cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.701284.v5
title: QAW: A Quality Assurance Workflow for Ontologies based on Detecting Semantic Regularities - Dataset on SNOMED

description: This page contains supplementary material for the ISWC 2013 submission with title: <em>"QAW: A Quality Assurance Workflow for Ontologies based on Detecting Semantic Regularities" </em>Eleni Mikroyannidi, Manuel Quesada-Mart ́ınez, Dmitry Tsarkov, Jesualdo Tomas Fernandez Breis, Robert Stevens, Ignazio Palmisano. The fileset contains the data for the qualitative and quantititative analysis that were presented in the paper. In the <strong>qualitative analysis</strong>, six lexical patterns (keywords) that were processed. These are: "chronic","acute", "absent", "present", "right", "left". For these ones the reader can browse and download the following data: 1. XML Files with the generic name "<strong>keyword"_syntactic_usage.xml</strong> which contains the detected syntactic regularities for the referencing asserted axioms of the entities that contain the corresponding keyword in their label. There should be 6 files in total (for each keyword). 2. XML Files with the generic name "<strong>keyword"_semantic_usage.xml</strong> which contains the detected syntactic regularities for the referencing asserted axioms of the entities that contain the keyword in their label. In the <strong>quantitative analysis</strong>, 308 lexical patterns were processed, and corresponding syntactic and semantic regularities were detected. The dataset that is available for the reader contains the following: 1. <strong>LexAnal_Snomed_2013_NoSensitiveAnalysis_Cov_0.1_100.0.xml</strong>, which contains all lexical patterns that could be detected in the SNOMED-CT version January 2013. 2. <strong>Snomed_2013_LexAnal_Full_0.1-0.4Perc_.xml</strong>, which contains all lexical patterns with 0.1%-0.4% lexical pattern threshold. 3. <strong>syntactic_regularities_dataset.zip</strong> which contains 308 xml files with the syntactic regularities that were generated by RIO. 4. <strong>semantic_regularities_dataset.zip</strong> which contains 308 xml files with the semantic regularities that were generated by RIO. 5. <strong>quantitative_syntactic_regularity_analysis.csv</strong>, which contains the syntactic regularity stat analysis for the 308 processed cases. 6.<strong> quantitative_semantic_regularity_analysis.csv, </strong>which contains the semantic regularity stat analysis for the 308 processed cases.
DOI: 10.6084/m9.figshare.701173
title: Northeastern North American bee species information on population trends and ecosystem services delivered

description: Regional-scale trend from Bartomeus et al. (2013) in relative abundance calculated over the period 1870-2011. Local-scale status from Burkle et al. (2013) shows extinction or persistence in Carleville, Illinois. Ecosystem Service Provider is classified in non-ESP (not ecosystem-service providers to the studied crops), ESP (occasionally ecosystem-service providers to the studied crops), and main ESP (among the species cumulatively responsible for 90% of the pollinator visitation to at least one of the studied crop) according to data from Garibaldi et al. (2013). See main article for references.
DOI: 10.6084/m9.figshare.701203.v1
title: Query Strategies to Support Context-Specific Views Through Stand-off Data Mappings

description: Complete set of graphs for the experiments in the paper with the same name.
DOI: 10.6084/m9.figshare.701284.v6
title: QAW: A Quality Assurance Workflow for Ontologies based on Detecting Semantic Regularities - Dataset on SNOMED

