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 link

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

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

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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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Organizing Committee

​Elizabeth Campolongo, Imageomics
Julie Christopher, UCSD, SDSC
Kevin Coakley, UCSD, SDSC
Daniel S. Katz, UIUC, NCSA
Christine Kirkpatrick, UCSD, SDSC
Josephine Namayanja, iHARP
Douglas Rao, North Carolina Institute for Climate Studies, NCSU
Lynne Schreiber, UCSD, SDSC
Karen Stocks, UCSD, SIO

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Workshop Sponsors