Making citizen science reliable

Despite the wealth of information generated and the many resulting scientific discoveries, citizen science arouses skepticism among professional scientists. For some people, asking non-scientists to make scientific observations or to follow scientific methods scrupulously sounds like a recipe for disaster. This prejudice against citizen science persists partly because scientists fear biases more than smallpox. At least smallpox is conspicuous: when it’s here, you know it. Biases, on the other hand, can be asymptomatic and therefore, they can stay invisible while at the same time spoiling datasets and compromising results and conclusions. 

Professional scientists have also questioned the ethics of partnering with volunteers, and the “motives and ambitions” of the volunteers themselves. The primary fear is that science and policy might be derived from unreliable data. For some, citizen science is worth more for its educational potential than for the science it can produce. 

Contrary to these clichés, a growing body of publications clearly shows that citizen science can produce data with accuracy equal to or surpassing that of professionals. It all depends on how it is done. For the Wild Lab Projects, data quality is an obsession, and we use our experience as scientists, our common sense, and the recommendations in the literature to aim for the highest level of reliability.  

Stepping away from the crowds (image: Franziska Barczyk)

Adapting to citizen science 

A very important first step in our projects is to co-design methods that are compatible with citizen science. This is a strong focus we share with our research partners during the early stage of the projects. Asking the participants too much is a no-win situation anyways: it leads to disappointing results for the scientists who need the data, and it is also discouraging for the participants. On the other hand, oversimplified methods are not so interesting for the scientists, but can boring for the participants. Together with our research partners, we find a compromise, satisfying for both parties and as ambitious as possible. 

Training and supervising the participants 

We train the participants, and we supervise them in the field. It has been shown repeatedly that this approach enhances data accuracy and credibility. It also benefits the participants who can better understand the protocols and the projects, ask questions and get answers right away, acquire new skills and feel that their efforts are useful and productive. We see training and supervision as a way to aim higher in terms of what we can accomplish scientifically and in the field. Also, by taking care of the logistics and providing equipment, we can simply do more. Supervising the participants also means that we are responsible for their safety and well-being, and that creates ideal conditions to focus on what needs to be done, and for the participants to relax and enjoy the experience. 

Getting better with time 

Most of our citizen science projects are long-term commitments. In the beginning, it takes time to establish a trustworthy relationship with a research partner, to understand each other’s motivations and needs, and to design adequate methods together. Although Wild Lab’s facilitators are educated scientists, they are not experts in all the topics, and depending on the topic it can take time for us to acquire enough knowledge to be able to lead the projects. It is a rewarding and stimulating exercise for us, and definitely an investment that pays off in the long run. Then regularly, our research partners share feedback with us, and we evaluate together the methods’ strengths and weaknesses, to adjust and improve them over time.  

We have no doubt that citizen science can be as reliable as mainstream science when designed and conducted adequately.  It is essential for us to collaborate very closely with our research partners, at the beginning of the projects when formulating the research questions and co-designing the methods, but also regularly during the entire life of the projects. This being said, citizen science must also be fun, engaging and rewarding! After all, what brings us together is the shared cause of caring for the Earth and these projects are opportunities to make a difference while making our life richer with enjoyable and interesting experiences.  

You want to read more about the topic? Here are relevant sources that inspired this article (there are tons of articles on the topic):  

Aceves-Bueno et al. (2017). The Accuracy of Citizen Science Data: A Quantitative Review. Bulletin of the Ecological Society of America, 98(4): 278-290. 

Bonney et al. (2014). Next Steps for Citizen Science. Science 343: 1436–37. 

Cohn (2008). Citizen Science: Can Volunteers Do Real Research? BioScience, Volume 58, Issue 3: 192–197. 

Danielsen et al. (2014). A Multicountry Assessment of Tropical Resource Monitoring by Local Communities. BioScience, Volume 64, Issue 3: 236–251 

Freitag et al. (2016). Strategies employed by citizen science programs to increase the credibility of their data. Citizen Science: Theory and Practice 1: 2. 

Kosmala et al. (2016). Assessing data quality in citizen science. Front Ecol Environ 2016; 14(10): 551–560. 

Resnik et al. (2015). A framework for addressing ethical issues in citizen science. Environmental Science & Policy 54 (2015) 475–481. 

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