A lossless data-management platform that describes relationships between properties, structures, and experimental processes in electronic laboratory notebooks has been developed by researchers at Japan's Waseda University and the National Institute for Materials Science. The notebook represents experimental events and related environmental parameters as knowledge graphs.
Researchers incorporated raw data from more than 500 experiments on superionic conductivity in organic lithium-ion electrolytes into the notebook; an artificial intelligence algorithm rendered the knowledge-graph data as machine-learnable datasets and uploaded them into a public archive.
From Waseda University
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