Full profile of 10,000 people in the US - download here, data schema here, with more than 40 data points including - Full Name - Education - Location - Work Experience History and many more!

There are additionally 258+ Million US people profiles available, visit the LinkDB product page here.

Our LinkDB database is an exhaustive database of publicly accessible LinkedIn people and companies profiles. It contains close to 500 Million people and companies profiles globally.

Here are a few use cases for this project:

1. Crowd Control: Use the "people_counting" model for monitoring and managing crowd sizes at public events such as concerts, sports events, or festivals. The model can provide real-time data on the number of people present, which can be used to control entry and enforce safety standards.

2. Retail Analysis: The model can be used in retail settings to monitor customer traffic in stores. Understanding peak hours and days can help management make decisions about staffing, product placement, and sales promotions.

3. Public Safety: Implement the model in surveillance systems of urban areas to detect overcrowded zones or unusual gathering which may indicate a potential public safety issue such as protests or accidents.

4. Transportation Planning: Use the model in public transportation planning. By counting the number of people on different routes or at different times, planners can effectively manage routes and schedules.

5. Smart Building Management: Implement the model in commercial and residential buildings to monitor occupancy rates and usage patterns. This data can be used for optimizing energy management and improving facilities management.

Summary

This dataset contains biographical information derived from articles on English Wikipedia as it stood in early June 2024. It was created as part of the Structured Contents initiative at Wikimedia Enterprise and is intended for evaluation and research use.

The beta sample dataset is a subset of the Structured Contents Snapshot focusing on people with infoboxes in EN wikipedia; outputted as json files (compressed in tar.gz).

We warmly welcome any feedback you have. Please share your thoughts, suggestions, and any issues you encounter on the discussion page for this dataset here on Kaggle.

Data Structure

• File name: wme_people_infobox.tar.gz
• Size of compressed file: 4.12 GB
• Size of uncompressed file: 21.28 GB

Noteworthy Included Fields: - name - title of the article. - identifier - ID of the article. - image - main image representing the article's subject. - description - one-sentence description of the article for quick reference. - abstract - lead section, summarizing what the article is about. - infoboxes - parsed information from the side panel (infobox) on the Wikipedia article. - sections - parsed sections of the article, including links. Note: excludes other media/images, lists, tables and references or similar non-prose sections.

The Wikimedia Enterprise Data Dictionary explains all of the fields in this dataset.

Stats

Infoboxes - Compressed: 2GB - Uncompressed: 11GB

Infoboxes + sections + short description - Size of compressed file: 4.12 GB - Size of uncompressed file: 21.28 GB

Article analysis and filtering breakdown: - total # of articles analyzed: 6,940,949 - # people found with QID: 1,778,226 - # people found with Category: 158,996 - people found with Biography Project: 76,150 - Total # of people articles found: 2,013,372 - Total # people articles with infoboxes: 1,559,985 End stats - Total number of people articles in this dataset: 1,559,985 - that have a short description: 1,416,701 - that have an infobox: 1,559,985 - that have article sections: 1,559,921

This dataset includes 235,146 people articles that exist on Wikipedia but aren't yet tagged on Wikidata as instance of:human.

Maintenance and Support

This dataset was originally extracted from the Wikimedia Enterprise APIs on June 5, 2024. The information in this dataset may therefore be out of date. This dataset isn't being actively updated or maintained, and has been shared for community use and feedback. If you'd like to retrieve up-to-date Wikipedia articles or data from other Wikiprojects, get started with Wikimedia Enterprise's APIs

Initial Data Collection and Normalization

The dataset is built from the Wikimedia Enterprise HTML “snapshots”: https://enterprise.wikimedia.com/docs/snapshot/ and focuses on the Wikipedia article namespace (namespace 0 (main)).

Who are the source language producers?

Wikipedia is a human generated corpus of free knowledge, written, edited, and curated by a global community of editors since 2001. It is the largest and most accessed educational resource in history, accessed over 20 billion times by half a billion people each month. Wikipedia represents almost 25 years of work by its community; the creation, curation, and maintenance of millions of articles on distinct topics. This dataset includes the biographical contents of English Wikipedia language editions: English https://en.wikipedia.org/, written by the community.

Attribution

Terms and conditions

Wikimedia Enterprise provides this dataset under the assumption that downstream users will adhere to the relevant free culture licenses when the data is reused. In situations where attribution is required, reusers should identify the Wikimedia project from which the content was retrieved as the source of the content. Any attribution should adhere to Wikimedia’s trademark policy (available at https://foundation.wikimedia.org/wiki/Trademark_policy) and visual identity guidelines (ava...

