Hyperspectral reflectance data, plants traits and weights for trained model implemented in: "Data driven discovery and quantification of hyperspectral leaf reflectance phenotypes across a maize diversity panel." https://doi.org/10.1002/ppj2.20106

Supplementary data for the paper: <i>"Data driven discovery and quantification of hyperspectral leaf reflectance phenotypes across a maize diversity panel."</i><b>"maize_2020_unl_reflectance_traits.csv</b> " contains hyperspectral reflectance data and molecular traits for a maize diversity panel grown in Lincoln, Nebraska in 2020.<b>"weights.h5"</b> are the weights of the autoencoder model used to reduce dimensionality of the hyperspectral reflectance data in this study.<b>"</b><b>maize_2020_unl_reflectance_traits.csv" </b>contains the latent variables/reduced dimensions of the hyperspectral reflectance data used in this study.<b>"</b><b>maize_2020_2021_reflectance_traits,csv" </b>contains hyperspectral reflectance, days to pollen and total grain mass (grams) data for a maize diversity panel grown in Lincoln, Nebraska in 2020 &amp; 2021.<b>"</b><b>sorghum_unl_reflectance_traits.csv" </b>contains hyperspectral reflectance data and molecular traits for a sorghum association panel grown in Lincoln, Nebraska in 2018 (Greenhouse conditions) &amp; 2020 (High and low nitrogen field conditions)