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

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 .

With the "Public Data" portal, Google provides a collection of visualization options for publicly accessible data sets. The statistical data sets from the areas of budget, demographics and economy from the OGD portal of the city of Vienna have been read in by Google and can be combined and displayed in various ways. Line, bar and bubble diagrams are possible as well as a representation on a geographical map. The data can be set in relation to each other and for records with time series an animation can be started over the time series. The individually created visualizations can be forwarded via a link or integrated into your own website.

Context

DataSF seeks to transform the way that the City of San Francisco works -- through the use of data.

https://datasf.org/about/

Content

This dataset contains the following tables: ['311_service_requests', 'bikeshare_stations', 'bikeshare_status', 'bikeshare_trips', 'film_locations', 'sffd_service_calls', 'sfpd_incidents', 'street_trees']

• This data includes all San Francisco 311 service requests from July 2008 to the present, and is updated daily. 311 is a non-emergency number that provides access to non-emergency municipal services.
• This data includes fire unit responses to calls from April 2000 to present and is updated daily. Data contains the call number, incident number, address, unit identifier, call type, and disposition. Relevant time intervals are also included. Because this dataset is based on responses, and most calls involved multiple fire units, there are multiple records for each call number. Addresses are associated with a block number, intersection or call box.
• This data includes incidents from the San Francisco Police Department (SFPD) Crime Incident Reporting system, from January 2003 until the present (2 weeks ago from current date). The dataset is updated daily. Please note: the SFPD has implemented a new system for tracking crime. This dataset is still sourced from the old system, which is in the process of being retired (a multi-year process).
• This data includes a list of San Francisco Department of Public Works maintained street trees including: planting date, species, and location. Data includes 1955 to present.

This dataset is deprecated and not being updated.

Fork this kernel to get started with this dataset.

Acknowledgements

http://datasf.org/

• https://cloud.google.com/bigquery/public-data/sfo-311
• https://cloud.google.com/bigquery/public-data/sffd-service-calls
• https://cloud.google.com/bigquery/public-data/sfpd-reports
• https://cloud.google.com/bigquery/public-data/sfo-trees

Dataset Source: SF OpenData. This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source - http://sfgov.org/ - and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

Banner Photo by @meric from Unplash.

Inspiration

Which neighborhoods have the highest proportion of offensive graffiti?

Which complaint is most likely to be made using Twitter and in which neighborhood?

What are the most complained about Muni stops in San Francisco?

What are the top 10 incident types that the San Francisco Fire Department responds to?

How many medical incidents and structure fires are there in each neighborhood?

What’s the average response time for each type of dispatched vehicle?

Which category of police incidents have historically been the most common in San Francisco?

What were the most common police incidents in the category of LARCENY/THEFT in 2016?

Which non-criminal incidents saw the biggest reporting change from 2015 to 2016?

What is the average tree diameter?

What is the highest number of a particular species of tree planted in a single year?

Which San Francisco locations feature the largest number of trees?

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.Learn more

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.

Community Connections Program: 100 Gigabit Speed (Google Fiber) Public Facilities administered through the City's Office of Telecom & Regulatory Affairs (TARA) and Google Fiber.

Universal Analytics data from Google Analytics for the CalHHS Open Data Portal. This data was captured using the depreciated Universal Analytics tool and is no longer available on the web via Google UI or Google APIs. It has been loaded here so that users and the metrics dashboard can access the data.

Context

NYC Open Data is an opportunity to engage New Yorkers in the information that is produced and used by City government. We believe that every New Yorker can benefit from Open Data, and Open Data can benefit from every New Yorker. Source: https://opendata.cityofnewyork.us/overview/

Content

Thanks to NYC Open Data, which makes public data generated by city agencies available for public use, and Citi Bike, we've incorporated over 150 GB of data in 5 open datasets into Google BigQuery Public Datasets, including:

• Over 8 million 311 service requests from 2012-2016

• More than 1 million motor vehicle collisions 2012-present

• Citi Bike stations and 30 million Citi Bike trips 2013-present

• Over 1 billion Yellow and Green Taxi rides from 2009-present

• Over 500,000 sidewalk trees surveyed decennially in 1995, 2005, and 2015

This dataset is deprecated and not being updated.

