I’ve used Indiana University’s Cushman Collection of photographs before, in my Palladio tutorial. Google Fusion tables, though, is a slightly simpler way for people to get started with data visualization. So here’s a quick tutorial that uses the same data to create a map and some simple charts.
You can also download this tutorial as a PDF or a Word document (in case you’d like to modify it).
As a teacher, I’ve always operated on the assumption that students are primarily interested in each other. Here’s a fun activity that takes advantage of that interest to teach students a little about data visualization. It’s an extremely unscientific Cosmo-style quiz, designed to show students which interests they have in common with each other. It’s just an introductory lesson, but it gives you a fun dataset to play with. You’ll probably want to split this among a few class sessions, since students will need at least one full class to just get familiar with Gephi.
Of course, it’s also a good chance to talk about how authoritative graphs like these can look, and whether the data these contain actually means much at all. (Probably not!)
Make a questionnaire for your students
I’d do this about a week before you do the dataviz lesson. I used Google Forms for this. Just to make things more fun, I called it the Mysterious DH Questionnaire. I asked five questions, each of which had five options. The possible answers were literally the first options that occurred to me.
Of course, you can choose whatever you want; just be sure you have a constrained list of choices (no write-ins).
Make your spreadsheet into a two-mode edge list
Now that you have your data, you want it in three different formats: 1) raw; 2) an edge list for a two-mode network graph; and 3) an edge list for a one-mode network graph. To get your two-mode list, use Open Refine to transpose columns across rows. The idea is to go from the layout shown in the above screenshot to …
… this one. It’s the same data, just rearranged into two columns.
Make your spreadsheet into a one-mode edge list
Then, if you want (you don’t have to, but it can help students see the difference between one-mode and two-node network graphs), you can project your two-mode edge list into a one-mode edge list, using Gephi and this tutorial from Shawn Graham.
Make an alluvial diagram
You can do this with the class. Use RAW to make alluvial diagrams from the raw dataset, experimenting with different categories. It’s fun to see the various relationships between, say, book and movie preferences.
Make network graphs
When the class is ready, move on to using the datasets to show which students have the most in common. Here’s a tutorial I prepared for students to use with this dataset (names have been blurred out). (And here’s a Word version of the Gephi tutorial, in case you’d like to alter it.)
Start with the two-mode network diagram, and when the class is ready, move on to the one-mode. Students really enjoyed seeing who had the most in common, examining the communities Gephi was able to detect, and comparing those communities to their own groups of friends.
I’m not organizing this event (Brittany Paris, of UCLA’s Information Studies department is), but wanted to give it my full support: A hackathon on police brutality data for L.A. County on Saturday, February 14, from 12 to 4 in the UCLA Decafe (1302 Perloff Hall). All are welcome. Register here.
I wish I had more time to write this, but I’ve been reading Saidiya Hartman’s Scenes of Subjection this week and have found that it’s brought some clarity to my thinking about the recent news and coverage of the Mike Brown and Eric Garner cases. In particular, it’s informed my thinking about the photographs circulating around these two tragedies: why they seem to compel some people but not others, and the limits of the ability of the photograph (and the video, in the Garner case) to convey deeply entrenched injustice.
So I thought I’d share these extended quotations, in case they’re helpful to anyone else.
Properly speaking, empathy is a projection of oneself into another in order to better understand the other … Yet empathy in important respects confounds Rankin’s efforts to identify with the enslaved because in making the slave’s suffering his own, Rankin begins to feel for himself rather than for those whom this exercise in imagination presumably is designed to reach. Moreover, by exploiting the vulnerability of the captive body as a vessel for the uses, thoughts, and feelings of others, the humanity extended to the slave inadvertently confirms the expectations and desires definitive of the relations of chattel slavery. … Put differently, the effort to counteract the commonplace callousness to black suffering requires that the white body be positioned in the place of the black body in order to make this suffering visible and intelligible. (18-19)
The photographs of Mike Brown’s and Eric Garner’s family, and the video of Eric Garner’s arrest, should, it seems, be enough to inspire widespread reevaluation of the justice system. These are human beings in terrible despair, and that should convey the depth and urgency of structural injustice. Yet somehow it isn’t and doesn’t. Again and again, we’ve seen these images submitted to what Tressie McMillan Cottom has called “the logic of stupid poor people”: picked apart, judged on someone else’s terms. If empathy is the act of transposing oneself into another’s body, than perhaps it has limits: We who are not continually besieged by state brutality cannot properly empathize; or if we can, then the very act obliterates the specific body we try to inhabit. The demand must consist of something stronger than identification or empathy. Justice, I guess? Deep and searching scrutiny of structure?
