20 datasets found
  1. n

    RUCC - Dataset - CKAN

    • nationaldataplatform.org
    Updated Jun 22, 2025
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    (2025). RUCC - Dataset - CKAN [Dataset]. https://nationaldataplatform.org/catalog/dataset/rucc
    Explore at:
    Dataset updated
    Jun 22, 2025
    Description

    The Rural-Urban Continuum Codes (RUCC), developed by the U.S. Department of Agriculture's Economic Research Service (ERS), classify U.S. counties by their level of urbanization and proximity to metropolitan areas. Counties are categorized as metropolitan or nonmetropolitan, with further divisions based on population size, urbanization level, and adjacency to metro regions. The RUCC provides a detailed framework that supports research and policy analysis in areas such as public health, sociology, regional planning, and economic development. It is widely used for identifying rural-urban disparities and integrates Census data, aligning with Office of Management and Budget (OMB) metro delineations for consistent updates. Its nuanced stratification is particularly valuable in studies like the Alzheimer's Disease Neuroimaging Initiative (ADNI), which explore the social determinants of health.

  2. f

    Characteristics of NCS-R participants by rurality classification*.

    • plos.figshare.com
    xls
    Updated Jun 4, 2023
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    Jennifer S. McCall-Hosenfeld; Sucharita Mukherjee; Erik B. Lehman (2023). Characteristics of NCS-R participants by rurality classification*. [Dataset]. http://doi.org/10.1371/journal.pone.0112416.t001
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jun 4, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Jennifer S. McCall-Hosenfeld; Sucharita Mukherjee; Erik B. Lehman
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    All analyses applied appropriate sample weights.Characteristics of NCS-R participants by rurality classification.

  3. Rapid Update Cycle (RUC) [13 km]

    • catalog.data.gov
    • data.cnra.ca.gov
    • +4more
    Updated Sep 19, 2023
    + more versions
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    NOAA National Centers for Environmental Information (Point of Contact); DOC/NOAA/NESDIS/NCEI > National Centers for Environmental Information, NESDIS, NOAA, U.S. Department of Commerce (Point of Contact) (2023). Rapid Update Cycle (RUC) [13 km] [Dataset]. https://catalog.data.gov/dataset/rapid-update-cycle-ruc-13-km1
    Explore at:
    Dataset updated
    Sep 19, 2023
    Dataset provided by
    United States Department of Commercehttp://www.commerce.gov/
    National Oceanic and Atmospheric Administrationhttp://www.noaa.gov/
    National Centers for Environmental Informationhttps://www.ncei.noaa.gov/
    National Environmental Satellite, Data, and Information Service
    Description

    The Rapid Update Cycle (RUC) weather forecast model was developed by the National Centers for Environmental Prediction (NCEP). On May 1, 2012, the RUC was replaced by NCEP's Rapid Refresh (RAP) weather forecast model. The RUC was designed to produce quick, short-term, weather forecasts using the most currently available observations. When it was first implemented in 1994, the model was run every three hours making forecasts out to 12 hours. By 2002, the RUC was run every hour, on the hour, producing 12-hour forecasts with a 1 hour temporal resolution. This dataset contains a 13 km horizontal resolution Lambert Conformal grid covering the Continental United States (CONUS) domain.

  4. f

    Adjusted odds ratios (aOR) for trauma exposure by rurality, NCS-R...

    • plos.figshare.com
    • figshare.com
    xls
    Updated May 31, 2023
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    Jennifer S. McCall-Hosenfeld; Sucharita Mukherjee; Erik B. Lehman (2023). Adjusted odds ratios (aOR) for trauma exposure by rurality, NCS-R participants*†. [Dataset]. http://doi.org/10.1371/journal.pone.0112416.t002
    Explore at:
    xlsAvailable download formats
    Dataset updated
    May 31, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Jennifer S. McCall-Hosenfeld; Sucharita Mukherjee; Erik B. Lehman
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    All analyses applied appropriate sample weights.†Odds ratios are adjusted for all other factors listed.Adjusted odds ratios (aOR) for trauma exposure by rurality, NCS-R participants†.

