A fun way to introduce DH students to dataviz

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

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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

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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 …

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… this one. It’s the same data, just rearranged into two columns.

Make your spreadsheet into a one-mode edge list

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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

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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

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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.

Photography and the limits of empathy: Reading Garner and Brown through Saidiya Hartman

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.

On the limits of empathy

Writing in response to a harrowing description of enslaved people by John Rankin:

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?

Getting started with Palladio

NOTE: Scroll down to get to the tutorial itself!

Updated November 2015 for Palladio 1.1. If you’d like to use this tutorial in the classroom, or if you want to alter it and make it your own, there’s a version on Github you can do whatever you want with.

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 active 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. Palladio 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 (http://voyant-tools.org/).

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 tools like Plot.ly or Tableau.

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 various measures of 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. First, though, a quick note that this tutorial does not cover some important features of Palladio, specifically its ability to link multiple data tables together, its timespan feature, and a feature that allows you to use multiple basemaps. Perhaps these will be the subject of a later tutorial!

A word on the dataset we’ll use, which you can find here.

This is a spreadsheet that contains the metadata for a portion of the Charles Weever Cushman Collection of photographs, located at Indiana University. The full Cushman Collection contains more than 14,500 Kodachrome photographs, taken between 1938 and 1969. Indiana University’s archivists were forward-thinking enough to place this data on Github, which is how we’re able to use it.

In order to make this data a little easier to work with, I’ve limited this spreadsheet to photographs taken between 1938 and 1955. I’ve also removed the “End Date” field to prevent confusion, changed the format of the date field, and added geocoordinates so that we can map the data more easily. For a great introduction to how to do some of this data manipulation on your own data, see this handout, developed by Owen Stephens on behalf of the British Library, which explains how to use the data-cleaning application OpenRefine.

A reminder that Palladio is still under development, so it can be buggy and slow! Some tips:

  • Work slowly. Wait for an option to finish loading before you click it again or click something else.
  • Do not refresh the page. You’ll lose your work.
  • On a related note: To start over, refresh the page.
  • Clicking on the Palladio logo will bring you to the Palladio homepage, but it won’t erase your work.

Navigate to Palladio.

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Go to palladio.designhumanities.org and click on Start.

Upload your spreadsheet.

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Click on the Load Spreadsheet or CSV tab and drag your spreadsheet onto the tab. (If you have an Excel spreadsheet, save it as a .csv file before uploading it.) Then press Load.

Hey, you imported your data!

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As you can see, each column in your spreadsheet is a different category of data. If you look closely, you’ll see that Palladio has automatically categorized your data as different datatypes: “IU Archives Number” is a number, for example, while “PURL” is a URL. And if you scroll down, you’ll see that “Geocoordinates” is Latlong.

Tell Palladio what kind of data you have.

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One of your data categories is a date, but Palladio hasn’t figured that out right away. We need to tell it, so that it treats this particular category as temporal data.

Click on the Date category. In the window that pops up, select Date from the Data type dropdown menu. Looks good! Click Done.

Hide some data

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We have a lot of categories here, and Palladio runs a little faster if it has fewer of them to deal with. (Plus it’s easier to see what you have.) Let’s hide some categories we won’t be using by clicking on the tiny eye to the right of the category name. I hid Archive Date, Description from Slide Note, Image Note, and Slide Condition. You can always go back and reveal these if you decide you want them after all.

Map your data!

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Click on the Map tab at the top of the window to go to the maps view of your data. Before we go on, let’s talk about what you see in the Map layers pane that appears in this window.

Palladio expects you to map your data in layers. This means that not only could you map one kind of thing, like photos; you could layer other kinds of things on top of that data. For example, it might be cool to have a layer of Cushman’s photos and a layer of interstate road networks, to see if Cushman traveled on highways. Palladio lets you do that!

But for the time being, we only have one layer: Cushman’s photos. So we’ll stick with that.

Map your data! (2)

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Let’s tell Palladio what we want in our layer. We can name the Layer whatever we want. I’ll call it Photos.