People Tracking & Object Detection dataset

The dataset comprises of annotated video frames from positioned in a public space camera. The tracking of each individual in the camera's view has been achieved using the rectangle tool in the Computer Vision Annotation Tool (CVAT).

The dataset is created on the basis of Real-Time Traffic Video Dataset

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2Fc5a8dc4f63fe85c64a5fead10fad3031%2Fpersons_gif.gif?generation=1690705558283123&alt=media" alt="">

Dataset Structure

• The images directory houses the original video frames, serving as the primary source of raw data.
• The annotations.xml file provides the detailed annotation data for the images.
• The boxes directory contains frames that visually represent the bounding box annotations, showing the locations of the tracked individuals within each frame. These images can be used to understand how the tracking has been implemented and to visualize the marked areas for each individual.

Data Format

The annotations are represented as rectangle bounding boxes that are placed around each individual. Each bounding box annotation contains the position ( xtl-ytl-xbr-ybr coordinates ) for the respective box within the frame. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F4f274551e10db2754c4d8a16dff97b33%2Fcarbon%20(10).png?generation=1687776281548084&alt=media" alt="">

👉 Legally sourced datasets and carefully structured for AI training and model development. Explore samples from our dataset of 95,000+ human images & videos - Full dataset

🚀 You can learn more about our high-quality unique datasets here

keywords: multiple people tracking, human detection dataset, object detection dataset, people tracking dataset, tracking human object interactions, human Identification tracking dataset, people detection annotations, detecting human in a crowd, human trafficking dataset, deep learning object tracking, multi-object tracking dataset, labeled web tracking dataset, large-scale object tracking dataset

Young people's open spaces (play areas) in York. For further information please visit City of York Council's website. *Please note that the data published within this dataset is a live API link to CYC's GIS server. Any changes made to the master copy of the data will be immediately reflected in the resources of this dataset.The date shown in the "Last Updated" field of each GIS resource reflects when the data was first published.

This data contains information about people involved in a crash and if any injuries were sustained. This dataset should be used in combination with the traffic Crash and Vehicle dataset. Each record corresponds to an occupant in a vehicle listed in the Crash dataset. Some people involved in a crash may not have been an occupant in a motor vehicle, but may have been a pedestrian, bicyclist, or using another non-motor vehicle mode of transportation. Injuries reported are reported by the responding police officer. Fatalities that occur after the initial reports are typically updated in these records up to 30 days after the date of the crash. Person data can be linked with the Crash and Vehicle dataset using the “CRASH_RECORD_ID” field. A vehicle can have multiple occupants and hence have a one to many relationship between Vehicle and Person dataset. However, a pedestrian is a “unit” by itself and have a one to one relationship between the Vehicle and Person table. The Chicago Police Department reports crashes on IL Traffic Crash Reporting form SR1050. The crash data published on the Chicago data portal mostly follows the data elements in SR1050 form. The current version of the SR1050 instructions manual with detailed information on each data elements is available here. Change 11/21/2023: We have removed the RD_NO (Chicago Police Department report number) for privacy reasons.

Dataset Overview

This dataset contains 417,937 biographical records of notable individuals, extracted from Wikidata using SPARQL queries via the Wikidata Query Service.

Key Selection Criteria:

• Timeframe: Individuals born in the 20th or 21st century (1901–present).
• Country of Birth: Entries must include the country_of_birth.
• Photo Availability: Each entry includes an associated image (image_url is mandatory).
• Profession Filter: Focused on individuals with occupations categorized in occupation_groups.csv (Science & Academia, Arts & Culture, Public Figures, Sports, Business).

Column Descriptions

Column	Description	Notes
wikidata_url	Unique Wikidata URL identifier for the entry	Mandatory
label	Primary name/label of the person (usually in English)	Mandatory
name_in_native_languages	Name(s) in the person’s native language(s)	;-separated values
pseudonyms	Alternative names or aliases used by the person	;-separated values
sex_or_gender	Gender information	Mandatory
date_of_birth	Birth date	Mandatory
place_of_birth	City or region of birth
country_of_birth	Country of birth	Mandatory
date_of_death	Death date (if applicable)
place_of_death	City or region of death (if applicable)
country_of_death	Country of death (if applicable)
citizenships	Nationalities or citizenships held	;-separated values
occupations	Specific occupations or roles	Mandatory, ;-separated
occupation_groups	Broad occupational categories	Mandatory, ;-separated
awards	Awards, honors, or recognitions received	;-separated values
signature_url	URL to an image of the person’s signature
image_url	URL to the person's image/portrait	Mandatory
date_of_image	Date when the image was created (if available)

Notes

The data may contain some number of inaccuracies, due to inconsistencies or errors in the original Wikidata entries. This can sometimes be seen in date fields, especially date_of_image.

