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915 results for “Graph”
EVI: Evidence Graph Ontology v1.0
<p>The Evidence Graph ontology (EVI v1.0) extends core concepts from the W3C Provenance Ontology PROV-O to describe evidence for correctness of findings in biomedical publications. The semantic data model in EVI is expressed using OWL2 Web Ontology Language (OWL2).</p> <p>The core PROV ontology concepts used in EVI are Entity, Activity, and Agent, with two sub-classes Person and Organization. The object properties in EVI are used to establish relations among instances, most of which are of type DigitalObject. Computations are activities performed on instances of other DigitalObjects, Software or Services.</p> <p>The latest release of EVI is v1.0 that can be accessed here at <a href="https://w3id.org/EVI">https://w3id.org/EVI.</a></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>
hybrid-graph-benchmark
<p>This is a benchmark dataset for evaluating <strong>hybrid-graph</strong> (hypergraph and hierarchical graph) learning algorithms. It contains:</p> <ul> <li>21 real-world higer-order graphs from the domains of biology, social media, and wikipedia</li> </ul> <p>For accessing additional functionalities, please access through <a href="https://github.com/Zehui127/hybrid-graph-benchmark">Project Page</a></p> <ul> <li>Built-in functionalities for preprocessing hybrid-graphs</li> <li>A framework to easily train and evaluate Graph Neural Networks</li> </ul>
Undirected Node Attributed Social Network Graph of Twitter Users interested in plastic pollution - created in the framework of the PlasticTwist project
<p>This dataset has been created in the framework of the Plastic Twist project (<a href="https://ptwist.eu/">Ptwist</a>) and more specifically using the Ptwist crowdsourcing application (<a href="https://crowdsourcing.plastictwist.com/">crowdsourcing.plastictwist.com/</a>). We are sharing the edge list and specific node attributes (hashtags) of Twitter users posting about plastic pollution. The dataset can be used for community detection,clustering, node importance, influence maximization tasks, etc. Each user is represented by a unique integer which has nothing to do with the official Twitter user ID. The dataset contains three (3) files: </p> <ul> <li>ptwist.edgelist: A list containing all the 1,362,863 edges between the users. When loaded they create an undirected graph of 800K+ users.</li> <li>node_attributes.txt: This file contains information about the hashtags used by each user. (e.g. "652003": ["SingleUsePlastic"] -> user 6529003 has used the hashtag SingleUsePlastic) </li> <li>annotated_graph: A pickle file which, when loaded, returns a <a href="https://networkx.github.io/">NetworkX</a> node attributed undirected graph.</li> </ul> <p> </p> <p> </p>
Graphs and Attributes used for the attribute-structure correlation pattern mining
<p>## SCPM: An implementation of an algorithm for structural correlation pattern mining.</p> <p>The structural correlation measures how a set of attributes induces dense subgraphs in an attributed graph. A structural correlation pattern is a dense subgraph induced by a particular attribute set. Structural correlation pattern mining is useful to analyze how different attribute sets are correlated to dense subgraphs in several real-life attributed graphs.</p> <p>**Relevant Publications**</p> <p>* Arlei Silva, Wagner Meira, Jr., and Mohammed J. Zaki. Structural correlation pattern mining for large graphs. In Proceedings of the Eighth Workshop on Mining and Learning with Graphs (MLG '10).</p> <p>* Arlei Silva, Wagner Meira, Jr., and Mohammed J. Zaki. Mining Attribute-structure Correlated Patterns in Large Attributed Graphs. In Proceedings of the VLDB Endowment (PVLDB '12).</p> <p>* Arlei Silva. Structural correlation pattern mining for large graphs. M.Sc Thesis, Computer Science Department, Universidade Federal de Minas Gerais, 2011.</p> <p>* Arlei Silva, Wagner Meira Jr. Structural correlation pattern mining for large graphs. Thesis and Dissertation Contest of the Brazilian Computer Society (CTD'12).</p> <p><br> ## HOW TO</p> <p>cd to trunk and run make<br> see README in trunk</p> <p><br> ## Datasets:</p> <p>### Description:</p> <p>#### ATTRIBUTE FILE:</p> <p>Format: Lists the attributes of each vertex from the graph.