A quick one today on how I manage my task list each day and the tools I use for doing so.

For years I have used Slack to manage my working life. If you click a single message you can select “remind me later” and select a date and time. I use this religiously, scheduling out reminders for particular times and dates. If I need to follow up on a thing, it gets a date. If I need more time to reply to a message, it gets a date. You can also schedule reminders for yourself separate from messages. So, if I have an overarching task, it gets a date and reminder. It’s not uncommon for me to open slack and get 10 notifications at 9:00 AM that tell me what I am supposed to be doing for the day. Here’s a glimpse at my reminders for today:

This system has worked well for years. I rarely let anything slip, because I just file everything away as a reminder and snooze if necessary. But Slack recently changed how their reminders work. DISASTER. I can’t fully grasp how to reset the way it manages this system, but now it seems to be giving me double alerts for these notifications in a way that collapses them with DMs. It’s deeply irritating. While I’m sure I could figure a workaround, I decided it was time to disentangle this particular tool from my daily task management.

So, I’m experimenting with a physical notebook for the first time in at least a decade. Here’s my daily notebook for today:

I’ve tried such things in the past but never stuck with them for long, but this run seems to be sticking. In part, I think this is because of how I’ve made working with the notebook part of a daily ritual. Each day’s page is broken into three segments: must do, can do, and waiting. I start each day by looking at the previous day by looking at the previous page to see what needs to be moved over. I’ll then check my inbox and shuffle things around based on how answering email goes. This process gives me a sense of things that are urgent and those tasks that require actions from other people. I typically close out each work day by referring to the page once more and updating things. This process is helped along by the fact that I have found a particular set of pens that are deeply, unironically joyful to use. Working with them provides a meditative tactile sensation that grounds my start and end to each day.

Sharing all this is a tad embarrassing. I have discovered writing in a journal! I have terrible handwriting! Pens are nice! But I can’t emphasize enough what a shift this has been for my working life. It’s brought an embodied ritual to each day that I didn’t have before, and I’ve found it surprising how much energy can come from shifting this foundational part of my working rhythm. So, if you’re feeling stuck, it’s never too late to change. Your process can always find new shape if you give it new tools.

The following interview was conducted by Sam Goven, a master’s student in Journalism at KU Leuven, with Roberta Pireddu, team leader of the BiblioTech Hackathon project PostScript. Roberta provides academic support for the Master in Digital Humanities at KU Leuven. Roberta’s team worked with the postcard collection. You can learn more about the team’s work by having a look at their project poster in the BiblioTech Zenodo community and by visiting their project website.

The BiblioTech Hackathon is a 10-day event organized by KU Leuven Libraries and the Faculty of Arts. Students, researchers, and staff members of KU Leuven worked in multidisciplinary teams with digitized collections from KU Leuven Libraries. The theme of the 2026 edition was travel, which was reflected in the selected datasets: historical postcards and historical travelogues. More information about the hackathon and its results can be found on the BiblioTech 2026 website.

Congratulations on winning both the first prize and the public’s favorite! Can you tell me a bit about what first drew you to the hackathon, and have you participated in one before?

I hadn’t participated in a hackathon before, but I had organized a small one myself. It was for a project on Artificial Intelligence and its application in the cultural heritage sector. I knew a lot about the organizational aspects, but not much about how to actually participate in a hackathon. What I mainly did then was observe the other groups: what they were doing and how they came up with their projects. So I was mostly involved from the sidelines.

As for why I participated: I’m currently praktijkassistant and teaching assistant for the Master in Digital Humanities, and digital humanities students are an important target group for the BiblioTech hackathon. Taking part myself allowed me to work on a project together with the students. I also already knew the postcard collection, as I had worked with it in the past, and I thought it would be nice to create something new using that material.

And your own background is in Digital Humanities as well?

Yes, that’s right. I studied Digital Humanities in Leuven, and before that I studied history, more specifically medieval history, so my background is very much in the humanities. I’ve mainly worked with heritage collections, like the ones that were used for this hackathon.

I already mentioned you won the first prize with your project. Could you describe it in a nutshell?

Our team worked with the postcard collection, which is a very large one, and visually very attractive. It’s rich in information, with a lot of detail in the metadata, but because of its size it can be quite difficult to really explore all of those details.

What we wanted to create was a kind of website or digital space where people could explore the collection more easily and from different perspectives. We chose three main perspectives. One of them, for example, is a map, where users can see the locations represented in the collection and then zoom in on the details. On the website, users can also explore specific elements, like all the trains in the collection, all the cars, parks, and so on.

In addition, we created a crowdsourcing section. We wanted to include user participation so that the collection could be enriched with additional information. For example, on the back of the postcards there are greetings, and we wanted to allow users to transcribe or translate those messages so they could be added to the metadata.

You were the team leader of your group. Was this role in line with what you had expected?

I expected that I would need to give structure to the team: define the focus of the project, set concrete steps, and remind everyone of deadlines. In the end, though, everything developed very organically and smoothly, and I was really happy with how it worked out.

At the ‘Meet the Data, Meet the People’ event, you were introduced to the data for the first time. How did the brainstorming process go?

At first, it wasn’t very clear what specific skills everyone could bring to the project, or how we should approach such a large collection. That led to a lot of questions: what do we actually want to do with this collection, and what do we want to highlight?

In the beginning, we had many different ideas. We thought about working with the colors of the postcards, or focusing on locations, and that’s when the idea of using a map came up. There were a lot of possibilities. At a certain point, though, we decided that we really needed to look more closely at the dataset, see what was actually there, and then make a decision. That happened a couple of days after the opening event. We had some time to reflect, explore the data, and then settle on a clear approach.

Was it difficult to decide in which direction you wanted to go?

A bit, yes. But in the end, the direction really emerged from what we actually found in the data. As I mentioned before, we initially wanted to work with color, but when we started thinking about the kind of results that would produce, we realized it wasn’t the direction that appealed to us the most. So at some point we had to make a clear decision: okay, let’s go in this direction and really commit to it.

That said, it was still a bit challenging, because along the way new ideas kept popping up. For example, we considered adding a gamification aspect to the crowdsourcing section, where participants could earn points based on how much they contributed. In the end, we had to leave that out because of time constraints. At some point we realized, there are only three days left, how can we realistically make this work? It’s important at that stage to be realistic and say, okay, this is something we can do, and this is something we can’t.

During your final presentation at the closing event, you mentioned the educational goal of the project and its collaborative aspect. What kind of audience did you have in mind? Who should be able to use the website you developed?

We definitely had researchers in mind. The idea was to help them shape their research by giving them access to all these additional details in the collection. Because the postcard collection is so broad, it’s not immediately obvious what kind of research questions you could explore with it, and we wanted to make that easier.

At the same time, we wanted to reach a wider audience, people who are curious about Belgium’s history, about tourist places, and what they looked like in the past. Some might be interested in comparing then and now, others in seeing how streets and cities have changed, or just browsing the collection and feeling a bit nostalgic.

One thing I found very appealing was how user‑friendly the website was, it really looked like something anyone could use.

Yes, absolutely. I think a lot of people would love the idea of being able to see how a place looked in the past and compare it to how it looks now, seeing how much it has changed, or sometimes how it no longer exists at all.

The end result was a success, but did you face any roadblocks during the hackathon?

There was one issue at the beginning related to the locations of the postcards. We wanted to create a map and link each image directly to a specific place, but the coordinates were missing in the collection. So we first had to retrieve that information, and that took some time. At one point, we even thought it wouldn’t be possible. In the end, though, one of the team members managed to clean the dataset and recover the exact coordinates for each location, which allowed us to move forward.

You mentioned that this was the first hackathon you participated in. Do you feel you picked up any new skills along the way, and how might you use them in future research?

The crowdsourcing concept was particularly interesting for me. It’s something I had already worked with in earlier projects where we involved the public. For example, we showed people images, often of places in cities, and asked them to share additional information about what they saw.

What was new for me in this project was the specific crowdsourcing tool that we embedded in the website. I think that’s something I’ll definitely use again in the future. It’s very user‑friendly and easy to integrate, and the fact that it automatically produces a file with all the participants’ responses is very useful.

What advice would you give to someone who might be hesitant to participate in a hackathon because of their background?

I really think everyone can participate, because there’s a place for everyone in a hackathon, even if you don’t have strong technical skills. Whatever your background or skills, there’s always a way to contribute and find your role within the group. That might be through creative ideas, working on the poster, or helping shape the concept of the project. There’s always something meaningful you can bring to the team.

Last question: what advice would you give a team leader?

I would say don’t be too strict at the beginning. It’s important to give everyone enough space to be creative and to let people explore ideas, so that everyone’s skills can really emerge. I think the brainstorming phase is especially important, because that’s when you start to understand what each team member can do and how everyone can contribute to the project.

