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Features of a FAIR vocabulary
Fuqi Xu1, Nick Juty2, Carole Goble2
1European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory, Wellcome Genome Campus, Cambridge, Hinxton, CB10 1SD, UK.
Defining and assessing FAIR vocabularies is crucial for data science. This study proposes features and assessment methods to guide the development of Findable, Accessible, Interoperable, and Reusable (FAIR) vocabularies.
Area of Science:
- Data Science
- Information Science
- Biomedical Informatics
Background:
- The Findable, Accessible, Interoperable, and Reusable (FAIR) Principles mandate FAIR vocabularies, but their definition remains unclear.
- A clear definition and assessment framework for FAIR vocabularies are needed to guide their development and improve data discoverability.
Purpose of the Study:
- To define features of FAIR vocabularies.
- To develop assessment approaches for FAIR vocabularies.
- To guide the development and evolution of FAIR vocabularies.
Main Methods:
- Differentiated data, data resources, and vocabularies in the context of FAIR.
- Examined the application of FAIR Principles to vocabularies.
- Aligned FAIR vocabulary requirements with Open Biomedical Ontologies principles.
- Proposed FAIR Vocabulary Features (FVFs).
- Designed assessment approaches by mapping FVFs with existing FAIR assessment indicators.
- Demonstrated evaluation and improvement using biomedical vocabularies.
Main Results:
- Proposed specific features for FAIR vocabularies.
- Developed indicators for assessing the FAIR levels of vocabularies.
- Mapped proposed features with existing FAIR assessment indicators.
- Demonstrated practical application in evaluating and improving biomedical vocabularies.
Conclusions:
- The study provides a framework for defining and assessing FAIR vocabularies.
- Identified use cases for vocabulary engineers to improve vocabulary quality.
- Offers guidance for the evolution of vocabularies to meet FAIR data principles.
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