Do you know?

Do you know how much time you spend on an app? Do you know the total use time of a day or average use time of an app?

What it consists of?

This data set consists of - how many times a person unlocks his phone. - how much time he spends on every app on every day. - how much time he spends on his phone.

It lists the usage time of apps for each day.

What we can do?

Use the test data to find the Total Minutes that we can use the given app in a day. we can get a clear stats of apps usage. This data set will show you about the persons sleeping behavior as well as what app he spends most of his time. with this we can improve the productivity of the person.

The dataset was collected from the app usage app.

[ADVISORY] IMPORTANT

Instructions for citation:

If you use this dataset anywhere in your work, kindly cite as the below: L. Gupta, "Google Play Store Apps," Feb 2019. [Online]. Available: https://www.kaggle.com/lava18/google-play-store-apps

Context

While many public datasets (on Kaggle and the like) provide Apple App Store data, there are not many counterpart datasets available for Google Play Store apps anywhere on the web. On digging deeper, I found out that iTunes App Store page deploys a nicely indexed appendix-like structure to allow for simple and easy web scraping. On the other hand, Google Play Store uses sophisticated modern-day techniques (like dynamic page load) using JQuery making scraping more challenging.

Content

Each app (row) has values for catergory, rating, size, and more.

Acknowledgements

This information is scraped from the Google Play Store. This app information would not be available without it.

Inspiration

The Play Store apps data has enormous potential to drive app-making businesses to success. Actionable insights can be drawn for developers to work on and capture the Android market!

Apple App Store Key StatisticsApps & Games in the Apple App StoreApps in the Apple App StoreGames in the Apple App StoreMost Popular Apple App Store CategoriesPaid vs Free Apps in Apple App...

How much time do people spend on social media? As of 2025, the average daily social media usage of internet users worldwide amounted to 141 minutes per day, down from 143 minutes in the previous year. Currently, the country with the most time spent on social media per day is Brazil, with online users spending an average of 3 hours and 49 minutes on social media each day. In comparison, the daily time spent with social media in the U.S. was just 2 hours and 16 minutes. Global social media usageCurrently, the global social network penetration rate is 62.3 percent. Northern Europe had an 81.7 percent social media penetration rate, topping the ranking of global social media usage by region. Eastern and Middle Africa closed the ranking with 10.1 and 9.6 percent usage reach, respectively. People access social media for a variety of reasons. Users like to find funny or entertaining content and enjoy sharing photos and videos with friends, but mainly use social media to stay in touch with current events friends. Global impact of social mediaSocial media has a wide-reaching and significant impact on not only online activities but also offline behavior and life in general. During a global online user survey in February 2019, a significant share of respondents stated that social media had increased their access to information, ease of communication, and freedom of expression. On the flip side, respondents also felt that social media had worsened their personal privacy, increased a polarization in politics and heightened everyday distractions.

Dataset Card for Macappstore Applications Descriptions

Mac App Store Applications descriptions extracted from the metadata from the public API.

Curated by: MacPaw Way Ltd.

Language(s) (NLP): Mostly EN, DE License: MIT

  Dataset Details

This dataset is a combined and refined Mac App Store Applications Metadata dataset subset. The main idea behind its creation is to separate the description texts of the macOS apps for the convenience of further analysis.… See the full description on the dataset page: https://huggingface.co/datasets/MacPaw/mac-app-store-apps-descriptions.

Dataset Card for Macappstore Applications Release Notes

Mac App Store Applications release notes extracted from the metadata from the public API.

Curated by: MacPaw Way Ltd.

Language(s) (NLP): Mostly EN, DE License: MIT

  Dataset Details

This dataset is a combined and refined Mac App Store Applications Metadata dataset subset. The main idea behind its creation is to separate the release notes texts of the macOS apps for the convenience of further analysis.… See the full description on the dataset page: https://huggingface.co/datasets/MacPaw/mac-app-store-apps-release-notes.

Which county has the most Facebook users?

              There are more than 378 million Facebook users in India alone, making it the leading country in terms of Facebook audience size. To put this into context, if India’s Facebook audience were a country then it would be ranked third in terms of largest population worldwide. Apart from India, there are several other markets with more than 100 million Facebook users each: The United States, Indonesia, and Brazil with 193.8 million, 119.05 million, and 112.55 million Facebook users respectively.

              Facebook – the most used social media

              Meta, the company that was previously called Facebook, owns four of the most popular social media platforms worldwide, WhatsApp, Facebook Messenger, Facebook, and Instagram. As of the third quarter of 2021, there were around 3,5 billion cumulative monthly users of the company’s products worldwide. With around 2.9 billion monthly active users, Facebook is the most popular social media worldwide. With an audience of this scale, it is no surprise that the vast majority of Facebook’s revenue is generated through advertising.

