Advanced Collaborative Support (ACS) is a scholarly service at the HTRC offering collaboration between external scholars and HTRC staff to solve challenging problems related to HTRC tools and services. By working together with scholars, we facilitate computational access to HathiTrust Research Center digital tools (HTRC) as well as the HathiTrust Digital Library (HTDL) based on individual scholarly need. ACS will drive innovation at the scholar's digital workbench for enhancing and developing new techniques for use within the HTRC platform. For questions, please send an email to firstname.lastname@example.org.
Fighting Fever in the Caribbean: Medicine and Empire, 1650-1902
Mariola Espinosa, University of Iowa
This project seeks to explore the history of yellow fever in the Caribbean by comparing how the disease was described by residents of the Caribbean to the European perspective, including through sentiment analysis of text referencing yellow fever. Her work will be visualized spatially in a map generated with support from the University of Iowa’s Digital Scholarship and Publishing Studio. She will build a corpus of text from the HathiTrust Digital Library related to yellow fever and filth in the Caribbean to track the use of the terms “filth” and “filthiness” from 1650 to 1902.
Project report: Fighting Fever in the Caribbean
Inside the Creativity Boom
Samuel Franklin, Brown University
This project will map the increasing use and shifting meanings of the words “creative” and “creativity,” with a particular focus on the twentieth century. A custom “creativity corpus” will be assembled and processed to identify linguistic patterns via a number of text analysis and natural language processing techniques. Brown’s project will make use of the functionality developed for HathiTrust + Bookworm.
The Chicago School: Wikification as the First Step in Text Mining in Architectural History
Dan Baciu, Illinois Institute of Technology
This project will look at the Chicago School of architecture and examine its history of reception over the last 75 years, as well as identify patterns in its international spread and influence. Baciu will use named entity recognition in his analysis, notably deploying the Wikifier tool on a large sample corpus of HathiTrust data for the first time.
Signal and Noise and Pride and Prejudice: Toward an Information History of Romantic Fiction
Dallas Liddle, Augsburg College
This project will test two hypotheses about information theory and information density as they relate to a digital humanities approach to studying Romantic-era British fiction. The concept of "information" used in mathematical information theory may help digital humanists evaluate the information density of textual forms. This project tests a theory that the popular and critical success of the novel in British print culture after 1815 may be related to increased information density and sophistication of information encoding in those years, especially via innovations introduced by Jane Austen and Walter Scott.
The Trace of Theory
Geoffrey Rockwell, Laura Mandell, Stefan Sinclair, Matthew Wilkens, Susan Brown, University of Alberta, Texas A&M University, University of Notre Dame
Rockwell, Mandell, Sinclair, Wilkens, and Brown aim to subset theoretical subsets from the HT public corpus and apply large-scale topic modeling on the subsets. The researchers will develop tools and computational methods for tracking the concept of "theory”.
Project report: The Trace of Theory project
Detecting Literary Plagiarisms: The Case of Oliver Goldsmith
Douglas Duhaime, University of Notre Dame
Duhaime will work on developing tools for detecting plagiarisms. He will focus on the case of Oliver Goldsmith, to detect the literary thefts of Goldsmith by using machine learning techniques.
Project report: Coming soon!
Taxonomizing the Texts: Towards Cultural-Scale Models of Full Text
Colin Allen, Jaimie Murdock, Indiana University Bloomington
Allen and Murdock will carry out a cultural-scale investigation and topic modeling on HT public-domain full text through random sampling to select collections according to the Library of Congress Subject Headings (LCSH).
Project report: Towards Cultural-Scale Models of Full-Text project