[ESIP-all] Call for Abstracts: FAIR in ML, AI Readiness, & Reproducibility Workshop

[Yuhan Douglas Rao]

Dear colleagues,

The FAIR in ML, AI Readiness, & Reproducibility Research Coordination
Network (FARR RCN <https://www.farr-rcn.org/>) 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 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 submit.

The "FARR Workshop” aims to make advancements in the areas of AI Readiness,
AI Reproducibility, and the intersection of the FAIR Principles and machine
learning (ML) through

   -

   Spurring new or deepened collaborations
   -

   Sharing best practices and lessons learned
   -

   Exploring research gaps and priorities



Call For Abstracts

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:

   -

   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.

Abstract Submissions are due by June 28, 2024
<https://forms.gle/gErSZ1qJUwM5GyhQA>

For more information on the workshop: https://www.farr-rcn.org/workshop24

Supported by NSF award # 2226453.

On behalf of the FARR RCN,

-Douglas
-- 
[image: NCICS] <http://ncics.org/> Yuhan (Douglas) Rao
*Research Scientist*
Pronoun <https://www.mypronouns.org/what-and-why/>: he/him/his
North Carolina State University <http://ncsu.edu/>
North Carolina Institute for Climate Studies (NCICS) <https://ncics.org/>
151 Patton Ave, Asheville, NC 28801
e: yrao5 at ncsu.edu
o: +1 828 271 4903
Schedule a meeting with me <https://calendly.com/douglas_rao>*I choose
to work on a flexible schedule and across a number of time zones. My
apologies for sending emails outside of your working hours.*
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