The goal of the Sumerian Network project has been to build reproducible models of 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 c. 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), this JupyterBook has a linear progression which applies various tools and methods in NLP and Open Science.
In order to make the study reproducible, the project was built using Python Jupyter Notebooks (Pérez and Granger 2007), hosted in a GitHub repository, to describe the tools and methods used in connection with the code and dataset, which result in a series of empirical network models. The book describes a series of steps for building a network from a collection of Sumerian Ur III administrative texts, which are curated digitally in three online 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.
The book is intended to help other researchers learn how to build similar networks using Python Jupyter Notebooks, which can be mounted to GoogleDrive and run in Google Colaboratory. 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.
Authors & Developers: Niek Veldhuis, PI (2017-2021), Adam Anderson (2017-2021), Yashila Bordag (2020-2021), Colman Bouton (2021), Jenny Chen (2018), Tiffany Chien (2017-2020), Lucie Choi (2018), Dalton Do (2017-2018), Zekai Fan (2018-2020), Kimberly Kao (2018), Jason Kha (2017), Anya Kulikov (2018-2021), Dominic Liu (2020-2021), Harini Rajan (2017-2019), Max Sullivan (2020), Aleksi Sahala (2019), Anjali Unnithan (2018-2021).
Sumerian Networks: A new JupyterBook for classifying text groups in the Drehem Archives
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