Blockchain technology is forecast to increase to nearly 1,000 trillion U.S. dollars by 2032, but this was lower than in a previous forecast. This is according to a market research forecast, focusing on blockchain with cloud applications for specific business segments. The numbers do not include decentralized applications such as blockchain gaming. Originally, a forecast from June 2022 predicted "blockchain technology" would reach 1,235 billion U.S. dollars by 2030, at a CAGR of 82.8 percent. A newer forecast from December 2023 predicts a value of 943 billion U.S. dollars in 2032 with a CAGR of 56.1 percent. The source does not explain this difference.

Dataset Card for LLM Blockchain Benchmark

  Dataset Summary

The Blockchain Benchmark Dataset is a comprehensive collection of data specifically curated for benchmarking Language Models (LMs) in the domain of blockchain technology. This dataset is designed to facilitate research and development in natural language understanding within the blockchain domain. A complete list of tasks: ['general-reasoning', 'code', 'math']

  Supported Tasks and Leaderboards

Model… See the full description on the dataset page: https://huggingface.co/datasets/revflask/blockchain-benchmark.

Context

Dash is a cryptocurrency governed by a decentralized autonomous organization (DAO) run by a subset of users, called "masternodes". The currency permits fast transactions that are difficult to trace. 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.

Content

You can access the data from BigQuery in your notebook with bigquery-public-data.crypto_dash dataset.

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_dash.[TABLENAME].

Acknowledgements

This dataset wouldn't be possible without the help of BigQuery and all of their contributions to public data.

From the Texas Work Group on Blockchain Matters, this is the report and proposed master plan to expand the blockchain industry in Texas in compliance with House Bill 1576, passed by the 87th Texas Legislature. This report examines the current blockchain industry in Texas, reviews the state’s current academic, educational, and workforce needs required to grow the industry, and identifies areas for economic growth and development opportunities presented by blockchain technology. The report contains legislative and policy recommendations aimed at encouraging the industry’s expansion and establishing regulatory and legal clarity to establish Texas as a leader in the blockchain technology and cryptocurrency space.

Context

Blockchain technology, first implemented by Satoshi Nakamoto in 2009 as a core component of Bitcoin, is a distributed, public ledger recording transactions. Its usage allows secure peer-to-peer communication by linking blocks containing hash pointers to a previous block, a timestamp, and transaction data. Bitcoin is a decentralized digital currency (cryptocurrency) which leverages the Blockchain to store transactions in a distributed manner in order to mitigate against flaws in the financial industry.

Nearly ten years after its inception, Bitcoin and other cryptocurrencies experienced an explosion in popular awareness. The value of Bitcoin, on the other hand, has experienced more volatility. Meanwhile, as use cases of Bitcoin and Blockchain grow, mature, and expand, hype and controversy have swirled.

Content

In this dataset, you will have access to information about blockchain blocks and transactions. All historical data are in the bigquery-public-data:crypto_bitcoin dataset. It’s updated it every 10 minutes. The data can be joined with historical prices in kernels. See available similar datasets here: https://www.kaggle.com/datasets?search=bitcoin.

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_bitcoin.[TABLENAME]. Fork this kernel to get started.

Method & Acknowledgements

Allen Day (Twitter | Medium), Google Cloud Developer Advocate & Colin Bookman, Google Cloud Customer Engineer retrieve data from the Bitcoin network using a custom client available on GitHub that they built with the bitcoinj Java library. Historical data from the origin block to 2018-01-31 were loaded in bulk to two BigQuery tables, blocks_raw and transactions. These tables contain fresh data, as they are now appended when new blocks are broadcast to the Bitcoin network. For additional information visit the Google Cloud Big Data and Machine Learning Blog post "Bitcoin in BigQuery: Blockchain analytics on public data".

Photo by Andre Francois on Unsplash.

