Dawn S. Opel is an Assistant Professor of Digital Media and User Experience in the Department of Writing, Rhetoric, and American Cultures at Michigan State University. She currently serves as a Policy Research Fellow at the Center for Health and Research Transformation at the University of Michigan. An action researcher, she works to improve the design of communication across healthcare, government, and nonprofit organizations for enhanced coordination of patient care. Recent published work appears in
Michael Simeone is the Director of Data Science and Analytics for Arizona State University Libraries and Assistant Research Professor (FSC) in the ASU Global Biosocial Complexity Initiative. He studies interdisciplinary data science and data visualization.
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This article illuminates the ways that digital humanities labs might foster
experiences for graduate students that fulfill what Alexander Reid (2002)
postulates as the central task
of the digital
humanities graduate education. We argue that while the digital humanities lab as
an institutional economic model does not necessarily promote a focus on graduate
student professionalization, it uniquely has the capacity to push back against
competing discourses of neoliberal vocationalism, funding and labor precarity on
one hand, and technological utopianism and tool fetishization on the other, to
train students agile, contextual, and rhetorical mindsets with which to enter
technologically-mediated workplaces and lives. To begin, we review the
discussion of digital humanities labs in the literature: digital humanities
institutional models, how these models are practiced, lab funding, and the
resultant position of labs as sites of training for graduate students. From
there, we offer a teaching case from the Lab’s fall 2015 “Stories from Data”
workshop in order to render visible a set of principles to guide
professionalization of graduate students in the digital humanities lab. We
conclude with reflections on how these principles might alter current
discussions of the success or failure of the Mellon Foundation and NEH ODH
digital humanities funding initiatives in the United States.
The place of a digital humanities lab in graduate study.
In
humanistic labs, centres, projects, and products,but he maintains a striking focus on the possibilities of the digital humanities (DH) lab. For Lane, the digital humanities lab provides an opportunity to bring a Big Science research model to the humanities, which includes a shift to
lab-basedknowledge production
lab ethos
less concerned about creating in-perpetuity funds than solving a large, intractable problem
Of course this is not the only value that institutions see in digital humanities
labs. One need only inspect the technologies in a given DH lab to know what
kinds of projects in which the lab is participating or able to support. A large,
funded project will support the tools and methods needed to get the specific
work of that project done. There may even be development of specific packages or
software. By contrast, a lab that fits the computing support model will offer a
broader range of technologies, tools, and personnel to fit a broad range of DH
interests. An example of the former can be found in the humanities
college-funded Digital Environmental Humanities Lab at California State
University-Northridge, where students under the mentorship of one professor used
XMIND software to visualize data related to the creation of the national park
system in order to expose the exclusion of people of color in this narrative
The goal of the Digital Humanities
Lab is to encourage and support innovative, interdisciplinary
research projects that make creative use of technology,
said
Colleen Boff, associate dean for University Libraries. We focus on supporting projects that are enhanced by
visualization tools, multimedia applications, and manipulating data
acquired through application programming interfaces
(API).
However, what do we see when we de-emphasize the institutional and faculty aspirations so often associated with DH labs? The relationship between institutional role and research activity operates amidst other dynamics of research variation, and the DH lab is not uniform across it. As Mary Jo Deegan notes, the literature in DH in the United States (she mentions specifically Gold’s edited collections
In this article, we consider what invisible or less visible work becomes
illuminated when practitioners understand the DH lab as a space for prioritizing
graduate student needs. Graduate student perspectives may be sufficiently
overlooked in the debates of central task
of DH — graduate
education
It is difficult to discuss the DH lab in a vacuum from the forces that create, shape, and reshape it over time. Here, we will review how the lab as a specific form of DH institution comes into being, with specific focus on funding models and projected inputs and outcomes from that funding. We will consider how the practice of DH in the United States and outside of it may affect the composition of labs and their graduate training. The graduate students who come to labor in the lab are a visible and direct result of the synergistic forces of funding and institutional shaping rather than consideration for the goals and objectives of graduate education. Based on these considerations, we will take a look at how various labs have approached graduate training and education as a part of that institutional model. We then argue that much of that graduate training and education is invisible because it is not tied to these funding models and resultant lab practices.
