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915 results for “graphs”
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>
Supplementary Data for NIPS Publication: Protein Interface Prediction using Graph Convolutional Networks.
<p>These data sets can be used to re-run the experiments from our paper, Protein Interface Prediction using Graph Convolutional Networks. The data are derived from protein complexes in the docking benchmark dataset v. 5.0. Each file is a python tuple that has been saved using cPickle and compressed using gzip.</p> <p>Links:</p> <p>Paper: https://papers.nips.cc/paper/7231-protein-interface-prediction-using-graph-convolutional-networks</p> <p>Poster: https://zenodo.org/record/1134154</p> <p>Code: https://github.com/fouticus/pipgcn</p> <p> </p> <p><strong>File Descriptions:</strong></p> <p>train.cpkl.gz and test.cpkl.gz have the data formatted for neighborhood based graph convolutions. The diffc_ files are the same data formatted for the diffusion convolutional neural networks that we compare against. </p> <p>train.cpkl.gz is a tuple of length 2:</p> <ul> <li>element 0 is a list of length 175 containing the PDB codes from the docking benchmark dataset</li> <li>element 1 is a list of length 175 containing features for each protein. Each element is a dictionary containing the following keys: <ul> <li>r_vertex: vertex (residue) features for the receptor. numpy array of shape (x, 70) where x is the number of residues in the receptor and 70 is the number of features.</li> <li>l_vertex: vertex (residue) features for the ligand. analogous to above, with shape (y, 70) where y is the number of residues in the ligand.</li> <li>complex_code: PDB code of the complex. matches the list of codes described above.</li> <li>l_edge: edge features for the neighborhood around each residue in the ligand. numpy array of shape (y, 20, 2) where y is defined as above. the second dimension is the edges to the 20 nearest neighboring residues, ordered by decreasing distance. The third dimension allows for two features per edge. </li> <li>r_edge: edge features for the neighborhood around each residue in the receptor. numpy array of shape (x, 20, 2) where x is as above. </li> <li>l_hood_indices: the index of the 20 closest residues to each residue, ordered by decreasing distance. numpy array of shape (y, 20, 1). "Index" means which row in l_vertex gives the vertex features for the closest neighbor, second closest neighbor, etc. </li> <li>r_hood_indices: analogous to above, shape (x, 20, 1).</li> <li>label: 1 or -1 label for each residue pair. numpy array of shape (x*y, 3). Each row looks like (i, j, k) where i is the index of the ligand residue, j is the index of the receptor residue, and k is either -1 (negative example) or 1 (positive example).</li> </ul> </li> </ul> <p>test.cpkl.gz matches the structure of train.cpkl.gz except it has the test set of 55 complexes. </p> <p>Descriptions of the vertex and edge features can be found in Appendix A of <a href="https://mountainscholar.org/handle/10217/185661">this.</a></p> <p>diffc_g2_p2_train.cpkl.gz is a tuple of length 2:</p> <ul> <li>element 0 is a list of the same 175 PDB codes as above. </li> <li>element 1 is a list of features for the 175 complexes. Each element is a dictionary of features with these keys: <ul> <li>r_vertex, l_vertex, complex_code, label: these are the same as described above. </li> <li>'r_power_series': Stacked diffusion matrices which are powers of the similarity matrix used in the DCNN method. numpy array of shape (x, 2, x) where x is the number of receptor residues. the middle dimension 2 indicates how many "hops" is used for that diffusion (1 vs. 2). In other words, element (i, 0, j) is the similarity after 1 hops between residues i and j. element (i, 1, j) is the similarity after 2 hops. See DCNN paper for details.</li> <li>'l_power_series': same as above but for the ligand. shape is (y, 2, y).</li> </ul> </li> </ul> <p>diffc_g2_p2_test.cpkl.gz is the same as diffc_g2_p2_train.cpkl.gz but for the 55 test complexes.</p> <p>diff_g2_p5_train.cpkl.gz and diff_g2_p5_test.cpkl.gz are the same as the p2 version above, except that the diffusion matrices have shape (x, 5, x) and (y, 5, y) because one of our comparisons against the DCNN model uses 5 hops instead of just 2. </p> <p> </p> <p>Note: these files were pickled with Python 2.7. If you're unpickling with Python 3.x you might have to specify encoding as 'latin1'. </p> <p> </p> <p>Please direct any questions to:</p> <ul> <li>Alex Fout (fout@colostate.edu)</li> <li>Jonathon Byrd (jonbyrd@colostate.edu)</li> <li>Basir Shariat (basir@cs.colostate.edu</li> <li>Asa Ben-Hur (asa@cs.colostate.edu)</li> </ul>
