Journey9ni/VLM-3R-DATA dataset hosted on Hugging Face and contributed by the HF Datasets community

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.

Dataset

This dataset was created by JAYAPRAKASHPONDY

Released under CC0: Public Domain

Contents

Dataset

This dataset was created by SeshuRaju 🧘‍♂️

Released under CC0: Public Domain

Contents

WorldSense: Evaluating Real-world Omnimodal Understanding for Multimodal LLMs

Jack Hong1, Shilin Yan1†, Jiayin Cai1, Xiaolong Jiang1, Yao Hu1, Weidi Xie2‡

†Project Leader
‡Corresponding Author

1Xiaohongshu Inc. 2Shanghai Jiao Tong University [🏠 Project Page] [📖 arXiv Paper] [🤗 Dataset] [🏆 Leaderboard]

  🔥 News

2025.02.07 🌟 We release WorldSense, the first benchmark for real-world omnimodal understanding of MLLMs.

  👀 WorldSense Overview

we… See the full description on the dataset page: https://huggingface.co/datasets/honglyhly/WorldSense.

Jules Dataset

## Overview

Jules Dataset is a dataset for instance segmentation tasks - it contains Pallet annotations for 2,140 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).

rufimelo/securecode-dataset dataset hosted on Hugging Face and contributed by the HF Datasets community

Gopalatius/bitcoin-historical-dataset dataset hosted on Hugging Face and contributed by the HF Datasets community

The dataset represents a compilation of user interaction data generated by users who participated in the project's pilot activities in Patras, Greece. Data was generated by users in the SMARTBUY app and includes information about users, stores, product categories, professions, and events.

The dataset comprises the following data: - users: user account data for the Patras pilot users - occupation: all possible occupations that the pilot users could choose from - stores: stores which participated in the Patras pilot - sel_products_cat: products uploaded to the SMARTBUY platform by retailers - events: geo-stamped and time-stamped descriptions of a user interaction event (for instance, "user_id 67 rated product_id 722 with rating 4 at location x1 at datetime y1", or "user_id 91 denoted product_id 78 as favorite at location x2 at datetime y2") - event_types: all possible event types captured by the SMARTBUY platform ('Product searches', 'Product views', 'Featured product', 'Products near you views', 'Product photos browsed', 'Product ratings', 'Clicks on Read More button to read product reviews', 'Clicks on Open map button', 'Clicks on Send this info by email button', 'Products denoted as Favorite')

Privacy-sensitive information such as user names, retailer owner names and store names and keywords searched are anonymized.

About this dataset

Context

The dataset tabulates the population of Rochester by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of Rochester across both sexes and to determine which sex constitutes the majority.

Key observations

There is a slight majority of female population, with 51.82% of total population being female. Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

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

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the Rochester is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Rochester total population. Please note that the sum of all percentages may not equal one due to rounding of values.

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 Rochester Population by Race & Ethnicity. You can refer the same here

The DARWIN dataset includes handwriting data from 174 participants. The classification task consists in distinguishing Alzheimer's disease patients from healthy people.

For what purpose was the dataset created?

The DARWIN dataset was created to allow researchers to improve the existing machine learning methodologies for the prediction of Alzheimer's disease via handwriting analysis.

Has Missing Values?

No

We delete the ID column.

Dataset Overview

This dataset, titled "Anabolic Steroids", provides a meticulously curated compilation of nearly 50 steroids. It includes detailed information on their original names, common names, medicinal applications, abuse potential, side effects, historical context, and relative molecular mass (RMM). The dataset aims to serve as a resource for exploring the dual nature of anabolic steroids—both their therapeutic benefits and their misuse in sports and bodybuilding.

Anabolic steroids are synthetic derivatives of testosterone that have been used for decades in medicine to treat conditions like anemia, muscle-wasting diseases, and hormone deficiencies. However, they are also widely abused for performance enhancement and aesthetic purposes. This dataset captures a comprehensive view of these compounds, making it valuable for researchers, educators, and data enthusiasts.

Data Science Applications

While this dataset is relatively small (approx 50 entries), it offers rich opportunities for exploratory analysis and domain-specific insights. Potential applications include:

• Exploratory Data Analysis (EDA):

  • Analyze trends in medicinal vs. non-medicinal use.
  • Study correlations between molecular mass and reported side effects.
  • Visualize the historical development of anabolic steroids over time.

• Domain-Specific Insights:

  • Examine the evolution of steroid formulations from the 1930s to the present.
  • Investigate patterns in therapeutic uses versus abuse potential.

• Educational Use:

  • Serve as a teaching tool for understanding data cleaning, visualization, and analysis.
  • Provide insights into the pharmacological and chemical properties of anabolic steroids.

Column Descriptors

1. Original Name: The scientific or chemical name of the steroid compound (e.g., Testosterone).
2. Common Name: The popular or brand name under which the steroid is marketed (e.g., Testoviron).
3. Medicinal Use: Approved therapeutic applications of the steroid (e.g., treating anemia or hormone replacement therapy).
4. Abused For: Non-medical uses often associated with performance enhancement or bodybuilding (e.g., bulking cycles, lean muscle retention).
5. Side Effects: Documented adverse effects resulting from steroid use or abuse (e.g., liver toxicity, gynecomastia).
6. History: A brief historical context about the steroid's development or usage (e.g., year introduced, medical approval status).
7. Relative Molecular Mass (g/mol): The molar mass of the steroid compound, useful for chemical analysis.

Ethically Mined Data

This dataset has been ethically compiled from publicly available sources such as scientific journals, chemical databases, and educational websites. No proprietary or confidential information has been included. The data was aggregated to ensure accuracy and relevance while respecting intellectual property rights.

