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 global number of smartphone users in was forecast to continuously increase between 2024 and 2029 by in total 1.8 billion users (+42.62 percent). After the fifteenth consecutive increasing year, the smartphone user base is estimated to reach 6.1 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 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 the Americas and Asia.

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

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.

Percentage of Canadians using a smartphone for personal use and selected habits of use during a typical day.

China is leading the ranking by number of smartphone users , recording 859.38 million users. Following closely behind is India with 700.58 million users, while Seychelles is trailing the ranking with 0.05 million users, resulting in a difference of 859.33 million users to the ranking leader, China. 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).

The number of smartphone users in Argentina was forecast to continuously increase between 2024 and 2029 by in total 3.1 million users (+7.54 percent). After the tenth consecutive increasing year, the smartphone user base is estimated to reach 44.15 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 information concerning Austria and Lithuania.

Quantitative and qualitative data sets for 24 sites across Ghana, Malawi and South Africa:
a) SPSS dataset on young people’s use of mobile phones in Ghana, Malawi and South Africa.  4626 cases (young people aged 7-25 years): 1568 Ghana; 1544 Malawi; 1514 South Africa.  719 variables (+ 11 ‘navigation facilitators’) b) 1,620 Qualitative transcripts from interviews with people of diverse ages, 8y upwards: individual interviews [using either i.theme checklist or ii call register checklist]; focus group interviews [not all sites]: 50-80 transcripts for most sites.

This research project, which commenced in August 2012, explored how the rapid expansion of mobile phone usage is impacting on young lives in sub-Saharan Africa. It builds directly on our previous research on children’s mobility within which baseline quantitative data and preliminary qualitative information was collected on mobile phone usage (2006-2010) across 24 research sites, as an adjunct to our wider study of children’s physical mobility and access to services.

In this study our focus is specifically on mobile phones and we cover a much wider range of phone-related issues, including changes in gendered and age patterns of phone use over time; phone use in building social networks (for instance to support job search); impacts on education, livelihoods, health status, safety and surveillance, physical mobility and possible connections to migration, youth identity, and questions of exploitation and empowerment associated with mobile phones.

Mixed-method, participatory youth-centred studies have been conducted in the same 24 sites as in our earlier work across Ghana, Malawi and South Africa (urban, peri-urban, rural, remote rural, in two agro-ecological zones per country). We have built on the baseline data for 9-18 year-olds gathered in 2006-2010, through repeat and extended studies, but also included additional studies with 19-25 year-olds (to capture changing usage and its impacts as our initial cohort move into their 20s).

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.

What is the SherLock dataset?

A long-term smartphone sensor dataset with a high temporal resolution. The dataset also offers explicit labels capturing the to activity of malwares running on the devices. The dataset currently contains 10 billion data records from 30 users collected over a period of 2 years and an additional 20 users for 10 months (totaling 50 active users currently participating in the experiment).

The primary purpose of the dataset is to help security professionals and academic researchers in developing innovative methods of implicitly detecting malicious behavior in smartphones. Specifically, from data obtainable without superuser (root) privileges. However, this dataset can be used for research in domains that are not strictly security related. For example, context aware recommender systems, event prediction, user personalization and awareness, location prediction, and more. The dataset also offers opportunities that aren't available in other datasets. For example, the dataset contains the SSID and signal strength of the connected WiFi access point (AP) which is sampled once every second, over the course of many months.

To gain full free access to the SherLock Dataset, follow these two steps:

1) Read, complete and sign the license agreement. The general restrictions are:

-The license lasts for 3 years, afterwhich the data must be deleted.

-Do not share the data with those who are not bound by the license agreement.

-Do not attempt to de-anonymize the individuals (volunteers) who have contributed the data.

-Any of your publication that benefit from the SherLock project must cite the following article: Mirsky, Yisroel, et al. "SherLock vs Moriarty: A Smartphone Dataset for Cybersecurity Research." Proceedings of the 2016 ACM Workshop on Artificial Intelligence and Security. ACM, 2016.

2)Send the scanned document as a PDF to bgu.sherlock@gmail.com and provide a gmail account to share a google drive folder with.

More information can be found here, or in this publication (download link).

