In July 2024, YouTube was the most popular Google app, generating approximately 13.65 million downloads from iOS and Google Play Store users during the month. Second-ranked YouTube Kids was downloaded 12.22 million times from global users. Mobile navigation service Google Maps ranked third, with approximately 11.9 million downloads worldwide during the examined period.

This is the second version of the Google Landmarks dataset (GLDv2), which contains images annotated with labels representing human-made and natural landmarks. The dataset can be used for landmark recognition and retrieval experiments. This version of the dataset contains approximately 5 million images, split into 3 sets of images: train, index and test

In 2024, India was the leading global market for the Google Play Store, with users in the region generating over ** billion downloads. Brazil followed, as Android users generated approximately ***** billion app downloads from the Google Play Store during the examined period. Despite being a strong market for iOS devices, the Google Play Store generated over ***** billion app downloads in the United States in 2024.

The dataset used in our article was uploaded on our Google Drive account and the download link is provided as follows:https://drive.google.com/drive/folders/1-tXCaPtv0vupjXeLvrEX2cGt3tCOqyiv?usp=sharingSix compressed files (named as clinical.zip, COVID-19.zip, CXR.zip, Influenza.zip, Normal.zip and Pneumonia.zip) were included in the download address with a total size of 31.7GB.The sample of this is study is a multi-modal dataset consisting of X-ray image data (X-data), CT image data (CT-data) and clinical indicators data (Clinical-data).The X-data of COVID-19 cases collected from the CCD contained 212 patients diagnosed with COVID-19. We also collected 5,100 normal cases and 3,100 pneumonia cases from the RSNA. In addition, The X-data collected from the Youan hospital contained 45 cases diagnosed with COVID-19, 503 normal cases, 435 cases diagnosed with pneumonia(not COVID-19 patients), and 145 cases diagnosed as influenza.We collected CT-data of 120 normal cases from the LUNA-16. We also collected the CT-data of 215 pneumonia cases from the ICNP. The CT-data collected from the Youan hospital contained 95 patients diagnosed with COVID-19, 50 patients diagnosed with influenza and 215 patients diagnosed with pneumonia.The Clinical-data contained 95 clinical indicators data pairs of COVID-19 (369 images of the lesion area and 95×5 clinical indicators).The persistent web links for the four public data repositories are listed as below:

CCD: (https://github.com/ieee8023/covid-chestxray-dataset)

RSNA: (https://www.kaggle.com/c/rsna-pneumonia-detection-challenge)

LUNA16: (https://luna16.grand-challenge.org/Data/)

ICNP: (https://data.mendeley.com/datasets/kk6y7nnbfs/1)

You can check the fields description in the documentation: current Full database: https://docs.dataforseo.com/v3/databases/google/full/?bash; Historical Full database: https://docs.dataforseo.com/v3/databases/google/history/full/?bash.

Full Google Database is a combination of the Advanced Google SERP Database and Google Keyword Database.

Google SERP Database offers millions of SERPs collected in 67 regions with most of Google’s advanced SERP features, including featured snippets, knowledge graphs, people also ask sections, top stories, and more.

Google Keyword Database encompasses billions of search terms enriched with related Google Ads data: search volume trends, CPC, competition, and more.

This database is available in JSON format only.

You don’t have to download fresh data dumps in JSON – we can deliver data straight to your storage or database. We send terrabytes of data to dozens of customers every month using Amazon S3, Google Cloud Storage, Microsoft Azure Blob, Eleasticsearch, and Google Big Query. Let us know if you’d like to get your data to any other storage or database.

App Download Key StatisticsApp and Game DownloadsiOS App and Game DownloadsGoogle Play App and Game DownloadsGame DownloadsiOS Game DownloadsGoogle Play Game DownloadsApp DownloadsiOS App...

Google

## Overview

Google is a dataset for object detection tasks - it contains Botella annotations for 491 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).

