Citizen science projects recruit members of the public as volunteers to process and produce datasets. These datasets must win the trust of the scientific community. The task of securing credibility involves, in part, applying standard scientific procedures to clean these datasets. However, effective management of volunteer behavior also makes a significant contribution to enhancing data quality. Through a case study of Galaxy Zoo, a citizen science project set up to generate datasets based on volunteer classifications of galaxy morphologies, this paper explores how those involved in running the project manage volunteers. The paper focuses on how methods for crediting volunteer contributions motivate volunteers to provide higher quality contributions and to behave in a way that better corresponds to statistical assumptions made when combining volunteer contributions into datasets. These methods have made a significant contribution to the success of the project in securing trust in these datasets, which have been well used by other scientists. Implications for practice are then presented for citizen science projects, providing a list of considerations to guide choices regarding how to credit volunteer contributions to improve the quality and trustworthiness of citizen science-produced datasets.
Thu, 2 Mar 17
Comments: 16 pages, 0 figures, published in International Journal of Digital Curation