Cross Cutting Themes

1
Cross Cutting Themes

• Jonathan Grudin
• John Walsh
• Deb Agarwal

2
Issues

• Why people collaborate, build a collaboratory
• What happens when people are forced to
  collaborate long distance (GAN, others)
• Readiness
• Collaboration vs. competition
• Where are the constraints, stresses, contention
• Technical history

3
Issues (continued)

• Evolution of Collaboratories
• Going through the types
• Garnering best practices
• Changes as the population grows
• Natural size (people and instruments)
• Idea size, more participation
• Rich get richer
• Value in being flexible
• CFAR, UARC, EMSL

4
Issues, continued

• What is domain specific, what is general
• What taxonomies/dimensions will be useful
• What is success
• The role of collaboratories in the research value
  chain
• How are collabs changing science
• Going from research or first adopters to
  production
• How to you insure success the second time.
• Sustainability

5
Issues (continued)

• Tension between
• Science policy
• Collab technology
• Social studies of scientists
• Evaluation of scientific efforts
• Incentives for participation
• How important is security really

6
Issues (continued)

• How do we generalize from these instances
• How do we be prescriptive

7
Theories (cause and effect)

• Diffusion of Innovation
• Trialability (EMSL)
• First adopters get burned (UARC)
• Who adopts, transmission to new people
• Cost/benefit as conditions of adoption
• Lifecycle
• Access, collaboration, integration, community
• Legitimate peripheral participation
• Other issues of mentoring, growth of a field

8
Opportunities

• Track the relationships between collaboratories
• UARC-SPARC-others
• Chickscope-Bugscope
• Shared technologies
• EMSL-GAN eLog others
• Why isnt more collaboration happening among
  collaboratories
• Glue to multiple online environments

9
Methodological issues

• Collect a standard set of things over all
  collaboratories on internal and external impacts
• Getting people to respond to evaluation surveys
• Can we quantify
• Data flow
• Infor flow
• Money

10
Methodological issues

• Resource Diagram
• Capture change over time
• Break them up
• Create prototypical
• What should we add to the C_at_G protocol
• Get more data on failure
• Who should the control groups be?

11
Other

• What are the priorities for funding
• Workshop proceedings (this workshop)
• Tools, social science, big science projects,
  shared facilities