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Dataset results
915 results for “graphs”
European Olfactory Knowledge Graph
<p>The European Olfactory Knowledge Graph (EOKG) includes information about smell from (digital) text and image collections from the European history (1600-1920), extracted in the context of the <a title="Odeuropa" href="https://odeuropa.eu/" target="_blank" rel="noopener">Odeuropa project</a> in a cultural heritage preservation perspective.</p> <p>It contains over 2,500,000 olfactory reference coming from over 43,000 images and 2,400,000 texts in six languages, organised according to the <a href="https://data.odeuropa.eu/ontology" target="_blank" rel="noopener">Odeuropa Ontology</a> and leveraging machine learning to recognise and categorise olfactory elements.</p> <h3>Additional Links</h3> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div>EOKG Vocabularies: <a href="https://vocab.odeuropa.eu/" target="_blank" rel="noopener noreferrer">https://vocab.odeuropa.eu/</a> (vocabulary browser)<br>Odeuropa Ontology: <a href="https://data.odeuropa.eu/ontology/" target="_blank" rel="noopener noreferrer">https://data.odeuropa.eu/ontology/</a> (data model)<br>EOKG API: <a href="https://grlc.eurecom.fr/api/Odeuropa/kg-api/" target="_blank" rel="noopener noreferrer">https://grlc.eurecom.fr/api/Odeuropa/kg-api/</a> (API)<br>EOKG technical report: <a href="https://odeuropa.eu/wp-content/uploads/2024/10/D4_3_European_Olfactory_Knowledge_Graph_v2_final.pdf">https://odeuropa.eu/wp-content/uploads/2024/10/D4_3_European_Olfactory_Knowledge_Graph_v2_final.pdf</a> (documentation)<br>Odeuropa Smell Explorer: <a href="https://explorer.odeuropa.eu/" target="_blank" rel="noopener noreferrer">https://explorer.odeuropa.eu/</a> (demonstrator)</div> </div> </div> </div> </div> </div> </div> </div> <div> </div> </div> </div> </div> </div>
Datasets for Paper "MetagenomicKG: a knowledge graph for metagenomic applications"
<p>This repository contains some required data that is used for building MetagenomicKG. Please see more details in <a href="https://github.com/KoslickiLab/MetagenomicKG">https://github.com/KoslickiLab/MetagenomicKG</a>.</p>
Assessing the Overlap of Science Knowledge Graphs: A Quantitative Analysis — exact and related matches
<p>Results of the 'Assessing the Overlap of Science Knowledge Graphs: A Quantitative Analysis' papers. There are 2 datasets:</p> <ul> <li>'exact_matches.csv': contains detailed information about the concepts present both in OpenAlex and OpenAIRE.</li> <li>'related_matches.csv': contains detailed information about the concepts from OpenAlex and OpenAIRE that were not present in both KGs but got aligned following the algorithm presented in the paper.</li> </ul> <p>The detailed information refers to the following column:</p> <ul> <li>Category1: name of the first category</li> <li>Source1: source of the first category ('OpenAlex' or 'OpenAIRE')</li> <li>Category2: name of the second category</li> <li>Source2: source of the first category ('OpenAlex' or 'OpenAIRE')</li> <li>Similarity: semantic similarity value of the two categories</li> <li>PapersInC1: number of papers from the collected dataset belonging to the first category</li> <li>PapersInC2: number of papers from the collected dataset belonging to the second category</li> <li>PapersInBoth: number of papers from the collected dataset belonging to both of the categories</li> <li>Agreement: the value of the agreement of the categories in the tw KGs (Intersection over Union)</li> </ul>
Cross-language Wikipedia link graph
