ERC Stone-Masters Project

We are pleased to announce that on Monday, 27 April, we will be hosting Hernán González Bordas (Université Bordeaux-Montaigne). During the Warsaw Seminar in Epigraphy and Papyrology, Hernán will give the paper titled: "Land and law in Roman North Africa: why epigraphy and paleography matter." Please, join us in person!

Venue: Wydział Archeologii Uniwersytet Warszawski, University of Warsaw, room 206
Time: 4:45–6:15pm

We have recently announced that Marina Bastero Acha has been awarded a five-year postdoctoral fellowship funded by the Basque Government. Now, we can happily let you know that Martyna's postdoc position will be taken over by Martyna Świerk. Martyna was selected in a competitive call and she will focus on exploring the workshops attested of North Africa in the coming months. Previously Martyna has already had a significant input into our work by performing a prosopographical research on artisans attested in mosaic inscriptions across the Late Antique Mediterranean.

Maciek Krawczyk , who has programmed our Digital Atlas of Workshops, recently attended the CCA conference in digital humanities in Vienna. He gave there the paper "Natural Schema Evolution vs Machine Learning Readiness: A Case Study from the Stone-Masters Project," where he explored how the experience of programming the Atlas allowed him to get new insights into the problem of evolution of database schemas. As he explains, most databases do not start large and complex.They begin quite simply — with a small set of fields designed to record the most important attributes of the material. But as the project develops, something very natural happens. New research questions appear. Typologies become more detailed. Additional datasets are integrated.And slowly, the schema begins to grow. After several years, the database often looks very different from what was originally designed. At the same time, it becomes harder for algorithms to identify stable and reliable patterns. As a result, analytical stability tends to decrease—not because the data are incorrect or poorly collected, but because they have become more expressive and more detailed. In other words, the dataset becomes increasingly powerful for human interpretation while simultaneously becoming more challenging for computational generalization.

https://2026.caaconference.org/