FAIR principles for AI models with a practical application for accelerated high energy diffraction microscopy

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...

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Bibliographic Details
Main Authors: Nikil Ravi, Pranshu Chaturvedi, E. A. Huerta, Zhengchun Liu, Ryan Chard, Aristana Scourtas, K. J. Schmidt, Kyle Chard, Ben Blaiszik, Ian Foster
Format: Article
Language:English
Published: Nature Portfolio 2022-11-01
Series:Scientific Data
Online Access:https://doi.org/10.1038/s41597-022-01712-9

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https://doi.org/10.1038/s41597-022-01712-9

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