Galaxy Zoo: Exploring the Motivations of Citizen Science Volunteers.

By M. Jordan Raddick, G. Bracey, P. Gay, Chris J. Lintott, P. Murray, Kevin Schawinski, A. Szalay, Jan vandenBerg. 2009

540 citations recorded by the source corpus.

The Galaxy Zoo citizen science website invites anyone with an Internet connection to participate in research by classifying galaxies from the Sloan Digital Sky Survey. As of April 2009, more than 200,000 volunteers had made more than 100 million galaxy classifications. In this paper, we present results of a pilot study into the motivations and demographics of Galaxy Zoo volunteers, and define a technique to determine motivations from free responses that can be used in larger multiple-choice surveys with similar populations. Our categories form the basis for a future survey, with the goal of determining the prevalence of each motivation.

Subfields: Ecological Modeling, Ecology, Evolution, Behavior and Systematics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/02eeffb065028dc31037728862af6334d7e24e6b

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Citations in the corpus

Understanding the Motivations and Satisfactions of Volunteers to Improve the Effectiveness of Citizen Science Programs (2015, background): “Many studies find a strong sense of values driving environmental volunteers (Miles et al. 1998; Haas 2000; Ryan, Kaplan, and Grese 2001; Bruyere 1024 D. R. Wright et al. and Rappe 2007; Measham and Barnett 2008; Young 2008; Raddick et al. 2010).”
Challenges in Data Crowdsourcing (2016, background): “For example, in the Christmas Bird Count (http://www.audubon.org/conservation/science/ christmas-bird-count) citizen scientists perform a bird census each year; Galaxy Zoo [41] recruits volunteers to identify galaxies in astronomical photographs.”
Crowdsourced Data Management: Industry and Academic Perspectives (2015, background): “As an example, volunteers were asked to help translate tweets during the Haiti earthquake [206], or help identify galaxies in astronomical images [154, 195].”
The Milky Way Project First Data Release: a bubblier Galactic disc (2012, methodology): “The task of recognising bubbles may eventually be handled by advanced machine-learning algorithms (e.g. Beaumont et al. 2011) but in the meantime the community of Zooniverse users are keen to contribute to astronomy and science (Raddick et al. 2010).”
Query-Oriented Data Cleaning with Oracles (2015, background): “Incentives can range from small payments to public recognition and social reputation to the desire to help scientiﬁc progress [51].”
Moderating with the Mob: Evaluating the Efficacy of Real-Time Crowdsourced Fact-Checking (2021, background): “Recently, the utility of crowd-sourcing has also gained momentum in a number of scientific fields, including medicine (Tucker et al. 2019), geology (Comber et al. 2016), and astronomy (Raddick et al. 2010).”
OpenML: networked science in machine learning (2014, background): “Finally, there are many diﬀerent reasons for citizen scientists to dedicate their free time [36].”
Evaluation on crowdsourcing research: Current status and future direction (2012, background): “For example, the motivation to participate in a citizen science project or a business-oriented crowdsourcing contest may differ widely (Brabham 2008b; Nov et al. 2011; Raddick et al. 2010).”