CIMMYT Research Data & Software Repository Network

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Publication Year: 2018

751 to 760 of 1,196 Results

New deep learning genomic prediction model for multi-traits with mixed binary, ordinal, and continuous phenotypes Oct 22, 2018 - CIMMYT Research Data Montesinos-López, Osval A.; Martín-Vallejo, Javier; Crossa, Jose; Gianola, Daniel; Hernández-Suarez, Carlos Moisés; Montesinos-López, Abelardo; Juliana, Philomin; Singh, Ravi, 2018, "New deep learning genomic prediction model for multi-traits with mixed binary, ordinal, and continuous phenotypes", https://hdl.handle.net/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.
Data_Set_1.RData.gz Oct 22, 2018 - New deep learning genomic prediction model for multi-traits with mixed binary, ordinal, and continuous phenotypes Gzip Archive - 4.3 MB - MD5: 231fdf85ad051b494deacd4886d55708
Data_Set_2.RData.gz Oct 22, 2018 - New deep learning genomic prediction model for multi-traits with mixed binary, ordinal, and continuous phenotypes Gzip Archive - 4.4 MB - MD5: 74cf012e823cf8ff5eff6a51f10d592c
Data_Set_3.RData.gz Oct 22, 2018 - New deep learning genomic prediction model for multi-traits with mixed binary, ordinal, and continuous phenotypes Gzip Archive - 6.8 MB - MD5: 4c158d81032c889c280f1918b5d1b16f
Data_Set_4.RData.gz Oct 22, 2018 - New deep learning genomic prediction model for multi-traits with mixed binary, ordinal, and continuous phenotypes Gzip Archive - 7.0 MB - MD5: bc2a4fce307e769e24b9f477e14592f6
Data_Set_5.RData.gz Oct 22, 2018 - New deep learning genomic prediction model for multi-traits with mixed binary, ordinal, and continuous phenotypes Gzip Archive - 6.8 MB - MD5: 0af143aab2914c4d065ef8ef9037632d
Data_Set_6.RData.gz Oct 22, 2018 - New deep learning genomic prediction model for multi-traits with mixed binary, ordinal, and continuous phenotypes Gzip Archive - 153.6 KB - MD5: d891bbd1db0577097ef9ce267581237f
Data_Set_7.RData.gz Oct 22, 2018 - New deep learning genomic prediction model for multi-traits with mixed binary, ordinal, and continuous phenotypes Gzip Archive - 261.2 KB - MD5: f0396ede52b49e1a91090a95e299bc31
SUPPLEMENTARY MATERIAL.docx Oct 22, 2018 - New deep learning genomic prediction model for multi-traits with mixed binary, ordinal, and continuous phenotypes MS Word - 358.9 KB - MD5: 6bfa2583115c2816b2945150fb882e77
Evaluating Radar Satellite Based Remote Sensing for Conservation Agriculture Adoption Detection in North and Central Mexico Oct 8, 2018 - CIMMYT Research Data Sonder, Kai; Schulthess, Urs; Chomé, Guillaume, 2018, "Evaluating Radar Satellite Based Remote Sensing for Conservation Agriculture Adoption Detection in North and Central Mexico", https://hdl.handle.net/11529/10548137, CIMMYT Research Data & Software Repository Network, V1 Conservation agriculture has been tested and out scaled for over 60 years in rural areas of Mexico, however the rate of adoption and dis adoption, as well as the current area under CA is unknown. Estimates range between 50,000 ha and over 800,000 ha depending on the source. Studi...

New deep learning genomic prediction model for multi-traits with mixed binary, ordinal, and continuous phenotypes

Oct 22, 2018 - CIMMYT Research Data

Montesinos-López, Osval A.; Martín-Vallejo, Javier; Crossa, Jose; Gianola, Daniel; Hernández-Suarez, Carlos Moisés; Montesinos-López, Abelardo; Juliana, Philomin; Singh, Ravi, 2018, "New deep learning genomic prediction model for multi-traits with mixed binary, ordinal, and continuous phenotypes", https://hdl.handle.net/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.

Data_Set_1.RData.gz

Oct 22, 2018 - New deep learning genomic prediction model for multi-traits with mixed binary, ordinal, and continuous phenotypes

Gzip Archive - 4.3 MB -

Data_Set_2.RData.gz

Oct 22, 2018 - New deep learning genomic prediction model for multi-traits with mixed binary, ordinal, and continuous phenotypes

Gzip Archive - 4.4 MB -

Data_Set_3.RData.gz

Oct 22, 2018 - New deep learning genomic prediction model for multi-traits with mixed binary, ordinal, and continuous phenotypes

Gzip Archive - 6.8 MB -

Data_Set_4.RData.gz

Oct 22, 2018 - New deep learning genomic prediction model for multi-traits with mixed binary, ordinal, and continuous phenotypes

Gzip Archive - 7.0 MB -

Data_Set_5.RData.gz

Oct 22, 2018 - New deep learning genomic prediction model for multi-traits with mixed binary, ordinal, and continuous phenotypes

Gzip Archive - 6.8 MB -

Data_Set_6.RData.gz

Oct 22, 2018 - New deep learning genomic prediction model for multi-traits with mixed binary, ordinal, and continuous phenotypes

Gzip Archive - 153.6 KB -

Data_Set_7.RData.gz

Oct 22, 2018 - New deep learning genomic prediction model for multi-traits with mixed binary, ordinal, and continuous phenotypes

Gzip Archive - 261.2 KB -

SUPPLEMENTARY MATERIAL.docx

Oct 22, 2018 - New deep learning genomic prediction model for multi-traits with mixed binary, ordinal, and continuous phenotypes

MS Word - 358.9 KB -

Evaluating Radar Satellite Based Remote Sensing for Conservation Agriculture Adoption Detection in North and Central Mexico

Oct 8, 2018 - CIMMYT Research Data

Sonder, Kai; Schulthess, Urs; Chomé, Guillaume, 2018, "Evaluating Radar Satellite Based Remote Sensing for Conservation Agriculture Adoption Detection in North and Central Mexico", https://hdl.handle.net/11529/10548137, CIMMYT Research Data & Software Repository Network, V1

Conservation agriculture has been tested and out scaled for over 60 years in rural areas of Mexico, however the rate of adoption and dis adoption, as well as the current area under CA is unknown. Estimates range between 50,000 ha and over 800,000 ha depending on the source. Studi...

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