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

Raquel Cedazo,Neal Reeves,Irene Celino,Ahmet Soylu,Dumitru Roman,Esteban Gonzalez,Shady Abd El Kader,Gloria Re Calegari,Damiano Scandolari,Oscar Corcho,Philip Turk,Elena Simperl

doi:10.1108/dta-10-2020-0253

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.

Highlights

Citizen Science (CS) describes the active engagement of volunteer participants within scientific research
We first analysed whether projects had a clearly defined problem which they sought to solve by gathering data or metadata from citizen volunteers
In the context of CS projects moving towards a co-creation approach, understanding how various assets are handled and shared within and outside of the projects they co-create will become a relevant aspect of managing CS projects in the future

Summary

Introduction

Citizen Science (CS) describes the active engagement of volunteer participants within scientific research. CS data are of significant value in the projects in which they are gathered, but for subsequent analysis and re-use (Wang et al, 2015). The vast majority of available data are from CS sources (Groom et al, 2017; Poisson et al, 2020). This is true of software, with CS projects developing a wide variety of software programs which are of value to professional scientists, and to lay people without the technical or scientific knowledge to develop tools of their own (Cooper et al, 2018; Zaman et al, 2020)

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