Inspiration

• How many bitcoins are sent each day?
• How many addresses receive bitcoin each day?
• Compare transaction volume to historical prices by joining with other available data sources

Bitcoin's blockchain size was close to reaching 5450 gigabytes in 2024, as the database saw exponential growth by nearly one gigabyte every few days. The Bitcoin blockchain contains a continuously growing and tamper-evident list of all Bitcoin transactions and records since its initial release in January 2009. Bitcoin has a set limit of 21 million coins, the last of which will be mined around 2140, according to a forecast made in 2017. Bitcoin mining: A somewhat uncharted world Despite interest in the topic, there are few accurate figures on how big Bitcoin mining is on a country-by-country basis. Bitcoin's design philosophy is at the heart of this. Created out of protest against governments and central banks, Bitcoin's blockchain effectively hides both the country of origin and the destination country within a (mining) transaction. Research involving IP addresses placed the United States as the world's most Bitcoin mining country in 2022 - but the source admits IP addresses can easily be manipulated using VPN. Note that mining figures are different from figures on Bitcoin trading: Africa and Latin America were more interested in buying and selling BTC than some of the world's developed economies. Bitcoin developments Bitcoin's trade volume slowed in the second quarter of 2023, after hitting a noticeable growth at the beginning of the year. The coin outperformed most of the market. Some attribute this to the announcement in June 203 that BlackRock filed for a Bitcoin ETF. This iShares Bitcoin Trust was to use Coinbase Custody as its custodian. Regulators in the United States had not yet approved any applications for spot ETFs on Bitcoin.

The global blockchain technology market size was estimated at USD 31.28 billion in 2024 and is projected to grow at a CAGR of 90.1% from 2025 to 2030

In many blockchains, e.g., Ethereum, Binance Smart Chain (BSC), the primary representation used for wallet addresses is a hardly memorable 40-digit hexadecimal string. As a result, users often select addresses from their recent transaction history, which enables blockchain address poisoning. The adversary first generates lookalike addresses similar to one with which the victim has previously interacted, and then engages with the victim to “poison” their transaction history. The goal is to have the victim mistakenly send tokens to the lookalike address, as opposed to the intended recipient. We develop a detection system and perform measurements over two years on Ethereum and BSC. We release the detection result dataset, including over 17 million attack attempts on Ethereum and successful payoff transfers. We also provide a jupyter notebook explaining 1) how to access the dataset, 2) how to produce descriptive statistics such as the number of poisoning transfers, and 3) how to manually verify the payoff transfer on Etherscan (BSCscan). This dataset will enable other researchers to validate our results as well as conduct further analysis.

This dataset contains bitcoin transfer transactions extracted from the Bitcoin Mainnet blockchain. Details of the datasets are given below: FILENAME FORMAT: The filenames have the following format: btc-tx- where For example file btc-tx-100000-149999-aa.bz2 and the rest of the parts if any contain transactions from block 100000 to block 149999 inclusive. The files are compressed with bzip2. They can be uncompressed using command bunzip2. TRANSACTION FORMAT: Each line in a file corresponds to a transaction. The transaction has the following format: BLOCK TIME FORMAT: The block time file has the following format: IMPORTANT NOTE: Public Bitcoin Mainnet blockchain data is open and can be obtained by connecting as a node on the blockchain or by using the block explorer web sites such as https://btcscan.org . The downloaders and users of this dataset accept the full responsibility of using the data in GDPR compliant manner or any other regulations. We provide the data as is and we cannot be held responsible for anything. NOTE: If you use this dataset, please do not forget to add the DOI number to the citation. If you use our dataset in your research, please also cite our paper: https://link.springer.com/chapter/10.1007/978-3-030-94590-9_14 @incollection{kilicc2022analyzing, title={Analyzing Large-Scale Blockchain Transaction Graphs for Fraudulent Activities}, author={K{\i}l{\i}{\c{c}}, Baran and {"O}zturan, Can and {\c{S}}en, Alper}, booktitle={Big Data and Artificial Intelligence in Digital Finance}, pages={253--267}, year={2022}, publisher={Springer, Cham} }

