AutoClimDS: Climate Data Science Agentic AI -- A Knowledge Graph is All You Need

• URL: http://arxiv.org/abs/2509.21553v1
• Date: Thu, 25 Sep 2025 20:38:23 GMT
• Title: AutoClimDS: Climate Data Science Agentic AI -- A Knowledge Graph is All You Need
• Authors: Ahmed Jaber, Wangshu Zhu, Karthick Jayavelu, Justin Downes, Sameer Mohamed, Candace Agonafir, Linnia Hawkins, Tian Zheng,
• Abstract summary: Climate data science faces persistent barriers stemming from fragmented nature of data sources, heterogeneous formats, and steep technical expertise required to identify, acquire, and process datasets.<n>We present a proof of concept for addressing these barriers through the integration of a curated knowledge graph (KG) with AI agents designed for cloud-native scientific research.
• Score: 1.1639172596200853
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Climate data science faces persistent barriers stemming from the fragmented nature of data sources, heterogeneous formats, and the steep technical expertise required to identify, acquire, and process datasets. These challenges limit participation, slow discovery, and reduce the reproducibility of scientific workflows. In this paper, we present a proof of concept for addressing these barriers through the integration of a curated knowledge graph (KG) with AI agents designed for cloud-native scientific workflows. The KG provides a unifying layer that organizes datasets, tools, and workflows, while AI agents -- powered by generative AI services -- enable natural language interaction, automated data access, and streamlined analysis. Together, these components drastically lower the technical threshold for engaging in climate data science, enabling non-specialist users to identify and analyze relevant datasets. By leveraging existing cloud-ready API data portals, we demonstrate that "a knowledge graph is all you need" to unlock scalable and agentic workflows for scientific inquiry. The open-source design of our system further supports community contributions, ensuring that the KG and associated tools can evolve as a shared commons. Our results illustrate a pathway toward democratizing access to climate data and establishing a reproducible, extensible framework for human--AI collaboration in scientific research.

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