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...

Dataset Card for Macappstore Applications Metadata

Mac App Store Applications Metadata sourced by the public API.

Curated by: MacPaw Inc.

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

  Dataset Details

This data aims to cover our internal company research needs and start collecting and sharing the macOS app dataset since we have yet to find a suitable existing one. Full application metadata was sourced by the public iTunes search API for the US, Germany, and… See the full description on the dataset page: https://huggingface.co/datasets/MacPaw/mac-app-store-apps-metadata.

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.

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

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!

googleplaystore.csv

googleplaystore_user_reviews.csv

More - Find More Exciting🙀 Datasets Here - An Upvote👍 A Dayᕙ(`▿´)ᕗ , Keeps Aman Hurray Hurray..... ٩(˘◡˘)۶Haha

Dataset

This dataset was created by skoc10

Contents

Dataset for the paper A Longitudinal Study of Removed Apps in iOS App Store (WWW 2021)

According to a January 2022 analysis of the leading dating apps downloaded from the Apple App Store, the Badoo mobile app was indexed as collecting the largest number of data types from its users' activity. Bumble and HER ranked second, with an index value of close to 68, respectively. Grindr had a reported index value of 62, while market leader Tinder was indexed with a value of 38.4.

Dataset

This dataset was created by Tanishq Kachiwala

Released under Apache 2.0

Contents

Dataset

This dataset was created by Amady Ba

Contents

According to a May 2022 analysis of period tracking apps distributed on the Apple App Store, commercial female health app Eve was indexed as collecting the largest number of data types from its users' activity. Glow and Ovia followed, with an index value of 64.8 and 62.4 points, respectively. Clover had a reported index value of 56.4, while market leader Flo was indexed with a value of 45.

Protein networks have become a popular tool for analyzing and visualizing the often long lists of proteins or genes obtained from proteomics and other high-throughput technologies. One of the most popular sources of such networks is the STRING database, which provides protein networks for more than 2000 organisms, including both physical interactions from experimental data and functional associations from curated pathways, automatic text mining, and prediction methods. However, its web interface is mainly intended for inspection of small networks and their underlying evidence. The Cytoscape software, on the other hand, is much better suited for working with large networks and offers greater flexibility in terms of network analysis, import, and visualization of additional data. To include both resources in the same workflow, we created stringApp, a Cytoscape app that makes it easy to import STRING networks into Cytoscape, retains the appearance and many of the features of STRING, and integrates data from associated databases. Here, we introduce many of the stringApp features and show how they can be used to carry out complex network analysis and visualization tasks on a typical proteomics data set, all through the Cytoscape user interface. stringApp is freely available from the Cytoscape app store: http://apps.cytoscape.org/apps/stringapp.

An Enumeration Operational Data Store (ODS) in DB2 using SUMS standards and architecture. MISF version for ad hoc reporting and standard MI reports.

Dataset

This dataset was created by ojshav saxena

Contents

The Automated Insights Dataset (AID) brings metadata from the 200 most downloaded free apps from each of the 32 categories on the Google Play Store, totaling 6400 apps, with information that goes beyond that presented by app stores, also bringing metadata from AppBrain. The User Interface Depth Dataset (UID) brings a high-quality sampling of the AID, and delves into the identification of 7540 components of 50 component types and the capture of 1948 screenshots of the interface of 400 apps. The component set was based on components of Google Material Design and Android Studio.

The datasets can be viewed in the spreadsheets named "Automated Insights Dataset (AID).xlsx" and "User Interface Depth Dataset (UID).xlsx".

The "UID - Screenshots.zip" file contains screenshots of the apps present in the UID, organized in folders by app IDs.

The "Source code of the developed tools.zip" file contains Python codes and complementary files used to collect the datasets.

The "Discarded apps.zip" file contains the apps discarded in the analysis, it presents screenshots of some apps, collected elements and the reasons that led to these apps being discarded.

The "Data explanation.zip" file contains graphical representations of the UID components and textual representations of each data present in the UID and AID, allowing a better understanding of the criteria used.

Evolution mensuelle des notes sur App Store et Google Play store des applications mobiles Banque Populaire et Caisse d'Epargne. Source : stores Apple et Google

This dataset provides the locations of the 36 accessible parking spaces included in the dlr SpaceFinder App. The app provides real-time information on the location & live availability of the 36 accessible parking spaces in Dún Laoghaire Town. Through the app, sensors at each of these locations will notify blue badge users whether the space is free or occupied and allow users can accurately pinpoint and navigate to available accessible parking spaces. Within this dataset, you'll find information on 36 distinct accessible parking locations in Dún Laoghaire Town. Each entry is defined by its unique ID and includes geographical data: latitude and longitude coordinates and ITM coordinates. The app is available on Apple and Android app stores.

The Measurable AI FoodPanda Food & Grocery Transaction dataset is a leading source of email receipts and transaction data, offering data collected directly from users via Proprietary Consumer Apps, with millions of opt-in users.

We source our email receipt consumer data panel via two consumer apps which garner the express consent of our end-users (GDPR compliant). We then aggregate and anonymize all the transactional data to produce raw and aggregate datasets for our clients.

Use Cases Our clients leverage our datasets to produce actionable consumer insights such as: - Market share analysis - User behavioral traits (e.g. retention rates) - Average order values - Promotional strategies used by the key players. Several of our clients also use our datasets for forecasting and understanding industry trends better.