Acknowledgements

The following sources were instrumental in compiling this dataset: 1. PubChem Database – For verifying chemical properties and molecular mass values. 2. Wikipedia – For historical context and general information on anabolic steroids. 3. NIST Chemistry WebBook – For accurate molecular mass values and chemical details. 4. Scientific Journals – Referenced for medicinal uses, side effects documentation, and abuse patterns. 5. DALL·E 3 by OpenAI – Used to generate illustrative images related to anabolic steroids to complement dataset visualizations.

Discouraging Steroid Usage and Highlighting Harms

The misuse of anabolic steroids poses significant health risks and ethical concerns. While anabolic steroids have legitimate medical applications, their abuse for performance enhancement or aesthetic purposes can lead to severe physical and psychological side effects. Common adverse effects include liver damage, cardiovascular strain, hormonal imbalances, infertility, aggression, and mental health issues such as depression. Prolonged misuse can also result in irreversible damage to vital organs and an increased risk of life-threatening conditions like heart attacks or strokes. Beyond individual health risks, steroid abuse undermines the integrity of sports and creates unfair advantages in competitive environments. It is crucial to prioritize natural methods of achieving fitness goals and seek professional guidance for any medical conditions requiring treatment.

Notes for Kaggle Users

This dataset is not intended for machine learning due to its small size but serves as an excellent resource for exploratory data analysis (EDA), visualization projects, and domain-specific research into anabolic steroids' pharmacology and societal impact.

Mountain Heritage location dataset — 4 locations in 1 states. Part of CREHQ's multi-unit intelligence platform covering retail, restaurant, financial services, and healthcare brands. Licensed access via enterprise API or dataset purchase. Training on CREHQ data is not permitted.

CDFW BIOS GIS Dataset, Contact: FAB Financial Assistance Branch, Description: This data contains summary information for Disadvantaged ($56,982) and Severely Disadvantaged ($42,737) communities. The thresholds are derived from American Community Survey 2014-18 (ACS 2014-18) 5-year estimates at the census place geographic level and the California State Median Household Income of $71,228.

The Construction Equipment dataset is a valuable resource designed for object detection tasks, which finds potential applications within the realms of the construction and surveillance industries. Comprising 318 images, this dataset encompasses a total of 3752 annotated objects, categorized into five distinct classes, such as crane, excavator, truck, tractor and other. This dataset serves as an essential tool for developing and testing object detection algorithms to enhance safety and efficiency within these industrial domains.

This is a video dataset of six basketball games. [Details] Category: Video / Subjects: Five men in their 20s to 40s / Format: mp4 [Notes] [Shooting Time] 2 hours, 5 minutes, 36 seconds
[Shooting Environment] Basketball court in a gymnasium
[Shooting Distance/Angle] Four fixed points A/B/C/D on the basketball court

Added new dataset OpenDataLog. The dataset stores detailed information regarding issues with the open data portal, new or changes to datasets on the portal as well as other information related to the City's Open Data Portal

The Dataset of Annotated Food Crops and Weed Images offers a comprehensive look at food crops and weeds in their early seedling stages. With 1,118 images and 7,853 manual annotations, it neatly classifies them into two primary categories: weed and crop. The dataset was collected in several locations in Latvia and describes eight weed and six food species.

These data include all datasets published for 'input4MIPs.CMIP6.ScenarioMIP.IAMC.IAMC-REMIND-MAGPIE-ssp534-over-1-1-supplemental-data' with the full Data Reference Syntax following the template 'activity_id.mip_era.target_mip.institution_id.source_id.realm.frequency.variable_id.grid_label'. The model IAMC-REMIND-MAGPIE-ssp534-over-1-1-supplemental-data (IAMC-REMIND-MAGPIE-ssp534-over-1-1-supplemental-data) was run by the IAMC (IAMC) in native nominal resolutions: unknown.

Individuals using the data must abide by the terms of use for CMIP6 data (https://pcmdi.llnl.gov/CMIP6/TermsOfUse). Details on any license restrictions are recorded as global attributes in the files.

These data include all datasets published for 'CMIP6.PMIP.MRI.MRI-ESM2-0.past1000' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The MRI-ESM2.0 climate model, released in 2017, includes the following components: aerosol: MASINGAR mk2r4 (TL95; 192 x 96 longitude/latitude; 80 levels; top level 0.01 hPa), atmos: MRI-AGCM3.5 (TL159; 320 x 160 longitude/latitude; 80 levels; top level 0.01 hPa), atmosChem: MRI-CCM2.1 (T42; 128 x 64 longitude/latitude; 80 levels; top level 0.01 hPa), land: HAL 1.0, ocean: MRI.COM4.4 (tripolar primarily 0.5 deg latitude/1 deg longitude with meridional refinement down to 0.3 deg within 10 degrees north and south of the equator; 360 x 364 longitude/latitude; 61 levels; top grid cell 0-2 m), ocnBgchem: MRI.COM4.4, seaIce: MRI.COM4.4. The model was run by the Meteorological Research Institute, Tsukuba, Ibaraki 305-0052, Japan (MRI) in native nominal resolutions: aerosol: 250 km, atmos: 100 km, atmosChem: 250 km, land: 100 km, ocean: 100 km, ocnBgchem: 100 km, seaIce: 100 km.

Individuals using the data must abide by terms of use for CMIP6 data (https://pcmdi.llnl.gov/CMIP6/TermsOfUse). The original license restrictions on these datasets were recorded as global attributes in the data files, but these may have been subsequently updated.