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Arazy O, Kaplan-Mintz K, Malkinson D, Nagar Y. A local community on a global collective intelligence platform: A case study of individual preferences and collective bias in ecological citizen science. PLoS One 2024; 19:e0308552. [PMID: 39186522 PMCID: PMC11346665 DOI: 10.1371/journal.pone.0308552] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Accepted: 07/26/2024] [Indexed: 08/28/2024] Open
Abstract
The collective intelligence of crowds could potentially be harnessed to address global challenges, such as biodiversity loss and species' extinction. For wisdom to emerge from the crowd, certain conditions are required. Importantly, the crowd should be diverse and people's contributions should be independent of one another. Here we investigate a global citizen-science platform-iNaturalist-on which citizens report on wildlife observations, collectively producing maps of species' spatiotemporal distribution. The organization of global platforms such as iNaturalist around local projects compromises the assumption of diversity and independence, and thus raises concerns regarding the quality of such collectively-generated data. We spent four years closely immersing ourselves in a local community of citizen scientists who reported their wildlife sightings on iNaturalist. Our ethnographic study involved the use of questionnaires, interviews, and analysis of archival materials. Our analysis revealed observers' nuanced considerations as they chose where, when, and what type of species to monitor, and which observations to report. Following a thematic analysis of the data, we organized observers' preferences and constraints into four main categories: recordability, community value, personal preferences, and convenience. We show that while some individual partialities can "cancel each other out", others are commonly shared among members of the community, potentially biasing the aggregate database of observations. Our discussion draws attention to the way in which widely-shared individual preferences might manifest as spatial, temporal, and crucially, taxonomic biases in the collectively-created database. We offer avenues for continued research that will help better understand-and tackle-individual preferences, with the goal of attenuating collective bias in data, and facilitating the generation of reliable state-of-nature reports. Finally, we offer insights into the broader literature on biases in collective intelligence systems.
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Affiliation(s)
- Ofer Arazy
- Department of Information Systems, The University of Haifa, Haifa, Israel
| | - Keren Kaplan-Mintz
- Department of Learning and Instructional Sciences, The University of Haifa, Haifa, Israel
| | - Dan Malkinson
- School of Environmental Sciences, The University of Haifa, Haifa, Israel
| | - Yiftach Nagar
- Department of Information Systems, The University of Haifa, Haifa, Israel
- School of Information Systems, Academic College of Tel Aviv-Jaffa, Tel Aviv-Yafo, Israel
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2
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Rozo Posada A, Faes C, Beutels P, Pepermans K, Hens N, Van Damme P, Neyens T. The effect of spatio-temporal sample imbalance in epidemiologic surveillance using opportunistic samples: An ecological study using real and simulated self-reported COVID-19 symptom data. Spat Spatiotemporal Epidemiol 2024; 50:100676. [PMID: 39181604 DOI: 10.1016/j.sste.2024.100676] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Revised: 05/19/2024] [Accepted: 07/08/2024] [Indexed: 08/27/2024]
Abstract
Open surveys complementing surveillance programs often yield opportunistically sampled data characterised by spatio-temporal imbalance. We set up our study to understand to what extent spatio-temporal statistical models using such data achieve in describing epidemiological trends. We used self-reported symptomatic COVID-19 data from two Belgian regions, Flanders and the Brussels-Capital Region. These data were collected in a large-scale open survey with spatio-temporally imbalanced participation rates. We compared incidence estimates of both self-reported symptoms and test-confirmed COVID-19 cases obtained through generalised linear mixed models correcting for spatio-temporal correlation. We additionally simulated symptom incidences under different sampling strategies to explore the impact of sample imbalance, sample size and disease incidence, on trend detection. Our study shows that spatio-temporal sample imbalance generally does not lead to bad model performances in spatio-temporal trend estimation and high-risk area detection. Except for low-incidence diseases, collecting large samples will often be more essential than ensuring spatio-temporally sample balance.
