AI-Ready Competency Framework for Biomedical Scientific Data Literacy.
- Author:
Zhe WANG
1
;
Zhi-Gang WANG
1
;
Wen-Ya ZHAO
1
;
Wei ZHOU
2
;
Sheng-Fa ZHANG
3
;
Xiao-Lin YANG
4
Author Information
- Publication Type:Review
- Keywords: AI-ready; FAIR principles; competency framework; data literacy; scientific data management
- MeSH: Artificial Intelligence; Humans; Biomedical Research
- From: Chinese Medical Sciences Journal 2025;40(3):203-210
- CountryChina
- Language:English
- Abstract: With the rise of data-intensive research, data literacy has become a critical capability for improving scientific data quality and achieving artificial intelligence (AI) readiness. In the biomedical domain, data are characterized by high complexity and privacy sensitivity, calling for robust and systematic data management skills. This paper reviews current trends in scientific data governance and the evolving policy landscape, highlighting persistent challenges such as inconsistent standards, semantic misalignment, and limited awareness of compliance. These issues are largely rooted in the lack of structured training and practical support for researchers. In response, this study builds on existing data literacy frameworks and integrates the specific demands of biomedical research to propose a comprehensive, lifecycle-oriented data literacy competency model with an emphasis on ethics and regulatory awareness. Furthermore, it outlines a tiered training strategy tailored to different research stages-undergraduate, graduate, and professional, offering theoretical foundations and practical pathways for universities and research institutions to advance data literacy education.
