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Comparative Investigation of Collaboratories: Cross-Cutting Themes

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  1. Comparative Investigation of Collaboratories: Cross-Cutting Themes June 20, 2003 University of Michigan Ann Arbor

  2. Reminder: Where We’ve Been • UM group – 15 years of experience with distributed collaboration • SOC project • ~40 Collaboratories at a Glance (C@G) • 10 in-depth studies • Sept. 02: SPARC/UARC, CFAR, Bugscope, EMSL • June 03: NEESgrid, InterMed, GriPhyN, iVDGL, AfCS, BIRN • “The Literature” • Your input

  3. What We Learned Here • Review Cross-cutting Themes • Modify • Refine • Eliminate • Add • Framework for generalizations • What leads to success, failure? • Source of design prescriptions • How to do the next one?

  4. Cross-cutting themes: From Prior Work • Collaboration readiness • Collaboration vs. competition in science • Bottom-up vs. top-down origins • Technology readiness • Experience with collaboration tools • Infrastructure readiness • Both technical and social • Common ground • Extent of shared knowledge; critical in interdisciplinary work • Coupling of work • The interdependencies among individuals

  5. Collaboration Readiness • Can a collaboratory be mandated by an external agency (e.g., funding source)? • NEESgrid – collaboratory capability as a condition of funding • High risk – details in presentation & discussion • History of collaboration • High energy physics vs. earthquake engineering • Science driven • AfCS • BIRN

  6. Common Ground • NEESgrid • Differences in terminology between CS & EE communities • InterMed • Importance of establishing shared vocabulary • Boundary objects, pidgins • GryPhyN, iVDGL • Too much common ground  Boundary objects as key concept [G. Bowker] • BIRN • Attention to metadata, ontology

  7. Cross-cutting Themes from SOC Analyses • What is success? • Detailed discussion in June 2001 workshop • What are the incentives for participation? • Survey study in progress • What kinds of collaboratories are there? • Taxonomy – presented later • How do collaboratories evolve? • Some ideas based on our taxonomy – presented later

  8. What is Success? • Use of the collaboratory tools • Software technology • Direct effects on the science • Science careers • Effects on learning, science education • Inspiration for other collaboratories • Learning about collaboratories in general • Effects on funding, public perception

  9. Measures of Success • GriPhyN, iVDGL • Persist beyond ITR funding • Spending less time on tools, more on science • BIRN • Cover story in Nature • Lots of publications • Multiple audiences • Beyond the scientists • Students, government, industry, general public • Collaboratory  NSF STC

  10. Incentives • AfCS • Alliance with Nature • BIRN • Guidance re publications • LHC • Shift in time scale of experiments • Implications for careers

  11. Evolution***** • Ecology of collaborations • Movement from limited to full collaboration • Data – wisdom hierarchy [G. Furnas] • Movement up and down over time and space • Relates to social vs. technical processes • Where did the field come from, where is it going? • Historical context as critical • Multi-tasking of individuals (G. Mark) • Time scale issues • AfCS – bioinformatics earlier? • BIRN –savings across successive BIRNs

  12. Wisdom Knowledge Information Data The world The relationships Practice and Expertise Distributed Research Centers Community Data Systems Shared Instruments

  13. Cross-cutting Themes from SOC Analyses • Do collaborations have an ideal size? • Collaboratories allow for larger ones • How do they scale? • What are various organizational models for how to structure collaboratories? • How does the control and flow of resources affect collaboratory success? • The money flow; the relation to the sponsor(s) • How much flexibility should be designed in? • What kinds of early commitments? • How much flexibility will funders allow?

  14. Ideal size • ATLAS • Collaboration of 2000 • But very organized • Beyond ATLAS? • Manhattan • Apollo • How many working groups can be supported? • Organizational science as source of clues • What does technology enable? • How to scale from literature on teams (G. Mark)

  15. Flexibility • Retrenchment, redefining of goals • G. Bowker – may be key to success • Funding models • AfCS – enough flexibility? (A. Prakash) • Adapting to new developments • InterMed – 1995 shift to focus on guidelines • AfCS – 2003 changing cells

  16. Cross-cutting Themes from SOC Analyses • How important are data issues in collaboratories? • Data seems to be a central component of all collaboratories • For what kind of work do you need real-time vs. asynchronous interactions? • How important is security? • What’s the mix of tailor-made vs. off-the-shelf tools?

  17. Data Issues • Metadata • Provenance • Persistence, archiving • Rationale for transformations • NEESgrid, GriPhyN, iVDGL, AfCS, BIRN • Details of size, usage – different software needs? • What level of processing? Different disciplines may vary [D. Sonnenwald] • Data sharing across jurisdictional boundaries – BIRN • IRB – data from humans • International

  18. Cross-cutting Themes from SOC Analyses • How crucial are platform issues? • What is the emerging role of middleware? • What is the role of emerging infrastructure such as the Grid? • How does one move from early prototypes to production versions of collaboratories? • Why isn’t there more reuse of collaboratory tools? • To what extent are the issues specific to science domain or are general?

  19. Moving to Production Versions • Tensions between CS and domain users • NEESgrid – “innovation vs. extrapolation” • GriPhyN & iVDGL • Moving beyond initial demo stages • Slow adoption • InterMed • Sustaining the investment • NEESgrid – NEES consortium infrastructure set up in advance • GriPhyN, iVDGL – seeking a sustaining support process • BIRN • Incentives • “build hardware” [J. Leigh] • Diffusion of Innovation literature

  20. Domain specificity • The unusual character of HEP • Long history – since Manhattan • Scale – LHC • Common knowledge, self-esteem, etc.

  21. New Issues • Human subjects issues • IRBs across jurisdictional boundaries • Need for new approach? • Management • NEESgrid – management lags implementation • InterMed – need for tight management • GriPhyN & iVDGL – hiring project managers • AfCS – charismatic management • BIRN – governance manual; adding steering committee • Vision • Who’s vision • “Acephalous” projects (G. Bowker) • Leadership issues – charisma

  22. New Issues • What kind of technology? • Specific applications vs. APIs • Generic collab vs collab in specialized tools (S. Poltrock) • Economics of the Grid (M. Cohen) • Standards as a unifying process • Politics of standards setting • BIRN in a box • “If you build it, they will come” • Highly flawed model • NEESgrid • InterMed • GriPhyN, iVDGL • Tied to incentives • Expectation management

  23. New Issues • Intellectual property • Who negotiates? • What are the arrangements? • Evaluation • Who does it? • Within the project – formative • Outside the project – summative • What is it? • Cross sectional • Longitudinal • Over what time period? • Lag effects, long term indirect effects • Be sophisticated • “science” talk vs. “informal” talk (G. Bowker)

  24. Biggest issues – my candidates • What is success? • Evolution – ecology • Transition to production versions, sustaining the vision • Data issues • How to manage collaboratories?

  25. Some SOC Issues • Are we asking the right questions? • Are we doing the right kinds of analyses? • Measures • Control groups • Are our representations useful? • Resource diagrams • Mix of science and engineering