In a recent attempt to introduce undergraduate students to the potential of a digital humanities (DH) visualization for their local history class, the students were tasked to plot their hometown on a Leaflet storymap (Dougherty and Ilyankou 2025; see Appendix for resources) and illustrate it with a photo (Figure 1). Some images were easy to find with available online sources like Wikimedia Commons, but others had more difficulty completing the assignment. In many cases, their small hometowns just weren’t represented online. One student uploaded his personal photos to Wikimedia Commons so he could complete the assignment. In the process, he unknowingly also changed the online landscape. Others pivoted, changed their topics, or found other places of interest in their towns, ones perhaps less representative of their place and experience. The final project then was maybe less authentic and their voices and perspectives were literally left off the map. This experience highlights many of the challenges inherent in digital humanities, minimal computing, and humanities pedagogy. How can students respond to data that isn’t even there? How can they create a new digital landscape that represents their own underrepresented pasts and experiences? How can students learn to question data as a flawed, conditional source and enrich our understanding of technology with a critical and inclusive eye? Although a number of new curricula aim to familiarize students with data-driven methods, students need more than the skills of data wrangling; they need a firm grounding in data ethics and critical data studies, which the humanities disciplines are well-placed to offer (Bhargava and D’Ignazio, n.d.; U.S. Census Bureau 2022). As the Leaflet storymap example illustrates, small assignments can leverage minimal tools for maximal outcomes, opening up high-level conversations about critical data studies even as they teach core technical skills and concepts. This article argues for pedagogies that pair minimal tools with small data as a vehicle for teaching critical, humanistic approaches to messy data, evaluation of data practices and tools, and considerations of technical sustainability issues, all within a humanistic framework.
Digital humanities, a wide-ranging field that encompasses both technical approaches to the humanities and humanistic approaches to technology, offers students a wealth of tools for digital research and storytelling. DH tools and methods can bring primary sources to life, help students identify patterns across large corpora, bring their scholarship to public audiences, and engage both critically and playfully with technology. But they also present significant barriers to many students. Many tools can be technically daunting, requiring high levels of specialized knowledge and confidence on the part of the students. These projects require students to think differently about time and project management as well, adding layers of technical labor and decision-making to the research and writing process. With experience, students develop the ability to troubleshoot technical difficulties; in the meantime, however, such difficulties add frustration that sometimes causes students to revert to a more traditional paper. Minimal, small data engagements respond to some of these challenges. A tool does not need to be complex to help students tap into high-level conversations about data ethics and DH. Assignments and activities that use and build on familiar tools like spreadsheets provide a gentle entry point into DH and data, creating pathways to more complex digital humanities projects for some students and cultivating critical data practices for all.
The small engagements with data that we describe below draw on the principles of minimal computing, a collection of approaches to computation centered around the question “what do we need,” and more precisely “what is enough?” (Gil 2015). The answers to these questions might take any number of shapes: efficient machines that minimize our environmental impacts; clear and uncluttered interfaces that render software more legible to new users; websites that forego bandwidth-hungry media to reduce the costs of accessing them (Sayers 2016). These approaches to minimalism, different and sometimes contradictory as they are, share a commitment to equity, access, and care. They also share an implicit rebuke of technological cultures of consumption and the drive to collect, monetize, and weaponize ever more data. Scholars have called out the harms inflicted by this fetishization of data, from water and energy usage to algorithmic bias to labor impacts, repeatedly over the last decade (O’Neil 2016, Noble 2018, D’Ignazio & Klein 2020, Monserrate 2022, Wei 2024). Now, with the widespread adoption of generative AI, data practices are newly in the public eye (Hao 2024, Gilliard 2024, Wittenberg 2025). In such an environment, the idea of “enough” is a value statement. We draw on minimal computing in the classroom to (1) introduce and scaffold core data skills and concepts one at a time, (2) equip students to critically evaluate ostensibly data-driven tools and research they encounter, and (3) encourage students to embody an ethics of care in their data practices, paired with an awareness of the material impacts that data and computational practices have on the environment and on their communities.
