The global number of smartphone users in was forecast to continuously increase between 2024 and 2029 by in total *** billion users (+***** percent). After the fifteenth consecutive increasing year, the smartphone user base is estimated to reach *** billion 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 *** 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 the Americas and Asia.

Percentage of smartphone users by selected smartphone use habits in a typical day.

this graph was created in R and Canva :

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Recent years have witnessed a rapid growth in the use of mobile devices, enabling people to access the Internet in various contexts. More than 77% of Americans now own a smartphone, with an increasing trend in terms of the time people spend on their phones. More recently, with the release of intelligent assistants such as Google Assistant, Apple Siri, and Microsoft Cortana, people are experiencing mobile search through a single voice-based interface. These systems introduce several research challenges. Given that people spend most of their times in apps and, as a consequence, most of their search interactions would be with apps (rather than a browser), one limitation is that users are unable to use a intelligent assistants to search within many apps.

This data base shows the responses of medical students from 7 countries of Latin America, about academic use of smartphones.

The number of smartphone users in the United Kingdom was forecast to continuously increase between 2024 and 2029 by in total *** million users (+**** percent). After the ninth consecutive increasing year, the smartphone user base is estimated to reach ***** 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 *** 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 information concerning Denmark and Latvia.

This dataset, titled "U.S. Consumer Cell Phone Data," provides anonymized records of consumer purchases and ownership of cell phones across the United States. Each entry in the dataset includes vital information such as the name, address, and purchase-related details of individual buyers. The data offers insights into consumer behavior, trends in cell phone adoption, and geographic patterns of mobile technology use.

Key features of the dataset include:

1. Name: The names of cell phone buyers, enabling linkages with other consumer records for demographic and behavioral analysis (while respecting privacy guidelines).

2. Address: Detailed geographic information, including street addresses, cities, states, and ZIP codes. This allows for regional analysis of purchasing trends and the potential for mapping market penetration of specific phone brands or models.

3. Device Details: While not explicitly stated in the description, such datasets often include information about the types of cell phones purchased, brands, models, and potentially the purchase date.

This dataset could be invaluable for businesses, marketers, and researchers aiming to understand consumer preferences, improve mobile marketing strategies, or tailor product offerings to specific regions.

In summary, the U.S. Consumer Cell Phone Data is a robust resource for analyzing the intersection of consumer behavior and mobile technology, provided that its usage adheres to relevant privacy and ethical standards.

In 2022, smartphone vendors sold around 1.39 billion smartphones were sold worldwide, with this number forecast to drop to 1.34 billion in 2023.

Smartphone penetration rate still on the rise

Less than half of the world’s total population owned a smart device in 2016, but the smartphone penetration rate has continued climbing, reaching 78.05 percent in 2020. By 2025, it is forecast that almost 87 percent of all mobile users in the United States will own a smartphone, an increase from the 27 percent of mobile users in 2010.

Smartphone end user sales

In the United States alone, sales of smartphones were projected to be worth around 73 billion U.S. dollars in 2021, an increase from 18 billion dollars in 2010. Global sales of smartphones are expected to increase from 2020 to 2021 in every major region, as the market starts to recover from the initial impact of the coronavirus (COVID-19) pandemic.

The smartphone helps workers balance the demands of their professional and personal lives but can also be a distraction, affecting productivity, wellbeing, and work-life balance. Drawing from insights on the impact of physical environments on object engagement, this study examines how the distance between the smartphone and the user influences interactions in work contexts. Participants (N = 22) engaged in two 5h knowledge work sessions on the computer, with the smartphone placed outside their immediate reach during one session. Results show that limited smartphone accessibility led to reduced smartphone use, but participants shifted non-work activities to the computer and the time they spent on work and leisure activities overall remained unchanged. These findings suggest that discussions on smartphone disruptiveness in work contexts should consider the specific activities performed, challenging narratives of ‘smartphone addiction’ and ‘smartphone overuse’ as the cause of increased disruptions and lowered work productivity.

This dataset is about books. It has 1 row and is filtered where the book is Bearing witness while black : African Americans, smartphones, and the new protest #Journalism. It features 7 columns including author, publication date, language, and book publisher.

Contains video data of ASL signs from PopSign v1.0 game category (test set only).

Video data before landmark data as part of the 2023 Google - Isolated Sign Language Recognition Competition https://www.kaggle.com/competitions/asl-signs See related landmark data here: https://www.kaggle.com/competitions/asl-signs/data

Information below mirrored from original source: https://signdata.cc.gatech.edu/view/datasets/popsign_v1_0

PopSign ASL v1.0

95% of deaf children are born to hearing parents. Since many hearing parents do not know sign, these deaf children are at risk for language acquisition delays resulting in cognitive issues. We are making an educational smartphone game PopSign that helps hearing parents practice their signing vocabulary.