<p>Wikipedia articles use Wikidata to list the links to the same article in other language versions. Therefore, each Wikipedia language edition stores the Wikidata Q-id for each article.</p> <p>This dataset constitutes a Wikipedia link graph where all the article identifiers are normalized to Wikidata Q-ids. It contains the normalized links from all Wikipedia language versions. Detailed link count statistics are attached. Note that articles that have no incoming nor outgoing links are not part of this graph.</p> <p>The format is as follows:</p> <p>Q-id of linking page (outgoing) <tab> Q-id of linked page (incoming) <tab> language version - dump date (20241101)</p> <p>This dataset was used to compute <a href="https://danker.s3.amazonaws.com/index.html">Wikidata PageRank</a>. More information can be found on the <a href="https://github.com/athalhammer/danker">danker</a> repository, where the source code of the link extraction as well as the PageRank computation is hosted.</p> <p>Example entries:<br><br>$ bzcat 2024-11-06.allwiki.links.bz2 | head</p> <p>1 107 ckbwiki-20241101<br>1 107 lawiki-20241101<br>1 107 ltwiki-20241101<br>1 107 tewiki-20241101<br>1 107 wuuwiki-20241101<br>1 111 hywwiki-20241101<br>1 11379 bat_smgwiki-20241101<br>1 11471 cdowiki-20241101<br>1 150 ckbwiki-20241101<br>1 150 lowiki-20241101</p> <p> </p> <p> </p>
Рис. 2. График вЗаимосвяЗи меЖду суммой средних месячных температур воды марта и апреля и датами начала нереста. Fig. 2. Graph of relationship between the sum of the average monthly water temperatures of March and April and the start dates of spawning. in Review of methods for the forecast of mollusk's spat productivity in sea-farms of Primorye and probable ways of their enhancement
Рис. 2. График вЗаимосвяЗи меЖду суммой средних месячных температур воды марта и апреля и датами начала нереста. Fig. 2. Graph of relationship between the sum of the average monthly water temperatures of March and April and the start dates of spawning.
Real-World Signed Graphs Annotated for Whole Graph Classification
<p><strong>Description: </strong>this corpus was designed as an experimental benchmark for a task of signed graph classification. It is composed of three datasets derived from external sources and adapted to our needs:</p> <ul> <li><strong>SpaceOrigin Conversations [1]: </strong>set of conversational graphs, each one associated to a situation of verbal abuse vs. normal situation. These conversations model interactions happening in chatrooms hosted by an MMORPG/ The graphs were originally unsigned: we attributed signed to the edges based on the polarity of the exchanged messages. </li> <li><strong>Correlation Clustering Instances [2]: </strong>set of graph generated randomly as instances of the Correlation Clustering problem, which consists in partitioning signed graphs. These graphs are not associated in any class in the original paper. We proposed a class based on certain features of the space of optimal solutions explored in [2].</li> <li><strong>European Parliament Roll-Calls [3]: </strong>vote networks extracted from the activity of French Members of the European Parliament. The original data does not have any class associated to the networks: we proposed one based on the number of political factions identified in each network in [3]. </li> </ul> <p>These data were used in [4] in order to train and assess various representation learning methods. The authors proposed Signed Graph2vec, a signed variant of Graph2vec; WSGCN, a whole-graph variant of Signed Graph Convolutional Networks (SGCN), and use an aggregated version of Signed Network Embeddings (SiNE) as a baseline. The article provides more information regarding the properties of the datasets, and how they were constituted.</p> <p><strong>Software: </strong>the software used to train the representation learning methods and classifiers is publicly available online: <a href="https://github.com/CompNet/SWGE">SWGE</a>.