\nSRER\nSanta Rita Experimental Range NEON\n1\n0\n1\n0\n1\n\n\nSTEI\nSteigerwaldt-Chequamegon NEON\n1\n0\n1\n0\n1\n\n\nSTER\nNorth Sterling NEON\n1\n0\n1\n0\n1\n\n\nSUGG\nLake Suggs NEON\n0\n1\n0\n0\n0\n\n\nSYCA\nSycamore Creek NEON\n0\n1\n0\n0\n0\n\n\nTALL\nTalladega National Forest NEON\n1\n0\n1\n1\n1\n\n\nTEAK\nLower Teakettle NEON\n1\n0\n1\n0\n1\n\n\nTECR\nTeakettle Creek - Watershed 2 NEON\n0\n1\n0\n0\n0\n\n\nTOMB\nLower Tombigbee River NEON\n0\n1\n0\n0\n0\n\n\nTOOK\nToolik Lake NEON\n0\n1\n0\n0\n0\n\n\nTOOL\nToolik Field Station NEON\n1\n0\n1\n0\n1\n\n\nTREE\nTreehaven NEON\n1\n0\n1\n0\n1\n\n\nUKFS\nUniversity of Kansas Field Station NEON\n1\n0\n1\n1\n1\n\n\nUNDE\nUniversity of Notre Dame Environmental Research Center NEON\n1\n0\n1\n0\n1\n\n\nWALK\nWalker Branch NEON\n0\n1\n0\n0\n0\n\n\nWLOU\nWest St Louis Creek NEON\n0\n1\n0\n0\n0\n\n\nWOOD\nChase Lake National Wildlife Refuge NEON\n1\n0\n1\n0\n1\n\n\nWREF\nWind River Experimental Forest NEON\n1\n0\n1\n0\n1\n\n\nYELL\nYellowstone National Park NEON\n1\n0\n1\n0\n1" - }, - { - "objectID": "targets.html#additional-variables", - "href": "targets.html#additional-variables", - "title": "What to forecast", - "section": "Additional variables", - "text": "Additional variables\n\nHourly water temperature\nDaily time-step variables measured in the monitored stream (Tunnel Branch; site_id = tubr)\n\nurl <- \"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/targets/project_id=neon4cast/duration=PT1H/aquatics-expanded-observations.csv.gz\"\n\naquatics_expanded <- read_csv(url, show_col_types = FALSE)\nglimpse(aquatics_expanded)\n\nRows: 1,412,364\nColumns: 6\n$ project_id <chr> \"neon4cast\", \"neon4cast\", \"neon4cast\", \"neon4cast\", \"neon4…\n$ site_id <chr> \"SUGG\", \"SUGG\", \"SUGG\", \"SUGG\", \"SUGG\", \"SUGG\", \"SUGG\", \"S…\n$ datetime <dttm> 2017-08-19 00:00:00, 2017-08-19 00:00:00, 2017-08-19 00:0…\n$ duration <chr> \"PT1H\", \"PT1H\", \"PT1H\", \"PT1H\", \"PT1H\", \"PT1H\", \"PT1H\", \"P…\n$ variable <chr> \"temperature\", \"temperature\", \"temperature\", \"temperature\"…\n$ observation <dbl> 29.570, 29.600, 29.610, 29.610, 29.610, 29.658, 29.510, 29…" - }, - { - "objectID": "performance.html", - "href": "performance.html", - "title": "Forecast performance", - "section": "", - "text": "This page visualizes the forecasts and forecast performance for the focal target variables." - }, - { - "objectID": "performance.html#sec-performance", - "href": "performance.html#sec-performance", - "title": "Forecast performance", - "section": "Most recent forecasts", - "text": "Most recent forecasts\nForecasts submitted on 2023-11-05\n\nTerrestrial: Net Ecosystem ExchangeTerrestrial: Latent Heat FluxPhenology: GreenessPhenology: RednessAquatics: water temperatureAquatics: dissolved oxygenAquatics: chrophyll-aBeetle community richnessBeetle community abundanceTicks: Amblyomma americanum\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nNULL\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nNULL\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nNULL\n\n\n\n\n\n\nNULL\n\n\n\n\n\n\nNULL" - }, - { - "objectID": "performance.html#forecast-analysis", - "href": "performance.html#forecast-analysis", - "title": "Forecast performance", - "section": "Forecast analysis", - "text": "Forecast analysis\nBelow are forecasts submitted 30 days ago and include the observations used to evaluate them. Mouse over to see the team id, scroll to zoom.\n\nTerrestrial: Net Ecosystem ExchangeTerrestrial: Latent Heat FluxPhenology: GreenessPhenology: RednessAquatics: water temperatureAquatics: disolved oxygenAquatics: chrophyll-aBeetle community richnessBeetle community abundanceTicks: Amblyomma americanum" - }, - { - "objectID": "performance.html#aggregated-scores", - "href": "performance.html#aggregated-scores", - "title": "Forecast performance", - "section": "Aggregated scores", - "text": "Aggregated scores\nAverage skill scores of each model across all sites.\n\nScores are shown by reference date and forecast horizon (in days).\n\nScores are averaged across all submissions of the model with a given horizon or a given reference_datetime using submissions made since 2023-10-07.\n\n\nTerrestrial: Net Ecosystem ExchangeTerrestrial: Latent Heat FluxPhenology: GreenessPhenology: RednessAquatics: water temperatureAquatics: dissolved oxygenAquatics: chrophyll-aBeetle community richnessBeetle community abundanceTicks: Amblyomma americanum\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nNULL\n\n\n\n\n\n\nNULL\n\n\n\n\n\n\nNULL" - }, - { - "objectID": "instructions.html", - "href": "instructions.html", - "title": "How to forecast", - "section": "", - "text": "We provide an overview of the steps for submitting with the details below:\n\nRegister at https://forms.gle/kg2Vkpho9BoMXSy57. You are not required to register if your forecast submission uses the word “example” in your model_id”. Any forecasts with “example” in the model_id will not be used in analyses.\nGenerate forecast with required columns.\nWrite the forecast to a file that follows the standardized naming format.\nSubmit forecast using a provided function.\nWatch forecast be evaluated as new data is collected." - }, - { - "objectID": "instructions.html#tldr-how-to-submit-a-forecast", - "href": "instructions.html#tldr-how-to-submit-a-forecast", - "title": "How to forecast", - "section": "", - "text": "We provide an overview of the steps for submitting with the details below:\n\nRegister at https://forms.gle/kg2Vkpho9BoMXSy57. You are not required to register if your forecast submission uses the word “example” in your model_id”. Any forecasts with “example” in the model_id will not be used in analyses.\nGenerate forecast with required columns.\nWrite the forecast to a file that follows the standardized naming format.\nSubmit forecast using a provided function.\nWatch forecast be evaluated as new data is collected." - }, - { - "objectID": "instructions.html#generating-a-forecast", - "href": "instructions.html#generating-a-forecast", - "title": "How to forecast", - "section": "2 Generating a forecast", - "text": "2 Generating a forecast\n\n2.1 Any approach is allowed\nYou can use any approach to make a prediction about the future conditions at any of the sites and variables\n\n\n2.2 Must include uncertainty\nForecasts require you to make a access of confidence in the prediction of the future. You can represent your confidence (i.e., uncertainty in forecast) using a distribution or numerically using an ensemble (or sample) of predictions.\n\n\n2.3 Any model drivers/covariates/features are welcome\nYou can use any data as inputs. This includes all the targets data available to date. All sensor-based target data is available in near-real time (1 day latency). You may want to use the updated target data to re-train a model or use in data assimilation.