“If [medieval] culture is regarded as a response to the environment then the elements in that environment to which it responded most vigorously were manuscripts.”
– C. S. Lewis, The Discarded Image: An Introduction to Medieval and Renaissance Literature
The Historical Medical Library, as part of the Philadelphia Area Consortium of Special Collections Libraries (PACSCL), is participating in a CLIR grant to digitize Western medieval and early modern manuscripts held by libraries in the greater Philadelphia area. The Library is lending thirteen medical manuscripts dating from c. 1220 to 1600 to this project, called Bibliotheca Philadelphiensis (BiblioPhilly). Our manuscripts will be digitized at the University of Pennsylvania’s Schoenberg Center for Electronic Text and Images (SCETI) and the digital images hosted through the University of Pennyslvania’s OPenn manuscript portal and dark-archived at Lehigh University.
The Historical Medical Library, as part of its role with the Medical Heritage Library (MHL), is working on a consortium wide digitization effort, in conjunction with the Internet Archive, to provide scholarly access to the entirety of the State Medical Society Journals published in the 20th century. For an introduction to this project, you can read my previous blog post.
In this post, I would like to explore what I began to discuss at the end of my last post: the application of computer aided text analysis techniques, also referred to as “text mining.” In this second-in-a-series of posts about the MHL project and the possibilities for digital scholarship, I will offer an introduction to some of the core concepts of text mining, as well as some easy-to-use, browser-based tools for getting started without the need for a high level of expertise, or specialized software. There will be a link to some more in-depth resources and processes at the end of this article for people interested in exploring some of these concepts and processes more fully.