How many cryptocurrencies are there? In short, there were over ***** as of September 2025, although there were many more digital coins in the early months of 2022. Note, however, that a large portion of cryptocurrencies might not be that significant. There are other estimates of roughly ****** cryptocurrencies existing, but most of these are either inactive or discontinued. Due to how open the creation process of a cryptocurrency is, it is relatively easy to make one. Indeed, the top 20 cryptocurrencies make up nearly ** percent of the total market. Why are there thousands of cryptocurrencies? Any private individual or company that knows how to write a program on a blockchain can technically create a cryptocurrency. That blockchain can be an existing one. Ethereum and Binance Smart Chain are popular blockchain platforms for such ends, including smart contracts within Decentralized Finance (DeFi). The ease of crypto creation allows some individuals to find solutions to real-world payment problems while others hope to make a quick profit. This explains why some crypto lack utility. Meme coins such as Dogecoin - named after a Japanese dog species - are an infamous example, with Dogecoin's creator coming out and stating the coin started as a joke. The many types of cryptocurrency Meme coins are but one group of cryptocurrencies. Other types include altcoins, utility tokens, governance tokens, and stablecoins. Altcoins are often measured against Bitcoin, as this refers to all crypto that followed Bitcoin - the first digital currency ever created. Utility tokens and governance tokens are somewhat connected to NFTs and the metaverse. A specific example is the MANA cryptocurrency, which allows real estate purchases in the Decentraland metaverse. Stablecoins refer to the likes of Tether, which are pegged to a real-world asset like the U.S. dollar. Such coins are meant to be less volatile than regular cryptocurrency.

Cryptocurrency historical datasets from January 2012 (if available) to October 2021 were obtained and integrated from various sources and Application Programming Interfaces (APIs) including Yahoo Finance, Cryptodownload, CoinMarketCap, various Kaggle datasets, and multiple APIs. While these datasets used various formats of time (e.g., minutes, hours, days), in order to integrate the datasets days format was used for in this research study. The integrated cryptocurrency historical datasets for 80 cryptocurrencies including but not limited to Bitcoin (BTC), Ethereum (ETH), Binance Coin (BNB), Cardano (ADA), Tether (USDT), Ripple (XRP), Solana (SOL), Polkadot (DOT), USD Coin (USDC), Dogecoin (DOGE), Tron (TRX), Bitcoin Cash (BCH), Litecoin (LTC), EOS (EOS), Cosmos (ATOM), Stellar (XLM), Wrapped Bitcoin (WBTC), Uniswap (UNI), Terra (LUNA), SHIBA INU (SHIB), and 60 more cryptocurrencies were uploaded in this online Mendeley data repository. Although the primary attribute of including the mentioned cryptocurrencies was the Market Capitalization, a subject matter expert i.e., a professional trader has also guided the initial selection of the cryptocurrencies by analyzing various indicators such as Relative Strength Index (RSI), Moving Average Convergence/Divergence (MACD), MYC Signals, Bollinger Bands, Fibonacci Retracement, Stochastic Oscillator and Ichimoku Cloud. The primary features of this dataset that were used as the decision-making criteria of the CLUS-MCDA II approach are Timestamps, Open, High, Low, Closed, Volume (Currency), % Change (7 days and 24 hours), Market Cap and Weighted Price values. The available excel and CSV files in this data set are just part of the integrated data and other databases, datasets and API References that was used in this study are as follows: [1] https://finance.yahoo.com/ [2] https://coinmarketcap.com/historical/ [3] https://cryptodatadownload.com/ [4] https://kaggle.com/philmohun/cryptocurrency-financial-data [5] https://kaggle.com/deepshah16/meme-cryptocurrency-historical-data [6] https://kaggle.com/sudalairajkumar/cryptocurrencypricehistory [7] https://min-api.cryptocompare.com/data/price?fsym=BTC&tsyms=USD [8] https://min-api.cryptocompare.com/ [9] https://p.nomics.com/cryptocurrency-bitcoin-api [10] https://www.coinapi.io/ [11] https://www.coingecko.com/en/api [12] https://cryptowat.ch/ [13] https://www.alphavantage.co/ This dataset is part of the CLUS-MCDA (Cluster analysis for improving Multiple Criteria Decision Analysis) and CLUS-MCDAII Project: https://aimaghsoodi.github.io/CLUSMCDA-R-Package/ https://github.com/Aimaghsoodi/CLUS-MCDA-II https://github.com/azadkavian/CLUS-MCDA