Bi-equivalent planar graphs data files
<p>The archive file contains files describing every bi-equivalent planar graph.<br>Each folder of the type LM contains the graph of valencies L and M.<br>Each sub-folder in these folder correspond to 1 graph.<br>Their content is:<br>- f.par : the PGC for the graph<br>- a txt file: file describing the connectivity of the graph (see below for the format).<br>- a svg file: vector graphic file for the graph.</p> <p>Syntax of the txt file:<br>Line 1 : the number of nodes for the graph<br>Line 2 : the nodes for the outside face (usually "0 1 2 P-1")<br>Following lines except last 2 lines: "n1 n2" : the indices of 2 nodes linked together.<br>Line -2 from the end: "N0 n1 n2 ... nn" : the list of nodes of the 1st type.<br>Last line: "N1 n1 n2 ... nn", the list of nodes of the 2nd type.</p>
MIRA-KG: A Knowledge Graph of Hypotheses and Findings for Social Demography Research
<p>A shift in scientific publishing from paper-based to knowledge-based practices promotes reproducibility, machine actionability and knowledge discovery. This is important for disciplines like social science, as study indicators are often social constructs such as race or education; hypothesis tests are challenging to compare in demographic research due to their limited temporal and spatial coverage; and natural language in research papers is often imprecise and ambiguous. Therefore, we present the MIRA-KG, consisting of: (1) an ontology for capturing social demography research, which links hypotheses and findings to evidence, (2) annotations of papers on health inequality in terms of the ontology, gathered by (i) prompting a Large Language Model to annotate paper abstracts using the ontology, (ii) mapping concepts to terms from NCBO BioPortal ontologies and GeoNames, and (iii) refining the final graph by a set of SHACL constraints, developed according to data quality criteria. The utility of the resource lies in its use for formally representing social demography research hypotheses, discovering research biases, discovery of knowledge, and the derivation of novel questions.<br><br>This dataset was generated using the code available on Github at <a href="https://w3id.org/mira/">https://w3id.org/mira/</a> at version v1.0. It uses the following ontology: <a href="https://w3id.org/mira/ontology/">https://w3id.org/mira/ontology/</a>. </p>
Dataset and Source Code for the Paper: A Framework for Developing Strategic Cyber Threat Intelligence from Advanced Persistent Threat Analysis Reports Using Graph-Based Algorithms
<p>Here are the data set and source code related to the paper: "A Framework for Developing Strategic Cyber Threat Intelligence from Advanced Persistent Threat Analysis Reports Using Graph-Based Algorithms"</p> <p>1- aptnotes-downloader.zip : contains source code that downloads all APT reports listed in https://github.com/aptnotes/data and https://github.com/CyberMonitor/APT_CyberCriminal_Campagin_Collections</p> <p>2- apt-groups.zip : contains all APT group names gathered from https://docs.google.com/spreadsheets/d/1H9_xaxQHpWaa4O_Son4Gx0YOIzlcBWMsdvePFX68EKU/edit?gid=1864660085#gid=1864660085 and https://malpedia.caad.fkie.fraunhofer.de/actors and https://malpedia.caad.fkie.fraunhofer.de/actors</p> <p>3- apt-reports.zip : contains all deduplicated APT reports gathered from https://github.com/aptnotes/data and https://github.com/CyberMonitor/APT_CyberCriminal_Campagin_Collections</p> <p>4- countries.zip : contains country name list.</p> <p>5- ttps.zip : contains all MITRE techniques gathered from https://attack.mitre.org/resources/attack-data-and-tools/</p> <p>6- malware-families.zip : contains all malware family names gathered from https://malpedia.caad.fkie.fraunhofer.de/families</p> <p>7- ioc-searcher-app.zip : contains source code that extracts IoCs from APT reports. Extracted IoC files are provided in report-analyser.zip. Original code repo can be found at https://github.com/malicialab/iocsearcher</p> <p>8- extracted-iocs.zip : contains extracted IoCs by ioc-searcher-app.zip</p> <p>9- report-analyser.zip : contains source code that searchs APT reports, malware families, countries and TTPs. I case of a match, it updates files in extracted-iocs.zip.</p> <p>10- cti-transformation-app.zip : contains source code that transforms files in extracted-iocs.zip to CTI triples and saves into Neo4j graph database.