Reinventing Discovery - Michael Nielsen [78]
Another example of citizen science is Project eBird, run by Cornell University’s Laboratory of Ornithology. eBird asks amateur birdwatchers to upload information about the birds they see to an online website: what species of bird they saw, when they saw it, and where they saw it. By combining all the submitted observations, eBird can build up an understanding of the world’s bird populations. This is another case where citizen science is building on an earlier tradition, this time a tradition of collaboration between amateur birdwatchers and professional ornithologists. But Project eBird is enabling this collaboration on an unprecedented scale, with participants so far reporting more than 30 million bird observations. About 2,500 birdwatchers are frequent contributors to the site, making 50 or more contributions, and tens of thousands of people regularly use the site. The data collected can be used, for example, to generate range maps showing the density of some particular species of bird in different locations. As eBird gathers more data (it began in 2002) such range maps will become increasingly useful for tracking the impact on birds of effects such as climate change, changes in nearby human population, and other environmental factors.
Yet another example of citizen science comes from the study of dinosaurs. Most dinosaur research concentrates on just one or a few fossils. In September of 2009, paleontologists Andy Farke, Mathew Wedel, and Mike Taylor had the idea of creating a large database containing information about many dinosaurs, by combining the results of hundreds or even thousands of scientific papers. Their hope was that the database could then be mined to answer many new questions. But instead of building the database on their own, they decided to harness the distributed knowledge and effort of a broader community of people. They started the Open Dinosaur Project, recruiting people from all over the world to, er, dig up papers about dinosaurs. As I write, they’re focusing on dinosaur limb measurements. If a volunteers finds a paper studying, say, a Stegosaurus specimen with a right femur that’s 1,242 millimeters in length, they would record that piece of data in the database. The project has thus created a list of measurements from 1,659 separate dinosaur specimens, contributed by 46 people, many amateurs. Their hope is that this will let them answer questions about (for example) the evolution of dinosaur locomotion. It’s still early days in the Open Dinosaur Project, and while data are being collected quickly, it’s too soon to say how useful the data will be. But it’s aother example of how a community containing both amateur and professional scientists can do more than either group could on their own.
From these and earlier examples, we see several distinct ways that citizen scientists are contributing to science. Citizen science can be a powerful way both to collect and also to analyze enormous data sets. In those data sets, citizen scientists can scout out the unusual and the unexpected, discoveries such as the voorwerp and the green peas, discoveries that would be difficult to program a computer to spot. Citizen science thus complements the tools of data-driven intelligence described in the last chapter.
Citizen scientists can also work to symbiotically extend the capability of those tools, as demonstrated by the Foldit players’ artistry in using the tools of protein-structure prediction. In another twist on this idea, the Zookeepers have recently used the Zooites’ galaxy classifications to train a computer algorithm to distinguish between spiral and elliptical galaxies. The preliminary results are promising, with the algorithm achieving 90 percent agreement with the human classifications. This result is interesting in part because future sky surveys from instruments such as the Large Synoptic Survey Telescope (the LSST, described on page 107) will produce vastly more data than even the huge crowd of volunteers at Galaxy Zoo can hope to analyze.