Context

Things like Block chain, Bitcoin, Bitcoin cash, Ethereum, Ripple etc are constantly coming in the news articles I read. So I wanted to understand more about it and this post helped me get started. Once the basics are done, the data scientist inside me started raising questions like:

How many cryptocurrencies are there and what are their prices and valuations? Why is there a sudden surge in the interest in recent days? So what next? Now that we have the price data, I wanted to dig a little more about the factors affecting the price of coins. I started of with Bitcoin and there are quite a few parameters which affect the price of Bitcoin. Thanks to Blockchain Info, I was able to get quite a few parameters on once in two day basis.

This will help understand the other factors related to Bitcoin price and also help one make future predictions in a better way than just using the historical price.

Content

The dataset has one csv file for each currency. Price history is available on a daily basis from April 28, 2013. This dataset has the historical price information of some of the top crypto currencies by market capitalization.

Date : date of observation Open : Opening price on the given day High : Highest price on the given day Low : Lowest price on the given day Close : Closing price on the given day Volume : Volume of transactions on the given day

Cryptocurrencies

Cryptocurrencies are fast becoming rivals to traditional currency across the world. The digital currencies are available to purchase in many different places, making it accessible to everyone, and with retailers accepting various cryptocurrencies it could be a sign that money as we know it is about to go through a major change.

In addition, the blockchain technology on which many cryptocurrencies are based, with its revolutionary distributed digital backbone, has many other promising applications. Implementations of secure, decentralized systems can aid us in conquering organizational issues of trust and security that have plagued our society throughout the ages. In effect, we can fundamentally disrupt industries core to economies, businesses and social structures, eliminating inefficiency and human error.

Content

The dataset contains all historical daily prices (open, high, low, close) for all cryptocurrencies listed on CoinMarketCap.

Acknowledgements

• Every Cryptocurrency Daily Market Price - I initially developed kernels for this dataset before making my own scraper and dataset so that I could keep it regularly updated.
• CoinMarketCap - For the data

Cryptocurrency data is a collection of information about crypto currency users. However, companies can filter this data by gender, age, and relationship status. This means they can find the right people easily. For example, companies can search for that group if they want to talk to young people. This filtering helps companies’ better reach specific groups of cryptocurrency users. Also, the data follows important rules called GDPR. These rules help make sure companies use it legally and safely. If any part of the data is not correct, the company can remove it. Cryptocurrency data is very useful for companies that want to connect with cryptocurrency users. By filtering the data, companies can reach the exact audience they want. They can focus on gender, age, or relationship status. Following GDPR rules helps protect both the company and the people in the database. This legal use of data builds trust between everyone. Regular updates keep the information fresh and relevant. Also, removing any wrong data keeps everything accurate. The WS Phone List helps you find contact information for businesses. This invaluable database can be found on List To Data. Cryptocurrency number database is a detailed collection of information about people who use cryptocurrencies like Bitcoin and Ethereum. It gathers data from reliable sources and includes links for easy access. Support is available 24/7 for any questions, so users can get the help they need. The database shares information only with consent, making it safe to use. Companies can take advantage of this database to connect with users and send them special offers and updates. The data is trustworthy and legal, and the database is regularly updated to provide the latest information. Overall, this database is essential for reaching the expanding community of cryptocurrency users. Get it from the List To Data website.

