scholarly journals Data Quality in Citizen Science Projects: Challenges and Solutions

2016 ◽  
Vol 4 ◽  
Author(s):  
Weigelhofer Gabriele ◽  
Pölz Eva-Maria
Keyword(s):  
Data Quality ◽  
Citizen Science ◽  
Science Projects

scholarly journals Assessing data quality in citizen science (preprint)

2016 ◽  
Author(s):  
Margaret Kosmala ◽  
Andrea Wiggins ◽  
Alexandra Swanson ◽  
Brooke Simmons
Keyword(s):  
Resource Management ◽  
Data Quality ◽  
Systematic Error ◽  
Citizen Science ◽  
Project Design ◽  
High Quality ◽  
Volunteer Training ◽  
Science Projects ◽  
Growing Body ◽  
Expert Validation

AbstractEcological and environmental citizen science projects have enormous potential to advance science, influence policy, and guide resource management by producing datasets that are otherwise infeasible to generate. This potential can only be realized, though, if the datasets are of high quality. While scientists are often skeptical of the ability of unpaid volunteers to produce accurate datasets, a growing body of publications clearly shows that diverse types of citizen science projects can produce data with accuracy equal to or surpassing that of professionals. Successful projects rely on a suite of methods to boost data accuracy and account for bias, including iterative project development, volunteer training and testing, expert validation, replication across volunteers, and statistical modeling of systematic error. Each citizen science dataset should therefore be judged individually, according to project design and application, rather than assumed to be substandard simply because volunteers generated it.

scholarly journals Training citizen scientists through an online game developed for data quality control

2019 ◽  
Author(s):  
Barbara Strobl ◽  
Simon Etter ◽  
H. J. Ilja van Meerveld ◽  
Jan Seibert
Keyword(s):  
Quality Control ◽  
Data Quality ◽  
Citizen Science ◽  
Training Methods ◽  
Online Game ◽  
Data Quality Control ◽  
Training Requirements ◽  
Science Projects ◽  
Other Information ◽  
Before And After

Abstract. Some form of training is often necessary in citizen science projects. While in many citizen science projects it is possible to keep tasks simple so that training requirements are minimal, some projects include more challenging tasks and, thus, require more extensive training. Training can hinder joining a project, and therefore most citizen science projects prefer to keep training requirements low. However, training may be needed to ensure good data quality. In this study, we evaluated if an online game that was originally developed for data quality control in a citizen science project, can be used for training for that project. More specifically, we investigated whether the CrowdWater game can be used to train new participants on how to use the virtual staff gauge in the CrowdWater smartphone app for the collection of water level class data. Within this app, the task of placing a virtual staff gauge to start measurements at a new location has proven to be challenging; however this is a crucial task for all subsequent measurements at this location. We analysed the performance of 52 participants in the placement of the virtual staff gauge before and after playing the online CrowdWater game as a form of training. After playing the game, the performance improved for most participants. This suggests that players learned project related tasks intuitively by observing actual gauge placements by other citizen scientists and thus acquired knowledge about how to best use the app instinctively. Interestingly, self-assessment was not a good proxy for the participants’ performance or performance increase. These results demonstrate the value of an online game for training, particularly when compared to other information materials, which are often not used extensively by citizen scientist. These findings are useful for the development of training strategies for other citizen science projects because they indicate that gamified approaches might provide valuable alternative training methods.

Surveying the citizen science landscape

First Monday ◽  
2014 ◽  
Author(s):  
Andrea Wiggins ◽  
Kevin Crowston
Keyword(s):  
Social Interaction ◽  
Data Quality ◽  
Citizen Science ◽  
The Internet ◽  
Project Design ◽  
Science Project ◽  
Funding Sources ◽  
Science Projects ◽  
Combined Features ◽  
Citizen Science Project

