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915 results for “graphs”
LabelGit: A dataset for software repositories classification using attributed dependency graphs
<p>A dataset for software repositories classification using attributed dependency graphs</p>
Data Set Knowledge Graph (DSKG)
<p>We present the <strong>Data Set Knowledge Graph (<a href="http://dskg.org">DSKG.org</a>)</strong>, an <strong>RDF</strong> <strong>dataset about datasets </strong>that are <strong>linked to publications</strong> (modeled in the Microsoft Academic Knowledge Graph, MAKG) that mention the datasets. The metadata of the datasets is based on datasets that are registered in <strong>OpenAIRE</strong> and <strong>Wikidata</strong>.</p> <p><strong>What exactly do we provide?</strong></p> <ol> <li>Periodically updated <strong><a href="http://dskg.org">RDF dump files</a></strong> of the Data Set Knowledge Graph.</li> <li><strong><a href="http://dskg.org">URI resolution</a></strong> of the Data Set Knowledge Graph within the Linked Open Data.</li> <li>A publicly accessible <strong><a href="http://dskg.org">SPARQL endpoint</a></strong> containing the latest Dataset Knowledge Graph data.</li> </ol> <p><strong>How big is the Dataset Knowledge Graph?</strong></p> <p>The <a href="http://dskg.org">Dataset Knowledge Graph</a> models, among others,</p> <ul> <li>2,208 datasets from all scientific disciplines</li> <li>813,551 links to 634,803 unique papers</li> <li>1,169 authors of datasets</li> <li>208 ORCID IDs.</li> </ul> <p><strong>Potential use cases:</strong></p> <ul> <li>Use the DSKG for the development of semantic search engines (e.g. use the metadata of the linked publications of the datasets for advanced search capabilities)</li> <li>Easier data integration by using the RDF standard vocabulary DCAT and by linking resources to other data sources (e.g., combining the DSKG with other dataset collections in RDF).</li> <li>Data analysis to measure and award the provisioning of datasets (e.g., determine the scientific influence of datasets and authors).</li> </ul>
Five graphs on speech acts in late-Georgian satires
<p>Data, R scripts, and image files for 'Five graphs on speech acts in late-Georgian satires', cradledincaricature.com, 21 April 2015 If you have any questions or queries, please email me at james.baker@bl.uk. All responses will be logged for the benefit of future researchers. Share nicely. James Baker (james.baker@bl.uk)</p> <p><br /> This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.</p>
facebook social network graph
<p>This dataset is also contained in http://snap.stanford.edu/data/egonets-Facebook.html, but it is converted to one by one single graphs in this dataset.</p> <p>Graph Example:</p> <p># graph_id, graph_activity, number_of_vertices, number_of_edges</p> <p>vertex_1， vertex_2, ... , vertex_n</p> <p>vertex_1 vertex_k edge_label vertex_2 vertex_m edge_label, ..., vertex_i, vertex_j, edge_label</p>
The Business of Satirical Prints in Late-Georgian England: network graphs
<p>These figures support research published in Chapter 8 of James Baker, <em>The Business of Satirical Prints in Late-Georgian England</em> (London: Palgrave, 2017). It contains figures made by James Baker in February 2013. These are figures 8.13-8.20 of <em>The Business of Satirical Prints</em>. Figures generated in Gephi using data deposited at http://dx.doi.org/10.5281/zenodo.49548. All errors are the fault of the author.</p>
Graphing and tabulating next-generation sequencing and genotyping data
