The number of smartphone users in the United States was forecast to continuously increase between 2024 and 2029 by in total 17.4 million users (+5.61 percent). After the fifteenth consecutive increasing year, the smartphone user base is estimated to reach 327.54 million users and therefore a new peak in 2029. Notably, the number of smartphone users of was continuously increasing over the past years.Smartphone users here are limited to internet users of any age using a smartphone. The shown figures have been derived from survey data that has been processed to estimate missing demographics.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of smartphone users in countries like Mexico and Canada.

The average time spent daily on a phone, not counting talking on the phone, has increased in recent years, reaching a total of * hours and ** minutes as of April 2022. This figure was expected to reach around * hours and ** minutes by 2024.

Alesco Phone ID: Your Comprehensive Identity Graph Solution

In today's complex data landscape, having a clear and accurate view of your customers is essential. Alesco Phone ID provides the foundation for building a robust Identity Graph that delivers unparalleled insights. Our database is a rich source of Identity Data, including Phone Number Data / Telemarketing Data, that enables you to connect with your audience more effectively.

At the heart of our solution is Identity Linkage Data. By combining advanced data matching techniques with a vast array of public and private data sources, we create a powerful Identity Graph that links Phone Number Data to real people. This enables you to build detailed customer profiles, identify new opportunities, and optimize your marketing campaigns.

With over 860 million Phone Number Data points, including landlines, mobiles, and VoIP, our database offers unmatched coverage. Our proprietary technology processes an impressive 100 million phone signals daily, ensuring data accuracy and freshness. This continuous validation process guarantees that your Identity Graph is always up-to-date.

To provide maximum flexibility, we offer our Phone ID database as an on-premise solution. This gives you complete control over your Identity Data and allows you to integrate it seamlessly into your existing systems.

By leveraging Alesco Phone ID, you can:

Enhance your customer understanding through a robust Identity Graph Improve campaign targeting and personalization with precise Phone Number Data Optimize your Telemarketing efforts with accurate contact information Strengthen fraud prevention and identity verification with reliable Identity Linkage Data

Ready to elevate your data strategy? Contact Alesco today to learn how our Phone ID database can be the cornerstone of your Identity Graph solution.

English(the United States) Scripted Monologue Smartphone speech dataset_Guiding, collected from monologue based on given prompts, covering smart car, smart home, voice assistant domains. Transcribed with text content and other attributes. Our dataset was collected from extensive and diversify speakers(344 speakers), geographicly speaking, enhancing model performance in real and complex tasks.Quality tested by various AI companies. We strictly adhere to data protection regulations and privacy standards, ensuring the maintenance of user privacy and legal rights throughout the data collection, storage, and usage processes, our datasets are all GDPR, CCPA, PIPL complied.

license: apache-2.0 tags: - africa - sustainable-development-goals - world-health-organization - development

  Individuals who own a mobile telephone (%)





  Dataset Description

This dataset provides country-level data for the indicator "5.b.1 Individuals who own a mobile telephone (%)" across African nations, sourced from the World Health Organization's (WHO) data portal on Sustainable Development Goals (SDGs). The data is presented in a wide format, where each row… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/mobile-phone-ownership-for-african-countries.

Knowing who your consumers are is essential for businesses, marketers, and researchers. This detailed demographic file offers an in-depth look at American consumers, packed with insights about personal details, household information, financial status, and lifestyle choices. Let's take a closer look at the data:

Personal Identifiers and Basic Demographics At the heart of this dataset are the key details that make up a consumer profile:

Unique IDs (PID, HHID) for individuals and households Full names (First, Middle, Last) and suffixes Gender and age Date of birth Complete location details (address, city, state, ZIP) These identifiers are critical for accurate marketing and form the base for deeper analysis.

Geospatial Intelligence This file goes beyond just listing addresses by including rich geospatial data like:

Latitude and longitude Census tract and block details Codes for Metropolitan Statistical Areas (MSA) and Core-Based Statistical Areas (CBSA) County size codes Geocoding accuracy This allows for precise geographic segmentation and localized marketing.

