Theme: CDH Methods Workshop

Computer programmes which predict the likely next words in sentences are a familiar part of everyday life for billions of people who encounter them in auto-complete tools for search engines and the predictive keyboards used by mobile phones and word processing software. These tools rely on “language models” developed by researchers in fields such as natural language processing (NLP) and information retrieval which assign probabilities to words in a sequence based on a specific set of “training data” (in this case a collection of texts where the frequencies of word pairings or three-word phrases have been calculated in advance).

Recent developments in machine learning have led to the creation of general language models trained on extremely large datasets which can now produce ‘synthetic’ texts, answer questions, summarise information without the need for lengthy or costly processes of training for each new task. The difficulties in distinguishing the outputs of these language models from texts written by humans has provoked widespread interest in the media. Researchers have experimented with prompting GPT-3, a language model developed by OpenAI to write short stories, answer philosophical questions and apparently propose potential medical treatments -although GPT-3 did have some difficulty with the question “how many eyes does a horse have?”. Meanwhile, The Guardian ‘commissioned’ an op-ed from GPT-3.

This Methods Workshop will explore the generation of ‘synthetic’ texts through presentations, discussion and demonstrations of text generation techniques which participants will be encouraged to try out for themselves during the sessions. We will also report back from the Ghost Fictions Guided Project, organised by Cambridge Digital Humanities Learning Programme in October and November this year. The project looks at how ideas about the distinction between ‘fact’, ‘fiction’ and ‘nonfiction’ are shaping the reception of text generation methods and aims to stimulate deeper critical engagement with machine learning by humanities researchers.

Prior knowledge of programming, computer science or Machine Learning is not required. In order to try out the text generation techniques demonstrated during the course you will need access to Google Drive (accessible via Raven login for University of Cambridge users).

The TEI (Text Encoding Initiative https://tei-c.org/) is a standard for the transcription and description of text bearing objects, and is very widely used in the digital humanities – from digital editions and manuscript catalogues to text mining and linguistic analysis. This course will take you through the basics of the TEI – what it is and what it can be used for – with a particular focus on uses in research, paths to publication (both web and print) and the use of TEI documents as a dataset for analysis. There will be a chance to create some TEI yourself as well as looking at existing projects and examples. The course will take place over two sessions a week apart – with an introductory taught session, then a chance to work on TEI records yourself, followed by a review and discussion session.

Text-mining is extracting information from unstructured text, such as books, newspapers, and manuscript transcriptions. This foundational course is aimed at students and staff who are new to text-mining, and presents a basic introduction to text-mining principles and methods, with coding examples and exercises in Python. To discuss the process, we will walk through a simple example of collecting, cleaning and analysing a text.

If you are interested in attending this course, please fill in, and return, the application form by Monday, 22 February 2021. Places will be prioritised for students and staff in the schools of Arts & Humanities, Humanities & Social Sciences, libraries and museums. If you study or work in a STEM department and use humanities or social sciences approaches you are also welcome to apply.

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.

Application forms should be returned to CDH Learning (learning@cdh.cam.ac.uk).

Social Network Analysis (SNA) is an exciting and rapidly growing methodology. You will find researchers in almost every faculty at the University of Cambridge applying SNA methods within their research. However, SNA researchers can only go so far before they must learn a coding language. Many SNA tools- descriptive metrics, visualisation techniques, and mathematical models- require researchers to use R. This session is for those researchers interested in SNA methods, but lack experience in the R environment.

While network visualisation is just one component of SNA, data visualisation can be a great gateway into a new programming language. This session will introduce you to the R environment by leading you through the creation of static network diagrams. The session is directed at beginners and basic R users that want to explore SNA tools in R.

Methods Workshop: Best Practices in Coding for Digital Humanities

Mary Chester-Kadwell (CDH Methods Fellow)

Please note this workshop has limited spaces and an application process in place. Application forms should be completed by Tuesday, 11 May 2021. Successful applicants will be notified by end-of-day Wednesday, 12 May 2021.

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. Places will be prioritised for students and staff in the schools of Arts & Humanities, Humanities & Social Sciences, libraries and museums. If you study or work in a STEM department and use humanities or social sciences approaches you are also welcome to apply.

If you are interested in attending this course, please fill in the application form.

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.

We are pleased to welcome Dr Ann Borda as a guest lecturer for this CDH Methods Workshop. Ann is the Participatory Health Lead in the Co-design Living Lab for Digital Health in the Centre for Digital Transformation of Health at the University of Melbourne. She is a Fellow of the Australasian Institute of Digital Health, Honorary Senior Research Associate at University College London, and sits on the policy committee of the Climate and Health Alliance. Ann formerly held collaborative positions in JISC and at the Science Museum London. Her research spans living lab and citizen science methods, and emerging participatory practices in digital health and culture.

There is an increasing presence in research incorporating participatory approaches to the production of knowledge. Participatory research is a range of methods framed within ideological perspectives. Its fundamental principles are that the subjects of the research become involved as partners in the process of the enquiry, and enacted through a set of social values. Participation can be classified by various degrees of involvement. Participatory activities can be expressed through various methods and approaches, such as co-design, citizen science, crowdsourcing, living labs, participatory action research and community-based participatory research, among others.

