Use the OpenWeb Ninja Google Play App Store Data API to access comprehensive data on Google Play Store, including Android Apps / Games, reviews, top charts, search, and more. Our extensive dataset provides over 40 app store data points, enabling you to gain deep insights into the market.

The App Store Data dataset includes all key app details:

App Name, Description, Rating, Photos, Downloads, Version Information, App Size, Permissions, Developer and Contact Information, Consumer Review Data.

Nowadays, mobile applications (a.k.a., apps) are used by over two billion users for every type of need, including social and emergency connectivity. Their pervasiveness in today world has inspired the software testing research community in devising approaches to allow developers to better test their apps and improve the quality of the tests being developed. In spite of this research effort, we still notice a lack of empirical analyses aiming at assessing the actual quality of test cases manually developed by mobile developers: this perspective could provide evidence-based findings on the future research directions in the field as well as on the current status of testing in the wild. As such, we performed a large-scale empirical study targeting 1,780 open-source Android apps and aiming at assessing (1) the extent to which these apps are actually tested, (2) how well-designed are the available tests, and (3) what is their effectiveness. The key results of our study show that mobile developers still tend not to properly test their apps, possibly because of time to market requirements. Furthermore, we discovered that the test cases of the considered apps have a low (i) design quality, both in terms of test code metrics and test smells, and (ii) effectiveness when considering code coverage as well as assertion density.

This dataset contains information on application install interactions of users in the Myket android application market. The dataset was created for the purpose of evaluating interaction prediction models, requiring user and item identifiers along with timestamps of the interactions. Hence, the dataset can be used for interaction prediction and building a recommendation system. Furthermore, the data forms a dynamic network of interactions, and we can also perform network representation learning on the nodes in the network, which are users and applications.

Data Creation

The dataset was initially generated by the Myket data team, and later cleaned and subsampled by Erfan Loghmani a master student at Sharif University of Technology at the time. The data team focused on a two-week period and randomly sampled 1/3 of the users with interactions during that period. They then selected install and update interactions for three months before and after the two-week period, resulting in interactions spanning about 6 months and two weeks.

We further subsampled and cleaned the data to focus on application download interactions. We identified the top 8000 most installed applications and selected interactions related to them. We retained users with more than 32 interactions, resulting in 280,391 users. From this group, we randomly selected 10,000 users, and the data was filtered to include only interactions for these users. The detailed procedure can be found in here.

Data Structure

The dataset has two main files.

• myket.csv: This file contains the interaction information and follows the same format as the datasets used in the "JODIE: Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks" (ACM SIGKDD 2019) project. However, this data does not contain state labels and interaction features, resulting in associated columns being all zero.
• app_info_sample.csv: This file comprises features associated with applications present in the sample. For each individual application, information such as the approximate number of installs, average rating, count of ratings, and category are included. These features provide insights into the applications present in the dataset.

Dataset Details

• Total Instances: 694,121 install interaction instances
• Instances Format: Triplets of user_id, app_name, timestamp
• 10,000 users and 7,988 android applications
• Item features for 7,606 applications

For a detailed summary of the data's statistics, including information on users, applications, and interactions, please refer to the Python notebook available at summary-stats.ipynb. The notebook provides an overview of the dataset's characteristics and can be helpful for understanding the data's structure before using it for research or analysis.

Top 20 Most Installed Applications

Package Name	Count of Interactions
com.instagram.android	15292
ir.resaneh1.iptv	12143
com.tencent.ig	7919
com.ForgeGames.SpecialForcesGroup2	7797
ir.nomogame.ClutchGame	6193
com.dts.freefireth	6041
com.whatsapp	5876
com.supercell.clashofclans	5817
com.mojang.minecraftpe	5649
com.lenovo.anyshare.gps	5076
ir.medu.shad	4673
com.firsttouchgames.dls3	4641
com.activision.callofduty.shooter	4357
com.tencent.iglite	4126
com.aparat	3598
com.kiloo.subwaysurf	3135
com.supercell.clashroyale	2793
co.palang.QuizOfKings	2589
com.nazdika.app	2436
com.digikala	2413