</p> <p><strong>References:</strong></p> <ol> <li>Papegnies, É.; Labatut, V.; Dufour, R. & Linarès, G. Conversational Networks for Automatic Online Moderation. <em>IEEE Transactions on Computational Social Systems, </em>2019<em>, </em>6:38-55. DOI: <a href="http://doi.org/10.1109/TCSS.2018.2887240">10.1109/TCSS.2018.2887240</a> ⟨<a href="https://hal.science/hal-01999546">hal-01999546</a>⟩</li> <li>Arınık, N.; Figueiredo, R. & Labatut, V. Multiplicity and Diversity: Analyzing the Optimal Solution Space of the Correlation Clustering Problem on Complete Signed Graphs. <em>Journal of Complex Networks, </em>2020<em>, </em>8(6):cnaa025. DOI: <a href="http://doi.org/10.1093/comnet/cnaa025">10.1093/comnet/cnaa025</a> ⟨<a href="https://hal.science/hal-02994011">hal-02994011</a>⟩</li> <li>Arınık, N.; Figueiredo, R. & Labatut, V. Multiple partitioning of multiplex signed networks: Application to European parliament votes. <em>Social Networks, </em>2020<em>, </em>60:83-102. DOI: <a href="http://doi.org/10.1016/j.socnet.2019.02.001">10.1016/j.socnet.2019.02.001</a> ⟨<a href="https://hal.science/hal-02082574">hal-02082574</a>⟩</li> <li>Cécillon, N.; Labatut, V.; Dufour, R. & Arınık, N. Whole-Graph Representation Learning For the Classification of Signed Networks. <em>IEEE Access</em>, 2024, 12:151303-151316. DOI: <a href="https://dx.doi.org/10.1109/ACCESS.2024.3472474">10.1109/ACCESS.2024.3472474</a> <a href="https://hal.archives-ouvertes.fr/hal-04712854" rel="nofollow">⟨hal-04712854⟩</a></li> </ol> <p><strong>Funding: </strong>part of this work was funded by a grant from the <em>Provence-Alpes-Côte-d'Azur</em> region (PACA, France) and the <em>Nectar de Code</em> company.</p> <p><strong>Citation: </strong>If you use this data or the associated source code, please cite article [4]:</p> <p><code>@Article{Cecillon2024,</code><br><code> author = {Cécillon, Noé and Labatut, Vincent and Dufour, Richard and Arınık, Nejat},</code><br><code> title = {Whole-Graph Representation Learning For the Classification of Signed Networks},</code><br><code> journal = {IEEE Access},</code><br><code> year = {2024},</code><br><code> volume = {12},</code><br><code> pages = {151303-151316},</code><br><code> doi = {10.1109/ACCESS.2024.3472474},</code><br><code>}</code></p>
SemTab 2024: Semantic Web Challenge on Tabular Data to Knowledge Graph Matching Data Sets - WikidataTables2024R1 and WikidataTables2024R2
<p>Data Sets from the ISWC 2024 Semantic Web Challenge on Tabular Data to Knowledge Graph Matching, Round 1, Wikidata Tables. Links to other datasets can be found on the challenge website: https://sem-tab-challenge.github.io/2024/ as well as the proceedings of the challenge published on CEUR.</p> <p>For details about the challenge, see: http://www.cs.ox.ac.uk/isg/challenges/sem-tab/</p> <p>For 2024 edition, see: https://sem-tab-challenge.github.io/2024/</p> <p>Note on License: This data includes data from the following sources. Refer to each source for license details:<br>- Wikidata https://www.wikidata.org/</p> <p>THIS DATA IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.</p>
Resources of IncRML: Incremental Knowledge Graph Construction from Heterogeneous Data Sources
<h2>IncRML resources</h2> <p>This Zenodo dataset contains all the resources of the paper 'IncRML: Incremental Knowledge Graph Construction from Heterogeneous Data Sources' submitted to the Semantic Web Journal's Special Issue on Knowledge Graph Construction. This resource aims to make the paper experiments fully reproducible through our <a href="https://github.com/kg-construct/exectool" target="_blank" rel="noopener">experiment tool</a> written in Python which was already used before in the <a href="https://doi.org/10.5281/zenodo.7837289" target="_blank" rel="noopener">Knowledge Graph Construction Challenge by the ESWC 2023 Workshop on Knowledge Graph Construction</a>. The exact Java JAR file of the RMLMapper (rmlmapper.jar) is also provided in this dataset which was used to execute the experiments. This JAR file was executed with Java OpenJDK 11.0.20.1 on Ubuntu 22.04.1 LTS (Linux 5.15.0-53-generic). Each experiment was executed 5 times and the median values are reported together with the standard deviation of the measurements.</p> <h2>Datasets</h2> <p>We provide both dataset dumps of the GTFS-Madrid-Benchmark and of real-life use cases from Open Data in Belgium.<br>GTFS-Madrid-Benchmark dumps are used to analyze the impact on execution time and resources, while the real-life use cases aim to verify the approach on different types of datasets since the GTFS-Madrid-Benchmark is a single type of dataset which does not advertise changes at all.