<p>Making figures and tables for publication. Each zip archive contains input data, shell script to initiate and log R script, one R script for generating several graphs and tables, and the output graphs and tables themselves.</p> <p>Data was generated by whole-genome resequencing of 22 individual D.melanogaster from Sussex-LHM population and 2 from the Sussex RG line, followed by read-mapping, then genotyping with Haplotype Caller and Genomestrip.</p> <p>Locations for raw data, code, logs, extended QC data:</p> <p>Sequence reads NCBI SRA268956</p> <p>NCBI dbSNP https://www.ncbi.nlm.nih.gov/projects/SNP/snp_viewBatch.cgi?sbid=1062461</p> <p>NCBI dbVar accession number pre-release nstd134</p> <p> </p> <p>The pre-print manuscript for this data is available on biorxiv: "Whole genome resequencing of a laboratory-adapted Drosophila melanogaster population sample" http://biorxiv.org/content/early/2016/10/17/081554 doi: http://dx.doi.org/10.1101/081554</p> <p> </p>
Raw data used for COI delineation of the Eupolybothrus species: Authors: Stoev et al. 2013 Data type: genomic The archive contains the following data: 1) fasta-Alignment as the basis for all analyses (.FASTA), 2) mega-file for the calculation of the genetic distances and the NJ tree (.MDSX), 3) NJ-tree in Newick format (.NWK), 4) graph of the TCS Software for the Statistical Parsimony method (.GRAPH) File: E_cavernicolus.rar from: Eupolybothrus cavernicolus Komerički & Stoev sp. n. (Chilopoda: Lithobiomorpha: Lithobiidae): the first eukaryotic species description combining transcriptomic, DNA barcoding and micro-CT imaging data - Biodiversity Data Journal 1: e1013 (28 October 2013) https://doi.org/10.3897/BDJ.1.e1013
<p>Authors: Stoev et al. 2013 Data type: genomic The archive contains the following data: 1) fasta-Alignment as the basis for all analyses (.FASTA), 2) mega-file for the calculation of the genetic distances and the NJ tree (.MDSX), 3) NJ-tree in Newick format (.NWK), 4) graph of the TCS Software for the Statistical Parsimony method (.GRAPH) File: E_cavernicolus.rar</p>
Liftover of DGRP D.melanogaster genotypes to reference genome assembly v6.0, with QC graph
<p>Output are vcf and plink-format genotype files. Also provided are the run code in bash and R, the logs, summary statistics, and a graph showing how the positions of SNPs have changed.</p>
Added graph to previous Alsop data set
<p>A previous plot compared CHU 14.67 MHz Doppler shift between the day before and day of eclipse.</p> <p>I also had available Doppler shift data from 8/19 (two days before). I added that to the graph to perhaps help detect or dismiss a previously observed effect. The two days before the eclipse were pretty comparable from a Doppler Shift standpoint.</p>
Traditional Chinese Medicine Multidimensional Knowledge Graph
<h3>Overview of the Traditional Chinese Medicine Multi-dimensional Knowledge Graph (TCM-MKG)</h3> <p>The <strong>Traditional Chinese Medicine Multi-dimensional Knowledge Graph (TCM-MKG)</strong> is a comprehensive, open-source data platform developed by Jingqi Zeng in November 2024. This platform aims to integrate and standardize a vast array of data from multiple sources, encompassing both traditional Chinese medicine (TCM) and modern biomedical sciences. By organizing and linking this diverse information, TCM-MKG acts as a bridge that connects the ancient wisdom of TCM with contemporary medical research and applications.</p> <h3>Key Features and Objectives:</h3> <ul> <li> <p><strong>Multi-source Data Integration</strong>: TCM-MKG consolidates data from over 30 authoritative resources, covering a broad spectrum of topics, including TCM terminology, Chinese patent medicines (CPM), Chinese herbal pieces (CHP), natural products (NP), chemical components, disease targets, and more. These data sources are carefully curated and interlinked, ensuring a rich, multi-dimensional view of TCM in relation to modern biomedical research. The platform incorporates data from reputable databases such as DrugBank, BioGRID, DisGeNET, STRING, and many others, ensuring that the TCM knowledge is not only expansive but also scientifically robust and cross-referenced with global biomedical standards.