BIDS' Adam Anderson and Niek Veldhuis have recently released the Sumerian Networks JupyterBook, a project which focuses on building reproducible models of socio-economic networks from the Ur III archives. Anya Kulikov, Yashila Bordag and Colman Bouton – students in CDSS’ Data Science Discovery Program – also contributed to the project, and since 2015 about 15 undergraduate researchers have engaged with the project, working on various aspects from data cleaning to network analysis.
The project began with ~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). The group applied various classification methods in order to delineate sub-archival data sets, known as "text groups" in the scholarly literature.
In order to make the study reproducible, the team used iPython Jupyter Notebooks (Pérez and Granger 2007) to describe the tools and methods used in connection with the code and dataset, as well as a tool for generating a series of empirical network models.
The results show that the key factor for success lies in building reproducible and replicable workflows, which allow for the combination of classification methods with scholarly input. For the most recent results of these reproducible network models, see their Jupyter Book, Sumerian Networks. The book is intended to help other researchers learn how such a network can be built using Python Jupyter Notebooks, which can be mounted to GoogleDrive and run in Google Colaboratory.
Sumerian Networks: Classifying Text Groups in the Drehem Archives
July 28, 2021 | Adam Anderson, Anya Kulikov, and Niek Veldhuis | Interdisciplinary Digital Engagement in Arts & Humanities (IDEAH)
Data Science Discovery Program Students Help Open Accounting Data from Ancient Mesopotamia
August 9, 2021 | Jon Bashor | CDSS News