Published March 3, 2023
| Version 1
Dataset
Open
Ensemble BLUP, Machine Learning, and Deep Learning Models Predict Maize Yield Better Than Each Model Alone.
Description
Data and scripts exploring ensembling strategies using the models developed in Kick et al., 2023 (see also 1, 2). Download all files to a single directory then run setup.sh or manually unzip using tar.
| Filename | Description |
| setup.sh | Simple script that unzips zipped directories |
| ext_data | Reduced data from Kick et al. 2023 |
| ext_data_notebooks | Contains python notebooks containing analysis and R markdown file containing visualization of results. Python and R data objects are written to allow results to be read in instead of re-generated. |
| output | Folder containing a placeholder file. |
This research used resources provided by the United States Department of Agriculture’s Agricultural Research Service (project number 5070-21000-041-000-D). The SCINet project of the USDA Agricultural Research Service (project number 0500-00093-001-00-D) was instrumental in the training of the models used in this work. In addition, we would like to acknowledge those presently and historically involved in generating data for the Genomes to Fields Initiative.
Files
Files
(3.8 GB)
| Name | Size | Download all |
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md5:05bdb783ee6514c8c072e47680af8ff7
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66 Bytes | Download |
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md5:100ae4ce1ac0009c19c43dbd4ba54c79
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551 Bytes | Download |
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md5:c70d338ef14c3c478ff4887502256ed7
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3.8 GB | Download |