Reinventing Discovery_ The New Era of Networked Science - Michael Nielsen [3]
Galaxy Zoo is just one of many online citizen science projects that are recruiting volunteers, most of them without scientific training, to help solve scientific research problems. We’ll see examples ranging across science, from volunteers who are using computer games to predict the shape of protein molecules, to volunteers who are helping understand how dinosaurs evolved. These are serious scientific projects, projects where large groups of volunteers with little scientific training can attack scientific problems beyond the reach of small groups of professionals. There’s no way a team of professionals could do what Galaxy Zoo does—even working full time, the pros don’t have the time to classify hundreds of thousands (or more) of galaxies. You might suppose they’d use computers to classify the galaxy images, but in fact the human volunteers classify the galaxies more accurately than even the best computer programs. So the volunteers at projects such as Galaxy Zoo are expanding the boundary of what scientific problems can be solved, and in so doing, changing both who can be a scientist and what it means to be a scientist. How far can the boundary between professional and amateur scientist be blurred? Will we one day see Nobel Prizes won by huge collaborations dominated by amateurs?
Citizen science is part of a larger shift in the relationship between science and society. Galaxy Zoo and similar projects are examples of institutions that are bridging the scientific community and the rest of society in new ways. We’ll see that online tools enable many other new bridging institutions, including open access publishing, which gives the public direct access to the results of science, and science blogging, which is helping create a more open and more transparent scientific community. What other new ways can we find to build bridges between science and the rest of society? And what will be the long-run impact of these new bridging institutions?
The story so far is an optimistic story of possibility, of new tools that are changing the world. But there’s a problem with this story, some major obstacles that prevent scientists from taking full advantage of online tools. To understand the obstacles, consider the studies linking genes to disease that we discussed earlier. There’s a crucial part of that story which I glossed over, but which is actually quite puzzling: why is it that biologists share genetic data in GenBank in the first place? When you think about it, it’s a peculiar choice: if you’re a professional biologist it’s to your advantage to keep data secret as long as possible. Why share your data online before you get a chance to publish a paper or take out a patent on your work? In the scientific world it’s papers and, in some fields, patents that are rewarded by jobs and promotions. Publicly releasing data typically does nothing for your career, and might even damage it, by helping your scientific competitors.
In part for these reasons, GenBank took off slowly after it was launched in 1982. While many biologists were happy to access others’ data in GenBank, they had little interest in contributing their own data. But that has changed over time. Part of the reason for the change was a historic conference held in Bermuda in 1996, and attended by many of the world’s leading biologists, including several of the leaders of the government-sponsored Human Genome Project. Also present was Craig Venter, who would later lead a private effort to sequence the human genome. Although many attendees weren’t willing to unilaterally make the first move to share all their genetic data in advance f publication, everyone could see that science as a whole would benefit enormously if open sharing of data became common practice.