GESIS Training Courses

Scientific Coordination

André Ernst

Administrative Coordination

Janina Götsche

Tools and Workflows for Reproducible Research in the Quantitative Social Sciences

Online via Zoom
General Topics
Course Level
Software used
Students: 200 €
Academics: 300 €
Commercial: 600 €
Additional links
Lecturer(s): Dr. Bernd Weiß, Dr. Johannes Breuer, Dr. Arnim Bleier

About the lecturer - Dr. Bernd Weiß

About the lecturer - Dr. Johannes Breuer

About the lecturer - Dr. Arnim Bleier

Course description

The focus of the course is on reproducible research in the quantitative social and behavioral sciences. Reproducibility here means that other researchers can fully understand and (re-)use your statistical analyses. The workflows and tools covered in this course will ultimately facilitate your work as they, e.g., allow you to automate analysis and reporting tasks. This course aims to introduce participants to tools and processes for reproducible research and enable them to use those for their work. In addition to a conceptual introduction to the methods and key terms around reproducible research, this course focuses on procedures for making a data analysis with R fully reproducible. We will cover questions about project organization (e.g., folder structures, naming schemes, documentation) and choosing and working with tools such as command-line interfaces (PowerShell, Bash, etc.) RStudio and R Markdown, Git and GitHub, Jupyter Notebooks, and Binder. Slides and materials of last year's workshop can be found on GitHub: (this year, however, we will not cover LaTeX).

Target group

The workshop is targeted at participants who have (at least some) experience with R and want to learn (more) about workflows and tools for making the results of their research reproducible.

Learning objectives

By the end of the course, participants should
  • have gained important insights into key concepts of reproducible research and recommended best practices
  • be able to work with state-of-the-art frameworks and tools, such as R Markdown, Jupyter, Git, and Binder
  • be able to publish reproducible computational analysis pipelines with R
    Organisational structure of the course:
    The workshop is structured into segments of instructive lectures and interactive hands-on sessions. During the interactive sessions, participants will, e.g., create Git repositories and learn how to collaborate on GitHub, work with R Markdown, and interact with and publish Jupyter notebooks. The lecturers will be available for support during hands-on segments and can also consult on participants' projects.


    Participants should have some basic knowledge of R. While this is not required per se, participants who have experience doing statistical analysis in R will benefit most from this course. To facilitate applying the methods covered in the course to their work, we recommend that participants ensure to install all necessary software on their computers.
    Software requirements:
    All software used in the workshop is available without cost as open-source under Windows, MacOS, and Linux systems. Detailed installation instructions for the installation and setup of the required software will be provided before starting course.


    Recommended readings