McCarthy Maureen S, Stephens Colleen, Dieguez Paula, Samuni Liran, Després-Einspenner Marie-Lyne, Harder Briana, Landsmann Anja, Lynn Laura K, Maldonado Nuria, Ročkaiová Zuzana, Widness Jane, Wittig Roman M, Boesch Christophe, Kühl Hjalmar S, Arandjelovic Mimi
Max Planck Institute for Evolutionary Anthropology Leipzig Germany.
Department of Human Evolutionary Biology Harvard University Cambridge Massachusetts USA.
Ecol Evol. 2020 Dec 16;11(4):1598-1608. doi: 10.1002/ece3.7128. eCollection 2021 Feb.
Citizen science has grown rapidly in popularity in recent years due to its potential to educate and engage the public while providing a means to address a myriad of scientific questions. However, the rise in popularity of citizen science has also been accompanied by concerns about the quality of data emerging from citizen science research projects. We assessed data quality in the online citizen scientist platform Chimp&See, which hosts camera trap videos of chimpanzees () and other species across Equatorial Africa. In particular, we compared detection and identification of individual chimpanzees by citizen scientists with that of experts with years of experience studying those chimpanzees. We found that citizen scientists typically detected the same number of individual chimpanzees as experts, but assigned far fewer identifications (IDs) to those individuals. Those IDs assigned, however, were nearly always in agreement with the IDs provided by experts. We applied the data sets of citizen scientists and experts by constructing social networks from each. We found that both social networks were relatively robust and shared a similar structure, as well as having positively correlated individual network positions. Our findings demonstrate that, although citizen scientists produced a smaller data set based on fewer confirmed IDs, the data strongly reflect expert classifications and can be used for meaningful assessments of group structure and dynamics. This approach expands opportunities for social research and conservation monitoring in great apes and many other individually identifiable species.
近年来，公民科学因具有教育公众并使其参与其中的潜力，同时为解决众多科学问题提供了一种途径，而迅速受到欢迎。然而，公民科学受欢迎程度的提高也伴随着对公民科学研究项目所产生数据质量的担忧。我们评估了在线公民科学家平台Chimp&See的数据质量，该平台托管着赤道非洲各地黑猩猩及其他物种的相机陷阱视频。特别是，我们将公民科学家对个体黑猩猩的检测和识别与研究这些黑猩猩多年的专家进行了比较。我们发现，公民科学家通常检测到的个体黑猩猩数量与专家相同，但为这些个体分配的识别（ID）却少得多。然而，所分配的那些ID几乎总是与专家提供的ID一致。我们通过从公民科学家和专家各自的数据集中构建社会网络来应用这些数据集。我们发现，这两个社会网络都相对稳健，具有相似的结构，并且个体网络位置呈正相关。我们的研究结果表明，尽管公民科学家基于较少的已确认ID生成了较小的数据集，但这些数据强烈反映了专家的分类，可用于对群体结构和动态进行有意义的评估。这种方法扩大了对大猩猩和许多其他可个体识别物种进行社会研究和保护监测的机会。