This dataset encompasses a wide-ranging collection of Google Play applications, providing a holistic view of the diverse ecosystem within the platform. It includes information on various attributes such as the title, developer, monetization features, images, app descriptions, data safety measures, user ratings, number of reviews, star rating distributions, user feedback, recent updates, related applications by the same developer, content ratings, estimated downloads, and timestamps. By aggregating this data, the dataset offers researchers, developers, and analysts an extensive resource to explore and analyze trends, patterns, and dynamics within the Google Play Store. Researchers can utilize this dataset to conduct comprehensive studies on user behavior, market trends, and the impact of various factors on app success. Developers can leverage the insights derived from this dataset to inform their app development strategies, improve user engagement, and optimize monetization techniques. Analysts can employ the dataset to identify emerging trends, assess the performance of different categories of applications, and gain valuable insights into consumer preferences. Overall, this dataset serves as a valuable tool for understanding the broader landscape of the Google Play Store and unlocking actionable insights for various stakeholders in the mobile app industry.

This dataset contains two tables: creative_stats and removed_creative_stats. The creative_stats table contains information about advertisers that served ads in the European Economic Area or Turkey: their legal name, verification status, disclosed name, and location. It also includes ad specific information: impression ranges per region (including aggregate impressions for the European Economic Area), first shown and last shown dates, which criteria were used in audience selection, the format of the ad, the ad topic and whether the ad is funded by Google Ad Grants program. A link to the ad in the Google Ads Transparency Center is also provided. The removed_creative_stats table contains information about ads that served in the European Economic Area that Google removed: where and why they were removed and per-region information on when they served. The removed_creative_stats table also contains a link to the Google Ads Transparency Center for the removed ad. Data for both tables updates periodically and may be delayed from what appears on the Google Ads Transparency Center website. About BigQuery This data is hosted in Google BigQuery for users to easily query using SQL. Note that to use BigQuery, users must have a Google account and create a GCP project. This public dataset is included in BigQuery's 1TB/mo of free tier processing. 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 . Download Dataset This public dataset is also hosted in Google Cloud Storage here and available free to use. Use this quick start guide to quickly learn how to access public datasets on Google Cloud Storage. We provide the raw data in JSON format, sharded across multiple files to support easier download of the large dataset. A README file which describes the data structure and our Terms of Service (also listed below) is included with the dataset. You can also download the results from a custom query. See here for options and instructions. Signed out users can download the full dataset by using the gCloud CLI. Follow the instructions here to download and install the gCloud CLI. To remove the login requirement, run "$ gcloud config set auth/disable_credentials True" To download the dataset, run "$ gcloud storage cp gs://ads-transparency-center/* . -R" 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 .

In 2022, Google Play users worldwide downloaded 110 billion mobile apps, down from 111.3 billion apps in 2021. Generating significantly more downloads than it’s iOS counterpart, the Apple App Store, Android apps on Google Play are available for a wider range of devices than those catering to Apple's closed ecosystem.

Google’s app sales continue to climb Google’s success was equally reflected in its app revenue which has steadily increased each year, reaching 38.6 billion U.S. dollars in 2020. Since 2019, the highest average number of apps released monthly on Google Play was 141.4 thousand in March 2019. Meanwhile, August 2021 saw the lowest number of apps released within the last two and a half years. Despite its success in the mobile app arena, the tech powerhouse still generates the vast majority of its revenues from advertising.

Social and dating apps are among most popular in 2021 In the first quarter of 2021, one year into the coronavirus pandemic and lockdowns, the number of available dating apps grew by nearly 24 percent. Meanwhile, travel apps saw relatively little growth during this period. Facebook-owned Instagram was the most downloaded Android app in July 2021. With travel restrictions still in place in many regions of the world, communication and social apps such as Snapchat, TikTok and Telegram also featured among the most popular apps downloaded that month.

Google's AudioSet consistently reformatted

During my work with Google's AudioSet(https://research.google.com/audioset/index.html)
I encountered some problems due to the fact that Weak (https://research.google.com/audioset/download.html) and
 Strong (https://research.google.com/audioset/download_strong.html) versions of the dataset used different csv formatting for the data, and that also labels used in the two datasets are different (https://github.com/audioset/ontology/issues/9) and also presented in files with different formatting.

This dataset reformatting aims to unify the formats of the datasets so that it is possible
to analyse them in the same pipelines, and also make the dataset files compatible
with psds_eval, dcase_util and sed_eval Python packages used in Audio Processing.

