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    • This is a Non-Federal dataset covered by different Terms of Use than Data.gov.

    Measurement and Effects of Supply Chain Bottlenecks Using Natural Language Processing

    Metadata Updated: December 18, 2024

    The Supply Chain Bottleneck Sentiment (SCB Sentiment) index measures supply chain bottlenecks by analyzing narratives from the Federal Reserve's Beige Books using machine learning and natural language processing techniques. The Beige Book summarizes the economic condition of each of the twelve Federal Reserve districts and aggregates narratives that are collected from business contacts.

    Access & Use Information

    Public: This dataset is intended for public access and use. Non-Federal: This dataset is covered by different Terms of Use than Data.gov. License: No license information was provided.

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    Dates

    Metadata Created Date December 18, 2024
    Metadata Updated Date December 18, 2024

    Metadata Source

    Harvested from federal

    Other Data Resources

    Additional Metadata

    Resource Type Dataset
    Metadata Created Date December 18, 2024
    Metadata Updated Date December 18, 2024
    Publisher Board of Governors of the Federal Reserve System
    Maintainer
    Identifier FRBC0001
    Data Last Modified 2024-10-24
    Public Access Level public
    Bureau Code 920:00
    Schema Version https://project-open-data.cio.gov/v1.1/schema
    Harvest Object Id bdec2946-023f-40d7-911a-63dbb924fbd2
    Harvest Source Id ca6963d5-5226-446e-869f-283e1b543a14
    Harvest Source Title federal
    Homepage URL https://www.federalreserve.gov/econres/notes/feds-notes/measurement-and-effects-of-supply-chain-bottlenecks-using-natural-language-processing-20230206.html
    Program Code 920:000
    Source Datajson Identifier True
    Source Hash d3a60e7b27bf50bb664c39f85ab8549328410e192541e1834a5928fab7e106f4
    Source Schema Version 1.1

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