Citizen science has seen enormous growth in recent years, in part due to the influence of the Internet, and a corresponding growth in interest. However, the few stand-out examples that have received attention from media and researchers are not representative of the diversity of the field as a whole, and therefore may not be the best models for those seeking to study or start a citizen science project. In this work, we present the results of a survey of citizen science project leaders, identifying sub-groups of project types according to a variety of features related to project design and management, including funding sources, goals, participant activities, data quality processes, and social interaction. These combined features highlight the diversity of citizen science, providing an overview of the breadth of the phenomenon and laying a foundation for comparison between citizen science projects and to other online communities.

scholarly journals Tackling Data Quality When Using Low-Cost Air Quality Sensors in Citizen Science Projects

2021 ◽  
Vol 9 ◽  
Author(s):  
Ågot K. Watne ◽  
Jenny Linden ◽  
Jens Willhelmsson ◽  
Håkan Fridén ◽  
Malin Gustafsson ◽  
...  
Keyword(s):  
Air Quality ◽  
Data Quality ◽  
Citizen Science ◽  
Low Cost ◽  
Quality Data ◽  
High Data ◽  
Science Projects ◽  
Air Quality Measurements ◽  
Air Quality Data ◽  
Intended Use

Using low-cost air quality sensors (LCS) in citizen science projects opens many possibilities. LCS can provide an opportunity for the citizens to collect and contribute with their own air quality data. However, low data quality is often an issue when using LCS and with it a risk of unrealistic expectations of a higher degree of empowerment than what is possible. If the data quality and intended use of the data is not harmonized, conclusions may be drawn on the wrong basis and data can be rendered unusable. Ensuring high data quality is demanding in terms of labor and resources. The expertise, sensor performance assessment, post-processing, as well as the general workload required will depend strongly on the purpose and intended use of the air quality data. It is therefore a balancing act to ensure that the data quality is high enough for the specific purpose, while minimizing the validation effort. The aim of this perspective paper is to increase awareness of data quality issues and provide strategies to minimizing labor intensity and expenses while maintaining adequate QA/QC for robust applications of LCS in citizen science projects. We believe that air quality measurements performed by citizens can be better utilized with increased awareness about data quality and measurement requirements, in combination with improved metadata collection. Well-documented metadata can not only increase the value and usefulness for the actors collecting the data, but it also the foundation for assessment of potential integration of the data collected by citizens in a broader perspective.

scholarly journals Training citizen scientists through an online game developed for data quality control

2020 ◽  
Vol 3 (1) ◽  
pp. 109-126 ◽  
Author(s):  
Barbara Strobl ◽  
Simon Etter ◽  
H. J. Ilja van Meerveld ◽  
Jan Seibert
Keyword(s):  
Quality Control ◽  
Data Quality ◽  
Citizen Science ◽  
Training Methods ◽  
Online Game ◽  
Data Quality Control ◽  
Training Requirements ◽  
Science Projects ◽  
Other Information ◽  
Before And After

Abstract. Some form of training is often necessary for citizen science projects. While in some citizen science projects, it is possible to keep tasks simple so that training requirements are minimal, other projects include more challenging tasks and, thus, require more extensive training. Training can be a hurdle to joining a project, and therefore most citizen science projects prefer to keep training requirements low. However, training may be needed to ensure good data quality. In this study, we evaluated whether an online game that was originally developed for data quality control in a citizen science project can be used for training for that project. More specifically, we investigated whether the CrowdWater game can be used to train new participants on how to place the virtual staff gauge in the CrowdWater smartphone app for the collection of water level class data. Within this app, the task of placing a virtual staff gauge to start measurements at a new location has proven to be challenging; however, this is a crucial task for all subsequent measurements at this location. We analysed the performance of 52 participants in the placement of the virtual staff gauge before and after playing the online CrowdWater game as a form of training. After playing the game, the performance improved for most participants. This suggests that players learned project-related tasks intuitively by observing actual gauge placements by other citizen scientists in the game and thus acquired knowledge about how to best use the app instinctively. Interestingly, self-assessment was not a good proxy for the participants' performance or the performance increase through the training. These results demonstrate the value of an online game for training. These findings are useful for the development of training strategies for other citizen science projects because they indicate that gamified approaches might provide valuable alternative training methods, particularly when other information materials are not used extensively by citizen scientists.