description: This page contains supplementary material for the ISWC 2013 submission with title: <em>"QAW: A Quality Assurance Workflow for Ontologies based on Detecting Semantic Regularities" </em>Eleni Mikroyannidi, Manuel Quesada-Mart ́ınez, Dmitry Tsarkov, Jesualdo Tomas Fernandez Breis, Robert Stevens, Ignazio Palmisano. The fileset contains the data for the qualitative and quantititative analysis that were presented in the paper. In the <strong>qualitative analysis</strong>, six lexical patterns (keywords) that were processed. These are: "chronic","acute", "absent", "present", "right", "left". For these ones the reader can browse and download the following data: 1. XML Files with the generic name "<strong>keyword"_syntactic_usage.xml</strong> which contains the detected syntactic regularities for the referencing asserted axioms of the entities that contain the corresponding keyword in their label. There should be 6 files in total (for each keyword). 2. XML Files with the generic name "<strong>keyword"_semantic_usage.xml</strong> which contains the detected syntactic regularities for the referencing asserted axioms of the entities that contain the keyword in their label. In the <strong>quantitative analysis</strong>, 308 lexical patterns were processed, and corresponding syntactic and semantic regularities were detected. The dataset that is available for the reader contains the following: 1. <strong>LexAnal_Snomed_2013_NoSensitiveAnalysis_Cov_0.1_100.0.xml</strong>, which contains all lexical patterns that could be detected in the SNOMED-CT version January 2013. 2. <strong>Snomed_2013_LexAnal_Full_0.1-0.4Perc_.xml</strong>, which contains all lexical patterns with 0.1%-0.4% lexical pattern threshold. 3. <strong>syntactic_regularities_dataset.zip</strong> which contains 308 xml files with the syntactic regularities that were generated by RIO. 4. <strong>semantic_regularities_dataset.zip</strong> which contains 308 xml files with the semantic regularities that were generated by RIO. 5. <strong>quantitative_syntactic_regularity_analysis.csv</strong>, which contains the syntactic regularity stat analysis for the 308 processed cases. 6.<strong> quantitative_semantic_regularity_analysis.csv, </strong>which contains the semantic regularity stat analysis for the 308 processed cases.
DOI: 10.6084/m9.figshare.701203.v2
title: Query Strategies to Support Context-Specific Views Through Stand-off Data Mappings

description: Raw results file used to generate the graphs. Complete set of graphs for the experiments in the paper with the same name.
DOI: 10.6084/m9.figshare.701203.v4
title: Query Strategies to Support Context-Specific Views Through Stand-off Data Mappings

description: Raw results file used to generate the graphs. Complete set of graphs for the experiments in the paper with the same name.
DOI: 10.6084/m9.figshare.701203.v3
title: Query Strategies to Support Context-Specific Views Through Stand-off Data Mappings

description: Raw results file used to generate the graphs. Complete set of graphs for the experiments in the paper with the same name.
DOI: 10.6084/m9.figshare.701284.v7
title: QAW: A Quality Assurance Workflow for Ontologies based on Detecting Semantic Regularities - Dataset on SNOMED

description: This page contains supplementary material for the ISWC 2013 submission with title: <em>"QAW: A Quality Assurance Workflow for Ontologies based on Detecting Semantic Regularities" </em>Eleni Mikroyannidi, Manuel Quesada-Mart ́ınez, Dmitry Tsarkov, Jesualdo Tomas Fernandez Breis, Robert Stevens, Ignazio Palmisano. The fileset contains the data for the qualitative and quantititative analysis that were presented in the paper. In the <strong>qualitative analysis</strong>, six lexical patterns (keywords) that were processed. These are: "chronic","acute", "absent", "present", "right", "left". For these ones the reader can browse and download the following data: 1. XML Files with the generic name "<strong>keyword"_syntactic_usage.xml</strong> which contains the detected syntactic regularities for the referencing asserted axioms of the entities that contain the corresponding keyword in their label. There should be 6 files in total (for each keyword). 2. XML Files with the generic name "<strong>keyword"_semantic_usage.xml</strong> which contains the detected syntactic regularities for the referencing asserted axioms of the entities that contain the keyword in their label. In the <strong>quantitative analysis</strong>, 308 lexical patterns were processed, and corresponding syntactic and semantic regularities were detected. The dataset that is available for the reader contains the following: 1. <strong>LexAnal_Snomed_2013_NoSensitiveAnalysis_Cov_0.1_100.0.xml</strong>, which contains all lexical patterns that could be detected in the SNOMED-CT version January 2013. 2. <strong>Snomed_2013_LexAnal_Full_0.1-0.4Perc_.xml</strong>, which contains all lexical patterns with 0.1%-0.4% lexical pattern threshold. 3. <strong>syntactic_regularities_dataset.zip</strong> which contains 308 xml files with the syntactic regularities that were generated by RIO. 4. <strong>semantic_regularities_dataset.zip</strong> which contains 308 xml files with the semantic regularities that were generated by RIO. 5. <strong>quantitative_syntactic_regularity_analysis.csv</strong>, which contains the syntactic regularity stat analysis for the 308 processed cases. 6.<strong> quantitative_semantic_regularity_analysis.csv, </strong>which contains the semantic regularity stat analysis for the 308 processed cases.
DOI: 10.6084/m9.figshare.701284.v9
title: QAW: A Quality Assurance Workflow for Ontologies based on Detecting Semantic Regularities - Dataset on SNOMED

