Like many of us here, I have an interdisciplinary background, in comparative literature and the computer sciences. Although I first really thought about data visualization in the context of the digital humanities, learning about it from Graduate Center Digital Fellows (like Micki Kaufman, who you’ll hear from in a bit), my first real work with it was in visualizing online networks that I helped to foster as a web developer for the Futures Initiative here at the Graduate Center.
The work that we do as a team is about building diverse communities, and collaborating by difference. We think that having multiple voices and diverse perspectives ensure that what we build is equitable, and also better — that it opens up new possibilities and ways of thinking and creating knowledge. We use a multi-site network built on the open-source Commons In A Box platform (developed here at the GC) as an online space on the open web to further expand our approaches to student-centered pedagogy. Using the programming language R and the D3 library, I visualized network data that I pulled from our SQL databases. This helped to show how we engaged across campuses in the City University of New York system, how graduate students worked as hinges that distributed multi-directional learning, where professors like the brilliant Cathy Davidson (here with us) could — yes, certainly teach — but also learn from the vast network of undergraduate students throughout CUNY, with all of their varied and complex expertise. The visualized data showed how students were wandering in and out of the networked sites, moving from course blogs about Chemistry and into Theatre, truly taking charge of their own learning in ways that are completely unheard of in so-called “learning management systems.”
From here, I turned to that dragon that is my dissertation. I am trained in medieval and early modern Italian literature, and a lot of my research involves looking at manuscripts and early printed books, and considering these as information systems that shape the reception of their authors in the eyes of their readers. The figure I study is a 14th-century Italian mystic writer, Catherine of Siena, who was also a community builder. As a woman of this period, she worked within the confines of Italian religious culture, but also pushed against it. An innovator of language, she was one of the first writers in the Italian vernacular during a time when Latin — the lingua franca of the rich, the educated, the powerful — was what you needed to read and write to be considered literate. Catherine, who worked with the poor, the sick, the imprisoned, insisted on writing in Italian even to the pope. In an era when most women who challenged the status quo were considered to be witches, she would eventually be made a saint. Why? Catherine built a network around her. We have nearly 400 extant letters of hers, that were sent to two popes, to kings, queens, prisoners, prostitutes, priests and nuns. That same network analysis and visualization that can bring to life an online community through querying databases and seeing how users interact with websites, can also be brought to bear on the nodes of letters and of people from Italian communities from the 14th century. Catherine connected disparate and diverse people together: she was a force that drove community engagement through the technology of writing.
In the media, lately I hear a lot that “data tell a story,” or that “data is beautiful.” We are using words associated with the humanities and the arts to talk about something that is typically connected with statistics, technology, and the sciences. But data visualization is inherently interdisciplinary. Data are multifold and voluminous and infinite, but to tell a story, to make it beautiful, we need to constrain it and to interpret it. At its best, data visualization is a synergy between the fields, a pathway through. It is a way that can make meaningful information accessible through a visual language that non-experts can begin to understand. We live in a time of information overload, and data visualization presents us with a way to compress that data into something that can potentially be easily digested, but we must do this in a rigorous and critical way. Allowing disciplines to overlap and combine, to be in conversation with each other, we can start to synthesize data, to visualize data, to open it up to new possibilities, new voices, and new ways of creating knowledge.