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REMI: Resource for Materials Informatics

Data provided by  National Institute of Standards and Technology

The REsource for Materials Informatics (REMI) will host a diverse collection of scripting notebooks (Jupyter, Matlab LiveScripts, etc.) for collecting, pre-processing, analyzing, and visualizing materials data. Notebooks are curated using tags aligned to Materials Science and Data Science topics. REMI emerged from the realization that both experts and novices wanted examples of using machine learning for science. Meanwhile, lots of experts are developing digital notebooks (e.g. Jupyter) to demonstrate step-by-step data collection, pre-processing, analysis and visualization. However, before REMI there were no indexed repositories of notebooks with such examples and no community to maintain, build or learn from these resources. REMI aims to fill this gap, enabling scientists to more easily pick up machine learning while also helping to build a community for sharing knowledge, discussing, debating, establish collaborations, and benchmarking methods.

About this Dataset

Updated: 2024-02-22
Metadata Last Updated: 2020-09-25 00:00:00
Date Created: N/A
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Data Provided by:
machine learning
Dataset Owner: N/A

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Table representation of structured data
Title REMI: Resource for Materials Informatics
Description The REsource for Materials Informatics (REMI) will host a diverse collection of scripting notebooks (Jupyter, Matlab LiveScripts, etc.) for collecting, pre-processing, analyzing, and visualizing materials data. Notebooks are curated using tags aligned to Materials Science and Data Science topics. REMI emerged from the realization that both experts and novices wanted examples of using machine learning for science. Meanwhile, lots of experts are developing digital notebooks (e.g. Jupyter) to demonstrate step-by-step data collection, pre-processing, analysis and visualization. However, before REMI there were no indexed repositories of notebooks with such examples and no community to maintain, build or learn from these resources. REMI aims to fill this gap, enabling scientists to more easily pick up machine learning while also helping to build a community for sharing knowledge, discussing, debating, establish collaborations, and benchmarking methods.
Modified 2020-09-25 00:00:00
Publisher Name National Institute of Standards and Technology
Contact mailto:aaron.kusne@nist.gov
Keywords Machine learning , data analysis , data processing , materials science , materials genome initiative 
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    "contactPoint": {
        "hasEmail": "mailto:aaron.kusne@nist.gov",
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    "landingPage": "https:\/\/pages.nist.gov\/remi\/",
    "description": "The REsource for Materials Informatics (REMI) will host a diverse collection of scripting notebooks (Jupyter, Matlab LiveScripts, etc.) for collecting, pre-processing, analyzing, and visualizing materials data. Notebooks are curated using tags aligned to Materials Science and Data Science topics. REMI emerged from the realization that both experts and novices wanted examples of using machine learning for science. Meanwhile, lots of experts are developing digital notebooks (e.g. Jupyter) to demonstrate step-by-step data collection, pre-processing, analysis and visualization. However, before REMI there were no indexed repositories of notebooks with such examples and no community to maintain, build or learn from these resources. REMI aims to fill this gap, enabling scientists to more easily pick up machine learning while also helping to build a community for sharing knowledge, discussing, debating, establish collaborations, and benchmarking methods.",
    "language": [
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    "title": "REMI: Resource for Materials Informatics",
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            "title": "DOI Access for REMI: Resource for Materials Informatics"
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    "accrualPeriodicity": "R\/P1W",
    "theme": [
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        "Mathematics and Statistics:Image and signal processing",
        "Mathematics and Statistics:Statistical analysis",
        "Mathematics and Statistics:Uncertainty quantification",
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    "issued": "2020-10-23",
    "keyword": [
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}

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