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Progress toward a comprehensive teaching approach to the FAIR data principles.

Hugh Shanahan1, Nancy Hoebelheinrich2, Angus Whyte3

  • 1Department of Computer Science, Royal Holloway, University of London, Egham, England TW20 0EX, UK.

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Summary
This summary is machine-generated.

This study evaluates educational frameworks and training initiatives for teaching FAIR data principles. It highlights the need for linking resources to frameworks, using community metadata, and fostering instructor collaboration for effective FAIR data skills education.

Keywords:
DSML 4: Production: Data science output is validated, understood, and regularly used for multiple domains/platforms

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Area of Science:

  • Data Science Education
  • Research Data Management
  • Open Science Training

Background:

  • Effective teaching of FAIR data principles is crucial for advancing research.
  • Existing educational frameworks and training initiatives require evaluation.
  • Discoverability and descriptive information of FAIR training materials vary significantly.

Purpose of the Study:

  • To evaluate current educational frameworks and training initiatives for FAIR data principles.
  • To analyze sources for FAIR training materials based on descriptive information.
  • To identify best practices for teaching FAIR data skills.

Main Methods:

  • Examined existing and developing educational frameworks focused on FAIR principles.
  • Analyzed training initiatives and sources for FAIR training materials.
  • Utilized FAIR4S for skills and competencies, analyzing target audiences and material descriptions.

Main Results:

  • Identified the importance of linking training resources to FAIR-related educational frameworks.
  • Highlighted the need for consistent descriptions of materials using community-based metadata.
  • Emphasized the value of developing an instructor community of practice.

Conclusions:

  • Linking resources to frameworks, consistent metadata, and instructor communities are key to effective FAIR data skills education.
  • Further development of educational resources and collaborative platforms is recommended.
  • Improved discoverability and description of FAIR training materials will enhance adoption.