  5. rucc.cricket - Historical whois Lookup

    • whoisdatacenter.com
    csv
    Updated Sep 18, 2024
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    AllHeart Web Inc (2024). rucc.cricket - Historical whois Lookup [Dataset]. https://whoisdatacenter.com/domain/rucc.cricket/
    Explore at:
    csvAvailable download formats
    Dataset updated
    Sep 18, 2024
    Dataset provided by
    AllHeart Web
    Authors
    AllHeart Web Inc
    License

    https://whoisdatacenter.com/terms-of-use/https://whoisdatacenter.com/terms-of-use/

    Time period covered
    Mar 15, 1985 - Jul 12, 2025
    Description

    Explore the historical Whois records related to rucc.cricket (Domain). Get insights into ownership history and changes over time.

  6. h

    FunBench

    • huggingface.co
    Updated Mar 2, 2025
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    AIMClab (2025). FunBench [Dataset]. https://huggingface.co/datasets/AIMClab-RUC/FunBench
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    Dataset updated
    Mar 2, 2025
    Dataset authored and provided by
    AIMClab
    License

    Attribution-NonCommercial-ShareAlike 4.0 (CC BY-NC-SA 4.0)https://creativecommons.org/licenses/by-nc-sa/4.0/
    License information was derived automatically

    Description

    [MICCAI2025] FunBench: Benchmarking Fundus Reading Skills of MLLMs FunBench is a novel visual question answering (VQA) benchmark designed to comprehensively evaluate MLLMs' fundus reading skills. Code and description are available at https://github.com/ruc-aimc-lab/FunBench

  7. NCEP RUC Model Forecast Imagery

    • data.ucar.edu
    image
    Updated Dec 26, 2024
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    Research Applications Laboratory (RAL); NCAR (2024). NCEP RUC Model Forecast Imagery [Dataset]. http://doi.org/10.26023/84CS-PJRV-VX0A
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    imageAvailable download formats
    Dataset updated
    Dec 26, 2024
    Dataset provided by
    University Corporation for Atmospheric Research
    Authors
    Research Applications Laboratory (RAL); NCAR
    Time period covered
    Jun 1, 2004 - Sep 20, 2004
    Area covered
    Description

    This data set contains RUC imagery from the NAME Field Catalog. The following imagery products are included: 200mb_wnd, 300mb_wnd, 500mb_wnd, 700mb_wnd, 850mb_wnd, sfc_precip.

  8. f

    Logistic regression model assessing associations between participant...

    • plos.figshare.com
    xls
    Updated Jun 4, 2023
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    Theresa E. Gildner; Elise J. Laugier; Zaneta M. Thayer (2023). Logistic regression model assessing associations between participant Rural-Urban Continuum Code (RUCC) and reported exercise change. [Dataset]. http://doi.org/10.1371/journal.pone.0243188.t003
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jun 4, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Theresa E. Gildner; Elise J. Laugier; Zaneta M. Thayer
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Logistic regression model assessing associations between participant Rural-Urban Continuum Code (RUCC) and reported exercise change.

  9. RUC Forecast Product Imagery

    • data.ucar.edu
    image
    Updated Dec 26, 2024
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    Research Applications Laboratory (RAL); NCAR (2024). RUC Forecast Product Imagery [Dataset]. http://doi.org/10.26023/PG12-SWFS-7W14
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    imageAvailable download formats
    Dataset updated
    Dec 26, 2024
    Dataset provided by
    University Corporation for Atmospheric Research
    Authors
    Research Applications Laboratory (RAL); NCAR
    Time period covered
    Feb 13, 2006 - May 18, 2006
    Area covered
    Description

    This dataset contains RUC forecast product model imagery collected during the MILAGRO field project.

