In the most recently reported fiscal year, Google's revenue amounted to 348.16 billion U.S. dollars. Google's revenue is largely made up by advertising revenue, which amounted to 264.59 billion U.S. dollars in 2024. As of October 2024, parent company Alphabet ranked first among worldwide internet companies, with a market capitalization of 2,02 billion U.S. dollars. Google’s revenue Founded in 1998, Google is a multinational internet service corporation headquartered in California, United States. Initially conceptualized as a web search engine based on a PageRank algorithm, Google now offers a multitude of desktop, mobile and online products. Google Search remains the company’s core web-based product along with advertising services, communication and publishing tools, development and statistical tools as well as map-related products. Google is also the producer of the mobile operating system Android, Chrome OS, Google TV as well as desktop and mobile applications such as the internet browser Google Chrome or mobile web applications based on pre-existing Google products. Recently, Google has also been developing selected pieces of hardware which ranges from the Nexus series of mobile devices to smart home devices and driverless cars. Due to its immense scale, Google also offers a crisis response service covering disasters, turmoil and emergencies, as well as an open source missing person finder in times of disaster. Despite the vast scope of Google products, the company still collects the majority of its revenue through online advertising on Google Site and Google network websites. Other revenues are generated via product licensing and most recently, digital content and mobile apps via the Google Play Store, a distribution platform for digital content. As of September 2020, some of the highest-grossing Android apps worldwide included mobile games such as Candy Crush Saga, Pokemon Go, and Coin Master.

In the first quarter of 2025, Google's revenue amounted to over 89.52 billion U.S. dollars, up from the 79.97 billion U.S. dollars registered in the same quarter a year prior. The company amounted to an annual revenue of 348.16 billion U.S. dollars throughout 2024, its highest value to date, with most of its earnings being powered by advertising through Google sites and its network. Google advertising The foundations of Google's earnings are its advertising revenues, generated through its Google Ads platform, which enables advertisers to display ads, product listings, and service offerings across its extensive network (properties, partner sites, and apps) to web users via programs like AdSense or AdSearch. In 2024, Google accounted for most of its parent company Alphabet's annual revenues with 234.2 billion U.S. dollars in Google website ad revenues alone. Other sources of revenue Google's multitude of income sources also includes digital content products and apps sold through the digital content distribution platform Google Play, as well as hardware including Chromecast devices and smartphones. Geographically, the biggest single country share of Alphabet’s revenue comes from the United States, and close to 30 percent of revenues originate from the EMEA region.

YouTube was launched in 2005. It was founded by three PayPal employees: Chad Hurley, Steve Chen, and Jawed Karim, who ran the company from an office above a small restaurant in San Mateo. The first...

In 2024, Meta Platforms generated a revenue of over 164 billion U.S. dollars, up from 134 billion USD in 2023. The majority of Meta’s profits come from its advertising revenue.Meta’s total Family of Apps revenue for 2022 amounted to 114 billion U.S. dollars. Additionally, Meta’s Reality Labs, the company’s VR division, generated around 2.1 billion dollars. Meta’s marketing expenditure for 2022 amounted to just over 15 billion U.S. dollars, up from 14 billion U.S. dollars in the previous year. Increasing audience base despite privacy misgivings Meta’s user numbers have continued to grow steadily throughout past years. In the fourth quarter of 2022, there was a total of 3.74 billion worldwide users across all of Meta’s platforms. For this same time frame, the company recorded 407 million monthly active users across Europe. Downloads of Meta’s app Oculus, for which virtual reality headsets are required, increased greatly from 2020 to 2021, reaching a total of 10.62 million downloads by the end of last year. Up until 2021, downloads had grown in a steady manner but from 2020 to 2021, they more than doubled.User numbers have increased despite data security issues and past controversy such as the Cambridge Analytica scandal in 2018. There remains skepticism surrounding the idea of the metaverse in which Meta aims to immerse itself. Of surveyed adults in the United States, the majority said that they were concerned about their privacy if Meta were to succeed in creating the metaverse.

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 .

App Revenue Key StatisticsMobile Ad SpendApp and Game RevenuesiOS App and Game RevenueGoogle Play App and Game RevenueGaming App RevenuesiOS Gaming App RevenueGoogle Play Gaming App RevenueApp...

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. The dataset was presented in our CVPR'20 paper. In this repository, we present download links for all dataset files and relevant code for metric computation. This dataset was associated to two Kaggle challenges, on landmark recognition and landmark retrieval. Results were discussed as part of a CVPR'19 workshop. In this repository, we also provide scores for the top 10 teams in the challenges, based on the latest ground-truth version. Please visit the challenge and workshop webpages for more details on the data, tasks and technical solutions from top teams.