Source and Licensing Notes

• All data in this dataset was derived from Wikidata. Wikidata content is available under the CC0 1.0 license.
• Images linked in image_url and signature_url are hosted on Wikimedia Commons and may have individual licenses (e.g., CC BY-SA, Public Domain). Please check the license terms on the source page before using.

Population Mid-year Estimates from the Office for National Statistics (ONS). These are the official estimates of the resident population in Lincolnshire. ONS uses information from the census and other data to produce these official mid-year population estimates every year between each census. These figures show how many people live in each local area and the population age-sex structure. This data is updated annually. Although the ONS data shows exact numbers, they are estimates so some rounding should be applied. For current Armed forces populations, two Ministry of Defence links are also shown below. The ONS 2021 Census link has Veterans data. Population Projections data sourced from ONS is also available on this platform. The Source link shown below is to the ONS Nomis website. It has user-friendly data query tools for a broad range of ONS and other datasets from official sources.

Population Mid-year Estimates from the Office for National Statistics (ONS). These are the official estimates of the resident population in Lincolnshire. ONS uses information from the census and other data to produce these official mid-year population estimates every year between each census. These figures show how many people live in each local area and the population age-sex structure. This data is updated annually. Although the ONS data shows exact numbers, they are estimates so some rounding should be applied. For current Armed forces populations, two Ministry of Defence links are also shown below. The ONS 2021 Census link has Veterans data. Population Projections data sourced from ONS is also available on this platform. The Source link shown below is to the ONS Nomis website. It has user-friendly data query tools for a broad range of ONS and other datasets from official sources.

Face Images Dataset

Description

10,109 people - face images dataset includes people collected from many countries. Multiple photos of each person’s daily life are collected, and the gender, race, age, etc. of the person being collected are marked.This Dataset provides a rich resource for artificial intelligence applications. It has been validated by multiple AI companies and proves beneficial for achieving outstanding performance in real-world applications. Throughout the process of Dataset collection, storage, and usage, we have consistently adhered to Dataset protection and privacy regulations to ensure the preservation of user privacy and legal rights. All Dataset comply with regulations such as GDPR, CCPA, PIPL, and other applicable laws. For more details, please refer to the link: https://www.nexdata.ai/datasets/computervision/1402?source=Kaggle

Data size

10,109 people, no less than 30 images per person

Race distribution

3,504 black people, 3,559 Indian people and 3,046 Asian people

Gender distribution

4,930 males, 5,179 females

Age distribution

most people are young aged, the middle-aged and the elderly cover a small portion

Collecting environment

including indoor and outdoor scenes

Data diversity

different face poses, races, accessories, ages, light conditions and scenes

Data format

.jpg, .png, .jpeg

Licensing Information

Commercial License

How many people use social media?

              Social media usage is one of the most popular online activities. In 2024, over five billion people were using social media worldwide, a number projected to increase to over six billion in 2028.

              Who uses social media?
              Social networking is one of the most popular digital activities worldwide and it is no surprise that social networking penetration across all regions is constantly increasing. As of January 2023, the global social media usage rate stood at 59 percent. This figure is anticipated to grow as lesser developed digital markets catch up with other regions
              when it comes to infrastructure development and the availability of cheap mobile devices. In fact, most of social media’s global growth is driven by the increasing usage of mobile devices. Mobile-first market Eastern Asia topped the global ranking of mobile social networking penetration, followed by established digital powerhouses such as the Americas and Northern Europe.

              How much time do people spend on social media?
              Social media is an integral part of daily internet usage. On average, internet users spend 151 minutes per day on social media and messaging apps, an increase of 40 minutes since 2015. On average, internet users in Latin America had the highest average time spent per day on social media.

