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Maize Genomes to Fields: 2014 and 2015 field season genotype, phenotype, environment, and inbred ear image datasets

OBJECTIVES: Crop improvement relies on analysis of phenotypic, genotypic, and environmental data. Given large, well-integrated, multi-year datasets, diverse queries can be made: Which lines perform best in hot, dry environments? Which alleles of specific genes are required for optimal performance in...

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Autores principales: AlKhalifah, Naser, Campbell, Darwin A., Falcon, Celeste M., Gardiner, Jack M., Miller, Nathan D., Romay, Maria Cinta, Walls, Ramona, Walton, Renee, Yeh, Cheng-Ting, Bohn, Martin, Bubert, Jessica, Buckler, Edward S., Ciampitti, Ignacio, Flint-Garcia, Sherry, Gore, Michael A., Graham, Christopher, Hirsch, Candice, Holland, James B., Hooker, David, Kaeppler, Shawn, Knoll, Joseph, Lauter, Nick, Lee, Elizabeth C., Lorenz, Aaron, Lynch, Jonathan P., Moose, Stephen P., Murray, Seth C., Nelson, Rebecca, Rocheford, Torbert, Rodriguez, Oscar, Schnable, James C., Scully, Brian, Smith, Margaret, Springer, Nathan, Thomison, Peter, Tuinstra, Mitchell, Wisser, Randall J., Xu, Wenwei, Ertl, David, Schnable, Patrick S., De Leon, Natalia, Spalding, Edgar P., Edwards, Jode, Lawrence-Dill, Carolyn J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6038255/
https://www.ncbi.nlm.nih.gov/pubmed/29986751
http://dx.doi.org/10.1186/s13104-018-3508-1
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author AlKhalifah, Naser
Campbell, Darwin A.
Falcon, Celeste M.
Gardiner, Jack M.
Miller, Nathan D.
Romay, Maria Cinta
Walls, Ramona
Walton, Renee
Yeh, Cheng-Ting
Bohn, Martin
Bubert, Jessica
Buckler, Edward S.
Ciampitti, Ignacio
Flint-Garcia, Sherry
Gore, Michael A.
Graham, Christopher
Hirsch, Candice
Holland, James B.
Hooker, David
Kaeppler, Shawn
Knoll, Joseph
Lauter, Nick
Lee, Elizabeth C.
Lorenz, Aaron
Lynch, Jonathan P.
Moose, Stephen P.
Murray, Seth C.
Nelson, Rebecca
Rocheford, Torbert
Rodriguez, Oscar
Schnable, James C.
Scully, Brian
Smith, Margaret
Springer, Nathan
Thomison, Peter
Tuinstra, Mitchell
Wisser, Randall J.
Xu, Wenwei
Ertl, David
Schnable, Patrick S.
De Leon, Natalia
Spalding, Edgar P.
Edwards, Jode
Lawrence-Dill, Carolyn J.
author_facet AlKhalifah, Naser
Campbell, Darwin A.
Falcon, Celeste M.
Gardiner, Jack M.
Miller, Nathan D.
Romay, Maria Cinta
Walls, Ramona
Walton, Renee
Yeh, Cheng-Ting
Bohn, Martin
Bubert, Jessica
Buckler, Edward S.
Ciampitti, Ignacio
Flint-Garcia, Sherry
Gore, Michael A.
Graham, Christopher
Hirsch, Candice
Holland, James B.
Hooker, David
Kaeppler, Shawn
Knoll, Joseph
Lauter, Nick
Lee, Elizabeth C.
Lorenz, Aaron
Lynch, Jonathan P.
Moose, Stephen P.
Murray, Seth C.
Nelson, Rebecca
Rocheford, Torbert
Rodriguez, Oscar
Schnable, James C.
Scully, Brian
Smith, Margaret
Springer, Nathan
Thomison, Peter
Tuinstra, Mitchell
Wisser, Randall J.
Xu, Wenwei
Ertl, David
Schnable, Patrick S.
De Leon, Natalia
Spalding, Edgar P.
Edwards, Jode
Lawrence-Dill, Carolyn J.
author_sort AlKhalifah, Naser
collection PubMed
description OBJECTIVES: Crop improvement relies on analysis of phenotypic, genotypic, and environmental data. Given large, well-integrated, multi-year datasets, diverse queries can be made: Which lines perform best in hot, dry environments? Which alleles of specific genes are required for optimal performance in each environment? Such datasets also can be leveraged to predict cultivar performance, even in uncharacterized environments. The maize Genomes to Fields (G2F) Initiative is a multi-institutional organization of scientists working to generate and analyze such datasets from existing, publicly available inbred lines and hybrids. G2F’s genotype by environment project has released 2014 and 2015 datasets to the public, with 2016 and 2017 collected and soon to be made available. DATA DESCRIPTION: Datasets include DNA sequences; traditional phenotype descriptions, as well as detailed ear, cob, and kernel phenotypes quantified by image analysis; weather station measurements; and soil characterizations by site. Data are released as comma separated value spreadsheets accompanied by extensive README text descriptions. For genotypic and phenotypic data, both raw data and a version with outliers removed are reported. For weather data, two versions are reported: a full dataset calibrated against nearby National Weather Service sites and a second calibrated set with outliers and apparent artifacts removed.