  10. Data from: Urban-rural continuum

    • figshare.com
    tiff
    Updated May 30, 2023
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    Andrea Cattaneo; Andy Nelson; Theresa McMenomy (2023). Urban-rural continuum [Dataset]. http://doi.org/10.6084/m9.figshare.12579572.v4
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    tiffAvailable download formats
    Dataset updated
    May 30, 2023
    Dataset provided by
    Figsharehttp://figshare.com/
    Authors
    Andrea Cattaneo; Andy Nelson; Theresa McMenomy
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    The urban–rural continuum classifies the global population, allocating rural populations around differently-sized cities. The classification is based on four dimensions: population distribution, population density, urban center location, and travel time to urban centers, all of which can be mapped globally and consistently and then aggregated as administrative unit statistics.Using spatial data, we matched all rural locations to their urban center of reference based on the time needed to reach these urban centers. A hierarchy of urban centers by population size (largest to smallest) is used to determine which center is the point of “reference” for a given rural location: proximity to a larger center “dominates” over a smaller one in the same travel time category. This was done for 7 urban categories and then aggregated, for presentation purposes, into “large cities” (over 1 million people), “intermediate cities” (250,000 –1 million), and “small cities and towns” (20,000–250,000).Finally, to reflect the diversity of population density across the urban–rural continuum, we distinguished between high-density rural areas with over 1,500 inhabitants per km2 and lower density areas. Unlike traditional functional area approaches, our approach does not define urban catchment areas by using thresholds, such as proportion of people commuting; instead, these emerge endogenously from our urban hierarchy and by calculating the shortest travel time.Urban-Rural Catchment Areas (URCA).tif is a raster dataset of the 30 urban–rural continuum categories for the urban–rural continuum showing the catchment areas around cities and towns of different sizes. Each rural pixel is assigned to one defined travel time category: less than one hour, one to two hours, and two to three hours travel time to one of seven urban agglomeration sizes. The agglomerations range from large cities with i) populations greater than 5 million and ii) between 1 to 5 million; intermediate cities with iii) 500,000 to 1 million and iv) 250,000 to 500,000 inhabitants; small cities with populations v) between 100,000 and 250,000 and vi) between 50,000 and 100,000; and vii) towns of between 20,000 and 50,000 people. The remaining pixels that are more than 3 hours away from any urban agglomeration of at least 20,000 people are considered as either hinterland or dispersed towns being that they are not gravitating around any urban agglomeration. The raster also allows for visualizing a simplified continuum created by grouping the seven urban agglomerations into 4 categories.Urban-Rural Catchment Areas (URCA).tif is in GeoTIFF format, band interleaved with LZW compression, suitable for use in Geographic Information Systems and statistical packages. The data type is byte, with pixel values ranging from 1 to 30. The no data value is 128. It has a spatial resolution of 30 arc seconds, which is approximately 1km at the equator. The spatial reference system (projection) is EPSG:4326 - WGS84 - Geographic Coordinate System (lat/long). The geographic extent is 83.6N - 60S / 180E - 180W. The same tif file is also available as an ESRI ArcMap MapPackage Urban-Rural Catchment Areas.mpkFurther details are in the ReadMe_data_description.docx

  11. h

    PhD

    • huggingface.co
    Updated Nov 22, 2024
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    AIMClab (2024). PhD [Dataset]. https://huggingface.co/datasets/AIMClab-RUC/PhD
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    Dataset updated
    Nov 22, 2024
    Dataset authored and provided by
    AIMClab
    License

    MIT Licensehttps://opensource.org/licenses/MIT
    License information was derived automatically

    Description

    [CVPR2025 Highlight] PhD: A ChatGPT-Prompted Visual hallucination Evaluation Dataset

    preprint

      🔥 PhD-webdataset
    

    To enhance usability and integration with evaluation frameworks like lmm-eval, we are pleased to offer a packaged version in webdataset format. This packaged version is designed to facilitate easier deployment and testing. For further details and access, please refer to our repository PhD-webdataset. Please note that the data in both repositories is completely… See the full description on the dataset page: https://huggingface.co/datasets/AIMClab-RUC/PhD.