Training offerings in DH labs relate closely to the mission of the lab itself. As
with the Bowling Green example, a computing support lab model tends to support
general training offerings for the academic community related to particular types of software or specialist tools
provided by the lab itself
The DH lab offers a space for experiential learning that challenges a more
traditional classroom environment
Stories from DataWorkshop
We consider the affordances and constraints from such a model by offering a
teaching case from our own Lab’s fall 2015 Stories from
Data
workshop in order to render visible a set of principles to
guide professionalization of graduate students in the DH lab. The context for
this case study is the Nexus Lab at Arizona State University. During our time
employed at the Nexus Lab (as Director and Postdoctoral Fellow), it worked
largely within the Big Humanities model: teams sought out large-scale and
long-term funding; faculty partnerships for research spanned humanities and STEM
disciplines; and upper administration aligned the lab with other units at the
University invested in broad-reaching and big-impact work. However, part of the
mission of the lab was to operate in a Big Humanities space while at the same
time integrating technologies and lessons learned from that activity into
graduate student training.
The Stories from Data workshop ran in the Fall Semester of 2015. The curriculum spanned fifteen weeks, beginning in the second week of classes. Hosted in the Lab, sessions took place from 1:00-3:00 p.m. on Friday afternoons. Attendance was free and open to all university staff, students, and faculty. We did not require consistent attendance in order to participate. We believe this contributed to enormous initial interest, from which emerged a steady cohort of staff, faculty, and students. The workshop began with seventy five participants, and after steady attrition, concluded with a group of twelve who remained consistent attendees and participants. Their backgrounds included rhetoric, sustainability, industrial engineering, computer science, and literature.
Stories from Data was an ongoing exercise in connecting participant expertise
with the process and tools for decision making with data. In many ways, we
sought to provide as much value as possible to participants by using the
workshop as a way of codifying into the recognizable skillset of
Our framing principle for the workshop was People make
decisions based on stories from data, not the data alone.
Our goals
for the workshop engaged the cognitive, cultural, design, and narrative
dimensions of visualization: 1) understand the tendencies and capabilities of
users; 2) produce visualizations that help us think, not merely present
information; and 3) learn how to tell a story from data that considers visual
and non-visual narrative parts.
Generally each session fell into two parts, a presentation/discussion and a
hands-on exercise. Each session lasted two to three hours. Consistently, we
referred to the audience of visualizations as
The first weeks of the workshop consisted of discussing and exploring the capabilities and limitations of visualization users. In this phase of the workshop, we used ineffective visualizations as negative examples. We solicited students to discuss the charts with the goal of understanding the relationship between the data and visual elements, identifying the story produced from the data, and producing recommendations for how to improve each example. For instance, we discussed the world economy voronoi plot (see Figure 1) as visually impressive, but hardly navigable visually. The circular shape of the chart and the irregular shapes of the cells make it difficult to understand the relationship between the data and the area assigned to each country. The voronoi layout also makes the countries with smaller economies crowded and illegible, possibly recreating the perception perhaps the visualization was hoping to remedy. Participants also raised a concern about the use of red and green colors together that would pose accessibility concerns for colorblind users. The group wondered what a bar chart or hierarchical chart might clarify about the data, as well as discussed potential questions that could mobilize a revised visualization in a story: is the goal to call attention to inequity? To power dynamics? To correlations among large economies?
