People data provides complete people information and gives the ability to link individual information to organizations and roles.

NamUs is the only national repository for missing, unidentified, and unclaimed persons cases. The program provides a singular resource hub for law enforcement, medical examiners, coroners, and investigating professionals. It is the only national database for missing, unidentified, and unclaimed persons that allows limited access to the public, empowering family members to take a more proactive role in the search for their missing loved ones.

Promo People inc. Whois Database, discover comprehensive ownership details, registration dates, and more for Promo People inc. with Whois Data Center.

Factori's Person API empowers businesses to enhance their contact database of Shopify and Klaviyo by enriching data. Simply input phone numbers, email addresses, hashed values, or name/company details, and receive comprehensive contact details in a standardized format. Fuel your marketing, sales, and customer relationship management activities with enriched contact information, including names, company details, job titles, contact information, social media profiles, and more. With optimized performance, robust error handling, and data security measures, Factori's Person API provides a seamless experience. Unlock valuable insights, personalize your outreach, and drive business growth effortlessly with Factori's Person API. Use Cases: Personalized Marketing: Enrich existing contact data with additional details such as social media profiles, educational background, or job titles. Tailor your marketing messages and campaigns to specific customer segments, improving personalization and engagement. Account-Based Marketing (ABM): Enhance your ABM strategy by enriching contact data of target accounts. Gain a comprehensive understanding of key stakeholders, their roles, and their preferences to deliver highly targeted and personalized campaigns. Sales Intelligence: Arm your sales team with enriched contact information to improve prospecting and sales conversations. Access valuable insights such as past experiences, interests, or industry expertise to establish meaningful connections and drive conversions. Data Cleansing and Validation: Ensure the accuracy and completeness of your contact database by enriching existing data with verified information. Update outdated or missing contact details, improving data quality and integrity. Market Research and Analysis: Enrich contact data to gain deeper insights into industry trends, job movements, or market dynamics. Analyze enriched data to identify patterns, opportunities, and market gaps for informed decision-making.

RegDB is used for Visible-Infrared Re-ID which handles the cross-modality matching between the daytime visible and night-time infrared images. The dataset contains images of 412 people. It includes 10 color and 10 thermal images for each person.

In 2022, there were 313,017 cases filed by the NCIC where the race of the reported missing was White. In the same year, 18,928 people were missing whose race was unknown.

What is the NCIC?

The National Crime Information Center (NCIC) is a digital database that stores crime data for the United States, so criminal justice agencies can access it. As a part of the FBI, it helps criminal justice professionals find criminals, missing people, stolen property, and terrorists. The NCIC database is broken down into 21 files. Seven files belong to stolen property and items, and 14 belong to persons, including the National Sex Offender Register, Missing Person, and Identify Theft. It works alongside federal, tribal, state, and local agencies. The NCIC’s goal is to maintain a centralized information system between local branches and offices, so information is easily accessible nationwide.

Missing people in the United States

A person is considered missing when they have disappeared and their location is unknown. A person who is considered missing might have left voluntarily, but that is not always the case. The number of the NCIC unidentified person files in the United States has fluctuated since 1990, and in 2022, there were slightly more NCIC missing person files for males as compared to females. Fortunately, the number of NCIC missing person files has been mostly decreasing since 1998.

This multi-camera surveillance dataset, the SAIVT-SoftBio database, was captured from an existing surveillance network, to enable the evaluation of person recognition and re-identification models in a reallife multi-camera surveillance environment.

The dataset consists of 150 people moving through a building environment, recorded by eight surveillance cameras. Each camera captures data at 25 frames per second, at a resolution of 704 x 576 pixels, and is calibrated using Tsai’s method. The placement of cameras is a real-life surveillance setup, and cameras have been placed to provide maximal coverage of the space (with some overlap) and observation of the entrances to the building. The dataset was collected in an uncontrolled manner, so subjects can travel any route through the building. Thus, the vast majority of subjects will only pass through a subset of the camera network and that subset varies from person to person. This provides a highly unconstrained environment in which to test person re-identification models.

The frames are recorded from when the subject enters the building through one of the three main doorways visible in Camera 4, Camera 7 and Camera 5/8, until they leave observation either through exiting the building or entering a lecture theatre. Any frames which are significantly occluded, have been omitted.

XML files are used to store information about the database to enable different evaluations to be easily performed based on which subset of the dataset fits the desired criteria. For each subject, an XML file is used to summarise the camera views and frame information which can be used to select subjects which fit the desired evaluation conditions (e.g. only subjects that exist in specific cameras or locations can be selected).

The overall dataset is also summarised in an XML file, which provides information on the camera calibration data for each subject.

