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In the game Foldit, players compete to build stable configurations of the biological molecules called proteins. Red spheres mark chemically unstable regions that require player attention. A chat interface allows groups of players to collaborate.
Test My Brain hosts various psychological studies like this one, of facial-recognition abilities. Detailed sleep-cycle data from the popular mobile-phone app iSleeping may soon play a role in clinical trials.
The app, developed by researchers in France, already monitors the sleep patterns of more than , people. Both scientists and supercomputers have long struggled to predict the three-dimensional structures of the biological molecules called proteins. But there are infinite structural possibilities for any given amino-acid chain, and a computer, searching through them, faces a daunting challenge.
An algorithm, Rosetta, sifted through the many possibilities while a screensaver showing the various protein-folding permutations kept users updated on its progress. Then something unexpected happened.
The result was Foldit , a game that let nonprofessionals try their hands at protein-folding problems that had stymied supercomputers. Foldit is part of a growing trend toward citizen science: enabling ordinary people, often without formal training, to contribute to scientific research in their spare time.
The range of involvement varies. Some citizen scientists donate idle time on their home computers for use in solving problems large in scale the search for intergalactic objects, as in Einstein home or small folding proteins. Other projects encourage participants to contribute small bits of data about themselves or their environments.
The Great Sunflower Project , for instance, provides a platform for logging and sharing observations of pollinators like bees and wasps. Active lay communities still exist in fields like astronomy and ornithology, she notes, and frequently, citizen science simply organizes what people already do. But the Internet and mobile phones now connect more people than ever before, changing how scientists and citizens interact. Foldit, by contrast, has players compete in teams to win challenges and climb leaderboards.
There are as many varieties of citizen science as there are of science. In some fields, researchers look to citizen volunteers for help sifting through the deluge of information from microscopes, satellites, and telescopes. In other fields—like ornithology, where lay observations posted on eBird contribute to detailed maps of bird migrations—analytic capabilities have outstripped the available data, and scientists are asking citizens to gather more.
Professionals may work side-by-side with small groups of dedicated amateurs in field experiments; alternatively, tens of thousands of citizen scientists participate from the comfort of their own homes, often in moments of boredom and procrastination.
She developed the website Test My Brain , which hosts psychological studies that have gathered more than , participants in the past five years see page Yet for most scientists and laymen, that concept remains foreign.
What, exactly, can untrained laypeople contribute to an endeavor as rarefied as scientific research? In the five years since Foldit fold. In one three-week challenge, they produced a near-exact model for a protein whose structure had eluded scientists for more than a decade. In another instance, they successfully redesigned an existing protein to increase its efficiency more than eighteenfold.
Player strategies, in turn, have been studied by researchers seeking to improve computer algorithms, and Foldit now is challenging its users to design proteins that have never existed in nature. Foldit players—most of whom have little to no biochemistry background and who play the game in their spare time—are authors on four scientific papers, and their gameplay has contributed to several more. The premise behind Foldit is that all human beings have advanced spatial-reasoning capabilities far beyond those of current computers, making protein-folding a visual and almost intuitive endeavor.
Your own player name rushes up through the ranks, and the adrenaline starts. In online challenges, an amino-acid sequence or partially folded protein is released to the entire Foldit community, and players work, usually in teams, to achieve the most stable configuration in the weeks or months allotted, swapping tips and frustrations in chat rooms and message boards.
The conversations you have with someone who has no scientific background at all, but has been playing Foldit for a while, are pretty high-level. The launch of the citizen-science project Galaxy Zoo was met with immediate success: a site crash. Spurred by the enormous number of images captured by telescopes each day, astronomers from Johns Hopkins University and, in England, the University of Portsmouth and the University of Oxford had developed a website to involve amateurs in classifying galaxies based on shape—and the turnout stunned them.
Initial traffic was 20 times what they had hoped for, and within 24 hours, online participants were tagging more than 60, images an hour. The citizen classifications, though useful, are not always ends in themselves. The new field of human computation aims to guide this integration of man and machine, combining inputs to tackle problems that neither humans nor computers can solve alone. Classically, computers have used entirely automated operations, but human computation involves tasks like image recognition or text analysis, where the exact process can be difficult to define through traditional programming commands.
Rather than explicitly coding the characteristics of a galaxy, for instance, researchers are developing machine-learning methods that enable computers to infer the appropriate patterns from human-generated training sets.
These bubbles, like galaxy shapes, are hard for computers to detect, but in an effort called the Milky Way Project , hosted by the citizen-science platform Zooniverse an expansion of the original Galaxy Zoo effort; www. Beaumont has used these contributions to build more sophisticated algorithms for bubble identification that will cut down on the need for human input: for instance, a computer might screen large datasets and present lay volunteers and experts with only the most ambiguous cases.
Human computation frequently taps into a phenomenon called crowdsourcing : small contributions from a large base of users—in this case, citizens—can collectively accomplish huge tasks impossible for a small, dedicated group. She plans to launch it this spring. She began by interviewing Harvard researchers across multiple disciplines. How do they currently train people?
