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100 results for “RDF”
RDF Representation of RNA Metabolism Evolution data - version 3 (diagrammed in https://zenodo.org/deposit/47641/)
<p>Version 3 (replaces http://doi.org/10.5281/zenodo.50496)<br> <br> Protein complexes involved in RNA Metabolism; individual proteins and their orthologues through a wide range of fungal species spanning much of the kingdom (using yeast as the primary seed for orthology search, and using the EMBL-EBI orthologue database to identify orthologues). For each family of orthologues, the protein domain structure is determined, and then the presence/absence of that domain is evaluated in each of the species. The data is presented in RDF, and is visualized in the form of Heat Maps in http://doi.org/10.5281/zenodo.47641</p>
Anwendungsprofil zur RDF-Serialisierung der Entwurfsversion des OstData-Metadatenschemas
<p>Dieses SHACL-basierte Anwendungsprofil legt Klassen und Eigenschaften aus der SPAR DataCite Ontology, DCAT (Data Catalog Vocabulary) und FaBiO (FRBR-aligned Bibliographic Ontology) zur RDF-Serialisierung der Entwurfsversion 1.0 des OstData-Metadatenschemas fest. Die Möglichkeit zur Integration von Daten aus Forschungsinformationssystemen wird durch Einsatz der FRAPO (Funding, Research Administration and Projects Ontology) demonstriert. Im wesentlichen sind in diesem Entwurf des Anwendungsprofils nur die Pflichtfelder berücksichtigt. Ggf. wurden Angaben verfeinert, um sie auf Elemente der nachgenutzten RDF-Ontologien abbilden zu können (z. B. project.time.start und project.time.end), oder exemplarisch ergänzt (z. B. project.acronym). OstData-interne Felder für administrative Metadaten wurden nicht in das Anwendungsprofil übernommen.</p>
OpenCitations Meta RDF dataset of agent roles metadata and its provenance information
<p>This dataset is a specialized subset of the OpenCitations Meta RDF data, focusing exclusively on data related to <strong>agent roles</strong> of bibliographic resources<strong> </strong>(<a href="http://purl.org/spar/pro/RoleInTime" target="_blank" rel="noopener">http://purl.org/spar/pro/RoleInTime</a>). These agents can be authors, editors, or publishers. It contains all the metadata and its provenance information, structured specifically around agent roles, in JSON-LD format.</p> <p>The inner folders are named through the <strong>supplier prefix</strong> of the contained entities. It is a prefix that allows you to recognize the entity membership index (e.g., OpenCitations Meta corresponds to <strong>06*0</strong>).</p> <p>After that, the folders have <strong>numeric names</strong>, which refer to the range of contained entities. For example, the 10000 folder contains entities from 1 to 10000. Inside, you can find the <strong>zipped </strong>RDF data.</p> <p>At the same level, additional folders containing the <strong>provenance </strong>are named with the same criteria already seen. Then, the 1000 folder includes the provenance of the entities from 1 to 1000. The provenance is located inside a folder called <strong>prov</strong>, also in zipped JSON-LD format.</p> <p>For example, data related to the entity is located in the folder /ar/06250/10000/1000/1000.zip, while information about provenance in /ar/06250/10000/1000/prov/se.zip</p> <p>Additional information about OpenCitations Meta at the <a href="https://opencitations.net/meta" target="_blank" rel="noopener">official webpage</a>.</p>
OpenCitations Meta RDF dataset of page numbers metadata and its provenance information