  12. Web map for Rural Urban Classification (RUC) of Local Authority District...

    • hub.arcgis.com
    Updated Mar 6, 2025
    + more versions
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    Office for National Statistics (2025). Web map for Rural Urban Classification (RUC) of Local Authority District Areas (LADs), England and Wales, 2021 [Dataset]. https://hub.arcgis.com/maps/a0df5dc88c53440ba2d56c0f9377fa8c
    Explore at:
    Dataset updated
    Mar 6, 2025
    Dataset authored and provided by
    Office for National Statisticshttp://www.ons.gov.uk/
    License

    https://www.ons.gov.uk/methodology/geography/licenceshttps://www.ons.gov.uk/methodology/geography/licences

    Area covered
    Description

    The Rural-Urban Classification is a Government Statistical Service product developed by the Office for National Statistics; the Department for Environment, Food and Rural Affairs; and the Welsh Assembly Government.Source: Office for National Statistics licensed under the Open Government Licence v.3.0.Contains OS data © Crown copyright 2025Links below to FAQ, Methodology and User GuideFAQ https://geoportal.statistics.gov.uk/documents/f359d48424664a1584dca319f3dac97f/aboutMethodology https://geoportal.statistics.gov.uk/documents/833a35f2a1ec49d98466b679ae0a0646/aboutUser Guide https://geoportal.statistics.gov.uk/documents/c8e8e6db38e04cb8937569d74bce277a/about

  13. h

    FlashRAG_datasets

    • huggingface.co
    Updated Nov 2, 2004
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    NLPIR Lab @ RUC (2004). FlashRAG_datasets [Dataset]. https://huggingface.co/datasets/RUC-NLPIR/FlashRAG_datasets
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    Dataset updated
    Nov 2, 2004
    Dataset authored and provided by
    NLPIR Lab @ RUC
    License

    Attribution-ShareAlike 4.0 (CC BY-SA 4.0)https://creativecommons.org/licenses/by-sa/4.0/
    License information was derived automatically

    Description

    ⚡FlashRAG: A Python Toolkit for Efficient RAG Research

    FlashRAG is a Python toolkit for the reproduction and development of Retrieval Augmented Generation (RAG) research. Our toolkit includes 36 pre-processed benchmark RAG datasets and 16 state-of-the-art RAG algorithms. With FlashRAG and provided resources, you can effortlessly reproduce existing SOTA works in the RAG domain or implement your custom RAG processes and components. For more information, please view our GitHub repo… See the full description on the dataset page: https://huggingface.co/datasets/RUC-NLPIR/FlashRAG_datasets.

  14. Rural-Urban Classification User Guide (2021)

    • geoportal.statistics.gov.uk
    • hub.arcgis.com
    Updated Mar 5, 2025
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    Office for National Statistics (2025). Rural-Urban Classification User Guide (2021) [Dataset]. https://geoportal.statistics.gov.uk/documents/c8e8e6db38e04cb8937569d74bce277a
    Explore at:
    Dataset updated
    Mar 5, 2025
    Dataset authored and provided by
    Office for National Statisticshttp://www.ons.gov.uk/
    License

    https://www.ons.gov.uk/methodology/geography/licenceshttps://www.ons.gov.uk/methodology/geography/licences

    Description

    The user guide is a high level summary of the 2021 Rural Urban Classification methodology, including a visual representation of the method, high level summary statistics and a brief explanation of the changes from the 2011 RUC. This is intended as a short introduction to the 2021 RUC, but for more detailed information users should consult the 2021 Rural Urban Classification of Statistical Geographies, England and Wales: Methodology document.

  15. Ruc 20601412714 Company profile with phone,email, buyers, suppliers, price,...

    • volza.com
    csv
    Updated Jun 27, 2025
    + more versions
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    Volza FZ LLC (2025). Ruc 20601412714 Company profile with phone,email, buyers, suppliers, price, export import shipments. [Dataset]. https://www.volza.com/company-profile/ruc-20601412714-14672810/
    Explore at:
    csvAvailable download formats
    Dataset updated
    Jun 27, 2025
    Dataset provided by
    Volza
    Authors
    Volza FZ LLC
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    2014 - Sep 30, 2021
    Variables measured
    Count of exporters, Count of importers, Sum of export value, Sum of import value, Count of export shipments, Count of import shipments
    Description

    Credit report of Ruc 20601412714 contains unique and detailed export import market intelligence with it's phone, email, Linkedin and details of each import and export shipment like product, quantity, price, buyer, supplier names, country and date of shipment.

  16. Rural-Urban Continuum Codes

    • catalog.data.gov
    • datadiscoverystudio.org
    • +4more
    Updated Apr 21, 2025
    + more versions
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    Economic Research Service, Department of Agriculture (2025). Rural-Urban Continuum Codes [Dataset]. https://catalog.data.gov/dataset/rural-urban-continuum-codes
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    Dataset updated
    Apr 21, 2025
    Dataset provided by
    Economic Research Servicehttp://www.ers.usda.gov/
    Description

    The 2013 Rural-Urban Continuum Codes form a classification scheme that distinguishes metropolitan counties by the population size of their metro area, and nonmetropolitan counties by degree of urbanization and adjacency to a metro area. The official Office of Management and Budget (OMB) metro and nonmetro categories have been subdivided into three metro and six nonmetro categories. Each county in the U.S. is assigned one of the 9 codes. This scheme allows researchers to break county data into finer residential groups, beyond metro and nonmetro, particularly for the analysis of trends in nonmetro areas that are related to population density and metro influence. The Rural-Urban Continuum Codes were originally developed in 1974. They have been updated each decennial since (1983, 1993, 2003, 2013), and slightly revised in 1988. Note that the 2013 Rural-Urban Continuum Codes are not directly comparable with the codes prior to 2000 because of the new methodology used in developing the 2000 metropolitan areas. See the Documentation for details and a map of the codes. An update of the Rural-Urban Continuum Codes is planned for mid-2023.

  17. The Troubles: RUC/PSNI personnel 1971-2011

    • statista.com
    Updated Jul 4, 2024
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    Statista (2024). The Troubles: RUC/PSNI personnel 1971-2011 [Dataset]. https://www.statista.com/statistics/1402088/ni-troubles-ruc-numbers/
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    Dataset updated
    Jul 4, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    1971 - 2011
    Area covered
    Northern Ireland
    Description

    From the early 1970s until the early 1980s, the number of police officers serving in the Royal Ulster Constabulary (RUC) more than doubled from around 4,000 to 8,000 full time officers, and it stayed at more than 8,000 officers until the end of the Troubles in 1998. The RUC was then superseded by the Police Service of Northern Ireland in 2001, and its numbers then dropped to around 7,000 officers in its first decade. Additionally, the RUC was supported by its reserve force, whose membership peaked at almost 5,000 personnel in the mid-1970s, and remained at over 4,000 until the end of the Troubles.

  18. o

    Index of Relative Rurality (IRR) for Multiple Geographies

    • openicpsr.org
    Updated Apr 9, 2025
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    W Jay Christian; Jeff Levy; Mary Kay Rayens; Ann Kingsolver; Ellen Hahn; Teresa Waters; Mikhail Koffarnus; Seth Himelhoch; Shyanika Rose (2025). Index of Relative Rurality (IRR) for Multiple Geographies [Dataset]. http://doi.org/10.3886/E226101V1
    Explore at:
    Dataset updated
    Apr 9, 2025
    Dataset provided by
    University of Kentucky College of Nursing and AppalTRUST
    University of Kentucky College of Arts & Sciences and AppalTRUST
    University of Kentucky College of Medicine and AppalTRUST
    Augusta University School of Public Health and AppalTRUST
    University of Chicago Biological Sciences Division and Pritzker School of Medicine and AppalTRUST
    University of Kentucky College of Public Health and AppalTRUST (Appalachian Tobacco Regulatory Science Team, the University of Kentucky TCORS [Tobacco Centers of Regulatory Science] Center)
    Authors
    W Jay Christian; Jeff Levy; Mary Kay Rayens; Ann Kingsolver; Ellen Hahn; Teresa Waters; Mikhail Koffarnus; Seth Himelhoch; Shyanika Rose
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    2020
    Area covered
    census tracts, and 3-digit ZCTAs, Counties, zip code tabulation areas (ZCTAs)
    Description

