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

Abstract

Data literacy, the ability to understand and effectively communicate with data, is crucial for researchers to interpret and validate data. However, low reproducibility in biomedical research is nowadays a significant issue, with major implications for scientific progress and the reliability of findings. Recognizing this, funding bodies such as the European Commission emphasize the importance of regular data management practices to enhance reproducibility. Establishing a standardized framework for statistical methods and data analysis is essential to minimize biases and inaccuracies. The FAIR principles (Findable, Accessible, Interoperable, Reusable) aim to enhance data interoperability and reusability, promoting transparent and ethical data practices. The study presented here aimed to train postgraduate students at the Universidad Europea de Madrid in data literacy skills and FAIR principles, assessing their application in master thesis projects. A total of 46 participants, including students and mentors, were involved in the study during the 2022–2023 academic year. Students were trained to prioritize FAIR data sources and implement Data Management Plans (DMPs) during their master’s thesis. An 11-item questionnaire was developed to evaluate the FAIRness of research data, showing strong internal consistency. The study found that integrating FAIR principles into educational curricula is crucial for enhancing research reproducibility and transparency. This approach equips future researchers with essential skills for navigating a data-driven scientific environment and contributes to advancing scientific knowledge.