Alesco Phone ID: Your Comprehensive Identity Graph Solution

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

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

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

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

By leveraging Alesco Phone ID, you can:

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

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

Exports of Handled Wireless Phone and Its Parts in China increased to 19353217.05 USD Thousand in February from 13483302.92 USD Thousand in December of 2023. This dataset includes a chart with historical data for China Exports of Handled Wireless Phone And Its Parts.

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.

Reference: Mingxiao Li, Song Gao, Feng Lu, Kang Liu, Hengcai Zhang, Wei Tu. (2021) Prediction of human activity intensity using the interactions in physical and social spaces through graph convolutional networks. International Journal of Geographical Information Science. X(X), XX-XX.Abstract: Dynamic human activity intensity information is of great importance in many location-based applications. However, two limitations remain in the prediction of human activity intensity. First, it is hard to learn the spatial interaction patterns across scales for predicting human activities. Second, social interaction can help model the activity intensity variation but is rarely considered in the existing literature. To mitigate these limitations, we proposed a novel dynamic activity intensity prediction method with deep learning on graphs using the interactions in both physical and social spaces. In this method, the physical interactions and social interactions between spatial units were integrated into a fused graph convolutional network to model multi-type spatial interaction patterns. The future activity intensity variation was predicted by combining the spatial interaction pattern and the temporal pattern of activity intensity series. The method was verified with a country-scale anonymized mobile phone dataset. The results demonstrated that our proposed deep learning method with combining graph convolutional networks and recurrent neural networks outperformed other baseline approaches. This method enables dynamic human activity intensity prediction from a more spatially and socially integrated perspective, which helps improve the performance of modeling human dynamics.

The work-cell is an essential industrial environment for testing wireless communication techniques in factory automation processes. A graph database approach to storing and analyzing network performance data from a manufacturing factory work-cell is introduced. A robotic testbed performs a pick-and-place task using two collaborative grade robot arms, machine emulators, and wireless communication devices. A graph database is implemented to capture network data and operational event data among the actors within the testbed. Using a proposed schema, the database is then populated with events from the testbed and the resulting graph is constructed. Query commands are then presented to examine and analyze network performance and relationships within the actors of the network. The resulting data from the experiments conducted are included in this dataset.

Related article: Bergroth, C., J��rv, O., Tenkanen, H., Manninen, M., Toivonen, T., 2022. A 24-hour population distribution dataset based on mobile phone data from Helsinki Metropolitan Area, Finland. Scientific Data 9, 39. In this dataset: We present temporally dynamic population distribution data from the Helsinki Metropolitan Area, Finland, at the level of 250 m by 250 m statistical grid cells. Three hourly population distribution datasets are provided for regular workdays (Mon ��� Thu), Saturdays and Sundays. The data are based on aggregated mobile phone data collected by the biggest mobile network operator in Finland. Mobile phone data are assigned to statistical grid cells using an advanced dasymetric interpolation method based on ancillary data about land cover, buildings and a time use survey. The data were validated by comparing population register data from Statistics Finland for night-time hours and a daytime workplace registry. The resulting 24-hour population data can be used to reveal the temporal dynamics of the city and examine population variations relevant to for instance spatial accessibility analyses, crisis management and planning. Please cite this dataset as: Bergroth, C., J��rv, O., Tenkanen, H., Manninen, M., Toivonen, T., 2022. A 24-hour population distribution dataset based on mobile phone data from Helsinki Metropolitan Area, Finland. Scientific Data 9, 39. https://doi.org/10.1038/s41597-021-01113-4 Organization of data The dataset is packaged into a single Zipfile Helsinki_dynpop_matrix.zip which contains following files: HMA_Dynamic_population_24H_workdays.csv represents the dynamic population for average workday in the study area. HMA_Dynamic_population_24H_sat.csv represents the dynamic population for average saturday in the study area. HMA_Dynamic_population_24H_sun.csv represents the dynamic population for average sunday in the study area. target_zones_grid250m_EPSG3067.geojson represents the statistical grid in ETRS89/ETRS-TM35FIN projection that can be used to visualize the data on a map using e.g. QGIS. Column names YKR_ID : a unique identifier for each statistical grid cell (n=13,231). The identifier is compatible with the statistical YKR grid cell data by Statistics Finland and Finnish Environment Institute. H0, H1 ... H23 : Each field represents the proportional distribution of the total population in the study area between grid cells during a one-hour period. In total, 24 fields are formatted as ���Hx���, where x stands for the hour of the day (values ranging from 0-23). For example, H0 stands for the first hour of the day: 00:00 - 00:59. The sum of all cell values for each field equals to 100 (i.e. 100% of total population for each one-hour period) In order to visualize the data on a map, the result tables can be joined with the target_zones_grid250m_EPSG3067.geojson data. The data can be joined by using the field YKR_ID as a common key between the datasets. License Creative Commons Attribution 4.0 International. Related datasets J��rv, Olle; Tenkanen, Henrikki & Toivonen, Tuuli. (2017). Multi-temporal function-based dasymetric interpolation tool for mobile phone data. Zenodo. https://doi.org/10.5281/zenodo.252612 Tenkanen, Henrikki, & Toivonen, Tuuli. (2019). Helsinki Region Travel Time Matrix [Data set]. Zenodo. http://doi.org/10.5281/zenodo.3247564