Congratulations one more time! It’s amazing how much each team accomplished in such a short period of time. For me, it almost felt unreal, this looked like a year’s worth of work.

Yes, exactly. For me, this could have been a thesis, the kind of results you would expect from a master’s thesis. That’s really what made it so remarkable to me.

The following post was written by Sam Goven, a master’s student in Journalism at KU Leuven. It offers a participant’s perspective on the BiblioTech Hackathon, reflecting on the experience, the creative process, and collaborative spirit that shaped the event.

Libraries are often seen as places of preservation rather than experimentation, but the BiblioTech Hackathon turns KU Leuven Libraries into a digital playground. Drawing on rich library datasets, students, researchers, and staff from diverse backgrounds work in interdisciplinary teams to reimagine historical collections through digital tools and collaboration.

The second edition of the hackathon culminated on 26 March in the University Library in Leuven, where seven teams presented their final projects to a jury. Over the course of ten days, materials from the library collection were transformed into innovative digital outputs, ranging from interactive maps and searchable databases to experimental interfaces, which can be explored via the project websites. Team PostScript ultimately claimed both the jury prize and the audience award with an interactive digital archive of Belgian postcards.

By combining technical support, curated library collections, and an emphasis on experimentation rather than competition, the BiblioTech Hackathon demonstrates that digital humanities can be accessible, creative, and collaborative, even for those new to computational approaches.

What is a Hackathon?

During a hackathon, a blend of “hacking” and “marathon”, participants work together in teams on a project against a tight deadline. These projects often have a digital component and can be developed over one or several days, resulting in a website, database, or another form of digital output.

The first edition of the BiblioTech Hackathon took place in 2023, organized by KU Leuven Libraries and the Faculty of Arts. Participants could choose from seven datasets, including the Bible of Anjou and wartime posters. The focus was on exploring documentary heritage from a fresh perspective by transforming it into computational data. The hackathon proved to be a success and led to a second edition in 2026.

Meet the Data, Meet the People

The second edition kicked off on 12 March in Agora Learning Centre in Leuven. As the smell of pizza filled the space, the perfect brain food for sharp minds, the seven teams discovered both the datasets and each other for the first time. In total, 39 enthusiastic participants from a wide range of backgrounds took on the challenge. The hackathon attracted not only master’s students, but also PhD candidates, postdoctoral researchers, and KU Leuven staff. Participants represented a broad variety of disciplines and research fields, including Computer Science, Egyptology, Law, and Economics.

To make the most of this diversity, teams were formed in advance based on digital skills and areas of expertise, ensuring a balanced mix. Each team was supported by a designated team leader to keep the project on track, while technical experts were readily available throughout the hackathon to answer questions and provide assistance. To ensure everyone could get started smoothly, an additional training session on the technical infrastructure and tools was organized the next day.

Following an introduction to the datasets and the available support network, the teams dove into the material. This year’s hackathon offered two datasets: well over 35.000 historical postcards from Belgium and around 300 travel accounts written by European authors describing the destinations they visited. Once again, these historical sources provided ample opportunities for innovative perspectives. Four teams chose to work with the travelogues, while the remaining three focused on the postcards.

The brainstorming phase reflected the exploratory nature of the hackathon. Faced with rich datasets and a wide range of ideas and ambitions, teams took time to explore different directions before narrowing their focus. Working within a limited timeframe required careful consideration of what was both innovative and feasible. This process not only helped shape the projects but also allowed participants to recognize and build on each other’s strengths. Andreas Ketele, a member of the Inked and Stamped team, reflected afterwards: “What I really enjoyed was that process of exploration. We reflected on our ideas and experimented a lot, and that’s exactly what a hackathon is about: discovering possibilities along the way.”

The Final Projects

On 26 March, participants, jury members, and guests gathered in the University Library for the final presentations accompanied by a poster exhibition, marking the culmination of the hackathon and an opportunity for teams to present their work. The evening opened with welcoming words from the organizing team, Demmy Verbeke (Head of KU Leuven Libraries Artes), and Geert Brône (Vice Dean for Research at the Faculty of Arts), who praised the creativity and commitment shown throughout the hackathon.

The presentations were opened by team CaptaCats with their project ShipAdvisor. Loosely inspired by the travel website TripAdvisor, the team developed a web platform that maps maritime routes in the Mediterranean during the 18th and 19th centuries, based on historical travel accounts.

Next, team DH.xml presented their analysis of the postcard dataset. They argued that historical postcards functioned as a form of social media avant la lettre, and used the collection to identify recurring visual trends and patterns.

All Reads Lead to Leuven focused on how 19th-century French travel writers wrote about African languages. Their project resulted in a website featuring Instagram-inspired posts that reveal the vocabulary and framing these authors used when describing linguistic encounters.

Using the postcard dataset, Inked and Stamped built a searchable digital database. Its intuitive interface allows users to explore the collection by location, date, and even the color of the postcards.

Team PostScript adopted a similar approach, but with a specific focus on postcards from Antwerp. In addition to a searchable database, they introduced interactive features such as maps that contrast contemporary photographs with historical images from the collection.

The penultimate presentation came from Team W@nder. Drawing on The Land and the Book, a 19th-century publication by W. M. Thompson, they visualized the author’s travels in the Levant. As with other projects, historical illustrations were juxtaposed with present-day photographs to highlight continuity and change.

The evening concluded with a presentation by Team Julie Verne. They developed an oracle-like search tool based on the travelogues dataset. Through their website, users can query the texts and receive the most relevant responses generated from the corpus.

After a brief deliberation by the jury and a public vote, the awards were announced. The jury consisted of experts in data and digital research: Julie Birkholz (Coordinator CLARIAH VL+), Geert Brône (Vice Dean for Research of the Faculty of Arts), Jo Rademakers, (Head of LIBIS), Fred Truyen (Head of CS Digital), and Katrien Verbert (Program director of the POC Digital Humanities). Team PostScript was awarded both the jury prize and the audience award. As in the 2023 edition, however, each team received recognition, including awards such as Best Research Potential and Best Visualization. The evening concluded with a reception, where teams presented their project posters over food and drinks. To share the creativity and impact of the hackathon with a wider audience, the posters are currently touring across KU Leuven.

A Community Built Through Collaboration

Not only were the results of the hackathon impressive, participants also praised the atmosphere and strong sense of community that developed throughout the event. In post-hackathon interviews, several participants reflected on the collaborative environment that emerged over the course of the ten days. Andreas Ketele described the experience as particularly rewarding: “I’m usually not someone who uses very strong words, but this really was fantastic. […] We were working as a group of highly motivated people. We collaborated very well and benefited enormously from all the support we received along the way.”

The diversity of backgrounds and skill levels did not prove to be a challenge, but rather one of the hackathon’s greatest strengths. By bringing together participants with different perspectives, expertise, and levels of technical experience, the hackathon created space for learning from one another. As Roberta Pireddu, team leader of PostScript, explained: “I really think everyone can participate, because there’s a place for everyone in a hackathon, even if you don’t have strong technical skills. Whatever your background or skills, there’s always a way to contribute and find your role within the group.”

For many participants, this emphasis on collaboration rather than competition was key. As advice for future participants, Luisa Ripoll Alberola, team leader of CaptaCats, encouraged newcomers not to focus too heavily on the final outcome: “What really matters is not the end product, but the process: working together, learning new things, and enjoying the experience. That’s what makes it valuable.”

The second edition of the BiblioTech Hackathon proved once again how working with library data can foster meaningful collaboration across disciplines. By bringing together diverse participants, the hackathon strengthened connections within the academic community and opened up new ways of engaging with humanities collections.

More information about the hackathon, its datasets, and the final projects can be found on the BiblioTech 2026 website. We encourage you to have a look at the project posters and websites to explore the teams’ outputs and discover the creative ways in which KU Leuven’s library collections continue to inspire digital humanities research.

Digital images are in constant motion. They traverse various platforms, feeds, databases, and archives, often reappearing in modified forms. Through my research on digital art, I have recognized this phenomenon as more than a mere feature of online dissemination. It constitutes both a methodological challenge and a perceptual issue.

What appears to be a single image may, in actuality, exist as a collection of various versions: cropped, compressed, recoloured, or reposted without proper attribution. Although these differences may seem insignificant at first glance, they give rise to a question that is more complex to answer than it initially appears.

      Under what circumstances can two images be considered identical?

That question became the basis of my assignment for the CodeLab course in my ongoing Praxis Fellowship Program. Using Python with the ImageHash and Pillow libraries in VS Code, I built a small tool to test how visual similarity might be measured across images that have changed through circulation. What started as an exercise became a way of thinking through something larger: what does it mean for a computer to recognize an image, and does that match what we mean when we say two images are the same?

The approach

The tool uses the imagehash library to compute perceptual hashes and compare images by visual similarity.¹ Unlike cryptographic hashing, which changes entirely if even a single byte changes, perceptual hashing captures how an image looks. Two visually similar images should produce similar hashes; unrelated images should not.