</p> <p>11- graph-db-backup.zip : contains volume folder of Neo4j Docker container. When it is mounted to a Docker container, all CTI database becomes reachable from Neo4j web interface. Here is how to run a Neo4j Docker container that mounts folder in the zip:</p> <p>docker run -d --publish=7474:7474 --publish=7687:7687 --volume={PATH_TO_VOLUME}/DEVIL_NEO4J_VOLUME/neo4j/data:/data --volume={PATH_TO_VOLUME}/DEVIL_NEO4J_VOLUME/neo4j/plugins:/plugins --volume={PATH_TO_VOLUME}/DEVIL_NEO4J_VOLUME/neo4j/logs:/logs --volume={PATH_TO_VOLUME}/DEVIL_NEO4J_VOLUME/neo4j/conf:/conf --env 'NEO4J_PLUGINS=["apoc","graph-data-science"]' --env NEO4J_apoc_export_file_enabled=true --env NEO4J_apoc_import_file_enabled=true --env NEO4J_apoc_import_file_use__neo4j__config=true --env=NEO4J_AUTH=none neo4j:5.13.0</p> <h4><strong>web interface: http://localhost:7474</strong></h4> <h4><strong>username: neo4j</strong></h4> <h4><strong>password: neo4j</strong></h4> <p> </p>
CNN Wild Park - Graph Neural Networks for Learning Equivariant Representations of Neural Networks
<p>This repository contains the <strong>CNN Wild Park</strong> dataset from the paper:</p> <blockquote> <p><strong>Graph Neural Networks for Learning Equivariant Representations of Neural Networks</strong><br><a href="https://mkofinas.github.io/">Miltiadis Kofinas</a>*, <a href="https://bknyaz.github.io/">Boris Knyazev</a>, <a href="https://www.cyanogenoid.com/">Yan Zhang</a>, <a href="https://yunlu-chen.github.io/">Yunlu Chen</a>, <a href="https://gertjanburghouts.github.io/">Gertjan J. Burghouts</a>, <a href="https://egavves.com/">Efstratios Gavves</a>, <a href="https://www.ceessnoek.info/">Cees G. M. Snoek</a>, <a href="https://davzha.netlify.app/">David W. Zhang</a>*<br><em>ICLR 2024</em> (oral)<br><a href="https://arxiv.org/abs/2403.12143">https://arxiv.org/abs/2403.12143</a><br><a href="https://github.com/mkofinas/neural-graphs">https://github.com/mkofinas/neural-graphs</a><br>*Joint first and last authors</p> </blockquote> <p>We introduce a new dataset of CNNs, which we term <em>CNN Wild Park</em>.<br>The dataset consists of 117,241 checkpoints from 2,800 CNNs, trained for up to 1,000 epochs on CIFAR10.<br>The CNNs vary in the number of layers, kernel sizes, activation functions, and residual connections between arbitrary layers.</p> <p>More specifically, we construct the CNN Wild Park dataset by training 2,800 small CNNs with different architectures for 200 to 1,000 epochs on CIFAR10. We retain a checkpoint of its parameters every 10 steps and also record the test accuracy. The CNNs vary by:</p> <ul> <li>Number of layers L in [2, 3, 4, 5] (note that this does not count the input layer).</li> <li>Number of channels per layer c_l in [4, 8, 16, 32].</li> <li>Kernel size of each convolution k_l in [3, 5, 7].</li> <li>Activation functions at each layer are one of ReLU, GeLU, tanh, sigmoid, leaky ReLU, or the identity function.</li> <li>Skip connections between two layers with at least one layer in between. Each layer can have at most one incoming skip connection. We allow for skip connections even in the case when the number of channels differ, to increase the variety of architectures and ensure independence between different architectural choices. We enable this by adding the skip connection only to the min(c_n, c_m) nodes.</li> </ul> <p>We divide the dataset into train/val/test splits such that checkpoints from the same run are <strong>not</strong> contained in both the train and test splits. </p> <div> </div> <div> </div>
The number of realizations of all Laman graphs with at most 12 vertices
<p>This data set consists of files for all Laman graphs (minimally rigid graphs) with at most 12 vertices and files for their Laman numbers (number of complex relaizations).</p> <p>The data is computed by a combinatorial algorithm of Capco, Gallet, Grasegger, Koutschan, Lubbes and Schicho (see <a href="https://doi.org/10.1137/17M1118312">10.1137/17M1118312</a> for a description and <a href="https://doi.org/10.5281/zenodo.1245506">10.5281/zenodo.1245506</a> for an implementation).</p>
Maven central dependency graph
<p>The Maven dependency graph is an open dataset of Maven Central artifacts, their dependencies, as well as other relationships. Its main intent is to domesticate the wild within and around the Maven central ecosystem, in particular, and JVM-based libraries at large, making it more harnessable to both academics and industry. It is intended to answer high-level research questions concerning artifacts releases, evolution, and usage trends over time. It can also be used to assist researchers in selecting relevant datasets, among the mass of existing software artifact, for assessing particular empirical software engineering challenges. The complexity of these questions can range from simple pattern matching to advanced big data analysis and machine learning techniques.<br> <br> The accompanying paper to this dataset is has been accepted for publication in the proceedings of the International Conference on Mining Software Repositories 2019 and has received the MSR 2019 Data Showcase Award. This paper is available for download on <a href="https://arxiv.org/abs/1901.05392">arXiv</a>.</p>