</p> <p> <VERTEX_ID>,<ATTRIBUTE_ID>,<ATTRIBUTE_ID>...,<ATTRIBUTE_ID></p> <p> Example: <br> 1,A,C <br> 2,A <br> 3,A,C,D <br> 4,A,D <br> 5,A,E <br> 6,A,B,C <br> 7,A,B,E <br> 8,A,B <br> 9,A,B <br> 10,A,B,D <br> 11,A,B</p> <p>#### GRAPH FILE:</p> <p>Format: Lists the neighbors of each vertex from the graph (adjacency list). Although the graph is undirected, each edge must be included in both directions.</p> <p> <VERTEX_ID>,<NEIGHBOR_ID>,<NEIGHBOR_ID>...,<NEIGHBOR_ID> <br> <br> Example: <br> 1,4 <br> 2,3 <br> 3,2,4,5,6,7 <br> 4,1,3,5,6 <br> 5,3,4,6 <br> 6,3,4,5,7,8,9,10 <br> 7,3,6,8,11 <br> 8,6,7,9,10,11 <br> 9,6,8,10,11 <br> 10,6,8,9,11 <br> 11,7,8,9,10</p> <p>### REAL DATASETS</p> <p>Lastfm:</p> <p>attributes: attrLastFm.csv.tar.bz2</p> <p>network: graphLastFm.csv.tar.gz</p> <p>DBLP:</p> <p>attributes: newAttrDBLP.csv.tar.bz2</p> <p>network: newGraphDBLP.csv.tar.bz2</p> <p>CITESEER:</p> <p>attributes: attrCiteseer.csv.tar.bz2</p> <p>network: graphCiteseer.csv.tar.bz2</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>
Vertex-transitive Graphs On Fewer Than 48 Vertices
<p><strong><em>Vertex-transitive Graphs On Fewer Than 48 Vertices</em></strong></p> <p>This dataset contains all the vertex-transitive graphs on 10-47 vertices.</p> <p>It consists of a collection of tar files, with names like</p> <p>alltrans26.tar</p> <p>meaning that this tar file contains all the vertex-transitive graphs on 26 vertices.</p> <p>Once the tar file is unpacked (using "tar xf alltrans26.tar") this will create a number of smaller gzipped files with names such as</p> <p>alltrans26_k=03.gz</p> <p>meaning that this file contains all the transitive graphs on 26 vertices with degree (valency) 3. </p> <p>Once the gzip file is unpacked using "gunzip alltrans26_k=03.gz" the resulting file contains all the graphs, one per line, in graph6 format (this format was invented by Brendan McKay and is recognised by SageMath). </p> <p>The first five lines of the file alltrans26_k=03 are as follows:</p> <pre>Ys???C????_CA?@?`?_GO?c?@_?Q??K??O@CG?aA?GAG@?OCCG?GGC?? Ys??WO@?O??O?J?E?A_H??A?C??O?????_?DC?AQ?AAA??oG?C_O?I?? Ys??WWG@?@?A?W?c??g?S?@??G???G??O??S??I?_?Ac??SS??OW??_? Ys?GGSG@?@?A?W?c??g?S?@??G???G??O??S??I?_?Ac??SS??OW??_? Ys?GOO?????c?Q?c?B?@_?I?A??G??A?@?CCC?OP?CCC?C_O?AOC?C_? </pre> <p>These can be directly used as input to SageMath with commands such as</p> <p>g = Graph("Ys???C????_CA?@?`?_GO?c?@_?Q??K??O@CG?aA?GAG@?OCCG?GGC??")</p> <p>No attempt has been made to reduce data storage by removing redundancy. So the tar file for vertex-transitive graphs on n vertices contains files for each feasible valency from 0 to n-1, despite the redundancy inherent in storing both a graph and its complement, and in storing both disconnected and connected graphs.</p> <p> </p>
Node2Vec model - Czech Wikidata (knowledge graph / concepts / l80 / rw40)
<p>Node2Vec embedding model trained on Czech wikidata (from October 2020) concepts using gensim implementation of Word2Vec with the following parameters for random walks:</p> <ul> <li>length of walk = 80</li> <li>number of random walks = 40</li> </ul>
Node2Vec model - Czech Wikidata (knowledge graph / concepts / l40 / rw10)
<p>Node2Vec embedding model trained on Czech wikidata (from October 2020) concepts using gensim implementation of Word2Vec with the following parameters for random walks:</p> <ul> <li>length of walk = 40</li> <li>number of random walks = 10</li> </ul>
Node2Vec model - Czech Wikidata (knowledge graph / labels / l80 / rw40)
<p>Node2Vec embedding model trained on Czech wikidata (from October 2020) labels using gensim implementation of Word2Vec with the following parameters for random walks:</p> <ul> <li>length of walk = 80</li> <li>number of random walks = 40</li> </ul>
Node2Vec model - Czech Wikidata (knowledge graph / labels / l160 / rw40)
<p>Node2Vec embedding model trained on Czech wikidata (from October 2020) labels using gensim implementation of Word2Vec with the following parameters for random walks:</p> <ul> <li>length of walk = 160</li> <li>number of random walks = 40</li> </ul>
Supplementary data files for manuscript titled "From spreadsheet lab data templates to knowledge graphs: A FAIR data journey in the domain of AMR research"
