ArticleF1000Research2017
Developing data interoperability using standards: A wheat community use case.
Article in F1000Research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Challenges for FAIR-compliant description and comparison of crop phenotype data with standardized controlled vocabularies.Database : the journal of biological databases and curation · 2021Article
- The Ontologies Community of Practice: A CGIAR Initiative for Big Data in Agrifood Systems.Patterns (New York, N.Y.) · 2020Article
- WTO, an ontology for wheat traits and phenotypes in scientific publications.Genomics & informatics · 2020Article
- Building a successful international research community through data sharing: The case of the Wheat Information System (WheatIS).F1000Research · 2020Article
- Applying FAIR Principles to Plant Phenotypic Data Management in GnpIS.Plant phenomics (Washington, D.C.) · 2019Article
- Dealing with multi-source and multi-scale information in plant phenomics: the ontology-driven Phenotyping Hybrid Information System.The New phytologist · 2019Article
- GrainGenes: centralized small grain resources and digital platform for geneticists and breeders.Database : the journal of biological databases and curation · 2019Article
- Linking the International Wheat Genome Sequencing Consortium bread wheat reference genome sequence to wheat genetic and phenomic data.Genome biology · 2018Article
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Authors and funding
21 authors.
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Abstract
In this article, we present a joint effort of the wheat research community, along with data and ontology experts, to develop wheat data interoperability guidelines. Interoperability is the ability of two or more systems and devices to cooperate and exchange data, and interpret that shared information. Interoperability is a growing concern to the wheat scientific community, and agriculture in general, as the need to interpret the deluge of data obtained through high-throughput technologies grows. Agreeing on common data formats, metadata, and vocabulary standards is an important step to obtain the required data interoperability level in order to add value by encouraging data sharing, and subsequently facilitate the extraction of new information from existing and new datasets. During a period of more than 18 months, the RDA Wheat Data Interoperability Working Group (WDI-WG) surveyed the wheat research community about the use of data standards, then discussed and selected a set of recommendations based on consensual criteria. The recommendations promote standards for data types identified by the wheat research community as the most important for the coming years: nucleotide sequence variants, genome annotations, phenotypes, germplasm data, gene expression experiments, and physical maps. For each of these data types, the guidelines recommend best practices in terms of use of data formats, metadata standards and ontologies. In addition to the best practices, the guidelines provide examples of tools and implementations that are likely to facilitate the adoption of the recommendations. To maximize the adoption of the recommendations, the WDI-WG used a community-driven approach that involved the wheat research community from the start, took into account their needs and practices, and provided them with a framework to keep the recommendations up to date. We also report this approach's potential to be generalizable to other (agricultural) domains.
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