The rapid proliferation of online content producing and sharing technologies resulted in an explosion of user-generated content (UGC), which now extends to scientific data. Citizen science, in which ordinary people contribute information for scientific research, epitomizes UGC. Citizen science projects are typically open to everyone, engage diverse audiences, and challenge ordinary people to produce data of highest quality to be usable in science. This also makes citizen science a very exciting area to study both traditional and innovative approaches to information quality management. With this paper we position citizen science as a leading information quality research frontier. We also show how citizen science opens a unique opportunity for the information systems community to contribute to a broad range of disciplines in natural and social sciences and humanities.

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Despite the importance of instruction for effective task completion in crowdsourcing, particularly for scientific work, little attention has been given to the design of instructional materials in crowdsourcing and citizen science. Consequences of inattention to tutorial design are further magnified by the diversity of citizen science volunteers. We use digital genre theory to identify the norms of tutorial design for the most abundant citizen science project type on the Zooniverse platform, camera trap image classification, where a highly-standardized task structure makes it a strong candidate as a specific genre of citizen science. Comparative content analysis of 14 projects' features, tutorial design, and supporting materials identified a great deal of uniformity in some respects (indicating an emergent genre) but surprising variation in others. As further evidence of an emergent genre, the amount of mentoring the science team received and specific task features of the project appeared to impact tutorial design and supporting resources. Our findings suggest that genre theory provides a useful lens for understanding crowd science projects with otherwise disparate characteristics and identifying instances where the digital medium can be deployed more effectively for task instruction.

This paper is the culmination of several facilitated exercises and meetings between external researchers and five citizen science (CS) project teams who analyzed existing data records to understand CS volunteers' accuracy and skills. CS teams identified a wide range of skill variables that were "hiding in plain sight" in their data records, and that could be explored as part of a secondary analysis, which we define here as analyses based on data already possessed by the project. Each team identified a small number of evaluation questions to explore with their existing data. Analyses focused on accurate data collection and all teams chose to add complementary records that documented volunteers' project engagement or the data collection context to their analysis. Most analyses were conducted as planned, and included a range of approaches from correlation analyses to general additive models. Importantly, the results from these analyses were then used to inform the design of both existing and new CS projects, and to inform the field more broadly through a range of dissemination strategies. We conclude by sharing ways that others might consider pursuing their own secondary analysis to help fill gaps in our current understanding related to volunteer skills.

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