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

翻译：历史资料浩如烟海。然而，无论是从历时性还是共时性角度，拼凑出人类知识演变与传播的全貌，始终是一项挑战，至今仅能在极为有限的情况下应对。由于人类专家数量有限，海量的资料使得全面研究难以实现。然而，随着大量历史资料现已数字化，借助人工智能进行历史分析迎来了充满希望的机遇。在本研究中，我们通过采用创新的机器学习（ML）技术，在分析大规模历史语料库方面迈出了关键一步，得以在宏大尺度上获取深刻的历史见解。我们的研究聚焦于“萨克罗博斯科收藏集”中的知识演变——该数字馆藏包含359部早期现代天文学教科书的印刷版，这些书籍曾在1472年至1650年间用于欧洲大学教学，共计约76,000页，其中大量页面包含天文学和计算用表格。基于机器学习的表格分析，有助于揭示这一时期欧洲大学所传授的数理天文学领域，知识与创新在时空演变中的重要方面。