The COVID lockdown was a boon for citizen science, a movement to engage average people in solving complex scientific problems. Chris Lintott, principal investigator and cofounder of Zooniverse, a 12-year-old citizen-science platform with nearly 2 million registered users, says that volunteer engagement— looking at images and quickly categorizing them—was up 40% last year; since April 2020, the number of projects created—covering astronomy, biology, wildlife conservation, humanities, and more—has roughly doubled. “Lots of people suddenly had spare time,” he says. Plus, much of the activity now takes place on Zooniverse’s mobile app, which launched in 2019 and currently receives a thousand times more “quick review” image submissions per volunteer than the website does. Lintott, who studies star formation in his day job as a professor of astrophysics at the University of Oxford (he’s also a presenter on BBC TV’s The Sky at Night, first discovered the power of crowdsourcing in 2007, when he launched a project called Galaxy Zoo, in which volunteers helped identify and classify nearly one million galaxies from blurry telescopic images. Ten years later, Lintott worked with postdoctoral researcher Helen Spiers to develop and launch Etch A Cell, a collaboration between researchers at Oxford and London’s Francis Crick Institute. The project asked volunteers to “segment” electron microscope images of cells, outlining the shape of the “nuclear envelope” a crucial and time-consuming step in building 3D cellular models to better understand diseases such as cancer. A peer-reviewed paper published in April 2021 (one of 150 that’s been published by Zooniverse project teams) showed that the data produced by untrained volunteers could be used to train a machine learning algorithm to detect the nuclear envelope much faster than using data produced by experts. Lintott believes that human insight combined with machine learning offers the best of both worlds for revealing undiscovered patterns, whether in stars or cells. “Machines can tell you what is unusual; humans can tell what’s interesting,” he says.
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