PhD / Postdoc / Good research practice

In this two-day course you learn the necessary concepts and techniques to make your research transparent, credible and reproducible. On the first day, we begin with a fundamental discussion of reproducibility and best practices for study design. You learn how to avoid typical biases and statistical pitfalls, to effectively write an analysis plan, and to use pre-registration, registered reports and reporting guidelines to your advantage. We discuss open science practices and techniques to manage and share your data effectively. On the second course day we cover techniques to improve computational reproducibility: versioning with git and dynamic reporting with R and Quarto. Participants will bring their laptops, and together we set them up to produce a minimal reproducible manuscript. Finally, we introduce recent research on reproducibility and on methods to conduct meta-research in your own field.

For further information on Reproducible Science, visit https://www.crs.uzh.ch/en.html.

For forther information on the two instructors, visit https://www.crs.uzh.ch/en/people/team/Rachel-Heyard.html
https://www.crs.uzh.ch/en/people/team/Fabio-Molo.html

Participants:

• 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

Fabio Molo, UZH & Rachel Heyard, UZH

This course is primarily aimed at PhD candidates and postdoctoral researchers in the empirical sciences. Knowledge of the R programming language is an advantage.
 

1 ECTS (has to be recognized by your faculty)

Conditions of participation:

• A no-show will lead to a blocked account and the cancellation of all other booked GRC courses.   
• You may only register for two courses per semester.

-> Further conditions