Search

Your search keyword '"Celeste M. Falcon"' showing total 7 results
Start Over Author "Celeste M. Falcon"
7 results on '"Celeste M. Falcon"'

1. Maize genomes to fields (G2F): 2014–2017 field seasons: genotype, phenotype, climatic, soil, and inbred ear image datasets

Author

Bridget A. McFarland, Naser AlKhalifah, Martin Bohn, Jessica Bubert, Edward S. Buckler, Ignacio Ciampitti, Jode Edwards, David Ertl, Joseph L. Gage, Celeste M. Falcon, Sherry Flint-Garcia, Michael A. Gore, Christopher Graham, Candice N. Hirsch, James B. Holland, Elizabeth Hood, David Hooker, Diego Jarquin, Shawn M. Kaeppler, Joseph Knoll, Greg Kruger, Nick Lauter, Elizabeth C. Lee, Dayane C. Lima, Aaron Lorenz, Jonathan P. Lynch, John McKay, Nathan D. Miller, Stephen P. Moose, Seth C. Murray, Rebecca Nelson, Christina Poudyal, Torbert Rocheford, Oscar Rodriguez, Maria Cinta Romay, James C. Schnable, Patrick S. Schnable, Brian Scully, Rajandeep Sekhon, Kevin Silverstein, Maninder Singh, Margaret Smith, Edgar P. Spalding, Nathan Springer, Kurt Thelen, Peter Thomison, Mitchell Tuinstra, Jason Wallace, Ramona Walls, David Wills, Randall J. Wisser, Wenwei Xu, Cheng-Ting Yeh, and Natalia de Leon

Subjects
Maize, Genome, Genotype, GBS, G × E, Hybrid, Medicine, Biology (General), QH301-705.5, Science (General), Q1-390
Abstract

Abstract Objectives Advanced tools and resources are needed to efficiently and sustainably produce food for an increasing world population in the context of variable environmental conditions. The maize genomes to fields (G2F) initiative is a multi-institutional initiative effort that seeks to approach this challenge by developing a flexible and distributed infrastructure addressing emerging problems. G2F has generated large-scale phenotypic, genotypic, and environmental datasets using publicly available inbred lines and hybrids evaluated through a network of collaborators that are part of the G2F’s genotype-by-environment (G × E) project. This report covers the public release of datasets for 2014–2017. Data description Datasets include inbred genotypic information; phenotypic, climatic, and soil measurements and metadata information for each testing location across years. For a subset of inbreds in 2014 and 2015, yield component phenotypes were quantified by image analysis. Data released are accompanied by README descriptions. For genotypic and phenotypic data, both raw data and a version without outliers are reported. For climatic data, a version calibrated to the nearest airport weather station and a version without outliers are reported. The 2014 and 2015 datasets are updated versions from the previously released files [1] while 2016 and 2017 datasets are newly available to the public.

Published
2020
Full Text
View/download PDF

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

Author

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, and Carolyn J. Lawrence-Dill

Subjects
Maize, Genome, Genotype, Environment, Breeding, Phenotype, Medicine, Biology (General), QH301-705.5, Science (General), Q1-390
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.

Published
2018
Full Text
View/download PDF

3. Erratum to: Relative utility of agronomic, phenological, and morphological traits for assessing genotype‐by‐environment interaction in maize inbreds

Author

Celeste M. Falcon, Shawn M. Kaeppler, Edgar P. Spalding, Nathan D. Miller, Nicholas Haase, Naser AlKhalifah, Martin Bohn, Edward S. Buckler, Darwin A. Campbell, Ignacio Ciampitti, Lisa Coffey, Jode Edwards, David Ertl, Sherry Flint‐Garcia, Michael A. Gore, Christopher Graham, Candice N. Hirsch, James B. Holland, Diego Jarquín, Joseph Knoll, Nick Lauter, Carolyn J. Lawrence‐Dill, Elizabeth C. Lee, Aaron Lorenz, Jonathan P. Lynch, Seth C. Murray, Rebecca Nelson, M. Cinta Romay, Torbert Rocheford, Patrick S. Schnable, Brian Scully, Margaret Smith, Nathan Springer, Mitchell R. Tuinstra, Renee Walton, Teclemariam Weldekidan, Randall J. Wisser, Wenwei Xu, and Natalia de Leon

Subjects
Agronomy and Crop Science
Published
2022

4. Maize genomes to fields (G2F): 2014–2017 field seasons: genotype, phenotype, climatic, soil, and inbred ear image datasets

Author

James C. Schnable, Rajandeep S. Sekhon, Nick Lauter, Rebecca Nelson, Patrick S. Schnable, Joseph E. Knoll, Michael A. Gore, Seth C. Murray, Brian T. Scully, Diego Jarquin, John K. McKay, Christopher Graham, Ignacio A. Ciampitti, Christina Poudyal, Kurt D. Thelen, Joseph L. Gage, Edgar P. Spalding, Naser Alkhalifah, Jessica Bubert, Shawn M. Kaeppler, Oscar Rodriguez, Celeste M. Falcon, Maria Cinta Romay, Natalia de Leon, Greg R. Kruger, Randall J. Wisser, Maninder P. Singh, Bridget A. McFarland, David S. Ertl, Kevin A. T. Silverstein, Nathan M. Springer, James B. Holland, Dayane C. Lima, Peter R. Thomison, Cheng Ting Yeh, Nathan D. Miller, Candice N. Hirsch, Jonathan P. Lynch, Torbert Rocheford, Mitchell R. Tuinstra, Elizabeth E. Hood, Sherry Flint-Garcia, David C. Hooker, Jode W. Edwards, David M. Wills, Margaret E. Smith, Stephen P. Moose, Aaron J. Lorenz, Elizabeth C. Lee, Wenwei Xu, Edward S. Buckler, Jason G. Wallace, Ramona Walls, and Martin O. Bohn

