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

This dataset (ml-latest-small) describes 5-star rating and free-text tagging activity from MovieLens, a movie recommendation service. It contains 100836 ratings and 3683 tag applications across 9742 movies. These data were created by 610 users between March 29, 1996 and September 24, 2018. This dataset was generated on September 26, 2018.

Users were selected at random for inclusion. All selected users had rated at least 20 movies. No demographic information is included. Each user is represented by an id, and no other information is provided.

The data are contained in the files - - links.csv - movies.csv - ratings.csv - tags.csv

This and other GroupLens data sets are publicly available for download at http://grouplens.org/datasets/.

License: This dataset is sourced from the GroupLens Research Group at the University of Minnesota. It is provided for non-commercial research and educational purposes only. License details can be found here under Usage License - https://files.grouplens.org/datasets/movielens/ml-latest-small-README.html

Important:

• This dataset is provided "as is" without warranty.
• For commercial use, please contact grouplens-info@umn.edu."

Citation F. Maxwell Harper and Joseph A. Konstan. 2015. The MovieLens Datasets: History and Context. ACM Transactions on Interactive Intelligent Systems (TiiS) 5, 4: 19:1–19:19. https://doi.org/10.1145/2827872

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.

LlamaLens: Specialized Multilingual LLM Dataset

This dataset supports the research presented in the paper LlamaLens: Specialized Multilingual LLM for Analyzing News and Social Media Content.

  Overview

LlamaLens is a specialized multilingual LLM designed for analyzing news and social media content. It focuses on 18 NLP tasks, leveraging 52 datasets across Arabic, English, and Hindi. This repository contains the English-language portion of the data.… See the full description on the dataset page: https://huggingface.co/datasets/QCRI/LlamaLens-English.

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

The Consumer Complaint Database is a collection of complaints about consumer financial products and services that we sent to companies for response. Complaints are published after the company responds, confirming a commercial relationship with the consumer, or after 15 days, whichever comes first. Complaints referred to other regulators, such as complaints about depository institutions with less than $10 billion in assets, are not published in the Consumer Complaint Database. The database generally updates daily.

Use this dataset to classify what product or service a complaint is pointing to, given the complaint narrative provided by the customer. Link: https://catalog.data.gov/dataset/consumer-complaint-database

Head Data Set 2

## Overview

Head Data Set 2 is a dataset for object detection tasks - it contains Heads QiDz annotations for 2,342 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.

This dataset contains 12,680 ultrasound images of Polycystic Ovary Syndrome (PCOS) cases, sourced from publicly available datasets. It is intended to support research in the development of machine learning models for automated PCOS detection and the application of Explainable AI techniques. The dataset can be used for training, validation, and testing purposes in medical image analysis, offering diverse cases for exploring classification accuracy in PCOS diagnosis.

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.

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.

Normangee State Bank location dataset: United States subset. Verified addresses and coordinates. Licensed via CREHQ Data Store.

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.

CAL_LID_L2_05kmALay-Standard-V5-00 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Lidar Level 2 5 km Aerosol Layer data product. This data product was collected using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. Within this aerosol layer product, generated at a horizontal resolution of 5 km, are two general classes of data: Column Properties (including position data and viewing geometry) and Layer Properties. The aerosol layer products consist of a sequence of column descriptors, each associated with a variable number of cloud layer descriptors. The column descriptors specify the temporal and geophysical location of the column of the atmosphere through which a given lidar pulse travels. Also included in the column descriptors are indicators of surface lighting conditions, information about the surface type, and the number of features (e.g., aerosol layers) identified within the column. For each feature within a column, a set of layer descriptors is reported. The layer descriptors provide information about the spatial and optical characteristics of a feature, such as base and top altitudes, integrated attenuated backscatter, and optical depth. CALIPSO was a partnership between NASA and the French Space Agency, CNES. CALIPSO was launched on April 28, 2006 to study the many roles played by clouds and aerosols in Earth’s climate and weather. It flew in the international A-Train constellation for coincident Earth observations from launch until September 13, 2018,when CALIPSO began lowering its orbit from 705 km to 688 km (428 miles) above the Earth to resume formation flying with CloudSat as part of the “C-Train”. The CALIPSO satellite carried three remote sensing instruments: the Cloud-Aerosol Lidar with Orthogonal Polarization(CALIOP), the Imaging Infrared Radiometer (IIR), and the Wide Field-of-View Camera (WFC). By mutual agreement between NASA and CNES, the CALIPSO science mission concluded on August1, 2023.

