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AgBioData consortium recommendations for sustainable genomics and genetics databases for agriculture
AgBioData consortium recommendations for sustainable genomics and genetics databases for agriculture
The future of agricultural research depends on data. The sheer volume of agricultural biological data being produced today makes excellent data management essential. Governmental agencies, publishers and science funders require data management plans for publicly funded research. Furthermore, the value of data increases exponentially when they are properly stored, described, integrated and shared, so that they can be easily utilized in future analyses. AgBioData (https://www.agbiodata.org) is a consortium of people working at agricultural biological databases, data archives and knowledgbases who strive to identify common issues in database development, curation and management, with the goal of creating database products that are more Findable, Accessible, Interoperable and Reusable. We strive to promote authentic, detailed, accurate and explicit communication between all parties involved in scientific data. As a step toward this goal, we present the current state of biocuration, ontologies, metadata and persistence, database platforms, programmatic (machine) access to data, communication and sustainability with regard to data curation. Each section describes challenges and opportunities for these topics, along with recommendations and best practices.
International audience
- University of California System United States
- The University of Texas at Austin United States
- University of Utah United States
- Iowa State University United States
- Arizona State University United States
Microsoft Academic Graph classification: Computer science Data management Best practice Interoperability Biological database computer.software_genre Biological data Data curation Database business.industry Metadata Data access business computer
[SDV.SA]Life Sciences [q-bio]/Agricultural sciences, Breeding, General Biochemistry, Genetics and Molecular Biology, Databases, Library and Information Studies, Genetic, Information and Computing Sciences, Surveys and Questionnaires, Databases, Genetic, Library And Information Studies, Metadata, [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], Data management and data science, [INFO.INFO-WB]Computer Science [cs]/Web, Agriculture, Genomics, Biological Sciences, Bioinformatics and computational biology, Data Format, [INFO.INFO-TT]Computer Science [cs]/Document and Text Processing, Gene Ontology, [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR], Zero Hunger, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], General Agricultural and Biological Sciences, Information Systems
[SDV.SA]Life Sciences [q-bio]/Agricultural sciences, Breeding, General Biochemistry, Genetics and Molecular Biology, Databases, Library and Information Studies, Genetic, Information and Computing Sciences, Surveys and Questionnaires, Databases, Genetic, Library And Information Studies, Metadata, [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], Data management and data science, [INFO.INFO-WB]Computer Science [cs]/Web, Agriculture, Genomics, Biological Sciences, Bioinformatics and computational biology, Data Format, [INFO.INFO-TT]Computer Science [cs]/Document and Text Processing, Gene Ontology, [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR], Zero Hunger, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], General Agricultural and Biological Sciences, Information Systems
Microsoft Academic Graph classification: Computer science Data management Best practice Interoperability Biological database computer.software_genre Biological data Data curation Database business.industry Metadata Data access business computer
99 references, page 1 of 10
1. Curty,R.G., Crowston,K., Specht,A. et al. (2017) Attitudes and norms affecting scientists' data reuse. PLoS One, 12, e0189288.
2. Leonelli,S. and Ankeny,R.A. (2012) Re-thinking organisms: the impact of databases on model organism biology. Stud. Hist. Philos. Biol. Biomed. Sci., 43, 29-36. [OpenAIRE]
3. MacPherson,K.A., Starr,B., Wong,E.D. et al. (2017) Outreach and online training services at the Saccharomyces Genome Database. Database, 2017, https://doi.org/10.1093/ database/bax002.
4. Wilkinson,M.D., Dumontier,M., Aalbersberg,I.J.J. et al. (2016) The FAIR Guiding Principles for scientific data management and stewardship. Sci. Data., 3, 160018.
5. Howe,D., Costanzo,M., Fey,P. et al. (2008) Big data: the future of biocuration. Nature, 455, 47-50.
6. Burge,S., Attwood,T.K., Bateman,A. et al. (2012) Biocurators and biocuration: surveying the 21st century challenges. Database, 2012, https://doi.org/10.1093/database/bar059.
7. Skrzypek,M.S. and Nash,R.S. (2015) Biocuration at the Saccharomyces genome database. Genesis, 53, 450-457.
8. Berardini,T.Z., Reiser,L., Li,D. et al. (2015) The Arabidopsis information resource: making and mining the “gold standard” annotated reference plant genome. Genesis, 53, 474-485.
9. Swarbreck,D., Wilks,C., Lamesch,P. et al. (2008) The Arabidopsis Information Resource (TAIR): gene structure and function annotation. Nucleic Acids Res., 36, D1009-D1014.
10. Berardini,T.Z. (2004) Functional annotation of the Arabidopsis genome using controlled vocabularies. Plant Physiol., 135, 745-755.
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