Would they be comfortable sharing data, and at what stage? I was thinking about what crowdsourcing could bring to science. One faculty member she interviewed was Charles Davis , professor of organismic and evolutionary biology and co-director of the Harvard University Herbaria HUH.
The source of this historical comparison? Detailed records kept by naturalists Henry David Thoreau in Concord, Massachusetts, in the nineteenth century, and Aldo Leopold in Dane County, Wisconsin, in the twentieth—among the few sources of information on long-term ecological change.
These valuable historical data gave the researchers detailed insight into the effects of climate change in the eastern United States over a year time span. But the work is far from done.
If the premise of the project—laypeople classifying images, whether plant specimens or interstellar bubbles—is beginning to sound familiar, Law would agree. Curio is built on the commonalities among disparate crowdsourcing projects. Many of his students, he says, are shocked when they encounter a specimen for the first time. They plan to reach out to local gardening and naturalist communities for volunteers, and the aim is for amateurs to interface with both botanical specimens and timely research questions.
Scientific discoveries come from unusual places; widespread evidence of prosopagnosia, or face blindness, came from an online forum. Shared experiences draw people together all the time, but this common thread was something new: the inability to recognize faces.
The phenomenon had been reported in the scientific literature, but almost entirely in connection with traumatic events like strokes. While a research assistant at University College London from to , Laura Germine of MGH helped develop a test of face recognition that ran online, not in the lab, to accommodate rapidly increasing public interest.
Before long, tens of thousands of people were participating. Most did not think themselves face-blind; they were simply curious about how they measured up. Yet many researchers were initially skeptical about data—especially of the sort requiring precisely timed responses—gathered in the unsupervised setting of the Internet. Unless scientists used recruited and compensated volunteers who were tested under carefully controlled conditions, how was it possible to know that subjects were not cheating, lying, or simply becoming distracted?
In response, Germine and colleagues published a study in that compared data from Test My Brain with data from studies conducted using traditional methods. Though the much larger Web samples showed slightly higher variance, the researchers found no consistent differences in other aspects of performance or data quality.
Web data, in fact, may have unique advantages, thanks to the diversity of its participants. The enormous sample sizes of Web data, on the other hand, can in fact help characterize cultural differences in areas like cognition and social behavior; for example, researchers from the CRCS have used an online platform called Lab in the Wild to quantify cultural preferences for website aesthetics. Some members of the medical community are beginning to take note.
Patients with chronic illnesses, for example, are frequently forced to become experts on their own conditions. Advances in DNA sequencing technology have made genetic information plentiful, but data about symptoms and disease outcomes remain in relatively short supply. He believes that citizens, in addition to going into forests or backyards to collect data, can help research by gathering information on themselves.
One of his newest projects, undertaken in collaboration with Guinan and the Fanconi Anemia Research Fund a patient-support and fundraising group , focuses on a rare, genetic blood disorder that puts patients at high risk of head and neck tumors.
Can you tell me ways to find it early? Patient self-monitoring, in addition to helping catch tumors early, may also contribute to medical research: Friend suspects that these patient journals may hold clues to understanding the course of the disease. Another project aims to use the popular iSleeping mobile phone app to gather data on the effect of sleep medications.
The app, developed by researchers in France, already monitors the sleep patterns of more than , people by analyzing snoring and user movement, effectively creating an automatic sleep log.
Friend hopes to enroll 1, users in a clinical trial that will make use of this detailed data. Other initiatives are taking similar approaches to soliciting patient contributions. The American Gut Project sends participants a kit with which to sample the bacteria living on and in their bodies; a related effort has recruited more than 1, volunteers to test the microorganisms in their homes. Yet the road to full partnership brings additional challenges. Fields like human computation are exploring how best to utilize lay participation and integrate it with traditional research, but citizen science in the Internet age carries all the ambiguities of the digital world—concerns about trustworthiness, privacy, intellectual property, the role of expertise in the age of Wikipedia.
As citizens assume more involved roles, these issues grow progressively more complex. Who would own a protein that a team of Foldit players helped design? One major question facing citizen science is that of citizen ownership. Leaving aside questions of authorship and intellectual property, amateur contributions to science tend to be narrowly circumscribed. Some citizens find unusual ways to make projects their own: Germine and Hartshorne, for instance, report that classroom teachers sometimes ask students to interpret the personal feedback scores from Test My Brain and Games With Words, or collect the scores as data sets for classroom analysis.
The researchers themselves receive feedback: participants often critique the study design or suggest their own interpretations of results. Other projects push the bounds of citizen participation.
Public Lab , an initiative of the MIT Center for Civic Media, takes a do-it-yourself approach to involving citizens in environmental science. The Internet has begun democratizing science in surprising ways; some researchers make comparisons to how personal computers have altered technology and society.
But for the most part, the question remains: is citizen science intended ultimately for the citizens or the scientists? The very reason for the growing popularity of citizen science—its usefulness in research endeavors—may paradoxically diminish the quality of engagement for its lay participants.
Bluntly put, in a time of tight federal funding, lay participation is cheap. Law points out that in most online projects, the scientists have never met their citizen participants. Most researchers involved with citizen science believe this vision is one worth seeking, whatever the way forward may be.
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