              What are the most popular social media platforms?
              Market leader Facebook was the first social network to surpass one billion registered accounts and currently boasts approximately 2.9 billion monthly active users, making it the most popular social network worldwide. In June 2023, the top social media apps in the Apple App Store included mobile messaging apps WhatsApp and Telegram Messenger, as well as the ever-popular app version of Facebook.

Dataset Card for StereoSet

  Dataset Summary

StereoSet is a dataset that measures stereotype bias in language models. StereoSet consists of 17,000 sentences that measures model preferences across gender, race, religion, and profession.

  Supported Tasks and Leaderboards

multiple-choice question answering

  Languages

English (en)

  Dataset Structure





  Data Instances

intersentence

{'bias_type': 'race', 'context': 'Many people live in… See the full description on the dataset page: https://huggingface.co/datasets/McGill-NLP/stereoset.

From adolescence to old age: who do we spend our time with?

To understand how social connections evolve throughout our lives, we can look at survey data on how much time people spend with others and who that time is spent with.

This dataset shows the amount of time people in the US report spending in the company of others, based on their age. The data comes from time-use surveys, where people are asked to list all the activities they perform over a full day and the people who were present during each activity. Currently, there is only data with this granularity for the US – time-use surveys are common across many countries, but what is special about the US is that respondents of the American Time Use Survey are asked to list everyone present for each activity.

The numbers in this chart are based on averages for a cross-section of US society – people are only interviewed once, but the dataset represents a decade of surveys, tabulating the average amount of time survey respondents of different ages report spending with other people.

Source

https://ourworldindata.org/time-with-others-lifetime by Esteban Ortiz-Ospina December 11, 2020

Know Your Customer Dataset, Face Detection and Re-identification

The dataset is created on the basis of Selfies and ID Dataset

80,000+ photos including 10,600+ document photos from 5,300 people from 28 countries. The dataset includes 2 photos of a person from his documents and 13 selfies. All people presented in the dataset are caucasian. The dataset contains a variety of images capturing individuals from diverse backgrounds and age groups.

Photo documents contains only a photo of a person. All personal information from the document is hidden

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F9ad166a8728e7299087a69793e420918%2FFrame%2015%20(1).png?generation=1712143714014867&alt=media" alt="">

Documents in the dataset

• Passports
• International passport
• Driver licenses
• Student cards
• Health certificate
• Pensioner's ID
• Pass to work
• Other documents

The dataset can be utilized for a wide range of tasks, including face recognition, emotion detection, age estimation, gender classification, or any problem related to human image analysis.

👉 Legally sourced datasets and carefully structured for AI training and model development. Explore samples from our dataset of 95,000+ human images & videos - Full dataset

Metadata for the full dataset:

• assignment_id - unique identifier of the media file
• worker_id - unique identifier of the person
• age - age of the person
• gender - gender of the person
• country - country of the person
• ethnicity - ethnicity of the person
• photo_1_extension, photo_2_extension, …, photo_15_extension - photo extensions in the dataset
• photo_1_resolution, photo_2_resolution, …, photo_15_resolution - photo resolution in the dataset

Statistics for the dataset

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F5a5be7a59953aa5e05014dbc88c7740b%2FFrame%2093.png?generation=1712832246364646&alt=media" alt="">

🧩 This is just an example of the data. Leave a request here to learn more

Content

The dataset consists of: - files - includes 7 folders corresponding to each person and including 15 images (2 id photos and 13 selfies), - .csv file - contains information about the images and people in the dataset

File with the extension .csv

• id: id of the person,
• age - age of the person,
• gender - gender of the person,
• country - country of the person,
• id_1, id_2: link to access id photos,
• selfie_1, selfie_2, ..., selfie_13: link to access each of the 13 selfies of the person

🚀 You can learn more about our high-quality unique datasets here

keywords: biometric system, biometric dataset, face recognition database, face recognition dataset, face detection dataset, facial analysis, object detection dataset, deep learning datasets, computer vision datset, human images dataset, human faces dataset, machine learning, image-to-image, re-identification, id photos, selfies and paired id, photos, id verification models, passport, id card image, digital photo-identification, caucasian people, caucasian dataset

Dataset Description:

This dataset includes two folders of images of people. One folder contains images of men, and the other contains images of women. The images include faces, upper bodies, and full bodies. This dataset can be used for various projects like gender recognition, human identification, and image classification.

Use Cases:

1. Gender Recognition: For algorithms that recognize gender based on images.
2. Human Identification: To improve models for identifying humans in images and videos.
3. Image Classification: For classifying images into categories of men and women.

