FAIRiCUBE Knowledge Base


Based on the needs, experiences and understanding of the FAIRiCUBE use cases, this Knowledge Base aims to share know-how on extracting insights from large/complex data collections using ML techniques.
Its interactive Query tool allows both experts and non-experts to easily discover and analyse the FAIRiCUBE resources for data analysis and processing (pipelines, pre-processing, ML models and algorithms …).
Its Tips & Tricks share the challenges faced by the use cases and related solutions, workarounds, failures and lessons learnt.


Query Tool

Enables interactive searches over the project's analysis and processing resources, based on different levels of technical expertise.
Use this tool to discover and analyse the processing resources of the use cases and their assets (datasets, pre-trained models, code libraries …) through the accurate descriptions contained in the related metadata.
The searches can be performed using keywords and pre-defined queries, or by creating a custom query (recommended for advanced users only).

Use the search tool of the FAIRiCUBE metadata Catalog to discover and analyze the data resources of the use cases


Tips & Tricks

Document the use cases challenges and related successes & failures, solutions & workarounds. Have a look!


External resources

Links to additional FAIRiCUBE resources:

Community Collaboration Platform

Provides access to the documentation of the FAIRiCUBE Hub and its components, with tutorials, examples, description of ML toolkits, a self-training library and much more. Access here.

Metadata Catalog

STAC metadata Catalog of the FAIRiCUBE datasets and processing resources. Access here.


GitHub organization for the FAIRiCUBE project: find there use cases repositories, with codes and algorithms, issues and discussions. Access here.