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Thomas, R. Q., Boettiger, C., Carey, C. C., Dietze, M. C., Johnson, L. R., Kenney, M. A., et al. (2023). The NEON Ecological Forecasting Challenge. Frontiers in Ecology and the Environment, 21(3), 112–113. https://doi.org/10.1002/fee.2616

We thank NEON for providing the freely available data and the EFI community for feedback on the design of the Challenge. This material is based upon work supported by the National Science Foundation under Grant DEB-1926388.

Page last updated on 2024-03-04

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Thomas, R. Q., Boettiger, C., Carey, C. C., Dietze, M. C., Johnson, L. R., Kenney, M. A., et al. (2023). The NEON Ecological Forecasting Challenge. Frontiers in Ecology and the Environment, 21(3), 112–113. https://doi.org/10.1002/fee.2616

We thank NEON for providing the freely available data and the EFI community for feedback on the design of the Challenge. This material is based upon work supported by the National Science Foundation under Grant DEB-1926388.

Page last updated on 2024-03-05

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diff --git a/search.json b/search.json index 001ca402e5..ed7ae056e0 100644 --- a/search.json +++ b/search.json @@ -11,7 +11,7 @@ "href": "performance.html#sec-performance", "title": "Forecast performance", "section": "Most recent forecasts", - "text": "Most recent forecasts\nOnly the top-performing models from the last 30 days are shown.\nForecasts submitted on 2024-03-02\n\nTerrestrial: net ecosystem exchangeTerrestrial: latent heat fluxPhenology: greennessPhenology: rednessAquatics: water temperatureAquatics: dissolved oxygenAquatics: chlorophyll-aBeetle community richnessBeetle community abundanceTicks: Amblyomma americanum\n\n\nForecast summaries are available here\n\n\n\n\n\n\n\n\nForecast summaries are available here\n\n\n\n\n\n\n\n\nForecast summaries are available here\n\n\n\n\n\n\n\n\nForecast summaries are available here\n\n\n\n\n\n\n\n\nForecast summaries are available here\n\n\n\n\n\n\n\n\nForecast summaries are available here\n\n\n\n\n\n\n\n\nForecast summaries are available here\n\n\n\n\n\n\n\n\nForecast summaries are available here\n\n\n\n\n\n\n\n\nForecast summaries are available here\n\n\n\n\n\n\n\n\nForecast summaries are available here" + "text": "Most recent forecasts\nOnly the top-performing models from the last 30 days are shown.\nForecasts submitted on 2024-03-03\n\nTerrestrial: net ecosystem exchangeTerrestrial: latent heat fluxPhenology: greennessPhenology: rednessAquatics: water temperatureAquatics: dissolved oxygenAquatics: chlorophyll-aBeetle community richnessBeetle community abundanceTicks: Amblyomma americanum\n\n\nForecast summaries are available here\n\n\n\n\n\n\n\n\nForecast summaries are available here\n\n\n\n\n\n\n\n\nForecast summaries are available here\n\n\n\n\n\n\n\n\nForecast summaries are available here\n\n\n\n\n\n\n\n\nForecast summaries are available here\n\n\n\n\n\n\n\n\nForecast summaries are available here\n\n\n\n\n\n\n\n\nForecast summaries are available here\n\n\n\n\n\n\n\n\nForecast summaries are available here\n\n\n\n\n\n\n\n\nForecast summaries are available here\n\n\n\n\n\n\n\n\nForecast summaries are available here" }, { "objectID": "performance.html#forecast-analysis", @@ -60,7 +60,7 @@ "href": "index.html#why-a-forecasting-challenge", "title": "Forecasting Challenge", "section": "Why a forecasting challenge?", - "text": "Why a forecasting challenge?\nOur vision is to use forecasts to advance theory and to support natural resource management. We can begin to realize this vision by creating and analyzing a catalog of forecasts from a range of ecological systems, spatiotemporal scales, and environmental gradients.\nOur forecasting challenge is platform for the ecological and data science communities to advance skills in forecasting ecological systems and for generating forecasts that contribute to a synthetic understanding of patterns of predictability in ecology. Rewards for contributing are skill advancement, joy, and potential involved in manuscripts. We do not currently crown winner nor offer financial awards.\nThe Challenge is an excellent focal project in university courses.\n \n\n\n\n\n\n\n\n\n\nTotal forecasts submitted to the NEON Challenge\n6124\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nMost recent data for model training\n2024-03-02\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nNumber of years of data for model training\n10.68\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nNumber of variables being forecasted\n10" + "text": "Why a forecasting challenge?