Place-Based Pedagogies
While these are global crises, there are good reasons to frame them pedagogically within local contexts. Local manifestations of global issues render those issues more tangible and impactful. For instance, the environmental impacts of generative AI take on a different light when a data center is proposed in the heart of your own city. Instead of a water bottle per query (Verma and Tan 2024), the stakes of that data center are rendered tangible in immediate and visceral ways: the creation and elimination of jobs, rising utility prices, environmental damage, and changes to the urban fabric. DH pedagogy can and should respond to local contingencies and help students articulate their relationship to place as residents, scholars, and advocates. For us, our local contexts include metro St. Louis and southwestern Illinois. Southern Illinois University Edwardsville (SIUE) is a regional public university located in St. Louis’s Metro East, on the Illinois side of the Mississippi River, which is within a major metropolitan area but culturally and physically divorced and decentered.
Collectively, the greater St. Louis region has a very uneven digital infrastructure distributed across the geographic, demographic, and economic boundaries that demarcate this region—boundaries with historic roots and ongoing manifestations (“Illinois Broadband Maps” 2024; Ernst & Young 2022). This impacts our DH teaching in several ways. The digital divide impacts the level of comfort and familiarity students bring to technology in the classroom; in addition, many of them access the internet primarily through a smartphone or a tablet, limiting the tools they can use for in-class activities and homework assignments. But the region also offers opportunities for students who cultivate skills for the local job market. The St. Louis region is home to a thriving tech industry, including the National Geospatial-Intelligence Agency, established corporations, and start-ups. Technical skills equip students to get jobs in these arenas, but DH approaches to data and technology equip them to carry out their work with attention to human impacts.
Our work is additionally informed by our respective positions within a DH center and a history department, where through formal courses and informal instruction we engage graduate and undergraduate students from across the university in discussions and applications of the digital humanities. Beyond core humanistic data literacies, which have currency across the curriculum, we also utilize minimal, small data engagements to build skills and confidence with DH for humanities majors and to scaffold larger projects that they might implement in a class or as a capstone assignment. Faculty and students both express interest in digital senior projects, for instance; but when their first encounter with DH is in their senior year, the accumulated barriers of time, technical learning curve, and project design and management often render a digital project unfeasible. Others struggle to imagine what a digital project might look like. Although we are both historians by training, we have found that those institutional contexts matter. Digital humanities as a discipline is not only more expansive in content, but often more methodologically focused. By contrast, history departments offer students deep engagements with content and disciplinary perspectives. The breadth of DH and depth of history generate important points of conversation and occasionally friction for students and faculty alike. Frequent low-stakes encounters with DH projects, coaching and consultation in the DH center, and small data assignments in multiple classrooms are yielding results, with more and more students considering and in some cases implementing a digital project.
Humanistic Approaches to Data
The following digital humanities activities and assignments centering small data sets equip students and instructors alike to work from the ground up, creating resources that introduce students to critical, humanistic data concepts through bitesize, hands-on exercises. There is a persistent focus in them on identifying issues of spatial and data justice. But digital pedagogies are not just a way to mitigate these increasingly heavy social realities. They also empower students to respond creatively, to build communities of practice, and to center their own knowledge and lived experience counter to technology’s tendency to aggregate people and data. As Viglianti et al. (2022) suggest, minimal computing need not be a deficit response to technological and economic constraints; instead, removing reliance on proprietary tools and infrastructures grants agency and power to students. Digital pedagogies done well can make students critical thinkers and literate users of new technologies so that they can intentionally shape the digital landscape instead of becoming victims of these ethical and technological pitfalls.
This is a crucial moment for such interventions, as existing infrastructures for data and information literacy are under threat. Libraries, which have historically provided robust data and information literacy training, are increasingly and systematically weakened by book bans, defunding, and staff reductions. This is a problem with both national and local manifestations. The state of Missouri made national headlines in 2023 when its legislature attempted to defund public libraries across the state following challenges to its extensive book bans (Smith 2023). Although the state of Illinois has vocally opposed book bans, local funding priorities have continued to strip away resources from libraries. Edwardsville, Ilinois’s District 7, for instance, has not employed a certified librarian since 2015 and relies on state resources and the local public library for some library services (Weaver 2024). The high school’s library space has since been dismantled and repurposed as classrooms. This lack of access to library resources continues at the post-secondary level. At Western Illinois University, for instance, all nine tenured or tenure-track library faculty were among the lay-offs announced in August 2024 (Palmer 2024). The defunding and deprioritization of libraries and librarians is a massive problem in its own right, which cannot be fully mitigated in the classroom. But in the absence of librarians, it is even more important that students develop data and information literacies and a critical eye toward AI and other emerging technologies.
