Cambridge Digital Humanities course timetable

Convenor: Liz Stevenson, CDH Methods Fellow 2024/25

Are you a humanities researcher or scholar with no coding experience who would like to begin using digital analysis tools productively, manageably, and in a way that meets your needs?

Come and join one of the limited places to create a toolbox of basic text mining skills and methods that you can apply to your own humanities research and a simple but clear understanding of online resources with which you can do this, such as

• EEBO,
• Github and
• The R Graph Gallery,

and coding languages and workspaces like

• R and
• R Studio.

We will cover the underlying basic theory and philosophy, equipping you with the commands to perform tasks such as authorship attribution and statistical analyses of literary materials. For example, the creation of topic models and execution of word frequency analyses along with other similar methods of investigation.

You will have the coding tools to create simple but attractive visualisations and graphs of your results.

About the convenor:

Liz is a fourth-year PhD candidate in Renaissance English at Darwin College, having studied English at Stanford University, USA, where she also completed a BA (2016) and MA (2017). Liz’s work revolves around the relationship between digital analysis and subjective understandings of meaning and physicality in Renaissance literature. She is currently completing her dissertation using R-based topic modelling, MFW & MDW analyses, and language field volume analyses to argue for the categorisation of Shakespeare’s plays according to linguistic fields on the basis of the plays’ atypical generic behaviour compared to broader Elizabeth and Jacobean stage works of literature.

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

The impact of well-crafted data visualisations has been well-documented historically. Florence Nightingale famously used charts to make her case for hospital hygiene in the Crimean War, while Dr John Snow’s bar charts of cholera deaths in London helped convince the authorities of the water-borne nature of the disease. However, as information designer Alberto Cairo notes, charts can also lie. This introductory Basics session presents the basic principles of data visualisation for researchers who are new to working with quantitative data.

• Principles and good practice in data visualisation

• Basic introduction to quantitative methods of data analysis

Ensuring long-term access to digital data is often a difficult task: both hardware and code decay much more rapidly than many other means of information storage. Digital data created in the 1980s is frequently unreadable, whereas books and manuscripts written in the 980s are still legible. This session explores good practice in data preservation and software sustainability and looks at what you need to do to ensure that the data you don’t want to keep is destroyed.

• Data and code sustainability

• Retention, archiving and re-use

• Data destruction

• Recap on the project life-cycle

The Programming Historian publishes novice-friendly, peer-reviewed tutorials that help humanists learn a wide range of digital tools, techniques, and workflows to facilitate research and teaching. This workshop will help you plan your next steps in learning the skills you’ll need to work with data. We’ll highlight PH lessons which will put into practice skills related to the previous sessions in the Basics series, discuss common problems and how to overcome obstacles, sources of peer-to-peer support and how to build a community around you as you work. This can be attended as a standalone session or as part of the DH Basics course.

This workshop, organised in collaboration with Dr Ann Borda (Alan Turing Institute), will provide an accessible, non-technical introduction to AI systems for working with images (such as image classification, analysis and generation) and discuss sources of bias and problems of interpretation. Through discussion and hands-on exercises, we will demonstrate some of the ways in which image-generation models reproduce bias and stereotypes. We will use a data justice lens to:

• Address the hidden assumptions, abilities, knowledges, and interpretations that shape how AI is represented in images and is used in image generation;
• Explore ways to make AI more accessible to different human capabilities;
• Investigate how to make AI outputs more representative of diverse populations, reflecting a broader range of lived experiences.