The Central Data Hub (CDH) is the central organizational structure within GreenRobust responsible for managing and ensuring the quality of research data generated across the project.
Core responsibilities:
- Oversight of the PPA experiments: The CDH oversees all activities of the PPA, determining the exact experimental conditions under which they are carried out.
- Data standardization: It coordinates how data is generated, handled, and analyzed so that different experiments across the cluster remain comparable with one another. This also helps keep the number of necessary repeat experiments to a minimum.
- FAIR principles: The CDH ensures that all generated datasets adhere to the FAIR principles (Findable, Accessible, Interoperable, Reusable), guaranteeing quality, comparability, and integrability.
- Approval process: Before researchers can run an experiment under the PPA, they must submit a detailed proposal through a CDH portal, which is reviewed and approved by the CDH planning group. Metadata must also be prepared according to CDH guidelines beforehand. Only once these prerequisites are met is a RobustID assigned, allowing the experiment to proceed.
- Quality control: Strict quality control measures are enforced during data acquisition to maintain data integrity.
- Data storage: All data is stored within ARCs (Annotated Research Contexts) in a federated data storage system, which supports structured data recording and accessibility for detailed analysis and machine learning applications.
Committee composition: The CDH committee is made up of GreenRobust PIs with experience in generating and handling big data — named examples include Kemen, Lohmann, Nahnsen, and Weigel.
Link to Research Projects: Insights gained from the PPA experiments and data management feed directly into the experimental design and analysis strategies of the other Research Projects, which also fall under CDH oversight.
In short: the CDH functions as GreenRobust’s data governance body — it ensures that all research data produced across the cluster follows unified, traceable, and quality-assured standards, and remains usable and comparable across studies.
