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915 results for “Graph”
Using Bidirected Graphs to Map Keywords
<p>This study attempts to demonstrate the significance of considering two-way relationships by proposing a keyword network formed using bidirected graphs and association rules to examine the two-way relationship of two or more keywords. A web application to visualize is accessible at <a href="http://www.coconut-libtool.com">www.coconut-libtool.com</a></p>
List of articles resulting from the Google Scholar search "graph based author name disambiguation" published after 1/1/2021
<p>This dataset contains the list of articles resulting from the Google Scholar search “graph based author name disambiguation” published after 1/1/2021. The list is provided for reproducibility of the survey article “Graph-based Methods for Author Name Disambiguation: A Survey” and it was obtained using the following Python script available at <a href="https://github.com/WittmannF/sort-google-scholar">https://github.com/WittmannF/sort-google-scholar</a>:</p> <blockquote> <p>$ python sortgs.py --kw “graph based author name disambiguation” --startyear 2021</p> </blockquote> <p>The command returned the CSV file that contains the first 94 publications matching the query (articles with corrupted metadata have been excluded), each with metadata about Title, Number of Citations, and Rank. The CSV contains a column that specified which articles have been eventually selected for the survey.</p>
Dataset: an overview of knowledge graphs in NFDI
<p>This dataset contains a list of knowledge graphs (KGs), KG software, KG publications and KG use cases in context of NFDI (German National Research Data Infrastructure). The related manuscript is submitted to a poster session at the <em>1st Conference on Research Data Infrastructure - Connecting Communities, </em>12. – 14. September 2023, Karlsruhe, Germany: 'Who is using Knowledge Graphs in NFDI? An overview by the Working Group "Knowledge Graphs"'.</p>
PheKnowLator Human Disease Knowledge Graph Benchmarks -- v1.0.0
<p><strong>PKT Human Disease Knowledge Graph Benchmark Builds (v1.0.0)</strong></p><p><strong>Build Date: September 03, 2019</strong></p><p>The KG Benchmark Builds can also be downloaded from Zenodo:<br>👉 <strong>KGs:</strong> <a href="https://doi.org/10.5281/zenodo.7030200">https://doi.org/10.5281/zenodo.7030200</a><br>👉 <strong>Embeddings:</strong> <a href="https://zenodo.org/record/7030189">https://zenodo.org/record/7030189</a></p><p> </p><p><strong>Required Input Documents</strong></p><ul><li>resource_info.txt</li><li>class_source_list.txt</li><li>instance_source_list.txt</li><li>ontology_source_list.txt</li></ul><p> </p><p><strong>Data</strong></p><p><strong>Data Download Date:</strong> November 30, 2018</p><p><i><strong>Ontologies</strong></i></p><ul><li><a href="http://purl.obolibrary.org/obo/go.owl">Gene Ontology</a></li><li><a href="http://purl.obolibrary.org/obo/hp.owl">Human Phenotype Ontology</a></li></ul><p><i><strong>Classes</strong></i></p><ul><li><a href="http://purl.obolibrary.org/obo/doid.owl">Human Disease Ontology</a></li><li><a href="http://geneontology.org/gene-associations/goa_human.gaf.gz">Gene Ontology: gene associations</a></li><li><a href="https://reactome.org/download/current/gene_association.reactome">Reactome: gene associations</a></li><li><a href="http://compbio.charite.de/jenkins/job/hpo.annotations.monthly/lastStableBuild/artifact/annotation/ALL_SOURCES_ALL_FREQUENCIES_genes_to_phenotype.txt">Human Phenotype Ontology: all source annotations - genes to phenotypes</a></li><li><a href="http://compbio.charite.de/jenkins/job/hpo.annotations.monthly/lastSt">Human Phenotype Ontology: all source annotations - diseases to genes to phenotypes</a></li></ul><p><i><strong>Instances</strong></i></p><ul><li><a href="http://ctdbase.org/reports/CTD_chem_gene_ixns.tsv.gz">CTD: chemicals-genes</a></li><li><a href="http://ctdbase.org/reports/CTD_chem_pathways_enriched.tsv.gz">CTD: chemicals-pathways</a></li><li><a href="http://ctdbase.org/reports/CTD_chemicals_diseases.tsv.gz">CTD: chemicals-diseases</a></li><li><a href="http://ctdbase.org/reports/CTD_genes_pathways.tsv.gz">CTD: genes-pathways</a></li><li><a href="http://ctdbase.org/reports/CTD_diseases_pathways.tsv.gz">CTD: diseases-pathways</a></li><li><a href="https://stringdb-static.org/download/protein.links.v10.5/9606.protein.links.v10.5.txt.gz">STRING DB: Proteins</a></li><li><a href="https://string-db.org/mapping_files/entrez_mappings/entrez_gene_id.vs.string.v10.28042015.tsv">String DB: entrez gene mappings</a></li></ul><p> </p><p><strong>Knowledge Graphs</strong></p><p><strong>Knowledge Representation</strong><br>We worked with a PhD-level biologist to develop a knowledge representation (see the figure below) that modeled mechanisms underlying human disease.</p><p> </p><p>To do this, we manually mapped all possible combinations of the following six node types:</p><ul><li>Humans Diseases</li><li>Human Phenotypes</li><li>Human Genes</li><li>Gene Ontology concepts</li><li>Reactome Pathways</li><li>Chemicals</li></ul><p>As shown in the figure above, the <a href="http://basic-formal-ontology.org/">Basic Formal Ontology</a> and <a href="https://github.com/oborel/obo-relations/">Relation Ontology</a> ontologies were then used to create edges between the node types.</p><p> </p><p>As shown in this figure, the following edge-types were created:</p><ul><li><strong>Phenotypes-Genes:</strong> The <a href="http://purl.obolibrary.org/obo/hp.owl">Human Phenotype Ontology (HP)</a> provides <a href="http://compbio.charite.de/jenkins/job/hpo.annotations.monthly/lastStableBuild/artifact/annotation/ALL_SOURCES_ALL_FREQUENCIES_genes_to_phenotype.txt">phenotype-Entrez gene annotations</a> that were used to map 6,651 HP classes to 120,288 Entrez genes.</li><li><strong>Phenotypes-Diseases:</strong> The <a href="http://purl.obolibrary.org/obo/hp.owl">HP</a> provides <a href="http://compbio.charite.de/jenkins/job/hpo.annotations.monthly/lastStableBuild/artifact/annotation/ALL_SOURCES_ALL_FREQUENCIES_diseases_to_genes_to_phenotypes.txt">HP-DOID-Gene annotations</a> that were used to map 5,438 HP concepts to 43,817 DOID concepts.</li><li><strong>Biological processes, Molecular Functions, and Cellular Locations-Genes:</strong> The <a href="http://purl.obolibrary.org/obo/go.owl">Gene Ontology (GO)</a> provides <a href="http://geneontology.org/gene-associations/goa_human.gaf.gz">GO-Gene annotations</a> that were used to map 17,505 GO concepts to 265,002 Entrez genes.