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Summary
This summary is machine-generated.

Data literacy is crucial for high-quality biomedical research and artificial intelligence (AI) readiness. This study proposes a data literacy competency model and tiered training strategy to address challenges in data governance and researcher education.

Keywords:
AI-readyFAIR principlescompetency frameworkdata literacyscientific data management

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Area of Science:

  • Biomedical Research
  • Data Science
  • Scientific Data Governance

Background:

  • Data-intensive research necessitates enhanced data literacy for improved data quality and AI readiness.
  • Biomedical data's complexity and privacy sensitivity demand robust data management skills.
  • Current scientific data governance faces challenges like inconsistent standards, semantic misalignment, and compliance awareness gaps.

Purpose of the Study:

  • To propose a comprehensive, lifecycle-oriented data literacy competency model for biomedical research.
  • To emphasize ethics and regulatory awareness within data literacy.
  • To outline a tiered training strategy for advancing data literacy education.

Main Methods:

  • Review of current trends in scientific data governance and policy.
  • Integration of existing data literacy frameworks with biomedical research specificities.
  • Development of a competency model and tiered training strategy.

Main Results:

  • A proposed data literacy competency model tailored for the biomedical domain.
  • Identification of key challenges in data governance and researcher training.
  • A tiered training strategy for undergraduate, graduate, and professional researchers.

Conclusions:

  • Structured training and practical support are essential to overcome data literacy challenges.
  • The proposed model and strategy can enhance data quality, AI readiness, and regulatory compliance in biomedical research.
  • Universities and research institutions can utilize this framework to advance data literacy education.