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AI Magazine Special Issue

The FARR (FAIR in ML, AI-readiness and Reproducibility) Research Coordination Network is seeking articles for a special issue in AI Magazine at the intersection of the FAIR Principles and Machine Learning, AI Readiness and AI Reproducibility.

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The special issue is interested in the following topics, with papers ideally addressing both research and practice aspects of more than one topic, and with other related topics also welcome:

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  • Improving AI reproducibility, and understanding sources of irreproducibility

  • FAIR data practices and leveraging large language models (LLMs) for increasing the FAIRness of domain-specific applications

  • Challenges and progress in making data AI-ready, including automation in dataset preparation

  • Defining and improving FAIR in the context of AI, such as for AI models

  • Metadata and AI, including potential standards for AI models

  • Private AI models tailored to specific contexts

  • Understanding and addressing gaps in standards and best practices across various fields

  • The role of institutional stakeholders, such as data repositories

 

Special Issue Editors

  • Geoffrey Fox, University of Virginia

  • Daniel S. Katz, NCSA & SCDS & iSchool, University of Illinois Urbana-Champaign

  • Christine R. Kirkpatrick, SDSC, University of California San Diego

  • Yuhun (Douglas) Rao, North Carolina Institute for Climate Studies, North Carolina State University

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Notes

  • Authors Do Not pay publication fees

  • Articles are open access

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Guidelines

AI Magazine prefers expository articles, 6000-9000 words in length (including references and author biographies), without a lot of mathematical formalism.

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​Tentative Schedule

  • Intent to submit: April 30, 2025

  • Papers due: June 16, 2025

  • Reviews due: August 15, 2025

  • Paper submission addressing comments: September 30, 2025

  • 2nd reviews: October 31, 2025

  • Final Papers: November 28, 2025

  • Publication: Spring 2026

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For more information: community@farr-rcn.org

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This work is supported through the National Science Foundation award # 2226453.

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