From the British Library Digital Scholarship Blog:
This post by the British Library’s Digital Curator for Western Heritage Collections, Dr Mia Ridge, reports on an experimental format designed to provide more flexible and timely training on fast-moving topics like text and data mining.
This post covers two topics – firstly, an update to the established format of sessions on our Digital Scholarship Training Programme (DSTP) to introduce ‘strands’ of related modules that cumulatively make up a ‘course’, and secondly, an overview of subjects we’ve covered related to content mining for digital scholarship with cultural heritage collections.
The Digital Research team have been running the DSTP for some years now. It’s been very successful but we know that it’s hard for people to get away for a whole day, so we wanted to break courses that might previously have taken 5 or 6 hours of a day into smaller modules. Shorter sessions (talks or hands-on workshops) only an hour or at most two long seemed to fit more flexibly into busy diaries. We can also reach more people with talks than with hands-on workshops, which are limited by the number of training laptops and the need to offer more individual
A ‘strand’ is a new, flexible format for learning and maintaining skills, with training delivered through shorter modules that combine to build attendees’ knowledge of a particular topic over time. We can repeat individual modules – for example, a shorter ‘Introduction to’ session might run more often, or target people with some existing knowledge for more advanced sessions.
Content mining for digital scholarship with cultural heritage collections
From the course blurb: ‘Content mining (sometimes ‘text and data mining’) is a form of computational processing that uses automated analytical techniques to analyse text, images, audio-visual material, metadata and other forms of data for patterns, trends and other useful information. Content mining methods have been applied to digitised and digital historic, cultural and scientific collections to help scholars answer new research questions at scale, analysing hundreds or hundreds of thousands of items. In addition to supporting new forms of digital scholarship that apply content mining methods, methods like Named Entity Recognition or Topic Modelling can make collection items more discoverable.
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