Kelling Steve, Johnston Alison, Hochachka Wesley M, Iliff Marshall, Fink Daniel, Gerbracht Jeff, Lagoze Carl, La Sorte Frank A, Moore Travis, Wiggins Andrea, Wong Weng-Keen, Wood Chris, Yu Jun
Cornell Lab of Ornithology, Cornell University, Ithaca, New York, United States of America.
British Trust for Ornithology, Thetford, Norfolk, England, United Kingdom.
PLoS One. 2015 Oct 9;10(10):e0139600. doi: 10.1371/journal.pone.0139600. eCollection 2015.
Volunteers are increasingly being recruited into citizen science projects to collect observations for scientific studies. An additional goal of these projects is to engage and educate these volunteers. Thus, there are few barriers to participation resulting in volunteer observers with varying ability to complete the project's tasks. To improve the quality of a citizen science project's outcomes it would be useful to account for inter-observer variation, and to assess the rarely tested presumption that participating in a citizen science projects results in volunteers becoming better observers. Here we present a method for indexing observer variability based on the data routinely submitted by observers participating in the citizen science project eBird, a broad-scale monitoring project in which observers collect and submit lists of the bird species observed while birding. Our method for indexing observer variability uses species accumulation curves, lines that describe how the total number of species reported increase with increasing time spent in collecting observations. We find that differences in species accumulation curves among observers equates to higher rates of species accumulation, particularly for harder-to-identify species, and reveals increased species accumulation rates with continued participation. We suggest that these properties of our analysis provide a measure of observer skill, and that the potential to derive post-hoc data-derived measurements of participant ability should be more widely explored by analysts of data from citizen science projects. We see the potential for inferential results from analyses of citizen science data to be improved by accounting for observer skill.
越来越多的志愿者被招募到公民科学项目中,为科学研究收集观测数据。这些项目的另一个目标是让志愿者参与进来并对其进行教育。因此,参与的障碍很少,这导致志愿者观测者完成项目任务的能力各不相同。为了提高公民科学项目成果的质量,考虑观测者之间的差异,并评估参与公民科学项目会使志愿者成为更好的观测者这一很少被检验的假设,将会很有帮助。在这里,我们提出一种基于参与公民科学项目eBird的观测者定期提交的数据来索引观测者变异性的方法,eBird是一个大规模监测项目,观测者在观鸟时收集并提交所观察到的鸟类物种清单。我们的观测者变异性索引方法使用物种累积曲线,即描述随着收集观测数据时间的增加报告的物种总数如何增加的曲线。我们发现,观测者之间物种累积曲线的差异等同于更高的物种累积率,尤其是对于难以识别的物种,并且随着持续参与,物种累积率会增加。我们认为,我们分析的这些特性提供了一种观测者技能的度量,并且来自公民科学项目数据的分析师应该更广泛地探索从事后数据得出参与者能力度量的潜力。我们认为,通过考虑观测者技能,对公民科学数据的分析得出的推断结果有改进的潜力。