From notes to knowledge by increasing the level of annotation at the source, tracking provenance using community standards. This will maximize data discoverability and reuse following the FAIR data principles.
The advantages of a well-maintained research data management according to FAIR principles are obvious. In this context, especially the annotation of metadata is essential. The completeness of these depends on the corresponding endpoint repository and is based on specific formats, checklists and terminologies. For individual researchers, the amount of different standards can easily become unmanageable. Therefore, we have made it our mission to keep track of the key data relevant for data publication by including them in our Annotated Research Context, enabling the individual researcher to comfortably use the prefabricated construct without having to worry about such trivialities. No idea how to create an ARC? Use our "4 steps to your ARC" guide!
No idea how to create an ARC?
Read trough our ArcCommander Wiki! 
Collaborative research and generation of comprehensive datasets is only possible through data sharing. Therefore, we envision a platform where data can be stored and jointly used prior to submission of the final data construct to an endpoint repository. This allows both the measurement service provider and a data steward, and at the same time a wet lab scientist, to add and logically connect their data to existing sets. Git's versioning capability allows each step to be tracked at any time, preserving the provenance of each dataset.
In the future, the collection of ARCs will be available in a searchable form. Based on specific data or metadata attributes, each scientist will have quick access to individual measurements, information or entire ARCs. The searchability of a single factor in different plants, experiments or under different conditions thus leads to the opening of “a whole new world” (Aladdin).
