Taxonomy of Data Quality Metrics in Digital Citizen Science

Krishna Vaddepalli; Victoria Palacin; Jari Porras; Ari Happonen

doi:10.1007/978-981-19-7660-5_34

Taxonomy of Data Quality Metrics in Digital Citizen Science

Krishna Vaddepalli^*, Victoria Palacin, Jari Porras, Ari Happonen

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Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussa › Conference contribution › Scientific › vertaisarvioitu

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Data quality is key in the success of a citizen science project. Valid datasets serve as evidence for scientific research. Numerous projects have highlighted the ways in which participatory data collection can cause data quality issues due to human day-to-day practices and biases. Also, these projects have used and reported a myriad of techniques to improve data quality in different contexts. Yet, there is a lack of systematic analyses of these experiences to guide the design and of digital citizen science projects. We mapped 35 data quality issues of 16 digital citizen science projects and proposed a taxonomy with 64 mechanisms to address data quality issues before, during and after the data collection in digital citizen science projects. This taxonomy is built upon the analysis of literature reports (N = 144), two urban experiments (participants = 280), and expert interviews (N = 11). Thus, we contribute to advance the development of systematic methods to improve the data quality in digital citizen science projects.

Alkuperäiskieli	Englanti
Otsikko	Intelligent Sustainable Systems - Selected Papers of WorldS4 2022
Toimittajat	Atulya K. Nagar, Dharm Singh Jat, Durgesh Kumar Mishra, Amit Joshi
Kustantaja	Springer
Sivut	391-410
Sivumäärä	20
ISBN (painettu)	978-981-19-7659-9
DOI - pysyväislinkit	https://doi.org/10.1007/978-981-19-7660-5_34
Tila	Julkaistu - 2023
OKM-julkaisutyyppi	A4 Artikkeli konferenssijulkaisussa
Tapahtuma	World Conference on Smart Trends in Systems, Security and Sustainability - London, Iso-Britannia Kesto: 24 elok. 2022 → 27 elok. 2022

Julkaisusarja

Nimi	Lecture Notes in Networks and Systems
Vuosikerta	578
ISSN (painettu)	2367-3370
ISSN (elektroninen)	2367-3389

Conference

Conference	World Conference on Smart Trends in Systems, Security and Sustainability
Lyhennettä	WS4
Maa/Alue	Iso-Britannia
Kaupunki	London
Ajanjakso	24/08/2022 → 27/08/2022

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10.1007/978-981-19-7660-5_34

https://lutpub.lut.fi/handle/10024/165179

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Vaddepalli, K., Palacin, V., Porras, J., & Happonen, A. (2023). Taxonomy of Data Quality Metrics in Digital Citizen Science. teoksessa A. K. Nagar, D. Singh Jat, D. K. Mishra, & A. Joshi (Toimittajat), Intelligent Sustainable Systems - Selected Papers of WorldS4 2022 (Sivut 391-410). (Lecture Notes in Networks and Systems; Vuosikerta 578). Springer. https://doi.org/10.1007/978-981-19-7660-5_34

@inproceedings{14dd82119c474b6d810522f7390026d9,

title = "Taxonomy of Data Quality Metrics in Digital Citizen Science",

abstract = "Data quality is key in the success of a citizen science project. Valid datasets serve as evidence for scientific research. Numerous projects have highlighted the ways in which participatory data collection can cause data quality issues due to human day-to-day practices and biases. Also, these projects have used and reported a myriad of techniques to improve data quality in different contexts. Yet, there is a lack of systematic analyses of these experiences to guide the design and of digital citizen science projects. We mapped 35 data quality issues of 16 digital citizen science projects and proposed a taxonomy with 64 mechanisms to address data quality issues before, during and after the data collection in digital citizen science projects. This taxonomy is built upon the analysis of literature reports (N = 144), two urban experiments (participants = 280), and expert interviews (N = 11). Thus, we contribute to advance the development of systematic methods to improve the data quality in digital citizen science projects.",

keywords = "Citizen science, Data quality, Data quality issues, Data quality mechanisms, Digital citizen science, Taxonomy",

author = "Krishna Vaddepalli and Victoria Palacin and Jari Porras and Ari Happonen",

note = "Funding Information: Acknowledgements Authors thank European Regional Development Funds and Regional Council of South Karelia for funding MINT project supporting experience collection and also AWARE and CroBoDITT CBC projects funded by the European Union, supporting manuscript finalization. Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; World Conference on Smart Trends in Systems, Security and Sustainability, WS4 ; Conference date: 24-08-2022 Through 27-08-2022",

