DIG 210: Data Culture

A new course for the Digital Studies program at Davidson College. Influences for the syllabus abound: Lisa Gitelman, Lauren Klein, Ben Schmidt, Matt Wilkens, and many other folks in the digital humanities.

Course Description

“Data” is often considered to be the domain of scientists and statisticians. But with the proliferation of databases across nearly all aspects of modern life, data has become an everyday concern. Bank accounts, FaceTime records, Snapchat posts, Xbox leaderboards, CatCard purchases, your DNA—at the heart of all them is data. To live today is to breathe and exhale data, wherever you go, online and off. And at the same time data has become a function of daily life, it has also become the subject of—and vehicle for—literary and artistic critiques.

This course explores the role of data and databases in contemporary culture, with an eye toward understanding how data shapes the way we perceive—and misperceive—the world. After historicizing the origins of modern databases in 19th century industrialization and census efforts, we will survey our present-day data landscape, considering data mining, data visualization, and database art. We will encounter nearly evangelical enthusiasm for “Big Data” but also rigorous criticisms of what we might call naïve empiricism. The ethical considerations of data collection and analysis will be at the forefront of our conversation, as will be issues surrounding privacy and surveillance.

Learning Goals

Upon completion of DIG 210, students will be able to:

  • Develop a timeline of the rise of databases
  • Compare the symbolic power of databases in contemporary life with their functional role
  • Analyze a data set with one the common digital tools for data analysis and visualization
  • Appraise arguments for and against “Big Data” as an interpretive mechanism
  • Experiment with the way identities are constructed through data
  • Evaluate competing claims about the ethical collection and use of social data

Required Reading

  • Various journal articles, book chapters, and online material, available through the library, Moodle, and the class website

Required Work

The graded work for DIG 210 will take several forms, detailed below: (1) class participation; (2) weekly blogging; (3) a data critique; (4) a “distant reading” using data analysis tools; and (5) a data-based project.

(1)  This class places a high premium on participation. It is essential that everyone has carefully considered the day’s material, attends class, and participates. I also expect students to bring the day’s readings to class, well-marked up with notes and annotations. Daily attendance is crucial for full participation. More than two absences will lower your class participation grade by at least one letter grade. More than five absences will result in a zero for your class participation grade. Participation is worth 20% of the final grade.

(2)  Each student will contribute to the weekly class blog. There will be three roles on the blog, and each week a quarter of the class will rotate through these roles (one group has the week off in terms of blogging). Students in one group (“Readers”) will post an approximately 250-word critical response to the week’s reading by Monday night at 10pm. Students in another group (“Responders”) will either respond to these posts or to our classroom discussion by Wednesday night at 10pm. A third group (“Observers”) will obsessively collect data about our class, ranging from the trivial (e.g. the number of people wearing red one day) to the substantive (e.g. the type of questions asked during a class discussion). The observers will then post this data onto our class blog at the end of every week. Blogging is worth 20% of the final grade.

(3)  The data critique is a close analysis of a large dataset, in which you investigate its sources, how it was collected, issues around privacy or ethics that come into play, the uses to which the data is put, and the ways it represents “truth” or “facts.” The data may be impersonal, such as census statistics, or it may be data that is closer to home, such as the Davidson residential hall energy usage, or even data about your Facebook network. The critique will be accompanied by a reflective statement. The data critique is worth 20% of your final grade.

(4)  The distant reading is playful attempt to construct meaning from a set of data without engaging the data on a local level. For example, you might “read” an unfamiliar Victorian novel by running it through various text analysis tools. Or you might create a network visualization of a hashtag. The distant reading will be accompanied by a reflective analysis of your distant reading and its methodology. The distant reading is worth 20% of your final grade.

(5)  The data-based project is not a database; rather, it is an opportunity to reconfigure a set of data into an argument about that data. In other words, your task is to transform an existing data set—through the interface, its visualization, and so on—in a way that highlights some underlying argument you want to make about the data. The project can be speculative, in the form of a mock-up or proof-of-concept. The data project will be accompanied by a 3-4 page analysis of your project. It is due at the end of the exam period, but you may hand it in earlier. The data-based project is worth 20% of your final grade.