Our work builds on existing frameworks for information and data literacy. The Association of College and Research Libraries Information Literacy Framework (2016) rests on six key concepts: that authority and expertise are constructed and contextual, that knowledge production is processual and iterative, that information has value (legal, economic, social), that research is also iterative, that scholarship is an ongoing conversation, and that search and information acquisition are strategic rather than straightforward. These propositions, which affirm the situated and constructed nature of information, are a firm foundation for disciplinary explorations. Abner (2020) has expanded on these and on related data literacy frameworks to articulate some of the key contributions DH makes to data literacy, suggesting three further pillars: that data is shaped by both humans and machines; that it offers only a partial vantage point to complex issues; and that data, like the research process that produces it, is iterative. To these, we add further extensions rooted in the fields of history and DH.
While each field brings its own disciplinary approaches to data and information literacies, the humanities disciplines offer crucial interventions in two categories. First, humanities data—that is, the data we derive from historical, literary, artistic, and other humanistic sources—reveals its messiness readily. Such data is often unstructured, mediated through multiple layers of transmission and interpretation, subject to contingencies of time and place, and subjective in any number of other ways. Indeed, Drucker (2015) has proposed that on the basis of these qualities, we relabel data (literally “given” in Latin) to capta (Latin for “taken”) to reveal the many points of intervention in the data lifecycle. This self-evident messiness forces data practitioners to confront the constructed and situated nature of data, facets that are obscured (although no less present) in more “scientific” data sets. As a result, humanist data practitioners have also cultivated humanistic approaches to data that lend value to any data-driven analysis: interrogating the sources, assumptions, arguments, and biases that shape all data; assessing and ethically addressing the human and environmental impacts of data and data practices; placing data and data practices in conversation with other historical and contemporary injustices; accounting for ethical considerations of access and privacy; and critically engaging with our technical infrastructures.
Underlying many of these concerns is an ethics of care, of a technical practice grounded in relationship and mutual responsibility. Our approaches to minimal computing and small data draw on existing conversations surrounding data sovereignty and digital ethics. We draw on D’Ignazio and Klein’s (2020) formulation of data feminism as a call to examine and critique the intersections of data and power, to elevate affect and embodiment as ways of knowing, and to render labor visible. Odumosu (2020) also identifies several obligations to those represented in the data we use: (1) to tend to those represented in or excluded from the sources; (2) to “welcome people into mindful encounters” with moments of historical trauma, encouraging people to engage with digitized materials in a way that centers emotion, affect, and relationship; and (3) to “guide people overall in the use and circulation of sensitive visual material,” particularly attending to whether we’re broadcasting destructive rhetoric that can likewise circulate without context. These are all steps developed out of a commitment to care and to relationship. This ethics of care is a necessary foundation for a humanities approach to data.
Catherine D’Ignazio (2017) has written persuasively about the power of creative data literacy pedagogies to engage students with these issues. In order to provide accessible learning opportunities for students from diverse populations, she argues that curricula should be focused, guided, invited, and expandable. DataBasic.io, a collaboration between D’Ignazio and Rahul Bhargava, employs these principles in the suite of data exploration tools and brief lesson plans. D’Ignazio and Bhargava’s tool-first approach is ideal for encouraging a sense of play and exploration in working with data. Our pedagogical approaches extend their work by building out modules that are similarly playful but rooted in humanistic content.
Critical evaluations of sources and projects like Odumosu’s and playful but powerful curricula like those developed by D’Ignazio and Bhargava speak to Paul Fyfe’s (2011) observation that pedagogies don’t need to be technically complex, or even technical, to effectively teach digital and data literacies. Fyfe’s digital pedagogy foregoes tool mastery in favor of closer engagement with the fundamental concepts underpinning DH computing, like categorizing, curating, and mining. The activities he describes are powerful tools for bridging the digital divide in the classroom. Likewise, material data visualization activities allow students to grapple with data outside the confines of software, instead thinking creatively and expansively about what forms might help to mediate both the individual lives represented in a data set and arguments about the whole. All of these activities constitute a form of “screwing around,” Stephen Ramsay’s (2014) formulation of DH pedagogy as play. Together, these referents outline a variety of methodologies for creating a pedagogical environment in which to explore themes of ethics, access, and digital flexibility so that students can see themselves in a new digital landscape and become makers of a new humanistic space.
