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49 results for “Bitcoin”
Pagerank Dataset for Bitcoin Blockchain - Part 2 of 2
<p><strong>Description</strong></p> <p>This dataset contains the Pagerank values and rankings of Bitcoin addresses and transaction IDs (TXID). It contains a total of 1.608.748.675 addresses or TXIDs.</p> <p>Part 1 is available at <a href="https://zenodo.org/record/6052811">https://zenodo.org/record/6052811</a></p> <p> </p> <p><strong>File format</strong></p> <p>The dataset is compressed with bzip2. It can be uncompressed using the command bunzip2. The dataset is divided into multiple files since it was large. The files are space-delimited plain text files and have the following five fields:</p> <p><Label> <Label type> <Rank> <Rank with ties> <Pagerank value></p> <p>Label: A alphanumeric Bitcoin address (e.g. 1DzTCMmWABEDM1rYFL1RgdLyE59jXMzEHV) or a 64 character hexadecimal transaction ID (e.g. 000000000fdf0c619cd8e0d512c7e2c0da5a5808e60f12f1e0d01522d2986a51) Type: String</p> <p>Label type: It's value is 0 if the label is transaction ID and 1 if the label is a Bitcoin address. Type: Integer</p> <p>Rank: Unique Pagerank rank where the ties (addresses having the same Pagerank value) are resolved by sorting the addresses. Type: Integer</p> <p>Rank with ties: Pagerank rank where the ties (addresses having the same Pagerank value) have the same rank. Type: Integer</p> <p>Pagerank value: Pagerank of the address and transaction IDs calculated using Pagerank algorithm. Type: Floating-point number</p> <p> </p> <p>Sample lines:</p> <p>000000000fdf0c619cd8e0d512c7e2c0da5a5808e60f12f1e0d01522d2986a51 0 427225664 266976712 0.979246<br> 1DzTCMmWABEDM1rYFL1RgdLyE59jXMzEHV 1 1114666798 508037940 0.877961</p> <p> </p> <p><strong>Dataset Generation</strong></p> <p>The Bitcoin transactions between blocks 0 (mined on 03.01.2009) and 713.999 (mined on 13.12.2021) are extracted. A transaction graph is constructed, where Bitcoin addresses and transaction IDs are nodes of the graph and the transaction inputs and outputs 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/6038419">Pagerank Dataset for Ethereum Blockchain</a>.</p> <p> </p>
Pagerank Dataset for Bitcoin Blockchain - Part 1 of 2
<p><strong>Description</strong></p> <p>This dataset contains the Pagerank values and rankings of Bitcoin addresses and transaction IDs (TXID). It contains a total of 1.608.748.675 addresses or TXIDs.</p> <p>Part 2 is available at <a href="https://zenodo.org/deposit/6077428">https://zenodo.org/deposit/6077428</a></p> <p> </p> <p><strong>File format</strong></p> <p>The dataset is compressed with bzip2. It can be uncompressed using the command bunzip2. The dataset is divided into multiple files since it was large. The files are space-delimited plain text files and have the following five fields:</p> <p><Label> <Label type> <Rank> <Rank with ties> <Pagerank value></p> <p>Label: A alphanumeric Bitcoin address (e.g. 1DzTCMmWABEDM1rYFL1RgdLyE59jXMzEHV) or a 64 character hexadecimal transaction ID (e.g. 000000000fdf0c619cd8e0d512c7e2c0da5a5808e60f12f1e0d01522d2986a51) Type: String</p> <p>Label type: It's value is 0 if the label is transaction ID and 1 if the label is a Bitcoin address. Type: Integer</p> <p>Rank: Unique Pagerank rank where the ties (addresses having the same Pagerank value) are resolved by sorting the addresses. Type: Integer</p> <p>Rank with ties: Pagerank rank where the ties (addresses having the same Pagerank value) have the same rank. Type: Integer</p> <p>Pagerank value: Pagerank of the address and transaction IDs calculated using Pagerank algorithm. Type: Floating-point number</p> <p> </p> <p>Sample lines:</p> <p>000000000fdf0c619cd8e0d512c7e2c0da5a5808e60f12f1e0d01522d2986a51 0 427225664 266976712 0.979246<br> 1DzTCMmWABEDM1rYFL1RgdLyE59jXMzEHV 1 1114666798 508037940 0.877961</p> <p> </p> <p>"head.txt" contains the first 10 lines of each file. "tail.txt" contains the last 10 lines of each file.</p> <p> </p> <p><strong>Dataset Generation</strong></p> <p>The Bitcoin transactions between blocks 0 (mined on 03.01.2009) and 713.999 (mined on 13.12.2021) are extracted. A transaction graph is constructed, where Bitcoin addresses and transaction IDs are nodes of the graph and the transaction inputs and outputs 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/6038419">Pagerank Dataset for Ethereum Blockchain</a>.</p> <p> </p>
