Skip to main content.

From Idea to Insight

The Making of Data by Design

Any digital project is the work of many hands. How can this labor be visualized? What remains out of sight?

In nearly every meeting, as different specialists moved in and out of the project, or between different facets of it, someone uttered the name of an absent presence. Over time, it seemed like an ephemeral citation, a calling-back-to. Some names were colleagues, friends, or even Tanvi and Nick's pet kitten, Tibia; others were scholars or artists no one had ever met who left an impression on one of the team members, like when Margy finished Hanya Yanaghihara's A Little Life in the early hours of the morning before a meeting the following afternoon. Though we did not explicitly consider the prospect earlier on, the echo of those who were not present week by week drove us to reflect on ways we could acknowledge how this project grew and grew out of a multitude of voices, hands, disciplines, technologies, and communities. Even while we were unsure of how to record it, how to account for all of those who touched it and us (as well as how they did so), we knew some sort of memory of the expanse that a project like this takes-one so concerned with data, memory, and embodiment-had to be recorded somehow.

And that is how you find yourself here, glimpsing a bit more in-depth at how Data by Design came to be. Your authors here are two of the lead contributors to this project: Margy Adams, a PhD Candidate in English at Emory, and Tanvi Sharma, currently a Research Fellow with the Atlanta Interdisciplinary Network and a recent graduate of NYU's Masters' program in Integrated Design & Media. While we cannot speak for all of the other contributors, who span multiple institutions over multiple years, we hope our words can stand for the additional perspectives behind the chapters that you see.

Any digital project is the work of many hands, eyes, ears, and, of course, the various technologies we employ to make it. When viewing a complicated website such as this one, with multiple interactions, toggles, and animations featured across each chapter, a question you might (understandably) have is: how did they make that? Here is an answer:


Pictured above is the technology “stack,” the various platforms and software tools we used to develop this website, and that it rests upon. We counted on different operating systems, libraries, servers, databases, browsers, and hosting platforms to bring this project to fruition. More specifically, this project was created with HTML, JavaScript, and CSS, using the React.js and Tailwind frameworks. We used a combination of D3, P5, and raw SVG to create the visualizations that appear on the site. It is hosted on a server maintained by Emory's Center for Digital Scholarship Center, using content delivered by AWS. You can find the entire project code on GitHub. So that is one answer to the question. But, as you well know by now after encountering the previous chapters, the work of these tools relies on how they are applied.

Beyond the official “tech stack,” there are more tools we used on a daily basis to help us build our work up to the implementation stage. Cumulatively, all team members used a number of various applications to produce ideas across (and beyond) the website_and also, crucially, among ourselves.

For example, Github proved crucial for us not only in the final deployment, but as we engineered the site, as it enabled us to implement our various ideas, play with various ways of refactoring pages and components, and generally collaborate on writing code.

But before any implementation was design, which we (and largely Tanvi) conducted using Figma. When designing the final visualization of the Trans-Atlantic Slave Trade database in the first chapter, for example, our weekly meetings often included a segment when Tanvi or our visualization prototyper, Shiyao, would share their screen to walk the remaining team members through new designs they had come up with. It was in these feedback sessions where we discussed how to complicate notions of time and linearity, the pros and cons of adapting a water metaphor, given the violence associated with it in the context of the Middle Passage , and the linguistic tension of orienting a map horizontally, from left to right, rather than vertically, so as to resist the user imposing a coordinate system upon what we intended as an abstract display. In this regard, Figma was a necessary tool, even if it is not granted entry into the official tech stack of the site.

Zotero was another necessary tool, both as the platform which preserved the notes, data, and images from Lauren's original archival research, and later, as she needed to share this information with the rest of the project team. In researching Shanawdithit's maps for the third chapter, for example, only Lauren went to the archive in St. John's, so Zotero became the platform that enabled the rest of the project team to collect and share information that could inform our own work as we developed the interactive components for the chapter.

Google Drive was also crucial in many ways throughout the project's development. In fact at first, Dan wrote a plugin that would enable us to author the chapters in Google Docs, with updating in real time. For me, Margy, Google sheets played a huge role. I wrote over 20 pages of alt-text for the images included in the site, which enriched our collective understandings of the images themselves.

And of course, iCalendar and Zoom were essential for the team to meet frequently and keep each other up to date with our individual and collective progress. These tools all bear the traces of our labor, which the visualization below brings to light.

