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

TL;DR

This paper presents 39 practical hints developed by the Agrisemantics Working Group to promote the use of semantic technologies for improving data interoperability in agriculture and nutrition, supporting FAIR principles.

Contribution

It introduces a comprehensive set of 39 recommendations for semantic resource use in agrifood, based on extensive landscape and use case studies, to enhance data sharing and reuse.

Findings

Adoption of hints facilitates semantic data interoperability in agriculture.

The recommendations support FAIR data principles in agrifood sciences.

Examples demonstrate practical implementation of the hints.

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.