description: This page contains supplementary material for the EKAW 2014 submission with title: <em>"QAW: A Quality Assurance Workflow for Ontologies based on Detecting Semantic Regularities" </em>Eleni Mikroyannidi, Manuel Quesada-Mart ́ınez, Dmitry Tsarkov, Jesualdo Tomas Fernandez Breis, Robert Stevens, Ignazio Palmisano. The detection of regularities was done with the Regularity Inspector for Ontologies framework. The project is open source and can be downloaded from the Links below. The fileset contains the data for the qualitative and quantititative analysis that were presented in the paper. In the <strong>qualitative analysis</strong>, six lexical patterns (keywords) that were processed. These are: "chronic","acute", "absent", "present", "right", "left". For these ones the reader can browse and download the following data: 1. XML Files with the generic name $<strong>keyword_syntactic_usage.xml</strong> which contains the detected syntactic regularities for the referencing asserted axioms of the entities that contain the corresponding keyword in their label. There should be 6 files in total (for each keyword). 2. XML Files with the generic name $<strong>keyword_semantic_usage.xml</strong> which contains the detected syntactic regularities for the referencing asserted axioms of the entities that contain the keyword in their label. 3. Text files with the generic name $<strong>keyword_syntactic_usage_readable.txt</strong>, which contains a more readable format with label rendering of the syntactic regularities. 3. Text files with the generic name $<strong>keyword_semantic_usage_readable.txt</strong>, which contains a more readable format with label rendering of the semantic regularities. In the <strong>quantitative analysis</strong>, 308 lexical patterns were processed, and corresponding syntactic and semantic regularities were detected. The dataset that is available for the reader contains the following: 1. <strong>LexAnal_Snomed_2013_NoSensitiveAnalysis_Cov_0.1_100.0.xml</strong>, which contains all lexical patterns that could be detected in the SNOMED-CT version January 2013. 2. <strong>Snomed_2013_LexAnal_Full_0.1-0.4Perc_.xml</strong>, which contains all lexical patterns with 0.1%-0.4% lexical pattern threshold. 3. <strong>syntactic_regularities_dataset.zip</strong> which contains 308 xml files with the syntactic regularities that were generated by RIO. 4. <strong>semantic_regularities_dataset.zip</strong> which contains 308 xml files with the semantic regularities that were generated by RIO. 5. <strong>quantitative_syntactic_regularity_analysis.csv</strong>, which contains the syntactic regularity stat analysis for the 308 processed cases. 6.<strong> quantitative_semantic_regularity_analysis.csv, </strong>which contains the semantic regularity stat analysis for the 308 processed cases.
DOI: 10.6084/m9.figshare.701284.v8
title: QAW: A Quality Assurance Workflow for Ontologies based on Detecting Semantic Regularities - Dataset on SNOMED

description: This page contains supplementary material for the ISWC 2013 submission with title: <em>"QAW: A Quality Assurance Workflow for Ontologies based on Detecting Semantic Regularities" </em>Eleni Mikroyannidi, Manuel Quesada-Mart ́ınez, Dmitry Tsarkov, Jesualdo Tomas Fernandez Breis, Robert Stevens, Ignazio Palmisano. The detection of regularities was done with the Regularity Inspector for Ontologies framework. The project is open source and can be downloaded from the Links below. The fileset contains the data for the qualitative and quantititative analysis that were presented in the paper. In the <strong>qualitative analysis</strong>, six lexical patterns (keywords) that were processed. These are: "chronic","acute", "absent", "present", "right", "left". For these ones the reader can browse and download the following data: 1. XML Files with the generic name "<strong>keyword"_syntactic_usage.xml</strong> which contains the detected syntactic regularities for the referencing asserted axioms of the entities that contain the corresponding keyword in their label. There should be 6 files in total (for each keyword). 2. XML Files with the generic name "<strong>keyword"_semantic_usage.xml</strong> which contains the detected syntactic regularities for the referencing asserted axioms of the entities that contain the keyword in their label. 3. Text files with the generic name <strong>"keyword"_syntactic_usage_readable.txt</strong>, which contains a more readable format with label rendering of the syntactic regularities. 3. Text files with the generic name <strong>"keyword"_semantic_usage_readable.txt</strong>, which contains a more readable format with label rendering of the semantic regularities. In the <strong>quantitative analysis</strong>, 308 lexical patterns were processed, and corresponding syntactic and semantic regularities were detected. The dataset that is available for the reader contains the following: 1. <strong>LexAnal_Snomed_2013_NoSensitiveAnalysis_Cov_0.1_100.0.xml</strong>, which contains all lexical patterns that could be detected in the SNOMED-CT version January 2013. 2. <strong>Snomed_2013_LexAnal_Full_0.1-0.4Perc_.xml</strong>, which contains all lexical patterns with 0.1%-0.4% lexical pattern threshold. 3. <strong>syntactic_regularities_dataset.zip</strong> which contains 308 xml files with the syntactic regularities that were generated by RIO. 4. <strong>semantic_regularities_dataset.zip</strong> which contains 308 xml files with the semantic regularities that were generated by RIO. 5. <strong>quantitative_syntactic_regularity_analysis.csv</strong>, which contains the syntactic regularity stat analysis for the 308 processed cases. 6.<strong> quantitative_semantic_regularity_analysis.csv, </strong>which contains the semantic regularity stat analysis for the 308 processed cases.
DOI: 10.6084/m9.figshare.700733
title: Preliminary cummeRbund Analysis of Anopheles gambiae midgut-specific RNA-seq bloodfed time-course 0-10h