Metadata, Title Pages, and Network Graph of the Digitized Content of the Berlin State Library (146,000 items)
<p>The data set has been downloaded via the OAI-PMH endpoint of the Berlin State Library/Staatsbibliothek zu Berlin’s Digitized Collections (<a href="https://digital.staatsbibliothek-berlin.de/oai">https://digital.staatsbibliothek-berlin.de/oai</a>) on March 1<sup>st</sup> 2019 and converted into common tabular formats on the basis of the provided Dublin Core metadata. It contains 146,000 records.</p> <p>In addition to the bibliographic metadata, representative images of the works have been downloaded, resized to a 512 pixel maximum thumbnail image and saved in JPEG format. The image data is split into title pages and first pages. Title pages have been derived from structural metadata created by scan operators and librarians. If this information was not available, first pages of the media have been downloaded. In case of multi-volume media, title pages are not available.</p> <p>In total, 141,206 images title/first pages are available.</p> <p> </p> <p>Furthermore, the tabular data has been cleaned and extended with geo-spatial coordinates provided by the OpenStreetMap project (<a href="https://www.openstreetmap.org">https://www.openstreetmap.org</a>). The actual data processing steps are summarized in the next section. For the sake of transparency and reproducibility, the original data taken from the OAI-PMH endpoint is still present in the table.</p> <p> </p> <p>To conclude with, various graphs in GML file format are available that can be loaded directly into graph analysis tools such as Gephi (<a href="https://gephi.org/">https://gephi.org/</a>).</p> <p> </p> <p>The implementation of the data processing steps (incl. graph creation) are available as a Jupyter notebook provided at <a href="https://github.com/elektrobohemian/SBBrowse2018/blob/master/DataProcessing.ipynb">https://github.com/elektrobohemian/SBBrowse2018/blob/master/DataProcessing.ipynb</a>.</p> <p> </p> <p>Tabular Metadata</p> <p> </p> <p>The metadata is available in Excel (cleanedData.xlsx) and CSV (cleanedData.csv) file formats with equal content.</p> <p>The table contains the following columns. Italique columns have not been processed.</p> <p>· <em>title</em> The title of the medium</p> <p>· <em>creator</em> Its creator (family name, first name)</p> <p>· <em>subject</em> A collection’s name as provided by the library</p> <p>· <em>type</em> The type of medium</p> <p>· <em>format</em> A MIME type for full metadata download</p> <p>· <em>identifier</em> An additional identifier (most often the PPN)</p> <p>· <em>language</em> A 3-letter language code of the medium</p> <p>· <em>date</em> The date of creation/publication or a time span</p> <p>· <em>relation</em> A relation to a project or collection a medium has been digitized for.</p> <p>· <em>coverage</em> The location of publication or origin (ranging from cities to continents)</p> <p>· <em>publisher</em> The publisher of the medium.</p> <p>· <em>rights</em> Copyright information.</p> <p>· <em>PPN</em> The unique identifier that can be used to find more information about the current medium in all information systems of Berlin State Library/Staatsbibliothek zu Berlin.</p> <p>· spatialClean In case of multiple entries in coverage, only the first place of origin has been extracted. Additionally, characters such as question marks, brackets, or the like have been removed. The entries have been normalized regarding whitespaces and writing variants with the help of regular expressions.</p> <p>· dateClean As the original date may contain various format variants to indicate unclear creation dates (e.g., time spans or question marks), this field contains a mapping to a certain point in time.</p> <p>· spatialCluster The cluster ID determined with the help of the Jaro-Winkler distance on the spatialClean string. This step is needed because the spatialClean fields still contain a huge amount of orthographic variants and latinizations of geographic names.</p> <p>· spatialClusterName A verbal cluster name (controlled manually).</p> <p>· latitude The latitude provided by OpenStreetMap of the spatialClusterName if the location could be found.</p> <p>· longitude The longitude provided by OpenStreetMap of the spatialClusterName if the location could be found.</p> <p>· century A century derived from the date.