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 ninth 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 Australia & Oceania and Asia.

In this dataset, we present temporally dynamic population distribution data from the Helsinki Metropolitan Area, Finland, at the level of 250 m by 250 m statistical grid cells. Three hourly population distribution datasets are provided for regular workdays (Mon – Thu), Saturdays and Sundays. The data are based on aggregated mobile phone data collected by the biggest mobile network operator in Finland. Mobile phone data are assigned to statistical grid cells using an advanced dasymetric interpolation method based on ancillary data about land cover, buildings and a time use survey. The data were validated by comparing population register data from Statistics Finland for night-time hours and a daytime workplace registry. The resulting 24-hour population data can be used to reveal the temporal dynamics of the city and examine population variations relevant to for instance spatial accessibility analyses, crisis management and planning.

Organization of data

The dataset is packaged into a single Zipfile Helsinki_dynpop_matrix.zip which contains following files:

1. HMA_Dynamic_population_24H_workdays.csv represents the dynamic population for average workday in the study area.
2. HMA_Dynamic_population_24H_sat.csv represents the dynamic population for average saturday in the study area.
3. HMA_Dynamic_population_24H_sun.csv represents the dynamic population for average sunday in the study area.
4. target_zones_grid250m_EPSG3067.geojson represents the statistical grid in ETRS89/ETRS-TM35FIN projection that can be used to visualize the data on a map using e.g. QGIS.

Column names

1. YKR_ID : a unique identifier for each statistical grid cell (n=13,231). The identifier is compatible with the statistical YKR grid cell data by Statistics Finland and Finnish Environment Institute.
2. H0, H1 ... H23 : Each field represents the proportional distribution of the total population in the study area between grid cells during a one-hour period. In total, 24 fields are formatted as “Hx”, where x stands for the hour of the day (values ranging from 0-23). For example, H0 stands for the first hour of the day: 00:00 - 00:59.
   The sum of all cell values for each field equals to 100 (i.e. 100% of total population for each one-hour period)

In order to visualize the data on a map, the result tables can be joined with the target_zones_grid250m_EPSG3067.geojson data. The data can be joined by using the field YKR_ID as a common key between the datasets.

License
Creative Commons Attribution 4.0 International.

Related datasets

Tenkanen, Henrikki, & Toivonen, Tuuli. (2019). Helsinki Region Travel Time Matrix [Data set]. Zenodo. http://doi.org/10.5281/zenodo.3247564

The dataset provides temporary migration estimates at the (origin*destination*time)-level in Senegal derived from mobile phone data for the period 2013-2015. Origin and destination locations are comprised of 39 cities and 112 rural areas of third-level administrative units (i.e., districts), defining the spatial resolution of the dataset. Estimates are provided for each half-month period, defining the temporal resolution of the dataset.

