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100 results for “RDF”
RDF dataset produced in the work "Exploring Adverse Outcome Pathways for Nanomaterials with semantic web technologies"
<p>Adverse Outcome Pathways (AOPs) have been proposed to facilitate mechanistic understanding of interactions of chemicals/materials with biological systems. Each AOP starts with a molecular initiating event (MIE) and possibly ends with adverse outcome(s) (AOs) via a series of key events (KEs). So far, the interaction of engineered nanomaterials (ENMs) with biomolecules, biomembranes, cells, and biological structures, in general, is not yet fully elucidated. There is also a huge lack of information on which AOPs are ENMs-relevant or -specific, despite numerous published data on toxicological endpoints they trigger, such as oxidative stress and inflammation. We propose to integrate related data and knowledge recently collected. Our approach combines the annotation of nanomaterials and their MIEs with ontology annotation to demonstrate how we can then query AOPs and biological pathway information for these materials. We conclude that a FAIR (Findable, Accessible, Interoperable, Reusable) representation of the ENM-MIE knowledge simplifies integration with other knowledge.</p>
Spanish Workers' Statute Legal Relations and RDF
<p>These datasets result from extracting legal events and relationships from the Spanish Workers' Statutes and structuring the extracted data in an RDF graph. After a set of experiments conducted with GPT-3.5 using scarce annotated data from previous works <a href="http://journal.sepln.org/sepln/ojs/ojs/index.php/pln/article/view/6432">[1]</a>, a 5-shot learning approach was applied to the full text of the Spanish Workers’ Statute. Approximately 1500 relations were extracted in a JSON format, structured into a dataset, and represented in an RDF graph.</p>
[RDF Triples] Courses given at the Vrije Universiteit Amsterdam 2020/2021
<p>This data set is published in Turtle format using triples. </p> <p>The dataset contains all the Courses and their information at the Vrije Universiteit Amsterdam for the university year 2020/2021. This information was gathered from the Vrije Universiteit Amsterdam online study guide </p> <p>A new vocabulary 'vu' was needed as some vocabularies did not allow full expression of the datas et.</p> <p>You can use SPARQL in order to fetch data from this dataset, the property variables one can user are:</p> <ul> <li>vu:offeredByFaculty <ul> <li>This property will return the faculty the course belongs to as a vu:<strong>FacultyName</strong>.</li> </ul> </li> <li>vu:taughtBy <ul> <li>This property will return the professors who teach that course as a vu:<strong>ProfessorName</strong>.</li> </ul> </li> <li>vu:courseContent <ul> <li>This property returns what a course is about as a string. </li> </ul> </li> <li>vu:courseLevel <ul> <li>This property returns the course level (between 100 and 600) as an integer.</li> </ul> </li> <li>vu:courseObjective <ul> <li>This property returns the objective of a course as a string.</li> </ul> </li> <li>vu:literature <ul> <li>This property returns the required literature of a course as a string.</li> </ul> </li> <li>vu:recommendedBackground <ul> <li>This poperty returns the recommended background of a course as a string.</li> </ul> </li> <li>vu:targetAudience <ul> <li>This property returns the target audience of a course as a string.</li> </ul> </li> <li>vu:teachingMethods <ul> <li>This property returns the teaching methods of a course as a string. </li> </ul> </li> </ul> <p>These are the by us created property variables. There are also some re-used variables which are:</p> <ul> <li>vuc:<strong>CourseID</strong> rdf:type teach:Course <ul> <li>This returns all courses when used in sparql.</li> </ul> </li> <li>dbo:language <ul> <li>This property returns the language in which a course is given as a dbr:<strong>LanguageName</strong></li> </ul> </li> <li>teach:academicTerm <ul> <li>This property is used in order to return the academic term in which a course is given as a string.</li> </ul> </li> <li>teach:courseTitle <ul> <li>This property returns the name of a course as a string.</li> </ul> </li> <li>teach:ects <ul> <li>This property returns the number of European credits one receives for a course as an integer.</li> </ul> </li> <li>teach:grading <ul> <li>This property returns the grading method of a course as a string. </li> </ul> </li> </ul> <p>This data set can be used in order to find specific information about courses without knowing the exact course name or code. For example, you can fetch all courses that have 3 credits and are in English, given at the faculty of Science. Therefore it becomes much easier for students that want to follow courses outside of their major/master, which also fit their requirements. In a normal situation, a student would need to search through all 2064 courses given at the VU to find a course that they like. </p>
