Chris Lintott is a Professor of Astrophysics at the University of Oxford, where he serves as Principal Investigator for Zooniverse, the world's largest and most successful citizen-science platform. Also a co-founder of crowd-sourced astronomy site Galaxy Zoo, Lintott is co-presenter of the BBC's long-running Sky at Night program. The views expressed are the author’s own, and do not necessarily represent the views of Qualcomm.
Three years ago, a Los Angeles resident noticed an unusual lizard hanging out on his front porch. Intrigued, he uploaded an image to iNaturalist, an online community where amateur and professional nature observers can swap discoveries and learn about what they see in world around them. The lizard photo, in particular, caught the interest of a herpetologist at the L.A. Natural History Museum, and the pair worked together to further log the activity of the creature. This citizen-science collaboration revealed something remarkable: The lizard, a gecko species native to tropical regions like Australia and Southeast Asia, was surviving and breeding in southern California.
It’s stories like this that have helped make iNaturalist the star of citizen science, a movement in which researchers crowdsource data collection and crunching to curious civilians. Mobile phones make this path to discovery seamless; a casual picture can lead to education (for citizens) and discovery (for scientists). Projects transform science from a distant concept carried out by men in white coats to a part of everyday life — something to which anyone can contribute a few spare moments.
At the same time, however, the digital revolution presents scientists with a new challenge. Widespread connectivity puts a massive amount of data at our fingertips. The sheer volume of it has grown and will continue to grow far beyond our capacity to make use of it. The next phase of citizen science will focus on finding ways for scientists to ask for targeted help, precisely where and when they need it.
Thankfully, projects like iNaturalist have already shown us that there’s plenty of help at the ready. In 2007, a small team and I launched Galaxy Zoo, a crowdsourced astronomy project that asks the public to sort galaxies by shape. (If you know the shape of a galaxy, you can understand a lot about its history.) Through both our website and subsequent mobile app, volunteers have since provided more than 100 million classifications, together outperforming the best work that current machine-learning technology can do. Even better, our participants are wonderfully distractible. When they find something unexpected, whether it’s a mysterious glowing cloud of gas or a new type of galaxy, they stop to discuss it. Those discussions have helped alert scientists when there’s something worth seeing, relieving the practical burden of wading through large datasets.
And those datasets are about to be become even more unwieldy. Let’s stick with an astronomical example to illustrate: The Large Synoptic Survey Telescope (LSST). Now under construction in Chile, the telescope will, when finished, collect 30 terabytes of images a night, creating an ever-changing movie of the sky. From this onslaught of data we should expect to receive millions of alerts a night, each bearing news of an asteroid, a planet around a distant star, or an explosion halfway to the edge of the observable universe.
With projects like LSST on the horizon, we need the largest possible crowd to take part. Even though our app, and many others like it, have been widely adopted, simply waiting for people to wander to our websites or download our apps won’t be enough. We need to find new ways to pull in users. Cancer Research UK does this by turning its data-crunching efforts into a smartphone game. Its app puts users at the helm of a spaceship on a quest to find and collect Element Alpha. In reality, however, players are actually doing something they couldn’t normally do: They’re helping the researchers identify patterns in the genetic information of tumors.
Tactics like this are effective, but we should be more ambitious. Our goal should be to break down the barrier that separates the public from science. And that’s something no game can do. This is where the idea of a targeted approach comes into play. Rather than asking volunteers to wade through seemingly endless troughs of data, specific calls-to-action will instill a drive and sense of purpose in the crowd.
As data flows in, algorithms can help sort out which chunks of it might require human attention. Then, based on factors like geographic location, the systems can identify who among the crowd would be best suited to respond to an alert. In fact, we’ve already proven the value of this type of focused tactic. Earlier this year, we launched a campaign on the Zooniverse network, which tapped users to help analyze satellite images to assist disaster relief efforts after a magnitude 7.8 earthquake hit Ecuador. Volunteers analyzed satellite images to help relief workers identify the most-affected areas and find passable roads. It’s easy to imagine a similar approach applied to wildlife preservation or an unexpected celestial event.
Conversely, onboard sensors on mobile devices can work to call scientists’ attention where it’s needed. For example, the Crayfis app uses a phone’s camera to help locate cosmic rays, mysterious showers of high-energy particles that enter the atmosphere from outer space. Meanwhile, scientists have worked for years to create the world’s largest seismograph by tapping into a vast network of laptops and mobile phones. Most recently, those efforts have led to U.C. Berkeley’s MyShake app; through a network of mobile-phone sensors, the researchers hope to identify seismic events at early stages. Ultimately, the data could help them understand the activity that precedes earthquakes and lead to the development of new early-warning systems.
These two modes for handling data — active, focused exploration and the passive use of the phone’s sensors — are mirrors of each other. They point to a future where a smooth exchange of information and knowledge between science and citizenry allows us to collaborate and respond as a global community. By making use of our skill as observers and pattern-seeking creatures, together we’ll better understand, and perhaps even improve, the world and the universe around us.