The distributed, cross-institutional User Support Network (USN) for NFDI4Earth is based on the existing and well embedded user support structures of the participating institutions. The USN serves as a single point of contact for user requests that could not be handled via OneStop4All and require individual consulting. By combining the distributed RDM knowledge of experts in the USN in conjunction with the Knowledge Hub, the NFDI4Earth team will convey the notion (knowledge) of a best practice for dealing with data and how data can be made FAIRer and open (by acknowledging privacy and legal issues).
Hence, the USN represents 2nd level support for NFDI4Earth. At the USN, ESS researchers find answers to questions related to all aspects of NFDI4Earth services (in collaboration with 2Interoperate), FAIR data and software tools. The USN approach covers the diversity of NFDI4Earth by involving many scientific backgrounds. Here, wide methodological knowledge and experience of heterogeneous data sets from observations, experiments and simulations can be found. The USN coalesces RDM support, i.e. institutional RDM help desks from many different (infrastructure) providers. It will use the Living Handbook, based on the Knowledge Hub, as its long-term memory, for adding and retrieving information but also for offering coherent support.
Our ambition is to provide a high-quality data management support network for ESS researchers. In this network, already existing support institutions contribute jointly. Incentives activate users to share their knowledge and experience with the network.
Support will encourage self-reflection regarding FAIR principles, i.e. is my own data FAIR and if not, what can I do, to achieve this? This part of the FAIR game is strongly associated with data curation, data quality (i.e. quality of description / metadata) and ingest procedures and options, since they provide the automated validation of appropriate data format and metadata description. Hence, to make comprehensive and long-tail data sets FAIRer and future-proof, and by doing so promote the cultural change towards FAIR and Open research data, a good understanding of data ingest procedures and options needs to be facilitated.