Face evaluation and first impression generation can be affected by multiple face elements such as invariant facial features, gaze direction and environmental context; however, the composite modulation of eye gaze and illumination on faces of different gender and ages has not been previously investigated. We aimed at testing how these different facial and contextual features affect ratings of social attributes. Thus, we created and validated the Bi-AGI Database, a freely available new set of male and female face stimuli varying in age across lifespan from 18 to 87 years, gaze direction and illumination conditions. Judgments on attractiveness, femininity-masculinity, dominance and trustworthiness were collected for each stimulus. Results evidence the interaction of the different variables in modulating social trait attribution, in particular illumination differently affects ratings across age, gaze and gender, with less impact on older adults and greater effect on young faces.

US Public Records contains records from Bedford, Hillsborough, New Hampshire, USA by U.S., Public Records Index, 1950-1993, Volume 1; Ancestry.com. U.S., Public Records Index, 1950-1993, Volume 1 [database on-line]. Lehi, UT, USA: Ancestry.com Operations, Inc., 2010.; Original data: Voter Registration Lists, Public Record Filings, Historical Residential Records, and Other Household Database Listings. - .

Libya LY: Labour Force data was reported at 2,403,125.000 Person in 2017. This records an increase from the previous number of 2,363,336.000 Person for 2016. Libya LY: Labour Force data is updated yearly, averaging 2,009,973.500 Person from Dec 1990 (Median) to 2017, with 28 observations. The data reached an all-time high of 2,403,125.000 Person in 2017 and a record low of 1,255,832.000 Person in 1990. Libya LY: Labour Force data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Libya – Table LY.World Bank.WDI: Labour Force. Labor force comprises people ages 15 and older who supply labor for the production of goods and services during a specified period. It includes people who are currently employed and people who are unemployed but seeking work as well as first-time job-seekers. Not everyone who works is included, however. Unpaid workers, family workers, and students are often omitted, and some countries do not count members of the armed forces. Labor force size tends to vary during the year as seasonal workers enter and leave.; ; Derived using data from International Labour Organization, ILOSTAT database and World Bank population estimates. Labor data retrieved in September 2018.; Sum; Data up to 2016 are estimates while data from 2017 are projections.

Comprehensive dataset of 1 Missing persons organizations in Hungary as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

Comprehensive dataset of 13 Missing persons organizations in Russia as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

GOArt (Göteborg Organ Art Center) is a research center that specializes in integrated studies of instruments and performance. The pipe organ and its related keyboard instruments - the clavichord, harpsichord, harmonium, and fortepiano - form the research field. The organ database system was developed 1995 - 2000. The main goal was to create a database structure in which it is possible to store both technical and historical information on organs. In addition to storing information on organs, it was important to be able to store information on archive documents and persons related to the instruments. The database thus consists of four main parts, divided into: technical information about the organs, historical information, other archival data, and information about persons linked to the organs. This structure makes it possible to select a certain point of time in history and to trace the historical process, in the database stored as a network between organs, documents, and persons. The user can navigate backwards or forwards in time and will always be informed about connections between organs, documents, and persons at every point in this historical network. The purpose of the database has been to serve as a documentation tool for results from research as well as a tool for analysis and evaluation. The database is also a source of information for future research projects. There are at present more than 200 historical Swedish organs, 870 archive documents and 1900 person records in the database.

Purpose:

The purpose of the database has been to serve as a documentation tool for results from research as well as a tool for analysis and evaluation. The collection of data to the database helps to preserve a unique Swedish cultural heritage for posterity, thus supporting sustainable development. The database information may help curators to make decisions with financial implications, as it may provide a basis for assessing the need and value of measures. The database will also contribute to the documentation and inventory of parts of organs that are unique and protected by the Antiquities Act.

UK-wide system used to store and share information and intelligence on those individuals who have been identified as posing a risk of serious harm to the public.

Flexible pricing available to meet all your business needs. Available for transactional orders or full licensing.

Fields Include: -Business Owner/Contact -Employee Size -Email -Ethnic Group of Contact Person -Executives by Title -Fax Number -Gender of Contact Person -Headquarters/Branches -Home Based Businesses -Minority Owner Businesses -NAICS code -Professional Specialties -Sales Volume -SIC code -Small Business Owner -Square Footage -Telephone Numbers -UCC Indicator -Website Address -Year Established

UHS: Avg Number of Person Per Household: Hohhot data was reported at 2.580 Person in 2012. This records a decrease from the previous number of 2.610 Person for 2011. UHS: Avg Number of Person Per Household: Hohhot data is updated yearly, averaging 2.840 Person from Dec 1992 (Median) to 2012, with 21 observations. The data reached an all-time high of 3.330 Person in 1994 and a record low of 2.580 Person in 2012. UHS: Avg Number of Person Per Household: Hohhot data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Household Survey – Table CN.HC: No of Person Per Household: City.