<p>This dataset is a specialized subset of the OpenCitations Meta RDF data, focusing exclusively on data related to <strong>page numbers</strong> of bibliographic resources, known as <strong>manifestations </strong>(<a href="http://purl.org/spar/fabio/Manifestation" target="_new">http://purl.org/spar/fabio/Manifestation</a>). It contains all the bibliographic metadata and its provenance information, structured specifically around manifestations (page numbers), in JSON-LD format.</p> <p>The inner folders are named through the <strong>supplier prefix</strong> of the contained entities. It is a prefix that allows you to recognize the entity membership index (e.g., OpenCitations Meta corresponds to <strong>06*0</strong>).</p> <p>After that, the folders have <strong>numeric names</strong>, which refer to the range of contained entities. For example, the 10000 folder contains entities from 1 to 10000. Inside, you can find the <strong>zipped </strong>RDF data.</p> <p>At the same level, additional folders containing the <strong>provenance </strong>are named with the same criteria already seen. Then, the 1000 folder includes the provenance of the entities from 1 to 1000. The provenance is located inside a folder called <strong>prov</strong>, also in zipped JSON-LD format.</p> <p>For example, data related to the entity is located in the folder /br/06250/10000/1000/1000.zip, while information about provenance in /br/06250/10000/1000/prov/se.zip</p> <p>Additional information about OpenCitations Meta at the <a href="https://opencitations.net/meta" target="_blank" rel="noopener">official webpage</a>.</p>
OpenCitations Meta RDF dataset of identifiers metadata and its provenance information
<p>This dataset is a specialized subset of the OpenCitations Meta RDF data, focusing exclusively on data related to <strong>identifiers </strong>(<a href="http://purl.org/spar/datacite/Identifier" target="_blank" rel="noopener">http://purl.org/spar/datacite/Identifier</a>) of bibliographic resources. It contains all the metadata and its provenance information, structured specifically around identifiers, in JSON-LD format.</p> <p>The inner folders are named through the <strong>supplier prefix</strong> of the contained entities. It is a prefix that allows you to recognize the entity membership index (e.g., OpenCitations Meta corresponds to <strong>06*0</strong>).</p> <p>After that, the folders have <strong>numeric names</strong>, which refer to the range of contained entities. For example, the 10000 folder contains entities from 1 to 10000. Inside, you can find the <strong>zipped </strong>RDF data.</p> <p>At the same level, additional folders containing the <strong>provenance </strong>are named with the same criteria already seen. Then, the 1000 folder includes the provenance of the entities from 1 to 1000. The provenance is located inside a folder called <strong>prov</strong>, also in zipped JSON-LD format.</p> <p>For example, data related to the entity is located in the folder /id/06250/10000/1000/1000.zip, while information about provenance in /id/06250/10000/1000/prov/se.zip</p> <p>Additional information about OpenCitations Meta at the <a href="https://opencitations.net/meta" target="_blank" rel="noopener">official webpage</a>.</p>
OpenCitations Meta RDF dataset of bibliographic resources metadata and its provenance information
<div> <p>This dataset is a specialized subset of the OpenCitations Meta RDF data, focusing exclusively on data related to <strong>bibliographic resources </strong>(<a href="http://purl.org/spar/fabio/Expression" target="_blank" rel="noopener">http:///purl.org/spar/fabio/Expression</a>). It contains all the metadata and its provenance information, structured specifically around bibliographic resources, in JSON-LD format.</p> <p>The inner folders are named through the <strong>supplier prefix</strong> of the contained entities. It is a prefix that allows you to recognize the entity membership index (e.g., OpenCitations Meta corresponds to <strong>06*0</strong>).</p> <p>After that, the folders have <strong>numeric names</strong>, which refer to the range of contained entities. For example, the 10000 folder contains entities from 1 to 10000. Inside, you can find the <strong>zipped </strong>RDF data.</p> <p>At the same level, additional folders containing the <strong>provenance </strong>are named with the same criteria already seen. Then, the 1000 folder includes the provenance of the entities from 1 to 1000. The provenance is located inside a folder called <strong>prov</strong>, also in zipped JSON-LD format.</p> <p>For example, data related to the entity is located in the folder /br/06250/10000/1000/1000.zip, while information about provenance in /br/06250/10000/1000/prov/se.zip</p> <p>Additional information about OpenCitations Meta at the <a href="https://opencitations.net/meta" target="_blank" rel="noopener">official webpage</a>.</p> <p> </p> </div>
