Citizen science entails the participation of the public and professional scientists in scientific activities in order to expand scientific knowledge and understanding. This involves participants adopting different roles for completing specific tasks which can shape overall learning experiences.
Citizen science (CS) activities have increasingly become diverse of both subject matter and objectives, creating diverse opportunities for people representing a variety of socio-economic backgrounds as well as experiences to come together and participate in science activities.
Nowadays, there are numerous forms of technology ranging from audio recorders to smartphones as well as technological platforms, e.g., social media, that equip citizen scientists with the necessary tools to carry out their activities or projects of interest.
Social networks, such as Twitter, are increasingly being investigated to capture online interactive participation. Although citizen science projects have been remarkably successful in advancing scientific knowledge, it is not known whether the educational aspect is considered in citizen science projects.
The first version of the CS Track database contains a comprehensive collection of CS projects in the European Union and H2020 Associated Countries for data extraction and further analysis. This data was collected to both analyse and better understand citizen science.
We follow a computational approach to assign research areas and categories to textual project descriptions on the web platform Zooniverse. Using this, we quantify the degree of multi-disciplinarity for 218 citizen science projects.
Are most of the citizen science projects only about environmental research? We answer this question by analysing descriptions of 218 Zooniverse projects using text analytics and identifying the predominant research area.