year = "2023",

doi = "10.1007/978-981-19-7660-5_34",

language = "English",

isbn = "978-981-19-7659-9",

series = "Lecture Notes in Networks and Systems",

publisher = "Springer",

pages = "391--410",

editor = "Nagar, {Atulya K.} and {Singh Jat}, Dharm and Mishra, {Durgesh Kumar} and Amit Joshi",

booktitle = "Intelligent Sustainable Systems - Selected Papers of WorldS4 2022",

address = "Germany",

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Vaddepalli, K, Palacin, V, Porras, J & Happonen, A 2023, Taxonomy of Data Quality Metrics in Digital Citizen Science. julkaisussa AK Nagar, D Singh Jat, DK Mishra & A Joshi (toim), Intelligent Sustainable Systems - Selected Papers of WorldS4 2022. Lecture Notes in Networks and Systems, Vuosikerta 578, Springer, Sivut 391-410, World Conference on Smart Trends in Systems, Security and Sustainability, London, Iso-Britannia, 24/08/2022. https://doi.org/10.1007/978-981-19-7660-5_34

Taxonomy of Data Quality Metrics in Digital Citizen Science. / Vaddepalli, Krishna; Palacin, Victoria; Porras, Jari et al.
Intelligent Sustainable Systems - Selected Papers of WorldS4 2022. toim. / Atulya K. Nagar; Dharm Singh Jat; Durgesh Kumar Mishra; Amit Joshi. Springer, 2023. s. 391-410 (Lecture Notes in Networks and Systems; Vuosikerta 578).

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussa › Conference contribution › Scientific › vertaisarvioitu

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T1 - Taxonomy of Data Quality Metrics in Digital Citizen Science

AU - Vaddepalli, Krishna

AU - Palacin, Victoria

AU - Porras, Jari

AU - Happonen, Ari

N1 - Funding Information: Acknowledgements Authors thank European Regional Development Funds and Regional Council of South Karelia for funding MINT project supporting experience collection and also AWARE and CroBoDITT CBC projects funded by the European Union, supporting manuscript finalization. Publisher Copyright: © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

PY - 2023

Y1 - 2023

N2 - Data quality is key in the success of a citizen science project. Valid datasets serve as evidence for scientific research. Numerous projects have highlighted the ways in which participatory data collection can cause data quality issues due to human day-to-day practices and biases. Also, these projects have used and reported a myriad of techniques to improve data quality in different contexts. Yet, there is a lack of systematic analyses of these experiences to guide the design and of digital citizen science projects. We mapped 35 data quality issues of 16 digital citizen science projects and proposed a taxonomy with 64 mechanisms to address data quality issues before, during and after the data collection in digital citizen science projects. This taxonomy is built upon the analysis of literature reports (N = 144), two urban experiments (participants = 280), and expert interviews (N = 11). Thus, we contribute to advance the development of systematic methods to improve the data quality in digital citizen science projects.

AB - Data quality is key in the success of a citizen science project. Valid datasets serve as evidence for scientific research. Numerous projects have highlighted the ways in which participatory data collection can cause data quality issues due to human day-to-day practices and biases. Also, these projects have used and reported a myriad of techniques to improve data quality in different contexts. Yet, there is a lack of systematic analyses of these experiences to guide the design and of digital citizen science projects. We mapped 35 data quality issues of 16 digital citizen science projects and proposed a taxonomy with 64 mechanisms to address data quality issues before, during and after the data collection in digital citizen science projects. This taxonomy is built upon the analysis of literature reports (N = 144), two urban experiments (participants = 280), and expert interviews (N = 11). Thus, we contribute to advance the development of systematic methods to improve the data quality in digital citizen science projects.

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KW - Data quality

KW - Data quality issues

KW - Data quality mechanisms

KW - Digital citizen science

KW - Taxonomy

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DO - 10.1007/978-981-19-7660-5_34

M3 - Conference contribution

AN - SCOPUS:85148025279

SN - 978-981-19-7659-9

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SP - 391

EP - 410

BT - Intelligent Sustainable Systems - Selected Papers of WorldS4 2022

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A2 - Singh Jat, Dharm

A2 - Mishra, Durgesh Kumar

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PB - Springer

T2 - World Conference on Smart Trends in Systems, Security and Sustainability

Y2 - 24 August 2022 through 27 August 2022

ER -

Taxonomy of Data Quality Metrics in Digital Citizen Science

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