Cambridge Digital Humanities course timetable

In this session we consider the practice of history in an age of large digitised collections. The discipline of history has long encompassed both quantitative and qualitative approaches; we ask: what are the opportunities and the risks of a working with sources as data? How can historians adapt their craft in the era of data science, and which questions should they be asking?

The first part of this session focuses on one of the most widely used types of historical source: newspapers; also one of the most misused in their digital form. We explore new methods for making sense of large newspaper collections, using Python. The second part asks what we can learn by converging large historical datasets in new combinations. Focusing on Ordnance Survey maps and the Census, we explore how machine learning makes possible new questions about nineteenth-century Britain.

About the convenor:

Daniel Wilson, Research Fellow at The Alan Turing Institute

Daniel Wilson is a historian of science and technology working on the politics and provenance of data and machines in the nineteenth, twentieth and twenty-first centuries. His work combines traditional close-reading and archival study with computational techniques. Current projects include using language models and other critical methods to explore historical text datasets, including the internationally important collections of the British Library.

This session is part of the workshop series 'Digital Methods for the Digital Humanist'. This series is integrated in the Postgraduate Researcher Development Programme in Digital Humanities organised by Cambridge Digital Humanities and generously co-funded by the Enhanced Funding Scheme. View the complete programme of CDH events here.