In response to socio-cultural, scholarly, and infrastructural pressures, our experiences and commitments lead us to several best practices for DH pedagogy that center small data and minimal computing. Such engagements with DH in the classroom should respond to local affordances, accounting for the opportunities, limitations, and cultures of local and regional communities. They should teach across the curriculum, making visible to students both the ways that technology extends humanistic research as well as how the humanities enhance STEM and social science fields. They should offer students the opportunity to intervene critically and creatively in the fields they study. And they should make explicit linkages between technologies, the humans who produced them, and the humans who use them. The assignments and activities below, a collaborative Leaflet map, spatial analysis, and re-envisioning traditional thesis projects, integrate these skills directly into curricula. Each assignment includes additional resources in the appendix.
Activities and Assignments
Mapping St. Louis’s cultural landscape with Leaflet
One way to render visible the linkages between technical systems and human users is to have students create a data set. In this activity, students work together to map the St. Louis region’s cultural landscape (Figure 2). This highlights the ways that data is shaped by people and as a result encodes our biases and assumptions. Students are charged to create a data set of culturally significant sites within the St. Louis metro area. The goal is for them to think about data cleaning from the machine’s perspective (ensuring our data is computationally viable) and from the humanistic perspective (interrogating how our data practices shape our outcomes).
This activity takes place entirely within the classroom, and it has been successful in both dedicated DH courses and workshops across the university. The technical side of it is fairly quick, although discussions can stretch on for ages! Prior to class, the instructor uses a GitHub template to set up a web page with a Leaflet map that draws its data from a Google sheet (Smith 2025). In class, each student adds at least one site in the metro area that they think of as a cultural landmark. They add a title, a brief description, and coordinates. After they’ve done that, the instructor refreshes the map page—and almost without fail, it breaks. When students copy in coordinates, they often introduce formatting issues that make the sheet impossible for the map to parse. This is an opportunity to talk about data cleaning for machines. One of the reasons we clean our data is to make sure that it is machine readable—that is, in the format the computer expects. The instructor and students clean the data together and refresh it until the map loads.
When the map is functional, students make observations about the distribution of the points. They note where the clusters are and what gaps remain. Do those indicate a lack of sources, or a lack of attention by the researchers (in this case, them)? In what other ways are the students’ choices, preferences, and biases evident in the map? They also engage critically with the relationship between computational data cleaning and humanistic concerns. How were they constrained by the columns on the spreadsheet (that is, by the instructor’s choices)? If they were creating the spreadsheet, what columns would they create, and what categories would they impose on their data? Despite the variety of courses in which this exercise is used, one of the most interesting trends is that no matter the classroom context, students consistently ignore St. Louis’s Metro East, the portion that lies in Illinois. This is ironic, since SIUE itself is located in the Metro East and many students call this region home, but it is not surprising. St. Louis exercises an outsized influence on the region’s mental geographies, and the Metro East is often relegated to the butt of a joke or the victim of racist cliches and stereotypes (Sumida and Jack 2019). Seeing their own mental geographies laid out on a map, that gaping void in the Metro East is the first thing students notice. It provides a valuable opportunity to talk about how data and narrative are often deployed in mutually reinforcing ways. But it also opens up conversations about how data might be leveraged in the opposite direction to effect narrative change.
Through this activity, students recognize that data is constructed, open to interrogation, and situated in time and place. It illuminates their own limitations as scholars and data practitioners, revealing the need for collaboration and multiple voices. By highlighting the obvious messiness of humanities data, as well as the lessons learned by digital humanists and critical data scholars over the last twenty years, students can learn to identify the better-hidden messiness of data in other fields, from finance to marketing to political polling (D’Ignazio 2017). Seeing their points on the map evolve in real time also highlights common themes, misconceptions, and how individual data points can create a virtual ecosystem that reifies and amplifies existing beliefs.
Spatial analysis with Google My Maps
While mapping the region’s cultural landscape helps students understand their own preconceptions about their communities, spatial tools can also equip them to recognize the structural inequities that shape their lives in material ways, both while they are here on campus and wherever they land in the future. Students use Google Maps and Google My Maps to analyze access to basic local resources and amenities (Figures 3 and 4). Spatial analysis is a method that uses mapping and geographic information systems (GIS) to analyze the relationships between people, places, and resources. Those relationships dictate what kind of access people have to things like grocery stores, banks, employment opportunities, green spaces, and more. In turn, those relationships are a great lens for exploring concepts of spatial justice (Soja 2010).
