Resumo

MANRIQUE-GOMEZ, Laura  e  BORJA GOMEZ, Jaime Humberto. Data Science for History: Datafication of Sources for a (Predictive) History. Hist. graf [online]. 2025, n.64, pp.97-145.  Epub 25-Fev-2025. ISSN 1405-0927.  https://doi.org/10.48102/hyg.vi64.541.

Historiography has traditionally relied on three pillars: document, data, and facts. These concepts gained new value in the late 20th-century with mass digitization efforts, leading to a redefinition of the role of data in history. A significant outcome of this shift was historians losing control over data and facts, resulting in a bifurcation of their roles into users and curators of digital data. This transformation brings forth two critical challenges: the need to re-evaluate the handling of both analog and digital data, and the exploration of how data science techniques can enhance historical research. This article explores the interplay between history and data science, focusing on databases as emergent historiographical narratives. We argue that the datafication of history is a fundamental result of data science’s integration into the field and with the advent of artificial intelligence, the predictive dimension of history is becoming the central point of disciplinary debate.

Palavras-chave : History; Digital History; Datafication; Data Science; Data.

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