BMC Research Notes (Jul 2018)
Maize Genomes to Fields: 2014 and 2015 field season genotype, phenotype, environment, and inbred ear image datasets
- Naser AlKhalifah,
- Darwin A. Campbell,
- Celeste M. Falcon,
- Jack M. Gardiner,
- Nathan D. Miller,
- Maria Cinta Romay,
- Ramona Walls,
- Renee Walton,
- Cheng-Ting Yeh,
- Martin Bohn,
- Jessica Bubert,
- Edward S. Buckler,
- Ignacio Ciampitti,
- Sherry Flint-Garcia,
- Michael A. Gore,
- Christopher Graham,
- Candice Hirsch,
- James B. Holland,
- David Hooker,
- Shawn Kaeppler,
- Joseph Knoll,
- Nick Lauter,
- Elizabeth C. Lee,
- Aaron Lorenz,
- Jonathan P. Lynch,
- Stephen P. Moose,
- Seth C. Murray,
- Rebecca Nelson,
- Torbert Rocheford,
- Oscar Rodriguez,
- James C. Schnable,
- Brian Scully,
- Margaret Smith,
- Nathan Springer,
- Peter Thomison,
- Mitchell Tuinstra,
- Randall J. Wisser,
- Wenwei Xu,
- David Ertl,
- Patrick S. Schnable,
- Natalia De Leon,
- Edgar P. Spalding,
- Jode Edwards,
- Carolyn J. Lawrence-Dill
Affiliations
- Naser AlKhalifah
- Iowa State University
- Darwin A. Campbell
- Iowa State University
- Celeste M. Falcon
- University of Wisconsin
- Jack M. Gardiner
- Iowa State University
- Nathan D. Miller
- University of Wisconsin
- Maria Cinta Romay
- Cornell University
- Ramona Walls
- University of Arizona
- Renee Walton
- Iowa State University
- Cheng-Ting Yeh
- Iowa State University
- Martin Bohn
- University of Illinois at Urbana-Champaign
- Jessica Bubert
- University of Illinois at Urbana-Champaign
- Edward S. Buckler
- Cornell University
- Ignacio Ciampitti
- Kansas State University
- Sherry Flint-Garcia
- USDA-ARS
- Michael A. Gore
- Cornell University
- Christopher Graham
- South Dakota State University
- Candice Hirsch
- University of Minnesota
- James B. Holland
- USDA-ARS
- David Hooker
- University of Guelph
- Shawn Kaeppler
- University of Wisconsin
- Joseph Knoll
- USDA-ARS
- Nick Lauter
- Iowa State University
- Elizabeth C. Lee
- University of Guelph
- Aaron Lorenz
- University of Nebraska
- Jonathan P. Lynch
- Pennsylvania State University
- Stephen P. Moose
- University of Illinois at Urbana-Champaign
- Seth C. Murray
- Texas A&M University
- Rebecca Nelson
- Cornell University
- Torbert Rocheford
- Purdue University
- Oscar Rodriguez
- University of Nebraska
- James C. Schnable
- University of Nebraska
- Brian Scully
- USDA-ARS
- Margaret Smith
- Cornell University
- Nathan Springer
- University of Minnesota
- Peter Thomison
- Ohio State University
- Mitchell Tuinstra
- Purdue University
- Randall J. Wisser
- University of Delaware
- Wenwei Xu
- Texas A&M AgriLife Research
- David Ertl
- Iowa Corn Growers Association
- Patrick S. Schnable
- Iowa State University
- Natalia De Leon
- University of Wisconsin
- Edgar P. Spalding
- University of Wisconsin
- Jode Edwards
- Iowa State University
- Carolyn J. Lawrence-Dill
- Iowa State University
- DOI
- https://doi.org/10.1186/s13104-018-3508-1
- Journal volume & issue
-
Vol. 11,
no. 1
pp. 1 – 5
Abstract
Abstract 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.
Keywords