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161 results for “graph dataset”
A biodiversity dataset graph: Biological Associations in TaxonWorks hash://sha256/e4a47c067d6c125da60c9a1b92b5eecdea539cb8666cd3aed99db347ae5b8ed0 hash://md5/686007de79cc2a49ab23fd3debe56e3f
<p>The intended use of this archive is to facilitate (meta-)analysis of Biological Associations captured in TaxonWorks [1]. TaxonWorks is an integrated web-based workbench for taxonomists and biodiversity scientists. It allows you to capture, organize, and enrich your data; share it with collaborators; and package it for analysis and publication. </p> <p>This dataset provides versioned snapshots of the TaxonWorks network as tracked by Preston [2,3,4] during 2024-05-07 using:</p> <pre><code>preston track -u https://sfg.taxonworks.org</code></pre> <p>. In addition, this dataset provides a processed version of the biological associations using the "preston tw-stream" command as generated by the following bash script:</p> <pre><code>#!/bin/bash # # Generates GloBI interaction JSON Lines from provided provenance log as generated by preston tw-stream. # /usr/local/bin/preston cat hash://sha256/c1b081afa6ea0f60570c24cca85c4d9acd91eeefe36b9cacd1fe53b6893ea154\ | /usr/local/bin/preston tw-stream </code></pre> <p><br>The script itself was executed using:</p> <pre><code>cat transform.sh | preston bash </code></pre> <p>The execution of this transform.sh script (with content id hash://sha256/6dfe3c4ebf877bed73aebbe88c7d388bf894c569578ed7b28ca68e57a6afe43b), as well as their results, is captured within this datasets also. A rdf/quads formatted machine readable version of the workflow execution description can be found via:</p> <pre><code>preston cat hash://sha256/e4a47c067d6c125da60c9a1b92b5eecdea539cb8666cd3aed99db347ae5b8ed0 </code></pre> <p>And, the resulting JSON Lines file has content id (or signature) hash://sha256/4c2b8642251ced5985660d63c565efa6e5a9bf3d12b3b0c0d9ac577905f5e897 and is also included as interactions.json to facilitate access. </p> <p>The first json record can be generated using:</p> <pre><code>preston cat hash://sha256/4c2b8642251ced5985660d63c565efa6e5a9bf3d12b3b0c0d9ac577905f5e897\ | head -n1\ | jq . </code></pre> <p>or, provided that the interactions.json has content id starting with hash://sha256/4c2b86...</p> <pre><code>cat interactions.json\ | head -n1\ | jq . </code></pre> <p>This produces the following (formatted) json object:</p> <pre><code>{<br> "http://www.w3.org/ns/prov#wasDerivedFrom": "hash://sha256/fdbf13dc5f3d9c5afbc03db62699e2ce2724c499b7d91d8b0bf31e39409b153a",<br> "http://www.w3.org/1999/02/22-rdf-syntax-ns#type": "application/vnd.taxonworks+json",<br> "referenceId": "https://sfg.taxonworks.org/api/v1/sources/213218",<br> "interactionId": "https://sfg.taxonworks.org/api/v1/biological_associations/227664",<br> "taxonRootsResolved": 2,<br> "referenceResolved": true,<br> "referenceCitation": "@article{213218,\n author = {Monzen, Kota},\n journal = {Annual Report of the Gakugei Faculty of the Iwate University},\n pages = {24-38},\n title = {Revision of the Japanese gall wasps with the descriptions of new genus, subgenus, species and subspecies (II). Cynipidae (Cynipinae) Hymenoptera.},\n volume = {6},\n year = {1954}\n}\n",<br> "interactionTypeId": "gid://taxon-works/BiologicalRelationship/69",<br> "interactionTypeName": "gall",<br> "sourceTaxonName": "Neuroterus hakonensis",<br> "sourceTaxonId": "gid://taxon-works/TaxonName/1174121",<br> "sourceTaxonRank": "species",<br> "sourceTaxonAuthorship": "Ashmead, 1904",<br> "sourceTaxonPath": "Root | Cynipidae | Neuroterus | Neuroterus hakonensis",<br> "sourceTaxonPathIds": "gid://taxon-works/TaxonName/623170 | gid://taxon-works/TaxonName/1170060 | gid://taxon-works/TaxonName/1170097 | gid://taxon-works/TaxonName/1174121",<br> "sourceTaxonPathNames": "nomenclatural rank | family | genus | species",<br> "targetTaxonName": "Quercus",<br> "targetTaxonId": "gid://taxon-works/TaxonName/1173543",<br> "targetTaxonRank": "genus",<br> "targetTaxonAuthorship": "",<br> "targetTaxonPath": "Root | Fagaceae | Quercus",<br> "targetTaxonPathIds": "gid://taxon-works/TaxonName/623170 | gid://taxon-works/TaxonName/1173542 | gid://taxon-works/TaxonName/1173543",<br> "targetTaxonPathNames": "nomenclatural rank | family | genus"<br>}<br></code></pre> <p>In this example, a claim is made that, according to https://sfg.taxonworks.org/api/v1/sources/213218 [6] Neuroterus hakonensis (a gall wasp) has a primary host in the genus of Quercus (oak tree). </p> <p>In total, 237,068 such claims can be found in the generated resource with alias interactions.json and content id starting with hash://sha256/4c2b86... .