              Facebook usage by device
              As of July 2021, it was found that 98.5 percent of active users accessed their Facebook account from mobile devices. In fact, almost 81.8 percent of Facebook audiences worldwide access the platform only via mobile phone. Facebook is not only available through mobile browser as the company has published several mobile apps for users to access their products and services. As of the third quarter 2021, the four core Meta products were leading the ranking of most downloaded mobile apps worldwide, with WhatsApp amassing approximately six billion downloads.

This data includes the name and location of active food service establishments and the violations that were found at the time of the inspection. Active food service establishments include only establishments that are currently operating. This dataset excludes inspections conducted in New York City (https://data.cityofnewyork.us/Health/Restaurant-Inspection-Results/4vkw-7nck), Suffolk County (http://apps.suffolkcountyny.gov/health/Restaurant/intro.html) and Erie County (http://www.healthspace.com/erieny). Inspections are a “snapshot” in time and are not always reflective of the day-to-day operations and overall condition of an establishment. Occasionally, remediation may not appear until the following month due to the timing of the updates. Update frequencies and availability of historical inspection data may vary from county to county. Some counties provide this information on their own websites and information found there may be updated more frequently. This dataset is refreshed on a monthly basis. The inspection data contained in this dataset was not collected in a manner intended for use as a restaurant grading system, and should not be construed or interpreted as such. Any use of this data to develop a restaurant grading system is not supported or endorsed by the New York State Department of Health. For more information, visit http://www.health.ny.gov/regulations/nycrr/title_10/part_14/subpart_14-1.htm or go to the “About” tab.

Our NFL Data product offers extensive access to historic and current National Football League statistics and results, available in multiple formats. Whether you're a sports analyst, data scientist, fantasy football enthusiast, or a developer building sports-related apps, this dataset provides everything you need to dive deep into NFL performance insights.

Key Benefits:

Comprehensive Coverage: Includes historic and real-time data on NFL stats, game results, team performance, player metrics, and more.

Multiple Formats: Datasets are available in various formats (CSV, JSON, XML) for easy integration into your tools and applications.

User-Friendly Access: Whether you are an advanced analyst or a beginner, you can easily access and manipulate data to suit your needs.

Free Trial: Explore the full range of data with our free trial before committing, ensuring the product meets your expectations.

Customizable: Filter and download only the data you need, tailored to specific seasons, teams, or players.

API Access: Developers can integrate real-time NFL data into their apps with API support, allowing seamless updates and user engagement.

Use Cases:

Fantasy Football Players: Use the data to analyze player performance, helping to draft winning teams and make better game-day decisions.

Sports Analysts: Dive deep into historical and current NFL stats for research, articles, and game predictions.

Developers: Build custom sports apps and dashboards by integrating NFL data directly through API access.

Betting & Prediction Models: Use data to create accurate predictions for NFL games, helping sportsbooks and bettors alike.

Media Outlets: Enhance game previews, post-game analysis, and highlight reels with accurate, detailed NFL stats.

Our NFL Data product ensures you have the most reliable, up-to-date information to drive your projects, whether it's enhancing user experiences, creating predictive models, or simply enjoying in-depth football analysis.

As of April 2024, almost 32 percent of global Instagram audiences were aged between 18 and 24 years, and 30.6 percent of users were aged between 25 and 34 years. Overall, 16 percent of users belonged to the 35 to 44 year age group.

              Instagram users

              With roughly one billion monthly active users, Instagram belongs to the most popular social networks worldwide. The social photo sharing app is especially popular in India and in the United States, which have respectively 362.9 million and 169.7 million Instagram users each.

              Instagram features

              One of the most popular features of Instagram is Stories. Users can post photos and videos to their Stories stream and the content is live for others to view for 24 hours before it disappears. In January 2019, the company reported that there were 500 million daily active Instagram Stories users. Instagram Stories directly competes with Snapchat, another photo sharing app that initially became famous due to it’s “vanishing photos” feature.
              As of the second quarter of 2021, Snapchat had 293 million daily active users.

Cristiano Ronaldo has one of the most popular Instagram accounts as of April 2024.

              The Portuguese footballer is the most-followed person on the photo sharing app platform with 628 million followers. Instagram's own account was ranked first with roughly 672 million followers.

              How popular is Instagram?

              Instagram is a photo-sharing social networking service that enables users to take pictures and edit them with filters. The platform allows users to post and share their images online and directly with their friends and followers on the social network. The cross-platform app reached one billion monthly active users in mid-2018. In 2020, there were over 114 million Instagram users in the United States and experts project this figure to surpass 127 million users in 2023.

