Data Quality in Citizen Science

Balázs, Bálint; Mooney, Peter; Nováková, Eva; Bastin, Lucy; Arsanjani, Jamal Jokar

doi:10.1007/978-3-030-58278-4_8

Cited by 83 publications

(84 citation statements)

References 60 publications

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“…The numerous examples of using data mining in citizen science projects include but are not limited to astronomy, life sciences, environmental sciences and oceanography (Franzen et al, 2021). While citizen science offers enormous opportunities, for example in training classification algorithms, there is also a need for rigorous procedures to ensure data quality (Balázs et al, 2021), as in any scientific research.…”

Section: Introductionmentioning

confidence: 99%

TeaTime4Schools: Using Data Mining Techniques to Model Litter Decomposition in Austrian Urban School Soils

et al. 2021

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Litter decomposition plays a pivotal role in the global carbon cycle, but is difficult to measure on a global scale, especially by citizen scientists. Here, citizen scientists, i.e., school students with their teachers, used the globally applied and standardized Tea Bag Index (TBI) method to collect data on litter decomposition in urban areas in Austria. They also sampled soils to investigate the linkages between litter decomposition and soil attributes. For this study, 54 sites were selected from the school experiments and assembled into a TBI dataset comprising litter decomposition rates (k), stabilization factors (S), as well as soil and environmental attributes. An extensive pre-processing procedure was applied to the dataset, including attribute selection and discretization of the decomposition rates and stabilization factors into three categories each. Data mining analyses of the TBI data helped reveal trends in litter decomposition. We generated predictive models (classification trees) that identified the soil attributes governing litter decomposition. Classification trees were developed for both of the litter decomposition parameters: decomposition rate (k) and stabilization factor (S). The main governing factor for both decomposition rate (k) and stabilization factor (S) was the sand content of the soils. The data mining models achieved an accuracy of 54.0 and 66.7% for decomposition rates and stabilization factors, respectively. The data mining results enhance our knowledge about the driving forces of litter decomposition in urban soils, which are underrepresented in soil monitoring schemes. The models are very informative for understanding and describing litter decomposition in urban settings in general. This approach may also further encourage participatory researcher-teacher-student interactions and thus help create an enabling environment for cooperation for further citizen science research in urban school settings.

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Section: Introductionmentioning

confidence: 99%

TeaTime4Schools: Using Data Mining Techniques to Model Litter Decomposition in Austrian Urban School Soils

et al. 2021

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“…Such findings draw attention to the necessity of an alignment between trainers before delivering workshops for general participants. Promoting continuous data quality assessment through the initiative (Balázs et al, 2021) is important to achieve robust data (Bonter and Cooper, 2012).…”

Section: Discussionmentioning

confidence: 99%

“…Combining the low response rate in the validation process (around 11%), along with the restricted number of participants who engaged in the monitoring days after the workshop, it is clear that interest or engagement with the initiative's goals is a limiting factor. Perhaps a possibility to have more participants involved in the validation process is to keep participants informed (and reminded) of the importance of this procedure for the applicability of the outcomes (Balázs et al, 2021). Also, it has been shown that communication about the application of the data gathered is fundamental to maintain participants engaged and interested (Schläppy et al, 2017).…”

Section: Discussionmentioning

confidence: 99%

Participatory Monitoring—A Citizen Science Approach for Coastal Environments

2021

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In this article the authors share their experiences, results, and lessons learned during the creation of a coastal biodiversity participatory monitoring initiative. Throughout 2019, we delivered five training workshops to 51 citizen scientists. Data collected by the citizens scientists were validated by checking its similarities against that gathered by specialists. High similarity values were found, indicating that, if proper training is provided, there is a great potential for citizen scientists to contribute biodiversity data with high value. During this process a certain level of variation in data produced by specialists was found, drawing attention to the need for prior alignment among specialists who may offer training for citizens. In addition, despite overall similar results between specialists and participants, some differences emerged in particular parts of the habitat; for example, the bivalve zones presented higher complexity and hence greater challenge. Identifying key challenges for participants is key to developing appropriate citizen science protocols. Here it is provided preliminary evidence that supports the use of the monitoring protocol to obtain biodiversity data gathered by citizen scientists, assuring its scientific quality. Enhancing participation by the community and specialists is key to further validate the approach and to effectively expand such protocols, enhancing the level of biodiversity data collection. In order to promote participation, and maintain citizen scientist engagement in the initiative, it is recommended the development of new investigations that assess the interests and motivations of the public to take part. It is also fundamentally important to have an effective strategy to communicate the results of participants’ monitoring and their applicability to local and global issues, thus maximizing the continuity of engagement of citizen scientists.

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“…Their supply and use can be expected to expand rapidly in the coming years, but this will require solutions to overcome issues around quality control and validation (Balázs et al, 2021;Wiggins et al, 2021. ) In the agriculture and food domain, crowdsourced data are more common in price data collection efforts, where agents or volunteers can be recruited to survey markets (UN Global Pulse, 2015; Zeug et al, 2017;Ochieng and Baulch, 2020).…”

Section: Crowdsourced and Citizen-generated Datamentioning

confidence: 99%

Agricultural Data Collection to Minimize Measurement Error and Maximize Coverage

Carletto¹,

Dillon²,

Zezza³

2021

Policy Research Working Papers

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The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Policy Research Working Paper 9745Advances in agricultural data production provide ever-increasing opportunities for pushing the research frontier in agricultural economics and designing better agricultural policy. As new technologies present opportunities to create new and integrated data sources, researchers face trade-offs in survey design that may reduce measurement error or increase coverage. This paper first reviews the econometric and survey methodology literatures that focus on the sources of measurement error and coverage bias in agricultural data collection. Second, it provides examples of how agricultural data structure affects testable empirical models. Finally, it reviews the challenges and opportunities offered by technological innovation to meet old and new data demands and address key empirical questions, focusing on the scalable data innovations of greatest potential impact for empirical methods and research. This paper is a product of the Development Data Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world.

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Data Quality in Citizen Science

Cited by 83 publications

References 60 publications

TeaTime4Schools: Using Data Mining Techniques to Model Litter Decomposition in Austrian Urban School Soils

TeaTime4Schools: Using Data Mining Techniques to Model Litter Decomposition in Austrian Urban School Soils

Participatory Monitoring—A Citizen Science Approach for Coastal Environments

Agricultural Data Collection to Minimize Measurement Error and Maximize Coverage

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