On the Ferguson hug
The simulation of consent in the context of extreme domination was an orchestration intent upon making the captive body speak the master’s truth as well as disproving the suffering of the enslaved. Thus a key aspect of the manifold uses of the body was its facility as a weapon used against the enslaved. (38)
The hug. It appears to have been staged, but that almost doesn’t matter; the excitement with which it was circulated as an emblem of hope says a lot about what we want black bodies to do at this moment.
On what we feel entitled to see
However, what I am trying to suggest is that if the scene of beating readily lends itself to an identification with the enslaved, it does so at the risk of fixing and naturalizing this condition of pained embodiment and … increased the difficulty of beholding black suffering since the endeavor to bring pain close exploits the spectacle of the body in pain and oddly confirms the spectral character of suffering and the inability to witness the captive’s pain. If, on the one hand, pain extends humanity to the dispossessed and the ability to sustain suffering leads to transcendence, on the other, the spectral and spectacular character of this suffering, or, in other words, the shocking and ghostly presence of pain, effaces and restricts black sentience. (21)
We demand, in an effort to convey the depth of injustice, the most exquisitely graphic images of brutality. Should we question our own right to scrutinize the body in pain, and our own hunger to view and circulate these images?
Palladio, a product of Stanford’s Humanities+Design Lab, is a web-based visualization tool for complex humanities data. Think of Palladio as a sort of Swiss Army knife for humanities data. It’s one package that includes a number of tools, each of which allows you to get a different angle on the same data.
Palladio is relatively new and still under development, which means that you will almost certainly encounter bugs! Still, it’s a very useful tool for getting a handle on a complicated dataset.
When Might Palladio be the Right Tool for You?
You have structured data.
Here, “structured data” means “data in a spreadsheet”: categorized, sorted, and stored in an Excel document or some other kind of spreadsheet application.
You’re interested in time, space, and relationships.
That’s where Palladio excels: showing you how various entities are connected across time and space.
Your data has many attributes.
Palladio’s really good at helping you uncover relationships among disparate attributes over time and space. For example, it can help you see that a diarist was especially interested in trees as he traveled through North Carolina, and especially interested in bats as he traveled through Arizona. One of Palladio’s most distinctive features is that it allows you to drill down through your data using faceted browsing.
When Might Palladio Not be the Right Tool for You?
You have unstructured data.
If you’re trying to analyze a long text, like a poem or a novel, Palladio won’t help you much. You’ll want to look for text analysis tools, like Voyant.
You just want to count things.
If you just want to make relatively simple charts and graphs, like a bar or pie chart, Palladio is too much tool for you! Instead, try using Excel’s built-in functions, or check out ManyEyes.
You want to present an interactive visualization.
One big limitation of Palladio is that you can’t embed or share the visualizations you create, except in static form. So while Palladio can help you explore and understand your data, it’s not great for presentation, at least not yet. Instead, try Google Fusion Tables, ManyEyes, or Tableau.
You want to create complex, fine-tuned maps and networks graphs.
While Palladio can produce maps and network graphs, you can’t customize them to any great extent, and you can’t perform sophisticated network analysis, such as calculating centrality. Instead, you might consider more sophisticated mapping tools, such as CartoDB or ArcGIS, and more sophisticated network analysis tools, such as Gephi and Cytoscape.
You hate bugs.
Palladio is still a baby, and you will almost certainly encounter some bugs. If you prefer not to use unstable software, you might investigate Google Fusion Tables or Tableau.
With that out of the way, we’re almost ready to get started using Palladio.
If you teach anything “digital,” you’ve probably had a similar experience: as soon as you mention Facebook, Twitter, or Snapchat, the conversation goes off the rails. Students want very much to share their own stories about these technologies. When they do, I hear lots of sweeping generalizations repeated back to me: that millennials never read, that the Internet has changed everything about social interactions, that none of the old rules apply.
After a few years of this, I got to thinking, OK, let’s really talk about this, but let’s actually do it right. What do we mean when we say “millennial”? How do we acknowledge the effect of technological change on culture without resorting to scorched-Earth, EVERYTHING-IS-NEW hyperbole? So here’s the course description for the class I’ll be offering this winter.
If all you knew about “millennials” was what you heard on the news, you’d think that college-aged people spent every waking hour texting and had never read anything longer than a Buzzfeed list. Of course, we know that isn’t true. People in their late teens and early twenties are as thoughtful, diverse, and interested in the world as anyone else. And the Internet isn’t evenly distributed. While some people count on near-seamless Internet connectivity, others can only access the Web sporadically.
Still, perhaps something about life is different for people who grew up with the Internet. So how do we think about these differences without defaulting to alarmist diatribes about sexting, or utopian proclamations about the Internet as a realm of boundless freedom? How do we talk about generational difference without flattening diversity or ascribing supernatural power to technology?
