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10,231 to 10,240 of 12,530 Results
Genomics and Genebank Workshop on the use of genotypic data to rationalize genebank collections: diversity gaps and duplicates
Genomics and Genebank Workshop on the use of genotypic data to rationalize genebank collections: diversity gaps and duplicates
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...
Use of High-Resolution Image Data Outperforms Vegetation Indices in Prediction of Maize Yield: Supplementary Methods
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.
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
01_Identificationmm.dta
Jun 22, 2017 - Nepal Rice Crop Cut and Survey Data 2016
Unknown - 48.5 KB - MD5: 470fba8575d2b169d9224e87683af7e0
05 Stata Files
02_Hhinformation.csv
Jun 22, 2017 - Nepal Rice Crop Cut and Survey Data 2016
Plain Text - 34.6 KB - MD5: 6f475fff74371c3be1086d9cb2eb9a73
06 CSV Files
02_Hhinformationmm.dta
Jun 22, 2017 - Nepal Rice Crop Cut and Survey Data 2016
Unknown - 25.5 KB - MD5: ebff4d5fa18c2bb6ea421f29d151be2b
05 Stata Files
Household information details
03_Ricelargeplotinfomm.csv
Jun 22, 2017 - Nepal Rice Crop Cut and Survey Data 2016
Plain Text - 161.6 KB - MD5: c6947df01f06ab2e1a9a851c085e9631
06 CSV Files
03_Ricelargeplotinfomm.dta
Jun 22, 2017 - Nepal Rice Crop Cut and Survey Data 2016
Unknown - 114.9 KB - MD5: 6c2552ae712dbee1525453bfd1d512af
05 Stata Files
Large plot for rice details
04_Fertzapplymm.csv
Jun 22, 2017 - Nepal Rice Crop Cut and Survey Data 2016
Plain Text - 37.7 KB - MD5: fd628067bfae4afe1724cfe62ba0d2bb
06 CSV Files
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