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Genomes to Fields 2022 Maize genotype by Environment Prediction Competition
OBJECTIVES: The Genomes to Fields (G2F) 2022 Maize Genotype by Environment (GxE) Prediction Competition aimed to develop models for predicting grain yield for the 2022 Maize GxE project field trials, leveraging the datasets previously generated by this project and other publicly available data. DATA...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10353085/ https://www.ncbi.nlm.nih.gov/pubmed/37461058 http://dx.doi.org/10.1186/s13104-023-06421-z |
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author | Lima, Dayane Cristina Washburn, Jacob D. Varela, José Ignacio Chen, Qiuyue Gage, Joseph L. Romay, Maria Cinta Holland, James Ertl, David Lopez-Cruz, Marco Aguate, Fernando M. de los Campos, Gustavo Kaeppler, Shawn Beissinger, Timothy Bohn, Martin Buckler, Edward Edwards, Jode Flint-Garcia, Sherry Gore, Michael A. Hirsch, Candice N. Knoll, Joseph E. McKay, John Minyo, Richard Murray, Seth C. Ortez, Osler A. Schnable, James C. Sekhon, Rajandeep S. Singh, Maninder P. Sparks, Erin E. Thompson, Addie Tuinstra, Mitchell Wallace, Jason Weldekidan, Teclemariam Xu, Wenwei de Leon, Natalia |
author_facet | Lima, Dayane Cristina Washburn, Jacob D. Varela, José Ignacio Chen, Qiuyue Gage, Joseph L. Romay, Maria Cinta Holland, James Ertl, David Lopez-Cruz, Marco Aguate, Fernando M. de los Campos, Gustavo Kaeppler, Shawn Beissinger, Timothy Bohn, Martin Buckler, Edward Edwards, Jode Flint-Garcia, Sherry Gore, Michael A. Hirsch, Candice N. Knoll, Joseph E. McKay, John Minyo, Richard Murray, Seth C. Ortez, Osler A. Schnable, James C. Sekhon, Rajandeep S. Singh, Maninder P. Sparks, Erin E. Thompson, Addie Tuinstra, Mitchell Wallace, Jason Weldekidan, Teclemariam Xu, Wenwei de Leon, Natalia |
author_sort | Lima, Dayane Cristina |
collection | PubMed |
description | OBJECTIVES: The Genomes to Fields (G2F) 2022 Maize Genotype by Environment (GxE) Prediction Competition aimed to develop models for predicting grain yield for the 2022 Maize GxE project field trials, leveraging the datasets previously generated by this project and other publicly available data. DATA DESCRIPTION: This resource used data from the Maize GxE project within the G2F Initiative [1]. The dataset included phenotypic and genotypic data of the hybrids evaluated in 45 locations from 2014 to 2022. Also, soil, weather, environmental covariates data and metadata information for all environments (combination of year and location). Competitors also had access to ReadMe files which described all the files provided. The Maize GxE is a collaborative project and all the data generated becomes publicly available [2]. The dataset used in the 2022 Prediction Competition was curated and lightly filtered for quality and to ensure naming uniformity across years. |
format | Online Article Text |
id | pubmed-10353085 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-103530852023-07-19 Genomes to Fields 2022 Maize genotype by Environment Prediction Competition Lima, Dayane Cristina Washburn, Jacob D. Varela, José Ignacio Chen, Qiuyue Gage, Joseph L. Romay, Maria Cinta Holland, James Ertl, David Lopez-Cruz, Marco Aguate, Fernando M. de los Campos, Gustavo Kaeppler, Shawn Beissinger, Timothy Bohn, Martin Buckler, Edward Edwards, Jode Flint-Garcia, Sherry Gore, Michael A. Hirsch, Candice N. Knoll, Joseph E. McKay, John Minyo, Richard Murray, Seth C. Ortez, Osler A. Schnable, James C. Sekhon, Rajandeep S. Singh, Maninder P. Sparks, Erin E. Thompson, Addie Tuinstra, Mitchell Wallace, Jason Weldekidan, Teclemariam Xu, Wenwei de Leon, Natalia BMC Res Notes Data Note OBJECTIVES: The Genomes to Fields (G2F) 2022 Maize Genotype by Environment (GxE) Prediction Competition aimed to develop models for predicting grain yield for the 2022 Maize GxE project field trials, leveraging the datasets previously generated by this project and other publicly available data. DATA DESCRIPTION: This resource used data from the Maize GxE project within the G2F Initiative [1]. The dataset included phenotypic and genotypic data of the hybrids evaluated in 45 locations from 2014 to 2022. Also, soil, weather, environmental covariates data and metadata information for all environments (combination of year and location). Competitors also had access to ReadMe files which described all the files provided. The Maize GxE is a collaborative project and all the data generated becomes publicly available [2]. The dataset used in the 2022 Prediction Competition was curated and lightly filtered for quality and to ensure naming uniformity across years. BioMed Central 2023-07-17 /pmc/articles/PMC10353085/ /pubmed/37461058 http://dx.doi.org/10.1186/s13104-023-06421-z Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Data Note Lima, Dayane Cristina Washburn, Jacob D. Varela, José Ignacio Chen, Qiuyue Gage, Joseph L. Romay, Maria Cinta Holland, James Ertl, David Lopez-Cruz, Marco Aguate, Fernando M. de los Campos, Gustavo Kaeppler, Shawn Beissinger, Timothy Bohn, Martin Buckler, Edward Edwards, Jode Flint-Garcia, Sherry Gore, Michael A. Hirsch, Candice N. Knoll, Joseph E. McKay, John Minyo, Richard Murray, Seth C. Ortez, Osler A. Schnable, James C. Sekhon, Rajandeep S. Singh, Maninder P. Sparks, Erin E. Thompson, Addie Tuinstra, Mitchell Wallace, Jason Weldekidan, Teclemariam Xu, Wenwei de Leon, Natalia Genomes to Fields 2022 Maize genotype by Environment Prediction Competition |
title | Genomes to Fields 2022 Maize genotype by Environment Prediction Competition |
title_full | Genomes to Fields 2022 Maize genotype by Environment Prediction Competition |
title_fullStr | Genomes to Fields 2022 Maize genotype by Environment Prediction Competition |
title_full_unstemmed | Genomes to Fields 2022 Maize genotype by Environment Prediction Competition |
title_short | Genomes to Fields 2022 Maize genotype by Environment Prediction Competition |
title_sort | genomes to fields 2022 maize genotype by environment prediction competition |
topic | Data Note |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10353085/ https://www.ncbi.nlm.nih.gov/pubmed/37461058 http://dx.doi.org/10.1186/s13104-023-06421-z |
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