A documentation for FAIR GPT, an AI assistant focused on guiding researchers and data stewards in making their datasets compliant with the FAIR (Findable, Accessible, Interoperable, and Reusable) principles.
FAIR GPT offers the following features:
- Metadata Review and Recommendations: Analyzes dataset metadata for compliance with international standards (DataCite, Dublin Core, Schema.org) and offers enhancements to ensure datasets are easily findable and identifiable.
- Data Structure Guidance: Provides advice on data organization, including naming conventions and file structures, to enhance data accessibility and usability.
- Documentation Assistance: Helps in creating detailed documentation that covers data collection, processing, anonymization, and usage, including exemplary readme files to accompany datasets.
- Repository Recommendations: Identifies appropriate data repositories for long-term storage, taking into account the subject area and specific data types, to ensure datasets are preserved in accessible and citable forms.
- FAIR Principles Evaluation: Employs advanced algorithms and external APIs to assess and score datasets' adherence to FAIR principles, offering a clear pathway toward improvement.
- Customized Training and Education: Provides tailored training sessions and educational resources on data management best practices, specific to the user's field or data type, promoting a deeper understanding of FAIR principles.
- Knowledge Graph Integration: Advises on linking dataset metadata with knowledge graphs like Wikidata, enhancing interoperability and the semantic enrichment of data.
- Legal and Ethical Guidance: Offers recommendations on licensing, data protection, and ethical considerations to ensure data sharing complies with legal standards and respects privacy.
- Data Policy Consultation: Assists organizations in developing or refining their data policies to support FAIR data management and sharing practices.
- Data Paper Publication Support: Recommends suitable data journals for publishing data papers, increasing the visibility and citation of datasets.
- Interactive FAIRness Assessment Tool: Includes an interactive tool for users to self-assess their datasets' FAIRness, providing instant feedback and actionable advice for each FAIR principle.
- Data management planning: Creates a data management plan using the metadata of data.
To get started with FAIR GPT, follow these steps:
- Get access to ChatGPT Plus: Buy subscription to ChatGPT Plus
- Open FAIR GPT
- Usage scenarios:
- Upload you metadata or part of data. Ask for help.
- Copy and paste your metadata or data into a prompt. Ask for help.
- Add a link to your data. Ask for assessment.
- Ask whatever you want about FAIR data.
- H2020 Programme Guidelines on FAIR Data Management in Horizon 2020
- "Turning FAIR into reality" Final report and action plan from the European Commission expert group on FAIR data
- Awesome-RDM GitHub Repo
- re3data Repositories API: This API provides access to repository data from re3data.org
- FAIR Enough API: API for evaluating resources with FAIR Enough service
- Wikidata API: Gets Wikidata QIDs for a given label (
wbgetentities) - TIB Central Terminology Service Search API: API for free text search over the ontologies
- FAIR-Checker: Retrieves current weather data for a location
If you're interested in improving FAIR GPT, please open an issue or make a pull request.
The documentation for FAIR GPT is available under the CC0 license.
- Shigapov, R. (2023, December 15). Optimizing FAIR data sharing with ChatGPT. ENGAGE.EU Webinar on FAIR data, Online. Zenodo. https://doi.org/10.5281/zenodo.10378143
- Shigapov, R. (2024, February 15). ChatGPT for FAIR Research Data. Research Data Management Seminars at the University of Mannheim, Online. Zenodo. https://doi.org/10.5281/zenodo.10664554