cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.5907172.v1
title: Complex Alignment Benchmark: GeoLink Dataset

description: This is a real-world dataset from the GeoLink project as a potential complex alignment benchmark. The dataset consists of two ontologies, the GeoLink Base Ontology (GBO) and the GeoLink Modular Ontology (GMO), that were developed in consultation with numerous domain experts. The manually created alignment between these two ontologies includes 1:1, 1:n, and m:n equivalence and subsumption correspondences. The reference alignment for this benchmark is available in both EDOAL and rules syntax.<br><b>-ReadMe.txt:</b> ReadMe file that introduces the structure of the files<b>-gbo.owl:</b> GeoLink Base Ontology<b>-gmo.owl:</b> GeoLink Modular Ontology<b>-gbo-gmo.edoal:</b> the alignments in EDOAL syntax<b>-gbo-gmo-alignment.rdf:</b> the alignments in rule syntax <br>
DOI: 10.6084/m9.figshare.5907172.v2
title: Complex Alignment Benchmark: GeoLink Dataset

description: This is a real-world dataset from the GeoLink project as a potential complex alignment benchmark. The dataset consists of two ontologies, the GeoLink Base Ontology (GBO) and the GeoLink Modular Ontology (GMO), that were developed in consultation with numerous domain experts. The manually created alignment between these two ontologies includes 1:1, 1:n, and m:n equivalence and subsumption correspondences. The reference alignment for this benchmark is available in both EDOAL and rules syntax.<br><b>-ReadMe.txt:</b> ReadMe file that introduces the structure of the files<b>-gbo.owl:</b> GeoLink Base Ontology<b>-gmo.owl:</b> GeoLink Modular Ontology<b>-gbo-gmo.edoal:</b> the alignments in EDOAL syntax<b>-gbo-gmo-alignment.rdf:</b> the alignments in rule syntax <br>
DOI: 10.6084/m9.figshare.5907172.v3
title: Complex Alignment Benchmark: GeoLink Dataset

description: This is a real-world dataset from the GeoLink project as a potential complex alignment benchmark. The dataset consists of two ontologies, the GeoLink Base Ontology (GBO) and the GeoLink Modular Ontology (GMO), that were developed in consultation with numerous domain experts. The manually created alignment between these two ontologies includes 1:1, 1:n, and m:n equivalence and subsumption correspondences. The reference alignment for this benchmark is available in both EDOAL and rules syntax.<br><b>-ReadMe.txt:</b> ReadMe file that introduces the structure of the files<b>-gbo.owl:</b> GeoLink Base Ontology<b>-gmo.owl:</b> GeoLink Modular Ontology<b>-gbo-gmo.edoal:</b> the alignments in EDOAL format<b>-gbo-gmo-alignment.rdf:</b> the alignments in rule syntax <br>
DOI: 10.6084/m9.figshare.5907172.v4
title: Complex Alignment Benchmark: GeoLink Dataset

description: This is a real-world dataset from the GeoLink project as a potential complex alignment benchmark. The dataset consists of two ontologies, the GeoLink Base Ontology (GBO) and the GeoLink Modular Ontology (GMO), that were developed in consultation with numerous domain experts. The manually created alignment between these two ontologies includes 1:1, 1:n, and m:n equivalence and subsumption correspondences. The reference alignment for this benchmark is available in both EDOAL and rules syntax.<br><b>-ReadMe.txt:</b> ReadMe file that introduces the structure of the files<b>-gbo.owl:</b> GeoLink Base Ontology<b>-gmo.owl:</b> GeoLink Modular Ontology<b>-gbo-gmo.edoal:</b> the alignments in EDOAL format<b>-gbo-gmo-alignment.rdf:</b> the alignments in rule syntax <br>
DOI: 10.6084/m9.figshare.5907172.v5
title: Complex Alignment Benchmark: GeoLink Dataset