The activity begins in the classroom and has been used in both DH courses and throughout the general curriculum through class visits to departments like Computer Management and Information Systems. In small groups, students choose an amenity that they use regularly. It can be anything—a grocery store, a gas station, a park, or anything else that they rely on. In Google My Maps (a version of Google Maps that allows the creation of custom maps with multiple layers of data), they search for that generic resource and create a map layer featuring all the local instances of that amenity. As a group, they make some observations: What does the distribution of those pins look like? Where are they clustered? Are they in proximity to the university? Are they distributed evenly throughout the area?
Having gathered some raw data about quantity and distribution of their amenity, they then turn to questions of access. They pick what they consider to be the best option, the one that best serves their needs. Often that’s simply the closest, but sometimes they have other priorities. One student mentioned that their preferred products are often out of stock at the local Target, for instance. Others note that although there are banks nearby, their own bank doesn’t have a local branch. Whichever they identify as their preferred location, they then turn to Google Maps to determine how accessible it is. How long would it take to drive there? Bike? Take the bus? What would it cost, accounting for gas, parking, transit fares, exertion, and other costs that might crop up? And crucially, within those parameters, who has access and who doesn’t?
The final stage of the activity is a reflection, due the following class period. Students consider the implications of their analysis and whether there are elements of spatial injustice at play. They consider whether the things they observed in the lab have impacted how they experience the university and its surrounding community, as well as whether they might impact the experiences of other students, faculty and staff, or community members. If they identified an element of spatial injustice, they speculate about root causes and how it might be addressed.
This assignment asks students to use easily available data and platforms to identify structural inequity in the communities they inhabit. One notable example was a Black student who chose to search for beauty supply shops. The nearest one that served her hair needs was in East St. Louis, some fourteen miles away and at minimum an hour-long commute by public transit. Two potential responses emerged from that discovery: to support more Black entrepreneurs who might open businesses closer to the university, and/or to enhance the public transit infrastructures and cross-county connectivity that made the commute so arduous. Those creative responses to structural inequities are what shift this assignment from disheartening to empowering. The assignment takes those gaps in the data that they engaged with critically in previous assignments, and it turns them into opportunities to imagine more just communities. As a result, students learn to see themselves as data practitioners and as advocates, learning skills that they can leverage both in a workplace and in their own communities as they move out into the world. The assignment taps into many of the practices we aim to cultivate. By centering students’ own local lived experience, it responds directly to local affordances, and by asking students to move from observation to explanation to solution, it empowers them to intervene in patterns of structural and systemic injustice that they themselves have identified.
These deep local interrogations reinforce the benefits and complexities of place-based, small data pedagogy. For the instructor, it offers a laboratory of sorts to dissect structures, economies of scale, and systems that either work or don’t work. For the students who call these places home, it requires a new critical eye that may disrupt their local knowledge and community culture. These conversations are mutually informative, but they can also generate frictions. Local residents have a better handle on how they use the space but instructors may lend a distant eye to better analyze systems and structures students are blind to. In both cases, care is necessary. Scholars need to remember that these places are not just geographies to study but dynamic relationships that people call home. Recognizing these dynamics is crucial to teaching regional data analysis for both the student and the teacher.
Digital history projects
Activities like the two mapping assignments above bring the humanities into courses across the curriculum, asking students in fields like data analytics and computer science to bring the humanities to bear on their work. Likewise, departments in the humanities (including the digital humanities) have integrated digital assignments into their courses as a means of broadening and deepening students’ engagement with humanistic sources and arguments. Over the past year, small data engagements in digital humanities and history courses have created pathways for history majors to broach producing larger-scale digital history projects in their senior seminar. Small data, low-stakes activities have built confidence with digital tools, helped students to see the applicability of data-driven methods to humanities disciplines, and illuminated a pathway from small-scale to large-scale digital projects.