In a world of increasing crime, many organizations are interested in examining incident details to learn from and prevent future crime. Our client, based in Los Angeles County, was interested in this exact thing. They asked us to examine the data to answer several questions; among them,
1 .what was the rate of increase or decrease in crime from 2020 to 2023, 2. which ethnicity or group of people were targeted the most

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10207857%2Fffb10f897f266803ba3829c42ca2ac09%2F2023-12-25%20174131.jpg?generation=1703493715977607&alt=media" alt="">

Very many people are coming to Japan. I am very happy. In this issue, I would like to show you the number of people coming to Japan by month and by year.

year:2023 data:government statistics

This dataset provides comprehensive information about the employment-to-population ratio and the actual population in the United States, spanning from 1979 to 2022.

The employment-to-population ratio signifies the percentage of the civilian noninstitutional population that is employed.

Interesting Task Ideas:

1. Take a closer look at how many people are employed compared to the total population, and see how it relates to different stuff like education, gender, race, and age groups.
2. Look into how employment has changed over time for specific groups of people, like women, different age groups, and different races or ethnicities.
3. See if there's any connection between the employment-to-population ratio and economic stuff like how much the country's making (GDP growth) or how many people are out of work (unemployment rates)
4. Find out if education levels have anything to do with how many people are employed in different racial and ethnic groups.
5. Dive into what happens to employment rates during tough times like a recession or when the economy is doing well.
6. Use the dataset to make predictions about what might happen with employment in the future.

If you find this dataset useful, please consider giving it an upvote! 😊💝

Checkout my other datasets

Poverty-Level Wages in the USA Dataset

Productivity and Hourly Compensation

150K TMDb TV Shows

900K TMDb Movies

Health Insurance Coverage in the USA

Column Descriptions

Field	Description	Type
year	The year for which the data is recorded	int
all	Employment-to-population ratio for the entire population	float
16-24	Employment-to-population ratio for individuals aged 16-24	float
25-54	Employment-to-population ratio for individuals aged 25-54	float
55-64	Employment-to-population ratio for individuals aged 55-64	float
65+	Employment-to-population ratio for individuals aged 65 years and older	float
less_than_hs	Employment-to-population ratio for individuals with less than a high school education	float
high_school	Employment-to-population ratio for individuals with a high school education	float
some_college	Employment-to-population ratio for individuals with some college education	float
bachelors_degree	Employment-to-population ratio for individuals with a bachelor's degree	float
advanced_degree	Employment-to-population ratio for individuals with an advanced degree	float
women	Employment-to-population ratio for women of all age groups	float
women_16-24	Employment-to-population ratio for women aged 16-24	float
women_25-54	Employment-to-population ratio for women aged 25-54	float
women_55-64	Employment-to-population ratio for women aged 55-64	float
women_65+	Employment-to-population ratio for women aged 65 years and older	float
women_less_than_hs	Employment-to-population ratio for women with less than a high school education	float
women_high_school	Employment-to-population ratio for women with a high school education	float
women_some_college	Employment-to-population ratio for women with some college education	float
women_bachelors_degree	Employment-to-population ratio for women with a bachelor's degree	float
women_advanced_degree	Employment-to-population ratio for women with an advanced degree	float
men	Employment-to-population ratio for men of all age groups	float
men_16-24	Employment-to-population ratio for men aged 16-24	float
men_25-54	Employment-to-population ratio for men aged 25-54	float
men_55-64	Employment-to-population ratio for men aged 55-64	float
men_65+	Employment-to-population ratio for men aged 65 years and older	float
men_less_than_hs	Employment-to-population ratio for men with less than a high school education	float
men_high_school	Employment-to-population ratio for men with a high school education	float
men_some_college	Employment-to-population ratio for men with some college education	float
men_bachelors_degree	Employment-to-population ratio for men with a bachelor's degree	float
men_advanced_degree	Employment-to-population ratio for men with a...

Collection of Thermal images with annotations and labels for human figures. The dataset can used to Object Detection models such as YoLo family object detection models, SSD models, ViT-Object Detection models (DeTr - Detection Transformer) and much more.

This data can be used for Human detection using Unmaned Aerial Vehicles (UAVs) and others.

This dataset gives a game-by-game attendance to every NCAA FBS game from 2001 to today. Big thanks to the SportsDataVerse whose cfbfastR package was used to get a majority of this data. NCAA Statistics was used to get current year attendance data.