The Alzheimer's Disease Multiclass Dataset contains approximately 44,000 MRI images categorized into four distinct classes based on the severity of Alzheimer's disease. This dataset is intended for use in machine learning model training and testing. All images are skull-stripped and clean of non-brain tissue.

Dataset Structure The dataset is organized into the following four directories, each representing a different class of disease severity: NonDemented: Contains 12,800 MRI images of subjects with no signs of dementia. VeryMildDemented: Contains 11,200 MRI images of subjects with very mild symptoms of dementia. MildDemented: Contains 10,000 MRI images of subjects with mild dementia. ModerateDemented: Contains 10,000 MRI images of subjects with moderate dementia.

Image Details Total Number of Images: 44,000 Image Format: MRI scans as .JPG files Image Usage: Suitable for training and testing machine learning models focused on classifying Alzheimer's disease stages.

Disease Severity Classification The dataset follows a severity ranking system for Alzheimer's disease: NonDemented: No dementia. Very Mild Demented: Early signs of dementia, very mild symptoms. Mild Demented: Clear signs of dementia, but still mild. Moderate Demented: More pronounced symptoms of dementia, moderate severity.

This dataset is an augmented and upsampled version of the dataset below: https://www.kaggle.com/datasets/uraninjo/augmented-alzheimer-mri-dataset-v2

This dataset was upsampled as the original dataset had a large class imbalance.

joekaw/test-dataset dataset hosted on Hugging Face and contributed by the HF Datasets community

VLM-3R Training Data

Training QA data for VLM-3R: vsibench_train/ (VSI-Bench-style tasks) and vstibench_train/ (VSTI-Bench tasks over ScanNet train split).

  Erratum (2026-07-13): corrected camera-position ground truth

A bug in the QA generation pipeline (reported by Jacob Yeung, CMU) extracted the camera center from camera-to-world poses using -R.T @ t instead of pose[:3, 3]. Answers in five vstibench_train files depended on the camera's world position and have… See the full description on the dataset page: https://huggingface.co/datasets/Journey9ni/VLM-3R-DATA.

farsi-asr/ganjoor-dataset dataset hosted on Hugging Face and contributed by the HF Datasets community

An english description is given below.

In diesem Datensatz sind alle ( 15) Klimastationen des gewählten Bundeslandes abgelegt. Je Station 10 Realisierungen. 150 Dateien mit je 4'241'439 Byte. Datensatz ist zip-gepackt.

Daten: (ASCII) Datasatz Kürzel : WR2010_EH5_1_A1B_MV_KL Datasatz Name : UBA-WETTREG ECHAM5/OM 20C + A1B Lauf 1 1961-2100 für das gewählte Bundesland, Klimastationen

Dateistruktur Klimastation: (Kopfzeilen) Stationsname Breite Länge Höhe Typ ta.mo.jahr TX TM TN RR RF PP DD SD NN FF

Stationslist: Stationsliste_MV_KL.txt Stationsnummer, Stationsname, Bundeslandkürzel, Breite, Länge, Stationshöhe,Typ

Es gibt keine Jahre mit Schalttag. Die Ausfallkennung ist -999.0

This data set is a pool of all ( 15) climate stations of the selected Federal State, specified in the entry_name. 10 realizations per station . 150 files with 4'292'439 Byte. Dataset is zip-compressed.

Data: (ASCII) Dataset acronym: WR2010_EH5_1_A1B_MV_KL Dataset name: UBA-WETTREG ECHAM5/OM 20C + A1B Run 1 realization 1961-2100 for the selected Federal State - climate stations

File structure climate stations: (header) station name Latitude Longitude height type ta.mo.jahr TX TM TN RR RF PP DD SD NN FF

Station list: Stationsliste_MV_KL.txt station number, name of station, Abbreviation of federal state, latitude, longitude, height over sea level,type

There are no leap years. Missing values are indicated with -999.0

Box Obstacle

## Overview

Box Obstacle is a dataset for object detection tasks - it contains Box Obstacle annotations for 266 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

COCO-Counterfactuals is a high quality synthetic dataset for multimodal vision-language model evaluation and for training data augmentation. Each COCO-Counterfactuals example includes a pair of image-text pairs; one is a counterfactual variation of the other. The two captions are identical to each other except a noun subject. The two corresponding synthetic images differ only in terms of the altered subject in the two captions. In our accompanying paper, we showed that the COCO-Counterfactuals dataset is challenging for existing pre-trained multimodal models and significantly increase the difficulty of the zero-shot image-text retrieval and image-text matching tasks. Our experiments also demonstrate that augmenting training data with COCO-Counterfactuals improves OOD generalization on multiple downstream tasks.

Global Football (Soccer) Data Lake

Cleaned, deduplicated, quality-gated football match data for BTTS / goals modelling. Sources: API-Football + football-data.co.uk. Pipeline & docs: https://github.com/eatpizzanot/soccer-dataset

673,966 fixtures (644,901 played), 271 leagues, 11,104 teams, 2008-06-07 - 2027-06-06. BTTS base rate 0.5063. xG fake-zeros removed; known_at leakage guard; 12-dimension QA gate (QUALITY_REPORT.md).

