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Journal Article

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

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Büttner,  Jochen
Department of Structural Changes of the Technosphere, Max Planck Institute of Geoanthropology, Max Planck Society;

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Citation

Eberle, O., Büttner, J., el-Hajj, H., Montavon, G., Müller, K.-R., & Valleriani, M. (2024). Historical insights at scale: a corpus-wide machine learning analysis of early modern astronomic tables. Science Advances, 10(43): eadj1719, pp. 1-16. doi:10.1126/sciadv.adj1719.


Cite as: https://hdl.handle.net/21.11116/0000-000F-FDCE-D
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 competition among educational institutions and market dynamics. This approach deepens our understanding by grounding insights in historical context, integrating with traditional methodologies. Case studies illustrate how communities embraced scientific advancements, reshaping astronomic and geographical views and exploring scientific roots amidst a changing world. An unsupervised ML model analyzes historical sources beyond human capacities through an atomization-recomposition approach.