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Affiliation(s)
- Alejandro Rozo Posada
- L-BioStat, I-BioStat, KU Leuven, Leuven, Belgium; Data Science Institute, Hasselt University, Hasselt, Belgium.
| | - Christel Faes
- Data Science Institute, Hasselt University, Hasselt, Belgium
| | - Philippe Beutels
- Centre for Health Economics Research and Modelling Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp, Antwerp, Belgium
| | - Koen Pepermans
- Social Sciences Faculty, University of Antwerp, Antwerp, Belgium
| | - Niel Hens
- Data Science Institute, Hasselt University, Hasselt, Belgium; Centre for Health Economics Research and Modelling Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp, Antwerp, Belgium
| | - Pierre Van Damme
- Centre for the Evaluation of Vaccination, Vaccine and Infectious Disease Institute, University of Antwerp, Antwerp, Belgium
| | - Thomas Neyens
- L-BioStat, I-BioStat, KU Leuven, Leuven, Belgium; Data Science Institute, Hasselt University, Hasselt, Belgium
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Sarker S, Krug LA, Islam KM, Basak SC, Huda ANMS, Hossain MS, Das N, Riya SC, Liyana E, Chowdhury GW. An integrated coastal ecosystem monitoring strategy: Pilot case in Naf-Saint Martin Peninsula, Bangladesh. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 913:169718. [PMID: 38163602 DOI: 10.1016/j.scitotenv.2023.169718] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 12/25/2023] [Accepted: 12/25/2023] [Indexed: 01/03/2024]
Abstract
Rapid population growth creating an excessive pressure on the marine environment and thus monitoring of marine ecosystem is essential. However, due to high technical and financial involvement, monitoring of coastal ecosystem is always challenging in developing countries. This study aims to develop an integrated coastal ecosystem monitoring system that combines scientific sampling, numerical model simulation and citizen science observations to monitor the coastal ecosystem of Bangladesh. This concept of integrated monitoring approach was piloted from January 2022 to April 2023 at the South East coastal zone of Bangladesh. Scientific sampling and numerical model simulations were performed for temperature and salinity data collection. Citizen science approach was employed to collect data on environmental conditions, fisheries, plankton, other marine resources, and plastic pollution. Numerical model simulations and citizen scientists observations of temperature and salinity showed good agreement with the scientifically collected data. In addition, citizen scientists observations on fisheries, plankton, other marine resources and plastic pollution were also in line with the existing database and previous studies. The proposed integrated monitoring approach presents a viable technique, creating a new avenue for coastal and marine ecosystem monitoring where infrastructural facilities are limited.
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Affiliation(s)
- Subrata Sarker
- Department of Oceanography, Shahjalal University of Science and Technology, Bangladesh.
| | - Lilian A Krug
- Partnership for Observation of the Global Ocean (POGO), United Kingdom; Algarve Centre of Marine Sciences (CCMAR), University of Algarve, Portugal
| | - Kazi Mainul Islam
- Department of Geography and Environment, Shahjalal University of Science and Technology, Bangladesh
| | - Shyamal Chandra Basak
- Bangladesh Civil Service (34th BCS, Administration Cadre), Government of the People's Republic of Bangladesh, Bangladesh
| | - A N M Samiul Huda
- Department of Oceanography, Shahjalal University of Science and Technology, Bangladesh
| | - Md Shahadat Hossain
- Department of Oceanography, Shahjalal University of Science and Technology, Bangladesh
| | - Nabanita Das
- Department of Oceanography, Shahjalal University of Science and Technology, Bangladesh
| | | | - Eurida Liyana
- Department of Oceanography, Shahjalal University of Science and Technology, Bangladesh
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Arazy O, Malkinson D. A Framework of Observer-Based Biases in Citizen Science Biodiversity Monitoring: Semi-Structuring Unstructured Biodiversity Monitoring Protocols. Front Ecol Evol 2021. [DOI: 10.3389/fevo.2021.693602] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Citizen science, whereby ordinary citizens participate in scientific endeavors, is widely used for biodiversity monitoring, most commonly by relying on unstructured monitoring approaches. Notwithstanding the potential of unstructured citizen science to engage the public and collect large amounts of biodiversity data, observers’ considerations regarding what, where and when to monitor result in biases in the aggregate database, thus impeding the ability to draw conclusions about trends in species’ spatio-temporal distribution. Hence, the goal of this study is to enhance our understanding of observer-based biases in citizen science for biodiversity monitoring. Toward this goals we: (a) develop a conceptual framework of observers’ decision-making process along the steps of monitor – > record and share, identifying the considerations that take place at each step, specifically highlighting the factors that influence the decisions of whether to record an observation (b) propose an approach for operationalizing the framework using a targeted and focused questionnaire, which gauges observers’ preferences and behavior throughout the decision-making steps, and (c) illustrate the questionnaire’s ability to capture the factors driving observer-based biases by employing data from a local project on the iNaturalist platform. Our discussion highlights the paper’s theoretical contributions and proposes ways in which our approach for semi-structuring unstructured citizen science data could be used to mitigate observer-based biases, potentially making the collected biodiversity data usable for scientific and regulatory purposes.