\nOur vision is to use forecasts to advance theory and to support natural resource management. We can begin to realize this vision by creating and analyzing a catalog of forecasts from a range of ecological systems, spatiotemporal scales, and environmental gradients.\nOur forecasting challenge is platform for the ecological and data science communities to advance skills in forecasting ecological systems and for generating forecasts that contribute to a synthetic understanding of patterns of predictability in ecology. Rewards for contributing are skill advancement, joy, and potential involved in manuscripts. We do not currently crown winner nor offer financial awards.\nThe Challenge is an excellent focal project in university courses.\n \n\n\n\n\n\n\n\n\n\nTotal forecasts submitted to the NEON Challenge\n6141\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nMost recent data for model training\n2024-03-03\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nNumber of years of data for model training\n10.68\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nNumber of variables being forecasted\n10" }, { "objectID": "index.html#the-challenge-is-a-platform", @@ -81,7 +81,7 @@ "href": "index.html#acknowledgements", "title": "Forecasting Challenge", "section": "Acknowledgements", - "text": "Acknowledgements\nThomas, R. Q., Boettiger, C., Carey, C. C., Dietze, M. C., Johnson, L. R., Kenney, M. A., et al. (2023). The NEON Ecological Forecasting Challenge. Frontiers in Ecology and the Environment, 21(3), 112–113. https://doi.org/10.1002/fee.2616 We thank NEON for providing the freely available data and the EFI community for feedback on the design of the Challenge. This material is based upon work supported by the National Science Foundation under Grant DEB-1926388. Page last updated on 2024-03-04" + "text": "Acknowledgements\nThomas, R. Q., Boettiger, C., Carey, C. C., Dietze, M. C., Johnson, L. R., Kenney, M. A., et al. (2023). The NEON Ecological Forecasting Challenge. Frontiers in Ecology and the Environment, 21(3), 112–113. https://doi.org/10.1002/fee.2616 We thank NEON for providing the freely available data and the EFI community for feedback on the design of the Challenge. This material is based upon work supported by the National Science Foundation under Grant DEB-1926388. Page last updated on 2024-03-05" }, { "objectID": "instructions.html", @@ -193,7 +193,7 @@ "href": "targets.html#sec-targets", "title": "What to forecast", "section": "Explore the targets and themes", - "text": "Explore the targets and themes\nInformation on the targets files for the five “themes” is below. In the tables,\n\n“duration” is the time-step of the variable where PT30M is a 30-minute mean, P1D is a daily mean, and P1W is a weekly total.\nThe “forecast horizon” is the number of days-ahead that we want you to forecast.\nThe “latency” is the time between data collection and data availability in the targets file\n\n\nTerrestrial fluxesAquaticsPhenologyBeetle communitiesTick populations\n\n\n\nThe exchange of water and carbon dioxide between the atmosphere and the land is akin to earth’s terrestrial ecosystems breathing rate and lung capacity. \nThe terrestrial flux theme challenges you to forecast the gas exchange at up to 47 sites across the U.S.\nThere are two variables and two time-steps (or duration) that you can forecast.\n\n\n\n\n\n\n\n\n\n\n\n\nvariable\nduration\nDescription\nforecast horizon\nLatency\n\n\n\n\nle\nP1D\ndaily mean latent heat flux (W/m2)\n30 days\n~ 5 days\n\n\nnee\nP1D\ndaily mean Net ecosystem exchange (gC/m2/day)\n30 days\n~ 5 days\n\n\nle\nPT30M\n30 minute mean latent heat flux (W/m2)\n10 days\n~ 5 days\n\n\nnee\nPT30M\n30 minute mean net ecosystem exchange (umol/m2/s)\n10 days\n~ 5 days\n\n\n\n\n\n\nDaily mean\nThe daily mean target file is located at the following URL.