Solution Publishing by Allforce UK B2B Contact Data is a sophisticated data product crafted specifically for businesses aiming to bolster their B2B marketing and outreach efforts. This data set amalgamates a wealth of information, providing businesses with unparalleled insights into their target audience, paving the way for more personalized and impactful marketing campaigns.

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Smartphone usage in the United Kingdom has increased across all age ranges since 2012, most noticeably among those aged 55-64 years of age. Whereas just nine percent of mobile phone users aged 55 to 64 years used a smartphone in 2012, this number rose to over 90 percent by 2023 and reached 93 percent in 2024. Smartphones are becoming more accessibleAs well as becoming more ubiquitous, smartphones are also becoming more accessible. In terms of price, the global average selling price of smartphones has fallen from 336.8 U.S. dollars in 2010, to 276.20 U.S. dollars in 2015. However, estimates available from 2019 predicted that the average selling price of smartphones worldwide will increase again and reach 317 U.S. dollars by 2021. The average selling price for smartphones in Europe was at around 373 euros in 2019. Smartphone usage in the UK    Smartphones are the Swiss army knife of digital devices, with their capabilities limited by the creativity of developers as much as it is the technology contained in the phone. In 2017, communications were the most popular ways to use a phone, however, 87 percent of users report using camera apps frequently, 85 percent report frequent use of browser apps, and 68 percent report frequent use of navigation apps.

The proportion of smartphone users out of those using a mobile phone in the United Kingdom (UK) increased notably from 2011 to 2024, from less than half in 2011 with 44 percent to 93 percent of mobile phone users using a smartphone in 2024.

Average weekly household expenditure on goods and services in the UK. Data are shown by region, age, income (including equivalised) group (deciles and quintiles), economic status, socio-economic class, housing tenure, output area classification, urban and rural areas (Great Britain only), place of purchase and household composition.

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This data set forms the basis of the paper 'The Hibernating Mobile Phone: Dead Storage as a Barrier to Efficient Electronic Waste Recovery'.

These results are from an online, self-completion questionnaire with mobile phone owners; distributed to a non-probability, purposive sample (i.e. aged between 18-25 years old, living and studying at a UK University, and owning a mobile phones. The survey was conducted during July 2015.

For a full description of the data collection techniques and our analysis of the data, please refer to the above paper.

This quarterly dataset for the UK fixed-line and mobile telecommunication markets contains data for aggregated call revenues, mobile phone and landline connections, call volumes, message volumes and subscriber numbers. The tables are published quarterly on the Ofcom website in pdf and csv formats.

Hedonic quality adjustment was first introduced in the Consumer Prices Index (CPI) in 2003 for PCs. Since then the use of hedonics has expanded in UK consumer price statistics to include a further five technology products; digital cameras, laptops, mobile phones, pay as you go phones, smartphones and tablet PCs. This article reviews the use of hedonic quality adjustment in consumer price indices in the UK and internationally. It also details the reasons for changing the method of quality adjustment for pay-as-you-go phones and digital cameras, from hedonic adjustment to class mean imputation, from March 2014 onwards.

Source agency: Office for National Statistics

Designation: National Statistics

Language: English

Alternative title: Review of hedonic quality adjustment

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.

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This dataset on mobile phone coverage is no longer current. It was published by Ofcom in 2013. More recent mobile phone coverage data is available in our 2017 Connected Nations report (formerly called Infrastructure Report), both on www.data.gov.uk and on the Ofcom website at https://www.ofcom.org.uk/research-and-data/multi-sector-research/infrastructure-research/connected-nations-2017

Smart homes contain programmable electronic devices (mostly IoT) that enable home au- tomation. People who live in smart homes benefit from interconnected devices by controlling them either remotely or manually/autonomously. However, high interconnectivity comes with an increased attack surface, making the smart home an attractive target for adversaries. NCC Group and the Global Cyber Alliance recorded over 12,000 attacks to log into smart home devices maliciously. Recent statistics show that over 200 million smart homes can be subjected to these attacks. Conventional security systems are either focused on network traffic (e.g., firewalls) or physical environment (e.g., CCTV or basic motion sensors), but not both. A key challenge in de- veloping cyber-physical security systems is the lack of datasets and test beds. For cyber-physical datasets to be meaningful, they need to be collected in real smart home environments. Due to the inherited difficulties and challenges (e.g. effort, costs, test-bed availability), such cyber-physical smart home datasets are quite rare. This paper aims to fill this gap by contributing a dataset we collected in a real smart home with annotated labels. This paper explains the process we followed to collect the data and how we organised them to facilitate wider use within research communities.A related article can be found at https://doi.org/10.3389/friot.2023.1275080

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.

Data being used in the paper: Vanhoof,M., Schoors, W., Van Rompaey, A., Ploetz, T., Smoreda, Z. (2018) Correcting mobility entropy for regional comparison of individual movement patterns. Journal of Urban Technology. Remark that the mobile phone data used in this paper are property of Orange Labs, France and are subject to a non-disclosure agreement. This data, therefore is not included here.

Description for the data set:User micromobility data setThe dataset measures the movement of a mobile phone from a single user for five different activities. For each activity, one five-minute clip and three one-minute clips have been recorded. Each motion has been captured by three cameras, each with a frame rate of 240 fps. The recorded motion from each camera is combined to form the motion in six degrees of freedom.Time: time step for each measurement, measured in seconds.Displacement in (X, Y, Z) Translation: Horizontal movement in x, y, z axes, measured in millimetres.Displacement in (X, Y, Z) Rotation: Rotational movement around x, y, z axes, measured in degrees.ContactFor any questions regarding the dataset, please contact us using the following email address:John Edward Wu – zceejew@ucl.ac.ukDr. Martyn Fice – m.fice@ucl.ac.uk

English(the united kingdom) Spontaneous Dialogue Smartphone speech dataset, collected from dialogues based on given topics. Transcribed with text content, timestamp, speaker's ID, gender and other attributes. Our dataset was collected from extensive and diversify speakers(around 500 native 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.

The data set contain network survey statistics from the county of Nottinghamshire for four major UK mobile operators. The data are collected from September 2022 till December 2022 and contain both 4G-LTE and 5G-NSA network information and their corresponding GPS location.

Success.ai’s Phone Number Data offers direct access to over 50 million verified phone numbers for professionals worldwide, extracted from our expansive collection of 170 million profiles. This robust dataset includes work emails and key decision-maker profiles, making it an essential resource for companies aiming to enhance their communication strategies and outreach efficiency. Whether you're launching targeted marketing campaigns, setting up sales calls, or conducting market research, our phone number data ensures you're connected to the right professionals at the right time.

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Worldwide mobile data pricing: The cost of 1GB of mobile data in 228 countries

Data from 5,554 mobile data plans in 228 countries were gathered and analysed by Cable.co.uk between 3 February and 25 February 2020. The average cost of one gigabyte (1GB) was then calculated and compared to form a worldwide mobile data pricing league table.

Source

https://www.cable.co.uk/mobiles/worldwide-data-pricing/#resources

Image Source

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.