Additionally, we drew from Jeff Johnson’s
Drawing from Ben Fry’s
Defining the problem, and evaluating that definition, is the key area where
we encourage the participants to apply their expertise. What data exists and
what visual roles it will play are necessary considerations. But what data
is missing, or the conceptualization of the problem itself required critical
thinking and reflection on participant expertise. During one session, we
presented the group with data and charts about the global production and
circulation of food from the International Center for Tropical Agriculture’s
2015 survey
The group consisted of participants with various expertise in rhetoric, sustainability, industrial engineering, computer science, and literature. By the end of the day’s session, the group did not ever reach the point of sketching out or brainstorming possible visuals. Instead, they conducted a lively discussion about the representative limitations of the data in its current state (see Figure 2). A sustainability view challenged the explanatory value of any visualizations that selectively presented countries, while rhetorical considerations advised against presenting all countries at once in a display that looked impressive but communicated little. Additionally, some were reluctant to show spatial relationships without also showing those relationships unfold over time. At stake in each of these threads in the conversation is how to make a series of representations (first data, then any visualization of that data) relevant and responsible to the disciplinary values of their expertise.
By mid semester, then, the group engaged in practices that saw visualization as a set of practices rather than a digital artifact or set of tools. Before any ink or pixels, there must be a relevant and coherent sequence of inquiries.
We used the concept of a
In the end, some participants excelled at programming, others at design, and
still others at the stories and rhetorical positioning of the analytical
narratives. No matter what the aptitude, discipline, and interests of the
participants, constructing the
The Stories from Data workshop provides a teaching case to make visible what the
work of professionalizing graduate students
Instead, we argue for centering graduate students and their development as
individuals, to prepare them for a wide variety of contingencies in their future
career paths. These principles do not invoke a particular set of skills, as the
tools and methods used in industry and in academia are just tools, not
development of the individual who may use them. As Goldman Sachs banking
director Matthew William Barrett said in an interview, I
used to joke that if you can find me someone who has a degree in figuring
out patterns of imagery in Chaucer’s Canterbury Tales, I can teach him to
break down a balance sheet in 30 minutes. What you want is a mind
By nature and design, a DH lab can serve as a space of contact for graduate
students from the humanities and STEM disciplines who are approaching issues
of shared concern. It is in this way that the DH lab becomes a contact zone,
much as Mary Louise Pratt described as social spaces
where cultures meet, clash, and grapple with each other, often in
contexts of highly asymmetrical relations of power, such as colonialism,
slavery, or their aftermaths as they are lived out in many parts of the
world today
This involves, in part, understanding the benefits from being trained in a
particular discipline and working with others who are trained according to
different disciplinary ideals. Often, it is in collaborating across
difference — mixing methods or juxtaposing competing ideas within the same
discussion — that it becomes possible to break through a problem.
Unfortunately, interdisciplinary collaboration is rarely taught or trained
to academics, and as a result, grant-funded research projects across
disciplines can fall apart. As Bendix et al. notes, Thrown into strange company without preparation, ongoing guidance, or
long-term professional incentives, researchers fall back onto
disciplinary habits and raise disciplinary defenses
This principle aligns with a concept suggested by Patricia Bizzell as she
argues how Pratt’s contact zone theory may be applied to graduate study.
Bizzell writes: It would also mean reorganizing graduate study [in
English] and professional scholarly work in ways I hardly dare to
suggest. I suppose that one would no longer become a specialist in
American literature, a
It is neither new nor controversial to say that the DH lab has the capacity for experiential learning. What is harder to abide is the work of combining that experiential building/making/hacking with rhetorical, critical, and contextual understanding of the very technologies we learn to build/make/hack. As discussed earlier, the strictures of the semester system and the traditional classroom render this work nearly impossible in the curriculum. The workshop setting allowed those who wanted to invest the time to do so. This allowed the workshop to spend its time in lengthy discussion and then hands on exercises scaffolded to lead to the building of data visualizations with several different tools and methods.
The development of rhetorical and critical mindsets, and a greater understanding of the context of the tools we use, often coincided with team-based experiences across disciplines. For example, when the cross-disciplinary team tasked with creating a visualization from the agricultural survey data could not agree on an interpretative approach to the dataset, it revealed that it is much more complicated to collaborate with a tool than to just use it alone. Several students from the course went home to gain a greater proficiency with d3.js, but everyone in the workshop gained a greater intercultural proficiency around the context of use of the tool, a proficiency that will serve graduate students in all disciplines well, wherever they may find themselves in career or life.