              Who uses Instagram?

              Instagram audiences are predominantly young – recent data states that almost 60 percent of U.S. Instagram users are aged 34 years or younger. Fall 2020 data reveals that Instagram is also one of the most popular social media for teens and one of the social networks with the biggest reach among teens in the United States.

              Celebrity influencers on Instagram
              Many celebrities and athletes are brand spokespeople and generate additional income with social media advertising and sponsored content. Unsurprisingly, Ronaldo ranked first again, as the average media value of one of his Instagram posts was 985,441 U.S. dollars.

Reference evapotranspiration per day with a spatial resolution of 0.1 degree. Unit: mm day-1. The dataset contains daily values for global land areas, excluding Antarctica, since 1979. The dataset has been prepared according to the FAO Penman - Monteith method as described in FAO Irrigation and Drainage Paper 56.

The input variables are part of the Agrometeorological indicators dataset produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) through the Copernicus Climate Change Service (C3S).

The Agrometeorological indicators dataset provides daily surface meteorological data for the period from 1979 to present as input for agriculture and agro-ecological studies. This dataset is based on the hourly ECMWF ERA5 data at surface level and is referred to as AgERA5. References: https://doi.org/10.24381/cds.6c68c9bb

The Copernicus Climate Change Service (C3S) aims to combine observations of the climate system with the latest science to develop authoritative, quality-assured information about the past, current and future states of the climate in Europe and worldwide. ECMWF operates the Copernicus Climate Change Service on behalf of the European Union and will bring together expertise from across Europe to deliver the service.

Data publication: 2021-10-30

Contact points:

Metadata Contact: AQUASTAT

Resource Contact: AQUASTAT

Data lineage:

Copernicus Agrometeorological data were aggregated to daily time steps at the local time zone and corrected towards a finer topography at a 0.1° spatial resolution. The correction to the 0.1° grid was realized by applying grid and variable-specific regression equations to the ERA5 dataset interpolated at 0.1° grid. The equations were trained on ECMWF's operational high-resolution atmospheric model (HRES) at a 0.1° resolution. This way the data is tuned to the finer topography, finer land use pattern and finer land-sea delineation of the ECMWF HRES model.

Resource constraints:

• The dataset contains modified Copernicus Climate Change Service information [1979-to date];

• Neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus information or data it contains.

More information on Copernicus License in PDF version at https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf

Online resources:

Download Reference Evapotranspiration - AgERA5 derived (Daily - ~10km)

Apple is one of the most influential and recognisable brands in the world, responsible for the rise of the smartphone with the iPhone. Valued at over $2 trillion in 2021, it is also the most valuable...

This data includes the name and location of food service establishments and the violations that were found at the time of their last inspection. This dataset excludes inspections conducted in New York City (see: https://nycopendata.socrata.com/), Suffolk County (http://apps.suffolkcountyny.gov/health/Restaurant/intro.html) and Erie County (http://www.healthspace.com/erieny). Inspections are a “snapshot” in time and are not always reflective of the day-to-day operations and overall condition of an establishment. Occasionally, remediation may not appear until the following month due to the timing of the updates. Some counties provide this information on their own websites and information found there may be updated more frequently. This dataset is refreshed on a monthly basis.

Last inspection data is the most recently submitted and available data.

For more information, check out http://www.health.ny.gov/regulations/nycrr/title_10/part_14/subpart_14-1.htm, or go to the "About" tab.

Here are a few use cases for this project:

1. Smart Shopping Assistant: Integrate the "Snack" model into a shopping app that allows users to quickly find and identify specific snack products on the shelves. By scanning the shelf with their smartphone's camera, users can receive real-time information about the snacks, such as nutritional facts, price comparison, and personalized recommendations based on dietary preferences.

2. Inventory Management: Retail stores can use the "Snack" model to automate inventory tracking and management. By regularly scanning shelves with a computer vision-equipped device, store owners can receive real-time updates on stock levels and identify which items need restocking or have expired.

3. Interactive Marketing Campaigns: Brands can use the "Snack" model to create augmented reality (AR) marketing experiences for consumers. By incorporating the model into AR apps, users can find hidden promotions and virtual rewards by scanning snack packages with their smartphone, creating fun and engaging brand experiences.

4. Dietary Monitoring: The "Snack" model can be integrated into a health tracking app that allows users to monitor their daily snack consumption more easily. By simply taking a photo of their snacks throughout the day, users can receive instant feedback on the nutritional content of their snacks, helping them make smarter snacking choices and maintain a healthier diet.

