FAIR词汇的特征。

IF 1.6 3区 工程技术 Q3 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Fuqi Xu, Nick Juty, Carole Goble, Simon Jupp, Helen Parkinson, Mélanie Courtot
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引用次数: 2

摘要

背景:可查找、可访问、可互操作和可重用(FAIR)原则明确要求使用FAIR词汇表,但具体是什么构成FAIR词汇表尚不清楚。能够定义FAIR词汇表,识别FAIR词汇表的特征,并提供针对这些特征的评估方法,可以指导词汇表的开发。结果:我们区分了FAIR使用的数据、数据资源和词汇表,考察了FAIR原则在词汇表中的应用,并将其要求与开放生物医学本体原则相结合,提出了FAIR词汇表特征。我们还通过将FVFs与现有的FAIR评估指标进行映射,设计了FAIR词汇表的评估方法。最后,我们将通过示例性生物医学词汇来演示如何使用它们来评估和改进词汇。结论:我们的工作提出了公平词汇的特征和相应的指标来评估不同类型词汇的公平水平,为词汇工程师确定了用例,并指导了词汇的演变。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Features of a FAIR vocabulary.

Background: The Findable, Accessible, Interoperable and Reusable(FAIR) Principles explicitly require the use of FAIR vocabularies, but what precisely constitutes a FAIR vocabulary remains unclear. Being able to define FAIR vocabularies, identify features of FAIR vocabularies, and provide assessment approaches against the features can guide the development of vocabularies.

Results: We differentiate data, data resources and vocabularies used for FAIR, examine the application of the FAIR Principles to vocabularies, align their requirements with the Open Biomedical Ontologies principles, and propose FAIR Vocabulary Features. We also design assessment approaches for FAIR vocabularies by mapping the FVFs with existing FAIR assessment indicators. Finally, we demonstrate how they can be used for evaluating and improving vocabularies using exemplary biomedical vocabularies.

Conclusions: Our work proposes features of FAIR vocabularies and corresponding indicators for assessing the FAIR levels of different types of vocabularies, identifies use cases for vocabulary engineers, and guides the evolution of vocabularies.

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来源期刊
Journal of Biomedical Semantics
Journal of Biomedical Semantics MATHEMATICAL & COMPUTATIONAL BIOLOGY-
CiteScore
4.20
自引率
5.30%
发文量
28
审稿时长
30 weeks
期刊介绍: Journal of Biomedical Semantics addresses issues of semantic enrichment and semantic processing in the biomedical domain. The scope of the journal covers two main areas: Infrastructure for biomedical semantics: focusing on semantic resources and repositories, meta-data management and resource description, knowledge representation and semantic frameworks, the Biomedical Semantic Web, and semantic interoperability. Semantic mining, annotation, and analysis: focusing on approaches and applications of semantic resources; and tools for investigation, reasoning, prediction, and discoveries in biomedicine.
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