Caveats: league history is uneven — check… See the full description on the dataset page: https://huggingface.co/datasets/eatpizzanot/soccer-dataset.

Electric Pole Side

## Overview

Electric Pole Side is a dataset for object detection tasks - it contains Electric Pole annotations for 271 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Test dataset for a manuscript: "Parvalbumin-expressing basal forebrain neurons mediate learning from negative experience" from Hegedüs et al (https://doi.org/10.1101/2023.03.31.535018).

This dataset contains a folder structure with tetrode recording, behavioral and fiber photometry data to test functions in https://github.com/hangyabalazs/PV_Pavlovian_analysis GitHub repository

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Disclaimer: PLEASE DO NOT USE THIS VERSION (Data has been made unavailable)

Another version, version 2.0.0, of this data set is available here, which contains the original data (+ details on the data set). The version published on the current page contains EEG data that has been pre-processed with a data-driven artifact removal procedure on a trial-by-trial basis. This can introduce slight differences across trials that can possibly be exploited by classifiers. Therefore, for proper benchmarking of auditory attention decoding algorithms that can learn to use such cues, it is recommended to NOT use this pre-processed data set, and instead use the unprocessed version 1.1.0 (the original data).

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This version of the data set was used in

Das, N., Vanthornhout, J., Francart, T., & Bertrand, A. “Stimulus-aware spatial filtering for single-trial neural response and temporal response function estimation in high-density EEG with applications in auditory research.” NeuroImage, Volume 204, 116211, Jan. 2020
https://doi.org/10.1016/j.neuroimage.2019.116211

The results and conclusions in the paper are not affected by the trial-by-trial application of the artifact removal procedure.

This dataset is based on the Sample Leads Dataset and is intended to allow some simple filtering by lead source. I had modified this dataset to support an upcoming Towards Data Science article walking through the process. Link to be shared once published.

EPIC Theatres location dataset: United States subset. Verified addresses, coordinates, and phones. Licensed via CREHQ Data Store.

midooy/RDRF-dataset dataset hosted on Hugging Face and contributed by the HF Datasets community

parthrautV/water-gemma-dataset dataset hosted on Hugging Face and contributed by the HF Datasets community

This dataset contains the digitized treatments in Plazi based on the original journal article Sawyer, Roy T. (2019): Observations on the terrestrial leech Haemopis septagon Sawyer & Shelley, 1976 (Annelida: Hirudinea) from the Outer Banks, North Carolina, USA, with a revision of the species. Zootaxa 4658 (2): 275-296, DOI: 10.11646/zootaxa.4658.2.4

Abstract

The terrestrial leech Haemopis septagon Sawyer & Shelley, 1976, is indigenous to the Great Dismal Swamp and environs of northeastern North Carolina and southeastern Virginia, USA. Ever since its discovery in 1895 at Lake Drummond in the Dismal Swamp, this elusive species has been recognized as taxonomically aberrant. For example, it is the only jawed leech in the United States with seven annuli between gonopores, and the only one with sixteen complete (5-annulate) segments, both highly conserved characters in the Hirudinidae.

The discovery of two populations of H. septagon in the Albemarle Peninsula in the Outer Banks region of North Carolina afforded an opportunity to investigate the taxonomy and biology of this inadequately characterized species. Its description in this study is the first comprehensive account of the external and internal anatomy of this species since its incomplete original description in 1976. This study is also an opportunity to correct errors in the incomplete original description, and to elucidate morphological and developmental variability of taxonomic significance. Evidence is presented for the first time of a possible aquatic or semi-aquatic form of H. septagon.

These Albemarle individuals were compared to the holotype from Durham County, NC, specimens from southeastern Virginia and a terrestrial leech recently reported from southern New Jersey. All of these fall within the variability demonstrated in this study for the Albemarle populations, and are accordingly recognized as the same species, H. septagon. Consequentially, Haemopis ottorum Wirchansky & Shain, 2010, is recognized as a junior synonym of Haemopis septagon.

This is the edited sequence set for the data in the cowbird dataset. Due to file size limitations, these are only the unique sequences. For the unedited dataset, please contact the authors of the manuscript: Hird et al. 2014. Sampling locality is more detectable than taxonomy or ecology in the brood-parasitic Brown-Headed Cowbird. PeerJ. DOI:10.7717/peerj.321. Available: https://peerj.com/articles/321/

Epstein Files OCR — Datasets 1–8 (Early Release)

ARCHIVE NOTICE This dataset is no longer maintained. Please refer to the Epstein Files — Complete OCR Dataset.

  Dataset Summary

This dataset contains page-level OCR output (as Markdown) from a public release of documents related to Jeffrey Epstein / the Epstein case. Each Markdown file represents one scanned page converted to text using an automated OCR pipeline. The dataset is designed for:

Question answering Information… See the full description on the dataset page: https://huggingface.co/datasets/ishumilin/epstein-files-ocr-datasets-1-8-early-release.

