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OzBarley: A genetic and phenotypic data resource capturing the Australian barley breeding history

  • Ute Baumann
  • , Elena Kalashyan
  • , Julian Schwerdt
  • , Amanda Box
  • , Chris Brien
  • , Kenneth Chalmers
  • , Stewart Coventry
  • , Aanandini Ganesalingam
  • , Jessica Hyles
  • , Brett Lobsey
  • , Haoyu Lou
  • , Sebastian Raubach
  • , Sarah Richmond
  • , George Sainsbury
  • , Paul D. Shaw
  • , Bjorg Sherman
  • , John R. Stephen
  • , Ben Trevaskis
  • , Robbie Waugh
  • , Matthew R. Tucker
  • Bettina Berger (Lead / Corresponding author)

Research output: Contribution to journalArticlepeer-review

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Abstract

OzBarley is a comprehensive genotype-to-phenotype resource to support research and enhance barley breeding by integrating genotypic and phenotypic data for gene discovery. This publicly available dataset comprises genotypic data from historical and modern elite barley cultivars of significance to Australian barley breeding. The phenotypic component includes high-throughput imaging and X-ray CT-based spike analysis, enabling trait measurements such as plant growth dynamics and seed morphology. Users can leverage genome-wide association studies (GWAS) and genomic selection to identify genetic variants associated with agronomically important traits in the OzBarley datasets, thereby accelerating targeted breeding strategies. The dataset is accessible for download under CC-BY 4.0 license and users are invited to contribute new data when using OzBarley plant material in their research. Through its FAIR-compliant design (Findable, Accessible, Interoperable, Reusable), OzBarley represents a resource to protect genotypes of historical relevance, explore the genetic architecture of adaptation to dryland environments, and to enhance knowledge of the resilience, yield, and quality of barley cultivars under diverse environmental conditions, contributing to global food security and agricultural sustainability.

Original languageEnglish
Article number703
Number of pages11
JournalScientific Data
Volume13
Early online date18 Mar 2026
DOIs
Publication statusE-pub ahead of print - 18 Mar 2026

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 2 - Zero Hunger
    SDG 2 Zero Hunger

ASJC Scopus subject areas

  • Statistics and Probability
  • Information Systems
  • Education
  • Computer Science Applications
  • Statistics, Probability and Uncertainty
  • Library and Information Sciences

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