1. New Approaches and Tools for Doing Networked Science
David Baker, Sid Banerjee, David Cooper, Ashish Goel, Elizabeth Lorns, Sandeep Neema, Andrew Sallans

3. 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.

4. 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

5. Incentives and Mechanisms for Opt-In Collaborations
A formal understanding of incentives in collaborative research vs competition, and innovative funding mechanisms
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.
Incentivizing diversity/exploration in research
Theoretical models? Connections with bandit problems?
Experiments in Fold-It/Topcoder competitions

6. 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 neuro-degenerative disease
Collaborative design and visualization
Experiments with different incentive mechanisms
Common standards and web APIs for data access and preparation

7. 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

8. Some Potential Participants
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)
home and home and
The Science Exchange network
Terry Tao (UCLA), Tim Gowers (Polymath)
Luis von Ahn (CMU – Crowdsourcing platforms, eg. Human Computation)