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摘要翻译：
在本文中，我们报告了研究数据联盟(RDA)Agrisemantics工作组的产出和采用情况，包括一系列建议，以促进采用语义技术和方法，实现农业和营养领域的数据互操作。从2016年到2019年，该小组聚集了信息技术和农业科学交叉点的研究人员和实践者，研究语义资源生命周期的各个方面：概念化、编辑、共享、标准化、服务、对齐、长期支持。首先，工作组实现了一个景观研究，一个关于语义在农业食品中的使用的研究，然后为语义资源的开发收集用例--一个涵盖词汇、术语、叙词表、本体的通用术语。对语义资源的用户和开发者以及语义资源服务的提供者，将所得到的需求合成为39个“提示”。我们相信，采纳这些建议将使农业食品科学进入必要的过渡，以利用数据生产、共享和重用，并采用公平数据原则。本文包括采用这些需求的例子，并讨论了它们对数据科学领域的贡献。
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英文标题：
《39 Hints to Facilitate the Use of Semantics for Data on Agriculture and
Nutrition》
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作者：
Caterina Caracciolo (FAO), Sophie Aubin (DipSO), Clement Jonquet
(LIRMM, UM, CNRS), Emna Amdouni (LIRMM, UM, CNRS), Romain David (MISTEA,
ERINHA-AISBL), Leyla Garcia (ZB MED), Brandon Whitehead, Catherine Roussey
(UR TSCF, INRAE), Armando Stellato, Ferdinando Villa (BC3)
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最新提交年份：
2020
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分类信息：
一级分类：Quantitative Biology 数量生物学
二级分类：Other Quantitative Biology 其他定量生物学
分类描述：Work in quantitative biology that does not fit into the other q-bio classifications
不适合其他q-bio分类的定量生物学工作
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一级分类：Computer Science 计算机科学
二级分类：Databases 数据库
分类描述：Covers database management, datamining, and data processing. Roughly includes material in ACM Subject Classes E.2, E.5, H.0, H.2, and J.1.
涵盖数据库管理、数据挖掘和数据处理。大致包括ACM学科类E.2、E.5、H.0、H.2和J.1中的材料。
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英文摘要：
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.
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PDF链接：
https://arxiv.org/pdf/2012.08325
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