What question did this study set out to answer?

The aim is to improve the accessibility and machine-readability of datasets in the RADAR repository by integrating RO-Crate and employing AI for metadata enhancement.

March 25, 2026Open Access

Advancing FAIR Digital Objects in RADAR: RO-Crate Integration, Machine-Actionable Access, and AI-Support for Metadata Annotation and FAIRness Assessment

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The aim is to improve the accessibility and machine-readability of datasets in the RADAR repository by integrating RO-Crate and employing AI for metadata enhancement.
Implemented RO-Crate for enhanced metadata structuring.
Developed streaming access for individual file retrieval from TAR archives.
Introduced fragment identifiers for precise file referencing through DOIs.
Utilized AI for automatic metadata improvement and FAIRness assessment.
Enhanced dataset access through machine-readable RO-Crate metadata.
Enabled streaming access for individual files, reducing download requirements.
Improved metadata quality with AI-driven suggestions for relevant terms.
Strengthened compliance with FAIR principles, aiding dataset discoverability across disciplines.

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Implication

This project enhances dataset accessibility and metadata quality in RADAR, improving interoperability and FAIR compliance.

Cite This Study

Schweikert et al. (2026) studied this question.

synapsesocial.com/papers/69c37bb3b34aaaeb1a67e5a0 https://doi.org/10.5281/zenodo.18964118

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Advancing FAIR Digital Objects in RADAR: RO-Crate Integration, Machine-Actionable Access, and AI-Support for Metadata Annotation and FAIRness Assessment

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Implication

Cite This Study

Advancing FAIR Digital Objects in RADAR: RO-Crate Integration, Machine-Actionable Access, and AI-Support for Metadata Annotation and FAIRness Assessment

Key Points

Discussion

Implication

Cite This Study

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