39 Hints to Facilitate the Use of Semantics for Data on Agriculture and Nutrition

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...

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Bibliographic Details
Main Authors: Caterina Caracciolo, Sophie Aubin, Clement Jonquet, Emna Amdouni, Romain David, Leyla Garcia, Brandon Whitehead, Catherine Roussey, Armando Stellato, Ferdinando Villa
Format: Article
Language:English
Published: Ubiquity Press 2020-12-01
Series:Data Science Journal
Subjects:
Online Access:https://datascience.codata.org/articles/1225
Description
Summary: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.
ISSN:1683-1470