description: This is a real-world dataset from the GeoLink project as a potential complex alignment benchmark. The dataset consists of two ontologies, the GeoLink Base Ontology (GBO) and the GeoLink Modular Ontology (GMO), that were developed in consultation with numerous domain experts. The manually created alignment between these two ontologies includes 1:1, 1:n, and m:n equivalence and subsumption correspondences. The reference alignment for this benchmark is available in both EDOAL and rules syntax.<br><b>-ReadMe.txt:</b> ReadMe file that introduces the structure of the files<b>-gbo.owl:</b> GeoLink Base Ontology<b>-gmo.owl:</b> GeoLink Modular Ontology<b>-gbo-gmo.edoal:</b> the alignments in EDOAL format<b>-gbo-gmo-alignment.rdf:</b> the alignments in rule syntax <br>
DOI: 10.6084/m9.figshare.5907172.v6
title: Complex Alignment Benchmark: GeoLink Dataset

description: This is a real-world dataset from the GeoLink project as a potential complex alignment benchmark. The dataset consists of two ontologies, the GeoLink Base Ontology (GBO) and the GeoLink Modular Ontology (GMO), that were developed in consultation with numerous domain experts. The manually created alignment between these two ontologies includes 1:1, 1:n, and m:n equivalence and subsumption correspondences. The reference alignment for this benchmark is available in both EDOAL and rules syntax.<br><b>-ReadMe.txt:</b> ReadMe file that introduces the structure of the files<b>-gbo.owl:</b> GeoLink Base Ontology<b>-gmo.owl:</b> GeoLink Modular Ontology<b>-gbo-gmo.edoal:</b> the alignments in EDOAL format<b>-gbo-gmo-alignment.rdf:</b> the alignments in rule syntax <br>
DOI: 10.6084/m9.figshare.5907172.v7
title: Complex Alignment Benchmark: GeoLink Dataset

description: This is a real-world dataset from the GeoLink project as a potential complex alignment benchmark. The dataset consists of two ontologies, the GeoLink Base Ontology (GBO) and the GeoLink Modular Ontology (GMO), that were developed in consultation with numerous domain experts. The manually created alignment between these two ontologies includes 1:1, 1:n, and m:n equivalence and subsumption correspondences. The reference alignment for this benchmark is available in both EDOAL and rules syntax.<br><b>-ReadMe.txt:</b> ReadMe file that introduces the structure of the files<b>-gbo.owl:</b> GeoLink Base Ontology<b>-gmo.owl:</b> GeoLink Modular Ontology<b>-gbo-gmo.edoal:</b> the alignments in EDOAL format<b>-gbo-gmo-alignment.rdf:</b> the alignments in rule syntax <br>
DOI: 10.6084/m9.figshare.5907172.v8
title: Complex Alignment Benchmark: GeoLink Dataset

description: This is a real-world dataset from the GeoLink project as a potential complex alignment benchmark. The dataset consists of two ontologies, the GeoLink Base Ontology (GBO) and the GeoLink Modular Ontology (GMO), that were developed in consultation with numerous domain experts. The manually created alignment between these two ontologies includes 1:1, 1:n, and m:n equivalence and subsumption correspondences. The reference alignment for this benchmark is available in both EDOAL and rules syntax.<br><b>-ReadMe.txt:</b> ReadMe file that introduces the structure of the files<b>-gbo.owl:</b> GeoLink Base Ontology<b>-gmo.owl:</b> GeoLink Modular Ontology<b>-gbo-gmo.edoal:</b> the alignments in EDOAL format<b>-gbo-gmo-alignment.rdf:</b> the alignments in rule syntax <br>
DOI: 10.6084/m9.figshare.5907172.v9
title: Complex Alignment Benchmark: GeoLink Dataset

description: This is a real-world dataset from the GeoLink project as a potential complex alignment benchmark. The dataset consists of two ontologies, the GeoLink Base Ontology (GBO) and the GeoLink Modular Ontology (GMO), that were developed in consultation with numerous domain experts. The manually created alignment between these two ontologies includes 1:1, 1:n, and m:n equivalence and subsumption correspondences. The reference alignment for this benchmark is available in both EDOAL and rules syntax.<br><b>-ReadMe.txt:</b> ReadMe file that introduces the structure of the files<b>-gbo.owl:</b> GeoLink Base Ontology<b>-gmo.owl:</b> GeoLink Modular Ontology<b>-gbo-gmo.edoal:</b> the alignments in EDOAL format<b>-gbo-gmo-alignment.rdf:</b> the alignments in rule syntax <br>
DOI: 10.6084/m9.figshare.5907172.v10
title: Complex Alignment Benchmark: GeoLink Dataset

