All Cambridge Digital Humanities courses

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.