Fork this kernel to get started with this dataset.

Acknowledgements

https://opendata.cityofnewyork.us/

https://cloud.google.com/blog/big-data/2017/01/new-york-city-public-datasets-now-available-on-google-bigquery

This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source - https://data.cityofnewyork.us/ - and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

By accessing datasets and feeds available through NYC Open Data, the user agrees to all of the Terms of Use of NYC.gov as well as the Privacy Policy for NYC.gov. The user also agrees to any additional terms of use defined by the agencies, bureaus, and offices providing data. Public data sets made available on NYC Open Data are provided for informational purposes. The City does not warranty the completeness, accuracy, content, or fitness for any particular purpose or use of any public data set made available on NYC Open Data, nor are any such warranties to be implied or inferred with respect to the public data sets furnished therein.

The City is not liable for any deficiencies in the completeness, accuracy, content, or fitness for any particular purpose or use of any public data set, or application utilizing such data set, provided by any third party.

Banner Photo by @bicadmedia from Unplash.

Inspiration

On which New York City streets are you most likely to find a loud party?

Can you find the Virginia Pines in New York City?

Where was the only collision caused by an animal that injured a cyclist?

What’s the Citi Bike record for the Longest Distance in the Shortest Time (on a route with at least 100 rides)?

https://cloud.google.com/blog/big-data/2017/01/images/148467900588042/nyc-dataset-6.png" alt="enter image description here"> https://cloud.google.com/blog/big-data/2017/01/images/148467900588042/nyc-dataset-6.png

Explore APISCRAPY, your AI-powered Google Map Data Scraper. Easily extract Business Location Data from Google Maps and other platforms. Seamlessly access and utilize publicly available map data for your business needs. Scrape All Publicly Available Data From Google Maps & Other Platforms.

View metadata for key information about this dataset.The city chose metrics based on the results of a 2020 survey of open data end users. OIT then developed datasets to track these metrics over time and a dashboard to display them visually and in a way accessible to a broad audience of users.For questions about this dataset, contact kistine.carolan@phila.gov. For technical assistance, email maps@phila.gov.

Context

Labeled datasets are useful in machine learning research.

Content

This public dataset contains approximately 9 million URLs and metadata for images that have been annotated with labels spanning more than 6,000 categories.

Tables: 1) annotations_bbox 2) dict 3) images 4) labels

Update Frequency: Quarterly

Querying BigQuery Tables

Fork this kernel to get started.

Acknowledgements

https://bigquery.cloud.google.com/dataset/bigquery-public-data:open_images

https://cloud.google.com/bigquery/public-data/openimages

APA-style citation: Google Research (2016). The Open Images dataset [Image urls and labels]. Available from github: https://github.com/openimages/dataset.

Use: The annotations are licensed by Google Inc. under CC BY 4.0 license.

The images referenced in the dataset are listed as having a CC BY 2.0 license. Note: while we tried to identify images that are licensed under a Creative Commons Attribution license, we make no representations or warranties regarding the license status of each image and you should verify the license for each image yourself.

Banner Photo by Mattias Diesel from Unsplash.

Inspiration

Which labels are in the dataset? Which labels have "bus" in their display names? How many images of a trolleybus are in the dataset? What are some landing pages of images with a trolleybus? Which images with cherries are in the training set?

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 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.了解详情

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?