</p> <h3>Benchmarks</h3> <ul> <li>GTFS-Madrid-Benchmark: change types with fixed data size and amount of changes: additions-only, modifications-only, deletions-only (11 versions)</li> <li>GTFS-Madrid-Benchmark: amount of changes with fixed data size: 0%, 25%, 50%, 75%, and 100% changes (11 versions)</li> <li>GTFS-Madrid-Benchmark: data size with fixed amount of changes: scales 1, 10, 100 (11 versions)</li> </ul> <h3>Real-world datasets</h3> <ul> <li>Traffic control center Vlaams Verkeerscentrum (Belgium): traffic board messages data (1 day, 28760 versions)</li> <li>Meteorological institute KMI (Belgium): weather sensor data (1 day, 144 versions)</li> <li>Public transport agency NMBS (Belgium): train schedule data (1 week, 7 versions)</li> <li>Public transport agency De Lijn (Belgium): busses schedule data (1 week, 7 versions)</li> <li>Bike-sharing company BlueBike (Belgium): bike-sharing availability data (1 day, 1440 versions)</li> <li>Bike-sharing company JCDecaux (EU): bike-sharing availability data (1 day, 1440 versions)</li> <li>OpenStreetMap (World): geographical map data (1 day, 1440 versions)</li> </ul> <h3>Ingestion</h3> <p>Real-world datasets LDES output was converted into SPARQL UPDATE queries and executed against Virtuoso to have an estimate for non-LDES clients how incremental generation impacted ingestion into triplestores.</p> <h2>Remarks</h2> <ol> <li>The first version of each dataset is always used as a baseline. All next versions are applied as an update on the existing version. The reported results are only focusing on the updates since these are the actual incremental generation.</li> <li>GTFS-Change-50_percent-{ALL, CHANGE}.tar.xz datasets are not uploaded as GTFS-Madrid-Benchmark scale 100 because both share the same parameters (50% changes, scale 100). Please use GTFS-Scale-100-{ALL, CHANGE}.tar.xz for GTFS-Change-50_percent-{ALL, CHANGE}.tar.xz</li> <li>All datasets are compressed with XZ and provided as a TAR archive, be aware that you need sufficient space to decompress these archives! 2 TB of free space is advised to decompress all benchmarks and use cases. The expected output is provided as a ZIP file in each TAR archive, decompressing these requires even more space (4 TB).</li> </ol> <h2>Reproducing</h2> <p>By using our <a href="https://github.com/kg-construct/exectool" target="_blank" rel="noopener">experiment tool</a>, you can easily reproduce the experiments as followed:</p> <ol> <li>Download one of the TAR.XZ archives and unpack them.</li> <li>Clone the GitHub repository of our experiment tool and install the Python dependencies with '<em>pip install -r requirements.txt'.</em></li> <li>Download the rmlmapper.jar JAR file from this Zenodo dataset and place it inside the experiment tool root folder.</li> <li>Execute the tool by running: '<em>./exectool --root=/path/to/the/root/of/the/tarxz/archive --runs=5 run</em>'. The argument '<em>--runs=5</em>' is used to perform the experiment 5 times.</li> <li>Once executed, you can generate the statistics by running: '<em>./exectool --root=/path/to/the/root/of/the/tarxz/archive stats</em>'.</li> </ol> <h2>Testcases</h2> <p>Testcases to verify the integration of RML and LDES with IncRML, see <a href="https://doi.org/10.5281/zenodo.10171394">https://doi.org/10.5281/zenodo.10171394</a></p>
OpenAIRE Graph: Dataset of funded products
<p>This dataset contains the metadata records about research products (research literature, data, software, other types of research products) with funding information available in the OpenAIRE Graph produced on July 2024.<br>Records are grouped by funder in a dedicated archive file (<funder acronym>.tar).