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.
Highlights
Data are important in agriculture, including fields such as precision agriculture (Shannon et al, 2020), climate modeling (Crosson et al, 2011; Nelson et al, 2014) and policy making
Caracciolo et al: 39 Hints to Facilitate the Use of Semantics for Data on Agriculture and Nutrition importance of data interoperability to researchers and practitioners working at the interface between data and information management and agriculture, we formed the Agrisemantics Working Group (WG) within the Research Data Alliance (RDA)
The Agrisemantics WG formulated two questions (Aubin et al, 2017b): Is there a specific semantics for agriculture? Is there a specific type of interoperability for agricultural data? we focused on semantics for agricultural data, understood as “data produced or used in agriculture and food systems, including data on agricultural production, or agronomic data relative to lab and field experiments, environmental conditions, soils or climate, just to mention a few relevant areas of data productions” (Aubin et al, 2017b)
Summary
Data are important in agriculture, including fields such as precision agriculture (Shannon et al, 2020), climate modeling (Crosson et al, 2011; Nelson et al, 2014) and policy making. The meaning of data is commonly expressed by semantic resources (aka semantic structures, or semantic artifacts, or more generally knowledge organization systems (Zeng, 2008)), typically sets of terms and definitions organized in ways that reflect views of the world adopted when collecting the data, suitable to the intended applications of the data Such structures vary from flat to hierarchical (taxonomies), to more complex structures supporting reasoning (ontologies). Providing explicit and machine-readable description of data makes it possible to programmatically integrate and reuse data With this in mind, the Agrisemantics WG formulated two questions (Aubin et al, 2017b): Is there a specific semantics for agriculture?
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