Total forecasts submitted to the NEON Challenge
77926
Most recent data for model training
2024-11-18
Number of years of data for model training
11.39
Number of variables being forecasted
10
We invite you to submit forecasts!
The NEON Ecological Forecasting Challenge is an open platform for the ecological and data science communities to forecast data from the National Ecological Observatory Network before they are collected.
The Challenge is hosted by the Ecological Forecasting Initiative Research Coordination Network and sponsored by the U.S. National Science Foundation.
Why a forecasting challenge?
Our 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.
Our 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.
The Challenge is an excellent focal project in university courses.
The Challenge is a platform
Our platform is designed to empower you to contribute by providing target data, numerical weather forecasts, and tutorials. We automatically score your forecasts using the latest NEON data. All forecasts and scores are publicly available through cloud storage and discoverable through our catalog.
Figure from Thomas et al. 2023
Contact
eco4cast.initiative@gmail.com
Acknowledgements
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 2024-11-20