Results 141 to 150 of about 746,734 (318)
Advanced Intelligent Discovery, EarlyView.Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley +1 more source
MALDI MS Data and Metadata from "A biological reading of a palimpsest"
, 2022 Sarah Fiddyment +2 more
openalex +1 more source
Advanced Intelligent Discovery, EarlyView.Large language models are transforming microbiome research by enabling advanced sequence profiling, functional prediction, and association mining across complex datasets. They automate microbial classification and disease‐state recognition, improving cross‐study integration and clinical diagnostics.
Jieqi Xing +4 more
wiley +1 more source
Reusing Data and Metadata to Create New Metadata Through Machine-Learning & Other Programmatic Methods [PDF]
Recent improvements in natural language processing (NLP) enable metadata to be created programmatically from reused original metadata or even the dataset itself.
Buonomo, Anthony R. +4 more
core +1 more source
opXRD: Open Experimental Powder X‐Ray Diffraction Database
Advanced Intelligent Discovery, EarlyView.We introduce the Open Experimental Powder X‐ray Diffraction Database, the largest openly accessible collection of experimental powder diffractograms, comprising over 92,000 patterns collected across diverse material classes and experimental setups. Our ongoing effort aims to guide machine learning research toward fully automated analysis of pXRD data ...
Daniel Hollarek +23 more
wiley +1 more source
Code4Lib Journal, 2016 This paper presents the concepts of metadata assessment and “quantification” and describes preliminary research results applying these concepts to metadata from the Digital Public Library of America (DPLA).
Corey A. Harper
doaj
Pedagogically informed metadata content and structure for learning and teaching
, 2007 In order to be able to search, compare, gap analyse, recommend, and visualise learning objects, learning resources, or teaching assets, the metadata structure and content must be able to support pedagogically informed reasoning, inference, and machine ...
Gilbert, Lester, Sitthisak, Onjira
core
Large Language Model in Materials Science: Roles, Challenges, and Strategic Outlook
Advanced Intelligent Discovery, EarlyView.Large language models (LLMs) are reshaping materials science. Acting as Oracle, Surrogate, Quant, and Arbiter, they now extract knowledge, predict properties, gauge risk, and steer decisions within a traceable loop. Overcoming data heterogeneity, hallucinations, and poor interpretability demands domain‐adapted models, cross‐modal data standards, and ...
Jinglan Zhang +4 more
wiley +1 more source
OMEinfo: global geographic metadata for -omics experiments [PDF]
Matthew Crown, Matthew Bashton
openalex +1 more source