Android maintained its position as the leading mobile operating system worldwide in the first quarter of 2025 with a market share of about ***** percent. Android's closest rival, Apple's iOS, had a market share of approximately ***** percent during the same period. The leading mobile operating systems Both unveiled in 2007, Google’s Android and Apple’s iOS have evolved through incremental updates introducing new features and capabilities. The latest version of iOS, iOS 18, was released in September 2024, while the most recent Android iteration, Android 15, was made available in September 2023. A key difference between the two systems concerns hardware - iOS is only available on Apple devices, whereas Android ships with devices from a range of manufacturers such as Samsung, Google and OnePlus. In addition, Apple has had far greater success in bringing its users up to date. As of February 2024, ** percent of iOS users had iOS 17 installed, while in the same month only ** percent of Android users ran the latest version. The rise of the smartphone From around 2010, the touchscreen smartphone revolution had a major impact on sales of basic feature phones, as the sales of smartphones increased from *** million units in 2008 to **** billion units in 2023. In 2020, smartphone sales decreased to **** billion units due to the coronavirus (COVID-19) pandemic. Apple, Samsung, and lately also Xiaomi, were the big winners in this shift towards smartphones, with BlackBerry and Nokia among those unable to capitalize.

Description for each of the variables:

1. Brands: The brands of smartphones included in the dataset.
2. Colors: The colors available for the smartphones.
3. Memory: The storage capacity of the smartphones, typically measured in gigabytes (GB) or megabytes (MB).
4. Storage: The internal storage capacity of the smartphones, often measured in gigabytes (GB) or megabytes (MB).
5. Rating: The user ratings or scores assigned to the smartphones, reflecting user satisfaction or performance.
6. Selling Price: The price at which the smartphones are sold to consumers.
7. Original Price: The original or list price of the smartphones before any discounts or promotions.
8. Mobile: Indicates whether the device is a mobile phone.
9. Discount: The discount applied to the original price to calculate the selling price.
10. Discount percentage: The percentage discount applied to the original price to calculate the selling price.

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.

This dataset encompasses mobile smartphone application (app) usage, collected from over 150,000 triple-opt-in first-party US Daily Active Users (DAU). Use it for measurement, attribution or surveying to understand the why. iOS and Android operating system coverage.

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

Context

Imagine you have to process bug reports about an application your company is developing, which is available for both Android and iOS. Could you find a way to automatically classify them so you can send them to the right support team?

Content

The dataset contains data from two StackExchange forums: Android Enthusiasts and Ask Differently (Apple). I pre-processed both datasets from the raw XML files retrieved from Internet Archive in order to only contain useful information for building Machine Learning classifiers. In the case of the Apple forum, I narrowed down to the subset of questions that have one of the following tags: "iOS", "iPhone", "iPad".

Think of this as a fun way to learn to build ML classifiers! The training, validation and test sets are all available, but in order to build robust models please try to use the test set as little as possible (only as a last validation for your models).

Acknowledgements

The image was retrieved from unsplash and made by @thenewmalcolm. Link to image here.

The data was made available for free under a CC-BY-SA 4.0 license by StackExchange and hosted by Internet Archive. Find it here.

This is a public release of Beiwe-generated data. The Beiwe Research Platform collects high-density data from a variety of smartphone sensors such as GPS, WiFi, Bluetooth, gyroscope, and accelerometer in addition to metadata from active surveys. A description of passive and active data streams, and a documentation concerning the use of Beiwe can be found here. This data was collected from an internal test study and is made available solely for educational purposes. It contains no identifying information; subject locations are de-identified using the noise GPS feature of Beiwe.

As part of the internal test study, data from 6 participants were collected from the start of March 21, 2022 to the end of March 28, 2022. The local time zone of this study is Eastern Standard Time. Each participant was notified to complete a survey at 9am EST on Monday, Thursday, and Saturday of the study week. An additional survey was administered on Tuesday at 5:15pm EST. For each survey, subjects were asked to respond to the prompt "How much time (in hours) do you think you spent at home?".