Subjects
0106 biological sciences, 0301 basic medicine, Genotype, G × E, Datasets as Topic, lcsh:Medicine, Context (language use), Environment, Biology, GBS, Data Note, Field metadata, Zea mays, 01 natural sciences, Genome, General Biochemistry, Genetics and Molecular Biology, Weather station, 03 medical and health sciences, README, Inbred, lcsh:Science (General), lcsh:QH301-705.5, 2. Zero hunger, lcsh:R, General Medicine, 15. Life on land, Field (geography), Hybrid, Maize, Metadata, Plant Breeding, Phenotype, 030104 developmental biology, lcsh:Biology (General), 13. Climate action, Outlier, Raw data, Cartography, Genome, Plant, 010606 plant biology & botany, lcsh:Q1-390
Abstract

Objectives Advanced tools and resources are needed to efficiently and sustainably produce food for an increasing world population in the context of variable environmental conditions. The maize genomes to fields (G2F) initiative is a multi-institutional initiative effort that seeks to approach this challenge by developing a flexible and distributed infrastructure addressing emerging problems. G2F has generated large-scale phenotypic, genotypic, and environmental datasets using publicly available inbred lines and hybrids evaluated through a network of collaborators that are part of the G2F’s genotype-by-environment (G × E) project. This report covers the public release of datasets for 2014–2017. Data description Datasets include inbred genotypic information; phenotypic, climatic, and soil measurements and metadata information for each testing location across years. For a subset of inbreds in 2014 and 2015, yield component phenotypes were quantified by image analysis. Data released are accompanied by README descriptions. For genotypic and phenotypic data, both raw data and a version without outliers are reported. For climatic data, a version calibrated to the nearest airport weather station and a version without outliers are reported. The 2014 and 2015 datasets are updated versions from the previously released files [1] while 2016 and 2017 datasets are newly available to the public.

Published
2020

5. Relative utility of agronomic, phenological, and morphological traits for assessing genotype‐by‐environment interaction in maize inbreds

Author

Shawn M. Kaeppler, Torbert Rocheford, Nicholas J. Haase, Diego Jarquin, Renee Walton, Sherry Flint-Garcia, Randall J. Wisser, Nick Lauter, Margaret E. Smith, Martin O. Bohn, Celeste M. Falcon, Carolyn J. Lawrence-Dill, Aaron J. Lorenz, Jode W. Edwards, Jonathan P. Lynch, Natalia de Leon, M. Cinta Romay, Ignacio A. Ciampitti, David S. Ertl, James B. Holland, Elizabeth C. Lee, Nathan D. Miller, Lisa Coffey, Brian T. Scully, Seth C. Murray, Nathan M. Springer, Wenwei Xu, Rebecca Nelson, Edgar P. Spalding, Patrick S. Schnable, Mitchell R. Tuinstra, Edward S. Buckler, Christopher Graham, Darwin A. Campbell, Teclemariam Weldekidan, Naser Alkhalifah, Joseph E. Knoll, Michael A. Gore, and Candice N. Hirsch

Subjects
0106 biological sciences, Agronomy, Relative utility, Phenology, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, 04 agricultural and veterinary sciences, Biology, Gene–environment interaction, 01 natural sciences, Agronomy and Crop Science, 010606 plant biology & botany
Published
2020

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

Author

Patrick S. Schnable, Edgar P. Spalding, Brian T. Scully, Rebecca Nelson, Ignacio A. Ciampitti, Jessica Bubert, Celeste M. Falcon, Torbert Rocheford, Christopher Graham, Carolyn J. Lawrence-Dill, David C. Hooker, Joseph E. Knoll, Ramona Walls, Michael A. Gore, Martin O. Bohn, Darwin A. Campbell, Margaret E. Smith, Natalia de Leon, Stephen P. Moose, Nick Lauter, Jode W. Edwards, Sherry Flint-Garcia, Cheng Ting Yeh, Mitchell R. Tuinstra, Oscar Rodriguez, Shawn M. Kaeppler, Randall J. Wisser, Naser Alkhalifah, James C. Schnable, Nathan M. Springer, Jonathan P. Lynch, Elizabeth C. Lee, Peter R. Thomison, Candice N. Hirsch, Renee Walton, Jack M. Gardiner, Wenwei Xu, Edward S. Buckler, Aaron J. Lorenz, Nathan D. Miller, James B. Holland, Maria Cinta Romay, Seth C. Murray, and David S. Ertl

Subjects
0106 biological sciences, 0301 basic medicine, Genotype, Datasets as Topic, lcsh:Medicine, Environment, Breeding, Biology, Data Note, computer.software_genre, Zea mays, 01 natural sciences, General Biochemistry, Genetics and Molecular Biology, Field (computer science), Weather station, Environmental data, Set (abstract data type), Soil, 03 medical and health sciences, Inbred, Inbreeding, Gene–environment interaction, lcsh:Science (General), lcsh:QH301-705.5, 2. Zero hunger, Genome, lcsh:R, Sequence Analysis, DNA, General Medicine, computer.file_format, 15. Life on land, Hybrid, Maize, Plant Breeding, Phenotype, 030104 developmental biology, lcsh:Biology (General), Outlier, Seasons, Data mining, Prediction, Raw data, computer, Genome, Plant, Comma-Separated Values, lcsh:Q1-390, 010606 plant biology & botany
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.

Published
2018

Catalog

Books, media, physical & digital resources
See catalog results