Care, capital, and COVID

In the spirit of letting no writing go to waste: my friends at Arizona State University asked me to lead a “design studio” on the future of work and caregiving. They asked me to write three introductions, one for each of three “movements,” during which the studio participants were invited to discuss questions about caregiving and labor. I included an image or a quote for each movement, which I’ve added to the post.

Thank you to the very professional and creative ASU staff—I don’t think I’ve ever participated in such a well-managed event. We even had a dress rehearsal!

I’m still very angry!! I will probably never stop being angry!!

1. Care and work under COVID

My son was born in the last week of February, 2020. It’s hard to believe now, but on the day he was born, COVID wasn’t on any of our minds. No one at the hospital was wearing a mask; it didn’t even occur to me to worry about it.

Woman sits in a chair, holding two children, one a child and one an infant.
March 2020, Los Angeles. Photo by Andy Wallace.

Of course, within a couple of weeks, all of that changed. My daughter, who was then seven years old, was home from school for what turned out to be a couple of years, and my partner and I were on our own with a newborn, more or less fending for ourselves while the world came crashing down around us.

My partner took this picture of me around that time, and one of the reasons I like it is that it expresses so vividly what it felt like at that moment: so much was at stake, and all of it seemed to depend on me, with no help coming from any direction.

This predicament—this absolute vacuum of support for caregiving—didn’t just happen overnight, though it may have felt like it at the time. As historians have told us, the neoliberal state steadily destroyed the social safety net over the second half of the twentieth century.

As a middle-class white woman, I was probably one of the last to feel its absence. To borrow the words of the legal scholar Lani Guinier, Black women were the canaries in this particular coal mine, as they so often are. But the destruction of the social safety net eventually came for us all. Austerity came steadily for poor people, disabled people, and people of color, as successive administrations shredded welfare, social programs, and labor protections in the name of self-reliance and the liberating forces of the free market. By 2020, it had come for me, too.

But as many of us discovered during the first weeks of COVID, we are none of us in actuality self-reliant. Hard though it may be for some of us to admit, we depend on many things that require legions of people to maintain. We need infrastructure, for example, like electricity, roads, and supply chains. We need a functioning public health system. And we all of us require, or have required, or will require care, along with a human being to supply it.

That carework—the work of attending and nurturing and loving the people who need us—is part of what scholars sometimes call “social reproduction.” Whatever else care work does, it also serves an irreplaceable economic function, in that it produces, maintains, and bolsters the functional members of society necessary to serve as workers and consumers. Giving this carework a name is itself a response to our pervasive unwillingness to acknowledge its importance or value. I felt this myself when I heard complaints about the inability of parents to be available for meetings at all hours. “Where the hell,” I wondered, “do they think the college students come from?”

Vital though this work is, our social system has persistently refused to acknowledge it and, indeed, mercilessly attacked our ability to perform it for each other, from requiring everyone to move long distances for jobs, to setting work hours via algorithm, to forcing workers to string together multiple gig jobs, to creating conditions in which child- and eldercare are both immiserating and scarce.

The political theorist Nancy Fraser calls this predicament “cannibal capitalism”: in its ruthless drive to extract natural resources and then charge us for them, capitalism has actually imperiled its own ability to persist. Who will power and maintain the economy if none of us can meet our own or others’ basic needs?

Cannibal capitalism makes its presence known via a stream of logical contradictions that become increasingly difficult to tolerate. We must be good parents, but we must commit ourselves to the grind; we must care for our elders, but we must move to wherever the jobs are; we must be loyal friends, but we’re too exhausted to see each other; we must work through the pain, but we are physically unable to do so.

The outlandishness of these fallacies became crystal-clear to me during the height of COVID, when people told me with a straight face, that I must be responsible for my daughter’s education and my son’s care while simultaneously bearing my work responsibilities. The actual, physical impossibility of this—its contradiction of the literal laws of physics—was never acknowledged. It was never so clear to me as in that moment that we have created the conditions for our own destruction.

Arguably, we missed our chance to emerge from COVID-era isolation having demanded and achieved a better way to live. But the cognitive dissonance of what we experienced bred a sheer, full-body, incandescent rage that is still with me, as perhaps it is also with you. Perhaps our task now is to transmute that rage into care—an expression of love that also carries within it the ability to expose the intolerable contradictions we labor under.

