Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
100
datasets available to search
ShareScore release 0.9.0
Dataset results
100 results for “rdf”
Contextual maritime data set (RDF triples)
<p>This data set is the RDF conversion w.r.t. the datAcron ontology, of the contextual maritime data available at https://zenodo.org/record/1167595 . It has been generated by the RDF-Gen method on the data sets describing sea ports (World Port Index, Ports of Brittany, SeaDataNet fishing ports) and protected regions (fishing areas, fishing interdiction, Natura2000).</p>
Reti Medievali Open Archive: Metadata in RDF-XML
<p>This RDF-XML dump contains metadata about the items deposited in <em>RM Open Archive</em> converted in Linked Open Data.</p> <p>RM <em>Open Archive</em> is an Open Access scholarly repository, which covers the whole range of medieval studies: social, economic, political and institutional history, as well as cultural, religious and gender representations and practices.</p> <p>RM <em>Open Archive</em> was realised in the frame of the PRIN 2010-2011 project <a href="http://www.medievistica.unina.it/"><em>Concepts, Practices and Institutions of a Discipline: Italian Medieval Studies in 19th and 20th Centuries</em></a>, coordinated by Prof. Roberto Delle Donne at "Federico II" University of Naples. <br> It is under the aegis of the following learned societies, which invite their members to deposit publications. </p> <p><em><a href="http://www.sismed.eu/it/">Società italiana degli storici medievisti</a></em> (Italian Society of Medievalists)</p> <p><em>Consulta per il Medioevo e l'Umanesimo latini</em> (Council for Latin Middle Ages and Humanism)</p> <p><em><a href="http://www.sifr.it/">Società Italiana di Filologia Romanza</a></em> (Italian Society of Romance Philology)</p> <p><em><a href="http://www.paleografi-diplomatisti.org/">Associazione Italiana dei Paleografi e Diplomatisti</a></em> (Italian Association of Paleography and Diplomatics)</p> <p><em><a href="http://www.mediaevistenverband.de/">Mediävistenverband e.V.</a></em> (German Association of Medievalists)</p>
Crunchbase in RDF: A Large Data Set About Jobs, Websites, Organizations, News, People, Products, and Acquisitions
<p><strong>CrunchBase</strong> in an online platform providing information about startups and technology companies, including related entities such as the products they sell, key people they employ, and investments they made and received.</p> <p>We provide here an <strong>RDF data set of Crunchbase</strong> as of October 2015. The data set contains information about</p> <ul> <li>1,946,435 jobs</li> <li>1,348,449 websites</li> <li>567,937 organizations</li> <li>519,763 news</li> <li>430,093 people</li> <li>60,076 products, and</li> <li>33,127 acquisitions.</li> </ul> <p>The data set has been used, among other things, for data integration with financial data sources to evaluate the performance of particular companies and for monitoring news to find statements that are not in Crunchbase as an RDF knowledge graph yet.</p> <p>Note that the provided data set was created in October 2015 when all Crunchbase data was <strong>licensed under Creative Commons Attribution-NonCommercial License 4.0 (CC-BY-NC) and partly under Creative Commons Attribution License 4.0 (CC-BY)</strong>. Also the provied<strong> data set is licensed under these licenses.</strong> Concerning licensing of current Crunchbase data, we can refer to <a href="https://about.crunchbase.com/terms-of-service/">https://about.crunchbase.com/terms-of-service/</a>.</p> <p>For <strong>more information</strong> about the data set, see our paper <a href="http://dbis.informatik.uni-freiburg.de/content/team/faerber/papers/CrunchBaseWrapper_SWJ2017.pdf">A Linked Data Wrapper for CrunchBase.</a></p> <p>When you use the data set, please <strong>cite</strong> us as follows:</p> <blockquote> <p>Michael Färber, Carsten Menne, Andreas Harth. “A Linked Data Wrapper for CrunchBase”. In: Semantic Web Journal 9(4). IOS Press, 2018, pp. 505–5015. (<a href="https://dblp.org/rec/bibtex/journals/semweb/FarberMH18">BibTeX entry at DBLP</a>)</p> </blockquote>
Wikidata Dump Dump rdf
<p> RDF dump of wikidata produced with <a href="https://tools.wmflabs.org/wdumps/">wdumps</a>. </p> <p> <br> <a href="https://tools.wmflabs.org/wdumps/dump/1568">View on wdumper</a> </p> <p> <b>entity count</b>: 0, <b>statement count</b>: 0, <b>triple count</b>: 0 </p>
Resources of "Declarative RDF Graph Construction: Materialization or Virtualization?"
