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 • 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? • Team formation • 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 • • • • • • • • • • • • • • • 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) [email protected] home and [email protected] home and [email protected] The Science Exchange network Terry Tao (UCLA), Tim Gowers (Polymath) Luis von Ahn (CMU – Crowdsourcing platforms, eg. Human Computation)