</p> <ul> <li>fundRef contains the following funders</li> </ul> <div> <ul> <li> <ul> <li>100007490 Bausch and Lomb Ireland</li> <li>100007630 College of Engineering and Informatics, National University of Ireland, Galway</li> <li>100007731 Endo International</li> <li>100007819 Allergan</li> <li>100008099 Food Safety Authority of Ireland</li> <li>100008124 Department of Jobs, Enterprise and Innovation</li> <li>100008303 Department for Economics, Northern Ireland</li> <li>100009098 Department of Foreign Affairs and Trade, Ireland</li> <li>100009099 Irish Aid</li> <li>100009770 National University of Ireland</li> <li>100010399 European Society of Cataract and Refractive Surgeons</li> <li>100010546 Deparment of Children and Youth Affairs, Ireland</li> <li>100010547 Irish Youth Justice Service</li> <li>100010993 Irish Nephrology Society</li> <li>100011096 Jazz Pharmaceuticals</li> <li>100011396 Irish College of General Practitioners</li> <li>100012733 National Parks and Wildlife Service</li> <li>100012734 Department for Culture, Heritage and the Gaeltacht, Ireland</li> <li>100012754 Horizon Pharma</li> <li>100012891 Medical Research Charities Group</li> <li>100012919 Epilepsy Ireland</li> <li>100012920 GLEN</li> <li>100012921 Royal College of Surgeons in Ireland</li> <li>100013029 Iris O'Brien Foundation</li> <li>100013206 Food Institutional Research Measure</li> <li>100013381 Irish Phytochemical Food Network</li> <li>100013433 Transport Infrastructure Ireland</li> <li>100013917 Society for Musicology in Ireland</li> <li>100014251 Humanities in the European Research Area</li> <li>100014364 National Children's Research Centre</li> <li>100014384 Amarin Corporation</li> <li>100014902 Irish Association for Cancer Research</li> <li>100015023 Ireland Funds</li> <li>100015278 Pfizer Healthcare Ireland</li> <li>100015319 Sport Ireland Institute</li> <li>100015442 Global Brain Health Institute</li> <li>100015992 St. Luke's Institute of Cancer Research</li> <li>100017144 Shell E and P Ireland</li> <li>100017897 Friedreich’s Ataxia Research Alliance Ireland</li> <li>100018064 Department of Tourism, Culture, Arts, Gaeltacht, Sport and Media</li> <li>100018172 Department of the Environment, Climate and Communications</li> <li>100018175 Dairy Processing Technology Centre</li> <li>100018270 Health Service Executive</li> <li>100018529 Alkermes</li> <li>100018542 Irish Endocrine Society</li> <li>100018754 An Roinn Sláinte</li> <li>100019428 Nabriva Therapeutics</li> <li>100019637 Horizon Therapeutics</li> <li>100020174 Health Research Charities Ireland</li> <li>100020202 UCD Foundation</li> <li>100020233 Ireland Canada University Foundation</li> <li>100022895 Health Research Institute, University of Limerick</li> <li>100022943 National Cancer Registry Ireland</li> <li>501100001581 Arts Council of Ireland</li> <li>501100001582 Centre for Ageing Research and Development in Ireland</li> <li>501100001583 Cystinosis Foundation Ireland</li> <li>501100001584 Department of Agriculture, Food and the Marine, Ireland</li> <li>501100001586 Department of Education and Skills, Ireland</li> <li>501100001587 Economic and Social Research Institute</li> <li>501100001588 Enterprise Ireland</li> <li>501100001591 Heritage Council</li> <li>501100001592 Higher Education Authority</li> <li>501100001593 Irish Cancer Society</li> <li>501100001594 Irish Heart Foundation</li> <li>501100001595 Irish Hospice Foundation</li> <li>501100001596 Irish Research Council for Science, Engineering and Technology</li> <li>501100001598 Mental Health Commission</li> <li>501100001599 National Council for Forest Research and Development</li> <li>501100001600 Research and Education Foundation, Sligo General Hospital</li> <li>501100001601 Royal Irish Academy</li> <li>501100001603 Sustainable Energy Authority of Ireland</li> <li>501100001604 Teagasc</li> <li>501100001627 Marine Institute</li> <li>501100001628 Central Remedial Clinic</li> <li>501100001629 Royal Dublin Society</li> <li>501100001630 Dublin Institute for Advanced Studies</li> <li>501100001631 University College Dublin</li> <li>501100001633 National University of Ireland, Maynooth</li> <li>501100001634 University of Galway</li> <li>501100001635 University of Limerick</li> <li>501100001636 University College Cork</li> <li>501100001637 Trinity College Dublin</li> <li>501100001638 Dublin City