In an effort to help combat COVID-19, we created a COVID-19 Public Datasets program to make data more accessible to researchers, data scientists and analysts. The program will host a repository of public datasets that relate to the COVID-19 crisis and make them free to access and analyze. These include datasets from the New York Times, European Centre for Disease Prevention and Control, Google, Global Health Data from the World Bank, and OpenStreetMap. Free hosting and queries of COVID datasets As with all data in the Google Cloud Public Datasets Program , Google pays for storage of datasets in the program. BigQuery also provides free queries over certain COVID-related datasets to support the response to COVID-19. Queries on COVID datasets will not count against the BigQuery sandbox free tier , where you can query up to 1TB free each month. Limitations and duration Queries of COVID data are free. If, during your analysis, you join COVID datasets with non-COVID datasets, the bytes processed in the non-COVID datasets will be counted against the free tier, then charged accordingly, to prevent abuse. Queries of COVID datasets will remain free until Sept 15, 2021. The contents of these datasets are provided to the public strictly for educational and research purposes only. We are not onboarding or managing PHI or PII data as part of the COVID-19 Public Dataset Program. Google has practices & policies in place to ensure that data is handled in accordance with widely recognized patient privacy and data security policies. See the list of all datasets included in the program

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

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

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. For more information please see this site.

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.

DISCLAIMER: The Financial Statement and Notes Data Sets contain information derived from structured data filed with the Commission by individual registrants as well as Commission-generated filing identifiers. Because the data sets are derived from information provided by individual registrants, we cannot guarantee the accuracy of the data sets. In addition, it is possible inaccuracies or other errors were introduced into the data sets during the process of extracting the data and compiling the data sets. Finally, the data sets do not reflect all available information, including certain metadata associated with Commission filings. The data sets are intended to assist the public in analyzing data contained in Commission filings; however, they are not a substitute for such filings. Investors should review the full Commission filings before making any investment decision.

Dimensions is the largest database of research insight in the world. It represents the most comprehensive collection of linked data related to the global research and innovation ecosystem available in a single platform. Because Dimensions maps the entire research lifecycle, you can follow academic and industry research from early stage funding, through to output and on to social and economic impact. Businesses, governments, universities, investors, funders and researchers around the world use Dimensions to inform their research strategy and make evidence-based decisions on the R&D and innovation landscape. With Dimensions on Google BigQuery, you can seamlessly combine Dimensions data with your own private and external datasets; integrate with Business Intelligence and data visualization tools; and analyze billions of data points in seconds to create the actionable insights your organization needs. Examples of usage: Competitive intelligence Horizon-scanning & emerging trends Innovation landscape mapping Academic & industry partnerships and collaboration networks Key Opinion Leader (KOL) identification Recruitment & talent Performance & benchmarking Tracking funding dollar flows and citation patterns Literature gap analysis Marketing and communication strategy Social and economic impact of research About the data: Dimensions is updated daily and constantly growing. It contains over 112m linked research publications, 1.3bn+ citations, 5.6m+ grants worth $1.7trillion+ in funding, 41m+ patents, 600k+ clinical trials, 100k+ organizations, 65m+ disambiguated researchers and more. The data is normalized, linked, and ready for analysis. Dimensions is available as a subscription offering. For more information, please visit www.dimensions.ai/bigquery and a member of our team will be in touch shortly. If you would like to try our data for free, please select "try sample" to see our openly available Covid-19 data.Learn more

Context

Bitcoin and other cryptocurrencies have captured the imagination of technologists, financiers, and economists. Digital currencies are only one application of the underlying blockchain technology. Like its predecessor, Bitcoin, the Ethereum blockchain can be described as an immutable distributed ledger. However, creator Vitalik Buterin also extended the set of capabilities by including a virtual machine that can execute arbitrary code stored on the blockchain as smart contracts.

Both Bitcoin and Ethereum are essentially OLTP databases, and provide little in the way of OLAP (analytics) functionality. However the Ethereum dataset is notably distinct from the Bitcoin dataset:

• The Ethereum blockchain has as its primary unit of value Ether, while the Bitcoin blockchain has Bitcoin. However, the majority of value transfer on the Ethereum blockchain is composed of so-called tokens. Tokens are created and managed by smart contracts.

