Arnaud Elizabeth, Laporte Marie-Angélique, Kim Soonho, Aubert Céline, Leonelli Sabina, Miro Berta, Cooper Laurel, Jaiswal Pankaj, Kruseman Gideon, Shrestha Rosemary, Buttigieg Pier Luigi, Mungall Christopher J, Pietragalla Julian, Agbona Afolabi, Muliro Jacqueline, Detras Jeffrey, Hualla Vilma, Rathore Abhishek, Das Roma Rani, Dieng Ibnou, Bauchet Guillaume, Menda Naama, Pommier Cyril, Shaw Felix, Lyon David, Mwanzia Leroy, Juarez Henry, Bonaiuti Enrico, Chiputwa Brian, Obileye Olatunbosun, Auzoux Sandrine, Yeumo Esther Dzalé, Mueller Lukas A, Silverstein Kevin, Lafargue Alexandra, Antezana Erick, Devare Medha, King Brian
Digital Solutions Team, Digital Inclusion Lever, Bioversity International, Montpellier Office, Montpellier, France.
Markets, Trade and Institutions Division (MTID), International Food Policy Research Institute (IFPRI), Washington, DC, USA.
Patterns (N Y). 2020 Sep 25;1(7):100105. doi: 10.1016/j.patter.2020.100105. eCollection 2020 Oct 9.
Heterogeneous and multidisciplinary data generated by research on sustainable global agriculture and agrifood systems requires quality data labeling or annotation in order to be interoperable. As recommended by the FAIR principles, data, labels, and metadata must use controlled vocabularies and ontologies that are popular in the knowledge domain and commonly used by the community. Despite the existence of robust ontologies in the Life Sciences, there is currently no comprehensive full set of ontologies recommended for data annotation across agricultural research disciplines. In this paper, we discuss the added value of the Ontologies Community of Practice (CoP) of the CGIAR Platform for Big Data in Agriculture for harnessing relevant expertise in ontology development and identifying innovative solutions that support quality data annotation. The Ontologies CoP stimulates knowledge sharing among stakeholders, such as researchers, data managers, domain experts, experts in ontology design, and platform development teams.
可持续全球农业和农业食品系统研究产生的异构多学科数据需要进行高质量的数据标记或注释，以便实现互操作性。根据FAIR原则的建议，数据、标签和元数据必须使用知识领域中流行且社区常用的受控词汇表和本体。尽管生命科学领域存在强大的本体，但目前尚无一套全面的本体推荐用于跨农业研究学科的数据注释。在本文中，我们讨论了国际农业研究磋商组织（CGIAR）农业大数据平台的本体实践社区（CoP）在利用本体开发相关专业知识和识别支持高质量数据注释的创新解决方案方面的附加价值。本体CoP促进了利益相关者之间的知识共享，这些利益相关者包括研究人员、数据管理人员、领域专家、本体设计专家和平台开发团队。
