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The way biomedical research is conducted has changed significantly in recent years. Two changes are central to Annelies Pieterman-Bos’s research: 1) datasets become larger and more complex, 2) there are increasing examples of questionable research practices. As a result, scientists and students need different skills.

In her dissertation, Annelies demonstrates that courses focused on data collection and analysis (data science) often focus on practical skills. Students learn to perform many different statistical analyses. Because of that focus, there is little room for understanding statistical concepts and decision-making in data analysis. Together with colleagues, Annelies developed and investigated a data science learning trajectory that placed greater focus on this understanding. One conclusion was that students seemed better able to assess what they do and do not master in statistics. This seems to be a valuable first step in preventing questionable practices.

Annelies also argues that another important step toward this end is teaching student to reflect on what makes a scientific study ‘good.’ This means that they need to think about the nature of science. However, little research has been done on how Dutch life sciences students think about this and what they learn about it. As a first step in this research area, Annelies studied implicit notions of the nature of science in student research reports and compared these with their explicit views. She identified some differences between these, which students found difficult to explain. Therefore, explicit reflection on these topics is important in university education.