The Google Trends dataset will provide critical signals that individual users and businesses alike can leverage to make better data-driven decisions. This dataset simplifies the manual interaction with the existing Google Trends UI by automating and exposing anonymized, aggregated, and indexed search data in BigQuery. This dataset includes the Top 25 stories and Top 25 Rising queries from Google Trends. It will be made available as two separate BigQuery tables, with a set of new top terms appended daily. Each set of Top 25 and Top 25 rising expires after 30 days, and will be accompanied by a rolling five-year window of historical data in 210 distinct locations in the United States. This Google dataset is hosted in Google BigQuery as part of Google Cloud's Datasets solution 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

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

Column 1

Source

Data sources that the publications retrieved. Values for this column are “Google Scholar”, “Scopus”, and “Web of Science”.

Column 2

Authors

The authors of the publications. This column is kept as additional information for verification of data. Not used in the analysis, it has not been standardized.

Column 3

Title

Titles of the publications. For non-English publications, English titles, if available, are kept in this column. Otherwise, the original titles have been entered. The headings were checked and errors and omissions were corrected. Corrected titles are marked in red.

Column 4

Title translated with Google Translate

In this Column, the English translated titles of the publications that do not have English titles are kept. Google Translate is used for detecting the language and translation. For publications with an English title, the expression [Title in English] has been entered. The translations of the original titles kept in this field were used in the analysis made through VOSviewer. It is marked in red as it is newly added data.

Column 5

Language

Language of the publications. The languages of all publications were checked, missing data were completed and errors were corrected. If the language of the publication could not be determined, the value is [Not found]. The cells with addition or correction are marked in red.

Column 6

Document type

Types of the documents. For all publications, publication type information was checked, missing ones were completed and corrections were made. All intervened cells are marked in red. Article and Review types are referred to as “Article” in the text.

Column 7

Full-text available

Values for this column are “Yes” and “No”. The values for this column are Yes and No. If there is access to the full text of the publication via the web, "Yes", otherwise the "No" value has been entered.

Column 8

On research evaluation

Values for this column are “Yes” and “No”. Using the title and/or abstract information, it was tried to determine whether the publications were related to the research evaluation. “Yes”, if found relevant, and “No” if not. It is marked in red as it is newly added data.

Column 9

Publication year

The publication years of the documents. If the publication years are missing, they have been completed. The current publication years have been checked and corrected if necessary. If the year of publication could not be found, it is indicated as [Not found].

Column 10

English abstract

Abstracts of the publications. If there is an accessible/available English abstract for the publication, it is kept in this column. [Not found/Not available] for missing values. Abstracts that were added, changed, corrected, or completed are marked in red.

Explore our public notebook content!

Meta Kaggle Code is an extension to our popular Meta Kaggle dataset. This extension contains all the raw source code from hundreds of thousands of public, Apache 2.0 licensed Python and R notebooks versions on Kaggle used to analyze Datasets, make submissions to Competitions, and more. This represents nearly a decade of data spanning a period of tremendous evolution in the ways ML work is done.

Why we’re releasing this dataset

By collecting all of this code created by Kaggle’s community in one dataset, we hope to make it easier for the world to research and share insights about trends in our industry. With the growing significance of AI-assisted development, we expect this data can also be used to fine-tune models for ML-specific code generation tasks.

Meta Kaggle for Code is also a continuation of our commitment to open data and research. This new dataset is a companion to Meta Kaggle which we originally released in 2016. On top of Meta Kaggle, our community has shared nearly 1,000 public code examples. Research papers written using Meta Kaggle have examined how data scientists collaboratively solve problems, analyzed overfitting in machine learning competitions, compared discussions between Kaggle and Stack Overflow communities, and more.

The best part is Meta Kaggle enriches Meta Kaggle for Code. By joining the datasets together, you can easily understand which competitions code was run against, the progression tier of the code’s author, how many votes a notebook had, what kinds of comments it received, and much, much more. We hope the new potential for uncovering deep insights into how ML code is written feels just as limitless to you as it does to us!

Sensitive data

While we have made an attempt to filter out notebooks containing potentially sensitive information published by Kaggle users, the dataset may still contain such information. Research, publications, applications, etc. relying on this data should only use or report on publicly available, non-sensitive information.