Housing and Property Data The dataset covers a lot of ground when it comes to housing, providing valuable insights for real estate professionals, lenders, and home service providers:

Homeownership status Dwelling type (single-family, multi-family, etc.) Property values (market, assessed, and appraised) Year built and square footage Room count, amenities like fireplaces or pools, and building quality This data is crucial for targeting homeowners with products and services like refinancing or home improvement offers.

Wealth and Financial Data For a deeper dive into consumer wealth, the file includes:

Estimated household income Wealth scores Credit card usage Mortgage info (loan amounts, rates, terms) Home equity estimates and investment property ownership These indicators are invaluable for financial services, luxury brands, and fundraising organizations looking to reach affluent individuals.

Lifestyle and Interests One of the most useful features of the dataset is its extensive lifestyle segmentation:

Hobbies and interests (e.g., gardening, travel, sports) Book preferences, magazine subscriptions Outdoor activities (camping, fishing, hunting) Pet ownership, tech usage, political views, and religious affiliations This data is perfect for crafting personalized marketing campaigns and developing products that align with specific consumer preferences.

Consumer Behavior and Purchase Habits The file also sheds light on how consumers behave and shop:

Online and catalog shopping preferences Gift-giving tendencies, presence of children, vehicle ownership Media consumption (TV, radio, internet) Retailers and e-commerce businesses will find this behavioral data especially useful for tailoring their outreach.

Demographic Clusters and Segmentation Pre-built segments like:

Household, neighborhood, family, and digital clusters Generational and lifestage groups make it easier to quickly target specific demographics, streamlining the process for market analysis and campaign planning.

Ethnicity and Language Preferences In today's multicultural market, knowing your audience's cultural background is key. The file includes:

Ethnicity codes and language preferences Flags for Hispanic/Spanish-speaking households This helps ensure culturally relevant and sensitive communication.

Education and Occupation Data The dataset also tracks education and career info:

Education level and occupation codes Home-based business indicators This data is essential for B2B marketers, recruitment agencies, and education-focused campaigns.

Digital and Social Media Habits With everyone online, digital behavior insights are a must:

Internet, TV, radio, and magazine usage Social media platform engagement (Facebook, Instagram, LinkedIn) Streaming subscriptions (Netflix, Hulu) This data helps marketers, app developers, and social media managers connect with their audience in the digital space.

Political and Charitable Tendencies For political campaigns or non-profits, this dataset offers:

Political affiliations and outlook Charitable donation history Volunteer activities These insights are perfect for cause-related marketing and targeted political outreach.

Neighborhood Characteristics By incorporating census data, the file provides a bigger picture of the consumer's environment:

Population density, racial composition, and age distribution Housing occupancy and ownership rates This offers important context for understanding the demographic landscape.

Predictive Consumer Indexes The dataset includes forward-looking indicators in categories like:

Fashion, automotive, and beauty products Health, home decor, pet products, sports, and travel These predictive insights help businesses anticipate consumer trends and needs.

Contact Information Finally, the file includes ke...

Abstract

To facilitate comparisons with the Latin America and the Caribbean (LAC) High-Frequency Surveys collected in 2021, harmonized versions of the COVID-19 High Frequency Phone Surveys 2022 Brazil databases have been produced. The databases follow the same structure as those for the countries in the region (for example, see: COVID-19 LAC High Frequency Phone Surveys 2021 (Wave 1)).

The Brazil 2021 COVID-19 Phone Survey was conducted to provide information on how the pandemic had been affecting Brazilian households in 2021, collecting information along multiple dimensions relevant to the welfare of the population (e.g. changes in employment and income, coping mechanisms, access to health and education services, gender inequalities, and food insecurity). A total of 2,166 phone interviews were conducted across all Brazilian states between July 26 and October 1, 2021. The survey followed an Random Digit Dialing (RDD) sampling methodology using a dual sampling frame of cellphone and landline numbers. The sampling frame was stratified by type of phone and state. Results are nationally representative for households with a landline or at least one cell phone and of individuals of ages 18 years and above who have an active cell phone number or a landline at home.

