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Comparative Investigation of Collaboratories: Cross-Cutting Themes. June 20, 2003 University of Michigan Ann Arbor. 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 - PowerPoint PPT Presentation
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SCHOOL OF INFORMATION .UNIVERSITY OF MICHIGAN
Comparative Investigation of Collaboratories:
Cross-Cutting ThemesJune 20, 2003
University of Michigan
Ann Arbor
SCHOOL OF INFORMATION .UNIVERSITY OF MICHIGAN
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
SCHOOL OF INFORMATION .UNIVERSITY OF MICHIGAN
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?
SCHOOL OF INFORMATION .UNIVERSITY OF MICHIGAN
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
SCHOOL OF INFORMATION .UNIVERSITY OF MICHIGAN
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
SCHOOL OF INFORMATION .UNIVERSITY OF MICHIGAN
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
SCHOOL OF INFORMATION .UNIVERSITY OF MICHIGAN
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
SCHOOL OF INFORMATION .UNIVERSITY OF MICHIGAN
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
SCHOOL OF INFORMATION .UNIVERSITY OF MICHIGAN
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
SCHOOL OF INFORMATION .UNIVERSITY OF MICHIGAN
Incentives
AfCS– Alliance with Nature
BIRN– Guidance re publications
LHC– Shift in time scale of experiments– Implications for careers
SCHOOL OF INFORMATION .UNIVERSITY OF MICHIGAN
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
SCHOOL OF INFORMATION .UNIVERSITY OF MICHIGAN
The relationships
Wisdom Knowledge Information Data The world
Shared Instruments
Distributed ResearchCenters
Practice and Expertise
Community Data Systems
SCHOOL OF INFORMATION .UNIVERSITY OF MICHIGAN
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?
SCHOOL OF INFORMATION .UNIVERSITY OF MICHIGAN
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)
SCHOOL OF INFORMATION .UNIVERSITY OF MICHIGAN
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
SCHOOL OF INFORMATION .UNIVERSITY OF MICHIGAN
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?
SCHOOL OF INFORMATION .UNIVERSITY OF MICHIGAN
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
SCHOOL OF INFORMATION .UNIVERSITY OF MICHIGAN
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?
SCHOOL OF INFORMATION .UNIVERSITY OF MICHIGAN
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
SCHOOL OF INFORMATION .UNIVERSITY OF MICHIGAN
Domain specificity
The unusual character of HEP– Long history – since Manhattan– Scale – LHC– Common knowledge, self-esteem, etc.
SCHOOL OF INFORMATION .UNIVERSITY OF MICHIGAN
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
SCHOOL OF INFORMATION .UNIVERSITY OF MICHIGAN
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
SCHOOL OF INFORMATION .UNIVERSITY OF MICHIGAN
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)
SCHOOL OF INFORMATION .UNIVERSITY OF MICHIGAN
Biggest issues – my candidates What is success? Evolution – ecology Transition to production versions,
sustaining the vision Data issues How to manage collaboratories?