When it comes to online participation in collective endeavors, 99% of us typically take a free ride.
From Wikipedia and YouTube to simple forum discussions, there is a persistent pattern known as the 90-9-1 principle. This means, for example, that of Wikipedia users, 90% only view content, 9% edit existing content, and 1% actually create new content. Inequity in effort, of vastly more people accessing collective information than contributing to it, is a persistent feature of online engagement.
Large-scale citizen-science projects, such as where ordinary people assist in genuine scientific research, when facilitated by the Internet, may not be exempt from the 1% rule of thumb. Despite the promise of app and web development to assist citizen scientists in data submission, the “build it and they will come” approaches fail because not enough people contribute to make such projects useful. Are there examples of online citizen-science projects that succeed on a big scale despite unequal participation? If so, how?
For answers, let’s take a look at eBird, a free, online citizen-science project run by the Cornell Lab of Ornithology. eBird began in 2002 and quickly became a global network within which bird watchers contribute their bird observations to a central database. Over 2.5 million people have engaged with eBird. Of those, 150,000 have submitted data (6%) and 25,000 (1%) have submitted 99% of data. The 1% includes the world’s best birders as well as less skilled but highly dedicated backyard bird watchers. For everyone else, eBird is free information, and there is lots of it.
Is eBird successful?
eBird is successful scientifically. Since 2006, eBird has grown 40% ever year, which makes it one of the fastest growing biodiversity datasets in existence. It has amassed over 140 million bird observations, with observations from every country on the planet. Researchers have written over 90 peer-reviewed publications using eBird.
eBird is successful for conservation. The last two State of the Birds reports, which relied on eBird data to examine species occurrence, habitat types, and land ownership at a level of detail never achieved before, inform decisions of the US Fish & Wildlife Service and the US Forest Service. The Nature Conservancy uses eBird data to identify which rice farmers in the Central Valley of California they should ask to flood their fields at the particular right time for migrating waterfowl.
eBird is successfully engaging bird watchers. eBird doesn’t ignore the 99% who don’t submit data. The most frequent use of the eBird database is by handheld apps that people use to figure out where to go birdwatching.
Recently I was scheduled to give an opening provocation for a workshop on technology for citizen science at the British Ecological Society meeting in London. A “provocation” is intended to provoke thoughts, emotions, and epiphanies in order to instigate deep discussion, in this case about how to use technology to make citizen science successful. Are more apps for submitting data really the answer? Should we try to break the 1% rule or engage the 99% in other ways? To prepare, I went to Steve Kelling, the head honcho of eBird. In Jack Nicolson style, he can deliver a one-liner that blankets a room in thought, which is then invariably followed by a succession of light-bulb moments of understanding. When I asked Kelling to explain the success of eBird, he sagely said, “When eBird stopped doing citizen science, it got successful.” (score!).
How could eBird succeed at citizen science by not doing citizen science?
Kelling’s counter-intuitive riddle reveals the Zen in the art of citizen science.
In Zen in the Art of Archery, the author, Eugen Herrigel wonders, “Do ‘I’ hit the goal, or does the goal hit me?” In other words, he learned to not think about aiming to hit the target, but to let the target find his arrow. Similarly, eBird doesn’t shoot a citizen science app at birders and tell them to do it in the name of science; eBird builds tools that show birders what’s possible with their collective observations and they find citizen science.
I needed to look at the eBird history and growth to understand. In the early years, 2002-2005, with the slogan “Birding For A Purpose,” the project failed to engage sufficient birders. In 2006, they changed their strategy, and more recently adopted the tagline “Birding in the 21st Century.” These slogans represent philosophies that can make or break a project. eBird moved from appealing to a birder’s sense of duty, and succeeded by helping birders embrace the excitement of maturing their hobby to impact the future.
The eBird team’s strategy involves the steady development of state-of-the-art tools that allow anybody to use eBird data, often with such ease that they don’t realize they are using it. Don’t imagine downloading megabytes of raw data files, but a map of recent observations that loads within a second. Providing useful data means the eBird team does the hard computational work in order to serve the data on a platter to meet a variety of appetites. Data utility means, for example, visualizations in the form of maps, tables, and intuitive graphics and apps that point you where to go to, say, find birds you personally have never seen before.
Simultaneously, the eBird team of top-notch birders have changed the norm of what of it means to be a birder. The project leaders show other birders how using eBird makes them better birders. And better birders make better science. For example, better birders submit complete checklists. Initially, 75% of submissions to eBird were incomplete checklists; now over 80% are complete.
An important lesson here for the field of citizen science is that the current heavy focus on app development for data entry may be misguided if it occurs without simultaneous development of tools for data access and use. After all, we already know that people love to collect natural-history observations. We know people love to share these observations. Acquiring data from the public is often a low hurdle. The harder challenge is ensuring that the 1% who take the time to contribute do indeed build databases useful to the communities of interest. The 1% disparity is not a call to Occupy citizen science so that everyone contributes data. It’s a call to Wiki-fy citizen science so that the 99% find it valuable.