</p> <p>· textCluster A text cluster ID on the basis of a k-means clustering relying on the title field with a vocabulary size of 125,000 using the tf*idf model and k=5,000.</p> <p>· creatorCluster A text cluster ID based on the creator field with k=20,000.</p> <p>· titleImage The path to the first/title page relative to the img/ subdirectory or None in case of a multi-volume work.</p> <p>Other Data</p> <p> </p> <p><em>graphs.zip</em></p> <p> </p> <p>Various pre-computed graphs.</p> <p><em> </em></p> <p><em>img.zip</em></p> <p> </p> <p>First and title pages in JPEG format.</p> <p> </p> <p><em>json.zip</em></p> <p> </p> <p>JSON files for each record in the following format:</p> <p> </p> <p>ppn "PPN57346250X"</p> <p>dateClean "1625"</p> <p>title "M. Georgii Gutkii, Gymnasii Berlinensis Rectoris Habitus Primorum Principiorum, Seu Intelligentia; Annexae Sunt Appendicis loco Disputationes super eodem habitu tum in Academia Wittebergensi, tum in Gymnasio Berlinensi ventilatae"</p> <p>creator "Gutke, Georg"</p> <p>spatialClusterName "Berlin"</p> <p>spatialClean "Berolini"</p> <p>spatialRaw "Berolini"</p> <p>mediatype "monograph"</p> <p>subject "Historische Drucke"</p> <p>publisher "Kallius"</p> <p>lat "52.5170365"</p> <p>lng "13.3888599"</p> <p>textCluster "45"</p> <p>creatorCluster "5040"</p> <p>titleImage "titlepages/PPN57346250X.jpg"</p>
The Software Heritage Graph Dataset
<p>Software Heritage is the largest existing public archive of software source<br> code and accompanying development history: it currently spans more than five<br> billion unique source code files and one billion unique commits, coming from<br> more than 80 million software projects.</p> <p>This is the Software Heritage graph dataset: a fully-deduplicated<br> Merkle DAG representation of the Software Heritage archive. The dataset links<br> together file content identifiers, source code directories, Version Control<br> System (VCS) commits tracking evolution over time, up to the full states of VCS<br> repositories as observed by Software Heritage during periodic crawls. The<br> dataset’s contents come from major development forges (including GitHub and<br> GitLab), FOSS distributions (e.g., Debian), and language-specific package<br> managers (e.g., PyPI). Crawling information is also included, providing<br> timestamps about when and where all archived source code artifacts have been<br> observed in the wild.</p> <p>The Software Heritage graph dataset is available in multiple formats, including<br> downloadable CSV dumps and Apache Parquet files for local use, as well as a<br> public instance on Amazon Athena interactive query service for ready-to-use<br> powerful analytical processing.</p> <p>By accessing the dataset, you agree with the Software Heritage <a href="https://www.softwareheritage.org/legal/users-ethical-charter/">Ethical Charter<br> for using the archive data</a>, and the <a href="https://www.softwareheritage.org/legal/bulk-access-terms-of-use/">terms of use for bulk access</a>.</p> <p>If you use this dataset for research purposes, please cite the following paper:</p> <ul> <li>Antoine Pietri, Diomidis Spinellis, Stefano Zacchiroli. <br> <em>The Software Heritage Graph Dataset: Public software development under one roof</em>. <br> In proceedings of <a href="http://2019.msrconf.org/">MSR 2019</a>: The 16th International Conference on Mining Software Repositories, May 2019, Montreal, Canada. Co-located with <a href="https://2019.icse-conferences.org/">ICSE 2019</a>. <br> <a href="https://upsilon.cc/~zack/research/publications/msr-2019-swh.pdf">preprint</a>, <a href="https://upsilon.cc/~zack/research/publications/msr-2019-swh.bib">bibtex</a></li> </ul> <p>You can also refer to the above paper for more information the dataset and sample queries.</p>
Title, Author, Publisher, Place of Publication, and Language-related Network Graphs of the Berlin State Library Main Catalog
<p>The dataset contains graphs in GML, GraphML, and a simple JSON format.</p> <p>For each of the following languages:</p> <ol> <li>cze</li> <li>dan</li> <li>dut</li> <li>eng</li> <li>fre</li> <li>fry</li> <li>ger</li> <li>gre</li> <li>ice</li> <li>ita</li> <li>lat</li> <li>nor</li> <li>pol</li> <li>por</li> <li>rum</li> <li>rus</li> <li>slo</li> <li>spa</li> <li>swe</li> </ol> <p>two graphs are made available linking</p> <ul> <li>author, publisher, and place of publication</li> <li>author, publisher, place of publication, and title</li> </ul> <p>Additionaly, a third graph links authors and publishers to the language of publication (incl. year of the publication).</p> <p>The core statistics of each graph are outlined in <em>social_analysis_statistics.csv</em>. The smallest graph (fry, author_publisher_location) has 298 nodes and 264 edges, while the largest (ger, author_publisher_location_title) has 2,499,943 nodes and 3,950,900 edges.</p> <p>The language graphs spans all languages and has 1,706,273 nodes and 1,827,759 edges.</p> <p>All graphs have been created by a Python script available <a href="https://github.com/elektrobohemian/CulturalAnalytics/blob/master/SocialAnalysisStabikat.ipynb">here.</a></p>
