FARR: FAIR in ML, AI Readiness, & Reproducibility Network Postcard

Kirkpatrick, Christine R.; Coakley, Kevin; Schreiber, Lynne; Christopher, Julie; Katz, Daniel; Stocks, Karen; Rao, Yuhan Douglas (2023). FARR: FAIR in ML, AI Readiness, & Reproducibility Network Postcard. In San Diego Supercomputer Center (SDSC) Research Data Services Materials Collection. UC San Diego Library Digital Collections. https://doi.org/10.6075/J0X92BG3

This postcard was distributed at conferences to disseminate project objectives and to promote awareness of AI reproducibility and ways to document implementation factors of AI-based research.

• 2022

• 2023

• Christopher, Julie
• Coakley, Kevin
• Katz, Daniel
• Kirkpatrick, Christine R.
• Rao, Yuhan Douglas
• Schreiber, Lynne
• Stocks, Karen

• San Diego Supercomputer Center (SDSC)

Acknowledgement to Odd Erik Gunderson for his work on the Source of Irreproducibility in Machine Learning: A Review, available at https://doi.org/10.48550/arxiv.2204.07610.

FARR is funded by the National Science Foundation under grants #2226453, 1928208.

• FAIR in ML, AI Readiness, & Reproducibility Research Coordination Network (FARR)

• Artificial intelligence (AI) readiness
• FAIR Principles
• Machine learning
• Research reproducibility

• text

• English

Doi: https://doi.org/10.6075/J0X92BG3

Primary associated publication

• Gundersen, Odd Erik; Coakley, Kevin; Kirkpatrick, Christine (2022). Sources of Irreproducibility in Machine Learning: A Review. arXiv. https://doi.org/10.48550/arxiv.2204.07610

Other resource

• FAIR in ML, AI Readiness, & Reproducibility Research Coordination Network (FARR) (farr-rcn.org)
• San Diego Supercomputer Center (SDSC) (sdsc.edu)

Creative Commons Attribution 4.0 International Public License

• Authors

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2023-01-11