Consumers from countries in Africa, Asia, and South America were most likely to be an owner of cryptocurrencies, such as Bitcoin, in 2025. This conclusion can be reached after combining ** different surveys from the Statista's Consumer Insights over the course of that year. Nearly one out of three respondents to Statista's survey in Nigeria, for instance, mentioned they either owned or use a digital coin, rather than *** out of 100 respondents in the United States. This is a significant change from a list that looks at the Bitcoin (BTC) trading volume in ** countries: There, the United States and Russia were said to have traded the highest amounts of this particular virtual coin. Nevertheless, African and Latin American countries are noticeable entries in that list too. Daily use, or an investment tool? The survey asked whether consumers either owned or used cryptocurrencies but does not specify their exact use or purpose. Some countries, however, are more likely to use digital currencies on a day-to-day basis. Nigeria increasingly uses mobile money operations to either pay in stores or to send money to family and friends. Polish consumers could buy several types of products with a cryptocurrency in 2019. Opposed to this is the country of Vietnam: Here, the use of Bitcoin and other cryptocurrencies as a payment method is forbidden. Owning some form of cryptocurrency in Vietnam as an investment is allowed, however. Which countries are more likely to invest in cryptocurrencies? Professional investors looking for a cryptocurrency-themed ETF were more often found in Europe than in the United or China, according to a survey in early 2020. Most of the largest crypto hedge fund managers with a location in Europe in 2020, were either from the United Kingdom or Switzerland - the country with the highest cryptocurrency adoption rate in Europe according to Statista's Global Consumer Survey. Whether this had changed by 2025 was not yet clear.

Investment cannot be separated from the level of return and risk inherent in assets. Today, investment instruments are not only stocks, currencies, bonds, deposits, savings and others. The beginning of Bitcoin’s emergence as a pioneer of Cryptocurrency was in 2009. Crypto assets are emerging rapidly and are accompanied by an increase in the number of transactions each period. The growth in the market capitalization value of crypto assets has also grown significantly. During COVID-19, many investments, such as stocks, experienced a decline due to market uncertainty. The results of this study prove that with the existence of COVID-19, the crypto market is not affected. Crypto is an attraction characterized by a high degree of fluctuation, and there is no limit to transactions in the open market 24 hours to trade. The Cryptocurrency market is currently a market that can provide short-term benefits to risk-taking investors, while the market in other investment instruments is declining. 78% of the value capitalization of the top 200 cryptocurrencies is represented by the top 9 cryptos used as samples in this study. So that if there is a decrease in these 9 cryptos, it will also have an impact on the overall capitalization value of crypto in the market. The future development of Cryptocurrencies will no longer be digital assets traded with many speculators who can control prices, it can even be digital money that can be used worldwide without any transaction fees and is controlled on a blockchain system. (2023-01-12)

This Dataset is described in Charting the Landscape of Online Cryptocurrency Manipulation. IEEE Access (2020), a study that aims to map and assess the extent of cryptocurrency manipulations within and across the online ecosystems of Twitter, Telegram, and Discord. Starting from tweets mentioning cryptocurrencies, we leveraged and followed invite URLs from platform to platform, building the invite-link network, in order to study the invite link diffusion process.

Please, refer to the paper below for more details.

Nizzoli, L., Tardelli, S., Avvenuti, M., Cresci, S., Tesconi, M. & Ferrara, E. (2020). Charting the Landscape of Online Cryptocurrency Manipulation. IEEE Access (2020).

This dataset is composed of:

~16M tweet ids shared between March and May 2019, mentioning at least one of the 3,822 cryptocurrencies (cashtags) provided by the CryptoCompare public API;

~13k nodes of the invite-link network, i.e., the information about the Telegram/Discord channels and Twitter users involved in the cryptocurrency discussion (e.g., id, name, audience, invite URL);

~62k edges of the invite-link network, i.e., the information about the flow of invites (e.g., source id, target id, weight).

With such information, one can easily retrieve the content of channels and messages through Twitter, Telegram, and Discord public APIs.

Please, refer to the README file for more details about the fields.

Bitcoin Cash is a cryptocurrency that allows more bytes to be included in each block relative to it’s common ancestor Bitcoin. 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. This dataset is part of a larger effort to make cryptocurrency data available in BigQuery through the Google Cloud Public Datasets program . The program is hosting several cryptocurrency datasets, with plans to both expand offerings to include additional cryptocurrencies and reduce the latency of updates. You can find these datasets by searching "cryptocurrency" in GCP Marketplace. For analytics interoperability, we designed a unified schema that allows all Bitcoin-like datasets to share queries. Interested in learning more about how the data from these blockchains were brought into BigQuery? Looking for more ways to analyze the data? Check out the Google Cloud Big Data blog post and try 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 .

Dogecoin is an open source peer-to-peer digital currency, favored by Shiba Inus worldwide. It is qualitatively more fun while being technically nearly identical to its close relative Bitcoin. 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. This dataset is part of a larger effort to make cryptocurrency data available in BigQuery through the Google Cloud Public Datasets program . The program is hosting several cryptocurrency datasets, with plans to both expand offerings to include additional cryptocurrencies and reduce the latency of updates. You can find these datasets by searching "cryptocurrency" in GCP Marketplace. For analytics interoperability, we designed a unified schema that allows all Bitcoin-like datasets to share queries. Interested in learning more about how the data from these blockchains were brought into BigQuery? Looking for more ways to analyze the data? Check out the Google Cloud Big Data blog post and try 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 .

Context

In the last few days, I have been hearing a lot of buzz around cryptocurrencies. Things like Block chain, Bitcoin, Bitcoin cash, Ethereum, Ripple etc are constantly coming in the news articles I read. So I wanted to understand more about it and this post helped me get started. Once the basics are done, the DS guy sleeping inside me (always lazy.!) woke up and started raising questions like

1. How many such cryptocurrencies are there and what are their prices and valuations?
2. Why is there a sudden surge in the interest in recent days? Is it due to the increase in the price in the last few days? etc.

For getting answers to all these questions (and if possible to predict the future prices ;)), I started getting the data from coinmarketcap about the cryptocurrencies.

Content

This dataset has the historical price information of some of the top cryptocurrencies by market capitalization. The currencies included are

• Bitcoin
• Ethereum
• Ripple
• Bitcoin cash
• Bitconnect
• Dash
• Ethereum Classic
• Iota
• Litecoin
• Monero
• Nem
• Neo
• Numeraire
• Stratis
• Waves

In case if you are interested in the prices of some other currencies, please post in comments section and I will try to add them in the next version. I am planning to revise it once in a week.

Dataset has one csv file for each currency. Price history is available on a daily basis from April 28, 2013 till Aug 07, 2017. The columns in the csv file are

• Date : date of observation
• Open : Opening price on the given day
• High : Highest price on the given day
• Low : Lowest price on the given day
• Close : Closing price on the given day
• Volume : Volume of transactions on the given day
• Market Cap : Market capitalization in USD

Acknowledgements

This data is taken from coinmarketcap and it is free to use the data.

Cover Image : Photo by Thomas Malama on Unsplash

Inspiration

Some of the questions which could be inferred from this dataset are:

1. How did the historical prices / market capitalizations of various currencies change over time?
2. Predicting the future price of the currencies
3. Which currencies are more volatile and which ones are more stable?
4. How does the price fluctuations of currencies correlate with each other?
5. Seasonal trend in the price fluctuations

# Crypto Price Monitoring Repository

This repository contains two CSV data files that were created to support the research titled "Price Arbitrage for DeFi Derivatives." This research is to be presented at the IEEE International Conference on Blockchain and Cryptocurrencies, taking place on 5th May 2023 in Dubai, UAE. The data files include monitoring prices for various crypto assets from several sources. The data files are structured with five columns, providing information about the symbol, unified symbol, time, price, and source of the price.

