FAIR principles for AI models with a practical application for accelerated high energy diffraction microscopy
Abstract A concise and measurable set of FAIR (Findable, Accessible, Interoperable and Reusable) principles for scientific data is transforming the state-of-practice for data management and stewardship, supporting and enabling discovery and innovation. Learning from this initiative, and acknowledgin...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2022-11-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-022-01712-9 |
| _version_ | 1828329886085808128 |
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| author | Nikil Ravi Pranshu Chaturvedi E. A. Huerta Zhengchun Liu Ryan Chard Aristana Scourtas K. J. Schmidt Kyle Chard Ben Blaiszik Ian Foster |
| author_facet | Nikil Ravi Pranshu Chaturvedi E. A. Huerta Zhengchun Liu Ryan Chard Aristana Scourtas K. J. Schmidt Kyle Chard Ben Blaiszik Ian Foster |
| author_sort | Nikil Ravi |
| collection | DOAJ |
| description | Abstract A concise and measurable set of FAIR (Findable, Accessible, Interoperable and Reusable) principles for scientific data is transforming the state-of-practice for data management and stewardship, supporting and enabling discovery and innovation. Learning from this initiative, and acknowledging the impact of artificial intelligence (AI) in the practice of science and engineering, we introduce a set of practical, concise, and measurable FAIR principles for AI models. We showcase how to create and share FAIR data and AI models within a unified computational framework combining the following elements: the Advanced Photon Source at Argonne National Laboratory, the Materials Data Facility, the Data and Learning Hub for Science, and funcX, and the Argonne Leadership Computing Facility (ALCF), in particular the ThetaGPU supercomputer and the SambaNova DataScale® system at the ALCF AI Testbed. We describe how this domain-agnostic computational framework may be harnessed to enable autonomous AI-driven discovery. |
| first_indexed | 2024-04-13T20:31:44Z |
| format | Article |
| id | doaj.art-d4f06a97624a4ca6b9b33142632fd44c |
| institution | Directory Open Access Journal |
| issn | 2052-4463 |
| language | English |
| last_indexed | 2024-04-13T20:31:44Z |
| publishDate | 2022-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj.art-d4f06a97624a4ca6b9b33142632fd44c2022-12-22T02:31:09ZengNature PortfolioScientific Data2052-44632022-11-01911910.1038/s41597-022-01712-9FAIR principles for AI models with a practical application for accelerated high energy diffraction microscopyNikil Ravi0Pranshu Chaturvedi1E. A. Huerta2Zhengchun Liu3Ryan Chard4Aristana Scourtas5K. J. Schmidt6Kyle Chard7Ben Blaiszik8Ian Foster9Data Science and Learning Division, Argonne National LaboratoryData Science and Learning Division, Argonne National LaboratoryData Science and Learning Division, Argonne National LaboratoryData Science and Learning Division, Argonne National LaboratoryData Science and Learning Division, Argonne National LaboratoryData Science and Learning Division, Argonne National LaboratoryData Science and Learning Division, Argonne National LaboratoryData Science and Learning Division, Argonne National LaboratoryData Science and Learning Division, Argonne National LaboratoryData Science and Learning Division, Argonne National LaboratoryAbstract A concise and measurable set of FAIR (Findable, Accessible, Interoperable and Reusable) principles for scientific data is transforming the state-of-practice for data management and stewardship, supporting and enabling discovery and innovation. Learning from this initiative, and acknowledging the impact of artificial intelligence (AI) in the practice of science and engineering, we introduce a set of practical, concise, and measurable FAIR principles for AI models. We showcase how to create and share FAIR data and AI models within a unified computational framework combining the following elements: the Advanced Photon Source at Argonne National Laboratory, the Materials Data Facility, the Data and Learning Hub for Science, and funcX, and the Argonne Leadership Computing Facility (ALCF), in particular the ThetaGPU supercomputer and the SambaNova DataScale® system at the ALCF AI Testbed. We describe how this domain-agnostic computational framework may be harnessed to enable autonomous AI-driven discovery.https://doi.org/10.1038/s41597-022-01712-9 |
| spellingShingle | Nikil Ravi Pranshu Chaturvedi E. A. Huerta Zhengchun Liu Ryan Chard Aristana Scourtas K. J. Schmidt Kyle Chard Ben Blaiszik Ian Foster FAIR principles for AI models with a practical application for accelerated high energy diffraction microscopy Scientific Data |
| title | FAIR principles for AI models with a practical application for accelerated high energy diffraction microscopy |
| title_full | FAIR principles for AI models with a practical application for accelerated high energy diffraction microscopy |
| title_fullStr | FAIR principles for AI models with a practical application for accelerated high energy diffraction microscopy |
| title_full_unstemmed | FAIR principles for AI models with a practical application for accelerated high energy diffraction microscopy |
| title_short | FAIR principles for AI models with a practical application for accelerated high energy diffraction microscopy |
| title_sort | fair principles for ai models with a practical application for accelerated high energy diffraction microscopy |
| url | https://doi.org/10.1038/s41597-022-01712-9 |
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