New Approaches and Tools for Doing Networked Science

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Presentation transcript:

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 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

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

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 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)