<p>Resources for the paper "Declarative RDF Graph Construction: Materialization or Virtualization?", currently under double-blind review, thus all information about the authors is redacted.</p> <ul> <li><code>GTFS-MADRID-BENCH.tar.xz</code>: GTFS Madrid Bench with scales 1, 25, 50</li> <li><code>BSBM.tar.xz</code>: BSBM benchmark with scales 5000, 10000, 15000</li> <li>SANTA: <ul> <li><code>PARALLEL-LOADING.tar.xz</code>: parallel loading parameter data with 1, 8, 16, 24, 32 cores.</li> <li><code>DUPLICATES.tar.xz</code>: duplicate values parameter data with 0%, 25%, 50%, 75% and 100% of the data containing duplicates.</li> <li><code>EMPTY-VALUES.tar.xz</code>: empty values parameter data with 0%, 25%, 50%, 75% and 100% of the data containing empty values.</li> <li><code>RAW-DATA.tar.xz</code>: scaling among data records & properties parameter data with 1000, 5000, 25000, 125000 records and 1, 5, 10, 15 properties.</li> <li><code>JOIN-1_1.tar.xz</code>: Join 1-1 data.</li> <li><code>JOIN-1-N.tar.xz</code>: Join 1-N data with relationship N: 3, 5, 10, 15.</li> <li><code>JOIN-N-1.tar.xz</code>: Join N-1 data with relationship N: 3, 5, 10, 15.</li> <li><code>JOIN-N-M.tar.xz</code>: Join N-M data with relationships N-M: 3-3, 3-5, 5-3, 10-5, 5-10.</li> <li><code>JOIN-DUPLICATES.tar.xz</code>: Join duplicates data with 0%, 25%, 50%, 75%, 100% of the joins generating duplicates.</li> <li><code>MAPPINGS.tar.xz</code>: Triples Maps & Predicate Object Maps mapping rules impact parameter data with 1 TM + 15 POMs, 15 TMs + 1 POM, 3 TMs + 5 POMs, 5 TMs + 3 POMs.</li> </ul> </li> <li>Graphs <ul> <li>GTFS-graphs.pdf: GTFS-Madrid-Bench query execution time graph in full resolution</li> <li>BSBM-graphs.pdf: BSBM query execution time graph in full resolution</li> </ul> </li> </ul> <p><strong>Note</strong>: These archives are heavily compressed, make sure you have enough disk space to unpack them.</p> <p>Our <a href="https://github.com/blindreviewing/eswc2023-materialization-virtualization">bench-executor tool</a> can execute these automatically for you, you need to set the <code>--root</code> parameter to the root directory of the unpacked archive and execute them with the <code>run</code> command or list them all with the <code>list</code> command. Extensive instructions on how to use the tool are available in the README of the linked repository.</p>
WikiPathways April 2023 Release - RDF data
<p>Archive of the WikiPathways April 2022 RDF data for all species as GPMLRDF and WPRDF (.ttl format). The data is licensed under the <a href="https://creativecommons.org/share-your-work/public-domain/cc0/">CCZero waiver</a>.</p>
VOYAGE: A Large Collection of Vocabulary Usage in Open RDF Datasets
<p><strong>List of files:</strong></p> <ul> <li>odps.json: for each of the accessed ODPs, its name, URL, API type, API URL, and the IDs of RDF datasets collected from it <ul> <li> <p>JSON structure: a list of objects, where each object contains the following attributes - 'name' (string), 'URL' (string), 'API type' (string), 'API URL' (string), and 'collected datasets IDs' (list of integers)</p> </li> </ul> </li> <li>datasets.json: for each of the crawled RDF datasets, its ID, title, description, author, license, dump file URLs, and PLDs <ul> <li> <p>JSON structure: a list of objects, where each object contains the following attributes - 'ID' (integer), 'title' (string), 'description' (string), 'author' (string), 'license' (string), 'dump file URLs' (list of strings), and 'PLDs' (list of strings)</p> </li> </ul> </li> <li>deduplicated_datasets.json: the IDs of the deduplicated RDF datasets and whether they are in the LOD Cloud <ul> <li> <p>JSON structure: a list of objects, where each object contains the following attributes - 'ID' (integer) and 'in LOD Cloud' (boolean)</p> </li> </ul> </li> <li>terms.json: the extracted classes, properties, and the IDs of RDF datasets using each term <ul> <li> <p>JSON structure: a list of objects, where each object contains the following attributes - 'term' (string), 'is class' (boolean), 'is property' (boolean), and 'used in dataset IDs' (list of integers)</p> </li> </ul> </li> <li>vocabularies.json: the extracted vocabularies, the classes and properties in each vocabulary, and the IDs of RDF datasets using each vocabulary <ul> <li> <p>JSON structure: a list of objects, where each object contains the following attributes - 'vocabulary' (string), 'classes' (list of strings), 'properties' (list of strings), and 'used in dataset IDs' (list of integers).