Geographic coverage

National level.

Analysis unit

Households and individuals of 18 years of age and older.

Sampling procedure

The sample is based on a dual frame of cell phone and landline numbers that was generated through a Random Digit Dialing (RDD) process and consisted of all possible phone numbers under the national phone numbering plan. Numbers were screened through an automated process to identify active numbers and cross-checked with business registries to identify business numbers not eligible for the survey. This method ensures coverage of all landline and cellphone numbers active at the time of the survey. The sampling frame was stratified by type of phone and state. See Sampling Design and Weighting document for more detail.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

Available in Portuguese. The questionnaire followed closely the LAC HFPS Questionnaire of Phase II Wave I but had some critical variations.

Global B2B Mobile Phone Number Database | 100M+ Verified Contacts | 95% Accuracy Forager.ai provides the world’s most reliable mobile phone number data for businesses that refuse to compromise on quality. With 100 million+ professionally verified mobile numbers refreshed every 3 weeks, our database ensures 95% accuracy – so your teams never waste time on dead-end leads.

Why Our Data Wins ✅ Accuracy You Can Trust 95% of mobile numbers are verified against live carrier records and tied to current job roles. Say goodbye to “disconnected number” voicemails.

✅ Depth Beyond Digits Each contact includes 150+ data points:

Direct mobile numbers

Current job title, company, and department

Full career history + education background

Location data + LinkedIn profiles

Company size, industry, and revenue

✅ Freshness Guaranteed Bi-weekly updates combat job-hopping and role changes – critical for sales teams targeting decision-makers.

✅ Ethically Sourced & Compliant First-party collected data with full GDPR/CCPA compliance.

Who Uses This Data?

Sales Teams: Cold-call C-suite prospects with verified mobile numbers.

Marketers: Run hyper-personalized SMS/WhatsApp campaigns.

Recruiters: Source passive candidates with up-to-date contact intel.

Data Vendors: License premium datasets to enhance your product.

Tech Platforms: Power your SaaS tools via API with enterprise-grade B2B data.

Flexible Delivery, Instant Results

API (REST): Real-time integration for CRMs, dialers, or marketing stacks

CSV/JSON: Campaign-ready files.

PostgreSQL: Custom databases for large-scale enrichment

Compliance: Full audit trails + opt-out management

Why Forager.ai? → Proven ROI: Clients see 62% higher connect rates vs. industry averages (request case studies). → No Guesswork: Test-drive free samples before committing. → Scalable Pricing: Pay per record, license datasets, or get unlimited API access.

B2B Mobile Phone Data | Verified Contact Database | Sales Prospecting Lists | CRM Enrichment | Recruitment Phone Numbers | Marketing Automation | Phone Number Datasets | GDPR-Compliant Leads | Direct Dial Contacts | Decision-Maker Data

Need Proof? Contact us to see why Fortune 500 companies and startups alike trust Forager.ai for mission-critical outreach.

About this dataset

Context

The dataset presents median income data over a decade or more for males and females categorized by Total, Full-Time Year-Round (FT), and Part-Time (PT) employment in Mobile County. It showcases annual income, providing insights into gender-specific income distributions and the disparities between full-time and part-time work. The dataset can be utilized to gain insights into gender-based pay disparity trends and explore the variations in income for male and female individuals.

Key observations: Insights from 2023

Based on our analysis ACS 2019-2023 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Mobile County, the median income for all workers aged 15 years and older, regardless of work hours, was $41,601 for males and $26,052 for females.

These income figures highlight a substantial gender-based income gap in Mobile County. Women, regardless of work hours, earn 63 cents for each dollar earned by men. This significant gender pay gap, approximately 37%, underscores concerning gender-based income inequality in the county of Mobile County.

