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Social Network Analysis with Digital Data new Tue 4 Feb 2020   11:00 [Places]

This course will provide a hands-on introduction to the field of Social Network Analysis, giving participants the opportunity to “learn by doing” the process of network data collection and analysis. After being introduced to the basic concepts, the participants will have the opportunity to explore all stages of a social network analysis project, including research design, essential measures, data collection and data analysis. The focus will be on the retrieval of electronic archival data (e.g. websites, digital archives and social media platforms) for non-programmers and on the production of network analysis with specialised software (e.g. Gephi). At the end, the participants will be equipped with the basic tools to perform meaningful visualisations and analyses of network data.

Sources to Data (Workshop) Wed 5 Jun 2019   11:00 Finished

This workshop will examine database creation from historical documents. Extracting data from these can be hard work and involves quite unusual skill combinations. You may need to digitise and transcribe from primary sources, and then design and build a database from scratch with the information. Other sources you use could already be digitised but may be arranged or filed in an unsuitable way for your project and therefore need conversion. We will look at techniques used when employing crumbling manuscripts, printed documents, books, or text searchable images, to harvest historical data. Techniques include manual data-entry, scanning and OCR, and handwritten text recognition systems.

Letters have been for centuries the main form of communication between scientists. Correspondence collections are a unique window into the social networks of prominent historical figures. What can digital social sciences and humanities reveal about the correspondence networks of 19th century scientists? This two-session intensive workshop will give participants the opportunity to explore possible answers to this question.

With the digitisation and encoding of personal letters, researchers have at their disposal a wealth of relational data, which we propose to study through social network analysis (SNA). The workshop will be divided in two sessions during which participants will “learn by doing” how to apply SNA to personal correspondence datasets. Following a guided project framework, participants will work on the correspondence collections of John Herschel and Charles Darwin. After a contextual introduction to the datasets, the sessions will focus on the basic concepts of SNA, data transformation and preparation, data visualisation and data analysis, with particular emphasis on “ego network” measures.

The two demonstration datasets used during the workshop will be provided by the Epsilon project, a research consortium between Cambridge Digital Library, The Royal Institution and The Royal Society of London aimed at building a collaborative digital framework for 19th century letters of science. The first dataset, the “Calendar of the Correspondence of Sir John Hershel Database at the Adler Planetarium”, is a collection of the personal correspondence of John Frederick William Herschel (1792-1871), a polymath celebrated for his contributions to the field of astronomy. Its curation process started in the 50s at the Royal Society and currently comprises 14.815 digitised letters encoded in extensible markup language (.xml) format. The second dataset, the “Darwin Correspondence Project” has been locating, researching, editing and publishing Charles Darwin’s letters since 1974. In addition to a 30-volume print edition, the project has also made letters available in .xml format.

The workshop will provide a step-by-step guide to analysing correspondence networks from these collections, which will cover:

- 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; - Analysis of the Ego Networks of John Herschel and Charles Darwin (requires UCINET)

About the speakers and course facilitators:

Anne Alexander is Director of Learning at Cambridge Digital Humanities

Hugo Leal is Methods Fellow at Cambridge Digital Humanities and Co-ordinator of the Cambridge Data School

Louisiane Ferlier is Digital Resources Manager at the Centre for the History of Science at the Royal Society. In her current role she facilitates research collaborations with the Royal Society collections, curates digital and physical exhibitions, as well as augmenting its portfolio of digital assets. A historian of ideas by training, her research investigates the material and intellectual circulation of ideas in the 17th and 18th centuries.

Elizabeth Smith is the Associate Editor for Digital Development at the Darwin Correspondence Project, where she contributed to the conversion of the Project’s work into TEI several years ago, and has since been collaborating with the technical director in enhancing the Darwin Project’s data. She is one of the co-ordinators of Epsilon, a TEI-based portal for nineteenth-century science letters.

No knowledge of prior knowledge of programming is required, instructions on software to install will be sent out before the workshop. Some exercises and preparation for the second session will be set during the first and participants should allow 2-3 hours for this. Please note, priority will be given to staff and students at the University of Cambridge for booking onto this workshop.

CDH Learning gratefully acknowledges the support of the Isaac Newton Trust and the Faculty of History for this workshop.

The Library as Data new Mon 15 Oct 2018   13:30 Finished

Discover the rich digital collections of Cambridge University Library and explore the methods and tools that researchers are using to analyse and visualise data.

The Library as Data: An overview new Wed 16 Oct 2019   11:00 Finished

Is the "digital library" more than a virtual rendering of the bookshelf or filing cabinet? Does the transformation of books into bytes and manuscripts into pixels change the way we create and share knowledge? This session introduces a conceptual toolkit for understanding the library collection in the digital age, and provides a guide to key methods for accessing, transforming and analysing the contents as data. Using the rich collections of Cambridge University Library as a starting point, we will explore:

  • Relations between digital and material texts and artefacts
  • Definitions of data and metadata
  • Methods for accessing data in bulk from digital collections
  • Understanding file formats and standards

The session will also provide an overview of the content in the rest of the term’s Library as Data programme, and introduce our annual call for applications to the Machine Reading the Archive Projects mentoring scheme.

The Library as Data: Digital Text Markup and TEI new Wed 23 Oct 2019   11:00 Finished

Text encoding, or the addition of semantic meaning to text, is a core activity in digital humanities, covering everything from linguistic analysis of novels to quantitative research on manuscript collections. In this session we will take a look at the fundamentals of text encoding – why we might want to do it, and why we need to think carefully about our approaches. We will also introduce the TEI (Text Encoding Initiative), the most commonly used standard for markup in the digital humanities, and look at some common research applications through examples.

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.

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