Michael Schulte-Mecklenbeck

Learning objectives The creation and communication of data visualizations is a critical step in any data analytic project. Modern open-source software packages offer ever more powerful data visualizations tools. When applied with psychological and design principles in mind, users competent in these tools can produce data visualizations that easily tell more than a thousand words. In this course, participants learn how to employ state-of-the-art data visualization tools within the programming language R to create stunning, publication-ready data visualizations that communicate critical insights about data. Prior to, during, and after the course participants work their own data visualization project. Course content Each day will contain a series of short lectures and demonstrations that introduce and discuss new topics. The bulk of each day will be dedicated to hands-on, step-by-step exercises to help participants ‘learn by doing’. In these exercises, participants will learn how to read-in and prepare data, how to create various types of static and interactive data visualizations, how to tweak them to exactly fit one’s needs, and how to embed them in digital reports. Accompanying the course, each participant will work on his or her own data visualization project turning an initial visualization sketch into a one-page academic paper featuring a polished, well-designed figure. To advance these projects, participants will be able to draw on support from the instructors in the afternoons of course days two to four.

The course addresses central aspects of good research practice and invites you to think about your own methods and academic habits. We discuss what qualifies as “good” research practice, mirroring current debates in various disciplines. The course is intended to shed light on these multi-faceted and stimulating debates and to familiarize participants with questions such as: Why is transparency and openness needed in research? What does “open” mean for different perspectives on knowledge generation (i.e., different disciplines, methods, etc.)? Why do we publish knowledge and findings at all, and how can such publications be made more accessible? Which other aspects of the research and teaching process can be designed “better” in accordance with the principles of good research practice? What are limits of openness in research?

With these questions comes the need for using certain tools to achieve transparency and openness. Collaborative work on Github for writing code and producing reproducible projects in RStudio will be introduce. We will also explore the ideas connected to pre-registration of your hypothesis and analysis plan on the open science framework (osf.io).