Bitcoin dust transactions
<p><strong>General information</strong></p> <p>This repository contains data regarding Bitcoin <em>dust</em> transactions. In the Bitcoin protocol, dust refers to the small amounts of currency that are lower than the fee required to spend them in a transaction. </p> <p>The repository comprises all transactions with at least one dust output or input. According to our definition, a dust output (or input) is considered dust if the associated amount is between 1 and 545 satoshis (where 1 satoshi = 10<sup>-8</sup> bitcoin). For more details about the definition of dust, see [1].</p> <ul> </ul> <p>All dust transactions have been extracted from the first 479,970 blocks of the Bitcoin blockchain, thus covering the time period between January 3rd, 2009 18:15 GMT and August 10th, 2017 18:03 GMT.</p> <p><strong>Data set description</strong></p> <table> <tbody> <tr> <td>File</td> <td>Description</td> </tr> <tr> <td>txs</td> <td>A text file containing a representation of all Bitcoin transactions that create and consume dust. See the description below for more information about the structure of this file.</td> </tr> <tr> <td>txs_addr_map.csv</td> <td>A CSV file that maps numeric address identifiers to real Bitcoin addresses. This file comprises all addresses appearing in the txs data set.</td> </tr> <tr> <td>labels.csv</td> <td>A CSV file containing categorical entity labels for Bitcoin addresses appeared in transactions between 2010 and 2018. This file has been derived from the <em>Entity-Address data set</em> [2, 3] (see also: <a href="https://github.com/Maru92/EntityAddressBitcoin">https://github.com/Maru92/EntityAddressBitcoin</a>).</td> </tr> <tr> <td>outputs_spent_stats.csv </td> <td>A CSV file containing statistics about all spent outputs in the first 479970 blocks of the Bitcoin blockchain. The file describes the durations of dust and non-dust outputs. The duration is defined as the difference between the height of the block where the output is spent and the height of the block where it was created. </td> </tr> <tr> <td>cluster_sizes_*.csv</td> <td>These CSV files contain information about clusters of addresses induced by Bitcoin transactions. They have been used for the clustering analysis presented in [4]. See <a href="https://github.com/mloporchio/DustAnalysis">this GitHub repository</a> for more information.</td> </tr> </tbody> </table> <p><strong>Transaction representation</strong></p> <p>The <em>txs</em> file contains a textual representation of dust transactions in the Bitcoin blockchain. Each row of the file corresponds to a transaction and is represented as a sequence of fields</p> <p><code>info:inputs:outputs</code></p> <p>with the following meaning.</p> <ol> <li> <p>The <code>info</code> section contains general information about the transaction. It is represented as a list of comma-separated fields, namely: <code>timestamp,blockId,txId,isCoinbase,fee,approxSize</code>.</p> <p>The meaning of the fields is the following:</p> <ol> <li><code>timestamp</code> represents the Unix timestamp of the block containing the transaction.</li> <li><code>blockId</code> represents the height of the block containing the transaction.</li> <li><code>txId</code> is a numeric value that univocally identifies the transaction.</li> <li><code>isCoinbase</code> is equal to 1 if the transaction is a coinbase transaction, 0 otherwise.</li> <li><code>fee</code> denotes the transaction fee, expressed in satoshis (i.e., the smallest bitcoin denomination).</li> <li><code>approximateSize</code> denotes the approximate size of the transaction (expressed in bytes).</li> </ol> </li> <li> <p>The <code>inputs</code> section contains a sequence of (0 or more) transaction inputs separated by a semicolon. Each input, in turn, is represented as a comma-separated string <code>addrId,amount,prevTxId,offset</code> where:</p> <ol> <li><code>addrId</code> represents the numeric identifier of the spending address;</li> <li><code>amount</code> is the amount of value associated with the input (expressed in satoshis);</li> <li><code>prevTxId</code> represents the numeric identifier of the transaction that created the output that is currently being spent;</li> <li><code>offset</code> represents the position, among all outputs of <code>prevTxId</code>, of the output that is currently being spent.