- Full-time workers, aged 15 years and older: In Mobile County, among full-time, year-round workers aged 15 years and older, males earned a median income of $60,256, while females earned $43,203, leading to a 28% gender pay gap among full-time workers. This illustrates that women earn 72 cents for each dollar earned by men in full-time roles. This analysis indicates a widening gender pay gap, showing a substantial income disparity where women, despite working full-time, face a more significant wage discrepancy compared to men in the same roles.

Surprisingly, the gender pay gap percentage was higher across all roles, including non-full-time employment, for women compared to men. This suggests that full-time employment offers a more equitable income scenario for women compared to other employment patterns in Mobile County.

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.

Gender classifications include:

• Male
• Female

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Variables / Data Columns

• Year: This column presents the data year. Expected values are 2010 to 2023
• Male Total Income: Annual median income, for males regardless of work hours
• Male FT Income: Annual median income, for males working full time, year-round
• Male PT Income: Annual median income, for males working part time
• Female Total Income: Annual median income, for females regardless of work hours
• Female FT Income: Annual median income, for females working full time, year-round
• Female PT Income: Annual median income, for females working part time

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 Mobile County median household income by race. You can refer the same here

About this dataset

Context

The dataset presents median income data over a decade or more for males and females categorized by Total, Full-Time Year-Round (FT), and Part-Time (PT) employment in Mobile City. It showcases annual income, providing insights into gender-specific income distributions and the disparities between full-time and part-time work. The dataset can be utilized to gain insights into gender-based pay disparity trends and explore the variations in income for male and female individuals.

Key observations: Insights from 2023

Based on our analysis ACS 2019-2023 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Mobile City, while the Census reported a median income of $29,773 for all male workers aged 15 years and older, data for females in the same category was unavailable due to an insufficient number of sample observations.

Given the absence of income data for females from the Census Bureau, conducting a thorough analysis of gender-based pay disparity in the city of Mobile City was not possible.

- Full-time workers, aged 15 years and older: In Mobile City, among full-time, year-round workers aged 15 years and older, males earned a median income of $31,094, while females earned $45,517

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.

Gender classifications include:

• Male
• Female

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Variables / Data Columns

• Year: This column presents the data year. Expected values are 2010 to 2023
• Male Total Income: Annual median income, for males regardless of work hours
• Male FT Income: Annual median income, for males working full time, year-round
• Male PT Income: Annual median income, for males working part time
• Female Total Income: Annual median income, for females regardless of work hours
• Female FT Income: Annual median income, for females working full time, year-round
• Female PT Income: Annual median income, for females working part time

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 Mobile City median household income by race. You can refer the same here

Overview: The QuitNowTXT text messaging program is designed as a resource that can be adapted to specific contexts including those outside the United States and in languages other than English. Based on evidence-based practices, this program is a smoking cessation intervention for smokers who are ready to quit smoking. Although evidence supports the use of text messaging as a platform to deliver cessation interventions, it is expected that the maximum effect of the program will be demonstrated when it is integrated into other elements of a national tobacco control strategy. The QuitNowTXT program is designed to deliver tips, motivation, encouragement and fact-based information via unidirectional and interactive bidirectional message formats. The core of the program consists of messages sent to the user based on a scheduled quit day identified by the user. Messages are sent for up to four weeks pre-quit date and up to six weeks post quit date. Messages assessing mood, craving, and smoking status are also sent at various intervals, and the user receives messages back based on the response they have submitted. In addition, users can request assistance in dealing with craving, stress/mood, and responding to slips/relapses by texting specific key words to the QuitNow. Rotating automated messages are then returned to the user based on the keyword. Details of the program are provided below. Texting STOP to the service discontinues further texts being sent. This option is provided every few messages as required by the United States cell phone providers. It is not an option to remove this feature if the program is used within the US. If a web-based registration is used, it is suggested that users provide demographic information such as age, sex, and smoking frequency (daily or almost every day, most days, only a few days a week, only on weekends, a few times a month or less) in addition to their mobile phone number and quit date. This information will be useful for assessing the reach of the program, as well as identifying possible need to develop libraries to specific groups. The use of only a mobile phone-based registration system reduces barriers for participant entry into the program but limits the collection of additional data. At bare minimum, quit date must be collected. At sign up, participants will have the option to choose a quit date up to one month out. Text messages will start up to 14 days before their specified quit date. Users also have the option of changing their quit date at any time if desired. The program can also be modified to provide texts to users who have already quit within the last month. One possible adaptation of the program is to include a QuitNowTXT "light" version. This adaptation would allow individuals who do not have unlimited text messaging capabilities but would still like to receive support to participate by controlling the number of messages they receive. In the light program, users can text any of the programmed keywords without fully opting in to the program. Program Design: The program is designed as a 14-day countdown to quit date, with subsequent six weeks of daily messages. Each day within the program is identified as either a pre-quit date (Q- # days) or a post-quit date (Q+#). If a user opts into the program fewer than 14 days before their quit date, the system will begin sending messages on that day. For example, if they opt in four days prior to their quit date, the system will send a welcome message and recognize that they are at Q-4 (or four days before their quit date), and they will receive the message that everyone else receives four days before their quit date. As the user progresses throughout the program, they will receive messages outlined in the text message library. Throughout the program, users will receive texts that cover a variety of content areas including tips, informational content, motivational messaging, and keyword responses. The frequency of messages incre