The global smartphone penetration in was forecast to continuously increase between 2024 and 2029 by in total 20.3 percentage points. After the fifteenth consecutive increasing year, the penetration is estimated to reach 74.98 percent and therefore a new peak in 2029. Notably, the smartphone penetration of was continuously increasing over the past years.The penetration rate refers to the share of the total population.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 smartphone penetration in countries like North America and the Americas.

App download rankings, usage metrics, and user engagement data (iOS/Android)

This dataset encompasses mobile web clickstream behavior on any browser, collected from over 150,000 triple-opt-in first-party US Daily Active Users (DAU). Use it for measurement, attribution or path to purchase and consumer journey understanding. Full URL deliverable available including searches.

Infant Crying smartphone speech dataset, collected by Android smartphone and iPhone, covering infant crying. Our dataset was collected from extensive and diversify speakers(201 people in total, with balanced gender distribution), 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.

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.

This dataset contains synthetic logs designed to simulate the activity of the Pegasus malware, providing a rich resource for cybersecurity research, anomaly detection, and machine learning applications. The dataset includes 1000 entries with 17 columns, each capturing detailed information about device usage, network traffic, and potential security events

Columns: user_id: Unique identifier for each user device_type: Type of device used (e.g., iPhone, Android) os_version: Operating system version of the device app_usage_pattern: Usage pattern of the applications (Low, Normal, High) timestamp: Timestamp of the recorded activity source_ip: Source IP address of the network traffic destination_ip: Destination IP address of the network traffic protocol: Network protocol used (e.g., HTTPS, FTP, SSH) data_volume: Volume of data transferred in the session log_type: Type of log entry (system, application, security) event: Specific event type (e.g., App Install, System Update, Logout, App Crash) event_description: Description of the event error_code: Error code associated with the event file_accessed: File path accessed during the event process: Process name involved in the event anomaly_detected: Description of any detected anomalies (e.g., Unknown Process Execution, Suspicious File Access) ioc: Indicators of Compromise (e.g., Pegasus Signature, Known Malicious IP)

This dataset is ideal for those looking to develop and test cybersecurity solutions, understand malware behavior, or create educational tools for cybersecurity training. The data captures various scenarios of potential malware activities, making it a versatile resource for a range of cybersecurity applications.

Overview

The MSCardio Seismocardiography Dataset is an open-access dataset collected as part of the Mississippi State Remote Cardiovascular Monitoring (MSCardio) study. This dataset includes seismocardiogram (SCG) signals recorded from participants using smartphone sensors, enabling scalable, real-world cardiovascular monitoring without requiring specialized equipment. The dataset aims to support research in SCG signal processing, machine learning applications in health monitoring, and cardiovascular assessment.

See the GitHub repository of this dataset for the latest updates: https://github.com/TaebiLab/MSCardio

Background

Cardiovascular diseases remain the leading cause of morbidity and mortality worldwide. SCG is a non-invasive technique that captures chest vibrations induced by cardiac activity and respiration, providing valuable insights into cardiac function. However, the scarcity of open-access SCG datasets has been a significant limitation for research in this field. The MSCardio dataset addresses this gap by providing real-world SCG signals collected via smartphone sensors from a diverse population.

