Michael Schulte-Mecklenbeck

In today’s data-driven world, the ability to effectively tell stories with data is essential for success in science and business. Data storytelling goes beyond visualizing numbers—it’s about crafting compelling narratives that resonate with your audience, guiding them through insights, and helping them make informed decisions. This course focuses on equipping participants with the skills to create impactful data stories–using the programming language R–that combine powerful visualization techniques with research or business context. By integrating principles of psychology, design, and communication, participants will learn how to translate data into clear, engaging narratives that drive action.

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.