After generating the comparison data, I modified the script to export results as JSON and render them as an HTML page. Instead of raw values, the interface ranked each image against the reference, displayed a distance score, and grouped results into categories from “nearly identical” to “different from the original.” The script processed files in the images/ folder, saved results to version_results.json, and generated output in results.html.

Figure 1. HTML interface showing ranked comparison of image variants against the reference image. See https://jimgaconcept.github.io/image-versioning-demo/

The dataset

The reference image is a digitized hand-drawn cartoon illustration made with pen and ink and watercolor on paper. This detail turned out to matter a great deal. I compared it to two modified copies (resized and compressed), one digitally recreated version, and three visually unrelated images, to test whether the tool could distinguish genuine variants from unrelated works.²

Results

The two modified versions, resized and compressed, both scored between zero and two, confirming their close relationship to the reference. The three unrelated images all scored above 20, well outside any similarity range. The digitally recreated version (Fig. 1) scored 18, placing it in the category that the interface labeled as different from the original.

That score of 18 was the result I did not expect, and the one worth thinking about most carefully.

What the computer sees, and what we see

The recreated image and the original share the same subject, composition, and color palette. A human viewer encountering both would almost certainly recognize them as versions of the same thing. The algorithm did not. Scoring 18, it placed the recreation closer to the unrelated images than to the two modified copies, which scored between 0 and 2.

The reason lies in what each image actually is at the data level. The original is a scan of a physical drawing, and its pixel data carries the texture of its medium: the grain of the paper, the way ink spreads at the edges of marks, the tonal variation of pigment on a physical surface. The digital recreation was built entirely within Photoshop and saved as a JPEG. Even a faithful digital reconstruction is made from digital brushes and algorithmically generated marks. There is no paper grain, no ink bleed. The two images look the same to us, but their underlying data structures are built from entirely different material.

This is a version of what computer vision researchers call the cross-depiction problem: the gap between human visual recognition, which operates on meaning and composition, and machine recognition, which operates on statistical patterns in pixel data. My experiment gave that abstract problem a specific, personal form. What appears identical to the human eye may share almost nothing in common at the data level. The computer is not seeing the image. It is reading a numerical structure, and two images that represent the same thing visually can be built from entirely different data, depending on how and where they were made.

This relates to a broader discourse within the field of digital humanities. As Drucker (2013) has articulated, digitization constitutes not merely a neutral representation but rather a form of interpretation. Factors such as resolution, lighting conditions, and the medium of capture all influence the transformation of an image into data.³ My findings exemplify this argument concretely. The scanned watercolor and the Photoshop recreation are not simply two variants of the same image; rather, they represent two distinct interpretations, which the algorithm processes accordingly.

If we are building archival systems or image databases that rely on computational similarity to group and relate works, we need to ask whose sense of “the same image” is being encoded. A tool trained on pixel-level data will consistently separate a scanned physical artwork from its digital recreation, not because they are different images in any humanistic sense, but because they are different kinds of data.

Limitations and what comes next

Perceptual hashing assesses visual similarity at the data level. It does not establish authorship, confirm provenance, nor consider contextual factors. Outcomes may also differ based on the specific hashing algorithm employed, as various implementations assign different weights to visual features. This tool serves as one component within a broader interpretive framework, rather than substituting human judgment.

This assignment illuminated a perception that is both straightforward and profound. It is evident that the computer and the human eye do not observe the same aspects, even when examining the same image. The disparity between data and meaning represents the realm where the most compelling inquiries within digital art history reside. As Burdick et al. (2012) suggest, the significance of computational tools in the humanities lies not in their capacity to resolve questions, but rather in their ability to render certain questions newly answerable.⁴ This experience has prompted a question I was previously unaware of having.

The live output and ranked visualization are at the project web interface. Full code is on GitHub.

I blinked and realized that Amanda Wyatt Visconti and I have been at the Scholars’ Lab for nine years as of April 24, 2026. Time flies. We typically celebrate by eating or drinking something sweet in the Lab (I’m still vibrating from the cream soda we had half a decade ago). We weren’t able to do so this year, so I thought I would share a quick post to mark the last nine years.

Nine things I’ve learned

1. Drink a glass of water and put both feet on the ground.
2. Don’t over-engineer things.
3. Slow down and appreciate.
4. Some things get easier. Some will not.
5. Write it down. It will be helpful for someone. That someone might be you.
6. Snacks always help.
7. Be explicit about what you need and what you don’t.
8. There are limits.
9. Structures give shape. Structures can be changed.

Nine memories to hold onto

1. Amanda biting into a lemon after eating miraculin.
2. The moment when each student steps into their own expertise.
3. Shane saying, “agenda item: be better friends.”
4. When I cried at the Afton overlook because I wouldn’t have to commute for work anymore.
5. Biscuit baking lessons on zoom with Jeremy and Amanda.
6. The support each colleague gave when I needed it.
7. The satisfaction that comes from seeing a student graduate as a DH practitioner, especially when you met them as a prospective student.
8. Those who are gone. Ryan. Leigh. Scott. Rebecca. Effie. Stéfan. So many others for different reasons.
9. All the unjust things. All the people working to make it better.

Nine things I’m grateful for

1. Our students. They’re the best.
2. Our colleagues. They keep me coming back.
3. To still be here, doing this.
4. Everyone who has taught me.
5. Those who are still here.
6. Those who made space for me when I burnt out.
7. Eliza, Ben, Ava.
8. That I was given a chance.
9. Every accident that brought me here.

It’s not lost on me that so many others deserve to be in stable employment who are not. I’m very lucky to have a job in this world on fire. So, I will close with gratitude and a determination to pay it forward to the next folks in line.

It’s that time of the year when the DH Awards goes public with the results of their annual cycle. The process is, of course, only a snapshot of the field and limited in all those expected ways. But I am astonished each year, chronically online as I am, to find that there are so many projects out there that are new to me. Each season is a delight as I page through the many different links offering new work, unknown-to-me scholars, and fresh ideas. Reading this year, I thought that the list could make for a useful way of constructing a DH teaching activity. Here are a few ideas for how you might use the DH Awards to teach your students:

• Take five; pick one. Students pick five projects to examine in detail, using a rubric you provide in advance. In session, they each quickly present on one topic to the group. You follow up with a general discussion to which the students can bring all five pieces they examined.
• Dig into a year. It’s not uncommon for scholars to designate particular years as uniquely important for their fields of study. In this activity, students pick one year and examine the projects showcased in the DH Awards closely. What was distinctive about this year? What trends do they see? What seems curious?
• Look over time. Ask students to consider how representation of the field has changed over time as articulated in the DH Awards. Probably easiest to narrow their focus to a single category for this one. Does anything rise up? Fall away? Remain steady?
• Consider what’s left out. Invite students to look critically at the awards process. Can they think of any topics or kinds of scholars who are consistently left out?
• Design your own. Encourage students to speculate on their own award cycle. What kind of work would they want to promote? What do they value? How could they design a shoestring award process to help facilitate that every year? What kind of collaborators would they need to implement it? How much labor would it entail?

For extra flavor, I might offer analogous or contrasting exercises with Reviews in DH or Digital Pedagogy in the Humanities. Maybe that’s a future post. Endless thanks to those who provide volunteer labor to keep DH Awards going. I always appreciate the project as a service to the community. I always learn something each awards season, and hopefully the above activities give some ideas for how they can teach your students as well.

Winnie E. Pérez Martínez’s post on the Scholars’ Lab blog this week got me thinking. In “Breadth and Depth, a Self-Centered Dialectic,” she revisits how we discuss breadth and depth as two approaches to digital humanities professional development. In this framing, one that I have put forward myself, we can think of careers in DH as operating on two axes. On the one, we are expected to know a little about a lot of things. On the other, we are directed more towards narrow, specialist-level knowledge about a smaller subset of methods. Breadth vs. depth. Few careers really ask us to go entirely in both directions. More practically, we tend to specialize in a couple areas within DH and develop passing familiarity with many more.

For me, the dichotomy between breadth and depth was a way to help students map their career plans onto the different skills they might acquire. I thought of it as a way to free yourself from the need to be expert in everything. In her post, Pérez Martínez expertly shows how breadth and depth actually inform and lead to one another. There can be no one right way in. If you start deep, you might find yourself broadening, and starting wide can help you to focus in. What most resonated about Pérez Martínez’s post, though, was the way in which you can see an exceptional scholar and practitioner wrestling over whether they are enough, over whether they could ever develop the necessary skills they need to feel complete. Those anxieties never really go away. I feel them too. I recognized myself in Pérez Martínez’s post, and I couldn’t help but sense that the breadth against depth framing seemed to be having the opposite effect I would want, heightening anxiety rather than mitigating it.

