Citizen science has the potential to expand the scope and scale of research in ecology and conservation, but many professional researchers remain skeptical of data produced by nonexperts. We devised an approach for producing accurate, reliable data from untrained, nonexpert volunteers. On the citizen science website www.snapshotserengeti.org, more than 28,000 volunteers classified 1.51 million images taken in a large-scale camera-trap survey in Serengeti National Park, Tanzania. Each image was circulated to, on average, 27 volunteers, and their classifications were aggregated using a simple plurality algorithm. We validated the aggregated answers against a data set of 3829 images verified by experts and calculated 3 certainty metrics-level of agreement among classifications (evenness), fraction of classifications supporting the aggregated answer (fraction support), and fraction of classifiers who reported "nothing here" for an image that was ultimately classified as containing an animal (fraction blank)-to measure confidence that an aggregated answer was correct. 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. Easily calculated analysis of variance and post-hoc Tukey tests indicated that the certainty metrics were significant indicators of whether each image was correctly classified or classifiable. Thus, the certainty metrics can be used to identify images for expert review. Bootstrapping analyses further indicated that 90% of images were correctly classified with just 5 volunteers per image. Species classifications based on the plurality vote of multiple citizen scientists can provide a reliable foundation for large-scale monitoring of African wildlife.
Una Estrategia Generalizada para la Producción, Cuantificación y Validación de los Datos de Ciencia Ciudadana a partir de Imágenes de Vida Silvestre
La ciencia ciudadana tiene el potencial de expandir el alcance y la escala de la investigación en la ecología y la conservación, pero muchos investigadores profesionales permanecen escépticos sobre los datos producidos por quienes no son expertos. Diseñamos una estrategia para generar datos precisos y fiables a partir de voluntarios no expertos y sin entrenamiento. En el sitio web de ciencia ciudadana
Keywords: Snapshot Serengeti; Zooniverse; big data; camera traps; conjunto de datos; crowdsourcing; cámaras trampa; data aggregation; data validation; datos grandes; image processing; procesamiento de imágenes; validación de datos.
© 2016 The Authors. Conservation Biology published by Wiley Periodicals, Inc. on behalf of Society for Conservation Biology.