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.