Description This dataset contains the Pagerank values and rankings of 147.098.561 Ethereum addresses. File format The dataset is compressed with bzip2. It can be uncompressed using the command bunzip2. It is a space-delimited plain text file and has the following four fields: Ethereum Address: A 42-character hexadecimal Ethereum address in the lowercase form (not in checksummed (mixed-case) form). E.g. 0x3f5ce5fbfe3e9af3971dd833d26ba9b5c936f0be rank: Unique Pagerank rank where the ties (addresses having the same Pagerank value) are resolved by sorting the addresses by hexadecimal value rank with ties: Pagerank rank where the ties (addresses having the same Pagerank value) have the same rank. Pagerank value: Pagerank of the address calculated using Pagerank algorithm. Dataset Generation The Ethereum transactions between blocks 0 (mined on 30.07.2015) and 13.799.999 (mined on 14.12.2021) are extracted. A transaction graph is constructed, where Ethereum addresses are nodes of the graph and the transactions are edges of the graph. Pagerank is applied on this transaction graph. This computation is performed using the system presented in the paper 'Parallel analysis of Ethereum blockchain transaction data using cluster computing'. Note If you use our dataset in your research, please cite our paper: https://link.springer.com/article/10.1007/s10586-021-03511-0 @article{kilic2022parallel, title={Parallel Analysis of Ethereum Blockchain Transaction Data using Cluster Computing}, journal={Cluster Computing}, author={K{\i}l{\i}{\c{c}}, Baran and {"O}zturan, Can and Sen, Alper}, year={2022}, month={Jan} } Other Datasets If you are interested, please also check out our Pagerank Dataset for Bitcoin Blockchain.

Уникальный идентификатор https://doi.org/10.5281/zenodo.4718440 Набор данных обновлен Dec 19, 2022 Набор данных предоставлен Zenodo Авторы Can Özturan; Can Özturan; Alper Şen; Alper Şen; Baran Kılıç; Baran Kılıç Лицензия Attribution 4.0 (CC BY 4.0) Информация о лицензии была получена автоматически Описание This dataset contains ether as well as popular ERC20 token transfer transactions extracted from the Ethereum Mainnet blockchain. Only send ether, contract function call, contract deployment transactions are present in the dataset. Miner reward (static block reward) and "uncle block inclusion reward" are added as transactions to the dataset. Transaction fee reward and "uncles reward" are not currently included in the dataset. Details of the datasets are given below: FILENAME FORMAT: The filenames have the following format: eth-tx- where For example file eth-tx-1000000-1099999.txt.bz2 contains transactions from block 1000000 to block 1099999 inclusive. The files are compressed with bzip2. They can be uncompressed using command bunzip2. TRANSACTION FORMAT: Each line in a file corresponds to a transaction. The transaction has the following format: units. ERC20 tokens transfers (transfer and transferFrom function calls in ERC20 contract) are indicated by token symbol. For example GUSD is Gemini USD stable coin. The JSON file erc20tokens.json given below contains the details of ERC20 tokens. Failed transactions are prefixed with "F-". BLOCK TIME FORMAT: The block time file has the following format: erc20tokens.json FILE: This file contains the list of popular ERC20 token contracts whose transfer/transferFrom transactions appear in the data files. ERC20 token list: USDT TRYb XAUt BNB LEO LINK HT HEDG MKR CRO VEN INO PAX INB SNX REP MOF ZRX SXP OKB XIN OMG SAI HOT DAI EURS HPT BUSD USDC SUSD HDG QCAD PLUS BTCB WBTC cWBTC renBTC sBTC imBTC pBTC IMPORTANT NOTE: Public Ethereum Mainnet blockchain data is open and can be obtained by connecting as a node on the blockchain or by using the block explorer web sites such as http://etherscan.io . The downloaders and users of this dataset accept the full responsibility of using the data in GDPR compliant manner or any other regulations. We provide the data as is and we cannot be held responsible for anything. NOTE: If you use this dataset, please do not forget to add the DOI number to the citation. If you use our dataset in your research, please also cite our paper: https://link.springer.com/article/10.1007/s10586-021-03511-0 @article{kilic2022parallel, title={Parallel Analysis of Ethereum Blockchain Transaction Data using Cluster Computing}, journal={Cluster Computing}, author={K{\i}l{\i}{\c{c}}, Baran and {"O}zturan, Can and Sen, Alper}, year={2022}, month={Jan} }