</p> <p>In addition, the archive preston.tar.gz to allow for batch download. The archive contains three types of files: index files, provenance logs and data files. In addition, index files have been individually included in this dataset publication to facilitate remote access. Index files provide a way to links provenance files in time to establish a versioning mechanism. Provenance files describe how, when, what and where the TaxonWorks content was retrieved. For more information, please visit https://preston.guoda.bio or https://doi.org/10.5281/zenodo.1410543 . </p> <p>To retrieve and verify the downloaded TaxonWorks biodiversity dataset graph, download preston.tar.gz. Then, extract the archive into a "data" folder. Alternatively, you can use the preston[2] command-line tool to "clone" this dataset using:</p> <pre><code>java -jar preston.jar clone --remote https://zenodo.org/record/11151783/files </code></pre> <p>After that, verify the index of the archive by reproducing the following provenance log history:</p> <pre><code> java -jar preston.jar history --log tsv</code></pre> <p>to be:</p> <pre><code>hash://sha256/e4a47c067d6c125da60c9a1b92b5eecdea539cb8666cd3aed99db347ae5b8ed0 http://www.w3.org/ns/prov#wasDerivedFrom hash://sha256/c1b081afa6ea0f60570c24cca85c4d9acd91eeefe36b9cacd1fe53b6893ea154 </code><br><code>hash://sha256/c1b081afa6ea0f60570c24cca85c4d9acd91eeefe36b9cacd1fe53b6893ea154 http://www.w3.org/ns/prov#wasDerivedFrom hash://sha256/a4d651aac5220487835e6178511886e98b845b2d98cb7c5447fb2b042e0654d2hash://sha256/a4d651aac5220487835e6178511886e98b845b2d98cb7c5447fb2b042e0654d2 http://www.w3.org/ns/prov#wasDerivedFrom hash://sha256/ab7550368905e7c919e70a306efbb97719a1edbba2cfe4c4515f635ebc0be4bb hash://sha256/a4d651aac5220487835e6178511886e98b845b2d98cb7c5447fb2b042e0654d2 http://www.w3.org/ns/prov#wasDerivedFrom hash://sha256/ab7550368905e7c919e70a306efbb97719a1edbba2cfe4c4515f635ebc0be4bb</code><br><code>hash://sha256/ab7550368905e7c919e70a306efbb97719a1edbba2cfe4c4515f635ebc0be4bb http://www.w3.org/ns/prov#wasDerivedFrom hash://sha256/ff5e709305e593c87711e897b6341b94e775e2f312aa6d4ae5ed6120babd6f5e urn:uuid:0659a54f-b713-4f86-a917-5be166a14110 http://purl.org/pav/hasVersion hash://sha256/ff5e709305e593c87711e897b6341b94e775e2f312aa6d4ae5ed6120babd6f5e </code></pre> <p><br>To check the integrity of the extracted archive, confirm that each line produce by the command "preston verify" produces lines as shown below, with each line including "CONTENT_PRESENT_VALID_HASH". Depending on hardware capacity, this may take a while.</p> <pre><code>java -jar preston.jar verify</code></pre> <p>Note that a copy of the java program "preston", preston.jar, is included in this publication. The program runs on java 8+ virtual machine using "java -jar preston.jar", or in short "preston". </p> <p>Files in this data publication:</p> <p>--- start of file descriptions ---</p> <p>-- description of archive and its contents (a rendition of this file) --<br>README</p> <p>-- biological associations indexed from TaxonWorks expressed in a GloBI [5] compatible JSON Lines file --<br>interactions.json</p> <p>-- first 10 biological associations indexed from TaxonWorks expressed in a GloBI [5] compatible JSON Lines file --<br>interactions-10.json</p> <p>-- executable java jar containing preston [2,3,4] v0.8.5-SNAPSHOT. --<br>preston.jar</p> <p>-- preston archive containing TaxonWorks data files, associated provenance logs and a provenance index --<br>preston.tar.gz</p> <p>-- individual provenance index files --</p> <p>1fed32bf78298d7ecc3d9f36d106f1d7d7773a8b9a5e47af6632f36c1f82adb5<br>29306c5c144c3d7fd21be344d8b6b554b6f6efa3b8f8f5c0b27cdf0e88785652<br>2a5de79372318317a382ea9a2cef069780b852b01210ef59e06b640a3539cb5a<br>d31ff1ef1dea88c5952181a4f30e7ea7862873aa5f66430451275aa6d08d329e<br>deb84d69224af488da585186f88cafc58e978db5f9897de624cc9b02c0c83742<br>e9c34683f1e826f68f841f3419bd5ee9c0fa18be04713a6fd3364f226c7c5f2f<br>f98d36a9dc7bd833c93b3b61130865628f7bc2f7bb0920e95afcd16fba3dc6a8<br>ffb41d48979ceb964fbfbeb68cb60b584b759950087fdcc012521b866249bc39</p> <p>--- end of file descriptions ---</p> <p>This work is funded in part by grant NSF OAC 1839201, NSF DBI 1901932, NSF DBI 1901926, and NSF DBI 2102006 from the National Science Foundation.<br> </p>
Event-QA: A Dataset for Event-Centric Question Answering over Knowledge Graphs
<p>Event-QA dataset contains 1000 semantic queries and the corresponding verbalisations for EventKG - a recently proposed event-centric knowledge graph containing over 970 thousand events.</p>
SCG Dataset from Graph Neural Networks in Supply Chain Analytics and Optimization: Concepts, Perspectives, Dataset and Benchmarks
<p><strong>Abstract:</strong> Graph Neural Networks (GNNs) have recently gained traction in transportation, bioinformatics, language and image processing, but research on their application to supply chain management remains limited. Supply chains are inherently graph-like, making them ideal for GNN methodologies, which can optimize and solve complex problems. The barriers include a lack of proper conceptual foundations, familiarity with graph applications in SCM, and real-world benchmark datasets for GNN-based supply chain research. To address this, we discuss and connect supply chains with graph structures for effective GNN application, providing detailed formulations, examples, mathematical definitions, and task guidelines. Additionally, we present a multi-perspective real-world benchmark dataset from a leading FMCG company in Bangladesh, focusing on supply chain planning. We discuss various supply chain tasks using GNNs and benchmark several state-of-the-art models on homogeneous and heterogeneous graphs across six supply chain analytics tasks. Our analysis shows that GNN-based models consistently outperform statistical ML and other deep learning models by around 10-30% in regression, 10-30% in classification and detection tasks, and 15-40% in anomaly detection tasks on designated metrics. With this work, we lay the groundwork for solving supply chain problems using GNNs, supported by conceptual discussions, methodological insights, and a comprehensive dataset.</p>
