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Popular media often portray “big data” as the exclusive province of information scientists, but data collection in the humanities can swiftly exceed the capacity of the human brain to analyze. Increasingly, humanists turn to digital tools to conduct quantitative research on literary texts, websites, tweets, images and sound recordings. How does one create or reuse a humanities data set? What tools are used to store, manipulate and process that data? How does one begin to analyze humanities research data and share findings in the form of visualizations? This course will explore some methodologies of quantitative analysis in the humanities using free and open source digital tools to yield insights into data that would otherwise be difficult to obtain. Through lectures, discussion, labs, and a digital final project, students will familiarize themselves with the tools of digital humanities scholarship and learn to form arguments on the basis of a few simple computational techniques.

Key Questions:

• What happens to history as it gets digitized?
• That is, what does history look like, what happens to our materials, and the stories we tell or the questions we ask, as we abstract further and further away from ‘In Real Life’?
• What does ‘digital history’ really mean?

How will we explore these questions? You will choose a real world object/building/site here in Ottawa that you can access and:

• progressively abstract it away from the real world with a series of technologies from photogrammetry to augmented reality 
• all the while attending lectures to learn the context of what we’re doing and why,
• annotating the readings collaboratively on the open web
• as you keep open notebooks reflecting on this progression
• so that you can build a digital experience of your understanding of your results
• for a public reveal to be held on campus at the end of term.

The sources for the history of our times are fragile. Joe Ricketts, the billionaire owner of DNAInfo and Gothamist, shut the local news publications down rather than tolerate a unionized workforce. For 11 minutes, Trump was kicked off Twitter. Ian Bogost sees in both episodes a symptom of a deeper problem: both are pulling on the same brittle levers that have made the contemporary social, economic, and political environment so lawless. As public historians, what are we to do about this? There are a lot of issues highlighted here, but let’s start at the most basic. It takes nothing to delete the record. The fragility of materials online is both a danger, and an opportunity, for us. Some scholars have “gone rogue” in trying to deal with this problem. That is to say, they neither sought nor obtained permission. They just scoped out a process, and did it. I initially called this class ‘guerrilla public digital history’ partly tongue in cheek. I imagined us doing some augmented reality type projects in public spaces. Reprogramming those public spaces. Using digital techs to surface hidden histories, and insert them into spaces where they didn’t ‘belong’. Counterprogramming. That was the ‘guerilla’ bit. I still want to do all that. But I think we’re going to have to do a bit more. Digital Public Historians have a role to play I suspect in countering the information power asymmetry. These ways are impromptu, without authorization. Rogue. Improvised. What is a ‘guerilla digital public history’? I don’t know. But we’re going to find out.

In this course you will learn to apply computational methods to create historical arguments. You will learn to work with historical data, including finding, gathering, manipulating, analyzing, visualizing, and arguing from data, with special attention to geospatial, textual, and network data. These methods will be taught primarily through scripting in the R programming language. While historical methods can be applied to many topics and time periods, they cannot be understood separate from how the discipline forms meaningful questions and interpretations, nor divorced from the particularities of the sources and histories of some specific topic. You will therefore work through a series of example problems using datasets from the history of the nineteenth-century U.S. religion, and you will apply these methods to a dataset in your own field of research.

How do you measure a book? Can machines read? Do we read prose texts now the way people read them in 1919 or in 1819? We are swimming in textual data that could change our understanding of the written word - if you have the right tools and know how to access and work with it. What could you learn to do with all these different forms of textuality, with all this data? Can you find connections between your current interests in literature and the perspectives that technology opens up, or the goals of your career? This course is meant to give you practice with a variety of methods and real-world scenarios to help you participate in digital projects, using both prepared materials and your own. The course fulfills an elective in the Graduate Certificate in Digital Humanities (DH). We want to introduce you to literary computational methods as part of digital humanities, no matter what previous familiarity you might have. You will find any of your previous studies of literature highly relevant and useful for participating in this course. No one needs to be or to become a programmer. You will begin with your own interests and skills and help us encounter, together, specific methods of digital reading or ways to analyze and visualize the data of texts, including topic modeling and XML markup. There is room in our plans for us to consider how our methods could be applied for selected writers or literary works or genres that you want to write about or work on, or that you have encountered in other courses or personal reading. A focus on literary DH in this course doesn’t cover the entire spectrum of possibilities for digital research. We hope you will be interested to inquire further, and follow your paths with different tools and methods beyond this course.

Tuesdays and Thursdays from 9:30am - 10:45am in Ruffner 175.

Some undergraduate course offerings can count toward your elective requirement, but that depends on the department and professor. If you'd like to take this course, contact the professor to see if they would allow you to take it and what they would require of your work in the course to ensure it counts at the graduate level.

Computers are universal media. Our intimacy with computers shapes how we think about our communities, histories, cultures, society, and ourselves. Learn to program these "thinking machines" as an act of philosophical inquiry and personal expression, challenging your beliefs about creativity, intelligence, randomness, and communication. Students with no previous programming experience are especially welcome!