description: This is a real-world dataset from the GeoLink project as a potential complex alignment benchmark. The dataset consists of two ontologies, the GeoLink Base Ontology (GBO) and the GeoLink Modular Ontology (GMO), that were developed in consultation with numerous domain experts. The manually created alignment between these two ontologies includes 1:1, 1:n, and m:n equivalence and subsumption correspondences. The reference alignment for this benchmark is available in both EDOAL and rules syntax.<br><b>-ReadMe.txt:</b> ReadMe file that introduces the structure of the files<b>-gbo.owl:</b> GeoLink Base Ontology<b>-gmo.owl:</b> GeoLink Modular Ontology<b>-gbo-gmo.edoal:</b> the alignments in EDOAL format<b>-gbo-gmo-alignment.rdf:</b> the alignments in rule syntax <br>
DOI: 10.6084/m9.figshare.5907172.v11
title: Complex Alignment Benchmark: GeoLink Dataset

description: This is a real-world dataset from the GeoLink project as a potential complex alignment benchmark. The dataset consists of two ontologies, the GeoLink Base Ontology (GBO) and the GeoLink Modular Ontology (GMO), that were developed in consultation with numerous domain experts. The manually created alignment between these two ontologies includes 1:1, 1:n, and m:n equivalence and subsumption correspondences. The reference alignment for this benchmark is available in both EDOAL and rules syntax.<br><b>-ReadMe.txt:</b> ReadMe file that introduces the structure of the files<b>-gbo.owl:</b> GeoLink Base Ontology<b>-gmo.owl:</b> GeoLink Modular Ontology<b>-gbo-gmo.edoal:</b> the alignments in EDOAL format<b>-gbo-gmo-alignment.rdf:</b> the alignments in rule syntax <br>
DOI: 10.6084/m9.figshare.5907172
title: GeoLink Dataset: Complex Ontology Alignment Benchmark

description: This is a real-world dataset from the GeoLink project as a potential complex alignment benchmark. The dataset consists of two ontologies, the GeoLink Base Ontology (GBO) and the GeoLink Modular Ontology (GMO), that were developed in consultation with numerous domain experts. The manually created alignment between these two ontologies includes 1:1, 1:n, and m:n equivalence and subsumption correspondences. The reference alignment for this benchmark is available in both EDOAL and rules syntax.<br><b>-ReadMe.txt:</b> ReadMe file that introduces the structure of the files<b>-gbo.owl:</b> GeoLink Base Ontology<b>-gmo.owl:</b> GeoLink Modular Ontology<b>-gboWithInstance.owl:</b> GeoLink Base Ontology with instance data populated<b>-gmoWithInstance.owl:</b> GeoLink Modular Ontology with instance data populated<b>-gbo-gmo.edoal:</b> the alignments in EDOAL format<b>-gbo-gmo-alignment.rdf:</b> the alignments in rule syntax <br><br>
DOI: 10.6084/m9.figshare.5907055
title: APPREAL (APProximate REduction of Automata and Languages) tool used to obtain experimental results in TACAS '18 paper: Approximate Reduction of Finite Automata for High-Speed Network Intrusion Detection

