An Approach to Improve the Quality of User-Generated Content of Citizen Science Platforms

Abstract

The quality of the user-generated content of citizen science platforms has been discussed widely among researchers. Content is categorized into data and information: data is content stored in a database of a citizen science platform, while information is context-dependent content generated by users. Understanding data and information quality characteristics and utilizing them during design improves citizen science platforms’ overall quality. This research investigates the integration of data and information quality characteristics into a citizen science platform for collecting information from the general public with no scientific training in the area where content is collected. The primary goal is to provide a framework for selecting and integrating data and information quality characteristics into the design for improving the content quality on platforms. The design and implementation of a citizen science platform that collects walking path conditions are presented, and the resulting implication is evaluated. The results show that the platform’s content quality can be improved by introducing quality characteristics during the design stage of the citizen science platform.

Digital Participation for Data Literate Citizens – A Qualitative Analysis of the Design of Multi-Project Citizen Science Platforms

“…A different angle for analyzing CS projects can be the analysis of underlying digital infrastructure. Many best practices have been developed for CS platforms to optimize their design for participation (Newman et al, 2010;Jennett and Cox 2016;Wald et al, 2016;Yadav and Darlington, 2016;Sturm et al, 2018;Skarlatidou et al, 2019;Musto and Dahanayake, 2021). Linking these principles to their relevance for learning and data literacy education allows us to assess platform designs according to their potential to promote citizens' data literacy.…”

“…Validate user-generated data (Skarlatidou et al, 2019;Musto and Dahanayake, 2021) Enable informal learning through the execution of tasks (Jennett et al, 2016) Empower learners to practically apply their skills to real-world data (Bhargava et al, 2015;Ridsdale et al, 2015;D'Ignazio, 2022) Facilitate entering user-generated data (Newman et al, 2010;Sturm et al, 2018;Skarlatidou et al, 2019) Enable data analysis and visualization (Newman et al, 2010;Wald et al, 2016;Skarlatidou et al, 2019;Musto and Dahanayake, 2021) Support (SU) Provide separate support pages (Skarlatidou et al, 2019) Enable formal learning (Jennett et al, 2016) Provide a guided tool (D'Ignazio and Bhargava, 2016) Provide educational material (Wald et al, 2016) Provide interactive tutorials and information (Jennett and Cox, 2016;Skarlatidou et al, 2019) Communi cation (CO) Enable communication between participants (Newman et al, 2010;Wald et al, 2016;Sturm et al, 2018;Skarlatidou et al, 2019) Enable informal learning through interaction (Jennett et al, 2016) Enable two-sided learning for an empowering effect (De Albuquerque and Almeida, 2020) Enable communication between participants and researchers (Newman et al, 2010;Yadav and Darlington, 2016;Sturm et al, 2018;Skarlatidou et al, 2019)…”

"Citizen Science as a Service? A Review of Multi-Project Citizen Science Platforms"

“…Enable communication between participants Newman et al, 2010;Skarlatidou et al, 2019;Wald, Longo, and Dobell, 2016) Enable communication between participants and researchers (Yadav and Darlington, 2016;Newman et al, 2010;Skarlatidou et al, 2019). Validate the credibility of users (Musto and Dahanayake, 2021;Newman et al, 2010) Data standards (DS)…”

“…Enable secure handling of private data (Yadav and Darlington, 2016;Musto and Dahanayake, 2021) Validate user-generated data (Skarlatidou et al, 2019;Musto and Dahanayake, 2021) Facilitate entering user-generated data (Newman et al, 2010;Skarlatidou et al, 2019) Enable data analysis and visualization (Musto and Dahanayake, 2021;Newman et al, 2010;Skarlatidou et al, 2019;Wald, Longo, and Dobell, 2016) Support information (SI)…”

A model of integrating college students’ consumerism education and ideological and political education by applying multivariate data fusion

“…The consumption method has changed from offline to online, and college students have been exposed to more and more financial products. Their consumption life can be described as mixed, which not only brings quickness and convenience to consumption life but also lays traps for consumption life, such as installment payment and debt consumption exist in college student groups from time to time, avoiding financial fraud and payment risks in consumption life, correcting over-consumption concept, blind consumption concept, impulsive The concept of consumption is an urgent task in the current ideological and political education work in colleges and universities [12]. By launching a deep education on the consumer outlook, we actively take measures in terms of value perception and value orientation to be able to prescribe the right remedy so that the education on the consumer outlook of college students can go in the same direction and with the same force as the ideological and political education of colleges and universities, to cultivate rational and peaceful consumers for the new era and cultivate builders and successors of the socialist cause with a sound mind and proper behavior for the new era [13].…”