5. Accessible Product Information for Visually Impaired Users: The "Snack" model can be used in apps designed for visually impaired individuals, allowing them to easily identify snack products and access relevant information about the product. By scanning the snack with their smartphone's camera, users could receive audio feedback containing product details such as ingredients, allergen information, and nutritional data.

Researchers have developed many experimental tasks for studying memory for intentions in the laboratory. Virtually all of these tasks prevent participants from using external tools and reminders. However, in everyday life we often use diaries or to-do lists, or we set up reminders in our environment. How do we decide whether or not to use these strategies? What consequences do they have for our behaviour? How much do individuals differ in whether or not they use reminders, and what can we do to influence this? While some studies have investigated these questions in brain-injured individuals, we know very little about healthy people. Yet, with new technologies such as smartphone reminder apps and wearable devices, we increasingly 'offload' intentions into the world around us. This proposal describes a systematic and detailed investigation of 'intention offloading'. Data from Gilbert, S.J., Bird, A., Carpenter, J., Fleming, S.M., Sachdeva, C., & Tsai, P.C. (2020). Optimal use of reminders: Metacognition, effort, and cognitive offloading. Journal of Experimental Psychology: General, 149, 501-517Every day, we form many intentions for behaviours that we plan to execute after a delay. These intentions might be postponed for just a few seconds (e.g. intending to add an attachment to an email before clicking 'send'), or minutes, days, or longer (e.g. intending to attend a planned hospital appointment). If we are to live independent, purposeful lives, it is essential that we are able to fulfil such intentions. Yet we all know how easily forgotten they are. This can have catastrophic effects in safety-critical fields such as nursing or aviation, and serious consequences for health-related behaviours such as remembering to take medication. Understanding how we fulfil delayed intentions will allow us to develop techniques to optimise this aspect of memory, and potentially compensate for any difficulties. Researchers have developed many experimental tasks for studying memory for intentions in the laboratory. Virtually all of these tasks prevent participants from using external tools and reminders. However, in everyday life we often use diaries or to-do lists, or we set up reminders in our environment. How do we decide whether or not to use these strategies? What consequences do they have for our behaviour? How much do individuals differ in whether or not they use reminders, and what can we do to influence this? While some studies have investigated these questions in brain-injured individuals, we know very little about healthy people. Yet, with new technologies such as smartphone reminder apps and wearable devices, we increasingly 'offload' intentions into the world around us. This proposal describes a systematic and detailed investigation of 'intention offloading'.

This data includes the name and location of active food service establishments and the violations that were found at the time of the inspection. Active food service establishments include only establishments that are currently operating. This dataset excludes inspections conducted in New York City (https://data.cityofnewyork.us/Health/Restaurant-Inspection-Results/4vkw-7nck), Suffolk County (http://apps.suffolkcountyny.gov/health/Restaurant/intro.html) and Erie County (http://www.healthspace.com/erieny). Inspections are a “snapshot” in time and are not always reflective of the day-to-day operations and overall condition of an establishment. Occasionally, remediation may not appear until the following month due to the timing of the updates. Update frequencies and availability of historical inspection data may vary from county to county. Some counties provide this information on their own websites and information found there may be updated more frequently. This dataset is refreshed on a monthly basis. The inspection data contained in this dataset was not collected in a manner intended for use as a restaurant grading system, and should not be construed or interpreted as such. Any use of this data to develop a restaurant grading system is not supported or endorsed by the New York State Department of Health. For more information, visit http://www.health.ny.gov/regulations/nycrr/title_10/part_14/subpart_14-1.htm or go to the “About” tab.

UiPad - UI Parsing and Accessibility Dataset

Curated by: MacPaw Way Ltd. Language(s): Mostly EN, UA License: MIT

Overview UiPad is a dataset created for the IASA Champ 2024 Challenge, focusing on the accessibility and interface understanding of MacOS applications. With growing interest in AI-driven user interface analysis, the dataset aims to bridge the gap in available resources for desktop app accessibility. While mobile apps and web platforms benefit from datasets like RICO and… See the full description on the dataset page: https://huggingface.co/datasets/MacPaw/UiPad.

As of January 2024, #love was the most used hashtag on Instagram, being included in over two billion posts on the social media platform. #Instagood and #instagram were used over one billion times as of early 2024.

2019 Novel Coronavirus COVID-19 (2019-nCoV) Visual Dashboard and Map:
https://www.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6

• Confirmed Cases by Country/Region/Sovereignty
• Confirmed Cases by Province/State/Dependency
• Deaths
• Recovered

Downloadable data:
https://github.com/CSSEGISandData/COVID-19

Additional Information about the Visual Dashboard:
https://systems.jhu.edu/research/public-health/ncov