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1 to 10 of 55 Results
Dataset / Software
Feb 1, 2019
Mondal, Suchismita; Krause, Margaret ; Juliana, Philomin; Poland, Jesse; Dreisigacker, Susanne; Singh, Ravi, 2018, "Use of hyperspectral reflectance-derived relationship matrices for genomic prediction of grain yield in wheat - data for publication", hdl:11529/10548109, CIMMYT Research Data & Software Repository Network, V2
Genomic, pedigree, grain yield and hyperspectral data for the manuscript
Dataset / Software
Sep 21, 2018
Basnet, Bhoja Raj; Crossa, José; Pérez-Rodríguez, Paulino; Manes, Yann; Singh, Ravi ; Rosyara, Umesh; Camarillo-Castillo, Fatima; Murua, Mercedes, 2018, "Supplemental data for hybrid wheat prediction using genomic, pedigree and environmental covariables interaction models", hdl:11529/10548129, CIMMYT Research Data & Software Repository Network, V1
Genomic prediction of hybrids unobserved in field evaluations is crucial. In this study, we used genomic G×E models for hybrid prediction, where similarity between lines was assessed by pedigree and molecular markers, and similarity between environments was accounted for by envir...
Dataset / Software
Jul 12, 2019
Howard, Reka; Gianola, Daniel; Montesinos-López, Osval; Juliana, Philomin; Singh, Ravi; Poland, Jesse; Shrestha, Sandesh; Perez-Rodriguez, Paulino; Crossa, Jose; Jarquin, Diego, 2019, "Replication Data for: Joint use of genome, pedigree and their interaction with environment for predicting the performance of wheat lines in new environments", hdl:11529/10548169, CIMMYT Research Data & Software Repository Network, V3
In this study, we evaluated genome-based prediction using 35,403 wheat lines from the Global Wheat Breeding Program of the International Maize and Wheat Improvement Center (CIMMYT). We implemented eight statistical models that included genome-wide molecular marker and pedigree in...
Dataset / Software
May 16, 2019
Singh, Ravi; Mondal, Suchismita; Crespo, Leonardo; Kummar, Uttam; Imtiaz, Muhammad; Lan, Caixia; Randhawa, Mandeep; Bhavani, Sridhar; Singh, Pawan K.; Huerta, Julio; He, Xinyao; Rahman, Mokhles; Pinto, Francisco; Perez Gonzalez, Lorena; Juliana, Philomin; Singh, Daljit; Lucas, Mark; Poland, Jesse, 2016, "Phenotypic data from trials conducted by the CIMMYT Bread Wheat Breeding Program", hdl:11529/10696, CIMMYT Research Data & Software Repository Network, V6
Phenotypic data were collected in on-station field trials for advanced breeding lines from the CIMMYT Bread Wheat breeding program over several years.
Dataset / Software
Oct 22, 2018
Montesinos-López, Osval A ; Martín-Vallejo, Javier; Crossa, Jose; Gianola, Daniel ; Hernández-Suárez, Carlos M.; Montesinos-López, Abelardo; Juliana, Philomin; Singh, Ravi, 2018, "New deep learning genomic prediction model for multi-traits with mixed binary, ordinal, and continuous phenotypes", hdl:11529/10548140, CIMMYT Research Data & Software Repository Network, V1
The seven data sets are wheat data from CIMMYT Global Wheat Breeding program. They comprise different traits, like days to heading, days to maturity, grain yield, grain color, different type of leaf and stripe rust in wheat. Also the trials were run in different environments.
Dataset / Software
Nov 12, 2018
Battenfield, Sarah; Sheridan, Jaime; Silva, Luciano ; Miclaus, Kelci; Dreisigacker, Susanne; Wolfinger, Russell; Peña, Roberto; Singh, Ravi; Jackson, Eric; Fritz, Allan; Guzmán, Carlos; Poland, Jesse, 2018, "Grain quality data from CIMMYT bread wheat breeding program (2010-2015)", hdl:11529/10548148, CIMMYT Research Data & Software Repository Network, V1
Bread wheat breeding lines from yield and elite yield trials are analyzed annually for grain quality traits at the Wheat Chemistry and Quality Laboratory of CIMMYT. The analysis done are the following: grain morphology (test weight and thousand kernel weight), grain hardness and...
Dataset / Software
Oct 30, 2018
Poland, Jesse; Dreisigacker, Susanne; Shrestha, Sandesh; Wu, Shuangye; Singh, Ravi; Mondal, Suchismita; Juliana, Philomin; Crossa, Jose; Rutkoski, Jessica, 2016, "Genotypic data from CIMMYT bread wheat breeding lines used in the Feed the Future Innovation Lab for Applied Wheat Genomics", hdl:11529/10695, CIMMYT Research Data & Software Repository Network, V2
Genetic profiling of wheat breeding lines from the CIMMYT bread wheat breeding program was carried out over several years. Unimputed genotypic data in the VCF format (CIMMYT-2013-2018.hmp.vcf) for 91,680 markers are available upon request. We could not upload and publish the unim...
Dataset / Software
Sep 2, 2019
Mondal, Suchismita; Dutta, Somak; Crespo-Herrera, Leonardo; Huerta-Espino, Julio; Braun, Hans J.; Singh, Ravi, 2019, "Fifty years of semi-dwarf spring wheat breeding at CIMMYT: Grain yield progress in optimum, drought and heat stress environments", hdl:11529/10548299, CIMMYT Research Data & Software Repository Network, V1
This dataset provides supplementary files related to fifty years of semi-dwarf spring wheat breeding at CIMMYT: Grain yield progress in optimum, drought and heat stress environments.
Dataset / Software
Aug 15, 2019
Crossa, Jose; Martini, Johannes; Gianola, Daniel; Pérez-Rodríguez, Paulino; Burgueño, Juan; Singh, Ravi; Juliana, Philomin; Montesinos-López, Osval; Cuevas, Jaime, 2019, "Deep kernel and deep learning for genomic-based prediction", hdl:11529/10548273, CIMMYT Research Data & Software Repository Network, V1
Deep learning (DL) is a promising method in the context of genomic prediction for selecting individuals early in time without measuring their phenotypes. iI this paper we compare the performance in terms of genome-based prediction of the DL method, deep kernel (arc-cosine kernel,...
Dataset / Software
Oct 26, 2018
Montesinos-López, Osval A ; Montesinos-López, Abelardo; Crossa, Jose; Cuevas, Jaime; Montesinos-López, José Cricelio; Gutiérrez, Zitlalli Salas; Lillemo, Morten; Juliana, Philomin; Singh, Ravi, 2018, "A Bayesian genomic multi-output regressor stacking model for predicting multi-trait multi-environment plant breeding data", hdl:11529/10548141, CIMMYT Research Data & Software Repository Network, V1
A new statistical model is presented for genomic prediction on maize and wheat data comprising multi-trait, multi-environment data.
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