<br> </li> </ol> </li> <li>The <code>outputs</code> section contains a sequence of (1 or more) transaction outputs separated by a semicolon. Each output, in turn, is represented as a comma-separated string <code>addrId,amount,scriptType</code> where:<br> <ol> <li><code>addrId</code> represents the numeric identifier of the receiving address;</li> <li><code>amount</code> is the amount of value associated with the output (expressed in satoshis);</li> <li><code>scriptType</code> is a numeric identifier representing the type of the script associated with the output (i.e., 0=UNKNOWN; 1=P2PK; 2=P2PKH; 3=P2SH; 4=RETURN; 5=EMPTY).</li> </ol> </li> </ol> <p><strong>Data analysis</strong></p> <p>Data included in this repository have been employed for the analyses presented in [4, 5]. <a href="https://github.com/mloporchio/DustAnalysis">This GitHub repository</a> contains several tools, written in Java and Python, for analyzing the data.</p> <p><strong>Cite this work</strong></p> <p>If the data included in this repository have been useful, please cite the following article in your work.</p> <pre>@article{loporchio2023bitcoin,   title={Is Bitcoin gathering dust? An analysis of low-amount Bitcoin transactions},   author={Loporchio, Matteo and Bernasconi, Anna and Di Francesco Maesa, Damiano and Ricci, Laura},   journal={Applied Network Science},   volume={8},   number={1},   pages={1--28},   year={2023},   publisher={SpringerOpen} } </pre> <p><strong>References</strong></p> <ol> <li>Pérez-Solà, Cristina, et al. "Another coin bites the dust: an analysis of dust in UTXO-based cryptocurrencies." <em>Royal Society open science</em> 6.1 (2019): 180817.</li> <li>Jourdan, Marc, et al. "Characterizing entities in the bitcoin blockchain." <em>2018 IEEE international conference on data mining workshops (ICDMW)</em>. IEEE, 2018.</li> <li>Jourdan, Marc, et al. "A probabilistic model of the bitcoin blockchain." <em>Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops</em>. 2019.</li> <li>Loporchio, Matteo, et al. "Is Bitcoin gathering dust? An analysis of low-amount Bitcoin transactions." <em>Applied Network Science</em> 8.1 (2023): 1-28.</li> <li>Loporchio, Matteo, et al. "An Analysis of Bitcoin Dust Through Authenticated Queries." <em>Complex Networks and Their Applications XI: Proceedings of The Eleventh International Conference on Complex Networks and their Applications: COMPLEX NETWORKS 2022—Volume 2</em>. Cham: Springer International Publishing, 2023.</li> </ol>
Dataset: An Empirical Analysis of Pool Hopping Behavior in the Bitcoin Blockchain
<p>We provide the first empirical analysis of pool hopping behavior among 15 mining pools throughout Bitcoin's history. Bitcoin mining is a critical activity that keeps the Bitcoin system secure, valid, and stable. Mining pools have emerged as major players that ensure that the Bitcoin system stays secure, valid, and stable. Individual miners join mining pools to benefit from a more stable and predictable income. Many questions remain open regarding how mining pools have evolved throughout Bitcoin's history and when and why miners join or leave mining pools. We propose a heuristic algorithm to extract the payout flow from mining pools and detect the pools' migration of miners. Our results showed that reward rules and pool fees influence miners' decisions to join, change, or exit from a mining pool, thus affecting the dynamics of mining pool market shares. Our analysis provides evidence that mining activity becomes an industry as miners' decisions follow classical economic rationale. </p>
Transaction Graph Dataset for the Bitcoin Blockchain - Part 2 of 4
