Between January and July 2024, Google received ****** requests for disclosure of user information from the United States federal agencies and courts. This is a slight decrease in comparison to the second half of 2023, in which over ****** requests were issued.

In the first half of 2024, Google received over 82,000 requests for disclosure of user information from the U.S. federal agencies and other government entities. The Indian government ranked second by the number of requests about user information disclosure sent to Google, followed by Germany.

Introduction

Google Drive Statistics: Google Drive, which was launched by Google in 2012, currently serves as the biggest pillar of cloud storage and collaboration. It has enabled users to access and keep files on a single platform that is made portable and synchronised across devices. By the end of 2024, its adoption rates will reflect users' inclination toward the integral roles it has assumed in their personal and professional lives.

The article discusses the Google Drive statistics, including user engagement and market presence, along with data security and performance in terms of finances.

In the second half of 2023, 82 percent of user data requests sent to Google by federal agencies and governments worldwide ended up with the disclosure of some information. In the first half of 2019, this figure stood at 73 percent.

Google Play Store dataset to explore detailed information about apps, including ratings, descriptions, updates, and developer details. Popular use cases include app performance analysis, market research, and consumer behavior insights.

Use our Google Play Store dataset to explore detailed information about apps available on the platform, including app titles, developers, monetization features, user ratings, reviews, and more. This dataset also includes data on app descriptions, safety measures, download counts, recent updates, and compatibility, providing a complete overview of app performance and features.

Tailored for app developers, marketers, and researchers, this dataset offers valuable insights into user preferences, app trends, and market dynamics. Whether you're optimizing app development, conducting competitive analysis, or tracking app performance, the Google Play Store dataset is an essential resource for making data-driven decisions in the mobile app ecosystem.

Dataset Features

• url: The URL link to the app’s detail page on the Google Play Store.
• title: The name of the application.
• developer: The developer or company behind the app.
• monetization_features: Information regarding how the app generates revenue (e.g., in-app purchases, ads).
• images: Links or references to images associated with the app.
• about: Details or a summary description of the app.
• data_safety: Information regarding data safety and privacy practices.
• rating: The overall rating of the app provided by its users.
• number_of_reviews: The total count of user reviews received.
• star_reviews: A breakdown of reviews by star ratings.
• reviews: Reviews and user feedback about the app.
• what_new: Information on the latest updates or features added to the app.
• more_by_this_developer: Other apps by the same developer.
• content_rating: The content rating which guides suitability based on user age.
• downloads: The download count or range indicating the app’s popularity.
• country: The country associated with the app listing.
• app_category: The category or genre under which the app is classified.

Distribution

• Data Volume: 17 Columns and 65.54M Rows
• Format: CSV

Usage

This dataset is ideal for a variety of applications:

• App Market Analysis: Enables market researchers to extract insights on app popularity, engagement, and trends across different categories.
• Machine Learning: Can be used by data scientists to build recommendation engines or sentiment analysis models based on app review data.
• User Behavior Studies: Facilitates academic or industrial research into user preferences and behavior with respect to mobile applications.

Coverage

• Geographic Coverage: global.

License

CUSTOM Please review the respective licenses below: 1. Data Provider's License - Bright Data Master Service Agreement

Who Can Use It

• Data Scientists: To train machine learning models for app popularity prediction, sentiment analysis, or recommendation systems.
• Researchers: For academic or scientific studies into market trends, consumer behavior, and app performance analysis.
• Businesses: For strategic analysis, developing market insights, or enhancing app development and user engagement strategies.

Suggested Dataset Name

1. Play store Insights
2. Android App Scope
3. Market Analytics
4. Play Store Metrics Vault

5. AppTrend360: Google Play Edition

Pricing

Based on Delivery frequency

~Up to $0.0025 per record. Min order $250

Approximately 10M new records are added each month. Approximately 13.8M records are updated each month. Get the complete dataset each delivery, including all records. Retrieve only the data you need with the flexibility to set Smart Updates.