</li><li><strong>Biological processes, Molecular Functions, and Cellular Locations-Pathways-Pathways:</strong> <a href="https://reactome.org/">Reactome</a> provides <a href="https://reactome.org/download/current/gene_association.reactome">GO-Gene links</a> that were used to map 17,906 pathways to 1,910 biological processes, molecular functions, and cellular locations.</li><li><strong>Chemicals-Pathways:</strong> The <a href="http://ctdbase.org/">Comparative Toxicogenomics Database (CTD)</a> provides <a href="http://ctdbase.org/reports/CTD_chem_pathways_enriched.tsv.gz">Chemical-pathway links</a> that were used to map 8,886 MESH concepts to 711,043 Reactome pathways.</li><li><strong>Chemicals-Genes:</strong> The <a href="http://ctdbase.org/">Comparative Toxicogenomics Database (CTD)</a> provides <a href="http://ctdbase.org/reports/CTD_chem_gene_ixns.tsv.gz">Chemical-Gene links</a> that were used to map 8,881 MESH concepts 410,379 Entrez genes.</li><li><strong>Chemicals-Diseases:</strong> The <a href="http://ctdbase.org/">Comparative Toxicogenomics Database (CTD)</a> provides <a href="http://ctdbase.org/reports/CTD_chemicals_diseases.tsv.gz">Chemical-Disease links</a> that were used to map 14,238 MESH concepts 1,216,900 DOID concepts.</li><li><strong>Genes-Genes:</strong> The<a href="https://string-db.org/">STRING Database</a> provides <a href="https://stringdb-static.org/download/protein.links.v10.5/9606.protein.links.v10.5.txt.gz">Gene-Gene links</a> that were used to create 594,100 gene-gene interactions. When generating these mappings, only the inferred protein-protein relationships considered to be high confidence were used (score of 700 or better).</li><li><strong>Genes-Disease:</strong> Mappings between genes and diseases were retrieved from <a href="http://www.disgenet.org/web/DisGeNET/menu">DisGeNet</a> via SPARQL endpoint and used to map 6,051 Entrez genes to 20,452 DOID concepts.</li><li><strong>Genes-Pathways:</strong> The <a href="http://ctdbase.org/">Comparative Toxicogenomics Database (CTD)</a> provides <a href="http://ctdbase.org/reports/CTD_genes_pathways.tsv.gz">Gene-Pathway links</a> that were used to map 110,370 Entrez genes to 107,029 Reactome pathways.</li><li><strong>Pathways-Disease:</strong> The <a href="http://ctdbase.org/">Comparative Toxicogenomics Database (CTD)</a> provides <a href="http://ctdbase.org/reports/CTD_diseases_pathways.tsv.gz">Pathway-Disease links</a> that were used to map 1,818 Reactome pathways to 106,727 DOID concepts.</li></ul><p> </p><p><strong>Knowledge Graph</strong><br>The knowledge graph represented above was built using the following steps: Merge Ontologies: Merge ontologies using the <a href="https://github.com/owlcollab/owltools/wiki">OWL Tools API</a><br>Express New Ontology Concept Annotations: Create new ontology annotations by asserting a relation between the instance and an instance of the ontology class. For example to assert the following relations:</p><blockquote><p><a href="https://www.ncbi.nlm.nih.gov/mesh/68009020">Morphine</a> --> <a href="https://www.ebi.ac.uk/ols/ontologies/ro/properties?iri=http%3A%2F%2Fpurl.obolibrary.org%2Fobo%2FRO_0002606">is substance that treats</a> --> <a href="https://hpo.jax.org/app/browse/term/HP:0002076">Migraine</a></p><p>We would need to create two axioms:</p><ul><li>isSubstanceThatTreats(Morphine, x1)</li><li>instanceOf(x1, Migraine)</li></ul></blockquote><p>While the instance of the HP class hemiplegic migraines can be treated as an anonymous node in the knowledge graph, we generate a new international resource identifier for each newly generated instance.</p><p><strong>Deductively Close Knowledge Graph:</strong> The knowledge graph is deductively closed by using the OWL 2 EL reasoner, ELK via Protégé v5.1.1. ELK is able to classify instances and supports inferences over class hierarchies and object properties. inference over disjointness, intersection, and existential quantification (ontology class hierarchies).</p><p><strong>Generate Edge List:</strong> The final step before exporting the edge list is to remove any nodes that are not biologically meaningful or would otherwise reduce the performance of machine learning algorithms and the algorithm used to generate embeddings.</p><p> </p><p>🚨 <strong>AVAILABLE FILES </strong>🚨Available KG benchmark files are zipped and listed below. For additional details on what each file contains, please see the associated Wiki page 👉 <a href="https://github.com/callahantiff/PheKnowLator/wiki/September-3,-2019">here</a>.</p>
A graph-based representation of the Hack Forums using Neo4j
<p>A graph-based representation of the Hack Forums using Neo4j.</p> <p>This dataset contains data to complement the paper "A Graph-based Stratified Sampling Methodology for the Analysis of (Underground) Forums" to appear in IEEE Transactions on Information Forensics and Security (TIFS). </p> <p>Due to ethical reasons, the data is anonymized and access to the actual content stored in the graph is subject to a formal data-sharing agreement with the Cambridge Cybercrime Centre. Please visit <a href="https://www.cambridgecybercrime.uk/process.html">this page</a> for more details on the process. </p>
Datasets for Paper "BenchTemp: A General Benchmark for Evaluating Temporal Graph Neural Networks"
<p>Datasets for Paper "BenchTemp: A General Benchmark for Evaluating Temporal Graph Neural Networks"<br> URL: https://github.com/qianghuangwhu/benchtemp</p> <p>Openreview: https://openreview.net/forum?id=rnZm2vQq31</p> <p><br> There are 19 (15+4) benchmark temporal graph datasets:<br> reddit,<br> wikipedia,<br> mooc,<br> lastfm,<br> enron,<br> SocialEvo,<br> uci,<br> CollegeMsg,<br> TaobaoSmall,<br> CanParl,<br> Contacts,<br> Flights,<br> UNtrade,<br> USLegis,<br> UNvote,</p> <p>DGraphFin,</p> <p>TaobaoLarge,</p> <p>YoutubeReddit,</p> <p>YoutubeRedditLarge</p> <p> </p> <p><br> Each dataset has three files:<br> 1. ml_{data_name}.csv - the csv file of the Temporal Graph.</p> <p>This file have five columns with properties:</p> <p>'u': the id of the user.<br> 'i': the id of the item.<br> 'ts': the timestamp of the interaction (edge) between the user and the item.<br> 'label': the label of the interaction (edge).<br> 'idx': the index of the interaction (edge).<br> For example:</p> <p>,u,i,ts,label,idx<br> 0,1,2,0.0,0.0,1<br> 1,1,3,0.0,0.0,2<br> 2,1,4,0.0,0.0,3<br> 2. ml_{data_name}.npy - the edge features corresponding to the interactions (edges) in the the Temporal Graph..</p> <p>3. ml_{data_name}_node.npy - the initialization node features of the Temporal Graph.</p>
