Are citizen science projects multi-disciplinary research activities? Article Swipe
YOU?
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· 2021
· Open Access
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· DOI: https://doi.org/10.5281/zenodo.4694223
· OA: W3156029728
<strong>The majority (58%) of Zooniverse projects is multi-disciplinary</strong> <em>Most of the projects cover between 1 and 5 research areas </em> Citizen science projects incorporate the perspectives of volunteers, practitioners, and scientists (among others) in different fields of science and applications. However, successful examples like “Galaxy Zoo” show that a narrow research orientation can help to streamline the crowd efforts and might lead to a higher scientific output. While examples like the Galaxy Zoo can be easily assigned to a single research area, other projects tend to have a multi-faceted or even multi-disciplinary orientation (e.g., the “Wild Mont-Blanc” project). In this work, we operationalised the question of multi-disciplinarity by assigning research areas to textual project descriptions based on computational text analytics. Thus, we identify a project as multi-disciplinary if it was assigned to more than one research area. <strong>How to interpret this data </strong> We analysed 218 project descriptions using text-analytics based on explicit semantic analysis [1]. The research areas are mapped using the web of science taxonomy. This consists of research categories with a certain number of research areas per category. Figure 1 shows the relative amount of multi-disciplinary projects. 58% of the projects have been assigned to more than one research area and thus count as multi-disciplinary. Figure 2 presents the concrete numbers of projects that have a certain number of research areas. That some projects have a high number of associated research areas is caused by unclear textual project descriptions, which include many specific terms that are bound to the associated research areas. <strong>References</strong> [1] Gabrilovich, E., & Markovitch, S. (2007, January). Computing semantic relatedness using wikipedia-based explicit semantic analysis. In <em>IJcAI</em> (Vol. 7, pp. 1606-1611). If you'd like to discuss further check out this graphical article in our eMagazine.
