Advancing Historical Research Through AI and Data-Centric Approaches

The rapid digitization of large source collections in the humanities over the last three decades has comprehensively transformed the discipline: The accessibility of primary sources has improved drastically, the pre-processing of research data has been revolutionized in some areas, and new transdisciplinary approaches have emerged and become possible. However, the theoretical grounding of these developments has not kept pace with the changed realities of the research process in many respects: most critically, the concept of "information" — central to computer science and computational methods — has so far been insufficiently received and theorized within historical methodology. In this contribution, we employ a concept from Science and Technology Studies — Bruno Latour's "circulating reference" — to analyze and render describable the processes of historical research within a digitized research environment. Through three case studies — AI-supported segmentation of Habsburg cadastral maps (1817–1861), computational analysis of the Hof- und Staatsschematismus (1702–1918), and the datafication of the Munich Special Court archive inventory — we demonstrate how and at which specific points historical research benefits from this framework, and what new insights it enables.