\n\nurl_P1D <- \"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/targets/project_id=neon4cast/duration=P1D/terrestrial_daily-targets.csv.gz\"\n\nYou can directly load it into R using the following\n\nterrestrial_targets <- read_csv(url_P1D, show_col_types = FALSE)\n\nThe file contains the following columns\n\n\n\n\n\nproject_id\nsite_id\ndatetime\nduration\nvariable\nobservation\n\n\n\n\nneon4cast\nBART\n2017-02-06\nP1D\nle\n11.5794551\n\n\nneon4cast\nBART\n2017-02-07\nP1D\nle\n4.8951620\n\n\nneon4cast\nBART\n2017-02-09\nP1D\nle\n7.5281656\n\n\nneon4cast\nBART\n2017-02-11\nP1D\nle\n1.1577581\n\n\nneon4cast\nBART\n2017-02-12\nP1D\nle\n0.1999174\n\n\nneon4cast\nBART\n2017-02-13\nP1D\nle\n10.9325370\n\n\n\n\n\nand the time series for the focal sites\n\nterrestrial_targets |> \n filter(site_id %in% terrestrial_focal_sites) |> \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\n30 minute\nThe 30 minute duration targets are designed for forecasting sub-daily carbon and water dynamics. The URL is found at:\n\nurl_PT30M <- \"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/targets/project_id=neon4cast/duration=PT30M/terrestrial_30min-targets.csv.gz\"\n\nLearn more at: https://projects.ecoforecast.org/neon4cast-docs/Terrestrial.html\n\n\n\n\nFreshwater surface water temperature, dissolved oxygen, and chlorophyll-a all influence drinking water quality, are critical for life in aquatic environments, and can represent the health of the ecosystem.\nThe aquatics theme challenges you to forecast daily mean water quality variables at up-to 7 lakes and 27 river/stream NEON sites.\n\n\n\n\n\n\n\n\n\n\n\n\nvariable\nduration\nDescription\nhorizon\nLatency\n\n\n\n\nchla\nP1D\ndaily mean Chlorophyll-a (ug/L)\n30 days\n~ 3 days\n\n\noxygen\nP1D\nSurface Mean Daily Dissolved Oxygen Concentration (mgL)\n30 days\n~ 3 days\n\n\ntemperature\nP1D\nSurface Mean Daily Water Temperature (Celsius)\n30 days\n~ 3 days\n\n\n\n\n\nThe daily mean target file is located at the following URL.\n\nurl <- \"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/targets/project_id=neon4cast/duration=P1D/aquatics-targets.csv.gz\"\n\nYou can directly load it into R using the following\n\naquatics_targets <- read_csv(url, show_col_types = FALSE)\n\nThe file contains the following columns\n\n\n\n\n\nproject_id\nsite_id\ndatetime\nduration\nvariable\nobservation\n\n\n\n\nneon4cast\nARIK\n2016-08-12\nP1D\noxygen\n3.402153\n\n\nneon4cast\nARIK\n2016-08-13\nP1D\noxygen\n4.156236\n\n\nneon4cast\nARIK\n2016-08-14\nP1D\noxygen\n4.071263\n\n\nneon4cast\nARIK\n2016-08-15\nP1D\noxygen\n3.909114\n\n\nneon4cast\nARIK\n2016-08-16\nP1D\noxygen\n3.862653\n\n\nneon4cast\nARIK\n2016-08-17\nP1D\noxygen\n4.354618\n\n\n\n\n\nand the time series for the focal sites\n\naquatics_targets |> \n filter(site_id %in% aquatics_focal_sites) |> \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 941 rows containing missing values or values outside the scale range\n(`geom_point()`).\n\n\n\n\n\nWater temperature at multiple depths measured at the UTC 00 hour are available for the 7 NEON lake sites. These data can be used for model development but will not be used for forecast evaluation.\n\nurl <- \"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/supporting_data/project_id=neon4cast/aquatics-expanded-observations.csv.gz\"\n\nLearn more at: https://projects.ecoforecast.org/neon4cast-docs/Aquatics.html\n\n\n\nPhenology (the changes in plant canopies over the year) has been identified as one of the primary ecological fingerprints of global climate change.\nThe greenness and redness, as measured by a camera looking down at the top of vegetation are a quantitative measure of phenology. The phenology theme challenges you to forecast daily mean greeness and/or redness at up-to 47 terrestrial NEON sites.\n\nurl <- \"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/targets/project_id=neon4cast/duration=P1D/phenology-targets.csv.gz\"\nread_csv(url, show_col_types = FALSE) |> \n distinct(variable, duration) |> \n left_join(target_metadata, by = c(\"variable\",\"duration\")) |> \n filter(variable %in% c(\"gcc_90\",\"rcc_90\")) |> \n select(-class) |> \n knitr::kable()\n\n\n\n\n\n\n\n\n\n\n\nvariable\nduration\nDescription\nhorizon\nLatency\n\n\n\n\ngcc_90\nP1D\nGreen chromatic coordinate is the ratio of the green digital number to the sum of the red, green, blue digital numbers from a digital camera.\n30 days\n~ 2 days\n\n\nrcc_90\nP1D\nRed chromatic coordinate is the ratio of the Red digital number to the sum of the red, green, blue digital numbers from a digital camera.\n30 days\n~ 2 days\n\n\n\n\n\nThe daily mean target file is located at the following URL.