From 6df57c81567455a73ffb3b91c571ef0f164be84e Mon Sep 17 00:00:00 2001 From: github-actions Date: Fri, 26 Jan 2024 07:15:25 +0000 Subject: [PATCH] update pages --- index.html | 6 +- performance.html | 106 ++++----- search.json | 210 +++++++++--------- sitemap.xml | 10 +- targets.html | 4 +- .../figure-html/unnamed-chunk-15-1.png | Bin 68789 -> 68250 bytes .../figure-html/unnamed-chunk-21-1.png | Bin 128575 -> 128429 bytes .../figure-html/unnamed-chunk-31-1.png | Bin 31796 -> 35147 bytes .../figure-html/unnamed-chunk-9-1.png | Bin 99116 -> 99453 bytes 9 files changed, 168 insertions(+), 168 deletions(-) diff --git a/index.html b/index.html index 8d70ff0953..a332f5c53a 100644 --- a/index.html +++ b/index.html @@ -198,7 +198,7 @@

Why a forecast

Total forecasts submitted to the NEON Challenge

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Why a forecast

Most recent data for model training

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Contact

Acknowledgements

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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 2024-01-25

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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 2024-01-26

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On this page

Most recent forecasts

Only the top performing models from the last 30 days are shown.

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If you want to see your forecasts or scores, the links can be found in our forecast catalog and scores catalog" - }, - { - "objectID": "performance.html#sec-performance", - "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 2024-01-24\n\nTerrestrial: Net Ecosystem ExchangeTerrestrial: Latent Heat FluxPhenology: GreenessPhenology: 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\nNULL\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\nNULL\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", - "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. Only the top five performing models are shown. Information on how to access the scores can be found in our catalog\n\nTerrestrial: Net Ecosystem ExchangeTerrestrial: Latent Heat FluxPhenology: GreenessPhenology: RednessAquatics: water temperatureAquatics: disolved 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" - }, - { - "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 in the last 30 days for the daily or sub-daily variables and the past year for the weekly variables\n\nLearn about the continuous ranked probability score here\n\nTerrestrial: Net Ecosystem ExchangeTerrestrial: Latent Heat FluxPhenology: GreenessPhenology: RednessAquatics: water temperatureAquatics: dissolved oxygenAquatics: chrophyll-aBeetle community richnessBeetle community abundanceTicks: Amblyomma americanum" - }, - { - "objectID": "learn-more.html", - "href": "learn-more.html", - "title": "Learn more", - "section": "", - "text": "Introductory tutorial for submitting to Challenge focused on aquatics theme: https://github.com/OlssonF/NEON-forecast-challenge-workshop. A webinar version of tutorial\nMore advanced tutorial for submitting to Challenge focused on terrestrial theme: https://github.com/rqthomas/FluxCourseForecast\nOther tutorial materials about ecological forecasting\n\n\n\n\n\nLewis, A., W. Woelmer, H. Wander, D. Howard, J. Smith, R. McClure, M. Lofton, N. Hammond, R. Corrigan, R.Q. Thomas, C.C. Carey. 