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

Dumitru Roman (SINTEF AS, Oslo, Norway)
Neal Reeves (King's College London, London, UK)
Esteban Gonzalez (Universidad Politécnica de Madrid, Madrid, Spain)
Irene Celino (Cefriel, Milan, Italy)
Shady Abd El Kader (SINTEF AS, Oslo, Norway)
Philip Turk (SINTEF AS, Oslo, Norway)
Ahmet Soylu (Oslo Metropolitan University, Oslo, Norway)
Oscar Corcho (Universidad Politécnica de Madrid, Madrid, Spain)
Raquel Cedazo (Universidad Politécnica de Madrid, Madrid, Spain)
Gloria Re Calegari (Cefriel, Milan, Italy)
Damiano Scandolari (Cefriel, Milan, Italy)
Elena Simperl (King's College London, London, UK)

Data Technologies and Applications

ISSN: 2514-9288

Article publication date: 5 May 2021

Issue publication date: 11 October 2021

322

Abstract

Purpose

Citizen 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/approach

The 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.

Findings

The 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/value

In 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.

Keywords

Acknowledgements

The work in this paper is partly funded by the H2020 project ACTION (grant number 824603). The authors thank the ACTION consortium partners for fruitful discussions related to analysing Citizen Science projects and particularly to the pilots' providers in the project.

Citation

Roman, D., Reeves, N., Gonzalez, E., Celino, I., Abd El Kader, S., Turk, P., Soylu, A., Corcho, O., Cedazo, R., Re Calegari, G., Scandolari, D. and Simperl, E. (2021), "An analysis of pollution Citizen Science projects from the perspective of Data Science and Open Science", Data Technologies and Applications, Vol. 55 No. 5, pp. 622-642. https://doi.org/10.1108/DTA-10-2020-0253

Publisher

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Emerald Publishing Limited

Copyright © 2021, Emerald Publishing Limited

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