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

Singapore Mobile Phone Statistics: TAS: Penetration Rate data was reported at 147.300 % in Aug 2018. This records a decrease from the previous number of 147.800 % for Jul 2018. Singapore Mobile Phone Statistics: TAS: Penetration Rate data is updated monthly, averaging 117.400 % from Jan 1997 (Median) to Aug 2018, with 260 observations. The data reached an all-time high of 156.300 % in Mar 2014 and a record low of 13.600 % in Jan 1997. Singapore Mobile Phone Statistics: TAS: Penetration Rate data remains active status in CEIC and is reported by Infocomm Media Development Authority of Singapore. The data is categorized under Global Database’s Singapore – Table SG.TB001: Telecommunication Statistics.

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

BackgroundThis study aimed to explore the adverse influences of mobile phone usage on pilots’ status, so as to improve flight safety.MethodsA questionnaire was designed, and a cluster random sampling method was adopted. Pilots of Shandong Airlines were investigated on the use of mobile phones. The data was analyzed by frequency statistics, linear regression and other statistical methods.ResultsA total of 340 questionnaires were distributed and 317 were returned, 315 of which were valid. The results showed that 239 pilots (75.87%) used mobile phones as the main means of entertainment in their leisure time. There was a significant negative correlation between age of pilots and playing mobile games (p

The population share with mobile internet access in North America was forecast to increase between 2024 and 2029 by in total 2.9 percentage points. This overall increase does not happen continuously, notably not in 2028 and 2029. The mobile internet penetration is estimated to amount to 84.21 percent in 2029. Notably, the population share with mobile internet access of was continuously increasing over the past years.The penetration rate refers to the share of the total population having access to the internet via a mobile broadband connection.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 population share with mobile internet access in countries like Caribbean and Europe.

This ds-graph represents this information for the Human Reference Atlas Cell Type Populations Effort (Börner et al. 2025). It provides sample registration information submitted by consortium members in single-cell atlassing efforts, including accurate sample sizes and positions (Bueckle et al. 2025). When combined with ref-organ data, this information helps create 3D visual tissue sample placements. Additionally, the sample information is linked to datasets from researchers' assay analyses that offer deeper insights into the tissue samples. The “ds” stands for “dataset.” ds-graphs represent datasets by tissue sample and donor. It is a dataset graph for the Human Reference Atlaspop Universe. It includes all datasets considered for Human Reference Atlaspop (not enriched).

Bibliography:

• Börner, Katy, Philip D. Blood, Jonathan C. Silverstein, Matthew Ruffalo, Rahul Satija, Sarah A. Teichmann, Gloria J. Pryhuber, et al. 2025. “Human BioMolecular Atlas Program (HuBMAP): 3D Human Reference Atlas Construction and Usage.” Nature Methods, March, 1–16. https://doi.org/10.1038/s41592-024-02563-5.
• Bueckle, Andreas, Bruce W. Herr II, Josef Hardi, Ellen M. Quardokus, Mark A. Musen, and Katy Börner. 2025. “Construction, Deployment, and Usage of the Human Reference Atlas Knowledge Graph for Linked Open Data.” bioRxiv. https://doi.org/10.1101/2024.12.22.630006.
• Lonsdale, John, Jeffrey Thomas, Mike Salvatore, Rebecca Phillips, Edmund Lo, Saboor Shad, Richard Hasz, et al. 2013. “The Genotype-Tissue Expression (GTEx) Project.” Nature Genetics 45 (6): 580–85. https://doi.org/10.1038/ng.2653.
• Börner, Katy, Andreas Bueckle, Bruce W. Herr II, Leonard E. Cross, Ellen M. Quardokus, Elizabeth G. Record, Yingnan Ju, et al. 2022. “Tissue Registration and Exploration User Interfaces in Support of a Human Reference Atlas.” Communications Biology 5 (1): 1369. https://doi.org/10.1038/s42003-022-03644-x.

