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8,821 to 8,830 of 12,513 Results
5TH HRWYT.7z
Aug 15, 2019 - 1st to 10th High Rainfall Wheat Yield Trial
7Z Archive - 1.6 MB - MD5: 98cab4cfa576c1f8e4c2252214726006
IWIN Summarized Data
Phenotypic traits raw data for all locations
6TH HRWYT.7z
Aug 15, 2019 - 1st to 10th High Rainfall Wheat Yield Trial
7Z Archive - 1.1 MB - MD5: 8974ade0bc00569496fa8937b5681629
IWIN Summarized Data
Phenotypic traits raw data for all locations
7TH HRWYT.7z
Aug 15, 2019 - 1st to 10th High Rainfall Wheat Yield Trial
7Z Archive - 1.3 MB - MD5: 6c8373fc03044a7dcef0f92f1c3f2132
IWIN Summarized Data
Phenotypic traits raw data for all locations
8TH HRWYT.7z
Aug 15, 2019 - 1st to 10th High Rainfall Wheat Yield Trial
7Z Archive - 1.4 MB - MD5: fd64fda3f3445f0fa9b3975b84a62225
IWIN Summarized Data
Phenotypic traits raw data for all locations
Deep kernel and deep learning for genomic-based prediction
Aug 15, 2019 - CIMMYT Research Data
Crossa, Jose; Martini, Johannes; Gianola, Daniel; Pérez-Rodríguez, Paulino; Burgueño, Juan; Singh, Ravi; Juliana, Philomin; Montesinos-López, Osval A.; Cuevas, Jaime, 2019, "Deep kernel and deep learning for genomic-based prediction", https://hdl.handle.net/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,...
Geno_eyt1516_1052linesand8402markers.7z
Aug 15, 2019 - Deep kernel and deep learning for genomic-based prediction
7Z Archive - 1.1 MB - MD5: ed49e029b92a168c4711b550b670e91b
Genotypic data_EYT1516
Geno_eyt1617_1040linesand8312markers.7z
Aug 15, 2019 - Deep kernel and deep learning for genomic-based prediction
7Z Archive - 1.0 MB - MD5: 35fa8334623ef10d5e87760c48f53630
Genotypic data_EYT1617
PHENOTYPIC DATA.7z
Aug 15, 2019 - Deep kernel and deep learning for genomic-based prediction
7Z Archive - 130.4 KB - MD5: d873938a21d1df115b0133fecb95509a
Phenotypic data
TAMASA_NOTs_Database_Nigeria_2015_2016.tab
Aug 15, 2019 - NG 2015 2016 NOTs soil leaf yield database
Tabular Data - 3.3 KB - 5 Variables, 24 Observations - UNF:6:8WnI5ertrYb+cnu08o0gxQ==
Nigeria PT Maize database 04022019.tab
Aug 15, 2019 - Nigeria Performance Trials 2016
Tabular Data - 3.1 KB - 5 Variables, 24 Observations - UNF:6:m584cAFq8CYyhkLLCAcFqA==
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