Coverage - Asia (Hong Kong, Taiwan, Singapore, Thailand, Malaysia, Philippines, Pakistan)

Granular Data Itemized, high-definition data per transaction level with metrics such as - Order value - Items ordered - No. of orders per user - Delivery fee - Service fee - Promotions used - Geolocation data and more

Aggregate Data - Weekly/ monthly order volume - Revenue delivered in aggregate form, with historical data dating back to 2018. All the transactional e-receipts are sent from the FoodPanda food delivery app to users’ registered accounts.

Most of our clients are fast-growing Tech Companies, Financial Institutions, Buyside Firms, Market Research Agencies, Consultancies and Academia.

Our dataset is GDPR compliant, contains no PII information and is aggregated & anonymized with user consent. Contact business@measurable.ai for a data dictionary and to find out our volume in each country.

This data set is from the One Big Store research project conducted by the App Studies Initiative researchers at the University of Toronto and Manchester Metropolitan University. Have the uneven global flows of capital in the cultural industries changed because of access to distribution platforms like the Apple iOS App Store? Based on a financial analysis of this data set containing three years of game app revenues (2015-2017), our research project asks two questions. First, were game developers and publishers able to generate revenue in their domestic markets in the App Store? Second, to what extent are game app developers from the Global South, historically at the periphery of the global game industry, able to capture value in the US, Canadian, and Dutch instances of the App Store? Our research project advances discussions on the political economy of platform-dependent cultural production by situating this case within broader conversations on cultural imperialism, demonstrating that local app store instances are part of “one big store;” a US-dominated and oriented space of distribution and consumption that effectively captures revenue in regional marketplaces. (2023-10-06)

The app is downloaded from the Apple Store and Google Play: Hello.tousanticovid.gouv.fr/ Description of the data This dataset informs for each day since the launch of the application on 2 June 2020: — Cumulative total of the number of registered applications minus the number of deregistrations. — Cumulative total of users notified by the application: the number of users notified by the application as risk contacts following exposure to COVID-19, since 2 June 2020. — Cumulative total of users reporting as COVID-19 cases per day: the number of users who reported as COVID-19 cases in the application, since 2 June 2020.

NoSQL Database Market was valued at $9.38 Billion in 2023, and is projected to reach $USD 86.48 Billion by 2032, at a CAGR of 28% from 2023 to 2032.

Our dataset provides detailed and precise insights into the business, commercial, and industrial aspects of any given area in the USA (Including Point of Interest (POI) Data and Foot Traffic. The dataset is divided into 150x150 sqm areas (geohash 7) and has over 50 variables. - Use it for different applications: Our combined dataset, which includes POI and foot traffic data, can be employed for various purposes. Different data teams use it to guide retailers and FMCG brands in site selection, fuel marketing intelligence, analyze trade areas, and assess company risk. Our dataset has also proven to be useful for real estate investment.- Get reliable data: Our datasets have been processed, enriched, and tested so your data team can use them more quickly and accurately.- Ideal for trainning ML models. The high quality of our geographic information layers results from more than seven years of work dedicated to the deep understanding and modeling of geospatial Big Data. Among the features that distinguished this dataset is the use of anonymized and user-compliant mobile device GPS location, enriched with other alternative and public data.- Easy to use: Our dataset is user-friendly and can be easily integrated to your current models. Also, we can deliver your data in different formats, like .csv, according to your analysis requirements. - Get personalized guidance: In addition to providing reliable datasets, we advise your analysts on their correct implementation.Our data scientists can guide your internal team on the optimal algorithms and models to get the most out of the information we provide (without compromising the security of your internal data).Answer questions like: - What places does my target user visit in a particular area? Which are the best areas to place a new POS?- What is the average yearly income of users in a particular area?- What is the influx of visits that my competition receives?- What is the volume of traffic surrounding my current POS?This dataset is useful for getting insights from industries like:- Retail & FMCG- Banking, Finance, and Investment- Car Dealerships- Real Estate- Convenience Stores- Pharma and medical laboratories- Restaurant chains and franchises- Clothing chains and franchisesOur dataset includes more than 50 variables, such as:- Number of pedestrians seen in the area.- Number of vehicles seen in the area.- Average speed of movement of the vehicles seen in the area.- Point of Interest (POIs) (in number and type) seen in the area (supermarkets, pharmacies, recreational locations, restaurants, offices, hotels, parking lots, wholesalers, financial services, pet services, shopping malls, among others). - Average yearly income range (anonymized and aggregated) of the devices seen in the area.Notes to better understand this dataset:- POI confidence means the average confidence of POIs in the area. In this case, POIs are any kind of location, such as a restaurant, a hotel, or a library. - Category confidences, for example"food_drinks_tobacco_retail_confidence" indicates how confident we are in the existence of food/drink/tobacco retail locations in the area. - We added predictions for The Home Depot and Lowe's Home Improvement stores in the dataset sample. These predictions were the result of a machine-learning model that was trained with the data. Knowing where the current stores are, we can find the most similar areas for new stores to open.How efficient is a Geohash?Geohash is a faster, cost-effective geofencing option that reduces input data load and provides actionable information. Its benefits include faster querying, reduced cost, minimal configuration, and ease of use.Geohash ranges from 1 to 12 characters. The dataset can be split into variable-size geohashes, with the default being geohash7 (150m x 150m).