</p> </li> </ul> </li> <li>edps.json: the extracted distinct EDPs and the IDs of RDF datasets using each EDP <ul> <li> <p>JSON structure: a list of objects, where each object contains the following attributes - 'classes' (list of strings), 'forward properties' (list of strings), 'backward properties' (list of strings), and 'used in dataset IDs' (list of integers)</p> </li> </ul> </li> <li>clusters.json: the clusters of vocabularies generated by MV-ITCC and LDA <ul> <li> <p>JSON structure: {"LDA": {"vocabularies": {VOCABULARY_CLUSTER_ID_1: [LIST_OF_VOCABULARIES], VOCABULARY_CLUSTER_ID_2: [LIST_OF_VOCABULARIES], ...}}, "MV-ITCC": {"vocabularies": {VOCABULARY_CLUSTER_ID_1: [LIST_OF_VOCABULARIES], VOCABULARY_CLUSTER_ID_2: [LIST_OF_VOCABULARIES], ...}, "dataset IDs": {DATASET_CLUSTER_ID_1: [LIST_OF_DATASET_IDS], DATASET_CLUSTER_ID_2: [LIST_OF_DATASET_IDS], ...}}}</p> </li> </ul> </li> </ul>
Results and log of LLM-KG-Bench runs described in article "Benchmarking the Abilities of Large Language Models for RDF Knowledge Graph Creation and Comprehension: How Well Do LLMs Speak Turtle?", Frey et al. 2023
<p>Results and log of LLM-KG-Bench runs described in article ""Benchmarking the Abilities of Large Language Models for RDF Knowledge Graph Creation and Comprehension: How Well Do LLMs Speak Turtle?", Frey et al. 2023, to appear in proceedings for workshop DL4KG@ISWC 2023.</p> <p>For data on task FactExtractStatic please contact authors.</p>
CLARA Knowledge Graph of licensed educational resources (using RDF-star, Standard reification, Singleton properties, or Named graphs)
<p><strong>CLARA</strong><br>This deposit is part of the <a href="https://project.inria.fr/clara/">CLARA project</a>. The CLARA project aims to empower teachers in the task of creating new educational resources. And in particular with the task of handling the licenses of reused educational resources.</p> <p>The present deposit contains the RDF files created using an RDF mapping (<a href="https://rml.io/">RML</a>) and a mapper (<a href="https://github.com/morph-kgc/morph-kgc">Morph-KGC</a>). It also contains the files JSON used as input. The corresponding pipeline can be found on <a href="https://gitlab.univ-nantes.fr/clara/pipeline">Gitlab</a>. The data used in that pipeline originate from <a href="https://www.x5gon.org/">X5GON</a>, a European project aiming to generate and gather open educational resources.</p> <p><strong>Knowledge graph content</strong><br>The present Knowledge Graph contains information about 45K Educational Resources (ERs) and 135K subjects (extracted from DBpedia).<br>That information contains </p> <ul> <li>the author,</li> <li>its title and description</li> <li>the license,</li> <li>a URL to the resource itself,</li> <li>the language of the ER,</li> <li>its mimetype,</li> <li>and finally which subject it talks about, and to what extent.</li> </ul> <p><br>That extent is given by two scores: a PageRank score and a Cosinus score.</p> <p>A particularity of the knowledge graph is its heavy use of RDF reification, across large multi-valued properties.<br>Thus four versions of the knowledge graph exist, using Standard reification, Singleton property, Named graphs, and RDF-star.</p> <p>The Knowledge Graph also contains <a href="https://databus.dbpedia.org/dbpedia/generic/categories">categories</a> originating from DBpedia. They help precise the subjects that are also extracted from DBpedia.</p> <p>The KG.zip files contain five types of files:</p> <ul> <li><strong>Authors_[</strong>X<strong>].nt</strong> - Those contain the authors' nodes, their type, and name.</li> <li><strong>ER_[</strong>X<strong>].nt/nq/ttl</strong> - Those contain the ERs and their information using the respective RDF reification model.