<p>This dataset contains bitcoin transfer transactions extracted from the Bitcoin Mainnet blockchain.</p> <p>Part1 is available at <a href="https://zenodo.org/deposit/7157356">https://zenodo.org/deposit/7157356</a><br> Part3 is available at <a href="https://zenodo.org/deposit/7158133">https://zenodo.org/deposit/7158133</a><br> Part4 is available at <a href="https://zenodo.org/deposit/7158328">https://zenodo.org/deposit/7158328</a></p> <p>Details of the datasets are given below:</p> <p><strong>FILENAME FORMAT:</strong></p> <p>The filenames have the following format:</p> <p>btc-tx-<start blockno>-<end blockno>-<part>.bz2 </p> <p>where <start blockno> is the starting block number, <end blockno> final block number, and <part> is the split part of the file. </p> <p>For example file btc-tx-100000-149999-aa.bz2 and the rest of the parts if any contain transactions from </p> <p>block 100000 to block 149999 inclusive. </p> <p>The files are compressed with bzip2. They can be uncompressed using command bunzip2.</p> <p> </p> <p><strong>TRANSACTION FORMAT:</strong></p> <p>Each line in a file corresponds to a transaction. The transaction has the following format:</p> <p><SYMBOL> <blockno> <txno> <from> <to> <value> </p> <p><br> <SYMBOL> Type of transaction (i.e. BTC-IN or BTC-OUT).</p> <p><blockno> Number of the block which contains the transaction. </p> <p><txno> Position of the transaction in the block (i.e. transaction number in the block).</p> <p><from> Source bitcoin address/transaction of the transfer.</p> <p><to> Destination bitcoin address/transaction of the transfer.</p> <p><value> Amount of transfer.</p> <p> </p> <p><strong>BLOCK TIME FORMAT:</strong></p> <p>The block time file has the following format:</p> <p><block no> <timestamp></p> <p><br> <block no> Number of the block. </p> <p><timestamp> Unix timestamp at which the block is mined as a hexadecimal number.</p> <p> </p> <p><strong>IMPORTANT NOTE:</strong></p> <p>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 <a href="https://btcscan.org">https://btcscan.org</a> . 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.</p> <p> </p> <p><strong>NOTE:</strong></p> <p>If you use this dataset, please do not forget to add the DOI number to the citation.</p> <p>If you use our dataset in your research, please also cite our paper: <a href="https://link.springer.com/chapter/10.1007/978-3-030-94590-9_14">https://link.springer.com/chapter/10.1007/978-3-030-94590-9_14</a></p> <pre><code>@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} }</code></pre> <p> </p>
Transaction Graph Dataset for the Bitcoin Blockchain - Part 1 of 4
<p>This dataset contains bitcoin transfer transactions extracted from the Bitcoin Mainnet blockchain.</p> <p>Part2 is available at <a href="https://zenodo.org/deposit/7157854">https://zenodo.org/deposit/7157854</a><br> Part3 is available at <a href="https://zenodo.org/deposit/7158133">https://zenodo.org/deposit/7158133</a><br> Part4 is available at <a href="https://zenodo.org/deposit/7158328">https://zenodo.org/deposit/7158328</a></p> <p>Details of the datasets are given below:</p> <p><strong>FILENAME FORMAT:</strong></p> <p>The filenames have the following format:</p> <p>btc-tx-<start blockno>-<end blockno>-<part>.bz2 </p> <p>where <start blockno> is the starting block number, <end blockno> final block number, and <part> is the split part of the file. </p> <p>For example file btc-tx-100000-149999-aa.bz2 and the rest of the parts if any contain transactions from </p> <p>block 100000 to block 149999 inclusive. </p> <p>The files are compressed with bzip2. They can be uncompressed using command bunzip2.</p> <p> </p> <p><strong>TRANSACTION FORMAT:</strong></p> <p>Each line in a file corresponds to a transaction. The transaction has the following format:</p> <p><SYMBOL> <blockno> <txno> <from> <to> <value> </p> <p><br> <SYMBOL> Type of transaction (i.e. BTC-IN or BTC-OUT).</p> <p><blockno> Number of the block which contains the transaction. </p> <p><txno> Position of the transaction in the block (i.e. transaction number in the block).</p> <p><from> Source bitcoin address/transaction of the transfer.</p> <p><to> Destination bitcoin address/transaction of the transfer.</p> <p><value> Amount of transfer.</p> <p> </p> <p><strong>BLOCK TIME FORMAT:</strong></p> <p>The block time file has the following format:</p> <p><block no> <timestamp></p> <p><br> <block no> Number of the block. </p> <p><timestamp> Unix timestamp at which the block is mined as a hexadecimal number.</p> <p> </p> <p><strong>IMPORTANT NOTE:</strong></p> <p>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 <a href="https://btcscan.org">https://btcscan.org</a> . 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.</p> <p> </p> <p><strong>NOTE:</strong></p> <p>If you use this dataset, please do not forget to add the DOI number to the citation.</p> <p>If you use our dataset in your research, please also cite our paper: <a href="https://link.springer.com/chapter/10.1007/978-3-030-94590-9_14">https://link.springer.com/chapter/10.1007/978-3-030-94590-9_14</a></p> <pre><code>@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} }</code></pre> <p> </p>