Datasets for Watset: Local-Global Graph Clustering with Applications in Sense and Frame Induction
<p>This dataset supplements the article “<a href="https://doi.org/10.1162/COLI_a_00354">Watset: Local-Global Graph Clustering with Applications in Sense and Frame Induction</a>” published in the Computational Linguistics journal:</p> <ul> <li> <p><code>watset-coli-lcc-performance.tsv</code>: runtime analysis</p> </li> <li> <p><code>watset-coli-synsets.zip</code>: synset induction experiment (note that <code>pairwise-{en-babelnet,ru-rwn}.pkl</code> files are excluded due to the licensing issues)</p> </li> <li> <p><code>watset-coli-triframes.zip</code>: semantic frame induction experiment</p> </li> <li> <p><code>watset-coli-classes.zip</code>: semantic class induction experiment</p> </li> </ul>
Datasets for Non-Parametric Class Completeness Estimators for Collaborative Knowledge Graphs
<p><strong>Non-Parametric Class Completeness Estimators for Collaborative Knowledge Graphs</strong></p> <p>This are intermediary datasets used for the calculation of the Class Completeness Estimators on Wikidata. For more information see: https://github.com/eXascaleInfolab/cardinal/</p> <p><strong>edits_wikidatawiki-20181001-pages.csv</strong></p> <p>This is an extract from <em>wikidatawiki-20181001-pages-meta-history</em> (All pages with complete page edit history (.bz2)) found at <a href="https://dumps.wikimedia.org/wikidatawiki/">https://dumps.wikimedia.org/wikidatawiki/</a>.</p> <p>The extract was created by the following SQL query:</p> <pre> SELECT page_title, rev_comment, rev_user_text, rev_timestamp FROM revisions WHERE rev_comment LIKE '%[[Property:%]]%[[Q%' ORDER BY rev_id INTO OUTFILE 'edits_wikidatawiki-20181001-pages.csv'; </pre> <p> </p> <p><strong>wikidata-20180813-all.json.bz2.universe.noattr.gt.bz2</strong></p> <p>This is a graph-tool representation of the WikiData graph. Output of <a href="https://github.com/eXascaleInfolab/cardinal/blob/master/1_create_inmemory_graph.py">https://github.com/eXascaleInfolab/cardinal/blob/master/1_create_inmemory_graph.py</a>.</p> <p><strong>observations_wikidatawiki-20181001-pages.pickle</strong></p> <p>Extracted observations. Output of <a href="https://github.com/eXascaleInfolab/cardinal/blob/master/2_extract_observations.py">https://github.com/eXascaleInfolab/cardinal/blob/master/2_extract_observations.py</a>.</p> <p> </p> <p><strong>estimates_wikidatawiki-20181001-pages.pickle</strong></p> <p>Extracted estimates. Output of <a href="https://github.com/eXascaleInfolab/cardinal/blob/master/3_calculate_estimates.py">https://github.com/eXascaleInfolab/cardinal/blob/master/3_calculate_estimates.py</a></p> <p> </p> <p><strong>results_wikidatawiki-20181001-pages.pickle </strong></p> <p>Results. Output of <a href="https://github.com/eXascaleInfolab/cardinal/blob/master/4_draw_graphs.py">https://github.com/eXascaleInfolab/cardinal/blob/master/4_draw_graphs.py</a></p>
Graph topological features extracted from expression profiles of neuroblastoma patients
<p><strong>Introduction</strong></p> <p>This dataset contains the data described in the paper titled "A deep neural network approach to predicting clinical outcomes of neuroblastoma patients." by Tranchevent, Azuaje and Rajapakse. More precisely, this dataset contains the topological features extracted from graphs built from publicly available expression data (see details below). This dataset does not contain the original expression data, which are available elsewhere. We thank the scientists who did generate and share these data (please see below the relevant links and publications).</p> <p> </p> <p><strong>Content</strong></p> <p>File names start with the name of the publicly available dataset they are built on (among "Fischer", "Maris" and "Versteeg"). This name is followed by a tag representing whether they contain raw data ("raw", which means, in this case, the raw topological features) or TF formatted data ("TF", which stands for TensorFlow). This tag is then followed by a unique identifier representing a unique configuration. The configuration file "Global_configuration.tsv" contains details about these configurations such as which topological features are present and which clinical outcome is considered.