Data generated by citizen science projects can be reliably high quality

December 1, 2016

Have you ever wanted to contribute to science? Citizen science is a type of science that includes people who have not been formally trained as scientists in doing scientific research. There are hundreds of citizen science projects that people can take part in, and most of them involve the collection or classification of data. Many projects are currently creating valuable scientific knowledge that can be used by policy-makers, resource managers, conservationists, and others. However, many professionals are skeptical of the quality of data produced by citizen scientist volunteers.

In this paper, we discuss strategies used by successful citizen science projects to ensure high data quality. These strategies include training and testing volunteers, using standardized equipment, having experts validate the data of volunteers, and having multiple volunteers make independent measurements of the same thing. Successful projects usually also have an iterative design stage, in which a project is tested and then tweaked and tested again to make sure 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 create high-quality data sets can produce data that are as good as data produced by professional scientists.

This is a plain language summary of the paper:

Kosmala, M., A. Wiggins, A. Swanson, B. Simmons. Assessing data quality in citizen science. Frontiers in Ecology and the Environment, 14:551–560. doi: 10.1002/fee.1436