• Ether value transfers are precise and direct, resembling accounting ledger debits and credits. This is in contrast to the Bitcoin value transfer mechanism, for which it can be difficult to determine the balance of a given wallet address.

• Addresses can be not only wallets that hold balances, but can also contain smart contract bytecode that allows the programmatic creation of agreements and automatic triggering of their execution. An aggregate of coordinated smart contracts could be used to build a decentralized autonomous organization.

Content

The Ethereum blockchain data are now available for exploration with BigQuery. All historical data are in the ethereum_blockchain dataset, which updates daily.

Our hope is that by making the data on public blockchain systems more readily available it promotes technological innovation and increases societal benefits.

Querying BigQuery tables

You can use the BigQuery Python client library to query tables in this dataset in Kernels. Note that methods available in Kernels are limited to querying data. Tables are at bigquery-public-data.crypto_ethereum.[TABLENAME]. Fork this kernel to get started.

Acknowledgements

Cover photo by Thought Catalog on Unsplash

Inspiration

• What are the most popularly exchanged digital tokens, represented by ERC-721 and ERC-20 smart contracts?
• Compare transaction volume and transaction networks over time
• Compare transaction volume to historical prices by joining with other available data sources like Bitcoin Historical Data

The COKI Language Dataset contains predictions for 122 million academic publications. The dataset consists of DOI, title, ISO language code and the fastText language prediction probability score.

Methodology
A subset of the COKI Academic Observatory Dataset, which is produced by the Academic Observatory Workflows codebase [1], was extracted and converted to CSV with Bigquery and downloaded to a virtual machine. The subset consists of all publications with DOIs in our dataset, including each publication’s title and abstract from both Crossref Metadata and Microsoft Academic Graph. The CSV files were then processed with a Python script. The titles and abstracts for each record were pre-processed, concatenated together and analysed with fastText. The titles and abstracts from Crossref Metadata were used first, with the MAG titles and abstracts serving as a fallback when the Crossref Metadata information was empty. Language was predicted for each publication using the fastText lid.176.bin language identification model [2]. fastText was chosen because of its high accuracy and fast runtime speed [3]. The final output dataset consists of DOI, title, ISO language code and the fastText language prediction probability score.

Query or Download
The data is publicly accessible in BigQuery in the following two tables:

• coki-data-share.language.doi_language
• coki-data-share.language.iso_language

When you make queries on these tables, make sure that you are in your own Google Cloud project, otherwise the queries will fail.

See the COKI Language Detection README for instructions on how to download the data from Zenodo and load it into BigQuery.

Code
The code that generated this dataset, the BigQuery schemas and instructions for loading the data into BigQuery can be found here: https://github.com/The-Academic-Observatory/coki-language

License
COKI Language Dataset © 2022 by Curtin University is licenced under CC BY 4.0.

Attributions
This work contains information from:

• Microsoft Academic Graph which is made available under the ODC Attribution Licence.
• Crossref Metadata via the Metadata Plus program. Bibliographic metadata is made available without copyright restriction and Crossref generated data under a CC0 licence. See metadata licence information for more details.

References
[1] https://doi.org/10.5281/zenodo.6366695
[2] https://fasttext.cc/docs/en/language-identification.html
[3] https://modelpredict.com/language-identification-survey

Context

Form 990 (officially, the "Return of Organization Exempt From Income Tax"1) is a United States Internal Revenue Service form that provides the public with financial information about a nonprofit organization. It is often the only source of such information. It is also used by government agencies to prevent organizations from abusing their tax-exempt status. Source: https://en.wikipedia.org/wiki/Form_990

Content

Form 990 is used by the United States Internal Revenue Service to gather financial information about nonprofit/exempt organizations. This BigQuery dataset can be used to perform research and analysis of organizations that have electronically filed Forms 990, 990-EZ and 990-PF. For a complete description of data variables available in this dataset, see the IRS’s extract documentation: https://www.irs.gov/uac/soi-tax-stats-annual-extract-of-tax-exempt-organization-financial-data.

Update Frequency: Annual

Fork this kernel to get started with this dataset.

