Methods of Promoting Learning and Data Quality in Citizen and Community Science

Abstract

Citizen science or community science (CS) programmes are engaging volunteers in specific stages of the scientific research, mostly data collection and processing. They are less likely to have an explicit objective to support and promote volunteers' learning. In response to that, “citizen inquiry” emphases citizens' learning and community education, by viewing CS as an opportunity to educate the general public in thinking and acting scientifically. In citizen inquiry, citizens can take part in all the stages of the scientific research, from setting up an inquiry of personal interest, to deciding on the methods of data collection, analysis, and reporting. To ensure data quality when non-professionals design their own or take part in existing investigations, we have designed a bespoke online technological solution, the nQuire platform (nquire.org.uk), with support from the Open University/BBC partnership. nQuire scaffolds the design of high quality scientific inquiries through an authoring functionality and a process of data quality review by experts. In this paper, we detail how nQuire can support data quality assurance and control. We present case studies of how data quality was managed in two projects: “Heatwaves: Are you coping?” and “Pollinator Watch.”

How IoT-Driven Citizen Science Coupled with Data Satisficing Can Promote Deep Citizen Science

“…The participants apply and gain only a somewhat shallow knowledge of science, shallower citizen science. In these CS studies, even though there may be a focus on user studies where citizens also act as co-creators of the study [27,28], the study design often limits citizens' ability to deepen their knowledge of science, to become better and more knowledgeable amateur citizen scientists. Fostering a deep citizen science approach, defined as enabling an ability to deepen citizens' understanding and quality of science, is needed.…”

“…In contrast, Ref. [28] considers how data quality could be improved in CS projects, albeit applied to weather rather than AQ measurements. They propose good practice guidelines concerning six aspects such as (a) giving equipment operational instructions in an accessible language, avoiding technical terminology; (b) providing technical details about different devices and their accuracy; (c) asking citizens to report contextual information such as the instrument they used and the operational conditions; (d) undertaking checks to enhance data quality during data analysis such as data cleaning to remove outliers; (e) comparing data points reported from multiple citizens' instruments in the same geographic location (postcode) to identify the degree of agreement amongst them.…”

“…Adopting the good practice guidelines to promote higher data quality in CS studies proposed by [28]: (a) We gave equipment operational instructions in an accessible language at workshops; (b) we did not provide technical details about different devices and their accuracy as it is far more complex to ascertain this for AQ data than temperature data, and in our study, we only used one type of device; (c) we asked citizens to report contextual information such as location, time but we needed to add more context info, such as temperature, humidity and a more fine-grained spatial context such as the spatial topology. However, for the latter, this could be very challenging for citizens to define in such a way that could be automated, fused and correlated; (d) we considered that removing outliers is not all that necessary since our goal is to 'satisfice', which was done.…”

Young people in iNaturalist: a blended learning framework for biodiversity monitoring

“…Community and Citizen Science (CCS) refers to a diverse set of participatory approaches to authentic research, ranging from collecting and analysing data for scientists to defining the focus and methodology of a study in community-led research (Herodotou et al, 2017 ; Ballard et al, 2017 ; Herodotou et al, 2021a ). An increasing body of studies has documented the positive impact online CCS participation has on science learning for volunteers such as enhanced science knowledge, development of scientific skills such as data collection and analysis, scientific literacy, as well as broader learning outcomes such as greater awareness and personal change (e.g.…”

“…Another study engaging young people with citizen-led inquiries on nQuire stressed the importance of mobile-friendly platform design to promote learning and participation (Aristeidou & Herodotou, 2018 ). A survey with 150 nQuire adult participants noted learning benefits from participation in online CCS studies (led by scientists) including awareness and behavioural change such as actions to provide a habitat for pollinators, yet challenges were noted when volunteers were asked whether they would create their own studies (Herodotou et al, 2021a ), raising the need for further support should CCS participation include ‘extreme citizen science’ forms (Haklay, 2013 ).…”