The use of computational methods in the Social Sciences has many roots. Classic examples include the fitting of measurement-, as well as structural models or the recognition of simulation as a third way of doing science. More recently, the availability of large organic datasets has led to the emergence of Computational Social Science. All these examples have in common that potentially compute-intensive and complex analysis pipelines are an essential part of the research. Reproducibility is the cornerstone of modern science. Yet more work is needed to make reproducibility the norm for complex computational analysis pipelines. GESIS Notebooks aims to close this gap in the Social Sciences by supporting reproducible computational analysis pipelines that enable researchers to focus on the methodological aspects of their work while increasing their visibility and following best open-science practices simultaneously. The service does not try to reinvent the wheel but reuses existing technology whenever possible. Execution, place, and content are the concepts of the service, and in the remainder, we will look at each of them.

Submitted to the KonsortSWD Call for Ideas https://www.konsortswd.de/aktuelles/neuigkeiten/call-sichtbarkeit-dateninfrastruktur/