Two-day course on Good Research Practices

This is a two day course. It will occur on Friday 12th of June and Friday 19th of June. 

Many seemingly established scientific results cannot be reproduced. Issues include lack of transparency, poor study design and methodology, and questionable research practices. As part of an ongoing reckoning with poor levels of reproducibility, it is becoming normative to 'follow open and reproducible research practices' in various research fields. But implementing reproducible research processes is not always easy.

In this course you learn the necessary concepts, techniques and best practices to make your research reproducible. We begin with a fundamental point of view on reproducibility and with considerations during study design. You learn how to avoid typical biases, how to eff ectively write an analysis plan, and use pre-registration and registered reports to your advantage.

The course also covers techniques to improve computational reproducibility: versioning with git and dynamic reporting with R and Quarto, and software containerization with Docker. Participants will bring their laptops, and together we set them up to produce a minimal reproducible manuscript.
We discuss open science practices and techniques to manage and share your data eff ectively. The course concludes with a journal club on diff erent 'failure modes' of science (e.g. publication bias or questionable research practices) and how to consider them in your own research.

In this two-day course you learn the necessary concepts and techniques to make your research transparent, credible and reproducible. You will :

• understand important concepts related to reproducibility,
• know what the main causes of irreproducible research are,
• understand principles of the design, pre-registration and reporting of a study,
• practice computational reproducibility techniques,
• know about 'failure modes' of science (e.g. publication bias) and understand how to avoid them in your own research.

The course is primarily aimed at PhD students in empirical research.
Students will need to bring a laptop to class (where they have admin or equivalent privileges).

The course is evaluated on a pass/fail basis. It can be followed in one of two tracks:

1 ECTS track: We expect course attendance and active participation

2 ECTS track: In addition to attendance and active participation, the course participants have to:

• Submit an analysis plan for one of their (planned or ongoing) research projects until Wednesday 17 June 2026. Instructions will be given during the first course day.
• Hold a presentation of 12 minutes (including discussion) on one article from the course literature list.

The course will be given with a minimum of 6 students. It is intended for a maximum of 12 students in the “2 ECTS track” and can accommodate a total maximum of 20 students.