The fourth and final workshop of the CESSDA Conference 2024, titled "From MyBinder to JupyterHub: Enhancing Reproducibility in Computational Social Science," was led by Arnim Bleier and Raniere Silva from GESIS - Leibniz Institute for the Social Sciences. This session provided a hands-on overview of tools and strategies to improve reproducibility in computational social science research.

The workshop demonstrated practical applications of MyBinder and JupyterHub, showcasing their ability to create interactive and reliable research environments. Participants engaged in live demonstrations and discussions, exploring the potential of these tools to enhance research transparency and replicability.

Bleier and Silva explained how MyBinder transforms a Git repository into a collection of interactive notebooks, simplifying the process of sharing and reproducing computational research. They also highlighted JupyterHub's capability to provide scalable and persistent access to computational resources, which is crucial for sustaining long-term research projects.

A significant feature discussed was the integration of BinderHub with JupyterHub. This integration allows for the creation of predefined images using Docker, ensuring a consistent environment for running notebooks. This feature is particularly useful for maintaining uniformity across different research setups and enhancing collaborative efforts.

During the hands-on segment, participants experienced the practical benefits of these tools firsthand. They learned how to  effectively use MyBinder and JupyterHub, and how these platforms can streamline their research workflows by ensuring consistent computational environments.

Throughout the workshop, the importance of reproducibility in computational social science was a key focus. The session concluded with discussions on how integrating BinderHub with JupyterHub offers a robust solution for managing computational resources and maintaining reproducibility.

Overall, the workshop highlighted the essential role of MyBinder and JupyterHub in advancing reproducibility in computational social science, providing researchers with the tools and insights needed to create dependable and transparent research environments.