The OCEAN mailing list data set: Network analysis spanning mailing lists and code repositories

• URL: http://arxiv.org/abs/2204.00603v1
• Date: Fri, 1 Apr 2022 17:50:15 GMT
• Title: The OCEAN mailing list data set: Network analysis spanning mailing lists and code repositories
• Authors: Melanie Warrick, Samuel F. Rosenblatt, Jean-Gabriel Young, Amanda Casari, Laurent H\'ebert-Dufresne, James Bagrow
• Abstract summary: We combine and standardize mailing lists of the Python community, resulting in 954,287 messages from 1995 to the present. To showcase the usefulness of these data, we focus on the CPython repository and merge the technical layer with the social layer. We discuss how these data provide a laboratory to test theories from standard organizational science in large open source projects.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Communication surrounding the development of an open source project largely occurs outside the software repository itself. Historically, large communities often used a collection of mailing lists to discuss the different aspects of their projects. Multimodal tool use, with software development and communication happening on different channels, complicates the study of open source projects as a sociotechnical system. Here, we combine and standardize mailing lists of the Python community, resulting in 954,287 messages from 1995 to the present. We share all scraping and cleaning code to facilitate reproduction of this work, as well as smaller datasets for the Golang (122,721 messages), Angular (20,041 messages) and Node.js (12,514 messages) communities. To showcase the usefulness of these data, we focus on the CPython repository and merge the technical layer (which GitHub account works on what file and with whom) with the social layer (messages from unique email addresses) by identifying 33% of GitHub contributors in the mailing list data. We then explore correlations between the valence of social messaging and the structure of the collaboration network. We discuss how these data provide a laboratory to test theories from standard organizational science in large open source projects.

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