Abstract Digital collections have fundamentally transformed historical research, enabling search with extraordinary precision across vast archives. While challenges such as limited digitization, transcription errors, and infrastructure vulnerabilities remain, significant progress has been achieved through national collections, open platforms and innovative research projects. For example, The Alan Turing Institute and British Library’s Living with Machines project (2018–23) demonstrated how interdisciplinary collaboration between historians, data scientists, and library professionals can develop powerful tools for analysing huge collections at scale. Emerging technologies, including Machine Learning and Large Language Models, are making sophisticated computational methods accessible to historians. However, research still benefits from careful attention to the documentation of digitization processes, selection biases, and technical constraints. The characteristics of historical records – ambiguous, incomplete, subjective and inconsistent – represent both challenges and opportunities for computational methods. Approaches for presenting historical records must resist oversimplification and retain their contextual richness. The future of digital history depends on historians actively engaging with and shaping these technologies. Through continued collaboration between cultural heritage institutions and researchers, the field can ensure that large-scale digital collections serve historical scholarship’s values while enabling innovative research methodologies.
Mia Ridge (Wed,) studied this question.