Blockchain in Small and Medium Business Market Outlook

The global blockchain in small and medium business market size was valued at approximately USD 1.5 billion in 2023 and is projected to reach USD 12.8 billion by 2032, growing at an impressive CAGR of 26.7% during the forecast period. The market growth is primarily driven by the increasing adoption of blockchain technology across various industry verticals, including retail, healthcare, manufacturing, and BFSI, which seek to enhance operational efficiency, transparency, and security. As small and medium enterprises (SMEs) continue to embrace digital transformation, blockchain offers cost-effective solutions that can revolutionize their operations, providing a significant competitive advantage.

One of the primary growth factors for the blockchain market in SMEs is the demand for enhanced supply chain management solutions. Blockchain technology offers unparalleled transparency and traceability, which are crucial for improving supply chain operations. SMEs can benefit from real-time tracking of goods, reduction of fraud, and improvement in inventory management, resulting in cost savings and increased efficiency. This capability is particularly appealing to SMEs in manufacturing and retail sectors, where supply chain efficiency directly influences profitability and customer satisfaction.

Another significant growth driver is the increasing need for secure and efficient payment systems. Blockchain's decentralized nature ensures transactions are secure, reducing the risk of fraud and unauthorized access. For SMEs, which often operate on thin margins and limited resources, blockchain-based payment solutions can enhance security and reduce transaction fees. Cryptocurrencies and decentralized finance (DeFi) platforms provide SMEs with alternative financial solutions, enabling direct transactions with customers and suppliers without relying on traditional banking systems.

Furthermore, the rise of smart contracts has contributed to the growing adoption of blockchain technology among SMEs. Smart contracts, which are self-executing with the terms of the agreement directly written into code, automate processes and reduce the need for intermediaries. This automation can lead to significant cost savings and efficiency improvements, particularly in legal and administrative tasks. Industries like BFSI and real estate are increasingly leveraging smart contracts to streamline operations, attract new customers, and improve service delivery, thus contributing to the market's growth.

Regionally, North America currently leads the blockchain market for SMEs, with Europe and Asia Pacific also showing promising growth. The mature digital ecosystem and supportive regulatory frameworks in North America have facilitated the rapid adoption of blockchain technology. In contrast, Asia Pacific's growth is propelled by the increasing number of startups and government initiatives promoting blockchain adoption across various sectors. Europe, with its focus on data privacy and security, is also witnessing significant blockchain adoption among SMEs, particularly in financial services and healthcare industries.

Component Analysis

The component segment of the blockchain in SMEs market is divided into platforms and services. Blockchain platforms are the foundational technology that powers the various applications of blockchain. These platforms provide the infrastructure necessary for developing decentralized applications (dApps) and smart contracts. As SMEs continue to explore and implement blockchain solutions, the demand for robust and scalable platforms is expected to grow. Established blockchain platforms like Ethereum, Hyperledger, and Corda, as well as emerging platforms, offer diverse functionalities and customization options that cater to the specific needs of SMEs, driving further market growth.

On the other hand, blockchain services encompass consultancy, development, integration, and managed services that guide SMEs through their blockchain adoption journey. Many SMEs lack the in-house expertise required to develop and implement blockchain solutions, making professional services crucial to successful deployment. Blockchain consultancy services assist SMEs in identifying suitable use cases, developing strategic roadmaps, and navigating the complexities of blockchain technology. Moreover, integration and managed services ensure seamless implementation and operation of blockchain solutions within the existing IT infrastructure of SMEs, minimizing disruptions and maximizing benefits.

