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

In 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

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?

Libraries.io gathers data on open source software from 33 package managers and 3 source code repositories. We track over 2.4m unique open source projects, 25m repositories and 121m interdependencies between them. This gives Libraries.io a unique understanding of open source software. In this release you will find data about software distributed and/or crafted publicly on the Internet. You will find information about its development, its distribution and its relationship with other software included as a dependency. You will not find any information about the individuals who create and maintain these projects. 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 .

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.

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. We've made available a number of tables (explained in detail below): 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. Example analyses are given below. This dataset is part of a larger effort to make data available in BigQuery through the Google Cloud Public Datasets program . OSM itself is produced as a public good by volunteers, and there are no guarantees about data quality. Interested in learning more about how these data were brought into BigQuery and how you can use them? Check out the sample queries below to get started. This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery .

The 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

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

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.

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:

used GitHub from the beginning (first commit not more than seven days before project creation date according to GHTorrent),

were active for at least one year (365 days) before the first build with Travis CI (before_ci),

used Travis CI at least for one year (during_ci),

had commit or merge activity on the default branch in both of these phases, and

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:

have Java or Ruby as their project language

used GitHub from the beginning (first commit not more than seven days before project creation date according to GHTorrent)

have commit activity for at least two years (730 days)

are engineered software projects (at least 10 watchers)

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.

Context

The CMS National Plan and Provider Enumeration System (NPPES) was developed as part of the Administrative Simplification provisions in the original HIPAA act. The primary purpose of NPPES was to develop a unique identifier for each physician that billed medicare and medicaid. This identifier is now known as the National Provider Identifier Standard (NPI) which is a required 10 digit number that is unique to an individual provider at the national level.

Once an NPI record is assigned to a healthcare provider, parts of the NPI record that have public relevance, including the provider’s name, speciality, and practice address are published in a searchable website as well as downloadable file of zipped data containing all of the FOIA disclosable health care provider data in NPPES and a separate PDF file of code values which documents and lists the descriptions for all of the codes found in the data file.

Content

The dataset contains the latest NPI downloadable file in an easy to query BigQuery table, npi_raw. In addition, there is a second table, npi_optimized which harnesses the power of Big Query’s next-generation columnar storage format to provide an analytical view of the NPI data containing description fields for the codes based on the mappings in Data Dissemination Public File - Code Values documentation as well as external lookups to the healthcare provider taxonomy codes . While this generates hundreds of columns, BigQuery makes it possible to process all this data effectively and have a convenient single lookup table for all provider information.

Fork this kernel to get started.

Acknowledgements

https://bigquery.cloud.google.com/dataset/bigquery-public-data:nppes?_ga=2.117120578.-577194880.1523455401

https://console.cloud.google.com/marketplace/details/hhs/nppes?filter=category:science-research

Dataset Source: Center for Medicare and Medicaid Services. 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 are the top ten most common types of physicians in Mountain View?

What are the names and phone numbers of dentists in California who studied public health?

These datasets are important to genomics researchers because they characterize several aspects of what the scientific community has learned to date about human sequence variants. Making this human annotation data freely available in GCP will enable researchers to focus less on data movement and management tasks associated with procuring this data and instead make immediate use of the data to better understand the clinical relevance of particular variant such as disease causing or protective variants (ClinVar), search a catalog of SNPs that have been identified in the human genome (dbSNP), and discover how frequently a particular variant occurs across the human population (1000Genomes, ESP, ExAC, gnomAD) This human annotation dataset contains both a mirror of the original Variant Call Files (VCF) files from NCBI, NHLBI Exome Sequencing Project (ESP) and ensembl as Google Cloud Storage (GCS) objects. In addition, these human sequence variants have also been translated into a particular variant table format and made available in Google BigQuery giving researchers the ability to use cloud technology and code repositories such as the Verily Life Sciences Annotation Toolkit to perform analyses in parallel. 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 . This public dataset is hosted in Google Cloud Storage and available free to use. Use this quick start guide to quickly learn how to access public datasets on Google Cloud Storage.

Ethereum is a crypto currency which leverages blockchain technology to store transactions in a distributed ledger. A blockchain is an ever-growing "tree" of blocks, where each block contains a number of transactions. To learn more, read the "Ethereum in BigQuery: a Public Dataset for smart contract analytics" blog post by Google Developer Advocate Allen Day. This dataset is part of a larger effort to make cryptocurrency data available in BigQuery through the Google Cloud Public Datasets program.

This dataset contains two files.

1) A python pickle file (github_dataset.zip) that contains Github repositories with datasets. Specifically, using Google’s public dataset copy of Github and the BigQuery service to build a list of repositories that have a CSV or XLSX or XLS file. We then used the GitHub API to collect nformation about each repository in this list. The resulting dataset consists of 87936 repositories that contain at least a CSV, XLSX or XLS file, alongside with information about their features (e.g. number of open and closed issues and license) from GitHub. This corpus had more than two million data files. We then excluded those files withless then ten rows, which was the case for 65537 repositories with a total of 1,467,240 data files.