Joining with Meta Kaggle

The files contained here are a subset of the KernelVersions in Meta Kaggle. The file names match the ids in the KernelVersions csv file. Whereas Meta Kaggle contains data for all interactive and commit sessions, Meta Kaggle Code contains only data for commit sessions.

File organization

The files are organized into a two-level directory structure. Each top level folder contains up to 1 million files, e.g. - folder 123 contains all versions from 123,000,000 to 123,999,999. Each sub folder contains up to 1 thousand files, e.g. - 123/456 contains all versions from 123,456,000 to 123,456,999. In practice, each folder will have many fewer than 1 thousand files due to private and interactive sessions.

The ipynb files in this dataset hosted on Kaggle do not contain the output cells. If the outputs are required, the full set of ipynbs with the outputs embedded can be obtained from this public GCS bucket: kaggle-meta-kaggle-code-downloads. Note that this is a "requester pays" bucket. This means you will need a GCP account with billing enabled to download. Learn more here: https://cloud.google.com/storage/docs/requester-pays

Questions / Comments

We love feedback! Let us know in the Discussion tab.

Happy Kaggling!

so if you have to have a G+ account (for YouTube, location services, or other reasons) - here's how you can make it totally private! No one will be able to add you, send you spammy links, or otherwise annoy you. You need to visit the "Audience Settings" page - https://plus.google.com/u/0/settings/audience You can then set a "custom audience" - usually you would use this to restrict your account to people from a specific geographic location, or within a specific age range. In this case, we're going to choose a custom audience of "No-one" Check the box and hit save. Now, when people try to visit your Google+ profile - they'll see this "restricted" message. You can visit my G+ Profile if you want to see this working. (https://plus.google.com/114725651137252000986) If you are not able to understand you can follow this website : http://www.livehuntz.com/google-plus/support-phone-number

Company Datasets for valuable business insights!

Discover new business prospects, identify investment opportunities, track competitor performance, and streamline your sales efforts with comprehensive Company Datasets.

These datasets are sourced from top industry providers, ensuring you have access to high-quality information:

• Owler: Gain valuable business insights and competitive intelligence. -AngelList: Receive fresh startup data transformed into actionable insights. -CrunchBase: Access clean, parsed, and ready-to-use business data from private and public companies. -Craft.co: Make data-informed business decisions with Craft.co's company datasets. -Product Hunt: Harness the Product Hunt dataset, a leader in curating the best new products.

We provide fresh and ready-to-use company data, eliminating the need for complex scraping and parsing. Our data includes crucial details such as:

• Company name;
• Size;
• Founding date;
• Location;
• Industry;
• Revenue;
• Employee count;
• Competitors.

You can choose your preferred data delivery method, including various storage options, delivery frequency, and input/output formats.

Receive datasets in CSV, JSON, and other formats, with storage options like AWS S3 and Google Cloud Storage. Opt for one-time, monthly, quarterly, or bi-annual data delivery.

With Oxylabs Datasets, you can count on:

• Fresh and accurate data collected and parsed by our expert web scraping team.
• Time and resource savings, allowing you to focus on data analysis and achieving your business goals.
• A customized approach tailored to your specific business needs.
• Legal compliance in line with GDPR and CCPA standards, thanks to our membership in the Ethical Web Data Collection Initiative.

Pricing Options:

Standard Datasets: choose from various ready-to-use datasets with standardized data schemas, priced from $1,000/month.

Custom Datasets: Tailor datasets from any public web domain to your unique business needs. Contact our sales team for custom pricing.

Experience a seamless journey with Oxylabs:

• Understanding your data needs: We work closely to understand your business nature and daily operations, defining your unique data requirements.
• Developing a customized solution: Our experts create a custom framework to extract public data using our in-house web scraping infrastructure.
• Delivering data sample: We provide a sample for your feedback on data quality and the entire delivery process.
• Continuous data delivery: We continuously collect public data and deliver custom datasets per the agreed frequency.

Unlock the power of data with Oxylabs' Company Datasets and supercharge your business insights today!

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.

In 2023, Google's ad revenue amounted to 264.59 billion U.S. dollars. The company generates advertising revenue through its Google Ads platform, which enables advertisers to display ads, product listings and service offerings across Google’s extensive ad network (properties, partner sites, and apps) to web users. Google advertising Advertising accounts for the majority of Google’s revenue, which amounted to a total of 305.63 billion U.S. dollars in 2023. The majority of Google's advertising revenue comes from search advertising. Google market share These revenue figures come as no surprise, as Google accounts for the majority of the online and mobile search market worldwide. As of September 2023, Google was responsible for more than 84 percent of global desktop search traffic. The company holds a market share of more than 80 percent in a wide range of digital markets, having little to no domestic competition in many of them. China, Russia, and to a certain extent, Japan, are some of the few notable exceptions, where local products are more preferred.

