39 Hints to Facilitate the Use of Semantics for Data on Agriculture and Nutrition. (11th December 2020)

• Record Type: Journal Article
• Authors: Caracciolo, Caterina; Aubin, Sophie; Jonquet, Clement; Amdouni, Emna; David, Romain; Garcia, Leyla; Whitehead, Brandon; Roussey, Catherine; Stellato, Armando; Villa, Ferdinando
• Is Part Of: Data science journal. Volume 19(2020)
• Publication Date: 2020-12-11
• Subjects: agrifood data -- FAIR data -- semantics -- semantic resources -- ontology -- vocabulary -- terminology -- thesauri -- ontology repository -- terminology service; Science -- Data processing -- Periodicals; Database management -- Periodicals; 502.85
• DOI: 10.5334/dsj-2020-047
• Languages: English
• ISSNs: 1683-1470

Abstract

In this paper, we report on the outputs and adoption of the Agrisemantics Working Group of the Research Data Alliance (RDA), consisting of a set of recommendations to facilitate the adoption of semantic technologies and methods for the purpose of data interoperability in the field of agriculture and nutrition. From 2016 to 2019, the group gathered researchers and practitioners at the crossing point between information technology and agricultural science, to study all aspects in the life cycle of semantic resources: conceptualization, edition, sharing, standardization, services, alignment, long term support. First, the working group realized a landscape study, a study of the uses of semantics in agrifood, then collected use cases for the exploitation of semantics resources – a generic term to encompass vocabularies, terminologies, thesauri, ontologies. The resulting requirements were synthesized into 39 "hints" for users and developers of semantic resources, and providers of semantic resource services. We believe adopting these recommendations will engage agrifood sciences in a necessary transition to leverage data production, sharing and reuse and the adoption of the FAIR data principles. The paper includes examples of adoption of those requirements, and a discussion of their contribution to the field of data science.