Replication Data for: Optimizing Genomic-Enabled Prediction: A Feature Weighting Approach for Enhancing within Family Accuracy
CIMMYT Research Data & Software Repository Network Dataverse OAI Archive
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Title |
Replication Data for: Optimizing Genomic-Enabled Prediction: A Feature Weighting Approach for Enhancing within Family Accuracy
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Identifier |
https://hdl.handle.net/11529/10548950
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Creator |
Montesinos-López, Osval A.
Crespo Herrera, Leonardo Abdiel Xavier, Alencar Godwa, Manje Beyene, Yoseph Saint Pierre, Carolina De la Rosa-Santamaria, Roberto Salinas-Ruiz, Josafhat Gerard, Guillermo Vitale, Paolo Dreisigacker, Susanne Lillemo, Morten Grignola, Fernando Sarinelli, Martin Pozzo, Ezequiel Quiroga, Marco Montesinos-López, Abelardo Crossa, Jose |
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Publisher |
CIMMYT Research Data & Software Repository Network
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Description |
This study provides supplemental data to support the study on Optimizing Genomic-Enabled Prediction: A Feature Weighting Approach for Enhancing within Family Accuracy.
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Subject |
Agricultural Sciences
Agricultural research Maize Sorghum Zea mays Genomic selection Genomic best linear unbiased predictor GBLUP |
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Language |
English
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Date |
2021
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Contributor |
Shrestha, Rosemary
Bill and Melinda Gates Foundation (BMGF) Accelerating Genetic Gains in Maize and Wheat for Improved Livelihoods (AG2MW) United States Agency for International Development (USAID) CGIAR Research Program on Wheat (WHEAT) CGIAR Research Program on Maize (MAIZE) Foundation for Research Levy on Agricultural Products (FFL) Agricultural Agreement Research Fund (JA) Global Maize Program (GMP) Global Wheat Program (GWP) Genetic Resources Program (GRP) Biometrics and Statistics Unit (BSU) |
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Type |
Experimental data
Genotypic data Phenotypic data |
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