Bar graphs of DEMIX database tile classifications
<p>The DEMIX database (Guth, 2023a) contains statistics from 6 test 1 arc second DEMs (ALOS, ASTER, CopDEM, FABDEM, NASADEM, and SRTM) compared to high resolution reference DEMs. The database contains 236 DEMIX tiles (Guth and others, 2023) and forms the basis for the ranking of global DEMs in Bielski and others (2023).</p> <p>A K-means clustering of the database using MICRODEM (Guth, 2023b, 2023c), and an additional set of 4 land cover and landform classifications (Table 1) for the 236 DEMIX tiles computed the percentage of each DEMIX tile in each classification category. Guth (2023d) has the raw data for the percentages of each category for each of the 236 tiles along with the K-means cluster assignments.</p> <p>This data set contains 3 figures for each of the 5 classification databases in Guth (2023d):</p> <ul> <li>Bar graph of the category percentages for each of the test areas. A composite version of these graphs is in Bielski and others (2023).</li> <li>Bar graph of the category percentages for each of the 236 test tiles. These graphs are too large to include on a single page with readable legends.</li> <li>Legend for the classification</li> </ul> <p> </p> <p>References:</p> <p>Bielski, C.; López-Vázquez, C.; Grohmann, C.H.; Guth. P.L.; and the TMSG DEMIX Working Group, 2023. DEMIX Method Ranks COPDEM, and FABDEM as Top 1” Global DEMs: <a href="https://arxiv.org/abs/2302.08425v3">https://arxiv.org/abs/2302.08425v3</a></p> <p>Guth, P. L., 2023a. DEMIX GIS Database Version 2 (2.0) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.8062008">https://doi.org/10.5281/zenodo.8062008</a></p> <p>Guth, P.L., 2023b. GIT-MICRODEM [Delphi source code, archived installation versions]. URL: https://github.com/prof-pguth/git_microdem </p> <p>Guth, P.L., 2023c, MICRODEM: Open-source GIS with a focus on Geomorphometry [download latest Win64 executable and CHM help file] URL: <a href="https://microdem.org/">https://microdem.org/</a></p> <p>Guth, P.L., 2023d, K-means clustering of the DEMIX data set (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.8283791</p> <p>Guth, Peter L., Peter Strobl, Kevin Gross, & Serge Riazanoff. (2023). DEMIX 10k Tile Data Set (1.0) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.7504791">https://doi.org/10.5281/zenodo.7504791</a></p>
Graphs for DCRs using SPD with shield for possible variation using standard and armoured fibre
<p>Dark Count Rate (DCR) graphs related to shield with possible variation using standard and armoured fibre for Single Photon Detector (SPD).</p>
Measuring coselectional constraint in learner corpora: A graph-based approach
<p>All data from the thesis, plots, scripts, rough draft of annotation guidelines (more is included in thesis). not all svgs are included yet, but can be computed from scripts + data. more data will be added in next version (after defense).</p>
WarSampo Knowledge Graph
<p>WarSampo Knowledge Graph includes harmonized data of different kinds concerning the Second World War in Finland, separated in different subgraphs representing events, actors, places, photographs, and other aspects and documentation of the war. The data covers the Winter War 1939-1940 against the Soviet attack, the Continuation War 1941-1944 where the occupied areas of the Winter War were temporarily regained, and the Lapland War 1944-1945, where the Finns pushed the German troops away from Lapland.</p> <p>To test and demonstrate its usefulness, this Knowledge Graph is in use in the semantic portal <a href="https://sotasampo.fi/en">WarSampo</a>, explained in more detail in the <a href="https://seco.cs.aalto.fi/projects/sotasampo/en/">project page</a>.</p> <p>Example SPARQL queries for the data:</p> <ul> <li><a href="http://yasgui.org/#query=PREFIX+skos%3A+%3Chttp%3A%2F%2Fwww.w3.org%2F2004%2F02%2Fskos%2Fcore%23%3E%0APREFIX+rdfs%3A+%3Chttp%3A%2F%2Fwww.w3.org%2F2000%2F01%2Frdf-schema%23%3E%0APREFIX+crm%3A+%3Chttp%3A%2F%2Fwww.cidoc-crm.org%2Fcidoc-crm%2F%3E%0APREFIX+articles%3A+%3Chttp%3A%2F%2Fldf.fi%2Fschema%2Fwarsa%2Farticles%2F%3E%0APREFIX+wet%3A+%3Chttp%3A%2F%2Fldf.fi%2Fwarsa%2Fevents%2Fevent_types%2F%3E%0APREFIX+dc%3A+%3Chttp%3A%2F%2Fpurl.org%2Fdc%2Felements%2F1.1%2F%3E%0A%0A%23+Events%2C+photographs+and+articles+that+are+situated+in+Vyborg%0ASELECT+DISTINCT+%3Ftype+%3Fresource+%3FprefLabel%0AWHERE+%7B%0A++%7B%0A++++%23+Events%0A++++BIND+(%3Chttp%3A%2F%2Fldf.fi%2Fwarsa%2Fplaces%2Fmunicipalities%2Fm_place_614%3E+as+%3Fvyborg)%0A++++%3Fresource+crm%3AP7_took_place_at+%3Fvyborg+%3B%0A++++++++++++++a+%3FtypeURI+%3B%0A++++++++++++++crm%3AP4_has_time-span+%3Ftimespan+.%0A++++FILTER(%3FtypeURI+!%3D+wet%3APhotography)%0A++++%3FtypeURI+rdfs%3AsubClassOf*+crm%3AE5_Event+.%0A++%7D%0A++UNION%0A++%7B%0A++++%23+Photographs%0A++++BIND+(%3Chttp%3A%2F%2Fldf.fi%2Fwarsa%2Fplaces%2Fmunicipalities%2Fm_place_614%3E+as+%3Fvyborg)%0A++++%3Fresource+%5Ecrm%3AP94_has_created%2Fcrm%3AP7_took_place_at+%3Fvyborg+%3B%0A++++++++++++++++++++++++++++++++++a+%3FtypeURI+.%0A++%7D%0A++UNION%0A++%7B%0A++++%23+Articles%0A++++BIND+(%3Chttp%3A%2F%2Fldf.fi%2Fwarsa%2Fplaces%2Fmunicipalities%2Fm_place_614%3E+as+%3Fvyborg)%0A++++%3Fresource+articles%3Aplace%2Fskos%3ArelatedMatch+%3Fvyborg+%3B%0A++++++++++++++++++++++++++++articles%3Aauthor+%3Fauthor+%3B%0A++++++++++++++++++++++++++++articles%3Aissue+%3Fissue+%3B%0A++++++++++++++++++++++++++++a+%3FtypeURI+.%0A++%7D%0A++OPTIONAL+%7B%0A++++%3FtypeURI+skos%3AprefLabel+%3Ftype+.%0A++++FILTER(langMatches(lang(%3Ftype)%2C+%22en%22))%0A++%7D%0A++OPTIONAL+%7B%0A++++%3FtypeURI+skos%3AprefLabel+%3Ftype+.%0A++++FILTER(langMatches(lang(%3Ftype)%2C+%22fi%22))%0A++%7D%0A++OPTIONAL+%7B%0A++++%3FtypeURI+skos%3AprefLabel+%3Ftype+.%0A++%7D%0A++OPTIONAL+%7B%0A++++%3Fresource+skos%3AprefLabel%7Cdc%3Atitle+%3FprefLabel+.%0A++++FILTER(langMatches(lang(%3FprefLabel)%2C+%22en%22))%0A++%7D%0A++OPTIONAL+%7B%0A++++%3Fresource+skos%3AprefLabel%7Cdc%3Atitle++%3FprefLabel+.%0A++++FILTER(langMatches(lang(%3FprefLabel)%2C+%22fi%22))%0A++%7D%0A++OPTIONAL+%7B%0A++++%3Fresource+skos%3AprefLabel%7Cdc%3Atitle+%3FprefLabel+.