OpenAIRE Graph: Dataset for research communities and initiatives
<p>This dataset contains metadata records of the OpenAIRE Graph relevant for the research communities and initiatives collaborating with OpenAIRE and with a public Community Gateway on <a href="https://connect.openaire.eu">OpenAIRE CONNECT</a> as of July 2025.</p> <p>Each file is a tar archive containing gzip files with one json per line. Each json is compliant to the schema available at <a href="https://doi.org/10.5281/zenodo.14891476" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.14891476</a></p> <table style="width: 100%; height: 822.939px;"> <tbody> <tr style="height: 19.5938px;"> <td style="width: 33.9453%; height: 19.5938px;"><strong>Research community name</strong></td> <td style="width: 38.9414%; height: 19.5938px;"><strong>File name</strong></td> <td style="width: 23.1067%; height: 19.5938px;"><strong>URL to the gateway</strong></td> </tr> <tr style="height: 19.5938px;"> <td style="width: 33.9453%; height: 19.5938px;">Argo France</td> <td style="width: 38.9414%; height: 19.5938px;">argo-france.tar</td> <td style="width: 23.1067%; height: 19.5938px;"><a href="https://argo-france.openaire.eu/">https://argo-france.openaire.eu</a></td> </tr> <tr style="height: 19.5938px;"> <td style="width: 33.9453%; height: 19.5938px;">Aurora University Alliance</td> <td style="width: 38.9414%; height: 19.5938px;">aurora.tar</td> <td style="width: 23.1067%; height: 19.5938px;"><a href="https://aurora.openaire.eu">https://aurora.openaire.eu</a></td> </tr> <tr style="height: 19.5938px;"> <td style="width: 33.9453%; height: 19.5938px;">Transport Research (EC projects BE OPEN and SciLake)</td> <td style="width: 38.9414%; height: 19.5938px;">beopen.tar</td> <td style="width: 23.1067%; height: 19.5938px;"><a href="https://beopen.openaire.eu">https://beopen.openaire.eu</a></td> </tr> <tr style="height: 19.5938px;"> <td style="width: 33.9453%; height: 19.5938px;">CIVICA Alliance</td> <td style="width: 38.9414%; height: 19.5938px;">civica.tar</td> <td style="width: 23.1067%; height: 19.5938px;"><a href="https://civica.openaire.eu" target="_blank" rel="noopener">https://civica.openaire.eu</a></td> </tr> <tr style="height: 19.5938px;"> <td style="width: 33.9453%; height: 19.5938px;">COVID 19</td> <td style="width: 38.9414%; height: 19.5938px;">covid-19.tar</td> <td style="width: 23.1067%; height: 19.5938px;"><a href="https://covid-19.openaire.eu">https://covid-19.openaire.eu</a></td> </tr> <tr style="height: 19.5938px;"> <td style="width: 33.9453%; height: 19.5938px;">DARIAH EU</td> <td style="width: 38.9414%; height: 19.5938px;">dariah.tar</td> <td style="width: 23.1067%; height: 19.5938px;"><a href="https://dariah.openaire.eu">https://dariah.openaire.eu</a></td> </tr> <tr style="height: 19.5938px;"> <td style="width: 33.9453%; height: 19.5938px;">Digital Humanities and Cultural Heritage</td> <td style="width: 38.9414%; height: 19.5938px;">dh-ch.tar</td> <td style="width: 23.1067%; height: 19.5938px;"><a href="https://dh-ch.openaire.eu">https://dh-ch.openaire.eu</a></td> </tr> <tr style="height: 19.5938px;"> <td style="width: 33.9453%; height: 19.5938px;">Virtual Twins in Health (EC project EDITH)</td> <td style="width: 38.9414%; height: 19.5938px;">dth.tar</td> <td style="width: 23.1067%; height: 19.5938px;"><a href="https://dth.openaire.eu">https://dth.openaire.eu</a></td> </tr> <tr style="height: 19.5938px;"> <td style="width: 33.9453%; height: 19.5938px;">European Digital Innovation Hubs Network ADRIA</td> <td style="width: 38.9414%; height: 19.5938px;">edih-adria.tar</td> <td style="width: 23.1067%; height: 19.5938px;"><a href="https://edih-adria.openaire.eu">https://edih-adria.openaire.eu</a></td> </tr> <tr style="height: 39.1875px;"> <td style="width: 33.9453%; height: 39.1875px;">European Geothermal Research and Innovation Search Engine </td> <td style="width: 38.9414%; height: 39.1875px;">egrise.tar</td> <td style="width: 23.1067%; height: 39.1875px;"><a href="https://egrise.openaire.eu">https://egrise.openaire.eu</a></td> </tr> <tr style="height: 19.5938px;"> <td style="width: 33.9453%; height: 19.5938px;">ELIXIR Greece</td> <td style="width: 38.9414%; height: 19.5938px;">elixir-gr.tar</td> <td style="width: 23.1067%; height: 19.5938px;"><a href="https://elixir-gr.openaire.eu">https://elixir-gr.openaire.eu</a></td> </tr> <tr style="height: 19.5938px;"> <td style="width: 33.9453%; height: 19.5938px;">Energy Planning (EC project SciLake)</td> <td style="width: 38.9414%; height: 19.5938px;">energy-planning_1.tar, energy_planning_2.tar</td> <td style="width: 23.1067%; height: 19.5938px;"><a href="https://energy-planning.openaire.eu/">https://energy-planning.openaire.eu</a></td> </tr> <tr style="height: 