University</li> <li>501100002736 Covidien</li> <li>501100002755 Brennan and Company</li> <li>501100002919 Cork Institute of Technology</li> <li>501100002959 Dublin City Council</li> <li>501100003036 Perrigo Company Charitable Foundation</li> <li>501100003037 Elan</li> <li>501100003496 HeyStaks Technologies</li> <li>501100003553 Gaelic Athletic Association</li> <li>501100003840 Irish Institute of Clinical Neuroscience</li> <li>501100003956 Aspect Medical Systems</li> <li>501100004162 Meath Foundation</li> <li>501100004210 Our Lady's Children's Hospital, Crumlin</li> <li>501100004321 Shire</li> <li>501100004981 Athlone Institute of Technology</li> <li>501100006518 Department of Communications, Energy and Natural Resources, Ireland</li> <li>501100006553 Collaborative Centre for Applied Nanotechnology</li> <li>501100006554 IDA Ireland</li> <li>501100006759 CLARITY Centre for Sensor Web Technologies</li> <li>501100009246 Technological University Dublin</li> <li>501100009269 Programme of Competitive Forestry Research for Development</li> <li>501100009315 Cystinosis Ireland</li> <li>501100010808 Geological Survey of Ireland</li> <li>501100011030 Alimentary Glycoscience Research Cluster</li> <li>501100011031 Alimentary Health</li> <li>501100011103 Rannís</li> <li>501100011626 Energy Policy Research Centre, Economic and Social Research Institute</li> <li>501100012354 Inland Fisheries Ireland</li> <li>501100014384 X-Bolt Orthopaedics</li> <li>501100014531 Physical Education and Sport Sciences Department, University of Limerick</li> <li>501100014710 PrecisionBiotics Group</li> <li>501100014745 APC Microbiome Institute</li> <li>501100014826 ADAPT - Centre for Digital Content Technology</li> <li>501100014827 Dormant Accounts Fund</li> <li>501100017501 FotoNation</li> <li>501100018641 Dairy Research Ireland</li> <li>501100018839 Irish Centre for High-End Computing</li> <li>501100019905 Galway University Foundation</li> <li>501100020270 Advanced Materials and Bioengineering Research</li> <li>501100020403 Irish Composites Centre</li> <li>501100020425 Irish Thoracic Society</li> <li>501100020570 College of Medicine, Nursing and Health Sciences, National University of Ireland, Galway</li> <li>501100020871 Bernal Institute, University of Limerick</li> <li>501100021102 Waterford Institute of Technology</li> <li>501100021110 Irish MPS Society</li> <li>501100021525 Insight SFI Research Centre for Data Analytics</li> <li>501100021694 Elan Pharma International</li> <li>501100021838 Royal College of Physicians of Ireland</li> <li>501100022542 Breakthrough Cancer Research</li> <li>501100022610 Breast Cancer Ireland</li> <li>501100022728 Munster Technological University</li> <li>501100023273 HRB Clinical Research Facility Galway</li> <li>501100023551 Cystic Fibrosis Ireland</li> <li>501100023970 Tyndall National Institute</li> <li>501100024242 Synthesis and Solid State Pharmaceutical Centre</li> <li>501100024313 Irish Rugby Football Union</li> <li>501100024834 Tusla - Child and Family Agency</li> </ul> </li> <li>AKA Academy of Finland</li> <li>ANR French National Research Agency (ANR)</li> <li>ARC Australian Research Council (ARC)</li> <li>ASAP Aligning Science Across Parkinson's</li> <li>CHISTERA CHIST-ERA</li> <li>CIHR Canadian Institutes of Health Research</li> <li>EC_ERASMUS+ European Commission - Erasmus+ funding stream</li> <li>EC_FP7 European Commission - FP7 funding stream</li> <li>EC_H2020 European Commission - H2020 funding stream</li> <li>EC_HE European Commission - HE funding stream</li> <li>EEA European Environment Agency</li> <li>EPA Environmental Protection Agency</li> <li>FCT Fundação para a Ciência e a Tecnologia, I.P.