• Monthly

New snapshot each month, 12 snapshots/year Paid monthly

• Quarterly

New snapshot each quarter, 4 snapshots/year Paid quarterly

• Bi-annual

New snapshot every 6 months, 2 snapshots/year Paid twice-a-year

• One-time purchase

New snapshot one-time delivery Paid once

In the U.S. public companies, certain insiders and broker-dealers are required to regularly file with the SEC. The SEC makes this data available online for anybody to view and use via their Electronic Data Gathering, Analysis, and Retrieval (EDGAR) database. The SEC updates this data every quarter going back to January, 2009. To aid analysis a quick summary view of the data has been created that is not available in the original dataset. The quick summary view pulls together signals into a single table that otherwise would have to be joined from multiple tables and enables a more streamlined user experience. This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets.詳細

Introduction

Google Fit Statistics: Google Fit, since its launch in 2014, formed the major platform of fitness and health for Google, enabling users to track several health metrics and pool data from several fitness apps and devices. In its continued evolution were added unique features like Heart Points, developed under the auspices of WHO and AHA, aimed at inducing physical activity.

Changes of much significance are due in 2024, marking a change in Google's very own approach to health data-keeping. In this article, we will enclose the Google Fit statistics.

Author: Víctor Yeste. Universitat Politècnica de Valencia.The object of this study is the design of a cybermetric methodology whose objectives are to measure the success of the content published in online media and the possible prediction of the selected success variables.In this case, due to the need to integrate data from two separate areas, such as web publishing and the analysis of their shares and related topics on Twitter, has opted for programming as you access both the Google Analytics v4 reporting API and Twitter Standard API, always respecting the limits of these.The website analyzed is hellofriki.com. It is an online media whose primary intention is to solve the need for information on some topics that provide daily a vast number of news in the form of news, as well as the possibility of analysis, reports, interviews, and many other information formats. All these contents are under the scope of the sections of cinema, series, video games, literature, and comics.This dataset has contributed to the elaboration of the PhD Thesis:Yeste Moreno, VM. (2021). Diseño de una metodología cibermétrica de cálculo del éxito para la optimización de contenidos web [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/176009Data have been obtained from each last-minute news article published online according to the indicators described in the doctoral thesis. All related data are stored in a database, divided into the following tables:tesis_followers: User ID list of media account followers.tesis_hometimeline: data from tweets posted by the media account sharing breaking news from the web.status_id: Tweet IDcreated_at: date of publicationtext: content of the tweetpath: URL extracted after processing the shortened URL in textpost_shared: Article ID in WordPress that is being sharedretweet_count: number of retweetsfavorite_count: number of favoritestesis_hometimeline_other: data from tweets posted by the media account that do not share breaking news from the web. Other typologies, automatic Facebook shares, custom tweets without link to an article, etc. With the same fields as tesis_hometimeline.tesis_posts: data of articles published by the web and processed for some analysis.stats_id: Analysis IDpost_id: Article ID in WordPresspost_date: article publication date in WordPresspost_title: title of the articlepath: URL of the article in the middle webtags: Tags ID or WordPress tags related to the articleuniquepageviews: unique page viewsentrancerate: input ratioavgtimeonpage: average visit timeexitrate: output ratiopageviewspersession: page views per sessionadsense_adunitsviewed: number of ads viewed by usersadsense_viewableimpressionpercent: ad display ratioadsense_ctr: ad click ratioadsense_ecpm: estimated ad revenue per 1000 page viewstesis_stats: data from a particular analysis, performed at each published breaking news item. Fields with statistical values can be computed from the data in the other tables, but total and average calculations are saved for faster and easier further processing.id: ID of the analysisphase: phase of the thesis in which analysis has been carried out (right now all are 1)time: "0" if at the time of publication, "1" if 14 days laterstart_date: date and time of measurement on the day of publicationend_date: date and time when the measurement is made 14 days latermain_post_id: ID of the published article to be analysedmain_post_theme: Main section of the published article to analyzesuperheroes_theme: "1" if about superheroes, "0" if nottrailer_theme: "1" if trailer, "0" if notname: empty field, possibility to add a custom name manuallynotes: empty field, possibility to add personalized notes manually, as if some tag has been removed manually for being considered too generic, despite the fact that the editor put itnum_articles: number of articles analysednum_articles_with_traffic: number of articles analysed with traffic (which will be taken into account for traffic analysis)num_articles_with_tw_data: number of articles with data from when they were shared on the media’s Twitter accountnum_terms: number of terms analyzeduniquepageviews_total: total page viewsuniquepageviews_mean: average page viewsentrancerate_mean: average input ratioavgtimeonpage_mean: average duration of visitsexitrate_mean: average output ratiopageviewspersession_mean: average page views per sessiontotal: total of ads viewedadsense_adunitsviewed_mean: average of ads viewedadsense_viewableimpressionpercent_mean: average ad display ratioadsense_ctr_mean: average ad click ratioadsense_ecpm_mean: estimated ad revenue per 1000 page viewsTotal: total incomeretweet_count_mean: average incomefavorite_count_total: total of favoritesfavorite_count_mean: average of favoritesterms_ini_num_tweets: total tweets on the terms on the day of publicationterms_ini_retweet_count_total: total retweets on the terms on the day of publicationterms_ini_retweet_count_mean: average retweets on the terms on the day of publicationterms_ini_favorite_count_total: total of favorites on the terms on the day of publicationterms_ini_favorite_count_mean: average of favorites on the terms on the day of publicationterms_ini_followers_talking_rate: ratio of followers of the media Twitter account who have recently published a tweet talking about the terms on the day of publicationterms_ini_user_num_followers_mean: average followers of users who have spoken of the terms on the day of publicationterms_ini_user_num_tweets_mean: average number of tweets published by users who spoke about the terms on the day of publicationterms_ini_user_age_mean: average age in days of users who have spoken of the terms on the day of publicationterms_ini_ur_inclusion_rate: URL inclusion ratio of tweets talking about terms on the day of publicationterms_end_num_tweets: total tweets on terms 14 days after publicationterms_ini_retweet_count_total: total retweets on terms 14 days after publicationterms_ini_retweet_count_mean: average retweets on terms 14 days after publicationterms_ini_favorite_count_total: total bookmarks on terms 14 days after publicationterms_ini_favorite_count_mean: average of favorites on terms 14 days after publicationterms_ini_followers_talking_rate: ratio of media Twitter account followers who have recently posted a tweet talking about the terms 14 days after publicationterms_ini_user_num_followers_mean: average followers of users who have spoken of the terms 14 days after publicationterms_ini_user_num_tweets_mean: average number of tweets published by users who have spoken about the terms 14 days after publicationterms_ini_user_age_mean: the average age in days of users who have spoken of the terms 14 days after publicationterms_ini_ur_inclusion_rate: URL inclusion ratio of tweets talking about terms 14 days after publication.tesis_terms: data of the terms (tags) related to the processed articles.stats_id: Analysis IDtime: "0" if at the time of publication, "1" if 14 days laterterm_id: Term ID (tag) in WordPressname: Name of the termslug: URL of the termnum_tweets: number of tweetsretweet_count_total: total retweetsretweet_count_mean: average retweetsfavorite_count_total: total of favoritesfavorite_count_mean: average of favoritesfollowers_talking_rate: ratio of followers of the media Twitter account who have recently published a tweet talking about the termuser_num_followers_mean: average followers of users who were talking about the termuser_num_tweets_mean: average number of tweets published by users who were talking about the termuser_age_mean: average age in days of users who were talking about the termurl_inclusion_rate: URL inclusion ratio