scholarly journals CitSci.org & PPSR Core: Sharing biodiversity observations across platforms

2021 ◽  
Vol 5 ◽  
Author(s):  
Brandon Budnicki ◽  
Gregory Newman
Keyword(s):  
Data Quality ◽  
Data Sharing ◽  
Citizen Science ◽  
Natural World ◽  
Use Cases ◽  
Community Surveys ◽  
Science Projects ◽  
Metadata Standards ◽  
Informatics Infrastructure ◽  
Science Association

CitSci.org is a global citizen science software platform and support organization housed at Colorado State University. The mission of CitSci is to help people do high quality citizen science by amplifying impacts and outcomes. This platform hosts over one thousand projects and a diverse volunteer base that has amassed over one million observations of the natural world, focused on biodiversity and ecosystem sustainability. It is a custom platform built using open source components including: PostgreSQL, Symfony, Vue.js, with React Native for the mobile apps. CitSci sets itself apart from other Citizen Science platforms through the flexibility in the types of projects it supports rather than having a singular focus. This flexibility allows projects to define their own datasheets and methodologies. The diversity of programs we host motivated us to take a founding role in the design of the PPSR Core, a set of global, transdisciplinary data and metadata standards for use in Public Participation in Scientific Research (Citizen Science) projects. Through an international partnership between the Citizen Science Association, European Citizen Science Association, and Australian Citizen Science Association, the PPSR team and associated standards enable interoperability of citizen science projects, datasets, and observations. Here we share our experience over the past 10+ years of supporting biodiversity research both as developers of the CitSci.org platform and as stewards of, and contributors to, the PPSR Core standard. Specifically, we share details about: the origin, development, and informatics infrastructure for CitSci our support for biodiversity projects such as population and community surveys our experiences in platform interoperability through PPSR Core working with the Zooniverse, SciStarter, and CyberTracker data quality data sharing goals and use cases. the origin, development, and informatics infrastructure for CitSci our support for biodiversity projects such as population and community surveys our experiences in platform interoperability through PPSR Core working with the Zooniverse, SciStarter, and CyberTracker data quality data sharing goals and use cases. We conclude by sharing overall successes, limitations, and recommendations as they pertain to trust and rigor in citizen science data sharing and interoperability. As the scientific community moves forward, we show that Citizen Science is a key tool to enabling a systems-based approach to ecosystem problems.

scholarly journals Citizens, Scientists, and Enablers: A Tripartite Model for Citizen Science Projects

Diversity ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 309
Author(s):  
Rhian A. Salmon ◽  
Samuel Rammell ◽  
Myfanwy T. Emeny ◽  
Stephen Hartley
Keyword(s):  
Case Studies ◽  
Nature Of Science ◽  
Citizen Science ◽  
Public Engagement ◽  
Science Communication ◽  
Tripartite Model ◽  
Science Project ◽  
Science Projects ◽  
Emerging Issues ◽  
Citizen Science Project

In this paper, we focus on different roles in citizen science projects, and their respective relationships. We propose a tripartite model that recognises not only citizens and scientists, but also an important third role, which we call the ‘enabler’. In doing so, we acknowledge that additional expertise and skillsets are often present in citizen science projects, but are frequently overlooked in associated literature. We interrogate this model by applying it to three case studies and explore how the success and sustainability of a citizen science project requires all roles to be acknowledged and interacting appropriately. In this era of ‘wicked problems’, the nature of science and science communication has become more complex. In order to address critical emerging issues, a greater number of stakeholders are engaging in multi-party partnerships and research is becoming increasingly interdisciplinary. Within this context, explicitly acknowledging the role and motivations of everyone involved can provide a framework for enhanced project transparency, delivery, evaluation and impact. By adapting our understanding of citizen science to better recognise the complexity of the organisational systems within which they operate, we propose an opportunity to strengthen the collaborative delivery of both valuable scientific research and public engagement.