Keep the map type as Points. If you happened to have data that depicted the movement of objects from place to place, you could do a point-to-point map. But we don’t have that kind of data.

If you click on the Places box, you should be able to choose Geocoordinates from the dropdown.

The Tooltip Label, which controls the label you see when your cursor hovers over a point, can be anything you want. I’ll call mine Genre 1, since that gives me some sense of what’s in the photo.

When you’ve done all this, press Add layer.

You have a map!

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Looking good! If you hover over a map point, you should get a tooltip.

Combine your map with a timeline.

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The ability to put data on a map is cool, but the real power of Palladio is the ability it gives you to explore the relationships of various features of your data through Facets and Timelines. Let’s start with a timeline, which is pretty much what it sounds like: a visualization of the distribution of your data over time.

Start by clicking on Timeline tab at the bottom of your screen. Group your data by Genre 1. Now you can see the distribution of photos over time. That’s interesting: looks like Cushman took a lot of photos in 1952.

Filter your data by date.

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On the bottom graph, use the crosshairs to drag (slowly!) from 1940 to 1942. A blue box appears to indicate that you’re filtering your data by date. You’ll notice that the points on the map repopulate to correspond with the timespan. You can even select multiple spans of time and see them visualized simultaneously!

If you want to temporarily collapse your timeline so that you can see the map better, click on the downward-pointing arrow on the right of the timeline pane. To get rid of the date filter, click on the pink “x” next to the datespan above the graph.

Note: If you’re unable to “grab” your timeline in order to filter it, it may help to lengthen your browser window.

Add a facet to further refine your data.

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You’ve now narrowed your data down to 1940–1942. Now let’s try filtering and visualizing your data using other attributes. We can do this with a Facet filter.

Click on the Facet tab. (You’ll probably want to compress your Timeline window by clicking on the downward-pointing arrow that appears on the upper right-hand corner of the pane.)

Click on the Dimensions menu.

Now select Genre 1, Topical Subject Heading 1, and Topical Subject Heading 2. (Actually, you can select whatever you want; I just think these are fun ones to try.)

Explore your facets.

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Working from left to right, the facet dimensions gradually narrow down the data displayed on the map. For example, in the image above, the map will show where Cushman took landscape photographs that contain both trees and shrubs. (Only on the East Coast and Great Lakes! Wonder why.)

Try playing with some other facets and altering your timeline. Find any interesting relationships?

(You might wonder about the Timespan tab, which is greyed out when we use Palladio with our dataset. If our records had start dates and end dates, the timespan function would display those dates as “lifespans.” Take a look at this video for an explanation: https://vimeo.com/101672780.)

Explore your data as a gallery.

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Maps are fun, but galleries can be useful, too, especially when you’re working with images. First, delete your time and facet filters by clicking on the tiny pink garbage can that appears at the lower right-hand corner of each pane. (You can also delete them by clicking on the pink X’s at the top of the filters pane.)

Now, click on the Gallery tab at the top of your window.

Change the categories your gallery displays.

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So far, not very useful. Let’s change the categories your gallery is displaying. For Title, choose City and State. For Subtitle, choose Genre 1. For Text, choose Description from Notebook. For Link URL, choose PURL. For Image URL, choose Image URL. If you’d like, you can sort your gallery by Date.

(Actually, you can put whatever you want on these gallery cards, but these are some categories I think are interesting.)

Filter your gallery by date and other attributes.

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You can filter your gallery in the same way that you filter your map. For example, in the above image, I’m looking at pictures taken in Chicago that contain both clouds and buildings.

View your data as a network diagram.

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Network diagrams are good for showing the relationships among entities. Often, those entities are people or objects, but we can use subject headings as our entities, too.

To view your data as a network diagram, get rid of your filters and then click on Graph. (Palladio is using the term “Graph” the way computer scientists do, to mean exclusively a network graph.)

Set the parameters of your network diagram.

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In order to create a network diagram, you need to tell Palladio which two attributes of your data you want to explore. For Source, choose Genre 1; for Target, choose Genre 2. Now you can see which genres tend to co-occur in Cushman’s photographs. You can click and drag the nodes (the circles) to explore your diagram.