2. Love like a prison

“I’d wager that you, too, can imagine something better than the norm that makes a prison for adults—especially women—out of their own commitment to children they love.”

Sophie Lewis, Abolish the Family

When I was a kid, because I was very fortunate, I experienced my mother’s love as a kind of white noise: ubiquitous, omnipresent, and not particularly interesting. It was the background hum against which more interesting things happened, as I figured out who I was and wanted to be.

So it was the shock of my life when, upon having kids, I discovered that from the other side, that same love feels anything but static. On the contrary, it is cataclysmic, like a tidal wave, an almost material thing. My children may experience this love, like I did, as radio static—I hope they do—but on my end, it’s a full-body experience that buckles my knees and takes my breath away.

So what does it mean to call this relationship a prison? I would guess that many of us who care for people we love can hazard a guess. The love itself is not oppressive; it’s the constant awareness that at a fundamental level, my children have only me and my partner. The weight of that responsibility is so immense that it makes it hard to breathe.

I feel the implicit threat when I hear about families who have been evicted from their homes, children with incarcerated parents, children who have been separated from their homes because of their parents’ poverty or despair. It could be me. I know that my race and class make these outcomes unlikely, but the pandemic also taught me that when everything falls apart, there is no one there to catch us. I am my children’s only bulwark against world-ending tragedy.

That’s part of what Sophie Lewis means when she talks about abolishing the family. She’s not talking about the abolition of reciprocity, but about the extension of that mutual care beyond the boundaries of the family unit. With the withdrawal and privatization of most forms of social support, the weight of more and more caregiving has been piled onto the family, which is already strained to breaking point. Many of us have only ourselves to offer care to elderly or disabled relatives. Many of us worry about who will care for us when we need it. Many of us have no break from the labor of caregiving, and many of us lie awake at night worrying about catastrophes that could destroy the lives of the people we love the most in this world.

What would life be like if that weight were lifted? What might you have the time and freedom to do? What kinds of relationships and infrastructure might be possible if the responsibility for some of that care were distributed elsewhere? Where might that care and reciprocity go?

We don’t have to abolish the family, not if you don’t want to. But I include this provocation as an encouragement to think really big as we imagine a more just landscape of caregiving. The weight of responsibility is so immense and so omnipresent that it can be hard to imagine what it would be like not to have to carry it. But for this provocation, I ask you to consider: What if you didn’t?

3. Exactly the wrong skills

Like all of you, I’ve worked hard my whole life. I was never a particularly rebellious kid, and so I diligently cultivated the talents that my teachers, and, later my employers, rewarded. I studied. I stayed late. I showed initiative. I prided myself on my self-reliance and sought to be intelligent, well-read, and well-spoken. That’s what I had learned to do, and building these skills seemed vital to my ability to succeed.

A black-and-white illustration of humans helping other humans with tools and food reads "Fuck rugged isolationism / Doomsday prep with love."
Tabitha Arnold, available here.

So it was a shock to me when I realized, during the early days of the pandemic, that I’ve spent my whole life cultivating exactly the wrong skills. Nothing—no amount of Googling, reading, working, or screaming—could compensate for the fact that I had failed to build a community. At school dropoff, I’d been too absorbed with thoughts of emails and lesson plans to get to know the other parents. Coming home late, dragging groceries up the stairs, I was too exhausted to contemplate chatting with the neighbors. I cherished my friends, but I prided myself on never asking for help, so we had no track record of reciprocity to build on. And yet now I desperately needed other people to help me cope and to share the burden we’d all been saddled with.

It struck me then that the talent the world will demand of my children is not study skills or work ethic, as I’d been taught; it’s the ability to give and receive help, and in doing so to create bonds of reciprocity. Their short lives have already been studded with disasters, from wildfires to pandemics, and in the coming years that number is only likely to grow. The world I’d grown up in taught me to respond by hoarding canned goods and squirreling away cash. But experts on disasters have been clear that the people who emerge most successfully from catastrophes are likely to do so because of the bonds they’ve formed with their neighbors and communities. The best way—the only way—to equip my children for survival is to teach them that we all depend on and must care for each other.