%0A++%7D%0A%7D+&contentTypeConstruct=text%2Fturtle&contentTypeSelect=application%2Fsparql-results%2Bjson&endpoint=http%3A%2F%2Fldf.fi%2Fwarsa%2Fsparql&requestMethod=POST&tabTitle=Query+1&headers=%7B%7D&outputFormat=table">Events, photographs and articles that are situated in Vyborg</a></li> <li><a href="http://yasgui.org/#query=PREFIX+%3A+%3Chttp%3A%2F%2Fldf.fi%2Fwarsa%2Factors%2F%3E+%09%0APREFIX+warsa%3A+%3Chttp%3A%2F%2Fldf.fi%2Fschema%2Fwarsa%2F%3E+%09%0APREFIX+atypes%3A+%3Chttp%3A%2F%2Fldf.fi%2Fwarsa%2Factors%2Factor_types%2F%3E+%09%0APREFIX+foaf%3A+%3Chttp%3A%2F%2Fxmlns.com%2Ffoaf%2F0.1%2F%3E+%09%0APREFIX+casualties%3A+%3Chttp%3A%2F%2Fldf.fi%2Fschema%2Fwarsa%2Fcasualties%2F%3E%09%0APREFIX+skos%3A+%3Chttp%3A%2F%2Fwww.w3.org%2F2004%2F02%2Fskos%2Fcore%23%3E+%09%0APREFIX+xsd%3A+%3Chttp%3A%2F%2Fwww.w3.org%2F2001%2FXMLSchema%23%3E+%09%0APREFIX+crm%3A+%3Chttp%3A%2F%2Fwww.cidoc-crm.org%2Fcidoc-crm%2F%3E+%09%0APREFIX+geo%3A+%3Chttp%3A%2F%2Fwww.w3.org%2F2003%2F01%2Fgeo%2Fwgs84_pos%23%3E%09%0A%0A%23+Place%2Fdate+distribution+for+casualties+of+the+1st+Division+and+its+subunits+in+time+interval+13.2.-13.3.1940%0ASELECT+%3Fplacename+%3Flat+%3Flon+(SUM(%3Fw)+AS+%3Fnum_casualties)+%3Fdate+WHERE+%7B+%09%0A++%7B%0A++++SELECT+%3Fplace+(COUNT(%3Fid)+AS+%3Fw)+%3Fdate+WHERE+%7B%0A++++++%3Aactor_1135+(%5Ecrm%3AP144_joined_with%2Fcrm%3AP143_joined)*+%3Fsubunit+.%0A%0A++++++%3Fid+a+warsa%3ADeathRecord+%3B%0A++++++++++casualties%3Aunit+%3Fsubunit+%3B%09%0A++++++++++warsa%3Adate_of_death+%3Fdate+.%0A%0A++++++FILTER(%3Fdate+%3E%3D+%221940-02-13%22%5E%5Exsd%3Adate+%26%26+%3Fdate+%3C%3D+%221940-03-13%22%5E%5Exsd%3Adate)+%09%09%0A%0A++++++%3Fid+casualties%3Amunicipality_of_death+%3Fplace+.%0A%0A++++%7D%09GROUP+BY+%3Fplace+%3Fw+%3Fdate%0A++%7D++%09%0A++FILTER+(%3Fw+%3E+0)+%0A++%3Fplace+skos%3AprefLabel+%3Fplacename+.%0A++OPTIONAL+%7B%0A++++%3Fplace+geo%3Alat+%3Flat+%3B+%0A+++++++++++geo%3Along+%3Flon+.%09%0A++%7D%0A%7D+GROUP+BY+%3Fplacename+%3Flat+%3Flon+%3Fweigth+%3Fdate+ORDER+BY+%3Fdate&contentTypeConstruct=text%2Fturtle&contentTypeSelect=application%2Fsparql-results%2Bjson&endpoint=http%3A%2F%2Fldf.fi%2Fwarsa%2Fsparql&requestMethod=POST&tabTitle=Query&headers=%7B%7D&outputFormat=table">Casualties of the 1st Division and its subunits in the time interval 13.2.-13.3.1940 by place and date</a></li> </ul> <p>WarSampo knowledge graph version history:</p> <ul> <li>1.0.0, November 2015: Initial public release</li> <li>1.1.0, November 2017: War cemeteries addition</li> <li>2.0.0, May 2018: Backwards-incompatible URI changes</li> <li>2.0.1, November 2019: Updated schema and VoiD descriptions</li> <li>2.1.0, November 2019: Prisoners of war addition</li> </ul> <p>Version 2.1.0 contains 14,322,426 triples.</p> <p>To combine the files into a single Turtle file on a Linux system:</p> <pre><code class="language-bash">find . -mindepth 2 -name "*.ttl" | xargs cat >> warsampo.ttl</code></pre> <p> </p>
A dataset based on two graph coverage criteria: prime-path and edge coverage
<p>This repository contains 462 instances from 6 projects. The dataset structure contains 43 columns, in which 18 columns are the source code metrics of the application methods under test, 18 columns are the source code metrics of test methods, and seven columns are the test case metrics. </p>
Selected survey papers for creating a knowledge graph
<p>This file contains the set of selected survey papers for populating a scholarly knowledge graph. This includes the paper title, table reference, source and full paper reference. </p>
Data for Bovine breed-specific augmented reference graphs facilitate accurate sequence read mapping and unbiased variant discovery
<p><strong>Description of the datasets</strong></p> <p>Data are organized as folders and compressed with tar.gz.</p> <p>There are two compressed data folder: <strong>data </strong>which used for cattle genome graphs experiment and <strong>data_human</strong> which we used for human genome graphs experiment. </p> <p><strong>Cattle genome graphs experiments</strong></p> <p>First you need to unzip the file using command <em>tar -xvzf data.tar.gz</em>. After unzipping, the data folder is organized as follows:</p> <ul> <li>Utilities: contain bovine ARS-UCD 1.2 fasta reference with the accompanying index.</li> <li>Bin: contain the softwares used in the paper (vg, liftover, vcf2diploid)</li> <li>Part1: data for analysis in variant prioritization section, further subdivided into: <ul> <li>vcf_sim: variant files from four animal in each breed used to simulate reads</li> <li>reads_sim: simulated short reads used for read mapping</li> <li>vcf_freq: variants augmented to graphs filtered based on allele frequency</li> </ul> </li> <li>Part2: data used for analysis in the section of graph mapping with breeds-filtered variants, further subdivided into: <ul> <li>vcf_breed: variant files used to graphs construction.</li> </ul> </li> <li>Part3: data used for analysis in the section of consensus genome, further subdivided into: <ul> <li>read_sims: simulated reads as in the part1, but the coordinates are liftovered to the new consensus genomes.</li> <li>reference: contain the original reference and consensus references.</li> <li>vcf_consensus: contain major allele variants to construct consensus genomes.</li> </ul> </li> <li>Part4: data analysis in the section of whole genome graph construction and variant genotyping. <ul> <li>vcf_construct: variants from chromosome 1-29 from 82 Brown Swiss used to construct BSW whole genome graph.</li> <li>BSW_graph: whole genome Brown Swiss graph with the three accompanying indexes (xg,gcsa, and gbwt).</li> </ul> </li> </ul> <p><strong>Human genome graphs experiments</strong></p> <p>First you need to unzip the <em>data_human</em> file using command <em>tar -xvzf data</em><em>_hum.tar.gz</em>. After unzipping, the data folder is organized as follows:</p> <ul> <li>reference: the g1k_v37 reference used as a graph backbone</li> <li>vcf_sim: variant files from four individuals in each population used to simulate reads</li> <li>reads_sim: simulated short reads used for read mapping</li> <li>vcf_freq: variants augmented to graphs filtered based on allele frequency</li> </ul>
The Updated Maven Central Dependency Graph