For better formatted documentation and source code of reformatting refer to https://github.com/bakhtos/GoogleAudioSetReformatted 

-Changes in dataset

All files are converted to tab-separated `*.tsv` files (i.e. `csv` files with `\t`
as a separator). All files have a header as the first line.

-New fields and filenames

Fields are renamed according to the following table, to be compatible with psds_eval:

Old field -> New field
YTID -> filename
segment_id -> filename
start_seconds -> onset
start_time_seconds -> onset
end_seconds -> offset
end_time_seconds -> offset
positive_labels -> event_label
label -> event_label
present -> present

For class label files, `id` is now the name for the for `mid` label (e.g. `/m/09xor`)
and `label` for the human-readable label (e.g. `Speech`). Index of label indicated
for Weak dataset labels (`index` field in `class_labels_indices.csv`) is not used.

Files are renamed according to the following table to ensure consisted naming
of the form `audioset_[weak|strong]_[train|eval]_[balanced|unbalanced|posneg]*.tsv`:

Old name -> New name
balanced_train_segments.csv -> audioset_weak_train_balanced.tsv
unbalanced_train_segments.csv -> audioset_weak_train_unbalanced.tsv
eval_segments.csv -> audioset_weak_eval.tsv
audioset_train_strong.tsv -> audioset_strong_train.tsv
audioset_eval_strong.tsv -> audioset_strong_eval.tsv
audioset_eval_strong_framed_posneg.tsv -> audioset_strong_eval_posneg.tsv
class_labels_indices.csv -> class_labels.tsv (merged with mid_to_display_name.tsv)
mid_to_display_name.tsv -> class_labels.tsv (merged with class_labels_indices.csv)

-Strong dataset changes

Only changes to the Strong dataset are renaming of fields and reordering of columns,
so that both Weak and Strong version have `filename` and `event_label` as first 
two columns.

-Weak dataset changes

-- Labels are given one per line, instead of comma-separated and quoted list

-- To make sure that `filename` format is the same as in Strong version, the following
format change is made:
The value of the `start_seconds` field is converted to milliseconds and appended to the `filename` with an underscore. Since all files in the dataset are assumed to be 10 seconds long, this unifies the format of `filename` with the Strong version and makes `end_seconds` also redundant.

-Class labels changes

Class labels from both datasets are merged into one file and given in alphabetical order of `id`s. Since same `id`s are present in both datasets, but sometimes with different human-readable labels, labels from Strong dataset overwrite those from Weak. It is possible to regenerate `class_labels.tsv` while giving priority to the Weak version of labels by calling `convert_labels(False)` from convert.py in the GitHub repository.

-License

Google's AudioSet was published in two stages - first the Weakly labelled data (Gemmeke, Jort F., et al. "Audio set: An ontology and human-labeled dataset for audio events." 2017 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE, 2017.), then the strongly labelled data (Hershey, Shawn, et al. "The benefit of temporally-strong labels in audio event classification." ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2021.)

Both the original dataset and this reworked version are licensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)

Class labels come from the AudioSet Ontology, which is licensed under CC BY-SA 4.0.

You can check the fields description in the documentation: current Keyword database: https://docs.dataforseo.com/v3/databases/google/keywords/?bash; Historical Keyword database: https://docs.dataforseo.com/v3/databases/google/history/keywords/?bash. You don’t have to download fresh data dumps in JSON or CSV – we can deliver data straight to your storage or database. We send terrabytes of data to dozens of customers every month using Amazon S3, Google Cloud Storage, Microsoft Azure Blob, Eleasticsearch, and Google Big Query. Let us know if you’d like to get your data to any other storage or database.

As of February 2025, Roblox was one of the most downloaded mobile games on Google Play in Thailand, with a download volume of more than one billion times. Roblox is a mobile game published by Roblox Corporation.

Fork this notebook to get started on accessing data in the BigQuery dataset by writing SQL queries using the BQhelper module.

Context

Google Patents Public Data, provided by IFI CLAIMS Patent Services, is a worldwide bibliographic and US full-text dataset of patent publications. Patent information accessibility is critical for examining new patents, informing public policy decisions, managing corporate investment in intellectual property, and promoting future scientific innovation. The growing number of available patent data sources means researchers often spend more time downloading, parsing, loading, syncing and managing local databases than conducting analysis. With these new datasets, researchers and companies can access the data they need from multiple sources in one place, thus spending more time on analysis than data preparation.

