Mapping Historical Networks

John Schaefer, CDH Methods Fellow 2026-27

John Schaefer is a PhD candidate in History and Philosophy of Science at the University of Cambridge, in collaboration with the Royal Botanic Gardens, Kew. His doctoral project analyses the social networks of colonial plant collectors during the long nineteenth century, using Kew Herbarium specimen data to investigate the global dynamics of plant and knowledge exchange in the British Empire. He holds an AB in History and Science from Harvard University and an MPhil in Digital Humanities from Cambridge.

This hands-on workshop provides a critical introduction to Social Network Analysis (SNA) tailored for researchers in the arts, humanities, and social sciences. It may be especially relevant for those who are working with large historical datasets, or keen to do so in the future.

Moving beyond the visualisation of simple "ego networks", this session balances practical approaches to SNA with critical data theory. Using a real-world dataset of plant collectors drawn from nineteenth-century botanical specimen labels, participants will learn to clean and transform raw data into relational node and edge lists with RStudio. We will then import this structured data into Gephi to build, filter, style, and analyse dynamic temporal networks. By the end of the session, researchers will gain both a computational pipeline to process their own historical data and the theoretical framework required to interpret complex network metrics and visualisations responsibly.

Prior to the session, participants must complete a 2-hour self-guided Moodle module to help familiarise themselves with key social network analysis concepts, as well as setting up RStudio and Gephi on their laptop. This helps to ensure we can spend more time on analysis and discussion in the workshop itself.

Key Learning Objectives:

• Evaluate qualitative research questions and historical sources through a network analysis framework.
• Calculate and critically interpret key network centrality metrics (degree, betweenness, eigenvector) and community detection algorithms (modularity).
• Navigate real-world data challenges such as missing data, source bias, entity disambiguation, and standardisation.
• Transform raw (meta)data into structured edge/node tables using pre-prepared R scripts in RStudio.
• Experiment with dynamic network visualisation, layout algorithms, and filtering in Gephi.

Participants will need to bring their own laptop and R, R Studio, and Gephi can be downloaded for free prior to the workshop.

Our CDH Methods workshops have limited places and are prioritised for students and staff at the University of Cambridge. However, if space is available, we welcome all participants who want to learn and apply digital methods and use digital tools in their research.

This session may be of particular interest to:

• PhD students in the Arts, Humanities and Social Sciences
• Early Career Researchers in the Arts, Humanities and Social Sciences

If you have specific accessibility needs for this event, please get in touch. We will do our best to accommodate any requests, however please note that the building is grade 2 listed and has 4 steps into the building so unfortunately wheelchair access is not available.

This workshop is part of our Methods Fellowship programme, which develops and delivers innovative teaching in digital methods. You can read more about the and view the complete series of .

Event venue & nearby stays

Cambridge Digital Humanities, 2 Trumpington Street, Cambridge, UK, United Kingdom

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