Matrix for federated research with Python libraries

Using the Matrix protocol for signaling and messaging makes a lot of sense for dynamic, inter-collaboration communication scenarios. In addition to the obvious human-to-human interactions facilitated by Matrix, there is also utility in machine-to-machine communication where federation is desirable; namely, where two independent entities want to communicate securely but do not share a trusted intermediary.

Imagine two researchers in separate organizations want to explore a parameter space of some physical system – using some interactive programming environment like a Jupyter notebook that allows them to run automated scans but also explore the data interactively – and they want to pool their results together so that they both benefit from the results obtained by the other in real-time.

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