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Automating FAIRness: A FAIRification Tool within the Language Resources Infrastructure

Summary

In many repositories, checking whether language resources meet the FAIR principles is still mostly manual. This is slow, hard to reproduce and does not scale as deposits grow. Generic automated checkers also miss the metadata conventions typical of linguistic data. The authors, from CNR-ILC, present the FAIRification Tool, developed by CLARIN-IT within the Italian H2IOSC infrastructure. It uses the F-UJI assessment service as its foundation and adds language-domain indicators tied to CMDI profiles and CLARIN controlled vocabularies. The tool combines automatic scoring with curator review, a feedback dashboard and certification thresholds. It is built into repository workflows, so improving FAIRness becomes a recurring cycle rather than a one-time audit. A first evaluation on a sample of linguistic datasets found FAIR scores rising by about 30–40%, with the strongest gains in findability and reusability. The work offers a reproducible, governance-aware way to raise the quality of language data across CLARIN, H2IOSC and EOSC.

Key points

  • The tool is based on F-UJI, the reference implementation of the FAIRsFAIR Data Object Assessment Metrics. These comprise 17 metrics spread over the four FAIR dimensions and are tested via landing pages, repository APIs and embedded JSON-LD/RDFa.
  • F-UJI is deliberately domain-agnostic. The FAIRification Tool adds linguistic-specific checks (CMDI profiles, CLARIN vocabularies), human validation, a dashboard and certification thresholds.
  • In the preliminary evaluation, FAIR scores improved by 30–40%, mainly in the Findable and Reusable dimensions.
  • The tool was built by CLARIN-IT within H2IOSC, which is funded by EU NextGeneration EU. It targets interoperability with CLARIN and EOSC.
  • Planned work includes DSpace 7 integration, visual dashboards, longitudinal monitoring, and support for multimodal and multilingual datasets. A live demo is planned for LREC 2026.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Linguistics & languages

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.63317/2ryf7gasio58

@inproceedings{melaccio2026automating,
  title = {{Automating FAIRness: A FAIRification Tool within the Language Resources Infrastructure}},
  author = {Melaccio, Daniele and Monachini, Monica},
  booktitle = {Proceedings of the Fifteenth Language Resources and Evaluation Conference},
  year = {2026},
  doi = {10.63317/2ryf7gasio58},
  url = {https://doi.org/10.63317/2ryf7gasio58}
}

References

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