Exploring the S4S community

The Science for Sustainability (S4S) community is very diverse in its members and research topics. To get a better understanding of the topics that the people of S4S do research on, we initialized a project on community network analysis. The project is centred around recent publications from authors associated with the S4S community.

Overall, we aim to help scientists to connect and get in touch with each other, led by their topic of expertise or their interdisciplinary work. By providing interactive visualisation tools we want to allow everybody to discover the diverse network of research topics and discover possibilities to cooperate.

Visualising the research topics

Below we present the first version of a keyword network which can be explored in the interactive graphic below or see a full version of the graphic. In the standard settings, the size of nodes represents the number of publications with this keyword and the colour of the so-called “community” (a way of identifying clusters within a network).

If you want to learn more about the network or change things in the visualisation you can activate the left panel by clicking the button in the top left. Here you have various options to re-colour, re-size and even filter the nodes according to their attributes. By clicking on nodes, you can view additional information (if the left panel is active).

Everything is still a work in progress; therefore, ideas and feedback are very welcome. You can use the questionnaire below to provide us with input!

Feedback form
Full version of the graph

With this first version we want to explore one specific use of the network method: the keyword network. Most publications nowadays have author keywords associated to them. These are terms which should represent the study as well as possible and are picked by the author(s) themselves. Additionally, keywords are added by the various journals and databases. Naturally, keywords are not used exclusively in one publication, but are rather re-used within others. This exact fact is what we use in the network: Each node (“dot”) is linked through edges (“lines”), where nodes represent the keywords and edges the co-occurrence in different publications. The number of co-occurrences determines how close the keywords will be displayed together.

We will elaborate further about the functionality and methodology of this project in future articles.


Torbjörn Kagel