OAR@ICRISATThe Ontologies Community of Practice: A CGIAR Initiative for Big Data in Agrifood SystemsEArnaudauthorM ALaporteauthorSKimauthorCAubertauthorSLeonelliauthorBMiroauthorLCooperauthorPJaiswalauthorGKrusemanauthorRShresthaauthorP LButtigiegauthorC JMungallauthorJPietragallaauthorAAgbonaauthorJMuliroauthorJDetrasauthorVHuallaauthorARathoreauthorR RDasauthorIDiengauthorGBauchetauthorNMendaauthorCPommierauthorFShawauthorDLyonauthorLMwanziaauthorHJuarezauthorEBonaiutiauthorBChiputwaauthorOObileyeauthorSAuzouxauthorE DYeumoauthorL AMuellerauthorKSilversteinauthorALafargueauthorEAntezanaauthorMDevareauthorBKingauthorThe increasing application to agrifood research data of the FAIR (findable, accessible, interoperable, and reusable) principles1 has led to the research community’s growing interest in using ontologies. FAIR principles indeed recommend that data must be described with commonly used, controlled vocabularies structured in thesauri and semantically rich ontologies. An ontology is a representation of a domain of knowledge where key concepts, as well as the relationships between those concepts, are defined.2 By providing standardized definitions for the terms used by scientists along with defined logical relationships among these terms, ontologies compile information about the content of a dataset that can be explicitly used by computers.3 Each concept has a Uniform Resource Identifier (URI) that uniquely identifies it as a web resource accessible by anyone for data labeling, to efficiently support consistent use of ontology terms within and across disciplines and domains. Therefore, annotating data with quality and widely used ontologies increases the findability, interoperability, and reusability of data.Data & Analytics2020-10World Scientific PublishersArticle

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