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  11. Fair Compliant Database Development For Human Microbiome Data Samples

FAIR compliant database development for human microbiome data samples

Mathieu Dorst1, Nathan Zeevenhooven1, Rory Wilding2

  • 1Informatics Institute, University of Amsterdam, Amsterdam, Netherlands.

Frontiers in Cellular and Infection Microbiology
|May 22, 2024

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View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces a real-time, FAIR-compliant database for human microbiome data. It addresses privacy concerns using GDPR regulations and enhances accessibility with a large language model for broader research application.

Area of Science:

  • Microbiology
  • Bioinformatics
  • Data Science

Background:

  • Sharing microbiome data accelerates innovation and reduces research costs.
  • Standardized, transparent, and accessible data are crucial for microbiome research.
  • Human microbiome and host-associated data require robust handling and storage solutions.

Purpose of the Study:

  • To develop a real-time, FAIR-compliant database for human microbiome and host-associated data.
  • To address privacy concerns and regulatory conflicts (e.g., GDPR) in data sharing.
  • To enhance data accessibility and usability for researchers and non-experts.

Main Methods:

  • Implementation of a real-time database using the open-source Supabase platform.
  • Development of protocols for FAIR (Findable, Accessible, Interoperable, Reusable) data compliance.
Keywords:
(meta)datadatabasefair principlesgeneral data protection regulation (GDPR)

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  • Integration of a large language model (LLM) for knowledge dissemination and user support.

    • Main Results:

    • A functional, FAIR-compliant database for human microbiome data is established.
    • Protocols ensure data privacy while maintaining compliance with regulations like GDPR.
    • The LLM facilitates non-expert interaction and knowledge sharing regarding the database.

    Conclusions:

    • The developed database promotes efficient and ethical sharing of human microbiome data.
    • FAIR principles and privacy-preserving methods are successfully integrated.
    • The LLM component enhances the database's utility and reach within the scientific community.
    microbiome
    pseudonymize
    real-time