Data Science Team
Predicting plant phenotypes using machine learning models and environmental and genomic data. Our role is to curate phenotypic and meteorological data, including TERRA REF, National Ecological Observatory Network phenology data, and National Phenology Network data, to be usable and well-documented by collaborators.
As part of this project, we participated in the Ecological Forecasting Initiative's phenology forecast competition.
Outputs
Publications
Thomas, R.Q., C. Boettiger, C.C. Carey, M.C. Dietze, L.R. Johnson, M.A. Kenney, J.S. Mclachlan, J.A. Peters, E.R. Sokol, J.F. Weltzin, A. Willson, W.M. Woelmer, and Challenge Contributors. 2022. The NEON Ecological Forecasting Challenge. ESS Open Archive. https://www.doi.org/10.22541/essoar.167079499.99891914/v1