About this dataset

Context

The dataset presents median household incomes for various household sizes in Badger, IA, as reported by the U.S. Census Bureau. The dataset highlights the variation in median household income with the size of the family unit, offering valuable insights into economic trends and disparities within different household sizes, aiding in data analysis and decision-making.

Key observations

• Of the 7 household sizes (1 person to 7-or-more person households) reported by the census bureau, Badger did not include 6, or 7-person households. Across the different household sizes in Badger the mean income is $90,854, and the standard deviation is $33,863. The coefficient of variation (CV) is 37.27%. This high CV indicates high relative variability, suggesting that the incomes vary significantly across different sizes of households.
• In the most recent year, 2023, The smallest household size for which the bureau reported a median household income was 1-person households, with an income of $34,167. It then further increased to $101,250 for 5-person households, the largest household size for which the bureau reported a median household income.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Household Sizes:

• 1-person households
• 2-person households
• 3-person households
• 4-person households
• 5-person households
• 6-person households
• 7-or-more-person households

Variables / Data Columns

• Household Size: This column showcases 7 household sizes ranging from 1-person households to 7-or-more-person households (As mentioned above).
• Median Household Income: Median household income, in 2023 inflation-adjusted dollars for the specific household size.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Badger median household income. You can refer the same here

Context

Action Recognition in video is known to be more challenging than image recognition problems. Unlike image recognition models which use 2D convolutional neural blocks, action classification models require additional dimensionality to capture the spatio-temporal information in video sequences. This intrinsically makes video action recognition models computationally intensive and significantly more data-hungry than image recognition counterparts. Unequivocally, existing video datasets such as Kinetics, AVA, Charades, Something-Something, HMDB51, and UFC101 have had tremendous impact on the recently evolving video recognition technologies. Artificial Intelligence models trained on these datasets have largely benefited applications such as behavior monitoring in elderly people, video summarization, and content-based retrieval. However, this growing concept of action recognition has yet to be explored in Intelligent Transportation System (ITS), particularly in vital applications such as incidents detection. This is partly due to the lack of availability of annotated dataset adequate for training models suitable for such direct ITS use cases. In this paper, the concept of video action recognition is explored to tackle the problem of highway incident detection and classification from live surveillance footage. First, a novel dataset - HWID12 (Highway Incidents Detection) dataset is introduced. The HWAD12 consists of 11 distinct highway incidents categories, and one additional category for negative samples representing normal traffic. The proposed dataset also includes 2780+ video segments of 3 to 8 seconds on average each, and 500k+ temporal frames. Next, the baseline for highway accident detection and classification is established with a state-of-the-art action recognition model trained on the proposed HWID12 dataset. Performance benchmarking for 12-class (normal traffic vs 11 accident categories), and 2-class (incident vs normal traffic) settings is performed. This benchmarking reveals a recognition accuracy of up to 88% and 98% for 12-class and 2-class recognition setting, respectively.

Data Acquisition

The Proposed Highway Incidents Detection Dataset (HWID12) is the first of its kind dataset aimed at fostering experimentation of video action recognition technologies to solve the practical problem of real-time highway incident detections which currently challenges intelligent transportation systems. The lack of such dataset has limited the expansion of the recent breakthroughs in video action classification for practical uses cases in intelligent transportation systems.. The proposed dataset contains more than 2780 video clips of length varying between 3 to 8 seconds. These video clips capture moments leading to, up until right after an incident occurred. The clips were manually segmented from accident compilations videos sourced from YouTube and other videos data platforms.

Content

There is one main zip file available for download. The zip file contains 2780+ video clips. 1) 12 folders
2) each folder represents an incident category. One of the classes represent the negative sample class which simulates normal traffic.

Terms and Conditions

• Videos provided in this dataset are freely available for research and education purposes only. Please be sure to properly credit the authors by citing the article below.
• Be sure to upvote this dataset if you find it useful by scrolling up and clicking the up-Arrow ^ sign at the top banner of the page, next to "New Notebook" button.
• Be sure to blur out all plate numbers before publishing any of the contents available in this dataset.

Acknowledgements

Any publication using this database must reference to the following journal manuscript:

• Landry Kezebou, Victor Oludare, Karen Panetta, James Intriligator, and Sos Agaian "Highway accident detection and classification from live traffic surveillance cameras: a comprehensive dataset and video action recognition benchmarking", Proc. SPIE 12100, Multimodal Image Exploitation and Learning 2022, 121000M (27 May 2022); https://doi.org/10.1117/12.2618943

Note: if the link is broken, please use http instead of https.

In Chrome, use the steps recommended in the following website to view the webpage if it appears to be broken https://www.technipages.com/chrome-enabledisable-not-secure-warning

Other relevant datasets VCoR dataset: https://www.kaggle.com/landrykezebou/vcor-vehicle-color-recognition-dataset VRiV dataset: https://www.kaggle.com/landrykezebou/vriv-vehicle-recognition-in-videos-dataset

For any enquires regarding the HWID12 dataset, contact: landrykezebou@gmail.com