Data Description

Study Population

• Total participants enrolled: 123
• Participants who uploaded data: 108 (46 males, 61 females, 1 unspecified)
• Age range: 18 to 62 years
• Total recordings uploaded: 515
• Unique recordings after duplicate removal: 502
• Platforms used: iOS and Android smartphones

Signal Data

• Axial vibrations in three directions (SCG) recorded using smartphone sensors
• Sampling frequency varies depending on the device capabilities
• Data synchronization is ensured for temporal accuracy
• Missing SCG data identified in certain recordings, addressed through preprocessing

Metadata

Each recording includes:

• Device model (e.g., iPhone Pro Max)
• Recording time (UTC) and time zone
• Platform (iOS or Android)
• General demographic details (gender, race, age, height, weight)

File Structure

The dataset is organized as follows:

MSCardio_SCG_Dataset/
│── info/
│ └── all_subject_data.csv # Consolidated metadata for all subjects
│── MSCardio/
│ ├── Subject_XXXX/ # Subject-specific folder
│ │ ├── general_metadata.json # Demographic and device information
│ │ ├── Recording_XXX/ # Individual recordings
│ │ │ ├── scg.csv # SCG signal data
│ │ │ ├── recording_metadata.json # Timestamp and device details

Data Collection Protocol

• Participants placed their smartphone on their chest while lying in a supine position.
• The app recorded SCG signals for approximately two minutes.
• Self-reported demographic data were collected.
• Data were uploaded to the study's cloud storage.

Usage and Applications

This dataset is intended for research in:

• SCG signal processing and feature extraction
• Machine learning applications in cardiovascular monitoring
• Investigating inter- and intra-subject variability in SCG signals
• Remote cardiovascular health assessment
• The Data_visualization.py script is provided for data visualization

Citation

If you use this dataset in your research, please cite:

@article{rahman2025MSCardio,
author = {Taebi, Amirtah{\`a} and Rahman, Mohammad Muntasir},
title = {MSCardio: Initial insights from remote monitoring of cardiovascular-induced chest vibrations via smartphones},
journal = {Data in Brief},
year = {2025},
publisher = {Elsevier}
}

Contact

For any questions regarding the dataset, please contact:

• Amirtahà Taebi and Mohammad Muntasir Rahman
• E-mail: ataebi@abe.msstate.edu, mmr510@msstate.edu
• Biomedical Engineering Program, Mississippi State University

---

This dataset is provided under an open-access license. Please ensure ethical and responsible use when utilizing this dataset for research.

This dataset encompasses mobile app usage, web clickstream and location visitation behavior, collected from over 150,000 triple-opt-in first-party US Daily Active Users (DAU). The only omnichannel meter at scale representing iOS and Android platforms.

CSV file with code smell occurrences per application. One file for iOS and one for Android. Analysis of open source applications.

Data abstract: This Zenodo upload contains the ADVIO data for benchmarking and developing visual-inertial odometry methods. The data documentation is available on Github: https://github.com/AaltoVision/ADVIO

Paper abstract: The lack of realistic and open benchmarking datasets for pedestrian visual-inertial odometry has made it hard to pinpoint differences in published methods. Existing datasets either lack a full six degree-of-freedom ground-truth or are limited to small spaces with optical tracking systems. We take advantage of advances in pure inertial navigation, and develop a set of versatile and challenging real-world computer vision benchmark sets for visual-inertial odometry. For this purpose, we have built a test rig equipped with an iPhone, a Google Pixel Android phone, and a Google Tango device. We provide a wide range of raw sensor data that is accessible on almost any modern-day smartphone together with a high-quality ground-truth track. We also compare resulting visual-inertial tracks from Google Tango, ARCore, and Apple ARKit with two recent methods published in academic forums. The data sets cover both indoor and outdoor cases, with stairs, escalators, elevators, office environments, a shopping mall, and metro station.

Attribution: If you use this data set in your own work, please cite this paper:

Santiago Cortés, Arno Solin, Esa Rahtu, and Juho Kannala (2018). ADVIO: An authentic dataset for visual-inertial odometry. In European Conference on Computer Vision (ECCV). Munich, Germany.

What’s Included

Introducing the English Product Image Dataset - a diverse and comprehensive collection of images meticulously curated to propel the advancement of text recognition and optical character recognition (OCR) models designed specifically for the English language.