Content

The Google Patents Public Data dataset contains a collection of publicly accessible, connected database tables for empirical analysis of the international patent system.

Acknowledgements

Data Origin: https://bigquery.cloud.google.com/dataset/patents-public-data:patents

For more info, see the documentation at https://developers.google.com/web/tools/chrome-user-experience-report/

“Google Patents Public Data” by IFI CLAIMS Patent Services and Google is licensed under a Creative Commons Attribution 4.0 International License.

Banner photo by Helloquence on Unsplash

The dataset provides 12 months (August 2016 to August 2017) of obfuscated Google Analytics 360 data from the Google Merchandise Store , a real ecommerce store that sells Google-branded merchandise, in BigQuery. It’s a great way analyze business data and learn the benefits of using BigQuery to analyze Analytics 360 data Learn more about the data The data includes The data is typical of what an ecommerce website would see and includes the following information:Traffic source data: information about where website visitors originate, including data about organic traffic, paid search traffic, and display trafficContent data: information about the behavior of users on the site, such as URLs of pages that visitors look at, how they interact with content, etc. Transactional data: information about the transactions on the Google Merchandise Store website.Limitations: All users have view access to the dataset. This means you can query the dataset and generate reports but you cannot complete administrative tasks. Data for some fields is obfuscated such as fullVisitorId, or removed such as clientId, adWordsClickInfo and geoNetwork. “Not available in demo dataset” will be returned for STRING values and “null” will be returned for INTEGER values when querying the fields containing no data.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

The Quick Draw Dataset is a collection of 50 million drawings across 345 categories, contributed by players of the game "Quick, Draw!". The drawings were captured as timestamped vectors, tagged with metadata including what the player was asked to draw and in which country the player was located.

Example drawings: https://raw.githubusercontent.com/googlecreativelab/quickdraw-dataset/master/preview.jpg" alt="preview">

Appendix A1-A16 report the top 10 highly cited articles in Google Patents from the Bing API search in the sixteen selected fields. It shows that there are some minority articles with many patent citations but with few or no Scopus citation.

The Google Maps dataset is ideal for getting extensive information on businesses anywhere in the world. Easily filter by location, business type, and other factors to get the exact data you need. The Google Maps dataset includes all major data points: timestamp, name, category, address, description, open website, phone number, open_hours, open_hours_updated, reviews_count, rating, main_image, reviews, url, lat, lon, place_id, country, and more.

In 2021, gaming apps accounted for 42 percent of global app downloads in the Google Play Store, representing the most downloaded app category for Android users. By 2026, downloads of mobile gaming apps on the Google Play Store are estimated to account for 44 percent of the total, while tool apps will account for roughly seven percent of all Android downloads.

Context

The Google Merchandise Store sells Google branded merchandise. The data is typical of what you would see for an ecommerce website.

Content

The sample dataset contains Google Analytics 360 data from the Google Merchandise Store, a real ecommerce store. The Google Merchandise Store sells Google branded merchandise. The data is typical of what you would see for an ecommerce website. It includes the following kinds of information:

Traffic source data: information about where website visitors originate. This includes data about organic traffic, paid search traffic, display traffic, etc. Content data: information about the behavior of users on the site. This includes the URLs of pages that visitors look at, how they interact with content, etc. Transactional data: information about the transactions that occur on the Google Merchandise Store website.

Fork this kernel to get started.

Acknowledgements

Data from: https://bigquery.cloud.google.com/table/bigquery-public-data:google_analytics_sample.ga_sessions_20170801

Banner Photo by Edho Pratama from Unsplash.

Inspiration

What is the total number of transactions generated per device browser in July 2017?

The real bounce rate is defined as the percentage of visits with a single pageview. What was the real bounce rate per traffic source?

What was the average number of product pageviews for users who made a purchase in July 2017?

What was the average number of product pageviews for users who did not make a purchase in July 2017?

What was the average total transactions per user that made a purchase in July 2017?

What is the average amount of money spent per session in July 2017?

What is the sequence of pages viewed?