Since computers revealed the possibility to collect and evaluate large data, there has been a significant increase in studies measuring the impact of academics. This study aims to analyse International Relations scholars and departments in Turkey by using the data from Google Scholar citation counts. Through this measurement, the study will generate a new ranking list as alternative to existing measurement lists. To control outcomes, Google-generated ranking lists will be compared with data generated from Social Science Citation Index (SSCI). Thus, the study aims to make a data-based contribution to the quality assessment literature, which has become increasingly popular in Turkey. Günümüzde bilgisayarlar geniş verileri toplama ve değerlendirme imkanını ortaya çıkarınca, akademisyenlerin etkisini ölçmeyi hedefleyen çalışmalarda ciddi bir artış oldu. Elinizdeki çalışma da Google Scholar (GS) atıf sayısı verileri üzerinden Türkiye’deki Uluslararası İlişkiler akademisyenlerini ve bölümlerini analiz etmeyi hedeflemektedir. Yapılacak bu analiz ile, mevcut ölçme listelerine alternatif olarak akademisyen ve bölümlerin yeni bir sıralanması ortaya konulmaktadır. GS verilerinden hareketle elde edilen sonuçlar, kontrol amacıyla Social Science Citation Index (SSCI) veri tabanından derlenen makale sayıları ve atıflar ile karşılaştırılmıştır. Böylelikle çalışma Türkiye özelinde gittikçe kapsamlı bir hale gelen nitelik değerlendirme literatürüne verilere dayalı bir katkı yapmayı hedeflemektedir

Meet Earth EngineGoogle Earth Engine combines a multi-petabyte catalog of satellite imagery and geospatial datasets with planetary-scale analysis capabilities and makes it available for scientists, researchers, and developers to detect changes, map trends, and quantify differences on the Earth's surface.SATELLITE IMAGERY+YOUR ALGORITHMS+REAL WORLD APPLICATIONSLEARN MOREGLOBAL-SCALE INSIGHTExplore our interactive timelapse viewer to travel back in time and see how the world has changed over the past twenty-nine years. Timelapse is one example of how Earth Engine can help gain insight into petabyte-scale datasets.EXPLORE TIMELAPSEREADY-TO-USE DATASETSThe public data archive includes more than thirty years of historical imagery and scientific datasets, updated and expanded daily. It contains over twenty petabytes of geospatial data instantly available for analysis.EXPLORE DATASETSSIMPLE, YET POWERFUL APIThe Earth Engine API is available in Python and JavaScript, making it easy to harness the power of Google’s cloud for your own geospatial analysis.EXPLORE THE APIGoogle Earth Engine has made it possible for the first time in history to rapidly and accurately process vast amounts of satellite imagery, identifying where and when tree cover change has occurred at high resolution. Global Forest Watch would not exist without it. For those who care about the future of the planet Google Earth Engine is a great blessing!-Dr. Andrew Steer, President and CEO of the World Resources Institute.CONVENIENT TOOLSUse our web-based code editor for fast, interactive algorithm development with instant access to petabytes of data.LEARN ABOUT THE CODE EDITORSCIENTIFIC AND HUMANITARIAN IMPACTScientists and non-profits use Earth Engine for remote sensing research, predicting disease outbreaks, natural resource management, and more.SEE CASE STUDIESREADY TO BE PART OF THE SOLUTION?SIGN UP NOWTERMS OF SERVICE PRIVACY ABOUT GOOGLE

Acknowledgement
These data are a product of a research activity conducted in the context of the RAILS (Roadmaps for AI integration in the raiL Sector) project which has received funding from the Shift2Rail Joint Undertaking under the European Union’s Horizon 2020 research and innovation programme under grant agreement n. 881782 Rails. The JU receives support from the European Union’s Horizon 2020 research and innovation program and the Shift2Rail JU members other than the Union.

Disclaimers
The information and views set out in this document are those of the author(s) and do not necessarily reflect the official opinion of Shift2Rail Joint Undertaking. The JU does not guarantee the accuracy of the data included in this document. Neither the JU nor any person acting on the JU’s behalf may be held responsible for the use which may be made of the information contained therein.