Weather and Air Quality data for Ireland as RDF data cube
<p>Weather, Air Pollution and Events data represented as RDF data cube. The original weather data has been downloaded from https://www.met.ie//climate/available-data/historical-data and the Air Quality data from <a href="https://discomap.eea.europa.eu/map/fme/AirQualityExport.htm">https://discomap.eea.europa.eu/map/fme/AirQualityExport.htm</a> and <a href="https://discomap.eea.europa.eu/map/fme/AirQualityExportAirbase.htm">https://discomap.eea.europa.eu/map/fme/AirQualityExportAirbase.htm</a>. The Events data refers to random events within the Republic of Ireland.</p> <p>The data has then been uplifted by running the {eeaMapping, metMapping, eventsMapping}.py scripts, which generate R2RML mappings to convert the CSV data to RDF. The mappings re-use vocabularies and ontologies that are W3C recommendations for dataset descriptions (DCAT, https://www.w3.org/TR/vocab-dcat-2/), statistical data (RDF Data Cube, https://www.w3.org/TR/vocab-data-cube/) and provenance data (PROV-O, https://www.w3.org/TR/prov-o/). The scripts use the R2RML engine from https://github.com/chrdebru/r2rml to execute the mappings which generate a data and metadata files for each of the datasets.</p> <p> </p>
RDF Reification Benchmark (REF) using the Biomedical Knowledge Repository (BKR)
<p>This resource can be used for benchmarking different RDF modelling solutions for statement-level metadata, namely: </p> <p>- RDF Reification,</p> <p>- Singleton Property,</p> <p>- RDF* (RDF-star). </p> <p> </p> <p>More details about this resource can be found in the following publication:</p> <p>Fabrizio Orlandi, Damien Graux, Declan O'Sullivan, "Benchmarking RDF Metadata Representations: Reification, Singleton Property and RDF*", <em>15th IEEE International Conference on Semantic Computing (ICSC)</em>, 2021.</p> <p>Pre-print available at: http://fabriziorlandi.net/pdf/2021/ICSC2021_REF-Benchmark.pdf</p> <p> </p> <p>The dataset contains 3 different versions of the Biomedical Knowledge Repository (BKR) knowledge graph, as described in:</p> <p>Vinh Nguyen, Olivier Bodenreider, Amit Sheth. "Don't Like RDF Reification? Making Statements About Statements Using Singleton Property" WWW 2014, doi: 10.1145/2566486.2567973.</p> <p>and,</p> <p>Satya S. Sahoo, Olivier Bodenreider, Pascal Hitzler, Amit Sheth and Krishnaprasad Thirunarayan. "Provenance Context Entity (PaCE): Scalable Provenance Tracking for Scientific RDF Data" in Sci Stat Database Manag. 2010; 6187: 461–470. doi: 10.1007/978-3-642-13818-8_32</p> <p> </p> <p>The 3 knowledge graphs dumps are packaged as Gzipped RDF files in Turtle (and Turtle*) syntax. </p> <p>BKR-R-fullKGdump.ttl.gz for the Reification method,</p> <p>BKR-S-fullKGdump.ttl.gz for the Singleton method,</p> <p>BKR-star-fullKGdump.ttls.gz for the RDF* (RDF-star) method.</p> <p> </p> <p>The RDF REiFication Benchmark (REF) includes also a set of SPARQL (and SPARQL*) queries that can be used to compare the performance of different triplestores.</p> <p>Details about the SPARQL queries, and the queries themselves, are included in the "REF-Benchmark.tar.gz" archive. The queries are named after the dataset they are designed for (BKR-R or BKR-S or BKR-star), plus they include a letter identifying the query set, and a query number. </p> <p>E.g. the query in the file "BKR-R_F-Q3.rq" is for the BKR-R (standard reification) dataset, it is part of the query set "F" and it is the number 3 of that set "F". Hence, the same query, but translated for the RDF* dataset in SPARQL* syntax, is contained in "BKR-star_F-Q3.rq".</p> <p>Sets "A" and "B" are derived from the queries introduced by V. Nguyen et al. in: "Don't Like RDF Reification? Making Statements About Statements Using Singleton Property" WWW 2014, doi: 10.1145/2566486.2567973. Set "F" has been designed more with RDF* in mind as part of this benchmark (see [Orlandi et al., ICSC 2021]) </p> <p> </p> <p> </p> <p> </p>