19.5938px;"> <td style="width: 33.9453%; height: 19.5938px;">Energy Research (EC project Enermaps)</td> <td style="width: 38.9414%; height: 19.5938px;">enermaps.tar</td> <td style="width: 23.1067%; height: 19.5938px;"><a href="https://enermaps.openaire.eu">https://enermaps.openaire.eu</a></td> </tr> <tr style="height: 39.1875px;"> <td style="width: 33.9453%; height: 39.1875px;">European University for Smart Urban Coastal Sustainability</td> <td style="width: 38.9414%; height: 39.1875px;">eu-conexus.tar</td> <td style="width: 23.1067%; height: 39.1875px;"><a href="https://eu-conexus.openaire.eu/">https://eu-conexus.openaire.eu</a></td> </tr> <tr style="height: 19.5938px;"> <td style="width: 33.9453%; height: 19.5938px;">European University of Technology+</td> <td style="width: 38.9414%; height: 19.5938px;">eut.tar</td> <td style="width: 23.1067%; height: 19.5938px;"><a href="https://eut.openaire.eu/">https://eut.openaire.eu</a></td> </tr> <tr style="height: 19.5938px;"> <td style="width: 33.9453%; height: 19.5938px;">EUTOPIA Alliance</td> <td style="width: 38.9414%; height: 19.5938px;">eutopia.tar</td> <td style="width: 23.1067%; height: 19.5938px;"><a href="https://eutopia.openaire.eu/">https://eutopia.openaire.eu</a></td> </tr> <tr style="height: 19.5938px;"> <td style="width: 33.9453%; height: 19.5938px;">FORTHEM Alliance</td> <td style="width: 38.9414%; height: 19.5938px;">forthem.tar</td> <td style="width: 23.1067%; height: 19.5938px;"><a href="https://forthem.openaire.eu">https://forthem.openaire.eu</a></td> </tr> <tr> <td style="width: 33.9453%;">[NEW] GoTriple </td> <td style="width: 38.9414%;">gotriple_1.tar, gotriple_2.tar</td> <td style="width: 23.1067%;"><a href="https://gotriple.openaire.eu/">https://gotriple.openaire.eu/</a></td> </tr> <tr style="height: 19.5938px;"> <td style="width: 33.9453%; height: 19.5938px;">Heritage Science (EC project IPERION HS)</td> <td style="width: 38.9414%; height: 19.5938px;">heritage-science.tar</td> <td style="width: 23.1067%; height: 19.5938px;"><a href="https://heritage-science.openaire.eu/">https://heritage-science.openaire.eu</a></td> </tr> <tr style="height: 39.1875px;"> <td style="width: 33.9453%; height: 39.1875px;">Institut national de recherche en informatique et en automatique (EC project GraspOS)</td> <td style="width: 38.9414%; height: 39.1875px;">inria.tar</td> <td style="width: 23.1067%; height: 39.1875px;"><a href="https://inria.openaire.eu" target="_blank" rel="noopener">https://inria.openaire.eu</a></td> </tr> <tr style="height: 19.5938px;"> <td style="width: 33.9453%; height: 19.5938px;">IPERION HS</td> <td style="width: 38.9414%; height: 19.5938px;">iperionhs.tar</td> <td style="width: 23.1067%; height: 19.5938px;"><a href="https://iperionhs.openaire.eu">https://iperionhs.openaire.eu</a></td> </tr> <tr style="height: 39.1875px;"> <td style="width: 33.9453%; height: 39.1875px;">Knowmad Institut</td> <td style="width: 38.9414%; height: 39.1875px;">knowmad_1.tar, knowmad_2.tar, knowmad_3.tar, knowmad_4.tar, knowmad_5.tar, knowmad_6.tar, knowmad_7.tar, knowmad_8.tar, knowmad_9.tar</td> <td style="width: 23.1067%; height: 39.1875px;"><a href="https://knowmad.openaire.eu/">https://knowmad.openaire.eu</a></td> </tr> <tr style="height: 19.5938px;"> <td style="width: 33.9453%; height: 19.5938px;">LifeWathc ERIC</td> <td style="width: 38.9414%; height: 19.5938px;">lifewatch-eric.tar</td> <td style="width: 23.1067%; height: 19.5938px;"><a href="https://lifewatch-eric.openaire.eu/">https://lifewatch-eric.openaire.eu</a></td> </tr> <tr style="height: 19.5938px;"> <td style="width: 33.9453%; height: 19.5938px;">European Marine Science</td> <td style="width: 38.9414%; height: 19.5938px;">mes.tar</td> <td style="width: 23.1067%; height: 19.5938px;"><a href="https://mes.openaire.eu">https://mes.openaire.eu</a></td> </tr> <tr style="height: 39.1875px;"> <td style="width: 33.9453%; height: 39.1875px;">Atmospheric Research Community (EC project NEANIAS)</td> <td style="width: 38.9414%; height: 39.1875px;">neanias-atmospheric.tar</td> <td style="width: 23.1067%; height: 39.1875px;"><a href="https://neanias-atmospheric.openaire.eu/">https://neanias-atmospheric.openaire.eu</a></td> </tr> <tr style="height: 19.5938px;"> <td style="width: 33.9453%; height: 19.5938px;">Space Research Community (EC project NEANIAS)</td> <td style="width: 38.9414%; height: 19.5938px;">neanias-space.tar</td> <td style="width: 23.1067%; height: 19.5938px;"><a href="https://neanias-space.openaire.eu/">https://neanias-space.openaire.eu</a></td> </tr> <tr style="height: 39.1875px;"> <td style="width: 33.9453%; height: 39.1875px;">Underwater Research Community (EC project NEANIAS)</td> <td style="width: 38.9414%; height: 39.1875px;">neanias-underwater.tar</td> <td style="width: 23.1067%; height: 39.1875px;"><a href="https://neanias-underwater.openaire.eu/">https://neanias-underwater.openaire.eu</a></td> </tr> <tr style="height: 19.5938px;"> <td style="width: 33.9453%; height: 19.5938px;">Netherlands Research Portal</td> <td style="width: 38.9414%; height: 19.5938px;">netherlands_1.tar</td> <td style="width: 23.1067%; height: 19.5938px;"><a href="https://netherlands.openaire.eu/">https://netherlands.openaire.eu</a></td> </tr> <tr