Comparison with SNAP Datasets

The Myket dataset introduced in this repository exhibits distinct characteristics compared to the real-world datasets used by the project. The table below provides a comparative overview of the key dataset characteristics:

Dataset	#Users	#Items	#Interactions	Average Interactions per User	Average Unique Items per User
Myket	10,000	7,988	694,121	69.4	54.6
LastFM	980	1,000	1,293,103	1,319.5	158.2
Reddit	10,000	984	672,447	67.2	7.9
Wikipedia	8,227	1,000	157,474	19.1	2.2
MOOC	7,047	97	411,749	58.4	25.3

The Myket dataset stands out by having an ample number of both users and items, highlighting its relevance for real-world, large-scale applications. Unlike LastFM, Reddit, and Wikipedia datasets, where users exhibit repetitive item interactions, the Myket dataset contains a comparatively lower amount of repetitive interactions. This unique characteristic reflects the diverse nature of user behaviors in the Android application market environment.

Citation

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

@misc{loghmani2023effect,
   title={Effect of Choosing Loss Function when Using T-batching for Representation Learning on Dynamic Networks}, 
   author={Erfan Loghmani and MohammadAmin Fazli},
   year={2023},
   eprint={2308.06862},
   archivePrefix={arXiv},
   primaryClass={cs.LG}
}

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%.

Beekeeping Log Mobile App Market Outlook

According to our latest research, the global Beekeeping Log Mobile App market size reached USD 54.2 million in 2024, driven by the growing digital transformation in agriculture and increasing awareness of sustainable beekeeping practices. The market is expanding at a robust CAGR of 13.1% from 2025 to 2033, and is expected to reach USD 160.5 million by the end of the forecast period. This growth is primarily fueled by rising adoption of smart technologies among both commercial and hobbyist beekeepers, as well as a surge in demand for data-driven hive management solutions.

The primary growth factor for the Beekeeping Log Mobile App market is the increasing need for efficient hive management and real-time monitoring solutions. As beekeeping becomes more complex due to climate change, disease outbreaks, and the need for higher productivity, beekeepers are turning to digital tools to streamline operations. Mobile apps equipped with features such as hive tracking, health monitoring, and inventory management allow users to maintain comprehensive records, optimize hive health, and enhance productivity. These apps not only help in reducing manual errors but also provide actionable insights through data analytics, enabling proactive decision-making and improved colony survival rates.

Another significant driver is the growing emphasis on sustainability and traceability in apiculture. With global concerns about pollinator decline and the ecological implications of beekeeping practices, stakeholders are increasingly adopting technologies that offer transparency and compliance with regulatory standards. Beekeeping log mobile apps facilitate detailed documentation of hive activities, treatments, and honey production, which is crucial for organic certification and quality assurance. Moreover, the integration of IoT devices with mobile platforms enables real-time data collection, further enhancing the ability to monitor environmental conditions and mitigate risks associated with hive losses.

The rising popularity of beekeeping as a hobby and the expansion of educational and research initiatives are also contributing to market growth. Urban and suburban beekeeping is on the rise, with more individuals seeking mobile solutions to manage small-scale operations. Educational institutions and research organizations are leveraging these apps to collect and analyze large datasets, supporting studies on bee health, behavior, and environmental impacts. The availability of user-friendly, customizable, and affordable mobile apps is making advanced beekeeping tools accessible to a broader audience, thus accelerating market penetration across various end-user segments.

Regionally, North America continues to dominate the Beekeeping Log Mobile App market, accounting for over 38% of the global revenue in 2024, followed by Europe and Asia Pacific. The high adoption rate in North America is attributed to the presence of large commercial apiaries, advanced technological infrastructure, and active beekeeping communities. Europe is witnessing steady growth due to stringent regulations on beekeeping practices and increasing investments in research and education. Meanwhile, Asia Pacific is emerging as a lucrative market, driven by rising awareness, government initiatives, and the proliferation of mobile technology in rural areas. The regional landscape is expected to evolve further with increasing collaborations between app developers, research institutions, and beekeeping associations.