Abstract

The fourth edition of the Global Findex offers a lens into how people accessed and used financial services during the COVID-19 pandemic, when mobility restrictions and health policies drove increased demand for digital services of all kinds.

The Global Findex is the world's most comprehensive database on financial inclusion. It is also the only global demand-side data source allowing for global and regional cross-country analysis to provide a rigorous and multidimensional picture of how adults save, borrow, make payments, and manage financial risks. Global Findex 2021 data were collected from national representative surveys of about 128,000 adults in more than 120 economies. The latest edition follows the 2011, 2014, and 2017 editions, and it includes a number of new series measuring financial health and resilience and contains more granular data on digital payment adoption, including merchant and government payments.

The Global Findex is an indispensable resource for financial service practitioners, policy makers, researchers, and development professionals.

Geographic coverage

National coverage

Analysis unit

Individual

Kind of data

Observation data/ratings [obs]

Sampling procedure

In most developing economies, Global Findex data have traditionally been collected through face-to-face interviews. Surveys are conducted face-to-face in economies where telephone coverage represents less than 80 percent of the population or where in-person surveying is the customary methodology. However, because of ongoing COVID-19 related mobility restrictions, face-to-face interviewing was not possible in some of these economies in 2021. Phone-based surveys were therefore conducted in 67 economies that had been surveyed face-to-face in 2017. These 67 economies were selected for inclusion based on population size, phone penetration rate, COVID-19 infection rates, and the feasibility of executing phone-based methods where Gallup would otherwise conduct face-to-face data collection, while complying with all government-issued guidance throughout the interviewing process. Gallup takes both mobile phone and landline ownership into consideration. According to Gallup World Poll 2019 data, when face-to-face surveys were last carried out in these economies, at least 80 percent of adults in almost all of them reported mobile phone ownership. All samples are probability-based and nationally representative of the resident adult population. Phone surveys were not a viable option in 17 economies that had been part of previous Global Findex surveys, however, because of low mobile phone ownership and surveying restrictions. Data for these economies will be collected in 2022 and released in 2023.

In economies where face-to-face surveys are conducted, the first stage of sampling is the identification of primary sampling units. These units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. To increase the probability of contact and completion, attempts are made at different times of the day and, where possible, on different days. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used. Respondents are randomly selected within the selected households. Each eligible household member is listed, and the hand-held survey device randomly selects the household member to be interviewed. For paper surveys, the Kish grid method is used to select the respondent. In economies where cultural restrictions dictate gender matching, respondents are randomly selected from among all eligible adults of the interviewer's gender.

In traditionally phone-based economies, respondent selection follows the same procedure as in previous years, using random digit dialing or a nationally representative list of phone numbers. In most economies where mobile phone and landline penetration is high, a dual sampling frame is used.

The same respondent selection procedure is applied to the new phone-based economies. Dual frame (landline and mobile phone) random digital dialing is used where landline presence and use are 20 percent or higher based on historical Gallup estimates. Mobile phone random digital dialing is used in economies with limited to no landline presence (less than 20 percent).

For landline respondents in economies where mobile phone or landline penetration is 80 percent or higher, random selection of respondents is achieved by using either the latest birthday or household enumeration method. For mobile phone respondents in these economies or in economies where mobile phone or landline penetration is less than 80 percent, no further selection is performed. At least three attempts are made to reach a person in each household, spread over different days and times of day.

Sample size for France is 1000.

Mode of data collection

Landline and mobile telephone

Research instrument

Questionnaires are available on the website.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country-specific margins of error, please refer to the Methodology section and corresponding table in Demirgüç-Kunt, Asli, Leora Klapper, Dorothe Singer, Saniya Ansar. 2022. The Global Findex Database 2021: Financial Inclusion, Digital Payments, and Resilience in the Age of COVID-19. Washington, DC: World Bank.