A 2 week data sample from a single user is provided on this Kaggle page. To access the full dataset for free, please visit our site. Note: The format of the sample dataset may differ from the full dataset.

We are publishing a walking activity dataset including inertial and positioning information from 19 volunteers, including reference distance measured using a trundle wheel. The dataset includes a total of 96.7 Km walked by the volunteers, split into 203 separate tracks. The trundle wheel is of two types: it is either an analogue trundle wheel, which provides the total amount of meters walked in a single track, or it is a sensorized trundle wheel, which measures every revolution of the wheel, therefore recording a continuous incremental distance.
Each track has data from the accelerometer and gyroscope embedded in the phones, location information from the Global Navigation Satellite System (GNSS), and the step count obtained by the device. The dataset can be used to implement walking distance estimation algorithms and to explore data quality in the context of walking activity and physical capacity tests, fitness, and pedestrian navigation. Methods The proposed dataset is a collection of walks where participants used their own smartphones to capture inertial and positioning information. The participants involved in the data collection come from two sites. The first site is the Oxford University Hospitals NHS Foundation Trust, United Kingdom, where 10 participants (7 affected by cardiovascular diseases and 3 healthy individuals) performed unsupervised 6MWTs in an outdoor environment of their choice (ethical approval obtained by the UK National Health Service Health Research Authority protocol reference numbers: 17/WM/0355). All participants involved provided informed consent. The second site is at Malm ̈o University, in Sweden, where a group of 9 healthy researchers collected data. This dataset can be used by researchers to develop distance estimation algorithms and how data quality impacts the estimation.

All walks were performed by holding a smartphone in one hand, with an app collecting inertial data, the GNSS signal, and the step counting. On the other free hand, participants held a trundle wheel to obtain the ground truth distance. Two different trundle wheels were used: an analogue trundle wheel that allowed the registration of a total single value of walked distance, and a sensorized trundle wheel which collected timestamps and distance at every 1-meter revolution, resulting in continuous incremental distance information. The latter configuration is innovative and allows the use of temporal windows of the IMU data as input to machine learning algorithms to estimate walked distance. In the case of data collected by researchers, if the walks were done simultaneously and at a close distance from each other, only one person used the trundle wheel, and the reference distance was associated with all walks that were collected at the same time.The walked paths are of variable length, duration, and shape. Participants were instructed to walk paths of increasing curvature, from straight to rounded. Irregular paths are particularly useful in determining limitations in the accuracy of walked distance algorithms. Two smartphone applications were developed for collecting the information of interest from the participants' devices, both available for Android and iOS operating systems. The first is a web-application that retrieves inertial data (acceleration, rotation rate, orientation) while connecting to the sensorized trundle wheel to record incremental reference distance [1]. The second app is the Timed Walk app [2], which guides the user in performing a walking test by signalling when to start and when to stop the walk while collecting both inertial and positioning data. All participants in the UK used the Timed Walk app.

The data collected during the walk is from the Inertial Measurement Unit (IMU) of the phone and, when available, the Global Navigation Satellite System (GNSS). In addition, the step count information is retrieved by the sensors embedded in each participant’s smartphone. With the dataset, we provide a descriptive table with the characteristics of each recording, including brand and model of the smartphone, duration, reference total distance, types of signals included and additionally scoring some relevant parameters related to the quality of the various signals. The path curvature is one of the most relevant parameters. Previous literature from our team, in fact, confirmed the negative impact of curved-shaped paths with the use of multiple distance estimation algorithms [3]. We visually inspected the walked paths and clustered them in three groups, a) straight path, i.e. no turns wider than 90 degrees, b) gently curved path, i.e. between one and five turns wider than 90 degrees, and c) curved path, i.e. more than five turns wider than 90 degrees. Other features relevant to the quality of collected signals are the total amount of time above a threshold (0.05s and 6s) where, respectively, inertial and GNSS data were missing due to technical issues or due to the app going in the background thus losing access to the sensors, sampling frequency of different data streams, average walking speed and the smartphone position. The start of each walk is set as 0 ms, thus not reporting time-related information. Walks locations collected in the UK are anonymized using the following approach: the first position is fixed to a central location of the city of Oxford (latitude: 51.7520, longitude: -1.2577) and all other positions are reassigned by applying a translation along the longitudinal and latitudinal axes which maintains the original distance and angle between samples. This way, the exact geographical location is lost, but the path shape and distances between samples are maintained. The difference between consecutive points “as the crow flies” and path curvature was numerically and visually inspected to obtain the same results as the original walks. Computations were made possible by using the Haversine Python library.

