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Published on: September 20, 2018
FAIRification on Trial? A Provenance and Reproducible Metadata Build Pipeline for Clinical Research
Tobechi Obinwanne1,2, Johannes Darms3,4, Juliane Fluck3,4
1Department of Medical Informatics, University Medical Center, Goettingen, Germany.
Studies in Health Technology and Informatics
|May 23, 2026
Summary
This study introduces a FAIRification pipeline for clinical trial registry metadata, improving machine readability and provenance tracking. The pipeline successfully converted 94.84% of metadata, enabling reproducible research and auditable transformations.
Area of Science:
- Health Informatics
- Data Science
- Clinical Research Informatics
Background:
- Clinical trial registries often lack machine-actionable structures, hindering large-scale data reuse and verification.
- Current systems prioritize human readability over automated processing and provenance management.
- Ensuring the reliability and reproducibility of clinical trial data is crucial for scientific advancement.
Purpose of the Study:
- To develop and evaluate a FAIRification pipeline for EU Clinical Trials Registry metadata.
- To preserve data provenance and enable the reproduction of research results.
- To demonstrate the conversion of clinical trial metadata into a standardized, machine-actionable model.
Main Methods:
- Development of a FAIRification pipeline to process clinical trial metadata.
- Application of the pipeline to trial data uploaded between 2022 and 2025.
- Utilizing W3C PROV-O for lineage tracking and RO-Crates for output packaging.
Main Results:
- Successfully converted 94.84% of trial metadata into the NFDI4Health metadata model.
- Achieved near-complete population of the target model's mandatory properties.
- Ensured W3C PROV-O lineage for all transformations, enabling auditable and repeatable outputs.
Conclusions:
- Clinical trial metadata should be treated as reproducible digital objects, not static narratives.
- The FAIRification pipeline enhances metadata reusability, verification, and auditability.
- Standardized, machine-actionable metadata is essential for reliable and federated data integration in clinical research.
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