Because Alex pursues highly specialised, bleeding edge research objectives, she has to leverage individual, bespoke one-of-a-kind-setups of equipment, methods and interfaces. Since she typically is solely responsible for a project, Alex wants to know exactly how her system and experiment function. She also commonly needs to adapt the system and experiment to meet her research objective. Alex needs highly flexible software or data models to represent and connect her experiment components, configurations and results. Therefore, Alex often rightfully chooses an iterative or agile approach to reach her research objective. Alex’s need for flexibility has to be balanced with  the necessities of compatibility and reusability. She must be able to trace the data-flow and configuration of her experiment, as well as adapt and repeat it. Alex mostly has to deal with at least a medium volume of data, with no upper limit (gigabytes to terabytes per year and project or more). Therefore Alex’s data rates are in almost all cases too high to manage the generated data manually, even when using document management or version control systems. Additionally, the amount of data is often too large to store or archive the entirety of a generated dataset.

The major objectives of this Task Area are to support Engineers like Alex and provide means to:

• facilitate re-use and integration of partial solutions (e.g. unified data transfer, increased compatibility, reusability) to improve the traceability of system functions, data flow and configuration. Lower the barrier to manipulate and adapt experiment setups.
• enable modular, self-documenting software setups (including representations of hardware setups), that automatically generate machine actionable metadata
• to store and retrieve specific subsets of (meta-)data in medium to large sized collections in an intuitive and performant way to encourage publishing and re-using data collections and facilitate the decoupling of code and data.