<p><strong>Maven Central Dependency Graph</strong></p> <p>This is an updated version of the artifact at https://zenodo.org/record/1489120</p> <p>The Maven dependency graph is an open dataset of Maven Central artifacts, their dependencies, as well as other relationships. Its main intent is to domesticate the wild within and around the Maven central ecosystem, in particular, and JVM-based libraries at large, making it more harnessable to both academics and industry. It is intended to answer high-level research questions concerning artifacts releases, evolution, and usage trends over time. It can also be used to assist researchers in selecting relevant datasets, among the mass of existing software artifact, for assessing particular empirical software engineering challenges. The complexity of these questions can range from simple pattern matching to advanced big data analysis and machine learning techniques.</p> <p>The accompanying paper to this dataset is has been accepted for publication in the proceedings of the International Conference on Mining Software Repositories 2019 and has received the MSR 2019 Data Showcase Award. This paper is available for download on <a href="https://arxiv.org/abs/1901.05392">arXiv</a>.</p> <p><strong>What is new?</strong></p> <p>The previous version included artifacts until September 6, 2018.<br> This version includes artifacts until September 10, 2019.</p> <p>This version includes license information as well as information about associated code repository.</p> <p>This version contains 4 201 392 artifacts (version) of 308116 distinct libraries from 47481 distinct group IDs.</p> <p>Note 33 638 artifacts represents version ranges and note actual versions. They can be filtered out by excluding version containing ','.</p> <p><br> <strong>Usage</strong></p> <p>Usage:</p> <ul> <li> Download the archive from zenodo</li> <li> Decompress the archive</li> </ul> <pre><code class="language-bash"># Pull the image and start the container docker run -d --name mm-neo4j -p 7474:7474 -p 7687:7687 -v /path/to/neo4j-data:/data --env=NEO4J_dbms_memory_heap_max__size=8g lyadis/mm-neo4j:latest</code></pre> <ul> <li> Open http://127.0.0.1:7474/browser/ in your browser</li> </ul>
A biodiversity dataset graph: Biodiverity Heritage Library (BHL)
<p>A biodiversity dataset graph: BHL</p> <p>The intended use of this archive is to facilitate (meta-)analysis of the Biodiversity Heritage Library (BHL). The Biodiversity Heritage Library improves research methodology by collaboratively making biodiversity literature openly available to the world as part of a global biodiversity community.</p> <p>This dataset provides versioned snapshots of the BHL network as tracked by Preston [2] between 2019-05-19 and 2020-05-09 using "preston update -u https://biodiversitylibrary.org".</p> <p>The archive consists of 256 individual parts (e.g., preston-00.tar.gz, preston-01.tar.gz, ...) to allow for parallel file downloads. The archive contains three types of files: index files, provenance logs and data files. In addition, index files have been individually included in this dataset publication to facilitate remote access. Index files provide a way to links provenance files in time to establish a versioning mechanism. Provenance files describe how, when, what and where the BHL content was retrieved. For more information, please visit https://preston.guoda.bio or https://doi.org/10.5281/zenodo.1410543 . </p> <p>To retrieve and verify the downloaded BHL biodiversity dataset graph, first concatenate all the downloaded preston-*.tar.gz files (e.g., cat preston-*.tar.gz > preston.tar.gz). Then, extract the archives into a "data" folder. Alternatively, you can use the preston[2] command-line tool to "clone" this dataset using:</p> <p>$ java -jar preston.jar clone --remote https://zenodo.org/record/3849560/files</p> <p>After that, verify the index of the archive by reproducing the following provenance log history:</p> <p>$ java -jar preston.jar history<br> <0659a54f-b713-4f86-a917-5be166a14110> <http://purl.org/pav/hasVersion> <hash://sha256/89926f33157c0ef057b6de73f6c8be0060353887b47db251bfd28222f2fd801a> .<br> <hash://sha256/41b19aa9456fc709de1d09d7a59c87253bc1f86b68289024b7320cef78b3e3a4> <http://purl.org/pav/previousVersion> <hash://sha256/89926f33157c0ef057b6de73f6c8be0060353887b47db251bfd28222f2fd801a> .<br> <hash://sha256/7582d5ba23e0d498ca4f55c29408c477d0d92b4fdcea139e8666f4d78c78a525> <http://purl.org/pav/previousVersion> <hash://sha256/41b19aa9456fc709de1d09d7a59c87253bc1f86b68289024b7320cef78b3e3a4> .<br> <hash://sha256/a70774061ccded1a45389b9e6063eb3abab3d42813aa812391f98594e7e26687> <http://purl.org/pav/previousVersion> <hash://sha256/7582d5ba23e0d498ca4f55c29408c477d0d92b4fdcea139e8666f4d78c78a525> .<br> <hash://sha256/007e065ba4b99867751d688754aa3d33fa96e6e03133a2097e8a368d613cd93a> <http://purl.org/pav/previousVersion> <hash://sha256/a70774061ccded1a45389b9e6063eb3abab3d42813aa812391f98594e7e26687> .<br> <hash://sha256/4fb4b4d8f1ae2961311fb0080e817adb2faa746e7eae15249a3772fbe2d662a1> <http://purl.org/pav/previousVersion> <hash://sha256/007e065ba4b99867751d688754aa3d33fa96e6e03133a2097e8a368d613cd93a> .<br> <hash://sha256/67cc329e74fd669945f503917fbb942784915ab7810ddc41105a82ebe6af5482> <http://purl.org/pav/previousVersion> <hash://sha256/4fb4b4d8f1ae2961311fb0080e817adb2faa746e7eae15249a3772fbe2d662a1> .<br> <hash://sha256/e46cd4b0d7fdb51ea789fa3c5f7b73591aca62d2d8f913346d71aa6cf0745c9f> <http://purl.org/pav/previousVersion> <hash://sha256/67cc329e74fd669945f503917fbb942784915ab7810ddc41105a82ebe6af5482> .<br> <hash://sha256/9215d543418a80510e78d35a0cfd7939cc59f0143d81893ac455034b5e96150a> <http://purl.org/pav/previousVersion> <hash://sha256/e46cd4b0d7fdb51ea789fa3c5f7b73591aca62d2d8f913346d71aa6cf0745c9f> .<br> <hash://sha256/1448656cc9f339b4911243d7c12f3ba5366b54fff3513640306682c50f13223d> <http://purl.org/pav/previousVersion> <hash://sha256/9215d543418a80510e78d35a0cfd7939cc59f0143d81893ac455034b5e96150a> .<br> <hash://sha256/7ee6b16b7a5e9b364776427d740332d8552adf5041d48018eeb3c0e13ccebf27> <http://purl.org/pav/previousVersion> <hash://sha256/1448656cc9f339b4911243d7c12f3ba5366b54fff3513640306682c50f13223d> .<br> <hash://sha256/34ccd7cf7f4a1ea35ac6ae26a458bb603b2f6ee8ad36e1a58aa0261105d630b1> <http://purl.org/pav/previousVersion> <hash://sha256/7ee6b16b7a5e9b364776427d740332d8552adf5041d48018eeb3c0e13ccebf27> .</p> <p>To check the integrity of the extracted archive, confirm that each line produce by the command "preston verify" produces lines as shown below, with each line including "CONTENT_PRESENT_VALID_HASH". Depending on hardware capacity, this may take a while.