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Data for: Citizen science as an ecosystem of engagement: Implications for learning and broadening participation

Citation

Allf, Bradley (2022), Data for: Citizen science as an ecosystem of engagement: Implications for learning and broadening participation, Dryad, Dataset, https://doi.org/10.5061/dryad.0gb5mkm3k

Abstract

The bulk of research on citizen science participants is project-centric, based on an assumption that volunteers experience a single project. Contrary to this assumption, survey responses (n=3,894) and digital trace data (n=3,649) from volunteers, who collectively engaged in 1,126 unique projects, revealed that multi-project participation was the norm. Only 23% of volunteers were singletons (who participated in only one project), and multi-project participants split evenly between disciplines specialists (39%) and discipline spanners (38% joined projects with different disciplinary topics), and unevenly between mode specialists (67%) and mode spanners (33% participated in online and offline projects). Public engagement was narrow: multi-project participants were eight times more likely to be white, and five times more likely to hold advanced degrees, than the general population. We propose a volunteer-centric framework that explores how the dynamic accumulation of experiences in a project ecosystem can support broad learning objectives and inclusive citizen science. 

Methods

The purpose of this project was to collect data about volunteers who do citizen science projects, particilarly the number and type of projects that these participants do, and what demographic communities these volunteers represent. There were four data sources: digital trace data from the website "SciStarter.org," a survey distributed to SciStarter volunteers, a survey distributed to volunteers with the project "The Christmas Bird Count" and volunteers with the project "Candid Critters." We used this data to create a list of citizen science projects, which we categorized according to disciplinary topic (ecology, astronomy, etc.) and participation mode (online or offline). We then categorized each volunteer in our data source according to how many projects they did, and whether the project(s) they did were from multiple disciplinary topics and modes. Finally, we used regression to assess what demographics and other factors predicted joining multiple projects, joining projects from multiple disciplines, and joining projects from multiple modes. 

Funding

National Science Foundation, Award: AISL # 1713562

National Science Foundation, Award: DGE-1746939