How IoT-Driven Citizen Science Coupled with Data Satisficing Can Promote Deep Citizen Science Article Swipe
Stefan Poslad
,
Tayyaba Irum
,
Patricia Charlton
,
Rafia Mumtaz
,
Muhammad Awais Azam
,
Hassan Zaidi
,
Christothea Herodotou
,
Guangxia Yu
,
Fesal Toosy
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/s22093196
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/s22093196
To study and understand the importance of Internet of Things-driven citizen science (IoT-CS) combined with data satisficing, we set up and undertook a citizen science experiment for air quality (AQ) in four Pakistan cities using twenty-one volunteers. We used quantitative methods to analyse the AQ data. Three research questions (RQ) were posed as follows: Which factors affect CS IoT-CS AQ data quality (RQ1)? How can we make science more inclusive by dealing with the lack of scientists, training and high-quality equipment (RQ2)? Can a lack of calibrated data readings be overcome to yield otherwise useful results for IoT-CS AQ data analysis (RQ3)? To address RQ1, an analysis of related work revealed that multiple causal factors exist. Good practice guidelines were adopted to promote higher data quality in CS studies. Additionally, we also proposed a classification of CS instruments to help better understand the data quality challenges. To answer RQ2, user engagement workshops were undertaken as an effective method to make CS more inclusive and also to train users to operate IoT-CS AQ devices more understandably. To address RQ3, it was proposed that a more feasible objective is that citizens leverage data satisficing such that AQ measurements can detect relevant local variations. Additionally, we proposed several recommendations. Our top recommendations are that: a deep (citizen) science approach should be fostered to support a more inclusive, knowledgeable application of science en masse for the greater good; It may not be useful or feasible to cross-check measurements from cheaper versus more expensive calibrated instrument sensors in situ. Hence, data satisficing may be more feasible; additional cross-checks that go beyond checking if co-located low-cost and calibrated AQ measurements correlate under equivalent conditions should be leveraged.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/s22093196
- https://www.mdpi.com/1424-8220/22/9/3196/pdf?version=1650853621
- OA Status
- gold
- Cited By
- 3
- References
- 28
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4224325739
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4224325739Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/s22093196Digital Object Identifier
- Title
-
How IoT-Driven Citizen Science Coupled with Data Satisficing Can Promote Deep Citizen ScienceWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-04-21Full publication date if available
- Authors
-
Stefan Poslad, Tayyaba Irum, Patricia Charlton, Rafia Mumtaz, Muhammad Awais Azam, Hassan Zaidi, Christothea Herodotou, Guangxia Yu, Fesal ToosyList of authors in order
- Landing page
-
https://doi.org/10.3390/s22093196Publisher landing page
- PDF URL
-
https://www.mdpi.com/1424-8220/22/9/3196/pdf?version=1650853621Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/1424-8220/22/9/3196/pdf?version=1650853621Direct OA link when available
- Concepts
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Citizen science, Satisficing, Data quality, Leverage (statistics), Quality (philosophy), Data science, Computer science, Internet of Things, Big data, Psychology, Data mining, Artificial intelligence, World Wide Web, Business, Marketing, Epistemology, Biology, Botany, Philosophy, Metric (unit)Top concepts (fields/topics) attached by OpenAlex
- Cited by
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3Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1, 2024: 1, 2023: 1Per-year citation counts (last 5 years)
- References (count)
-
28Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.classification | 134 |
| abstract_inverted_index.recommendations | 208 |
| abstract_inverted_index.understandably. | 174 |
| abstract_inverted_index.recommendations. | 205 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 89 |
| corresponding_author_ids | https://openalex.org/A5018639589 |
| countries_distinct_count | 3 |
| institutions_distinct_count | 9 |
| corresponding_institution_ids | https://openalex.org/I166337079 |
| citation_normalized_percentile.value | 0.48553354 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |