FAIR in ML, AI Readiness, & Reproducibility (FARR) Workshop

The FAIR in ML, AI Readiness, & Reproducibility Research Coordination Network (FARR RCN) welcomes computer scientists, geoscientists, research data practitioners, geosciences data and tool repositories / providers, and computing infrastructure providers and research tool builders to participate in FARR's in-person workshop on April 8-9, 2026 at the AGU Conference Center in Washington DC. Communities outside of geosciences with similar challenges, as well as industry, government, and non-profits with a stake in these topics are also encouraged to attend.   

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Purpose:

The FARR Workshop 2026 aims to make advances in the areas of AI Readiness, AI Reproducibility, and the intersection of the FAIR Principles and ML through

• Spurring new or deepened collaborations

• Sharing best practices and lessons learned

• Contributing to a roadmap that will serve as a guide for community-led efforts 

• Exploring research gaps, priorities and next steps

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Should you attend?

• Are you interested in learning about or working towards AI readiness?

• Do you want to share AI models, or use others' models?

• Are you concerned about reproducibility of work that uses AI models?

• Do you have experiences in these areas that you think others would benefit from?

If you are interested in those topics, this workshop will be a good opportunity to connect with others.

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Intent to Participate/ Call For Abstracts -  due by February 6, 2026

The FARR RCN seeks presentations and posters focused on AI Readiness, AI Reproducibility and FAIR Principles & ML, especially in the geosciences, including but not limited to topics such as:

• Foundation models (FM), especially as they relate to building FMs, methods for benchmarking, maintaining, extending, data formats, and related considerations

• Data-centric AI, especially as it relates to research priorities and signals for data repositories and resource providers

• Using ML and Knowledge Engineering to add context or structure to data 

• Applying the FAIR principles to data, workflows, and models for AI/ML, and techniques for automation and validation

• What AI Readiness means for geoscience repositories and related providers: challenges, success stories, and lessons learned

• Community approaches to AI reproducibility

• AI reproducibility and refactoring for LLMs and Gen-AI

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Logistics

This meeting will be in-person only. There is no registration fee. Breakfast and lunch will be provided. Travel support will be made available for a limited number of early career researchers. Please note that space is limited at this event and we might not be able to accommodate all applications.

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Posters

Posters should be no larger than 45 inches x 45 inches (114 cm x 114 cm).

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Accommodations: reservations due by March 6, 2026, while supplies last

• Normandy Hotel - $189/night +tax, 8 min walk, Group booking coming soon

• Generator Washington DC - $209/night+tax, 4 min walk, Group booking coming soon

• Churchill Hotel - $219/night +tax, 5 min walk, Group booking coming soon

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Registration

There is no registration fee, but participants should register by March 6, 2026 to allow for planning logistics such as space, food and name badges. link coming soon

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We adhere to the Community Participation Guidelines of our partner, GO FAIR US

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Venue

AGU Conference Center

2000 Florida Ave. NW, Washington, D.C. 20009

Directions to the AGU Conference Center

Closest Metro station: Dupont Circle

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