style="height: 19.5938px;"> <td style="width: 33.9453%; height: 19.5938px;">[NEW] Neuroscience<br>(EC project SciLake - former Neuroinformatics community is now a subcommunity of Neuroscience)</td> <td style="width: 38.9414%; height: 19.5938px;">neuroscience_1.tar</td> <td style="width: 23.1067%; height: 19.5938px;"><a href="https://neuroscience.openaire.eu">https://neuroscience.openaire.eu</a></td> </tr> <tr style="height: 39.1875px;"> <td style="width: 33.9453%; height: 39.1875px;">North American Studies</td> <td style="width: 38.9414%; height: 39.1875px;">north-american-studies.tar</td> <td style="width: 23.1067%; height: 39.1875px;"><a href="https://north-american-studies.openaire.eu">https://north-american-studies.openaire.eu</a></td> </tr> <tr style="height: 39.1875px;"> <td style="width: 33.9453%; height: 39.1875px;">Rural Digital Europe (EC project DESIRA)</td> <td style="width: 38.9414%; height: 39.1875px;">rural-digital-europe.tar</td> <td style="width: 23.1067%; height: 39.1875px;"><a href="https://rural-digital-europe.openaire.eu/">https://rural-digital-europe.openaire.eu</a></td> </tr> <tr style="height: 19.5938px;"> <td style="width: 33.9453%; height: 19.5938px;">Sustainable Development Solutions Network - Greece </td> <td style="width: 38.9414%; height: 19.5938px;">sdsn-gr.tar</td> <td style="width: 23.1067%; height: 19.5938px;"><a href="https://sdsn-gr.openaire.eu/">https://sdsn-gr.openaire.eu</a></td> </tr> <tr style="height: 19.5938px;"> <td style="width: 33.9453%; height: 19.5938px;">Technological University Network</td> <td style="width: 38.9414%; height: 19.5938px;">tunet.tar</td> <td style="width: 23.1067%; height: 19.5938px;"><a href="https://tunet.openaire.eu">https://tunet.openaire.eu</a></td> </tr> <tr> <td style="width: 33.9453%;">[NEW] UNITE! University Alliance</td> <td style="width: 38.9414%;">unite.tar</td> <td style="width: 23.1067%;"><a href="https://unite.openaire.eu">https://unite.openaire.eu</a></td> </tr> <tr style="height: 19.5938px;"> <td style="width: 33.9453%; height: 19.5938px;">University of the Arctic (UArctic) </td> <td style="width: 38.9414%; height: 19.5938px;">uarctic_1.tar, uarctic_2.tar</td> <td style="width: 23.1067%; height: 19.5938px;"><a href="https://uarctic.openaire.eu/">https://uarctic.openaire.eu</a></td> </tr> </tbody> </table>
Advection datasets from "Multi-scale rotation-equivariant graph neural networks for unsteady Eulerian fluid dynamics"
<p>Advection datasets from the paper:<br> Multi-scale rotation-equivariant graph neural networks for unsteady Eulerian fluid dynamics (https://doi.org/10.1063/5.0097679)</p> <p>The datasets are:<br> - AdvBox<br> - AdvInBox<br> - AdvTaylor<br> - AdvCircle<br> - AdvCircleAng<br> - AdvSquare<br> - AdvEllipseH<br> - AdvEllipseV<br> - AdvSpline<br> - AdvSquareAndCircle<br> - Adv3Circles</p> <p>Check the "README.txt" file for information on how the simulations are organised. The features of each dataset and how they were generated are explained in the journal publication.</p> <p> </p> <p>To cite these datasets, use the following reference:</p> <p>Mario Lino, Stathi Fotiadis, Anil A. Bharath, and Chris Cantwell. "Multi-scale rotation-equivariant graph neural networks for unsteady Eulerian fluid dynamics". Physics of Fluids, 34 (2022).</p> <pre><code>@article{lino2022multi, author = {Lino, Mario and Fotiadis, Stathi and Bharath, Anil A. and Cantwell, Chris}, title = {{Multi-scale rotation-equivariant graph neural networks for unsteady Eulerian fluid dynamics}}, journal = {Physics of Fluids}, volume = {34}, year = {2022}, url = {https://doi.org/10.1063/5.0097679}, }</code></pre> <p><br> </p>
Dataset for Min-Deviation-Flow in Bi-directed Graphs for T-Mesh Quantization
<p>Dataset for the paper <a href="https://www.algohex.eu/publications/bimdf-quantization/"><strong>Min-Deviation-Flow in Bi-directed Graphs for T-Mesh Quantization</strong></a>, <em>Martin Heistermann, Jethro Warnett, David Bommes</em>, ACM Transactions on Graphics, Volume 42, Issue 4 (2023), <a href="https://doi.org/10.1145/3592437">DOI 10.1145/3592437</a></p> <p>Both archive files (.zip and .tar.zst) have identical contents.</p> <p>Contents:</p> <ul> <li>Results of running various algorithmus variants on the "300" dataset <ul> <li>Intermediate data (e.g., patch decompositions)</li> <li>Final meshes</li> <li>Logfiles</li> <li>Metadata in json format (e.g., utilized settings, achieved energies, detailed runtimes)</li> </ul> </li> <li>Meshes used in all figures</li> </ul> <p> </p>
Protein Graphs Dataset from PDB
<p>This dataset contains the protein graphs constructed from PDB, the Protein Data Bank (www.rcsb.org/pdb), used in the paper:</p> <p>Nilothpal Talukder and <strong>Mohammed J. Zaki</strong>. <strong>A distributed approach for graph mining in massive networks.</strong> <em>Data Mining and Knowledge Discovery: Special Issue on ECML/PKDD 2016 Journal Track Papers</em>, 30(5):1024–1052, 2016. URL: <a href="http://link.springer.com/article/10.1007/s10618-016-0466-x">http://link.springer.com/article/10.1007/s10618-016-0466-x</a>.</p> <p>The format of graphs is as follows:</p> <p>t # GID</p> <p>v VID VLABEL</p> <p>e VID1 VID2 ELABEL</p> <p>where</p> <p>GID is a graph identifier (integer)</p> <p>VID is a vertex identifier (integer) with VLABEL its vertex label (integer)</p> <p>VID1 VID2 denotes an edge between the two vertices, with ELABEL the edge label (integer)</p>