Acknowledgements

https://bigquery.cloud.google.com/dataset/bigquery-public-data:irs_990

https://cloud.google.com/bigquery/public-data/irs-990

Dataset Source: U.S. Internal Revenue Service. This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source - http://www.data.gov/privacy-policy#data_policy - 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 @rawpixel from Unplash.

Inspiration

What organizations filed tax exempt status in 2015?

What was the revenue of the American Red Cross in 2017?

This dataset is based on the TravisTorrent dataset released 2017-01-11 (https://travistorrent.testroots.org), the Google BigQuery GHTorrent dataset accessed 2017-07-03, and the Git log history of all projects in the dataset, retrieved 2017-07-16 and 2017-07-17.

We selected projects hosted on GitHub that employ the Continuous Integration (CI) system Travis CI. We identified the projects using the TravisTorrent data set and considered projects that:

1. used GitHub from the beginning (first commit not more than seven days before project creation date according to GHTorrent),
2. were active for at least one year (365 days) before the first build with Travis CI (before_ci),
3. used Travis CI at least for one year (during_ci),
4. had commit or merge activity on the default branch in both of these phases, and
5. used the default branch to trigger builds.

To derive the time frames, we employed the GHTorrent Big Query data set. The resulting sample contains 113 projects. Of these projects, 89 are Ruby projects and 24 are Java projects. For our analysis, we only consider the activity one year before and after the first build.

We cloned the selected project repositories and extracted the version history for all branches (see https://github.com/sbaltes/git-log-parser). For each repo and branch, we created one log file with all regular commits and one log file with all merges. We only considered commits changing non-binary files and applied a file extension filter to only consider changes to Java or Ruby source code files. From the log files, we then extracted metadata about the commits and stored this data in CSV files (see https://github.com/sbaltes/git-log-parser).

We also retrieved a random sample of GitHub project to validate the effects we observed in the CI project sample. We only considered projects that:

1. have Java or Ruby as their project language
2. used GitHub from the beginning (first commit not more than seven days before project creation date according to GHTorrent)
3. have commit activity for at least two years (730 days)
4. are engineered software projects (at least 10 watchers)
5. were not in the TravisTorrent dataset

In total, 8,046 projects satisfied those constraints. We drew a random sample of 800 projects from this sampling frame and retrieved the commit and merge data in the same way as for the CI sample. We then split the development activity at the median development date, removed projects without commits or merges in either of the two resulting time spans, and then manually checked the remaining projects to remove the ones with CI configuration files. The final comparision sample contained 60 non-CI projects.

This dataset contains the following files:

tr_projects_sample_filtered_2.csv
A CSV file with information about the 113 selected projects.

tr_sample_commits_default_branch_before_ci.csv
tr_sample_commits_default_branch_during_ci.csv
One CSV file with information about all commits to the default branch before and after the first CI build. Only commits modifying, adding, or deleting Java or Ruby source code files were considered. Those CSV files have the following columns:

project: GitHub project name ("/" replaced by "_").
branch: The branch to which the commit was made.
hash_value: The SHA1 hash value of the commit.
author_name: The author name.
author_email: The author email address.
author_date: The authoring timestamp.
commit_name: The committer name.
commit_email: The committer email address.
commit_date: The commit timestamp.
log_message_length: The length of the git commit messages (in characters).
file_count: Files changed with this commit.
lines_added: Lines added to all files changed with this commit.
lines_deleted: Lines deleted in all files changed with this commit.
file_extensions: Distinct file extensions of files changed with this commit.

tr_sample_merges_default_branch_before_ci.csv
tr_sample_merges_default_branch_during_ci.csv
One CSV file with information about all merges into the default branch before and after the first CI build. Only merges modifying, adding, or deleting Java or Ruby source code files were considered. Those CSV files have the following columns:

project: GitHub project name ("/" replaced by "_").
branch: The destination branch of the merge.
hash_value: The SHA1 hash value of the merge commit.
merged_commits: Unique hash value prefixes of the commits merged with this commit.
author_name: The author name.
author_email: The author email address.
author_date: The authoring timestamp.
commit_name: The committer name.
commit_email: The committer email address.
commit_date: The commit timestamp.
log_message_length: The length of the git commit messages (in characters).
file_count: Files changed with this commit.
lines_added: Lines added to all files changed with this commit.
lines_deleted: Lines deleted in all files changed with this commit.
file_extensions: Distinct file extensions of files changed with this commit.
pull_request_id: ID of the GitHub pull request that has been merged with this commit (extracted from log message).
source_user: GitHub login name of the user who initiated the pull request (extracted from log message).
source_branch : Source branch of the pull request (extracted from log message).

