Cambridge Digital Humanities course timetable

Convenors: Leah Brainerd & Alex Gushurst-Moore (CDH Methods Fellow)

Centuries of ceramics. Millenia of maquettes. How do we grapple with large datasets? Join archaeologist Leah Brainerd and art historian Alex Gushurst-Moore to increase your computational literacy, learn how to scrape data from collections databases, and interpret that data through visual means.

Over two, two-hour sessions, you will be introduced to:

• Collections databases: what they are, how they are built, and how to navigate them
• Web-scraping: how do you go from a webpage on the internet to a dataset on your computer? A basic introduction to how web-scraping with R *Statistics works with a worked example, ethics of data, and learn how to evaluate a website for future data collection
• Data visualisation software: what options are available and how to use the open-source, online system mapping tool, Kumu
• Cultural evolutionary theory: cultural evolution is the change of culture over time; explore a theoretical perspective that views cultural information as an evolutionary process which teaches us, through cultural transmission, more about human decision making

The workshop will take place over two sessions. The first session (30 January) will cover collections databases and web-scraping. The second session (6 February) will cover data visualisation and cultural evolutionary theory. These sessions will consist of practical tutorials and discussion with the course leads. After each session, participants will be given an optional task to try out new skills acquired, on which they can receive feedback from the course organisers.

Convenor: Dita N. Love (CDH Methods Fellow)

Sarah Ahmed and Jackie Stacey wrote that “speaking out about injustice, trauma, pain and grief have become crucial aspects of contemporary life which have transformed notions of what it means to be a subject, what it means to speak, and how we can understand the formation of communities and collectives” (p.2, 2001) in the introduction of the special issue Testimonial Cultures. These workshops ask therefore: what does it mean to centre survivor-knowledge, and witness together the aftermath of intersecting violence, when language and traditional methods often fail to re-present the experience of trauma? How can we avoid tokenising creative-digital research under the pressures of a precarious academy and creative sector?

Convenor: Dita N. Love (CDH Methods Fellow)

Sarah Ahmed and Jackie Stacey wrote that “speaking out about injustice, trauma, pain and grief have become crucial aspects of contemporary life which have transformed notions of what it means to be a subject, what it means to speak, and how we can understand the formation of communities and collectives” (p.2, 2001) in the introduction of the special issue Testimonial Cultures. These workshops ask therefore: what does it mean to centre survivor-knowledge, and witness together the aftermath of intersecting violence, when language and traditional methods often fail to re-present the experience of trauma? How can we avoid tokenising creative-digital research under the pressures of a precarious academy and creative sector?

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.