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19 results for “Ethereum”
Pagerank Dataset for Ethereum Blockchain
<p><strong>Description</strong></p> <p>This dataset contains the Pagerank values and rankings of 147.098.561 Ethereum addresses.</p> <p> </p> <p><strong>File format</strong></p> <p>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:</p> <p><Ethereum Address> <rank> <rank with ties> <Pagerank value></p> <p>Ethereum Address: A 42-character hexadecimal Ethereum address in the lowercase form (not in checksummed (mixed-case) form). E.g. 0x3f5ce5fbfe3e9af3971dd833d26ba9b5c936f0be</p> <p>rank: Unique Pagerank rank where the ties (addresses having the same Pagerank value) are resolved by sorting the addresses by hexadecimal value</p> <p>rank with ties: Pagerank rank where the ties (addresses having the same Pagerank value) have the same rank.</p> <p>Pagerank value: Pagerank of the address calculated using Pagerank algorithm.</p> <p> </p> <p><strong>Dataset Generation</strong></p> <p>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'.</p> <p> </p> <p><strong>Note</strong></p> <p>If you use our dataset in your research, please cite our paper: <a href="https://link.springer.com/article/10.1007/s10586-021-03511-0">https://link.springer.com/article/10.1007/s10586-021-03511-0</a></p> <pre><code>@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} }</code></pre> <p> </p> <p><strong>Other Datasets</strong></p> <p>If you are interested, please also check out our <a href="https://zenodo.org/record/6052811">Pagerank Dataset for Bitcoin Blockchain</a>.</p>
Dataset: A Fly in the Ointment: An Empirical Study on the Characteristics of Ethereum Smart Contracts Code Weaknesses and Vulnerabilities
<p>Dataset: A Fly in the Ointment: An Empirical Study on the Characteristics of Ethereum Smart Contracts Code Weaknesses and Vulnerabilities</p> <p>Majd Soud, Grischa Liebel, Mohammad Hamdaqa<br> majd18@ru.is, grischal@ru.is, mhamdaqa@polymtl.ca.</p> <p>This Dataset includes the following: </p> <p>1. "labeling.xml" files that represents the data for Categories of vulnerabilities in Smart Contracts for four data sources (i.e., Common Vulnerability and Exposure (CVE), Smart Contract Weakness Classification Registry (SWC), Stack Overflow, and GitHub)<br> XML files structure:<br> The XML files can be opened used any editor or any code editor (e.g. Visual Studio Code).</p> <p>1. Each file has a root that is <Card_Table></Card_Table> which contains all the cards we labeled. <br> 2. Each card is represented by the <Card></Card> and contains the following:<br> - The tag marked by <Tag> represents the keyword that was used to search and collect the card from StackOverflow. <br> - The URL marked by <URL> of the URL link which contains all the information of the labeled vulnerability. <br> - The other tages marked by <OtherTags> that shows all the tags used in the post on Stack Overflow. <br> - The expert labeling for the categories of vulnerabilities in each card is represented by <CategoryExpertLabel><br> - In more details, some records has the <SecondExpertCategoryLabel> that represents the second expert labeling for the categories of vulnerabilities.<br> - The <CategoryAgreement> used to calculate the inter-rater agreement between the two labelers. <br> </p>
BF skip indexes for Ethereum
<p><strong>General information</strong></p> <p>This repository includes all data needed to reproduce the experiments presented in [1].<br>The paper describes the<em> BF skip index,</em> a data structure based on Bloom filters [2] that can be used for answering inter-block queries on blockchains efficiently. The article also includes a historical analysis of <code>logsBloom</code> filters included in the Ethereum block headers, as well as an experimental analysis of the proposed data structure. The latter was conducted using the data set of events generated by the <a href="https://etherscan.io/address/0x06012c8cf97bead5deae237070f9587f8e7a266d">CryptoKitties Core</a> contract, a popular decentralized application launched in 2017 (and also one of the first applications based on NFTs).</p> <p>In this description, we use the following abbreviations (also adopted throughout the paper) to denote two different sets of Ethereum blocks.</p> <ol> <li>D1: set of all Ethereum blocks between height 0 and 14999999.</li> <li>D2: set of all Ethereum blocks between height 14000000 and 14999999.</li> </ol> <p>Moreover, in accordance with the terminology adopted in the paper, we define the set of <em>keys </em>of a block as the set of all contract addresses and log topics of the transactions in the block. As defined in [3], log topics comprise event signature digests and the indexed parameters associated with the event occurrence.</p> <p><strong>Data set description</strong></p> <table> <tbody> <tr> <td>File</td> <td>Description</td> </tr> <tr> <td>filters_ones_0-14999999.csv.xz</td> <td>Compressed CSV file containing the number of ones for each logsBloom filter in D1.</td> </tr> <tr> <td>receipt_stats_0-14999999.csv.xz</td> <td>Compressed CSV file containing statistics about all transaction receipts in D1.</td> </tr> <tr> <td>Approval.csv</td> <td>CSV file containing the Approval event occurrences for the CryptoKitties Core contract in D2.