Multiple datasets are available regarding walking activity recognition among other daily living tasks. However, few studies are published with datasets that focus on the distance for both indoor and outdoor environments and that provide relevant ground truth information for it. Yan et al. [4] introduced an inertial walking dataset within indoor scenarios using a smartphone placed in 4 positions (on the leg, in a bag, in the hand, and on the body) by six healthy participants. The reference measurement used in this study is a Visual Odometry System embedded in a smartphone that has to be worn at the chest level, using a strap to hold it. While interesting and detailed, this dataset lacks GNSS data, which is likely to be used in outdoor scenarios, and the reference used for localization also suffers from accuracy issues, especially outdoors. Vezovcnik et al. [5] analysed estimation models for step length and provided an open-source dataset for a total of 22 km of only inertial walking data from 15 healthy adults. While relevant, their dataset focuses on steps rather than total distance and was acquired on a treadmill, which limits the validity in real-world scenarios. Kang et al. [6] proposed a way to estimate travelled distance by using an Android app that uses outdoor walking patterns to match them in indoor contexts for each participant. They collect data outdoors by including both inertial and positioning information and they use average values of speed obtained by the GPS data as reference labels. Afterwards, they use deep learning models to estimate walked distance obtaining high performances. Their results share that 3% to 11% of the data for each participant was discarded due to low quality. Unfortunately, the name of the used app is not reported and the paper does not mention if the dataset can be made available.

This dataset is heterogeneous under multiple aspects. It includes a majority of healthy participants, therefore, it is not possible to generalize the outcomes from this dataset to all walking styles or physical conditions. The dataset is heterogeneous also from a technical perspective, given the difference in devices, acquired data, and used smartphone apps (i.e. some tests lack IMU or GNSS, sampling frequency in iPhone was particularly low). We suggest selecting the appropriate track based on desired characteristics to obtain reliable and consistent outcomes.

This dataset allows researchers to develop algorithms to compute walked distance and to explore data quality and reliability in the context of the walking activity. This dataset was initiated to investigate the digitalization of the 6MWT, however, the collected information can also be useful for other physical capacity tests that involve walking (distance- or duration-based), or for other purposes such as fitness, and pedestrian navigation.

The article related to this dataset will be published in the proceedings of the IEEE MetroXRAINE 2024 conference, held in St. Albans, UK, 21-23 October.

This research is partially funded by the Swedish Knowledge Foundation and the Internet of Things and People research center through the Synergy project Intelligent and Trustworthy IoT Systems.

Abstract

To facilitate the use of data collected through the high-frequency phone surveys on COVID-19, the Living Standards Measurement Study (LSMS) team has created the harmonized datafiles using two household surveys: 1) the country’ latest face-to-face survey which has become the sample frame for the phone survey, and 2) the country’s high-frequency phone survey on COVID-19.

The LSMS team has extracted and harmonized variables from these surveys, based on the harmonized definitions and ensuring the same variable names. These variables include demography as well as housing, household consumption expenditure, food security, and agriculture. Inevitably, many of the original variables are collected using questions that are asked differently. The harmonized datafiles include the best available variables with harmonized definitions.

Two harmonized datafiles are prepared for each survey. The two datafiles are: 1. HH: This datafile contains household-level variables. The information include basic household characterizes, housing, water and sanitation, asset ownership, consumption expenditure, consumption quintile, food security, livestock ownership. It also contains information on agricultural activities such as crop cultivation, use of organic and inorganic fertilizer, hired labor, use of tractor and crop sales.
2. IND: This datafile contains individual-level variables. It includes basic characteristics of individuals such as age, sex, marital status, disability status, literacy, education and work.

Geographic coverage

National

Analysis unit

• Households
• Individuals

Universe

The survey covered all de jure households excluding prisons, hospitals, military barracks, and school dormitories.

