Interoperable Infrastructure in the AI Era
Last updated on 2025-11-18 | Edit this page
Overview
Questions
- What are the requirements for an AI-ready data infrastructure in climate science?
- Why is interoperability crucial for AI applications in climate science?
- What are the key elements of an AI-ready interoperable data infrastructure?
Objectives
- Understand the requirements for an AI-ready data infrastructure in climate science.
- Recognize the importance of interoperability for AI applications in climate science.
- Identify the key elements of an AI-ready interoperable data infrastructure.
In this episode you will learn about :
AI needs
- Large-scale multidimensional datasets
- Consistent CF metadata
- Chunked cloud-native formats
- STAC-like discoverability
- Stable APIs for pipeline automation
Challenges
- Data fragmentation
- Lack of standardization
- FAIR gaps
- Poorly documented repositories
Key elements of an AI-ready infrastructure
- Standarized metadata (e.g CF convention)
- Community formats
- Cloud-native layouts
- Stable and well documented APIs
- STAC catalogs
- Versioning & identifiers
Interoperability enables AI-ready infrastructure
Interoperability determines:
- Efficient access
- Reproducibility
- Integrability
- Trust in results
Examples
- FAIR-EO (FAIR Earth Observations) (https://oscars-project.eu/projects/fair-eo-fair-open-and-ai-ready-earth-observation-resources)
- AI-ready data infrastructure requires large-scale multidimensional datasets, consistent CF metadata, chunked cloud-native formats, STAC-like discoverability, and stable APIs for pipeline automation.
- Interoperability is crucial for AI applications in climate science as it enables efficient data access, reproducibility of results, integrability of diverse datasets, and trust in AI-driven insights.
- Key elements of an AI-ready interoperable data infrastructure include adherence to community formats, cloud-native layouts, stable APIs, comprehensive data catalogs, and robust versioning and identifier systems.