TY - JOUR
KW - big data
KW - Citizen Science
KW - crowdsourcing
KW - data quality
KW - ecology
KW - verification
AU - Baker Emily
AU - Drury Jonathan P.
AU - Judge Johanna
AU - Roy David B.
AU - Smith Graham C.
AU - Stephens Philip A.
AB - 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.

BT - Citizen Science: Theory and Practice
DO - 10.5334/cstp.351
IS - 1
LA - en
M1 - 1
N1 - Number: 1 Publisher: Ubiquity Press
N2 - 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.

PY - 2021
T2 - Citizen Science: Theory and Practice
TI - The Verification of Ecological Citizen Science Data: Current Approaches and Future Possibilities
UR - http://theoryandpractice.citizenscienceassociation.org/articles/10.5334/cstp.351/
VL - 6
SN - 2057-4991
ER -