<div>This data repository contains all the necessary supplementary files for the manuscript titled "<strong>From spreadsheet lab data templates to knowledge graphs: A FAIR data journey in the domain of AMR research.</strong>"</div> <div> </div> <div>The repository is a copy of the <a href="https://github.com/IMI-COMBINE/template2graphs">GitHub page</a> with the source code used to generate the graph and additional files required for the Lab Data Template.</div> <div> </div> <div>Below we provide a brief overview of the data files in the `additional folder` and their underlying purpose:</div> <div> <ul> <li>The <strong>Data Survey</strong> collects relevant project and data set information to set up a Data Management Plan. It can serve as an input for Lab Data Template development.</li> <li>The <strong>Lab Data Templates</strong> facilitate the collection of AMR research data (in vivo and in vitro) in several sub-tables. The Excel format is compatible with upload procedures into the data repository 'grit' and serves as input for a knowledge graph workflow.</li> <li>The <strong>Data dictionary</strong> is connected to the Lab Data Templates and ensures harmonized data entries. In addition, the dictionaries collect metadata beyond the content of the Lab Data Template (e.g. bacterial strain information or compound information) and link to ontologies where possible.</li> <li>The <strong>FAIR assessments</strong> have been used as a primer for improving the template. This report is generated using the FAIR-DSM model.</li> </ul> </div> <div>The templates have been used during the IMI2 GNA NOW project to collect information and have been improved according to FAIR standards in collaboration with the IMI FAIRplus project ("post FAIRification").</div>
Goblin: Neo4J Maven Central dependency graph
<p>This repository contains a Neo4j dump of Maven Central dependency graph generated using <a href="https://github.com/Goblin-Ecosystem/goblinDependencyMiner">goblinDependencyMiner</a>.<br>To import this graph into neo4j, <strong>please use a version 4.x</strong>.</p> <p>Our dependency graph structure and metamodel are shown in images "goblin_dg_structure" and "metamodel".</p> <p>The latest available version dates from April 20, 2025, contains <span>16,939,391</span> nodes (712,509 libraries and 16,226,882 releases) and <span>152,434,085</span> edges (136,207,203 dependencies and 16,226,882 versioning edges).</p> <p>This repository contains two dump of the database:</p> <ul> <li><strong>goblin_maven_20_04_25.dump: </strong>This dataset contains the entire Maven Central dependency graph.</li> <li><strong>with_metrics_goblin_maven_20_04_25.dump</strong>: This dataset is the same as the previous one, but enriched with new “AddedValue” nodes (49,393,155 new nodes) representing the following metrics: CVE (dated may 13, 2025), freshness, popularity and speed. More information in this <a href="https://github.com/Goblin-Ecosystem/goblinTutorial">tutorial</a>.</li> </ul> <p>More details in the dedicated paper: <strong>Goblin: A Framework For Enriching And Querying the Maven Central Dependency Graph </strong>(https://doi.org/10.1145/3643991.3644879)<strong> </strong>- 21st International Conference on Mining Software Repositories (MSR'24).<br>If you use it, please <strong>cite</strong> this paper: <a href="https://dl.acm.org/doi/10.1145/3643991.3644879">https://dl.acm.org/doi/10.1145/3643991.3644879</a></p> <p>⚠️ This dataset is the subject of the <strong>Mining Challenge at the MSR 2025 conference</strong>, more information <a href="https://2025.msrconf.org/track/msr-2025-mining-challenge">here</a>.</p>
Link-prediction on Biomedical Knowledge Graphs
<p>Release of code and experimental data from the paper <em>Towards Linking Graph Topology to Model Performance for Biomedical Knowledge Graph Completion </em>(<em>Machine Learning for Life and Material Sciences</em> workshop @ ICML2024) and <a href="https://arxiv.org/abs/2409.04103" rel="nofollow">The Role of Graph Topology in the Performance of Biomedical Knowledge Graph Completion Models</a>.</p> <div> <div>Knowledge Graph Completion has been increasingly adopted as a useful method for several tasks in biomedical research, like drug repurposing or drug-target identification. To that end, a variety of datasets and Knowledge Graph Embedding models has been proposed over the years. However, little is known about the properties that render a dataset useful for a given task and, even though theoretical properties of Knowledge Graph Embedding models are well understood, their practical utility in this field remains controversial. We conduct a comprehensive investigation into the topological properties of publicly available biomedical Knowledge Graphs and establish links to the accuracy observed in real-world applications. By releasing all model predictions we invite the community to build upon our work and continue improving the understanding of these crucial applications.