Project GeRDI collection in RDF
<p>This collection contains the metadata harvested in the context of Project GeRDI (https://www.gerdi-project.eu/). The dataset records are organized based on the institutions that provide them. This allows to download and use any provider of interest. In addition to the 11 dataset providers, the dataset also contains provenance information (neatly stored in the "provenance" named graph) for the conversion process, which is not part of the original dataset collectoin and was generated at the end of the conversion process.</p>
Statistical Data from Muenster (2010-2014) as RDF
<p>This dataset is a <strong>curated statistical dataset </strong>from the Muenster City Council in RDF (Resource Description Framework) format. The City Council of Muenster has provided statistical datasets about Muenster covering the period 2010-2014 in PDF (Portable Document Format). Since PDF is not machine readable, students from the University of Muenster have tried to convert this statistical data into RDF, making therefore the data consumable by machines. The dataset covers five different topics:</p> <ul> <li>Unemployment in Muenster</li> <li>Population in Muenster</li> <li>Migration in Muenster</li> <li>Households of Muenster</li> <li>Employees subject to social insurance in Muenster</li> </ul> <p>As proofs of the usefulness of the RDF data, the students built some nice visualizations. The visualizations can be accessed from:</p> <ul> <li>https://git.io/vD547 (unemployment)</li> <li>https://git.io/vD545 (population)</li> <li>https://git.io/vD54d (migration)</li> <li>https://git.io/vD54b (households)</li> <li>https://git.io/vD5Bv (employees subject to social insurance)</li> </ul>
EOS+TOV_crust-DD2-RDF_infty_265_555
<p>This work has been supported by the Polish National Science Centre (NCN) under grant No. 2019/33/B/ST9/03059.</p>
Resource Description Framework (RDF) Modeling of Named Entity Co-occurrences in Biomedical Literature and Its Integration with PubChemRDF
<p>This Zenodo record contains the co-occurrence RDF data generated in the work described in the paper “<strong>A resource description framework (RDF) model of named entity co-occurrences in biomedical literature and its integration with PubChemRDF</strong>” by Li et al., published in the Journal of Cheminformatics (<a href="https://doi.org/10.1186/s13321-025-01017-0" target="_blank" rel="noopener">https://doi.org/10.1186/s13321-025-01017-0</a>). It also contains the SPARQL query examples, the RDF schema in SHACL and ShEx, and the validation scripts.</p> <p>All content in this Zenodo record is for archival purposes. The latest version of the co-occurrence RDF data and other PubChemRDF data can be accessed via the PubChem FTP site (<a href="https://ftp.ncbi.nlm.nih.gov/pubchem/RDF/" target="_blank" rel="noopener">https://ftp.ncbi.nlm.nih.gov/pubchem/RDF/</a>). The up-to-date RDF schema in various formats is available on the PubChemRDF Schema page (<a href="https://pubchem.ncbi.nlm.nih.gov/docs/rdf-schema" target="_blank" rel="noopener">https://pubchem.ncbi.nlm.nih.gov/docs/rdf-schema</a>). A set of SPARQL query examples can be found on the PubChemRDF use case pages (<a href="https://pubchem.ncbi.nlm.nih.gov/docs/rdf-use-cases" target="_blank" rel="noopener">https://pubchem.ncbi.nlm.nih.gov/docs/rdf-use-cases</a>).</p>
National Edition of Aldo Moro's works (RDF Dataset)
<p>A Turtle file that contains structural, intertextual and contextual data about the National Edition of Aldo Moro's works.</p>
Original data and RML mapping used to generate RDF results for K-CAP 2021
<p>These materials include the following:</p> <ul> <li>The csvs used to track the accepted papers, authors and resources in K-CAP 2021</li> <li>The RML mappings generated to transform them to RDF (paths to data will have to be adjusted)</li> <li>The resultant RDF file generated when applying the mapping (out.ttl)</li> </ul>
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>
Wikidata subset with revision history information [RDF]
