Conceptual modeling principles for crowdsourcing
Proceedings of the 1st international workshop on Multimodal crowd sensing
Free as in puppies: compensating for ict constraints in citizen science
Proceedings of the 2013 conference on Computer supported cooperative work
Capturing quality: retaining provenance for curated volunteer monitoring data
Proceedings of the 17th ACM conference on Computer supported cooperative work & social computing
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Data quality is a primary concern for researchers employing a public participation in scientific research (PPSR) or ``citizen science'' approach. This mode of scientific collaboration relies on contributions from a large, often unknown population of volunteers with variable expertise. In a survey of PPSR projects, we found that most projects employ multiple mechanisms to ensure data quality and appropriate levels of validation. We created a framework of 18 mechanisms commonly employed by PPSR projects for ensuring data quality, based on direct experience of the authors and a review of the survey data, noting two categories of sources of error (protocols, participants) and three potential intervention points (before, during and after participation), which can be used to guide project design.