RDF version of the data from Choi, JS., Ha, M.K., Trinh, T.X. et al. Towards a generalized toxicity prediction model for oxide nanomaterials using integrated data from different sources. Sci Rep 8, 6110 (2018)
<p>RDF version of the data from Choi, JS., Ha, M.K., Trinh, T.X. et al. Towards a generalized toxicity prediction model for oxide nanomaterials using integrated data from different sources. Sci Rep 8, 6110 (2018)</p>
SeaLiT Knowledge Graphs - Maritime History Data in RDF using a CIDOC-CRM extension (SeaLiT Ontology)
<p><strong>SeaLiT Knowledge Graphs</strong> is an RDF dataset of maritime history data that has been transcribed (and then transformed) from original archival sources in the context of the <a href="http://www.sealitproject.eu/">SeaLiT Project</a> (Seafaring Lives in Transition, Mediterranean Maritime Labour and Shipping, 1850s-1920s). The underlying data model is the <a href="https://zenodo.org/record/5964240">SeaLiT Ontology</a>, an extension of the ISO standard <strong>CIDOC-CRM</strong> (ISO 21127:2014) for the modelling and integration of maritime history information. </p> <p>The knowledge graphs integrate data of totally 16 different types of archival sources:</p> <ul> <li>Crew Lists <ul> <li>Crew and displacement list (Roll)</li> <li>Crew List (Ruoli di Equipaggio)</li> <li>General Spanish Crew List</li> </ul> </li> <li>Registers / Lists <ul> <li>Students Register</li> <li>Civil Register</li> <li>Register of Maritime Personnel</li> <li>Register of Maritime Workers (Matricole della gente di mare)</li> <li>Sailors Register (Libro de registro de marineros)</li> <li>Naval Ship Register List</li> <li>Seagoing Personnel</li> <li>Lists of ships</li> </ul> </li> <li>Censuses <ul> <li>Census La Ciotat</li> <li>First National all-Russian Census of the Russian Empire</li> </ul> </li> <li>Payrolls <ul> <li>Payrolls of private archives and libraries in Greece</li> <li>Payrolls of Russian Steam Navigation and Trading Company</li> </ul> </li> <li>Employment records <ul> <li>Shipyards of Messageries Maritimes, La Ciotat</li> </ul> </li> </ul> <p>More information about the archival sources are available through the <a href="https://sealitproject.eu/dictionary-of-source-types-list">SeaLiT website</a>. Data exploration applications over these sources are also publicly available (<a href="https://catalogues.sealitproject.eu/">SeaLiT Catalogues</a>, <a href="http://rs.sealitproject.eu/">SeaLiT ResearchSpace</a>). </p> <p>Data from these archival sources has been transcribed in tabular form and then curated by historians of SeaLiT using the <a href="https://www.ics.forth.gr/isl/fast-cat">FAST CAT</a> system. The transcripts (records), together with the curated vocabulary terms and entity instances (ships, persons, locations, organizations), are then transformed to RDF using the SeaLiT Ontology as the target (domain) model. To this end, the corresponding schema mappings between the original schemata and the ontology were defined using the <a href="https://github.com/isl/x3ml">X3ML</a> mapping definition language, that were subsequently used for delivering the RDF datasets. </p> <p>More information about the FAST CAT system and the data transcription, curation and transformation processes can be found in the following paper:</p> <blockquote> <p>P. Fafalios, K. Petrakis, G. Samaritakis, K. Doerr, A. Kritsotaki, Y. Tzitzikas, M. Doerr, "FAST CAT: Collaborative Data Entry and Curation for Semantic Interoperability in Digital Humanities", ACM Journal on Computing and Cultural Heritage, 2021. <a