“In the long history of humankind: those who learned to collaborate and improvise most effectively have prevailed.” — Charles Darwin
Open, collaborative networking accelerates scientific discovery. In few fields does speed matter more than biomedical research, where individual labs and clinics hold critical clues to life-saving therapies. A rapid discovery approach, combining shared data and the expertise of specialists and clinicians, can deliver personalized solutions to help today’s patients. Our solutions include tools, a publishing venue, and incentives.
By using a carefully designed architecture of attention, tools “enable us to scale up creative conversation, so connections that would ordinarily require fortuitous serendipity instead happen as a matter of course… amplifying collective intelligence.”1
Tools on the rapid learning platform allow users to:
- Post research results and clinical observations, discuss with peers, and update with new evidence and insights
- Join and form communities based on topics and group identities, sharing with narrower or wider circles of access
- Exchange highly contextualized dialog on posted results, and rate and nominate research for publication
Rapid discovery requires rapid dissemination, “real-time” peer review, and iteration of results – a new mode of publishing that tracks users’ analyses and insights from inception to implementation. Context and interpretation are provided through expert consensus in state-of-the-art reviews.
The pilot journal, Collaborative Research, will be peer reviewed, open access and Pubmed indexed, and will feature:
- Evidence Reviews that contextualize the latest findings by special-interest groups on the platform with the latest published literature – searchable across communities and continually updated
- “Micro-results” supporting the Reviews – single experiments and analyses that can later be published in traditional high-impact papers
- Cases Central, a computable clearinghouse of treatments and outcomes reported in data-driven templates – case reports, N-of-1 studies, negative trial results
“All that’s needed for open science to succeed is for the sharing of scientific knowledge in new media to carry the same kind of cachet that papers do today.”1
To promote and reward collaboration we’re developing a “C-score” that measures the quality and quantity of collaborators’ contributions to group efforts. The score will take into account the full variety of tasks occurring on the collaboration platform – sharing, commenting, rating, reviewing, and authoring. It will be formulated by reputation experts drawing from the insights of mathematicians, computer scientists, economists, and social scientists, and vetted by funding officials, academic administrators, and researchers themselves (see executive summary).
1 Michael Nielsen, “Reinventing Discovery: The New Era of Networked Science,” Princeton Univ. Press, 2011.