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.

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 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.

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?".

Social media companies are starting to offer users the option to subscribe to their platforms in exchange for monthly fees. Until recently, social media has been predominantly free to use, with tech companies relying on advertising as their main revenue generator. However, advertising revenues have been dropping following the COVID-induced boom. As of July 2023, Meta Verified is the most costly of the subscription services, setting users back almost 15 U.S. dollars per month on iOS or Android. Twitter Blue costs between eight and 11 U.S. dollars per month and ensures users will receive the blue check mark, and have the ability to edit tweets and have NFT profile pictures. Snapchat+, drawing in four million users as of the second quarter of 2023, boasts a Story re-watch function, custom app icons, and a Snapchat+ badge.

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

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.

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.

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.

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.

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.

The dataset available through the Sightings Map of Invasive Plants in Portugal results from the Citizen Science platform INVASORAS.PT, which records sightings of invasive plants in Portugal (mainland and Archipelagos of Madeira and Azores). This platform was originally created in 2013, in the context of the project “Plantas Invasoras: uma ameaça vinda de fora” (Media Ciência nº 16905), developed by researchers from Centre for Functional Ecology of University of Coimbra and of Coimbra College of Agriculture of the Polytechnic Institute of Coimbra. Currently this project is over, but the platform is maintained by the same team. Sightings are reported by users who register at the platform and submit them, either directly on the website (https://invasoras.pt/pt/mapeamento) or using an app for Android (https://play.google.com/store/apps/details?id=pt.uc.invasoras2) and iOS (https://apps.apple.com/pt/app/plantas-invasoras-em-portugal/id1501776731) devices. Only validated sightings are available on the dataset. Validation is made based on photographs submitted along with the sightings by experts from the platform INVASORAS.PT team. As with all citizen science projects there is some risk of erroneous records and duplication of sightings.

1285 Indonesian native speakers participated in the recording with authentic accent. The recorded script is designed by linguists and cover a wide range of topics including generic, interactive, on-board and home. The text is manually proofread with high accuracy. It matches with mainstream Android and Apple system phones. The data set can be applied for automatic speech recognition, and machine translation scenes.Format：16kHz, 16bit, uncompressed wav, mono channelRecording Environment：quiet indoor environment, low background noise, without echoRecording Content：oral category; human-machine interaction category; smart home command and in-car command category; numbers; news categoryPopulation：1,285 speakers totally, with 47% male and 53% female; and 77.3% speakers of all are in the age group of 18-25,22.3% speakers of all are in the age group of 26-45, 0.4% speakers of all are in the age group of 46-60;Device：Android mobile phone, iPhoneLanguage：IndonesianApplication Scene：speech recognition, voiceprint recognition

The number of smartphone users in the Philippines was forecast to increase between 2024 and 2029 by in total 5.6 million users (+7.29 percent). This overall increase does not happen continuously, notably not in 2026, 2027, 2028 and 2029. The smartphone user base is estimated to amount to 82.33 million users in 2029. 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 Thailand and Indonesia.

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.

