Speaker
Description
Automating instrumentation is a big challenge for any lab. In established labs, there is often large amount of existing infrastructure, with the benefits of automation only tangible after several components of a set-up are automated. In smaller labs, automation is often hampered by lack of personnel and know-how.
Here, we present tomato, an open-source, python-based, cross-platform instrument automation framework that is part of the dgbowl suite of tools for digital (electro)-catalysis. With tomato, an incremental approach to instrument automation is possible, as tomato outputs data in NetCDF format. This means tomato's outputs are fully consistent with the standardised data output format of our FAIR data parser, yadg.
We present two case studies for tomato: our work on the integration of off-the-shelf battery testing infrastructure with a workflow management toolkit as part of the Aurora project, and our progress in incremental automation of our cavity operando conductivity set-up, COCoS. The case studies illustrate how painless it is to both deploy tomato as well as integrate it in more complex workflows.