Dataset for Creating a Scholarly Knowledge Graph from Survey Article Tables
<ul> <li><strong>Selected papers.csv</strong><br> This file lists all selected survey papers used to create the knowledge graph. The file contains paper titles, table references (of the tables that are extracted), sources and the reference to the survey paper (either a DOI, or a full textual reference)<br> </li> <li><strong>ORKG comparisons.csv</strong><br> All comparisons imported in the ORKG are listed in this file. Per survey paper, multiple tables could be extracted, and therefore multiple comparisons are created. The files lists the internal IDs and the URL to the comparisons. <br> </li> <li><strong>Ingested papers.csv</strong><br> A full list of all individual papers extracted from the survey articles. The file contains paper titles and their respective URL in the ORKG. </li> </ul>
OpenAIRE Graph Dataset
<p>The OpenAIRE Graph is exported as several files, so you can download the parts you are interested into.</p> <p><strong>publication_[part].tar</strong>: metadata records about research literature (includes types of publications listed <a href="http://api.openaire.eu/vocabularies/dnet:result_typologies/publication">here</a>)<br><strong>dataset_[part].tar</strong>: metadata records about research data (includes the subtypes listed <a href="http://api.openaire.eu/vocabularies/dnet:result_typologies/dataset">here</a>) <br><strong>software.tar</strong>: metadata records about research software (includes the subtypes listed <a href="http://api.openaire.eu/vocabularies/dnet:result_typologies/software">here</a>)<br><strong>otherresearchproduct_[part].tar</strong>: metadata records about research products that cannot be classified as research literature, data or software (includes types of products listed <a href="http://api.openaire.eu/vocabularies/dnet:result_typologies/other">here</a>)<br><strong>organization.tar</strong>: metadata records about organizations involved in the research life-cycle, such as universities, research organizations, funders.<br><strong>datasource.tar</strong>: metadata records about data sources whose content is available in the OpenAIRE Graph. They include institutional and thematic repositories, journals, aggregators, funders' databases.<br><strong>project.tar</strong>: metadata records about project grants.<br><strong>relation_[part].tar</strong>: metadata records about relations between entities in the graph.<br><strong>communities_infrastructures.tar</strong>: metadata records about research communities and research infrastructures</p> <p>Each file is a tar archive containing gz files, each with one json per line. Each json is compliant to the schema available at <a href="http://doi.org/10.5281/zenodo.14608526">http://doi.org/10.5281/zenodo.14608526</a>. The documentation for the model is available at <a href="https://graph.openaire.eu/docs/data-model/">https://graph.openaire.eu/docs/data-model/</a></p> <p>Learn more about the OpenAIRE Graph at <a href="https://graph.openaire.eu">https://graph.openaire.eu</a>.</p> <p>Discover the graph's content on <a href="https://explore.openaire.eu">OpenAIRE EXPLORE</a> and our <a href="https://develop.openaire.eu">API for developers</a>.</p> <p>This deposition contains:</p> <ul> <li>203,014,258 publications,</li> <li>82,899,003 datasets,</li> <li>751,829 software,</li> <li>30,805,759 other research products,</li> <li>154,636 datasources,</li> <li>3,668,019 projects,</li> <li>449,798 organizations,</li> <li>36 communities,</li> <li>7,172,449,797 relations</li> </ul> <p> </p>
Datasets of synthetic task graphs for evaluating a reliability and latency multi-objective task allocation framework
<p>These datasets of synthetic task graphs were generated to evaluate the performance and scalability of a multi-objective task allocation approach for workflow applications of various structures and sizes in a system based on the edge-hub-cloud paradigm. The targeted architecture comprised an edge device (e.g., a single-board computer attached to an unmanned aerial vehicle (UAV)) interacting with a hub device (e.g., a laptop), which in turn communicated with a more computationally capable cloud server. The objectives were the maximization of the overall reliability and the minimization of the overall latency of the application, under memory, storage, energy, and task precedence constraints. We considered that a percentage of the tasks required fixed allocation on the edge or hub device. Each task had a different vulnerability factor (i.e., probability of failure) on each device.</p> <p>We generated nine task graphs of serial, parallel, and mixed (a combination of serial and parallel) structure with 10, 100, and 1000 nodes, utilizing the Task Graphs For Free (TGFF) random task graph generator [1]. Additional task parameters (e.g., execution time, power consumption, vulnerability factor, memory, storage, output data size) were included post-generation, using representative random values. More details are provided in README.txt.</p> <p>Note: These datasets are released under a Creative Commons Attribution license. If you utilize these datasets in your work, please cite us using the corresponding Zenodo DOI https://doi.org/10.5281/zenodo.10357101.</p> <p>References:</p> <p>[1] R. P. Dick, D. L. Rhodes and W. Wolf, "TGFF: Task graphs for free," Proceedings of the Sixth International Workshop on Hardware/Software Codesign (CODES/CASHE'98), Seattle, WA, USA, 1998, pp. 97-101, doi: 10.1109/HSC.1998.666245.</p>