This dataset is a collection of user reviews for various Google Apps available on the Play Store. It provides detailed insights into user feedback, ratings, and engagement with different applications. The dataset's primary purpose is to offer a rich resource for understanding user sentiment, identifying app performance issues, and tracking user satisfaction over time. It is a valuable asset for analytics and natural language processing tasks related to app reviews.

Columns

• reviewId: A unique identifier for each individual user review.
• userName: The name of the user who submitted the review.
• userImage: The URL pointing to the user's profile image.
• content: The textual review provided by the user about the app.
• score: The numerical rating given by the user for the app, typically on a scale of 1 to 5.
• thumbsUpCount: The total number of likes or "thumbs up" received by that specific review.
• reviewCreatedVersion: The version of the app that was being reviewed at the time the review was created.
• at: The date and time when the user's review was created.
• replyContent: The textual content of the reply provided by the app developer to the user's review. A significant portion of reviews do not have a developer reply.
• repliedAt: The date and time when the developer's reply was issued. Many entries in this column are null, indicating no developer response.

Distribution

The dataset contains over 90,000 app reviews. The score column shows a distribution across ratings, with substantial counts for scores like 1.00-1.20, 2.00-2.20, 3.00-3.20, 4.00-4.20, and 4.80-5.00. For thumbsUpCount, the majority of reviews have a relatively low number of likes (0-720), but there are instances with significantly higher counts, reaching up to over 14,000 likes. The reviewCreatedVersion column shows a variety of app versions, with some being more frequently reviewed than others. Review creation dates span a period from April 2014 to February 2021, with a notable increase in review volume towards the later years, particularly between May 2020 and February 2021.

Usage

This dataset is ideal for: * Sentiment analysis of app reviews. * Natural Language Processing (NLP) tasks, such as topic modelling, text classification, and entity recognition. * App performance monitoring and identifying user pain points. * Market research on user satisfaction and trends in app usage. * Developing AI and Machine Learning models for predicting app ratings or automatically classifying feedback.