2022. Increased adoption of best practices in ecological forecasting enables comparisons of forecastability across systems. Ecological Applications 32: e02500 https://doi.org/10.1002/eap.2500\nLewis, A. S. L., Rollinson, C. R., Allyn, A. J., Ashander, J., Brodie, S., Brookson, C. B., et al. (2023). The power of forecasts to advance ecological theory. Methods in Ecology and Evolution, 14(3), 746–756. https://doi.org/10.1111/2041-210X.13955\n\n\n\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\nThomas, R.Q, R.P. McClure, T.N. Moore, W.M. Woelmer, C. Boettiger, R.J. Figueiredo, R.T. Hensley, C.C. Carey. Near-term forecasts of NEON lakes reveal gradients of environmental predictability across the U.S. Frontiers in Ecology and Environment 21: 220–226. https://doi.org/10.1002/fee.2623\nWheeler, K., M. Dietze, D. LeBauer, J. Peters, A.D. Richardson, R.Q. Thomas, K. Zhu, U. Bhat, S. Munch, R.F Buzbee, M. Chen, B. Goldstein, J.S. Guo, D. Hao, C. Jones, M. Kelly-Fair, H. Liu, C. Malmborg, N. Neupane. D. Pal, A. Ross, V. Shirey, Y. Song, M. Steen, E.A. Vance, W.M. Woelmer, J. Wynne and L. Zachmann. Predicting Spring Phenology in Deciduous Broadleaf Forests: An Open Community Forecast Challenge.\n\n\n\nDietze, M., R.Q. Thomas, J. Peters, C. Boettiger, A. Shiklomanov, and J. Ashander. 2023. A community convention for ecological forecasting: output files and metadata v1.0. Ecosphere 14: e4686 https://doi.org/10.1002/ecs2.4686\n\n\n\nMoore, T.N., R.Q. Thomas, W.M. Woelmer, C.C Carey. 2022. Integrating ecological forecasting into undergraduate ecology curricula with an R Shiny application-based teaching module. Forecasting 4:604-633. https://doi.org/10.3390/forecast4030033\nPeters, J. and R.Q. Thomas. 2021. Going Virtual: What We Learned from the Ecological Forecasting Initiative Research Coordination Network Virtual Workshop. Bulletin of the Ecological Society of America 102: e01828 https://doi.org/10.1002/bes2.1828\nWillson, A.M., H. Gallo, J.A. Peters, A. Abeyta, N. Bueno Watts, C.C. Carey, T.N. Moore, G. Smies, R.Q. Thomas, W.M. Woelmer, and J.S. McLachlan. 2023. Assessing opportunities and inequities in undergraduate ecological forecasting education. Ecology and Evolution 13: e10001. https://doi.org/10.1002/ece3.10001\nWoelmer, W. M., Bradley, L. M., Haber, L. T., Klinges, D. H., Lewis, A. S. L., Mohr, E. J., et al. (2021). Ten simple rules for training yourself in an emerging field. PLOS Computational Biology, 17(10), e1009440. https://doi.org/10.1371/journal.pcbi.1009440\nWoelmer, W.M., T.N. Moore, M.E. Lofton, R.Q. Thomas, and C.C. Carey. 2023. Embedding communication concepts in forecasting training increases students’ understanding of ecological uncertainty Ecosphere 14: e4628 https://doi.org/10.1002/ecs2.4628" - }, - { - "objectID": "learn-more.html#tutorials", - "href": "learn-more.html#tutorials", - "title": "Learn more", - "section": "", - "text": "Introductory tutorial for submitting to Challenge focused on aquatics theme: https://github.com/OlssonF/NEON-forecast-challenge-workshop. A webinar version of tutorial\nMore advanced tutorial for submitting to Challenge focused on terrestrial theme: https://github.com/rqthomas/FluxCourseForecast\nOther tutorial materials about ecological forecasting" - }, - { - "objectID": "learn-more.html#research-from-the-ecological-forecasting-initiative-research-coordination-network.", - "href": "learn-more.html#research-from-the-ecological-forecasting-initiative-research-coordination-network.", - "title": "Learn more", - "section": "", - "text": "Lewis, A., W. Woelmer, H. Wander, D. Howard, J. Smith, R. McClure, M. Lofton, N. Hammond, R. Corrigan, R.Q. Thomas, C.C. Carey. 