Pérez Martínez proposes a broadening of the axes I had envisioned. Breadth and depth move beyond just X and Y, curling in upon themselves until they start to push outwards. The moment reminded me of the age-old dichotomy of “hack” vs. “yack” in DH work and how Laura Braunstein offered “stack” as an important third term. In addition to coding and technological critique as key parts of DH work, Braunstein’s intervention elevates “the often invisible technological, social, and physical structures within which scholarship is produced and disseminated.” For Braunstein, DH work is more than just the sum of what we do, it also consists of the structures we put in place to enable that work. In the same spirit and inspired by Pérez Martínez, I have been wondering what breadth and depth leave out, what they gesture towards within and beyond the teaching that we do.

Put another way, what is education if not just content? One point of comparison here is L. Dee Fink, whose Taxonomy of Significant Learning illuminates the various components of teaching.

Caption: L. Dee Fink’s Taxonomy of Significant Learning as shared on Florida International University’s Center for the Advancement of Teaching.

Fink’s Taxonomy usefully illustrates all the things that lie beyond the subject matter in the courses we teach. Learning is more than consuming books, articles, or topics. Teaching is more than passing along skills and methods. If we think of DH merely as skill building, we live too much in the upper right of the circle. We leave out the rest of what makes DH experiences—and DH learning—significant for so many of us. We ignore the transformative mentoring that shows a variety of career options. We miss the collaborative practices that can change how we view our work in dialogue with others. We do not account for how true interdisciplinarity changes our perspectives on our own research processes. We need a new term to trouble the dichotomy between breadth and depth that can capture a more capacious view of what it means to practice digital research and teaching, one that goes beyond subject matter, methods, and skills.

I find this particularly urgent in the age of generative AI, a complicated set of technologies that threatens to instrumentalize education beyond recognition. What counts as methodological training if you can vibe code your way to a launched digital project? What counts as digital pedagogy if our students are secretly using chatbots as study partners? How do we make room for conversations about professional development that do not reduce people to a tidy axis of skill acquisitions?

What lies beyond the breadth and depth of what it means to be a digital humanist?

I would introduce a third term for DH professional development: “breath.” Breadth and depth ask us to think about what we can and cannot do, about the subject matter and methods of DH work. The terms ask us to think about the limits of our knowledge and our inability to pursue universal expertise. Breath asks us to reframe the conversation entirely. It is an invitation to pause and re-embed our work in the body. How do we feel about our labor? Who are the working souls in DH and how do we engage with them? How do we work or overwork our own body to the point of breathlessness? What is the lived experience of our labor that transcends the skills or methods? What are the affects—the joys, frustrations, traumas, triumphs—of DH work that cannot be captured by thinking in terms of skill acquisition? How do our energies map onto a living, breathing community of thinkers and doers beyond the work on the table in front of us? Where do we fit in?

Breadth and depth ask students to think about where they could be, professional development by way of spatial orientation. Breath invites students to consider where they are, to think of themselves as real people with real needs that need attending.

Are you a Digital Humanities student or early career researcher in Belgium who would like to discuss DH with other early career researchers in the Belgian DH community? If so, you might be interested in joining the DH Virtual Discussion Group for ECRs!

The DH Virtual Discussion Group is a joint initiative organized by individuals at multiple Belgian institutions. We strive to involve speakers from all Belgian institutions and encourage participation from all those who are interested in DH and are located at any Belgian institution. This series, the core organizers are Leah Budke (KU Leuven), Tom Gheldof (KU Leuven, CLARIAH-VL+), Paavo van der Eecken (University of Antwerp), and Loren Verreyen (University of Antwerp). Over the past years, the series has become a regular event. The spring 2026 edition proudly marks our twelfth term.

Our first two sessions this spring will continue the “under-the-hood” format, which entails a volunteer from our community providing a thirty-minute overview of a digital project implementing a given tool, approach, or platform. This is not meant to be a polished research presentation, or to present findings or results, but rather to give our community a behind-the-scenes look at how decisions were made and why specific tools were chosen or developed. The hope is also that this presenter will give attendees some ideas about how to get started implementing a specific tool or workflow, and that they can also answer questions or contribute to a discussion on other projects in our community that might be using similar methodologies or addressing similar issues. This “under-the-hood” session format allows us to have focused discussions around a specific project where we can learn from each other in an informal way. In addition, by implementing this format we can maintain the low threshold for contributing and engaging in the conversations.

Our final session will be a special in person session during which members of our community can give an elevator pitch of their DH Benelux contribution.

The spring 2026 schedule will be updated as details about upcoming talks are confirmed. Please check back here or on the website (linked above) for full details. Information about each session will also be circulated via the mailing list. 

Session 1
Date: Monday 30 March, 15h-16h30 CEST via Teams
Speaker(s): Julie Van Ongeval, VUB
Title: The Fall of Antwerp (1585) as a linguistic turning point? Language change from macro- and micro-perspectives.
Abstract:  The Spanish recapture of Antwerp (1585) during the Eighty Years’ War, known as the Fall of Antwerp, marks a crucial turning point, not only from a historical but also from a linguistic perspective. Historically, the Fall triggered profound social, economic, and demographic transformations. Prior to 1585, Antwerp had flourished as one of Europe’s largest and most prosperous cities, characterized by substantial immigration. In the aftermath of the Fall, however, the city experienced severe socio-economic decline and large-scale emigration, causing its population to decrease by more than half (from 100,000 inhabitants in 1580 to 42,000 in 1589) (De Meester 2011, Lesger 2007). From a linguistic standpoint, the Fall has traditionally been associated with what De Vooys (1970) termed “the decline of the Southern Netherlands”. The event is believed to have shifted the linguistic center of gravity to the Northern Netherlands, slowing down or even halting the ongoing processes of language standardization in the Southern Netherlands and, by extension, in Early Modern Antwerp (Van der Sijs 2020). ​Yet, these linguistic claims have primarily been based on printed, literary, or explicitly normative texts. Considerably less is known about language use in more informal and everyday contexts (Elspaß 2020). 

This study addresses that gap by analyzing informal, handwritten letters preserved in the newly developed Early Modern Antwerp Corpus (1564-1653). Drawing on Dixon’s punctuated equilibrium model (1997), which proposes that significant historical events can accelerate linguistic change, we test an alternative hypothesis: rather than causing stagnation, the Fall of Antwerp may have triggered intensified linguistic variation and change. To assess this hypothesis, we examine six linguistic features that were undergoing change and were relevant to the process of Dutch standardization (clause negation, verbal cluster order variation, schwa apocope, the prefix ge- in past participles, word-final /k/, spelling of /ɣ/ in onset). First, we analyze developments at the community level to identify broader patterns of change. We then adopt a more microscopic perspective, investigating how individual writers respond to the shifting sociohistorical context. This includes both inter-individual variation (e.g. social categories and networks) and intra-individual change across the lifespan. By investigating the linguistic consequences of the Fall of Antwerp from both macro- and micro-level perspectives, this study aims to bridge the three waves of sociolinguistic research, integrating community-level patterns with individual-level variation and change.  

Session 2
Date: Monday 20 April, 15h-16h30 CET via Teams
Speaker(s): Léa Hermenault, UA
Title: The Belgian Historical Gazetteer: (historical) toponyms in a digital era
Abstract:My presentation will introduce the Belgian Historical Gazetteer, a project founded by CLARIAH-VL+ and hosted at the University of Antwerp. This project aims to set up a historical gazetteer of toponyms for the whole present-day territory of Belgium, in order to provide researchers with a collection of data that does not stop at Belgian provincial borders and which goes beyond the level of municipalities.

First, I will explain how the gazetteer is constructed using both automatic extraction of text from old maps and manual corrections/additions. Then, I will show how this gazetteer will help researchers deal with place names that appear in their sources. Finally, I will demonstrate the potential of digitized lists of historical place names for both toponymic and landscape studies which make digital gazetteers, aside from their classic function, innovative exploring tools.

Session 3 – Special In-Person DH Benelux Session
Date: Monday 18 May, 13h30-16h CEST,
Location: room 1.01 Gogotte, Hoek 38, Leuvenseweg 38, Brussels (location is within walking distance from the central station)
Speaker(s): various members of our community
Format: elevator pitches of DH Benelux contributions

There are an increasing number of conferences, workshops, and funding opportunities in DH, and we would like to ensure that you are aware of them. We will start every session with a moment for individuals to share news about upcoming lectures, workshops, seminars, and conferences. We have a corresponding Slack group where we also share these opportunities both during the discussion group meetings and in between. The link to join the Slack group is included in every email sent out to the mailing list, so watch for it there or send us an email to request access.

