PT  - JOURNAL ARTICLE
AU  - Margaret Kosmala
AU  - Andrea Wiggins
AU  - Alexandra Swanson
AU  - Brooke Simmons
TI  - Assessing data quality in citizen science (preprint)
AID  - 10.1101/074104
DP  - 2016 Jan 01
TA  - bioRxiv
PG  - 074104
4099  - http://biorxiv.org/content/early/2016/09/08/074104.short
4100  - http://biorxiv.org/content/early/2016/09/08/074104.full
AB  - Ecological and environmental citizen science projects have enormous potential to advance science, influence policy, and guide resource management by producing datasets that are otherwise infeasible to generate. This potential can only be realized, though, if the datasets are of high quality. While scientists are often skeptical of the ability of unpaid volunteers to produce accurate datasets, a growing body of publications clearly shows that diverse types of citizen science projects can produce data with accuracy equal to or surpassing that of professionals. Successful projects rely on a suite of methods to boost data accuracy and account for bias, including iterative project development, volunteer training and testing, expert validation, replication across volunteers, and statistical modeling of systematic error. Each citizen science dataset should therefore be judged individually, according to project design and application, rather than assumed to be substandard simply because volunteers generated it.