\nAs this is a genuine forecasting challenge, you will need forecasted drivers if your model uses drivers as inputs. If you are interested in using forecasted meteorology, we have developed an R package (https://github.com/FLARE-forecast/RopenMeteo) that can be used to access ensemble weather forecasts or seasonal forecasts for the sites. The R package is a wrapper around the open-meteo.com API. The README in the R package repo provides instructions for use.\n\n\n2.4 Forecasts can be for a range of horizons\nForecasts can be submitted at the daily time step 1 day to 10 years ahead\n\n\n2.5 Forecasts can be submitted everyday\nSince forecasts can be submitted everyday, automation is important. We provide an example GitHub repository that can be used to automate your forecast with GitHub Actions. It also includes the use of a custom Docker Container eco4cast/rocker-neon4cast:latest that has many of the packages and functions needed to generate and submit forecasts." - }, - { - "objectID": "instructions.html#forecast-file-format", - "href": "instructions.html#forecast-file-format", - "title": "How to forecast", - "section": "3 Forecast file format", - "text": "3 Forecast file format\nThe file is a csv format with the following columns:\n\nproject_id: use neon4cast\nmodel_id: the short name of the model defined as the model_id in the file name (see below) and in your registration. The model_id should have no spaces. model_id should reflect a method to forecast one or a set of target variables and must be unique to the vera4cast Challenge\ndatetime: forecast timestamp. Format %Y-%m-%d %H:%M:%S with UTC as the time zone. Forecasts submitted with a %Y-%m-%d format will be converted to a full datetime assuming UTC mid-night.\nreference_datetime: The start of the forecast; this should be 0 times steps in the future. There should only be one value of reference_datetime in the file. Format is %Y-%m-%d %H:%M:%S with UTC as the time zone. Forecasts submitted with a %Y-%m-%d format will be converted to a full datetime assuming UTC mid-night.\nduration: the time-step of the forecast. Use the value of P1D for a daily forecast, P1W for a weekly forecast, and PT30M for 30 minute forecast. This value should match the duration of the target variable that you are forecasting. Formatted as ISO 8601 duration\nsite_id: code for site (bvre, fcre, or tubr)\nfamily name of the probability distribution that is described by the parameter values in the parameter column (see list below for accepted distribution). An ensemble forecast as a family of ensemble. See note below about family\nparameter the parameters for the distribution (see note below about parameter column) or the number of the ensemble member. For example the parameters for normal are mu and sigma.\nvariable: standardized variable name\nprediction: forecasted value for the parameter in the parameter column" - }, - { - "objectID": "instructions.html#representing-uncertainity", - "href": "instructions.html#representing-uncertainity", - "title": "How to forecast", - "section": "4 Representing uncertainity", - "text": "4 Representing uncertainity\nUncertainty is represented through the family - parameter columns\n\n4.0.1 Parameteric forecast\nFor a parametric forecast with the normal distribution, the family column uses the word normal to designate a normal distribution and the parameter column must have values of mu and sigma for each forecasted variable, site_id, depth and time combination.\nParameteric forecasts for binary variables should use bernoulli as the distribution.\nThe following names and parameterization of the distribution are supported (family: parameters):\n\nlognormal: mu, sigma\nnormal: mu,sigma\nbernoulli: prob\nbeta: shape1, shape2\nuniform: min, max\ngamma: shape, rate\nlogistic: location, scale\nexponential: rate\npoisson: lambda\n\nIf you are submitting a forecast that is not in the supported list we recommend using the ensemble format and sampling from your distribution to generate a set of ensemble members that represents your distribution.\n\n\n4.0.2 Ensemble (or sample) forecast\nEnsemble (or sample) forecasts use the family value of ensemble and the parameter values are the ensemble index.\nWhen forecasts using the ensemble family are scored, we assume that the ensemble is a set of equally likely realizations that are sampled from a distribution is comparable to that of the observations (i.e., no broad adjustments are required to make the ensemble more consistent with observations). This is referred to as a “perfect ensemble” in Bröcker and Smith (2007). Ensemble (or sample) forecasts are transformed to a probability distribution function (e.g., dressed) using the default methods in the scoringRules R package (empirical version of the quantile decomposition for the crps and kernel density estimation using a Gaussian kernel for the logs). Kernel density estimation uses the default bandwidth produced by the bw.nrd function in the R stats package.\n\n\n4.1 Supported distributions\nThe following names and parameterization of the distribution are supported (family: parameters):\n\nlognormal: mu, sigma\nnormal: mu,sigma\nbernoulli: prob\nbeta: shape1, shape2\nuniform: min, max\ngamma: shape, rate\nlogistic: location, scale\nexponential: rate\npoisson: lambda\n\n\n\n4.2 Example forecasts\nHere is an example of a forecast that uses a normal distribution:\n\ndf <- readr::read_csv(\"https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/forecasts/raw/T20231001231345_daily-2023-10-01-climatology.csv.gz\", show_col_types = FALSE)\ndplyr::glimpse(df)\n\nRows: 288\nColumns: 11\n$ model_id <chr> \"climatology\", \"climatology\", \"climatology\", \"clima…\n$ datetime <date> 2023-10-02, 2023-10-02, 2023-10-02, 2023-10-02, 20…\n$ reference_datetime <date> 2023-10-01, 2023-10-01, 2023-10-01, 2023-10-01, 20…\n$ site_id <chr> \"bvre\", \"bvre\", \"bvre\", \"bvre\", \"bvre\", \"bvre\", \"bv…\n$ variable <chr> \"Chla_ugL_mean\", \"Chla_ugL_mean\", \"Temp_C_mean\", \"T…\n$ family <chr> \"normal\", \"normal\", \"normal\", \"normal\", \"normal\", \"…\n$ parameter <chr> \"mu\", \"sigma\", \"mu\", \"sigma\", \"mu\", \"sigma\", \"mu\", …\n$ prediction <dbl> 10.041987, 2.587292, 18.331126, 2.531732, 9.541139,…\n$ depth_m <dbl> 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1…\n$ project_id <chr> \"vera4cast\", \"vera4cast\", \"vera4cast\", \"vera4cast\",…\n$ duration <chr> \"P1D\", \"P1D\", \"P1D\", \"P1D\", \"P1D\", \"P1D\", \"P1D\", \"P…\n\n\nFor an ensemble (or sample) forecast, the family column uses the word ensemble to designate that it is a ensemble forecast and the parameter column is the ensemble member number (1, 2, 3 …)\n\ndf <- readr::read_csv(\"https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/forecasts/raw/T20231001231348_daily-2023-10-01-persistenceRW.csv.gz\", show_col_types = FALSE)\ndplyr::glimpse(df)\n\nRows: 28,800\nColumns: 11\n$ model_id <chr> \"persistenceRW\", \"persistenceRW\", \"persistenceRW\", …\n$ datetime <dttm> 2023-10-02, 2023-10-03, 2023-10-04, 2023-10-05, 20…\n$ reference_datetime <dttm> 2023-10-01, 2023-10-01, 2023-10-01, 2023-10-01, 20…\n$ site_id <chr> \"bvre\", \"bvre\", \"bvre\", \"bvre\", \"bvre\", \"bvre\", \"bv…\n$ family <chr> \"ensemble\", \"ensemble\", \"ensemble\", \"ensemble\", \"en…\n$ parameter <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …\n$ variable <chr> \"Chla_ugL_mean\", \"Chla_ugL_mean\", \"Chla_ugL_mean\", …\n$ prediction <dbl> 10.59539, 13.04463, 13.86989, 13.81281, 14.09787, 1…\n$ depth_m <dbl> 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1…\n$ project_id <chr> \"vera4cast\", \"vera4cast\", \"vera4cast\", \"vera4cast\",…\n$ duration <chr> \"P1D\", \"P1D\", \"P1D\", \"P1D\", \"P1D\", \"P1D\", \"P1D\", \"P…" - }, - { - "objectID": "instructions.html#submission-process", - "href": "instructions.html#submission-process", - "title": "How to forecast", - "section": "5 Submission process", - "text": "5 Submission process\nIndividual forecast files can be uploaded any time.