Containing a total of 2000 images, this English OCR dataset offers diverse distribution across different types of front images of Products. In this dataset, you'll find a variety of text that includes product names, taglines, logos, company names, addresses, product content, etc. Images in this dataset showcase distinct fonts, writing formats, colors, designs, and layouts.

To ensure the diversity of the dataset and to build a robust text recognition model we allow limited (less than five) unique images from a single resource. Stringent measures have been taken to exclude any personally identifiable information (PII) and to ensure that in each image a minimum of 80% of space contains visible English text.

Images have been captured under varying lighting conditions – both day and night – along with different capture angles and backgrounds, to build a balanced OCR dataset. The collection features images in portrait and landscape modes.

All these images were captured by native English people to ensure the text quality, avoid toxic content and PII text. We used the latest iOS and Android mobile devices above 5MP cameras to click all these images to maintain the image quality. In this training dataset images are available in both JPEG and HEIC formats.

Along with the image data, you will also receive detailed structured metadata in CSV format. For each image, it includes metadata like image orientation, county, language, and device information. Each image is properly renamed corresponding to the metadata.

The metadata serves as a valuable tool for understanding and characterizing the data, facilitating informed decision-making in the development of English text recognition models.

We're committed to expanding this dataset by continuously adding more images with the assistance of our native English crowd community.

If you require a custom product image OCR dataset tailored to your guidelines or specific device distribution, feel free to contact us. We're equipped to curate specialized data to meet your unique needs.

Furthermore, we can annotate or label the images with bounding box or transcribe the text in the image to align with your specific project requirements using our crowd community.

This Image dataset, created by FutureBeeAI, is now available for commercial use.

Leverage the power of this product image OCR dataset to elevate the training and performance of text recognition, text detection, and optical character recognition models within the realm of the English language. Your journey to enhanced language understanding and processing starts here.

80 Hours - French(Canada) Spontaneous Dialogue Smartphone speech dataset

Description

French(Canada) Spontaneous Dialogue Smartphone speech dataset, collected from dialogues based on given topics, covering 20+ domains. Transcribed with text content, speaker's ID, gender, age and other attributes. Our dataset was collected from extensive and diversify speakers(126 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. For more details, please refer to the link: https://www.nexdata.ai/datasets/speechrecog/1302?source=Kaggle

Specifications

Format

16kHz, 16 bit, wav, mono channel;

Content category

Dialogue based on given topics;

Recording condition

Low background noise (indoor);

Recording device

Android smartphone, iPhone;

Speaker

126 native speakers in total, 48% male and 52% female;

Country

Canada(CAN);

Language(Region) Code

fr-CA;

Language

French;

Features of annotation

Transcription text, timestamp, speaker ID, gender, noise,PII redacted.

Accuracy Rate

Word Accuracy Rate (WAR) 98%

Licensing Information

Commercial License

The dataset to be published was generated through exploratory case studies conducted on wrist-worn devices from three vendors: Huawei, Amazfit, and Xiaomi. The specific devices investigated include the Huawei Fit 2 Smartwatch and Band 7, Amazfit Band 7, and Xiaomi Watch 3. These devices operate on different operating systems, namely Android Wear, Zepp OS, and Wear OS.

The data collection period for each device varies, with Huawei having approximately one year of data collected, while the other devices have shorter durations. All wrist-wear devices from different vendors were connected to an iPhone 11 mobile device, which acted as the host device. The iPhone facilitated data synchronization and provided access to the data through the respective health applications provided by the vendors.

To extract the data, MD-NEXT was employed, and the extracted data was further analyzed using the MD-RED tool. These tools were chosen due to their recognized forensically sound capabilities. As a result, the dataset contains data that is considered suitable for use in digital forensics fields.

Overall, the dataset provides valuable information obtained from wrist-worn devices, covering multiple vendors, operating systems, and data collection periods. Researchers in the digital forensics field can utilize this dataset for various investigative and analytical purposes.