</div> <div> </div> <div>Experiments were conducted on six datasets: five from the biomedical domain (<a href="../records/268568">Hetionet</a>, <a href="https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/IXA7BM">PrimeKG</a>, <a href="../records/4077338">PharmKG</a>, <a href="../records/5361324">OpenBioLink2020 HQ</a>, <a href="../records/7011027">PharMeBINet</a>) and one trivia KG (<a href="https://aclanthology.org/W15-4007.pdf">FB15k-237</a>). All datasets were randomly split into training, validation and test set (80% / 10% / 10%; in the case of PharMeBINet, 99.3% / 0.35% / 0.35% to mitigate the increased inference cost on the larger dataset).</div> <div>On each dataset, five different KGE models were compared: <a href="https://dl.acm.org/doi/10.5555/2999792.2999923">TransE</a>, <a href="https://arxiv.org/abs/1412.6575">DistMult</a>, <a href="https://arxiv.org/abs/1902.10197">RotatE</a>, <a href="https://arxiv.org/abs/2209.08271">TripleRE</a>, <a href="https://dl.acm.org/doi/10.5555/3504035.3504256">ConvE</a>. Hyperparameters were tuned on the validation split (see final train configurations in <code>train/scripts</code>). We release results for tail predictions on the test split. In particular, each test query <code>(h,r,?)</code> is scored against all entities in the KG and we compute the rank of the score of the correct completion <code>(h,r,t)</code> , after masking out scores of other <code>(h,r,t')</code> triples contained in the graph.</div> <div>Note: the ranks provided are computed as the average between the optimistic and pessimistic ranks of triple scores.</div> <div> </div> <div>Inside <code>experimental_data.zip</code>, the following files are provided.</div> <div> <ul> <li><code>datasets/{dataset}</code>: a folder for each dataset, containing <ul> <li><code>{dataset}_preprocessing.ipynb</code>: a Jupyter notebook for downloading and preprocessing the datasets. In particular, this generates the custom label->ID mapping for entities and relations, and the numerical tensor of <code>(h_ID,r_ID,t_ID)</code> triples for all edges in the graph, which can be used to compute graph topological metrics (e.g., using <a href="https://github.com/graphcore-research/kg-topology-toolbox">kg-topology-toolbox</a>) and compare them with the edge prediction accuracy.</li> <li><code>test_ranks.csv</code>: csv table with columns <code>["h", "r", "t"]</code> specifying the head, relation, tail IDs of the test triples, and columns <code>["DistMult", "TransE", "RotatE", "TripleRE", "ConvE"]</code> with the rank of the ground-truth tail in the ordered list of predictions made by the five KGE models;</li> <li><code>entity_dict.csv</code>: list of entity labels, ordered by entity ID (as generated in the preprocessing notebook);</li> <li><code>relation_dict.csv</code>: list of relation labels, ordered by relation ID (as generated in the preprocessing notebook).</li> </ul> </li> <li><code>train</code>: code to reproduce training (and validation) of the five KGE models, using the <a href="https://github.com/graphcore-research/bess-kge">BESS-KGE</a> distribution framework. <ul> <li><code>train/scripts</code>: executable scripts, with specifications of the final hyperparameters for all models and datasets.</li> </ul> </li> <li><code>notebooks</code>: Jupyter notebooks for data analysis and generation of all the figures in the paper.</li> </ul> <p>The separate <code>top_100_tail_predictions.zip</code> archive contains, for each of the test queries in the corresponding <code>test_ranks.csv</code> table, the IDs of the top-100 tail predictions made by each of the five KGE models, ordered by decreasing likelihood. The predictions are released in a <code>.npz</code> archive of numpy arrays (one array of shape <code>(n_test_triples, 100)</code> for each of the KGE models). </p> </div> </div>
NeSy4VRD: A Multifaceted Resource for Neurosymbolic AI Research using Knowledge Graphs in Visual Relationship Detection
