公民科学生态数据的贝叶斯项目反应模型
Bayesian item response models for citizen science ecological data
摘要：
从人群中提取的所谓“公民科学”数据在包括生态学在内的许多领域越来越受欢迎。然而，这些信息的质量经常受到科学界许多人的争论。因此，现代公民科学的实施需要衡量用户的熟练程度，说明任务的难度。我们介绍了一个新的方法框架的项目反应和线性逻辑检验模型与公民科学数据用于生态学研究。这种方法适应空间自相关项目内的困难，并产生相关的生态措施的物种和网站相关的困难，歧视的权力和猜测行为。这些，以及对主体能力的估计，可以更好地管理这些程序，并提供更深入的见解。本文还强调了项目响应模型通过分而治之来适应大数据。我们发现，建议的方法优于传统的项目反应模型的RMSE，准确性，WAIC的基础上留一交叉验证模拟和实证数据。我们提出了一个全面的实施，在坦桑尼亚的塞伦盖蒂物种识别的案例研究。提供R和Stan代码以实现完全再现性。多个统计插图和可视化，让从业者外推到广泛的公民科学生态问题。
英文摘要：
So-called 'citizen science' data elicited from crowds has become increasingly popular in many fields including ecology. However, the quality of this information is being frequently debated by many within the scientific community. Therefore, modern citizen science implementations require measures of the users' proficiency that account for the difficulty of the tasks. We introduce a new methodological framework of item response and linear logistic test models with application to citizen science data used in ecology research. This approach accommodates spatial autocorrelation within the item difficulties and produces relevant ecological measures of species and site-related difficulties, discriminatory power and guessing behavior. These, along with estimates of the subject abilities allow better management of these programs and provide deeper insights. This paper also highlights the fit of item response models to big data via divide-and-conquer. We found that the suggested methods outperform the traditional item response models in terms of RMSE, accuracy, and WAIC based on leave-one-out cross-validation on simulated and empirical data. We present a comprehensive implementation using a case study of species identification in the Serengeti, Tanzania. The R and Stan codes are provided for full reproducibility. Multiple statistical illustrations and visualizations are given which allow practitioners the extrapolation to a wide range of citizen science ecological problems.
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