Transaction Graph Dataset for the Bitcoin Blockchain - Part 4 of 4
<p>This dataset contains bitcoin transfer transactions extracted from the Bitcoin Mainnet blockchain.</p> <p>Part1 is available at <a href="https://zenodo.org/deposit/7157356">https://zenodo.org/deposit/7157356</a><br> Part2 is available at <a href="https://zenodo.org/deposit/7157854">https://zenodo.org/deposit/7157854</a><br> Part3 is available at <a href="https://zenodo.org/deposit/7158133">https://zenodo.org/deposit/7158133</a></p> <p>Details of the datasets are given below:</p> <p><strong>FILENAME FORMAT:</strong></p> <p>The filenames have the following format:</p> <p>btc-tx-<start blockno>-<end blockno>-<part>.bz2 </p> <p>where <start blockno> is the starting block number, <end blockno> final block number, and <part> is the split part of the file. </p> <p>For example file btc-tx-100000-149999-aa.bz2 and the rest of the parts if any contain transactions from </p> <p>block 100000 to block 149999 inclusive. </p> <p>The files are compressed with bzip2. They can be uncompressed using command bunzip2.</p> <p> </p> <p><strong>TRANSACTION FORMAT:</strong></p> <p>Each line in a file corresponds to a transaction. The transaction has the following format:</p> <p><SYMBOL> <blockno> <txno> <from> <to> <value> </p> <p><br> <SYMBOL> Type of transaction (i.e. BTC-IN or BTC-OUT).</p> <p><blockno> Number of the block which contains the transaction. </p> <p><txno> Position of the transaction in the block (i.e. transaction number in the block).</p> <p><from> Source bitcoin address/transaction of the transfer.</p> <p><to> Destination bitcoin address/transaction of the transfer.</p> <p><value> Amount of transfer.</p> <p> </p> <p><strong>BLOCK TIME FORMAT:</strong></p> <p>The block time file has the following format:</p> <p><block no> <timestamp></p> <p><br> <block no> Number of the block. </p> <p><timestamp> Unix timestamp at which the block is mined as a hexadecimal number.</p> <p> </p> <p><strong>IMPORTANT NOTE:</strong></p> <p>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 <a href="https://btcscan.org">https://btcscan.org</a> . 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.</p> <p> </p> <p><strong>NOTE:</strong></p> <p>If you use this dataset, please do not forget to add the DOI number to the citation.</p> <p>If you use our dataset in your research, please also cite our paper: <a href="https://link.springer.com/chapter/10.1007/978-3-030-94590-9_14">https://link.springer.com/chapter/10.1007/978-3-030-94590-9_14</a></p> <pre><code>@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} }</code></pre>
Transaction Graph Dataset for the Bitcoin Blockchain - Part 3 of 4
<p>This dataset contains bitcoin transfer transactions extracted from the Bitcoin Mainnet blockchain.</p> <p>Part1 is available at <a href="https://zenodo.org/deposit/7157356">https://zenodo.org/deposit/7157356</a><br> Part2 is available at <a href="https://zenodo.org/deposit/7157854">https://zenodo.org/deposit/7157854</a><br> Part4 is available at <a href="https://zenodo.org/deposit/7158328">https://zenodo.org/deposit/7158328</a></p> <p>Details of the datasets are given below:</p> <p><strong>FILENAME FORMAT:</strong></p> <p>The filenames have the following format:</p> <p>btc-tx-<start blockno>-<end blockno>-<part>.bz2 </p> <p>where <start blockno> is the starting block number, <end blockno> final block number, and <part> is the split part of the file. </p> <p>For example file btc-tx-100000-149999-aa.bz2 and the rest of the parts if any contain transactions from </p> <p>block 100000 to block 149999 inclusive. </p> <p>The files are compressed with bzip2. They can be uncompressed using command bunzip2.