</p> </li> <li> <p><strong>Standardized Design for Global Interoperability</strong>: TCM-MKG adheres to international data standards and integrates with widely-used global medical classification systems such as ICD-11, UMLS, MeSH, and DOID. This ensures that the platform’s data is globally comparable and facilitates easy integration with international research efforts, promoting collaboration and knowledge exchange across the fields of TCM and modern medicine.</p> </li> <li> <p><strong>Open Source and Collaborative</strong>: In line with its mission to enhance transparency and accessibility, TCM-MKG is open-sourced in a structured tabular format. This allows researchers worldwide to freely access, contribute to, and expand upon the data, fostering interdisciplinary collaboration and accelerating innovation in both TCM research and modern medicine.</p> </li> <li> <p><strong>Advanced Analytical Capabilities</strong>: By leveraging the power of knowledge graph technology and graph-based intelligence algorithms, TCM-MKG supports deep data mining and relational reasoning. Researchers can uncover hidden associations between TCM components, diseases, and targets, providing insights into the mechanisms of herbal interactions and offering new pathways for drug discovery and therapeutic research.</p> </li> </ul> <h3>Personal Research Application:</h3> <p>Using the TCM-MKG platform, I conducted a study titled <strong>"Graph Neural Networks for Quantifying Compatibility Mechanisms in Traditional Chinese Medicine."</strong> This research applied advanced graph intelligence algorithms to quantitatively assess the compatibility mechanisms of Chinese herbal formulas (CHF). The study provides fresh insights into the underlying principles of TCM herbal combinations.</p> <p>This research has been published:</p> <p><strong>Zeng, J., & Jia, X. (2025). Quantifying compatibility mechanisms in traditional Chinese medicine with interpretable graph neural networks. <em>Journal of Pharmaceutical Analysis</em>, 101342. <a href="https://doi.org/10.1016/j.jpha.2025.101342">https://doi.org/10.1016/j.jpha.2025.101342</a></strong></p> <p>The code and methodology for this research have been open-sourced and are available on <a href="https://github.com/ZENGJingqi/GraphAI-for-TCM" target="_new" rel="noopener">GitHub</a>.</p> <h3>Acknowledgments:</h3> <p>This work benefited from the integration of data from numerous open-access and authoritative databases. We acknowledge the valuable contributions of resources such as DrugBank, BindingDB, BioGRID, DisGeNET, and many others. These datasets provided essential insights into TCM, modern drug chemistry, genetics, diseases, and related fields, forming the foundation for the traditional Chinese medicine multi-dimensional knowledge graph (TCM-MKG) used in this study. Furthermore, we utilized the PSICHIC model (https://github. com/huankoh/PSICHIC) to analyze the binding interactions between components and targets. Full citations for these resources are included.</p> <h3>Contact Information:</h3> <p>For further inquiries or more detailed information, please feel free to contact:<br><strong>Email</strong>: <a rel="noopener">zjingqi@163.com</a></p> <p> </p>
ParliamentSampo Knowledge Graph
<p>The ParliamentSampo Knowledge Graph includes data regarding Finnish Parliamentary debates and actors. The RDF data has been converted using data from the Parliament of Finland's open data services and Wikidata.</p> <p>The Knowledge Graph contains harmonized data of</p> <ol> <li>speeches from the plenary sessions of the Finnish Parliament 1907–2024, and</li> <li>members and organizations of the Parliament.</li> </ol> <p>The data model is designed for representing speeches, interruptions, items (on agenda), documents and other aspects related to plenary session speeches and minutes as well as member of the parliament and biographical information about them focusing on their political career.