Of course, a large part of how this website came together is still missing from this account: the people who employed these tools, guided by their decisions and their expertise. Both in terms of specific features, and in terms of the people involved, there is far more that we could say. Taking a page from Du Bois, who credited his students' labor in the “data portraits” featured in Chapter Five, here we visualize the people of this project itself.

This visualization shows that it is not just the aforementioned platforms that connect people, though that is of course a part of it; people's own networks connect us. And, while we cannot name every individual who may be connected to this project, for that constellation is too vast to visualize, we can give you an idea of its expanse.


And now, you, too, are a part of our constellation. We are happy you are here!

A very particular postlude from Margy: Though I in no way can assume a neutral role in observing the final numerous (potentially uncountable) pushes of this project over the last year and a half, I am compelled by the ways composing this conclusion itself coincides with the idea that visualizations are not simply something we see. As a sound studies scholar, Tina Campt's Listening to Images for years has served as a guide for me, and it especially came into focus as I began working on this project. Operating from a Black feminist standpoint, she says that the future real conditional-the grammatical “what will have had to happen”-is “an existential grammatical practice of grappling with precarity, while maintaining an active commitment to the every labor of creating an alternative future. Indeed, it is this grammatical practice of futurity that constitutes my definition of freedom” (116, emphasis in original). It seems to me, upon reflection, that this chapter is an attempt to attend to the precarity of the “every labor,” that which is quotidian, taken for granted, or quiet, to invoke Kevin Quashie's book The Sovereignty of Quiet. Further, the very argument of this project, that data visualizations have points of view, seems to itself create the possibility for an alternative future-one where visualization can act as a language that propels us towards freedom.

Campt sets up a practice of “listening to images” as an encounter that emphasizes “looking beyond what we see and attuning our senses to the other affective frequencies…of images and how they move, touch, and connect us to the event of the photo” (9). While we need to be careful of how we apply this framework to images that are not identification photos, the primary subcategory which Campt studies, the ethic of looking “beyond” is crucial to disrupting alleged objectivity in all visualizations of data, especially those that pertain to marginalized populations and the production of histories. Listening to images allows us to understand visualizations not as neutral presentations, but instead as representations of a particular position through which the data came to be collected. To reiterate the foremost point of this project: data visualizations inevitably fall short of presenting a totalizing picture of the visualized content because that task in itself, completely capturing data, is vacuous.

However, instead of viewing this as a problem to solve, other possibilities for visualization emerge when we focus on why we know that prospect is impossible: we are able to read data visualizations in multiple, sometimes contradictory, even infinite ways. Reading-or, as Campt and I prefer, listening-to the visual, a mode assumed to be beyond linguistic and sonic capture, drives how we help make the visual produce meaning. To combine Campt's ideas with literary and cultural scholar Hortense Spillers', visualization becomes a grammar-a language, a form of expression-that does not end once the image is created; in fact, it keeps living through our interpretations of and interactions with it. Campt's project “question[s] the grammar of the camera (as both an event of photography and a photographed event) as well as the haptic temporalities of photographic capture as pernicious instruments of knowledge production” (8-9). Simply, she does not only consider what the photograph is of, but also the contexts in which the photograph was taken and the “pernicious” device-the camera-that took the photo in order to glean what the act of the recording itself might tell us. Likewise, with data visualization we must be thoughtful of the contexts in which the data was recorded and, similarly, the “pernicious” device-the strategies and traditions of data visualization-to ultimately reflect on the systems in place that allowed one hand to document and another to be (un)documented. How does the image speak? What is it saying, what is it hiding? And why might it be hiding certain things?

Thinking of visualization as a grammar enables creators of data visualizations to convey rather than overdetermine, to gesture towards rather than eclipse. It also allows witnesses to actively build up worlds of meaning with creators rather than receive information through a hierarchical, passive structure. Reconstruing visualization as a grammar means that the unknown becomes central, but instead of fearing it, we might instead expose the contours around it that made it so and what we can do to produce knowledge anew. Just as a Danish-language novel transforms into another novel altogether when translated into English, which loses some meaning but gains others in the process, we can conceive of data visualization as a grammar that itself exceeds capture, a linguistic exercise in translation, an acknowledgement of the expanse-the lives, the land, the lessons-beyond what is pictured.