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Findable, Accessible, Interoperable, and Reusable (FAIR) Data Principles

The U.S. Department of Energy (DOE) announced $8.5 Million for FAIR Data to Advance Artificial Intelligence for Science. The five projects funded will Advance Artificial Intelligence and aimed to make artificial intelligence (AI) models and data more accessible and reusable to accelerate AI research and development (R). They focussed on applying Findable, Accessible, Interoperable, and Reusable (FAIR) Data Principles so that science data can drive innovations in AI.

The FAIR principles were originally proposed and endorsed in 2016 by an international collaboration of universities, industry, funding agencies, and scholarly publishers.

Dr. Chris Fall, Director of DOE’s Office of Science, said: “One of the major scientific challenges of our time is being able to access and effectively analyze mounting quantities of data. The FAIR Data Principles are an effective way of enhancing access to data. The projects announced today will apply these principles and thereby maximize their usefulness to science.”

Selected projects cover a range of topics, including high-performance computing, materials science, high energy physics, and microbial science. Projects will make science data FAIR and develop frameworks to systematically study the relationships between data and AI models, aiming at a deeper understanding of how AI works and how it can be applied.

Projects were chosen by competitive peer review under DOE Funding Opportunity Announcement, “FAIR Data and Models for Artificial Intelligence and Machine Learning,” and a companion announcement for DOE laboratories, sponsored by the Office of Advanced Scientific Computing Research (ASCR) within DOE’s Office of Science.

Funding totals $8.5 million in the Fiscal Year 2020 dollars for projects lasting one to three years. A list of projects can be found on the ASCR homepage under the heading “What’s New.”

Source: Press Release
Department of Energy
energy.gov
Date: August 10, 2020
(202) 586-4940


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