Grading

The final grade will be calculated in the following manner:

  • Participation: 20%
  • Blogging: 20%
  • Data Critique: 20%
  • Distant Reading: 20%
  • Data-based Project: 20%

I will evaluate the blog posts according to the following 0-4 point scale:

RATING CHARACTERISTICS
4 The blog post is focused and coherently integrates examples with explanations or analysis. The post demonstrates awareness of its own limitations or implications, and it considers multiple perspectives when appropriate. The post includes at least one rhetorically useful image or media clip that illustrates—rather than trivializes—its point.
3 The blog post is reasonably focused, and explanations or analysis are mostly based on examples or other evidence. Fewer connections are made between ideas, and though new insights are offered, they are not fully developed. The post reflects moderate engagement with the topic.
2 The blog post is mostly description or summary, without consideration of alternative perspectives, and few connections are made between ideas. The entry reflects passing engagement with the topic.
1 The blog post is unfocused, or simply rehashes previous comments, and displays no evidence of student engagement with the topic.
0 No Credit. The blog post is missing, late, or consists of one or two disconnected sentences.

Every other assignment will be given a letter grade that has a percentage equivalent:

A = 95% /A- = 90%
B+ = 88% / B = 85% / B- = 80%
C+ = 78% / C = 75% / C- = 70%
D+ = 68% / D = 65% /F = below 60%

Inclusive learning

I am committed to the principle of inclusive learning. This means that our classroom, our virtual spaces, our practices, and our interactions be as inclusive as possible. Mutual respect, civility, and the ability to listen and observe others carefully are crucial to inclusive learning.

Any student with particular needs should contact Nance Longworth (x2129), the Academic Access and Disability Resources Coordinator, at the start of the semester. The Dean of Students’ office will forward any necessary information to me. Then you and I can work out the details of any accommodations needed for this course.

Academic Integrity

Students at Davidson College abide by an Honor Code. The principle of academic integrity is taken very seriously and violations are treated gravely. What does academic integrity mean in this course? Essentially this: when you are responsible for a task, you will perform that task. When you rely on someone else’s work in an aspect of the performance of that task, you will give full credit in the proper, accepted form.

Another aspect of academic integrity is the free play of ideas. Vigorous discussion and debate are encouraged in this course, with the firm expectation that all aspects of the class will be conducted with civility and respect for differing ideas, perspectives, and traditions. When in doubt (of any kind) please ask for guidance and clarification.

Classroom Courtesy

While this course embraces the digital world it also recognizes that digital tools and environments complicate personal interactions. Studies have shown that students who use laptops in class often receive lower grades than those who don’t. Even more worrisome are studies that show laptop users distract students around them. I permit laptops and tablets in class, but only when used for classroom activities, such as note-taking or class readings. Occasionally I may ask students to turn off all digital devices.

Text messaging or other cell phone use is unacceptable. Any student whose phone rings during class or who texts in class will be responsible for kicking off the next class day’s discussion.

Late arrivals or early departures from class are disruptive and should be avoided.


DIG 210 Calendar

History of data

Week 1 (January 13 and 15)
  • Key Concepts: Data as a Thing, History of Databases
  • John Durham Peters, “Information: Notes Toward a Critical History,” Journal of Communication Inquiry 12 (1988): 9-23.
  • Alex Wright, “Networks and Hierarchies” from Glut: Mastering Information through the Ages (2007)
  • Stephen Fortune, “A Brief History of Databases” (2014)
  • Jorge Luis Borges, “The Library of Babel” and “The Aleph”
Week 2 (January 20 and 22)
  • Key Concepts: Data as Discourse, Data versus Narrative,
  • Daniel Rosenberg, “Data before the Fact” from “Raw Data” is an Oxymoron, ed. Lisa Gitelman (MIT Press, 2013)
  • Mark Poster, “Databases as Discourse” from The Second Media Age (1995)
  • Lev Manovich, “The Database” from The Language of New Media (2001)
Week 3 (January 27 and 29)
  • Key Concepts: Databases and Archives
  • Alan Liu, “Transcendental Data: Toward a Cultural History and Aesthetics of the New Encoded Discourse.” Critical Inquiry 31.1 (2004): 49–84.
  • Marlene Manoff, “Archive and Database as Metaphor: Theorizing the Historical Record.” portal: Libraries and the Academy 10.4 (2010): 385–398.
  • Jerome McGann, “Database, Interface, and Archival Fever.” PMLA 122.5 (2007): 1588–1592.
  • Lauren Klein, “The Image of Absence: Archival Silence, Data Visualization, and James Hemings.” American Literature 85, no. 4 (January 1, 2013): 661–88.
  • Micki Kaufman, “‘Everything on Paper Will be Used Against Me’: Quantifying Kissinger” (2014)

