At MFour, our Behavioral Data stands out for its uniqueness and depth of insights. What makes our data genuinely exceptional is the combination of several key factors:

• First-Party Opt-In Data: Our data is sourced directly from our opt-in panel of consumers who willingly participate in research and provide observed behaviors. This ensures the highest data quality and eliminates privacy concerns. CCPA compliant.

• Unparalleled Data Coverage: With access to 3B+ billion events, we have an extensive pool of participants who allow us to observe their brick + mortar location visitation, app + web smartphone usage, or both. This large-scale coverage provides robust and reliable insights.

• Our data is generally sourced through our Surveys On The Go (SOTG) mobile research app, where consumers are incentivized with cash rewards to participate in surveys and share their observed behaviors. This incentivized approach ensures a willing and engaged panel, leading to the highest-quality data.

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.

A dataset consisting of 751,500 English app reviews of 12 online shopping apps. The dataset was scraped from the internet using a python script. This ShoppingAppReviews dataset contains app reviews of the 12 most popular online shopping android apps: Alibaba, Aliexpress, Amazon, Daraz, eBay, Flipcart, Lazada, Meesho, Myntra, Shein, Snapdeal and Walmart. Each review entry contains many metadata like review score, thumbsupcount, review posting time, reply content etc. The dataset is organized in a zip file, under which there are 12 json files and 12 csv files for 12 online shopping apps. This dataset can be used to obtain valuable information about customers' feedback regarding their user experience of these financially important apps.

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.

WebApp

## Overview

WebApp is a dataset for object detection tasks - it contains SlackJira annotations for 202 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Austin Energy’s free, interactive web app allows customers to monitor their daily energy usage, view their bill history, and see a future forecast of their energy bill cost. They can also set alerts to warn them of an upcoming rate tier change.

Austin Energy Web App users can also download Green Button Data which can help them better understand their energy usage and take action towards savings. This data set shows the number of web app users by month.

Learn more about the app at http://powersaver.austinenergy.com/wps/portal/psp/residential/learn/free-home-energy-management-options/alerts-and-tips-help-you-manage-your-energy-costs

[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!

Context

Data-driven models help mobile app designers understand best practices and trends, and can be used to make predictions about design performance and support the creation of adaptive UIs. This paper presents Rico, the largest repository of mobile app designs to date, created to support five classes of data-driven applications: design search, UI layout generation, UI code generation, user interaction modeling, and user perception prediction. To create Rico, we built a system that combines crowdsourcing and automation to scalably mine design and interaction data from Android apps at runtime. The Rico dataset contains design data from more than 9.3k Android apps spanning 27 categories. It exposes visual, textual, structural, and interactive design properties of more than 66k unique UI screens. To demonstrate the kinds of applications that Rico enables, we present results from training an autoencoder for UI layout similarity, which supports query-by-example search over UIs.

Content

Rico was built by mining Android apps at runtime via human-powered and programmatic exploration. Like its predecessor ERICA, Rico’s app mining infrastructure requires no access to — or modification of — an app’s source code. Apps are downloaded from the Google Play Store and served to crowd workers through a web interface. When crowd workers use an app, the system records a user interaction trace that captures the UIs visited and the interactions performed on them. Then, an automated agent replays the trace to warm up a new copy of the app and continues the exploration programmatically, leveraging a content-agnostic similarity heuristic to efficiently discover new UI states. By combining crowdsourcing and automation, Rico can achieve higher coverage over an app’s UI states than either crawling strategy alone. In total, 13 workers recruited on UpWork spent 2,450 hours using apps on the platform over five months, producing 10,811 user interaction traces. After collecting a user trace for an app, we ran the automated crawler on the app for one hour.

Acknowledgements

UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN https://interactionmining.org/rico

Inspiration

The Rico dataset is large enough to support deep learning applications. We trained an autoencoder to learn an embedding for UI layouts, and used it to annotate each UI with a 64-dimensional vector representation encoding visual layout. This vector representation can be used to compute structurally — and often semantically — similar UIs, supporting example-based search over the dataset. To create training inputs for the autoencoder that embed layout information, we constructed a new image for each UI capturing the bounding box regions of all leaf elements in its view hierarchy, differentiating between text and non-text elements. Rico’s view hierarchies obviate the need for noisy image processing or OCR techniques to create these inputs.

The dataset includes the data obtained for the randomized controlled study conducted from September to October 2018 in Universidad Nacional de La Matanza, Buenos Aires, with the aim to confirm efficacy of the web-based platform e-status in Probability and Statistics courses.

Results appear in the manuscript Measuring the effectiveness of online problem solving for improving academic performance in a probability course, from the same authors. Hopefully published in the coming months.

Students data have been anonymised, by removing ID numbers. Moreover, gender of students has been deleted, and age categorized into one of three classes (until 22 years, between 23 and 25, more than 25).

This dataset is having data of customers who buys clothes online. The store offers in-store style and clothing advice sessions. Customers come in to the store, have sessions/meetings with a personal stylist, then they can go home and order either on a mobile app or website for the clothes they want.

The company is trying to decide whether to focus their efforts on their mobile app experience or their website.

This dataset provides monthly volumes at the national level for federal fiscal years 2016 on for Internet Change of Address. The dataset includes only Internet Change of Address transactions. It should be noted that, in addition to using our online Change of Address application, the public might change an address by calling our 800 number, visiting a field office, or mailing us the request. This dataset pertains only to the online alternative.

