Historical insights at scale: A corpus-wide machine learning analysis of early modern astronomic tables.

ABSTRACT: Understanding the evolution and dissemination of human knowledge over time faces challenges due to the abundance of historical materials and limited specialist resources. However, the digitization of historical archives presents an opportunity for AI-supported analysis. This study advances historical analysis by using an atomization-recomposition method that relies on unsupervised machine learning and explainable AI techniques. Focusing on the "Sacrobosco Collection," consisting of 359 early modern printed editions of astronomy textbooks from European universities (1472-1650), totaling 76,000 pages, our analysis uncovers temporal and geographic patterns in knowledge transformation. We highlight the relevant role of astronomy textbooks in shaping a unified mathematical culture, driven by co

SUBMITTER: Eberle O 

PROVIDER: S-EPMC11498222 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature