6 datasets found
  1. h

    genz-slang-dataset

    • huggingface.co
    Updated Oct 2, 2024
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    GMLB trio 2024 (2024). genz-slang-dataset [Dataset]. https://huggingface.co/datasets/MLBtrio/genz-slang-dataset
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Oct 2, 2024
    Dataset authored and provided by
    GMLB trio 2024
    Description

    Dataset Details

    This dataset contains a rich collection of popular slang terms and acronyms used primarily by Generation Z. It includes detailed descriptions of each term, its context of use, and practical examples that demonstrate how the slang is used in real-life conversations. The dataset is designed to capture the unique and evolving language patterns of GenZ, reflecting their communication style in digital spaces such as social media, text messaging, and online forums. Each… See the full description on the dataset page: https://huggingface.co/datasets/MLBtrio/genz-slang-dataset.

  2. h

    Sindhi-Handwritten-Alphabets

    • huggingface.co
    Updated Feb 16, 2025
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    Mudasir Murtaza (2025). Sindhi-Handwritten-Alphabets [Dataset]. https://huggingface.co/datasets/maddy2104/Sindhi-Handwritten-Alphabets
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    Dataset updated
    Feb 16, 2025
    Authors
    Mudasir Murtaza
    License

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

    Description

    This dataset is a rich collection of handwritten Sindhi alphabet images, carefully curated to capture a diverse range of writing styles. The dataset includes samples from multiple generations, including Gen X, Millennials, Gen Z, and Gen Alpha, ensuring a broad representation of handwriting variations. Additionally, it encompasses contributions from individuals of different genders and varying levels of handwriting proficiency, making it highly valuable for research in handwriting recognition… See the full description on the dataset page: https://huggingface.co/datasets/maddy2104/Sindhi-Handwritten-Alphabets.

  3. PixART Images ( 512x512 )

    • kaggle.com
    Updated May 8, 2025
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    Aryan Kaushik 005 (2025). PixART Images ( 512x512 ) [Dataset]. https://www.kaggle.com/datasets/aryankaushik005/pixart
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    May 8, 2025
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Aryan Kaushik 005
    Description

    PixArt Generated Image Dataset

    This dataset contains a collection of AI-generated images created using the PixArt-α model. PixArt-α is an open-source, transformer-based diffusion model developed by Huawei's Noah’s Ark Lab for high-quality text-to-image generation. Images were generated using various prompts and images have been classified in various categories.

    Dataset Overview

    • Total Images: 2111 ( 2109 images + 2 txt files )
    • Resolution: 512×512 pixels
    • Format: jpg
    • Generation Model: [PixArt-alpha / PixArt-XL]

    PixArt-α

    PixArt-α is a state-of-the-art diffusion model that combines the strengths of diffusion techniques and transformer architectures to generate high-fidelity images from textual prompts. It supports generation at resolutions up to 1024×1024, although this dataset contains 512×512 resolution images for efficiency.

    Use Cases

    • Image classification and object recognition
    • Synthetic dataset generation
    • Few-shot learning experiments
    • Data augmentation for deep learning models
    • Benchmarking generative models

    Categories

    The dataset includes the following 11 image categories:

    • automobile
    • bird
    • cat
    • deer
    • dog
    • frog
    • horse
    • plane
    • ship
    • truck
    • truck_2

    Each folder represents a category and contains multiple 512×512 images generated by prompting PixArt-α with relevant class descriptions.

  4. a

    Renewables: Planned Distributed Energy Generation

    • laep-datahub-alpha-cityhall.hub.arcgis.com
    Updated Mar 21, 2025
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    GREATER LONDON AUTHORITY (2025). Renewables: Planned Distributed Energy Generation [Dataset]. https://laep-datahub-alpha-cityhall.hub.arcgis.com/datasets/renewables-planned-distributed-energy-generation
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    Dataset updated
    Mar 21, 2025
    Dataset authored and provided by
    GREATER LONDON AUTHORITY
    Area covered
    Description

    Author:BEIS (now DESNZ)Creation date:2023Date of source data harvest:2023Temporal coverage:2008-2027Spatial Resolution:Project locationsGeometry:PointSource data URL:Renewable Energy Planning Database: quarterly extract - GOV.UKData terms of use:Open Government Licence v3 - Dataset can be shared openly for re-use for commercial and non-commercial purposes, with appropriate attribution.Data attribution:- Dataset processed by Arup as part of the West London sub-regional LAEP, 2023.- Contains DESNZ data licensed under the Open Government Licence v3.0.Workflow Diagram:Available: pngComments:The data and analysis developed for the sub-regional LAEP was undertaken using data available at the time and will need to be refined for a full Phase 2 LAEP.Whilst every effort has been made to ensure the quality and accuracy of the data, the Greater London Authority is not responsible for any inaccuracies and/or mistakes in the information provided.

  5. O

    DEPRECATED: dGen (Distributed Generation Market Demand) Model Data: Alpha...

    • data.openei.org
    • catalog.data.gov
    archive, website
    Updated Apr 1, 2020
    + more versions
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    Stanley; Das; Sigrin; McCabe; Stanley; Das; Sigrin; McCabe (2020). DEPRECATED: dGen (Distributed Generation Market Demand) Model Data: Alpha Release [Dataset]. https://data.openei.org/submissions/8202
    Explore at:
    archive, websiteAvailable download formats
    Dataset updated
    Apr 1, 2020
    Dataset provided by
    National Renewable Energy Laboratory
    Open Energy Data Initiative (OEDI)
    USDOE Office of Energy Efficiency and Renewable Energy (EERE), Multiple Programs (EE)
    Authors
    Stanley; Das; Sigrin; McCabe; Stanley; Das; Sigrin; McCabe
    License

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

    Description

    DEPRECATED. DO NOT USE. See current version at https://dx.doi.org/10.7799/1812548. See active link below in the resources section. Open sourced data needed to run the basic alpha release version of the dGen model. Includes a pre-generated agent file of 100,000 agents in pickle file format along with the base schema and table data in parquet format that are needed to create a postgreSQL database for the model to interact with.

  6. Dataset of "Photoelectrochemical generation of H2O2 using hematite (α-Fe2O3)...

    • zenodo.org
    • data.niaid.nih.gov
    bin, csv, tiff, txt
    Updated May 29, 2025
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    Tomáš Imrich; Karel Bouzek; Karel Bouzek; Šárka Paušová; Šárka Paušová; Tomáš Imrich (2025). Dataset of "Photoelectrochemical generation of H2O2 using hematite (α-Fe2O3) and gas diffusion electrode (GDE)" [Dataset]. http://doi.org/10.5281/zenodo.13164575
    Explore at:
    tiff, csv, txt, binAvailable download formats
    Dataset updated
    May 29, 2025
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Tomáš Imrich; Karel Bouzek; Karel Bouzek; Šárka Paušová; Šárka Paušová; Tomáš Imrich
    License

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

    Time period covered
    Aug 9, 2024
    Description

    In contrast to the industrial-scale production of H2O2 the electrochemical or photoelectrochemical synthesis is environmentally friendly. In the present work,
    the photoelectrochemical generation of H2O2 was studied by combining the hematite (α-Fe2O3/FTO/glass) photoanode and gas diffusion electrode (GDE) modified by
    incorporation of tin (II) phthalocyanine (SnPc) in its hydrophilic layer. The experiments were carried out in a photoelectrochemical cell with two compartments
    separated by a proton exchange membrane under applied bias and AM1.5 irradiation (100 mW/cm2). The generated amount of H2O2 was determined by chemical analysis
    (visible light spectrophotometry) of the electrolyte. As a tool to determine the efficiency of such a process, the Faradaic efficiency (FE) was calculated. The
    best configuration used air as an inlet gas for GDE and phosphate buffer (pH 6.4) as an electrolyte in the cathodic compartment. The combination of hematite and
    GDE (with SnPc) was the most effective in H2O2 photoelectrochemical generation. The highest value of FE was 52.4 % for GDE (O2 reduction to H2O2) and 0.4 % for
    hematite photoanode (H2O oxidation to H2O2).

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GMLB trio 2024 (2024). genz-slang-dataset [Dataset]. https://huggingface.co/datasets/MLBtrio/genz-slang-dataset

genz-slang-dataset

MLBtrio/genz-slang-dataset

Explore at:
CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
Dataset updated
Oct 2, 2024
Dataset authored and provided by
GMLB trio 2024
Description

Dataset Details

This dataset contains a rich collection of popular slang terms and acronyms used primarily by Generation Z. It includes detailed descriptions of each term, its context of use, and practical examples that demonstrate how the slang is used in real-life conversations. The dataset is designed to capture the unique and evolving language patterns of GenZ, reflecting their communication style in digital spaces such as social media, text messaging, and online forums. Each… See the full description on the dataset page: https://huggingface.co/datasets/MLBtrio/genz-slang-dataset.

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