Big Data and Society Abstract & Indexing - ResearchHelpDesk - Big Data & Society (BD&S) is open access, peer-reviewed scholarly journal that publishes interdisciplinary work principally in the social sciences, humanities and computing and their intersections with the arts and natural sciences about the implications of Big Data for societies. The Journal's key purpose is to provide a space for connecting debates about the emerging field of Big Data practices and how they are reconfiguring academic, social, industry, business, and government relations, expertise, methods, concepts, and knowledge. BD&S moves beyond usual notions of Big Data and treats it as an emerging field of practice that is not defined by but generative of (sometimes) novel data qualities such as high volume and granularity and complex analytics such as data linking and mining. It thus attends to digital content generated through online and offline practices in social, commercial, scientific, and government domains. This includes, for instance, the content generated on the Internet through social media and search engines but also that which is generated in closed networks (commercial or government transactions) and open networks such as digital archives, open government, and crowdsourced data. Critically, rather than settling on a definition the Journal makes this an object of interdisciplinary inquiries and debates explored through studies of a variety of topics and themes. BD&S seeks contributions that analyze Big Data practices and/or involve empirical engagements and experiments with innovative methods while also reflecting on the consequences for how societies are represented (epistemologies), realized (ontologies) and governed (politics). Article processing charge (APC) The article processing charge (APC) for this journal is currently 1500 USD. Authors who do not have funding for open access publishing can request a waiver from the publisher, SAGE, once their Original Research Article is accepted after peer review. For all other content (Commentaries, Editorials, Demos) and Original Research Articles commissioned by the Editor, the APC will be waived. Abstract & Indexing Clarivate Analytics: Social Sciences Citation Index (SSCI) Directory of Open Access Journals (DOAJ) Google Scholar Scopus

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

Company Datasets for valuable business insights!

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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.
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Field Name

Description

StateName

Name of the state (Oklahoma)

date

Date of the data point (YYYY-MM-DD)

covid-19_OK

The search interest in the term "COVID-19" in Oklahoma on the given date

sars-cov-2_OK

The search interest in the term "SARS-CoV-2" in Oklahoma on the given date

coronavirus_OK

The search interest in the term "coronavirus" in Oklahoma on the given date

Omicron_OK

The search interest in the term "Omicron" in Oklahoma on the given date

Delta_OK

The search interest in the term "Delta" in Oklahoma on the given date

Fever_OK

The search interest in the term "fever" in Oklahoma on the given date

fatigue_OK

The search interest in the term "fatigue" in Oklahoma on the given date

diarrhea_OK

The search interest in the term "diarrhea" in Oklahoma on the given date

pneumonia_OK

The search interest in the term "pneumonia" in Oklahoma on the given date

sore throat_OK

The search interest in the term "sore throat" in Oklahoma on the given date

loss of smell_OK

The search interest in the term "loss of smell" in Oklahoma on the given date

loss smell_OK

Another variation for tracking the search interest in "loss of smell" in Oklahoma on the given date

loss taste_OK

The search interest in the term "loss of taste" in Oklahoma on the given date

cough_OK

The search interest in the term "cough" in Oklahoma on the given date

nasal congestion_OK

The search interest in the term "nasal congestion" in Oklahoma on the given date

Pytrends is an unofficial Google Trends API for Python. It enables users to programmatically fetch Google Trends data, which can be useful for various applications such as market research, academic studies, and tracking public interest in specific topics over time. Benefits of Using Pytrends: Automated Data Collection: Pytrends allows for automated and repeatable data collection from Google Trends, saving time and effort compared to manual extraction.

Customizable Queries: Users can specify keywords, timeframes, geographic locations, and other parameters to tailor the data to their specific needs.

Integration with Data Analysis Tools: Pytrends data can be easily integrated with tools like pandas for further analysis, visualization, and reporting.

Real-Time Insights: By regularly updating and analyzing Google Trends data, users can gain real-time insights into public interest and behavior, which is valuable for decision-making and research.

Context

Stack Overflow is the largest online community for programmers to learn, share their knowledge, and advance their careers.

Content

Updated on a quarterly basis, this BigQuery dataset includes an archive of Stack Overflow content, including posts, votes, tags, and badges. This dataset is updated to mirror the Stack Overflow content on the Internet Archive, and is also available through the Stack Exchange Data Explorer.

Fork this kernel to get started with this dataset.

Acknowledgements

Dataset Source: https://archive.org/download/stackexchange

https://bigquery.cloud.google.com/dataset/bigquery-public-data:stackoverflow

https://cloud.google.com/bigquery/public-data/stackoverflow

Banner Photo by Caspar Rubin from Unplash.

Inspiration

What is the percentage of questions that have been answered over the years?

What is the reputation and badge count of users across different tenures on StackOverflow?

What are 10 of the “easier” gold badges to earn?

Which day of the week has most questions answered within an hour?