About this dataset

Context

The dataset presents the mean household income for each of the five quintiles in Amherst, New York, as reported by the U.S. Census Bureau. The dataset highlights the variation in mean household income across quintiles, offering valuable insights into income distribution and inequality.

Key observations

• Income disparities: The mean income of the lowest quintile (20% of households with the lowest income) is 18,852, while the mean income for the highest quintile (20% of households with the highest income) is 296,153. This indicates that the top earners earn 16 times compared to the lowest earners.
• *Top 5%: * The mean household income for the wealthiest population (top 5%) is 495,426, which is 167.29% higher compared to the highest quintile, and 2627.98% higher compared to the lowest quintile.

Content

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

Income Levels:

• Lowest Quintile
• Second Quintile
• Third Quintile
• Fourth Quintile
• Highest Quintile
• Top 5 Percent

Variables / Data Columns

• Income Level: This column showcases the income levels (As mentioned above).
• Mean Household Income: Mean household income, in 2023 inflation-adjusted dollars for the specific income level.

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 Amherst town median household income. You can refer the same here

About this dataset

Context

The dataset presents the mean household income for each of the five quintiles in Winchester, VA, as reported by the U.S. Census Bureau. The dataset highlights the variation in mean household income across quintiles, offering valuable insights into income distribution and inequality.

Key observations

• Income disparities: The mean income of the lowest quintile (20% of households with the lowest income) is 14,125, while the mean income for the highest quintile (20% of households with the highest income) is 215,015. This indicates that the top earners earn 15 times compared to the lowest earners.
• *Top 5%: * The mean household income for the wealthiest population (top 5%) is 344,621, which is 160.28% higher compared to the highest quintile, and 2439.79% higher compared to the lowest quintile.

Content

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

Income Levels:

• Lowest Quintile
• Second Quintile
• Third Quintile
• Fourth Quintile
• Highest Quintile
• Top 5 Percent

Variables / Data Columns

• Income Level: This column showcases the income levels (As mentioned above).
• Mean Household Income: Mean household income, in 2023 inflation-adjusted dollars for the specific income level.

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 Winchester median household income. You can refer the same here

This dataset is about books. It has 2 rows and is filtered where the book is Represent : art and identity among the black upper-middle class. It features 7 columns including author, publication date, language, and book publisher.

2018-2019 Class Size District report for middle and high school grades by program type, number of students, number of classes and average class size.

Class size distribution information on middle and high school classes by borough

2018-2019 Class Size Borough report for middle and high school grades by program type, number of students, number of classes and average class size.

