Project description
Creating a user-friendly FAIRiCUBE HUB
According to the European Commission’s Digital Europe Programme, a data space refers to data infrastructure with tailored governance mechanisms enabling secure and cross-border access to key data sets in the targeted thematic area. The EU-funded FAIRiCUBE project aims to unleash potential environmental, biodiversity and climate data through dedicated European data spaces. To do this, the project will create FAIRiCUBE HUB, a crosscutting platform and framework for data ingestion, provision, analysis, processing and dissemination. A key focus will be the development of tools that enable users who are not familiar with the worlds of Earth Observation and machine learning to scope the requirements and costs of their desired analyses.
Objective
The core objective of FAIRiCUBE is to enable players from beyond classic Earth Observation (EO) domains to provide, access, process, and share gridded data and algorithms in a FAIR and TRUSTable manner. To reach this objective, we propose creating the FAIRiCUBE HUB, a crosscutting platform and framework for data ingestion, provision, analysis, processing, and dissemination, to unleash the potential of environmental, biodiversity and climate data through dedicated European data spaces. Within this project, TRL 7 will be attained, together with the necessary governance aspects to assure continued maintenance of the FAIRiCUBE HUB beyond the project lifespan.
This project’s goal is to leverage the power of Machine Learning (ML) operating on multi-thematic datacubes for a broader range of governance and research institutions from diverse fields, who at present cannot easily access and utilize these potent resources. Selected use cases will illustrate how data-driven projects can benefit from cube formats, infrastructure, and computational benefits. They will guide us in creating a user-friendly FAIRiCUBE HUB, which is tightly integrated to the common European data spaces, providing relevant stakeholders an overview of both data and processing modules readily available to be applied to these data sources. Tools enabling users not intimately familiar with the worlds of EO and ML to scope the requirements and costs of their desired analyses will be implemented, easing uptake of these resources by a broader community. The FAIR sharing of results with the community will be fostered by providing easy to use tools and workflows directly in the FAIRiCUBE HUB.
Fields of science
Keywords
Programme(s)
Funding Scheme
HORIZON-IA - HORIZON Innovation ActionsCoordinator
2027 Kjeller
Norway