Computational social science is only as strong as its reproducibility and algorithmic transparency. As automated systems and opaque commercial algorithms increasingly shape social science data, researchers face a dual challenge: auditing black-box systems while ensuring their own computational workflows remain transparent, reproducible, and accessible.
At the 3rd MethodsNET Conference 2026 (28–30 October, Louvain-la-Neuve, Belgium), our Statistical & Computational Track addresses these challenges head-on. Designed for researchers, academics, and doctoral students across disciplines, this track provides actionable, hands-on training to upgrade computational workflows using open-source toolkits, automated review frameworks, and cloud execution environments.
Featured Sessions
| Session Format | Title | Session Overview | Expert Speakers |
|---|---|---|---|
| Demonstration Session | Auditing Commercial Search Systems | A hands-on introduction to the open-source RAT Toolkit for collecting and evaluating search engine results and AI summaries. Participants will learn how to systematically audit commercial search systems to ensure data collection integrity in social science research. | Sebastian Sünkler, Oliver Koop (Hamburg University of Applied Sciences) |
| Demonstration Session | Towards Automating the Reproducibility Review of Computational Methods | This session addresses the persistent reproducibility gap in computational research. The team introduces emerging frameworks and tools designed to help automate the reproducibility review process, establishing higher methodological standards for the discipline. | Aleksandra Pawlik, Ahrabhi Kathirgamalingam, Felix Victor Münch (GESIS – Leibniz Institute for the Social Sciences) |
| Short Training Session | Explore, Run, Share: Methods Hub’s Interactive Environments | A practical, interactive tutorial on leveraging Jupyter4NFDI and myBinder. Attendees will discover how cloud-based interactive environments make computational research fully reproducible, portable, and effortless to share with the global community. | Aleksandra Pawlik, Muhammad Taimoor Khan, Ahrabhi Kathirgamalingam, Felix Victor Münch, Arnim Bleier (GESIS – Leibniz Institute for the Social Sciences) |
Key Event Details
Dates: 28–30 October 2026
Location: UCLouvain, Louvain-la-Neuve, Belgium
Track: Statistical & Computational Track