Skip to content

UB-Mannheim/FAIR-GPT

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

11 Commits
 
 
 
 
 
 

Repository files navigation

FAIR GPT

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.

Features

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.

Getting Started

To get started with FAIR GPT, follow these steps:

  1. Open FAIR GPT
  2. 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.
    • Ask "create data and code availability statements for my data and codes".
    • Ask "create an RDM part for my research project proposal".

The uploaded knowledge

Actions (External APIs)

How to Contribute

If you're interested in improving FAIR GPT, please open an issue or make a pull request.

License

The documentation for FAIR GPT is available under the CC0 license.

Presentations

Preprint

Releases

No releases published

Packages

No packages published