For detailed information, visit the Tucson Equity Priority Index StoryMap.Download the layer's data dictionaryWhat is the Tucson Equity Priority Index (TEPI)?The Tucson Equity Priority Index (TEPI) is a tool that describes the distribution of socially vulnerable demographics. It categorizes the dataset into 5 classes that represent the differing prioritization needs based on the presence of social vulnerability: Low (0-20), Low-Moderate (20-40), Moderate (40-60), Moderate-High (60-80) High (80-100). Each class represents 20% of the dataset’s features in order of their values. The features within the Low (0-20) classification represent the areas that, when compared to all other locations in the study area, have the lowest need for prioritization, as they tend to have less socially vulnerable demographics. The features that fall into the High (80-100) classification represent the 20% of locations in the dataset that have the greatest need for prioritization, as they tend to have the highest proportions of socially vulnerable demographics. How is social vulnerability measured?The Tucson Equity Priority Index (TEPI) examines the proportion of vulnerability per feature using 11 demographic indicators:Income Below Poverty: Households with income at or below the federal poverty level (FPL), which in 2023 was $14,500 for an individual and $30,000 for a family of fourUnemployment: Measured as the percentage of unemployed persons in the civilian labor forceHousing Cost Burdened: Homeowners who spend more than 30% of their income on housing expenses, including mortgage, maintenance, and taxesRenter Cost Burdened: Renters who spend more than 30% of their income on rentNo Health Insurance: Those without private health insurance, Medicare, Medicaid, or any other plan or programNo Vehicle Access: Households without automobile, van, or truck accessHigh School Education or Less: Those highest level of educational attainment is a High School diploma, equivalency, or lessLimited English Ability: Those whose ability to speak English is "Less Than Well."People of Color: Those who identify as anything other than Non-Hispanic White Disability: Households with one or more physical or cognitive disabilities Age: Groups that tend to have higher levels of vulnerability, including children (those below 18), and seniors (those 65 and older)An overall percentile value is calculated for each feature based on the total proportion of the above indicators in each area. How are the variables combined?These indicators are divided into two main categories that we call Thematic Indices: Economic and Personal Characteristics. The two thematic indices are further divided into five sub-indices called Tier-2 Sub-Indices. Each Tier-2 Sub-Index contains 2-3 indicators. Indicators are the datasets used to measure vulnerability within each sub-index. The variables for each feature are re-scaled using the percentile normalization method, which converts them to the same scale using values between 0 to 100. The variables are then combined first into each of the five Tier-2 Sub-Indices, then the Thematic Indices, then the overall TEPI using the mean aggregation method and equal weighting. The resulting dataset is then divided into the five classes, where:High Vulnerability (80-100%): Representing the top classification, this category includes the highest 20% of regions that are the most socially vulnerable. These areas require the most focused attention. Moderate-High Vulnerability (60-80%): This upper-middle classification includes areas with higher levels of vulnerability compared to the median. While not the highest, these areas are more vulnerable than a majority of the dataset and should be considered for targeted interventions. Moderate Vulnerability (40-60%): Representing the middle or median quintile, this category includes areas of average vulnerability. These areas may show a balanced mix of high and low vulnerability. Detailed examination of specific indicators is recommended to understand the nuanced needs of these areas. Low-Moderate Vulnerability (20-40%): Falling into the lower-middle classification, this range includes areas that are less vulnerable than most but may still exhibit certain vulnerable characteristics. These areas typically have a mix of lower and higher indicators, with the lower values predominating. Low Vulnerability (0-20%): This category represents the bottom classification, encompassing the lowest 20% of data points. Areas in this range are the least vulnerable, making them the most resilient compared to all other features in the dataset.

Introduction

Welcome to the Middle Eastern Multi-Year Facial Image Dataset, thoughtfully curated to support the development of advanced facial recognition systems, biometric identification models, KYC verification tools, and other computer vision applications. This dataset is ideal for training AI models to recognize individuals over time, track facial changes, and enhance age progression capabilities.

Facial Image Data

This dataset includes over 5,000+ high-quality facial images, organized into individual participant sets, each containing:

Diversity & Representation

Image Quality & Capture Conditions

To ensure model generalization and practical usability, images in this dataset reflect real-world diversity:

Metadata

Each participant’s dataset is accompanied by rich metadata to support advanced model training and analysis, including:

Use Cases & Applications

This dataset is highly valuable for a wide range of AI and computer vision applications:

Secure & Ethical Collection

Dataset Updates & Customization

To keep pace with evolving AI needs, this dataset is regularly updated and customizable. Custom data collection options include:

Introduction

Welcome to the Middle Eastern Human Facial Images Dataset, curated to advance facial recognition technology and support the development of secure biometric identity systems, KYC verification processes, and AI-driven computer vision applications. This dataset is designed to serve as a robust foundation for real-world face matching and recognition use cases.

Facial Image Data

The dataset contains over 1500 facial image sets of Middle Eastern individuals. Each set includes:

Diversity & Representation

Image Quality & Capture Conditions

All images were captured with real-world variability to enhance dataset robustness:

Metadata

Each participant’s data is accompanied by rich metadata to support AI model training, including:

This metadata enables targeted filtering and training across diverse scenarios.

Use Cases & Applications

This dataset is ideal for a wide range of AI and biometric applications:

Secure & Ethical Collection

Dataset Updates & Customization

To meet evolving AI demands, this dataset is regularly updated and can be customized. Available options include:

Introduction

Welcome to the Middle Eastern Human Face with Occlusion Dataset, carefully curated to support the development of robust facial recognition systems, occlusion detection models, biometric identification technologies, and KYC verification tools. This dataset provides real-world variability by including facial images with common occlusions, helping AI models perform reliably under challenging conditions.