</li> <li><strong>categories_skos_[</strong>X<strong>].ttl</strong> - Those contain the hierarchy of DBpedia categories.</li> <li><strong>categories_labels.ttl </strong>- This file contains additional information about the categories.</li> <li><strong>categories_article.ttl</strong> - This file contains the RDF triples that link the DBpedia subjects to the DBpedia categories.</li> </ul> <p> </p> <p><strong>JSON content</strong></p> <p>The original dataset was cut into multiple JSON files in order to make its processing easier. DBpedia categories were extracted as RDF and aren't present in the JSON files.<br><br>There are two types of files in the input-json.zip file:</p> <ul> <li><strong>authors_[</strong>X<strong>].json</strong> - Which lists the authors names</li> <li><strong>ER_[</strong>X<strong>].json</strong> - Which lists the ERs and their related information.<br>That information contains: <ul> <li>their <em>title.</em></li> <li>their <em>description.</em></li> <li>their <em>language</em> (and <em>language_detected</em>, only the first one is used in the pipeline here).</li> <li>their <em>license.</em></li> <li>their <em>mimetype.</em></li> <li>the <em>authors.</em></li> <li>the <em>date</em> of creation of the resource.</li> <li>a <em>url</em> linking to the resource itself.</li> <li>the subjects (named <em>concepts</em>) associated with the resource. With the corresponding scores.</li> </ul> </li> </ul> <p> </p> <p>If you do use this dataset, you can cite the corresponding paper:</p> <ul> <li>Kieffer, M., Fakih, G. & Serrano-Alvarado, P. (2023). Evaluating Reification with Multi-valued Properties in a Knowledge Graph of Licensed Educational Resources. Semantics, Leipzig, Germany.</li> </ul>
OpenAIRE ScholeXplorer data dump in RDF
<p>This dataset contains the RDF representation of OpenAIRE's ScholeXplore data dump from May 5th, 2019 (https://zenodo.org/record/2674330#.XrkfP6gzZPY).</p>
RDF Knowledge Graph SemOpenAlex-SemanticWeb
<p>SemOpenAlex-SemanticWeb is a subset of the RDF knowledge graph <a href="(https://semopenalex.org/">SemOpenAlex</a> (version from 2023-04-24) modeling the Semantic Web Community.</p><p>SemOpenAlex-SemanticWeb is a suitable database for creating heterogeneous graph machine learning datasets (e.g., used for GNN-based recommendation and similar tasks).</p><p>The RDF knowledge graph consists of 21,978,026 semantic RDF triples including the following entities: works (95,575 entities), semantic web authors (19,970 entities), concepts (38,050 entities), sources (10,739 entities), institutions (5,846 entities) and publishers (786 entities).</p><p>Besides the RDF knowledge graph, we provide TransE embeddings for entities and relations (see embeddings.zip).</p><p>More information can be found at <a href="https://github.com/davidlamprecht/semopenalex-semanticweb">https://github.com/davidlamprecht/semopenalex-semanticweb</a>.</p>
WikiPathways Nov 2020 Release - RDF data (ALL)
<p>Archive of the WikiPathways November 2020 RDF data for all species and all pathways as WPRDF (.ttl format, first unzip before using!). The data is licensed under the <a href="https://creativecommons.org/share-your-work/public-domain/cc0/">CCZero waiver</a>. This file contains data of the following pathway databases: Reactome, LIPID MAPS, WikiPathways.</p>
The results of direct mappings of panama paper to RDF and RDF-star
<p>In the paper 'Converting Property Graphs to RDF: A Preliminary Study of the Practical Impact of Different Mappings', we have used mappings introduced by Nguyen et al. [1], and Hartig [2]. Data sets are the results of direct mappings of the real-world LPG with data about the Panama Papers[3]. </p> <p>Shahrzad Khayatbashi, Sebastián Ferrada, and Olaf Hartig. 2022. Converting Property Graphs to RDF: A Preliminary Study of the Practical Impact of Different Mappings. In Joint Workshop on Graph Data Management Experiences & Systems (GRADES) and Network Data Analytics (NDA) (GRADES & NDA’22), June 12, 2022, Philadelphia, PA, USA. ACM, New York, NY, USA, 8 pages. https://doi.org/10.1145/3534540.3534695</p> <p> </p> <p>References:</p> <p>[1] Vinh Nguyen, Hong Yung Yip, Harsh Thakkar, Qingliang Li, Evan Bolton, and Olivier Bodenreider. 