This "dataset" has been created for scientific purposes only - and WITHOUT ANY COMMERCIAL purposes - to study the potentials of Deep Learning and Transfer Learning approaches. We are NOT re-distributing any video or audio; our files just contain pointers and indications needed to reproduce our study. The authors DO NOT ASSUME any responsibility for the use that other researchers or users will make of these data.

General Info
The CSV files contained in this folder (and subfolders) compose the Level Crossing (LC) Warning Bell (WB) Dataset.

When using any of these data, please mention:

De Donato, L., Marrone, S., Flammini, F., Sansone, C., Vittorini, V., Nardone, R., Mazzariello, C., and Bernaudine, F., "Intelligent Detection of Warning Bells at Level Crossings through Deep Transfer Learning for Smarter Railway Maintenance", Engineering Applications of Artificial Intelligence, Elsevier, 2023

Content of the folder
This folder contains the following subfolders and files.

"Data Files" contains all the CSV files related to the data composing the LCWB Dataset:

• WB_data.csv (WB_labels.csv): representing data of the "Warning Bell (WB)" class;
• NA_data.csv (NA_labels.csv): representing data of the "No Alarm (NA)" class;
• GE_data.csv (GE_labels.csv): representing data of the "GEneric alarm (GE)" class.

"LCWB Dataset" contains all the JSON files that show how the aforementioned data have been distributed among training, validation, and test sets:

• IT_Distribution.json and UK_distribution.json respectively show how Italian (IT) WBs and British (UK) WBs have been distributed;
• The same goes for NA_Distribution.json and GE_Distribution.json, which show the distribution of NA and GE data respectively;
• DatasetDistribution.json simply incorporates the content of the aforementioned JSON files in a unique file that can be exploited to obtain exactly the same dataset we adopted in our analyses.

"Additional Files" contains some CSV files related to data we adopted to further test the deep neural network leveraged in the aforementioned manuscript:

• FR_DE_data.csv (FR_DE_labels.csv): representing data that have been used to test the generalisation performances of the network we exploited on LC WBs related to countries that were not considered in the training phase.
• Noises_data.csv (Noises_labels.csv): representing the noises that were considered to study the behaviour of the network in case of noisy data.

CSV Files Structure
Each "XX_labels.csv" file contains, for each entry, the following information:

• The identifier ("index") of the sub-class (which is not relevant in our case);
• The code-name ("mid") of the class, which is used in the "XX_data.csv" file to indicate the sub-class of a specific audio;
• The extended name of the class ("display_name").

Worth mentioning, sub-classes do not have a specific purpose in our task. They have been kept to maintain as much as possible the structure of the "class_labels_indices.csv" file provided by AudioSet. The same applies to the "XX_data.csv" files, which have roughly the same structures of "Evaluation", "Balanced train", and "Unbalanced train" AudioSet CSV files.

Indeed, each "XX_data.csv" file contains, for each entry, the following information:

• ID: the identifier of the entry;
• YTID: the YouTube identifier of the video;
• start_seconds and end_seconds: which delimit the portion of audio (extracted from YTID) which is of interest for this task;
• positive_labels: the label(s) associated with the audio.

Credits
The structure of the CSV files contained in this dataset, as well as part of their content, was inspired by the CSV files composing the AudioSet dataset which is made available by Google Inc. under a Creative Commons Attribution 4.0 International (CC BY 4.0) license, while its ontology is available under a Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) license.

Particularly, from AudioSet, we retrieved:

• The structure of the CSV files as discussed above.
• Data contained in GE_data.csv (which is a minimal portion of data made available by AudioSet) as well as the related 19 classes (in GE_labels.csv) which we selected among the hundreds of classes included in the AudioSet ontology.

Pointers contained in "XX_data.csv" files other than GE_data.csv have been retrieved manually from scratch. Then, the related "XX_labels.csv" files have been created consequently.

More about downloading the AudioSet dataset can be found here.

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?

CHIRPS is an abbreviation for Climate Hazards Group InfraRed Precipitation with Station Data (Version 2.0 final). The CHIRPS is a 30+ year quasi-global rainfall dataset and incorporates 0.05° resolution satellite imagery with in-situ station data to create gridded rainfall time series for trend analysis and seasonal drought monitoring. The data of the CHIRPS pentad is derived from Google Earth Engine with earth engine snippet as https://code.earthengine.google.com/?scriptPath=Examples%3ADatasets%2FUCSB-CHG_CHIRPS_PENTAD . With the dataset in a global format, it is clipped with the Cambodia boundary and generated the data visualized chart through the obtained data.