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Note - all Consumer packages can include necessary PII (address, email, phone, DOB, etc.) for merging, linking, and activation of the data.

BIGDBM Privacy Policy: https://bigdbm.com/privacy.html

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These are the fields available within the RampedUp Global dataset.

CONTACT DATA: Personal Email Address - We manage over 115 million personal email addresses Professional Email - We manage over 200 million professional email addresses Home Address - We manage over 20 million home addresses Mobile Phones - 65 million direct lines to decision makers Social Profiles - Individual Facebook, Twitter, and LinkedIn Local Address - We manage 65M locations for local office mailers, event-based marketing or face-to-face sales calls.

JOB DATA: Job Title - Standardized titles for ease of use and selection Company Name - The Contact's current employer Job Function - The Company Department associated with the job role Title Level - The Level in the Company associated with the job role Job Start Date - Identify people new to their role as a potential buyer

EMPLOYER DATA: Websites - Company Website, Root Domain, or Full Domain Addresses - Standardized Address, City, Region, Postal Code, and Country Phone - E164 phone with country code Social Profiles - LinkedIn, CrunchBase, Facebook, and Twitter

FIRMOGRAPHIC DATA: Industry - 420 classifications for categorizing the company’s main field of business Sector - 20 classifications for categorizing company industries 4 Digit SIC Code - 239 classifications and their definitions 6 Digit NAICS - 452 classifications and their definitions Revenue - Estimated revenue and bands from 1M to over 1B Employee Size - Exact employee count and bands Email Open Scores - Aggregated data at the domain level showing relationships between email opens and corporate domains. IP Address -Company level IP Addresses associated to Domains from a DNS lookup

CONSUMER DATA: Education - Alma Mater, Degree, Graduation Date Skills - Accumulated Skills associated with work experience
Interests - Known interests of contact Connections - Number of social connections. Followers - Number of social followers

Download our data dictionary: https://rampedup.io/our-data

Related article: Bergroth, C., Järv, 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., Järv, 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

Järv, 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

Records of one cellphone user in the mobile phone data.

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Source code, example screenprobe.csv data file, and README.txt for processing, visualising and analysing smartphone use data. csv2data.m converts ScreenProbe.csv to usable data, while barcode.m allows visualisations to be generated. descriptives.m generates descriptive statistics that can be used for quantitative analysis. Source code requires Matlab version 2014b or later, but does not require any specific toolboxes. (ZIP)

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Mobile phone log data used to mine contextual behavioral rules of individual mobile phone users

The article "A systematic review of the educational use of mobile phones in times of COVID-19" aims to review what research has delved into the educational use of mobile phones during the COVID-19 pandemic. To do this, 38 papers indexed in the Journal Citation Reports database between 2020 and 2021 were analyzed. These works were categorized into the following categories: the mobile phone as part of educational innovation, improvement of results and academic performance, positive attitude towards mobile phone use in education, and risks and/or barriers to mobile phone use. The conclusions show that most teaching innovation experiences focus more on the device than on the student. Beyond its innovative nature, the mobile phone became a tool to allow access and continuity of training during the pandemic, especially in post-compulsory and higher education.

This data set is composed of the table with the references used for the review.

ABSTRACT: With the popularization of low-cost mobile and wearable sensors, prior studies have utilized such sensors to track and analyze people's mental well-being, productivity, and behavioral patterns. However, there still is a lack of open datasets collected in-the-wild contexts with affective and cognitive state labels such as emotion, stress, and attention, which would limit the advances of research in affective computing and human-computer interaction. This work presents K-EmoPhone, an in-the-wild multi-modal dataset collected from 77 university students for seven days. This dataset contains (i) continuous probing of peripheral physiological signals and mobility data measured by commercial off-the-shelf devices; (ii) context and interaction data collected from individuals' smartphones; and (iii) 5,582 self-reported affect states, such as emotion, stress, attention, and disturbance, acquired by the experience sampling method. We anticipate that the presented dataset will contribute to the advancement of affective computing, emotion intelligence technologies, and attention management based on mobile and wearable sensor data.