\n\nurl <- \"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/targets/project_id=neon4cast/duration=P1D/phenology-targets.csv.gz\"\n\nYou can directly load it into R using the following\n\nphenology_targets <- read_csv(url, show_col_types = FALSE)\n\nThe file contains the following columns\n\n\n\n\n\nproject_id\nsite_id\ndatetime\nduration\nvariable\nobservation\n\n\n\n\nneon4cast\nABBY\n2017-05-30\nP1D\ngcc_90\n0.41659\n\n\nneon4cast\nABBY\n2017-05-31\nP1D\ngcc_90\n0.41570\n\n\nneon4cast\nABBY\n2017-06-01\nP1D\ngcc_90\n0.41780\n\n\nneon4cast\nABBY\n2017-06-02\nP1D\ngcc_90\n0.41539\n\n\nneon4cast\nABBY\n2017-06-03\nP1D\ngcc_90\n0.42216\n\n\nneon4cast\nABBY\n2017-06-04\nP1D\ngcc_90\n0.41659\n\n\n\n\n\nand the time series for the focal sites\n\nphenology_targets |> \n filter(site_id %in% phenology_focal_sites) |> \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 8170 rows containing missing values or values outside the scale range\n(`geom_point()`).\n\n\n\n\n\nLearn more at: https://projects.ecoforecast.org/neon4cast-docs/Phenology.html\n\n\n\nSentinel species (such as beetles) can give forewarning of environmental risk to humans, so are particularly useful for such monitoring and forecasting efforts because they can provide surrogates for other co-located components of biodiversity.\nThe beetles theme challenges you to forecast weekly ground beetles (Family: Carabidae) abundance and richness (two measures of biodiversity) at up-to 47 terrestrial NEON sites.\n\n\n\n\n\n\n\n\n\n\n\n\nvariable\nduration\nDescription\nhorizon\nLatency\n\n\n\n\nabundance\nP1W\nTotal number of carabid individuals per trap-night, estimated each week of the year at each NEON site\n1 year\n~ 6 months\n\n\nrichness\nP1W\nTotal number of unique ‘species’ in a sampling bout for each NEON site each week.\n1 year\n~ 6 months\n\n\n\n\n\nThe daily mean target file is located at the following URL.\n\nurl <- \"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/targets/project_id=neon4cast/duration=P1W/beetles-targets.csv.gz\"\n\nYou can directly load it into R using the following\n\nbeetles_targets <- read_csv(url, show_col_types = FALSE)\n\nThe file contains the following columns\n\n\n\n\n\nproject_id\nsite_id\ndatetime\nduration\nvariable\nobservation\n\n\n\n\nneon4cast\nABBY\n2016-09-12\nP1W\nabundance\n1.0489796\n\n\nneon4cast\nABBY\n2016-09-12\nP1W\nrichness\n14.0000000\n\n\nneon4cast\nABBY\n2016-09-26\nP1W\nabundance\n4.4535714\n\n\nneon4cast\nABBY\n2016-09-26\nP1W\nrichness\n13.0000000\n\n\nneon4cast\nABBY\n2017-05-01\nP1W\nabundance\n0.0553571\n\n\nneon4cast\nABBY\n2017-05-01\nP1W\nrichness\n10.0000000\n\n\n\n\n\nand the time series for the focal sites\n\nbeetles_targets |> \n filter(site_id %in% beetles_focal_sites) |> \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\nLearn more at: https://projects.ecoforecast.org/neon4cast-docs/Beetles.html\n\n\n\nTarget species for the tick population forecasts are Amblyomma americanum nymphal ticks. A. americanum is a vector of ehrlichiosis, tularemia, and southern tick-associated rash illness. The species is present in the eastern United States, and their populations are expanding. There is a correlation between tick population abundance and disease incidence, meaning forecasts for tick abundance have the potential to aid in our understanding of disease risk through time and space.\nThe beetles theme challenges you to forecast weekly Amblyomma americanum nymphal tick abundance at up-to 9 terrestrial NEON sites.\n\n\n\n\n\n\n\n\n\n\n\n\nvariable\nduration\nDescription\nhorizon\nLatency\n\n\n\n\namblyomma_americanum\nP1W\nThe density of Amblyomma americanum nymphs per week (ticks per 1600m^2)\n1 year\n~ 6 months\n\n\n\n\n\nThe weekly target file is located at the following URL.\n\n\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/targets/project_id=neon4cast/duration=P1W/ticks-targets.csv.gz\"\n\n[1] \"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/targets/project_id=neon4cast/duration=P1W/ticks-targets.csv.gz\"\n\n\nYou can directly load it into R using the following\n\nticks_targets <- read_csv(url, show_col_types = FALSE)\n\nThe file contains the following columns\n\n\n\n\n\n\n\n\n\n\n\n\n\nproject_id\nsite_id\ndatetime\nduration\nvariable\nobservation\n\n\n\n\nneon4cast\nBLAN\n2015-04-20\nP1W\namblyomma_americanum\n0.000000\n\n\nneon4cast\nBLAN\n2015-05-11\nP1W\namblyomma_americanum\n9.815951\n\n\nneon4cast\nBLAN\n2015-06-01\nP1W\namblyomma_americanum\n10.000000\n\n\nneon4cast\nBLAN\n2015-06-08\nP1W\namblyomma_americanum\n19.393939\n\n\nneon4cast\nBLAN\n2015-06-22\nP1W\namblyomma_americanum\n3.137255\n\n\nneon4cast\nBLAN\n2015-07-13\nP1W\namblyomma_americanum\n3.661327\n\n\n\n\n\nand the time series for the focal sites\n\nticks_targets |> \n filter(site_id %in% ticks_focal_sites) |> \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\nLearn more at: https://projects.ecoforecast.org/neon4cast-docs/Ticks.html" + "text": "Explore the targets and themes\nInformation on the targets files for the five “themes” is below. In the tables,\n\n“duration” is the time-step of the variable where PT30M is a 30-minute mean, P1D is a daily mean, and P1W is a weekly total.