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Machine-learning analysis tracks the evolution of 16th-century European astronomical thought

Phys.org reports on how BIFOLD Berlin and the Max Planck Institute for the History of Science implement machine learning methods to analyze networks of early modern astronomy in Europe. Their approach enabled them to process 76,000 pages from textbooks of the period, filled with handwritten texts, graphs, and charts—a corpus so large that "it would take much too long for a small team of humans to study."