Please use this identifier to cite or link to this item: https://dl.eusset.eu/handle/20.500.12015/3142
Title: The Types, Roles, and Practices of Documentation in Data Analytics Open Source Software Libraries: A Collaborative Ethnography of Documentation Work
Authors: Geiger, R. Stuart
Varoquaux, Nelle
Mazel-Cabasse, Charlotte
Holdgraf, Chris
Keywords: documentation;standards;invisible work;motivations;peer production;collaboration;infrastructure;ethnography;open source
Issue Date: 2018
Publisher: Springer, London
metadata.dc.relation.ispartof: Computer Supported Cooperative Work 27(3-4)- ECSCW 2018: Proceedings of the 16th European Conference on Computer Supported Cooperative Work
Series/Report no.: ECSCW
Abstract: Computational research and data analytics increasingly relies on com- plex ecosystems of open source software (OSS) “libraries” – curated collections of reusable code that programmers import to perform a specific task. Software docu- mentation for these libraries is crucial in helping programmers/analysts know what libraries are available and how to use them. Yet documentation for open source soft- ware libraries is widely considered low-quality. This article is a collaboration between CSCW researchers and contributors to data analytics OSS libraries, based on ethno- graphic fieldwork and qualitative interviews. We examine several issues around the formats, practices, and challenges around documentation in these largely volunteer- based projects. There are many different kinds and formats of documentation that exist around such libraries, which play a variety of educational, promotional, and organizational roles. The work behind documentation is similarly multifaceted, in- cluding writing, reviewing, maintaining, and organizing documentation. Different aspects of documentation work require contributors to have different sets of skills and overcome various social and technical barriers. Finally, most of our intervie- wees do not report high levels of intrinsic enjoyment for doing documentation work (compared to writing code). Their motivation is affected by personal and project- specific factors, such as the perceived level of credit for doing documentation work versus more ‘technical’ tasks like adding new features or fixing bugs. In studying documentation work for data analytics OSS libraries, we gain a new window into the changing practices of data-intensive research, as well as help practitioners better understand how to support this often invisible and infrastructural work in their pro jects.
metadata.dc.identifier.doi: 10.1007/s10606-018-9333-1
ISSN: ISSN 0925-9724
metadata.mci.conference.sessiontitle: Long Papers
metadata.mci.conference.location: Nancy, France
metadata.mci.conference.date: 4-8 June 2018
Appears in Collections:ECSCW 2018 Long Papers

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