</td> </tr> <tr> <td>Birth.csv</td> <td>CSV file containing the Birth event occurrences for the CryptoKitties Core contract in D2.</td> </tr> <tr> <td>Pregnant.csv</td> <td>CSV file containing the Pregnant event occurrences for the CryptoKitties Core contract in D2.</td> </tr> <tr> <td>Transfer.csv</td> <td>CSV file containing the Transfer event occurrences for the CryptoKitties Core contract in D2.</td> </tr> <tr> <td>events.xz</td> <td>Compressed binary file containing information about all contract events in D2.</td> </tr> <tr> <td>keys.xz</td> <td>Compressed binary file containing information about all keys in D2.</td> </tr> </tbody> </table> <p><strong>File structure</strong></p> <p>We now describe the structure of the files included in this repository.</p> <ul> <li>filters_ones_0-14999999.csv.xz is a compressed CSV file with 15 million rows (one for each block in D1) and 3 columns. Note that it is not necessary to decompress this file, as the provided code is capable of processing it directly in its compressed form. The columns have the following meaning. <ol> <li><code>blockId</code>: the identifier of the block.</li> <li><code>timestamp</code>: timestamp of the block.</li> <li><code>numOnes</code>: number of bits set to 1 in the <code>logsBloom</code> filter of the block.</li> </ol> </li> <li>receipt_stats_0-14999999.csv.xz is a compressed CSV file with 15 million rows (one for each block in D1) and 5 columns. As for the previous file, it is not necessary to decompress this file. <ol> <li><code>blockId</code>: the identifier of the block.</li> <li><code>txCount</code>: number of transactions included in the block.</li> <li><code>numLogs</code>: number of event logs included in the block.</li> <li><code>numKeys</code>: number of keys included in the block.</li> <li><code>numUniqueKeys</code>: number of distinct keys in the block (useful as the same key may appear multiple times).</li> </ol> </li> <li>All CSV files related to the CryptoKitties Core events (i.e., Approval.csv, Birth.csv, Pregnant.csv, Transfer.csv) have the same structure. They consist of 1 million rows (one for each block in D2) and 2 columns, namely: <ol> <li><code>blockId</code>: identifier of the block.</li> <li><code>numOcc</code>: number of event occurrences in the block.</li> </ol> </li> <li>events.xz is a compressed binary file describing all <em>unique</em> event occurrences in the blocks of D2. The file contains 1 million data chunks (i.e., one for each Ethereum block). Each chunk includes the following information. Do note that this file only records <em>unique</em> event occurrences in each block, meaning that if an event from a contract is triggered more than once within the same block, there will be only one sequence within the corresponding chunk. <ol> <li><code>blockId</code>: identifier of the block (4 bytes).</li> <li><code>numEvents</code>: number of event occurrences in the block (4 bytes).</li> <li>A list of <code>numEvent</code> sequences, each made up of 52 bytes. A sequence represents an event occurrence and is indeed the concatenation of two fields, namely: <ol> <li>Address of the contract triggering the event (20 bytes).</li> <li>Event signature digest (32 bytes).</li> </ol> </li> </ol> </li> <li>keys.xz is a compressed binary file describing all unique keys in the blocks of D2. As for the previous file, duplicate keys only appear once. The file contains 1 million data chunks, each representing an Ethereum block and including the following information. <ol> <li><code>blockId</code>: identifier of the block (4 bytes)</li> <li><code>numAddr</code>: number of unique contract addresses (4 bytes).</li> <li><code>numTopics</code>: number of unique topics (4 bytes).</li> <li>A sequence of <code>numAddr</code> addresses, each represented using 20 bytes.</li> <li>A sequence of <code>numTopics</code> topics, each represented using 32 bytes.</li> </ol> </li> </ul> <p><strong>Notes</strong></p> <p>For space reasons, some of the files in this repository have been compressed using the XZ compression utility. Unless otherwise specified, these files need to be decompressed before they can be read. Please make sure you have an application installed on your system that is capable of decompressing such files.</p> <p><strong>Cite this work<br></strong></p> <p>If the data included in this repository have been useful, please cite the following article in your work.</p> <pre><code>@article{loporchio2025skip, title={Skip index: Supporting efficient inter-block queries and query authentication on the blockchain}, author={Loporchio, Matteo and Bernasconi, Anna and Di Francesco Maesa, Damiano and Ricci, Laura}, journal={Future Generation Computer Systems}, volume={164}, pages={107556}, year={2025}, publisher={Elsevier} }</code></pre> <p><strong>References</strong></p> <ol> <li>Loporchio, Matteo et al. "Skip index: supporting efficient inter-block queries and query authentication on the blockchain". Future Generation Computer Systems 164 (2025): 107556. <a href="https://doi.org/10.1016/j.future.2024.107556" target="_blank" rel="noopener">https://doi.org/10.1016/j.future.2024.107556</a></li> <li>Bloom, Burton H. "Space/time trade-offs in hash coding with allowable errors." <em>Communications of the ACM</em> 13.7 (1970): 422-426.</li> <li>Wood, Gavin. "Ethereum: A secure decentralised generalised transaction ledger." <em>Ethereum project yellow paper</em> 151.2014 (2014): 1-32.</li> </ol>
