After (a) preparing and selecting documents for the corpus on a historical basis (encompassing monographs, book series and journal articles)—digitizing, and OCR-ing with tesseract where necessary—we (b) machine translate the non-German texts into German using the Google Translate API. As de Vries, Schoonvelde, and Schumacher (2018) argue for topic-modelling in general, and Malaterre (2021) for the special use-case of history of science, modern machine translations deliver useful results that are reliable for multilingual topic-modelling. Additionally, we plan to assess our translation accuracy with Malaterre’s proposed “Semantic Topology Preservation Test” (2021). Then, we (c) preprocess the corpus: Following a general cleaning of common OCR-errors and stop words, we reduce the corpus to lemmatized adjectives and nouns via spaCy’s POS tagging and lemmatization algorithms. We assume that the conceptual topics we aim to explore are mostly expressed in nouns and adjectives (see Jockers 2013, Malaterre et al. 2020). The preprocessed documents are then (d) analyzed with LDA topic-modelling, using gensim’s MALLET-wrapper and (e) analyzed with top2vec, to cluster the documents thematically – enabling a different granularity and perspective, since top2vec does not treat the documents as bags-of-words and tends to generate few more general topics (see Angelov 2020). Finally, (f) we calculate document embeddings using UMAP and (g) visualize the embedding as an interactive scatter plot (with the option of time-period slices) with Bokeh, since the heterogeneity of our corpus does not allow for a simple linear visualization. We enrich the scatter plot with metadata for a mouse-over pop-up window, generated from the most important topics for each document, and color the documents by their top2vec cluster, complementing the visual clustering and topological distribution the document embedding shows. Thus, we create an interactive tool for exploration, hoping to motivate future research.