Kind of data

Sample survey data [ssd]

Sampling procedure

See “Uganda - National Panel Survey 2019-2020” and “Uganda - High-Frequency Phone Survey on COVID-19 2020-2021” documentations available in the Microdata Library for details.

Mode of data collection

Computer Assisted Personal Interview [capi]

Cleaning operations

Uganda National Panel Survey 2019-2020 and Uganda High-Frequency Phone Survey on COVID-19 2020-2021 data were harmonized following the harmonization guidelines (see “Harmonized Datafiles and Variables for High-Frequency Phone Surveys on COVID-19” for more details).

The high-frequency phone survey on COVID-19 has multiple rounds of data collection. When variables are extracted from multiple rounds of the survey, the originating round of the survey is noted with “_rX” in the variable name, where X represents the number of the round. For example, a variable with “_r3” presents that the variable was extracted from Round 3 of the high-frequency phone survey. Round 0 refers to the country’s latest face-to-face survey which has become the sample frame for the high-frequency phone surveys on COVID-19. When the variables are without “_rX”, they were extracted from Round 0.

Response rate

See “Uganda - National Panel Survey 2019-2020” and “Uganda - High-Frequency Phone Survey on COVID-19 2020-2021” documentations available in the Microdata Library for details.

PurposeStudies have reported that knowledge and skills to operate smartphones among people with profound visual loss are limited especially in low- to middle-income countries as many important functions of smartphones are unknown to them. This report presents smartphone use, its challenges, and enablers in two persons with profound visual impairment while executing their daily routine and instrumental living activities amidst the COVID-19 pandemic.Case selection and interviewDuring the lockdown period, we provided tele (vision) rehabilitation service. From the list of the callers, we purposely selected two callers with significant visual impairment, one woman and one man, to allow us to gather rich information related to smartphone use, enablers, and challenges faced during the usage. A semistructured interview was done to obtain insights into the information. The selection criteria were (1) continuous smartphone use independently for more than 5 years; (2) graduation-level education or higher; and (3) no additional disabilities.DiscussionWe found substantial use of smartphones in executing their daily and instrumental daily living activities by these two participants. The extent of the use of mainstream apps for various tasks was almost equivalent to what we observed among sighted persons. The most important enabling factors were the presence of a screen reader “TalkBack” on Android phones and data connection of the mobile, followed by the ability to assess multiple languages using the text-to-speech feature. A supportive environment from peers or family members is important for the beginner. Poor battery backup, frequent unwanted ads or pop-ups while using the phone, not readable contents with a screen reader, e.g., CAPTCHA, and slow or unresponsiveness of the screen reader were frequent challenges faced by them. Both cases reported that around 80% of daily solutions were helped by using a smartphone.ConclusionsThe current advances in accessible technology of smartphones enable an individual with profound visual loss to use them almost equivalently as a sighted person. To reduce the gap in digital inclusion, people with visual impairment should be encouraged to use the smartphone for their daily solutions with attention to proper training.

The number of smartphone users in Turkey was forecast to continuously increase between 2024 and 2029 by in total 9.3 million users (+12.38 percent). After the ninth consecutive increasing year, the smartphone user base is estimated to reach 84.37 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 further information concerning Bulgaria and Serbia.

Summary

FSboard is an American Sign Language fingerspelling dataset situated in a mobile text entry use case, collected from 147 paid and consenting Deaf signers using Pixel 4A selfie cameras in a variety of environments. At >3 million characters in length and >250 hours in duration, FSboard is the largest fingerspelling recognition dataset to date by a factor of >10x.

We previously hosted a Kaggle competition using MediaPipe Holistic landmarks for the FSboard data; this release now includes the underlying RGB videos and val/test sets.

See the our paper for a more complete exposition of the dataset: FSboard: Over 3 million characters of ASL fingerspelling collected via smartphones

The dataset consists of several categories of synthetically generated phrases (examples in the table below, not real PII) recorded as video clips of ASL fingerspelling (example frames in the figure below, faces blurred here but not in the dataset).

