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.

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.

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.

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

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.

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.

Differences between operating systems (Android, iOS, Mac OS, and Windows; Study 2).

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.

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.

The SYRCityline requests is a very large dataset This includes requests for city services which have been made by residents through SYRCityline, which utilizes SeeClickFix software. Service requests can be made at:https://seeclickfix.com/syracuseData DictionaryCreated at local - When this complaint or service was requested. (This is also in the format of MM/DD/YYYY - HH:MM(AM/PM)).Address - Address of the service request or complaint, provided by the community member.Summary - String that users select to categorize the nature of their complaint. Can be either Large or Bulk Items, Illegal Setouts, Sewer Back-ups, Weekly Trash Pickup, Large or Bulk Items - Skipped Pickup, Home & Building Maintenance, Sewer-related Concerns, Recyclling, Other Housing & Property Maintenance Concern, Streeth Lights, or Other.Rating - The number of followers on the Request in SeeClickFix.Description - Write up of the service request or complaint, provided by the community member.Agency Name - What type of City Department was this complaint assigned to. These include:Streets, Sidewalks & TransportationGarbage, Recycling & GraffitiHousing & Property MaintenanceFeedback to the CityParking & VehiclesGreen Spaces, Trees & Public UtilitiesWater & SewageAnimalsURL - Unique website address (url) that the complaint as well as comments from the City personnel can be viewed at.Latitude - Latitude GPS coordinate where the address is.Longitude - Longitude GPS coordinate where the address is.Export tagged places - Which quadrant of the city is this address matched to (Northeast, Southeast, Northwest, or Southwest).Acknowledged at local - When this complaint or service request was acknowledged by the City department.Closed at local - When this complaint or service request was marked as being closed by the City department.Minutes to acknowledged - The amount of time, in minutes, after it was Created at Local to being marked Acknowledged at local.Minutes to closed - The amount of time, in minutes, after service request was created at local to when it was marked as Closed at local.Assignee name - Which city Department was assigned to this request.Category - How was this request categorized. This can be Potholes, Large or Bulk Items, Water-related Concerns, Home & Building Maintenance, Street Lights, Weekly Trash Pickup, Public Trash Can, Yard Waste, Report Litter on Private Land, among other categories.SLA Limit - This is the limit assigned by the City of Syracuse, that puts a limit on how a request can stay in the list of tasks untouched. That amount of time, in hours, SeeClickFix will forward the request to the department head as well as an administrator to help ensure that requests are addressed in a timely manner.Report source - How this service request was obtained: Either Web-Mobile, iPhone, Portal, Web-Desktop, Android, or Request Form.Dataset OwnerOrganization: Department of Public Works (DPW)Position: Data Program ManagerCity: Syracuse, NYE-Mail Address:opendata@syrgov.net

The Mobile Games Dataset is a meticulously curated collection of 100+ top-rated mobile games spanning various genres. This dataset provides a valuable resource for game developers, researchers, and enthusiasts interested in exploring trends and patterns within the mobile gaming industry. Each entry includes the game name, developer, genre, and rating, offering a comprehensive overview of some of the most popular and critically acclaimed mobile games available today.

Column Descriptions:

• Game Name: The title of the mobile game.

  • Type: String
  • Example: "Candy Crush Saga"

• Developer: The name of the company or individual who developed the game.

  • Type: String
  • Example: "King"

• Genre: The category or type of game, indicating the primary gameplay mechanics.

  • Type: String
  • Example: "Puzzle"

• Rating: The average user rating of the game, typically on a scale from 1 to 5.

  • Type: Float
  • Example: 4.6

Descriptives of study 1 variables separated for operating systems (Android, iOS).

Global Cloud Mobile Backend as a Service (BaaS) Market size was $3.0 Billion in 2022 and is slated to hit $7.3 Billion by the end of 2030 with a CAGR of nearly 24.1%.

Dataset Card for ScreenSpot

GUI Grounding Benchmark: ScreenSpot. Created researchers at Nanjing University and Shanghai AI Laboratory for evaluating large multimodal models (LMMs) on GUI grounding tasks on screens given a text-based instruction.

  Dataset Details





  Dataset Description

ScreenSpot is an evaluation benchmark for GUI grounding, comprising over 1200 instructions from iOS, Android, macOS, Windows and Web environments, along with annotated element types… See the full description on the dataset page: https://huggingface.co/datasets/rootsautomation/ScreenSpot.

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