</p> <p>This dataset is available on a public SPARQL endpoint (<a href="http://ldf.fi/semparl/sparql"><em>http://ldf.fi/semparl/sparql</em></a>).</p> <p>To test and demonstrate its usefulness, this Knowledge Graph is in use in the semantic portal <a href="https://parlamenttisampo.fi/">ParliamentSampo</a>, explained in more detail in the <a href="https://seco.cs.aalto.fi/projects/semparl/en/">project page</a>.</p> <p>The Knowledge Graph can be downloaded also as CSV and XML files. See the dataset page on <a href="https://www.ldf.fi/dataset/semparl">LDF.fi</a> for more details.</p> <div> <div><strong>Version history</strong></div> <ul> <li>1.0.0, February 2023: Initial public release</li> <li>1.0.1, April 2024: README.md addition</li> <li>1.1.0, December 2024: added speeches from end of the year 2022, parliamentary session 2023, plenary sessions 112/1999, 120/1999 and 54/2016; changes to speeches in the plenary session 118/1999; fixes to "group of speaker" of speeches</li> <li>1.2.0, February 2025: added speeches from the parliamentary session 2024</li> <li>1.2.1, June 2025: fixes to speeches in plenary sessions 86–132/1999 (In dataset versions 1.1.0 and 1.2.0 some speeches were erroneously mixed: the same speech id had content of two speeches. This was due to processing the source data of the plenary sessions both from PDF files and HTML files. The current data on these plenary sessions is based only on the HTML source data.)</li> </ul> </div>
The Yelp Collaborative Knowledge Graph
<p>This is the The Yelp Collaborative Knowledge Graph (YCKG) - a transformation of the Yelp Open Dataset into RDF format using Y2KG. </p> <p>The full YCKG dataset can be found in <code>yelp.ttl.gz</code> and <code>yckg.tar.xz</code></p> <p><strong>Paper Abstract</strong></p> <p>The Yelp Open Dataset (YOD) contains data about businesses, reviews, and users from the Yelp website and is available for research purposes. This dataset has been widely used to develop and test Recommender Systems (RS), especially those using Knowledge Graphs (KGs), e.g., integrating taxonomies, product categories, business locations, and social network information. Unfortunately, researchers applied naive or wrong mappings while converting YOD in KGs, consequently obtaining unrealistic results. Among the various issues, the conversion processes usually do not follow state-of-the-art methodologies, fail to properly link to other KGs and reuse existing vocabularies. In this work, we overcome these issues by introducing Y2KG, a utility to convert the Yelp dataset into a KG. Y2KG consists of two components. The first is a dataset including (1) a vocabulary that extends Schema.org with properties to describe the concepts in YOD and (2) mappings between the Yelp entities and Wikidata. The second component is a set of scripts to transform YOD in RDF and obtain the Yelp Collaborative Knowledge Graph (YCKG). The design of Y2KG was driven by 16 core competency questions. YCKG includes 150k businesses and 16.9M reviews from 1.9M distinct real users, resulting in over 244 million triples (with 144 distinct predicates) for about 72 million resources, with an average in-degree and out-degree of 3.3 and 12.2, respectively.</p> <p><strong>Links</strong></p> <p>Latest GitHub release: <a href="https://github.com/MadsCorfixen/The-Yelp-Collaborative-Knowledge-Graph">https://github.com/MadsCorfixen/The-Yelp-Collaborative-Knowledge-Graph/releases/latest</a></p> <p>PURL domain: <a href="https://purl.prod.archive.org/domain/yckg">https://purl.archive.org/domain/yckg</a></p> <p><strong>Files</strong></p> <ul> <li>Graph Data Triple Files <ul> <li><code>yelp.ttl.gz</code> full dataset</li> <li><code>yckg.tar.xz</code> full dataset</li> </ul> </li> <li>One sample file for each of the Yelp domains (Businesses, Users, Reviews, Tips and Checkins), each containing 20 entities. <ul> <li><code>yelp_schema_mappings.nt.gz</code> containing the mappings from Yelp categories to Schema things.