A data science approach to mitigating data challenges in serious gaming

Citizen science initiatives offer an unprecedented scale of volunteer-driven data collection but often face scrutiny regarding their methodology, research design, data collection, and analysis. Addressing these concerns, this paper adopts a data science approach to process and enhance the integrity of data generated from citizen science projects, particularly in non-traditional settings such as serious gaming. We present a methodological framework that employs data science techniques to effectively mitigate data noisiness and coverage biases, issues commonly associated with citizen science datasets. The paper features a case study involving a collaboration with JGM (Jeffery Griffin Meijer), a serious gaming company based in the Netherlands, specializing in creating experiential learning environments through escape room scenarios. JGM’s mission is to enhance team performance by providing data on communication, collaboration, and leadership. This partnership exemplifies a novel form of citizen science, where participants not only engage in gameplay but also contribute data used for scientific analysis. By co-creating research questions and reflecting on team dynamics, JGM facilitates the generation of meaningful data that informs both scientific and practical outcomes. This article outlines rigorous data preprocessing workflows implemented from a data science standpoint to ensure data quality. The processed dataset, comprising 291 observations and 55 variables, is a blueprint for enhancing data reliability in citizen science endeavors. In summary, this paper demonstrates how data science methods can make citizen science projects more reliable and replicable. We encourage further exploration of the intersection between citizen science and data science to improve research quality.