Text-mining is extracting information from unstructured text, such as books, newspapers, and manuscript transcriptions. This foundational course is aimed at students and staff new to text-mining. It presents a basic introduction to text-mining principles and methods, with coding examples and exercises in Python. To discuss the process, we will walk through a simple example of collecting, cleaning and analysing a text.

If you are interested in attending this course, please request a place and complete the application form, submitting it by the end of Monday, 7 March 2022. Successful applicants will be notified by the end-of-day Thursday, 10 March 2022. Preparatory materials will be released on Thursday, 17 March 2022. Places will be prioritised for students and staff in the schools of Arts & Humanities, Humanities & Social Sciences, libraries and museums. However, if you study or work in a STEM department and use humanities or social sciences approaches, you are also welcome to apply.

Methods Workshop: Best Practices in Coding for Digital Humanities

Mary Chester-Kadwell (CDH Research Software Engineering Coordinator)

Please note this workshop has limited spaces and an application process in place. Application forms should be completed by noon Wednesday, 4 May 2022 (you can only access this form by signing into your University Google Account). Successful applicants will be notified by end-of-day Monday, 9 May 2021.

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 complete the application form.

Jessica M. Parr, PhD (Simmons University and The Programming Historian)

We welcome Jessica Parr as a guest lecturer for this Methods Workshop, where we will discuss mapping techniques for scholars of the transatlantic slave trade. It will open with a discussion of addressing the Eurocentricity of geospatial techniques and the archives. We will then discuss strategies for reading against the archive to locate Black voices and strategies for determining geospatial coordinates from primary sources. Finally, the workshop will conclude with a demonstration of how to create maps in Tableau and some discussion of data ethics.

Please apply for a place if you would like to attend, on registration, you will be asked to complete and submit an information form (which will remain open until 10 am Monday, 14 February 2022), places are limited and selected on a rolling basis, we would suggest early completion.  We will confirm participation week commencing Monday, 21 February 2022.

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.

Following the introductory session, this second 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 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 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: 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 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.

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/])

Speaker: Mark Algee-Hewitt, Associate Professor of English and Director of the Stanford Literary Lab.

About this Methods workshop

At the heart of many of the current computational models of language usage, from generative A.I. to recommendation engines, are large language models that relate hundreds of thousands, or millions, of words to each other based on shared contexts. Mysterious products of complex modelling algorithms, these objects raise a number of practical (and ethical) questions for Humanities scholars: How are these language models created? What kinds of relationships does their math encode? How do biases in the corpus affect the model? And how can we effectively use them to answer humanities-based questions?

In this workshop, we will explore these questions using a medium-sized language embedding model trained on a corpus of novels. Using approachable code in the R software environment, participants will learn how to manipulate a model, assess similarities and difference within it, visualise relationships between words and even train their own embeddings.

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. No prior knowledge of programming is required.

We will focus on the technical, ethical and societal implications of embedding Machine Learning systems for classifying and generating texts and images into the world of work, with a particular emphasis on the impact of Large Language Models such as ChatGPT. We will explore these text generation systems in the context of longer histories of AI, including the ‘deep learning revolution’ in image-based Machine Learning systems which laid the foundations for popular text-to-image generation models such as StableDiffusion.

Participants will have the chance to both learn more about how AI works and also discuss what the embedding of such systems into labour processes, management structures, resource allocation systems may mean for how society works.

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.

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.

Convenor: Dr Estara Arrant (Cambridge University Library)

This session is aimed at researchers who have never done any coding before. We will explore basic principles and approaches to navigating and working with code, using the popular programming language Python. Participants will use the Jupyter Notebooks platform to learn how to analyse texts. This will provide participants with a working foundation in the fundamentals of coding in Humanities research. The software we will use is free to download and compatible with most computers, and we will provide support in installation and setup before the class.

Convenor: Dr Estara Arrant (Cambridge University Library)

This session is aimed at researchers with minimal coding experience or who have not done any coding but have data they want to explore and visualise. However, you do not need to have a full set of data to benefit from this class. You will learn the fundamentals of conducting a basic analysis of Humanities-related data in the R language, including prepping and tidying data and generating useful graphs which communicate information about your research to others.  You will also gain a basic overview of the R programming language, which will provide you with principles that you can take forward to learn more advanced data analysis methods. The software we will use (RStudio) is free to download and is compatible with most computers. We will provide installation support and guidance. You will need your own laptop.

Convenor: Dr Giulia Grisot (Cambridge Digital Humanities)

This workshop will delve into the intricacies of sentiment analysis using R, offering participants a comprehensive understanding of this text mining technique and a chance to gain hands-on experience with sentiment scoring methodologies and advanced sentiment visualisation. Designed for intermediate R users, this session aims to equip attendees with the requisite skills to extract nuanced insights from textual data through the lens of R programming. You will need your own laptop.