Big Data

Week 4 (February 3 and 5)
Week 5 (February 10 and 12)
  • Key Concepts: Text Mining, Distant Reading
  • Franco Moretti, “Introduction to ‘Learning to Read Data.’” Victorian Studies 54.1 (2011): 78–78.
  • Franco Moretti, selections from Graphs, Maps, Trees (2005)
  • Jean-Baptiste Michel et al. “Quantitative Analysis of Culture Using Millions of Digitized Books from Science (2010)
  • Matthew Jockers, selections from Macroanalysis: Digital Methods and Literary History (2013)

Data Visualization

Week 6 (February 17 and 19)
  • Key Concepts: Visualization, Quantification
  • Lev Manovich, “What is Visualization?” from Visual Studies 26.1 (2011)
  • Edward Tufte, selections from The Visual Display of Quantitative Information (2001)
  • Nadav Hochman, and Lev Manovich. “Zooming into an Instagram City: Reading the Local through Social Media.” First Monday 18.7 (2013) <http://firstmonday.org/ojs/index.php/fm/article/view/4711>.
Week 7 (February 24 and 26 )
  • Key Concepts: Maps and Timelines
  • Daniel Rosenberg and Anthony Grafton, “Time in Print,” “A New Chart of History,” and “Frontier Lines,” from Cartographies of Time: A History of the Timeline (2010)
  • Lev Manovich, “Data Stream, Database, Timeline” (2012)
  • Edward Tufte, “Visual Confections: Juxtapositions from the Ocean of the Streams of Story,” from Visual Explanations: Images and Quantities, Evidence and Narrative
  • Mark Monmonier, from How to Lie with Maps (1996)
  • Johanna Drucker, “Humanities Approaches to Graphical Display” from Digital Humanities Quarterly (2011)
Week 8 (March 3 and 5)
  • Spring Break

Small Data

Week 9 (March 10 and 12)
Week 10 (March 17 and 19)

Database Art

Week 11 (March 24 and 26)
  • Sharon Daniel, “The Database: An Aesthetic of Dignity” from Database Aesthetics: Art in the Age of Information Overflow (2007)
  • Joyce Walker, “Narratives in the Database: Memorializing September 11th Online.” Computers and Composition 24.2 (2007): 121–153.
  • Gephi and GraphViz workshops
Week 12 (March 31 and April 2)
Week 13 (April 7 and April 9)
  • No Class on April 7 (Easter Break)

Ethics and Privacy

Week 14 (April 14 and 16)
  • Michel Foucault, selections from Discipline and Punish
  • Jason Nolan, Steve Mann, and Barry Wellman, “Sousveillance: Wearable and Digital Tools in Surveilled Environments” from Small Tech, eds. Byron Hawk, David M. Reider, and Ollie Oviedo
  • “Data Privacy, Machine Learning, and the Destruction of Mysterious Humanity.” John Foreman, Data Scientist. N. p., n.d. Web. 23 Feb. 2014 . <http://www.john-foreman.com/1/post/2014/02/data-privacy-machine-learning-and-the-destruction-of-mysterious-humanity.html>.
  • Alexander Galloway and Eugene Thacker, “Protocol, Control, and Networks.” Grey Room 17 (2004): 6–29.
  • Rita Raley, “Dataveillance and Countervailance” from “Raw Data” Is an Oxymoron, ed. Lisa Gitelman (2013)
Week 15 (April 21 and 23)
Week 16 (April 28 and 30)
  • Workshop on final Data-Based Project
Final Exam Period
  • * Data-Based Project due by May 13 *

Header Image: “Why We Feel What We Feel” from We Feel Fine, by Jonathan Harris and Sep Kamvar

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