Rhetorical and critical mindsets are also developed through the exposure to other disciplinary orientations towards technology. When humanities students critiqued the racial or gendered characteristics of a data visualization, students in STEM — not frequently exposed to cultural concerns — took notice. The reverse also took place, where students in STEM readily noticed that humanities students would be much more recalcitrant when it was time to build, instead reading or talking through the hands on components of the exercises. On the other hand, STEM students were much quicker to build first, question later (or never), which humanities students also found difficult to understand. However, over time, it helped each set to see how certain practices and activities made certain disciplines more comfortable, and how that might translate to the building of technologies and other products and services in the workplace.
By distancing the lab from both sponsored research and academic advancement (meaning, the workshop was not geared toward either), we aimed to create a workshop that was a space for graduate students to explore the material conditions of their education and employment. Inevitably — and likely because no one in the room was in a position of power over anyone else, as the workshop was not-for-credit and lab professionals were not affiliated with the disciplines or the tenure stream in the university — discussions became as much about labor in and out of the academy as well as the tools and methods for performing that labor.
Why do graduate students come to the DH lab? When graduate students arrived
in our lab, they voiced concerns. Worried about the academic job market,
about the kinds of work in industry they feel they may or may not be
prepared to do, and about the kinds of menial work they already had, as RAs
or as staff at the university. We are not alone, as Nowviskie writes about
The Scholars’ Lab at the University of Virginia: In the Scholars’ Lab, we work for the betterment of
the individual graduate students who come our way — conscious that
they are laboring within larger systems that are broken and that can
wound them. We want to broaden then their options and help them
build the technical and conceptual skills that will enable their
active engagement with the humanities well into its digital future.
We hope to render them more capable of constructing new systems and
of resisting inappropriate ones, from wherever they may
land.
In the last several years, several essays have been written about the state of
the DH initiative in the United States and its successes and challenges. In
2011, Alan Liu issued a report and a critique of DH. He asked, Are the digital humanities ready to live up to their
responsibility to represent the humanities and higher education as the
latter negotiates a new relation to postindustrial society?
lacks almost all cultural-critical awareness
and those
working from new media studies, who are indiscriminately
critical of society and global informational
empire
without
sufficient focus on the specifically institutional — in this case, higher
education — issues at stake
The workshop model and its set of principles for professionalization offer an opportunity to visualize a DH that does a different kind of work than what is often targeted in these criticisms. Much of the unrest and debate mentioned above focuses almost exclusively on past or current PI-driven, sponsored research projects in DH. What a workshop like Stories from Data makes visible is how DH might inform a digital future populated with professionals who do different kinds of work, in the academy, in industry, and in public life. The graduate students we trained experienced a curriculum that was skill-oriented, but anchored in a commitment to critical thinking and articulating their own expertise to the skills at hand. As graduate students assessing their professional trajectories and employment prospects, they experienced and interrogated the political, economic, and cultural realities of working with digital technologies, inside and outside of the humanities. They recognize the changing face of the neoliberalized university as well as industry hiring practices that largely reward experience with up-to-date technology tools devoid of the context of use. And, by working as a place for student development, the DH lab had given them opportunities to prepare for both.
Indeed, a key value of this kind of training is that it connects
Finally, and perhaps most importantly, we hope to mentor graduate students who
can render visible to the public how the academy contributes to knowledge
production vis-à-vis technology. This is not a new argument; in fact, many
digital humanists on large-scale projects echo this call to improve the public’s
understanding of and engagement in DH projects.
We need academia to step up to fill in the gaps in our collective understanding about the new role of technology in shaping our lives...It’s absolutely within the abilities of academic research to study such examples and to push against the most obvious statistical, ethical or constitutional failures and dedicate serious intellectual energy to finding solutions
Contact Zonesand English Studies.
This ever more amorphous thing called Digital Humanities: Whither the Humanities Project?