<p>This dataset is composed of 300 instances from the 100 most important classes in Wikidata, for a total of around 30000 entities and 390000 triples. The dataset is geared towards knowledge graph refinement models that leverage edit history information from the graph. There are two versions of the dataset:</p> <ul> <li>The <strong>static</strong> version (files postfixed with '_static') contains the simple statements of each entity fetched from Wikidata.</li> <li>The <strong>dynamic</strong> version (files postfixed with '_dynamic') contains information about the operations and revisions made to these entities, and the triples that were added or removed.</li> </ul> <p>Each version is split into three subsets: train, validation (val), and test. Each split contains every entity from the dataset. The train split contains the first 70% of revisions made to each entity, the validation split contains the 70% to 85% revisions, and the test set contains the last 15% revisions.</p> <p>This is a sample from the static datasets:</p> <pre><code>wd:Q217432 a uo:entity ; wdt:P1082 1.005904e+06 ; wdt:P1296 "0052280" ; wdt:P1791 wd:Q18704103 ; wdt:P18 "Pitakwa.jpg" ; wdt:P244 "n80066826" ; wdt:P571 "+1912-00-00T00:00:00Z" ; wdt:P6766 "421180027" .</code></pre> <p>Each entity has the type <em>uo:entity</em>, and contains the statements added during that time period following Wikidata's data model.</p> <p>In the following code snippet we show an example from the dynamic dataset:</p> <pre><code>uo:rev703872813 a uo:revision ; uo:timestamp "2018-06-28T22:31:32Z" . uo:op703872813_0 a uo:operation ; uo:fromRevision uo:rev703872813 ; uo:newObject wd:Q82955 ; uo:opType uo:add ; uo:revProp wdt:P106 ; uo:revSubject wd:Q6097419 . uo:op703878666_0 a uo:operation ; uo:fromRevision uo:rev703878666 ; uo:opType uo:remove ; uo:prevObject wd:Q1108445 ; uo:revProp wdt:P460 ; uo:revSubject wd:Q1147883 .</code></pre> <p>This dataset is composed of revisions, which have a timestamp. Each revision is composed of 1 to n operations, in which there is a change to a statement from the entity. There are two types of operations: <em>uo:add</em> and <em>uo:remove</em>. In both cases, the property and the subject being modified are shown with the <em>uo:revProp</em> and <em>uo:revSubject</em> properties. In the case of additions, <em>uo:newObject</em> and <em>uo:prevObject</em> properties are added to show the previous and new objects after the addition. In the case of removals, there is a <em>uo:prevObject </em>property to record the object that was removed.</p>
Gene coexpression data in ATTED-II (RDF data)
<p>These are the gene coexpression data in Notation3 format, also provided in Virtuoso in ATTED-II. Details of the data can be found on the download page in ATTED-II. https://atted.jp/download/</p> <p>Example:</p> <p>@prefix m2r: <http://med2rdf.org/ontology/med2rdf#> .<br> @prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> .<br> @prefix dcterms: <http://purl.org/dc/terms/> .<br> @prefix obo: <http://purl.obolibrary.org/obo/> .<br> @prefix ncbigene: <http://identifiers.org/ncbigene/> .<br> @prefix sio: <http://semanticscience.org/resource/> .<br> @prefix atted: <https://atted.jp/dataset/> .<br> @prefix attedo: <https://atted.jp/ontology/> .<br> @prefix xsd: <http://www.w3.org/2001/XMLSchema#> .</p> <p>atted:Ath-u.c3-0<br> dcterms:identifier "Ath-u.c3-0" ;<br> dcterms:issued "2022-04-07"^^xsd:date .</p> <p>[]<br> attedo:gene ncbigene:836761 ;<br> attedo:gene ncbigene:2745723 ;<br> attedo:lsmr_score 5.6464 ;<br> attedo:dataset atted:Ath-u.c3-0 ;<br> a attedo:CoExpressedGenePair .</p> <p>[]<br> attedo:gene ncbigene:836827 ;<br> attedo:gene ncbigene:2745723 ;<br> attedo:lsmr_score 5.2684 ;<br> attedo:dataset atted:Ath-u.c3-0 ;<br> a attedo:CoExpressedGenePair .</p>
Gene coexpression data in COXPRESdb (RDF data)
<p>These are the gene coexpression data in Notation3 format, also provided in Virtuoso in COXPRESdb. Details of the data can be found on the download page in COXPRESdb. https://coxpresdb.jp/download/</p> <p>Example:</p> <p>@prefix m2r: <http://med2rdf.org/ontology/med2rdf#> .<br> @prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> .<br> @prefix dcterms: <http://purl.org/dc/terms/> .<br> @prefix obo: <http://purl.obolibrary.org/obo/> .<br> @prefix ncbigene: <http://identifiers.org/ncbigene/> .<br> @prefix sio: <http://semanticscience.org/resource/> .<br> @prefix coxpresdb: <https://coxpresdb.jp/dataset/> .<br> @prefix coxpresdbo: <https://coxpresdb.jp/ontology/> .<br> @prefix xsd: <http://www.w3.org/2001/XMLSchema#> .</p> <p>coxpresdb:Hsa-u.c4-0<br> dcterms:identifier "Hsa-u.c4-0" ;<br> dcterms:issued "2022-06-08"^^xsd:date .</p> <p>[]<br> coxpresdbo:gene ncbigene:9 ;<br> coxpresdbo:gene ncbigene:10 ;<br> coxpresdbo:lsmr_score 6.33 ;<br> coxpresdbo:dataset coxpresdb:Hsa-u.c4-0 ;<br> a coxpresdbo:CoExpressedGenePair .</p> <p>[]<br> coxpresdbo:gene ncbigene:41 ;<br> coxpresdbo:gene ncbigene:100 ;<br> coxpresdbo:lsmr_score 2.49 ;<br> coxpresdbo:dataset coxpresdb:Hsa-u.c4-0 ;<br> a coxpresdbo:CoExpressedGenePair .</p>