</li> <li><code>schema_hierarchy.nt.gz</code> containing the full hierarchy of the mapped Schema things.</li> <li><code>yelp_wiki_mappings.nt.gz</code> containing the mappings from Yelp categories to Wikidata entities.</li> <li><code>wikidata_location_mappings.nt.gz</code> containing the mappings from Yelp locations to Wikidata entities.</li> </ul> </li> <li>Graph Metadata Triple Files <ul> <li><code>yelp_categories.ttl</code> contains metadata for all Yelp categories.</li> <li><code>yelp_entities.ttl</code> contains metadata regarding the dataset</li> <li><code>yelp_vocabulary.ttl</code> contains metadata on the created Yelp vocabulary and properties.</li> </ul> </li> <li>Utility Files <ul> <li><code>yelp_category_schema_mappings.csv</code>. This file contains the 310 mappings from Yelp categories to Schema types. These mappings have been manually verified to be correct.</li> <li><code>yelp_predicate_schema_mappings.csv</code>. This file contains the 14 mappings from Yelp attributes to Schema properties. These mappings are manually found.</li> <li><code>ground_truth_yelp_category_schema_mappings.csv</code>. This file contains the ground truth, based on 200 manually verified mappings from Yelp categories to Schema things. The ground truth mappings were used to calculate precision and recall for the semantic mappings.</li> <li><code>manually_split_categories.csv</code>. This file contains all Yelp categories containing either a & or /, and their manually split versions. The split versions have been used in the semantic mappings to Schema things.</li> </ul> </li> </ul>
Minigraph pangenome graphs for HPRC samples
<p>Minigraph pangenome graphs and derived results for HPRC samples. Glenn Hickey generated the graphs. Heng Li produced the per-assembly variant calls and annotated the alleles. See 00README.txt for file description.</p>
Data Set for the Journal Article "Automated Preparation of Nanoscopic Structures: Graph-Based Sequence Analysis, Mismatch Detection, and pH-Consistent Protonation with Uncertainty Estimates"
<p>This repository containes the data generated by ASAP and discussed in the journal article [Csizi, K.-S. and Reiher, M., 2023, arXiv:2307.16344], including Cartesian coordinates of training and test set molecules, and MD trajectories. </p>
dMC-Juniata-hydroDL River Graph Dataset
<p>This release is the data for the dMC-Juniata-hydroDL2 project (https://zenodo.org/doi/10.5281/zenodo.10183448). There is a readme inside of the .zip file which contains instructions for how to use this dataset. </p>
Expanding the chemical space using a Chemical Reaction Knowledge Graph
<p>This contains:</p><ul><li>the reaction graph dataset used to train the link prediction model</li></ul><p>Homepage: https://github.com/MolecularAI/reaction-graph-link-prediction</p>
Dataset for monkeys A and B from AMAG: Additive, Multiplicative and Adaptive Graph Neural Network For Forecasting Neuron Activity
<p>ECoG data from two monkeys, affi (A) and beignet (B) used in AMAG: Additive, Multiplicative and Adaptive Graph Neural Network For Forecasting Neuron Activity. Jingyuan Li, Leo Scholl, Trung Le, Pavithra Rajeswaran, Amy L Orsborn, and Eli Shlizerman. NeurIPS. 2023. https://openreview.net/forum?id=7ntI4kcoqG</p><p>See also https://github.com/shlizee/AMAG</p>
Temporal Patterns and Trends in Corporate Donations Using PageRank and Node Similarity Graph Algorithm