\nTeams will submit their forecast csv files through an R function.\nThe function is available using the following code\n\nremotes::install_github(\"eco4cast/neon4cast\")\n\nThe submit function is\n\nlibrary(neon4cast)\nneon4cast::submit(forecast_file = \"your_file.csv\")\n\nIf you will be submitting multiple forecasts using the same model_id, use the following\n\nneon4cast::submit(forecast_file = \"your_file.csv\",\n first_submission = FALSE)" - }, - { - "objectID": "instructions.html#post-submission", - "href": "instructions.html#post-submission", - "title": "How to forecast", - "section": "6 Post submission", - "text": "6 Post submission\n\n6.1 Processing\nAfter submission, our servers will process uploaded files by converting to a parquet format on our public s3 storage. A pub_datetime column will be added that denotes when a forecast was submitted.\n\n\n6.2 Evaluation\nAll forecasts are scored daily using new data until the full horizon of the forecast has been scored. Forecasts are scored using the crps and logs functions in the scoringRules R package.\n\n\n6.3 Comparsion\nForecast performance can be compared to the performance of baseline models. We are automatically submitting the following baseline models:\n\nclimatology: the normal distribution (mean and standard deviation) of that day-of-year in the historical observations\npersistenceRW: a random walk model that assumes no change in the mean behavior. The random walk is initialized using the most resent observation.\n\nOur forecast performance page includes evaluations of all submitted models.\n\n\n6.4 Catalog\nInformation for accessing the forecasts and scores can be found on our forecast catalog page." - } -] \ No newline at end of file diff --git a/dashboard/docs/site_libs/bootstrap/bootstrap-icons.css b/dashboard/docs/site_libs/bootstrap/bootstrap-icons.css deleted file mode 100644 index c3bfba6522..0000000000 --- a/dashboard/docs/site_libs/bootstrap/bootstrap-icons.css +++ /dev/null @@ -1,1981 +0,0 @@ -/*! - * Bootstrap Icons v1.10.5 (https://icons.getbootstrap.com/) - * Copyright 2019-2023 The Bootstrap Authors - * Licensed under MIT (https://github.com/twbs/icons/blob/main/LICENSE) - */ - -@font-face { - font-display: block; - font-family: "bootstrap-icons"; - src: -url("./bootstrap-icons.woff?1fa40e8900654d2863d011707b9fb6f2") format("woff"); -} - -.bi::before, -[class^="bi-"]::before, -[class*=" bi-"]::before { - display: 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-.bi-sign-turn-right-fill::before { content: "\f831"; } -.bi-sign-turn-right::before { content: "\f832"; } -.bi-sign-turn-slight-left-fill::before { content: "\f833"; } -.bi-sign-turn-slight-left::before { content: "\f834"; } -.bi-sign-turn-slight-right-fill::before { content: "\f835"; } -.bi-sign-turn-slight-right::before { content: "\f836"; } -.bi-sign-yield-fill::before { content: "\f837"; } -.bi-sign-yield::before { content: "\f838"; } -.bi-ev-station-fill::before { content: "\f839"; } -.bi-ev-station::before { content: "\f83a"; } -.bi-fuel-pump-diesel-fill::before { content: "\f83b"; } -.bi-fuel-pump-diesel::before { content: "\f83c"; } -.bi-fuel-pump-fill::before { content: "\f83d"; } -.bi-fuel-pump::before { content: "\f83e"; } -.bi-0-circle-fill::before { content: "\f83f"; } -.bi-0-circle::before { content: "\f840"; } -.bi-0-square-fill::before { content: "\f841"; } -.bi-0-square::before { content: "\f842"; } -.bi-rocket-fill::before { content: "\f843"; } 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content: "\f857"; } -.bi-sign-intersection-y-fill::before { content: "\f858"; } -.bi-sign-intersection-y::before { content: "\f859"; } -.bi-sign-intersection::before { content: "\f85a"; } -.bi-sign-merge-left-fill::before { content: "\f85b"; } -.bi-sign-merge-left::before { content: "\f85c"; } -.bi-sign-merge-right-fill::before { content: "\f85d"; } -.bi-sign-merge-right::before { content: "\f85e"; } -.bi-sign-no-left-turn-fill::before { content: "\f85f"; } -.bi-sign-no-left-turn::before { content: "\f860"; } -.bi-sign-no-parking-fill::before { content: "\f861"; } -.bi-sign-no-parking::before { content: "\f862"; } -.bi-sign-no-right-turn-fill::before { content: "\f863"; } -.bi-sign-no-right-turn::before { content: "\f864"; } -.bi-sign-railroad-fill::before { content: "\f865"; } -.bi-sign-railroad::before { content: "\f866"; } -.bi-building-add::before { content: "\f867"; } -.bi-building-check::before { content: "\f868"; } -.bi-building-dash::before { content: "\f869"; } -.bi-building-down::before { content: "\f86a"; } -.bi-building-exclamation::before { content: "\f86b"; } -.bi-building-fill-add::before { content: "\f86c"; } -.bi-building-fill-check::before { content: "\f86d"; } -.bi-building-fill-dash::before { content: "\f86e"; } -.bi-building-fill-down::before { content: "\f86f"; } -.bi-building-fill-exclamation::before { content: "\f870"; } -.bi-building-fill-gear::before { content: "\f871"; } -.bi-building-fill-lock::before { content: "\f872"; } -.bi-building-fill-slash::before { content: "\f873"; } -.bi-building-fill-up::before { content: "\f874"; } -.bi-building-fill-x::before { content: "\f875"; } -.bi-building-fill::before { content: "\f876"; } -.bi-building-gear::before { content: "\f877"; } -.bi-building-lock::before { content: "\f878"; } -.bi-building-slash::before { content: "\f879"; } -.bi-building-up::before { content: "\f87a"; } -.bi-building-x::before { content: "\f87b"; } -.bi-buildings-fill::before { content: "\f87c"; } -.bi-buildings::before { content: "\f87d"; } -.bi-bus-front-fill::before { content: "\f87e"; } -.bi-bus-front::before { content: "\f87f"; } -.bi-ev-front-fill::before { content: "\f880"; } -.bi-ev-front::before { content: "\f881"; } -.bi-globe-americas::before { content: "\f882"; } -.bi-globe-asia-australia::before { content: "\f883"; } -.bi-globe-central-south-asia::before { content: "\f884"; } -.bi-globe-europe-africa::before { content: "\f885"; } -.bi-house-add-fill::before { content: "\f886"; } -.bi-house-add::before { content: "\f887"; } -.bi-house-check-fill::before { content: "\f888"; } -.bi-house-check::before { content: "\f889"; } -.bi-house-dash-fill::before { content: "\f88a"; } -.bi-house-dash::before { content: "\f88b"; } -.bi-house-down-fill::before { content: "\f88c"; } -.bi-house-down::before { content: "\f88d"; } -.bi-house-exclamation-fill::before { content: "\f88e"; } -.bi-house-exclamation::before { content: "\f88f"; } -.bi-house-gear-fill::before { content: "\f890"; } -.bi-house-gear::before { content: "\f891"; } -.bi-house-lock-fill::before { content: "\f892"; } -.bi-house-lock::before { content: "\f893"; } -.bi-house-slash-fill::before { content: "\f894"; } -.bi-house-slash::before { content: "\f895"; } -.bi-house-up-fill::before { content: "\f896"; } -.bi-house-up::before { content: "\f897"; } -.bi-house-x-fill::before { content: "\f898"; } -.bi-house-x::before { content: "\f899"; } -.bi-person-add::before { content: "\f89a"; } -.bi-person-down::before { content: "\f89b"; } -.bi-person-exclamation::before { content: "\f89c"; } -.bi-person-fill-add::before { content: "\f89d"; } -.bi-person-fill-check::before { content: "\f89e"; } -.bi-person-fill-dash::before { content: "\f89f"; } -.bi-person-fill-down::before { content: "\f8a0"; } -.bi-person-fill-exclamation::before { content: "\f8a1"; } -.bi-person-fill-gear::before { content: "\f8a2"; } -.bi-person-fill-lock::before { content: "\f8a3"; } -.bi-person-fill-slash::before { content: "\f8a4"; } -.bi-person-fill-up::before { content: "\f8a5"; } -.bi-person-fill-x::before { content: "\f8a6"; } -.bi-person-gear::before { content: "\f8a7"; } -.bi-person-lock::before { content: "\f8a8"; } -.bi-person-slash::before { content: "\f8a9"; } -.bi-person-up::before { content: "\f8aa"; } -.bi-scooter::before { content: "\f8ab"; } -.bi-taxi-front-fill::before { content: "\f8ac"; } -.bi-taxi-front::before { content: "\f8ad"; } -.bi-amd::before { content: "\f8ae"; } -.bi-database-add::before { content: "\f8af"; } -.bi-database-check::before { content: "\f8b0"; } -.bi-database-dash::before { content: "\f8b1"; } -.bi-database-down::before { content: "\f8b2"; } -.bi-database-exclamation::before { content: "\f8b3"; } -.bi-database-fill-add::before { content: "\f8b4"; } -.bi-database-fill-check::before { content: "\f8b5"; } -.bi-database-fill-dash::before { content: "\f8b6"; } -.bi-database-fill-down::before { content: "\f8b7"; } -.bi-database-fill-exclamation::before { content: "\f8b8"; } -.bi-database-fill-gear::before { content: "\f8b9"; } -.bi-database-fill-lock::before { content: "\f8ba"; } -.bi-database-fill-slash::before { content: "\f8bb"; } -.bi-database-fill-up::before { content: "\f8bc"; } -.bi-database-fill-x::before { content: "\f8bd"; } -.bi-database-fill::before { content: "\f8be"; } -.bi-database-gear::before { content: "\f8bf"; } -.bi-database-lock::before { content: "\f8c0"; } -.bi-database-slash::before { content: "\f8c1"; } -.bi-database-up::before { content: "\f8c2"; } -.bi-database-x::before { content: "\f8c3"; } -.bi-database::before { content: "\f8c4"; } -.bi-houses-fill::before { content: "\f8c5"; } -.bi-houses::before { content: "\f8c6"; } -.bi-nvidia::before { content: "\f8c7"; } -.bi-person-vcard-fill::before { content: "\f8c8"; } -.bi-person-vcard::before { content: "\f8c9"; } -.bi-sina-weibo::before { content: "\f8ca"; } -.bi-tencent-qq::before { content: "\f8cb"; } -.bi-wikipedia::before { content: "\f8cc"; } diff --git 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body-start body-content-start] minmax(0px, 1fr) [body-content-end body-end body-end-outset page-end-inset page-end screen-end-inset] 1.5em [screen-end]}body:not(.floating):not(.docked) .page-columns.toc-left .page-columns{display:grid;gap:0;grid-template-columns:[screen-start] 1.5em [screen-start-inset page-start page-start-inset body-start-outset body-start body-content-start] minmax(0px, 1fr) [body-content-end body-end body-end-outset page-end-inset page-end screen-end-inset] 1.5em [screen-end]}nav[role=doc-toc]{display:none}}body,.page-row-navigation{grid-template-rows:[page-top] max-content [contents-top] max-content [contents-bottom] max-content [page-bottom]}.page-rows-contents{grid-template-rows:[content-top] minmax(max-content, 1fr) [content-bottom] minmax(60px, max-content) [page-bottom]}.page-full{grid-column:screen-start/screen-end 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Be={placement:"bottom",modifiers:[],strategy:"absolute"};function Re(){for(var t=arguments.length,e=new Array(t),i=0;ij.on(t,"mouseover",d))),this._element.focus(),this._element.setAttribute("aria-expanded",!0),this._menu.classList.add(Je),this._element.classList.add(Je),j.trigger(this._element,"shown.bs.dropdown",t)}hide(){if(c(this._element)||!this._isShown(this._menu))return;const t={relatedTarget:this._element};this._completeHide(t)}dispose(){this._popper&&this._popper.destroy(),super.dispose()}update(){this._inNavbar=this._detectNavbar(),this._popper&&this._popper.update()}_completeHide(t){j.trigger(this._element,"hide.bs.dropdown",t).defaultPrevented||("ontouchstart"in document.documentElement&&[].concat(...document.body.children).forEach((t=>j.off(t,"mouseover",d))),this._popper&&this._popper.destroy(),this._menu.classList.remove(Je),this._element.classList.remove(Je),this._element.setAttribute("aria-expanded","false"),U.removeDataAttribute(this._menu,"popper"),j.trigger(this._element,"hidden.bs.dropdown",t))}_getConfig(t){if(t={...this.constructor.Default,...U.getDataAttributes(this._element),...t},a(Ue,t,this.constructor.DefaultType),"object"==typeof t.reference&&!o(t.reference)&&"function"!=typeof t.reference.getBoundingClientRect)throw new TypeError(`${Ue.toUpperCase()}: Option "reference" provided type "object" without a required "getBoundingClientRect" method.`);return t}_createPopper(t){if(void 0===Fe)throw new TypeError("Bootstrap's dropdowns require Popper (https://popper.js.org)");let e=this._element;"parent"===this._config.reference?e=t:o(this._config.reference)?e=r(this._config.reference):"object"==typeof this._config.reference&&(e=this._config.reference);const i=this._getPopperConfig(),n=i.modifiers.find((t=>"applyStyles"===t.name&&!1===t.enabled));this._popper=qe(e,this._menu,i),n&&U.setDataAttribute(this._menu,"popper","static")}_isShown(t=this._element){return t.classList.contains(Je)}_getMenuElement(){return V.next(this._element,ei)[0]}_getPlacement(){const t=this._element.parentNode;if(t.classList.contains("dropend"))return ri;if(t.classList.contains("dropstart"))return ai;const e="end"===getComputedStyle(this._menu).getPropertyValue("--bs-position").trim();return t.classList.contains("dropup")?e?ni:ii:e?oi:si}_detectNavbar(){return null!