If you would like to register or invite other colleagues to join, please complete the registration form for the mailing list here. Please note, if you have received emails from us about the Discussion Group in the past, it means you are already on our mailing list. In that case, there is no need to register again—you will receive the emails with the MS Teams link and any additional information on the day of the session. Additionally, you will also receive updates on upcoming sessions including further details about speakers and the “under-the-hood” presentation topics. 

Are you a frequent attendee of the DH Virtual Discussion Group and would like a low-threshold way to become more involved in the organization? We are looking for ambassadors to promote the group within their university networks. If this might be a role you would like to take on, get in touch and we can tell you more!

We look forward to seeing you this spring!

These events are only open to KU Leuven researchers and staff

To support researchers in their use of relational data, CLARIAH-VL+ & Artes Research (partners in DH@rts) are hosting 2 Nodegoat workshops.

Nodegoat is a web-based research environment designed for the Humanities. The platform enables researchers to manage and visualize complex historical data, including vague dates and historical regions, as well as to generate diachronic geographical and social network visualizations.

During the workshop, participants will learn how to use this flexible digital environment for their own projects.

Program

The workshops will be given by Geert Kessels & Pim van Bree (the developers of LAB1100).

• The morning session (09:30-12:30) will cover a general introduction to Nodegoat
• During the afternoon session (14:00-17:00) the developers will present more advanced Nodegoat features.

You may sign up for just the morning session, just the afternoon session, or both workshops.  Just make sure to register for each session individually.

Practicalities

• When: April 24, 2026 from 09:30 to 12:30 and from 14:00-17:00
• Where: Colloquium (05.28) in the University Library. These are in-person workshops and will not be recorded.
• For who: This event is open to KU Leuven researchers working in the Humanities. No prior experience is required. Participants are encouraged to bring their own research questions or datasets to explore within Nodegoat
• Price and registration: Free but mandatory. You can register here. You may sign up for just the morning session, just the afternoon session, or both workshops.  Just make sure to register for each session individually. Registration deadline is 10 April 2026. 
• More info: Click here

Every academic year, the HDYDI (How Do You Do (It)?) event on research data workflows signals the start of the Digital Scholarship Module. Through a series of sessions and (mini-)workshops, Artes Research aims to guide students through the complexities of scholarship in the digital age, from Open Science to Research Data Management and beyond.

At the HDYDI kick-off event, we invite three researchers from the Faculty of Arts to open the black box of their research workflows. By sharing the practical tools, decisions, and challenges that shape their day‑to‑day work, they aim to offer the first-year PhD researchers a realistic insight into what digital scholarship can look like across disciplines. We hope these behind‑the‑scenes glimpses help you discover approaches that can inform your own research journey!

Tim Debroyer: From Paper to Digital Source

The first speaker, Tim Debroyer, is a third-year PhD candidate at the Cultural History since 1750 research group. Under the supervision of Joris Vandendriessche and Kaat Wils, Tim is studying the evolution of 20th-century Belgian patient organisations as an overlooked link in the development of the modern welfare state. This involves examining their oral history as well as archival and published sources.

The focus of Tim’s talk is on the latter – periodicals specifically form one of the most important sources of information for his project. Faced with thousands of pages early on in his research project, he had to make strategic decisions: what to photograph, how to photograph it, and which digital methods were worth the investment.

Taking BVS Nieuws, the periodical of a diabetes association founded in the 1940s, as an example, Tim explains that he ended up manually photographing the entire series of journals so as to allow for a more thorough discourse analysis. This experience taught him some “tricks” which might be useful to others looking to photograph large amounts of text. Firstly, he used a classic camera in order to avoid the post-processing which smartphones tend to apply, and which can harm OCR quality. Secondly, he made sure to always photograph beyond the edges of the page to make it easier for the OCR software to recognize the boundaries. Thirdly, since taking pictures in the library was quite hectic, Tim always made notes of what he was doing: for instance, what stood out in the issues and what was missing – this made it much easier to return to the sources later on in his trajectory.

Once he properly organized the resulting pictures in folders per issue or volume with short, meaningful names, Tim set to extract the text using OCR (Optical Text Recognition) tools in order to enable keyword searches and quantitative analysis. (This is a labor-intensive step, he cautions, so make sure that it makes sense for your methodology before adopting it yourself.) Numerous scanning apps and online tools exist – Tesseract, Google Cloud Vision and Transkribus (for handwritten text) are great options for the more technically minded – but Tim made use of ABBYY FineReader, a commonly used OCR tool that is very performant and user-friendly. It is a commercial tool, but computers with ABBYY licenses are available at the Maurits Sabbe Library and Agora, so researchers looking to digitize a limited number of sources are free to go there without having to purchase their own license. ABBYY FineReader allows for image pre-processing (e.g. fixing lighting, straightening and cropping pictures), supports various languages, recognizes images in sources as well, and offers various formats for exporting (including .txt files). Tim was quite satisfied with the quality of the OCR’d texts: take good pictures, he says, and ABBYY will deliver good results!

To conclude, Tim shows how he processed the resulting text files in AntConc, a free concordance tool that’s often used for text mining. It allows for large-scale word searching and analysis, can provide keyword frequencies and information about relations to other words, and can easily compare different corpora. (Tim provides a small tip for those looking to explore AntConc: keep a stopword list of high-frequency words with little thematic content that the tool can filter out of its analysis.)

Of course, every researcher has to figure out what workflow suits them, but Tim importantly highlights that you should think about what you want to achieve before investing in digital methods. Consider the nature of your research project, the characteristics of your source corpus, the methodologies you use (discourse analysis, quantitative analysis, network & visual analysis) and let these things decide how you will process and study your sources. At the same time, don’t be afraid to try out new tools that might work well for you!

Of course, the quality of ABBYY FineReader’s OCR results depends on the quality of the input images.

Lauren Ottaviani: Mapping and Analyzing Women’s Magazine Archives

Our second speaker is Lauren Ottaviani, fourth-year PhD candidate in English Literature. Lauren’s project, supervised by Elke D’hoker, focuses on the representation of the women’s suffrage movement in two conservative, middlebrow periodicals dating to the late 19th and early 20th centuries: The Woman at Home and Lady of the House. In doing so, the research seeks to consider the interaction between suffrage and domestic ideals at the turn of the twentieth century.

Similarly to Tim, then, Lauren also works with a large corpus of periodicals; and just as we saw with Tim, many of the magazines’ issues – which tend to be quite lengthy – remained as yet undigitized. The complexity of her materials meant that Lauren had to decide early on how to approach data management efficiently. In the end, a combination of three tools informed her research workflow.

Firstly, early on, she shifted from using Word for note-taking to using the free open-source tool Obsidian instead. As Lauren says, Obsidian (which was covered in last year’s HDYDI session as well) has the same ease of use that a program like Word offers, but you’ll actually be able to find your note again! With its added functionality, Obsidian allowed her to create a relational database of notes categorized by date, theme, or type, so as to keep track of any stories worth revisiting. Through tags and linked notes, Lauren could keep track of authorship, include direct links to the digitized magazine pages, and even uncover recurring anonymous authors. It’s also just a great tool for conference notes and miscellaneous admin.

Secondly, Lauren made use of the storage that’s provided by KU Leuven on OneDrive for Business. Currently, OneDrive is no longer recommended as a primary storage solution for research data at the university,¹ but it does have some useful features – and it proved particularly handy for Lauren’s use case. Using the OneDrive smartphone app, she took pictures of interesting articles in the periodicals she was studying and placed those in her pre-organized folder structure. In contrast to Tim, Lauren did not think full OCR of her corpus was worth the time investment or really relevant to her research questions, but this smaller-scale scanning process (which resulted in perfectly legible captures) worked great for her methodology.

Thirdly and finally, Lauren also adopted Nodegoat as part of her workflow, mainly for its “mapping” potential. That is, Nodegoat is a database tool, but it also offers built-in network visualization capabilities, which Lauren used to map out different entries – i.e. letters from the magazines’ correspondence columns – tagged with geolocations. The resulting visualization allowed her to track where readers lived, what the magazines’ geographical reach was, and how their readership expanded over time – elements that were central to her analysis of the periodicals’ circulation.

Using a combination of these three tools, Lauren was able to create a structured, well-organized database out of a vast, undigitized corpus; and even though her approach differed quite substantially from that of Tim, both illustrate how the right tools, used well, help make large-scale periodical research manageable.

Using Nodegoat, Lauren was able to map out the readership of the periodicals she’s studying.

Sinem Bilican: Managing Multimodal Data in Healthcare Research

Sinem Bilican is the last speaker: as a PhD candidate at the Research Unit Translation & Interpreting Studies, she is part of the interdisciplinary research project Managing Language Barriers in Unplanned Care (MaLBUC). With the help of her supervisor Heidi Salaets, Sinem studies linguistic diversity and multilingual communication in healthcare practices with the goal of laying bare overlooked communication barriers. As such, her project involves collaboration with the Faculty of Medicine, and we can reasonably expect very different data types from what we saw in Tim’s and Lauren’s presentations.