Dataset of Linkability Networks of Ethereum Accounts Involved in NFT trading of Top 15 NFT Collections
<p>The data is organized in 32 files:</p> <ul> <li> <p>collection_metadata.txt stores basic information about each collection that was analyzed. The graph data of the collection is stored in a file named as the nickname of the collection (slug column in collection_metadata.txt). The most important columns/properties are: rank, slug – nickname, creation date, address, and volume data, ...</p> </li> <li> <p>15 files reporting NFT ownership transfer, the names corresponding to the nicknames of the collections in collection_metadata.txt.</p> </li> <li> <p>15 files with graph data, the names correspond to the nicknames of the collections in collection_metadata.txt. Each file gives the address linkage graph of one of the top 15 collections according to monetary volume on Opensea marketplace, computed as presented in methodology, exclusively on Ethereum blockchain.</p> </li> </ul>
Dataset for Gas-centered Mutation Testing of Ethereum Smart Contracts
<p>This dataset contains additional material for the paper entitled "<em>Gas-centered Mutation Testing of Ethereum Smart Contracts</em>", published by Journal of Software: Evolution and Process. This material includes:</p> <ul> <li><strong>Mutation tool</strong>, with the implementation of our gas-centered mutation operators.</li> <li><strong>Mutation operators - Example</strong>, with the code excerpts used to illustrate the gas perturbation caused by the mutation operators.</li> <li><strong>Smart contract - Accelerator_0b58e2.dir</strong>, with the mutants generated in a smart contract and their execution results.</li> <li><strong>Smart contracts_features.xlsx,</strong> with the complete list of smart contracts and their characteristics.</li> </ul>
Comprehensive Ethereum Execution Data for Object-Centric Process Mining of Decentralized Applications (DApps)
<p>The dataset pertains to the collection and analysis of blockchain execution data, particularly from Ethereum-based Decentralized Applications (DApps). This data includes transactions, transaction receipts, and detailed transaction traces, documenting the execution steps performed by the Ethereum Virtual Machine (EVM). Such traces are essential for understanding the interaction between smart contracts and accounts, including Contract Accounts (CAs) and Externally Owned Accounts (EOAs).</p> <p>A blockchain is an append-only ledger that chronologically records data in blocks. Each block contains transactions that signify state transitions, and transaction receipts that provide a hashed result of these transitions to ensure uniform results across different executions. The dataset includes a classification of Ethereum accounts, detailing the functions and interactions between EOAs and CAs, where CAs deploy and execute smart contract code.</p> <p>The dataset captures the granular operational data of blockchain transactions, such as function calls, contract creations, and log entries generated by smart contracts. These details are crucial for creating object-centric event logs, aiding in process mining and analysis to bridge the gap between theoretical process models and actual execution.</p> <p>Contract creations and function calls are fundamental components of the dataset. The former documents the deployment of smart contracts, including the mechanics of contract updates and additions through various design patterns. Function calls between accounts are also extensively logged, providing insights into the flow of Ethereum's native token, Ether, and other transactional data within the blockchain.</p> <p>Delegated calls and log entries represent more specialized interactions within Ethereum, where delegated calls allow contracts to use code from other contracts to manipulate their own state, supporting upgradeable contract designs. Log entries, specified within smart contract code, facilitate the communication of contract execution details to external systems.</p> <p>To handle the diverse and dynamic nature of blockchain data, the dataset employs the Object-Centric Event Log (OCEL) format. This format accommodates multiple object types in a single log, addressing issues such as event divergence and convergence, typical of traditional single-case logs. The latest version, OCEL 2.0, supports documenting dynamic object roles and relationships, improving the fidelity of logs in capturing blockchain operations.</p> <p>In summary, the dataset is structured to support a comprehensive analysis of blockchain behaviors, particularly focusing on Ethereum DApps. It is tailored to assist researchers and practitioners in understanding and analyzing the decentralized execution of smart contracts and the associated data flows within the blockchain environment.</p>
Dataset of transactions of 10 Ethereum addresses controlled by a private key, each has at least 2000 output transactions, which include a transfer of cryptocurrency, and all transactions are performed within no longer than three months period.