There are many reasons why we should continue to propose and promote DH projects for history majors. First, many of our undergraduate students do not go into academic careers where research papers are necessary or even valued. Second, history departments in general are working to keep the discipline workforce ready and modern, answering the dreaded question, “what do you do with a history major?” In recent years, we have updated our course descriptions to include non-thesis projects as viable products. Finally, integrating DH pedagogy into the curriculum at all levels is responsible teaching that keeps elements of rigor, primary source analysis, critical thinking, and communication skills at the forefront while also preparing them to utilize their skills in a variety of applications after graduation–digital storytelling and data analysis as but one example. Digital projects help students humanize historical data, think intentionally about K-12 and public audiences, and develop creative approaches to public history that leverage the affordances of digital platforms that go beyond the printed page. Despite these benefits, we have encountered many obstacles to students who want to embark on this journey. Through the implementation of small data assignments at earlier stages, we are starting to see some students branch out and leave the traditional paper behind.
For many years, the History Department has been trying to expand students’ visions of a senior assignment beyond the traditional thesis, and barriers have been present at both the student and faculty level. In a technical sense, we often underestimate that students lack digital fluency. Many of our students are coming to college never having used the Microsoft Office suite, while others may have never worked with a spreadsheet—let alone engaged in code, thought about HTML, or seen the back end of a website. In addition, history students, we’ve found, lack the project management skills to coordinate the scaffolding required for DH and do not consider both the scale and scope of the project. Creating a capstone-level StoryMap or TimelineJS product requires research and production, an experience many of them have not encountered. Our history students are familiar with the process of creating a research paper because of years of training in middle and high school, but generally haven’t encountered this next step. Some students who attempted a project left time for the research but did not have enough time before the deadline to make sure, for example, that the photos appeared properly—a very time-consuming element. Students also have not seen many projects that match the scope they can achieve in a semester. Many examples from DH centers are multi-year, well-funded, collaborative endeavors, not an individually researched and produced product. There are plenty of 20-page papers students can look to for scope and content, but DH projects seem foreign and overwhelming.
Faculty also can adjust expectations to make digital projects easier to complete and successful. The fact that students often come to us without having used seemingly foundational tools like Microsoft Office is also a challenge for faculty, because it forces us out of our comfort zone. Paradigms for digital workspaces are shifting, and whether that’s fundamentally a student problem or a faculty problem is a matter of perspective. There are good reasons that students might have deviated from what we anticipate: Microsoft Office products are proprietary and prohibitively expensive. Google, although still proprietary, has done an impressive job of claiming the school market with free replacements. While we need to prepare students for the workforce beyond the university, the debate over whether that should be knowledge of proprietary software like the Microsoft suite or open-source programs for digital humanities projects is still a friction with which we grapple. And this is only one example of how different access, priorities, and assumptions about the purpose of technology might produce different expectations between students and faculty. Small data assignments provide low-stakes ways for faculty and students to identify and hammer out those gaps and points of friction, establishing shared technical frameworks together. We can also adjust the syllabus to factor in time for production. Our syllabus maps out progress toward completion in a thesis-driven timeline. Offering a timeline also appropriate to the cadence of digital project management can help students succeed by the deadline and with a project they can be proud of.
Additionally, non-DH faculty can broaden their expectations of what “rigor” and “product” mean. At SIUE, like at other universities, many faculty were trained in a “traditional” manner, creating articles and books and thus teaching their students to create articles and books (Waltzer 2012). Reworking our own expectations of what scholarship looks like is sometimes a large hurdle and presents pedagogical restraints. In this sense, “minimal computing” is a challenge that runs through faculty expertise and disciplinary expectations. Not only have faculty been primed to value the research paper as the most rigorous output, they also often struggle to support digital projects. Setting the scope of a digital project, determining a reasonable timeline and workplan, and helping students troubleshoot digital tools all present departures from the norm and additions to an already significant workload. If these sound like many of the same challenges that students face, that’s no coincidence. Managing these challenges to minimal computing needs to be interwoven throughout undergraduate curricula and faculty development to encourage their use.
Similarly, we can continue to focus on skills learned and not just content mastered. Our history department rubric for graduating seniors does not require a paper per se. Students need to demonstrate knowledge of relevant related literature, show evidence of conducting primary research, craft a well-formed historical question that is critical and novel, and present their findings in appropriate written and oral form to their peers and teachers. All of these goals are easily proven in a DH project. Small data projects at the lower-level courses can teach faculty and students how to break into digital formats with minimal computing and prepare them for larger projects at a capstone level. With the support of the IRIS Center for Digital Humanities, faculty have been holding guest workshops and classroom visits to all of our history majors to introduce the idea of a digital humanities project in lieu of a written thesis as a final project. Even if they do not create a digital capstone project, these smaller-scale class projects teach the same transferable skills that serve them beyond the discipline and the classroom.