That’s something I’m working on in recent years: getting to know my neighbors, learning to give and ask for help, building more enduring friendships, learning about mutual aid and networks of community support. It seems so small, but it is frankly excruciatingly difficult for me, as someone who’s both shy and trained to be self-reliant. But to me, it seems like the most meaningful way I can start to build a world in which care is not an individual burden but a shared source of strength.

I know a lot of people are much better at this stuff than I am, and a lot of you have already done this work. But maybe there are other things you have in mind as steps toward the world you want to see. So many of us are stretched so thin and life can be so overwhelming. But I wonder, what feels realistic and do-able for you in this moment?

Teaching technical skills online

Here I am, still blogging like some kind of caveman. I guess I should be using Substack or Medium or something, but maybe blogs will come back in style, like other artifacts of the ’00s.

Anyway, in the past, when people asked me whether I could teach my digital humanities classes online, I hemmed and hawed. Tools like web-based visualization software have made it easier to share work across platforms, and heaven knows there are plenty of cloud-based collaboration tools out there.

The thing that worried me was teaching new tech skills, which is a big part of my classes, and particularly my Intro to DH classes. I am super, super picky about how to do this, as I’ve mentioned before. My feeling is, I get one shot to teach the students this new skill, and if something goes badly wrong, I’ve not only missed my shot, but I may inadvertently lead someone to believe they’re not capable of learning the skill. It’s why I teach every single skill myself, rather than invite people to give workshops; I just know exactly how I want it done.

Continue reading “Teaching technical skills online”

Sitting with the rage

Have I ever felt this angry or trapped in my entire life? Certainly—let me cut you off right there at the pass—the world has seen greater cruelties and outrages. “Broken childcare infrastructure” barely makes the list of world-historical tragedies.

And yet for sheer absurdity, for the unbelievable stupidness of this problem, for our steadfast refusal to acknowledge the giant fucking impossible disaster hanging over all of our heads—for that, this year should win some kind of award.

Let me back up. California’s public schools are all currently online, as they should be. I have a seven-year-old daughter who’s in Zoom school. I also have a six-month-old baby. I also have a full-time job, as does my husband.

Continue reading “Sitting with the rage”

Several new publications

I’ve published several things in the last few months, and thanks to UC’s institutional repository, I’ve been able to make them available to everyone.


“See No Evil”

Logic Magazine no. 4 (buy a copy of this great magazine!)

This is a piece for general readership that investigates the software behind today’s massive, sprawling supply chains. I’m finishing the academic version and hope to have it out soonish.


“Prostitutes, Charity Girls, and The End of the Road: Hostile Worlds of Sex and Commerce in an Early Sexual Hygiene Film”

In Health Education Films in the Twentieth Century. Editors: Bonah C, Cantor D, Laukötter A. 173-187. University of Rochester Press, Rochester, NY 2018.

This piece looks at an American sexual hygiene film from 1919, using it to illustrate the fraught relationship between sex and money in post-World War I American culture. The publishers sent me a discount code if you want to buy a copy of the book. Use BB130 here to get 30% off. (Or just get it from the library!)


“Digital Humanities”

In The Craft of Criticism: Critical Media Studies in Practice. Editors: Kearney MC and Kackman M. 331-346. Routledge, New York, NY 2018.

This is an overview, history, and typology of digital humanities within the field of media studies. It also contains a step-by-step walkthrough of a digital humanities project I created. I think this will be really helpful for anyone trying to figure out what the heck DH is and how people go about building DH projects.

Scaling up DH101

Over the last few years, enrollment in my Introduction to Digital Humanities class has been trending steadily upward, as has enrollment in the minor itself. Last spring, we had an unexpected surge in enrollment in the minor, and many of those students needed to take DH101 right away. We had to scramble a bit to accommodate everyone. After considering a few possibilities, we more or less doubled the size of our Intro class, from 45 to 88 students. We were fortunate to enlist an excellent T.A., Dustin O’Hara, to teach two sections, and my fabulous longtime co-conspirator, Francesca Albrezzi, took the other two. (We have lectures twice a week and section once a week.)

Even with the expanded class size, we had to turn lots of people away; I suspect we could fill another DH101 class in the spring, if we had the faculty bandwidth to teach it.

