Tools for Efficient Workflows, Smooth Collaboration and Optimized Research Outputs

How can we create efficient workflows and facilitate optimal collaboration in teams? How can we ensure that our research processes, our data collection, and our complex (Big) Data analyses can be re-traced by ourselves and other researchers, both in the near and distant future? How can we build our analyses in a way that they can be run reliably and stably on other researchers' computers, regardless of the hardware and software environment? Efficient and reproducible workflows are essential to keep up with the increasing amount of data and complexity of analyses. In recent years, exciting new tools have emerged that enable effective data management and research collaboration. Not only do these new tools help us streamline our workflows, but they also make our research outputs more visible, citable, and sustainable. From the first day we begin adapting our research practices, we benefit from greater efficiency, ease of tracing our research progress, and smoother collaboration with other researchers. In the long run, these practices help us maintain a high quality of research outputs and meet the replicability and transparency standards that an increasing number of journals require for publication. This course provides participants with the skills to harness the potential of new tools that help create efficient workflows and optimize research outputs. It equips them with a toolkit to conduct research that is well organized and documented, and can be readily disseminated and reproduced, both when working on independent projects and in collaborations with others.

Course leader

Dr. Julia Schulte-Cloos, Lukas Lehner

Target group

Researchers at any stage of their career, who rely on data-driven approaches in their work.

Course aim

By the end of the course participants will:
- confidently master tools that enable efficient workflows and collaboration;
- be able to write executable code and create automatable reports using RMarkdown, Pandoc, and Lua;
- be able to collaborate effectively with other researchers and document work processes with version control through Git and DVC;
- have an in-depth understanding of key Git operations, including branching, merging, forking, resolving merge conflicts;
- rely on Veracrypt for advanced data protection and encryption;
- be able to effectively disseminate their findings online, e.g. on their own academic website created using GitHub Pages, Hugo, and Blogdown;
- successfully containerize their projects using Docker and Binder;
- understand how to ensure interoperability of programming languages when generating reports;
- be able to rely on the command line and shell scripts for advanced programming and to solve tricky computational issues.

Fee info

EUR 500: Students
EUR 750: Academics

Scholarships

none