  Social navigation datasets are necessary to assess social navigation
algorithms and train machine learning algorithms. Most of the currently
available datasets target pedestrians' movements as a pattern to be replicated
by robots. It can be argued that one of the main reasons for this to happen is
that compiling datasets where real robots are manually controlled, as they
would be expected to behave when moving, is a very resource-intensive task.
Another aspect that is often missing in datasets is symbolic information that
could be relevant, such as human activities, relationships or interactions.
Unfortunately, the available datasets targeting robots and supporting symbolic
information are restricted to static scenes. This paper argues that simulation
can be used to gather social navigation data in an effective and cost-efficient
way and presents a toolkit for this purpose. A use case studying the
application of graph neural networks to create learned control policies using
supervised learning is presented as an example of how it can be used.
