The Jupyter Notebooks operate by executing code blocks that search and harvest library data from the Swiss Library Service Platform (SLSP) online catalog in MARC XML format, accessed through an SRU interface – a procedure similar to using the Alma API (Hyams and Pilko 2022, 1). The SLSP-Alma database is a comprehensive library catalog, aggregating data from a nationwide network of Swiss libraries, boasting over 25 million media items. This catalog is a treasure trove of resources for scholars, researchers, and enthusiasts alike, surpassing the main Swisscovery website, which includes over six million media items and is the default search platform for many library users. The intuitive nature of these Jupyter Notebooks (Boscoe et al. 2017, 1) facilitates tapping into a more extensive repository of catalog data, as the notebooks are renowned for their seamless integration of code alongside explanatory rich text. While users may require modest to advanced programming skills to fully utilize these notebooks, they cater to a broad audience of tech-savvy researchers eager to explore the rich data offered by the SLSP-Alma catalog. By utilizing these Jupyter Notebooks, researchers gain improved capabilities in building topic-specific corpora (Oberbichler and Pfanzelter 2021, 4), visualizing data, conducting literature reviews and compiling bibliographies, as compared to traditional methods of searching via the Swisscovery website.