Abstract
Citizen science initiatives span a wide range of topics, designs, and research needs. Despite this heterogeneity, there are several common barriers to the uptake and sustainability of citizen science projects and the information they generate. One key barrier often cited in the citizen science literature is data quality. Open-source tools for the analysis, visualization, and reporting of citizen science data hold promise for addressing the challenge of data quality, while providing other benefits such as technical capacity-building, increased user engagement, and reinforcing data sovereignty. We developed an operational citizen science tool called the Community Water Data Analysis Tool (CWDAT)—a R/Shiny-based web application designed for community-based water quality monitoring. Surveys and facilitated user-engagement were conducted among stakeholders during the development of CWDAT. Targeted recruitment was used to gather feedback on the initial CWDAT prototype’s interface, features, and potential to support capacity building in the context of community-based water quality monitoring. Fourteen of thirty-two invited individuals (response rate 44%) contributed feedback via a survey or through facilitated interaction with CWDAT, with eight individuals interacting directly with CWDAT. Overall, CWDAT was received favourably. Participants requested updates and modifications such as water quality thresholds and indices that reflected well-known barriers to citizen science initiatives related to data quality assurance and the generation of actionable information. Our findings support calls to engage end-users directly in citizen science tool design and highlight how design can contribute to users’ understanding of data quality. Enhanced citizen participation in water resource stewardship facilitated by tools such as CWDAT may provide greater community engagement and acceptance of water resource management and policy-making.
Highlights
Citizen science (CS) encompasses a wide range of topics and investigations, from ornithology to astronomy to meteorology [1]
The ability of citizens and CS initiatives to independently analyze, interpret, and communicate reliable, actionable results from their own high-quality data has been identified as a key challenge for community-based monitoring [17] and the output of reliable, actionable information has been observed as an important driver for citizen science volunteers [5]
The novelty of Community Water Data Analysis Tool (CWDAT), relative to other open-source tools in the field of citizen science, lies in its ability to read-in users’ data, its standalone nature, its ability to statistically compare between data sources, its interactive visualization and reporting capabilities, and its specific focus on the field of community-based water quality monitoring
Summary
Citizen science (CS) encompasses a wide range of topics and investigations, from ornithology to astronomy to meteorology [1] Despite this heterogeneity, certain barriers are common to many citizen science initiatives [2]. Specific data concerns include comparisons of data from different sources [12], differing metadata standards [13], species identifications [14], and factors such as uncertainty, accuracy, bias, and precision [2,15]. Despite these well-known challenges, Fonte et al (2015) [16] noted an overall dearth of guidance on CS data quality control and quality assurance (QAQC). The ability of citizens and CS initiatives to independently analyze, interpret, and communicate reliable, actionable results from their own high-quality data has been identified as a key challenge for community-based monitoring [17] and the output of reliable, actionable information has been observed as an important driver for citizen science volunteers [5]
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