Our dataset is the largest collection of isolated sign videos collected using mobile phones. We are using the data to train recognition models for use in smartphone applications, including the PopSign game. PopSign and related educational technology teach hearing parents and deaf children to sign, reducing developmental problems.

From original paper:

PopSign ASL v1.0 collects examples of 250 isolated American Sign Language signs using the selfie camera on Pixel 4A smartphones in a variety of environments. It is the largest isolated sign language dataset publicly available, the first to focus on one-handed signing with smartphones, and one of the few of its size that has been manually reviewed.

Papers

• PopSign v1.0 Paper
• PopSign v1.0 Supplemental Paper

Digitally semi-literate means those people who face challenges in digital enablement and are not too familiar with using smartphones for text message communication. Any progress to reduce the difficulty of their smartphone usage can help these people. These people are over one billion worldwide. The dataset contains text messages in English (some of these are translations of local text messages) from semi-literate Indian users. The dataset has been derived from face to face surveys primarily. Only 10% by online surveys since these people are not comfortable in doing online surveys.

Excessive use of smartphones has been associated with a number of negative consequences for individuals. Some of these consequences relate to many symptoms of behavioral addictions. The present study aims to investigate whether participants with high levels of smartphone addiction may have difficulty with their ability to wield the self-control that is needed to restrict smartphone use compared to participants with lower levels of smartphone addiction. Specifically, we expect that people with high levels of smartphone addiction may have problems in refraining from using a smartphone. In addition, we expect people with a high level of smartphone addiction may show deficiencies in cognitive tasks such as memory, executive control, and visual and auditory attention.
An ABA design was used to analyze the effects of smartphone withdrawal. The first A refers to baseline measurements: Visual RT, Auditory RT, Go/No-Go RT and N-Back RT and Eriksen flanker RT. The B refers to 3-days of smartphone withdrawal, the second A refers to the same measurements used in the baseline. In addition, several standardized scales were administered, among them: Smartphone addiction scale-short version (SAS-SV), Fear of missing out scale (FoMOs), Procrastination scale, Psychological General Well-Being Index. One hundred and one participants took part in the study. Based on median split they were divided into two groups: high levels of smartphone addiction and low levels of smartphone addiction. Moreover, thanks to an app installed on the participant's smartphone, it was possible to measure levels of compliance with the task. Results indicate that participants with low levels of smartphone addiction show less difficulty in their ability to wield the self-control needed to withdraw smartphone use and faster reaction times on cognitive tests than participants with high levels of smartphone dependence. Moreover, the profile of participants with high levels of smartphone addiction shows higher scores on the FoMOs and Procrastination scale, and lower scores in the Psychological General Well-Being Index. The results are discussed in light of self-regulation theory.

Internet users (per 100 people) Internet users are individuals who have used the Internet (from any location) in the last 12 months. Internet can be used via a computer, mobile phone, personal digital assistant, games machine, digital TV etc.

SourceInternational Telecommunication Union, World Telecommunication/ICT Development Report and database, and World Bank estimates. License Open Aggregation Method: Weighted average Development Relevance: The digital and information revolution has changed the way the world learns, communicates, does business, and treats illnesses. New information and communications technologies (ICT) offer vast opportunities for progress in all walks of life in all countries - opportunities for economic growth, improved health, better service delivery, learning through distance education, and social and cultural advances. Today's smartphones and tablets have computer power equivalent to that of yesterday's computers and provide a similar range of functions. Device convergence is thus rendering the conventional definition obsolete. Comparable statistics on access, use, quality, and affordability of ICT are needed to formulate growth-enabling policies for the sector and to monitor and evaluate the sector's impact on development. Although basic access data are available for many countries, in most developing countries little is known about who uses ICT; what they are used for (school, work, business, research, government); and how they affect people and businesses. The global Partnership on Measuring ICT for Development is helping to set standards, harmonize information and communications technology statistics, and build statistical capacity in developing countries. However, despite significant improvements in the developing world, the gap between the ICT haves and have-nots remains. General Comments: Please cite the International Telecommunication Union for third-party use of these data. Limitations and Exceptions: Operators have traditionally been the main source of telecommunications data, so information on subscriptions has been widely available for most countries. This gives a general idea of access, but a more precise measure is the penetration rate - the share of households with access to telecommunications. During the past few years more information on information and communication technology use has become available from household and business surveys. Also important are data on actual use of telecommunications services. Ideally, statistics on telecommunications (and other information and communications technologies) should be compiled for all three measures: subscriptions, access, and use. The quality of data varies among reporting countries as a result of differences in regulations covering data provision and availability. Discrepancies may also arise in cases where the end of a fiscal year differs from that used by ITU, which is the end of December of every year. A number of countries have fiscal years that end in March or June of every year. Periodicity: Annual Statistical Concept and Methodology: The Internet is a world-wide public computer network. It provides access to a number of communication services including the World Wide Web and carries email, news, entertainment and data files, irrespective of the device used (not assumed to be only via a computer - it may also be by mobile phone, PDA, games machine, digital TV etc.). Access can be via a fixed or mobile network.

