Wednesday, January 21, 2015

More Research on the Economic Value and Engagement of Citizen Scientists

Regular readers know that I'm very interested in the factors that get people involved in citizen science projects and keeps them motivated to stay.  By its very nature this is one of the most important issues citizen science needs to master.  It was also of the first research areas I was interested in and it played an important role in my "Keys to Citizen Science Success" and later poster presentation. So its great to see some good, peer-reviewed research coming out on these very same issues.

My concern has always been that while many people may initially be interested in the general idea of citizen science, and may find a project that sparks some initial activity, it doesn't take long before they lose the passion and leave the project (or the field) altogether.  My gut has always told me this is a problem and now some recent research backs me up.  Apparently it really is true that while most people start projects with a rush of excitement it quickly fades and the actual work is done by a just a small handful of individuals.

The paper's official title is "Crowd science user contribution patterns and their implications", written by Henry Sauermann and Chiara Franzoni and published this month in the Proceedings of the National Academy of Sciences.  A popular press article from Ars Technica sums it up well, "Most participants in 'citizen science' projects give up almost immediately".  Similarly, in the words of the paper's authors, "...we find that most contributors participate only once and with little effort, leaving a relatively small share of users who return responsible for most of the work."  All this fits right in with some of my previous findings that found "Motivate the User" was an important key to successful citizen science projects. As I said at the time:

"People need a push sometimes. Just because they joined your project and have learned how to participate, that doesn't mean they'll stick around. This is especially true for projects where users perform the same tasks multiple times (such as identifying whale songs or counting craters). Users may lose interest after 10-15 minute. So project scientists need ways to keep things interesting by offering new goals as initial ones are met."

There is a lot to this article with data from over 100,000 users spread over six months on seven different Zooniverse sites and a comparison with sites like CitizenSort. As popular and highly successful citizen science platforms these are a great place to start.  However, one issue I have with the authors' analysis is that the participants involvement is at the "Contributory" level of citizen science.  You can read much more about the levels of citizen science involvement here, but this is one of the early stages that don't require much involvement from participants.  Of course people can CHOOSE to get heavily involved, but they can perform the required tasks with minimal training or follow-up, and without a large time/financial commitment. So the findings that many people don't stick with projects may be a self-fulfilling prophecy for these types of citizen science research.

It should also be noted that the authors attempted to quantify the financial impact of the citizen scientists' work. Depending on the model used, they found an average savings of over $200,000 per project. For all projects analyzed there was a total savings of over $1.5 million, or $15 per initial participant.

As someone interested in developing citizen science business models, evidence demonstrating the value of citizen science in either reducing the costs of research is very important.  Given the highly leveraged nature of these projects, where the large majority of costs are in initial software development and raw data collection, a $15 per user benefit is quite tempting.  Adding some costs for marketing and to handle the bandwidth for submitting individual results, this  cost-effective business tool can be used to both save money for companies AND support investments in additional citizen science projects.  In other words, benefiting people on both sides of the research process.

Interestingly, I'd be remiss if I didn't say that the researchers talk about some early evidence (from previous papers) hinting that may be stronger motivators than financial incentives.  I'm pretty skeptical of this for a variety of reasons, but obviously I can't pre-judge.  For instance, it only looks at projects at the "Contributory - Novice - Entry Stage" level of citizen science research. So I'll be taking a closer look at those portions in a future post.

Just one more reason for you to keep coming back!


  1. Thanks for your thoughts on Citizen Science. I'm involved in Gotham Whale, a CS project monitoring whales around New York City. It's a retirement project that is demanding more and more time (and money). I'm finding that building the organization is as much effort as doing the science. When personal funds aren't able to keep up, I'm planning for revenue generators: memberships, t-shirts etc. All of which take $$ and effort to start up. Fortunately I have a really sexy project (everyone loves whales) and have a committed group of helpers. I've also connected with a non-profit org that lets me work under their status, so I could pursue donations. Filing for 501(c)3 status takes some money as well. So monetizing a CS project is really important, even if one isn't interested in making any $$. The cost to operate can be substantial. I'm very interested in other's experience and will be happy to share ours. I'm sorry I can't get to San Jose for the conference, but look forward to what may come out of that. I'm particularly interested in this subject ($$) but also in mechanisms for publication of CS findings that may not get past rigorous peer reviewed journals (which sometimes can be biased against CS) but can offer very valuable insights.
    keep up the good work and I'll stay tuned.
    Paul L. Sieswerda
    Director, Gotham Whale

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  2. In a study we conducted using data collected from Galaxy Zoo and The Milky Way Project, we also found that only a small proportion of volunteers return after the first day and that such volunteers are responsible for most of the work performed in the projects (Please see "Volunteers' Engagement in Human Computation for Astronomy Projects" Motivated by this finding, we decided to further investigate the behavior of such volunteers by using data mining algorithms. We found that they can be clustered in 5 distinct engagement profiles, which differ among them in several ways in terms of both contribution behavior and aggregated work. We present and analyze the engagement profiles in a paper entitled "Finding Volunteers' Engagement Profiles in Human Computation for Citizen Science Projects" (

  3. Thank you for the reference! That fits in nicely with some of the thinking I've been doing, with yours quantifying the relative engagement levels in much more precise methods than my purely quantitative ones. I'll have to write much more about what you've found here.

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