Switzerland is leading the ranking by population share with mobile internet access , recording 95.06 percent. Following closely behind is Ukraine with 95.06 percent, while Moldova is trailing the ranking with 46.83 percent, resulting in a difference of 48.23 percentage points to the ranking leader, Switzerland. The penetration rate refers to the share of the total population having access to the internet via a mobile broadband connection.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 NCI1 dataset comes from the cheminformatics domain, where each input graph is used as representation of a chemical compound: each vertex stands for an atom of the molecule, and edges between vertices represent bonds between atoms. This dataset is relative to anti-cancer screens where the chemicals are assessed as positive or negative to cell lung cancer. Each vertex has an input label representing the corresponding atom type, encoded by a one-hot-encoding scheme into a vector of 0/1 elements.

The dataset provided here is an output of the Track & Know project, shared with the scientific community. The dataset consists of aggregate origin-destination (OD) flows of private cars in London augmented with feature data describing city locations and dyadic relations between them. The geographical location of each cell in the OD graph is not provided, for privacy protection, since the extension of each area is relatively small.

The dataset was first used in the following publication:  Gevorg Yeghikyan, Felix L. Opolka, Bruno Lepri, Mirco Nanni, Pietro Lio`. Learning

Mobility Flows from Urban Features with Spatial Interaction Models and Neural Networks. 2020 IEEE International Conference on Smart Computing (SMARTCOMP), to appear.

This dataset (CSV) shows prices of iPhone 3GS mobile mobile phone including data in Australia in July 2009. An Excel version is linked below. Fields include: telco, price per month, data per month, price per GB of data (calculated), excess price per GB, description, link to URL. The iPhone 3GS is used as a comparison across several telco's pricing. Prices are included for several types of plans. Graphs of this dataset in an Microsoft Excel version of this data are linked below.

Andrew Wharton's US Consumer Email Databases provide over 650 million current and active email address records in our 36-month Production Email Database, and additionally, over 1.4 billion historical records in our Legacy Email Database. These databases offer a comprehensive look-back at the digital and terrestrial identity information associated with a consumer. This Identity Graph Data has been collected from website registrations and is 100% opted-in for Third Party Uses.

The Email Address Data is fully populated with email addresses, HEMS (MD5, Sha1, Sha256), first name, last name, postal address (primary and secondary), IP Address, and Time Stamps for Last Registration, Verification, and First Seen. Additionally, our email address information assets can be linked with our Date-of-Birth and Phone Number databases to provide a powerful solution for consumer identity recognition and verification platforms through Identity Linkage Data.

As an add-on to our current and historical information, we also offer a database of hard-bounce email addresses. These are email addresses that have hard-bounced during our large-scale email campaign deployments or were identified as hard-bounces during our email verification processes. This database provides over 400 million unproductive email addresses useable as a part of suppression or fraud identification applications.

Our Email Information Assets are utilized by major Identity Graph Data and Identity Linkage platforms due to our comprehensive information that links the email address to consumer identity and IP Address information. This Identity Graph Data provides a robust alternative approach when faced with third-party cookie deprecation in the digital ecosystem.

Our digital advertising partners leverage this information to understand where their clients' customers and prospects are online and align media and content with consumer behavior. The additional Email Address Data, mobile phone numbers, and IP Addresses also work to increase the reach of your Digital Audience Data.

This Identity Graph Data has the scale and depth to help drive the creation of new platforms and products and provide significant enhancements to existing platforms. By utilizing our extensive Email Address Data and Identity Linkage Data, you can ensure precise consumer identity recognition and verification, making your marketing campaigns more effective and far-reaching.

Contact us at successdelivered@andrewswharton.com or visit us at www.andrewswharton.com to learn more about how our Identity Graph Data, Email Address Data, Identity Linkage Data, and Digital Audience Data can meet your marketing needs.