Ten simple rules for making a vocabulary FAIR

Cox, Simon J.; González-Beltrán, Alejandra N.; Magagna, Barbara; Marinescu, Maria-Cristina

doi:10.1371/journal.pcbi.1009041

Cited by 32 publications

(20 citation statements)

References 15 publications

(19 reference statements)

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“…Other recent related works on FAIR principles for semantic resources include a list of functional metrics and recommendations for Linked Open Data Knowledge Organization Systems (LOD KOS) products proposed in 2020 [21], a list of ten simple rules for making a vocabulary FAIR [22]. Finally, the DBPedia Archivo tool [23], an ontology archive also released at the end of 2020 that aims to help developers and consumers in "implementing FAIR ontologies on the Web.…”

Section: Specific Fairness Assessment Approachesmentioning

confidence: 99%

FAIR or FAIRer? An Integrated Quantitative FAIRness Assessment Grid for Semantic Resources and Ontologies

Amdouni

Jonquet

2022

Metadata and Semantic Research

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In open science, the expression "FAIRness assessment" refers to evaluating to which degree a digital object is Findable, Accessible, Interoperable, and Reusable. Standard vocabularies or ontologies are a key element to achieving a high level of FAIRness (FAIR Principle I2) but as with any other data, ontologies have themselves to be FAIR. Despite the recent interest in the open science and semantic Web communities for this question, we have not seen yet a quantitative evaluation method to assess and score the level of FAIRness of ontologies or semantic resources in general (e.g., vocabularies, terminologies, thesaurus). The main objective of this work is to provide such a method to guide semantic stakeholders in making their semantic resources FAIR. We present an integrated quantitative assessment grid for semantic resources and propose candidate metadata properties -taken from the MOD ontology metadata model-to be used to make a semantic resource FAIR. Aligned and nourished with relevant FAIRness assessment state-of-the-art initiatives, our grid distributes 478 credits to the 15 FAIR principles in a manner which integrates existing generic approaches for digital objects (i.e., FDMM, SHARC) and approaches dedicated to semantic resources (i.e., 5-stars V, MIRO, FAIRsFAIR, Poveda et al.). The credits of the grid can then be used for implementing FAIRness assessment methods and tools.

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Section: Specific Fairness Assessment Approachesmentioning

confidence: 99%

FAIR or FAIRer? An Integrated Quantitative FAIRness Assessment Grid for Semantic Resources and Ontologies

Amdouni

Jonquet

2022

Metadata and Semantic Research

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“…This will enable interoperability with other systems and support integrating and exchanging data in a straightforward way. To reduce the ambiguity in diverse data representations, we rely on common formats (such as JSON-LD [ 19 ]), and common terminologies or FAIR vocabularies [ 20 ] that provide clear definitions and persistent identifiers for the terms. For example, we use the provenance vocabulary (PROV-O) to faithfully represent the entities, activities and people involved in producing a research output.…”

Section: Introductionmentioning

confidence: 99%

FAIR data pipeline: provenance-driven data management for traceable scientific workflows

Mitchell

Lahiff

Cummings

et al. 2022

Phil. Trans. R. Soc. A.

Self Cite

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Modern epidemiological analyses to understand and combat the spread of disease depend critically on access to, and use of, data. Rapidly evolving data, such as data streams changing during a disease outbreak, are particularly challenging. Data management is further complicated by data being imprecisely identified when used. Public trust in policy decisions resulting from such analyses is easily damaged and is often low, with cynicism arising where claims of ‘following the science’ are made without accompanying evidence. Tracing the provenance of such decisions back through open software to primary data would clarify this evidence, enhancing the transparency of the decision-making process. Here, we demonstrate a Findable, Accessible, Interoperable and Reusable (FAIR) data pipeline. Although developed during the COVID-19 pandemic, it allows easy annotation of any data as they are consumed by analyses, or conversely traces the provenance of scientific outputs back through the analytical or modelling source code to primary data. Such a tool provides a mechanism for the public, and fellow scientists, to better assess scientific evidence by inspecting its provenance, while allowing scientists to support policymakers in openly justifying their decisions. We believe that such tools should be promoted for use across all areas of policy-facing research. This article is part of the theme issue ‘Technical challenges of modelling real-life epidemics and examples of overcoming these’.

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“…Garijo and Poveda-Villalon [7] discussed detailed requirements of ontology URIs and versioning strategies, as well as the formatting of those ontologies. Furthermore,"Ten simple rules for making a vocabulary FAIR [8]" for converting print-based or other forms of legacy vocabularies to FAIR vocabularies have also been proposed.…”

Section: Introductionmentioning

confidence: 99%

Features of a FAIR vocabulary

Juty

Goble

et al. 2022

Preprint

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Background: The FAIR Principles explicitly require the use of FAIR vocabularies, but what precisely constitutes a FAIR vocabulary remains unclear. Being able todefine 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 (OBO) Principles, and propose FAIR Vocabulary Features (FVFs). We also design assessment approaches for FAIR vocabularies by mapping the FVFs with existing FAIR assessment indicators. Finally, we demonstrate how FVFs can be used for evaluating and improving vocabularies using exemplar biomedical vocabularies.Conclusions: The FAIR Vocabulary Features and corresponding indicators can be used to assess the FAIR levels of different types of vocabularies, identify use cases for vocabulary engineers, and guide the evolution of vocabularies.

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Ten simple rules for making a vocabulary FAIR

Cited by 32 publications

References 15 publications

FAIR or FAIRer? An Integrated Quantitative FAIRness Assessment Grid for Semantic Resources and Ontologies

FAIR or FAIRer? An Integrated Quantitative FAIRness Assessment Grid for Semantic Resources and Ontologies

FAIR data pipeline: provenance-driven data management for traceable scientific workflows

Features of a FAIR vocabulary

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