Platform Analysis

The Platform segment in the Beekeeping Log Mobile App market is categorized into iOS, Android, and Web-based solutions. The proliferation of smartphones and mobile devices has made iOS and Android the primary platforms for beekeeping app deployment. Among these, Android holds a slightly larger market share due to the widespread adoption of affordable Android devices, esp

AI Coaching Running App Market Outlook

According to our latest research, the AI Coaching Running App market size reached USD 1.47 billion in 2024 globally, demonstrating robust momentum. The market is expected to grow at a compelling CAGR of 18.6% from 2025 to 2033, with the forecasted market size projected to reach USD 6.1 billion by 2033. This remarkable growth is primarily driven by the increasing adoption of digital fitness solutions, the integration of artificial intelligence in personal health management, and the surge in demand for personalized training experiences. The AI Coaching Running App market is witnessing exponential expansion as users seek advanced, data-driven, and interactive running solutions to enhance their fitness journeys.

The primary growth factor fueling the AI Coaching Running App market is the widespread proliferation of smartphones and wearable devices, which serve as the foundational platforms for these applications. As consumers become more health-conscious and seek convenient ways to monitor and improve their fitness, the demand for intelligent running apps that provide real-time feedback and customized coaching has soared. Integration with wearable technologies, such as smartwatches and fitness bands, allows these apps to collect granular biometric data, enabling AI algorithms to deliver highly personalized recommendations and adaptive training plans. This technological synergy is not only enhancing user engagement but also fostering long-term retention and loyalty, as runners benefit from tailored guidance that evolves with their progress.

Another significant driver is the evolution of AI and machine learning capabilities within the fitness sector. Modern AI Coaching Running Apps leverage sophisticated algorithms to analyze vast datasets, including users' running patterns, physiological responses, and environmental factors. These insights empower the apps to offer dynamic coaching, predictive injury prevention, and motivational support, transforming the running experience into a holistic, interactive journey. The ability to gamify workouts, provide social challenges, and integrate with virtual communities further amplifies user motivation and accountability. As a result, both amateur and professional runners are increasingly gravitating towards AI-powered solutions that offer a competitive edge and continuous improvement.

The COVID-19 pandemic has also played a pivotal role in accelerating the adoption of AI Coaching Running Apps, as lockdowns and social distancing measures forced individuals to seek alternative methods for maintaining their fitness routines. The shift towards remote and hybrid work models has contributed to a lasting change in exercise habits, with more people opting for flexible, app-based running programs that fit their schedules. Additionally, the growing emphasis on preventive healthcare and wellness has prompted healthcare providers, fitness centers, and corporate wellness programs to incorporate AI-driven running apps into their offerings. This trend is expected to persist, driving sustained growth and innovation in the market as stakeholders recognize the value of data-driven, accessible fitness solutions.

Regionally, North America dominates the AI Coaching Running App market, accounting for the largest share in 2024, followed closely by Europe and Asia Pacific. The high penetration of smartphones, advanced digital infrastructure, and a strong culture of fitness and wellness contribute to the region's leadership. However, Asia Pacific is emerging as the fastest-growing market, propelled by a burgeoning middle class, increasing health awareness, and rapid digitalization. Latin America and the Middle East & Africa are also witnessing steady adoption, albeit at a slower pace, as local players and global brands expand their footprints in these regions. The global landscape is characterized by intense competition, continuous innovation, and strategic partnerships aimed at capturing diverse user segments and unlocking new revenue streams.

Platform Analysis

The Platform segment of the AI Coaching Running App market is primarily categorized into iOS, Android, and Web-based platforms, each playing a distinct role in shaping user adoption and engagement. iOS-based apps have historically captured a significant market share, particularly in developed regions such as North America and Western Europe, where Apple devices are prevalent. The seamless

In the fourth quarter of 2024, TikTok generated around 186 million downloads from users worldwide. Initially launched in China first by ByteDance as Douyin, the short-video format was popularized by TikTok and took over the global social media environment in 2020. In the first quarter of 2020, TikTok downloads peaked at over 313.5 million worldwide, up by 62.3 percent compared to the first quarter of 2019. TikTok interactions: is there a magic formula for content success? In 2024, TikTok registered an engagement rate of approximately 4.64 percent on video content hosted on its platform. During the same examined year, the social video app recorded over 1,100 interactions on average. These interactions were primarily composed of likes, while only recording less than 20 comments per piece of content on average in 2024. The platform has been actively monitoring the issue of fake interactions, as it removed around 236 million fake likes during the first quarter of 2024. Though there is no secret formula to get the maximum of these metrics, recommended video length can possibly contribute to the success of content on TikTok. It was recommended that tiny TikTok accounts with up to 500 followers post videos that are around 2.6 minutes long as of the first quarter of 2024. While, the ideal video duration for huge TikTok accounts with over 50,000 followers was 7.28 minutes. The average length of TikTok videos posted by the creators in 2024 was around 43 seconds. What’s trending on TikTok Shop? Since its launch in September 2023, TikTok Shop has become one of the most popular online shopping platforms, offering consumers a wide variety of products. In 2023, TikTok shops featuring beauty and personal care items sold over 370 million products worldwide. TikTok shops featuring womenswear and underwear, as well as food and beverages, followed with 285 and 138 million products sold, respectively. Similarly, in the United States market, health and beauty products were the most-selling items, accounting for 85 percent of sales made via the TikTok Shop feature during the first month of its launch. In 2023, Indonesia was the market with the largest number of TikTok Shops, hosting over 20 percent of all TikTok Shops. Thailand and Vietnam followed with 18.29 and 17.54 percent of the total shops listed on the famous short video platform, respectively. 

Abstract This study explores how hedonic mobile applications (apps) compare to utilitarian apps in consumer evaluation. We posit that achieving a set of passionate consumers is a pre-cursor to product success in markets, whereas technology frustration is a negative hindrance to the product success. Also, we argue that technology frustration may act as a negative complementing factor to consumer passion, and this effect is higher for hedonic products than utilitarian products. We contextualize our study to the android apps, and used a dataset that tracked 19,121 apps in the android market for three months and coded our variables from this dataset. We conducted empirical analysis and found support for our hypotheses. This study contributes to the information systems and marketing literature in providing a new dimension associated with consumer evaluation of digital products, and draws evaluative comparisons between hedonic and utilitarian digital products.

This dataset presents a comprehensive survey of ride-hailing app users in Pakistan, capturing their experiences, preferences, and behavior regarding these services. With the increasing reliance on digital transportation solutions, ride-hailing apps have transformed urban mobility in the country. This dataset aims to provide insights into how users interact with these services, what factors influence their choices, and how satisfied they are with their overall experience.

The dataset includes key variables such as demographic details (age, gender, occupation), ride frequency, preferred ride-hailing apps, pricing perceptions, and service quality evaluations. Additionally, it explores factors like waiting time, ride availability, safety concerns, and customer support satisfaction. Understanding these elements is crucial for identifying gaps in service and improving user experience.

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Researchers, data analysts, and industry professionals can leverage this dataset to study market trends, assess customer satisfaction, and explore areas for service enhancement. It can also be used for predictive modeling, sentiment analysis, and business strategy development in the ride-hailing industry. Policymakers and urban planners may find it useful for transportation planning and infrastructure development.

This dataset is ideal for exploring consumer behavior, evaluating competition among ride-hailing services, and identifying the key drivers behind customer retention and loyalty. Whether you're conducting academic research, working on a business case study, or developing a machine-learning model, this dataset offers valuable insights into the evolving landscape of ride-hailing in Pakistan.