==this._element.closest(".navbar")}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_getPopperConfig(){const t={placement:this._getPlacement(),modifiers:[{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"offset",options:{offset:this._getOffset()}}]};return"static"===this._config.display&&(t.modifiers=[{name:"applyStyles",enabled:!1}]),{...t,..."function"==typeof this._config.popperConfig?this._config.popperConfig(t):this._config.popperConfig}}_selectMenuItem({key:t,target:e}){const i=V.find(".dropdown-menu .dropdown-item:not(.disabled):not(:disabled)",this._menu).filter(l);i.length&&v(i,e,t===Ye,!i.includes(e)).focus()}static jQueryInterface(t){return this.each((function(){const e=hi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}static clearMenus(t){if(t&&(2===t.button||"keyup"===t.type&&"Tab"!==t.key))return;const e=V.find(ti);for(let i=0,n=e.length;ie+t)),this._setElementAttributes(di,"paddingRight",(e=>e+t)),this._setElementAttributes(ui,"marginRight",(e=>e-t))}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t)[e];t.style[e]=`${i(Number.parseFloat(s))}px`}))}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,"paddingRight"),this._resetElementAttributes(di,"paddingRight"),this._resetElementAttributes(ui,"marginRight")}_saveInitialAttribute(t,e){const i=t.style[e];i&&U.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=U.getDataAttribute(t,e);void 0===i?t.style.removeProperty(e):(U.removeDataAttribute(t,e),t.style[e]=i)}))}_applyManipulationCallback(t,e){o(t)?e(t):V.find(t,this._element).forEach(e)}isOverflowing(){return this.getWidth()>0}}const pi={className:"modal-backdrop",isVisible:!0,isAnimated:!1,rootElement:"body",clickCallback:null},mi={className:"string",isVisible:"boolean",isAnimated:"boolean",rootElement:"(element|string)",clickCallback:"(function|null)"},gi="show",_i="mousedown.bs.backdrop";class bi{constructor(t){this._config=this._getConfig(t),this._isAppended=!1,this._element=null}show(t){this._config.isVisible?(this._append(),this._config.isAnimated&&u(this._getElement()),this._getElement().classList.add(gi),this._emulateAnimation((()=>{_(t)}))):_(t)}hide(t){this._config.isVisible?(this._getElement().classList.remove(gi),this._emulateAnimation((()=>{this.dispose(),_(t)}))):_(t)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_getConfig(t){return(t={...pi,..."object"==typeof t?t:{}}).rootElement=r(t.rootElement),a("backdrop",t,mi),t}_append(){this._isAppended||(this._config.rootElement.append(this._getElement()),j.on(this._getElement(),_i,(()=>{_(this._config.clickCallback)})),this._isAppended=!0)}dispose(){this._isAppended&&(j.off(this._element,_i),this._element.remove(),this._isAppended=!1)}_emulateAnimation(t){b(t,this._getElement(),this._config.isAnimated)}}const vi={trapElement:null,autofocus:!0},yi={trapElement:"element",autofocus:"boolean"},wi=".bs.focustrap",Ei="backward";class Ai{constructor(t){this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}activate(){const{trapElement:t,autofocus:e}=this._config;this._isActive||(e&&t.focus(),j.off(document,wi),j.on(document,"focusin.bs.focustrap",(t=>this._handleFocusin(t))),j.on(document,"keydown.tab.bs.focustrap",(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,j.off(document,wi))}_handleFocusin(t){const{target:e}=t,{trapElement:i}=this._config;if(e===document||e===i||i.contains(e))return;const n=V.focusableChildren(i);0===n.length?i.focus():this._lastTabNavDirection===Ei?n[n.length-1].focus():n[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?Ei:"forward")}_getConfig(t){return t={...vi,..."object"==typeof t?t:{}},a("focustrap",t,yi),t}}const Ti="modal",Oi="Escape",Ci={backdrop:!0,keyboard:!0,focus:!0},ki={backdrop:"(boolean|string)",keyboard:"boolean",focus:"boolean"},Li="hidden.bs.modal",xi="show.bs.modal",Di="resize.bs.modal",Si="click.dismiss.bs.modal",Ni="keydown.dismiss.bs.modal",Ii="mousedown.dismiss.bs.modal",Pi="modal-open",ji="show",Mi="modal-static";class Hi extends B{constructor(t,e){super(t),this._config=this._getConfig(e),this._dialog=V.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._ignoreBackdropClick=!1,this._isTransitioning=!1,this._scrollBar=new fi}static get Default(){return Ci}static get NAME(){return Ti}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||j.trigger(this._element,xi,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isAnimated()&&(this._isTransitioning=!0),this._scrollBar.hide(),document.body.classList.add(Pi),this._adjustDialog(),this._setEscapeEvent(),this._setResizeEvent(),j.on(this._dialog,Ii,(()=>{j.one(this._element,"mouseup.dismiss.bs.modal",(t=>{t.target===this._element&&(this._ignoreBackdropClick=!0)}))})),this._showBackdrop((()=>this._showElement(t))))}hide(){if(!this._isShown||this._isTransitioning)return;if(j.trigger(this._element,"hide.bs.modal").defaultPrevented)return;this._isShown=!1;const t=this._isAnimated();t&&(this._isTransitioning=!0),this._setEscapeEvent(),this._setResizeEvent(),this._focustrap.deactivate(),this._element.classList.remove(ji),j.off(this._element,Si),j.off(this._dialog,Ii),this._queueCallback((()=>this._hideModal()),this._element,t)}dispose(){[window,this._dialog].forEach((t=>j.off(t,".bs.modal"))),this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new bi({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new Ai({trapElement:this._element})}_getConfig(t){return t={...Ci,...U.getDataAttributes(this._element),..."object"==typeof t?t:{}},a(Ti,t,ki),t}_showElement(t){const e=this._isAnimated(),i=V.findOne(".modal-body",this._dialog);this._element.parentNode&&this._element.parentNode.nodeType===Node.ELEMENT_NODE||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0,i&&(i.scrollTop=0),e&&u(this._element),this._element.classList.add(ji),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,j.trigger(this._element,"shown.bs.modal",{relatedTarget:t})}),this._dialog,e)}_setEscapeEvent(){this._isShown?j.on(this._element,Ni,(t=>{this._config.keyboard&&t.key===Oi?(t.preventDefault(),this.hide()):this._config.keyboard||t.key!==Oi||this._triggerBackdropTransition()})):j.off(this._element,Ni)}_setResizeEvent(){this._isShown?j.on(window,Di,(()=>this._adjustDialog())):j.off(window,Di)}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(Pi),this._resetAdjustments(),this._scrollBar.reset(),j.trigger(this._element,Li)}))}_showBackdrop(t){j.on(this._element,Si,(t=>{this._ignoreBackdropClick?this._ignoreBackdropClick=!1:t.target===t.currentTarget&&(!0===this._config.backdrop?this.hide():"static"===this._config.backdrop&&this._triggerBackdropTransition())})),this._backdrop.show(t)}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(j.trigger(this._element,"hidePrevented.bs.modal").defaultPrevented)return;const{classList:t,scrollHeight:e,style:i}=this._element,n=e>document.documentElement.clientHeight;!n&&"hidden"===i.overflowY||t.contains(Mi)||(n||(i.overflowY="hidden"),t.add(Mi),this._queueCallback((()=>{t.remove(Mi),n||this._queueCallback((()=>{i.overflowY=""}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;(!i&&t&&!m()||i&&!t&&m())&&(this._element.style.paddingLeft=`${e}px`),(i&&!t&&!m()||!i&&t&&m())&&(this._element.style.paddingRight=`${e}px`)}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=Hi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}j.on(document,"click.bs.modal.data-api",'[data-bs-toggle="modal"]',(function(t){const