Inspiration

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• Battery_Power: Total energy a battery can store in one time measured in mAh
• Clock_Speed: speed at which microprocessor executes instructions
• FC: Front Camera mega pixels
• Int_Memory: Internal Memory in Gigabytes
• Mobile_D: Mobile Depth in cm
• Mobile_W: Mobile Weight
• Cores: Number of cores of processor
• PC: Primary Camera mega pixels
• Pixel_H: Pixel Resolution Height
• Pixel_W: Pixel Resolution Width
• Ram: Random Access Memory in Megabytes
• Screen_H: Screen Height of mobile in cm
• Screen_W: Screen Width of mobile in cm
• Talk_Time: Longest time that a single battery charge will last when you are
• Four_G: Has Four_G or not
• Three_G: Has Three_G or not
• Touch_Screen: Has Touch Screen or not
• Dual_SIM: Has Dual SIM support or not
• Bluetooth: Has Bluetooth or not
• WiFi: Has WiFi or not
• Price_Range: ???

Description: Welcome to this comprehensive dataset exploring the detrimental effects of defective products on both product revenue and global brand image. This dataset presents an in-depth examination of the consequences of product defects on businesses and their market reputation, providing valuable insights for researchers, analysts, and stakeholders.

Key Features:

The dataset contains detailed records of various products that were found to be defective, along with information on their respective industries and categories.

Gain a deeper understanding of customer sentiments and feedback related to defective products. Analyze customer reviews, complaints, and social media reactions to gauge the extent of damage to brand perception.

The dataset covers a significant time period, allowing for the analysis of trends and long-term consequences of product defects on financial performance and brand reputation.

The dataset includes information on how users responded to defective product incidents. Analyze the effectiveness of recall campaigns, corrective actions, and communication strategies.

There are a total of 17 questions in the survey, addressing the following categories:Internet useMobile phone use (smartphones & basic voice/SMS phones)Awareness and use of WikipediaGeneral demographicsThe survey collected 2500 total responses, representing populations in 5 geographical regions served by 3 mobile Iraqi operators. 3 language choices (Arabic, English, Kurdish) were provided.Here are the main questions this survey was designed to answer. However, analyzing the full data set allows you to conduct more in-depth data explorations and gain meaningful insights beyond the points presented here.What is the actual number of people who use the internet?(Real-world behavior makes this difficult to measure from industry reports, since people might have access to the internet through school, friends, internet cafés, public Wifi, etc.)For internet users: What do people mostly use the internet for?For non-internet users: Why not use the internet?How many people use smartphones?Do people with smartphones use the internet from just Wifi? Or just cellular service?How many people think that they don’t use the internet, but still use Facebook or WhatsApp?How many people have heard of Wikipedia? What do they use it for? How often?If they have heard of Wikipedia, but aren’t using it, why not?Compared to previous phone surveys in other countries, the 2017 Iraq phone survey presented new questions.What are people’s awareness of other major internet brands in comparison to Wikipedia?Can people find online content in their preferred language?How does data cost impact internet use?

This data set is supplement to this Scientific Reports article.

The data set provides estimates of country-level daily mobility metrics (uncertainty included) for 17 countries from March 11, 2020 to present. Estimates are based on more than 3.8 million smartphone trajectories.

Metrics:

Estimated daily average travelled distance by people.

Estimated percentage of people who did not move during the 24 hours of the day.

Countries: Argentina (ARG), Chile (CHL), Colombia (COL), Costa Rica (CRI), Ecuador (ECU), Greece (GRC), Guatemala (GTM), Italy (ITA), Mexico (MEX), Nicaragua (NIC), Panama (PAN), Peru (PER), Philippines (PHL), Slovenia (SVN), Turkey (TUR), United States (USA) and Venezuela (VEN).

