1. Citizen science projects have become increasingly popular in many fields, including ecology. However, the quality of this information is frequently debated within the scientific community. Modern citizen science implementations therefore require measures of the users' proficiency.
2. We introduce a new methodological framework of item response that quantifies a citizen scientist's ability, taking into account the difficulty of the task. We focus on citizen science programs involving the classification of images. Our approach accommodates spatial autocorrelation within the item difficulties, and provides deeper insights and relevant ecological measures of species and site-related difficulties, discriminatory power and guessing behaviour. The identification of very capable versus less skilled participants can facilitate selective use of data in analyses and more targeted training programs for citizen scientists. This paper also addresses challenges in fitting such models to very large datasets.
3. 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.
4. 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 extrapolation to a wide range of citizen science ecological problems.

中文翻译：

---

使用项目响应模型了解公民科学观察数据的可靠性

1. 公民科学项目在包括生态学在内的许多领域越来越受欢迎。然而，这些信息的质量在科学界经常引起争论。因此，现代公民科学的实施需要衡量用户的熟练程度。
2. 我们引入了一种新的项目响应方法框架，该框架可以量化公民科学家的能力，同时考虑到任务的难度。我们专注于涉及图像分类的公民科学计划。我们的方法适应了项目难度内的空间自相关，并提供了更深入的洞察力和物种的相关生态测量以及与场地相关的困难、辨别力和猜测行为。识别非常有能力的参与者与不太熟练的参与者可以促进在分析和更有针对性的公民科学家培训计划中选择性地使用数据。本文还解决了将此类模型拟合到非常大的数据集的挑战。
3. 我们发现，基于对模拟和经验数据的留一法交叉验证，建议的方法在 RMSE、准确性和 WAIC 方面优于传统的项目响应模型。
4. 我们使用坦桑尼亚塞伦盖蒂的物种鉴定案例研究提出了一个全面的实施方案。提供 R 和 Stan 代码以实现完全可重复性。给出了多个统计插图和可视化，可以推断出广泛的公民科学生态问题。