</p> <p>The code associated to the same manuscript that uses these data is at <a href="https://gitlab.com/biomodlih/SingalunDeep">https://gitlab.com/biomodlih/SingalunDeep</a>. The procedure by which the raw data are transformed into the TensorFlow ready data is described in the paper.</p> <p> </p> <p><strong>File format</strong></p> <p>All files are TSV files that correspond to matrices with samples as rows and features as columns (or clinical data as columns for clinical data files). The data files contain various sets of topological features that were extracted from the sample graphs (or Patient Similarity Networks - PSN). The clinical files contain relevant clinical outcomes.</p> <p>The raw data files only contain the topological data. For instance, the file "Fischer_raw_2d0000_data_tsv" contains 24 values for each sample corresponding to the 12 centralities computed for both the microarray (<em>Fischer-M</em>) and RNA-seq (<em>Fischer-R</em>) datasets. The TensorFlow ready files do not contain the sample identifiers in the first column. However, they contain two extra columns at the end. The first extra column is the sample weights (for the classifiers and because we very often have a dominant class). The second extra column is the class labels (binary), based on the clinical outcome of interest.</p> <p> </p> <p><strong>Dataset details</strong></p> <p>The <em>Fischer</em> dataset is used to train, evaluate and validate the models, so the dataset is split into train / eval / valid files, which contains respectively 249, 125 and 124 rows (samples) of the original 498 samples. In contrast, the other two datasets (<em>Maris</em> and <em>Versteeg</em>) are smaller and are only used for validation (and therefore have no training or evaluation file).</p> <p>The <em>Fischer</em> dataset also has more data files because various configurations were tested (see manuscript). In contrast, the validation, using the <em>Maris</em> and <em>Versteeg</em> datasets is only done for a single configuration and there are therefore less files.</p> <p>For <em>Fischer</em>, a few configurations are listed in the global configuration file but there is no corresponding raw data. This is because these items are derived from concatenations of the original raw data (see global configuration file and manuscript for details).</p> <p> </p> <p><strong>References</strong></p> <p>This dataset is associated with Tranchevent L., Azuaje F.. Rajapakse J.C., A deep neural network approach to predicting clinical outcomes of neuroblastoma patients.</p> <p>If you use these data in your research, please do not forget to also cite the researchers who have generated the original expression datasets.</p> <p><em>Fischer</em> dataset:</p> <ul> <li>Zhang W. et al., Comparison of RNA-seq and microarray-based models for clinical endpoint prediction. Genome Biology 16(1) (2015). doi:10.1186/s13059-015-0694-1</li> <li>Wang C. et al., The concordance between RNA-seq and microarray data depends on chemical treatment and transcript abundance. Nat. Biotechnol. 32(9), 926–932. doi:10.1038/nbt.3001</li> </ul> <p><em>Versteeg</em> dataset:</p> <ul> <li>Molenaar J.J. et al., Sequencing of neuroblastoma identifies chromothripsis and defects in neuritogenesis genes. Nature 483(7391), 589–593. doi:10.1038/nature10910</li> </ul> <p><em>Maris</em> dataset:</p> <ul> <li>Wang Q. et al., Integrative genomics identifies distinct molecular classes of neuroblastoma and shows that multiple genes are targeted by regional alterations in DNA copy number. Cancer Res. 66(12), 6050–6062. doi:10.1158/0008-5472.CAN-05-4618</li> </ul>
OpenAire Research Graph linked with OpenAlex
<p>This package contains linked datasets of OpenAire Research Graph and OpenAlex. </p> <p>Files descriptions:</p> <p>- author_to_publication_dic.json contains a mapping of authors to their publications</p> <p>- downloads_views_dic.json contains mappings of the publication id to the number of its downloads and views</p> <p>- id_doi_dic.json contains a mapping of the publication id to its doi</p> <p>- merged1..5.json contain all publication data from the OARG dataset</p> <p>- necessary_fields_dic.json contains extracted publications’ fields necessary for the work</p> <p>- oarg_ref_rel_dic.json contains mapping of publication id to referenced and related work present in OpenAlex dataset</p> <p>- openalex_found_publications5_4.json contains all data on found publications from the OpenAlex</p> <p>- publication_to_author_dic.json contains a mapping of publications to their authors</p>
Storage conditions affect the composition of the lyophilized secretome of multipotent mesenchymal stromal cells - graphs