comparison_project_sample_800.csv
A CSV file with information about the 800 projects in the comparison sample.

commits_default_branch_before_mid.csv
commits_default_branch_after_mid.csv
One CSV file with information about all commits to the default branch before and after the medium date of the commit history. Only commits modifying, adding, or deleting Java or Ruby source code files were considered. Those CSV files have the same columns as the commits tables described above.

merges_default_branch_before_mid.csv
merges_default_branch_after_mid.csv
One CSV file with information about all merges into the default branch before and after the medium date of the commit history. Only merges modifying, adding, or deleting Java or Ruby source code files were considered. Those CSV files have the same columns as the merge tables described above.

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

GitHub is how people build software and is home to the largest community of open source developers in the world, with over 12 million people contributing to 31 million projects on GitHub since 2008. This 3TB+ dataset comprises the largest released source of GitHub activity to date. It contains a full snapshot of the content of more than 2.8 million open source GitHub repositories including more than 145 million unique commits, over 2 billion different file paths, and the contents of the latest revision for 163 million files, all of which are searchable with regular expressions. 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 .

Dataset Card for notional-python

  Dataset Summary

The Notional-python dataset contains python code files from 100 well-known repositories gathered from Google Bigquery Github Dataset. The dataset was created to test the ability of programming language models. Follow our repo to do the model evaluation using notional-python dataset.

  Languages

Python

  Dataset Creation





  Curation Rationale

Notional-python was built to provide a dataset for… See the full description on the dataset page: https://huggingface.co/datasets/notional/notional-python.

StackExchange Dataset

Working doc: https://docs.google.com/document/d/1h585bH5sYcQW4pkHzqWyQqA4ape2Bq6o1Cya0TkMOQc/edit?usp=sharing

BigQuery query (see so_bigquery.ipynb): CREATE TEMP TABLE answers AS SELECT * FROM bigquery-public-data.stackoverflow.posts_answers WHERE LOWER(Body) LIKE '%arxiv%';

CREATE TEMPORARY TABLE questions AS SELECT * FROM bigquery-public-data.stackoverflow.posts_questions;

SELECT * FROM answers JOIN questions ON questions.id = answers.parent_id;

NOTE:… See the full description on the dataset page: https://huggingface.co/datasets/ag2435/stackexchange.

Context

Adapted from Wikipedia: OpenStreetMap (OSM) is a collaborative project to create a free editable map of the world. Created in 2004, it was inspired by the success of Wikipedia and more than two million registered users who can add data by manual survey, GPS devices, aerial photography, and other free sources.

To aid researchers, data scientists, and analysts in the effort to combat COVID-19, Google is making a hosted repository of public datasets including OpenStreetMap data, free to access. To facilitate the Kaggle community to access the BigQuery dataset, it is onboarded to Kaggle platform which allows querying it without a linked GCP account. Please note that due to the large size of the dataset, Kaggle applies a quota of 5 TB of data scanned per user per 30-days.

Content

This is the OpenStreetMap (OSM) planet-wide dataset loaded to BigQuery.

Tables: - history_* tables: full history of OSM objects. - planet_* tables: snapshot of current OSM objects as of Nov 2019.

The history_* and planet_* table groups are composed of node, way, relation, and changeset tables. These contain the primary OSM data types and an additional changeset corresponding to OSM edits for convenient access. These objects are encoded using the BigQuery GEOGRAPHY data type so that they can be operated upon with the built-in geography functions to perform geometry and feature selection, additional processing.

Resources

You can read more about OSM elements on the OSM Wiki. This dataset uses BigQuery GEOGRAPHY datatype which supports a set of functions that can be used to analyze geographical data, determine spatial relationships between geographical features, and construct or manipulate GEOGRAPHYs.

