Taxonomy of Data Quality Metrics in Digital Citizen Science
Loading...
Access rights
openAccess
URL
Journal Title
Journal ISSN
Volume Title
Conference article in proceedings
This publication is imported from Aalto University research portal.
View publication in the Research portal (opens in new window)
Other link related to publication (opens in new window)
View publication in the Research portal (opens in new window)
Other link related to publication (opens in new window)
Date
Major/Subject
Mcode
Degree programme
Language
en
Pages
20
391-410
391-410
Series
Intelligent Sustainable Systems - Selected Papers of WorldS4 2022, Lecture Notes in Networks and Systems, Volume 578
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.Description
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.
Other note
Citation
Vaddepalli , K , Palacin , V , Porras , J & Happonen , A 2023 , Taxonomy of Data Quality Metrics in Digital Citizen Science . in A K Nagar , D Singh Jat , D K Mishra & A Joshi (eds) , Intelligent Sustainable Systems - Selected Papers of WorldS4 2022 . Lecture Notes in Networks and Systems , vol. 578 , Springer , pp. 391-410 , World Conference on Smart Trends in Systems, Security and Sustainability , London , United Kingdom , 24/08/2022 . https://doi.org/10.1007/978-981-19-7660-5_34