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.

This metric shows the proportion of residential and small business premises with a reliable 4G signal from one of the four mobile network operators (EE, Vodafone, O2 or Three). ie, for this proportion of the area only one network is available. For overall coverage see all operators metric. It has been produced by Ofcom, using data provided by mobile operators referenced against the Ordnance Survey of Great Britain (OSGB) Coordinate System, premises coverage is calculated from the postal delivery points taken from the Ordnance Survey AddressBase database. Mobile coverage produced by operators are based on theoretical models, calibrated using measurements of actual performance. However, consumers use mobile phones in many different situations - indoors, outdoors, on the move, in cars, as pedestrians along roads in built up areas and in wide open spaces all of which can affect coverage. Due to variations in mobile performance over time, the file should not be regarded as a definitive and fixed view of the UK's mobile infrastructure. However, the information provided in this file may be useful in identifying variations in mobile performance by geography.Data is Powered by LG Inform Plus and automatically checked for new data on the 3rd of each month.

Key information about United Kingdom Number of Subscriber Mobile

• United Kingdom Number of Subscriber Mobile was reported at 84,300,000.000 Person in Dec 2023
• This records an increase from the previous number of 81,700,000.000 Person for Dec 2022
• UK Number of Subscriber Mobile data is updated yearly, averaging 11,859,500.000 Person from Dec 1960 to 2023, with 52 observations
• The data reached an all-time high of 84,300,000.000 Person in 2023 and a record low of 0.000 Person in 1984
• UK Number of Subscriber Mobile data remains active status in CEIC and is reported by World Bank
• The data is categorized under World Trend Plus’s Association: Telecommunication Sector – Table UK.World Bank.WDI: Telecommunication

Mobile cellular telephone subscriptions are subscriptions to a public mobile telephone service that provide access to the PSTN using cellular technology. The indicator includes (and is split into) the number of postpaid subscriptions, and the number of active prepaid accounts (i.e. that have been used during the last three months). The indicator applies to all mobile cellular subscriptions that offer voice communications. It excludes subscriptions via data cards or USB modems, subscriptions to public mobile data services, private trunked mobile radio, telepoint, radio paging and telemetry services.;International Telecommunication Union (ITU) World Telecommunication/ICT Indicators Database;Sum;Please cite the International Telecommunication Union for third-party use of these data.

Coverage data from mobile and fixed telecoms operators, as of May 2019, including coverage by type and extent. Limited data available for Glasgow such asdata zone. Sourced from Ofcom and provided via the Open Government License.

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

This dataset contains the location of mobile phone masts in Camden. Data is provided annually by the MOA (Mobile Operators Association). Attribution includes the operator name, site address and ward name. Some sites are duplicated where operators share the same infrastructure.

This BibTeX file contains the corpus of papers that cite CRAWDAD wireless network datasets, as used in the paper: Tristan Henderson and David Kotz. Data citation practices in the CRAWDAD wireless network data archive. Proceedings of the Second Workshop on Linking and Contextualizing Publications and Datasets, London, UK, September 2014. Most of the fields are standard BibTeX fields. There are two that require further explanation. "citations" - this field contains the citations for a paper as countedby Google Scholar as of 24 September 2014. "keywords" - this field contains a set of tags indicating data citation practice. These are as follows:- "uses_crawdad_data" - this paper uses a CRAWDAD dataset- "cites_insufficiently" - this paper does not meet our sufficiency criteria- "cites_by_description" - this paper cites a dataset by description rather than dataset identifier- "cites_canonical_paper" - this paper cites the original ("canonical") paper that collected a dataset, rather than pointing to the dataset- "cites_by_name" - this paper cites a dataset by a colloquial name rather than dataset identifier- "cites_crawdad_url" - this paper cites the main CRAWDAD URL rather than a particular dataset- "cites_without_url" - this paper does not provide a URL for dataset access- "cites_wrong_attribution" - this paper attributes a dataset to CRAWDAD, Dartmouth etc rather than the dataset authors- "cites_vaguely" - this paper cites the used datasets (if any) too vaguely to be sufficient If you have any questions about the data, please contact us atcrawdad@crawdad.org

Air Quality (AQ) is a very topical issue for many cities and has a direct impact on the health of its citizens. We propose to investigate the air quality of a large UK city using low-cost commodity Particulate Matter (PM) sensors, and compare them with government operated air quality stations. In this pilot deployment we design and build six AQ IoT devices, each with four different low-cost PM sensors and deploy them at two locations within the city. These devices are equipped with LoRaWAN wireless network transceivers to test city scale Low-Power Wide-Area Network network coverage. We conclude that some low-cost PM sensors are viable for monitoring AQ and demonstrate that our device design can be used via LoRaWAN to facilitate more granular city coverage without limitations of network access. Based on these findings we intend to deploy a larger LoRaWAN enabled Air Quality sensor network deployment across the city.

