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7,001 to 7,010 of 13,434 Results
Occupation, Earnings and Expenditure of the Daily Wage-based and Salaried Workforce in Farm and Nonfarm Sectors: Data from Bangladesh
Jul 2, 2021 - CIMMYT Research Data
Mottaleb, Khondoker, 2021, "Occupation, Earnings and Expenditure of the Daily Wage-based and Salaried Workforce in Farm and Nonfarm Sectors: Data from Bangladesh", https://hdl.handle.net/11529/10548580, CIMMYT Research Data & Software Repository Network, V2, UNF:6:VuYitGu/m5MaZzpomQImhQ== [fileUNF]
At present nearly half of the world’s population is under some form of government restriction to curb the spread of COVID-19, an extremely contagious disease. In Bangladesh, in the wake of five deaths and 48 infections from COVID-19, between March 24 and May 30, 2020 the governme...
Databrief.tab
Jul 2, 2021 - Occupation, Earnings and Expenditure of the Daily Wage-based and Salaried Workforce in Farm and Nonfarm Sectors: Data from Bangladesh
Tabular Data - 26.5 MB - 100 Variables, 50736 Observations - UNF:6:mKWkVUZMsT89xggNPKdF1A==
Complete database used in the study.
Description.tab
Jul 2, 2021 - Occupation, Earnings and Expenditure of the Daily Wage-based and Salaried Workforce in Farm and Nonfarm Sectors: Data from Bangladesh
Tabular Data - 6.7 KB - 5 Variables, 100 Observations - UNF:6:CD50RAS0nCw7fWM7xIYPwA==
Description of the variables.
esimat.do
Jul 2, 2021 - Occupation, Earnings and Expenditure of the Daily Wage-based and Salaried Workforce in Farm and Nonfarm Sectors: Data from Bangladesh
Stata Syntax - 9.5 KB - MD5: fb46a2e5d49754c25c59caca02cef19e
Replication Data for: Genetic dissection of quantitative resistance to common rust (Puccinia sorghi) in tropical maize (Zea mays L.) by combined GWAS, linkage mapping, and genomic prediction
Jun 30, 2021 - CIMMYT Research Data
Ren, Jiaojiao; Li, Zhimin; Wu, Penghao; Zhang, Ao; Liu, Yubo; Hu, Guanghui; Cao, Shiliang; Qu, Jingtao; Dhliwayo, Thanda; Zheng, Hongjian; Olsen, Michael Scott; Boddupalli, Prasanna; San Vicente, Felix; Zhang, Xuecai, 2021, "Replication Data for: Genetic dissection of quantitative resistance to common rust (Puccinia sorghi) in tropical maize (Zea mays L.) by combined GWAS, linkage mapping, and genomic prediction", https://hdl.handle.net/11529/10548575, CIMMYT Research Data & Software Repository Network, V1
Significant grain yield losses and poor grain quality can be caused by Common rust a major foliar disease in maize. This dataset provides the genotypic and phenotypic data that were used to perform genome-wide association studies (GWAS) and linkage analysis mapping to dissect the...
282DTMA unimputed genotype.hmp.txt.gz
Jun 30, 2021 - Replication Data for: Genetic dissection of quantitative resistance to common rust (Puccinia sorghi) in tropical maize (Zea mays L.) by combined GWAS, linkage mapping, and genomic prediction
Gzip Archive - 54.7 MB - MD5: 87598a5446f27039cb6c7d7d0ee60a72
Genotypic_data
DH1191_unimputed_genotype.hmp.txt.gz
Jun 30, 2021 - Replication Data for: Genetic dissection of quantitative resistance to common rust (Puccinia sorghi) in tropical maize (Zea mays L.) by combined GWAS, linkage mapping, and genomic prediction
Gzip Archive - 31.5 MB - MD5: 6ee524ab79fcb209b63cd04c9e0bebfb
Genotypic_data
DH1_phenotype_pedigre.txt
Jun 30, 2021 - Replication Data for: Genetic dissection of quantitative resistance to common rust (Puccinia sorghi) in tropical maize (Zea mays L.) by combined GWAS, linkage mapping, and genomic prediction
Plain Text - 19.1 KB - MD5: bee9c9c96027d7a49a635bf44d19adca
Phenotypic_data
DH1-phenotype.xlsx.gz
Jun 30, 2021 - Replication Data for: Genetic dissection of quantitative resistance to common rust (Puccinia sorghi) in tropical maize (Zea mays L.) by combined GWAS, linkage mapping, and genomic prediction
Gzip Archive - 60.0 KB - MD5: e3b1a7cbfc3df9c3c92bdc039320b060
Phenotypic_data
DTMA-phenotype.xlsx.gz
Jun 30, 2021 - Replication Data for: Genetic dissection of quantitative resistance to common rust (Puccinia sorghi) in tropical maize (Zea mays L.) by combined GWAS, linkage mapping, and genomic prediction
Gzip Archive - 220.5 KB - MD5: 84bd382cb5242a949b80291a2a200636
Phenotypic_data
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