2) A python pickle file (processed_dataset.zip) containing the feature information necessary to train a machine learning model to predict reuse on these Github datasets

Source code can be found at: https://github.com/laurakoesten/Dataset-Reuse-Indicators

For a full description of the content see:

Koesten, Laura and Vougiouklis, Pavlos and Simperl, Elena and Groth, Paul, Dataset Reuse: Translating Principles to Practice. Available at SSRN: https://ssrn.com/abstract=3589836 or http://dx.doi.org/10.2139/ssrn.3589836

Ethereum Classic is a cryptocurrency with shared history with the Ethereum cryptocurrency. On technical merits, the two cryptocurrencies are nearly identical, differing only in programming language features supported by the Ethereum Virtual machine which is used to write smart contracts. This dataset contains the blockchain data in their entirety, pre-processed to be human-friendly and to support common use cases such as auditing, investigating, and researching the economic and financial properties of the system. Interested in learning more about how Cloud Public Data is working to make data from blockchains and cryptocurrencies more accessible? Check out our blog post on the Google Cloud Big Data Blog and try the sample query below to get started. 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 .

Overview

BigQuery is Google's fully managed, NoOps, low cost analytics database. With BigQuery you can query terabytes and terabytes of data without having any infrastructure to manage, or needing a database administrator.

BigQuery Machine Learning BQML is where data analysts can create, train, evaluate, and predict with machine learning models with minimal coding.

In this you will explore millions of New York City yellow taxi cab trips available in a BigQuery Public Dataset. You will create a machine learning model inside of BigQuery to predict the fare of the cab ride given your model inputs and evaluate the performance of your model and make predictions with it.

perform the following tasks:

Query and explore the public taxi cab dataset. Create a training and evaluation dataset to be used for batch prediction. Create a forecasting (linear regression) model in BQML. Evaluate the performance of your machine learning model.

There are several model types to choose from:

Forecasting numeric values like next month's sales with Linear Regression (linear_reg). Binary or Multiclass Classification like spam or not spam email by using Logistic Regression (logistic_reg). k-Means Clustering for when you want unsupervised learning for exploration (kmeans).

Note: There are many additional model types used in Machine Learning (like Neural Networks and decision trees) and available using libraries like TensorFlow. At this time, BQML supports the three listed above. Follow the BQML roadmap for more information.

For reference sake of you we also released notebook which is available in this try to explore from that .use AutoMl foundational Models to automatically selecting important features from dataset and Model selection .

you can also go with spectral clustering algorithms upcourse it is not an unsupervised task but it is correlated ,visualize the Fare trip prices .so that cab drive easily identifies fare trips in their respective locations .

Build a Forecasting model which helps for cab drives like (uber,rapido) which reach their customers easily and short time

Dataset : ⏱️ 'trip_duration': How long did the journey last?[in Seconds] 🛣️ 'distance_traveled': How far did the taxi travel?[in Km] 🧑‍🤝‍🧑 'num_of_passengers': How many passengers were in the taxi? 💵 'fare': What's the base fare for the journey?[In INR] 💲 'tip': How much did the driver receive in tips?[In INR] 🎀 'miscellaneous_fees': Were there any additional charges during the trip?e.g. tolls, convenience fees, GST etc.[In INR] 💰 'total_fare': The grand total for the ride (this is your prediction target!).[In INR] ⚡ 'surge_applied': Was there a surge pricing applied? Yes or no?

IF IT IS USEFUL UPVOTE THE DATASET. THANK YOU!

The American Community Survey (ACS) is an ongoing survey that provides vital information on a yearly basis about our nation and its people by contacting over 3.5 million households across the country. The resulting data provides incredibly detailed demographic information across the US aggregated at various geographic levels which helps determine how more than $675 billion in federal and state funding are distributed each year. Businesses use ACS data to inform strategic decision-making. ACS data can be used as a component of market research, provide information about concentrations of potential employees with a specific education or occupation, and which communities could be good places to build offices or facilities. For example, someone scouting a new location for an assisted-living center might look for an area with a large proportion of seniors and a large proportion of people employed in nursing occupations. Through the ACS, we know more about jobs and occupations, educational attainment, veterans, whether people own or rent their homes, and other topics. Public officials, planners, and entrepreneurs use this information to assess the past and plan the future. For more information, see the Census Bureau's ACS Information Guide . This public dataset is hosted in Google BigQuery as part of the Google Cloud Public Datasets Program , with Carto providing cleaning and onboarding support. It 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 .

Spider 2.0 is a comprehensive code generation agent task that includes 632 examples. The agent has to interactively explore various types of databases, such as BigQuery, Snowflake, Postgres, ClickHouse, DuckDB, and SQLite. It is required to engage with complex SQL workflows, process extensive contexts, perform intricate reasoning, and generate multiple SQL queries with diverse operations, often exceeding 100 lines across multiple interactions.