Experimental public transit transport performance statistics by 200 metre grids for a subset of urban centres in Great Britain, with the following fields (Note: These data are experimental, please see the Methods and Known Limitations/Caveats Sections for more details).AttributeDescriptionidUnique IdentifierpopulationGlobal Human Settlement Layer population estimate downsampled to 200 metre (represents the total population across adjacent 100 metre cells)access_popThe total population that can reach the destination cell within 45 minutes using the public transit network (origins within 11.25 kilometres of the destination cell)proxim_popThe total population within an 11.25 kilometre radius of the destination celltrans_perfThe transport performance of the 200 metre cell. The percentage ratio of accessible to proximal populationcity_nmName of the urban centrecountry_nmName of the country that the urban centre belongs toMethods:

For more information please visit:

· Python Package: https://github.com/datasciencecampus/transport-network-performance

· Docker Image: https://github.com/datasciencecampus/transport-performance-docker

Known Limitations/Caveats:

These data are experimental – see the ONS guidance on experimental statistics for more details. They are being published at this early stage to involve potential users and stakeholders in assessing their quality and suitability. The known caveats and limitations of these experimental statistics are summarised below.

Urban Centre and Population Estimates:

· Population estimates are derived from data using a hybrid method of satellite imagery and national censuses. The alignment of national census boundaries to gridded estimates introduce measurement errors, particularly in newer housing and built-up developments. See section 2.5 of the GHSL technical report release 2023A for more details.

Public Transit Schedule Data (GTFS):

· Does not include effects due to delays (such as congestion and diversions).

· Common GTFS issues are resolved during preprocessing where possible, including removing trips with unrealistic fast travel between stops, cleaning IDs, cleaning arrival/departure times, route name deduplication, dropping stops with no stop times, removing undefined parent stations, and dropping trips, shapes, and routes with no stops. Certain GTFS cleaning steps were not possible in all instances, and in those cases the impacted steps were skipped. Additional work is required to further support GTFS validation and cleaning.

Transport Network Routing:

· “Trapped” centroids: the centroid of destination cells on very rare occasions falls on a private road/pathway. Routing to these cells cannot be performed. This greatly decreases the transport performance in comparison with the neighbouring cells. Potential solutions include interpolation based on neighbouring cells or snapping to the nearest public OSM node (and adjusting the travel time accordingly). Further development to adapt the method for this consideration is necessary.

Please also visit the Python package and Docker Image GitHub issues pages for more details.

How to Contribute:

We hope that the public, other public sector organisations, and National Statistics Institutions can collaborate and build on these data, to help improve the international comparability of statistics and enable higher frequency and more timely comparisons. We welcome feedback and contribution either through GitHub or by contacting datacampus@ons.gov.uk.

The Gaming Taxonomy contains a broad scope of Gaming related topics, based on the user's browser and mobile app activity through last 30 days. There are classical Demographic, Game Genre, Title and Studio segments. However, we provide also plenty of specific User Types, which contain e.g. Hardcore Gamers, Big Spenders or Parents of Gamers. There are also audiences categorized by specific Hardware Products and Brands, based on the Intent of these devices' purchase. Moreover, we offer segments for Virtual Reality, interest in Gaming Subscriptions, Payments, Micropayments, Devices and Platforms. We also cover the area of E-sports Enthusiasts and Fandoms Members. In spirit of looking beyond simple game genres, we categorize Games according to their Theme (e.g. Historical), which is definitely important aspects of user experience and purchase decisions. Since Mobile Gaming is a very important part of the Gaming Industry, we distinct special Mobile Gaming segments, which are analogous to the ordinary Gaming segments, with additional categorizations of the Telecommunication Network Providers.

Our data base include millions of profiles divided into popular categories. You can choose which target groups you want to reach. Segments based on users' interests, purchase intentions or demography. Contact us to check all the possibilities: team@oan.pl

How you can use our data?

There are two main areas where you can use our data: • marketers - targeting online campaigns With our high-quality audience data, you can easily reach specific audiences across the world in programmatic campaigns. Show them personalized ads adjusted to their specific profiles. • ad tech companies - enriching 1st party data or using our raw data by your own data science team

We are ready for a cookieless era. We already gather and provide non-cookie ID - for example Universal IDs, CTV IDs or Mobile IDs.

Walking Paths/routes

This "unpublished data" record referred to Urban Paths, which has been published for a number of years, as part of ITN. See https://data.gov.uk/dataset/os-mastermap-integrated-transport-network-layer1

In addition, OS has published a detailed path network of public rights of way in national parks. Currently only available via partners, and as part of OS Maps (web, mobile): https://os.uk/business-and-government/products/os-detailed-path-network.html; https://osmaps.os.uk/

This dataset contains the Matlab Code and result figures for the paper 'Semi-dynamic Green Resource Management in Downlink Heterogeneous Networks by Group Sparse Power Control', published in IEEE Journal of Selected Areas in Communications in May 2016.

Please see included 'read_me.odt' for details on the data set. Data was produced in Matlab.