Workshop Lectures on Transparent Research and Open Science

About

Sharing data, code and materials of studies has become synonymous with the idea of Open Science. Proper replicability of study results and reproducibility of analysis code is often a required step in the publication process. Many new tools have been developed to guide researchers in open practices throughout the research cycle – the goal of this course is to explain and demonstrate these tools and provide many practical applications so that students can make their own work open and reproducible. We will first explore the logic of the empirical method and provide you with the necessary skills to make your data openly available, properly share your code and material. Collaborative work on github for writing code and producing reproducible projects in RStudio will also be explored. On top of that, we will introduce you to the idea of pre-registration of your hypothesis and analysis plan on the open science framework (osf.io), applying the principles of Open Science to your own work.

Course leader

Michael Schulte-Mecklenbeck

Target group

Master | PhD | Postdoc | Professional

Fee info