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Complex scientific experiments often require setting up several pieces of hardware and software that must work together seamlessly. It is crucial not only to connect these components correctly, but also to design the setup in a way that others can easily replicate and adapt for similar experiments.
Striking a balance between a system that works correctly and one that can be quickly adjusted and understood by other users can be difficult. This is especially relevant when systems incorporating complicated software and hardware are to be designed and used by scientists who may not have expertise in those areas.
Dimitriadis et al. set out to create a software tool that would help researchers of all disciplines build and run complex experiments more easily. The resulting platform, known as Heron, lets users create setups by combining visual building blocks representing parts of an experiment. These blocks are arranged in what is called a Knowledge Graph, which shows how different steps in the experiment connect in a way that closely mirrors the thought process of the researcher.
This approach makes experiments quicker to set up, easier to update, and more transparent for others to replicate or understand, especially in fields like robotics or neuroscience where complex setups are common. It also results in code that is easier to understand, maintain and share with others. These factors will help Heron to enhance how reproducible experimental setups are and allow researchers to use combinations of hardware and software that would be difficult to achieve otherwise.