## Data Files

There are two CSV data files in this repository (one for each date):

1. `Pricemon_results_2022_11_01.csv`
2. `Pricemon_results_2022_11_08.csv`

## Data Format

Both data files have the same format and structure, with the following five columns:

1. `symbol`: The trading symbol for the crypto asset (e.g., BTC, ETH).
2. `unified_symbol`: A standardized symbol used across different platforms.
3. `time`: Timestamp for when the price data was recorded (in UTC format).
4. `price`: The price of the crypto asset at the given time (in USD).
5. `source`: The name of the price source for the data.

## Price Sources

The `source` column in the data files refers to the provider of the price data for each record. The sources include:

- `chainlink`: Chainlink Price Oracle
- `mycellium`: Built-in oracle of the Mycellium platform
- `bitfinex`: Bitfinex cryptocurrency exchange
- `ftx`: FTX cryptocurrency exchange
- `binance`: Binance cryptocurrency exchange

## Usage

You can use these data files for various purposes, such as analyzing price discrepancies across different sources, identifying trends, or developing trading algorithms. To use the data, simply import the CSV files into your preferred data processing or analysis tool.

### Example

Here's an example of how you can read and display the data using Python and the pandas library:

import pandas as pd

# Read the data from CSV file
data = pd.read_csv('Pricemon_results_2022_11_01.csv')

# Display the first 5 rows of the data
print(data.head())`

## Acknowledgements

These datasets were recorded and supported by Datamint company (value-added on-chain data provider) and its team.

## Contributing

If you have any suggestions or find any issues with the data, please feel free to contact authors.

Authors, through Twitter API, collected this database over eight months. These data are tweets of over 50 experts regarding market analysis of 40 cryptocurrencies. These experts are known as influencers on social networks such as Twitter. The theory of Behavioral economics shows that the opinions of people, especially experts, can impact the stock market trend (here, cryptocurrencies). Existing databases often cover tweets related to one or more cryptocurrencies. Also, in these databases, no attention is paid to the user's expertise, and most of the data is extracted using hashtags. Failure to pay attention to the user's expertise causes the irrelevant volume to increase and the neutral polarity to increase considerably. This database has a main table named "Tweets1" with 11 columns and 40 tables to separate comments related to each cryptocurrency. The columns of the main table and the cryptocurrency tables are explained in the attached document. Researchers can use this dataset in various machine learning tasks, such as sentiment analysis and deep transfer learning with sentiment analysis. Also, this data can be used to check the impact of influencers' opinions on the cryptocurrency market trend. The use of this database is allowed by mentioning the source. Also, in this version, we have added the excel version of the database and Python code to extract the names of influencers and tweets. in Version(3): In the new version, three datasets related to historical prices and sentiments related to Bitcoin, Ethereum, and Binance have been added as Excel files from January 1, 2023, to June 12, 2023. Also, two datasets of 52 influential tweets in cryptocurrencies have been published, along with the score and polarity of sentiments regarding more than 300 cryptocurrencies from February 2021 to June 2023. Also, two Python codes related to the sentiment analysis algorithm of tweets with Python have been published. This algorithm combines RoBERTa pre-trained deep neural network and BiGRU deep neural network with an attention layer (see code Preprocessing_and_sentiment_analysis with python).

This comprehensive dataset offers a thorough and meticulous analysis of Bitcoin transactions, providing a detailed and all-encompassing view. It delves into crucial metrics such as transaction volume, fees, and the overall activity of the network, shedding light on the pulse of the cryptocurrency world. The daily updates not only reflect the dynamic nature of this digital landscape but also make this dataset an essential tool for a diverse range of individuals. Whether you're an astute financial expert conducting in-depth market analyses, a curious researcher unraveling the complexities of the blockchain, or simply a passionate cryptocurrency enthusiast eager to stay informed, this dataset caters to your needs.

If you require further insights or have any inquiries regarding this dataset, please don't hesitate to contact us at info@blockchair.com. Our team is dedicated to assisting you and ensuring you maximize the value of the information provided.

