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Developing a framework for investigating citizen science through a combination of web analytics and social science methods—The CS Track perspective

Over the past decade, Citizen Science (CS) has shown great potential to transform the power of the crowd into knowledge of societal value. Many projects and initiatives have produced high quality scientific results by mobilizing peoples' interest in science to volunteer for the public good. Few...

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Autores principales: De-Groot, Reuma, Golumbic, Yaela N., Martínez Martínez, Fernando, Hoppe, H. Ulrich, Reynolds, Sally
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9581138/
https://www.ncbi.nlm.nih.gov/pubmed/36277734
http://dx.doi.org/10.3389/frma.2022.988544
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author De-Groot, Reuma
Golumbic, Yaela N.
Martínez Martínez, Fernando
Hoppe, H. Ulrich
Reynolds, Sally
author_facet De-Groot, Reuma
Golumbic, Yaela N.
Martínez Martínez, Fernando
Hoppe, H. Ulrich
Reynolds, Sally
author_sort De-Groot, Reuma
collection PubMed
description Over the past decade, Citizen Science (CS) has shown great potential to transform the power of the crowd into knowledge of societal value. Many projects and initiatives have produced high quality scientific results by mobilizing peoples' interest in science to volunteer for the public good. Few studies have attempted to map citizen science as a field, and assess its impact on science, society and ways to sustain its future practice. To better understand CS activities and characteristics, CS Track employs an analytics and analysis framework for monitoring the citizen science landscape. Within this framework, CS Track collates and processes information from project websites, platforms and social media and generates insights on key issues of concern to the CS community, such as participation patterns or impact on science learning. In this paper, we present the operationalization of the CS Track framework and its three-level analysis approach (micro-meso-macro) for applying analytics techniques to external data sources. We present three case studies investigating the CS landscape using these analytical levels and discuss the strengths and limitations of combining web-analytics with quantitative and qualitative research methods. This framework aims to complement existing methods for evaluating CS, address gaps in current observations of the citizen science landscape and integrate findings from multiple studies and methodologies. Through this work, CS Track intends to contribute to the creation of a measurement and evaluation scheme for CS and improve our understanding about the potential of analytics for the evaluation of CS.
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spelling pubmed-95811382022-10-20 Developing a framework for investigating citizen science through a combination of web analytics and social science methods—The CS Track perspective De-Groot, Reuma Golumbic, Yaela N. Martínez Martínez, Fernando Hoppe, H. Ulrich Reynolds, Sally Front Res Metr Anal Research Metrics and Analytics Over the past decade, Citizen Science (CS) has shown great potential to transform the power of the crowd into knowledge of societal value. Many projects and initiatives have produced high quality scientific results by mobilizing peoples' interest in science to volunteer for the public good. Few studies have attempted to map citizen science as a field, and assess its impact on science, society and ways to sustain its future practice. To better understand CS activities and characteristics, CS Track employs an analytics and analysis framework for monitoring the citizen science landscape. Within this framework, CS Track collates and processes information from project websites, platforms and social media and generates insights on key issues of concern to the CS community, such as participation patterns or impact on science learning. In this paper, we present the operationalization of the CS Track framework and its three-level analysis approach (micro-meso-macro) for applying analytics techniques to external data sources. We present three case studies investigating the CS landscape using these analytical levels and discuss the strengths and limitations of combining web-analytics with quantitative and qualitative research methods. This framework aims to complement existing methods for evaluating CS, address gaps in current observations of the citizen science landscape and integrate findings from multiple studies and methodologies. Through this work, CS Track intends to contribute to the creation of a measurement and evaluation scheme for CS and improve our understanding about the potential of analytics for the evaluation of CS. Frontiers Media S.A. 2022-10-05 /pmc/articles/PMC9581138/ /pubmed/36277734 http://dx.doi.org/10.3389/frma.2022.988544 Text en Copyright © 2022 De-Groot, Golumbic, Martínez Martínez, Hoppe and Reynolds. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Research Metrics and Analytics
De-Groot, Reuma
Golumbic, Yaela N.
Martínez Martínez, Fernando
Hoppe, H. Ulrich
Reynolds, Sally
Developing a framework for investigating citizen science through a combination of web analytics and social science methods—The CS Track perspective
title Developing a framework for investigating citizen science through a combination of web analytics and social science methods—The CS Track perspective
title_full Developing a framework for investigating citizen science through a combination of web analytics and social science methods—The CS Track perspective
title_fullStr Developing a framework for investigating citizen science through a combination of web analytics and social science methods—The CS Track perspective
title_full_unstemmed Developing a framework for investigating citizen science through a combination of web analytics and social science methods—The CS Track perspective
title_short Developing a framework for investigating citizen science through a combination of web analytics and social science methods—The CS Track perspective
title_sort developing a framework for investigating citizen science through a combination of web analytics and social science methods—the cs track perspective
topic Research Metrics and Analytics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9581138/
https://www.ncbi.nlm.nih.gov/pubmed/36277734
http://dx.doi.org/10.3389/frma.2022.988544
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