For APT, post-processing is mostly executed with proprietary software tools, which can often be opaque in their execution and have often limited performance and preclude the facile deployment of novel data processing methods. There is a need to agree on a more opened data format and metadata conventions as a critical prerequisite. Development of machine-learning optimized hardware and software remains plagued by the use of proprietary, specific data format, which limits usage across software, techniques and communities. And this should include the raw data, not only what has already been processed. As such, the community will have push to provide a complete set of tools equivalent to the currently available integrated beginning-to-end workflows, i.e., from an experiment to a publishable image, yet these will have to be open and extendable to include machine-learning steps and fully documented to also include traceable information regarding the sample and the specimen with appropriate metadata. A prerequisite is also the use of open and documented data formats. As a preliminary efforts in this directions, let us mention here Paraprobe ,