We are now seeing the results of early small data engagements in the senior seminar. In the spring 2025 semester, two students seriously considered a DH format for their senior product. They had previously created small-scale StoryMaps in another class so they could visualize what something might look like. One explored how students at the University of Illinois Champaign-Urbana reported on World War I before, during, and after America’s involvement through their student newspaper, and another proposed a comparison of representations of militancy within the suffrage movement in the United States and the Soviet Union in the 1910s. Were these digital projects perfect? No. In the end, while the first student incorporated the various Daily Illini newspaper articles into separate entries, put together a basic timeline of these articles in conjunction with historic events related to WWI, and recognized some of the relevant literature, he was unable to load images or direct links to the newspaper database because he underestimated the programming time, even with a user-friendly, open-source program. The other student parsed her data in preparation for the project but after seeing a visualization of her research, decided to create a more traditional thesis paper instead of a digital project. Both of these can be considered successes. These students broke out of their comfort zones to try something new, overcoming a pedagogical and intellectual barrier. Both ultimately selected an appropriate presentation of their research based solely on the richness of their own small data sets and research. By Fall 2025, three students in the history senior capstone class have embarked on digital projects at the start of the semester because they had exposure to small data/small scope projects in earlier classes. While we haven’t surveyed the students, they have self-reported that their prior experience and completion of Leaflet StoryMaps and TimelineJS projects helped them envision storytelling through a non-thesis project. While their work is still in progress at this writing, their initiative is an exciting and promising development that we hope will continue.
Conclusion
The value of minimal DH pedagogy is clear, for the humanities and for fields like data ethics and critical technology studies alike. Humanities data is messy. Our sources are full of conflicting information, ambiguous references, emotion, and lived experiences. Those things make them valuable for the humanist, but very difficult to combine with methods of data analysis and data visualization that usually produce predictable structures. Assignments that rely on minimal tools and small data teach students to recognize, interrogate, and respond to that messiness, whether they encounter it in a small dataset of their own making or in data produced by and for corporate or governmental interests in the world at large. Such tasks rely not only on a technical understanding of data processes but on humanistic skills of interrogating source bias, authorial intent, and their own scholarly biases.
The St. Louis region is also an excellent place-based laboratory in which to explore these issues. The deep infrastructural digital divide and the thriving tech industry at times seem at odds with each other. The professional sphere works in its own ecosystem, seemingly oblivious to their neighbors who don’t even have access to the internet outside their public libraries. This perceived vacuum can be a place of production. SIUE’s students often come from these digital deserts, and as they learn about these tech skills, they can bring them back home, integrating their own experiences and informing their communities. Like many institutions, SIUE therefore has an obligation to be the bridge between these two spheres, and we can help mitigate the chasm.
However, place-based pedagogies are scalable and replicable. We hope that these assignments are reusable in their current form; but even more, we hope that they serve as models for linking the local and global in the context of data practices. What might spatial justice look like in a former sundown town? What biases might students carry into the classroom in Hastings, NE, vs. San Francisco? The classroom engagements we have described are a small but powerful and flexible toolkit for engaging students with minimal computing and small data in ways that tap into a region’s affordances, encourage students to think across the curriculum, and empower students to be active, critical, and informed users of technical infrastructure.
Through these engagements, we respond to the questions posed by minimal computing: What do we need? What is enough? Like many of the manifestations of minimal computing, these questions are sometimes in tension with one another. The former invites us to ask for things: to identify the supports and scaffolds that make complex technologies more accessible, to demand more robust and equitable infrastructures, and to free tools and data from proprietary systems. The latter invites constraints: Who do we harm, silence, or exclude through our practices? For what reasons do we want more? How do we know when we have enough? Minimal computing does not offer us or our students easy answers to these questions, but through continued critical engagement, it does offer us the tools to develop our own digital ethics of care attuned to the communities and ecosystems to which we belong.
Appendix: Resources and Curriculum
Hometown mapping with Leaflet Storymaps (Fowler)
Learning objectives
In this activity early in the semester, students add a brief description and image of their hometown to a collective storymap. In Fowler’s History of Illinois class, it serves multiple objectives:
- Seeing their hometowns displayed on the collective storymap helps students forge connections with one another and identify shared experiences as well as how their individual experiences of nearby locations might differ in surprising ways.