Coverage

The dataset offers global coverage for app reviews. The time range for review creation spans from 10th April 2014 to 4th February 2021. While developer replies are included, the data on repliedAt primarily indicates a single latest date (4th February 2021) with the majority being null, suggesting that developer reply timestamps are not as broadly distributed across the dataset as review creation times.

License

CC0

Who Can Use It

• App Developers: To understand user feedback, identify bugs, and improve app features.
• Data Analysts: For trends analysis, user behaviour insights, and reporting.
• Researchers: In fields like computer science, internet studies, and data analytics for academic studies on online reviews.
• Machine Learning Engineers: To train models for sentiment analysis, user support automation, or content moderation.
• Product Managers: To gather insights for product iteration and strategic planning.

Dataset Name Suggestions

• Google Play Store App Reviews
• Play Store User Feedback
• Google Apps Ratings and Reviews
• Mobile App Review Data
• Playstore Review Analytics Data

Attributes

Original Data Source: Google Apps Playstore Reviews

In the U.S. public companies, certain insiders and broker-dealers are required to regularly file with the SEC. The SEC makes this data available online for anybody to view and use via their Electronic Data Gathering, Analysis, and Retrieval (EDGAR) database. The SEC updates this data every quarter going back to January, 2009. To aid analysis a quick summary view of the data has been created that is not available in the original dataset. The quick summary view pulls together signals into a single table that otherwise would have to be joined from multiple tables and enables a more streamlined user experience. This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets.Dowiedz się więcej

Google Suite is an umbrella Information System by which USAID receives multiple Google services per USAID's subscription contract. Business services include but are not limited to: Business email through Gmail, Video and voice conferencing, Secure team messaging, Shared calendars, Documents, spreadsheets, and presentations, Unlimited cloud storage, and Smart search across G Suite with Cloud Search. Security and administration controls include: Control how long your email messages and on-the-record chats are retained. Specify policies for your entire domain or based on organizational units, date ranges, and specific terms. Archive and set retention policies for emails and chats, Security center for G Suite, eDiscovery for emails, chats, and files, Audit reports to track user activity, Data loss prevention for Gmail, Data loss prevention for Drive Hosted S/MIME for Gmail, Integrate Gmail with compliant third-party archiving tools, Enterprise-grade access control with security key enforcement, and Gmail log analysis in BigQuery

In the U.S. public companies, certain insiders and broker-dealers are required to regularly file with the SEC. The SEC makes this data available online for anybody to view and use via their Electronic Data Gathering, Analysis, and Retrieval (EDGAR) database. The SEC updates this data every quarter going back to January, 2009. To aid analysis a quick summary view of the data has been created that is not available in the original dataset. The quick summary view pulls together signals into a single table that otherwise would have to be joined from multiple tables and enables a more streamlined user experience. This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets.Scopri di più

Google Workspace Market Outlook

The global market size of Google Workspace was estimated to be around USD 3.2 billion in 2023 and is projected to reach approximately USD 9.8 billion by 2032, growing at a compound annual growth rate (CAGR) of 13.1% during the forecast period. The growth of the Google Workspace market is largely driven by the increasing trend of remote work, the need for streamlined business communication, and the growing adoption of cloud-based solutions.

One of the primary growth factors for the Google Workspace market is the increasing trend towards remote and hybrid work models. The COVID-19 pandemic had a significant impact on how businesses operate, pushing a vast number of organizations to adopt remote working solutions. Google Workspace offers a comprehensive suite of productivity tools that enable seamless collaboration and communication among remote teams. This shift is not merely a temporary change but is expected to persist, thereby driving sustained demand for cloud-based productivity suites like Google Workspace.

Additionally, the emphasis on digital transformation across various industries is another crucial driver. Companies are increasingly moving away from traditional paper-based workflows and manual processes to digital solutions that offer greater efficiency and scalability. Google Workspace provides an integrated platform that supports this transformation by offering tools for document creation, storage, and sharing, all within a secure and accessible environment. This transition is particularly attractive for small and medium enterprises (SMEs) looking to scale operations without a significant investment in IT infrastructure.

Furthermore, the growing emphasis on data security and compliance is propelling the adoption of Google Workspace. Enterprises today are highly conscious of the need to secure their data and comply with industry regulations. Google Workspace addresses these concerns with robust security features, including data encryption, two-factor authentication, and administrative controls. These features make it a favored choice among organizations that prioritize data security and regulatory compliance, adding another layer to its market growth.

