Uncommon Knowledge and Open Innovation: Building a Science Commons

Knowledge on the internet has a tendency to form connections, to snap into greater and greater structures. New formats in the semantic web bring great promise to convert portions of the scientific canon into machine-readable formats, at the same time that new collaborative lightweight methodologies allow us to represent scientific arguments and knowledge formation in real time. But the entire culture of scholarly communication resists this transformation. This first part of this talk will lay out how an interlocking set of controls based on intellectual property, incentive structures, and business models has left scholars and scientists with a stolidly analog set of metaphors, digitized and controlled, preventing the emergence of the kind of value creation we take for granted with the web.

The transformational power of getting into cyberspace for scholarly communication is huge. If we can start to leverage both the power of the crowd and the power of technological enhancement more efficiently - i.e., without the high transaction costs, permission barriers, and information exclusion - then the mathematical odds of someone, somewhere making a breakthrough discovery go up. That could be innovations in scholarly communication itself - perhaps a new way to index information, like Google represented in the late 1990s. It could also be innovations in the science itself. There are a lot of smart people in this world who don't have access to integrated information - who can't afford access to the literature, or to the costly indexing and integration services that surround it. The second part of this talk will describe how a digital commons can help to lower transaction costs and increase transaction flow in scholarly knowledge.

What we are doing here is reducing the time and cost at which the Kuhnian revolution cycles operate - dumb ideas get exposed faster, and good ideas get validated faster. This is about the only way to accelerate those revolutions that does not rely on magical thinking: if we can make the things we know more useful in the evaluation of hypotheses and models, we are simply increasing the mathematical odds of discovery. This is the transformational potential. It is treating the literature and data online as elements in a vast periodic table of knowledge, a common reference point against which we can test how things fit together. The third part of this talk will describe experiences “from the road” at Science Commons.