Open Science policy, data sharing, and institutional research practice
From September 2026, I will be leading a project at Eindhoven University of Technology investigating how institutional policies shape researchers’ data- and code-sharing practices. Dutch universities recognize the value of open science for improving scientific efficiency and impact. Yet, we still need to better understand which policies and implementation strategies are most effective in practice. By combining policy analysis, behavioral research, and stakeholder engagement, I hope this project will shed some light on that question.
Web application for research coding and evidence synthesis
Artificial intelligence has considerable potential to improve science. One way it can do so is by making the invisible visible. When appraising a literature, it can be extremely valuable to know, for instance, what percentage of studies were preregistered, corrected for multiple testing, or made their data and code openly available. Yet, short of manually coding these methodological indicators and summarizing the results, such valuable insights have until now been largely out of reach. Moreover, systematic reviews can quickly become outdated, often do not capture this kind of methodological detail, and tend to rely on rigid inclusion and exclusion criteria.
Recognizing that AI may offer a solution, I have been developing a web application that would provide researchers with near-instant summaries of a literature across dozens of methodological dimensions. Want to better understand the methodological nuances of research domain? The hope is that the application will allow researchers to enter a research question and, with the click of a button, learn how many relevant studies preregistered their hypotheses, shared their data and code, corrected for multiple testing, or used particular research designs. It could also show how research practices are changing over time.