This class takes on this question by examining other moments of big technological change — film, television, telephone — and comparing them to the way we talk about technology today. We’ll also read the best writing about what it means to be a young adult in our current moment, and we’ll unpack the notions of “adolescence” and “young adulthood,” which turn out to be historically contingent categories themselves. Our goal is to develop a vocabulary for talking about technological and cultural change that accommodates the diversity and contingency of human experience.
There are some books and articles that seem like no-brainers (danah boyd’s It’s Complicated, much of the stuff on the Selfie Syllabus, Emily Bazelon’s Sticks and Stones), but I’m curious to hear from other people, too. What’s the best, least alarmist, most nuanced work you’ve read about adolescence and the digital age? I’m interested both in work that comments on adolescence and the digital age in its present moment, and work that shows how this moment has been constructed.
Thanks a million to the University of North Texas’s Spencer Keralis for inviting me to come speak at Digital Frontiers, a great conference in Northern Texas! I’m having an excellent time. Here’s the talk I gave today.
Around springtime, when universities are making offers for jobs that start in the fall, I tend to get a few similar emails. I’m junior enough that I know a lot of people just leaving grad school (whether it’s library school, a Ph.D. program, or a master’s program) and as universities continue to build DH centers, these people are getting snapped up to help spark DH activity elsewhere. So around May, they’re emailing me (and probably a lot of other people, too) to ask: Where do I start? What do I need to know?
I’ve been frank, as you may know, about what I think of taking someone fresh out of grad school, giving her a temporary gig, and expecting her to be the sole torchbearer for some amorphous DH initiative. In brief, it’s a bad idea, for a lot of different reasons. It’s not fair to the person you’re hiring, who will spend her entire tenure trying desperately to impress you at this impossible task so she can keep her job. And it’s not fair to your university community, which deserves continuity, focus, and the attention of someone who cares about the big picture.
But a number of people have good gigs that involve an element of community-building. And there are also a lot of people who’ve been working in libraries or other units for some time and are newly tasked with the responsibility of building interest in and capacity for digital humanities on their campus.
So for awhile now, I’ve had a mental list of things that I tell my friends who are getting started on the job of starting a DH initiative on their campus. If at all possible, I try to do it over a drink. This work is not easy, and it’s very sensitive, and I’ve only learned what I know by making terrible mistakes.
So in a minute, I’ll give you that list of suggestions for building and sustaining a digital humanities community at a university. (more…)
Over the years, I’ve spent a lot of time investigating the history of lobotomy, and particularly the kinds of visual evidence doctors used to support this practice. It’s part of the book I’m finishing, Depth Perception, which is broadly about the ways doctors have used film and photography during the twentieth century. In one of my chapters, I write about the lobotomist Walter Freeman, who was a prolific photographer, describing what he thought his patient photographs showed, and how our understandings differ today.
I get a lot of questions about lobotomy from people who find me on the Web, and I know other people who specialize in the subject do, too. I thought it might be helpful for me to write down some of the answers to the most frequent questions I get about the practice of lobotomy in the United States.
I’m sorry to say that I can’t answer individual questions on this subject, but I do provide references to some excellent books on the subject below.
What is a lobotomy?
The term “lobotomy” (often used interchangeably with “psychosurgery” during the period in which it was practiced) refers to an operation that severs the connections to and from the prefrontal cortex, in the anterior part of the brain’s frontal lobe. Generally, it was performed in one of two ways. From 1936 to 1945, lobotomies were generally performed by drilling two holes in the skull, near the patient’s temples, inserting a long instrument called a leucotome, and severing the connections to and from the prefrontal cortex. From 1945 until 1967, lobotomies were generally performed by inserting a long, thin instrument into the back of a patient’s eyeball, puncturing the thin orbital plate above the eye and rotating the instrument so that it destroyed the connections to the brain’s frontal lobe. This second type of lobotomy is called the transorbital lobotomy.1
After I wrote my original “How Did They Make That?” post, on some common types of DH projects, I got to thinking about whether there might be ways to help people reverse-engineer digital projects on their own. I used a talk I gave at CUNY as an excuse to think of some of these ways. This presentation, a modified version of that talk, is the result.
Incidentally, I propose a drinking game: whenever you see my tiny Skype avatar taking a sip of coffee, take a drink.
Erratum: The Negro Travelers’ Green Book is a project of the University of South Carolina Libraries, not the University of Southern California, as I keep saying. Also, just a note that while I focus on the mapping elements of that project, they’ve also done a beautiful job digitizing the book itself.
In the event, the class was terrifically generative and fulfilling — for me, and, I hope, for the students. While the memory of the class is still fresh, I wanted to jot down a few notes about some new-ish (for me) elements I introduced into this class, and how well I thought they worked.