href="https://doi.org/10.1145/3461460">https://doi.org/10.1145/3461460</a> [<a href="http://users.ics.forth.gr/~fafalios/files/pubs/fafaliosJOCCH2021.pdf">pdf</a>, <a href="http://users.ics.forth.gr/~fafalios/files/bibs/fafaliosJOCCH2021.bib">bib</a>]</p> </blockquote> <p>The RDF dataset is provided as a set of TriG files per record per archival source. For each record, the dataset provides: i) one trig file for the record's data (<em>records.trig</em>), ii) one trig file for the record's (curated) vocabulary terms (<em>vocabularies.trig</em>), and iii) four trig files for the record's (curated) entity instances (<em>ships.trig, persons.trig, persons.trig, organizations.trig</em>).</p> <p>We also provide the RDFS files of the used ontologies (SeaLiT Ontology verson 1.0, CIDOC-CRM version 7.1.1). </p>
NUTS-RDF in GeoSPARQL
<p>The <a href="https://ec.europa.eu/eurostat/web/nuts/background">NUTS</a> (Nomenclature of territorial units for statistics classification) representing the regions in the EU have been converted from NeoGeo to GeoSPARQL structures using a Construct SPARQL query. The purpose of the conversion is to enable <a href="https://opengeospatial.github.io/ogc-geosparql/geosparql11/spec.html">GeoSPARQL spatial reasoning features</a> for geometry-based queries.</p> <p>The nuts-rdf file used as input in the conversion process is http://nuts.geovocab.org/data/0.91/nuts-rdf-0.91.ttl, which contains a dump from all the regions in the EU defined in 2013.</p> <p>The nuts-rdf file has been uploaded to a triplestore, and then, the following Construct SPARQL query was run to generate the EU-nuts-rdf-geosparql.ttl file.</p> <p>PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#><br> PREFIX nuts: <http://nuts.geovocab.org/id/><br> PREFIX ngeo: <http://geovocab.org/geometry#><br> PREFIX geo: <http://www.w3.org/2003/01/geo/wgs84_pos#><br> PREFIX geosparql: <http://www.opengis.net/ont/geosparql#><br> PREFIX dc: <http://purl.org/dc/elements/1.1/><br> PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#><br> PREFIX ramon: <http://rdfdata.eionet.europa.eu/ramon/ontology/></p> <p>CONSTRUCT { <br> ?entity a geosparql:Feature ;<br> geosparql:hasGeometry ?entityGeo ;<br> rdfs:label ?label ;<br> ramon:name ?name ;<br> ramon:level ?level ;<br> ramon:code ?code .<br> ?entityGeo a geosparql:Geometry ;<br> dc:rights ?entityrights ;<br> geosparql:asWKT ?polygon .<br> }<br> WHERE {<br> {<br> SELECT ?entity (GROUP_CONCAT(?lonlatpair; separator=", ") AS ?lonlatlist)<br> WHERE {<br> {<br> SELECT ?entity ?list<br> WHERE{<br> ?entity ngeo:geometry/ngeo:exterior/ngeo:posList ?list .<br> }<br> }<br> ?list rdf:rest* [ rdf:first ?item ] .<br> ?item geo:long ?lon ;<br> geo:lat ?lat .<br> BIND(CONCAT(?lon, " ", ?lat) AS ?lonlatpair)<br> }<br> GROUP BY ?entity<br> }<br> # Get entity descriptors<br> ?entity ngeo:geometry/dc:rights ?entityrights .<br> ?entity rdfs:label ?label ;<br> ramon:name ?name ;<br> ramon:level ?level ;<br> ramon:code ?code .<br> # Define uri for the geometry<br> BIND(IRI(CONCAT(STR(?entity), "-geo")) AS ?entityGeo)<br> BIND(STRDT(CONCAT("POLYGON ((",STR(?lonlatlist), "))"), geosparql:wktLiteral)AS ?polygon)<br> }</p>
List.MID: A MIDI-Based Benchmark for Evaluating RDF Lists
<p>Linked lists represent a countable number of ordered values, and are among the most important abstract data types in computer science. With the advent of RDF as a highly expressive knowledge representation language for the Web, various implementations for RDF lists have been proposed. Yet, there is no benchmark so far dedicated to evaluate the performance of triple stores and SPARQL query engines on dealing with ordered linked data. Moreover, essential tasks for evaluating RDF lists, like generating datasets containing RDF lists of various sizes, or generating the same RDF list using different modelling choices, are cumbersome and unprincipled. In this paper, we propose List.MID, a systematic benchmark for evaluating systems serving RDF lists. List.MID consists of a dataset generator, which creates RDF list data in various models and of different sizes; and a set of SPARQL queries. The RDF list data is coherently generated from a large, community-curated base collection of Web MIDI files, rich in lists of musical events of arbitrary length. We describe the List.MID benchmark, and discuss its impact and adoption, reusability, design, and availability.</p>
