DataPLANT submitted three proposals following the Call for Papers of the "E-Science-Tage 2021: Share Your Research Data" scheduled for beginning of March in Heidelberg. The consortium plans to participate in the workshop suggested by the NFDI directorate to present the fundamental plant research community as part of the future research data management landscape. Such an integrated RDM landscape and services enables reproducible research, the linking of interdisciplinary expertise, the sharing of research for comparison and integration of different analysis results and metadata studies, taking advantage of the immense additional knowledge gained from them. Additionally, we suggested a short paper on the DataPLANT data steward model, as a core element of a holistic strategy for managing research data in the field of plant research. Research groups will profit from direct support in their daily tasks ranging from data organization to the selection of the proper tools, workflows and standards. Data stewards play a special hinge role between service providers, individual researchers, groups and the wider community. They also help bridging the gap between researchers and technical systems. The coordinated deployment of data stewards supports the adherence to good scientific practice among the research community.

Following the Kick-Off Task Area 2 "Software / Services" we started to draft an outline for the Annotated Research Context (ARC) as a starting point for experiments. An ARC captures the complete research cycle in a structured way, meeting the FAIR requirements whilst trying to mimic the way an individual researcher experimentsworks. ARCs are self-contained and include assay and /measurement data, workflows and computation results, accompanied by metadata in one package. Their structure allows full user-control over all metadata and facilitates usability, access, publication and sharing of the research. Thereby, ARCs are a practical implementation of existing standards leveraging the advantages of the “ISA model”, “research crates” and the “Common Workflow language”. The ARC concept relies on a structure that partitions assay, workflow and results for granular reuse and development. Assays cover biological, experimental and instrumental data including its self-contained description using the ISA model. Similarly, workflows describe all digital steps of a study and contain application code, script and/or any other executable description of an analysis providing the highest degree of flexibility for the scientists.