<p><strong>NeSy4VRD</strong></p> <p>NeSy4VRD is a multifaceted, multipurpose resource designed to foster neurosymbolic AI (NeSy) research, particularly NeSy research using Semantic Web technologies such as OWL ontologies, OWL-based knowledge graphs and OWL-based reasoning as symbolic components. The NeSy4VRD research resource pertains to the <em>computer vision</em> field of AI and, within that field, to the application tasks of <em>visual relationship detection (VRD) and scene graph generation</em>.</p> <p>Whilst the core motivation of the NeSy4VRD research resource is to foster computer vision-based NeSy research using Semantic Web technologies such as OWL ontologies and OWL-based knowledge graphs, AI researchers can readily use NeSy4VRD to either: 1) pursue computer vision-based NeSy research without involving Semantic Web technologies as symbolic components, or 2) pursue computer vision research without NeSy (i.e. pursue research that focuses purely on deep learning alone, without involving symbolic components of any kind). This is the sense in which we describe NeSy4VRD as being <em>multipurpose</em>: it can readily be used by diverse groups of computer vision-based AI researchers with diverse interests and objectives.</p> <p>The NeSy4VRD research resource in its entirety is distributed across two locations: Zenodo and GitHub.</p> <p> </p> <p><strong>NeSy4VRD on Zenodo: the NeSy4VRD dataset package</strong></p> <p>This entry on Zenodo hosts the <em>NeSy4VRD dataset package</em>, which includes the <em>NeSy4VRD dataset</em> and its companion <em>NeSy4VRD ontology</em>, an OWL ontology called VRD-World.</p> <p>The <em>NeSy4VRD dataset</em> consists of an image dataset with associated visual relationship annotations. The images of the <em>NeSy4VRD dataset</em> are the same as those that were once publicly available as part of the <a href="https://cs.stanford.edu/people/ranjaykrishna/vrd/">VRD</a> dataset. The NeSy4VRD visual relationship annotations are a highly customised and quality-improved version of the original VRD visual relationship annotations. The <em>NeSy4VRD dataset</em> is designed for computer vision-based research that involves detecting objects in images and predicting relationships between ordered pairs of those objects. A visual relationship for an image of the <em>NeSy4VRD dataset</em> has the form <'subject', 'predicate', 'object'>, where the 'subject' and 'object' are two objects in the image, and the 'predicate' describes some relation between them. Both the 'subject' and 'object' objects are specified in terms of bounding boxes and object classes. For example, representative annotated visual relationships are <'person', 'ride', 'horse'>, <'hat', 'on', 'teddy bear'> and <'cat', 'under', 'pillow'>.</p> <p>Visual relationship detection is pursued as a computer vision application task in its own right, and as a building block capability for the broader application task of scene graph generation. Scene graph generation, in turn, is commonly used as a precursor to a variety of enriched, downstream visual understanding and reasoning application tasks, such as image captioning, visual question answering, image retrieval, image generation and multimedia event processing.</p> <p>The <em>NeSy4VRD ontology</em>, VRD-World, is a rich, well-aligned, companion OWL ontology engineered specifically for use with the <em>NeSy4VRD dataset.</em> It directly describes the domain of the <em>NeSy4VRD dataset</em>, as reflected in the NeSy4VRD visual relationship annotations. More specifically, all of the object classes that feature in the NeSy4VRD visual relationship annotations have corresponding classes within the VRD-World OWL class hierarchy, and all of the predicates that feature in the NeSy4VRD visual relationship annotations have corresponding properties within the VRD-World OWL object property hierarchy. The rich structure of the VRD-World class hierarchy and the rich characteristics and relationships of the VRD-World object properties together give the VRD-World OWL ontology rich inference semantics. These provide ample opportunity for OWL reasoning to be meaningfully exercised and exploited in NeSy research that uses OWL ontologies and OWL-based knowledge graphs as symbolic components. There is also ample potential for NeSy researchers to explore supplementing the OWL reasoning capabilities afforded by the VRD-World ontology with Datalog rules and reasoning.