In 2022, over half of the web traffic was accessed through mobile devices. By reducing the energy consumption of mobile web apps, we can not only extend the battery life of our devices, but also make a significant contribution to energy conservation efforts. For example, if we could save only 5% of the energy used by web apps, we estimate that it would be enough to shut down one of the nuclear reactors in Fukushima. This paper presents a comprehensive overview of energy-saving experiments and related approaches for mobile web apps, relevant for researchers and practitioners. To achieve this objective, we conducted a systematic literature review and identified 44 primary studies for inclusion. Through the mapping and analysis of scientific papers, this work contributes: (1) an overview of the energy-draining aspects of mobile web apps, (2) a comprehensive description of the methodology used for the energy-saving experiments, and (3) a categorization and synthesis of various energy-saving approaches.

Percentage of Internet users who have experienced selected personal effects in their life because of the Internet and the use of social networking websites or apps, during the past 12 months.

In today's digital landscape, data transparency and compliance are paramount. Organizations across industries are striving to maintain trust and adhere to regulations governing data privacy and security. To support these efforts, we present our comprehensive Ads.txt and App-Ads.txt dataset.

Key Benefits of Our Dataset:

• Coverage: Our dataset offers a comprehensive view of the Ads.txt and App-Ads.txt files, providing valuable information about publishers, advertisers, and the relationships between them. You gain a holistic understanding of the digital advertising ecosystem.
• Multiple Data Formats: We understand that flexibility is essential. Our dataset is available in multiple formats, including .CSV, .JSON, and more. Choose the format that best suits your data processing needs.
• Global Scope: Whether your business operates in a single country or spans multiple continents, our dataset is tailored to meet your needs. It provides data from various countries, allowing you to analyze regional trends and compliance.
  • Top-Quality Data: Quality matters. Our dataset is meticulously curated and continuously updated to deliver the most accurate and reliable information. Trust in the integrity of your data for critical decision-making.
  • Seamless Integration: We've designed our dataset to seamlessly integrate with your existing systems and workflows. No disruptions—just enhanced compliance and efficiency.

The Power of Ads.txt & App-Ads.txt: Ads.txt (Authorized Digital Sellers) and App-Ads.txt (Authorized Sellers for Apps) are industry standards developed by the Interactive Advertising Bureau (IAB) to increase transparency and combat ad fraud. These files specify which companies are authorized to sell digital advertising inventory on a publisher's website or app. Understanding and maintaining these files is essential for data compliance and the prevention of unauthorized ad sales.

How Can You Benefit? - Data Compliance: Ensure that your organization adheres to industry standards and regulations by monitoring Ads.txt and App-Ads.txt files effectively. - Ad Fraud Prevention: Identify unauthorized sellers and take action to prevent ad fraud, ultimately protecting your revenue and brand reputation. - Strategic Insights: Leverage the data in these files to gain insights into your competitors, partners, and the broader digital advertising landscape. - Enhanced Decision-Making: Make data-driven decisions with confidence, armed with accurate and up-to-date information about your advertising partners. - Global Reach: If your operations span the globe, our dataset provides insights into the Ads.txt and App-Ads.txt files of publishers worldwide.

Multiple Data Formats for Your Convenience: - CSV (Comma-Separated Values): A widely used format for easy data manipulation and analysis in spreadsheets and databases. - JSON (JavaScript Object Notation): Ideal for structured data and compatibility with web applications and APIs. - Other Formats: We understand that different organizations have different preferences and requirements. Please inquire about additional format options tailored to your needs.

Data That You Can Trust:

We take data quality seriously. Our team of experts curates and updates the dataset regularly to ensure that you receive the most accurate and reliable information available. Your confidence in the data is our top priority.

Seamless Integration:

Integrate our Ads.txt and App-Ads.txt dataset effortlessly into your existing systems and processes. Our goal is to enhance your compliance efforts without causing disruptions to your workflow.

In Conclusion:

Transparency and compliance are non-negotiable in today's data-driven world. Our Ads.txt and App-Ads.txt dataset empowers you with the knowledge and tools to navigate the complexities of the digital advertising ecosystem while ensuring data compliance and integrity. Whether you're a Data Protection Officer, a data compliance professional, or a business leader, our dataset is your trusted resource for maintaining data transparency and safeguarding your organization's reputation and revenue.

Get Started Today:

Don't miss out on the opportunity to unlock the power of data transparency and compliance. Contact us today to learn more about our Ads.txt and App-Ads.txt dataset, available in multiple formats and tailored to your specific needs. Join the ranks of organizations worldwide that trust our dataset for a compliant and transparent future.

This dataset tracks the updates made on the dataset "Fee-for-Service Web App Quick User Guide" as a repository for previous versions of the data and metadata.

This dataset tracks the updates made on the dataset "Fee-for-Service Web App Quick User Guide" as a repository for previous versions of the data and metadata.

Add a description of the item.

Use multiple paragraphs if necessary.

Metadata

Percentage of Canadians who have experienced selected personal effects in their life because of the Internet and the use of social networking websites or apps, during the past 12 months.

The research examines transit agencies’ open data provision, transit agencies’ relationship with developers of transit apps as open data users, and transit apps as open data products in the context of legislated disability regulations in public transportation. Our investigation focused on transit agencies of 50 of the most populous cities in the United States. We used data collected from transit agencies websites, open data portals, smartphone app distribution platforms such as Google Play and the App Store, and the open web. Description of each dataset is available in the document titled "Data Description".

Dataset

This dataset was created by Varun Bagga

Contents

This web app supports the Tempe Public Art. The app allows for the navigation of Tempe Public Art locations and artist information. Points on this web map helps visualize locations of Tempe's diverse collection of permanent and temporary public art. Tempe Public Art promotes artistic expression, bringing people together to strengthen Tempe's sense of community and place.This web app is supported by the Tempe Public Art Map: Public Art MapPublic Art Homepage: Tempe Public Art