</p> <p> </p> <p><strong>TRANSACTION FORMAT:</strong></p> <p>Each line in a file corresponds to a transaction. The transaction has the following format:</p> <p><SYMBOL> <blockno> <txno> <from> <to> <value> </p> <p><br> <SYMBOL> Type of transaction (i.e. BTC-IN or BTC-OUT).</p> <p><blockno> Number of the block which contains the transaction. </p> <p><txno> Position of the transaction in the block (i.e. transaction number in the block).</p> <p><from> Source bitcoin address/transaction of the transfer.</p> <p><to> Destination bitcoin address/transaction of the transfer.</p> <p><value> Amount of transfer.</p> <p> </p> <p><strong>BLOCK TIME FORMAT:</strong></p> <p>The block time file has the following format:</p> <p><block no> <timestamp></p> <p><br> <block no> Number of the block. </p> <p><timestamp> Unix timestamp at which the block is mined as a hexadecimal number.</p> <p> </p> <p><strong>IMPORTANT NOTE:</strong></p> <p>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 <a href="https://btcscan.org">https://btcscan.org</a> . 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.</p> <p> </p> <p><strong>NOTE:</strong></p> <p>If you use this dataset, please do not forget to add the DOI number to the citation.</p> <p>If you use our dataset in your research, please also cite our paper: <a href="https://link.springer.com/chapter/10.1007/978-3-030-94590-9_14">https://link.springer.com/chapter/10.1007/978-3-030-94590-9_14</a></p> <pre><code>@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} }</code></pre>
Ransomware Payments in the Bitcoin Ecosystem
<p>This dataset contains 7,222 Bitcoin seed addresses related to 67 ransomware families as well as addresses that were identified by applying the expansion procedure describe in this paper: https://arxiv.org/pdf/1804.04080.pdf</p>
Bitcoin Dataset without Missing Values
<p>This dataset contains the potential influencers of the bitcoin price. There are a total of 18 daily time series including hash rate, block size, mining difficulty etc. It also encompasses public opinion in the form of tweets and google searches mentioning the keyword bitcoin. The data is scraped from the interactive web-graphs available at https://bitinfocharts.com. </p> <p>The original dataset contains missing values and they have been replaced by carrying forward the corresponding last seen observations (LOCF method).</p>
Bitcoin Dataset with Missing Values
<p>This dataset contains the potential influencers of the bitcoin price. There are a total of 18 daily time series including hash rate, block size, mining difficulty etc. It also encompasses public opinion in the form of tweets and google searches mentioning the keyword bitcoin. The data is scraped from the interactive web-graphs available at https://bitinfocharts.com.</p>
Bitcoin Historical Prices Dataset
<p>The following dataset contains the attributes:</p> <ol> <li>Date: Specific date to be observed for the corresponding price.</li> <li>Open: The opening price for the day</li> <li>High: The maximum price it has touched for the day</li> <li>Low: The minimum price it has touched for the day</li> <li>Close: The closing price for the day</li> <li>percent_change_24h: Percentage change for the last 24hours</li> <li>Volume: Volume of Bitcoin traded at the date</li> <li>Market Cap: Market Value of traded Bitcoin</li> </ol>
Dataset: Bitcoin Depot Inc. (BTMWW) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Valkyrie Bitcoin Futures Leveraged Strategy ETF (BTFX) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Valkyrie Bitcoin Fund (BRRR) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Global X Blockchain & Bitcoin Strategy ETF (BITS) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: CYBER HORNET S&P 500 and Bitcoin 75/25 Strategy ETF (ZZZ) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Valkyrie Bitcoin Miners ETF (WGMI) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Simplify Bitcoin Strategy PLUS Income ETF (MAXI) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: iShares Bitcoin Trust (IBIT) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
ScienceDex guides
Understand access before you commit
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.