This was my first time teaching a true lecture course. In previous versions of DH101, I’ve been able to alternate between dispensing information and turning discussion over to the students. While we still had discussions in the larger DH101, I could no longer pretend this was a seminar.

I expected the large class size to be a challenge, but I think the bigger challenge was the classroom itself. We were lucky to find a room at all, given how late we transitioned to a larger class size, but we were stuck with a very conventional lecture hall, with bolted-down seats in immovable rows. It at least had modern AV equipment, but the room was a significant challenge. In my previous classroom, students’ seats were arranged in 10 or so group tables, so it was easy to alternate between hands-on work and all-eyes-up-front lecturing. Now we had no choice but to sit lecture-style.

I did what I could to ameliorate the situation. I was able to reserve the Young Research Library main conference room on a few occasions, which gave us a chance to work more collaboratively. And I did continue asking the students to check in, share work with each other, and discuss issues in small groups in the lecture hall. But the space just didn’t really lend itself to that kind of thing. This was a real bummer for me, and probably for the students, too.

The classroom arrangement actually set us back significantly in terms of technical skills, too. I wasn’t really comfortable asking students to learn technical stuff when I couldn’t circulate freely in the classroom to see how they were doing. I don’t think a lecture hall is a good environment for learning new skills on your computer, since it’s so easy to get stuck and have no way to signal for help without stopping the entire class. So technical tutorials had to be reserved for section, for the rare occasions when I could reserve the Library conference room, and for a few at-home lessons. As a result, I wasn’t able to teach the students as many skills as I have in years past.

I also struggled to check in with students as much as I’ve been used to doing. Their group project is always really challenging for them, and every project is very different. Since I’m the one who picks out the datasets, I usually like to work at least a little bit with every group. But with so many students, I had a hard time devoting attention to everyone. The result was more confusion about the assignment and expectations than in previous years, and a couple of group meltdowns. Everyone pushed through and got to the finish line, thanks in large part to the TAs’ hard work, but it was more stressful than it needed to be.

The students’ final project showcase this week reassured me that, yes, they did learn what I wanted them to, and, yes, they did learn how to do serious research and think critically about data. I loved hearing them explain what they did and how they overcame challenges, and I was really excited to hear their newfound confidence in discussing technical matters. Still, as always, it’s my errors that stand out to me.

If the class remains this size next year (and I’m still the one teaching it), there are a few things I’d do differently.

  • Rethink the final assignment. This is tough, because I’ve loved giving them “real” data, and I believe they benefit from the intense labor of making meaning from messy, incomplete, but important datasets. But I’m not sure it’s realistic for me to assemble and augment this many datasets every year. And I worry about the groups getting the attention they need to complete this very complex project when there are so many people to check in with. The alternative that makes sense to me is some kind of digital portfolio, in which students create their own examples of multiple kinds of digital work and surround it with critical commentary.
  • Undecorate the Christmas tree a little bit. As the years have gone by, I’ve tossed more and more assignments in the syllabus. I don’t think the class is more work, necessarily, but there are a lot of things to turn in and a lot of dates and assignments to remember. It’s too much. I think I could cut the blog post assignments down to just a few and simplify the final project a lot.
  • Think about asking students to complete technical modules at home. I usually like to be with students when they’re learning a new technical skill, but that wasn’t always possible. On a few occasions, I had students walk through (very carefully written) tutorials themselves at home, and they seemed to do OK. I think I could do more of this, as long as I’m cautious.
  • Get a different classroom! I don’t think we actually have a great classroom for a group this size at UCLA, but what I imagine would work well is a large room, with lots of space for my TAs and me to circulate, and multiple large tables where students can sit in groups. Multiple screens would be awesome, so that students could quickly draft and share work, but honestly, I’d happily take a large, empty room with tables and chairs, preferably one that we don’t have to set up and tear down every time (ugh).

