Combining Physical and Digital Data Collection for Citizen Science Climate Research. (9th March 2022)

• Record Type: Journal Article
• Authors: Killen, Heather; Chang, Lucy; Soul, Laura; Barclay, Richard
• Is Part Of: Citizen science. Volume 7:Number 1(2022)
• Publication Date: 2022-03-09
• Subjects: citizen science -- technology -- physical sample -- digital data -- iNaturalist -- protocol design; Science -- Citizen participation -- Periodicals; Volunteer workers in science -- Periodicals; 507.2
• DOI: 10.5334/cstp.422
• Languages: English
• ISSNs: 2057-4991

Abstract

In this paper, we present our experience designing and implementing a hybrid citizen science protocol combining local data collection reported digitally with the return of physical samples by mail. Our project, Fossil Atmospheres, housed within the Paleobiology Department of the Smithsonian Institution's National Museum of Natural History, sought to complete a broad geographic collection of Ginkgo biloba L. leaves to better understand climate change over time. We also wished to leverage and test the affordances of using an established online platform as a technological tool for research-quality data collection. Participants were asked to find a local ginkgo tree and, using a hybrid protocol, collect leaf samples and record site data, including photos, GPS coordinates, and tree characteristics, using the iNaturalist online platform. Participants then returned their leaf samples by mail. Fossil Atmospheres received 562 leaf samples from 352 participants. These samples, representing 36 states, met our target geographic transects and reflected the known habitat range of living ginkgo in the United States. We were able to successfully pair a large majority of received samples to their corresponding digital data records, allowing us to include 88% of the samples received within the Fossil Atmospheres data set. These results greatly exceeded our project goals. The hybrid protocol model we present, based on our experiences, indicates that using tools like iNaturalist provides multiple benefits that meet or exceed more traditional data collection models, including increases in the scale of data that can be collected, data accuracy, and data completeness, uniformity, usability, and accessibility.