Beyond mere retrieval, the notebooks offer robust capabilities for metadata analysis, leveraging natural language processing (NLP) techniques (Jentsch and Porada 2021, 89). NLP libraries enable in-depth analysis of textual metadata, including curated keywords of titles visualized as word clouds centered around the initial search term. Frequency and correlation across all bibliographic fields are available in bar, pie and line charts, aiding in trend and distribution interpretation. Moreover, the notebooks include features for similarity analysis, semantic visualization, and world map views, unveiling inherent insights and patterns within the dataset. Finally, tables and figures are exportable as PDFs, enhancing the accessibility and shareability of analytical findings for dissemination in reports, presentations, or scholarly articles. Reviewing these results allows users to discern thematic trends, popular authors, temporal patterns and genre preferences within the library holdings. For instance, the spreadsheet search results for the query “Heidi Johanna Spyri” can be imported into a notebook for detailed data analysis. The analysis reveals that the most frequent publishing location is Zurich, with “Hemma” being the most common publisher. The majority of the media are physical books written in German, primarily published between 2010 and 2020. Interestingly, a small fraction (1.68%) of the media consists of physical and electronic audio recordings and computer files. The geographical focus of these works is predominantly on Switzerland and the Alps, with some references to Japan and the USA. Key terms associated with this search include “grandfather,” “orphan,” “alpine pasture”, “German” and “friendship,” which are illustrated in a word cloud. Such insights are valuable to various stakeholders, such as researchers, especially in computational linguistics and digital humanities, librarians involved in collection development and enthusiasts exploring literary landscapes (Herrmann et al. 2021, 2).