Citizen science projects involve multiple individuals, many of whom are not trained as scientists, who collect, categorise, transcribe, or analyse scientific data (Bonney, et al., 2014). Around the world, thousands of research projects are engaging millions of individual citizen scientists as volunteers who collect and processes data as part of scientific enquiries (Silvertown, 2009). Most citizen science projects obtain or manage scientific information at scales that would be impossible for individual researchers or research teams. This can take the form of individuals collecting data across large areas (even continents!), participants categorizing vast quantities of online data, or groups of volunteers tackling local problems under localized research programmes (Bonney, et al., 2014) (Dickinson, et al., 2012). Approaches to citizen science can include community‐based monitoring, as well as “crowd‐sourcing” help for scientific tasks using the internet (Dickinson, et al., 2012).
Projects that involve citizen scientists are especially prevalent in ecology and environmental sciences, however they can cover a breadth of topics from “microbiomes to native bees to water quality to galaxies” (Silvertown, 2009) (Bonney, et al., 2014). In the field of ecology citizen science is an important way to combine scientific research, environmental education, and natural history observation. The large scale that in made possible by citizen science projects expands the potential for spatial research. Citizen science has been used in biological studies of climate change, landscape ecology, phenology, rare and invasive species, disease, populations, communities, and ecosystems. Citizen science and the resulting data is often viewed as a public good that is generated through increasingly collaborative tools and resources, while simultaneously supporting public participation in science.
The widespread use of citizen science projects speaks to the validity of the data. However, there are theories that data collected by citizen scientists can be biased by being aligned with the citizens’ preferences rather than scientific objectives (Xue, Davies, Fink, & Gomes, 2016). Sometimes important information to account for the inevitable biases that observers introduce during data collection can be lacking. Therefore, citizen science projects must gather information about the observation process as well as the data. Regardless of what is being monitored, when citizen science projects collect small amounts of basic information about how observations are made, the scientific value of the data collected improves dramatically (Kelling, et al., 2019).
eBird is one of the world’s largest biodiversity-related science projects. It is a global citizen science project that collects information on bird occurrences distribution, abundance, habitat use, and trends, as well as contextual information on the observation process (eBird, 2021) (Kelling, et al., 2019). Birders enter when, where, and how they went birdwatching and fill out a checklist of all the birds they saw or heard. Gloablly, more than 100 million sightings are contributed annually by eBirders, with an approximate participation growth of 20% each year. Observers are presented with a list of likely birds for that date and area when they enter sightings. These filter lists are developed by bird distribution experts and, when unusual birds are seen or high counts are reported, the records are reviewed by regional experts (eBird, 2021).
Many citizen science programmes seek to incentivize volunteers to contribute to the research. This can take the form of both extrinsic and intrinsic incentives. Examples of extrinsic incentives are those which turn the citizen science programme into a game or competition, such as by providing score boards, progressive ranks, or badges (Beza, et al., 2017). Many projects adopt designs where the incentives draw on intrinsic motivation rather than the extrinsic motivation of scoreboards and social rewards. Intrinsic motivation takes the form of the citizen scientist’s interest in learning, developing skills, and social exchange, resulting in inherent satisfaction (Beza, et al., 2017). Incentives such as scoreboards, personalised maps, checklists can increase recruitment rates and the number of observations recorded by individual citizen scientists. However, there are financial and logistical costs associated with designing and implementing such programmes (Steger, Butt, & Hooten, 2017).
Incentives can be used in citizen science programmes to both increase participation and reduce biases. For example, if a particular area was under-sampled, incentives could be introduced to increase the number of citizen scientists visiting the area (Xue, Davies, Fink, & Gomes, 2016). eBird provides game-like incentives such as personal bird lists, user rankings, and rare bird alerts. When eBird introduced these features they saw a strong increase in participant numbers (Beza, et al., 2017). When researchers introduced a game called Avicaching to eBird they saw a great response from the birding community. The results of the study showed that the game incentives were effective at encouraging bird watchers to visit under-sampled areas and therefore helped to alleviate the issue with eBird’s data bias (Xue, Davies, Fink, & Gomes, 2016).
Citizen science is a powerful tool to increase the scale and scope of research projects, particular in the fields of ecology and the environment (Bonney, et al., 2014). However, there are issue with data bias and lack of information about the collection process. Incentives that are built into citizen science projects can alleviate the pressures of these biases by encouraging users to perform data collection in a certain way using rewards such as scoreboards and personal development opportunities (Beza, et al., 2017).
Beza, E., Steinke, J., van Etten, J., Reidsma, P., Fadda, C., Mittra, S., . . . Kooistra, L. (2017). What are the prospects for citizen science in agriculture? Evidence from three continents on motivation and mobile telephone use of resource-poor farmers. PLoS ONE.
Bonney, R., Shirk, J. L., Phillips, T. B., Wiggins, A., Ballard, H. L., Miller-Rushing, A. J., & Parrish, J. K. (2014). Next Steps for Citizen Science. Science, 1436-1437.
Dickinson, J. L., Shirk, J., Bonter, D., Bonney, R., Crain, R. L., Martin, J., . . . Purcell, K. (2012). The current state of citizen science as a tool for ecological research and public engagement. Frontiers in Ecology and the Environment, 291-297.
eBird. (2021). About eBird. Retrieved from eBird: https://ebird.org/about
Kelling, S., Johnston, A., Bonn, A., Fink, D., Ruiz-Gutierrez, V., Bonney, R., . . . Guralnick, R. (2019). Using Semistructured Surveys to Improve Citizen Science Data for Monitoring Biodiversity. BioScience, 170–179.
Silvertown, J. (2009). A new dawn for citizen science. Trends in Ecology and Evolution, 467-471.
Steger, C., Butt, B., & Hooten, M. B. (2017). Safari Science: assessing the reliability of citizen science data for wildlife surveys. Journal of Applied Ecology, 2053-2062.
Xue, Y., Davies, I., Fink, D., & Gomes, C. P. (2016). Science, Avicaching: A Two Stage Game for Bias Reduction in Citizen. International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS) , 776-785.