Large-scale attributed graph & hypergraph datasets: TWeibo, Amazon2M, Amazon, MAG-PM
<p>Here we provide additional large-scale datasets used in our work "A Versatile Framework for Attributed Network Clustering via K-Nearest Neighbor Augmentation", along with the index files for constructing KNN graphs using ScaNN and Faiss.</p> <p>Usage:</p> <p>cd ANCKA/</p> <p>unzip ~/Download_path/ANCKA_data.zip -d data/</p>
Tree Annotation Vocabulary (TAV) - Knowledge Graph and Annotated Dataset
<p>This dataset contains all the files used in developing the Tree-KG, the knowledge graph to capture the tree annotations in the works of Vladimir Nabokov. </p> <p>In the Annotated Dataset folder, 6 spreadsheets in excel (.xlsx) format are provided. They are numbered. Note that annotated data are all in English as the consulted works are the English translations of the literary works of Nabokov.</p> <p>(1) contains the tree annotations from the novels originally written in Russian by Vladimir Nabokov.</p> <p>(2) contains the tree annotations from the novels originally written in English by Vladimir Nabokov.</p> <p>(3) contains the tree annotations from the short stories originally written in Russian and English by Vladimir Nabokov.</p> <p>(4) is the knowledge base (KB) developed to link the annotated trees to Wikidata and DBPedia.</p> <p>(5) is the benchmarking results of some entity recognition tools. It includes the relevant passages from Nabokov's novels that were used in the experiments as well as the prompts used in getting the results.</p> <p>(6) represents the complete bibliographic details of the works of Vladimir Nabokov (https://thenabokovian.org/abbreviations).</p> <p>In the Ontology Versions folder, four ontology (TAV) files in turtle (.ttl) format are provided. They are all numbered and dated to represent their different versions. Some sample SPARQL queries are provided in a .txt file. The KG was developed on Protégé. </p> <p>(1) contains the essential schema for the TAV vocabulary.</p> <p>(2) contains the schema for TAV vocabulary with links to external vocabularies (Schema.Org; Open Annotation, etc.). </p> <p>(3) contains the Tree-KG in so far it reflects data from three novels (Mary; King, Queen, Knave; Glory).</p> <p>(4) contains the entire Tree-KG based on all the works mentioned in the excel sheets (20 books).</p> <p>(5) contains some sample SPARQL queries (.txt) file.</p>
Dataset variants used in "Task-Driven Knowledge Graph Filtering Improves Prioritizing Drugs for Repurposing"
<p>This file contains all datasets and variants thereof used in the linked paper. We do not take credit for constructing the datasets, which has been done by the respective original authors (<a href="https://github.com/hetio/hetionet">https://github.com/hetio/hetionet</a>, <a href="https://github.com/gnn4dr/DRKG">https://github.com/gnn4dr/DRKG</a>). For our work we produced modified versions (called "subset" in the file) by applying our metapath based filtering approach. For validation purposed we also constructed ablation versions where one specific type of entities is missing (i.e. "nogene", "noside", etc).</p>
UC Santa Barbara Invertebrate Zoology Collection (UCSB-IZC) Data Archive and Biodiversity Dataset Graph hash://md5/10663911550bb52a0f5741993f82db9d hash://sha256/80c0f5fc598be1446d23c95141e87880c9e53773cb2e0b5b54cb57a8ea00b20c
<p>A biodiversity dataset graph: UCSB-IZC</p> <p>The intended use of this archive is to facilitate (meta-)analysis of the UC Santa Barbara Invertebrate Zoology Collection (UCSB-IZC). UCSB-IZC is a natural history collection of invertebrate zoology at Cheadle Center of Biodiversity and Ecological Restoration, University of California Santa Barbara.</p> <p>This dataset provides versioned snapshots of the UCSB-IZC network as tracked by Preston [2,3] between 2021-10-08 and 2021-11-04 using [preston track "https://api.gbif.org/v1/occurrence/search/?datasetKey=d6097f75-f99e-4c2a-b8a5-b0fc213ecbd0"].</p> <p>This archive contains 14349 images related to 32533 occurrence/specimen records. See included sample-image.jpg and their associated meta-data sample-image.json [4].</p> <p>The images were counted using:</p> <p>$ preston cat hash://sha256/80c0f5fc598be1446d23c95141e87880c9e53773cb2e0b5b54cb57a8ea00b20c\<br> | grep -o -P ".*depict"\<br> | sort\<br> | uniq\<br> | wc -l</p> <p>And the occurrences were counted using:</p> <p>$ preston cat hash://sha256/80c0f5fc598be1446d23c95141e87880c9e53773cb2e0b5b54cb57a8ea00b20c\<br> | grep -o -P "occurrence/([0-9])+"\<br> | sort\<br> | uniq\<br> | wc -l</p> <p>The archive consists of 256 individual parts (e.g., preston-00.tar.gz, preston-01.tar.gz, ...) to allow for parallel file downloads. The archive contains three types of files: index files, provenance files and data files. Only two index and provenance files are included and have been individually included in this dataset publication. Index files provide a way to links provenance files in time to establish a versioning mechanism.