\nThe “forecast horizon” is the number of days-ahead that we want you to forecast.\nThe “latency” is the time between data collection and data availability in the targets file\n\n\nTerrestrial fluxesAquaticsPhenologyBeetle communitiesTick populations\n\n\n\nThe exchange of water and carbon dioxide between the atmosphere and the land is akin to earth’s terrestrial ecosystems breathing rate and lung capacity. \nThe terrestrial flux theme challenges you to forecast the gas exchange at up to 47 sites across the U.S.\nThere are two variables and two time-steps (or duration) that you can forecast.\n\n\n\n\n\n\n\n\n\n\n\n\nvariable\nduration\nDescription\nforecast horizon\nLatency\n\n\n\n\nle\nP1D\ndaily mean latent heat flux (W/m2)\n30 days\n~ 5 days\n\n\nnee\nP1D\ndaily mean Net ecosystem exchange (gC/m2/day)\n30 days\n~ 5 days\n\n\nle\nPT30M\n30 minute mean latent heat flux (W/m2)\n10 days\n~ 5 days\n\n\nnee\nPT30M\n30 minute mean net ecosystem exchange (umol/m2/s)\n10 days\n~ 5 days\n\n\n\n\n\n\nDaily mean\nThe daily mean target file is located at the following URL.\n\nurl_P1D <- \"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/targets/project_id=neon4cast/duration=P1D/terrestrial_daily-targets.csv.gz\"\n\nYou can directly load it into R using the following\n\nterrestrial_targets <- read_csv(url_P1D, show_col_types = FALSE)\n\nThe file contains the following columns\n\n\n\n\n\nproject_id\nsite_id\ndatetime\nduration\nvariable\nobservation\n\n\n\n\nneon4cast\nBART\n2017-02-06\nP1D\nle\n11.5794551\n\n\nneon4cast\nBART\n2017-02-07\nP1D\nle\n4.8951620\n\n\nneon4cast\nBART\n2017-02-09\nP1D\nle\n7.5281656\n\n\nneon4cast\nBART\n2017-02-11\nP1D\nle\n1.1577581\n\n\nneon4cast\nBART\n2017-02-12\nP1D\nle\n0.1999174\n\n\nneon4cast\nBART\n2017-02-13\nP1D\nle\n10.9325370\n\n\n\n\n\nand the time series for the focal sites\n\nterrestrial_targets |> \n filter(site_id %in% terrestrial_focal_sites) |> \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\n30 minute\nThe 30 minute duration targets are designed for forecasting sub-daily carbon and water dynamics. The URL is found at:\n\nurl_PT30M <- \"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/targets/project_id=neon4cast/duration=PT30M/terrestrial_30min-targets.csv.gz\"\n\nLearn more at: https://projects.ecoforecast.org/neon4cast-docs/Terrestrial.html\n\n\n\n\nFreshwater surface water temperature, dissolved oxygen, and chlorophyll-a all influence drinking water quality, are critical for life in aquatic environments, and can represent the health of the ecosystem.\nThe aquatics theme challenges you to forecast daily mean water quality variables at up-to 7 lakes and 27 river/stream NEON sites.\n\n\n\n\n\n\n\n\n\n\n\n\nvariable\nduration\nDescription\nhorizon\nLatency\n\n\n\n\nchla\nP1D\ndaily mean Chlorophyll-a (ug/L)\n30 days\n~ 3 days\n\n\noxygen\nP1D\nSurface Mean Daily Dissolved Oxygen Concentration (mgL)\n30 days\n~ 3 days\n\n\ntemperature\nP1D\nSurface Mean Daily Water Temperature (Celsius)\n30 days\n~ 3 days\n\n\n\n\n\nThe daily mean target file is located at the following URL.\n\nurl <- \"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/targets/project_id=neon4cast/duration=P1D/aquatics-targets.csv.gz\"\n\nYou can directly load it into R using the following\n\naquatics_targets <- read_csv(url, show_col_types = FALSE)\n\nThe file contains the following columns\n\n\n\n\n\nproject_id\nsite_id\ndatetime\nduration\nvariable\nobservation\n\n\n\n\nneon4cast\nARIK\n2016-08-12\nP1D\noxygen\n3.402153\n\n\nneon4cast\nARIK\n2016-08-13\nP1D\noxygen\n4.156236\n\n\nneon4cast\nARIK\n2016-08-14\nP1D\noxygen\n4.071263\n\n\nneon4cast\nARIK\n2016-08-15\nP1D\noxygen\n3.909114\n\n\nneon4cast\nARIK\n2016-08-16\nP1D\noxygen\n3.862653\n\n\nneon4cast\nARIK\n2016-08-17\nP1D\noxygen\n4.354618\n\n\n\n\n\nand the time series for the focal sites\n\naquatics_targets |> \n filter(site_id %in% aquatics_focal_sites) |> \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 941 rows containing missing values or values outside the scale range\n(`geom_point()`).