Other miscellaneous thoughts about this year’s DH101:

  • As part of their annotated bibliography, each student needed to not only write a blurb about each of their sources, but actually obtain the book or article and submit a photo of themselves holding it. We called those “shelfies.” I’m just tired of reading book summaries that are obviously pulled from the snippets students could read on Google Books. This seemed to work really well. Students STRUGGLED to find their sources, as I expected, and waited too long, as I expected, but a number of students told us that this was the first time they’d located or checked out a book in their college career. As we did last year, we held a “research-a-thon” to help get them going on this, and while I made a mistake by holding the event during midterms week, the librarians and I were able to personally escort a number of students up to the stacks and help them read a call number.
  • Students took to network analysis more than they have in years past, perhaps because a number of them were simultaneously taking an SNA class in the Sociology department. I’m happy with the lesson plan I’ve developed to introduce network analysis, which uses a questionnaire about their favorite books, movies, and musicians to develop a homophilic network graph to show how they’re all connected. (I recorded last year’s network analysis lecture and you can see it here.)
  • For the last couple of years, it’s been clear that the hardest thing about the final assignment for my students is getting started — understanding what kind of work is necessary to start asking questions of a dataset, and how to alternate between secondary research and data analysis. The DataBasic suite really helps with this, but I think they could use step-by-step instructions to get started. Perhaps I’ll take that on at some point.
  • I just did not have the wherewithal (or the funding) to schedule a pizza-dinner hackathon, as I’ve done in previous years, but I found a simple alternative that they seemed to appreciate. I convened an evening meeting to which each group had to send at least one representative and checked in with each group that way. Then, at the same time every week, I invited each group to sign up for dedicated help with me. It worked well and allowed me to work intensively with a few groups.
  • You probably guessed this, but with a lecture this size, you need to make every announcement multiple times and send email followups, and even then, students will plead total ignorance.
  • For the last few years, I’ve started off the class with a reading from Hayden White, about the essential unknowability of history. This year I switched it up and had them read the first chapter of Michel-Rolph Trouillot’s Silencing the Past, in part because Trouillot explicitly deals with power and race in ways that White doesn’t. They really struggled to understand Trouillot, but it seemed to make an impression on them, too.
  • Of the DH projects we examined together, the one they all seemed to like the most was Gabriela Aceves Sepúlveda’s [Re]Activating Mama Pina’s Cookbook. I think they liked its consideration of the materiality of data, the questions about what “counts” as data, and the beautiful design. Also, partly because so many of my students are people of color themselves, they appreciate it when I can pull in projects from and about other people of color.

Data Packages for DH Beginners

The quarter is off and running again at lightning speed. At UCLA, we’re on the quarter system, and things move fast — just 10 weeks to get through all your material. I’m teaching DH101 again this year, and, as usual, it’s a race against the clock. The profile of my students changes a bit every year, but the typical student who enters my DH101 classroom has facility with Word, PowerPoint, maybe Excel, maybe some of the Adobe suite, but not a ton of other computer stuff. By the end of the quarter, my goal is to get them working with and thinking critically about structured data, data cleaning, data visualization, mapping, and web design.

I’ve written about this before: working in groups, my students are assigned a dataset at the beginning of the quarter. They learn how to work with it as the quarter progresses, doing a lot of secondary contextual research, interviewing an expert about it, manipulating the data, and finally building a website that makes a scholarly humanistic argument with the support of the data. You can see the mechanics of this on my course website.

People often ask me about the data I use, and indeed, that is a story in itself. I have 88 students this year, and since I don’t like any group to have more than seven people in it, I have 12 groups, each of which needs a dataset. (Really, some of them can share the same dataset; I don’t know why I get weird about this.) And they can’t just use any dataset. In fact, most of the data out there is inappropriate for them.

Here is what I look for in a dataset for my students:

