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Historical materials are abundant. Yet, piecing together how human knowledgehas evolved and spread both diachronically and synchronically remains achallenge that can so far only be very selectively addressed. The vast volumeof materials precludes comprehensive studies, given the restricted number ofhuman specialists. However, as large amounts of historical materials are nowavailable in digital form there is a promising opportunity for AI-assistedhistorical analysis. In this work, we take a pivotal step towards analyzingvast historical corpora by employing innovative machine learning (ML)techniques, enabling in-depth historical insights on a grand scale. Our studycenters on the evolution of knowledge within the `Sacrobosco Collection' -- adigitized collection of 359 early modern printed editions of textbooks onastronomy used at European universities between 1472 and 1650 -- roughly 76,000pages, many of which contain astronomic, computational tables. An ML basedanalysis of these tables helps to unveil important facets of thespatio-temporal evolution of knowledge and innovation in the field ofmathematical astronomy in the period, as taught at European universities.

Insightful analysis of historical sources at scales beyond human capabilities using unsupervised Machine Learning and XAI