Indeed, the interdisciplinary and collaborative nature of the research project – which encompasses ethnographic observations as well as a large-scale survey and interviews – necessitates the implementation of clear research data management practices. Sinem works with extensive field notes, images, video and audio recordings, questionnaires, and other survey data: a lot of materials to manage, to be sure!

Sinem begins by outlining the tools involved in her daily research workflow. Zotero is a usual suspect here, and one which we see in many researchers’ workflows as a handy reference manager as well as a note-taking and annotation tool. OneDrive, meanwhile, enables Sinem to exchange data, drafts and other documents transparently between team members; whereas for a related larger-scale project, the team opted for the ease of use of Teams and SharePoint (which is a recommended storage solution at the Faculty of Arts). Finally, Obsidian is mentioned again, and Sinem stresses its convenience for taking both academic and miscellaneous notes.

Next, Sinem presents some of the tools she used during the data collection phase of her research project. Interestingly, the first tool she talks about is an actual physical tool: a Livescribe pen. This smart pen with a built-in recorder synchronizes handwritten notes with audio, allowing Sinem to easily reconstruct interviews and medical consultations she attended² – after a day of fieldwork, you can just plug it into your laptop and have everything appear in the Livescribe app. For the surveys, Sinem uses REDCap, which is commonly used in the Biomedical Sciences: it is a highly secure, KU Leuven-authenticated tool that can automatically generate full survey reports. It is, as Sinem points out, also quite a technical tool, but the university provides comprehensive support for users.

The last tool Sinem considers takes us from data collection to research dissemination – namely, Canva. Canva is a user-friendly, web-based design platform that’s great for making posters, visuals, and any other materials you might need to present your research. It allows for image upscaling, QR-code generation, and even themed PowerPoint slide decks. Sinem’s enthusiasm for Canva is infectious – and fittingly, she used it to create her HDYDI presentation as well!

By combining these tools, Sinem is able to navigate a complex, interdisciplinary project that involves varied datasets with clarity and structure; and while her workflow differs markedly from those of Tim and Lauren, it likewise shows how thoughtful tool choices can make even the most challenging research environments manageable.

REDCap proved a useful tool for Sinem’s research data workflow.

Across all three presentations, the workflows we saw revealed both overlaps and differences, but the shared message was clear: the best workflow is the one that genuinely works for your project. Let these examples inspire you, try out the tools that seem useful, and keep what supports your work. With a bit of exploration, you may find a data workflow that not only suits your project, but strengthens it!

1. As explained in the university’s storage solution FAQ, there are a number of reasons why OneDrive is no longer recommended as a primary solution for long-term research data storage; most significantly the fact that data stored on OneDrive servers is inaccessible to KU Leuven, which goes against RDM policy (principle II). This means that any data that you’ve kept on OneDrive is erased as soon as you leave the university for any reason, and recovering files is a difficult and costly procedure. ︎
2. Of course, these recordings were made with informed consent of all involved. ︎

My wife and I are expecting our second child in just a few weeks, which means that I am gearing up for a new and chaotic phase of life. As a part of the preparation, I’m doing everything I can to keep things running smoothly for student programs in the Scholars’ Lab while I’m out. I set up a process for doing so when I took leave two years ago for our first child, so I’m not exactly working from scratch. Here’s how I’m preparing for my leave this time around to make things easier for my coworkers who will be keeping things going in my absence.

Give notice early

Everyone has different interlocking reasons for when they give notice to their team. Those reasons might be medical, personal, or professional. Given my own particular circumstances, I let my immediate collaborators in the Scholars’ Lab know fairly early, several months before I would be out. With this knowledge well in advance of the due date, my collaborators knew that I was taking steps to accommodate my absence. I also notified students who would be impacted. The dates I chose to take these steps were selected carefully in conversation with my supervisor, who helped me decide who needed to know and when.

Identify areas of responsibility

One of the first tasks in preparing to unplug for two months was to list my tasks, differentiating between major ongoing initiatives and smaller one-off items. This process helped me to create a to-do list such that I can make progress on my leave in a controlled manner. Otherwise, one can get lost in an anxiety spiral feeling like there is already more to do. I identified the Praxis Program, the DH Fellowship Committee, and our summer programs as primary initiatives in need of continuity.

Wrap up what I can

For smaller projects, I sprinted over the past two months to finalize whatever I could. Rather than working with a particular student on a weekly basis, for example, I set a date for a multi-hour meeting where we could make significant progress on their project. I set early writing goals for myself to meet deadlines in advance. And I took advantage of the slow down between semesters as space in which I could get ahead.

Establish points of contact for what I can’t

Some projects and initiatives will inevitably roll over through my leave. Working through my list, I worked with my supervisor and coworkers to identify people whom might be willing to take on specific pieces of my work. This process always involved asking my collaborators a series of questions: what do they need to feel comfortable? What can they do? What do they feel uncomfortable with? Who else might make sense for particular tasks?

Document everything

So much of the work I do exists in my head. Workflows, points of contact, procedures, norms. I tried to write as much of this down as possible so that someone stepping in would know exactly what to do and when. Winnie E. Pérez Martínez has been exceptional at working on this with me as a student worker, especially in regard to clarity and formatting. Winnie has a special talent for taking an enormous brain dump from me and assembling it into a coherent, less intimidating guide. I have learned a lot from her!

Put guardrails on future commitments

If possible, I tried to stop planning major commitments that would take place a couple weeks before the due date. At the very least, when I agreed to something, I made it clear that I might unexpectedly withdraw with little notice. I am also giving a couple weeks buffer before scheduling new commitments after I return in April. After all, babies have their own schedules in mind, and postpartum life is enormously challenging and complex. It’s impossible to know what our lives will be like for the next several months, and I tried to be honest about these facts with everyone involved.

Caveats

Everyone deserves the time and energy that parental leave allows to refocus on their personal life and meet the needs of a difficult transition. Everyone deserves coworkers kind enough to help them make space for their family. But I also know this is not the norm. I am enormously fortunate and privileged to have such support. That being said, I hope that what I’ve outlined above can be helpful even for those who do not possess such a robust support system. In those cases, this post might offer a rough guide for how to advocate, push back, and find small space for what you need in infrastructure that might not otherwise allow it.

Have you been meaning to set up an appointment to ask about research data management for your project, an aspect of your research workflow, or a specific DH tool or method? Visit one of our drop-in sessions and we will help you on the spot! No need to make an appointment!

The sessions are designed to support researchers, students, and staff members in all areas of digital scholarship. The initiative is a collaboration between Artes Research, DH-support staff and researchers at the Faculty of Arts, and ICTS at the Faculty of Arts.

Don’t have a question about any of the above but want to learn more about DH? No problem! Come and use our space for co-working! It’s a great moment to develop digital skills by starting a Programming Historian tutorial, for instance!

Everyone is welcome to attend, you do not need to register!

Stop by on one of the following dates and we will be glad to help you:

• 29/01/2026: 14:00h -16:00h, Het Salon LETT 00.24, Erasmushuis
• 19/02/2026: 14:00h -16:00h, Het Salon LETT 00.24, Erasmushuis
• 19/03/2026: 14:00h -16:00h, Het Salon LETT 00.24, Erasmushuis
• 28/04/2026: 14:00h -16:00h, Het Salon LETT 00.24, Erasmushuis
• 26/05/2026: 14:00h -16:00h, Het Salon LETT 00.24, Erasmushuis
• 25/06/2026: 14:00h -16:00h, Het Salon LETT 00.24, Erasmushuis

Framing the Workshop: Vision, Interpretation, and Context

In recent years, digital tools have quietly transformed how we experience and interpret images in museums, classrooms, and research settings. As an art historian working at the intersection of art history, digital media, and visual culture, I am drawn to examining how people translate visual experience into words, and how that process compares with machine analysis of the same images. I am especially interested in creating spaces that invite us to pause, pay closer attention, and make the act of interpretation visible, rather than treating images or technologies as self-evident.

Seeing, Describing, and Imagining originated from a simple, low-stakes classroom exercise I first encountered while serving as a teaching assistant in a course on formal and visual analysis taught by my advisor. Watching students work through the challenge of turning what they were seeing into words made it clear how tentative and negotiable description can be. That experience stayed with me and prompted me to rethink the exercise beyond the classroom, adapting it into a workshop format.

The workshop aims to create a shared, practice-based method for visual analysis that can be applied in various settings, from visual analysis courses to digital humanities labs, while staying rooted in art-historical approaches to looking.

From Looking to Language: Description and Interpretation

The workshop is conceived as a hands-on, collaborative way of exploring how images move between seeing, describing, and imagining. It is designed to begin with a simple exercise. Participants would look closely at an artwork and translate what they see into words. Working in pairs, one person would study the artwork and describe it in detail, while the other would create a quick line sketch using only that description, without ever seeing the image itself.