2019. Singleton Property Graph: Adding A Semantic Web Abstraction Layer to Graph Databases. In Proceedings of the Blockchain enabled Semantic Web Workshop (BlockSW) and Contextualized Knowledge Graphs (CKG) Workshop.</p> <p>[2] Olaf Hartig. 2017. Foundations of RDF* and SPARQL*: An Alternative Approach to Statement-Level Metadata in RDF. In Proceedings of the 11th Alberto Mendelzon International Workshop on Foundations of Data Management and the Web (AMW).</p> <p>[3] https://offshoreleaks.icij.org/pages/database</p>
PRISMS RDF dump
<p>RDF dump of the entire PRISMS knowledge graph in N-Quads format. <a href="https://www.prisms.digital/">www.prisms.digital</a></p>
Fig. 9 Diagnostic and putative RDF fragments. a DIP-V-16113 in Ornamental feathers in Cretaceous Burmese amber: resolving the enigma of rachis-dominated feather structure
Fig. 9 Diagnostic and putative RDF fragments. a DIP-V-16113 overview and close-up of the tip of the feather (inset); b DIP-V-17232 overview with prominent rachidial ridge and deep C-shaped rachis; c DIP-V-15130 overview; d DIP-V-15137 overview; e DIP-V-15141 overview; f DIP-V-15142 overview; g DIP-V-15159 overview; h DIP-V-16178 overview, arrowheads mark lateral margins of rachis at both ends. Scale bars = 2 mm in (a and h); 1 mm in (b); 5 mm in (c–g)
Fig. 8 Diagnostic RDF fragments. a DIP-V-15125 in Ornamental feathers in Cretaceous Burmese amber: resolving the enigma of rachis-dominated feather structure
Fig. 8 Diagnostic RDF fragments. a DIP-V-15125 overview, arrowhead marks twist in rachis; b details of barbs and barbules in DIP-V-15125; c DIP- V-16115 overview; d DIP-V-17121 overview; e detail of DIP-V-17121 rachis and barbs, where they are cross-cut by polished surface of amber (left margin of d), arrow indicates rachidial ridge; f DIP-V-17138 overview; g DIP-V-17127 feather section; h DIP-V-16207 overview. Scale bars = 1 mm in (a); 0.5 mm in (b); 5 mm in (c, d and g); 0.2 mm in (e); 2 mm in (f and h)
Lithuania RDF assets
<p>These files are the result of the data conversion efforts made during the Open Government Intelligence Project. Specifically, these files correspond to the Lithuanian Pilot. </p> <p> </p> <p> </p>
WikiPathways June 2019 Release - GPML and RDF files
<p>Archive of the WikiPathways June 2019 GPML files for <em>Homo sapiens</em> and the GPMLRDF and WPRDF translations. The data is available as CCZero.</p>
Bibliographie du genre romanesque français 1751-1800 - RDF model
<p>RDF model of a bibliography of French novels from the 18th century:</p> <p>Martin, Angus; Mylne, Vivienne; Frautschi, Richard (1977): Bibliographie du genre romanesque français 1751–1800. London, Paris: Mansell, France expansion.</p> <p>To show possibilities of interlinking, some authors are linked to VIAF.</p> <p> </p>
Linked Papers With Code: The Latest in Machine Learning as an RDF Knowledge Graph
<p><strong>Linked Papers With Code (LPWC)</strong> is an <strong>RDF knowledge graph </strong>that comprehensively models the research field of <strong>machine learning</strong>. It contains information about almost 400,000 machine learning <strong>publications</strong>, including the <strong>tasks</strong> addressed, the <strong>datasets</strong> utilized, the <strong>methods</strong> implemented, and the <strong>evaluations</strong> conducted, along with their <strong>results</strong>. The data set is based on <strong>Papers With Code</strong> and licensed under the CC BY-SA 4.0 license. Furthermore, we provide <strong>knowledge graph embeddings</strong> for entities and relations represented in LPWC.</p><p>More information can be found at <a href="https://linkedpaperswithcode.com/"><strong>https://linkedpaperswithcode.com/</strong></a> and in the ISWC'23 publication <a href="https://linkedpaperswithcode.com/"><strong>"</strong></a><a href="https://aifb.kit.edu/web/Inproceedings3993"><strong>Linked Papers With Code: The Latest in Machine Learning as an RDF Knowledge Graph".</strong></a></p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.