We have achieved significant technical and scientific progress through the establishment of the FAIRiCUBE Hub and the data science work of five use cases (UCs) addressing European Green Deal priority actions. The following highlights key activities and achievements in our pursuit of technical excellence.
FAIRiCUBE Hub
We have established the architecture of the FAIRiCUBE Hub, which serves as the project’s cornerstone. The Hub is a crosscutting platform for data ingestion, provision, analysis, processing, and dissemination, enabling the use of environmental, biodiversity, and climate data through European data spaces. Progress has been made in defining its structure and functionality, including identifying core services under the FAIRiCUBE umbrella. Initial steps toward harmonizing and integrating these services ensure usability and accessibility.
Holistic Meta-Data Management Approach
A key objective of FAIRiCUBE has been to streamline the collection, ingestion, alignment, and availability of metadata alongside Earth observation and socio-economic data, addressing a core F.A.I.R. principle. User-friendly routines for data and metadata ingestion have been developed, and efforts extended to include analysis and processing metadata. A semi-automatic monitoring system for computing resources ensures optimal performance. The FAIRiCUBE Knowledge Base (KB) provides unified access to metadata, supporting accessibility and data-driven insights.
F.A.I.R. and Open Documentation of Data Science Work
The FAIRiCUBE Hub serves as the main infrastructure for executing and documenting UC data science work, acting both as a demonstrator and a source of project execution insights. Through the UCs, challenges such as data availability, ingestion bottlenecks, resource requirements, and service interoperability have been identified and addressed, strengthening platform robustness. Technical and scientific UC work is published through dedicated GitHub repositories and FAIRiCUBE communication tools. For machine learning applications, transparent documentation of processes and results represents a significant step forward for open science.
Enabling Data Science Resources for Non-Specialists
FAIRiCUBE is committed to making data science resources accessible beyond specialist communities. This includes guidance for integrating data science into workflows and access to essential resources such as data catalogues, storage, compute, and ML tools. Documentation of UC work supports future projects on the FAIRiCUBE Hub. Some aspects of data science workflows still require technical expertise; while not all subtasks of creating ML-ready data cubes can be automated, documentation and guidelines will assist future users.