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Molenberghs G, Buyse M, Abrams S, Hens N, Beutels P, Faes C, Verbeke G, Van Damme P, Goossens H, Neyens T, Herzog S, Theeten H, Pepermans K, Abad AA, Van Keilegom I, Speybroeck N, Legrand C, De Buyser S, Hulstaert F. Infectious diseases epidemiology, quantitative methodology, and clinical research in the midst of the COVID-19 pandemic: Perspective from a European country. Contemp Clin Trials 2020; 99:106189. [PMID: 33132155 PMCID: PMC7581408 DOI: 10.1016/j.cct.2020.106189] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2020] [Revised: 10/04/2020] [Accepted: 10/16/2020] [Indexed: 01/08/2023]
Abstract
Starting from historic reflections, the current SARS-CoV-2 induced COVID-19 pandemic is examined from various perspectives, in terms of what it implies for the implementation of non-pharmaceutical interventions, the modeling and monitoring of the epidemic, the development of early-warning systems, the study of mortality, prevalence estimation, diagnostic and serological testing, vaccine development, and ultimately clinical trials. Emphasis is placed on how the pandemic had led to unprecedented speed in methodological and clinical development, the pitfalls thereof, but also the opportunities that it engenders for national and international collaboration, and how it has simplified and sped up procedures. We also study the impact of the pandemic on clinical trials in other indications. We note that it has placed biostatistics, epidemiology, virology, infectiology, and vaccinology, and related fields in the spotlight in an unprecedented way, implying great opportunities, but also the need to communicate effectively, often amidst controversy.
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Affiliation(s)
- Geert Molenberghs
- Interuniversity Institute for Biostatistics and statistical Bioinformatics, Data Science Institute, Hasselt University, Belgium; Interuniversity Institute for Biostatistics and statistical Bioinformatics, KU Leuven, Belgium
| | - Marc Buyse
- Interuniversity Institute for Biostatistics and statistical Bioinformatics, Data Science Institute, Hasselt University, Belgium; International Drug Development Institute, Belgium; CluePoints, Belgium.
| | - Steven Abrams
- Interuniversity Institute for Biostatistics and statistical Bioinformatics, Data Science Institute, Hasselt University, Belgium; Global Health Institute, Department of Epidemiology and Social Medicine, University of Antwerp, Belgium
| | - Niel Hens
- Interuniversity Institute for Biostatistics and statistical Bioinformatics, Data Science Institute, Hasselt University, Belgium; Centre for Health Economics Research and Modelling of Infectious Diseases, University of Antwerp, Belgium; Vaccine & Infectious Disease Institute, University of Antwerp, Belgium
| | - Philippe Beutels
- Centre for Health Economics Research and Modelling of Infectious Diseases, University of Antwerp, Belgium; Vaccine & Infectious Disease Institute, University of Antwerp, Belgium
| | - Christel Faes
- Interuniversity Institute for Biostatistics and statistical Bioinformatics, Data Science Institute, Hasselt University, Belgium
| | - Geert Verbeke
- Interuniversity Institute for Biostatistics and statistical Bioinformatics, Data Science Institute, Hasselt University, Belgium; Interuniversity Institute for Biostatistics and statistical Bioinformatics, KU Leuven, Belgium
| | - Pierre Van Damme
- Centre for Health Economics Research and Modelling of Infectious Diseases, University of Antwerp, Belgium; Vaccine & Infectious Disease Institute, University of Antwerp, Belgium
| | | | - Thomas Neyens
- Interuniversity Institute for Biostatistics and statistical Bioinformatics, Data Science Institute, Hasselt University, Belgium; Interuniversity Institute for Biostatistics and statistical Bioinformatics, KU Leuven, Belgium
| | - Sereina Herzog
- Centre for Health Economics Research and Modelling of Infectious Diseases, University of Antwerp, Belgium; Vaccine & Infectious Disease Institute, University of Antwerp, Belgium
| | - Heidi Theeten
- Centre for Health Economics Research and Modelling of Infectious Diseases, University of Antwerp, Belgium; Vaccine & Infectious Disease Institute, University of Antwerp, Belgium