description: Preliminary cummeRbund Analysis of Anopheles gambiae midgut-specific RNA-seq bloodfed time-course 0-10h
DOI: 10.6084/m9.figshare.700714
title: Graph Sampling for Linked Data: SP2Bench Results

description: Results for Graph Sampling for Linked Data, a submission for ISWC 2013<br>We apply different rewrite methods to transform our RDF graph into an unweighted directed graph with unlabelled edges. The rewritten graph is analysed using different standard network algorithms, after which the weights are aggregated back to the RDF triples.<br>We measure the quality of the subgraph using query logs, where we calculate the recall by executing each query on the subgraph as well as the original graph.<br>Navigatable results (i.e. html) are available here:<br>http://data2semantics.github.io/GraphSampling/
DOI: 10.6084/m9.figshare.700733.v1
title: Preliminary cummeRbund Analysis of Anopheles gambiae midgut-specific RNA-seq bloodfed time-course 0-10h

description: Preliminary cummeRbund Analysis of Anopheles gambiae midgut-specific RNA-seq bloodfed time-course 0-10h
DOI: 10.6084/m9.figshare.700712
title: Graph Sampling for Linked Data: Semantic Web Dog Food Results

description: Results for Graph Sampling for Linked Data, a submission for ISWC 2013<br>We apply different rewrite methods to transform our RDF graph into an unweighted directed graph with unlabelled edges. The rewritten graph is analysed using different standard network algorithms, after which the weights are aggregated back to the RDF triples.<br>We measure the quality of the subgraph using query logs, where we calculate the recall by executing each query on the subgraph as well as the original graph.<br>Navigatable results (i.e. html) are available here:<br>http://data2semantics.github.io/GraphSampling/
DOI: 10.6084/m9.figshare.700710
title: Graph Sampling for Linked Data: Linked Movie Database Results

description: Results for Graph Sampling for Linked Data, a submission for ISWC 2013<br>We apply different rewrite methods to transform our RDF graph into an unweighted directed graph with unlabelled edges. The rewritten graph is analysed using different standard network algorithms, after which the weights are aggregated back to the RDF triples.<br>We measure the quality of the subgraph using query logs, where we calculate the recall by executing each query on the subgraph as well as the original graph.<br>Navigatable results (i.e. html) are available here:<br>http://data2semantics.github.io/GraphSampling/
DOI: 10.6084/m9.figshare.700669
title: Graph Sampling for Linked Data: DBPedia Results

description: Results for Graph Sampling for Linked Data, a submission for ISWC 2013 We apply different rewrite methods to transform our RDF graph into an unweighted directed graph with unlabelled edges. The rewritten graph is analysed using different standard network algorithms, after which the weights are aggregated back to the RDF triples. We measure the quality of the subgraph using query logs, where we calculate the recall by executing each query on the subgraph as well as the original graph. Navigatable results (i.e. html) are available here:<br>http://data2semantics.github.io/GraphSampling/
DOI: 10.6084/m9.figshare.700662
title: Rhodopsin photocascade

description: This LaTeX document generates a figure of the conformational changes in Rhodopsin following absorption of a photon(hV). Following absorption of light, the 11-cis-retinal chromophore isomerizes to the all-trans configuration within 200 fs and then relaxes thermally through a series of spectrally well-defined intermediates to the active Meta II state, which binds and activates the G protein transducin. Hydrolysis of the Schiffs base linkage generates opsin and frees all-trans retinal. BSI blue light sensitive intermediate. Adapted from Smith (2010)
DOI: 10.6084/m9.figshare.700712.v2
title: Graph Sampling for Linked Data: Semantic Web Dog Food Results