</p> <p>Use of the <em>NeSy4VRD ontology</em>, VRD-World, in conjunction with the <em>NeSy4VRD dataset </em>is, of course, purely optional, however. Computer vision AI researchers who have no interest in NeSy, or NeSy researchers who have no interest in OWL ontologies and OWL-based knowledge graphs, can ignore the <em>NeSy4VRD ontology</em> and use the <em>NeSy4VRD dataset </em>by itself.</p> <p>All computer vision-based AI research user groups can, if they wish, also avail themselves of the other components of the NeSy4VRD research resource available on GitHub.</p> <p> </p> <p><strong>NeSy4VRD on GitHub: open source infrastructure supporting extensibility, and sample code</strong></p> <p>The NeSy4VRD research resource incorporates additional components that are companions to the <em>NeSy4VRD dataset package</em> here on Zenodo. These companion components are available at <a href="https://github.com/djherron/NeSy4VRD/">NeSy4VRD on GitHub</a>. These companion components consist of:</p> <ul> <li>comprehensive open source Python-based infrastructure supporting the extensibility of the NeSy4VRD visual relationship annotations (and, thereby, the extensibility of the <em>NeSy4VRD ontology</em>, VRD-World, as well)</li> <li>open source Python sample code showing how one can work with the NeSy4VRD visual relationship annotations in conjunction with the <em>NeSy4VRD ontology</em>, VRD-World, and RDF knowledge graphs.</li> </ul> <p>The NeSy4VRD infrastructure supporting extensibility consists of:</p> <ul> <li>open source Python code for conducting deep and comprehensive analyses of the <em>NeSy4VRD dataset</em> (the VRD images and their associated NeSy4VRD visual relationship annotations)</li> <li>an open source, custom-designed <em>NeSy4VRD protocol</em> for specifying visual relationship annotation customisation instructions declaratively, in text files</li> <li>an open source, custom-designed <em>NeSy4VRD workflow, </em>implemented using Python scripts and modules, for applying small or large volumes of customisations or extensions to the NeSy4VRD visual relationship annotations in a configurable, managed, automated and repeatable process.</li> </ul> <p>The purpose behind providing comprehensive infrastructure to support extensibility of the NeSy4VRD visual relationship annotations is to make it easy for researchers to take the <em>NeSy4VRD dataset</em> in new directions, by further enriching the annotations, or by tailoring them to introduce new or more data conditions that better suit their particular research needs and interests. The option to use the NeSy4VRD extensibility infrastructure in this way applies equally well to each of the diverse potential NeSy4VRD user groups already mentioned.</p> <p>The NeSy4VRD extensibility infrastructure, however, may be of particular interest to NeSy researchers interested in using the <em>NeSy4VRD ontology</em>, VRD-World, in conjunction with the <em>NeSy4VRD dataset. </em>These researchers can of course tailor the VRD-World ontology if they wish without needing to modify or extend the NeSy4VRD visual relationship annotations in any way. But their degrees of freedom for doing so will be limited by the need to maintain alignment with the NeSy4VRD visual relationship annotations and the particular set of object classes and predicates to which they refer. If NeSy researchers want full freedom to tailor the VRD-World ontology, they may well need to tailor the NeSy4VRD visual relationship annotations first, in order that alignment be maintained.</p> <p>To illustrate our point, and to illustrate our vision of how the NeSy4VRD extensibility infrastructure can be used, let us consider a simple example. It is common in computer vision to distinguish between <em>thing</em> objects (that have well-defined shapes) and <em>stuff</em> objects (that are amorphous). Suppose a researcher wishes to have a greater number of <em>stuff</em> object classes with which to work. Water is such a <em>stuff</em> object. Many VRD images contain water but it is not currently one of the annotated object classes and hence is never referenced in any visual relationship annotations. So adding a <em>Water</em> class to the class hierarchy of the VRD-World ontology would be pointless because it would never acquire any instances (because an object detector would never detect any). However, our hypothetical researcher could choose to do the following:</p> <ul> <li>use the analysis functionality of the NeSy4VRD extensibility infrastructure to find images containing water (by, say, searching