e=n(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),j.one(e,xi,(t=>{t.defaultPrevented||j.one(e,Li,(()=>{l(this)&&this.focus()}))}));const i=V.findOne(".modal.show");i&&Hi.getInstance(i).hide(),Hi.getOrCreateInstance(e).toggle(this)})),R(Hi),g(Hi);const Bi="offcanvas",Ri={backdrop:!0,keyboard:!0,scroll:!1},Wi={backdrop:"boolean",keyboard:"boolean",scroll:"boolean"},$i="show",zi=".offcanvas.show",qi="hidden.bs.offcanvas";class Fi extends B{constructor(t,e){super(t),this._config=this._getConfig(e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get NAME(){return Bi}static get Default(){return Ri}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||j.trigger(this._element,"show.bs.offcanvas",{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._element.style.visibility="visible",this._backdrop.show(),this._config.scroll||(new fi).hide(),this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add($i),this._queueCallback((()=>{this._config.scroll||this._focustrap.activate(),j.trigger(this._element,"shown.bs.offcanvas",{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(j.trigger(this._element,"hide.bs.offcanvas").defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.remove($i),this._backdrop.hide(),this._queueCallback((()=>{this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._element.style.visibility="hidden",this._config.scroll||(new fi).reset(),j.trigger(this._element,qi)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_getConfig(t){return t={...Ri,...U.getDataAttributes(this._element),..."object"==typeof t?t:{}},a(Bi,t,Wi),t}_initializeBackDrop(){return new bi({className:"offcanvas-backdrop",isVisible:this._config.backdrop,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:()=>this.hide()})}_initializeFocusTrap(){return new Ai({trapElement:this._element})}_addEventListeners(){j.on(this._element,"keydown.dismiss.bs.offcanvas",(t=>{this._config.keyboard&&"Escape"===t.key&&this.hide()}))}static jQueryInterface(t){return this.each((function(){const e=Fi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}j.on(document,"click.bs.offcanvas.data-api",'[data-bs-toggle="offcanvas"]',(function(t){const e=n(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),c(this))return;j.one(e,qi,(()=>{l(this)&&this.focus()}));const i=V.findOne(zi);i&&i!==e&&Fi.getInstance(i).hide(),Fi.getOrCreateInstance(e).toggle(this)})),j.on(window,"load.bs.offcanvas.data-api",(()=>V.find(zi).forEach((t=>Fi.getOrCreateInstance(t).show())))),R(Fi),g(Fi);const Ui=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Vi=/^(?:(?:https?|mailto|ftp|tel|file|sms):|[^#&/:?]*(?:[#/?]|$))/i,Ki=/^data:(?:image\/(?:bmp|gif|jpeg|jpg|png|tiff|webp)|video\/(?:mpeg|mp4|ogg|webm)|audio\/(?:mp3|oga|ogg|opus));base64,[\d+/a-z]+=*$/i,Xi=(t,e)=>{const i=t.nodeName.toLowerCase();if(e.includes(i))return!Ui.has(i)||Boolean(Vi.test(t.nodeValue)||Ki.test(t.nodeValue));const n=e.filter((t=>t instanceof RegExp));for(let t=0,e=n.length;t{Xi(t,r)||i.removeAttribute(t.nodeName)}))}return n.body.innerHTML}const Qi="tooltip",Gi=new Set(["sanitize","allowList","sanitizeFn"]),Zi={animation:"boolean",template:"string",title:"(string|element|function)",trigger:"string",delay:"(number|object)",html:"boolean",selector:"(string|boolean)",placement:"(string|function)",offset:"(array|string|function)",container:"(string|element|boolean)",fallbackPlacements:"array",boundary:"(string|element)",customClass:"(string|function)",sanitize:"boolean",sanitizeFn:"(null|function)",allowList:"object",popperConfig:"(null|object|function)"},Ji={AUTO:"auto",TOP:"top",RIGHT:m()?"left":"right",BOTTOM:"bottom",LEFT:m()?"right":"left"},tn={animation:!0,template:'

',trigger:"hover focus",title:"",delay:0,html:!1,selector:!1,placement:"top",offset:[0,0],container:!1,fallbackPlacements:["top","right","bottom","left"],boundary:"clippingParents",customClass:"",sanitize:!0,sanitizeFn:null,allowList:{"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],div:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},popperConfig:null},en={HIDE:"hide.bs.tooltip",HIDDEN:"hidden.bs.tooltip",SHOW:"show.bs.tooltip",SHOWN:"shown.bs.tooltip",INSERTED:"inserted.bs.tooltip",CLICK:"click.bs.tooltip",FOCUSIN:"focusin.bs.tooltip",FOCUSOUT:"focusout.bs.tooltip",MOUSEENTER:"mouseenter.bs.tooltip",MOUSELEAVE:"mouseleave.bs.tooltip"},nn="fade",sn="show",on="show",rn="out",an=".tooltip-inner",ln=".modal",cn="hide.bs.modal",hn="hover",dn="focus";class un extends B{constructor(t,e){if(void 0===Fe)throw new TypeError("Bootstrap's tooltips require Popper (https://popper.js.org)");super(t),this._isEnabled=!0,this._timeout=0,this._hoverState="",this._activeTrigger={},this._popper=null,this._config=this._getConfig(e),this.tip=null,this._setListeners()}static get Default(){return tn}static get NAME(){return Qi}static get Event(){return en}static get DefaultType(){return Zi}enable(){this._isEnabled=!0}disable(){this._isEnabled=!1}toggleEnabled(){this._isEnabled=!this._isEnabled}toggle(t){if(this._isEnabled)if(t){const e=this._initializeOnDelegatedTarget(t);e._activeTrigger.click=!e._activeTrigger.click,e._isWithActiveTrigger()?e._enter(null,e):e._leave(null,e)}else{if(this.getTipElement().classList.contains(sn))return void this._leave(null,this);this._enter(null,this)}}dispose(){clearTimeout(this._timeout),j.off(this._element.closest(ln),cn,this._hideModalHandler),this.tip&&this.tip.remove(),this._disposePopper(),super.dispose()}show(){if("none"===this._element.style.display)throw new Error("Please use show on visible elements");if(!this.isWithContent()||!this._isEnabled)return;const t=j.trigger(this._element,this.constructor.Event.SHOW),e=h(this._element),i=null===e?this._element.ownerDocument.documentElement.contains(this._element):e.contains(this._element);if(t.defaultPrevented||!i)return;"tooltip"===this.constructor.NAME&&this.tip&&this.getTitle()!==this.tip.querySelector(an).innerHTML&&(this._disposePopper(),this.tip.remove(),this.tip=null);const n=this.getTipElement(),s=(t=>{do{t+=Math.floor(1e6*Math.random())}while(document.getElementById(t));return t})(this.constructor.NAME);n.setAttribute("id",s),this._element.setAttribute("aria-describedby",s),this._config.animation&&n.classList.add(nn);const o="function"==typeof this._config.placement?this._config.placement.call(this,n,this._element):this._config.placement,r=this._getAttachment(o);this._addAttachmentClass(r);const{container:a}=this._config;H.set(n,this.constructor.DATA_KEY,this),this._element.ownerDocument.documentElement.contains(this.tip)||(a.append(n),j.trigger(this._element,this.constructor.Event.INSERTED)),this._popper?this._popper.update():this._popper=qe(this._element,n,this._getPopperConfig(r)),n.classList.add(sn);const l=this._resolvePossibleFunction(this._config.customClass);l&&n.classList.add(...l.split(" ")),"ontouchstart"in document.documentElement&&[].concat(...document.body.children).forEach((t=>{j.on(t,"mouseover",d)}));const c=this.tip.classList.contains(nn);this._queueCallback((()=>{const t=this._hoverState;this._hoverState=null,j.trigger(this._element,this.constructor.Event.SHOWN),t===rn&&this._leave(null,this)}),this.tip,c)}hide(){if(!this._popper)return;const t=this.getTipElement();if(j.trigger(this._element,this.constructor.Event.HIDE).defaultPrevented)return;t.classList.remove(sn),"ontouchstart"in document.documentElement&&[].concat(...document.body.children).forEach((t=>j.off(t,"mouseover",d))),this._activeTrigger.click=!1,this._activeTrigger.focus=!1,this._activeTrigger.hover=!1;const e=this.tip.classList.contains(nn);this._queueCallback((()=>{this._isWithActiveTrigger()||(this._hoverState!