</li> <li>FWF Austrian Science Fund</li> <li>HRB Health Research Board</li> <li>HRZZ Croatian Science Foundation</li> <li>INCA Institut National du Cancer</li> <li>IRC Irish Research Council</li> <li>IReL Irish Research eLibrary</li> <li>MESTD Ministry of Education, Science and Technological Development of Republic of Serbia</li> <li>MZOS TOADDNAME</li> <li>NHMRC National Health and Medical Research Council (NHMRC)</li> <li>NIH National Institutes of Health</li> <li>NSERC Natural Sciences and Engineering Research Council of Canada</li> <li>NSF National Science Foundation</li> <li>NWO Netherlands Organisation for Scientific Research (NWO)</li> <li>SFI Science Foundation Ireland</li> <li>SNSF Swiss National Science Foundation</li> <li>SSHRC Social Sciences and Humanities Research Council</li> <li>TARA Tara Expeditions Foundation</li> <li>TIBITAK Türkiye Bilimsel ve Teknolojik Araştırma Kurumu</li> <li>UKRI UK Research and Innovation</li> <li>WT Wellcome Trust</li> </ul> </div> <p>Each tar archive contains gzip files with one json record per line. Json records are compliant with the schema available at <a href="https://doi.org/10.5281/zenodo.14608710">https://doi.org/10.5281/zenodo.14608710</a>.</p> <p>You can also search and browse this dataset (and more) in the <a href="https://explore.openaire.eu">OpenAIRE EXPLORE portal</a> and via the <a href="https://develop.openaire.eu">OpenAIRE API</a>.</p> <p> </p>
PrimeKGQA, the dataset from paper: Bridging the Gap: Generating a Comprehensive Biomedical Knowledge Graph Question Answering Dataset
<p>Despite the plethora of resources such as large-scale corpora and manually curated Knowledge Graphs (KGs), the ability to perform reasoning with natural language inputs over biomedical graphs remains challenging due to insufficient training data. We propose a novel method for automatically constructing a Biomedical Knowledge Graph Question Answering (BioKGQA) dataset sourced from PrimeKG, the largest precision medicine-oriented KG. In total,<br>we create 83999 question-answer pairs along with their respective SPARQL queries. Our approach generates a diverse array of contextually relevant questions covering a wide spectrum of biomedical concepts and levels of complexity. We evaluate our method based on automatic metrics alongside manual annotations. We establish novel standards tailored for KGQA systems to highlight the linguistic correctness and semantical faithfulness of the generated questions based on extracted KG facts. The compiled dataset – PrimeKGQA – serves as a valuable benchmarking resource for advancing knowledge-driven biomedical research and evaluating KGQA system.</p>
BOCK: Biological networks and Oligogenic Combinations as a Knowledge graph
<p>BOCK is a knowledge graph integrating oligogenic disease information (originally from the Oligogenic Disease Database (Natchtegael et al. 2022)) together with multiple biological networks and ontologies.</p> <p>Compared to more generic knowledge graphs, we selected specifically networks relevant to understand the molecular mechanisms of epistasis, placing genes as the central entities, and focused on trusted resources describing a large set of human genes and their interactions.</p> <p>All entities in the KG are linked to their source database entry via an URI (Uniform Resource Identifier) to facilitate integrations within larger bioinformatics linked data repositories.</p> <p>BOCK 2.0 integrates recent versions of the used ontologies and databases, as well as additional pathway-specific (The Reactome Pathway Knowledgebase 2024, Milacic et al.) and tissue-specific information (COXPRESdb v8, Obayashi et al.). Additionally the database used for the coexpression relation between genes, has been replaced by COXPRESdb v8.</p> <p>We provide BOCK 2.0 in three formats:</p> <ol> <li><strong>GraphML (Graph Markup Language)</strong>: a network format enabling the fast import of the KG by multiple libraries (e.g networkx) and tools (e.g Cytoscape).</li> <li><strong>XML (Extensible Markup Language)</strong>: a text-encoding system that is human-readable and compatible with many systems.</li> <li><strong>Neo4J import files</strong>: tab-separated files that can be easily imported into Neo4J using the neo4j-admin utils.</li> </ol> <p> </p>
PubMed-Temporal: A dynamic graph dataset with node-level features
Open the record for dataset details and reuse information.