Facial Image Data

The dataset comprises over 3,000 high-quality facial images, organized into participant-wise sets. Each set includes:

Diversity & Representation

Image Quality & Capture Conditions

To ensure robustness and real-world utility, images were captured under diverse conditions:

Metadata

Each image is paired with detailed metadata to enable advanced filtering, model tuning, and analysis:

This rich metadata helps train models that can recognize faces even when partially obscured.

Use Cases & Applications

This dataset is ideal for a wide range of real-world and research-focused applications, including:

Secure & Ethical Collection

Dataset

Income of individuals by age group, sex and income source, Canada, provinces and selected census metropolitan areas, annual.

Student Performance & Behavior Dataset

This dataset is real data of 5,000 records collected from a private learning provider. The dataset includes key attributes necessary for exploring patterns, correlations, and insights related to academic performance.

Columns: 01. Student_ID: Unique identifier for each student. 02. First_Name: Student’s first name. 03. Last_Name: Student’s last name. 04. Email: Contact email (can be anonymized). 05. Gender: Male, Female, Other. 06. Age: The age of the student. 07. Department: Student's department (e.g., CS, Engineering, Business). 08. Attendance (%): Attendance percentage (0-100%). 09. Midterm_Score: Midterm exam score (out of 100). 10. Final_Score: Final exam score (out of 100). 11. Assignments_Avg: Average score of all assignments (out of 100). 12. Quizzes_Avg: Average quiz scores (out of 100). 13. Participation_Score: Score based on class participation (0-10). 14. Projects_Score: Project evaluation score (out of 100). 15. Total_Score: Weighted sum of all grades. 16. Grade: Letter grade (A, B, C, D, F). 17. Study_Hours_per_Week: Average study hours per week. 18. Extracurricular_Activities: Whether the student participates in extracurriculars (Yes/No). 19. Internet_Access_at_Home: Does the student have access to the internet at home? (Yes/No). 20. Parent_Education_Level: Highest education level of parents (None, High School, Bachelor's, Master's, PhD). 21. Family_Income_Level: Low, Medium, High. 22. Stress_Level (1-10): Self-reported stress level (1: Low, 10: High). 23. Sleep_Hours_per_Night: Average hours of sleep per night.

The Attendance is not part of the Total_Score or has very minimal weight.

Calculating the weighted sum: Total Score=a⋅Midterm+b⋅Final+c⋅Assignments+d⋅Quizzes+e⋅Participation+f⋅Projects

Dataset contains: - Missing values (nulls): in some records (e.g., Attendance, Assignments, or Parent Education Level). - Bias in some Datae (ex: grading e.g., students with high attendance get slightly better grades). - Imbalanced distributions (e.g., some departments having more students).

Note: - The dataset is real, but I included some bias to create a greater challenge for my students. - Some Columns have been masked as the Data owner requested. "Students_Grading_Dataset_Biased.csv" contains the biased Dataset "Students Performance Dataset" Contains the masked dataset

CIFAR-100 Dataset

1. Overview

• CIFAR-100 is an extension of the CIFAR-10 dataset, with more classes and finer-grained categorization.
• It contains 100 classes, making it more challenging than CIFAR-10, which has only 10 classes.
• Each image in CIFAR-100 is labeled with both a fine label (specific category) and a coarse label (broader category, such as animals or vehicles).

2. Dataset Details

• Number of Images: 60,000 color images in total.
  • 50,000 for training.
  • 10,000 for testing.
• Image Size: Each image is a small 32x32 pixel RGB (color) image.
• Classes: 100 classes, grouped into 20 superclasses.
  • Each superclass contains 5 related classes.

3. Fine and Coarse Labels

• Fine Labels: The dataset has specific categories, such as 'apple', 'bicycle', 'rose', etc.
• Coarse Labels: These are broader categories, like 'fruit', 'flower', 'vehicle', etc.

4. Applications

• Image Classification: Used for training models to classify images into their respective categories.
• Feature Extraction: Useful for benchmarking feature extraction techniques in computer vision.
• Transfer Learning: Often used to pre-train models for other similar tasks.
• Deep Learning Research: Commonly used to test architectures like CNNs (Convolutional Neural Networks).

5. Challenges

• The images are very small (32x32 pixels), making it harder for models to learn intricate details.
• High class count (100) increases classification complexity.
• Intra-class variability and inter-class similarity make it a challenging dataset for classification.