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F20954272%2F2a7512937441315b8ddf742e9d02195d%2Ffs-blurred.png?generation=1739550608040254&alt=media" alt="">

Responsible Use

While facial expressions are an essential component of sign language and are therefore included in the dataset, we ask that you blur the signers’ faces when publicizing examples. You should not attempt to reidentify the signers or use their likenesses to generate and publish other content (deepfakes). Please be culturally respectful of the Deaf/Hard of Hearing community in your use of the dataset and do not exaggerate the significance of improving ASL fingerspelling performance, which is only one small component of American Sign Language.

Landmarks

Landmarks were extracted using MediaPipe Holistic . They are provided as tf.train.SequenceExample entries in TFRecordio files. There is also a script which converts these TFRecordio files to Parquet files in a similar format to the one used in the previous Kaggle Competition. Since each entry in the Parquet file represents a single landmark frame, the script also produces a supplemental csv file with video level information.

Sensitive Content Filtering

The synthetic URLs generated in the dataset were created by recombining parts from real URLs. As such, the full breadth of content available on the internet is represented. It is important not to infantilize the Deaf community, and therefore important to ensure that any applications in this space is able to produce arbitrary output. Imagine the frustration when your keyboard r*****s to produce certain ducking words. However, it's also important to ensure that an application doesn't easily produce offensive unintended content. In an effort to facilitate people making sane decisions with this data, we've run a sensitive content filter and keyword searches on the phrases used and manually reviewed the result to produce a boolean tag "sensitiveContent" which is available in the json files. Please ensure that the Deaf community is involved in the creation of any applications targeted to them.

Attribution

If you use FSboard in your work, please cite: @misc{georg2024fsboard3millioncharacters, title={FSboard: Over 3 million characters of ASL fingerspelling collected via smartphones}, author={Manfred Georg and Garrett Tanzer and Saad Hassan and Maximus Shengelia and Esha Uboweja and Sam Sepah and Sean Forbes and Thad Starner}, year={2024}, eprint={2407.15806}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2407.15806}, }

Liverpool City Council has device counters deployed throughout the city centre to understand how the city is used. Data in this dashboard is derived from Meshed nCounters and CCTV retrofitted with an algorithm developed by University of Wollongong. The algorithm has used machine learning to identify objects in the camera viewfinder and categorise them into pedestrian, vehicle or bicycle. This transforms the CCTV into visual sensors. The nCounters generate data by counting the number of Wi-Fi signals emitted by non-identifiable mobile devices within a specified proximity and performing certain filtering and processing. No individuals are identified by either method. Both methods have advantages and limitations.Advantages of the nCounter method:Can provide insightful data on crowd sizes and individualsAn individual with one device will be counted onceThe nCounter can report the average ‘dwell time’ of the deviceLimitations of the nCounter method:individuals without devices will not be counted (for example young children or people without smart phones),if someone is carrying a smart phone which is in aeroplane mode or switched off then it will not be counted, andindividuals with multiple devices will be counted by the number of devices they have. For example, one person may have two smart phones, therefore the individual will be counted more than once.The visual sensors (CCTV) count the number of bicycles, people and vehicles in the location.Advantages of the visual sensor method:Can provide insightful data on pedestrian, vehicle and bicycle numbers,Re-uses existing common technology on city streets without further visual clutter,Does not rely on individuals carrying their own devices, so useful in areas with lower technology uptakeLimitations of visual sensor method:Individuals can be counted multiple times as they exit and re-enter the camera viewfinder,The machine learning cannot differentiate between bicycles and motorbikesData collected in this dataset can be visualised in the Liverpool City Centre activity dashboard

Success.ai offers a comprehensive, enterprise-ready B2B leads data solution, ideal for businesses seeking access to over 150 million verified employee profiles and 170 million work emails. Our data empowers organizations across industries to target key decision-makers, optimize recruitment, and fuel B2B marketing efforts. Whether you're looking for UK B2B data, B2B marketing data, or global B2B contact data, Success.ai provides the insights you need with pinpoint accuracy.

Tailored for B2B Sales, Marketing, Recruitment and more: Our B2B contact data and B2B email data solutions are designed to enhance your lead generation, sales, and recruitment efforts. Build hyper-targeted lists based on job title, industry, seniority, and geographic location. Whether you’re reaching mid-level professionals or C-suite executives, Success.ai delivers the data you need to connect with the right people.

