Volunteers in citizen science (CS) have different kinds of motivations for participation, ranging from e.g. topic interest, supporting science to personal recognition. Information regarding the existence and characteristics of motivation profile groups of volunteers remains unclear.
Citizen science (CS) projects encompass a variety of different disciplines (e.g. health, biology, education). However, it is not clear whether volunteers’ working i.e. professional discipline is related to the discipline of their selected project.
Citizen science engages adults of all age groups. According to participants, which are technologies they use to participate in CS activities? What are the differences between different age groups and the technologies they use?
We analysed 17,122 Citizen Science tweets filtered with Health keywords in order to answer the following questions: What are the most used hashtags in the e-Health discussion? Are hashtags normally used alone or alongside others on the same theme?
In this article, we analysed linkages between tweets and SDGs in our dataset by means of classification algorithms. In addition, the network of retweets for each SDG is provided.
Through this Twitter analysis it was possible to demonstrate that some SDGs are much more discussed than others among the Citizen Science community of Twitter.
Analysing the discourse in discussion forums of CS projects can help to understand underlying patterns of collaborative knowledge creation and to identify highly engaged users. We ask the question: What can the quantitative analysis of forum data tell us about these patterns?
How can we make the CS Track database of Citizen Science projects interactively accessible for the purposes of interest-driven retrieval, navigation and comparative analysis as well as for checking and correcting existing information items and adding new ones? To achieve this, we have developed the Analytics Workbench.
It is expected that the discussion boards in online CS projects provide a space for knowledge-building.
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.
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