Hirai-Labs/alpr-vlm-instruct-dataset dataset hosted on Hugging Face and contributed by the HF Datasets community

A subset of the LendingClub DataSet obtained from Kaggle: https://www.kaggle.com/wordsforthewise/lending-club

LendingClub is a US peer-to-peer lending company, headquartered in San Francisco, California. It was the first peer-to-peer lender to register its offerings as securities with the Securities and Exchange Commission (SEC), and to offer loan trading on a secondary market. LendingClub is the world's largest peer-to-peer lending platform.

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.

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).

dvs/90sclub-dataset dataset hosted on Hugging Face and contributed by the HF Datasets community

Dataset

This dataset was created by Singh Prince Rinku

Released under Other (specified in description)

Contents

What this collection is: A curated, binary-classified image dataset of grayscale (1 band) 400 x 400-pixel size, or image chips, in a JPEG format extracted from processed Sentinel-1 Synthetic Aperture Radar (SAR) satellite scenes acquired over various regions of the world, and featuring clear open ocean chips, look-alikes (wind or biogenic features) and oil slick chips.

This binary dataset contains chips labelled as: - "0" for chips not containing any oil features (look-alikes or clean seas)
- "1" for those containing oil features.

This binary dataset is imbalanced, and biased towards "0" labelled chips (i.e., no oil features), which correspond to 66% of the dataset. Chips containing oil features, labelled "1", correspond to 34% of the dataset.

Why: This dataset can be used for training, validation and/or testing of machine learning, including deep learning, algorithms for the detection of oil features in SAR imagery. Directly applicable for algorithm development for the European Space Agency Sentinel-1 SAR mission (https://sentinel.esa.int/web/sentinel/missions/sentinel-1 ), it may be suitable for the development of detection algorithms for other SAR satellite sensors.

Overview of this dataset: Total number of chips (both classes) is N=5,630 Class 0 1 Total 3,725 1,905

Further information and description is found in the ReadMe file provided (ReadMe_Sentinel1_SAR_OilNoOil_20221215.txt)

About this dataset

Context

The dataset presents the the household distribution across 16 income brackets among four distinct age groups in Texarkana: Under 25 years, 25-44 years, 45-64 years, and over 65 years. The dataset highlights the variation in household income, offering valuable insights into economic trends and disparities within different age categories, aiding in data analysis and decision-making..

Key observations

• Upon closer examination of the distribution of households among age brackets, it reveals that there are 548(4.77%) households where the householder is under 25 years old, 3,613(31.43%) households with a householder aged between 25 and 44 years, 4,300(37.41%) households with a householder aged between 45 and 64 years, and 3,034(26.39%) households where the householder is over 65 years old.
• In Texarkana, the age group of 45 to 64 years stands out with both the highest median income and the maximum share of households. This alignment suggests a financially stable demographic, indicating an established community with stable careers and higher incomes.

Content

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

Income brackets:

• Less than $10,000
• $10,000 to $14,999
• $15,000 to $19,999
• $20,000 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $59,999
• $60,000 to $74,999
• $75,000 to $99,999
• $100,000 to $124,999
• $125,000 to $149,999
• $150,000 to $199,999
• $200,000 or more

Variables / Data Columns

• Household Income: This column showcases 16 income brackets ranging from Under $10,000 to $200,000+ ( As mentioned above).
• Under 25 years: The count of households led by a head of household under 25 years old with income within a specified income bracket.
• 25 to 44 years: The count of households led by a head of household 25 to 44 years old with income within a specified income bracket.
• 45 to 64 years: The count of households led by a head of household 45 to 64 years old with income within a specified income bracket.
• 65 years and over: The count of households led by a head of household 65 years and over old with income within a specified income bracket.

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 Texarkana median household income by age. You can refer the same here

Pen Dataset

## Overview

Pen Dataset is a dataset for object detection tasks - it contains Pen annotations for 304 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 [Public Domain license](https://creativecommons.org/licenses/Public Domain).

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

About this dataset

Context

The dataset presents the distribution of median household income among distinct age brackets of householders in Washburn town. Based on the latest 2019-2023 5-Year Estimates from the American Community Survey, it displays how income varies among householders of different ages in Washburn town. It showcases how household incomes typically rise as the head of the household gets older. The dataset can be utilized to gain insights into age-based household income trends and explore the variations in incomes across households.

Key observations: Insights from 2023

In terms of income distribution across age cohorts, in Washburn town, the median household income stands at $96,250 for householders within the 25 to 44 years age group, followed by $61,250 for the 45 to 64 years age group. Notably, householders within the 65 years and over age group, had the lowest median household income at $38,000.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Age groups classifications include:

• Under 25 years
• 25 to 44 years
• 45 to 64 years
• 65 years and over

Variables / Data Columns

• Age Of The Head Of Household: This column presents the age of the head of household
• Median Household Income: Median household income, in 2023 inflation-adjusted dollars for the specific age group

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 Washburn town median household income by age. 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.

11 Original

## Overview

11 Original is a dataset for object detection tasks - it contains Person annotations for 225 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).

All states (including the District of Columbia) are required to provide data to The Centers for Medicare & Medicaid Services (CMS) on a range of Medicaid and Children’s Health Insurance Program (CHIP) indicators related to key application, eligibility, enrollment and call center processes. These data reflect enrollment activity for all populations receiving comprehensive Medicaid and CHIP benefits in all states, as well as state program performance. States submit this data via the Performance Indicator dataset. Further information about this dataset is available at: https://www.medicaid.gov/medicaid/national-medicaid-chip-program-information/medicaid-chip-enrollment-data/performance-indicator-technical-assistance/index.html.

Dataset First

## Overview

Dataset First is a dataset for object detection tasks - it contains Dataset First annotations for 280 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).

Snakes

## Overview

Snakes is a dataset for classification tasks - it contains Snakes annotations for 3,061 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 [BY-NC-SA 4.0 license](https://creativecommons.org/licenses/BY-NC-SA 4.0).

Saudi_uniform 2

## Overview

Saudi_uniform 2 is a dataset for instance segmentation tasks - it contains Thoob Shmag Person N3o7 annotations for 560 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).

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.

Train_14

## Overview

Train_14 is a dataset for object detection tasks - it contains Car Truck Bike Bus annotations for 300 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).

Weed Detector Seg

## Overview

Weed Detector Seg is a dataset for instance segmentation tasks - it contains Weed BWS9 annotations for 299 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).