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/

This dataset presents coral fragment surface area calculations utilizing two methods (tin foil and Image J) and corresponding zooxanthellae count data. Three coral species were utilized in this experiment: the octocoral, Eunicea flexuosa, and two hard coral species, Acropora cervicornis and Orbicella faveolata.

After May 3, 2024, this dataset and webpage will no longer be updated because hospitals are no longer required to report data on COVID-19 hospital admissions, and hospital capacity and occupancy data, to HHS through CDC’s National Healthcare Safety Network. Data voluntarily reported to NHSN after May 1, 2024, will be available starting May 10, 2024, at COVID Data Tracker Hospitalizations.

This time series dataset includes viral COVID-19 laboratory test [Polymerase chain reaction (PCR)] results from over 1,000 U.S. laboratories and testing locations including commercial and reference laboratories, public health laboratories, hospital laboratories, and other testing locations. Data are reported to state and jurisdictional health departments in accordance with applicable state or local law and in accordance with the Coronavirus Aid, Relief, and Economic Security (CARES) Act (CARES Act Section 18115).

Data are provisional and subject to change.

Data presented here is representative of diagnostic specimens being tested - not individual people - and excludes serology tests where possible. Data presented might not represent the most current counts for the most recent 3 days due to the time it takes to report testing information. The data may also not include results from all potential testing sites within the jurisdiction (e.g., non-laboratory or point of care test sites) and therefore reflect the majority, but not all, of COVID-19 testing being conducted in the United States.

Sources: CDC COVID-19 Electronic Laboratory Reporting (CELR), Commercial Laboratories, State Public Health Labs, In-House Hospital Labs

Data for each state is sourced from either data submitted directly by the state health department via COVID-19 electronic laboratory reporting (CELR), or a combination of commercial labs, public health labs, and in-house hospital labs. Data is taken from CELR for states that either submit line level data or submit aggregate counts which do not include serology tests.

Côte d'Ivoire administrative level 0-3 boundaries (COD-AB) dataset version 01.

This dataset is structured into 3 levels:
- Admin 1: 33 District, Autonomous City
- Admin 2: 108 Region
- Admin 3: 510 Department

Dates associated with this dataset:
- 30 October 2025: dataset reviewed for accuracy and completeness
- 06 July 2018: valid for use by the humanitarian community
- 06 August 2018: last edit to the dataset before publishing
- 06 August 2018: boundaries created by the source

Contributed by OCHA West and Central Africa (ROWCA).
Quality assured, configured, and published by OCHA Field Information Services (FIS) and HDX.

Part of the dataset: Global Subnational Administrative Boundaries

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.

This dataset contains simulation data generated from two individual-based stochastic models for malware propagation in an Internet-of-Things (IoT) network: a continuous-time Gillespie SIR model and a discrete-time Monte Carlo SIR model. For each modeling framework, two variants are included: the standard version (Gil / LMC) and the version incorporating the Topological Overlap Measure (TOM) (GilT / LMCTOM). All simulations are executed on a 128-node IoT communication network generated as a power-law cluster graph. Each simulation is stored as an independent CSV file in pivoted format, where rows represent network nodes and columns represent temporal steps produced by the algorithm (event steps in the Gillespie method and iteration steps in the Monte Carlo method). The dataset is suitable for research on malware propagation, stochastic processes on networks, graph-based machine learning models, and cybersecurity analytics.

The IMISEM dataset contains 828 indicators on the migration policies of 32 polities from Europe, South East Asia and Latin America and the Caribbean. The IMISEM project adopts a comprehensive view of migration policy that includes both its emigrant/ emigration and immigrant/ immigration sides, bridging for the first time the two sides of migration policy. Thus, the dataset includes indicators that measure emigration policies (exit policies and control of outflows), immigration policies (entry policies and control of inflows), emigrant policies (rights granted, services offered and obligations imposed on non-resident citizens), immigrant policies (mainly, rights granted to non-citizen residents) and citizenship policies (mainly, access to naturalization for immigrants and retention of citizenship by emigrants). The main sources used to complete the IMISEM questionnaires are legal sources (i.e. laws, regulations). Legal sources are complemented with secondary sources (for instance, policy reports) and interviews with experts. The IMISEM Dataset is one of the main outputs of the “The Every Immigrant is an Emigrant Project (IMISEM)” funded by the Leibniz Gemeinschaft and carried out at the GIGA German Institute for Global and Area Studies between 2017 and 2020.