The Verification of Ecological Citizen Science Data: Current Approaches and Future Possibilities

David B. Roy,Johanna Judge,Jonathan P. Drury,Philip A. Stephens,Graham C. Smith,Emily Baker

doi:10.5334/cstp.351

Abstract

Citizen science schemes enable ecological data collection over very large spatial and temporal scales, producing datasets of high value for both pure and applied research. However, the accuracy of citizen science data is often questioned, owing to issues surrounding data quality and verification, the process by which records are checked after submission for correctness. Verification is a critical process for ensuring data quality and for increasing trust in such datasets, but verification approaches vary considerably between schemes. Here, we systematically review approaches to verification across ecological citizen science schemes that feature in published research, aiming to identify the options available for verification, and to examine factors that influence the approaches used. We reviewed 259 schemes and were able to locate verification information for 142 of those. Expert verification was most widely used, especially among longer-running schemes, followed by community consensus and automated approaches. Expert verification has been the default approach for schemes in the past, but as the volume of data collected through citizen science schemes grows and the potential of automated approaches develops, many schemes might be able to implement approaches that verify data more efficiently. We present an idealised system for data verification, identifying schemes where this system could be applied and the requirements for implementation. We propose a hierarchical approach in which the bulk of records are verified by automation or community consensus, and any flagged records can then undergo additional levels of verification by experts.

Highlights

In the current polarised political and media environment (Iyengar and Massey 2019), with public access to a vast choice of information sources (Huber et al 2019), there is an increasing need for effective public engagement and science communication
Verification is a critical process for ensuring data quality of, and trust in, citizen science datasets (Gilfedder et al 2019), enabling those datasets to be used in environmental research, management, and policy development (Tweddle et al 2012)
For each citizen science scheme, we identified the following attributes: number of species recorded through the scheme, number of occurrence records collected through the scheme, data type, number of participants, geographical extent, and duration in years

Summary

Introduction

In the current polarised political and media environment (Iyengar and Massey 2019), with public access to a vast choice of information sources (Huber et al 2019), there is an increasing need for effective public engagement and science communication. There is, an argument for the democratisation of science, to make information accessible to everyone, to engage the public in scientific issues, and to involve them in scientific research endeavours (Mason and Garbarino 2016; Salomon et al 2018). Scientists can involve the public in the research process through gaining insight into local knowledge and value systems, and through volunteer contributions to data collection and interpretation (Kimura and Kinchy 2016). One way that public engagement is increasingly embedded in ecological research is through data collection by members of the public. The public can contribute to species monitoring and biological recording, documenting species’ occurrences to track species’ distribution, abundance, and/or phenology (Sutherland, Roy, and Amano 2015)

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