The Henslowe-Allyen Digitisation Project

2000 digital photographs of manuscript pages in the single most inportant theatrical archive in the age of Shakespeare, as well as 15 digital essays by world-leading scholars

Early modern Oxford bindings in twenty-first century markup

Purpose – The Bodleian Binders Book contains nearly 150 pages of seventeenth century library records, revealing information about the binders used by the library and the thousands of bindings they produced. The purpose of this paper is to explore a pilot project to survey and record bindings information contained in the Binders Book. Design/methodology/approach – A sample size of seven pages (91 works, 65 identifiable bindings) to develop a methodology for surveying and recording bindings listed in the manuscript. To create a successful product that would be useful to bindings researchers, it addressed questions of bindings terminology and the role of the library in the knowledge creation process within the context that text encoding is changing the landscape of library functions. Text encoding formats were examined, and a basic TEI (Text Encoding Initiative) transcription was produced. This facilitates tagging of names and titles and the display of transcriptions with text images. Findings – Encoding was found not only to make the manuscript content more accessible, but to allow for the construction of new knowledge: characteristic Oxford binding traits were revealed and bindings were matched to binders. Plans for added functionality were formed. Originality/value – This research presents a “big picture” analysis of Oxford bindings as a result of text encoding and the foundation for qualitative and statistical analysis. It exemplifies the benefits of interdisciplinary methods – in this case from Digital Humanities – to enhance access to and interpretation of specialist materials and the library's provenance record.

A survey of data mining techniques for social media analysis

Social network has gained remarkable attention in the last decade. Accessing social network sites such as Twitter, Facebook LinkedIn and Google+ through the internet and the web 2.0 technologies has become more affordable. People are becoming more interested in and relying on social network for information, news and opinion of other users on diverse subject matters. The heavy reliance on social network sites causes them to generate massive data characterised by three computational issues namely; size, noise and dynamism. These issues often make social network data very complex to analyse manually, resulting in the pertinent use of computational means of analysing them. Data mining provides a wide range of techniques for detecting useful knowledge from massive datasets like trends, patterns and rules [44]. Data mining techniques are used for information retrieval, statistical modelling and machine learning. These techniques employ data pre-processing, data analysis, and data interpretation processes in the course of data analysis. This survey discusses different data mining techniques used in mining diverse aspects of the social network over decades going from the historical techniques to the up-to-date models, including our novel technique named TRCM. All the techniques covered in this survey are listed in the Table.1 including the tools employed as well as names of their authors.