\n\n\n\n\n\nWater temperature at multiple depths measured at the UTC 00 hour are available for the 7 NEON lake sites. These data can be used for model development but will not be used for forecast evaluation.\n\nurl <- \"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/supporting_data/project_id=neon4cast/aquatics-expanded-observations.csv.gz\"\n\nLearn more at: https://projects.ecoforecast.org/neon4cast-docs/Aquatics.html\n\n\n\nPhenology (the changes in plant canopies over the year) has been identified as one of the primary ecological fingerprints of global climate change.\nThe greenness and redness, as measured by a camera looking down at the top of vegetation are a quantitative measure of phenology. The phenology theme challenges you to forecast daily mean greeness and/or redness at up-to 47 terrestrial NEON sites.\n\nurl <- \"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/targets/project_id=neon4cast/duration=P1D/phenology-targets.csv.gz\"\nread_csv(url, show_col_types = FALSE) |> \n distinct(variable, duration) |> \n left_join(target_metadata, by = c(\"variable\",\"duration\")) |> \n filter(variable %in% c(\"gcc_90\",\"rcc_90\")) |> \n select(-class) |> \n knitr::kable()\n\n\n\n\n\n\n\n\n\n\n\nvariable\nduration\nDescription\nhorizon\nLatency\n\n\n\n\ngcc_90\nP1D\nGreen chromatic coordinate is the ratio of the green digital number to the sum of the red, green, blue digital numbers from a digital camera.\n30 days\n~ 2 days\n\n\nrcc_90\nP1D\nRed chromatic coordinate is the ratio of the Red digital number to the sum of the red, green, blue digital numbers from a digital camera.\n30 days\n~ 2 days\n\n\n\n\n\nThe daily mean target file is located at the following URL.\n\nurl <- \"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/targets/project_id=neon4cast/duration=P1D/phenology-targets.csv.gz\"\n\nYou can directly load it into R using the following\n\nphenology_targets <- read_csv(url, show_col_types = FALSE)\n\nThe file contains the following columns\n\n\n\n\n\nproject_id\nsite_id\ndatetime\nduration\nvariable\nobservation\n\n\n\n\nneon4cast\nABBY\n2017-05-30\nP1D\ngcc_90\n0.41659\n\n\nneon4cast\nABBY\n2017-05-31\nP1D\ngcc_90\n0.41570\n\n\nneon4cast\nABBY\n2017-06-01\nP1D\ngcc_90\n0.41780\n\n\nneon4cast\nABBY\n2017-06-02\nP1D\ngcc_90\n0.41539\n\n\nneon4cast\nABBY\n2017-06-03\nP1D\ngcc_90\n0.42216\n\n\nneon4cast\nABBY\n2017-06-04\nP1D\ngcc_90\n0.41659\n\n\n\n\n\nand the time series for the focal sites\n\nphenology_targets |> \n filter(site_id %in% phenology_focal_sites) |> \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 8172 rows containing missing values or values outside the scale range\n(`geom_point()`).\n\n\n\n\n\nLearn more at: https://projects.ecoforecast.org/neon4cast-docs/Phenology.html\n\n\n\nSentinel species (such as beetles) can give forewarning of environmental risk to humans, so are particularly useful for such monitoring and forecasting efforts because they can provide surrogates for other co-located components of biodiversity.\nThe beetles theme challenges you to forecast weekly ground beetles (Family: Carabidae) abundance and richness (two measures of biodiversity) at up-to 47 terrestrial NEON sites.\n\n\n\n\n\n\n\n\n\n\n\n\nvariable\nduration\nDescription\nhorizon\nLatency\n\n\n\n\nabundance\nP1W\nTotal number of carabid individuals per trap-night, estimated each week of the year at each NEON site\n1 year\n~ 6 months\n\n\nrichness\nP1W\nTotal number of unique ‘species’ in a sampling bout for each NEON site each week.\n1 year\n~ 6 months\n\n\n\n\n\nThe daily mean target file is located at the following URL.\n\nurl <- \"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/targets/project_id=neon4cast/duration=P1W/beetles-targets.csv.gz\"\n\nYou can directly load it into R using the following\n\nbeetles_targets <- read_csv(url, show_col_types = FALSE)\n\nThe file contains the following columns\n\n\n\n\n\nproject_id\nsite_id\ndatetime\nduration\nvariable\nobservation\n\n\n\n\nneon4cast\nABBY\n2016-09-12\nP1W\nabundance\n1.0489796\n\n\nneon4cast\nABBY\n2016-09-12\nP1W\nrichness\n14.0000000\n\n\nneon4cast\nABBY\n2016-09-26\nP1W\nabundance\n4.4535714\n\n\nneon4cast\nABBY\n2016-09-26\nP1W\nrichness\n13.0000000\n\n\nneon4cast\nABBY\n2017-05-01\nP1W\nabundance\n0.0553571\n\n\nneon4cast\nABBY\n2017-05-01\nP1W\nrichness\n10.0000000\n\n\n\n\n\nand the time series for the focal sites\n\nbeetles_targets |> \n filter(site_id %in% beetles_focal_sites) |> \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\nLearn more at: https://projects.ecoforecast.org/neon4cast-docs/Beetles.html\n\n\n\nTarget species for the tick population forecasts are Amblyomma americanum nymphal ticks. A. americanum is a vector of ehrlichiosis, tularemia, and southern tick-associated rash illness. The species is present in the eastern United States, and their populations are expanding. There is a correlation between tick population abundance and disease incidence, meaning forecasts for tick abundance have the potential to aid in our understanding of disease risk through time and space.