Randomized trial investigates data literacy and management skills in physics curriculum, highlighting their importance for students' future success.
In recent years, the field of physics is part of a transformation shaped by the rapid growth of data-driven research methods as well as vast and complex datasets which are now an integral part of advancing knowledge. High-quality data are also a key prerequisite for artificial intelligence being part of future research, a new era which is called the 4th paradigm of science. In order to be part of it, students and researchers alike must acquire and exercise novel skills of data literacy (DL) and research data management (RDM). DL encompasses a broad range of competencies, from data generation and storage to analysis, interpretation, and ethical and legal aspects of data sharing. For physics students, this skill set is essential. It enables them to understand and leverage data responsibly, ensuring their work is sustainable, reproducible and valuable within the broader scientific community. RDM further enhances these competencies by focusing on best practices for managing data across its lifecycle. There is broad consensus that data should be organized along the FAIR principles, ensuring Findability, Accessibility, Interoperability, and Reusability. All of these competencies are intricately linked to and ultimately part of good scientific practice. The document highlights key integration points across Bachelor's, Master's, and doctoral levels to equip students with the data skills needed to succeed in both academic and professional environments.
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FAIRmat et al. (2026) studied this question.