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Recent advances in machine learning are allowing computer vision and humanities researchers to develop new tools and methods for exploring digital image collections. Neural network models are now able to match, differentiate and classify images at scale in ways which would have been impossible a few years ago. This session introduces the IIIF image data framework, which has been developed by a consortium of the world’s leading research libraries and image repositories, and demonstrates a range of different machine learning- based methods for exploring digital image collections. We will also discuss some of the ethical challenges of applying computer vision algorithms to cultural and historical image collections. Topics covered will include:

  • Unlocking image collections with the IIIF image data framework
  • Machine Learning: a very short introduction
  • Working with images at scale: ethical and methodological challenges
  • Applying computer vision methods to digital collections

This session focusses on providing photography skills for those undertaking archival research. Dr Oliver Dunn has experience spanning a decade filming documents for major academic research projects. He will go over practical approaches to finding and ordering materials in the archive, methods of handling and filming them, digital file storage, and transcription strategies. The focus is very much on low-tech approaches and small budgets. We’ll consider best uses of smartphones, digital cameras and tripods. The session is held in the IT training room at the University Library.

Correspondence collections are a unique window into the social networks of prominent historical figures. With the digitisation and encoding of personal letters, researchers have at their disposal a wealth of relational data, which can be studied using social network analysis.

This session will introduce and demonstrate foundational concepts, methods and tools in social network analysis using datasets prepared from the Darwin Correspondence collection. Topics covered will include

  • Explanation of the encoding procedures and rationale following the Text Encoding Initiative guidelines
  • Preparation and transformation of .xml files for analysis with an open source data wrangler
  • Rendering of network visualisations using an open source SNA tool

No knowledge of prior knowledge of programming is required, instructions on software to install will be sent out before the session

Image big data are increasingly being used to understand the built and natural environment and to observe behaviours within it. Data sources include satellite and airborne imagery, 360 street views, and fixed video or time lapse traffic and CCTV cameras. While some of these sources are newer than others what has been changing are the quality of the images, the geographical coverage, and the potential for assessing changes over time. At the same time improvements in machine learning have made it possible to turn images into quantitative data at scale.

In this workshop we will explore the challenges that researchers face when using images at scale to understand environments and behaviours, building on work at Cambridge to estimate cycling levels, using satellite data to estimate motor vehicle volume, and planned data collection in Kenya using 360 cameras.