Speaker
Description
Citizen Science data are widely used in ecological research. Yet, the term “Citizen Science data” is often applied as a broad label that overlooks the vast diversity of how such data are collected.
Citizen Science datasets vary widely in quality, ranging from opportunistic point observations to structured recording schemes and well-curated datasets. Broad generalizations risk undervaluing the substantial effort and expertise invested by many contributors. High‐quality datasets, typically requiring rigorous protocols and time‐intensive repeated sampling can be collected by both citizen and professional scientists, but may become less visible when Citizen Science is primarily framed through opportunistic observations.
On our poster we present an overview of papers published in the last decade that state to use Citizen Science data in their research. We show that the use of Citizen Science datasets is generally increasing, but to a much larger extent in favor of datasets using unstructured data. In analysing narratives about Citizen Science data in the assessed papers, we show that the framing of the term is inadequate and overgeneralizing in many instances.
We argue that Citizen Science data should refer only to the origin of the data. That means, that citizen science data are collected by volunteers. Differentiation is needed regarding quality, scope, and bias that must be linked explicitly to the specific data type and sampling design. Greater precision in terminology is essential to reflect the heterogeneity of Citizen Science datasets and to appropriately acknowledge the contributions of expert volunteers.
| Status Group | Senior Scientist |
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