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Image by Maddison McMurrin

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

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The FAIR in ML, AI Readiness, & Reproducibility Research Coordination Network (FARR RCN) 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 October 9-10, 2024 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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Who should 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 questions, this workshop will be a good opportunity to connect with others.

 

Purpose:

The "FARR Workshop” aims to make advancement 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

  • Exploring research gaps and priorities 

  

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Call For Abstracts - due June 28, 2024

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

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  • 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

 

Oral Presentations/Posters

As with many scientific meetings and conferences, these presentations will be the key communication platform.  The date/time of your presentations will be communicated to presenters upon acceptance. Please note: there will be a limited number of 10-15 minute oral presentations.

 

Working Sessions (including tutorials)

Working sessions (90-120 minutes) may consist of mini hack-a-thons, development sprints, tutorials or other kinds of sessions in which the community is engaged to discuss particular questions, provide feedback on new technologies, or evaluate new frameworks for FAIR policies, procedures, or workflows. The goal of working sessions is to move forward on AI Readiness, AI Reproducibility, and FAIR Principles and ML.

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Abstract Submissions are due by June 28, 2024

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Logistics:

Workshop duration:

Wednesday, October 9th at 9:00 am - 5:00 pm

Thursday, October 10th at 9:00 am - 3:00 pm

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Attendance at the meeting will be in-person only. There is no registration fee for this meeting. Breakfast and lunch will be provided. Travel support will be made available for a limited number of early career researchers. 

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Intent to participate - Applications are due by June 28, 2024

Please note that space is limited at this event and we might not be able to accommodate all applications. 

 

Accommodations: reservations due by September 6, 2024, while supplies last

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