| | - Koen Pepermans
- Centre for Health Economics Research and Modelling of Infectious Diseases, University of Antwerp, Belgium; Vaccine & Infectious Disease Institute, University of Antwerp, Belgium
| | - Ariel Alonso Abad
- Interuniversity Institute for Biostatistics and statistical Bioinformatics, KU Leuven, Belgium
| | | | | | - Catherine Legrand
- Institute of Statistics, Biostatistics and Actuarial Sciences, UC Louvain, Belgium
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Barnagaud J, Geniez P, Cheylan M, Crochet P. Climate overrides the effects of land use on the functional composition and diversity of Mediterranean reptile assemblages. DIVERS DISTRIB 2020. [DOI: 10.1111/ddi.13176] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
Affiliation(s)
- Jean‐Yves Barnagaud
- CEFE, Univ Montpellier, CNRS, EPHE‐PSL, IRD, Univ Paul Valéry Montpellier 3 Montpellier France
| | - Philippe Geniez
- CEFE, Univ Montpellier, CNRS, EPHE‐PSL, IRD, Univ Paul Valéry Montpellier 3 Montpellier France
| | - Marc Cheylan
- CEFE, Univ Montpellier, CNRS, EPHE‐PSL, IRD, Univ Paul Valéry Montpellier 3 Montpellier France
| | - Pierre‐André Crochet
- CEFE, Univ Montpellier, CNRS, EPHE, IRD, Univ Paul Valéry Montpellier 3 Montpellier France
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Henckel L, Bradter U, Jönsson M, Isaac NJB, Snäll T. Assessing the usefulness of citizen science data for habitat suitability modelling: Opportunistic reporting versus sampling based on a systematic protocol. DIVERS DISTRIB 2020. [DOI: 10.1111/ddi.13128] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023] Open
Affiliation(s)
- Laura Henckel
- Swedish Species Information Centre (ArtDatabanken) Swedish University of Agricultural Sciences (SLU) Uppsala Sweden
| | - Ute Bradter
- Swedish Species Information Centre (ArtDatabanken) Swedish University of Agricultural Sciences (SLU) Uppsala Sweden
| | - Mari Jönsson
- Swedish Species Information Centre (ArtDatabanken) Swedish University of Agricultural Sciences (SLU) Uppsala Sweden
| | | | - Tord Snäll
- Swedish Species Information Centre (ArtDatabanken) Swedish University of Agricultural Sciences (SLU) Uppsala Sweden
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Data-derived metrics describing the behaviour of field-based citizen scientists provide insights for project design and modelling bias. Sci Rep 2020; 10:11009. [PMID: 32620931 PMCID: PMC7334204 DOI: 10.1038/s41598-020-67658-3] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2019] [Accepted: 06/11/2020] [Indexed: 11/09/2022] Open
Abstract
Around the world volunteers and non-professionals collect data as part of environmental citizen science projects, collecting wildlife observations, measures of water quality and much more. However, where projects allow flexibility in how, where, and when data are collected there will be variation in the behaviour of participants which results in biases in the datasets collected. We develop a method to quantify this behavioural variation, describing the key drivers and providing a tool to account for biases in models that use these data. We used a suite of metrics to describe the temporal and spatial behaviour of participants, as well as variation in the data they collected. These were applied to 5,268 users of the iRecord Butterflies mobile phone app, a multi-species environmental citizen science project. In contrast to previous studies, after removing transient participants (those active on few days and who contribute few records), we do not find evidence of clustering of participants; instead, participants fall along four continuous axes that describe variation in participants' behaviour: recording intensity, spatial extent, recording potential and rarity recording. Our results support a move away from labelling participants as belonging to one behavioural group or another in favour of placing them along axes of participant behaviour that better represent the continuous variation between individuals. Understanding participant behaviour could support better use of the data, by accounting for biases in the data collection process.
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