An Introduction to Digital Methods for Bibliography

• March 8 Digital resources for printed books: an introduction (Matthew Symonds) 

This session is designed to give participants a critical introduction to digital resources for bibliographical study, with a particular focus on the databases, tools and issues that intersect with CELL’s research. The session will be built around a critical discussion of the creation and technical underpinnings of Book Owners Online and its use of Semantic MediaWiki. We will also use work in progress, as we examine the data schemas that inform Shaping Scholarship to discuss the creation and utilisation of databases for bibliographical research. Readings covering the practical and theoretical aspects of will be provided beforehand in order to inform discussion. The session will assume no prior experience of the resources or technologies discussed on the part of participants. 

• March 15 Working with Bibliographical Data I: Data Fundamentals (Christopher Ohge)

This session focuses on what is often called ‘software carpentry’—basic and fundamental skills for working with data. These include working with the command line interface (CLI), working with the version control software Git, and cleaning data with OpenRefine. Taken together, these skills give the bibliographer more control over their data and the ability to clean it and to store it in an open source repository.

• April 5 Working with Bibliographical Data II: Enhancing the TEI Header with <bibl> and <msDesc> (Christopher Ohge)

This session focuses on encoding bibliographic metadata using the Text Encoding Initiative (TEI) Guidelines. Such metadata is encoded in the TEI Header, which allows the encoder to record important source data such as bibliographic descriptions, manuscript details, and other important information such as people and place names. The TEI is the most widely used standard for the encoding of scholarly texts, so this session will provide the fundamentals of TEI XML encoding while also focusing on the bibliographical features of TEI.

• April 19 Computer vision and the printed book (Giles Bergel)

Nodegoat is a web-based research environment for the humanities. You can use it to create and import data, build data models, and visualise the data through maps and social network graphs. You can read more at: https://nodegoat.net/about  Some examples of the nodegoat software in action can be seen here: https://nodegoat.net/usecases. In this unit, we will explore the use of nodegoat for working with data relating to early printed books and manuscripts in Europe. This will include looking critically at potential data sources, building a data model, importing and creating data, and developing map and network visualisations. We will also work on reconciling vocabularies for names and titles from different sources. We will also discuss some of the broader issues around bibliographical data and software requirements for bibliographical research. This will be a hands-on unit, with all participants being given an account on the nodegoat.net server. No local software installation will be required. Some familiarity with humanities databases and CSV spreadsheets will be an advantage.

• May 3 Manuscript Collation with VCEditor (Dot Porter  and Alberto Campagnolo)

The structure of premodern codex manuscripts is a vital part of their materiality, which in addition to being an important aspect of their long history can also help us understand their textual and artistic contents.  In our workshop we will talk about the importance of structural description - also called collation - and how the VisColl project can help manuscript scholars describe the collation of manuscripts by using VCEditor. The workshop will include videos on how collation can be determined, on the history of collation description and the VisColl project, and how existing collation formulas and manuscript descriptions can be used as a basis for building models. During the synchronous portion of the workshop, we will walk through the full process of determining collation from a manuscript, building a model in VCEditor, linking the model to digital images, and linking text and illustration information to the model.

• May 17  Using nodegoat to map the publication and ownership of early printed books and manuscripts (Toby Burrows

Introduces computer vision for bibliographical tasks including annotating, comparing, segmenting, matching and classifying digital images of printed documents. Applications include: finding illustrations, ornaments, text-blocks and other page regions; visual collation; visual search; and classifying the content of printed images using machine learning. Participants may follow the course either through online demos or by installing free and open-source software maintained by the University of Oxford’s Visual Geometry Group. No prior knowledge of computer vision, machine learning or coding is required.
Introduces computer vision for bibliographical tasks including annotating, comparing, segmenting, matching and classifying digital images of printed documents. Applications include: finding illustrations, ornaments, text-blocks and other page regions; visual collation; visual search; and classifying the content of printed images using machine learning. Participants may follow the course either through online demos or by installing free and open-source software maintained by the University of Oxford’s Visual Geometry Group. No prior knowledge of computer vision, machine learning or coding is required.

Introduces computer vision for bibliographical tasks including annotating, comparing, segmenting, matching and classifying digital images of printed documents. Applications include: finding illustrations, ornaments, text-blocks and other page regions; visual collation; visual search; and classifying the content of printed images using machine learning. Participants may follow the course either through online demos or by installing free and open-source software maintained by the University of Oxford’s Visual Geometry Group. No prior knowledge of computer vision, machine learning or coding is required.

Introduces computer vision for bibliographical tasks including annotating, comparing, segmenting, matching and classifying digital images of printed documents. Applications include: finding illustrations, ornaments, text-blocks and other page regions; visual collation; visual search; and classifying the content of printed images using machine learning. Participants may follow the course either through online demos or by installing free and open-source software maintained by the University of Oxford’s Visual Geometry Group. No prior knowledge of computer vision, machine learning or coding is required.