This Location Data & Foot traffic dataset available for all countries include enriched raw mobility data and visitation at POIs to answer questions such as:

-How often do people visit a location? (daily, monthly, absolute, and averages). -What type of places do they visit ? (parks, schools, hospitals, etc) -Which social characteristics do people have in a certain POI? - Breakdown by type: residents, workers, visitors. -What's their mobility like enduring night hours & day hours?
-What's the frequency of the visits partition by day of the week and hour of the day?

Extra insights -Visitors´ relative income Level. -Visitors´ preferences as derived by their visits to shopping, parks, sports facilities, churches, among others.

Overview & Key Concepts Each record corresponds to a ping from a mobile device, at a particular moment in time and at a particular latitude and longitude. We procure this data from reliable technology partners, which obtain it through partnerships with location-aware apps. All the process is compliant with applicable privacy laws.

We clean and process these massive datasets with a number of complex, computer-intensive calculations to make them easier to use in different data science and machine learning applications, especially those related to understanding customer behavior.

Featured attributes of the data Device speed: based on the distance between each observation and the previous one, we estimate the speed at which the device is moving. This is particularly useful to differentiate between vehicles, pedestrians, and stationery observations.

Night base of the device: we calculate the approximated location of where the device spends the night, which is usually their home neighborhood.

Day base of the device: we calculate the most common daylight location during weekdays, which is usually their work location.

Income level: we use the night neighborhood of the device, and intersect it with available socioeconomic data, to infer the device’s income level. Depending on the country, and the availability of good census data, this figure ranges from a relative wealth index to a currency-calculated income.

POI visited: we intersect each observation with a number of POI databases, to estimate check-ins to different locations. POI databases can vary significantly, in scope and depth, between countries.

Category of visited POI: for each observation that can be attributable to a POI, we also include a standardized location category (park, hospital, among others). Coverage: Worldwide.

Delivery schemas We can deliver the data in three different formats:

Full dataset: one record per mobile ping. These datasets are very large, and should only be consumed by experienced teams with large computing budgets.

Visitation stream: one record per attributable visit. This dataset is considerably smaller than the full one but retains most of the more valuable elements in the dataset. This helps understand who visited a specific POI, characterize and understand the consumer's behavior.

Audience profiles: one record per mobile device in a given period of time (usually monthly). All the visitation stream is aggregated by category. This is the most condensed version of the dataset and is very useful to quickly understand the types of consumers in a particular area and to create cohorts of users.

English(Latin America) Scripted Monologue Smartphone speech dataset, collected from monologue based on given scripts, covering generic domain, human-machine interaction, smart home command and control, in-car command and control, numbers and other domains. Transcribed with text content and other attributes. Our dataset was collected from extensive and diversify speakers(281 people in total), geographicly speaking, enhancing model performance in real and complex tasks.Quality tested by various AI companies. We strictly adhere to data protection regulations and privacy standards, ensuring the maintenance of user privacy and legal rights throughout the data collection, storage, and usage processes, our datasets are all GDPR, CCPA, PIPL complied.

