(1) a lack of established data and file formats. Experimental raw data is typically acquired in instrumentspecific file formats. Extracting all potentially relevant data for machine-learning workflows is often not possible or impaired. Community efforts have been undertaken to establish open data formats . An alternative effort aims at read-function libraries that support multiple formats . For storing analysis output, ideally in conjunction with input data, no standard exists. Similarly, exchanging data between different data management systems is severely hindered by inherent heterogeneity in data structures and metadata, in the naming and unit convention of data fields, and by assuming implicit context (e.g., providing an instrument's name rather than its measurement parameters).