A generalized approach for producing, quantifying, and validating citizen science data from wildlife images
This article assesses an approach for producing accurate, reliable data from untrained, nonexpert volunteers: more than 28,000 volunteers classified 1.51 million online images taken in a large-scale camera-trap survey in Serengeti National Park, Tanzania. The aggregated answers were validated against a data set of images classified by experts. Overall, aggregated volunteer answers agreed with the expert-verified data on 98% of images, but accuracy differed by species commonness such that rare species had higher rates of false positives and false negatives. The authors therefore conclude that species classifications based on the plurality vote of multiple citizen scientists can provide a reliable foundation for large-scale monitoring of African wildlife.