Transcript New Ways of Doing Science?

New Approaches and Tools for
Doing Networked Science
David Baker, Sid Banerjee, David
Cooper, Ashish Goel, Elizabeth Lorns,
Sandeep Neema, Andrew Sallans
The Need
• Social networks and Internet communications have
revolutionized many other collaborative tasks
• Spectacular success from early experiments
(PolyMath/FoldIt)
– Opt-In science: anyone can choose to participate if they have
the right expertise
• Growing number of collaborators in scientific work
• Data sources are growing exponentially
• Automated tools for discovering scientific information (eg.
DeepDive) show early promise.
The time seems ripe for DARPA investment in a program.
Automated Knowledge Assistant
– Exploring or learning a new field
• What are key papers and concepts in a new field?
• Unsolved problems and experts
– Concept Maps and Visualization Tools
– Augmented search, where searching reveals
related concepts, key research results, other
communities which have studied the same
concept
– Using humans to direct the creation of this map
– Identifying research that needs reproduction
Incentives and Mechanisms for Opt-In
Collaborations
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A formal understanding of incentives in collaborative research
vs competition, and innovative funding mechanisms
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Understanding the nature of rewards: intellectual credit, intellectual
property, funding
Endogenous creation of rewards
Team formation
How to encourage confirmatory as well as exploratory research?
Understanding and designing large-scale crowdsourced
research frameworks — drawing lessons from Fold-It, etc.
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Incentivizing diversity/exploration in research
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Theoretical models? Connections with bandit problems?
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Team formation
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Experiments in Fold-It/Topcoder competitions
Platforms and Experimentation
• Existing Examples
– The Science Exchange Network
– Topcoder/FoldIt
– Polymath/K Base
• A platform for collaborative opt-in research among experts
• A platform for opt-in research among the general public
– Eg. Expanding the FoldIt paradigm to drug discovery and neurodegenerative disease
– Collaborative design and visualization
• Experiments with different incentive mechanisms
• Common standards and web APIs for data access and
preparation
Validation and integrity of research
– Goal: Improving reproducibility of biological
research
– How
• Replication studies
• Experiment with separation of experiment creator and
conductor
• Characterization of biological protocols in terms of
reproducibility
• Automating Reproducibility
• Tools for capturing workflow
Some Potential Participants
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Astronomy community (eg. Chris Lintott – Zooniverse)
David Baker and collaborators (FoldIt/eteRNA)
Michael Bernstein (Stanford -- Crowdsourcing platforms. Eg. Collaborative writing)
Center for Open Science
Distributed Biology Team
Yiling Chen (Harvard – User Generated Content)
Ashish Goel (Stanford – markets and social algorithms)
Arpita Ghosh (Cornell – User Generated Content)
Karim Lakhani (Harvard – Topcoder experiments)
Sandeep Neema (Vanderbilt – collaborative visualization)
Chris Re (DeepDive)
SETI@ home and Rosetta@ home and Folding@home
The Science Exchange network
Terry Tao (UCLA), Tim Gowers (Polymath)
Luis von Ahn (CMU – Crowdsourcing platforms, eg. Human Computation)