The Citizen Science / Crowdsourcing Spectrum

(originally published at

The Center for Policy Informatics was recently awarded two seed grants by the Arizona State University Office of the Vice President for Entrepreneurship & Innovation under their Citizen Science & Citizen Engagement (CSCE) Grant Program. One project is the “Citizen Science to Forecast the Future of a Desert City” and the other is the “Crowdsourcing the Next Great Citizen Science Project” project.

The CSCE grant program describes citizen science as involving public participation in research, which can include data collection, data interpretation and data analysis by community members. What is implied with the growth of citizen science in the past ten years is that the Internet is the likely or understood mechanism that facilitates this participation.

Crowdsourcing is a term coined in 2006 to describe the process of taking a task normally performed by employees and allocating it to volunteers – the crowd – using the Internet. You send out the request for volunteers, allocate the task and collect responses – all using the web.

So is citizen science the same as crowdsourcing? Why not?

I’d like to make the case here that there is an important connection between the terms “crowdsourcing” and “citizen science” – or, to be more precise, between what I call “scientific crowdsourcing” and “Web2.0-enabled citizen science”. My interest here is in trying to more precisely define the type of citizen science we’re talking about, and where it intersects with crowdsourcing.

First off, what strikes me as unhelpful is that there’s a noticeable preference in the citizen science movement against the term crowdsourcing, as though crowdsourcing were something less serious than citizen science. For example, the Citizen Science Alliance – the organization behind the Zooniverse – very precisely and carefully does not use the term crowdsourcing in its writings. Crowdsourcing is for making t-shirts, citizen science is for identifying galaxies. Maybe it’s just me, but I think there should be a more objective difference between the two terms.

The distinction I draw is that mass collaboration in science can be organized along a spectrum of intensity for participation intersected by a spectrum of technology mechanism.


On the one right-hand end of this spectrum is the tradition of citizen science; when supported by new technology platforms, I label this as “Web2.0-enabled citizen science”. This is closely aligned with the semantic meaning of the term “citizen scientist” – i.e., the citizen or member of the public doing the work of a scientist and contributing their observations to those with the position and authority to make use of them. When not reliant on new web technologies, this is how we used to think of citizen science: a great example of this is the amateur astronomer Robert Owen Evans who holds the record for visual discoveries of supernovae; or perhaps the volunteer who travels to an archeological site, learns the techniques of working carefully amongst a dig site and spends many hours contributing to that work. When mediated by web technology, the implication is that participation in the science endeavor requires a level of sophistication or commitment comparable to Robert Evans or the archeological dig participant.
At the other end of the spectrum are projects that require the powers of human perception, reasoning and pattern recognition (that still outperform the most powerful machine computers) – e.g., interpreting imagery or accurately transcribing handwriting. Pre-web examples are not very common, but would include the National Audubon Society’s annual Christmas Bird Count. In the Web2.0 setting, this harnessing of human cognitive ability can be obscure to the individual (e.g., the reCAPTCHA system for helping to digitize text), presented in a game interface where the participant is motivated not by the output but by the game itself (e.g., the idea of “games with a purpose” or “serious games”) or focused on the engagement of public participants in science endeavors through low-intensity pattern recognition tasks (e.g., Galaxy Zoo). The best, earliest example of this approach is NASA’s Clickworkers project that has spawned a genre of volunteer scientific crowdsourcing.

The new citizen science movement has provide a range of ways for people to engage with science. This is a good strategy for maximizing contributions and follows the principle of “trajectories of participation” that allow participants to engage at the level of intensity they are interested and capable of. But we need to be a little more careful in what we call these approaches because that care will translate into better design approaches for engaging people in scientific collaborations (Tanya Kelley and I discussed more on design considerations for engaging participation in an earlier blog post). Calling every mass collaboration effort related to science “citizen science” will result in sub-optimal design choices and less effective participant engagement.

Scientific crowdsourcing examples usually include tasks that:
  • are comprised of a large number of discrete, simple human-based computations or applications of human pattern recognition,
  • require very little time on the part of the volunteer to learn how to complete the task and actually complete one instance of the task, and
  • give the volunteer some measure of reward, and a sense of accomplishment and of having contributed to a larger undertaking through a very simple, short interaction.

In marrying Web2.0 and citizen science, and changing the nature of the science / public interaction, a new paradigm of scientific crowdsourcing has emerged in which the medium for communicating science tasks, and the mechanism for amateur citizen scientists to communicate their responses back, is now embodied in a Web2.0 infrastructure. This combination makes possible the tapping of a distributed network of human processing power that can accomplish tasks of high-volume, low-intensity analysis.


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