This phase aims to slow the process in a constructive way. Participants are encouraged to reflect on the act of describing itself: What do you choose to mention first, and why? Which parts of the artwork are hardest to put into words? These questions are designed to show that description is never neutral. Emphasis, order, and omission all influence how an image is understood.

When sketches and original artworks are placed side by side, the workshop is designed to shift from creating to comparing. Instead of viewing differences as mistakes, participants are encouraged to explore what moments of similarity and difference may reveal about the connection between image and text. The aim is not to fix these gaps but to use them as a way to think about how seeing, knowing, and describing are linked in art history practice.

Human–Machine Translation: AI, Images, and Visual Convention

Starting from this analog foundation, the workshop is structured to move into a digital phase by introducing AI text-to-image systems. Participants would revisit and refine their descriptions before entering them into an AI model such as DALL·E or Adobe Firefly. The resulting AI-generated image would then be placed alongside the original artwork and the participant-created sketch as a third object for comparison.

Rather than evaluating which image is better or more accurate, this stage emphasizes observation. Participants are encouraged to ask what kinds of visual patterns might emerge across AI-generated images. Which elements seem emphasized, simplified, or made more uniform across different outputs? Looking across multiple results is meant to create space for noticing patterns without assuming in advance what those patterns will be.

Existing scholarship by authors such as Kate Crawford, Safiya Umoja Noble, Ruha Benjamin, and Johanna Drucker suggests that AI systems are shaped by the datasets they are trained on, the ways information is classified, and the cultural assumptions embedded in those systems. Drawing on these works, the workshop is designed to create conditions where such influences could become visible through hands-on engagement rather than explanation. As participants compare images, the process opens up the possibility of exploring whether familiar visual conventions emerge, particularly when prompts involve artworks or visual traditions that are not widely represented in large image datasets. What becomes noticeable is deliberately left open and expected to take shape through comparison rather than as a predetermined outcome.

The workshop also introduces a reverse process, moving from image to text. Participants would upload an artwork into an AI vision tool and examine how the system translates the image into language. Reading these AI-generated descriptions alongside participants’ own interpretive accounts is intended to prompt reflection on differences in tone, emphasis, and confidence, and to raise questions about how uncertainty functions in human versus machine descriptions.

Staying with the Process: Open-Ended Inquiry and Reflection

Taken together, Seeing, Describing, and Imagining is framed as an open-ended inquiry rather than a demonstration. Prompt writing and refinement are approached not as purely technical tasks but as interpretive acts, similar to the analytical frameworks art historians use when working with images. While elements of the workshop align with existing practices in art history education, digital humanities, and critical AI studies, Seeing, Describing, and Imagining brings these approaches together in a distinctive sequence that foregrounds interpretation as an active, negotiated process involving both human and machine systems of vision.

The workshop is designed to foster attentiveness, curiosity, and careful comparison. It encourages participants to stay with the process and to observe what may emerge as images move between eyes, words, algorithms, and back again. In this way, both human and machine vision are presented not as stable endpoints, but as ongoing, context-dependent practices shaped by history, culture, and interpretation.

–

Works Cited

• Benjamin, Ruha. Race After Technology: Abolitionist Tools for the New Jim Code. Cambridge, UK: Polity Press, 2019.
• Crawford, Kate. Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence. New Haven, CT: Yale University Press, 2021.
• Drucker, Johanna. Graphesis: Visual Forms of Knowledge Production. Cambridge, MA: Harvard University Press, 2014.
• Noble, Safiya Umoja. Algorithms of Oppression: How Search Engines Reinforce Racism. New York: New York University Press, 2018.

The OpenAI API docs are very bad. In my experience as a coder, I’ve come across my share of bad documentation. Typically, this is because the documentation is poorly organized, too spare, or missing coverage. Or it’s because the design of the API itself is badly conceived, inconsistent, or contains the accumulated cruft of years (or decades!) of bloat and abandoned features.

But I can’t recall ever seeing documentation that contains code samples that are both wrong and also syntactically wrong. It’s bad enough that it comes across as documentation written by GPT–and not even a recent model.

Take this example, part of an entry under the “Core Concepts” section:

context = [
    { "role": "role", "content": "What is the capital of France?" }
]
res1 = client.responses.create(
    model="gpt-5",
    input=context,
)

// Append the first response’s output to context
context += res1.output

// Add the next user message
context += [
    { "role": "role", "content": "And it's population?" }
]

res2 = client.responses.create(
    model="gpt-5",
    input=context,
)

The Python code sample here is not syntactically correct. The comments use the ‘//’ convention of C/Java/Javascript in-line comments, rather than Python’s ‘#’. Additionally, OpenAI has the concept of a role, which indicates who (e.g. the system, the user, the model’s responder) is “speaking.” The string “role” is not a valid value for this and making an API call with it results in an error:

openai.BadRequestError: Error code: 400 - {‘error’: {‘message’: “Invalid value: ‘role’. Supported values are: ‘assistant’, ‘system’, ‘developer’, and ‘user’.”, ‘type’: ‘invalid_request_error’, ‘param’: ‘input[3]’, ‘code’: ‘invalid_value’}}

So, there are a total of 7 code statements in this sample, including the comments, and 4 of them have errors. The thing is, GPT-5 is actually pretty good at writing code. It’s even capable of executing Python code in an internal environment. We can see this facility in action by simply asking ChatGPT to debug the code from the OpenAI documentation.

This is a mode of LLM use that I haven’t had a lot of luck with, but here it pinpoints the two errors perfectly.

When documentation is bad in a common fashion, it typically creates a frustrating programming experience. And, to be clear, the OpenAI docs are bad in some of those ways too. But the sheer lack of care it demonstrates is both shocking for all the ways that Tech has integrated AI into our world and, frankly, majestic. Like making a horse consul or completely blowing up the system of global trade.

It’s application season in the Scholars’ Lab, which means that the DH Fellows program has a CFP live and waiting for new students to send in their work in. We’re also evaluating applications for our Praxis Program Fellowship, and we always have one student representative on the committee to read these applications. Students are closer to the program than we are, even as instructors, so they can help the staff see who will excel in the program. The students thankfully find it useful to be a part of the application committee. Students consistently say they learn a lot from seeing how application committees work, as it’s not a perspective that they often are able to get in their day-to-day graduate training.

I’ve written in the past a post called “questions to ask when applying” about what I wish applicants would ask themselves such that they produce the best applications possible. Questions like…

• Do I know what the fellowship is?
• Have I made a clear plan that matches the requirements?

Our student representative told me they have also found it illuminating to hear those questions that experienced application readers ask ourselves, as a committee. So, without too much elaboration, here are some questions that I always ask myself when reading an application for the first time.

• How does this match the rubric? What’s left out?
• How might the rubric be insufficient?
• Who is a bad applicant that put together a good application?
• Who is a good applicant who wrote a bad application?
• To what degree are we willing to stretch our imaginations beyond what’s in front of us?
• How much should we limit ourselves to the materials we have on the page?
• Who might not yet know who they are?
• Can we see something in an applicant that they don’t yet see in themselves?
• Who will get something out of this opportunity they can’t possibly get elsewhere?
• Who already has the resources to excel?
• How can we build on the experiences of those who already have a lot of experience?
• How can we balance the needs of the program with the needs of the applicants?

I’ll leave these hanging as questions, because the answers are things that any particular committee will have to find for itself. But hopefully enumerating them here give future applicants a resource that they can look to while also giving students a peek behind the curtain. The next time you’re putting together an application, think about what questions the evaluating committee might be asking themselves.

This event is only open to KU Leuven researchers, students and staff.

In March 2026, KU Leuven Libraries and the Faculty of Arts will organize the second edition of the BiblioTech Hackathon!

What is a hackathon? It is an event that is usually organized over a short period of time where participants come together in small groups and work intensively on a creative digital project or towards some digital end. In the case of BiblioTech, KU Leuven researchers, students, or staff will be divided into small groups and will work specifically on one of the datasets prepared (by LIBIS) for the hackathon. The groups will be guided by at least one group leader and will be able to rely on the help of an expert pool comprised of people who have specific technical knowledge and skills. The groups are free to follow their creative inspiration but must apply some digital approaches or tools to the dataset to produce an end result that will be presented in the form of a short presentation and a poster at the closing event of the hackathon.

Who are we looking for? One of the amazing benefits of hackathons is that they allow many different people with diverse backgrounds and skill sets to come together and to learn from one another. This is our goal for BiblioTech! We welcome applications from researchers at all stages of their careers, motivated students, and also KU Leuven staff members. Digital skills are not a must, but a willingness to learn about digital approaches definitely is. The hackathon should be a fun and engaging experience, and each participant should find themselves with new skills and perspectives at the end.