Mental health screening and diagnostic apps can provide an opportunity to reduce strain on mental health services, improve patient well-being, and increase access for underrepresented groups. Despite promise of their acceptability, many mental health apps on the market suffer from high dropout due to a multitude of issues. Understanding user opinions of currently available mental health apps beyond star ratings can provide knowledge which can inform the development of future mental health apps. This study aimed to conduct a review of current apps which offer screening and/or aid diagnosis of mental health conditions on the Apple app store (iOS), Google Play app store (Android), and using the m-health Index and Navigation Database (MIND). In addition, the study aimed to evaluate user experiences of the apps, identify common app features and determine which features are associated with app use discontinuation. The Apple app store, Google Play app store, and MIND were searched. User reviews and associated metadata were then extracted to perform a sentiment and thematic analysis. The final sample included 92 apps. 45.65% (n = 42) of these apps only screened for or diagnosed a single mental health condition and the most commonly assessed mental health condition was depression (38.04%, n = 35). 73.91% (n = 68) of the apps offered additional in-app features to the mental health assessment (e.g., mood tracking). The average user rating for the included apps was 3.70 (SD = 1.63) and just under two-thirds had a rating of four stars or above (65.09%, n = 442). Sentiment analysis revealed that 65.24%, n = 441 of the reviews had a positive sentiment. Ten themes were identified in the thematic analysis, with the most frequently occurring being performance (41.32%, n = 231) and functionality (39.18%, n = 219). In reviews which commented on app use discontinuation, functionality and accessibility in combination were the most frequent barriers to sustained app use (25.33%, n = 19). Despite the majority of user reviews demonstrating a positive sentiment, there are several areas of improvement to be addressed. User reviews can reveal ways to increase performance and functionality. App user reviews are a valuable resource for the development and future improvements of apps designed for mental health diagnosis and screening.

Hypertension AI Coach App Market Outlook

According to our latest research, the global hypertension AI coach app market size reached USD 1.37 billion in 2024, supported by increasing adoption of digital health solutions and rising prevalence of hypertension worldwide. The market is projected to expand at a robust CAGR of 23.4% during the forecast period, reaching an estimated USD 10.29 billion by 2033. This remarkable growth is driven by technological advancements in artificial intelligence, growing awareness about hypertension management, and the increasing integration of digital therapeutics into everyday healthcare practices.

The primary growth factor fueling the hypertension AI coach app market is the escalating global burden of hypertension, which affects over 1.3 billion adults, according to the World Health Organization. As hypertension remains a leading risk factor for cardiovascular diseases, stroke, and kidney failure, there is a critical demand for effective, accessible, and personalized management tools. AI-powered coach apps provide real-time monitoring, tailored lifestyle recommendations, and medication adherence reminders, significantly enhancing patient outcomes. Their ability to aggregate and analyze large datasets enables the delivery of proactive interventions, reducing the incidence of hypertension-related complications and healthcare costs. This technological leap is particularly appealing to healthcare providers and patients seeking convenient, non-invasive, and cost-effective management solutions.

Another significant driver is the integration of hypertension AI coach apps with wearable devices and remote monitoring technologies. The proliferation of smartwatches, fitness trackers, and connected blood pressure monitors has enabled seamless data collection and continuous health tracking. AI-driven apps leverage this data to offer dynamic feedback and predictive analytics, empowering users to make informed lifestyle choices. Additionally, these apps foster patient engagement and self-management, which are crucial for chronic disease control. The ability to personalize interventions based on individual health profiles and behavioral patterns has been shown to increase adherence to treatment plans and promote sustainable healthy habits, further propelling market growth.

Regulatory support and increasing investments in digital health infrastructure are also accelerating the adoption of hypertension AI coach apps. Governments and healthcare organizations worldwide are recognizing the potential of AI-driven solutions to bridge gaps in traditional care models, especially in underserved and remote areas. Initiatives to promote telemedicine, digital therapeutics, and mobile health applications are creating a conducive environment for innovation and market expansion. Furthermore, strategic collaborations between technology firms, healthcare providers, and pharmaceutical companies are facilitating the development and deployment of sophisticated AI coaching platforms, enhancing their credibility and scalability in the global marketplace.

