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Citizen science in environmental and ecological sciences
Summary
This review describes how citizen science, where non-professional volunteers help collect data, is being applied in environmental and ecological research. Citizen science projects have contributed to monitoring pollution, biodiversity, and water quality across large geographic areas. The approach is relevant to microplastic research because trained volunteers can help sample and catalog microplastic contamination across many locations that professional scientists cannot cover alone.
Citizen science is an increasingly acknowledged approach applied in many scientific domains, and particularly within the environmental and ecological sciences, in which non-professional participants contribute to data collection to advance scientific research. We present contributory citizen science as a valuable method to scientists and practitioners within the environmental and ecological sciences, focusing on the full life cycle of citizen science practice, from design to implementation, evaluation and data management. We highlight key issues in citizen science and how to address them, such as participant engagement and retention, data quality assurance and bias correction, as well as ethical considerations regarding data sharing. We also provide a range of examples to illustrate the diversity of applications, from biodiversity research and land cover assessment to forest health monitoring and marine pollution. The aspects of reproducibility and data sharing are considered, placing citizen science within an encompassing open science perspective. Finally, we discuss its limitations and challenges and present an outlook for the application of citizen science in multiple science domains. Contributory citizen science is a method in which non-professional participants contribute to data collection in whole or in part to advance scientific research. This Primer outlines the use of citizen science in the environmental and ecological sciences, discussing participant engagement, data quality assurance and bias correction.
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