The in

Step into the fascinating realm of Bitcoin outputs with this extraordinary dataset, which offers a comprehensive and nuanced perspective. It sheds light on the intricate mechanisms of the Bitcoin system, presenting itself as a constantly updated and priceless resource in the ever-evolving world of blockchain technology. This compilation serves as a veritable fountain of knowledge, tailored to cater to a diverse range of users. Whether you're a seasoned financial expert meticulously analyzing output dynamics, a dedicated researcher delving into the complexities of output configurations, or a fervent blockchain enthusiast seeking to grasp the pivotal elements of this groundbreaking technology, rest assured that this meticulously curated dataset has been crafted with your needs in mind.

For any further details or inquiries about this output dataset, please connect with us at info@blockchair.com. Our dedicated team is always available to guide and ensure you harness the full potential of the information at hand.

The blockchain market size is recorded to be USD 15.20 billion in 2024 and is expected to reach USD 776.47 billion by 2035, at a CAGR of 42.98%.

Dataset ReadMe

Overview

• This data was collected to study the structural properties of blockchain overlay networks. It contains several snapshots of seven different networks from the period 26 Jun 2020 to 20 July 2020.
• The networks included are (alphabetically): Bitcoin, Bitcoin Cash, Dash, Dogecoin, Ethereum, Litecoin, and ZCash.

• Studying the graph characteristics of these networks is beneficial;

  • It helps us evaluate the system's performance, robustness, and scalability by examining the network structure, node distribution, and communication patterns.
  • This analysis helps us identify bottlenecks and find ways to optimize efficiency and throughput.

  Moreover, understanding the vulnerabilities and attack possibilities unique to these networks allows us to develop proactive defense mechanisms and mitigate potential threats.

Data collection method: ask all reachable nodes continuously for their known peers. In Bitcoin's parlor, we send GETADDR messages and store all ADDR replies, drawing a connection between the sending node to all ip addresses contained in the ADDR message.

Data Description

All IP addresses have been replaced by numbers (NodeID) for ethical reasons. NodeIDs are consistent accross all files. The same NodeID corresponds to the same ip in ALL files (if present). Filenames contain the timestamp and the corresponding network. The date-time format is YYYYMMDD-HHMISS.

• File Contents: The edgelist files store information about the structure of the connectivity graph. Each file represents an edgelist of a graph at the specified time-stamp. Each line in a file corresponds the the list of known peers to a node. The NodeID of the node is the first number of each line. Example: the following line

  S N1 N2 N3 N4

  means that node S knows of nodes N1..N4; their ip addresses were included in S's ADDR responses.

To process the files in snap and networkx proper transformations have to be made. Please read the relevant documentation to find the appropriate input.

Research

This dataset has been used in the following works: - @inproceedings{aris_ssec,
author = {Paphitis, Aristodemos and Kourtellis, Nicolas and Sirivianos, Michael}, title = {Graph Analysis of Blockchain {P2P} Overlays and their Security Implications}, booktitle = {Proceedings of the 9th International Symposium on Security and Privacy in Social Networks and Big Data (SocialSec 2023)}, series = {Lecture Notes in Computer Science}, volume = {13983}, publisher = {Springer Nature}, year = {2023}, }

• @inproceedings{aris_nss,
  author = {Paphitis, Aristodemos and Kourtellis, Nicolas and Sirivianos, Michael}, title = {Resilience of Blockchain Overlay Networks}, booktitle = {Proceedings of the 17th International Conference on Network and System Security (NSS 2023)},
  series = {Lecture Notes in Computer Science}, volume = {14097}, publisher = {Springer Nature}, year = {2023}, }

License and Attribution

• You are free to share and adapt according to CC BY (4.0) https://creativecommons.org/licenses/by/4.0/

• Please cite as:

  Aristodemos Paphitis, Nicolas Kourtellis, and Michael Sirivianos. A First Look into the Structural Properties of Blockchain P2P Overlays. DOI:https://doi.org/10.6084/m9.figshare.23522919

