Citizen scientists’ data can be just as good as the professionals’

Margaret Kosmala, a postdoctoral fellow in the Department of Organismic and Evolutionary Biology at Harvard University, Cambridge, MA, shares this Frontiers Focus on assessing data quality in citizen science.

The UK Ladybird Survey and the Lost Ladybug Project record occurrences of Coccinellids reported by volunteers. Credit S. Nygaard CCBY.

Have you ever wanted to do scientific research, even though you have never trained as a scientist? Citizen science gives people with no science background the chance to get involved in collecting or classifying data to create valuable scientific knowledge that can be used by policy-makers, resource managers, conservationists, and others. However, many professionals are skeptical about the quality of data produced by citizen scientist volunteers.

In this paper, we discuss strategies used in successful citizen science projects to ensure good data quality. These strategies include training and testing volunteers, using standardized equipment, having experts validate the data produced by volunteers, and having multiple volunteers make independent measurements of the same thing. Successful projects typically repeat certain stages of the work, testing, then tweaking and testing again, to make sure that the data being produced is useful. And most citizen science projects produce a large amount of data, so it is also possible to use statistics to increase data quality. We show that citizen science projects that actively try to ensure high-quality data sets can produce results that are just as good as those produced by professional scientists.

Margaret Kosmala, Andrea Wiggins, Alexandra Swanson, Brooke Simmons (2016) Assessing data quality in citizen science. Front Ecol Environ 14(10): 551–560, doi:10.1002/fee.1436