for images whose visual relationships refer to object classes such as 'boat', 'surfboard', 'sand', 'umbrella', etc.);</li> <li>use free image analysis software (such as GIMP, at gimp.org) to get bounding boxes for instances of water in these images;</li> <li>use the <em>NeSy4VRD protocol</em> to specify new visual relationships for these images that refer to the new 'water' objects (e.g. <'boat', 'on', 'water'>);</li> <li>use the <em>NeSy4VRD workflow</em> to introduce the new object class 'water' and to apply the specified new visual relationships to the sets of annotations for the affected images;</li> <li>introduce class Water to the class hierarchy of the VRD-World ontology (using, say, the free Protege ontology editor);</li> <li>continue experimenting, now with the added benefit of the additional <em>stuff</em> object class 'water';</li> <li>contribute the enriched set of NeSy4VRD visual relationship annotations, and the enriched companion VRD-World ontology, to research communities.</li> </ul> <p> </p> <p><strong>Information pertaining to the VRD dataset</strong></p> <p>Information about the original VRD dataset is available <a href="https://cs.stanford.edu/people/ranjaykrishna/vrd/">here</a>. </p> <p>Public availability of the VRD images (via information accessible from that location) ceased sometime in the latter part of 2021. We thank Dr. Ranjay Krishna, one of the principals associated with the VRD dataset, for granting us permission to re-establish the public availability of the VRD images as part of NeSy4VRD.</p> <p>The original VRD visual relationship annotations are still publicly available from that location. But our deep analysis of those annotations, driven by our desire to design a robust companion ontology, revealed them to be highly problematic in many ways that made credible ontology modelling infeasible. They were also found to be replete with all manner of errors. The NeSy4VRD visual relationship annotations are far superior and we recommend them over the original VRD annotations to anyone contemplating conducting research using the VRD images. The NeSy4VRD annotations also have the added benefit of the rich, well-aligned companion <em>NeSy4VRD ontology</em>, VRD-World, for those whose research requires such a companion ontology.</p> <p>Researchers wishing to use the original VRD dataset may still do so. They can access the VRD images here, from within the <em>NeSy4VRD dataset</em> on Zenodo, and access the VRD visual relationship annotations from the location in the link.</p> <p><em>A note of caution</em>: the <em>NeSy4VRD ontology</em>, VRD-World, is <em>not</em><strong> </strong>compatible with the original VRD visual relationship annotations and cannot be used in conjunction with them. The VRD-World ontology has been engineered in relation to the highly customised and quality-improved NeSy4VRD visual relationship annotations. The customisations that were applied include ones that introduced many new object classes, merged some of the existing object classes, introduced one new predicate, and changed several predicate names.</p> <p>However, researchers can, if they wish, use the NeSy4VRD extensibility infrastructure (described above) to undertake their own customisation and quality-improvement exercise with respect to the original VRD visual relationship annotations. This is precisely how the NeSy4VRD visual relationship annotations were created in the first place. The primary intended use case of NeSy4VRD's extensibility infrastructure, however, is for researchers to use the NeSy4VRD visual relationship annotations as their starting point, and to take these annotations forward with onward customisations and extensions, as illustrated in the example use case given above.</p> <p> </p> <p> </p>
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>
Results of KROWN: Knowledge Graph Construction Benchmark
