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The FAIR Lesson Plan Handbook: Open Educational Resources for FAIR Training.

Martijn G Kersloot1,2, Mijke Jetten3, Stephan Nylinder4

  • 1Amsterdam UMC location University of Amsterdam, Department of Medical Informatics, Meibergdreef 9, Amsterdam, The Netherlands.

Studies in Health Technology and Informatics
|May 17, 2025
PubMed
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This handbook provides practical guidance for implementing FAIR Data Principles in life sciences. It offers a framework and lesson plans to train researchers on making data Findable, Accessible, Interoperable, and Reusable.

Area of Science:

  • Life Sciences
  • Data Science
  • Research Training

Background:

  • The FAIR Data Principles are crucial for effective data management in research.
  • There is a recognized need for practical, actionable guidance on implementing these principles.
  • Existing resources often lack structured training frameworks for life sciences organizations.

Purpose of the Study:

  • To develop a practical framework and comprehensive lesson plans for training researchers on the FAIR Data Principles.
  • To create a community-driven, evolving resource to support the adoption of FAIR practices.
  • To empower life sciences research organizations to build robust FAIR data training programs.

Main Methods:

  • A collaborative effort involving over 50 contributors.
Keywords:
FAIR Data PrinciplesOpen Educational ResourcesResearch Data ManagementTraining

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  • Development through a series of hackathons and collaborative working sessions.
  • Creation of structured lesson plans covering various aspects of FAIR data.

    • Main Results:

    • The FAIR Lesson Plan Handbook was developed as a result of the collaborative effort.
    • Lesson plans cover FAIR generics, Findable, Accessible, Interoperable, Reusable data, FAIR software, and data repositories.
    • The Handbook is hosted on GitHub, signifying a community-driven and evolving resource.

    Conclusions:

    • The FAIR Lesson Plan Handbook provides essential practical guidance for implementing FAIR Data Principles.
    • The resource empowers trainers and researchers to integrate FAIR practices into their workflows.
    • This community-driven initiative supports the broader adoption of FAIR data standards in life sciences research.