Not only the southern part of Europa felt the heat but also the FAIRiCUBE project team while preparing 9 deliverables and thereby accomplishing 4 milestones in a single month (https://lnkd.in/ePHbRFap).
Due to careful planning and early start of the preparation & validation, we are proud to report that all deliverables were submitted in time and at expected high quality. Naturally, some of the deliverables will be further updated during the project.
Highlights of the deliverable marathon comprise of the documentation of the readiness of the FAIRiCUBE Hub (https://lnkd.in/e9ravZ2T) which is one of the core pillars of the project and the place where we connect and interact with all the components of the data driven scientific work (ingestion, provision, documentation, processing, storage, etc.).
We have further finalized a first revision of the documentation of the data science work carried out but the use cases (https://lnkd.in/e-aQSGmu). In order to address the individual research questions formulated by the domain experts and use case owners, a full data exploratory data analysis was conducted which supported the development of a machine learning strategy tailored to the characteristics of the input data.
We especially focused on the documentation of the software and hardware needs and consumed resources in the execution of all data science work which is always unknown at project start but essential information for the estimation of costs and runtime. Finally, we continued to think about how to exploit the synergies of running 4 thematically different use cases on our FAIRiCUBE Hub and what lessons we learned and – equally importantly – how to teach future use cases to operate on FAIRiCUBE Hub through workshops and academic coursework. After all, we believe that every scientist that works on large regular earth observation or environmental data will benefit from our project and the FAIRiCUBE Hub!