\nThe beetles theme challenges you to forecast weekly Amblyomma americanum nymphal tick abundance at up-to 9 terrestrial NEON sites.\n\n\n\n\n\n\n\n\n\n\n\n\nvariable\nduration\nDescription\nhorizon\nLatency\n\n\n\n\namblyomma_americanum\nP1W\nThe density of Amblyomma americanum nymphs per week (ticks per 1600m^2)\n1 year\n~ 6 months\n\n\n\n\n\nThe weekly target file is located at the following URL.\n\n\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/targets/project_id=neon4cast/duration=P1W/ticks-targets.csv.gz\"\n\n[1] \"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/targets/project_id=neon4cast/duration=P1W/ticks-targets.csv.gz\"\n\n\nYou can directly load it into R using the following\n\nticks_targets <- read_csv(url, show_col_types = FALSE)\n\nThe file contains the following columns\n\n\n\n\n\n\n\n\n\n\n\n\n\nproject_id\nsite_id\ndatetime\nduration\nvariable\nobservation\n\n\n\n\nneon4cast\nBLAN\n2015-04-20\nP1W\namblyomma_americanum\n0.000000\n\n\nneon4cast\nBLAN\n2015-05-11\nP1W\namblyomma_americanum\n9.815951\n\n\nneon4cast\nBLAN\n2015-06-01\nP1W\namblyomma_americanum\n10.000000\n\n\nneon4cast\nBLAN\n2015-06-08\nP1W\namblyomma_americanum\n19.393939\n\n\nneon4cast\nBLAN\n2015-06-22\nP1W\namblyomma_americanum\n3.137255\n\n\nneon4cast\nBLAN\n2015-07-13\nP1W\namblyomma_americanum\n3.661327\n\n\n\n\n\nand the time series for the focal sites\n\nticks_targets |> \n filter(site_id %in% ticks_focal_sites) |> \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\nLearn more at: https://projects.ecoforecast.org/neon4cast-docs/Ticks.html" }, { "objectID": "targets.html#explore-the-sites", diff --git a/sitemap.xml b/sitemap.xml index 9422d9debd..01221e0e8d 100644 --- a/sitemap.xml +++ b/sitemap.xml @@ -2,23 +2,23 @@ https://projects.ecoforecast.org/neon4cast-ci/catalog.html - 2024-03-03 + 2024-03-04 https://projects.ecoforecast.org/neon4cast-ci/targets.html - 2024-03-03 + 2024-03-04 https://projects.ecoforecast.org/neon4cast-ci/instructions.html - 2024-03-03 + 2024-03-04 https://projects.ecoforecast.org/neon4cast-ci/performance.html - 2024-03-03 + 2024-03-04 https://projects.ecoforecast.org/neon4cast-ci/index.html - 2024-03-03 + 2024-03-04 https://radiantearth.github.io/stac-browser/#/external/raw.githubusercontent.com/eco4cast/neon4cast-ci/main/catalog/catalog.json @@ -580,6 +580,10 @@ https://radiantearth.github.io/stac-browser/#/external/raw.githubusercontent.com/eco4cast/neon4cast-ci/main/catalog/scores/models/collection.json 2024-02-23 + + https://radiantearth.github.io/stac-browser/#/external/raw.githubusercontent.com/eco4cast/neon4cast-ci/main/catalog/scores/models/model_items/GAM_air_wind.json + 2024-02-23 + https://radiantearth.github.io/stac-browser/#/external/raw.githubusercontent.com/eco4cast/neon4cast-ci/main/catalog/scores/models/model_items/GLEON_JRabaey_temp_physics.json 2024-02-23 @@ -592,6 +596,10 @@ https://radiantearth.github.io/stac-browser/#/external/raw.githubusercontent.com/eco4cast/neon4cast-ci/main/catalog/scores/models/model_items/GLEON_physics.json 2024-02-23 + + https://radiantearth.github.io/stac-browser/#/external/raw.githubusercontent.com/eco4cast/neon4cast-ci/main/catalog/scores/models/model_items/MALM.json + 2024-02-23 + https://radiantearth.github.io/stac-browser/#/external/raw.githubusercontent.com/eco4cast/neon4cast-ci/main/catalog/scores/models/model_items/MAL_model_unc.json 2024-02-23 @@ -600,10 +608,18 @@ https://radiantearth.github.io/stac-browser/#/external/raw.githubusercontent.com/eco4cast/neon4cast-ci/main/catalog/scores/models/model_items/PEG.json 2024-02-23 + + https://radiantearth.github.io/stac-browser/#/external/raw.githubusercontent.com/eco4cast/neon4cast-ci/main/catalog/scores/models/model_items/Sujan_forecast.json + 2024-02-23 + https://radiantearth.github.io/stac-browser/#/external/raw.githubusercontent.com/eco4cast/neon4cast-ci/main/catalog/scores/models/model_items/TESTclimatology.json 2024-02-23 + + https://radiantearth.github.io/stac-browser/#/external/raw.githubusercontent.com/eco4cast/neon4cast-ci/main/catalog/scores/models/model_items/TSLM_seasonal_JM.json + 2024-02-23 + https://radiantearth.github.io/stac-browser/#/external/raw.githubusercontent.com/eco4cast/neon4cast-ci/main/catalog/scores/models/model_items/USGSHABs1.json 2024-02-23 @@ -620,6 +636,10 @@ https://radiantearth.github.io/stac-browser/#/external/raw.githubusercontent.com/eco4cast/neon4cast-ci/main/catalog/scores/models/model_items/baseline_ensemble.json 2024-02-23 + + https://radiantearth.github.io/stac-browser/#/external/raw.githubusercontent.com/eco4cast/neon4cast-ci/main/catalog/scores/models/model_items/bee_bake_RFModel_2024.json + 2024-02-23 + https://radiantearth.github.io/stac-browser/#/external/raw.githubusercontent.com/eco4cast/neon4cast-ci/main/catalog/scores/models/model_items/bet_example_lm_temp2m_CMCC_CM2_VHR4.json 2024-02-23 @@ -808,6 +828,10 @@ https://radiantearth.github.io/stac-browser/#/external/raw.githubusercontent.com/eco4cast/neon4cast-ci/main/catalog/scores/models/model_items/glm_aed_v1.json 2024-02-23 + + https://radiantearth.github.io/stac-browser/#/external/raw.githubusercontent.com/eco4cast/neon4cast-ci/main/catalog/scores/models/model_items/hotdeck.json + 2024-02-23 + https://radiantearth.github.io/stac-browser/#/external/raw.githubusercontent.com/eco4cast/neon4cast-ci/main/catalog/scores/models/model_items/icon_seamless.json 2024-02-23 @@ -820,6 +844,10 @@ https://radiantearth.github.io/stac-browser/#/external/raw.githubusercontent.com/eco4cast/neon4cast-ci/main/catalog/scores/models/model_items/lasso.json 2024-02-23 + + https://radiantearth.github.io/stac-browser/#/external/raw.githubusercontent.com/eco4cast/neon4cast-ci/main/catalog/scores/models/model_items/lm_AT_WTL_WS.json + 2024-02-23 + https://radiantearth.github.io/stac-browser/#/external/raw.githubusercontent.com/eco4cast/neon4cast-ci/main/catalog/scores/models/model_items/mean.json 2024-02-23 @@ -828,6 +856,10 @@ https://radiantearth.github.io/stac-browser/#/external/raw.githubusercontent.com/eco4cast/neon4cast-ci/main/catalog/scores/models/model_items/mkricheldorf_example.json 2024-02-23 + + https://radiantearth.github.io/stac-browser/#/external/raw.githubusercontent.com/eco4cast/neon4cast-ci/main/catalog/scores/models/model_items/mlp1_wtempforecast_LF.json + 2024-02-23 + https://radiantearth.github.io/stac-browser/#/external/raw.githubusercontent.com/eco4cast/neon4cast-ci/main/catalog/scores/models/model_items/neon4cast_example.json 2024-02-23 @@ -956,6 +988,14 @@ https://radiantearth.github.io/stac-browser/#/external/raw.githubusercontent.com/eco4cast/neon4cast-ci/main/catalog/scores/models/model_items/tg_temp_lm_all_sites.json 2024-02-23 + + https://radiantearth.github.io/stac-browser/#/external/raw.githubusercontent.com/eco4cast/neon4cast-ci/main/catalog/scores/models/model_items/tidymodels_ML.json + 2024-02-23 + + + https://radiantearth.github.io/stac-browser/#/external/raw.githubusercontent.com/eco4cast/neon4cast-ci/main/catalog/scores/models/model_items/tidymodels_lm.json + 2024-02-23 + https://radiantearth.github.io/stac-browser/#/external/raw.githubusercontent.com/eco4cast/neon4cast-ci/main/catalog/scores/models/model_items/tslm.json 2024-02-23 @@ -964,6 +1004,10 @@ https://radiantearth.github.io/stac-browser/#/external/raw.githubusercontent.com/eco4cast/neon4cast-ci/main/catalog/scores/models/model_items/xgboost_parallel.json 2024-02-23 + + https://radiantearth.github.io/stac-browser/#/external/raw.githubusercontent.com/eco4cast/neon4cast-ci/main/catalog/scores/models/model_items/zimmerman_proj1.json + 2024-02-23 + https://radiantearth.github.io/stac-browser/#/external/raw.githubusercontent.com/eco4cast/neon4cast-ci/main/catalog/sites/collection.json 2024-02-23 diff --git a/targets.html b/targets.html index 0ddcbf22bf..4b51afe146 100644 --- a/targets.html +++ b/targets.html @@ -689,7 +689,7 @@

30 minute

facet_grid(variable~site_id, scales = "free_y") + theme_bw()
-
Warning: Removed 8170 rows containing missing values or values outside the scale range
+
Warning: Removed 8172 rows containing missing values or values outside the scale range
 (`geom_point()`).
@@ -971,8 +971,8 @@

30 minute

Explore the sites

-
- +
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The following table lists all the sites in the NEON Ecological Forecasting Challenge. The columns with “theme” names incidate whether that site is included in that theme’s target file.

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