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.

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

Grocery Shelves Dataset

Dataset comprises 5,000+ images of grocery shelves captured in various grocery stores and supermarkets under different lighting conditions. It is designed for research in object detection and product recognition, providing valuable insights into the retail industry for enhancing computer vision applications.

By utilizing this dataset, users can improve their understanding of deep learning methods and develop more effective vision applications tailored to the retail sector. - Get the data

Example of the data

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F22059654%2F7e72fe74d53eeb40dc28e6a315bcf49b%2FFrame%20184%20(1).png?generation=1734837963888145&alt=media" alt=""> Each image is accompanied by an XML-annotation indicating the labeled types of product for each image in the dataset. Each image has an attribute of the product(boolean): facing, flipped, occluded.

💵 Buy the Dataset: This is a limited preview of the data. To access the full dataset, please contact us at https://unidata.pro to discuss your requirements and pricing options.

Researchers can leverage this dataset to advance their work in object detection and product recognition, ultimately contributing to the development of smarter grocery delivery systems and enhanced shopping experiences for consumers. It includes a diverse range of shelf images that reflect real-world grocery market environments, making it an invaluable resource for researchers and developers focused on image classification and computer vision tasks.

🌐 UniData provides high-quality datasets, content moderation, data collection and annotation for your AI/ML projects

Detect Mix

## Overview

Detect Mix is a dataset for object detection tasks - it contains Crack annotations for 1,448 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).

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

About this dataset

Context

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

Key observations

There is a slight majority of female population, with 50.64% 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 Merced is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Merced 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 Merced Population by Race & Ethnicity. You can refer the same here

This is a training data file for Self-RAG that generates outputs to diverse user queries as well as reflection tokens to call the retrieval system adaptively and criticize its own output and retrieved passages. Self-RAG is trained on our 150k diverse instruction-output pairs with interleaving passages and reflection tokens using the standard next-token prediction objective, enabling efficient and stable learning with fine-grained feedback. At inference, we leverage reflection tokens covering… See the full description on the dataset page: https://huggingface.co/datasets/selfrag/selfrag_train_data.

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 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.

The Synthetic Employee Attrition Dataset is a simulated dataset designed for the analysis and prediction of employee attrition. It contains detailed information about various aspects of an employee's profile, including demographics, job-related features, and personal circumstances.

The dataset comprises 74,498 samples, split into training and testing sets to facilitate model development and evaluation. Each record includes a unique Employee ID and features that influence employee attrition. The goal is to understand the factors contributing to attrition and develop predictive models to identify at-risk employees.

This dataset is ideal for HR analytics, machine learning model development, and demonstrating advanced data analysis techniques. It provides a comprehensive and realistic view of the factors affecting employee retention, making it a valuable resource for researchers and practitioners in the field of human resources and organizational development.

FEATURES:

Employee ID: A unique identifier assigned to each employee. Age: The age of the employee, ranging from 18 to 60 years. Gender: The gender of the employee Years at Company: The number of years the employee has been working at the company. Monthly Income: The monthly salary of the employee, in dollars. Job Role: The department or role the employee works in, encoded into categories such as Finance, Healthcare, Technology, Education, and Media. Work-Life Balance: The employee's perceived balance between work and personal life, (Poor, Below Average, Good, Excellent) Job Satisfaction: The employee's satisfaction with their job: (Very Low, Low, Medium, High) Performance Rating: The employee's performance rating: (Low, Below Average, Average, High) Number of Promotions: The total number of promotions the employee has received. Distance from Home: The distance between the employee's home and workplace, in miles. Education Level: The highest education level attained by the employee: (High School, Associate Degree, Bachelor’s Degree, Master’s Degree, PhD) Marital Status: The marital status of the employee: (Divorced, Married, Single) Job Level: The job level of the employee: (Entry, Mid, Senior) Company Size: The size of the company the employee works for: (Small,Medium,Large) Company Tenure: The total number of years the employee has been working in the industry. Remote Work: Whether the employee works remotely: (Yes or No) Leadership Opportunities: Whether the employee has leadership opportunities: (Yes or No) Innovation Opportunities: Whether the employee has opportunities for innovation: (Yes or No) Company Reputation: The employee's perception of the company's reputation: (Very Poor, Poor,Good, Excellent) Employee Recognition: The level of recognition the employee receives:(Very Low, Low, Medium, High)

Attrition: Whether the employee has left the company, encoded as 0 (stayed) and 1 (Left).

Sketch Detection

## Overview

Sketch Detection is a dataset for object detection tasks - it contains Sketch annotations for 1,499 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).

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

Dataset Card for "Darija-Stories-Dataset"

Darija (Moroccan Arabic) Stories Dataset is a large-scale collection of stories written in Moroccan Arabic dialect (Darija).

  Dataset Description

Darija (Moroccan Arabic) Stories Dataset contains a diverse range of stories that provide insights into Moroccan culture, traditions, and everyday life. The dataset consists of textual content from various chapters, including narratives, dialogues, and descriptions. Each story chapter is… See the full description on the dataset page: https://huggingface.co/datasets/alielfilali01/Darija-Stories-Dataset.

