Articles | Volume 27, issue 19
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


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-658', Jonathan Paul, 22 May 2023
    • AC1: 'Reply on RC1', Jessica Eisma, 13 Jul 2023
  • RC2: 'Comment on egusphere-2023-658', Björn Weeser, 19 Jun 2023
    • AC2: 'Reply on RC2', Jessica Eisma, 13 Jul 2023

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
ED: Publish subject to minor revisions (further review by editor) (28 Jul 2023) by Wouter Buytaert
AR by Jessica Eisma on behalf of the Authors (31 Jul 2023)  Author's response   Author's tracked changes   Manuscript 
ED: Publish as is (29 Aug 2023) by Wouter Buytaert
AR by Jessica Eisma on behalf of the Authors (29 Aug 2023)  Manuscript 
Short summary
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.