Online-Panel-Anbieter sind mit einer ständig wachsenden Zahl von technischen Geräten, Betriebssystemen und Internet-Browsern, mit denen Panel-Mitglieder auf das Internet zugreifen und E-Mails abrufen, konfrontiert. Mobile Geräte wie Smartphones bieten eine Vielzahl von innovativen Forschungsdesigns durch neue Teilnahme-Modi, Interviewgelegenheiten und Datenquellen. Dieser vorteilhafte ´vorsätzliche´ Einsatz von mobilen Geräten zur Datenerfassung wird durch die potenzielle Bedrohung für die Datenqualität von ´unbeabsichtigten mobilen Befragten´ ausgeglichen. Unabsichtlich nehmen mobile Befragte an einer herkömmlichen Online-Umfrage per Mobilgerät teil. Vor allem die Beschränkungen von Smartphones in Sachen Display-Größe und Dateneingabekomfort führen zu Bedenken in Bezug auf den Nutzen von Mobil-Teilnahmen. Aber eine Ablehnung der mobilen Zugänglichkeit zu Umfragen könnte die Zugänglichkeit zu Umfragen verringern, die eine systematische Verzerrung der Stichprobe verursachen. Die wenigen zur Verfügung stehenden empirischen Studien haben keine aussagekräftigen Ergebnisse über das Ausmaß unbeabsichtigter Mobil Teilnahmen und deren Auswirkungen auf die Datenqualität. Das Papier spricht sowohl Bedrohungen der Validität an und beschreibt ebenso die Ergebnisse von vier Studien im deutschen Teil des von Harris Interactive AG durchgeführten Online-Panels. Themen: Beginn Panel-Mitgliedschaft; geschätzte Anzahl eigener Umfrageteilnahmen; privat genutzte technische Geräte (Desktop PC, Notebook, Netbook, Tablet PC, Apple iPhone, Black Berry, Smartphone (Betriebssystem: Android, Microsoft oder Symbian); geschätzte Häufigkeit des Internetzugriffs pro Woche mit diesen Geräten; genutzter Browser für den privaten Internetzugang per Smartphone; privater Internetzugang via Smartphone zuhause bzw. unterwegs per WLAN oder Mobilfunknetz; privater Mobilfunkvertrag mit Internet-Flatrate; ausreichendes monatliches Highspeedvolumen; geplanter Internetzugriff per Smartphone; Umfrageteilnahme per Smartphone in der Vergangenheit und Art der Einladung zu dieser Umfrage (z.B. durch E-Mail, Anwendung aus Online-Panel); Thema der Umfrage; Art der technischen Probleme während der Umfrageteilnahme; durch technische Probleme verursachter Verzicht auf die Umfrageteilnahme; Installation einer Umfrage-App und Gründe für die Deinstallation; Einstellung zur Nutzung von Smartphones (Skala: regelmäßiger Abrufen privater E-Mails via Smartphone, mehr Zeit online aufgrund des Smartphones, ausreichend gute Darstellung der meisten Webseiten, keine Probleme beim Ausfüllen von Online-Formularen, mehr Komfort durch Apps als durch Smartphone-Browser, Präferenz von optimierten Webseiten für mobile Geräte, fehlende Nutzerfreundlichkeit vieler Webseiten beim Zugriff via Smartphone, Smartphone-Nutzung für private Internetnutzung, offen für Umfragen per Smartphone, Wunsch nach Wahlmöglichkeit zwischen Smartphone und PC für jede Umfrage, Umfrageteilnahme per Smartphone ist zu unkomfortabel, Bereitschaft zur Teilnahme an Umfragen von höchstens 10 Minuten Dauer). Demographie: Geschlecht; Alter; Haushaltsgröße; höchster Schulabschluss; Bundesland; Bevölkerungszahl des Wohnortes (Ortsgröße). Experiment: Umfrage fertiggestellt; Experiment 1: Angeleiteter Zugang zur Befragung; Experiment 2: Dauer der Befragung. Weiterhin verkodet wurden: Dauer zwischen der Umfrageeinladung und den ersten Zugang zur Umfrage; Teilnahmedauer; Anzahl spontaner Markennennungen; Anzahl der Unstimmigkeiten bei den Fragen zu Marken (Bekanntheit, Relevanz, erste Wahl). Online panel providers are confronted with an ever-increasing number of technical devices, operating systems and internet browsers with which panel members access the internet and retrieve emails. Mobile devices such as Smartphones offer a wide variety of innovative research designs by means of new participation modes, interview occasions and data sources. This beneficial “intentional” use of mobile devices as data collection mode is counterbalanced by the potential threat to data quality by “unintentional mobile respondents”.Unintentional mobile respondents participate in a conventional online survey per mobile device. Especially the limitations inherent to Smartphones in terms of display size and data entry comfort raise concerns regarding the usefulness of mobile participations. But a rejection of mobile survey accesses may decrease the accessibility of surveys causing systematic sample biases.The few empirical studies available do not yield conclusive results regarding the extent of unintentional mobile participations and their impact on data quality. The paper addresses both validity threats and reports results of four studies conducted in the German section of the online panel of Harris Interactive AG. CASI (Computerunterstützte Selbstbefragung)- Interaktiver Selbstausfüller Interactive self-administered questionnaire: CASI (Computer Assisted Self-Interview) Mitglieder der deutschen Sektion des Online-Panels von Harris Interactive AG Members of the German section of the online panel of Harris Interactive AG Auswahlverfahren Kommentar: Einfache Zufallsauswahl aus Online-Panel

The smartphone penetration in the Philippines was forecast to continuously decrease between 2024 and 2029 by in total 6.4 percentage points. According to this forecast, in 2029, the penetration will have decreased for the fourth consecutive year to 65.75 percent. 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 Laos and Malaysia.