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Automated writing in the age of Machine Learning new Mon 7 Dec 2020   11:30 Finished

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).

Methods Workshop: TEI workshop new Mon 18 Jan 2021   10:00 Finished

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.

Doing Qualitative Research Online new Mon 1 Feb 2021   14:00 Finished

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.

Social Network Analysis (SNA) new Thu 18 Feb 2021   11:00 Finished

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.

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