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spelling pubmed-60382552018-07-12 Maize Genomes to Fields: 2014 and 2015 field season genotype, phenotype, environment, and inbred ear image datasets AlKhalifah, Naser Campbell, Darwin A. Falcon, Celeste M. Gardiner, Jack M. Miller, Nathan D. Romay, Maria Cinta Walls, Ramona Walton, Renee Yeh, Cheng-Ting Bohn, Martin Bubert, Jessica Buckler, Edward S. Ciampitti, Ignacio Flint-Garcia, Sherry Gore, Michael A. Graham, Christopher Hirsch, Candice Holland, James B. Hooker, David Kaeppler, Shawn Knoll, Joseph Lauter, Nick Lee, Elizabeth C. Lorenz, Aaron Lynch, Jonathan P. Moose, Stephen P. Murray, Seth C. Nelson, Rebecca Rocheford, Torbert Rodriguez, Oscar Schnable, James C. Scully, Brian Smith, Margaret Springer, Nathan Thomison, Peter Tuinstra, Mitchell Wisser, Randall J. Xu, Wenwei Ertl, David Schnable, Patrick S. De Leon, Natalia Spalding, Edgar P. Edwards, Jode Lawrence-Dill, Carolyn J. BMC Res Notes Data Note OBJECTIVES: Crop improvement relies on analysis of phenotypic, genotypic, and environmental data. Given large, well-integrated, multi-year datasets, diverse queries can be made: Which lines perform best in hot, dry environments? Which alleles of specific genes are required for optimal performance in each environment? Such datasets also can be leveraged to predict cultivar performance, even in uncharacterized environments. The maize Genomes to Fields (G2F) Initiative is a multi-institutional organization of scientists working to generate and analyze such datasets from existing, publicly available inbred lines and hybrids. G2F’s genotype by environment project has released 2014 and 2015 datasets to the public, with 2016 and 2017 collected and soon to be made available. DATA DESCRIPTION: Datasets include DNA sequences; traditional phenotype descriptions, as well as detailed ear, cob, and kernel phenotypes quantified by image analysis; weather station measurements; and soil characterizations by site. Data are released as comma separated value spreadsheets accompanied by extensive README text descriptions. For genotypic and phenotypic data, both raw data and a version with outliers removed are reported. For weather data, two versions are reported: a full dataset calibrated against nearby National Weather Service sites and a second calibrated set with outliers and apparent artifacts removed. BioMed Central 2018-07-09 /pmc/articles/PMC6038255/ /pubmed/29986751 http://dx.doi.org/10.1186/s13104-018-3508-1 Text en © The Author(s) 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Data Note
AlKhalifah, Naser
Campbell, Darwin A.
Falcon, Celeste M.
Gardiner, Jack M.
Miller, Nathan D.
Romay, Maria Cinta
Walls, Ramona
Walton, Renee
Yeh, Cheng-Ting
Bohn, Martin
Bubert, Jessica
Buckler, Edward S.
Ciampitti, Ignacio
Flint-Garcia, Sherry
Gore, Michael A.
Graham, Christopher
Hirsch, Candice
Holland, James B.
Hooker, David
Kaeppler, Shawn
Knoll, Joseph
Lauter, Nick
Lee, Elizabeth C.
Lorenz, Aaron
Lynch, Jonathan P.
Moose, Stephen P.
Murray, Seth C.
Nelson, Rebecca
Rocheford, Torbert
Rodriguez, Oscar
Schnable, James C.
Scully, Brian
Smith, Margaret
Springer, Nathan
Thomison, Peter
Tuinstra, Mitchell
Wisser, Randall J.
Xu, Wenwei
Ertl, David
Schnable, Patrick S.
De Leon, Natalia
Spalding, Edgar P.
Edwards, Jode
Lawrence-Dill, Carolyn J.
Maize Genomes to Fields: 2014 and 2015 field season genotype, phenotype, environment, and inbred ear image datasets
title Maize Genomes to Fields: 2014 and 2015 field season genotype, phenotype, environment, and inbred ear image datasets
title_full Maize Genomes to Fields: 2014 and 2015 field season genotype, phenotype, environment, and inbred ear image datasets
title_fullStr Maize Genomes to Fields: 2014 and 2015 field season genotype, phenotype, environment, and inbred ear image datasets
title_full_unstemmed Maize Genomes to Fields: 2014 and 2015 field season genotype, phenotype, environment, and inbred ear image datasets
title_short Maize Genomes to Fields: 2014 and 2015 field season genotype, phenotype, environment, and inbred ear image datasets
title_sort maize genomes to fields: 2014 and 2015 field season genotype, phenotype, environment, and inbred ear image datasets
topic Data Note
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6038255/
https://www.ncbi.nlm.nih.gov/pubmed/29986751
http://dx.doi.org/10.1186/s13104-018-3508-1
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