This comprehensive dataset offers a thorough and meticulous analysis of Dogecoin transactions, providing a detailed and all-encompassing view. It delves into crucial metrics such as transaction volume, fees, and the overall activity of the network, shedding light on the pulse of the cryptocurrency world. The daily updates not only reflect the dynamic nature of this digital landscape but also make this dataset an essential tool for a diverse range of individuals. Whether you're an astute financial expert conducting in-depth market analyses, a curious researcher unraveling the complexities of the blockchain, or simply a passionate cryptocurrency enthusiast eager to stay informed, this dataset caters to your needs.

If you require further insights or have any inquiries regarding this dataset, please don't hesitate to contact us at info@blockchair.com. Our team is dedicated to assisting you and ensuring you maximize the value of the information provided.

This comprehensive dataset offers a thorough and meticulous analysis of Litecoin transactions, providing a detailed and all-encompassing view. It delves into crucial metrics such as transaction volume, fees, and the overall activity of the network, shedding light on the pulse of the cryptocurrency world. The daily updates not only reflect the dynamic nature of this digital landscape but also make this dataset an essential tool for a diverse range of individuals. Whether you're an astute financial expert conducting in-depth market analyses, a curious researcher unraveling the complexities of the blockchain, or simply a passionate cryptocurrency enthusiast eager to stay informed, this dataset caters to your needs.

If you require further insights or have any inquiries regarding this dataset, please don't hesitate to contact us at info@blockchair.com. Our team is dedicated to assisting you and ensuring you maximize the value of the information provided.

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 .

The cryptocurrency market surpassed the barrier of $100 billion market capitalization in June 2017, after months of steady growth. Despite its increasing relevance in the financial world, a comprehensive analysis of the whole system is still lacking, as most studies have focused exclusively on the behaviour of one (Bitcoin) or few cryptocurrencies. Here, we consider the history of the entire market and analyse the behaviour of 1469 cryptocurrencies introduced between April 2013 and May 2017. We reveal that, while new cryptocurrencies appear and disappear continuously and their market capitalization is increasing (super-)exponentially, several statistical properties of the market have been stable for years. These include the number of active cryptocurrencies, market share distribution and the turnover of cryptocurrencies. Adopting an ecological perspective, we show that the so-called neutral model of evolution is able to reproduce a number of key empirical observations, despite its simplicity and the assumption of no selective advantage of one cryptocurrency over another. Our results shed light on the properties of the cryptocurrency market and establish a first formal link between ecological modelling and the study of this growing system. We anticipate they will spark further research in this direction.

Delving into the realm of Litecoin inputs, this dataset provides a broad and detailed view. Highlighting the intricate workings of the blockchain system, it stands as a continuously updated, invaluable asset in the ever-changing landscape of blockchain. This resource is a wellspring of information, suitable for various users. Whether you're a financial professional examining input dynamics, a researcher navigating the subtleties of input structures, or a blockchain enthusiast keen on understanding this technology's core components, this dataset is tailored for your exploration.

For any additional information or questions about this input dataset, please reach out to us at info@blockchair.com. Our devoted team is on standby to assist, ensuring you gain the utmost value from the data presented.

Litecoin is a cryptocurrency and distributed ledger system (blockchain) that is nearly identical to Bitcoin. Litecoin uses the scrypt algorithm (vs. Bitcoin’s SHA256) for proof-of-work and has a 2.5 minute block time (vs. Bitcoin’s 10 minute block time). Aside from these differences, it is nearly identical to Bitcoin.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. This dataset is part of a larger effort to make cryptocurrency data available in BigQuery through the Google Cloud Public Datasets program . The program is hosting several cryptocurrency datasets, with plans to both expand offerings to include additional cryptocurrencies and reduce the latency of updates. You can find these datasets by searching "cryptocurrency" in GCP Marketplace. For analytics interoperability, we designed a unified schema that allows all Bitcoin-like datasets to share queries. Interested in learning more about how the data from these blockchains were brought into BigQuery? Looking for more ways to analyze the data? Check out the Google Cloud Big Data blog post and try 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 .