In this context, exploiting automatic code generation from machine-readable data format definitions -in conjunction with ontologies and knowledge graphs -could be a game-changer to speed up the development, as they reduce the human effort in defining standards and implementing corresponding code for possibly different programming languages. Similarly, the trend towards higher abstraction in machine-learning software should be exploited to generate processing metadata. When the transformation chain is built at run-time via high-level objects (which later generate the actual code for the hardware at hand on the fly), the high-level representation should automatically annotate the data output with the details of the processing chain. At a higher level, workflow and data management tools must be adapted to deal with the specific challenges of experimental data. As experimental data sets can become very large, moving or copying around entire data sets is prohibitive. In most cases, raw data will be stored close to where it was generated. Computational resources for advanced machine learning might be located elsewhere, and only need part of the data, or specifically pre-processed data that reduces transfer size via dimensionality reduction or compressed sensing. Thus, workflows that deal with both distributed data and distributed computation will be needed, while maintaining consistency in metadata and ensuring that data access across computer systems is reliably authenticated to avoid premature publication or leakage of confidential data.