6. File Format

• The dataset is usually available in Python-friendly formats like .pkl or .npz.
• It can also be downloaded and loaded using frameworks like TensorFlow or PyTorch.

7. Example Classes

Some example classes include: - Animals: beaver, dolphin, otter, elephant, snake. - Plants: apple, orange, mushroom, palm tree, pine tree. - Vehicles: bicycle, bus, motorcycle, train, rocket. - Everyday Objects: clock, keyboard, lamp, table, chair.

This dataset belongs to the following dissertation: Barend Johannes Geyser (2017). The model for the accompaniment of seekers with a Christian background into silence in their quest for wholeness. Radboud UniversityData gathering has taken place by means of phenomenological interviews , observations and making field notes during the interviews, as well as video-stimulated recall. The interview transcripts are written in the South African language.This dataset contains the interview transcripts.The researcher decided to select participants who were starting with their second half of life, thus from 40 to 55 years of age. The participants are all from a Christian background and they were all living in the Northern suburbs of Johannesburg, which means that they are from the socio- economic middle class and upper middle class. There are 3 women and 5 men interviewed. The interviews involve the conscious selection of certain participants. In this instance, the participants are seekers that ask for accompaniment into silence. They are all Christian seekers on a quest for wholeness and investigating the possibility of the practice of silence as an aid in their quest. They all attempted to practice silence in some or other way for at least three years.In addition to the eight interview transcripts, a read me text is added to explain the context of the dataset.

2017- 2018 Class Size Report District Middle And High School Class Size Distribution

This dataset captures simulated student interaction data within a virtual learning environment (VLE), focusing on behavioral indicators related to academic engagement. It includes a variety of features that reflect how students participate in online courses, such as time spent on the platform, quiz scores, forum activity, and content completion.

Each entry is labeled with an engagement level—Low, Medium, or High—based on aggregated interaction metrics. The dataset supports exploratory analysis and the development of data-driven strategies to understand and improve student engagement in virtual settings.

🔑 Key Features: Time Spent Weekly: Average number of minutes a student spends on the platform.

Quiz Score Average: Mean score across online assessments.

Forum Posts: Number of contributions to discussion forums.

Video Watched Percent: Percentage of course video content completed.

Assignments Submitted: Count of assignments submitted on time.

Login Frequency: Number of logins per week.

Session Duration Average: Average duration per platform session.

Device Type: Platform used to access the content (e.g., Desktop, Mobile).

Course Difficulty: Self-reported or platform-defined difficulty of enrolled courses.

Region: Geographic classification (Urban, Suburban, Rural).

Engagement Level: Categorical label indicating Low, Medium, or High engagement.

This dataset can assist educators, researchers, and learning platform designers in understanding key behavioral patterns that influence student participation and success in online learning environments.

https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/tMkThSz65WoD6GdwIMwZy.png" alt="eebb440c-36c8-4ed3-b7e3-2ae1dab37ccc.png">

Deepfake-vs-Real-60K

Deepfake-vs-Real-60K is a large-scale image classification dataset designed to distinguish between deepfake and real facial images. The dataset includes approximately 60,000 high-quality images, comprising 30,000 fake (deepfake) and 30,000 real images, to support the development of robust deepfake detection models.

By providing a well-balanced and diverse collection, Deepfake-vs-Real-60K aims to enhance classification accuracy and improve generalization for AI-based deepfake detection systems.

Label Mappings

• ID to Label:
  {0: 'Fake', 1: 'Real'}
• Label to ID:
  {'Fake': 0, 'Real': 1}

Dataset Composition

The Deepfake-vs-Real-60K dataset is composed of modular subsets derived from:

• Deepfakes-QA-Patch1
• Deepfakes-QA-Patch2

These curated subsets ensure high diversity and quality, allowing models trained on this dataset to perform effectively across varied real-world scenarios.

