Abstract
Data quality (DQ) is a major concern in citizen science (CS) programs and is often raised as an issue among critics of the CS approach. We examined CS programs and reviewed the kinds of data they produce to inform CS communities of strategies of DQ control. From our review of the literature and our experiences with CS, we identified seven primary types of data contributions. Citizens can carry instrument packages, invent or modify algorithms, sort and classify physical objects, sort and classify digital objects, collect physical objects, collect digital objects, and report observations. We found that data types were not constrained by subject domains, a CS program may use multiple types, and DQ requirements and evaluation strategies vary according to the data types. These types are useful for identifying structural similarities among programs across subject domains. We conclude that blanket criticism of the CS data quality is no longer appropriate. In addition to the details of specific programs and variability among individuals, discussions can fruitfully focus on the data types in a program and the specific methods being used for DQ control as dictated or appropriate for the type. Programs can reduce doubts about their DQ by becoming more explicit in communicating their data management practices.
Highlights
Citizen science encompasses a variety of activities in which citizens are involved in doing science (Shirk et al, 2012; Haklay, 2013; Thiel et al, 2014; Cooper, 2016)
We describe each of these types and turn to the implications for Data quality (DQ) requirements and project design
Our analysis suggests that the general criticism about data quality in citizen science (CS) programs is more of a concern in the four remaining data types
Summary
Citizen science encompasses a variety of activities in which citizens are involved in doing science (Shirk et al, 2012; Haklay, 2013; Thiel et al, 2014; Cooper, 2016). Part of the excitement about CS is the number of scientific disciplines that have adopted a citizen science approach. Astronomy has used CS to map galaxies (Galaxy Zoo), chemistry to understand protein folding (FoldIt), computer science to refine algorithms (SciPy), ecology to document coral reef biodiversity (REEF), environmental science to monitor water quality (Acid Rain Monitoring Project), and geography to map features of cities (OpenStreetMap). Pocock et al (2017) identified over 500 CS projects in the ecology and environmental area alone. CS is a rapidly expanding field involving over 1,000 advertised projects (Scistarter websites). Pocock et al (2017) identified over 500 CS projects in the ecology and environmental area alone.
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