Covered period: from March 11, 2020 to present.

Temporal resolution: daily.

Temporal smoothing:

No smoothing.

7-day moving average.

14-day moving average.

21-day moving average.

28-day moving average.

Uncertainty: 95% bootstrap confidence interval.

Data ownership

Anonymized data on smartphone trajectories are collected, owned and managed by Futura Innovation SRL. Smartphone trajectories are stored and analyzed on servers owned by Futura Innovation SRL and not shared with third parties, including the author of this repository and his organization (University of Bergamo).

Contribution

Ilaria Cremonesi of Futura Innovation SRL is the data owner and data manager.

Francesco Finazzi of University of Bergamo developed the statistical methodology for the data analysis and the algorithms implemented on Futura Innovation SRL servers.

Repository update

CSV files of this repository are regularly produced by Futura Innovation SRL and published by the repository's author after validation.

The number of smartphone users in Ireland was forecast to continuously increase between 2024 and 2029 by in total 0.3 million users (+6.15 percent). After the seventh consecutive increasing year, the smartphone user base is estimated to reach 5.22 million users and therefore a new peak in 2029. 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 information concerning Serbia and Sweden.

The number of smartphone users in France was forecast to continuously increase between 2024 and 2029 by in total 3.2 million users (+5.96 percent). After the fifteenth consecutive increasing year, the smartphone user base is estimated to reach 56.89 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 Belgium and Luxembourg.

7 Types of Human Segmentation

The dataset includes 7 different types of image segmentation of people in underwear. For women, 4 types of labeling are provided, and for men, 3 types of labeling are provided. The dataset solves tasks in the field of recommendation systems and e-commerce.

👉 Legally sourced datasets and carefully structured for AI training and model development. Explore samples from our dataset - Full dataset

Types of labeling

Women I - distinctively detailed labeling of women. Special emphasis is placed on distinguishing the internal, external side, and lower breast depending on the type of underwear. The labeling also includes the face and hair, hands, forearms, shoulders, armpits, thighs, shins, underwear, accessories, and smartphones.

![https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2Fe157d0b7db89497f85c9b2d79d301086%2Fgirls_1_227.png?generation=1681741881080579&alt=media" alt="">

Women II - labeling of images of women with attention to the side abs area (highlighted in gray on the labeling). The labeling also includes the face and hair, hands, forearms, thighs, underwear, accessories, and smartphones.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F901d120c0273ea9a5a328fff15e26583%2Fgirls_2_-1087839647-1867563540.png?generation=1681741958025976&alt=media" alt="">

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F75fb0412edf631adce5f42ab6b9e8052%2Fwomen_fat_image_56570.png?generation=1681742864993159&alt=media" alt="">

Women III - primarily labeling of underwear. In addition to the underwear itself, the labeling includes the face and hair, abdomen, and arms and legs.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F6f32a06f0754a5a116fc994feae8c6f1%2Fgirls_5_111.png?generation=1681742011331681&alt=media" alt="">

Women IV - labeling of both underwear and body parts. It includes labeling of underwear, face and hair, hands, forearms, body, legs, as well as smartphones and tattoos.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F0dc22fcfd8b6e4fad3aa1806d14223ef%2Fgirls_6_image_4534.png?generation=1681742073295272&alt=media" alt="">

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F4a398547e13c555fdad142f521e62a5f%2Fsports_girls_IadpBSd3mI%20(1).png?generation=1681742947264828&alt=media" alt="">

Men I - labeling of the upper part of men's bodies. It includes labeling of hands and wrists, shoulders, body, neck, face and hair, as well as phones and accessories.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F3dae9889adb2b1415353769ccdd9c01b%2Fman_regular_1532667_38709335.png?generation=1681742128995529&alt=media" alt="">

Men II - more detailed labeling of men's bodies. The labeling includes hands and wrists, shoulders, body and neck, head and hair, underwear, tattoos and accessories, nipple and navel area.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2Fa57123e41066aa277bfeac140f4457da%2Fmen_1_3046.png?generation=1681742173310957&alt=media" alt="">

Men Neuro - labeling produced by a neural network for subsequent correction by annotators.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F5281cd644bc3f5949aaa9c40fb1cafd4%2F4595%20(1).png?generation=1681742187215164&alt=media" alt="">

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keywords: body segmentation dataset, human part segmentation dataset, human semantic part segmentation, human body segmentation data, human body segmentation deep learning, computer vision dataset, people images dataset, biometric data dataset, biometric dataset, images database, image-to-image, people segmentation, machine learning