From a regional perspective, North America holds a significant share of the Google Workspace market, driven by high adoption rates of cloud solutions and advanced IT infrastructure. However, the Asia Pacific region is expected to exhibit the highest growth rate during the forecast period. Factors such as increasing digitalization, economic growth, and rising awareness about the benefits of cloud-based productivity tools are contributing to the expansion of the Google Workspace market in this region.

Component Analysis

The Google Workspace suite comprises various components, including Gmail, Google Drive, Google Docs, Google Meet, Google Calendar, and others. Each of these components plays a critical role in driving the market growth, catering to different aspects of business productivity and communication. Gmail, for instance, remains one of the most widely used email services globally, known for its user-friendly interface and robust spam filters. Its integration with other Google Workspace tools enhances its functionality, making it a cornerstone of the suite's offering.

Google Drive is another crucial component, offering cloud storage solutions that enable users to store, share, and access files from anywhere. The demand for cloud storage solutions has surged, driven by the need for remote access and data backup. Google Drive's integration with Google Docs, Sheets, and Slides allows for real-time collaboration, which is a significant selling point for enterprises looking to improve team productivity.

Google Docs, Sheets, and Slides form the core of Google Workspace's productivity tools, allowing users to create and edit documents, spreadsheets, and presentations in real-time. These tools offer a collaborative environment where multiple users can work on the same document simultaneously, significantly enhancing workflow efficiency. The ease of use and accessibility of these tools make them popular choices for businesses of all sizes.

Google Meet has seen a substantial increase in usage, particularly in light of the COVID-19 pandemic. As businesses shifted to remote work, the need for reliable video conferencing solutions became paramount. Google Meet offers high-quality video and audio, along with features like screen sharing and meeting recording, making it a robust tool for v

Comprehensive dataset analyzing psychological patterns, cognitive triggers, and behavioral preferences of Google Maps users in Colorado Springs, including demographic psychology, seasonal patterns, and decision-making frameworks.

This is the dataset used for paper: "A Recommender System of Buggy App Checkers for App Store Moderators", published on the International Conference on Mobile Software Engineering and Systems (MOBILESoft) in 2015.

Dataset Collection We built a dataset that consists of a random sample of Android app metadata and user reviews available on the Google Play Store on January and March 2014. Since the Google Play Store is continuously evolving (adding, removing and/or updating apps), we updated the dataset twice. The dataset D1 contains available apps in the Google Play Store in January 2014. Then, we created a new snapshot (D2) of the Google Play Store in March 2014.

The apps belong to the 27 different categories defined by Google (at the time of writing the paper), and the 4 predefined subcategories (free, paid, new_free, and new_paid). For each category-subcategory pair (e.g. tools-free, tools-paid, sports-new_free, etc.), we collected a maximum of 500 samples, resulting in a median number of 1.978 apps per category.

For each app, we retrieved the following metadata: name, package, creator, version code, version name, number of downloads, size, upload date, star rating, star counting, and the set of permission requests.

In addition, for each app, we collected up to a maximum of the latest 500 reviews posted by users in the Google Play Store. For each review, we retrieved its metadata: title, description, device, and version of the app. None of these fields were mandatory, thus several reviews lack some of these details. From all the reviews attached to an app, we only considered the reviews associated with the latest version of the app —i.e., we discarded unversioned and old-versioned reviews. Thus, resulting in a corpus of 1,402,717 reviews (2014 Jan.).

Dataset Stats Some stats about the datasets:

• D1 (Jan. 2014) contains 38,781 apps requesting 7,826 different permissions, and 1,402,717 user reviews.

• D2 (Mar. 2014) contains 46,644 apps and 9,319 different permission requests, and 1,361,319 user reviews.

Additional stats about the datasets are available here.

Dataset Description To store the dataset, we created a graph database with Neo4j. This dataset therefore consists of a graph describing the apps as nodes and edges. We chose a graph database because the graph visualization helps to identify connections among data (e.g., clusters of apps sharing similar sets of permission requests).

In particular, our dataset graph contains six types of nodes: - APP nodes containing metadata of each app, - PERMISSION nodes describing permission types, - CATEGORY nodes describing app categories, - SUBCATEGORY nodes describing app subcategories, - USER_REVIEW nodes storing user reviews. - TOPIC topics mined from user reviews (using LDA).

