The New Science of Sharing

Companies such as Novartis and Intel are at the forefront of Science 2.0 by encouraging open systems of collaboration

by Don Tapscott and Anthony D. Williams

Earlier this month, Swiss drugmaker Novartis did something rather unusual—and almost unheard of in the high-stakes, highly competitive world of Big Pharma. After investing millions trying to unlock the genetic basis of type 2 diabetes, the company released all of its raw data on the Internet. This means anyone (or any company) with the inclination is free to use the data—no strings attached.

Type 2 diabetes and related cardiovascular risk factors—including obesity, high blood pressure, and high cholesterol—are among the most common and most costly public health challenges in the industrialized world. Pinpointing their precise genetic origins could unlock a treasure trove of new medicines and result in a major windfall for Novartis (NVS) shareholders.

So why the giveaway? "These discoveries are but a first step," says Mark Fishman, president of the Novartis Institute for BioMedical Research. "To translate this study's provocative identification of diabetes-related genes into the invention of new medicines will require a global effort."

In other words, the research conducted by Novartis and its university partners at MIT and Lund University in Sweden merely sets the stage for the more complex and costly drug identification and development process. According to researchers, there are far more leads than any one lab could possibly follow up alone. So by placing its data in the public domain, Novartis hopes to leverage the talents and insights of a global research community to dramatically scale and speed up its early-stage R&D activities.

Collaborative Commons

It's worth noting that Novartis didn't reveal everything. For example, it didn't give away three years' worth of its own observations on the data, which gives it a substantial lead time on other companies attempting to exploit the research. Meanwhile, the close ties and goodwill that Novartis has fostered with the research community studying diabetes will give it an advantage over competitors as it moves to the next stage of research.

The Novartis collaboration is just one example of a deep transformation in science and invention. Just as the Enlightenment ushered in a new organizational model of knowledge creation, the same technological and demographic forces that are turning the Web into a massive collaborative work space are helping to transform the realm of science into an increasingly open and collaborative endeavor. Yes, the Web was, in fact, invented as a way for scientists to share information. But advances in storage, bandwidth, software, and computing power are pushing collaboration to the next level. Call it Science 2.0.

The Earth System Grid (ESG), for example, is an experimental data network that integrates supercomputing power with large-scale data and analysis servers for scientists collaborating on climate studies. Once the first of its kind, the project is now one of several virtual collaborative environments that link distributed centers, users, models, and data throughout the U.S. Data for the project is being collected from a wide range of sources, including ground and satellite-based sensors, computer-generated simulations, and thousands of independent scientists. The grid will accelerate the execution of climate models 100-fold and allow scientists to perform high-resolution, long-duration simulations that harness the community's distributed data systems. The ESG's founders anticipate the project will revolutionize our understanding of global climate change.

Wider Peer Review

Indeed, in just about every discipline, plummeting computing and collaboration costs are encouraging the formation of large-scale research networks. A decade ago, disciplines such as astronomy were still driven by small groups of scientists keeping observational data proprietary and publishing individual results. With projects like the Sloan Digital Sky Survey, astronomy is now organized around massive data sets that are shared and coded by the community.