This dataset contains two significant mobile gestures for brain-mobile phone interfaces (BMPIs: (i) motor imagery of tapping on the screen of a mobile device and (ii) motor imagery of swiping down with a thumb on the screen of a mobile device. The raw EEG signals were recorded using the Emotiv EPOC Flex (Model 1.0) headset with saline-based sensors and Emotiv Pro (2.5.1.227) software. The sampling rate is 128 Hz. Each epoch contains 3.5 s signals. The first 1 s signal is recorded before the MI task starts (5 s to 6 s interval in the timing plan), and the next 2.5 s signal is recorded during the MI execution (6 s to 8.5 s interval in the timing plan). Please refer to the reference study below for details.The file names are constructed as follows. For example, taking "D01_s1" and "D01" in the file name refers to subject "01", and "s1" refers to session 1 ("s2" refers to session 2). The label data is given in a separate folder in Matlab format.The data is provided in two different forms for use (the desired is preferable):The set_files folder contains the data prepared for import in EEGLAB. EEGLAB must be installed, and the set files must be imported to access the data. The data is in epoched format in 3D (channels, sample_points, trials). With the EEGLAB interface, all the data can be accessed, and EEGLAB functions can be executed. Also, the EEG variable, which is built after importing the *.set file, contains all the information about the experiment. With the EEG.data variable, epoched data in the dimensions (channels, sample_points, trials) can be accessed.The mat_files folder contains data in mat file format. In these files, epoched data is stored in a 3-D array of size (channels, sample_points, trials). You can access the data as follows. For example, all data from the first session of subject D01 can be retrieved as follows. Load the mat file with the load('D01_s1.mat') code, and access the data using the EEG variable in the workspace. For instance, 13x448 x101 sized epoched data (channels, sample_points, trials) can be retrieved with the command EEG.data. Other information about the experiments and subjects is also included in the fields of the EEG variable.This research was supported by the Turkish Scientific and Research Council (TUBITAK) under project number 119E397.The following article must be used in academic studies with reference. Permission must be obtained for use in commercial studies.Journal: Neural Computing and Applications.DOI : 10.1007/s00521-024-10917-5.Title : MI-BMPI motor imagery brain–mobile phone dataset and performance evaluation of voting ensembles utilizing QPDM.

In 2024, smartphones across the globe used an average of 20.29 gigabytes of mobile data per month, up from 17.27 gigabytes the previous year. This figure is expected to reach 23.78 gigabytes in 2025, and 42.38 gigabytes by 2029.

North America registered the highest mobile data consumption per connection in 2023, with the average connection consuming ** gigabytes per month. This figure is set to triple by 2030, driven by the adoption of data intensive activities such as 4K streaming.

Context

Data-driven models help mobile app designers understand best practices and trends, and can be used to make predictions about design performance and support the creation of adaptive UIs. This paper presents Rico, the largest repository of mobile app designs to date, created to support five classes of data-driven applications: design search, UI layout generation, UI code generation, user interaction modeling, and user perception prediction. To create Rico, we built a system that combines crowdsourcing and automation to scalably mine design and interaction data from Android apps at runtime. The Rico dataset contains design data from more than 9.3k Android apps spanning 27 categories. It exposes visual, textual, structural, and interactive design properties of more than 66k unique UI screens. To demonstrate the kinds of applications that Rico enables, we present results from training an autoencoder for UI layout similarity, which supports query-by-example search over UIs.

Content

Rico was built by mining Android apps at runtime via human-powered and programmatic exploration. Like its predecessor ERICA, Rico’s app mining infrastructure requires no access to — or modification of — an app’s source code. Apps are downloaded from the Google Play Store and served to crowd workers through a web interface. When crowd workers use an app, the system records a user interaction trace that captures the UIs visited and the interactions performed on them. Then, an automated agent replays the trace to warm up a new copy of the app and continues the exploration programmatically, leveraging a content-agnostic similarity heuristic to efficiently discover new UI states. By combining crowdsourcing and automation, Rico can achieve higher coverage over an app’s UI states than either crawling strategy alone. In total, 13 workers recruited on UpWork spent 2,450 hours using apps on the platform over five months, producing 10,811 user interaction traces. After collecting a user trace for an app, we ran the automated crawler on the app for one hour.

Acknowledgements

UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN https://interactionmining.org/rico

Inspiration

The Rico dataset is large enough to support deep learning applications. We trained an autoencoder to learn an embedding for UI layouts, and used it to annotate each UI with a 64-dimensional vector representation encoding visual layout. This vector representation can be used to compute structurally — and often semantically — similar UIs, supporting example-based search over the dataset. To create training inputs for the autoencoder that embed layout information, we constructed a new image for each UI capturing the bounding box regions of all leaf elements in its view hierarchy, differentiating between text and non-text elements. Rico’s view hierarchies obviate the need for noisy image processing or OCR techniques to create these inputs.

In 2021, the global annual cellular data usage is projected to reach roughly *** thousand petabytes (PB), with approximately *** thousand petabytes coming from the use of mobile handsets, in other words, mobile phones. Tablets and cellular IoT devices currently do not compare to mobile phones in terms of data usage, but they are expected to grow in the upcoming years.

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China Internet User Structure: Mobile Phone: Student data was reported at 34.300 % in Jun 2011. This records a decrease from the previous number of 35.600 % for Dec 2010. China Internet User Structure: Mobile Phone: Student data is updated semiannually, averaging 34.300 % from Dec 2009 (Median) to Jun 2011, with 3 observations. The data reached an all-time high of 35.600 % in Dec 2010 and a record low of 32.300 % in Dec 2009. China Internet User Structure: Mobile Phone: Student data remains active status in CEIC and is reported by China Internet Network Information Center. The data is categorized under China Premium Database’s Information and Communication Sector – Table CN.ICE: Internet: Internet User Structure: Mobile Phone.

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