Analysis materials for "Defininig a Knowledge Graph Development Process through a Systematic Review"
<p>This depository stores the analysis materials for the article "Defining a Knowledge Graph Development Process through a Systematic Review". It includes:</p> <ul> <li><strong>Analysis of KG development process - Articles.csv </strong>- a table of summary of the articles included in the systematic review.</li> <li><strong>Analysis of KG development process - Tasks by level (count).csv</strong> - a table of counting the frequency of the tasks in the knowledge graph development process.</li> <li><strong>Analysis of KG development process - Tasks by level count (synonyms) (1).csv </strong>- a table of counting the frequency of the tasks in the knowledge graph development process after its been adjusted to synonyms.</li> <li><strong>Knowledge graph development processes - </strong>a folder of process figures from the articles that have been included in the systematic review.</li> </ul>
Zero-Shot Information Extraction to Enhance a Knowledge Graph Describing Silk Textiles - English and Spanish neighborhood sub-graphs
<p>Two language-specific sub-graphs (English and Spanish) based on the ConceptNet Knowledge Graph. These two files are required to run the code for reproducing the results reported in the paper <a href="https://aclanthology.org/2021.latechclfl-1.16/">"Zero-Shot Information Extraction to Enhancea Knowledge Graph Describing Silk Textiles"</a> at the <a href="https://sighum.wordpress.com/events/latech-clfl-2021/">LaTeCH-CLfL 2021</a> workshop co-located with <a href="https://2021.emnlp.org/">EMNLP 2021</a>.</p>
OC-782K: Knowledge Graph of "Scientometrics" modelled according to the OpenCitations Data Model
<p>This dataset is a knowledge graph extracted from a <a href="https://static.aminer.cn/misc/na-data-kdd18.zip">t</a>riplestore covering information about the journal <em>Scientometrics</em> and modelled according to the OpenCitations Data Model. The original triplestore is available <a href="https://doi.org/10.5281/zenodo.5151264">here</a>. This KG was extracted for a research project on knowledge graph embeddings (KGEs) for author disambiguation. Structural triples of the knowledge graph are split into training, testing and validation for applying representation learning methods. Textual literals and numeric literals were stored separately in order to implement multimodal approaches for KGEs (see <a href="https://arxiv.org/abs/1802.00934">arXiv:1802.00934</a>). For the same reason, textual literals and numeric literals are already stored into sentence embeddings and a numeric matrix respectively in the files <em>textual_literals.npy </em>and <em>numeric_literals.npy</em>. The file <em>and_eval</em><em>.json </em>contains the evaluation dataset used for evaluating our AND architecture. For the script used to gather this dataset see the GitHub repository: <a href="https://github.com/sntcristian/and-kge/tree/main/aminer">https://github.com/sntcristian/and-kge/tree/main/open-citations</a>.</p>
#energy_graph on renewable shares in electricity and CO2 emission factors in Australia and Germany
<p>This is the little graph I used for my #energy_graph tweet, including the underlying data, in an Excel file. Here is the tweet: https://twitter.com/WPSchill/status/1464368711817740298?s=20. And here is last year's tweet: https://twitter.com/WPSchill/status/1336633040676720640?s=20</p> <p>I occasionally tweet stuff like this. Follow me, if you like ;) https://twitter.com/WPSchill</p>
SILKNOW Knowledge Graph
<p>SILKNOW is a research project that aims at improving the understanding, conservation and dissemination of the<br> European silk heritage from the 15th to the 19th century. The SILKNOW knowledge graph (KG) lies at<br> the center of the application of Semantic Web technologies and computing research to the needs of museums and every other user of this knowledge. The underlying data model is based on CIDOC-CRM and data mappings which are realised and implementedwith conversion tools developed for SILKNOW.<br> <br> Full instructions on how to deploy this KG can be found in the README.md file. See also <a href="http://See also https://github.com/silknow/knowledge-base">https://github.com/silknow/knowledge-base</a></p>
The WASABI Dataset and RDF Knowledge Graph
<p>The WASABI Dataset and RDF Knowledge Graph is rich dataset describing more than 2 millions commercial songs, 200K albums and 77K artists (mainly from pop/rock culture). It comprises data extracted from music databases on the Web, and resulting from the processing of song lyrics and from audio analysis.</p> <p>This is version 2 of the dataset. It consists of two representation formats:</p> <ul> <li>The JSON format provides all data extracted from the MongoDB database that backs up the web application</li> <li>The RDF Knowledge Graph that represents the same data following the WASABI ontology.</li> </ul> <p>WASABI project homepage: http://wasabihome.i3s.unice.fr/</p> <p>Github: https://github.com/micbuffa/WasabiDataset</p>
Grid-graph modeling of emergent neuromorphic dynamics and heterosynaptic plasticity in memristive nanonetworks - Dataset
<p>This is the dataset of "Grid-graph modeling of emergent neuromorphic dynamics and heterosynaptic plasticity in memristive nanonetworks"</p>
Dataset for WWW2022 accepted paper "SelfKG: Self-Supervised Entity Alignment in Knowledge Graphs"
<p>Datasets for WWW2022 accepted paper "SelfKG: Self-Supervised Entity Alignment in Knowledge Graphs"</p> <p>The code repository is <a href="https://github.com/THUDM/SelfKG">here</a>, and our paper is <a href="https://arxiv.org/abs/2203.01044">here</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.