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83 results for “blockchain”
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>
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>
BTC-Blockchain-Network-Data
<p>File GKvolatility-2011-2017.txt contains the Garmann-Klass dailiy volatility.</p> <p>File BTC-BlockchainData_2017.txt contains the processed BTC blockchain transaction network.</p> <p>Column 1 = source node id<br> Column 2 = target node id<br> Column 3 = transaction time - unix timestamp of the transaction (seconds since<br> % 1970-01-01)<br> Column 4 = amount of bitcoins trasferred from source to node at transaction time</p> <p>For details about the data and citation:<br> ino Antulov-Fantulin, Dijana Tolic, Matija Piskorec, Zhang Ce, Irena Vodenska, Inferring short-term volatility indicators from Bitcoin blockchain, Complex Networks and Their Applications VII. COMPLEX NETWORKS 2018. Studies in Computational Intelligence, vol 813. Springer, https://doi.org/10.1007/978-3-030-05414-4_41</p> <p>Link on arxiv: https://arxiv.org/abs/1809.07856</p>
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>
MeshDapp: Blockchain-enabled Payment System for Wireless Mesh Networks - Dataset
<p>The MeshDapp dataset includes data from the MeshDapp project - Fed4Fire+ Open Call 9 winner:</p> <p>Data includes: Network Measurement from the CityLab - Antwerp testbed and IRIS (TCD) testbed. </p> <p>MeshDapp Paper:https://mvdsi.seeu.edu.mk/mselimi/papers/DAPPS20ms.pdf </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>
Improving the data access control using blockchain for healthcare domain
<p>This research reviews the importance of blockchains in healthcare as they provide infinite possibilities to individuals, companies, and governments.</p>
Blockchain and Smart Contracts Topics on Reddit
<p>This dataset includes posts related to Ethereum, Stellar, Algorand, Hyperledger, and smart contracts subreddits. The dataset includes URL, title, author, number of comments, and technology (Ethereum, Stellar, etc.). This dataset is used to spot relevant smart contracts topics Reddit's community discusses. Please consider that this dataset will be extended with more information and smart contracts platform-related subreddits.</p>
Unblocking Breakthroughs: How DEIP Are Using Blockchain and Open Science to Provide Innovation Evaluation
<p><strong>Episode Summary:</strong></p> <p>In this episode we talk to Alex Shkor about his company DEIP which is a peer review platform and blockchain protocol for the evaluation of intellectual capital, research, and innovation. We discussed why Alex created this platform, what the potential for digital research and big data is, and how Open Science can accelerate research. </p> <p><strong>Episode Links: </strong></p> <p><a href="https://deip.world/">DEIP</a></p>
Data from: Building trust takes time: Limits to arbitrage for blockchain-based assets
<p><span>The dataset contains all historical order book snapshots and blockchain network information used to generate the results for the paper "Building Trust takes Time". </span></p> <p><span>A blockchain replaces central counterparties with time-consuming consensus pro</span><span>tocols to record the transfer of ownership.</span> <span>This settlement latency slows cross-</span><span>exchange trading, exposing arbitrageurs to price risk. Off-chain settlement, instead, </span><span>exposes arbitrageurs to costly default risk. We show with Bitcoin network and or</span><span>der book data that cross-exchange price differences coincide with periods of high </span><span>settlement latency, asset flows chase arbitrage opportunities, and price differences </span><span>across exchanges with low default risk are smaller. Blockchain-based trading thus </span><span>faces a dilemma: reliable consensus protocols require time-consuming settlement </span><span>latency, leading to arbitrage limits. Circumventing such arbitrage costs is possible </span><span>only</span> <span>by reinstalling trusted intermediation, which mitigates default risk.</span></p>
Dataset created in the context of the project "Stable methodologies to evaluate and measure quality, interoperability, blockchain and reuse of open data in the agricultural field"
<p>The project "Stable methodologies to evaluate and measure quality, interoperability, blockchain and reuse of open data in the agricultural field", whose website is https://datause.es/, is a project funded by the Ministry of Science and Innovation - State Research Agency, with reference PID2019-105708RB-C22.</p> <p>Within the framework of the project, a bibliographic search is carried out in all thematic categories of the Web of Science (WoS) related to agriculture and related areas. The search equation included the following categories:</p> <p><strong>WC </strong>= (FOOD SCIENCE TECHNOLOGY OR PLANT SCIENCES OR FORESTRY OR AGRICULTURAL ENGINEERING OR AGRONOMY OR HORTICULTURE OR AGRICULTURE DAIRY ANIMAL SCIENCE OR AGRICULTURE MULTIDISCIPLINARY OR AGRICULTURAL ECONOMICS POLICY) </p> <p>This data set shows the distribution of journals and the quartile they occupy in each of the thematic categories in 2019, with the aim of serving researchers in this area and for future data mining.</p>
Dataset: Decentralized Cross-Blockchain Asset Transfers with Transfer Confirmation
<p>Dataset resulting from the experiments conducted in "Decentralized Cross-Blockchain Asset Transfers with Transfer Confirmation"</p>
Outputs of the paper "Blockchain-enabled Server-less Federated Learning"
<p>This repository contains the different outputs generated for the paper "Blockchain-enabled Server-less Federated Learning", submitted to Computer Networks (COMNET) journal. Two types of outputs are provided:</p> <ol> <li><strong>Blockchain queue simulator (output_queue_simulator):</strong> results of the simulations done in the batch-service queue simulator (https://github.com/fwilhelmi/batch_service_queue_simulator) to characterize the queue latency of blockchain applications.</li> <li><strong>Tensorflow (output_tensorflow):</strong> results of the simulations done in Tensorflow Federated (TFF), resulting from the application of different models to the federated EMNIST dataset.</li> </ol> <p>Each folder also includes the scripts used to execute the corresponding simulations. For more details, see the repository in https://github.com/fwilhelmi/blockchain_enabled_federated_learning</p>
Literaturrecherche: Blockchain & Health Care (Anlage 2 zur MHBA-Masterthesis)
<p>1. quantitative semantische Analysedaten (in ZIP-Datei) : Blockchain[title] AND health care in https://pubmed.ncbi.nlm.nih.gov/</p> <p>2. Exegese von „cases“ (Fallstudien) und „proof of concepts“ (Funktionsnachweise)</p> <p>mit den Suchstrings (TXT-Datei) nach</p> <p>a) Ansätzen zur Patienten- resp. Behandlungsdokumentation (EHRs)</p> <p>b) Ansätzen zur Prozessoptimierung (supply chain)</p> <p>c) Ansätzen in der Medizintechnik und Digital Health (Internet of Medical Things)</p> <p>sowie finale Liste (CSV-Datei) der eingeschlossenen und ausgeschlossenen Arbeiten.</p> <p>3. Ergebnisse der Autoren-/Länderanalyse der Publikationen (Formate: CSV, TXT)</p>
Dataset: Global Blockchain Acquisition Corp. (GBBKW) 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 Blockchain Acquisition Corp. (GBBKR) 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.