</p> <p>$ java -jar preston.jar verify<br> hash://sha256/e0c131ebf6ad2dce71ab9a10aa116dcedb219ae4539f9e5bf0e57b84f51f22ca file:/home/preston/preston-bhl/data/e0/c1/e0c131ebf6ad2dce71ab9a10aa116dcedb219ae4539f9e5bf0e57b84f51f22ca OK CONTENT_PRESENT_VALID_HASH 49458087 hash://sha256/e0c131ebf6ad2dce71ab9a10aa116dcedb219ae4539f9e5bf0e57b84f51f22ca<br> hash://sha256/1a57e55a780b86cff38697cf1b857751ab7b389973d35113564fe5a9a58d6a99 file:/home/preston/preston-bhl/data/1a/57/1a57e55a780b86cff38697cf1b857751ab7b389973d35113564fe5a9a58d6a99 OK CONTENT_PRESENT_VALID_HASH 25745 hash://sha256/1a57e55a780b86cff38697cf1b857751ab7b389973d35113564fe5a9a58d6a99<br> hash://sha256/85efeb84c1b9f5f45c7a106dd1b5de43a31b3248a211675441ff584a7154b61c file:/home/preston/preston-bhl/data/85/ef/85efeb84c1b9f5f45c7a106dd1b5de43a31b3248a211675441ff584a7154b61c OK CONTENT_PRESENT_VALID_HASH 519892 hash://sha256/85efeb84c1b9f5f45c7a106dd1b5de43a31b3248a211675441ff584a7154b61c<br> hash://sha256/251e5032afce4f1e44bfdc5a8f0316ca1b317e8af41bdbf88163ab5bd2b52743 file:/home/preston/preston-bhl/data/25/1e/251e5032afce4f1e44bfdc5a8f0316ca1b317e8af41bdbf88163ab5bd2b52743 OK CONTENT_PRESENT_VALID_HASH 787414 hash://sha256/251e5032afce4f1e44bfdc5a8f0316ca1b317e8af41bdbf88163ab5bd2b52743</p> <p>Note that a copy of the java program "preston", preston.jar, is included in this publication. The program runs on java 8+ virtual machine using "java -jar preston.jar", or in short "preston".</p> <p>Files in this data publication:</p> <p>--- start of file descriptions ---</p> <p>-- description of archive and its contents (this file) --<br> README</p> <p>-- executable java jar containing preston[2] v0.1.15. --<br> preston.jar</p> <p>-- preston archives containing BHL data files, associated provenance logs and a provenance index --<br> preston-[00-ff].tar.gz</p> <p>-- individual provenance index files --<br> 2a5de79372318317a382ea9a2cef069780b852b01210ef59e06b640a3539cb5a<br> 2b1104cb7749e818c9afca78391b2d0099bbb0a32f2b348860a335cd2f8f6800<br> 4081bc59dff58d63f6a86c623cb770f01e9a355a42495b205bcb538cd526190f<br> 47a2816f8b5600b24487093adcddfea12434cc4f270f3ab09d9215fbdd546cd2<br> 6f99a1388823fca745c9e22ac21e2da909a219aa1ace55170fa9248c0276903c<br> 7ae46d7cd9b5a0f5889ba38bac53c82e591b0bdf8b605f5e48c0dce8fb7b717f<br> 82903464889fea7c53f53daedf4e41fa31092f82619edeb3415eb2b473f74af3<br> 9e8c86243df39dd4fe82a3f814710eccf73aa9291d050415408e346fa2b09e70<br> a8308fbf4530e287927c471d881ce0fc852f16543d46e1ee26f1caba48815f3a<br> bcec6df2ea7f74e9a6e2830d0072e6b2fbe65323d9ddb022dd6e1349c23996e2<br> cfe47c25ec0210ac73c06b407beb20d9c58355cb15bae427fdc7541870ca2e4e<br> f73fc9e70bce8f21f0c96b8ef0903749d8f223f71343ab5a8910968f99c9b8b6</p> <p>--- end of file descriptions ---</p> <p><br> References</p> <p>[1] Biodiversity Heritage Library (BHL, https://biodiversitylibrary.org) accessed from 2019-05-19 to 2020-05-09 with provenance hash://sha256/34ccd7cf7f4a1ea35ac6ae26a458bb603b2f6ee8ad36e1a58aa0261105d630b1.<br> [2] https://preston.guoda.bio, https://doi.org/10.5281/zenodo.1410543 .</p> <p><br> This work is funded in part by grant NSF OAC 1839201 from the National Science Foundation.</p>
A biodiversity dataset graph: DataONE
<p>A biodiversity dataset graph: DataONE</p> <p>The intended use of this archive is to facilitate (meta-)analysis of the Data Observation Network for Earth (DataONE). DataONE is a distributed infrastructure that provides information about earth observation data.</p> <p>This dataset provides versioned snapshots of the DataONE network as tracked by Preston [2] between 2018-11-06 and 2020-05-07 using "preston update -u https://dataone.org".</p> <p>The archive consists of 256 individual parts (e.g., preston-00.tar.gz, preston-01.tar.gz, ...) to allow for parallel file downloads. The archive contains three types of files: index files, provenance logs and data files. In addition, index files have been individually included in this dataset publication to facilitate remote access. Index files provide a way to links provenance files in time to establish a versioning mechanism. Provenance files describe how, when, what and where the DataONE content was retrieved. For more information, please visit https://preston.guoda.bio or https://doi.org/10.5281/zenodo.1410543 . </p> <p>To retrieve and verify the downloaded DataONE biodiversity dataset graph, first concatenate all the downloaded preston-*.tar.gz files (e.g., cat preston-*.tar.gz > preston.tar.gz). Then, extract the archives into a "data" folder. Alternatively, you can use the preston[2] command-line tool to "clone" this dataset using:</p> <p>$ java -jar preston.jar clone --remote https://zenodo.org/record/3849494/files</p> <p>After that, verify the index of the archive by reproducing the following provenance log history:</p> <p>$ java -jar preston.jar history<br> <0659a54f-b713-4f86-a917-5be166a14110> <http://purl.org/pav/hasVersion> <hash://sha256/8c67e0741d1c90db54740e08d2e39d91dfd73566ea69c1f2da0d9ab9780a9a9f> .<br> <hash://sha256/3ed3acaca7ac57f546d0b8877c1927ab5e08c23eccaa8219600c59c77a72c685> <http://purl.org/pav/previousVersion> <hash://sha256/8c67e0741d1c90db54740e08d2e39d91dfd73566ea69c1f2da0d9ab9780a9a9f> .<br> <hash://sha256/857753997a7595a1b372b05641b58a25d9408b7ff08d557ce1fe8b73e4bd383f> <http://purl.org/pav/previousVersion> <hash://sha256/3ed3acaca7ac57f546d0b8877c1927ab5e08c23eccaa8219600c59c77a72c685> .<br> <hash://sha256/7ee0376f4c3f7aeeda36927a5211395e5da8201e810e8c7e638a0fe23d001e88> <http://purl.org/pav/previousVersion> <hash://sha256/857753997a7595a1b372b05641b58a25d9408b7ff08d557ce1fe8b73e4bd383f> .<br> <hash://sha256/68b4974d8ab7c4c7a7a4305065839b60ba460aaa862590b34c67877738feba90> <http://purl.org/pav/previousVersion> <hash://sha256/7ee0376f4c3f7aeeda36927a5211395e5da8201e810e8c7e638a0fe23d001e88> .<br> <hash://sha256/060a76d56255bf9482c951748c91291fddeeb20f180632132be1344e081b2372> <http://purl.org/pav/previousVersion> <hash://sha256/68b4974d8ab7c4c7a7a4305065839b60ba460aaa862590b34c67877738feba90> .<br> <hash://sha256/29357bdfab4548025f8a5743301f5c3c9146fa436c39e3c9e019fb9409ac9c42> <http://purl.org/pav/previousVersion> <hash://sha256/060a76d56255bf9482c951748c91291fddeeb20f180632132be1344e081b2372> .<br> <hash://sha256/3669cd95100d1d533eb8953ff4ec5092cbd8addb8879b3e6262191148a8a3ebb> <http://purl.org/pav/previousVersion> <hash://sha256/29357bdfab4548025f8a5743301f5c3c9146fa436c39e3c9e019fb9409ac9c42> .<br> <hash://sha256/8dc1663299359d271cb1b4c14ad521d0f1be67743689dd18016543dc1e097efb> <http://purl.org/pav/previousVersion> <hash://sha256/3669cd95100d1d533eb8953ff4ec5092cbd8addb8879b3e6262191148a8a3ebb> .<br> <hash://sha256/dc4903e8afee651db1d9bf509f20503bf9c8e89679c4bcffb46d5b97440cb6de> <http://purl.org/pav/previousVersion> <hash://sha256/8dc1663299359d271cb1b4c14ad521d0f1be67743689dd18016543dc1e097efb> .<br> <hash://sha256/f3bed9db3092c744604df5f50248a2ec36e564fe78a65f45c4190283bd61c807> <http://purl.org/pav/previousVersion> <hash://sha256/dc4903e8afee651db1d9bf509f20503bf9c8e89679c4bcffb46d5b97440cb6de> .