    This project introduces researchers and other users to the Index of Relative Rurality (IRR), a continuous, multi-dimensional, and scalable measure for characterizing the rurality of areas or regions in the United States. First proposed by Waldorf in 2006,[1] and later operationalized by Waldorf & Kim,[2,3] the IRR is an alternative to categorical measures such as the Rural-Urban Continuum Codes (RUCC), Rural-Urban Commuting Areas (RUCA), and Frontier and Remote (FAR) Codes, or binary classifications researchers derive from them. We are distributing these data because IRR values for some of these US geographies have not been available previously, and because we want to clearly and fully document the data sources and methods necessary to calculate the IRR. 1. Waldorf, B.S., 2006. A continuous multi-dimensional measure of rurality: Moving beyond threshold measures. Accessed 3/26/2025 at https://ageconsearch.umn.edu/record/21383?v=pdf. 2. Waldorf, B. and Kim, A., 2015. Defining and measuring rurality in the US: From typologies to continuous indices. In Commissioned paper presented at the Workshop on Rationalizing Rural Area Classifications, Washington, DC. Accessed 3/26/2025 at http://sites.nationalacademies.org/cs/groups/dbassesite/documents/webpage/dbasse_168031.pdf. 3. Kim, A. and Waldorf, B., 2023. The Index of Relative Rurality (IRR): US County Data for 2020. Accessed 3/26/2025 at https://zenodo.org/records/7675745. DOI: 10.5281/zenodo.7675745

  19. h

    OlymMATH

    • huggingface.co
    Updated Mar 28, 2025
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    RUC-AIBOX (2025). OlymMATH [Dataset]. https://huggingface.co/datasets/RUC-AIBOX/OlymMATH
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    Dataset updated
    Mar 28, 2025
    Dataset authored and provided by
    RUC-AIBOX
    License

    MIT Licensehttps://opensource.org/licenses/MIT
    License information was derived automatically

    Description

    Challenging the Boundaries of Reasoning: An Olympiad-Level Math Benchmark for Large Language Models

    This is the official huggingface repository for Challenging the Boundaries of Reasoning: An Olympiad-Level Math Benchmark for Large Language Models by Haoxiang Sun, Yingqian Min, Zhipeng Chen, Wayne Xin Zhao, Zheng Liu, Zhongyuan Wang, Lei Fang, and Ji-Rong Wen. We have also released the OlymMATH-eval dataset on HuggingFace 🤗, together with a data visualization tool OlymMATH-demo… See the full description on the dataset page: https://huggingface.co/datasets/RUC-AIBOX/OlymMATH.

  20. W

    Rural Urban Classification of Cambridgeshire

    • cloud.csiss.gmu.edu
    • data.europa.eu
    • +1more
    csv, xls
    Updated Jan 4, 2020
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    United Kingdom (2020). Rural Urban Classification of Cambridgeshire [Dataset]. https://cloud.csiss.gmu.edu/uddi/dataset/rural-urban-classification-of-cambridgeshire
    Explore at:
    csv(86375), csv(21196), csv(4283), xls(365056)Available download formats
    Dataset updated
    Jan 4, 2020
    Dataset provided by
    United Kingdom
    License

    http://reference.data.gov.uk/id/open-government-licencehttp://reference.data.gov.uk/id/open-government-licence

    Area covered
    Cambridgeshire
    Description

    The 2011 rural-urban classification was released in August 2013. It is a revised version of the classification produced after the 2001 Census, but with additional detail in the urban domain. The product was sponsored by a cross-Government working group comprising Department for Environment, Food and Rural Affairs, Department of the Communities and Local Government, Office for National Statistics and the Welsh Government.

    The data is available at three geographical levels:

    Output Area

    Output areas are treated as ‘urban’ if they were allocated to a 2011 built-up area with a population of 10,000 or more. The urban domain is then further sub-divided into three broad morphological types based on the predominant settlement component. As with the previous version of the classification, the remaining ‘rural’ output areas are grouped into three broad morphological types based on the predominant settlement component. The classification also categorises output areas based on context – i.e. whether the wider surrounding area of a given output area is sparsely populated or less sparsely populated.