description: Results for Graph Sampling for Linked Data, a submission for ISWC 2013<br>We apply different rewrite methods to transform our RDF graph into an unweighted directed graph with unlabelled edges. The rewritten graph is analysed using different standard network algorithms, after which the weights are aggregated back to the RDF triples.<br>We measure the quality of the subgraph using query logs, where we calculate the recall by executing each query on the subgraph as well as the original graph.<br>Navigatable results (i.e. html) are available here:<br>http://data2semantics.github.io/GraphSampling/
DOI: 10.6084/m9.figshare.700714.v2
title: Graph Sampling for Linked Data: SP2Bench Results

description: Results for Graph Sampling for Linked Data, a submission for ISWC 2013<br>We apply different rewrite methods to transform our RDF graph into an unweighted directed graph with unlabelled edges. The rewritten graph is analysed using different standard network algorithms, after which the weights are aggregated back to the RDF triples.<br>We measure the quality of the subgraph using query logs, where we calculate the recall by executing each query on the subgraph as well as the original graph.<br>Navigatable results (i.e. html) are available here:<br>http://data2semantics.github.io/GraphSampling/
DOI: 10.6084/m9.figshare.700669.v2
title: Graph Sampling for Linked Data: DBPedia Results

description: Results for Graph Sampling for Linked Data, a submission for ISWC 2013 We apply different rewrite methods to transform our RDF graph into an unweighted directed graph with unlabelled edges. The rewritten graph is analysed using different standard network algorithms, after which the weights are aggregated back to the RDF triples. We measure the quality of the subgraph using query logs, where we calculate the recall by executing each query on the subgraph as well as the original graph. Navigatable results (i.e. html) are available here:<br>http://data2semantics.github.io/GraphSampling/
DOI: 10.6084/m9.figshare.700714.v1
title: Graph Sampling for Linked Data: SP2Bench Results

description: Results for Graph Sampling for Linked Data, a submission for ISWC 2013<br>We apply different rewrite methods to transform our RDF graph into an unweighted directed graph with unlabelled edges. The rewritten graph is analysed using different standard network algorithms, after which the weights are aggregated back to the RDF triples.<br>We measure the quality of the subgraph using query logs, where we calculate the recall by executing each query on the subgraph as well as the original graph.<br>Navigatable results (i.e. html) are available here:<br>http://data2semantics.github.io/GraphSampling/
DOI: 10.6084/m9.figshare.700662.v2
title: Rhodopsin photocascade

description: This LaTeX document generates a figure of the conformational changes in Rhodopsin following absorption of a photon(hV). Following absorption of light, the 11-cis-retinal chromophore isomerizes to the all-trans configuration within 200 fs and then relaxes thermally through a series of spectrally well-defined intermediates to the active Meta II state, which binds and activates the G protein transducin. Hydrolysis of the Schiffs base linkage generates opsin and frees all-trans retinal. BSI blue light sensitive intermediate. Adapted from Smith (2010)
DOI: 10.6084/m9.figshare.700669.v1
title: Graph Samping for Linked Data: DBPedia Results

description: Results for Graph Sampling for Linked Data, a submission for ISWC 2013 We apply different rewrite methods to transform our RDF graph into an unweighted directed graph with unlabelled edges. The rewritten graph is analysed using different standard network algorithms, after which the weights are aggregated back to the RDF triples. We measure the quality of the subgraph using query logs, where we calculate the recall by executing each query on the subgraph as well as the original graph. Navigatable results (i.e. html) are available here:<br>http://data2semantics.github.io/GraphSampling/
DOI: 10.6084/m9.figshare.700712.v1
title: Graph Sampling for Linked Data: Semantic Web Dog Food Results

description: Results for Graph Sampling for Linked Data, a submission for ISWC 2013<br>We apply different rewrite methods to transform our RDF graph into an unweighted directed graph with unlabelled edges. The rewritten graph is analysed using different standard network algorithms, after which the weights are aggregated back to the RDF triples.<br>We measure the quality of the subgraph using query logs, where we calculate the recall by executing each query on the subgraph as well as the original graph.<br>Navigatable results (i.e. html) are available here:<br>http://data2semantics.github.io/GraphSampling/