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891 to 900 of 1,250 Results
Unknown - 4.8 MB - MD5: 4e160b17df1cac51514ae95a10ef9970
Genotypic Data
Adobe PDF - 784.5 KB - MD5: 5bc3ea70c7a931c1b6cd24be89ee4202
Data set
Bangladesh data
Unknown - 411.5 KB - MD5: 7c74abfae547c301eb2533157827010e
Data set
India data
Unknown - 33.3 KB - MD5: eaf1c98a6f820b3f94ac3dddc1aba4fb
Data set
Pakistan Data
Unknown - 29.1 KB - MD5: 5f41726d80ef2ca17f2a402d927225b2
Data set
Simulatilon file for India, Pakistan and Bangladesh
Jun 28, 2017 - CIMMYT Research Data
The Genomics and Genebank Workshop Planning Committee; Payne, Thomas; Hearne, Sarah; Abberton, Michael; Wenzl, Peter; Bramel, Paula; Ellis, Dave, 2017, "Genomics and Genebank Workshop on the use of genotypic data to rationalize genebank collections: diversity gaps and duplicates", https://hdl.handle.net/11529/10939, CIMMYT Research Data & Software Repository Network, V3
Genotyping and re-sequencing are among a suite of tools used to enable rapid and cost-effective tool to study genetic diversity. This workshop will explore its use in the genetic curation of accessi ons within and between collection(s). With such information across global collect...
Jun 27, 2017 - CIMMYT Research Software
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.
Unknown - 695.0 KB - MD5: ad2a90490e7c619747d1690bddd1cd04
Raw data in R format
HTML - 2.0 MB - MD5: 6ec2dfc08226ef6b760d0b76c2040591
R- code to analyze data step by step
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