</p> <p>To retrieve and verify the downloaded UCSB-IZC biodiversity dataset graph, first download preston-*.tar.gz. Then, extract the archives into a "data" folder. Alternatively, you can use the Preston [2,3] command-line tool to "clone" this dataset using:</p> <p>$ java -jar preston.jar clone --remote https://archive.org/download/preston-ucsb-izc/data.zip/,https://zenodo.org/record/5557670/files,https://zenodo.org/record/5660088/files/</p> <p>After that, verify the index of the archive by reproducing the following provenance log history:</p> <p>$ java -jar preston.jar history<br> <urn:uuid:0659a54f-b713-4f86-a917-5be166a14110> <http://purl.org/pav/hasVersion> <hash://sha256/d5eb492d3e0304afadcc85f968de1e23042479ad670a5819cee00f2c2c277f36> .<br> <hash://sha256/80c0f5fc598be1446d23c95141e87880c9e53773cb2e0b5b54cb57a8ea00b20c> <http://purl.org/pav/previousVersion> <hash://sha256/d5eb492d3e0304afadcc85f968de1e23042479ad670a5819cee00f2c2c277f36> .</p> <p>To check the integrity of the extracted archive, confirm that each line produce by the command "preston verify" produces lines as shown below, with each line including "CONTENT_PRESENT_VALID_HASH". Depending on hardware capacity, this may take a while.</p> <p>$ java -jar preston.jar verify<br> hash://sha256/ce1dc2468dfb1706a6f972f11b5489dc635bdcf9c9fd62a942af14898c488b2c file:/home/jhpoelen/ucsb-izc/data/ce/1d/ce1dc2468dfb1706a6f972f11b5489dc635bdcf9c9fd62a942af14898c488b2c OK CONTENT_PRESENT_VALID_HASH 66438 hash://sha256/ce1dc2468dfb1706a6f972f11b5489dc635bdcf9c9fd62a942af14898c488b2c<br> hash://sha256/f68d489a9275cb9d1249767244b594c09ab23fd00b82374cb5877cabaa4d0844 file:/home/jhpoelen/ucsb-izc/data/f6/8d/f68d489a9275cb9d1249767244b594c09ab23fd00b82374cb5877cabaa4d0844 OK CONTENT_PRESENT_VALID_HASH 4093 hash://sha256/f68d489a9275cb9d1249767244b594c09ab23fd00b82374cb5877cabaa4d0844<br> hash://sha256/3e70b7adc1a342e5551b598d732c20b96a0102bb1e7f42cfc2ae8a2c4227edef file:/home/jhpoelen/ucsb-izc/data/3e/70/3e70b7adc1a342e5551b598d732c20b96a0102bb1e7f42cfc2ae8a2c4227edef OK CONTENT_PRESENT_VALID_HASH 5746 hash://sha256/3e70b7adc1a342e5551b598d732c20b96a0102bb1e7f42cfc2ae8a2c4227edef<br> hash://sha256/995806159ae2fdffdc35eef2a7eccf362cb663522c308aa6aa52e2faca8bb25b file:/home/jhpoelen/ucsb-izc/data/99/58/995806159ae2fdffdc35eef2a7eccf362cb663522c308aa6aa52e2faca8bb25b OK CONTENT_PRESENT_VALID_HASH 6147 hash://sha256/995806159ae2fdffdc35eef2a7eccf362cb663522c308aa6aa52e2faca8bb25b</p> <p>Note that a copy of the java program "preston", preston.jar, is included in this publication. The program runs on java 8+ virtual machine using "java -jar preston.jar", or in short "preston".</p> <p>Files in this data publication:</p> <p>--- start of file descriptions ---</p> <p>-- description of archive and its contents (this file) --<br> README</p> <p>-- executable java jar containing preston [2,3] v0.3.1. --<br> preston.jar</p> <p>-- preston archive containing UCSB-IZC (meta-)data/image files, associated provenance logs and a provenance index --<br> preston-[00-ff].tar.gz</p> <p>-- individual provenance index files --<br> 2a5de79372318317a382ea9a2cef069780b852b01210ef59e06b640a3539cb5a</p> <p>-- example image and meta-data --<br> sample-image.jpg (with hash://sha256/916ba5dc6ad37a3c16634e1a0e3d2a09969f2527bb207220e3dbdbcf4d6b810c)<br> sample-image.json (with hash://sha256/f68d489a9275cb9d1249767244b594c09ab23fd00b82374cb5877cabaa4d0844)</p> <p>--- end of file descriptions ---</p> <p><br> References</p> <p>[1] Cheadle Center for Biodiversity and Ecological Restoration (2021). University of California Santa Barbara Invertebrate Zoology Collection. Occurrence dataset https://doi.org/10.15468/w6hvhv accessed via GBIF.org on 2021-11-04 as indexed by the Global Biodiversity Informatics Facility (GBIF) with provenance hash://sha256/d5eb492d3e0304afadcc85f968de1e23042479ad670a5819cee00f2c2c277f36 hash://sha256/80c0f5fc598be1446d23c95141e87880c9e53773cb2e0b5b54cb57a8ea00b20c.<br> [2] https://preston.guoda.bio, https://doi.org/10.5281/zenodo.1410543 .<br> [3] MJ Elliott, JH Poelen, JAB Fortes (2020). Toward Reliable Biodiversity Dataset References. Ecological Informatics. https://doi.org/10.1016/j.ecoinf.2020.101132<br> [4] Cheadle Center for Biodiversity and Ecological Restoration (2021). University of California Santa Barbara Invertebrate Zoology Collection. Occurrence dataset https://doi.org/10.15468/w6hvhv accessed via GBIF.org on 2021-10-08. https://www.gbif.org/occurrence/3323647301 . hash://sha256/f68d489a9275cb9d1249767244b594c09ab23fd00b82374cb5877cabaa4d0844 hash://sha256/916ba5dc6ad37a3c16634e1a0e3d2a09969f2527bb207220e3dbdbcf4d6b810c</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>
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>
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>
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.