Furthermore, there are five types of relationships between APP nodes and each of the remaining nodes:

• USES_PERMISSION relationships between APP and PERMISSION nodes
• HAS_REVIEW between APP and USER_REVIEW nodes
• HAS_TOPIC between USER_REVIEW and TOPIC nodes
• BELONGS_TO_CATEGORY between APP and CATEGORY nodes
• BELONGS_TO_SUBCATEGORY between APP and SUBCATEGORY nodes

Dataset Files Info

Neo4j 2.0 Databases

googlePlayDB1-Jan2014_neo4j_2_0.rar

googlePlayDB2-Mar2014_neo4j_2_0.rar We provide two Neo4j databases containing the 2 snapshots of the Google Play Store (January and March 2014). These are the original databases created for the paper. The databases were created with Neo4j 2.0. In particular with the tool version 'Neo4j 2.0.0-M06 Community Edition' (latest version available at the time of implementing the paper in 2014).

Neo4j 3.5 Databases

googlePlayDB1-Jan2014_neo4j_3_5_28.rar

googlePlayDB2-Mar2014_neo4j_3_5_28.rar Currently, the version Neo4j 2.0 is deprecated and it is not available for download in the official Neo4j Download Center. We have migrated the original databases (Neo4j 2.0) to Neo4j 3.5.28. The databases can be opened with the tool version: 'Neo4j Community Edition 3.5.28'. The tool can be downloaded from the official Neo4j Donwload page.

  In order to open the databases with more recent versions of Neo4j, the databases must be first migrated to the corresponding version. Instructions about the migration process can be found in the Neo4j Migration Guide.

  First time the Neo4j database is connected, it could request credentials. The username and pasword are: neo4j/neo4j

OpenWeb Ninja's Google Images Data (Google SERP Data) API provides real-time image search capabilities for images sourced from all public sources on the web.

The API enables you to search and access more than 100 billion images from across the web including advanced filtering capabilities as supported by Google Advanced Image Search. The API provides Google Images Data (Google SERP Data) including details such as image URL, title, size information, thumbnail, source information, and more data points. The API supports advanced filtering and options such as file type, image color, usage rights, creation time, and more. In addition, any Advanced Google Search operators can be used with the API.

OpenWeb Ninja's Google Images Data & Google SERP Data API common use cases:

• Creative Media Production: Enhance digital content with a vast array of real-time images, ensuring engaging and brand-aligned visuals for blogs, social media, and advertising.

• AI Model Enhancement: Train and refine AI models with diverse, annotated images, improving object recognition and image classification accuracy.

• Trend Analysis: Identify emerging market trends and consumer preferences through real-time visual data, enabling proactive business decisions.

• Innovative Product Design: Inspire product innovation by exploring current design trends and competitor products, ensuring market-relevant offerings.

• Advanced Search Optimization: Improve search engines and applications with enriched image datasets, providing users with accurate, relevant, and visually appealing search results.

OpenWeb Ninja's Annotated Imagery Data & Google SERP Data Stats & Capabilities:

• 100B+ Images: Access an extensive database of over 100 billion images.

• Images Data from all Public Sources (Google SERP Data): Benefit from a comprehensive aggregation of image data from various public websites, ensuring a wide range of sources and perspectives.

• Extensive Search and Filtering Capabilities: Utilize advanced search operators and filters to refine image searches by file type, color, usage rights, creation time, and more, making it easy to find exactly what you need.

• Rich Data Points: Each image comes with more than 10 data points, including URL, title (annotation), size information, thumbnail, and source information, providing a detailed context for each image.

Any work using this dataset should cite the following paper:

In the first half of 2024, 86 percent of user data requests sent to Google by government agencies in the United States resulted in the disclosure of some data. Overall, the percentage of user data requests in the U.S. with some disclosure has increased in recent years.

In the second half of 2023, Google received more than 216 thousand requests for disclosure of user information from federal agencies and governments worldwide. In the same period, the number of accounts subject to those requests was approximately 441 thousand.

This dataset contains current and historical demographic data on Google's workforce since the company began publishing diversity data in 2014. It includes data collected for government reporting and voluntary employee self-identification globally relating to hiring, retention, and representation categorized by race, gender, sexual orientation, gender identity, disability status, and military status. In some instances, the data is limited due to various government policies around the world and the desire to protect Googler confidentiality. All data in this dataset will be updated yearly upon publication of Google’s Diversity Annual Report . Google uses this data to inform its diversity, equity, and inclusion work. More information on our methodology can be found in the Diversity Annual Report. This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery .