All Cambridge Digital Humanities courses

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/])

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.

This Methods Workshop explores primary data collection using digital and online qualitative methods. Teaching methods for detailed assessment of the suitability of online platforms for the collection of research data. Considering not only general ethical issues, privacy, encryption, terms and conditions but also inclusivity for neurodivergent and vulnerable participants.

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: Tom Kissock (CDH Methods Fellow)

This Methods Workshop will offer Video Data Analysis for Social Science and Humanities students. It’s a relatively new, broad, and innovative multi-disciplinary methodology that helps students understand how video fits into modern research both inside and outside academia. For example, Cisco has estimated that video will make up 80% of internet traffic and 17.1% of it will be live video which is a 15-fold increase since 2017; therefore, it’s a tool that cannot be overlooked when conducting research.

Tom will address how to use video ethically, for example:

• Informed consent
• Storage
• Privacy

and also practically;

• Building timelines
• Coding schemes
• Presenting research findings

Tom will also plans to include a lesson focussed on viewing livestreams in a reflexive manner as this is a huge topic in the TikTok era

About the convenor: Tom has fifteen years’ experience as a Director, Executive Producer, and Livestream expert for the BBC, YouTube, NBC, and Cisco; coupled with seven years’ experience researching video witnessing and human rights abuses. In 2020 he received his MSc in Globalization and Latin American Development from UCL where his research used Video Data Analysis as a research methodology. He tracked how populist politicians in Brazil built misinformation campaigns by strategically cross-sharing videos to avoid journalistic questioning as a symbolic accountability mechanism during the 2018 presidential elections.

His PhD in Sociology at the University of Cambridge is a loose extension of his MSc, but explores positive aspects of streaming advocacy, such as how Indigenous video activists in Brazil use live video on platforms like Instagram, TikTok, and Kwai to reach audiences to discuss climate change, the environment, and land rights. He is interested in how video can produce knowledge and, subsequently how societies value different knowledge through the process of video witnessing. In his spare time, he serves as the Executive Producer of Declarations: Human Rights Podcast (part of Cambridge’s Centre for Governance and Human Rights), has given lectures on live streaming and human rights at MIT, UCL, and the University of Essex, and has written pieces for LatAM Dialogue and the Latin American Bureau.

Convenor: Dr Eleanor Dare (CDH Methods Fellow)

This Methods Workshop will invite participants to originate innovative research methods using virtual and augmented reality technologies underpinned by theoretical and pedagogic understandings. The session is conceived in recognition of an increasing interest in using virtual and extended reality (VR and XR) to create collaborative research spaces that span different locations, time zones, and spatiality. Such spaces might be used to investigate the impact of design, architecture and location on education or new ways to teach an array of subjects, from language to mathematics to performance, AI ethics and music.

About the convenor: Eleanor is currently the Co-Convenor for Arts, Creativity and Education at the University of Cambridge, Faculty of Education, they are also the Senior Teaching Associate: Educational Technologies, Arts and Creativity, lecturing and supervising on MPHIL Arts, Creativities and Education, MPhil Knowledge, Power and Politics, and MEd Transforming Practice. Eleanor is module lead for AI and Education, a Personal and Professional Development course at Cambridge.

Eleanor Dare’s research addresses the implications of digital technology and virtuality as a material for collaboration, critical-educational games development, performance, worldbuilding and pedagogic experimentation. Eleanor has been involved in several AHRC/EPSRC/ESRC/Arts Council/British Council funded projects investigating aspects of virtual and extended reality as well as projects with the Mozilla Foundation (AI-Musement/Monstrous 2022-2023), Theatre in the Mill Bradford (Bussing Out, 2022) and the Big Telly Theatre Company (via the Arts Council of Northern Ireland) for Rear Windows, forthcoming.

Dr Anne Alexander, Cambridge Digital Humanities

Places are limited and participants must complete this form in order to participate in addition to booking online. We will write and confirm your participation by email. Bookings will remain open until 10am, 11 October 2021; However, participants are encouraged to apply early as demand is likely to be high.

This online 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. It is designed as a preparatory session for potential applicants to our Interaction with Machine Learning Guided Project which will run in Lent Term 2022 in collaboration with the Department of Computer Science and Technology. However, it can also be booked as a standalone session.

Interactive Fiction (IF) stories let readers decide which paths the story should follow, featuring non-linear narrative design. The discipline combines the excitement of post-structuralist narratives with the power of creative coding, making it a perfect introduction for participants more familiar with one field than the other. In this workshop, led by Methods Fellow Claire Carroll, we’ll explore both parser-based (rooted in reader instructions and terminal response) and choice-based (hyperlink or multiple choice-driven) IF and work together to write our own interactive fiction. The workshop will also introduce participants to the passionate IF community, which offers advice and support to experienced writers and newcomers alike.

This CDH Basics session explores how data which you have captured rather than created yourself, is likely to need cleaning up before you can use it effectively. This short session will introduce you to the basic principles of creating structured datasets and walk through some case studies in data cleaning with OpenRefine, a powerful open source tool for working with messy data.

Machine vision systems can potentially help humanities researchers see historical and cultural image collections differently, and could provide tools to answer new research questions. This CDH Basics session provides an introductory overview of basic tasks in machine vision, such as Image Classification, Object Detection and Image Captioning within a critical framework highlighting the challenges of algorithmic bias and the limits of automation as a method for humanistic enquiry.

This workshop will examine strategies for transforming a variety of sources into structured digital data, ranging from crumbling manuscripts to printed documents and books.

Leonardo Impett, Cambridge Digital Humanities

Application forms should be returned to CDH Learning (learning@cdh.cam.ac.uk) by Friday 22 May 2020. Successful applicants will be notified by 26 May 2020.