- Recognizing that the majority of their fellow students come from a small geographic radius reinforces and creates a sense of community in the classroom and at SIUE.
- Understanding where we come from individually and collectively helps us to identify the limits of the knowledge we've acquired through lived experience.
- The small-scale engagement with a digital humanities project—in this case, adding a single row to a spreadsheet—doesn’t provoke much friction, and any technical challenges that arise are easily addressed.
Technical components
This activity uses the Leaflet Storymaps with Google Sheets template produced by Jack Dougherty and Ilya Ilyankou, one of several in their Hands-On Data Visualization. Setting up the storymap requires a GitHub account and a Google account. Students only need access to a device that can edit a Google sheet.
- GitHub template repository: https://github.com/handsOnDataViz/leaflet-storymaps-with-google-sheets/.
Resources
- There is a freely available tutorial in Hands-On Data Visualization, with clear instructions and screenshots for each step of the process as well as an example project. Jack Dougherty and Ilya Ilyankou, “Leaflet Storymaps with Google Sheets,” in Hands-On Data Visualization (O’Reilly 2021, updated 2025), https://handsondataviz.org/leaflet-storymaps-with-google-sheets.html.
- For students (or instructors!) who struggle with HTML, the WYSIWYG HTML editor is a very helpful resource. Entering and formatting text in the left-hand editor generates HTML in the right-hand editor, which can be copied and pasted into the spreadsheet. If the generated HTML spans multiple lines (which will paste into multiple cells in a spreadsheet), the Compress HTML button at the bottom of the editor condenses all the code to a single line. WYSIWYG HTML Editor, https://wysiwyghtml.com/.
Mapping St. Louis’s cultural landscape with Leaflet (Smith)
Learning objectives
Students construct a dataset documenting their local cultural landscape, adding sites that they find culturally significant to a Leaflet map via a Google spreadsheet. They analyze the resulting map to identify the ramifications of their assumptions, biases, and habits on the data.
- By collaboratively cleaning the data to make it machine-readable, students understand data cleaning from the computational perspective—that is, making sure that data is well-formatted and in the anticipated structure so that the computer can parse it.
- As they analyze the resulting map, students also understand data cleaning from the humanistic perspective—that is, recognizing how they have individually and collectively shaped the data and encoded their own assumptions and biases in it.
- The discussion helps students think about bias from multiple perspectives. No single student is responsible for the patterns on the map; they are the reflection of collective beliefs.
- But through that discussion, students also develop skills of recognizing that collective bias and of addressing it in their own data practices.
Technical components
Like the Leaflet storymap, this is also a GitHub template developed by Smith. Setting it up requires cloning (that is, copying) the repository and Google sheet and making a small update to the code to update the Google sheet link. Instructions are included in the repository.
- GitHub repository: https://github.com/msmith0913/teachingwithmaps
Resources
- The repository includes a walk-through for setting up the map, as well as troubleshooting tips for some common issues.
- If setting up the GitHub repository seems too daunting, a modified, code-free version could be done with Google MyMaps by having students add points within the custom map. This would lower the technical barriers for both students and faculty; however, it would also eliminate discussions of cleaning data to make it machine-readable.
Spatial analysis with Google My Maps (Smith)
Learning objectives
This two-part assignment asks students to first map the distribution of a particular amenity in their area using Google My Maps, then consider who has access to that amenity and what barriers they might face in accessing it.
- Students learn to use freely available and familiar tools to access and visualize data that impacts them and their communities.
- By exploring access through multiple modalities, students consider multiple vantage points in relation to the same data points.
- In discussions about the roots of the patterns they observe, students frequently look to the past and learn to read their community as a palimpsest, with the consequences (good and bad) of a community’s past written on its current infrastructure.
- By moving from observation to explanation to solution, students start to see injustice not as inevitable but as the result of choices, ignorance or misuse of data, and uneven investments—all of which can be changed.
Technical components
This activity requires a Google account for the My Maps portion. It does not require any technical setup on the part of the instructor, but it does require clear instructions for students.
Resources
- A walk-through of the assignment and the instructions I’ve drafted for my students are in the GitHub repository: https://github.com/msmith0913/spatial-analysis-google-mymaps.