API Features:

• Real-Time Updates: Our APIs deliver real-time updates, ensuring that the contact data your business relies on is always current and accurate.
• High Volume Handling: Designed to support up to 860k API calls per day, our system is built for scalability and responsiveness, catering to enterprises of all sizes.
• Flexible Integration: Easily integrate with CRM systems, marketing automation tools, and other enterprise applications to streamline your workflows and enhance productivity.

Benefits of the EU Premium Dataset:

Targeted Reach: Reach potential leads with detailed insights including email addresses, phone numbers, job titles, and more, specifically within the EU markets. Enhanced Lead Quality: Every profile is thoroughly verified, enhancing the quality of your outreach and increasing the likelihood of successful engagements. Best Price Guarantee: We are committed to providing these extensive services at the most competitive prices, ensuring that you receive the best value for your investment.

Key Categories Served: B2B sales leads – Identify decision-makers in key industries, B2B marketing data – Target professionals for your marketing campaigns, Recruitment data – Source top talent efficiently and reduce hiring times, CRM enrichment – Update and enhance your CRM with verified, updated data, Global reach – Coverage across 195 countries, including the United States, United Kingdom, Germany, India, Singapore, and more.

Global Coverage with Real-Time Accuracy: Success.ai’s dataset spans a wide range of industries such as technology, finance, healthcare, and manufacturing. With continuous real-time updates, your team can rely on the most accurate data available: 150M+ Employee Profiles: Access professional profiles worldwide with insights including full name, job title, seniority, and industry. 170M Verified Work Emails: Reach decision-makers directly with verified work emails, available across industries and geographies, including Singapore and UK B2B data. GDPR-Compliant: Our data is fully compliant with GDPR and other global privacy regulations, ensuring safe and legal use of B2B marketing data.

Key Data Points for Every Employee Profile: Every profile in Success.ai’s database includes over 20 critical data points, providing the information needed to power B2B sales and marketing campaigns: Full Name, Job Title, Company, Work Email, Location, Phone Number, LinkedIn Profile, Experience, Education, Technographic Data, Languages, Certifications, Industry, Publications & Awards.

Use Cases Across Industries: Success.ai’s B2B data solution is incredibly versatile and can support various enterprise use cases, including: B2B Marketing Campaigns: Reach high-value professionals in industries such as technology, finance, and healthcare. Enterprise Sales Outreach: Build targeted B2B contact lists to improve sales efforts and increase conversions. Talent Acquisition: Accelerate hiring by sourcing top talent with accurate and updated employee data, filtered by job title, industry, and location. Market Research: Gain insights into employment trends and company profiles to enrich market research. CRM Data Enrichment: Ensure your CRM stays accurate by integrating updated B2B contact data. Event Targeting: Create lists for webinars, conferences, and product launches by targeting professionals in key industries.

Use Cases for Success.ai's Contact Data - Targeted B2B Marketing: Create precise campaigns by targeting key professionals in industries like tech and finance. - Sales Outreach: Build focused sales lists of decision-makers and C-suite executives for faster deal cycles. - Recruiting Top Talent: Easily find and hire qualified professionals with updated employee profiles. - CRM Enrichment: Keep your CRM current with verified, accurate employee data. - Event Targeting: Create attendee lists for events by targeting relevant professionals in key sectors. - Market Research: Gain insights into employment trends and company profiles for better business decisions. - Executive Search: So...

Switzerland Internet Users: Individuals: % of Population data was reported at 89.135 % in 2016. This records an increase from the previous number of 87.479 % for 2015. Switzerland Internet Users: Individuals: % of Population data is updated yearly, averaging 65.100 % from Dec 1990 (Median) to 2016, with 27 observations. The data reached an all-time high of 89.135 % in 2016 and a record low of 0.596 % in 1990. Switzerland Internet Users: Individuals: % of Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Switzerland – Table CH.World Bank: Telecommunication. Internet users are individuals who have used the Internet (from any location) in the last 3 months. The Internet can be used via a computer, mobile phone, personal digital assistant, games machine, digital TV etc.; ; International Telecommunication Union, World Telecommunication/ICT Development Report and database.; Weighted average; Please cite the International Telecommunication Union for third-party use of these data.