scholarly journals A Conceptual Probabilistic Framework for Annotation Aggregation of Citizen Science Data

Mathematics ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 875
Author(s):  
Jesus Cerquides ◽  
Mehmet Oğuz Mülâyim ◽  
Jerónimo Hernández-González ◽  
Amudha Ravi Shankar ◽  
Jose Luis Fernandez-Marquez
Keyword(s):  
Data Quality ◽  
Citizen Science ◽  
Graphical Model ◽  
Real Life ◽  
Scientific Journals ◽  
Probabilistic Framework ◽  
Science Data ◽  
Label Aggregation ◽  
Evaluation Of Data ◽  
Model Formalism

Over the last decade, hundreds of thousands of volunteers have contributed to science by collecting or analyzing data. This public participation in science, also known as citizen science, has contributed to significant discoveries and led to publications in major scientific journals. However, little attention has been paid to data quality issues. In this work we argue that being able to determine the accuracy of data obtained by crowdsourcing is a fundamental question and we point out that, for many real-life scenarios, mathematical tools and processes for the evaluation of data quality are missing. We propose a probabilistic methodology for the evaluation of the accuracy of labeling data obtained by crowdsourcing in citizen science. The methodology builds on an abstract probabilistic graphical model formalism, which is shown to generalize some already existing label aggregation models. We show how to make practical use of the methodology through a comparison of data obtained from different citizen science communities analyzing the earthquake that took place in Albania in 2019.

scholarly journals The impact of data quality filtering of opportunistic citizen science data on species distribution model performance

2021 ◽  
Vol 444 ◽  
pp. 109453
Author(s):  
Camille Van Eupen ◽  
Dirk Maes ◽  
Marc Herremans ◽  
Kristijn R.R. Swinnen ◽  
Ben Somers ◽  
...  
Keyword(s):  
Data Quality ◽  
Citizen Science ◽  
Species Distribution ◽  
Species Distribution Model ◽  
Model Performance ◽  
Distribution Model ◽  
Science Data ◽  
Quality Filtering ◽  
The Impact

scholarly journals CWDAT—An Open-Source Tool for the Visualization and Analysis of Community-Generated Water Quality Data

2021 ◽  
Vol 10 (4) ◽  
pp. 207
Author(s):  
Annie Gray ◽  
Colin Robertson ◽  
Rob Feick
Keyword(s):  
Water Quality ◽  
Data Quality ◽  
Capacity Building ◽  
Open Source ◽  
Citizen Science ◽  
Water Resource ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
User Engagement ◽  
Community Based

Citizen science initiatives span a wide range of topics, designs, and research needs. Despite this heterogeneity, there are several common barriers to the uptake and sustainability of citizen science projects and the information they generate. One key barrier often cited in the citizen science literature is data quality. Open-source tools for the analysis, visualization, and reporting of citizen science data hold promise for addressing the challenge of data quality, while providing other benefits such as technical capacity-building, increased user engagement, and reinforcing data sovereignty. We developed an operational citizen science tool called the Community Water Data Analysis Tool (CWDAT)—a R/Shiny-based web application designed for community-based water quality monitoring. Surveys and facilitated user-engagement were conducted among stakeholders during the development of CWDAT. Targeted recruitment was used to gather feedback on the initial CWDAT prototype’s interface, features, and potential to support capacity building in the context of community-based water quality monitoring. Fourteen of thirty-two invited individuals (response rate 44%) contributed feedback via a survey or through facilitated interaction with CWDAT, with eight individuals interacting directly with CWDAT. Overall, CWDAT was received favourably. Participants requested updates and modifications such as water quality thresholds and indices that reflected well-known barriers to citizen science initiatives related to data quality assurance and the generation of actionable information. Our findings support calls to engage end-users directly in citizen science tool design and highlight how design can contribute to users’ understanding of data quality. Enhanced citizen participation in water resource stewardship facilitated by tools such as CWDAT may provide greater community engagement and acceptance of water resource management and policy-making.

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