Hydrometeorological time series and catchment attributes from the CABra dataset. The manuscript of "CABra: a novel large-sample dataset for Brazilian catchments" is under review in Hydrology and Earth System Sciences (HESS) journal.

Here we present the Catchments Attributes for Brazil (CABra), which is a large-sample dataset for Brazilian catchments that includes long-term data (30 years) for 735 catchments in eight main catchment attribute classes (climate, streamflow, groundwater, geology, soil, topography, land-use and land-cover, and hydrologic disturbance). We have collected and synthesized data from multiple sources (ground stations, remote sensing, and gridded datasets). To prepare the dataset, we delineated all the catchments using the Multi-Error-Removed Improved-Terrain Digital Elevation Model and the coordinates of the streamflow stations provided by the Brazilian Water Agency (ANA), where only the stations with 30 years (1980-2010) of data and less than 10% of missing records were included. Catchment areas range from 9 to 4,800,000 km² and the mean daily streamflow varies from 0.02 to 9 mm day^-1. Several signatures and indices were calculated based on the climate and streamflow data. Additionally, our dataset includes boundary shapefiles, geographic coordinates, and drainage areas for each catchment, aside from more than 100 attributes within the attribute classes.

Data can also be accessed at: thecabradataset.shinyapps.io/CABra

* This version includes water demand in CABra catchments for 2020 and 2040 (projection).

This dataset contains counts of deaths for California as a whole based on information entered on death certificates. Final counts are derived from static data and include out-of-state deaths to California residents, whereas provisional counts are derived from incomplete and dynamic data. Provisional counts are based on the records available when the data was retrieved and may not represent all deaths that occurred during the time period. Deaths involving injuries from external or environmental forces, such as accidents, homicide and suicide, often require additional investigation that tends to delay certification of the cause and manner of death. This can result in significant under-reporting of these deaths in provisional data.

The final data tables include both deaths that occurred in California regardless of the place of residence (by occurrence) and deaths to California residents (by residence), whereas the provisional data table only includes deaths that occurred in California regardless of the place of residence (by occurrence). The data are reported as totals, as well as stratified by age, gender, race-ethnicity, and death place type. Deaths due to all causes (ALL) and selected underlying cause of death categories are provided. See temporal coverage for more information on which combinations are available for which years.

The cause of death categories are based solely on the underlying cause of death as coded by the International Classification of Diseases. The underlying cause of death is defined by the World Health Organization (WHO) as "the disease or injury which initiated the train of events leading directly to death, or the circumstances of the accident or violence which produced the fatal injury." It is a single value assigned to each death based on the details as entered on the death certificate. When more than one cause is listed, the order in which they are listed can affect which cause is coded as the underlying cause. This means that similar events could be coded with different underlying causes of death depending on variations in how they were entered. Consequently, while underlying cause of death provides a convenient comparison between cause of death categories, it may not capture the full impact of each cause of death as it does not always take into account all conditions contributing to the death.

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

200+ Financial Indicators of US Stocks (2018)

Context

Algorithmic trading space is buzzing with new strategies. Companies have spent billions in infrastructure and R&D to be able to jump ahead of the competition and beat the market. Finding value in stocks is an art that very few mastered. Can a computer do that?

Content

This dataset contains 200+ financial indicators that are commonly found in the 10-K filings each publicly traded company releases yearly, for a period of US stocks for 2018.

## Target Variables The dataset includes two class labels: 1. PRICE VAR [%]: Lists the percent price variation for 2018 2. class: Binary classification for each stock where: - 1: Identifies stocks that an hypothetical trader should BUY - 0: Identifies stocks that an hypothetical trader should NOT BUY

Important Notes

• Some financial indicator values might be missing
• Contains outliers with extreme values (possibly due to mistyping)
• Price variations are calculated from the first trading day of 2018 to the last trading day of 2018

The Allen Brain Observatory – Visual Coding is a large-scale, standardized survey of physiological activity across the mouse visual cortex, hippocampus, and thalamus. It includes datasets collected with both two-photon imaging and Neuropixels probes, two complementary techniques for measuring the activity of neurons in vivo. The two-photon imaging dataset features visually evoked calcium responses from GCaMP6-expressing neurons in a range of cortical layers, visual areas, and Cre lines. The Neuropixels dataset features spiking activity from distributed cortical and subcortical brain regions, collected under analogous conditions to the two-photon imaging experiments. We hope that experimentalists and modelers will use these comprehensive, open datasets as a testbed for theories of visual information processing.

some abs. Visit https://dataone.org/datasets/urn%3Auuid%3Ae6f97c09-945e-40b7-8a0f-8551618ea78c for complete metadata about this dataset.

For each isolate, MIC testing was performed in three independent experiments. The mean MIC value obtained from these independent biological replicates was used as the representative MIC for analysis.