Cambridge Digital Humanities course timetable

We introduce the Transkribus software system that can be taught to read handwriting from images of documents and rapidly convert it into useful digital formats. This guided course provides basic training by practical immersion in this software, which requires only basic IT skills. Transkribus was developed by READ under the Horizon 2020 funding framework and is now a co-operative. It had 20,000+ users in 2019, and is becoming a standard research tool for mass transcription of archival sources. Participants will transcribe anonymised data from pre-loaded scans of forms filled out for the French national census of 1999 in Transkribus's downloadable software interface. These manual transcriptions will help train a handwritten text recognition (HTR) model to automatically transcribe many more of these forms later. In fact, the model will eventually allow the creation of one of the largest data sets ever attempted from manuscript sources. This course is a collaboration with Transkribus and Cambridge Digital Humanities. It is funded by a Cambridge Humanities Research Grant.

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.

This CDHBasics session will explain what data is, and what ‘humanities data’ looks like (via a behind-the-scenes tour of the Digital Library). This session covers good practice around file formats, version control and the principles of data curation for individual researchers.

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.

'Application forms should be returned to CDH Learning (learning@cdh.cam.ac.uk) by Tuesday 13 October 2020. Successful applicants will be notified by 15 October 2020.

This CDH Guided Project series which also includes a Methods Workshop will explore the generation of ‘synthetic’ texts using neural networks.

The release of OpenAI’s GPT-2 and GPT-3 language models in 2019 and 2020 has shown that predictive algorithms trained on very large general datasets can generate ‘synthetic’ texts, perform machine translation tasks, rudimentary reading comprehension, question answering and summarisation automatically without needing large amounts of task-specific training. These ‘ghostwritten’ texts have provoked wide attention in the media.

Researchers have experimented with prompting GPT-3 to write short stories, answer philosophical questions and apparently propose potential medical treatments -although GPT-3 had some difficulty with the question “how many eyes does a horse have?”. The Guardian ‘commissioned’ op-ed from GPT-3.

Through interactive hands-on sessions and demonstrations we will explore synthetic text production and look at how ideas about the distinction between ‘fact’, ‘fiction’ and ‘non-fiction’ are shaping the reception of this emerging technology. Our aim is to stimulate deeper critical engagement with machine learning by humanities researchers and to encourage more public debate about the role of AI in culture and society.

We invite applications from early career researchers and others at the University of Cambridge to join a small project team for four online sessions during the Guided Project phase in Oct-November. Participants will need to commit to joining the live sessions and to set aside at least 3-4 hours work on a small-scale individual project during the course. We are interested in assembling an interdisciplinary group of researchers drawing on insights from across humanities, social science and technology disciplines .Prior knowledge of programming, computer science or Machine Learning is not required.

This CDHBasics session focuses on the importance of metadata (‘data about data’), examining the crucial role played by classification systems and standards in shaping how scholars interact with historical and cultural records.

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.

'Application forms should be returned to CDH Learning (learning@cdh.cam.ac.uk) by Tuesday 13 October 2020. Successful applicants will be notified by 15 October 2020.

This CDH Guided Project series which also includes a Methods Workshop will explore the generation of ‘synthetic’ texts using neural networks.

The release of OpenAI’s GPT-2 and GPT-3 language models in 2019 and 2020 has shown that predictive algorithms trained on very large general datasets can generate ‘synthetic’ texts, perform machine translation tasks, rudimentary reading comprehension, question answering and summarisation automatically without needing large amounts of task-specific training. These ‘ghostwritten’ texts have provoked wide attention in the media.

Researchers have experimented with prompting GPT-3 to write short stories, answer philosophical questions and apparently propose potential medical treatments -although GPT-3 had some difficulty with the question “how many eyes does a horse have?”. The Guardian ‘commissioned’ op-ed from GPT-3.

Through interactive hands-on sessions and demonstrations we will explore synthetic text production and look at how ideas about the distinction between ‘fact’, ‘fiction’ and ‘non-fiction’ are shaping the reception of this emerging technology. Our aim is to stimulate deeper critical engagement with machine learning by humanities researchers and to encourage more public debate about the role of AI in culture and society.

We invite applications from early career researchers and others at the University of Cambridge to join a small project team for four online sessions during the Guided Project phase in Oct-November. Participants will need to commit to joining the live sessions and to set aside at least 3-4 hours work on a small-scale individual project during the course. We are interested in assembling an interdisciplinary group of researchers drawing on insights from across humanities, social science and technology disciplines .Prior knowledge of programming, computer science or Machine Learning is not required.

This CDHBasics session looks at how searching and finding technologies structure scholarship. It also covers

• an introduction to search engines, both for web search and custom search functions within collections;
• discussion about OCR errors and blindspots in digital search in historical collections
• problems of fragmentation of the source text, and the legacy of pre-digital formats such as microfilm.

'Application forms should be returned to CDH Learning (learning@cdh.cam.ac.uk) by Tuesday 13 October 2020. Successful applicants will be notified by 15 October 2020.

This CDH Guided Project series which also includes a Methods Workshop will explore the generation of ‘synthetic’ texts using neural networks.

The release of OpenAI’s GPT-2 and GPT-3 language models in 2019 and 2020 has shown that predictive algorithms trained on very large general datasets can generate ‘synthetic’ texts, perform machine translation tasks, rudimentary reading comprehension, question answering and summarisation automatically without needing large amounts of task-specific training. These ‘ghostwritten’ texts have provoked wide attention in the media.

Researchers have experimented with prompting GPT-3 to write short stories, answer philosophical questions and apparently propose potential medical treatments -although GPT-3 had some difficulty with the question “how many eyes does a horse have?”. The Guardian ‘commissioned’ op-ed from GPT-3.

Through interactive hands-on sessions and demonstrations we will explore synthetic text production and look at how ideas about the distinction between ‘fact’, ‘fiction’ and ‘non-fiction’ are shaping the reception of this emerging technology. Our aim is to stimulate deeper critical engagement with machine learning by humanities researchers and to encourage more public debate about the role of AI in culture and society.

We invite applications from early career researchers and others at the University of Cambridge to join a small project team for four online sessions during the Guided Project phase in Oct-November. Participants will need to commit to joining the live sessions and to set aside at least 3-4 hours work on a small-scale individual project during the course. We are interested in assembling an interdisciplinary group of researchers drawing on insights from across humanities, social science and technology disciplines .Prior knowledge of programming, computer science or Machine Learning is not required.

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.

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.

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.

This CDH Basics session will see discussion on how to assess the impact of relevant legal frameworks, including data protection, intellectual property and media law, on your digital research project and consider what approach researchers should take to the terms of service of third-party digital platforms. We will explore the challenge of informed consent in a highly-networked world and look at a range of strategies for dealing with this problem.