FAIR GPT: AI-Driven FAIR Data Management

• FAIR GPT is a virtual consultant that operationalizes FAIR principles by integrating external APIs for metadata curation, FAIRness scoring, and repository selection.
• It features a modular architecture with specialized microservices for metadata standards, documentation templates, and licensing guidance to improve data stewardship.
• The system enhances research data management efficiency, achieving up to 30% time savings and a 20% boost in metadata completeness during evaluations.

FAIR GPT is a virtual consultant for research data management designed to operationalize the FAIR (Findable, Accessible, Interoperable, Reusable) principles in the context of scientific data stewardship. By integrating with external APIs for vocabulary control, FAIRness assessment, and repository selection, as well as offering automation for documentation and licensing guidance, FAIR GPT targets key barriers in FAIR adoption. The system is built as a custom-augmented ChatGPT agent with modular architecture, aimed at reducing manual overhead and increasing the precision of FAIR compliance workflows (Shigapov et al., 2024).

1. Motivation and Context

The FAIR principles have become the de facto standard for scientific data management since their introduction by Wilkinson et al. (2016). Implementation in practice, however, is impeded by technical and organizational obstacles: lack of expertise in metadata standards, inconsistent use of controlled vocabularies, repository and licensing selection challenges, and insufficient documentation. FAIR GPT addresses these gaps by providing an AI-powered virtual consultant to support researchers, data stewards, and library personnel in preparing and evaluating data assets for FAIR compliance, automating metadata improvement, FAIRness assessment, repository selection, and RDM (Research Data Management) documentation tasks.

2. System Architecture and Integration

FAIR GPT leverages a ChatGPT-based front end with custom instructions, routing user queries to specialized microservices and external APIs. Its architecture includes the following modular components:

• ChatGPT Core: Natural language interface and reasoning layer.
• Metadata Module: Interfaces with the TIB Terminology Service API for vocabulary recommendations and Wikidata API for identifier validation.
• FAIRness Assessment Module: Aggregates per-principle sub-scores from FAIR-Checker and FAIR-Enough APIs (Findability, Accessibility, Interoperability, Reusability).
• Repository Recommendation Module: Calls the re3data API for repository metadata and applies a heuristic scoring function over subject fit, certification, licensing, and cost.
• Documentation Generator: Employs standardized templates (Horizon 2020 DMP guidelines, “Turning FAIR into Reality” best practices) for README files, codebooks, and management plans.
• Data Licensing Advisor: Draws upon a curated set of open licenses (e.g., Creative Commons, MIT, Apache 2.0).
• Instruction & Knowledge Assets: Hosted on GitHub for discoverability and version control.

The system is deployed via ChatGPT custom instructions on OpenAI, with supporting microservices implemented in Node.js or Python to broker API requests.

3. FAIRness Assessment Methodology

Assessment is triggered by input of a dataset URL or DOI. The workflow involves:

• External Scoring: RESTful calls to both FAIR-Checker and FAIR-Enough APIs, each returning graded metrics for the four principles: Findability (F1), Accessibility (F2), Interoperability (F3), and Reusability (F4).
• Aggregate Score: Conceptual formula

$$F = \alpha \cdot F_1 + \beta \cdot F_2 + \gamma \cdot F_3 + \delta \cdot F_4$$

where $\alpha + \beta + \gamma + \delta = 1$; the paper does not specify the precise aggregation weights.

• Operational Checks:
  • Metadata Quality: Required presence of schema.org or Dublin Core fields, use of controlled vocabularies, and persistent identifiers (DOIs, ORCIDs).
  • Dataset Structure: Folder hierarchy depth, naming conventions, versioning.
  • Interoperability: Usage of open file formats (CSV, JSON-LD), semantic annotations (RDF, JSON Schema).
  • Accessibility: License metadata presence and machine-readable protocols (OAI-PMH, HTTPS).
  • Reusability: License clarity, provenance, and completeness of documentation.

4. Metadata, Documentation, and Guidance

FAIR GPT automates critical components of metadata curation and RDM documentation:

• Schema Selection: Recommends DCAT, DataCite, or schema.org, with mandatory fields: title, authors (including ORCID), abstract, keywords, collection dates, license, funding, and methods summaries.
• Controlled Vocabulary Alignment: Subject keywords are mapped to TIB and Wikidata identifiers.
• Documentation Templates:
  • README.md: Title, overview, data structure, usage, license, and citation.
  • Codebook: Tabular format enumerating variable attributes, types, units, missing codes, and descriptions.
  • Data Management Plan: Six-section outline based on Horizon 2020 standards.

5. Repository Recommendation Logic

Selection of data repositories is guided by:

• Criteria: Re3data’s subject metadata, CoreTrustSeal certification, access and cost model, technical affordances (API availability, metadata depth, versioning support).
• Heuristic Ranking:

$$\text{Score}(\text{repo}) = w_1 \cdot \text{match}_\text{subject} + w_2 \cdot \text{certification} + w_3 \cdot \text{license}_\text{support} + w_4 \cdot \text{cost}_\text{factor}$$

with all scores normalized to $[0,1]$ and $\sum w_i = 1$.

• Rule-based Example: Genomics subjects recommend EMBL-EBI BioStudies; open social science data suggest Zenodo or OSF; otherwise, fallback to generalist repositories (Zenodo, Figshare).

6. Evaluation and Observed Impact

FAIR GPT's efficacy has been qualitatively validated by the University of Mannheim Research Data Center, reporting approximately 30% time savings per dataset review and a 20% increase in metadata completeness. However, the paper does not report quantitative metrics such as precision, recall, or F1-score, nor any controlled user studies. Noted limitations include a 5–10% error rate for novel domain vocabulary recommendations and residual hallucinations in edge cases (Shigapov et al., 2024).

7. Use Cases, Limitations, and Future Enhancements

Use Cases

• Researcher Assessment: Automated FAIRness evaluation for individual datasets, yielding per-principle scores and actionable remediation advice.
• Data Steward Review: Metadata snippet improvement, enforcing completeness, identifier mapping, and license attribution.

Limitations

• Absence of formal provenance tracing for recommendations.
• Rapidly evolving RDM standards necessitate ongoing update of instruction and template repositories.
• No upload or assessment of sensitive data due to privacy constraints.
• Currently no public API for batch or automated RDM pipeline integration.

Future Directions

• Development of provenance linkage between recommendations and their source APIs or guidelines.
• Batch processing support via a public REST API.
• Sensitive data handling modules, including GDPR compliance.
• Domain-specific plugins for tailored schema, vocabulary, and repository recommendations.
• User-configurable FAIRness weighting schemes.

8. Significance in the Research Data Management Ecosystem

FAIR GPT is positioned as a bridge between RDM best practices and scalable AI automation. By tightly coupling authoritative external APIs, strict use of controlled vocabularies, and standard metadata guidelines, the system operationalizes the FAIR principles in a reproducible and auditable manner. Its modular integration blueprint demonstrates a practical pathway for institutions to elevate the quality, discoverability, and reusability of scientific datasets within interoperable research infrastructures (Shigapov et al., 2024).