==on&&t.remove(),this._cleanTipClass(),this._element.removeAttribute("aria-describedby"),j.trigger(this._element,this.constructor.Event.HIDDEN),this._disposePopper())}),this.tip,e),this._hoverState=""}update(){null!==this._popper&&this._popper.update()}isWithContent(){return Boolean(this.getTitle())}getTipElement(){if(this.tip)return this.tip;const t=document.createElement("div");t.innerHTML=this._config.template;const e=t.children[0];return 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"+t+" required");return n}}}},function(t,e,n){var r=n(8),i=n(11),o=n(43);t.exports=r?function(t,e,n){return i.f(t,e,o(1,n))}:function(t,e,n){return t[e]=n,t}},function(t,e,n){var r=n(3),i=n(8),o=n(11).f;r({target:"Object",stat:!0,forced:Object.defineProperty!==o,sham:!i},{defineProperty:o})},function(t,e,n){var r=n(26),i=TypeError;t.exports=function(t){if(r(t))throw i("Can't call method on "+t);return t}},function(t,e){t.exports=function(t){return null==t}},function(t,e,n){var r=n(4),i=n(7),o=function(t){return i(t)?t:void 0};t.exports=function(t,e){return arguments.length<2?o(r[t]):r[t]&&r[t][e]}},function(t,e,n){var r=n(1);t.exports=r({}.isPrototypeOf)},function(t,e,n){var r=n(97),i=n(18),o=n(181);r||i(Object.prototype,"toString",o,{unsafe:!0})},function(t,e,n){var r,i=n(12),o=n(113),s=n(90),a=n(56),u=n(169),c=n(88),f=n(67),l=f("IE_PROTO"),h=function(){},d=function(t){return" - - - - - - - - - - - - - - - - - - - - - - - - -
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What to forecast

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library(tidyverse)
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Targets files

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Terrestrial fluxes

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url <- "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/targets/project_id=neon4cast/duration=P1D/terrestrial_daily-targets.csv.gz"
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terrestrial_targets <- read_csv(url, show_col_types = FALSE)
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glimpse(terrestrial_targets)
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Rows: 111,244
-Columns: 6
-$ project_id  <chr> "neon4cast", "neon4cast", "neon4cast", "neon4cast", "neon4…
-$ site_id     <chr> "BART", "BART", "BART", "BART", "BART", "BART", "BART", "B…
-$ datetime    <dttm> 2017-02-02, 2017-02-02, 2017-02-03, 2017-02-03, 2017-02-0…
-$ duration    <chr> "P1D", "P1D", "P1D", "P1D", "P1D", "P1D", "P1D", "P1D", "P…
-$ variable    <chr> "le", "nee", "le", "nee", "le", "nee", "le", "nee", "le", …
-$ observation <dbl> 2.7087720, 0.5801368, 8.4720864, 0.6873984, 6.6410325, 0.6…
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variabledurationDescription
leP1Ddaily mean latent heat flux (W/m2)
neeP1Ddaily mean Net ecosystem exchange (gC/m2/day)
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terrestrial_targets |> 
-  filter(site_id %in% c("HARV", "OSBS")) |> 
-  ggplot(aes(x = datetime, y = observation)) +
-  geom_point() +
-  facet_grid(variable~site_id, scales = "free_y") +
-  theme_bw()
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Aquatics

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url <- "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/targets/project_id=neon4cast/duration=P1D/aquatics-targets.csv.gz"
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aquatics_targets <- read_csv(url, show_col_types = FALSE)
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glimpse(aquatics_targets)
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Rows: 58,082
-Columns: 6
-$ project_id  <chr> "neon4cast", "neon4cast", "neon4cast", "neon4cast", "neon4…
-$ site_id     <chr> "ARIK", "ARIK", "ARIK", "ARIK", "ARIK", "ARIK", "ARIK", "A…
-$ datetime    <dttm> 2016-08-17, 2016-08-18, 2016-08-19, 2016-08-20, 2016-08-2…
-$ duration    <chr> "P1D", "P1D", "P1D", "P1D", "P1D", "P1D", "P1D", "P1D", "P…
-$ variable    <chr> "temperature", "temperature", "temperature", "temperature"…
-$ observation <dbl> 24.55817, 20.60144, 20.14269, 17.72215, 17.73065, 19.86279…
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variabledurationDescription
temperatureP1DSurface Mean Daily Water Temperature (Celsius)
chlaP1Ddaily mean Chlorophyll-a (ug/L)
oxygenP1DSurface Mean Daily Dissolved Oxygen Concentration (mgL)
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aquatics_targets |> 
-  filter(site_id %in% c("BARC", "CRAM")) |> 
-  ggplot(aes(x = datetime, y = observation)) +
-  geom_point() +
-  facet_grid(variable~site_id, scales = "free_y") +
-  theme_bw()
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Warning: Removed 219 rows containing missing values (`geom_point()`).
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Phenology

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url <- "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/targets/project_id=neon4cast/duration=P1D/phenology-targets.csv.gz"
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phenology_targets <- read_csv(url, show_col_types = FALSE)
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glimpse(phenology_targets)
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Rows: 266,678
-Columns: 6
-$ project_id  <chr> "neon4cast", "neon4cast", "neon4cast", "neon4cast", "neon4…
-$ site_id     <chr> "ABBY", "ABBY", "ABBY", "ABBY", "ABBY", "ABBY", "ABBY", "A…
-$ datetime    <dttm> 2016-01-29, 2016-01-30, 2016-01-31, 2016-02-01, 2016-02-0…
-$ duration    <chr> "P1D", "P1D", "P1D", "P1D", "P1D", "P1D", "P1D", "P1D", "P…
-$ variable    <chr> "gcc_90", "gcc_90", "gcc_90", "gcc_90", "gcc_90", "gcc_90"…
-$ observation <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
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variabledurationDescription
gcc_90P1DNA
rcc_90P1DNA
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phenology_targets |> 
-  filter(site_id %in% c("HARV", "OSBS")) |> 
-  ggplot(aes(x = datetime, y = observation)) +
-  geom_point() +
-  facet_grid(variable~site_id, scales = "free_y") +
-  theme_bw()
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Warning: Removed 1672 rows containing missing values (`geom_point()`).
