Integration of Philosophy of Science in Biomedical Data Science Education to Foster Better Scientific Practice

Abstract

Biomedical data science education faces the challenge of preparing students for conducting rigorous research with increasingly complex and large datasets. At the same time, philosophers of science face the challenge of making their expertise accessible for scientists in such a way that it can improve everyday research practice. Here, we investigate the possibility of approaching these challenges together. In current and proposed approaches to biomedical data science education, we identify a dominant focus on only one aspect of conducting scientific research: understanding and using data, research methods, and statistical methods. We argue that this approach cannot solve biomedical data science’s challenge and we propose to shift the focus to four other aspects of conducting research: making and justifying decisions in research design and implementation, explaining their epistemic and non-epistemic effects, balancing varying responsibilities, and reporting scientific research. Attending to these aspects requires learning on different dimensions than solely learning to apply techniques (first dimension). It also requires learning to make choices (second dimension) and to understand the rationale behind choices (third dimension). This could be fostered by integrating philosophical training in biomedical data science education. Furthermore, philosophical training fosters a fourth dimension of learning, namely, understanding the nature of science. In this article, we explain how we identified the five aspects of conducting research and the four dimensions of learning, and why attending to the fourth dimension is essential. We discuss educational approaches to attend to all aspects and dimensions, and present initial design principles to implement these approaches.

Improving STEM Education through Resource Activation: A Study of Culturally Relevant Teaching for Critical Data Literacy in a High School Science Classroom

“…In the context of bioinformatics and broader STEM fields, critical data literacy extends beyond fundamental data manipulation to encompass the critical appraisal of data by acknowledging its ethical and societal dimensions. This aspect is crucial in bioinformatics, where data interpretations can significantly influence societal well-being and ethical considerations (Pieterman-Bos & van Mil, 2023). For example, this involves researchers and practitioners critically evaluating the sources of data, considering the potential biases and limitations in the data collection process, and being transparent about these aspects in their analyses and interpretations (Bain et al, 2022).…”