RDF Linked Data representation of GC-MS data from the 'Rose Genome' article published in Nature genetics, June, 2018
<p>This dataset corresponds to the RDF Linked Data representation of the measurements of 61 known metabolites (all annotated with resolvable CHEBI identifiers and InChi strings), measured by gas chromatography mass-spectrometry (GC-MS) in 6 different Rose cultivars (all annotated with resolvable NCBITaxonomy Identifiers) and 3 organism parts (all annotated with resolvable Plant Ontology identifiers). The quantitation types are annotated with resolvable <a href="https://github.com/ISA-tools/stato">STATO</a> terms. Most of the semantics resources belong to the <a href="http://obofoundry.org">OBO foundry</a>.</p> <p>The transformation to RDF was performed on a Frictionless Tabular Data Package (<a href="https://frictionlessdata.io/specs/tabular-data-package/">https://frictionlessdata.io/specs/tabular-data-package/)</a>, holding the data extracted from a supplementary material table, available from <a href="https://static-content.springer.com/esm/art%3A10.1038%2Fs41588-018-0110-3/MediaObjects/41588_2018_110_MOESM3_ESM.zip">https://static-content.springer.com/esm/art%3A10.1038%2Fs41588-018-0110-3/MediaObjects/41588_2018_110_MOESM3_ESM.zip</a> and published alongside the Nature Genetics manuscript identified by the following doi: <a href="https://doi.org/10.1038/s41588-018-0110-3">https://doi.org/10.1038/s41588-018-0110-3</a>, published in June 2018. This supplementary material table was deposited to Zenodo and is identified by the following doi: <a href="https://doi.org/10.5281/zenodo.2598799">https://doi.org/10.5281/zenodo.2598799</a></p> <p>This dataset is used to demonstrate how to make data Findable, Accessible, Discoverable and Interoperable (FAIR) and how Frictionless Tabular Data Package representations can be easily mobilised for reanalysis and data science.</p> <p>It is associated to the following project: <a href="https://github.com/proccaserra/rose2018ng-notebook">https://github.com/proccaserra/rose2018ng-notebook</a> with all the necessary information, executable code and tutorials in the form of Jupyter notebooks.</p>
RDF version of the data from Hagar I. Labouta et al. Meta-Analysis of Nanoparticle Cytotoxicity via Data-Mining the Literature. NanoImpact (2019)
<p>This is an RDFied version of the dataset published by Hagar I. Labouta et al. Meta-Analysis of Nanoparticle Cytotoxicity via Data-Mining the Literature. NanoImpact (2019).</p> <p>The original dataset publication DOI: <a href="https://doi.org/10.1021/acsnano.8b07562">https://doi.org/10.1021/acsnano.8b07562</a></p> <p>The Original publication authors: Hagar I. Labouta, Nasimeh Asgarian, Kristina Rinker, and David T. Cramb</p>
RDF version of the data from Choi, JS. et al. Towards a generalized toxicity prediction model for oxide nanomaterials using integrated data from different sources (2018)
<p>This is an RDFied version of the dataset published in Choi, JS., Ha, M.K., Trinh, T.X. et al. Towards a generalized toxicity prediction model for oxide nanomaterials using integrated data from different sources. Sci Rep 8, 6110 (2018)</p> <p>The original dataset publication DOI: <a href="https://doi.org/10.1038/s41598-018-24483-z">https://doi.org/10.1038/s41598-018-24483-z</a></p> <p>The Original publication authors: Jang-Sik Choi, My Kieu Ha, Tung Xuan Trinh, Tae Hyun Yoon & Hyung-Gi Byun</p>
RDF version of the data from Anastasios G. et al. Computational enrichment of physicochemical data for the development of a zeta-potential read-across predictive model with Isalos Analytics Platform. NanoImpact (2021).
