The goal of the Sumerian Network project has been to build reproducible socio-economic networks from the Ur III archives, and to further refine these models to more accurately reflect the actors and entities active in these unprovenanced archives over the 80-year period in the 21st century BCE. Beginning with ca. 15,000 transliterated texts from the site of Drehem (known in antiquity as Puzriš-Dagān), which are curated online in three databases (the Open Richly Annotated Cuneiform Corpus (ORACC), the Database of Neo-Sumerian Texts (BDTNS), and the Cuneiform Digital Library Initiative (CDLI)), we applied various classification methods in order to delineate sub-archival data sets, known as text groups in the scholarly literature. In order to make our study reproducible, we used iPython Jupyter Notebooks (Pérez and Granger 2007) to describe the tools and methods we use in connection with the code and dataset, as well as a tool for generating a series of empirical network models.
The primary question which we pursue in this article is how one can use reproducible and replicable workflows for discovering the optimal classifications of the text groups from an unprovenanced archival context. We describe how we leverage existing scholarship to help validate our findings, both in terms of published work as well as through workshops with hands-on tutorials.
Our results show that the key factor for success lies in building reproducible and replicable workflows. This allows for the combination of classification methods with scholarly input. For the most recent results of these reproducible network models, see our Jupyter Book, Sumerian Networks.