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Tales of the Field: Building Small Science Cyberinfrastructure
 

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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    Tales of the Field: Building Small Science Cyberinfrastructure Tales of the Field: Building Small Science Cyberinfrastructure Presentation Transcript

    • Tales of the Field: Building Small Science Cyberinfrastructure Andrea Wiggins iSchool @ Syracuse University 31 October, 2009
    • 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/
    • 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
    • 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/
    • 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/
    • 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
    • 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/
    • 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
    • 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/
    • 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
    • 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