Pieces of the “Partial Puzzle”: Notes on editing a journal special issue on ancient data

In a 2020 chapter for the edited volume Digital Approaches to Teaching the Ancient Mediterranean, Lisl Walsh argues for a singular, disciplinary-specific benefit of studying the ancient world at university using the metaphor of solving—or better, attempting to solve—a jigsaw puzzle. She calls the idea “partial-puzzle analytics.” Walsh writes that immersing oneself in the study of antiquity “often necessitates an acknowledgement of the relative scarcity of its evidence and the consequent necessity for multidisciplinary approaches to the evidence we do have.” She continues with the metaphor itself, that studying antiquity can be “like trying to put together a 500-piece puzzle when we only have five pieces and no picture to guide us.”

Earlier this year, I co-edited a special issue of the Journal of Open Humanities Data dedicated to “Representing the Ancient World through Data” (with colleagues Andrea Farina, Paola Marongiu, and Martina Astrid Rodda): 16 data papers and two research papers—collecting, organizing, and publishing information about antiquity in formats that make it computationally tractable or otherwise available for further systematic quantitative and qualitative study. In preparing the special issue, Walsh’s chapter came to mind because in effect the data published here are the pieces of the “partial puzzle.” I was reminded throughout the process—as I am generally as an editorial board member of JOHD—of the responsibility we have as scholars of the past to make those extant puzzle pieces as available, accessible, and easy to employ in further research as possible.

In the call for papers, the editors noted our intention to “define the ancient world in its broadest sense,” and so we find a great deal of variety in the special issue. We have data papers covering different times, places, cultures, languages, and more, including papers on Mesopotamian place names, Hellenistic epigrams, Latin literary criticism (to which I am also a contributor), archaeological data management, and 19th-century classical commentaries, among others. Yet they all share in common the incompleteness, fragmentariness, sparsity, or other kinds of partialness that define ancient-world data. Unlike in a modern data-science context, where we may be able to collect more data in the future or perhaps wait until more datapoints emerge, data representing the ancient world is more often than not a reflection of the accidence of survival. What is not extant is for all intents and purposes intractably and irrecoverably lost. It is true that some data from antiquity remains undiscovered, unrecognized, unnoticed, uncontextualized, uncollected, and so on. And we very well may see the datasets published in this special issue expand in time as these scholarly negotiations with respect to discovery or recognition or contextualization are worked out. This is perhaps the most vital work available to the data-minded ancient-world researcher. But for the moment we need to recognize that the rows in today’s spreadsheet or the values in today’s JSON file, such as we find documented in the data papers presented in the special issue, only account for what we have: the missing values, so to speak, must also be kept in mind, a kind of ghost data that guides our analyses and interpretations through their conspicuous absences.

Reflection on the data we have—and the data we do not—helps, I would argue, to establish more clearly what gives ancient-world data its specific character and why it is valuable to bring together such a diverse collection of papers into a single special issue. Moreover, this diversity, whether through wide disciplinary coverage or wide methodological coverage, fits well with the kind of digital and computational work on offer at ISAW, where research is directed at both “both historical connections and patterns, as well as socially illuminating comparisons,” and for our purposes here, this includes data-driven connections, patterns, and comparisons. Reading the data papers in this special collection—and better, downloading the datasets themselves and incorporating them into our own research workflows—invites us to reflect on how such datasets and their constituent datapoints fit into the “partial puzzle.”