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Beetle communities

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url <- "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/targets/project_id=neon4cast/duration=P1W/beetles-targets.csv.gz"
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beetles_targets <- read_csv(url, show_col_types = FALSE)
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glimpse(beetles_targets)
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Rows: 5,280
-Columns: 6
-$ project_id  <chr> "neon4cast", "neon4cast", "neon4cast", "neon4cast", "neon4…
-$ site_id     <chr> "ABBY", "ABBY", "ABBY", "ABBY", "ABBY", "ABBY", "ABBY", "A…
-$ datetime    <dttm> 2016-09-12, 2016-09-12, 2016-09-26, 2016-09-26, 2017-05-0…
-$ duration    <chr> "P1W", "P1W", "P1W", "P1W", "P1W", "P1W", "P1W", "P1W", "P…
-$ variable    <chr> "abundance", "richness", "abundance", "richness", "abundan…
-$ observation <dbl> 0.3599440, 14.0000000, 0.8171691, 13.0000000, 0.2012987, 1…
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variabledurationDescription
abundanceP1WTotal number of carabid individuals per trap-night, estimated each week of the year at each NEON site
richnessP1WTotal number of unique ‘species’ in a sampling bout for each NEON site each week.
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beetles_targets |> 
-  filter(site_id %in% c("HARV", "OSBS")) |> 
-  ggplot(aes(x = datetime, y = observation)) +
-  geom_point() +
-  facet_grid(variable~site_id, scales = "free_y") +
-  theme_bw()
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Tick populations

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url <- "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/targets/project_id=neon4cast/duration=P1W/ticks-targets.csv.gz"
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ticks_targets <- read_csv(url, show_col_types = FALSE)
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glimpse(ticks_targets)
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Rows: 622
-Columns: 6
-$ project_id  <chr> "neon4cast", "neon4cast", "neon4cast", "neon4cast", "neon4…
-$ site_id     <chr> "BLAN", "BLAN", "BLAN", "BLAN", "BLAN", "BLAN", "BLAN", "B…
-$ datetime    <dttm> 2015-04-20, 2015-05-11, 2015-06-01, 2015-06-08, 2015-06-2…
-$ duration    <chr> "P1W", "P1W", "P1W", "P1W", "P1W", "P1W", "P1W", "P1W", "P…
-$ variable    <chr> "amblyomma_americanum", "amblyomma_americanum", "amblyomma…
-$ observation <dbl> 0.000000, 9.815951, 10.000000, 19.393939, 3.137255, 3.6613…
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variabledurationDescription
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ticks_targets |> 
-  filter(site_id %in% c("BLAN", "OSBS")) |> 
-  ggplot(aes(x = datetime, y = observation)) +
-  geom_point() +
-  facet_grid(variable~site_id, scales = "free_y") +
-  theme_bw()
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Sites

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site_list <- read_csv("../neon4cast_field_site_metadata.csv", show_col_types = FALSE) |> 
-  rename(site_id = field_site_id) |> 
-  select(site_id, field_site_name, terrestrial, aquatics, phenology, ticks, beetles)
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site_idfield_site_nameterrestrialaquaticsphenologyticksbeetles
ABBYAbby Road NEON10101
ARIKArikaree River NEON01000
BARCLake Barco NEON01000
BARRUtqiaġvik NEON10101
BARTBartlett Experimental Forest NEON10101
BIGCUpper Big Creek NEON01000
BLANBlandy Experimental Farm NEON10111
BLDEBlacktail Deer Creek NEON01000
BLUEBlue River NEON01000
BLWABlack Warrior River NEON01000
BONACaribou-Poker Creeks Research Watershed NEON10101
CARICaribou Creek NEON01000
CLBJLyndon B. Johnson National Grassland NEON10101
COMOComo Creek NEON01000
CPERCentral Plains Experimental Range NEON10101
CRAMCrampton Lake NEON01000
CUPERio Cupeyes NEON01000
DCFSDakota Coteau Field Site NEON10101
DEJUDelta Junction NEON10101
DELADead Lake NEON10101
DSNYDisney Wilderness Preserve NEON10101
FLNTFlint River NEON01000
GRSMGreat Smoky Mountains National Park NEON10101
GUANGuanica Forest NEON10101
GUILRio Guilarte NEON01000
HARVHarvard Forest & Quabbin Watershed NEON10101
HEALHealy NEON10101
HOPBLower Hop Brook NEON01000
JERCThe Jones Center At Ichauway NEON10101
JORNJornada Experimental Range NEON10101
KINGKings Creek NEON01000
KONAKonza Prairie Agroecosystem NEON10101
KONZKonza Prairie Biological Station NEON10111
LAJALajas Experimental Station NEON10101
LECOLeConte Creek NEON01000
LENOLenoir Landing NEON10111
LEWILewis Run NEON01000
LIROLittle Rock Lake NEON01000
MARTMartha Creek NEON01000
MAYFMayfield Creek NEON01000
MCDIMcDiffett Creek NEON01000
MCRAMcRae Creek NEON01000
MLBSMountain Lake Biological Station NEON10101
MOABMoab NEON10101
NIWONiwot Ridge NEON10101
NOGPNorthern Great Plains Research Laboratory NEON10101
OAESMarvin Klemme Range Research Station NEON10101
OKSROksrukuyik Creek NEON01000
ONAQOnaqui NEON10101
ORNLOak Ridge NEON10111
OSBSOrdway-Swisher Biological Station NEON10111
POSEPosey Creek NEON01000
PRINPringle Creek NEON01000
PRLAPrairie Lake NEON01000
PRPOPrairie Pothole NEON01000
PUUMPu’u Maka’ala Natural Area Reserve NEON10101
REDBRed Butte Creek NEON01000
RMNPRocky Mountains NEON10101
SCBISmithsonian Conservation Biology Institute NEON10111
SERCSmithsonian Environmental Research Center NEON10111
SJERSan Joaquin Experimental Range NEON10101
SOAPSoaproot Saddle NEON10101
SRERSanta Rita Experimental Range NEON10101
STEISteigerwaldt-Chequamegon NEON10101
STERNorth Sterling NEON10101
SUGGLake Suggs NEON01000
SYCASycamore Creek NEON01000
TALLTalladega National Forest NEON10111
TEAKLower Teakettle NEON10101
TECRTeakettle Creek - Watershed 2 NEON01000
TOMBLower Tombigbee River NEON01000
TOOKToolik Lake NEON01000
TOOLToolik Field Station NEON10101
TREETreehaven NEON10101
UKFSUniversity of Kansas Field Station NEON10111
UNDEUniversity of Notre Dame Environmental Research Center NEON10101
WALKWalker Branch NEON01000
WLOUWest St Louis Creek NEON01000
WOODChase Lake National Wildlife Refuge NEON10101
WREFWind River Experimental Forest NEON10101
YELLYellowstone National Park NEON10101
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Additional variables

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Hourly water temperature

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Daily time-step variables measured in the monitored stream (Tunnel Branch; site_id = tubr)

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url <- "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/targets/project_id=neon4cast/duration=PT1H/aquatics-expanded-observations.csv.gz"
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-aquatics_expanded <- read_csv(url, show_col_types = FALSE)
-glimpse(aquatics_expanded)
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Rows: 1,412,364
-Columns: 6
-$ project_id  <chr> "neon4cast", "neon4cast", "neon4cast", "neon4cast", "neon4…
-$ site_id     <chr> "SUGG", "SUGG", "SUGG", "SUGG", "SUGG", "SUGG", "SUGG", "S…
-$ datetime    <dttm> 2017-08-19 00:00:00, 2017-08-19 00:00:00, 2017-08-19 00:0…
-$ duration    <chr> "PT1H", "PT1H", "PT1H", "PT1H", "PT1H", "PT1H", "PT1H", "P…
-$ variable    <chr> "temperature", "temperature", "temperature", "temperature"…
-$ observation <dbl> 29.570, 29.600, 29.610, 29.610, 29.610, 29.658, 29.510, 29…
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CC-BY

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