Free People is a bohemian-inspired women's clothing brand that caters to a free-spirited, artistic customer. The Free People business model revolves around creating unique, vintage-inspired apparel and accessories that embody a sense of individuality and self-expression. Free People clothing emphasizes high-quality materials, intricate details, and flowing silhouettes, offering a distinct aesthetic that sets them apart from mainstream fashion. Beyond clothing, Free People cultivates a lifestyle brand through visually captivating catalogs, social media engagement, and a curated online experience that reflects their bohemian ethos. To reach their target audience, Free People operates a mix of retail stores, a strong online presence, and wholesale partnerships with department stores. You can download the complete list of key information about Free People locations, contact details, services offered, and geographical coordinates, beneficial for various applications like store locators, business analysis, and targeted marketing. The Free People data you can download includes:

Identification & Location: 


  store_name, address, address_line_2, city, state, zip_code, latitude, longitude, country, country_code, county, geo_accuracy


Contact Information: 


  phone_number, 


Operational Details & Services: 


  store_hours,  

As part of its enforcement efforts, OFAC publishes a list of individuals and companies owned or controlled by, or acting for or on behalf of, targeted countries. It also lists individuals, groups, and entities, such as terrorists and narcotics traffickers designated under programs that are not country-specific. Collectively, such individuals and companies are called "Specially Designated Nationals" or "SDNs." Their assets are blocked and U.S. persons are generally prohibited from dealing with them.

1) Introduction

The GESDPD is a depth images database containing 22000 frames , that simulates to have been taken with a sensor in an elevated front position, in an indoor environment. It was designed to fulfill the following objectives: • Allow the train and evaluation of people detection algorithms based on depth , or RGB-D data, without the need of manually labeling. • Provide quality synthetic data to the research community in people detection tasks. The people detection task can also be extended to practical applications such as video-surveillance, access control, people flow analysis, behaviour analysis or event capacity management.

2) General contents

GESDPD is composed of 22000 depth synthetic images, that simulates to have been taken with a sensor in an elevated front position, in a rectangular, indoor working environment. These have been generated using the simulation software Blender. The synthetic images show a room with different persons walking in different directions. The camera perspective is not stationary, it moves around the room along the database, which avoids a constant background. Some examples of the different views are shown in the next figures. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1411532%2Fdf423fca8672eab818d38a456ad36546%2Fsala_blend.png?generation=1578587944416132&alt=media" alt="">

3) Quantitative details on the database content are provided below.

• Number of frames: 22000

• Number of different people: 4 (3 men and 1 woman)

• Number of labeled people: 20800

• Image resolution: 320 × 240 pixels

For each image, the are provided the depth map and the ground truth including the position of each person in the scene.

To give you an idea on what to expect, the next figure shows some examples of images from the dataset. In this figure, depth values are represented in millimeters, using a colormap. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1411532%2F30197eb1dbbfb1e6e5bcbf0d5354b3f2%2Fsample-synthetic-images.png?generation=1578588087445161&alt=media" alt="">

4) ** Geometry details** As it has been said before, the dataset simulates to have been taken with a sensor in an elevated front position, in a rectangular indoor working environment. Specifically, the camera was placed at a height of 3 meters, and it rotates along the sequence. Regarding the room (whose distribution is shown in figure the next figure), its dimensions are 8.56 × 5.02m, and it has a height of 3.84m. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1411532%2Fa1ebe9f4de9d06e508b3105bfd9973f9%2Fdistribution.png?generation=1578588287335433&alt=media" alt="">

5) File Formats

5.1) Depth data

The depth information (distance to the camera plane) in stored as a .png image, in which each pixel represent the depth value in millimeters as a (little endian) unsigned integer of two bytes. Its values range from 0 to 15000.

5.2) Position Ground Truth Data

The ground truth information is also provided as a .png file, with the same dimensions that the gener- ated images (320 × 240 pixels). The ground truth files have in their names the same number than the corresponding depth files. For labeling people positions, there have been placed Gaussian functions over the centroid of the head of each person in the scene, so that the centroid corresponds to the 2D position of the center of the head and has a normalized value of one. The standard deviation has a value of 15 pixels for all the Gaussians, regardless of the size of each head and the distance from the head to the camera. This value has been calculated based on an estimated value of the average diameter of a person head, taking into account anthropometric considerations. It is worth to highlight that, when two heads are very closely or overlapping with each other, instead of adding both Gaussian functions, the maximum value of them prevail. That modification provides a set of Gaussians that are always separated, so that the CNN can learn to generate that separation between Gaussians in its output. The next figure shows an example of two Gaussian functions. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1411532%2F935aa3e17689e9183fe181fc50b76239%2Fgaussian.png?generation=1578588393522470&alt=media" alt="">

6) Disclaimer, Licensing, Request and Contributions This document and the data provided are work in progress and provided as is. The GEINTRA Synthetic Depth People Detection (GESDPD) Database (and accompanying files and documentation) by David Fuentes-Jiménez, Roberto Martín-López, Cristina Losada-Gutiérrez, Javier Macías-Guarasa and Carlos A. Luna is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

If you make use of this database and/or its related documentation,...