Articles | Volume 27, issue 19
https://doi.org/10.5194/hess-27-3565-2023
https://doi.org/10.5194/hess-27-3565-2023
Research article
 | 
09 Oct 2023
Research article |  | 09 Oct 2023

A Bayesian model for quantifying errors in citizen science data: application to rainfall observations from Nepal

Jessica A. Eisma, Gerrit Schoups, Jeffrey C. Davids, and Nick van de Giesen

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Citizen scientists often submit high-quality data, but a robust method for assessing data quality is needed. This study develops a semi-automated program that characterizes the mistakes made by citizen scientists by grouping them into communities of citizen scientists with similar mistake tendencies and flags potentially erroneous data for further review. This work may help citizen science programs assess the quality of their data and can inform training practices.