<p>Corporate donations wield considerable influence within political arenas, shaping policies and influencing decision-making processes. This study uses Neo4j, an advanced graph database tool, to explore a comprehensive company dataset, focusing on unraveling temporal patterns and evolving trends in corporate contributions. Visual representations, such as bar charts, reveal significant fluctuations in donations, indicating potential cyclic patterns occurring every six years. The study explores intricate relationships between donor entities and recipients, highlighting diverse donation patterns—both focused and widespread. The study's derived PageRank scores offer a comprehensive portrayal of the varying degrees of influence among diverse entities receiving donations within the network. Notably, the Conservative and Unionist Party emerges as the most prominent entity, boasting a striking score of 1.86, indicating a substantial influx of financial support likely to significantly shape its political endeavors. Despite a lower score of 0.62, the Labor Party still signifies a noteworthy level of financial backing, albeit less extensive than its counterpart. In contrast, the Liberal Democrats, The In Campaign Ltd, and Network for Animals Ltd exhibit comparatively restrained financial backing, warranting deeper investigation into the factors affecting their funding. Moreover, undisclosed findings regarding 170 similarity scores using Node Similarity algorithm disclose a prevalent similarity trend among entities, notably observed between Company 1 and Company 2, implying potential synergistic partnerships in donation-related endeavors. This high similarity often indicates shared values, highlighting prospects for collaborative initiatives or partnerships to augment positive impacts. Utilizing these insights supports the formulation of targeted donation strategies, circumventing donation redundancies, and ensuring optimal resource allocation for maximal societal benefit within specified sectors.</p> <p>Keywords—Company Dataset, Corporate Donations, Neo4j, Node Similarity, PageRank, Political Influence </p> <p> </p>
A LARGE INTEGRATED KNOWLEDGE GRAPH OF ECNOMICS, FINANCE AND BANKING
<p>Creating the first release to obtain a DOI on Zenodo</p>
PheKnowLator Human Disease Knowledge Graph Benchmarks Archive
<h2><strong>PKT Human Disease KG Benchmark Builds</strong></h2> <p>The PheKnowLator (PKT) Human Disease KG (PKT-KG) was built to model mechanisms of human disease, which includes the Central Dogma and represents multiple biological scales of organization including molecular, cellular, tissue, and organ. The knowledge representation was designed in collaboration with a PhD-level molecular biologist (<a href="https://user-images.githubusercontent.com/8030363/195469903-86598760-40b7-4126-857c-3d6368305a86.png">Figure</a>). </p> <p>The <strong>PKT Human Disease KG</strong> was constructed using 12 OBO Foundry ontologies, 31 Linked Open Data sets, and results from two large-scale experiments (<a href="https://doi.org/10.48550/arXiv.2307.05727">Supplementary Material</a>). The 12 OBO Foundry ontologies were selected to represent chemicals and vaccines (i.e., ChEBI and Vaccine Ontology), cells and cell lines (i.e., Cell Ontology, Cell Line Ontology), gene/gene product attributes (i.e., Gene Ontology), phenotypes and diseases (i.e., Human Phenotype Ontology, Mondo Disease Ontology), proteins, including complexes and isoforms (i.e., Protein Ontology), pathways (i.e., Pathway Ontology), types and attributes of biological sequences (i.e., Sequence Ontology), and anatomical entities (Uberon ontology). The RO is used to provide relationships between the core OBO Foundry ontologies and database entities.