To highlight one kind of node in order to distinguish between the two, click on the Highlight checkbox. To size nodes according to the number of objects they represent, click on the Size nodes checkbox.

And you can filter your diagram in the same way you filtered your map and gallery.

Share your work.

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Unfortunately, you can’t embed interactive Palladio diagrams on webpages, but you can produce static images, either by taking a screenshot or clicking on the Download link, which allows you to download an svg file. An svg is an image, and you can post it or share it as you like.

Download your work

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Palladio doesn’t save your data, but you can export your data model — the way you configured your data and upload it again later. This will save you the trouble of configuring your dataset the next time you want to work with it.

To do this, click on Download. This will download a file with the extension .json. The next time you use Palladio, you can upload this file (on the Palladio homepage) in order to open your project where you left off.

Other cool things Palladio can do

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Palladio has some other cool capabilities we haven’t discussed here. The image above shows one that I like: the ability to use other georeferenced maps (in this case an old railroad map from the New York Public Library) as basemaps. Here’s a tutorial on how to do that.

Other cool things you can do with Palladio:

  • work with multiple tables of data, connected relationally
  • export lists of data using the same filtering mechanisms we used for visualizations
  • create point-to-point maps
  • visualize spans of time with the timespan feature

Finished? Awesome! Now is a good time to see if anyone else in the room needs a hand. While you’re looking for people to help out, see if you can answer the following questions by visualizing the dataset:

  • When did Cushman take the most photographs?
  • Where did Cushman take the most photographs?
  • Can you connect travels or photographs with events in Cushman’s life? You can read about him here.
  • When and where did Cushman take photographs of landscape features, like trees, clouds, and the sky?
  • When and where did Cushman tend to take photographs of people?
  • Can you map Cushman’s travels to a particular road or interstate highway? How would you do this?
  • What other information would you need to fully understand this data? How might you obtain that information?

And check out the way in which my undergraduate students used the Cushman dataset as the basis for their final project!

New course for winter: Selfies, Snapchat, and Cyberbullies: Coming of Age Online

Photo: "Selfie," by Loren Kerns
Photo: “Selfie,” by Loren Kerns

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.

Here and There: Creating DH Community

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. Continue reading “Here and There: Creating DH Community”

Frequently asked questions about lobotomy

Image from the manuscript for Walter Freeman and James Watts' second edition of Psychosurgery (1950).
Image from the manuscript for Walter Freeman and James Watts’ second edition of Psychosurgery (1950).

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. Pressman, Jack David. Last Resort: Psychosurgery and the Limits of Medicine. Cambridge History of Medicine. Cambridge, U.K: Cambridge University Press, 1998.]

Continue reading “Frequently asked questions about lobotomy”

How Did They Make That? The Video!

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.

Special thanks to my all-star cast: Rachel Deblinger, Moya Bailey, and Elijah Meeks; and to Matt Gold at CUNY for inviting me to give the talk.

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.

Reflections on my digital materiality and labor class

Group photo on top of One Wilshire.
DH150 on the roof of One Wilshire. Photo by Craig Dietrich.

I was really glad to get the chance to teach a special topics course on Digital Labor, Materiality, and Urban Space last quarter. I’ve been thinking about this class for years, and the syllabus is the (imperfect) culmination of lots and lots of reading and thinking.

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.

Continue reading “Reflections on my digital materiality and labor class”

How Did They Make That? at CUNY, March 27, 2014

Screen Shot 2014-03-27 at 4.19.59 PMHere’s a list of links for my talk at the CUNY graduate center, for the audience members who’d like to follow along:

My original “How Did They Make That?” post (with Dot Porter’s Zotero library!)

UCLA Digital Humanities 101

Ben Schmidt, A Year of Ships

University of South Carolina Digital Libraries, Negro Travelers’ Green Book Map

Radu Suciu, Medical Case Studies on Renaissance Melancholy

Kieran Healy, A Co-Citation Network for Philosophy

Rachel Deblinger, Memories/Motifs

Stephanie Evans and Moya Bailey, SWAG Diplomacy

Stanford University Library, Kindred Britain