An analysis of pollution Citizen Science projects from the perspective of Data Science and Open Science

Abstract

PurposeCitizen Science – public participation in scientific projects – is becoming a global practice engaging volunteer participants, often non-scientists, with scientific research. Citizen Science is facing major challenges, such as quality and consistency, to reap open the full potential of its outputs and outcomes, including data, software and results. In this context, the principles put forth by Data Science and Open Science domains are essential for alleviating these challenges, which have been addressed at length in these domains. The purpose of this study is to explore the extent to which Citizen Science initiatives capitalise on Data Science and Open Science principles.Design/methodology/approachThe authors analysed 48 Citizen Science projects related to pollution and its effects. They compared each project against a set of Data Science and Open Science indicators, exploring how each project defines, collects, analyses and exploits data to present results and contribute to knowledge.FindingsThe results indicate several shortcomings with respect to commonly accepted Data Science principles, including lack of a clear definition of research problems and limited description of data management and analysis processes, and Open Science principles, including lack of the necessary contextual information for reusing project outcomes.Originality/valueIn the light of this analysis, the authors provide a set of guidelines and recommendations for better adoption of Data Science and Open Science principles in Citizen Science projects, and introduce a software tool to support this adoption, with a focus on preparation of data management plans in Citizen Science projects.

The potential of citizen science to transform science: Lessons for a sustainable future

“…In 2016, a set of guiding principles were instantiated with the aim to establish a standard for scientific data, ensuring that it is FAIR: Findable, Accessible, Interoperable and Reusable (Wilkinson et al., 2016). Nevertheless, it has been shown that despite the development of tools to facilitate open science, abounding toolkits and data and code repositories, the potential of citizen science to be practiced as open science is not always realised by those responsible for the projects, with missing information in published outcomes having a significant impact on the openness of the research projects that were analysed (Suter et al., 2023) and low uptake of complex data management plan tools (Roman et al., 2021). However, CS projects such as StreetSpectra provide an exemplary testbed for the usability of open science infrastructure and for testing how FAIR research is.…”

“…Non-specialists and volunteers are less likely to "work through" obfuscated or confusing datasets and technological quirks and difficulties; they are an unforgiving audience for collaborative platforms. Working with non-specialist citizen scientists on open science allows for infrastructure to be developed that supports proper scientific method while streamlining processes, such as in the co-creation process of the ACTION DMP tool(Roman et al, 2021),…”

Biogeosciences Perspectives on Integrated, Coordinated, Open, Networked (ICON) Science

“…Representation among different genders, backgrounds, nationalities, and career stages expands perspectives in a project (Sandbrook et al., 2019 ). Local non‐scientists can be great assets to projects, bringing valuable contextual information (Roman et al., 2021 ). The American Geophysical Union's Thriving Earth Exchange offers one opportunity for scientists to connect with communities seeking science support to resolve challenges that require the expertise of scientists, such as issues associated with municipal water quality and community forest health.…”

Real-Time Environmental Monitoring Platform for Wellness and Preventive Care in a Smart and Sustainable City with an Urban Landscape Perspective: The Case of Developing Countries

“…Finally, for citizens to go beyond the provided solutions, they need to develop the capacity to use the data and create innovative solutions for their own needs. Based on this perspective, the last dimension focuses on capacity building [3,16,43,53], as shown in Figure 1.…”