Key Features

• ~30,000 Deepfake images (label 0)
• ~30,000 Real images (label 1)
• Designed for image classification tasks
• Supports training, evaluation, and benchmarking of deepfake detection models
• Ensures balanced class distribution and high-quality samples

Citation

If you use this dataset in your research or project, please cite it as follows:

@misc{prithiv_sakthi_2025,
  author    = { Prithiv Sakthi },
  title    = { Deepfake-vs-Real-60K (Revision 1c14d74) },
  year     = 2025,
  url     = { https://huggingface.co/datasets/prithivMLmods/Deepfake-vs-Real-60K },
  doi     = { 10.57967/hf/5313 },
  publisher  = { Hugging Face }
}

License

This dataset is licensed under the Apache License 2.0.

For more details, see the license.

Dataset Page

Explore and download the dataset here:
https://huggingface.co/datasets/prithivMLmods/Deepfake-vs-Real-60K

2017-18 Final Class Size Report City Middle and High School Class Size Distribution

**Dataset Overview ** The Titanic dataset is a widely used benchmark dataset for machine learning and data science tasks. It contains information about passengers who boarded the RMS Titanic in 1912, including their age, sex, social class, and whether they survived the sinking of the ship. The dataset is divided into two main parts:

Train.csv: This file contains information about 891 passengers who were used to train machine learning models. It includes the following features:

PassengerId: A unique identifier for each passenger Survived: Whether the passenger survived (1) or not (0) Pclass: The passenger's social class (1 = Upper, 2 = Middle, 3 = Lower) Name: The passenger's name Sex: The passenger's sex (Male or Female) Age: The passenger's age Sibsp: The number of siblings or spouses aboard the ship Parch: The number of parents or children aboard the ship Ticket: The passenger's ticket number Fare: The passenger's fare Cabin: The passenger's cabin number Embarked: The port where the passenger embarked (C = Cherbourg, Q = Queenstown, S = Southampton) Test.csv: This file contains information about 418 passengers who were not used to train machine learning models. It includes the same features as train.csv, but does not include the Survived label. The goal of machine learning models is to predict whether or not each passenger in the test.csv file survived.

**Data Preparation ** Before using the Titanic dataset for machine learning tasks, it is important to perform some data preparation steps. These steps may include:

Handling missing values: Some of the features in the dataset have missing values. These values can be imputed or removed, depending on the specific task. Encoding categorical variables: Some of the features in the dataset are categorical variables, such as Pclass, Sex, and Embarked. These variables need to be encoded numerically before they can be used by machine learning algorithms. Scaling numerical variables: Some of the features in the dataset are numerical variables, such as Age and Fare. These variables may need to be scaled to ensure that they are on the same scale. Data Visualization

Data visualization can be a useful tool for exploring the Titanic dataset and gaining insights into the data. Some common data visualization techniques that can be used with the Titanic dataset include:

Histograms: Histograms can be used to visualize the distribution of numerical variables, such as Age and Fare. Scatter plots: Scatter plots can be used to visualize the relationship between two numerical variables. Box plots: Box plots can be used to visualize the distribution of a numerical variable across different categories, such as Pclass and Sex. Machine Learning Tasks

The Titanic dataset can be used for a variety of machine learning tasks, including:

Classification: The most common task is to use the train.csv file to train a machine learning model to predict whether or not each passenger in the test.csv file survived. Regression: The dataset can also be used to train a machine learning model to predict the fare of a passenger based on their other features. Anomaly detection: The dataset can also be used to identify anomalies, such as passengers who are outliers in terms of their age, social class, or other features.

The dataset typically includes the following columns:

PassengerId: A unique identifier for each passenger. Survived: This column indicates whether a passenger survived (1) or did not survive (0). Pclass (Ticket class): A proxy for socio-economic status, with 1 being the highest class and 3 the lowest. Name: The name of the passenger. Sex: The gender of the passenger. Age: The age of the passenger. (Note: There might be missing values in this column.) SibSp: The number of siblings or spouses the passenger had aboard the Titanic. Parch: The number of parents or children the passenger had aboard the Titanic. Ticket: The ticket number. Fare: The amount of money the passenger paid for the ticket.

The main goal of using this dataset is to predict whether a passenger survived or not based on various features. It serves as a popular introductory dataset for those learning data analysis, machine learning, and predictive modeling. Keep in mind that the dataset may be subject to variations and updates, so it's always a good idea to check the Kaggle website or dataset documentation for the most recent information.