2022. Increased adoption of best practices in ecological forecasting enables comparisons of forecastability across systems. Ecological Applications 32: e02500 https://doi.org/10.1002/eap.2500\nLewis, A. S. L., Rollinson, C. R., Allyn, A. J., Ashander, J., Brodie, S., Brookson, C. B., et al. (2023). The power of forecasts to advance ecological theory. Methods in Ecology and Evolution, 14(3), 746–756. https://doi.org/10.1111/2041-210X.13955\n\n\n\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\nThomas, R.Q, R.P. McClure, T.N. Moore, W.M. Woelmer, C. Boettiger, R.J. Figueiredo, R.T. Hensley, C.C. Carey. Near-term forecasts of NEON lakes reveal gradients of environmental predictability across the U.S. Frontiers in Ecology and Environment 21: 220–226. https://doi.org/10.1002/fee.2623\nWheeler, K., M. Dietze, D. LeBauer, J. Peters, A.D. Richardson, R.Q. Thomas, K. Zhu, U. Bhat, S. Munch, R.F Buzbee, M. Chen, B. Goldstein, J.S. Guo, D. Hao, C. Jones, M. Kelly-Fair, H. Liu, C. Malmborg, N. Neupane. D. Pal, A. Ross, V. Shirey, Y. Song, M. Steen, E.A. Vance, W.M. Woelmer, J. Wynne and L. Zachmann. Predicting Spring Phenology in Deciduous Broadleaf Forests: An Open Community Forecast Challenge.\n\n\n\nDietze, M., R.Q. Thomas, J. Peters, C. Boettiger, A. Shiklomanov, and J. Ashander. 2023. A community convention for ecological forecasting: output files and metadata v1.0. Ecosphere 14: e4686 https://doi.org/10.1002/ecs2.4686\n\n\n\nMoore, T.N., R.Q. Thomas, W.M. Woelmer, C.C Carey. 2022. Integrating ecological forecasting into undergraduate ecology curricula with an R Shiny application-based teaching module. Forecasting 4:604-633. https://doi.org/10.3390/forecast4030033\nPeters, J. and R.Q. Thomas. 2021. Going Virtual: What We Learned from the Ecological Forecasting Initiative Research Coordination Network Virtual Workshop. Bulletin of the Ecological Society of America 102: e01828 https://doi.org/10.1002/bes2.1828\nWillson, A.M., H. Gallo, J.A. Peters, A. Abeyta, N. Bueno Watts, C.C. Carey, T.N. Moore, G. Smies, R.Q. Thomas, W.M. Woelmer, and J.S. McLachlan. 2023. Assessing opportunities and inequities in undergraduate ecological forecasting education. Ecology and Evolution 13: e10001. https://doi.org/10.1002/ece3.10001\nWoelmer, W. M., Bradley, L. M., Haber, L. T., Klinges, D. H., Lewis, A. S. L., Mohr, E. J., et al. (2021). Ten simple rules for training yourself in an emerging field. PLOS Computational Biology, 17(10), e1009440. https://doi.org/10.1371/journal.pcbi.1009440\nWoelmer, W.M., T.N. Moore, M.E. Lofton, R.Q. Thomas, and C.C. Carey. 2023. Embedding communication concepts in forecasting training increases students’ understanding of ecological uncertainty Ecosphere 14: e4628 https://doi.org/10.1002/ecs2.4628" } ] \ No newline at end of file diff --git a/sitemap.xml b/sitemap.xml index 43ab0eb5e3..d9afe90172 100644 --- a/sitemap.xml +++ b/sitemap.xml @@ -2,23 +2,23 @@ https://projects.ecoforecast.org/neon4cast-ci/catalog.html - 2024-01-24 + 2024-01-25 https://projects.ecoforecast.org/neon4cast-ci/targets.html - 2024-01-24 + 2024-01-25 https://projects.ecoforecast.org/neon4cast-ci/instructions.html - 2024-01-24 + 2024-01-25 https://projects.ecoforecast.org/neon4cast-ci/performance.html - 2024-01-24 + 2024-01-25 https://projects.ecoforecast.org/neon4cast-ci/index.html - 2024-01-24 + 2024-01-25 https://radiantearth.github.io/stac-browser/#/external/raw.githubusercontent.com/eco4cast/neon4cast-ci/main/catalog/catalog.json diff --git a/targets.html b/targets.html index 4fca733ef4..e4de12cbff 100644 --- a/targets.html +++ b/targets.html @@ -969,8 +969,8 @@

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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