Visualizing "Big Data" in the Arts and Humanities
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Publisher:Ohio State University. Humanities Institute
Series/Report no.:Ohio State University. Humanities Institute. Digital Arts and Humanities Working Group. Lectures
Panelists David Staley (Associate Professor, The Ohio State University Department of History), Jessie Labov (Assistant Professor, The Ohio State University Department of Slavic and East European Languages and Cultures), and H. Lewis Ulman (Associate Professor, The Ohio State University Department of English) explored the place of data visualization as a form of humanities scholarship, with visualization as the hermeneutic act that allows humanists to read "big data." The panel described the concept of a Humanities Visualization Studio at The Ohio State University to conduct such humanistic readings of big data. Questions centered around defining "big data" in the context of the humanities, how humanists read big data, how our interests and goals with reading big data differ from that of scientists, and how visualization and visual hermeneutics is critical to the effort to read that data. As we read our texts, humanists seek not better models or predictive certainty, but rather patterns of interpretive insight. Reading big data is an occasion for humanists to assert our approach to knowledge: to champion the value of meaning, interpretation and insight in contrast to the logic of scientific prediction and control.
Visualizing "Big Data" in the Arts and Humanities recorded in four parts. [Part One:] Reading Big Data as a Humanist: The Humanities Visualization Studio (David J. Staley). This presentation explored the concept of a Humanities Visualization Studio for The Ohio State University and its potential role in leveraging "Big Data" to bring new insights and knowledge building in the humanities. Staley offered analysis of the unique approaches humanists might bring to working with large data sets and developing patterns of interpretive insight. He presented the concept of distant reading or macro-level reading as a methodology that is growing in relevance, initiating radical transformation of texts and enabled by emergent technologies. Staley further suggested the important role a Humanities Visualization Studio would have in bringing people together to collaborate, pool resources and move forward in concert. [Part Two:] Stanford’s Digital Humanities Labs (Jessie Labov). Labov shared her experience with Stanford’s Humanities Lab (http://humanitieslab.stanford.edu/admin/directory.html), Beyond Search (https://beyondsearch.stanford.edu/) and Stanford Literary Lab (http://litlab.stanford.edu/) as examples of how digital humanities labs can address issues involved in working with humanities big data. These initiatives are explored as examples of how such efforts to bring a humanities community together can work (or not). Labov suggested three lessons to be learned from Stanford’s experience: there is a risk in developing digital humanities spaces and equipment stores which are not solidly based in viable research projects, that more successful efforts are built around researchers and their existing work and interests and that an institution needs to let these environments evolve as communities and community centers. [Part Three:] Thinking Metaphorically about Data (H. Lewis Ulman). Ulman offered examples of different humanities visualization projects as way of examining the concept of "big data" and how we might set an agenda for a visualization studio. Interrogating the concepts of "close," "distant," "wide," and "deep" reading, he suggests that it is not the size of the data set but the broadness of the opportunities for investigation that should determine the kind of projects to be addressed by the humanities visualization studio. Ulman demonstrated how applying visualization techniques that provide a "wide" view of the data found in the Digital Archive of Literacy Narratives can offer new insights and conclusions. Similarly, investigating single words in Louisa A. Doane's Journal of Two Ocean Voyages (1850-52) against the "wide" canvas of the Google Books database increased students understanding of historical context and meaning. He suggested that archival finding aids can be made more understandable and facilitate research more effectively by employing visualization tools. Finally, by exploring the process of transforming "deep" text-encoding markup into reading or "surface" versions of texts, Ulman showed how electronic textual editions in themselves are visualizations of complex or "big" data. In these ways, data sets that seem small in size can have larger meaning through data visualization. [Part Four:] Visualization Q&A session. A portion of the Q&A session following the presentations of the panel for Visualizing "Big Data" in the Arts and Humanities records audience insights into the meaning behind the term "big data" and how we might develop initiatives at The Ohio State University.
Reading Big Data as a Humanist: The Humanities Visualization Studio (David J. Staley) -- Stanford’s Digital Humanities Labs (Jessie Labov) -- Thinking Metaphorically about Data (H. Lewis Ulman) -- Visualization Q&A session.
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