Exposing the Science in Citizen Science: Fitness to Purpose and Intentional Design

Abstract

Citizen science is a growing phenomenon. With millions of people involved and billions of in-kind dollars contributed annually, this broad extent, fine grain approach to data collection should be garnering enthusiastic support in the mainstream science and higher education communities. However, many academic researchers demonstrate distinct biases against the use of citizen science as a source of rigorous information. To engage the public in scientific research, and the research community in the practice of citizen science, a mutual understanding is needed of accepted quality standards in science, and the corresponding specifics of project design and implementation when working with a broad public base. We define a science-based typology focused on the degree to which projects deliver the type(s) and quality of data/work needed to produce valid scientific outcomes directly useful in science and natural resource management. Where project intent includes direct contribution to science and the public is actively involved either virtually or hands-on, we examine the measures of quality assurance (methods to increase data quality during the design and implementation phases of a project) and quality control (post hoc methods to increase the quality of scientific outcomes). We suggest that high quality science can be produced with massive, largely one-off, participation if data collection is simple and quality control includes algorithm voting, statistical pruning, and/or computational modeling. Small to mid-scale projects engaging participants in repeated, often complex, sampling can advance quality through expert-led training and well-designed materials, and through independent verification. Both approaches-simplification at scale and complexity with care-generate more robust science outcomes.

Citizen science in ecology: a place for humans in nature

“…Many citizen science projects report their findings in more than just peer‐reviewed publications 85,86 . Wiggins et al .…”

Disparate Environmental Monitoring as a Barrier to the Availability and Accessibility of Open Access Data on the Tidal Thames

“…It calls for public access to the methods and data collected by government, industry, and academia, particularly those that feed into policy decisions [6,9,10]. Allowing open access to data, reports, and findings increases research visibility and encourages more participatory research [1,10,11]. Public engagement, especially in the natural sciences, is rapidly growing in sectors such as non-governmental organizations and charities through citizen science activities [7,8,11].…”

“…Besides contributing to new research, citizen science has also been used to empower local communities and redirect research priorities and policy [1,[12][13][14]. Presently, citizens are not just involved in data collection; their participation is expanding to data analysis and interpretation [11,14,15]. Groups of citizens concerned about environmental issues can now access the technologies and data necessary to develop their own analyses, draw their own conclusions, and share their findings on social media [6,7,11,15].…”

“…Presently, citizens are not just involved in data collection; their participation is expanding to data analysis and interpretation [11,14,15]. Groups of citizens concerned about environmental issues can now access the technologies and data necessary to develop their own analyses, draw their own conclusions, and share their findings on social media [6,7,11,15]. Environmental data, however, are often challenging to analyze because of the complexity of the measured systems and other factors with which citizens may be unfamiliar, such as seasonality, extreme weather events, and natural variation.…”

Crowdsourcing Without Data Bias: Building a Quality Assurance System for Air Pollution Symptom Mapping Toward an SDG Indicator

“…Therefore, data quality protocols are an essential part of crowdsourcing-driven research. Although participatory research faces methodological challenges such as biases in data collection(Nimbalkar and Tripathi, 2016, English et al, 2018), CS has been proven to be a source of trusted geospatial data(Sheppard and Terveen, 2011, Lin et al, 2015, Kosmala et al, 2016, Parrish et al, 2018, including for health risk mapping(Maantay, 2007, Keddem et al, 2015, Palmer et al, 2017 and risks caused by poor air quality(Bastl et al, 2017, Penza et al, 2017, Khasha et al, 2018, Kankanamge et al, 2019. The data quality determines its usefulness(Choi et al, 2016, Chmielewski et al, 2018.…”