Digital Humanities at MIT—Seeing Data in New Light

Image of world map with "push-pin" icons in various locations, connected together by colored lines.

The interface for the app NewsConnect, that aims to visually represent national connections presented in world news articles. (Image from student project in CMS.633 Digital Humanities, courtesy of Meghana Bhat and Karleigh Moore. Used with permission.)

By Joe Pickett, OCW Publication Director

The application of computers to the Humanities has spawned many sophisticated tools that have advanced understanding in a variety of disciplines and created striking new approaches to visualization using digital media. From analyzing vast libraries of texts to assembling huge collections of images, digital technologies have unearthed discoveries and revealed patterns that were unimaginable in the pre-digital era.

How does MIT factor in all this? The School of Humanities, Arts and Social Sciences has long championed digital initiatives, and its website showcases a gallery of digital humanities that are ongoing under its aegis.

Among these is CMS.633 Digital Humanities. OCW has just published a website showcasing the Spring 2015 iteration of this course, taught by Dr. Kurt Fendt and Andy Kelleher Stuhl, of MIT’S Hyperstudio in the Comparative Media Studies / Writing department. The OCW course site includes readings and assignments, links to a variety of tools and data banks, and design documents and presentations of student projects.

How does the course work?

The instructors share insights about the course’s structure on their This Course at MIT page:

“In the first half of the semester, students read theoretical texts and we discuss core concepts in digital humanities, such as data representation, digital archives, information visualization, and user interaction . . . During the second half of the semester, students apply the concepts they’ve learned in real-world contexts through the development of digital humanities projects that meet scholarly, educational, or public needs. In developing their projects, we ask students to follow a process based in design thinking.”

Fendt and Stuhl require students to work on their projects in groups “because [group work] mirrors how digital humanists typically work in the field: collaboratively.”

Unique connections

Perhaps not surprisingly, most students taking CMS.633 are science or engineering majors, not humanities majors, but this is in many ways a blessing, says Fendt, as it opens a two-way street between disciplines:

“MIT students tend to focus on how they can use tools from the digital humanities to visualize data in their own fields in innovative ways. Additionally, they often contribute approaches and tools from their different fields that influence how we think about data in the humanities. This kind of knowledge sharing and adaptation is very exciting and is part of what makes digital humanities at MIT unique.”

Fine Tuning                               

Fendt cites the example of two astrophysics students who used a technique normally used to spot pulsars and applied it to data from the Comédie-Française Registers Project, which makes available over a century of daily records of this theater troupe.

“They overlaid two periods of data from the Comédie-Française Registers Project to see spikes that would be unnoticeable if you just examined the data in traditional ways. We thought it was a brilliant approach. It really captures what Digital Humanities is all about: taking approaches from other fields, and tuning them to make them appropriate for humanities data.”

Interpreted Data

And what about the other way around? In what ways do Digital Humanities affect the thinking of science and engineering majors?

“Typically, when students work with data in technical fields they often take the data as given items that need to be processed. We’re trying to help students think more critically about those data. When they encounter a data set, we want them to ask questions such as: What has been eliminated from the original data set? Is this data set therefore already an interpretation of the original data?  . . .  How do visual representations impact my interpretation of the data? In short, we help students realize that data are not neutral or objective. They are always filtered through an interpretive lens.”

The stars, as we imagine them, must be beaming.

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