    Urban: Major Conurbation (A1) Urban: Minor Conurbation (B1) Urban: City and Town (C1) Urban: City and Town in a Sparse Setting (C2) Rural: Town and Fringe (D1) Rural: Town and Fringe in a Sparse Setting (D2) Rural: Village (E1) Rural: Village in a Sparse Setting (E2) Rural: Hamlets and Isolated Dwellings (F1) Rural: Hamlets and Isolated Dwellings in a Sparse Setting (F2)

    Lower Layer Super Output Areas (LSOA)

    The 2011 rural-urban classification of lower layer super output areas was released in August 2013. It is a revised version of the classification produced after the 2001 Census, but with additional detail in the urban domain. This product was sponsored by a cross-Government working group comprising Department for Environment, Food and Rural Affairs, Department of the Communities and Local Government, Office for National Statistics and the Welsh Government. The classification at LSOA level is built from the RUC at OA level (the most detailed version of the classification). Assignments of LSOA to urban or rural categories are made by reference to the category to which the majority of their constituent OA are assigned. In the RUC at OA level, output areas are treated as ‘urban’ if they were allocated to a 2011 built-up area with a population of 10,000 or more. The urban domain is then further sub-divided into three broad morphological types based on the predominant settlement component. As with the previous version of the classification, the remaining ‘rural’ output areas are grouped into three broad morphological types based on the predominant settlement component. At the LSOA scale settlement form is less homogenous than at OA level and so there are just two rural settlement types. The classification also categorises output areas based on context – i.e. whether the wider surrounding area of a given output area is sparsely populated or less sparsely populated.

    Urban: Major Conurbation (A1) Urban: Minor Conurbation (B1) Urban: City and Town (C1) Urban: City and Town in a Sparse Setting (C2) Rural Town and Fringe (D1) Rural Town and Fringe in a Sparse Setting (D2) Rural Village and Dispersed (E1) Rural Village and Dispersed in a Sparse Setting (E2) Middle Layer Super Output Areas (MSOA)

    The 2011 rural-urban classification of middle layer super output areas was released in August 2013. It is a revised version of the classification produced after the 2001 Census, but with additional detail in the urban domain. This product was sponsored by a cross-Government working group comprising Department for Environment, Food and Rural Affairs, Department of the Communities and Local Government, Office for National Statistics and the Welsh Government. The classification at MSOA level is built from the RUC at OA level (the most detailed version of the classification). Assignments of MSOA to urban or rural categories are made by reference to the category to which the majority of their constituent OA are assigned. In the RUC at OA level, output areas are treated as ‘urban’ if they were allocated to a 2011 built-up area with a population of 10,000 or more. The urban domain is then further sub-divided into three broad morphological types based on the predominant settlement component. As with the previous version of the classification, the remaining ‘rural’ output areas are grouped into three broad morphological types based on the predominant settlement component. At the MSOA scale settlement form is less homogenous than at OA level and so there are just two rural settlement types. The classification also categorises output areas based on context – i.e. whether the wider surrounding area of a given output area is sparsely populated or less sparsely populated.

    Urban: Major Conurbation (A1) Urban: Minor Conurbation (B1) Urban: City and Town (C1) Urban: City and Town in a Sparse Setting (C2) Rural Town and Fringe (D1) Rural Town and Fringe in a Sparse Setting (D2) Rural Village and Dispersed (E1) Rural Village and Dispersed in a Sparse Setting (E2)

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(2025). RUCC - Dataset - CKAN [Dataset]. https://nationaldataplatform.org/catalog/dataset/rucc

RUCC - Dataset - CKAN

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Dataset updated
Jun 22, 2025
Description

The Rural-Urban Continuum Codes (RUCC), developed by the U.S. Department of Agriculture's Economic Research Service (ERS), classify U.S. counties by their level of urbanization and proximity to metropolitan areas. Counties are categorized as metropolitan or nonmetropolitan, with further divisions based on population size, urbanization level, and adjacency to metro regions. The RUCC provides a detailed framework that supports research and policy analysis in areas such as public health, sociology, regional planning, and economic development. It is widely used for identifying rural-urban disparities and integrates Census data, aligning with Office of Management and Budget (OMB) metro delineations for consistent updates. Its nuanced stratification is particularly valuable in studies like the Alzheimer's Disease Neuroimaging Initiative (ADNI), which explore the social determinants of health.

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