Content of this repository This is the repository that contains the scripts and dataset for the MSR 2019 mining challenge Github Repository with the software used : here. ======= DATASET The dataset was retrived utilizing google bigquery and dumped to a csv file for further processing, this original file with no treatment is called jsanswers.csv, here we can find the following information : 1. The Id of the question (PostId) 2. The Content (in this case the code block) 3. the lenght of the code block 4. the line count of the code block 5. The score of the post 6. The title A quick look at this files, one can notice that a postID can have multiple rows related to it, that's how multiple codeblocks are saved in the database. Filtered Dataset: Extracting code from CSV We used a python script called "ExtractCodeFromCSV.py" to extract the code from the original csv and merge all the codeblocks in their respective javascript file with the postID as name, this resulted in 336 thousand files. Running ESlint Due to the single threaded nature of ESlint, we needed to create a script to run ESlint because it took a huge toll on the machine to run it on 336 thousand files, this script is named "ESlintRunnerScript.py", it splits the files in 20 evenly distributed parts and runs 20 processes of esLinter to generate the reports, as such it generates 20 json files. Number of Violations per Rule This information was extracted using the script named "parser.py", it generated the file named "NumberofViolationsPerRule.csv" which contains the number of violations per rule used in the linter configuration in the dataset. Number of violations per Category As a way to make relevant statistics of the dataset, we generated the number of violations per rule category as defined in the eslinter website, this information was extracted using the same "parser.py" script. Individual Reports This information was extracted from the json reports, it's a csv file with PostID and violations per rule. Rules The file Rules with categories contains all the rules used and their categories.

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

Form 990 is used by the United States Internal Revenue Service to gather financial information about nonprofit/exempt organizations. This BigQuery dataset can be used to perform research and analysis of organizations that have electronically filed Forms 990, 990-EZ and 990-PF. For a complete description of data variables available in this dataset, see the IRS’s extract documentation . 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 .

Context

The Metropolitan Museum of Art, better known as the Met, provides a public domain dataset with over 200,000 objects including metadata and images. In early 2017, the Met debuted their Open Access policy to make part of their collection freely available for unrestricted use under the Creative Commons Zero designation and their own terms and conditions.

Content

This dataset provides a new view to one of the world’s premier collections of fine art. The data includes both image in Google Cloud Storage, and associated structured data in two BigQuery two tables, objects and images (1:N). Locations to images on both The Met’s website and in Google Cloud Storage are available in the BigQuery table.

Fork this kernel to get started with this dataset.

https://cloud.google.com/blog/big-data/2017/08/images/150177792553261/met03.png" alt=""> https://cloud.google.com/blog/big-data/2017/08/images/150177792553261/met03.png

Acknowledgements

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

https://console.cloud.google.com/launcher/details/the-metropolitan-museum-of-art/the-met-public-domain-art-works

This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source — http://www.metmuseum.org/about-the-met/policies-and-documents/image-resources — 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 @danieltong from Unplash.

Inspiration

What are the types of art by department?

What are the earliest photographs in the collection?

What was the most prolific period for ancient Egyptian Art?

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?

Context

Taxicabs in Chicago, Illinois, are operated by private companies and licensed by the city. There are about seven thousand licensed cabs operating within the city limits. Licenses are obtained through the purchase or lease of a taxi medallion which is then affixed to the top right hood of the car. Source: https://en.wikipedia.org/wiki/Taxicabs_of_the_United_States#Chicago

Content

This dataset includes taxi trips from 2013 to the present, reported to the City of Chicago in its role as a regulatory agency. To protect privacy but allow for aggregate analyses, the Taxi ID is consistent for any given taxi medallion number but does not show the number, Census Tracts are suppressed in some cases, and times are rounded to the nearest 15 minutes. Due to the data reporting process, not all trips are reported but the City believes that most are. See http://digital.cityofchicago.org/index.php/chicago-taxi-data-released for more information about this dataset and how it was created.

Fork this kernel to get started.

Acknowledgements

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

https://cloud.google.com/bigquery/public-data/chicago-taxi

Dataset Source: City of Chicago

This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source —https://data.cityofchicago.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 Ferdinand Stohr from Unplash.

Inspiration

What are the maximum, minimum and average fares for rides lasting 10 minutes or more? Which drop-off areas have the highest average tip? How does trip duration affect fare rates for trips lasting less than 90 minutes?

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