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Lessons Learned from a Decade of FLOSS Data Collection

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Big Data Factories

Part of the book series: Computational Social Sciences ((CSS))

Abstract

In 2004 a collaborative research team based at Syracuse University and Elon University began collecting and sharing data in order to understand how free/libre open source software (FLOSS) is made. Embodying some of the same FLOSS ethos, this team created a public-facing repository for their own data and analyses and encouraged other researchers to use it and contribute to it. This chapter tells the story of how the FLOSSmole project began, where the data comes from and what we have learned from it, and how the project has grown and changed over the years. In addition to capturing snapshots of the current state of the FLOSS landscape, FLOSSmole also serves as a mirror to the larger FLOSS ecosystem, since changes in FLOSSmole’s mission and goals over the years necessarily reflect some of the cultural and technological changes taking place in FLOSS itself. As such, FLOSSmole will continue to face many challenges in the future, including the continual need to provide broader access and more sophisticated and relevant data and analyses and to do all this in a way that is sustainable and community driven.

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Author information

Authors and Affiliations

  1. Syracuse University School of Information Studies, Hinds Hall 348, Syracuse, NY, 13244, USA

    Kevin Crowston

  2. Department of Computing Sciences, Elon University, Elon, NC, 27244, USA

    Megan Squire

Authors
  1. Kevin Crowston
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  2. Megan Squire
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Correspondence to Megan Squire .

Editor information

Editors and Affiliations

  1. Purdue University, West Lafayette, Indiana, USA

    Sorin Adam Matei

  2. Technopôle Brest-Iroise, IMT Atlantique (Telecom Bretagne), Brest Cedex 3, France

    Nicolas Jullien

  3. Computer Science, University of Missouri, Columbia, Missouri, USA

    Sean P. Goggins

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Crowston, K., Squire, M. (2017). Lessons Learned from a Decade of FLOSS Data Collection. In: Matei, S., Jullien, N., Goggins, S. (eds) Big Data Factories. Computational Social Sciences. Springer, Cham. https://doi.org/10.1007/978-3-319-59186-5_7

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  • DOI: https://doi.org/10.1007/978-3-319-59186-5_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59185-8

  • Online ISBN: 978-3-319-59186-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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