<p>This is an RDFied version of the dataset published by Anastasios G. et al. Computational enrichment of physicochemical data for the development of a zeta-potential read-across predictive model with Isalos Analytics Platform. NanoImpact (2021).</p> <p>The original dataset publication DOI: <a href="https://doi.org/10.1016/j.impact.2021.100308">https://doi.org/10.1016/j.impact.2021.100308</a></p> <p>The Original publication authors: Anastasios G. Papadiamantis, Antreas Afantitis, Andreas Tsoumanis, Eugenia Valsami-Jones, Iseult Lynch, Georgia Melagraki</p>
RDF version of the data from Saarimaki et al. Manually curated transcriptomics data collection for toxicogenomic assessment of engineered nanomaterials (Version 1.0.0) [Zenodo Dataset] (2020)
<p>This is an RDFied version of the dataset published by Saarimaki et al. Manually curated transcriptomics data collection for toxicogenomic assessment of engineered nanomaterials (Version 1.0.0) [Zebodo Dataset] (2020)</p> <p>The original dataset publication DOI: <a href="http://doi.org/10.5281/zenodo.4146981">http://doi.org/10.5281/zenodo.4146981</a></p> <p>The Original publication authors: Saarimaki, Laura Aliisa, Federico, Antonio, Lynch, Iseult, Papadiamantis, Anastasios G., Tsoumanis, Andreas, Melagraki, Georgia, Afantitis, Antreas, Serra, Angela, & Greco, Dario</p>
RDF version of the data from Anastasios G. Papadiamantis et al. Predicting Cytotoxicity of Metal Oxide Nanoparticles Using Isalos Analytics Platform (2020)
<p>This is an RDFied version of the dataset published in Papadiamantis, A.G. et al. Predicting Cytotoxicity of Metal Oxide Nanoparticles Using Isalos Analytics Platform. <em>Nanomaterials</em> <strong>2020</strong>, <em>10</em>, 2017.</p> <p>The original dataset publication DOI: <a href="https://doi.org/10.3390/nano10102017">https://doi.org/10.3390/nano10102017</a></p> <p>The Original publication authors: Papadiamantis, A.G.; Jänes, J.; Voyiatzis, E.; Sikk, L.; Burk, J.; Burk, P.; Tsoumanis, A.; Ha, M.K.; Yoon, T.H.; Valsami-Jones, E.; Lynch, I.; Melagraki, G.; Tämm, K.; Afantitis, 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>
Flowchart on the methodology used to conduct the literature search on provenance representation models and change-tracking in RDF
<p>The image contains a flowchart on the methodology used to conduct the literature search on provenance representation models and change-tracking in RDF. This methodology was used in the Abstract submitted to the DH2023 conference in Graz.</p>
ScienceDex guides
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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.