Helmholtz Knowledge Graph: RDF data dump
<p>Under this base DOI we regularly publish full RDF data dumps of the Helmholtz-Knowledge Graph.<br>Data dumps are typical associated with major releases, or major data updates.</p> <p>Dumps are serialized in .ttl and compressed with gzip.<br><br>For more information on deployment and documentation as well as data access see:<br>Search UI: https://search.unhide.helmholtz-metadaten.de/<br>SPARQL endpoint: https://sparql.unhide.helmholtz-metadaten.de/<br>Documentation: https://docs.unhide.helmholtz-metadaten.de/<br>Software: https://codebase.helmholtz.cloud/hmc/hmc-public/unhide</p>
Benchmark datasets for RDF load time evaluation (RiverBench)
<div> <p>Datasets to be used for reproducing the RDF load time benchmark, using the code here: <a href="https://github.com/Ostrzyciel/rdf4led-riverbench">https://github.com/Ostrzyciel/rdf4led-riverbench</a></p> <p>The datasets were obtained from <a href="https://w3id.org/riverbench/v/2.0.1/profiles/flat-triples" rel="nofollow">RiverBench profile <code>flat-triples</code> version 2.0.1</a>. <strong>The detailed licensing and authorship information for each individual dataset is available on <a href="https://w3id.org/riverbench/v/2.0.1/datasets" rel="nofollow">RiverBench's website</a>.</strong> The most restrictive license that applies to any of the datasets is CC BY-SA.</p> <p>Benchmark results: <a href="https://doi.org/10.5281/zenodo.12087112">https://doi.org/10.5281/zenodo.12087112</a></p> </div>
BiGe-Onto RDF dataset
<p>RDF dataset generated by the BiGe-Onto project. It contains approximately 4.3M of triplets extracted from different data sets belonging to <a href="https://obis.org/">OBIS</a> and <a href="https://www.gbif.org/">GBIF</a>. It also contains information on oceanographic campaigns of the Argentine initiative <a href="http://www.pampazul.gob.ar/">Pampa Azul</a></p>
Adverse Outcome Pathway Wiki RDF
<p>This dataset is the RDF generated from the AOP-Wiki data release (<a href="https://aopwiki.org/downloads">aopwiki.org/downloads</a>). It was generated using a Jupyter notebook that is available on GitHub (<a href="https://github.com/marvinm2/AOPWikiRDF">github.com/marvinm2/AOPWikiRDF</a>), and the process and additional description of the RDF have been published (<a href="https://doi.org/10.1089/aivt.2021.0010">doi.org/10.1089/aivt.2021.0010</a>).</p>
MiMoTextBase RDF Dump
<p>This is the RDF-Dump of the knowledge graph "<a href="https://data.mimotext.uni-trier.de/wiki/Main_Page">MiMoTextBase</a>" created within the project "Mining and Modeling Text" (2019-2023, <a href="https://mimotext.uni-trier.de/">MiMoText</a>) at the University Trier. As the project focusses on French Enlightenment novels, the MiMoTextBase - a wikibase instance - contains items on novels and their respective authors of the years 1751-1800. The RDF-Dump is a copy of all entries within the MiMoTextBase and will be updated as the graph grows.</p>
Ontology files in .owl, rdf and xml format demonstrating the texts, documents and works (=entities) ontology
<p>See various writings by Robinson concerning this ontology (e.g. , <a href="https://wiki.usask.ca/pages/viewpage.action?pageId=1324745355">Creating and Implementing an Ontology of Documents and Texts (ADHO 2018)</a>.</p> <p>Note revision of 10/21: removal of parts of document, work, text. dc:hasPart and dc:isPartOf make this redundant.</p>
ECARTICO as RDF triples
<p>ECARTICO is a comprehensive collection of structured biographical data concerning painters, engravers, printers, book sellers, gold- and silversmiths and others involved in the ‘cultural industries’ of the Low Countries in the sixteenth and seventeenth centuries. The database contains rich structured data on almost 60 000 persons. All data is available on the Web as Linked Open Data in the RDFa format. The file stored here, contains all data in the N-triples RDF serialization format.</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.