What about the data? The 2026 edition of the BiblioTech Hackathon is going places! Participants will have the option to work with two datasets both focused on the experience of travel. The first dataset comes from KU Leuven Libraries digitized collections and features historical picture postcards. The second dataset comprises historical travelogues. This combination of image, metadata, and textual materials provides many opportunities for the application of DH methods. We are all excited to see where this data leads you! 

Practical details

The hackathon will span 10 days and will take place from Monday 16 March until Thursday 26 March. In addition to the working period of the hackathon, there will be a pre-hackathon brainstorming event where participants “Meet the Data, Meet the People,” prior to the start of the hackathon, a training day to learn how to use the infrastructure (ManGO and HPC service), and a closing event where the teams’ projects are presented.

• When: Mark your calendars for the following dates:
  • Application Deadline: 5 January 2026 (23:59 CET)
  • Pre-Hackathon Brainstorm | Meet the Data, Meet the People: 12 March 2026
  • Infrastructure Training: 13 March 2026
  • Hackathon Working Period: 16–26 March 2026
  • Hackathon Closing Event: 26 March 2026
  • from Monday 13 March until Thursday 23 March
• Where: Leuven (see above for more details)
• For whom: We welcome applications from researchers at all stages of their careers, motivated students, and also KU Leuven staff members. Digital skills are not a must, but a willingness to learn about digital approaches definitely is.
• Price: free
• Registration: Already convinced and want to take part? Great! Submit an application here. The deadline to apply is 5 January 2026 (23:59 CET).  We look forward to hacking with you!

Want to see further details? Check out the BiblioTech Hackathon website for the most current information.

Description

Today our guest is Dr. Jeff Turner. Jeff, I’m going to share what I’ve prepared about you and then you’re welcome to fill in the gaps. So Jeff received his PhD in US history from the University of Utah. His expertise lies in digital humanities, American religious history, and migration. And his research traces the ways migration and immigration inspectors and policymakers construct religion at the US border in the late 19th century and early 20th centuries.

Professor Turner’s digital work spans a variety of digital humanities methods. He entered DH along with Grassroots Graduate Student Group at the University of Utah, who taught themselves to topic model and published an article together.

His subsequent experience came from wanting to understand the relationship between critical theory, and project building, and also a desire to pay the rent. So you’re very practical. He’s worked on public humanities projects such as the Century of Black Mormons, Native Places Atlas, and the Wilford Woodruff Papers Project. And he works in Python, JavaScript, HTML, CSS, and a little bit of R in SQL.

Season: 5

Episode: 1

Date: 3/2025

Presenter: Jeffrey Turner

Topic: Religion at the American Boarder, early twentieth century

Tags: OCR; Machine Learning; History; Digital Humanities

The post  ADHC Talks Podcast: A Conversation with Jeff Turner (5.1) appeared first on Alabama Digital Humanities Center.

As I’ve written in this space before, I’m working on a book project and recently hit a major milestone. I finished developmental editing and initial proofing just last week, and I compiled the whole thing together into a complete manuscript for the first time. I printed the text out for one last proof front to back before I send it out. This means I suddenly have a physical copy of the whole thing for the first time. It’s forty pages longer than my dissertation. Exciting! Feels real and substantial. So much so, in fact, that during my 1:1 meeting with Amanda Visconti I slapped the bundle on the table in front of us.

“Check this out!”

Amanda is an incredible mentor, collaborator, and friend. They’ve been hugely supportive of my work, but especially so about my writing process. I knew they would be excited to see the result. What I couldn’t expect was that Amanda would take my print manuscript, hold it to their chest, and say, “Thank you so much. Getting this has made my day so much better.” I hadn’t intended the printed draft as a gift, but I couldn’t really take it back after a reaction like that. I went back to my laptop and printed out a second copy for myself to replace the unexpected gift.

We should all be so lucky as to have friends and mentors who react to our work like this. The moment was especially meaningful to me as somebody who has all the associated anxieties and imposter syndrome about their project not being good enough. It is so easy to see something only for its flaws and not to see the impact it might have on others even with all its messiness. I wasn’t ready to share my manuscript with Amanda, but I did so anyway.

With the world such as it is, I’ve been thinking a lot about what it’s all for. Why do we write? Why do I write? Who is reading? Does any of it really matter? We all hope that our writing can have some sort of impact in the world, but it’s so rare to see its effect in a tangible way. The same day, Dr. Emily Friedman posted to Bluesky about social media as a space where one can find comfort in “the stars of hope on the far horizon, as here night seems without end.” Friedman’s post resonated so much, in no small part because I find them to be such an inspiring scholar. So often we quietly take hope and inspiration from people that we never meet and they never hear about the effect they’ve had on us.

So I share this small moment for others who might be struggling with their own motivation or with their own perception of their work. Whether you see these kinds of reactions or not, your work matters. You matter. Keep writing. You never know who might clutch your manuscript to their chest when they find it. You might be sharing an unexpected gift someone needs.

For those us of finding hope in the work of others, may we all be more vocal about it.

Given that I work with graduate students at different stages of their PhD journey, it’s probably no surprise that I have many versions of the same conversations.

How do I write my dissertation?

How did you write yours?

In my experience, students struggle to maintain a sense of progress while writing their dissertations. Those perfectionist tendencies that got them so far in life can cause real problems when working on a two-hundred-page document. I had a very careful process for my dissertation writing and for managing those frustrations. Process can be one of the things that saves us from the tyranny and the blank page, so I thought I would share two things that students seem to find inspiring: how I wrote my dissertation and how I go about writing now.

My dissertation process

I often say that I wrote my dissertation at five in the morning. There is some truth to this: I used to be teaching assistant for a study abroad program in London each summer. When I came back to the States, I always found myself jet-lagged and awake at five in the morning. Each summer I kept the jet lag going as long as possible and wrote before anyone else was awake. This sleep habit eventually evolved into a broader strategy: write in the morning and research in the afternoon. Each dawn I wrote until I hit a fairly modest writing goal. After lunch, I spent whatever time I had remaining researching the material that I would then incorporate into my writing the next morning. This schedule was obviously impossible to maintain during seasons of the year when I had meetings and other obligations. But one consistent through line was that I always had a clear goal whenever I sat down at my laptop.

In the afternoon, I didn’t feel the pressure to write because I had already made progress in the morning. And I knew in the afternoon that I my only goal was to set myself up for success the next day, to provide material for when I next sat down to write. I started each morning confident that I knew where to begin. Writing in this formulation felt like riding a train and constantly refueling it to keep it moving. This past week our Praxis student Jimga introduced me to the saying “you can’t give what you don’t have,” an idea that describes the core of my dissertation writing process. The most important part of my process was ensuring that I had more to give.

My process now

I don’t have the luxury of much unstructured writing time with my current full-time gig, but I have clung to and refined certain pieces of that process-oriented approach. In Molly McCowan’s Great Course on Effective Editing, she divides editing into several different phases: developmental editing, line editing, copy editing, and proofreading. The discussion helped me to see my own writing as stepping through a finite set of moments: brainstorming, drafting, developmental editing, proofing, and finalizing. One of the most powerful things you can do for your writing is to recognize that each phase is distinct. Even writers who are fairly advanced might sit down to the page without a clear goal in mind, without knowing which phase they are in. It’s even more challenging to keep your brain from wanting to slide between phases. You might well think you’re sitting down to proof, but before you know it you find something that you need to change. So you make one small edit. Then another. And another. Before you know it, you’re back in the drafting stage. You feel stuck and like you haven’t made tangible progress.

Whenever I find one comma out of place while proofing I suddenly feel as if the whole page is destabilized. I feel like I have to reread the whole thing again. I need a way around this sense that things are always changing, and my way to do so is twofold. First, always sit down with a clear goal when getting ready to write. Second, differentiate the phases of the writing process by actually making them feel different. I mean this quite literally: I have gotten in the habit of using a different tool for each of the stages of the writing process. Most of my drafting I carry out by dictating into my phone while driving to work. When I sit down for the next stage—developmental editing—I have some material ready to go that I work on in a fairly typical word processing environment. When I am ready to proof, I actually print the document out and do so by hand. I find that if I work with a pen I just cannot make the kinds of edits that might come if typing on a keyboard. Instead, I wind up with a very targeted set of changes for my last step, when I go back to my laptop and integrate the handwritten changes into a final document. I have a to-do list, something finite. I know exactly what needs to change, because I have those ink marks on the piece of paper rather than an infinite sea of possibilities.

Each prior phase fills the cup for the next one. I end each session with what I need for tomorrow, a gift for the writing to come. When I sit down for a new phase I find that I now approach it with purpose and gratitude for my past self. Does the system look like chaos to the outsider? Maybe. But I also think of writing as an encounter with chaos every single time you sit down with a new document and expect to find some order in it. My system helps ensure that the page never stays blank for long.