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Commonplace Cultures http://commonplacecultures.org Digging into 18th-century literary culture Sun, 19 Jun 2016 10:18:53 +0000 en-US hourly 1 https://wordpress.org/?v=4.5.9 Search Commonplace Cultures Database http://commonplacecultures.org/?p=189 http://commonplacecultures.org/?p=189#respond Tue, 07 Jun 2016 10:07:02 +0000 The Commonplace Cultures project database is now live at commonplacecultures.uchicago.edu. This database represents preliminary results at identifying “commonplaces” and other shared passages in the Gale-Cengage Eighteenth Century Collections Online (ECCO) corpus. Documentation and several use-case examples to follow soon.

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Digging into Data Round Three http://commonplacecultures.org/?p=55 http://commonplacecultures.org/?p=55#respond Sat, 18 Jan 2014 05:39:44 +0000 http://commonplacecultures.org/?p=55 [Read more...]]]> We are very pleased to have been selected as one of the 14 round three Digging into Data Challenge projects. Our project, “Commonplace Cultures:Mining Shared Passages in the 18th Century using Sequence Alignment and Visual Analytics”, is a partnership between the ARTFL Project and Computation Institute (CI) at the University of Chicago and the University of Oxford’s e-Research Centre (OeRC) and Voltaire Foundation (VF). Bringing together world-class centres for Enlightenment studies (ARTFL, VF) and multi-disciplinary computing applications (CI, OeRC), the team consists of 18th-century scholars: Robert Morrissey (PI, Chicago) and Nicholas Cronk (Co-I, Oxford); computer scientists: Min Chen (PI, Oxford) and Ian Foster (Co-I, Chicago); and digital humanists: Mark Olsen (Chicago), and Glenn Roe (ANU), among other participants. The interdisciplinary makeup of the team will allow for the innovative combination of data-intensive visual analytics, large-scale text mining, and deep scholarly engagement, emphasizing the close coupling of visualization and data analysis in a feedback loop facilitated by intuitive interaction with humanities scholars. This project represents a natural outgrowth from previous research into sequence alignment and other text mining approaches in large text collections, led by Morrissey in Chicago, and, more recently, a second-round Digging into Data project, led by Chen in Oxford, that applies multivariate data visualization techniques to poetry.

We will update this site with more news on the project’s development in the coming months.

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