<br> <hash://sha256/e3c7b3b14b2b792e3e2e560a1b2bef059ac93f777dee616b836317bc9cbfcbf7> <http://purl.org/pav/previousVersion> <hash://sha256/f3bed9db3092c744604df5f50248a2ec36e564fe78a65f45c4190283bd61c807> .<br> <hash://sha256/631a4531e7bb052816d28454bbeec3428d5e7bfd1f148c4f21ce63a6cf86c650> <http://purl.org/pav/previousVersion> <hash://sha256/e3c7b3b14b2b792e3e2e560a1b2bef059ac93f777dee616b836317bc9cbfcbf7> .<br> <hash://sha256/87de0898919d2212977a586965e930ae45bdd1366073591c808c208a635e2814> <http://purl.org/pav/previousVersion> <hash://sha256/631a4531e7bb052816d28454bbeec3428d5e7bfd1f148c4f21ce63a6cf86c650> .<br> <hash://sha256/79ec3ee370a0d38311bc352af07a36380cd3aa04dc98154cf723bbc73d12ee77> <http://purl.org/pav/previousVersion> <hash://sha256/87de0898919d2212977a586965e930ae45bdd1366073591c808c208a635e2814> .<br> <hash://sha256/e54b360a4ca84a4503e4c10a8a8cca062c130be7429c8fe6ea1e0e82fe113e12> <http://purl.org/pav/previousVersion> <hash://sha256/79ec3ee370a0d38311bc352af07a36380cd3aa04dc98154cf723bbc73d12ee77> .<br> <hash://sha256/2910f784f84e112f124a56ce54bd06b76e510f90276629d2d144ce29e326d80f> <http://purl.org/pav/previousVersion> <hash://sha256/e54b360a4ca84a4503e4c10a8a8cca062c130be7429c8fe6ea1e0e82fe113e12> .<br> <hash://sha256/bcb0bdff0689cfb06f586d057703e41d1c6ba409867232217081dd8cb5053c87> <http://purl.org/pav/previousVersion> <hash://sha256/2910f784f84e112f124a56ce54bd06b76e510f90276629d2d144ce29e326d80f> .<br> <hash://sha256/a12f8c7fbf4fbfa71536c7e1b2614a35454dac6a7fe9e1cc0b4df41ab2269bef> <http://purl.org/pav/previousVersion> <hash://sha256/bcb0bdff0689cfb06f586d057703e41d1c6ba409867232217081dd8cb5053c87> .<br> <hash://sha256/2b5c445f0b7b918c14a50de36e29a32854ed55f00d8639e09f58f049b85e50e3> <http://purl.org/pav/previousVersion> <hash://sha256/a12f8c7fbf4fbfa71536c7e1b2614a35454dac6a7fe9e1cc0b4df41ab2269bef> .</p> <p>To check the integrity of the extracted archive, confirm that each line produce by the command "preston verify" produces lines as shown below, with each line including "CONTENT_PRESENT_VALID_HASH". Depending on hardware capacity, this may take a while.</p> <p>$ java -jar preston.jar verify<br> hash://sha256/e55c1034d985740926564e94decd6dc7a70f779a33e7deb931553739cda16945 file:/home/preston/preston-dataone/data/e5/5c/e55c1034d985740926564e94decd6dc7a70f779a33e7deb931553739cda16945 OK CONTENT_PRESENT_VALID_HASH 21580 hash://sha256/e55c1034d985740926564e94decd6dc7a70f779a33e7deb931553739cda16945<br> hash://sha256/d0ddcc2111b6134a570bcc7d89375920ef4d754130cecc0727c79d2b05a9f81f file:/home/preston/preston-dataone/data/d0/dd/d0ddcc2111b6134a570bcc7d89375920ef4d754130cecc0727c79d2b05a9f81f OK CONTENT_PRESENT_VALID_HASH 2035 hash://sha256/d0ddcc2111b6134a570bcc7d89375920ef4d754130cecc0727c79d2b05a9f81f<br> hash://sha256/472de9d1c9fd7e044aac409abfbfff9f12c6b69359df995d431009580ffb0f53 file:/home/preston/preston-dataone/data/47/2d/472de9d1c9fd7e044aac409abfbfff9f12c6b69359df995d431009580ffb0f53 OK CONTENT_PRESENT_VALID_HASH 1935 hash://sha256/472de9d1c9fd7e044aac409abfbfff9f12c6b69359df995d431009580ffb0f53<br> hash://sha256/b29879462cd43862129c5cf9b149c41ecd33ffef284a4dbea4ac1c0f90108687 file:/home/preston/preston-dataone/data/b2/98/b29879462cd43862129c5cf9b149c41ecd33ffef284a4dbea4ac1c0f90108687 OK CONTENT_PRESENT_VALID_HASH 1553 hash://sha256/b29879462cd43862129c5cf9b149c41ecd33ffef284a4dbea4ac1c0f90108687</p> <p><br> Note that a copy of the java program "preston", preston.jar, is included in this publication. The program runs on java 8+ virtual machine using "java -jar preston.jar", or in short "preston".</p> <p>Files in this data publication:</p> <p>--- start of file descriptions ---</p> <p>-- description of archive and its contents (this file) --<br> README</p> <p>-- executable java jar containing preston[2] v0.1.15. --<br> preston.jar</p> <p>-- preston archives containing DataONE data files, associated provenance logs and a provenance index --<br> preston-[00-ff].tar.gz</p> <p>-- individual provenance index files --<br> 2a5de79372318317a382ea9a2cef069780b852b01210ef59e06b640a3539cb5a<br> 2aecaf289def0e23a27058bf7715f226ef9189905f0be13228174825633125cf<br> 2f65ae542401d4c2daf1bca70de640211da6749188f67d28ea71acd7d8ba070b<br> 35eb1e17e2bf3e71212cde35bdb03e8a6545a57483ea3c1633929257b70cf637<br> 3d38b70198e448674be6a63d14b9817f3a956f48bba7418fa7baa086a56c05b7<br> 66ad3e5e904740f1e835ac6718dda4279e0c24b204ea0d1113cda1352a5072ba<br> 7466a35e42dea7e2be068060ec0c926f9a8686388ed504ef5c6c990c1ba4e8d0<br> 81161d9746c2a5823641c436e773fb4508516b055da85f4494b38c545349da39<br> 8bf062872ce958545d361e9d53a552ffb025ac29ab875caad1157c0995d34f66<br> a90eed8d70c54c8e554f2dfde4fceb434eda162d9615d62de96ded2344f88a78<br> c33ef5e29100b323412f1f3bc66908c8e01e4f0d1db4ea3685d2fffc47981dd6<br> c84dffef20fec958255e759db6445fc469d73695674a33ae6f7e567a088c9fe0<br> d362d599d72000c4feb464db5a669b12e15fc3ca1a49b1e7d4d6f7d6d5d15411<br> d9378616636be3686bbabd5bf29d50f0ef0e5ceb5ddd7dfce47f7e755b596b7d<br> da26fa6e7371385ed3f61af9a766221c833060d59dfd4869bbd7110f95f288db<br> e4103a75627857de3ee2e317429108611c244fc448c01d1d7bf652115c3b8a55<br> eb368fedb8f100210dd968edcf80f4d13cab3dd64135a6ab744102cf15e68c94<br> f13ab4bca04f894ae8eabb51fa01b4dfbc69f717eabc9896c728e2ba39c4db27<br> f493baf276892a199a0b0d078359f64a38fe8ad3f807921f8d41ef73f7343b1f<br> ff92b6c06ae5286bd2f1db679e0fcc4da294acb9bc01b2e9522378d99218c2e3</p> <p>--- end of file descriptions ---</p> <p><br> References</p> <p>[1] Data Observation Network for Earth (DataONE, https://dataone.org) accessed from 2018-11-06 to 2020-05-07 with provenance hash://sha256/2b5c445f0b7b918c14a50de36e29a32854ed55f00d8639e09f58f049b85e50e3.<br> [2] https://preston.guoda.bio, https://doi.org/10.5281/zenodo.1410543 .</p> <p><br> This work is funded in part by grant NSF OAC 1839201 from the National Science Foundation.</p>
ArCo Knowledge Graph v0.1
<p>Version 0.1 of the ArCo knowledge graph contains the ontology network and the data about the cultural properties catalogued by the Italian Institute of the General Catalogue and Documentation.</p> <p>Data are represented with RDF and by using N-Triples as syntax.</p> <p>The ontologies of the network are modelled with OWL 2 and serialised with the RDF/XML syntax.</p> <p>The ontology network is released along with alignments to other ontologies/vocabularies in the Semantic Web. Those alignments are provided within separate OWL files.</p> <p>The data are contained into a single RDF dump serialised as N-TRIPLES.</p> <p>Additionally, the release provides the links between ArCO entities and other entities published in other datasets in the Linked Open Data cloud. Such links are represented by using owl:sameAs axioms and serialised as N-TRIPLES into a separate file.</p>
