Tales of the Field: Building Small Science Cyberinfrastructure

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Society for the Social Studies of Science cyberinfrastructure methods panel presentation on experiences building small science cyberinfrastructure and reflections on implications for other …

Society for the Social Studies of Science cyberinfrastructure methods panel presentation on experiences building small science cyberinfrastructure and reflections on implications for other pre-paradigmatic domains.

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  • 1. Tales of the Field: Building Small Science Cyberinfrastructure Andrea Wiggins iSchool @ Syracuse University 31 October, 2009
  • 2. Free/Libre Open Source Software
    • FLOSS development
      • Large-scale social phenomenon of “collaborative” software development
    • Observing FLOSS research
      • Reflexive examination of small scholarly community studying FLOSS development
      • Specifically working on building CI for FLOSS research
    http://www.flickr.com/photos/pmtorrone/304696349/
  • 3. eScience Proof of Concept
    • (some) FLOSS research is a good candidate for eScience approaches to doing the work
      • Lots of data due to scale of phenomenon
      • Research community ethos of sharing
        • Data repositories
        • Research paper archive
        • Analysis artifacts
  • 4. FLOSS Research Community
    • Little Science
      • Interdisciplinary: primarily software engineering, but also social sciences across a wide spectrum
      • Fairly small community: under 500 researchers worldwide
    http://www.flickr.com/photos/circulating/997909242/
  • 5. FLOSS Data
    • Many types of data, focus here on digital “trace” data
      • Archival, secondary
      • By-product of FLOSS work, easy to get but hard to use
    • Federated repositories of repositories (RoRs)
      • Data for research drawn from hosting “forges”
      • ~1 TB across 3 RoRs
    http://www.flickr.com/photos/smiteme/2379630899/
  • 6. Research Methods & Tools
    • Methods used with RoR data vary, but are generally quantitative
      • Correlational studies
      • Longitudinal analysis
      • Code metrics
    • Two main approaches
      • Bespoke scripts or tools
      • eScience workflow tools
  • 7. Barriers to Uptake
    • Little Science
      • Lack of agreement over epistemology, RQs, methods, tools
      • Researcher isolation, few incentives to collaborate
    • Bimodal distribution of skills
      • “ I can’t possibly do that! I can’t write code!”
      • “ Why bother? I just write my own Python script; you should too.”
    http://www.flickr.com/photos/noner/1739876378/
  • 8. Technology Skills Required
    • Taverna
    • SVN
    • (more) SSH, Unix terminal, XML
    • R, plus packages
    • SQL, relational DB management
    • Java & Eclipse (just enough)
    • OWL, RDF, SPARQL
    • Knowledge of opaque data sources
  • 9. Implications for Small Sciences
    • Critical mass
      • Need stewardship, dedicated resources
    • Skills gap
      • eScience tools require fairly high technology competency
    • Convergence of research
      • Common questions, modes of research
    • Motivations to contribute
      • Academic credit
    http://www.flickr.com/photos/askpang/327577395/
  • 10. Potential Solutions
    • $$$
      • Maintaining and developing resources is not free, even if they are freely shared
    • Curricular integration
      • Broaden contributor base by drawing on students through coursework
    • Deliberately cultivate a community
      • Train PhD students early in their studies
    • Mechanisms to incentivize contribution
  • 11. Conclusions
    • Without external imperatives, CI for little science seems unlikely to emerge unaided
    • CI requires standardization and movement toward normal science, which may be premature or simply inappropriate for many social sciences
    • Benefits for early adopters: tools support efficient collaboration, enable rigorous research provenance, permit analysis replication, and speed time to results