Cambridge Digital Humanities course timetable

This Methods Workshop will introduce advanced techniques used for the digitisation and preservation of archival material. The first workshop will introduce the following topics:

• Copyrights and sensitive data considerations
• Understanding Photography basics
• Digitisation Imaging Standards
• Scene and capture calibration
• Image post-processing
• Taking usable images in any conditions
• Principles and Digital Preservation good practice

Completing the workshop will give participants a good understanding of archival photography best practices. You will gain a strong professional vocabulary to discuss imaging and a toolkit to assess image quality.

A second session, bookable separately, will focus on how to adopt those principles to the projects chosen by the participants. This will cover learning a practical approach to taking images fit for purpose in any conditions with available resources. It may also address any more advanced imaging topics such as image stitching, Optical Character Recognition, Multispectral Imaging, or photogrammetry if these are in the interest of the participants. It will also be an opportunity to visit the Digital Content Unit at Cambridge University Library.

Convenor: Orla Delaney (CDH Methods Fellow)

What does it mean to prioritise small data over big data?

Cultural heritage datasets, such as museum databases and digital archives, seem to resist the quantitative methods we usually associate with data science work, asking to be read and explored rather than aggregated and analysed. This workshop provides participants with a non-statistical toolkit that will enable them to approach, critique, and tell the story of a cultural heritage dataset.

Together we will consider approaches to the database from the history of science and technology, media archaeology, and digital ethnography. This will be done alongside an overview of practical considerations relevant to databasing in the sector, such as standards like FAIR (Findable, Accessible, Interoperable, Reusable) and CARE (Collective Benefit, Authority to Control, Responsibility, Ethics), specific technologies like linked data, and the results of recent projects aiming to criticise and diversify the underpinning technologies of cultural heritage databases. This workshop is aimed both at cultural heritage professionals and students, and at data science researchers interested in introducing a qualitative approach to their work.

This project begins from the premise that ‘transparency’ is not clear at all. Transparency is historically mediated, culturally constructed, and ideologically complex. Understood expansively, transparency is enmeshed with a variety of functions and associations, having been mobilised as a political call to action; a design methodology; a radical practice of digital disruption; an ideological tool of surveillance; a corporate strategy of diversion; an aesthetics of obfuscation; a cultural paradigm; a programming protocol; a celebration of Enlightenment rationality; a tactic for spatialising data; an antidote to computational black boxing; an ethical cliché; and more.

Across two workshops, we will explore the multidimensionality and intractability of transparency and investigate how the demand for more of it—in our algorithms, computational systems, and culture more broadly—can encode assumptions about the liberational capacity of restoring representation to the invisible. As a group we will conduct a survey of transparency and its political ramifications to digital culture by learning about its conceptual genealogies; interrogating its relevance to art and architecture; questioning its limits as an ethical imperative; and mapping it as a contemporary strategy of anti/mediation. Drawing on a combination of artworks, historical texts, cultural touchstones, and moving images, these workshops will give participants an opportunity to attend to transparency’s complex configurations within contemporary culture through a media theoretical lens. This project is designed to facilitate collaborative study; foster inter-disciplinary discourse; promote experimental learning; and develop a more theoretically nuanced and historically grounded starting point for critiquing transparency and its operations within digital culture.

Convenor: Estara Arrant (CDH Methods Fellow)

This methods workshop will teach students three powerful machine learning algorithms appropriate for Humanities research projects. These algorithms are designed to help you identify and explore meaningful patterns and correlations in your research material and are appropriate for descriptive, qualitative data sets of almost any size. These algorithms are applicable to virtually any Humanities field or research question.

• Multiple Correspondence Analysis: automatically identifies correlations and differences between specific data elements. This helps one to understand how different features (‘variables’ or ‘characteristics’) of one’s data are related to each other, and how strong their relationships are. This can be useful in almost any research project. For example, in a sociological dataset, this analysis could help clarify relationships between specific demographic characteristics (race, gender, political affiliation) and socioeconomic status (working class, education level, income bracket).
• K-modes clustering and hierarchical clustering: finding groups of similarity and relationship within the entirety of your data. Clustering helps one to identify which variables/characteristics group together, and which do not, and the degree of difference between groups. For example, such clustering could allow an art historian to see how paintings from one decade are characterised by style and artist, as contrasted to paintings from another decade (thus tracking shifts in artistic trends over time)

This workshop will specifically cover the following: Determining when your research could benefit from machine learning analysis. Designing a good methodology and running the analysis. Interpreting the results and determining if they are meaningful. Producing a useful visualisation (graphic) of the results. Communicating the findings to other scholars in the Humanities in an accessible way. Students will actively implement a small research project using a practice dataset and are encouraged to try out the methods in their current research. They will learn the basics of running the analysis in R’s powerful programming language.

Convenor: Mary Chester-Kadwell -  Lead Research Software Engineer, Cambridge Digital Humanities

Please note this workshop has limited spaces and an application process in place. Application forms should be completed by noon, Sunday, 12 March 2023. Successful applicants will be notified by the end-of-day Tuesday, 14 March 2023. 

This course introduces best practices and techniques to help you better manage your code and data, and develop your project into a usable, sustainable, and reproducible workflow for research.

Developing your coding practice is an ongoing process throughout your career. This intermediate course is aimed at students and staff who use coding in research, or plan on starting such a project soon. We present an introduction to a range of best practices and techniques to help you better manage your code and data, and develop your project into a usable, sustainable, and reproducible workflow. All the examples and exercises will be in Python.

If you are interested in attending this course, please fill in the application form. Please ensure you are logged onto your University Google account to access the form further help here

This in-person workshop will provide an accessible, non-technical introduction to Machine Learning systems, aimed primarily at graduate students and researchers in the humanities, arts and social sciences.

Key topics covered in the sessions will include:

• Situating Machine Learning in the longer history of Artificial Intelligence
• Machine Learning system architectures
• The challenges of dimension reduction, classification and generalisation
• Sources of bias and problems of interpretation
• Machine Learning applications and their societal consequences

During the session participants will be encouraged to work through practical exercises in image classification. No prior knowledge of programming is required. Participants wishing to run the experiments for themselves will need access to a laptop, but no special software is required, just an up-to-date web browser and an internet connection. We will be using Google Colab for the text generation experiments which you have access to via your Raven log-in. The image classification experiments will require a GitHub account ([sign up here https://github.com/])