CIMMYT Research Software

Free, open access repository of research software produced and developed by CIMMYT scientists.

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61 to 70 of 78 Results

sampleLowNaGID_Trim.bin Feb 14, 2018 - An informational view of accession rarity and allele specificity in germplasm banks for management and conservation Unknown - 5.8 MB - MD5: 256a7c0e69ebd16cc88b80d6fea8e6cb
sampleLowNaGID_Trim.tab Feb 14, 2018 - An informational view of accession rarity and allele specificity in germplasm banks for management and conservation Tabular Data - 64.1 MB - 7988 Variables, 4126 Observations - UNF:6:v+RWYoZQ/W6oh+hfVGd6uQ==
Use of High-Resolution Image Data Outperforms Vegetation Indices in Prediction of Maize Yield: Supplementary Methods Jun 27, 2017 Fernando Aguate; Samuel Trachsel; Lorena González-Pérez; Juan Burgueño; José Crossa; Mónica Balzarini; David Gouache; Matthieu Bogard; Gustavo de los Campos, 2017, "Use of High-Resolution Image Data Outperforms Vegetation Indices in Prediction of Maize Yield: Supplementary Methods", https://hdl.handle.net/11529/10972, CIMMYT Research Data & Software Repository Network, V1 This is the supplementary methods of "Use of High-Resolution Image Data Outperforms Vegetation Indices in Prediction of Maize Yield" published in Crop Science · May 2017, DOI: 10.2135/cropsci2017.01.0007. It includes the raw data in R format and the R-code for the analysis.
dataAguateCropSci2017.RData Jun 27, 2017 - Use of High-Resolution Image Data Outperforms Vegetation Indices in Prediction of Maize Yield: Supplementary Methods Unknown - 695.0 KB - MD5: ad2a90490e7c619747d1690bddd1cd04 Raw data in R format
getResults.html Jun 27, 2017 - Use of High-Resolution Image Data Outperforms Vegetation Indices in Prediction of Maize Yield: Supplementary Methods HTML - 2.0 MB - MD5: 6ec2dfc08226ef6b760d0b76c2040591 R- code to analyze data step by step
SI-R Codes for Computing Selection Indices in R Jan 7, 2017 Alvarado, Gregorio; Pérez-Elizalde, Sergio; Cerón, Jesús, 2015, "SI-R Codes for Computing Selection Indices in R", https://hdl.handle.net/11529/10352, CIMMYT Research Data & Software Repository Network, V4 SI is a collection of R codes to compute several selection indices. It comes together a powerpoint explanation of the indices and one example.
A Genomic Bayesian Multi-trait and Multi-environment Model Nov 3, 2016 Montesinos-López, Osval A.; Montesinos-López, Abelardo; Crossa, Jose; Toledo, Fernando; Pérez-Hernández, Óscar; Eskridge, Kent; Rutkoski, Jessica, 2016, "A Genomic Bayesian Multi-trait and Multi-environment Model", https://hdl.handle.net/11529/10646, CIMMYT Research Data & Software Repository Network, V4 When plant scientists record information on multiple genotypes evaluated in multiple environments, a multi-environment single trait for assessing genotype × environment interaction (G×E) model is usually employed. Comprehensive models that simultaneously take into account the cor...
BMTME_0.0.3.zip Nov 3, 2016 - A Genomic Bayesian Multi-trait and Multi-environment Model Unknown - 752.6 KB - MD5: 25aeb1392abc4fa975c56149fe439651
Data.Maize.RDATa Apr 4, 2016 - A Genomic Bayesian Multi-trait and Multi-environment Model Unknown - 6.6 KB - MD5: 97a610a51a111b780accbdc7facc1f8f
Data.Trigo.RDATa Apr 4, 2016 - A Genomic Bayesian Multi-trait and Multi-environment Model Unknown - 19.1 KB - MD5: f543faa4b1f3c7b38397ae6e5309da2d

sampleLowNaGID_Trim.bin

Feb 14, 2018 - An informational view of accession rarity and allele specificity in germplasm banks for management and conservation

Unknown - 5.8 MB -

sampleLowNaGID_Trim.tab

Feb 14, 2018 - An informational view of accession rarity and allele specificity in germplasm banks for management and conservation

Tabular Data - 64.1 MB - 7988 Variables, 4126 Observations -

Use of High-Resolution Image Data Outperforms Vegetation Indices in Prediction of Maize Yield: Supplementary Methods

Jun 27, 2017

Fernando Aguate; Samuel Trachsel; Lorena González-Pérez; Juan Burgueño; José Crossa; Mónica Balzarini; David Gouache; Matthieu Bogard; Gustavo de los Campos, 2017, "Use of High-Resolution Image Data Outperforms Vegetation Indices in Prediction of Maize Yield: Supplementary Methods", https://hdl.handle.net/11529/10972, CIMMYT Research Data & Software Repository Network, V1

This is the supplementary methods of "Use of High-Resolution Image Data Outperforms Vegetation Indices in Prediction of Maize Yield" published in Crop Science · May 2017, DOI: 10.2135/cropsci2017.01.0007. It includes the raw data in R format and the R-code for the analysis.

dataAguateCropSci2017.RData

Jun 27, 2017 - Use of High-Resolution Image Data Outperforms Vegetation Indices in Prediction of Maize Yield: Supplementary Methods

Unknown - 695.0 KB -

Raw data in R format

getResults.html

Jun 27, 2017 - Use of High-Resolution Image Data Outperforms Vegetation Indices in Prediction of Maize Yield: Supplementary Methods

HTML - 2.0 MB -

R- code to analyze data step by step

SI-R Codes for Computing Selection Indices in R

Jan 7, 2017

Alvarado, Gregorio; Pérez-Elizalde, Sergio; Cerón, Jesús, 2015, "SI-R Codes for Computing Selection Indices in R", https://hdl.handle.net/11529/10352, CIMMYT Research Data & Software Repository Network, V4

SI is a collection of R codes to compute several selection indices. It comes together a powerpoint explanation of the indices and one example.

A Genomic Bayesian Multi-trait and Multi-environment Model

Nov 3, 2016

Montesinos-López, Osval A.; Montesinos-López, Abelardo; Crossa, Jose; Toledo, Fernando; Pérez-Hernández, Óscar; Eskridge, Kent; Rutkoski, Jessica, 2016, "A Genomic Bayesian Multi-trait and Multi-environment Model", https://hdl.handle.net/11529/10646, CIMMYT Research Data & Software Repository Network, V4

When plant scientists record information on multiple genotypes evaluated in multiple environments, a multi-environment single trait for assessing genotype × environment interaction (G×E) model is usually employed. Comprehensive models that simultaneously take into account the cor...

BMTME_0.0.3.zip

Nov 3, 2016 - A Genomic Bayesian Multi-trait and Multi-environment Model

Unknown - 752.6 KB -

Data.Maize.RDATa

Apr 4, 2016 - A Genomic Bayesian Multi-trait and Multi-environment Model

Unknown - 6.6 KB -

Data.Trigo.RDATa

Apr 4, 2016 - A Genomic Bayesian Multi-trait and Multi-environment Model

Unknown - 19.1 KB -

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