Abstract
The growing public interest in biodiversity monitoring has led to a significant increase in initiatives that unite citizen scientists, researchers, and machine learning technologies. In this context, we introduce WildLIVE!, a dynamic biomonitoring and citizen science project. In WildLIVE!, participants analyze a vast array of images from a long-term camera trapping project in Bolivia to investigate the impacts of shifting environmental factors on wildlife. From 2020 to 2023, more than 850 participants registered for WildLIVE!, contributing nearly 9,000 hours of voluntary work. We explore the motivators and sentiments of participant engagement and discuss the key strategies that have contributed to the project’s initial success. The findings from a questionnaire highlight that the primary motivational factors for our participants are understanding and knowledge, as well as engagement and commitment. However, expressions of positive and negative sentiments can be found regarding involvement. Participants appeared to be driven primarily by a desire for intellectual growth and emotional fulfillment. Factors crucial to the success of this digital citizen science project include media exposure, creating emotional connections through virtual and in-person communication with participants, and visibility on public citizen science portals. Moreover, the project’s labeled dataset serves as a valuable resource for machine learning, aiding the development of a new platform that is compliant with the FAIR principles. WildLIVE! not only contributes to outcomes in science, society, and nature conservation, but also demonstrates the potential of creating a collaborative bridge between the general public, scientific research, biodiversity conservation, and advanced technological applications.
Funder
Deutsche Forschungsgemeinschaft
Reference67 articles.
1. The role of citizen science and deep learning in camera trapping;Sustainability,2021
2. Citizen science in ecology: a place for humans in nature;Annals of the New York Academy of Sciences,2020
3. Wildlife insights: A platform to maximize the potential of camera trap and other passive sensor wildlife data for the planet;Environmental Conservation,2020
4. Anderson, C. (2008). Wired. The end of theory: the data deluge makes the scientific method obsolete. Retrieved from http://www.wired.com/2008/06/pb-theory [last accessed 11 June 2023]
5. Chimp&See: An online citizen science platform for large-scale, remote video camera trap annotation of chimpanzee behaviour, demography and individual identification;PeerJ Preprints,2016
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