<p>Figures for artcile Rogulska, O., Vackova, I., Prazak, S. <em>et al.</em> Storage conditions affect the composition of the lyophilized secretome of multipotent mesenchymal stromal cells. <em>Sci Rep</em> 14, 10243 (2024). https://doi.org/10.1038/s41598-024-60787-z</p> <p><span>Fig. 1. Preservation of the growth factors and cytokines in the lyophilized MSC-derived conditioned medium after 3 months of storage at different temperatures (N=3). Data is presented as Median (Q1; Q3). * - the values are significantly (p<0.05) lower compared to the -80°C group; # - the values are significantly (p<0.05) lower compared to the -20°C group. The values are related to non-lyophilized MSC-sec stored for the same time (3 months) in a frozen state at −80°C.</span></p> <p><span> </span><span>Fig. 2. The preservation of growth factors and cytokines in the lyophilized MSC-derived conditioned medium after 30 months of storage at different temperatures (N=3). Data is presented as Median (Q1; Q3). * - the values are significantly (p<0.05) lower compared to the -80°C group; # - the values are significantly (p<0.05) lower compared to the -20°C group; ° - the values are significantly (p<0.05) lower compared to the 4°C group. The values are related to non-lyophilized MSC-sec stored for the same time (30 months) in a frozen state at −80°C.</span></p> <p><span> </span><span>Fig. 3. The preservation levels of the whole lyophilized MSC-sec cocktail, determined as the median of the preservation values for each of the MSC-sec components. The v</span><span>alues are related to non-lyophilized MSC-sec stored for the same time in a frozen state at −80°C.</span></p>
The ScRAP yeast reference pangenome graphs
<p>The yeast reference pangenome graphs deposited here are constructed by <a href="https://www.evomicslab.org/">Evomics Lab</a> using the high-quality genome assemblies of globally-sampled <em>Saccharomyces cerevisiae </em>strains described in both the original <em>S. cerevisiae</em> Reference Assembly Panel (ScRAP; See <a title="O&rsquo;Donnell et al. Nature Genetics, 2023" href="https://www.nature.com/articles/s41588-023-01459-y" target="_blank" rel="noopener">O’Donnell et al. Nature Genetics, 2023</a>) and its future extensions. </p> <p>The detailed construction specification for each graphs are described below:</p> <p>1) ScRAP_v20230121_163asm.minigraph.gfa</p> <p>This graph was built upon 163 yeast genome assemblies from 142 strains. Briefly, we took the <em>S. cerevisiae</em> reference genome (denoted as 'SGDref') retrieved from the Saccharomyces genome database (SGD) as well as 162 assemblies from our recently released <em>S. cerevisiae</em> Reference Assembly Panel (ScRAP; See <a title="O&rsquo;Donnell et al. Nature Genetics, 2023" href="https://www.nature.com/articles/s41588-023-01459-y" target="_blank" rel="noopener">O’Donnell et al. Nature Genetics, 2023</a>) to construct reference pangenome graph by using <a href="https://github.com/lh3/minigraph">minigraph</a> with the command ‘minigraph -cxggs -l 5000’. With the SGDref as the reference genome, we incrementally added those 162 assemblies of other <em>S. cerevisiae</em> strains into the graph according to their phylogenetic distances to SGDref. We further mapped each input assembly to the graph by minigraph with the command ‘minigraph -cxasm’ to calculate the corresponding mapping depth. </p> <p> </p>
OpenAIRE Graph: dataset for research community in Virtual Human Twins
<p>This dataset contains metadata records of publications, research data, software and projects relevant for the research community in Virtual Twins in health.<br>The dump contains the records available in the <a href="https://dth.openaire.eu/" target="_blank" rel="noopener">OpenAIRE Gateway on Digital Twins in Health</a> of the <a href="https://www.edith-csa.eu/" target="_blank" rel="noopener">EDITH CSA project </a>of the European Commission (grant agreement n. 101083771).</p> <p>Records are identified via full-text mining and inference techniques applied to the <a href="https://graph.openaire.eu/">OpenAIRE Graph</a>.<br>The OpenAIRE Graph is one of the largest Open Access collections of metadata records and links between publications, datasets, software, projects, funders, and organizations, aggregating thousands of scholarly data sources world-wide.</p> <p>The dump consists of a tar archive containing gzip files with one json per line.<br>Each json is compliant to the schema available at <a href="https://doi.org/10.5281/zenodo.10519297">https://doi.org/10.5281/zenodo.10519297</a>.</p>
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.