Making scientific data accessible, discoverable, explorable, and understandable is central to good scientific practice. At the same time, increasing spatial and temporal resolutions of models, observations, and remote sensing products are driving rapidly growing data volumes and complexity. Earth system science data are also increasingly used beyond research, for decision support, knowledge transfer, and applications involving stakeholders and other end users.

These developments create challenges across the entire data and information chain, from infrastructures and metadata to interoperable services, analysis and visualization tools, and user-oriented applications. AI and agent-based workflows add a new dimension: data and services must be not only accessible and understandable to humans, but also discoverable, interpretable, and usable by AI applications and autonomous agents. This requires research infrastructures that are machine-actionable, interoperable, robust, and trustworthy, while significant gaps and barriers towards genuinely AI-ready infrastructures remain.

This session brings together scientists, research software engineers, data infrastructure experts, AI researchers, and practitioners working on platforms and tools that make Earth system science data accessible, discoverable, interoperable, explorable, trustworthy, and actionable.

We welcome contributions on:

Open-source platforms for data discovery, exploration, analysis, and visualization
AI-ready research data infrastructures and platforms
Machine-actionable metadata, semantic descriptions, APIs, and standardized interfaces
Robust and trustworthy services addressing provenance, quality, reliability, and reproducibility
Gaps, barriers, and show-stoppers towards AI-ready infrastructures, and approaches to overcome them
Decision-support systems and co-designed solutions for scientific and non-scientific users
Integration and discovery across distributed and decentralized infrastructures
AI-assisted data discovery, exploration, visualization, and access

We particularly encourage contributions based on community-driven standards, interoperable interfaces, open-source technologies, and FAIR principles, including approaches that address remaining challenges towards robust, trustworthy, and AI-ready Earth system science infrastructures.