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FAIR data management: a framework for fostering data literacy in biomedical sciences education.

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Training postgraduate students in data literacy and FAIR principles improved research reproducibility. This enhances data management and transparency for future scientists.

Keywords:
Academic researchBiomedical educationData literacyData stewardshipFAIR principlesMaster’s thesis

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Area of Science:

  • Biomedical Research
  • Data Science Education

Background:

  • Low reproducibility is a significant issue in biomedical research, impacting scientific progress and reliability.
  • Funding bodies advocate for data management practices to improve reproducibility.
  • Standardized statistical methods and data analysis are vital to minimize bias and inaccuracies.

Purpose of the Study:

  • To train postgraduate students in data literacy and FAIR principles.
  • To assess the application of these skills in master's thesis projects.
  • To enhance research reproducibility and transparency in biomedical studies.

Main Methods:

  • A training program on data literacy and FAIR principles was implemented for 46 postgraduate students and mentors.
  • Students were trained to prioritize FAIR data sources and create Data Management Plans (DMPs).
  • An 11-item questionnaire assessed the FAIRness of research data, demonstrating strong internal consistency.

Main Results:

  • The study successfully trained students in data literacy and FAIR principles.
  • Integration of FAIR principles into the curriculum was found to be crucial.
  • The developed questionnaire effectively evaluated the FAIRness of research data.

Conclusions:

  • Integrating FAIR principles into educational curricula is essential for improving research reproducibility and transparency.
  • This training equips future researchers with vital data skills for a data-driven scientific landscape.
  • The study contributes to advancing scientific knowledge through improved data practices.