</p> <p>The <strong>PKT Human Disease KG</strong> contained 18 node types and 33 edge types. Note that the number of nodes and edge types reflects those that are explicitly added to the core set of OBO Foundry ontologies and does not take into account the node and edge types provided by the ontologies. These nodes and edge types were used to construct 12 different PKT Human Disease benchmark KGs by altering the Knowledge Model (i.e., class- vs. instance-based), Relation Strategy (i.e., standard vs. inverse relations), and Semantic Abstraction (i.e., OWL-NETS (yes/no) with and without Knowledge Model harmonization [OWL-NETS Only vs. OWL-NETS + Harmonization]) parameters. Benchmarks within the PheKnowLator ecosystem are different versions of a KG that can be built under alternative knowledge models, relation strategies, and with or without semantic abstraction. They provide users with the ability to evaluate different modeling decisions (based on the prior mentioned parameters) and to examine the impact of these decisions on different downstream tasks.</p> <p>The Figures and Tables explaining attributes in the builds can be found <a href="https://github.com/callahantiff/PheKnowLator/wiki/Archived-Builds">here</a>.</p> <p> </p> <h3><strong>Build Data Access</strong></h3> <h4><strong>Important Build Information</strong></h4> <p>The benchmarks were originally built and stored using Google Cloud Platform (GCP) resources. For details and a complete description of this process, can be found on GitHub (<a href="https://github.com/callahantiff/PheKnowLator/tree/master/builds#readme">here</a>). Note that we have developed this Zenodo-based archive for the builds. While the original GCP resources contained all of the resources needed to generate the builds, due to the file size upload limits associated with each archive, we have limited the uploaded files to the KGs, associated metadata, and log files. The list of resources, including their URLs, and date of download, can all be found in the logs associated with each build.</p> <p>🗂 For additional information on the KG file types please see the following <a href="https://github.com/callahantiff/PheKnowLator/wiki/KG-Construction#table-knowledge-graph-build-output">Wiki page</a>, which is also available as a download from this repository (PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx). </p> <h4><strong>v1.0.0</strong></h4> <ul> <li>KGs: <a href="../doi/10.5281/zenodo.7030200">https://zenodo.org/doi/10.5281/zenodo.7030200</a></li> <li>Embeddings: <a href="../doi/10.5281/zenodo.7030188">https://zenodo.org/doi/10.5281/zenodo.7030188</a></li> </ul> <h4><strong>All Other Build Versions</strong></h4> <p><strong>Class-based Builds</strong></p> <p><em>Standard Relations</em></p> <ul> <li>OWL Build <ul> <li>v2.0.0: <a href="../doi/10.5281/zenodo.7029957">MAY2020</a><a href="../record/8178783">; </a><a href="../doi/10.5281/zenodo.8180239">JAN2021</a>; <a href="../doi/10.5281/zenodo.8180539">FEB2021</a></li> <li>v2.1.0: <a href="../doi/10.5281/zenodo.8180774">MAY2021</a>;<a href="../doi/10.5281/zenodo.8180825"> JUN2021</a>; <a href="../doi/10.5281/zenodo.8180972">JUL2021</a>; <a href="../doi/10.5281/zenodo.8183987">AUG2021</a>;<a href="../doi/10.5281/zenodo.8184090"> SEP2021</a></li> <li>v3.0.2: <a href="../doi/10.5281/zenodo.8184131">OCT2021</a>; <a href="../doi/10.5281/zenodo.8184205">NOV2021</a></li> </ul> </li> <li>OWL-NETS Build <ul> <li>v2.0.0: <a href="../doi/10.5281/zenodo.7029953">MAY2020</a><a href="../record/8178783">; </a><a href="../doi/10.5281/zenodo.8180255">JAN2021</a>; <a href="../doi/10.5281/zenodo.8180545">FEB2021</a></li> <li>v2.1.0: <a href="../doi/10.5281/zenodo.8180772">MAY2021</a>; <a href="../doi/10.5281/zenodo.8180827">JUN2021</a>; <a href="../doi/10.5281/zenodo.8180974">JUL2021</a>; <a href="../doi/10.5281/zenodo.8183989">AUG2021</a>; <a href="../doi/10.5281/zenodo.8184088">SEP2021</a></li> <li>v3.0.2: <a href="../doi/10.5281/zenodo.8184133">OCT2021</a>; <a href="../doi/10.5281/zenodo.8184208">NOV2021</a></li> </ul> </li> </ul> <p><em>Inverse Relations</em></p> <ul> <li>OWL Build <ul> <li>v2.0.0: <a href="../doi/10.5281/zenodo.7029893">MAY2020</a><a href="../record/8178783">; </a><a href="../doi/10.5281/zenodo.8180269">JAN2021</a>; <a