Regionally, North America dominates the hypertension AI coach app market, accounting for the largest share in 2024, followed by Europe and Asia Pacific. The high prevalence of hypertension, advanced healthcare infrastructure, and widespread smartphone penetration in North America have driven early adoption of AI health applications. Europe is witnessing significant growth due to supportive regulatory frameworks and increased investment in digital health technologies. Meanwhile, Asia Pacific is emerging as a lucrative market, fueled by a rising hypertensive population, rapid urbanization, and expanding digital connectivity. Latin America and the Middle East & Africa are also showing promising potential, albeit at a relatively nascent stage, as awareness and access to digital health solutions continue to improve.

Platform Analysis

The hypertension AI coach app market is segmented by platform into iOS, Android, and web-ba

Seed Germination Journal App Market Outlook

According to our latest research, the global Seed Germination Journal App market size reached USD 215 million in 2024, driven by rising digitalization in agriculture and increasing interest in home gardening. The market is experiencing robust growth, registering a CAGR of 14.8% from 2025 to 2033. By the end of 2033, the market is expected to attain a value of USD 661 million. This expansion is fueled primarily by the integration of advanced features such as data analytics, photo logging, and community sharing, which are enhancing user engagement and providing actionable insights for both hobbyists and commercial growers.

One of the primary growth drivers for the Seed Germination Journal App market is the increasing adoption of digital tools in agriculture and horticulture. As more individuals and organizations recognize the importance of data-driven decision-making in optimizing plant growth and yield, the demand for digital record-keeping and monitoring solutions has surged. These apps enable users to meticulously track the germination process, monitor environmental conditions, and adjust their practices based on real-time analytics. The proliferation of smartphones and improved internet connectivity, even in rural and semi-urban areas, has further widened access to these applications, making them an indispensable tool for both novice and seasoned growers.

Additionally, the growing trend of urban gardening and sustainable living is significantly boosting the Seed Germination Journal App market. With urban populations seeking ways to reconnect with nature and ensure food security, home gardening has become a popular pursuit. These apps cater to the needs of urban gardeners by offering intuitive interfaces, educational resources, and interactive features that simplify the germination process. The integration of community sharing and social features fosters a sense of belonging and encourages knowledge exchange, which in turn drives higher user retention and app engagement. Educational institutions are also leveraging these platforms to support experiential learning, further expanding the market’s reach.

The rise of precision agriculture and research initiatives focused on plant biology and genetics is another critical factor propelling market growth. Research organizations and commercial growers are increasingly relying on Seed Germination Journal Apps to collect accurate, time-stamped data on seed performance, germination rates, and growth patterns. The ability to analyze large datasets and generate actionable insights is invaluable for optimizing crop selection, improving yield forecasts, and advancing breeding programs. As the agricultural sector continues to embrace digital transformation, the adoption of specialized journal apps is expected to accelerate, particularly in regions with advanced agri-tech ecosystems.

Regionally, North America holds a significant share of the Seed Germination Journal App market, followed closely by Europe and Asia Pacific. The high adoption rate in North America is attributed to the region’s advanced digital infrastructure, strong culture of home gardening, and the presence of leading agri-tech companies. Europe’s market is bolstered by government initiatives promoting sustainable agriculture and digital innovation in education. Meanwhile, Asia Pacific is emerging as a lucrative market due to its large agricultural base, increasing smartphone penetration, and growing interest in urban farming. Latin America and the Middle East & Africa are also witnessing steady growth, supported by rising awareness and gradual improvements in digital literacy.

Platform Analysis

The Seed Germination Journal App market is segmented by platform into iOS, Android, and Web-based solutions, each catering to distinct user preferences and technological ecosystems. iOS-based applications have gained significant traction among users who prefer the Apple ecosystem, benefiting from robust security feature