Overview

The goal of this project was to create a structured dataset which can be used to train computer vision models to detect electronic waste devices, i.e., e-waste or Waste Electrical and Electronic Equipment (WEEE). Due to the often-subjective differences between e-waste and functioning electronic devices, a model trained on this dataset could also be used to detect electronic devices in general. However, it must be noted that for the purposes of e-waste recognition, this dataset does not differentiate between different brands or models of the same type of electronic devices, e.g. smartphones, and it also includes images of damaged equipment.

The structure of this dataset is based on the UNU-KEYS classification Wang et al., 2012, Forti et al., 2018. Each class in this dataset has a tag containing its corresponding UNU-KEY. This dataset structure has the following benefits: 1. It allows the user to easily classify e-waste devices regardless of which e-waste definition their country or organization uses, thanks to the correlation between the UNU-KEYS and other classifications such as the HS-codes or the EU-6 categories, defined in the WEEE directive; 2. It helps dataset contributors focus on adding e-waste devices with higher priority compared to arbitrarily chosen devices. This is because electronic devices in the same UNU-KEY category have similar function, average weight and life-time distribution as well as comparable material composition, both in terms of hazardous substances and valuable materials, and related end-of-life attributes Forti et al., 2018. 3. It gives dataset contributors a clear goal of which electronic devices still need to be added and a clear understanding of their progress in the seemingly endless task of creating an e-waste dataset.

This dataset contains annotated images of e-waste from every UNU-KEY category. According to Forti et al., 2018, there are a total of 54 UNU-KEY e-waste categories.

Description of Classes

At the time of writing, 22. Apr. 2024, the dataset has 19613 annotated images and 77 classes. The dataset has mixed bounding-box and polygon annotations. Each class of the dataset represents one type of electronic device. Different models of the same type of device belong to the same class. For example, different brands of smartphones are labelled as "Smartphone", regardless of their make or model. Many classes can belong to the same UNU-KEY category and therefore have the same tag. For example, the classes "Smartphone" and "Bar-Phone" both belong to the UNU-KEY category "0306 - Mobile Phones". The images in the dataset are anonymized, meaning that no people were annotated and images containing visible faces were removed.

The dataset was almost entirely built by cloning annotated images from the following open-source Roboflow datasets: [1]-[91]. Some of the images in the dataset were acquired from the Wikimedia Commons website. Those images were chosen to have an unrestrictive license, i.e., they belong to the public domain. They were manually annotated and added to the dataset.

Cite This Project

This work was done as part of the PhD of Dimitar Iliev, student at the Faculty of German Engineering and Industrial Management at the Technical University of Sofia, Bulgaria and in collaboration with the Faculty of Computer Science at Otto-von-Guericke-University Magdeburg, Germany.

If you use this dataset in a research paper, please cite it using the following BibTeX: @article{iliev2024EwasteDataset, author = "Iliev, Dimitar and Marinov, Marin and Ortmeier, Frank", title = "A proposal for a new e-waste image dataset based on the unu-keys classification", journal = "XXIII-rd International Symposium on Electrical Apparatus and Technologies SIELA 2024", year = 2024, volume = "23", number = "to appear", pages = {to appear} note = {under submission} }

Contribution Guidelines

Image Collection

1. Choose a specific electronic device type to add to the dataset and find its corresponding UNU-KEY. * The chosen type of device should have a characteristic design which an object detection model can learn. For example, CRT monitors look distinctly different than flat panel monitors and should therefore belong to a different class, regardless that they are both monitors. In contrast, LED monitors and LCD monitors look very similar and are therefore both labelled as Flat-Panel-Monitor in this dataset.
2. Collect images of this type of device. * Take note of the license of those images and their author/s to avoid copyright infringement. * Do not collect images with visible faces to protect personal data and comply w

Many have argued that digital technologies such as smartphones and social media are addictive. We develop an economic model of digital addiction and estimate it using a randomized experiment. Temporary incentives to reduce social media use have persistent effects, suggesting social media are habit forming. Allowing people to set limits on their future screen time substantially reduces use, suggesting self-control problems. Additional evidence suggests people are inattentive to habit formation and partially unaware of self-control problems. Looking at these facts through the lens of our model suggests that self-control problems cause 31 percent of social media use.