We are a home for Earth science data and computing professionals. Our sessions bring together the community for hands-on, interdisciplinary deep dives as we explore "Innovation to Impact" this year. Learn more about ESIP: esipfed.org
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Share your trained models as cloud-executable functions with DOIs with the NSF Funded Garden Service. You can bring your models to the session and together we will package them up and publish them as hosted Python functions. The Garden platform makes it easy for other researchers to find, understand, and try out your models.
If you don’t have a model to share, feel free to stop by to learn how to use Garden to help make your research more FAIR. Value to Session Participants: Researchers will be able to turn their trained models into citable research objects and extend the reach of their research by allowing others in the field to try the model out and possibly integrate them into their own work. Researchers will learn methods to most simply share their models and make them reusable by the community.
Recommended Ways to Prepare: Gather up your trained models Obtain a Hugging Face account
This session explores AI applications in geoscience, focusing on automated data analysis workflows and deposit type classification techniques using NSAI guided by KGs. We will demonstrate how AI agents leverage open LLMs to perform mineral data analysis and showcase a neuro-symbolic approach that embeds knowledge graphs of deposit rules within neural networks to enhance mineral deposit classification accuracy.
Value to Session Participants: Participants will gain insights into scalable AI-driven workflows and the integration of NSAI guided by KGs to enhance geochemical-based deposit type classification, exploring practical applications for automating data analysis and enhancing classification in geoscience and beyond.
Recommended Ways to Prepare: We will provide a Jupyter Notebook demo for our audience to interact with.