This course will introduce graduate students, early-career researchers, and professionals in the humanities to the technologies of image recognition and machine vision, including recent developments in machine vision research in the past half-decade. The course will seek to combine a technical understanding of how machine vision systems work, with a detailed understanding of the possibilities they open to research and study in the humanities, and with a critical exploration of the social, political and ideological dimensions of machine vision.

Learning outcomes

By the end of the course, students should be able to:

• Understand the basic tasks of machine vision, such as Image Classification, Object Detection, Image-to-Image Translation, Image Captioning, Image Segmentation etc.
• Understand the fundamental technical operations of image processing and machine vision: the pixel encoding of images, Gaussian and convolutional filters,
• Explore critical aspects of machine vision in a technically-informed way: e.g. the problems in algorithmic bias brought about by featureless convolutional networks
• Develop and run their own simple machine vision and image processing pipelines, in a visual programming language compiling to Python
• Understand the potential synergies and limitations of machine vision applications in humanities research and cultural heritage institutions

The afterlife of your research data forms a vitally important part of your research project. Research funders and academic journal publishers are often strongly committed to the re-use of data and are reluctant to fund or publish research where datasets are not accessible for the purposes of peer review or further use. Yet the push for open data exists in tension with the expectations of data protection law which requires transparency from researchers about how long they will retain personal data. This session will explore good practice in data sharing and archiving as well as introducing sources of further information and advice within the University on this topic.

Garbage in, garbage out! Your output is as good or as bad as your input. Data collected from online sources is often dirty and messy. Discover how to clean and organise your data. After transforming raw data into a structured dataset, you will be ready to perform data analysis.

Application forms https://www.cdh.cam.ac.uk/file/cdhdelvingintomassivedaapplicationdocx should be returned to CDH Learning (learning@cdh.cam.ac.uk) by Tuesday 6 October 2020. Successful applicants will be notified by Thursday 8 October 2020.

Massive digital archives such as the Internet Archive offer researchers tantalising possibilities for the recovery of lost, forgotten and neglected literary texts. Yet the reality can be very frustrating due to limitations in the design of the archives and the tools available for exploring them. This programme supports researchers in understanding the issues they are likely to encounter and developing practical methods for delving into massive digital archives.

Following the introductory Methods Workshops, held on 21st November 2023, this session will focus on how to adopt the 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.

This session addresses the technical and ethical aspects of digital data collection and wrangling – two fundamental stages in the lifecycle of a digital research project. Participants will be introduced to online data sources and practices of internet-mediated data collection, including retrieving data from social media platforms. As data collected from online sources is often dirty and messy, we will also provide a short practical introduction to the process of transforming raw data into a clean and structured dataset using free and open-source software.

This session is a primer on digital data collection. The goal is to become familiar with online data sources and practices of internet-mediated data collection, including retrieving data from social media platforms.

The shelf-life of your dataset dictates the longevity of your findings. Sharing your data and assuring its integrity is a fundamental part of a digital research project. In this session we will discuss the principles of open data, channels for data dissemination and the fundamentals of data preservation.

This intensive workshop will provide an overview of a range of applications of digital mapping in historical research projects and introduce GIS tools and software.

This CDHBasics session explores the lifecycle of a digital research project across the stages of design;

• data capture
• transformation
• analysis
• presentation and preservation

it also introduces tactics for embedding ethical research principles and practices at each stage of the research process.

Find out how to shape a digital research project from scratch. This session will introduce the building blocks of online research design, from the several methodologies available to conduct the research to the ethical guidelines that should underpin our projects.

What happens to practices of qualitative research when interactions between researcher and research subject are largely mediated? From observations of users’ interactions on social media platforms, to interviews conducted through WhatsApp or Skype, digital communications offer both opportunities and challenges for qualitative research in a wide range of disciplines across the Social Sciences and Humanities. This methods workshop will explore a wide range of topics including:

• Establishing trust and credibility
• Engaging with digital gatekeepers
• Navigating blurred boundaries between ‘private’ and ‘public’
• Re-conceptualising ‘researchers’, the ‘research field’ and ‘ research subjects’
• Identity, anonymity and visibility - implications for research practice
• Mitigation strategies: from data parsimony to creative obfuscation
• Self-care for researchers in online research
• Embedding ethical research practice across the project lifecycle

The workshop will take place over two sessions, an introductory seminar and discussion led by Dr Anne Alexander on 1 February, after which participants will be asked to complete a short reflective piece of work assessing their own research design and identifying areas where they feel they need further help and advice. The second session on 8 February will be participant led including small group and plenary discussions exploring strategies for dealing with challenges identified by participants.

Participants should set aside around 1 hour between the two sessions to complete and submit their self-assessment.

Participants are strongly encouraged to attend the CDH Basics session Privacy, information security and consent: a guide for researchers with Dr Anne Alexander on 26 January in advance of the Methods Workshop.

We are currently reformatting our Learning programme for remote teaching; this will require some rescheduling so bookings will reopen and new sessions will be created for online courses as soon as possible. In the interim we would encourage you to register your interest so as to be notified of the new schedule. Please be aware that we hope to run many of our courses online, but that this is dependent on staff availability and resources so please be aware we may have to postpone or cancel some sessions

This workshop will develop your coding practice from testing ideas to creating an efficient workflow for your code, data and analysis. If you are using Jupyter Notebooks (but even if you’re not) this workshop will demonstrate how to better manage your code using good programming practices, and package your code into a program that is easier and quicker to run for lots of data and more reliable.

Required preparation (instructions provided): Python 3 installed on laptop; a text editor or IDE installed on laptop; git installed on laptop and signed up for GitHub; a short internet-based exercise in working with the command line.