• bibtex:

  @misc{paphitis_first_nodate,
  author = {Paphitis, Aristodemos and Kourtellis, Nicolas and Sirivianos, Michael}, title = {A First Look into the Structural Properties of Blockchain {P2P} Overlays}, howpublished = {Public dataset with figshare}, doi = {10.6084/m9.figshare.23522919}, }

Contact Information

• Aris Paphitis: am.paphitis@edu.cut.ac.cy
• https://netsysci.cut.ac.cy/aristodemos.paphitis/

Bitcoin is a crypto currency leveraging blockchain technology to store transactions in a distributed ledger. A blockchain is an ever-growing tree of blocks. Each block contains a number of transactions. To learn more, read the Bitcoin Wiki . 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. To further interoperate with Ethereum and ERC-20 token transactions, we also created some views that abstract the blockchain ledger to be presented as a double-entry accounting ledger. 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 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 .

In 2021, global spending on blockchain solutions is projected to reach 6.6 billion dollars. Forecasts suggest that spending on blockchain solutions will continue to grow in the coming years, reaching almost 19 billion U.S. dollars by 2024.

What is blockchain?

Widely known for its association with cryptocurrencies such as Bitcoin, blockchain technology is simply an electronic list of connected records and verified records. Some of the benefits of this electronic “ledger” are that it is tamper-evident and can be efficiently updated online due to its nature as a decentralized network across many devices. These features make the technology perfect for data validation, data access, and identity protection, which serve as blockchain’s most common use cases. Enterprises around the world have begun to adopt private blockchain for internal purposes such as record keeping and intra-company transactions, as well as public blockchain like Bitcoins in their payment processes.

The business of blockchain

Given the potential of the technology and the widespread business interest in the capabilities it can provide, blockchain has become a huge market in its own right, even at this relatively early stage of the technology’s development. Promising blockchain startup companies regularly accumulate hundreds of millions of dollars of investment in their initial offerings, with particularly successful ones such as EOS raking in multiple billions.

As of 2021, 45 percent of respondents stated that their companies were working on secure information exchange as a use case based on blockchain technology, making it the most popular use case of the technology. Digital currency such as Bitcoin and Ethereum, asset tracking and management etc. are also common blockchain use cases.

The global blockchain technology market is projected to increase from a size of US$ 19.7 billion in 2024 to 2,450.4 billion by the end of 2034, expanding rapidly at a CAGR of 62% between 2024 and 2034.

Report Attributes	Details
Blockchain Technology Market Size (2024E)	US$ 19.7 Billion
Projected Market Value (2034F)	US$ 2,450.4 Billion
Global Market Growth Rate (2024 to 2034)	62% CAGR
China Market Value (2034F)	US$ 268.9 Billion
Canada Market Growth Rate (2024 to 2034)	62.7% CAGR
North America Market Share (2024E)	23.9%
East Asia Market Value (2034F)	US$ 566 Billion
Key Companies Profiled	IBM Corp. Microsoft Corp. The Linux Foundation BTL Group Ltd. Chain Inc. Monax Ripple Global Arena Holding, Inc.

Country-wise Insights

Attribute	United States
Market Value (2024E)	US$ 2.1 Billion
Growth Rate (2024 to 2034)	62.7% CAGR
Projected Value (2034F)	US$ 271.5 Billion

Attribute	China
Market Value (2024E)	US$ 2.2 Billion
Growth Rate (2024 to 2034)	62% CAGR
Projected Value (2034F)	US$ 268.9 Billion

Category-wise Insights

Attribute	Infrastructure & Protocols
Segment Value (2024E)	US$ 11.8 Billion
Growth Rate (2024 to 2034)	60.9% CAGR
Projected Value (2034F)	US$ 1,370 Billion

Attribute	Public Cloud
Segment Value (2024E)	US$ 12.2 Billion
Growth Rate (2024 to 2034)	60.6% CAGR
Projected Value (2034F)	US$ 1,390 Billion