Spanish(Latin America) Scripted Monologue Smartphone speech dataset, collected from monologue based on given scripts, covering generic domain, human-machine interaction, smart home command and in-car command, numbers, news and other domains. Transcribed with text content and other attributes. Our dataset was collected from extensive and diversify speakers(1,630 people in total, such as Mexicans, Colombians, etc.), geographicly speaking, enhancing model performance in real and complex tasks.Quality tested by various AI companies. We strictly adhere to data protection regulations and privacy standards, ensuring the maintenance of user privacy and legal rights throughout the data collection, storage, and usage processes, our datasets are all GDPR, CCPA, PIPL complied.

Liveness Detection Replay Dataset (3K+ Attacks, 1.5K People)

Liveness detection dataset of Replay attacks performed on Mobile devices. This dataset consists of 1,500 individuals who provided selfies, followed by 3,000 replay display attacks executed across 15 different mobile devices. These attacks are captured from a diverse range of devices, spanning low, medium, and high-end mobile phones, providing extensive variation in screen types, lighting, and environmental conditions… See the full description on the dataset page: https://huggingface.co/datasets/AxonData/Replay_attack_mobile.

Apple is one of the most influential and recognisable brands in the world, responsible for the rise of the smartphone with the iPhone. Valued at over $2 trillion in 2021, it is also the most valuable...

As people increasingly communicate via asynchronous non-spoken modes on mobile devices, particularly text messaging (e.g., short message service (SMS)), longstanding assumptions and practices of social measurement via telephone survey interviewing are being challenged. This dataset contains 1,282 cases, 634 cases that completed an interview and 648 cases that were invited to participate, but did not start or complete an interview on their iPhone. Participants were randomly assigned to answer 32 questions from US social surveys via text messaging or speech, administered either by a human interviewer or by an automated interviewing system. 10 interviewers from the University of Michigan Survey Research Center administered voice and text interviews; automated systems launched parallel text and voice interviews at the same time as the human interviews were launched. The key question was how the interview mode affected the quality of the response data, in particular the precision of numerical answers (how many were not rounded), variation in answers to multiple questions with the same response scale (differentiation), and disclosure of socially undesirable information. Texting led to higher quality data--fewer rounded numerical answers, more differentiated answers to a battery of questions, and more disclosure of sensitive information--than voice interviews, both with human and automated interviewers. Text respondents also reported a strong preference for future interviews by text. The findings suggest that people interviewed on mobile devices at a time and place that is convenient for them, even when they are multitasking, can give more trustworthy and accurate answers than those in more traditional spoken interviews. The findings also suggest that answers from text interviews, when aggregated across a sample, can tell a different story about a population than answers from voice interviews, potentially altering the policy implications from a survey. Demographic variables include participants' gender, race, education level, and household income.

Echo’s Mobility dataset maps movement trends across 18.8M+ POIs in the U.S., offering unmatched visibility into how people interact with physical spaces over time.

With GDPR-compliant, non-PII data, this dataset links visits and visitors to precise locations — helping brands uncover foot traffic trends, brand loyalty, and cross-visitation behavior for smarter strategy and planning.

Key data points include: - Visit counts, unique visitors, dwell time - Frequency and cross-visitation patterns - Monthly or quarterly updates - Optional custom Activity Analyses - Non-PII, fully GDPR-compliant

Ideal for market research, retail planning, and customer journey modeling, this U.S.-focused dataset helps companies unlock actionable insights from real-world mobility patterns.

Welcome to BatchData, your trusted source for comprehensive US homeowner data, contact information, and demographic data, all designed to empower political campaigns. In the fast-paced world of politics, staying ahead and targeting the right audience is crucial for success.