  1. It has to be a CSV (or able to be wrangled into a CSV). My beginners want to be able to double-click on their dataset and see…something that they can work with. CSVs are great because they open in Excel, which is familiar to most students and allows them to immediately start doing things like filtering and simple manipulation. Plus, you can drop a CSV into almost any visualization tool. I can use a relational database, but I usually just give the students the spreadsheet that results from a query, since I just don’t have time in the quarter to teach them about more complicated data structures. Likewise, if a dataset is XML, I’ll just flatten it. But I prefer not to have to deal with this because, like I said, 12 datasets.
  2. Around 2,000 records is ideal. Here’s why: I want the dataset to be big enough that it’s too labor-intensive for the students to manipulate it by hand, but not so big that it breaks Excel. Really, I can work with bigger sets, too, but students do tend to get very anxious about working with datasets that big. Any number of fields is fine (actually more is better) because students understand fairly quickly that they can choose which fields to examine.
  3. It has to be…humanities-ish. You and I probably know that one could make a humanities argument about municipal water data, or public health information, but it takes a little bit of sophistication to get there. The most “natural” kind of analysis for these kinds of datasets would be urban planning or public health kinds of questions, and it’s too difficult for me to push students toward the kind of open-ended humanities questions I want them to pursue. It’s far easier if the data is about art, books, movies — subjects that are the traditional province of the humanities.
  4. It’s nice if it’s something they care about. I have confidence that my students will eventually become interested in any subject, once they really dig into it, but I can forestall a lot of grumbling if I can give them a dataset that’s immediately compelling to them. Things they like: fashion, food, performance, books from their youth, cartoons, comic books, TV, movies.

You can see this year’s datasets at the bottom of this page. I do not just give my students their datasets in raw form. I cut the sets down to an appropriate number of records, if necessary, and then I give them the dataset along with a “project brief,” which contains:

  1. Information about the provenance and composition of the data.
  2. The name and contact info of an expert on that subject who has agreed to allow my students to interview them.
  3. The names and contact info of librarians who can help them.
  4. The name and contact info of UCLA’s mapping specialist.
  5. Two or three secondary sources to get them going on their research. I also teach them how to citation-chain.

Here is an example of a “data package,” with the contact info removed.

If you’re thinking this is kind of an absurd amount of work for the instructor, you’re right. I really feel the students need this apparatus around their dataset, but I end up spending a good chunk of my summer hunting down data, persuading friends (and strangers) to serve as subject experts, and researching secondary sources.

Even with all of this scaffolding, students get very anxious about the project assignment, just because it’s so new to them. I’ve learned to expect it, to warn them that they’ll feel anxious about it, and to reassure them that if they’re hitting project milestones, they’ll get to the finish line on time, even if they feel at sea.

Sorry for the dashed-off blog post; I’ve been meaning to write about this for some time and finally had a few (just a few!) minutes!

New job, same school! (Same office, even!)

View from window with trees and blue sky.
Never gonna give you up, beloved office window! I fought hard for this! (Used to be Chris Kelty’s! [I did not kick him out.])
I can never keep my mouth shut, so this announcement already made the rounds on social media, but I’m really excited about my remodeled job title: as of July 1, I’m an assistant professor of Information Studies and Digital Humanities (still at UCLA!). For those who care about such things, the appointment is 100% in IS, but I’ll continue to do half my teaching for the DH program.

I’m really happy. I’ve always loved my job at UCLA, but over the last few years, I’ve grown increasingly invested in a couple of research projects: the first, on the way data works under supply-chain capitalism; and the second, on what “data” means for the humanities more broadly. My new position will give me the time and resources I need to work on these projects. I’ve always felt very close to the i-School at UCLA — both to the people and to the questions they’re asking. It’s a really good fit.

When I came to UCLA for my job interview, Todd Presner, who became my boss, told me that the job I was interviewing for seemed to make sense for someone to hold for about five years. Five-and-a-half years later, that seems about right to me. I wasn’t really sure where I’d go after that time elapsed; I came close to moving into a higher-level administrative job, but in the end, I felt pulled to research and teaching.

I’m just really, really glad I had that option, and really glad I get to do it at UCLA, a place I genuinely and cheesily care about a lot. I’m very grateful for the mentors I’ve had to help me figure out how to navigate all this, and especially for Todd (who hates it when I say this).

Best of all: UCLA will be replacing “my” position as coordinator of the DH program, although I know they won’t be looking for a Miriam clone. I’m extremely excited about what it will mean to bring in someone with different and fresh ideas about DH. You can see, I think, that this is a pretty significant investment in DH at UCLA, and I think it will be good for all of us.

New tutorials on network analysis with Cytoscape

The Cytoscape interface, featuring a pane on the left with buttons and a graph diagram on the right
I find the Cytoscape interface more intuitive than Gephi’s, although in both cases, you need to have a basic understanding of key NA terms.

