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.

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.

Top Flutter Packages Dataset

Flutter is an open source framework by Google for building beautiful, natively compiled, multi-platform applications from a single codebase. It is gaining quite a bit of popularity because of ability to code in a single language and have it running on Android/iOS and web as well. This dataset contains a snapshot of Top 5000+ flutter/dart packages hosted on Flutter package repository The dataset was scraped in August-2024. We aim to use this dataset to… See the full description on the dataset page: https://huggingface.co/datasets/deepklarity/top-flutter-packages.

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/lett9468/test2.

🇦🇹 오스트리아 English With the ‘Join in!’ app for iPhone, Android and Windows Phone, citizens can report shortcomings or grievances, such as potholes, to their community in a non-bureaucratic and quick manner. For Vienna, the maps of the city of Vienna are offered via the city map interface with a wealth of value-added functions and unprecedented accuracy. The notes are handed over from the app to the Viennese city administration. ### Availability * Apple iOS from version 4.1 * Google Android version 2.1 and higher and * Microsoft Windows Phone from version 7.0

This dataset encompasses 2.5 billion annual data points on location visits, app usage, and mobile web clickstream activities. Collected from over 100,000 triple-opt-in first-party U.S. Daily Active Users (DAU), it offers a robust foundation for understanding consumer behaviors.

At its core, this dataset contains unstructured event-level data, capturing both brick-and-mortar and app + web visits and interactions. The data is collected from both iOS and Android smartphones, providing an in-depth analysis and interpretation of validated consumer behaviors.

One of the key strengths of this dataset, is its utilization of OmniTraffic technology, which seamlessly integrates location, app, and web behaviors from individual consumers. By meticulously tracking the "who, what, where and when" of both online and offline visits, it provides comprehensive insights into consumer journeys.

Moreover, this dataset goes beyond mere observation by incorporating validated behaviors to uncover the underlying motivations driving consumer decisions. This deeper understanding of "the why" behind behaviors sets it apart, offering invaluable insights into consumer preferences and trends.

• 4+ year history
• iOS & Android

The primary use cases and verticals of our Behavioral Data Product are diverse and varied. Some key applications include:

• Data Acquisition and Modeling: Our data helps businesses acquire valuable insights into consumer behavior and enables modeling for various research objectives.

• Shopper Data Analysis: By understanding purchase behavior and patterns, businesses can optimize their strategies, improve targeting, and enhance customer experiences.

• Media Consumption Insights: Our data provides a deep understanding of viewer behavior and patterns across popular platforms like YouTube, Amazon Prime, Netflix, and Disney+, enabling effective media planning and content optimization.

• App Performance Optimization: Analyzing app behavior allows businesses to monitor usage patterns, track key performance indicators (KPIs), and optimize app experiences to drive user engagement and retention.

• Location-Based Targeting: With our detailed location data, businesses can map out consumer visits to physical venues and combine them with web and app behavior to create predictive ad targeting strategies.

• Audience Creation for Ad Placement: Our data enables the creation of highly targeted audiences for ad campaigns, ensuring better reach and engagement with relevant consumer segments.

The Behavioral Data Product complements our comprehensive suite of data solutions in the broader context of our data offering. It provides granular and event-level insights into consumer behaviors, which can be combined with other data sets such as survey responses, demographics, or custom profiling questions to offer a holistic understanding of consumer preferences, motivations, and actions.

MFour's Behavioral Data empowers businesses with unparalleled consumer insights, allowing them to make data-driven decisions, uncover new opportunities, and stay ahead in today's dynamic market landscape.

STL10 - Segmentation

Please consider sponsoring this repo so that we can continue to develop high-quality datasets for the AI and ML research. To become a sponsor: GitHub Sponsors Buy me a coffee You can also sponsor us by downloading our free application, Etiqueta, to your devices: Etiqueta on iOS or Apple Chip Macs Etiqueta on Android This repo contains segmented images for the labeled part of the STL-10 Dataset.
If you are looking for STL10-Labeled variant of the dataset… See the full description on the dataset page: https://huggingface.co/datasets/semihyagli/STL10-Segmented.

Paper | Code | Dataset | Project

  Dataset Card for MONDAY

MONDAY (Mobile OS Navigation Task Dataset for Agents from YouTube) is a cross-platform mobile navigation dataset for training vision-language models. This dataset contains

20K curated list of videos of mobile navigation tasks from YouTube, including Android and iOS devices. 333K detected scenes, each representing a temporally segmented step within a mobile navigation task. 313K identified actions, including touch, scroll… See the full description on the dataset page: https://huggingface.co/datasets/runamu/MONDAY.

AURA-Classification

  Dataset Description

The AURA (App User Review in Arabic) Classification dataset is a collection of 2,900 Arabic-language app reviews collected from various mobile applications. This dataset is primarily designed for text classification tasks.

  Features

The dataset includes the following fields:

review: The text of the review in Arabic.

appName: The name of the application being reviewed.

platform: The platform (iOS or Android) where the… See the full description on the dataset page: https://huggingface.co/datasets/irfan-ahmad/AURA-Classification.