<p>Starting from the block Nr. 8578851, we have evaluated every block to find enough unique addresses that satisfy the following requirements. The address that appeared in a block must be controlled by private key (i.e., CA), have at least 2000, and not more than 6000 outgoing transactions (i.e., digitally signed), which however include a transfer of any amount of cryptocurrency (i.e., Ether) and must be performed within a period no longer than three months. The whole dataset includes a total of 30505 transactions.</p> <p>Each transaction has data about:</p> <ul> <li>transaction hash</li> <li>block number</li> <li>timestamp (Unix),</li> <li>date and time (m/dd/yyyy)</li> <li>from (Ethereum address)</li> <li>to (Ethereum address)</li> <li>output value (ETH)</li> <li>transaction fee (ETH)</li> <li>transaction fee (USD)</li> <li>historical price (USD)</li> </ul>
ENS Ethereum Name Service Logo
ENS Ethereum Name Service Logo with shiny glass materials and noise texture Source: Objaverse 1.0 / Sketchfab
Extracted Ethereum transactions for July 2019 from the Ethereum in BigQuery public dataset
<p>Extracted Ethereum transactions for July 2019 from the Ethereum in BigQuery public dataset.</p>
Ethereum Transactions Data
<p>These data have been used to analyze the data one popular oracle to predict the Gas price, along with the Ethereum transactions’ and blocks’ data.</p>
An empirical approach and practical framework for a decentralized Ethereum Ecosystem Index (EEI) datasets and codes
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Gas Fees and Unconfirmed Transactions in Ethereum: A Proof-of-Stake (PoS) Focus}
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Cotitzacions en viu i paraules claus de notícies relaciones per Bitcoin, Ethereum, S&P500 i Nasdaq
<p>Realtime dataset that contains crypto metrics, market metrics and news articles word analysis</p>
Empirical Review of Automated Analysis Tools on 47,587 Ethereum Smart Contracts
<p>This dataset contains the full output of the execution of 9 state-of-the-art automated analysis tools on 47,518 Solidity contracts.</p> <p>The data structure is as follows</p> <pre><code>├─ results │ └─ <tool_name> │ └─ <dataset_name> │ └─ <contract_address> │ ├─ <result.log> # stdout of the analysis │ └─ <result.json> # parsable output analysis</code></pre> <p> </p>
Transaction history of the first 10 million blocks of the Ethereum Blockchain
<p>This dataset contains the transaction history for the first 10,000,000 blocks of the Ethereum Blockchain. The dataset is structured into 10 consecutive CSV files, which sequentially hold the transactions of 1,000,000 blocks. The information stored for each transaction includes the sending and receiving addresses of the transaction, the transferred value, the corresponding block number, a timestamp, and further information about the target address.</p>
Ethereum Transaction Data used for End-user Comprehension of Transfer Risks in Smart Contracts
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Interface-based Classification of Token Contracts on Ethereum
<p>Dataset for the bachelor thesis "Interface-based Classification of Token Contracts on Ethereum".</p> <p><strong>Contents:</strong></p> <p><em>type_<x>_rules.json</em>: Rules for Type <x>.</p> <p><em>type_1_rules_extended.json</em>: Type I rules extended by functions within Levenshtein distance of 1 of original Type I rules.</p> <p><em>word_occurences.json</em>: Words sorted by how many unique functions they occur in.</p> <p><em>word_coverage_potential.json</em>: Words sorted by how many function occurrences functions including the word make up.</p> <p><em>categorized_functions_with_origins.json</em>: each categorized function, including key statistics such as the number of skeletons implementing them. Each function includes a list of Type I, II, III rules that captured it.</p> <p><em>top_rules_per_category.json</em>: The rules for each category sorted by how many occurrences they captured.</p> <p><em>skeletons_with_compliance.json</em>: Each skeleton's known functions and events and information about what standards they comply with.</p>
Ethereum Action
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AutoMESC: Automatic Framework for Mining and Classifying Ethereum Smart Contract Vulnerabilities and Their Fixes
<p>AutoMESC Dataset: Automatic Framework for Mining and Classifying Ethereum Smart Contract Vulnerabilities and Their<br> Fixes<br> <br> </p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.