For some reason I got it into my head to write a bunch of tutorials on using Cytoscape for network analysis. They’re now all up on Github. (I’ve been moving to Github for tutorials because they’re easier to update there.)

I started writing these for the students in my spring-quarter class and, even though the class is over, I’ve been adding to them compulsively. They’ll take you from zero to an interactive, web-based network graph, with stops along the way for projecting a two-mode network to a one-mode network and working with node attributes. (If you don’t know what any of that stuff means, they explain that, too.)

There’s a bit of a Gephi-versus-Cytoscape battle right now among people who do network analysis. I actually started out on Cytoscape, only because I found it slightly more intuitive, and switched to Gephi when I discovered most people used that. But in recent years, I’ve had a really hard time dealing with Gephi. First, there was the Legendary Java Problem, and although the new version is purportedly more stable, I actually just cannot get it to work on my Mac and have frankly kind of lost the will to keep trying.

Cytoscape is Fine. It’s designed for scientists, really, and other people who care very much about statistical measures of networks, which to be honest, I don’t really care that much about. (I don’t think most humanists trust these measures anyway, so I don’t see much point in hammering on them.) I find Cytoscape’s web service, CyNetShare, to be pretty janky-looking, but … you can interact with the network diagram, so that’s good, I guess.

To be honest, I’ve been slowly making the switch from Gephi/Cytoscape/etc. to R’s igraph package, and to D3 for displaying networks on the web, just because they’re so much nicer looking. One thing I like about Cytoscape is that after you’ve measured various aspects of your network, you can export JSON that’s set up specifically for D3’s popular force-layout network.

When I was visiting Stanford last winter, I got to see a preview of a network analysis tool that the Humanities + Design team is building, and I really liked the way they placed the emphasis on exploration and discovery, rather than statistical measures. I’ll be looking forward to seeing the release of that tool (I think it’s called Idiographic?), since I do feel that humanists have different interests when it comes to networks than scientists or social scientists.

New book chapter

arclight-cover-page-001-e1464474821563I’m really proud to have a new chapter in an open-access volume edited by Eric Hoyt and Charles Acland called The Arclight Guidebook to Media History and the Digital Humanitiespublished by the UK press REFRAME. The chapter, which is called “How is a Digital Project Like a Film?” is really about data and narrative. What does it mean to tell stories with data? On what basis can we call data-based narratives true, and where do they necessarily lie? And what role does the interface play in all of this?

The full TOC includes lots of great stuff, including pieces by Deb Verhoeven, Haidee Wasson, Greg Waller, and Lea Jacobs. I think it does a nice job bridging the gap between traditional film studies and other forms of scholarship, and I’m very pleased to be included.

Materials on Image-Mining for Medical History

Last week, I taught the image-mining portion of the Images and Texts in Medical History workshop at the National Library of Medicine. I am far from an expert on OpenCV, the open-source computer-vision library. But as usual, that didn’t stop me from attempting to teach it.

The materials I created for the workshop include detailed instructions on how to use OpenCV to extract images from scanned journal pages (using a script written by Chris Adams), as well as a detailed breakdown of how to use the Python OpenCV library to take the average color of an image. I’ve also included links to my favorite resources on OpenCV and computer-vision in general. (My experience has been that there are a lot of really terrible tutorials out there, so I’ve tried to link only to those that are actually helpful.)

Ben Schmidt taught the text-mining portion of the workshop, and his materials are really great. His handouts in particular are concise, opinionated rundowns of the strengths and weaknesses of various forms of text analysis.

In preparation for the workshop, Ben and I created a virtual machine, provisioned via Vagrant with all the dependencies and data the participants needed. If you’d like to install the VM, it has everything you need for both Ben and my portions of the workshop, and the instructions should be pretty clear. (The VM is based on one that Andrew Goldstone created for his Literary Data class.)

The process of getting the VM installed on participants’ own computers was … complicated. We learned many things about Vagrant and VirtualBox, including the fact that Windows 7 and 8 don’t come with any way to handle SSH.

It was definitely the most technically complex workshop either of us have attempted (to a group of about 50!!). It was definitely not hitch-free, but it was really satisfying to see participants get excited about computer vision, and to talk about ways they might use these techniques in their own research.