At BatchData, we understand the importance of having the most accurate, up-to-date, and relevant data to help you make informed decisions and connect with your constituents effectively. With our robust data offerings, political campaign agencies can easily reach both homeowners and renters, using direct contact information such as cell phone numbers, emails, and mailing addresses.

The Power of Data in Political Campaigns In the digital age, political campaigns are increasingly reliant on data-driven strategies. Precise targeting, tailored messaging, and efficient outreach have become the cornerstones of successful political campaigning. BatchData equips political campaign agencies with the tools they need to harness the power of data in their campaigns, enabling them to make the most of every interaction. Harness the power of voter data and campaign & election data to effectively run political campaigns.

Key Features of BatchData 1. US Homeowner Data At BatchData, we understand that having access to accurate and comprehensive homeowner data is the bedrock of a successful political campaign. Our vast database includes information on homeowners across the United States, allowing you to precisely target this key demographic. With our homeowner data, you can segment your campaign and craft messages that resonate with this audience. Whether you're running a local, state, or national campaign, our homeowner data is an invaluable asset.

1. Contact Information 258M Phone Numbers (US Phone Number Data) BatchData doesn't just stop at providing you with demographic data; we go a step further by giving you direct contact information. We offer cell phone numbers, email addresses, and mailing addresses, ensuring that you can connect with your audience on multiple fronts. This multifaceted approach allows you to engage with potential voters in a way that suits their preferences and lifestyles. Whether you want to send targeted emails, reach out through phone calls, or even send physical mailers, BatchData has you covered with both the data and the tools. (See BatchDialer for more Info).

2. Demographic Data In addition to homeowner data and contact information, BatchData provides a treasure trove of demographic data. You can refine your campaign strategy by tailoring your messages to specific demographics, including age, gender, income, religious preferences, and more. Our demographic data helps you understand your audience better, allowing you to craft compelling messages that resonate with their values and interests.

3. Targeting Both Homeowners and Renters We understand that not all political campaigns are exclusively focused on homeowners. That's why BatchData caters to a diverse range of campaign needs. Whether your campaign is directed at homeowners or renters, our data sets have you covered. You can effectively target a broader spectrum of the population, ensuring that your message reaches the right people, regardless of their housing status.

Flexible Data Delivery Methods BatchData understands that political campaigns are time-sensitive, and efficiency is paramount. That's why we offer a variety of data delivery methods to suit your specific needs.

1. API Integration For real-time access to data, our API integration is your go-to solution. Easily integrate BatchData's data into your campaign management systems, ensuring that you always have the latest information at your fingertips.

2. Bulk File Delivery When you require a large volume of data in one go, our bulk file delivery option is ideal. We'll deliver the data to you in a format that's easy to import into your campaign databases, allowing you to work with a comprehensive dataset on your terms.

3. S3 Data Storage If you prefer to host your data in an S3 bucket, BatchData can seamlessly deliver your datasets to the cloud storage location of your choice. This option ensures that your data is readily available whenever you need it.

4. Self-Service List Building Our self-service list building tool empowers you to create custom lists based on your specific criteria. You have the flexibility to choose the data elements you need, ensuring that your campaign efforts are tailored to your goals.

5. File Exporting Need to download data for offline use or share it with your team? Our file exporting feature lets you export data in a user-friendly format, making it easy to work with.

6. On-Demand Concierge Services For those campaigns that require a more personalized touch, BatchData offers on-demand concierge services. Our experienced team is here to assist you in building lists, refining your targeting, and providing support as needed. This ...

Mobile Face Liveness Detection

The dataset consists of videos featuring individuals wearing various types of masks. Videos are recorded under different lighting conditions and with different attributes (glasses, masks, hats, hoods, wigs, and mustaches for men). In the dataset, there are 4 types of videos filmed on mobile devices:

2D mask with holes for eyes - demonstration of an attack with a paper/cardboard mask (mask) 2D mask with holes for eyes, nose, and mouth - demonstration… See the full description on the dataset page: https://huggingface.co/datasets/TrainingDataPro/on-device-face-liveness-detection.