Instituto Nacional de Investigación Agropecuaria (INIA) Rice Breeding Program Historical Dataset

Breeding programs generate vast amounts of data which are often scattered in separate files. This hinders the application of modern breeding tools such as multi-environment analyses and genomic selection. This dataset is the result of consolidating 23 years of phenotypic, pedigree, and genomic records from the Uruguayan national rice breeding program. All the available data from 1997 to 2020 corresponding to field trials, blast nurseries, laboratory analyses of milling and cooking quality, pedigree information, and genomic information for selected advanced breeding lines are gathered. Records of 996 trials in 12 locations over a span of 23 years, 91,636 field plots with information on 14 phenotypic variables, pedigree for 19,447 genotypes, and genomic information regarding 61,260 SNP markers for 965 genotypes were recovered. The dataset is structured in Trials, Phenotypes, Lines, Genomic Information, and SNP Tables. Genotype identification has been coded.Data files are in txt format.Funding provided by: Agencia Nacional de Investigación e InnovaciónCrossref Funder Registry ID: http://dx.doi.org/10.13039/100008725Award Number: FSDA_1_2018_1_154120Funding provided by: Instituto Nacional de Investigación AgropecuariaCrossref Funder Registry ID: http://dx.doi.org/10.13039/501100011003Award Number: AZ13Funding provided by: Instituto Nacional de Investigación AgropecuariaCrossref Funder Registry ID: http://dx.doi.org/10.13039/501100011003Award Number: AZ35A detailed description on data collection and processing is available in the corresponding manuscript submitted to Crop Science journal. Briefly, for data unification a uniform format spreadsheet was designed with standardized names for fields, creating one spreadsheet per trial. An R code was created and used to merge trial's data into a single table. All tables were then unified into one with all the available IRBP information. A relational database structure was defined by identifying the elements that constituted the system and the key fields that connected them. Variable names and levels of all categorical variables were standardized