CoDEx: A Comprehensive Knowledge Graph Completion Benchmark
<p>This repository hosts the <strong>relational-only part</strong> of the CoDEx benchmark, which was presented at the EMNLP 2020 conference. You can access the paper <a href="https://www.aclweb.org/anthology/2020.emnlp-main.669.pdf">here</a> and the full dataset, including text and pretrained models, <a href="https://bit.ly/2EPbrJs">on GitHub</a>.</p> <p>Abstract:</p> <p><em>We present CoDEx, a set of knowledge graph completion datasets extracted from Wikidata and Wikipedia that improve upon existing knowledge graph completion benchmarks in scope and level of difficulty. In terms of scope, CoDEx comprises three knowledge graphs varying in size and structure, multilingual descriptions of entities and relations, and tens of thousands of hard negative triples that are plausible but verified to be false. To characterize CoDEx, we contribute thorough empirical analyses and benchmarking experiments. First, we analyze each CoDEx dataset in terms of logical relation patterns. Next, we report baseline link prediction and triple classification results on CoDEx for five extensively tuned embedding models. Finally, we differentiate CoDEx from the popular FB15K-237 knowledge graph completion dataset by showing that CoDEx covers more diverse and interpretable content, and is a more difficult link prediction benchmark. Data, code, and pretrained models are available <a href="https://bit.ly/2EPbrJs">here</a>.</em></p>
Enhanced Bug Prediction in JavaScript Programs with Hybrid Call-Graph Based Invocation Metrics (Training Dataset)
<p>This dataset consists of multiple files which contain bug prediction training data.</p> <p>The entries in the dataset are JavaScript functions either being buggy or non-buggy. Bug related information was obtained from the project EsLint contained in BugsJS (https://github.com/BugsJS/eslint). The buggy instances were collected throughout the lifetime of the project, however we added non-buggy entries from the latest version which is tagged as fix (entries which were previously included as buggy were not included as non-buggy later on).</p> <p>The dataset is based on hybrid call graphs which are constructed by https://github.com/sed-szeged/hcg-js-framework. The result of this tool is a call graph where the edges are associated with a confidence level which shows how likely the given edge is a valid call edge.</p> <p>We used different threshold values from which we considered the edges to be valid. The following threshold values were used:</p> <ul> <li>0.00</li> <li>0.05</li> <li>0.20</li> <li>0.30</li> </ul> <p>The prefix in the dataset file names are coming from the used threshold. The the datasets include coupling metrics NII (Nubmer of Incoming Invocations) and NOI (Number of Outgoing Invocations) which were calculated by a static source code analyzer called SourceMeter. Hybrid counterparts of these metrics (HNII and HNOI) are based on the given threshold values.</p> <p>There are four variants for all of these datasets:</p> <ul> <li>Both static (NII, NOi) and hybrid (HNII, HNOI) coupling metrics are included with additional static source code metrics and information about the entries (file without any postfix). Column contained only in this dataset are: <ul> <li>ID</li> <li>Name</li> <li>Longname</li> <li>Parent ID</li> <li>Component ID</li> <li>Path</li> <li>Line</li> <li>Column</li> <li>EndLine</li> <li>EndColumn</li> </ul> </li> <li>Both static (NII, NOi) and hybrid (HNII, HNOI) coupling metrics are included with additional static source code metrics (file with '_h+s' postfix)</li> <li>Only static (NII, NOI) coupling metrics are included with additional static source code metrics (file with '_s' postfix)</li> <li>Only hybrid (HNII, HNOI) coupling metrics are included with additional static source code metrics (file with '_h' postfix)</li> </ul> <p>Static source code metrics which are contained in all dataset are the following:</p> <ul> <li>McCC - McCabe Cyclomatic Complexity</li> <li>NL - Nesting Level</li> <li>NLE - Nesting Level Else If</li> <li>CD - Comment Density</li> <li>CLOC - Comment Lines of Code</li> <li>DLOC - Documentation Lines of Code</li> <li>TCD - Total Comment Density (Comment Lines in an emedded function will be also considered)</li> <li>TCLOC - Total Comment Lines of Code (Comment Lines in an emedded function will be also considered)</li> <li>LLOC - Logical Lines of Code (Comment and empty lines not counted)</li> <li>LOC - Lines of Code (Comment and empty lines are counted)</li> <li>NOS - Number of Statements</li> <li>NUMPAR - Number of Parameters</li> <li>TLLOC - Logical Lines of Code (Lines in embedded functions are also counted)</li> <li>TLOC - Lines of Code (Lines in embedded functions are also counted)</li> <li>TNOS - Total Number of Statements (Statements in embedded functions are also counted)</li> </ul>
Supporting data for Novel functional sequences uncovered through a bovine multi-assembly graph
<p><strong>Description of the datasets</strong></p> <p>Data are organized as a folder and compressed with tar.gz.</p> <p>You need to unzip the folder using the command <em>tar -xz</em><em>v</em><em>f</em> data.tar.gz. Unzipping will output a folder named <em>data_tidy</em>, which is organized as follow:</p> <ul> <li>graph.gfa : Graph in GFA format constructed from 6 cattle assemblies</li> <li>nonref.fa : Non-reference sequences extracted from the graph</li> <li>nonref.fa.masked: Hard masked repetitive regions version of nonref.fa</li> <li>nonref_woflanking.fa: Nonref.fa without flanking sequences</li> <li>nonref_woflanking.fa.masked: Masked version of nonref_woflanking.fa</li> <li>augustus_predict.gtf: Annotated gene models of Augustus from non-ref sequences</li> <li>augustus_prot.fa: Protein fasta of the predicted gene models from Augustus</li> <li>breeds_assembled.gtf: Annotation of the StringTie assembled across-breed transcriptome</li> <li>breeds_expressed.tsv: Expression data of breeds_assembled.gtf</li> <li>de_assembled.gtf: Annotation of the StringTie assembled differentially-expressed transcriptome on non-ref sequences</li> <li>de_expression.tsv: Differential expression results from de_assembled.gtf</li> <li>variant_nonref.tsv: Variants called from non-ref sequences (-1, 0, 1, 2 indicates no call, hom ref, het, and hom alt respectively)</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.