<p>In this Zenodo repository we present the results of using KROWN to benchmark popular RDF Graph Materialization systems such as RMLMapper, RMLStreamer, Morph-KGC, SDM-RDFizer, and Ontop (in materialization mode). </p> <h1>What is KROWN 👑?</h1> <p>KROWN 👑 is a benchmark for materialization systems to construct Knowledge Graphs from (semi-)heterogeneous data sources using declarative mappings such as<a href="http://w3id.org/rml/portal"> RML</a>.</p> <p>Many benchmarks already exist for virtualization systems e.g.<a href="https://github.com/oeg-upm/gtfs-bench"> GTFS-Madrid-Bench</a>,<a href="https://ontop-vkg.org/npd-benchmark/"> NPD</a>,<a href="http://wbsg.informatik.uni-mannheim.de/bizer/berlinsparqlbenchmark/"> BSBM</a> which focus on complex queries with a single declarative mapping. However, materialization systems are unaffected by complex queries since their input is the dataset and the mappings to generate a Knowledge Graph. Some specialized datasets exist to benchmark specific limitations of materialization systems such as duplicated or empty values in datasets e.g.<a href="https://doi.org/10.57702/4c9ivpgs"> GENOMICS</a>, but they do not cover all aspects of materialization systems. Therefore, it is hard to compare materialization systems among each other in general which is where KROWN 👑 comes in! </p> <h1>Results</h1> <p>The raw results are available as ZIP archives, the analysis of the results are available in the spreadsheet <em>results.ods</em>.</p> <h2>Evaluation setup</h2> <p>We generated several scenarios using <a href="https://github.com/kg-construct/KROWN/tree/main/data-generator">KROWN’s data generator</a> and executed them 5 times with <a href="https://github.com/kg-construct/KROWN/tree/main/execution-framework">KROWN’s execution framework</a>. All experiments were performed on Ubuntu 22.04 LTS machines (Linux 5.15.0, x86_64) with each Intel(R) Xeon(R) CPU E5-2650 v2 @ 2.60GHz, 48 GB RAM memory, and 2 GB swap memory. The output of each materialization system was set to N-Triples.</p> <h2>Materialization systems</h2> <p>We selected the most popular maintained materialization systems for constructing RDF graphs for performing our experiments with KROWN:</p> <ul> <li> <p>RMLMapper</p> </li> <li> <p>RMLStreamer</p> </li> <li> <p>Morph-KGC</p> </li> <li> <p>SDM-RDFizer</p> </li> <li> <p>OntopM (Ontop in materialization mode)</p> </li> </ul> <p><strong>Note</strong>: KROWN is flexible and allows adding any other materialization system, see <a href="https://github.com/kg-construct/KROWN/tree/main/execution-framework">KROWN’s execution framework</a> documentation for more information.</p> <h2>Scenarios</h2> <p>We consider the following scenarios:</p> <ul> <li> <p>Raw data: number of rows, columns and cell size</p> </li> <li> <p>Duplicates & empty values: percentage of the data containing duplicates or empty values</p> </li> <li> <p>Mappings: Triples Maps (TM), Predicate Object Maps (POM), Named Graph Maps (NG).</p> </li> <li> <p>Joins: relations (1-N, N-1, N-M), conditions, and duplicates during joins</p> </li> </ul> <p><strong>Note</strong>: KROWN is flexible and allows adding any other scenario, see <a href="https://github.com/kg-construct/KROWN/tree/main/data-generator">KROWN’s data generator documentation</a> for more information.</p> <p>In the table below we list all parameter values we used to configure our scenarios:</p> <div> <table> <tbody> <tr> <td> <p><strong>Scenario</strong></p> </td> <td> <p><strong>Parameter values</strong></p> </td> </tr> <tr> <td> <p>Raw data: rows</p> </td> <td> <p>10K, 100K, 1M, 10M</p> </td> </tr> <tr> <td> <p>Raw data: columns</p> </td> <td> <p>1, 10, 20, 30</p> </td> </tr> <tr> <td> <p>Raw data: cell size</p> </td> <td> <p>500, 1K, 5K, 10K </p> </td> </tr> <tr> <td> <p>Duplicates: percentage</p> </td> <td> <p>0%, 25%, 50%, 75%, 100%</p> </td> </tr> <tr> <td> <p>Empty values: percentage</p> </td> <td> <p>0%, 25%, 50%, 75%, 100%</p> </td> </tr> <tr> <td> <p>Mappings: TMs + 5POMs</p> </td> <td> <p>1, 10, 20, 30 TMs</p> </td> </tr> <tr> <td> <p>Mappings: 20TMs + POMs</p> </td> <td> <p>1, 3, 5, 10 POMs</p> </td> </tr> <tr> <td> <p>Mappings: NG in SM</p> </td> <td> <p>1, 5, 10, 15 NGs</p> </td> </tr> <tr> <td> <p>Mappings: NG in POM</p> </td> <td> <p>1, 5, 10, 15 NGs</p> </td> </tr> <tr> <td> <p>Mappings: NG in SM/POM</p> </td> <td> <p>1/1, 5/5, 10/10, 15/15 NGs</p> </td> </tr> <tr> <td> <p>Joins: 1-N relations</p> </td> <td> <p>1-1, 1-5, 1-10, 1-15</p> </td> </tr> <tr> <td> <p>Joins: N-1 relations</p> </td> <td> <p>1-1, 5-1, 10-1, 15-1</p> </td> </tr> <tr> <td> <p>Joins: N-M relations </p> </td> <td> <p>3-3, 3-5, 5-3, 10-5, 5-10</p> </td> </tr> <tr> <td> <p>Joins: join conditions</p> </td> <td> <p>1, 5, 10, 15</p> </td> </tr> <tr> <td> <p>Joins: join duplicates</p> </td> <td> <p>0, 5, 10, 15</p> </td> </tr> </tbody> </table> </div> <h1> </h1>
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.