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

TY - JOUR

T1 - An analysis of pollution Citizen Science projects from the perspective of Data Science and Open Science

AU - Roman, Dumitru

AU - Reeves, Neal

AU - Gonzalez, Esteban

AU - Celino, Irene

AU - Abd El Kader, Shady

AU - Turk, Philip

AU - Soylu, Ahmet

AU - Corcho, Oscar

AU - Cedazo, Raquel

AU - Re Calegari, Gloria

AU - Scandolari, Damiano

AU - Simperl, Elena

N1 - Funding Information: As per our recommendations in the previous section, one way to ensure a coherent and successful plan for CS projects is to define a Data Management Plan (DMP). A DMP document is often the standard way to explicitly define a strategy for data collection, processing and archiving, in line with the best practices of data science and open science. In several contexts, including the research and innovation projects supported by the NSF and by the Horizon 2020 Programme, the DMP is also a contractual obligation and a reference template is available to guide the compiler to cover all the necessary aspects, from a summary of data, to FAIR principle compliance to security and other aspects. The DMP compiled at the beginning of the project serves also as a guide throughout the project execution and should be kept up-to-date as soon as the activities progress. Funding Information: 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. Publisher Copyright: © 2021, Emerald Publishing Limited. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.

PY - 2021/10/11

Y1 - 2021/10/11

N2 - 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.

AB - 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.

KW - Citizen Science

KW - Data management plan

KW - Data science

KW - Open science

KW - Pollution projects

KW - Software

UR - http://www.scopus.com/inward/record.url?scp=85106224517&partnerID=8YFLogxK

U2 - 10.1108/DTA-10-2020-0253

DO - 10.1108/DTA-10-2020-0253

M3 - Article

AN - SCOPUS:85106224517

SN - 2514-9288

VL - 55

SP - 622

EP - 642

JO - Data Technologies and Applications

JF - Data Technologies and Applications

IS - 5

ER -