href="../doi/10.5281/zenodo.8180550">FEB2021</a></li> <li>v2.1.0: <a href="../doi/10.5281/zenodo.8180766">MAY2021</a>; <a href="../doi/10.5281/zenodo.8180829">JUN2021</a>; <a href="../doi/10.5281/zenodo.8180976">JUL2021</a>;<a href="../doi/10.5281/zenodo.8183991"> AUG2021</a>; <a href="../doi/10.5281/zenodo.8184086">SEP2021</a></li> <li>v3.0.2: <a href="../doi/10.5281/zenodo.8184135">OCT2021</a>; <a href="../doi/10.5281/zenodo.8184210">NOV2021</a></li> </ul> </li> <li>OWL-NETS Build <ul> <li>v2.0.0: <a href="../doi/10.5281/zenodo.7029921">MAY2020</a><a href="../record/8178783">; </a><a href="../doi/10.5281/zenodo.8180279">JAN2021</a>; <a href="../doi/10.5281/zenodo.8180555">FEB2021</a></li> <li>v2.1.0: <a href="../doi/10.5281/zenodo.8180768">MAY2021</a>; <a href="../doi/10.5281/zenodo.8180833">JUN2021</a>; <a href="../doi/10.5281/zenodo.8180982">JUL2021</a>; <a href="../doi/10.5281/zenodo.8183993">AUG2021</a>; <a href="../doi/10.5281/zenodo.8184084">SEP2021</a></li> <li>v3.0.2: <a href="../doi/10.5281/zenodo.8184137">OCT2021</a>; <a href="../doi/10.5281/zenodo.8184212">NOV2021</a></li> </ul> </li> </ul> <p><strong>Instance-based Builds</strong></p> <p><em>Standard Relations</em></p> <ul> <li>OWL Build <ul> <li>v2.0.0: <a href="../doi/10.5281/zenodo.7029941">MAY2020</a><a href="../record/8178783">; </a><a href="../doi/10.5281/zenodo.8180333">JAN2021</a>;<a href="../doi/10.5281/zenodo.8180558"> FEB2021</a></li> <li>v2.1.0: <a href="../doi/10.5281/zenodo.8180764">MAY2021</a>; <a href="../doi/10.5281/zenodo.8180835">JUN2021</a>; <a href="../doi/10.5281/zenodo.8180984">JUL2021</a>; <a href="../doi/10.5281/zenodo.8183995">AUG2021</a>; <a href="../doi/10.5281/zenodo.8184082">SEP2021 </a></li> <li>v3.0.2: <a href="../doi/10.5281/zenodo.8184139">OCT2021</a>; <a href="../doi/10.5281/zenodo.8184216">NOV2021 </a></li> </ul> </li> <li>OWL-NETS Build <ul> <li>v2.0.0: <a href="../doi/10.5281/zenodo.7029939">MAY2020</a><a href="../record/8178783">; </a><a href="../doi/10.5281/zenodo.8180335">JAN2021</a>;<a href="../doi/10.5281/zenodo.8180564"> FEB2021</a></li> <li>v2.1.0: <a href="../doi/10.5281/zenodo.8180762">MAY2021</a>; <a href="../doi/10.5281/zenodo.8180837">JUN2021</a>; <a href="../doi/10.5281/zenodo.8180986">JUL2021</a>; <a href="../doi/10.5281/zenodo.8183997">AUG2021</a>; <a href="../doi/10.5281/zenodo.8184080">SEP2021</a></li> <li>v3.0.2: <a href="../doi/10.5281/zenodo.8184141">OCT2021</a>; <a href="../doi/10.5281/zenodo.8184218">NOV2021</a></li> </ul> </li> </ul> <p><em>Inverse Relations</em></p> <ul> <li>OWL Build <ul> <li>v2.0.0: <a href="../doi/10.5281/zenodo.7029945">MAY2020</a><a href="../record/8178783">; </a><a href="../doi/10.5281/zenodo.8180338">JAN2021</a>; <a href="../doi/10.5281/zenodo.8180588">FEB2021</a></li> <li>v2.1.0: <a href="../doi/10.5281/zenodo.8180758">MAY2021</a>; <a href="../doi/10.5281/zenodo.8180878">JUN2021</a>; <a href="../doi/10.5281/zenodo.8180992">JUL2021</a>; <a href="../doi/10.5281/zenodo.8184001">AUG2021</a>; <a href="../doi/10.5281/zenodo.8184078">SEP2021 </a></li> <li>v3.0.2: <a href="../doi/10.5281/zenodo.8184143">OCT2021</a>; <a href="../doi/10.5281/zenodo.8184220">NOV2021</a></li> </ul> </li> <li>OWL-NETS Build <ul> <li>v2.0.0: <a href="../doi/10.5281/zenodo.7029919">MAY2020</a><a href="../record/8178783">; </a><a href="../doi/10.5281/zenodo.8180340">JAN2021</a>; <a href="../doi/10.5281/zenodo.8180584">FEB2021</a></li> <li>v2.1.0: <a href="../doi/10.5281/zenodo.8180756">MAY2021</a>; <a href="../doi/10.5281/zenodo.8180823">JUN2021</a>; <a href="../doi/10.5281/zenodo.8180996">JUL2021</a>; <a href="../doi/10.5281/zenodo.8184003">AUG2021</a>; <a href="../doi/10.5281/zenodo.8184076">SEP2021 </a></li> <li>v3.0.2: <a href="../doi/10.5281/zenodo.8184145">OCT2021</a>; <a href="../doi/10.5281/zenodo.8184222">NOV2021</a></li> </ul> </li> </ul>
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.