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FAIR-Checker: supporting digital resource findability and reuse with Knowledge Graphs and Semantic Web standards.

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FAIR-Checker is a new web tool that automatically assesses the Findable, Accessible, Interoperable, and Reusable (FAIR) principles for digital resources. It helps researchers improve metadata quality for better data sharing and reuse in life sciences.

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Area of Science:

  • Life Sciences
  • Bioinformatics
  • Digital Resource Management

Background:

  • Open Science and Reproducibility initiatives necessitate machine-actionable metadata for biological digital resources.
  • FAIR principles (Findable, Accessible, Interoperable, Reusable) are crucial for data and metadata sharing, with defined metrics.
  • Automated FAIRness assessment is challenging due to technical expertise and time constraints.

Purpose of the Study:

  • To introduce FAIR-Checker, a web-based tool for assessing the FAIRness of digital resource metadata.
  • To provide modules for metadata evaluation, recommendations, and quality improvement.
  • To leverage Semantic Web technologies for automated FAIR metric assessment.

Main Methods:

  • Development of a web-based tool, FAIR-Checker.
  • Implementation of Semantic Web standards, including SPARQL queries and SHACL constraints.
  • Creation of 'Check' and 'Inspect' modules for metadata evaluation and user assistance.

Main Results:

  • FAIR-Checker automatically assesses FAIRness metrics for metadata.
  • The tool identifies missing, necessary, or recommended metadata for various resource types.
  • Evaluation demonstrated utility in improving individual resource FAIRification and analyzing large-scale bioinformatics software descriptions.

Conclusions:

  • FAIR-Checker simplifies and automates the FAIRness assessment of metadata.
  • The tool supports researchers in enhancing metadata quality, promoting data sharing and reuse.
  • It contributes to the broader goals of Open Science and Reproducibility in the life sciences.