description: This is an artifact containing the tool APPREAL (APProximate REduction of Automata and Languages) used to obtain experimental results in the following paper accepted for publication at TACAS'18:<br>M. Ceska, V. Havlena, L. Holik, O. Lengal, and T. Vojnar. <i>Approximate Reduction of Finite Automata for High-Speed Network Intrusion Detection.</i><br>The approach outlines in the related TACAS'18 paper is implemented in a Python prototype in this repository. Other file type include openly accessible C-specific <b>.c</b> and<b> .h </b>formats, .html, Perl (.pcre) and openly-accessible text file formats.<br>The subdirectory <b>.src </b>contains source code relating to the reduction of NFAs (nondeterministic finite automata) obtained from PCREs (Perl compatible regular expressions) that occur in [Snort](https://www.snort.org) rules:<br>* `experiments/` - the setting of our experiments* `netbench/` - the Netbench tool that we use to transform PCREs into NFAs* `preproc/` - a bunch of small programs used for pre-processing network traffic PCAP files* `reduce/` - the tool performing the reduction itself* `regexps/` - regular expressions that we have collected<br>The subdirectory <b>.packages </b>contains required software packages and a shell file with bash script (install.sh) to install these packages,<br><b>Virtual Machine</b><br>The artifact is prepared to run on the TACAS'18 artifact evaluation virtual machine available here: https://doi.org/10.6084/m9.figshare.5896615.v1<br>2048 MiB of memory should be sufficient to reproduce the results from Tables 1a and 1b in the related proceedings paper, for reproducing the experiments from Tables 2 and 3, it is recommended to set the memory to 8192 MiB or more.<br>Detailed instructions on how to reproduce results from the TACAS'18 paper are available in <b>README.txt</b><b><br></b><b>Background</b><b><br></b>The related TACAS'18 paper considers the problem of approximate reduction of non-deterministic automata that appear in hardware-accelerated network intrusion detection systems (NIDSes). We define an error distance of a reduced automaton from the original one as the probability of packets being incorrectly classified by the reduced automaton (wrt the probabilistic distribution of packets in the network traffic). We use this notion to design an approximate reduction procedure that achieves a great size reduction (much beyond the state-of-the-art language preserving techniques) with a controlled and small error. We have implemented our approach and evaluated it on use cases from SNORT, a popular NIDS. Our results provide experimental evidence that the method can be highly efficient in practice, allowing NIDSes to follow the rapid growth in the speed of networks.<br>
DOI: 10.5281/zenodo.4008442
title: Supply and demand shocks in the COVID-19 pandemic: An industry and occupation perspective

description: Supply and demand shocks in the COVID-19 pandemic: An industry and occupation perspective<br> R. Maria del Rio-Chanona, Penny Mealy, Anton Picheler, Francois Lafond, J. Doyne Farmer<br> contact:<br> Results <br> The supply, demand, and total shocks at the industry and occupation level are in files: industry_variables_and_shock.csv<br> occupation_variables_and_shock.csv To reproduce our results we also include<br> The employment data between industries and occupations<br> industry_occupation_employment.csv<br> The classification of work activities<br> iwa_remotelabor_labels.csv<br> The essential score of industries at the NAICS 4d level<br> essential_score_industries_naics_4d_rev.csv<br> Update with respect to the previous version We have now included our code to reproduce our study from scratch.
DOI: 10.5281/zenodo.3744959
title: Supply and demand shocks in the COVID-19 pandemic: An industry and occupation perspective

description: Supply and demand shocks in the COVID-19 pandemic: An industry and occupation perspective<br> R. Maria del Rio-Chanona, Penny Mealy, Anton Picheler, Francois Lafond, J. Doyne Farmer<br> contact:<br> Results <br> The supply, demand, and total shocks at the industry and occupation level are in files: industry_variables_and_shock.csv<br> occupation_variables_and_shock.csv To reproduce our results we also include<br> The employment data between industries and occupations<br> industry_occupation_employment.csv<br> The classification of work activities<br> iwa_remotelabor_labels.csv<br> The essential score of industries at the NAICS 4d level<br> essential_score_industries_naics_4d_rev.csv<br> Update with respect to the previous version We have now included our code to reproduce our study from scratch.
DOI: 10.6084/m9.figshare.5906461
title: Determination of 105 antibiotic, anti-inflammatory, antiparasitic agents and tranquilizers by LC-MS/MS based on an acidic QuEChERS-like extraction

description: A procedure for screening 105 veterinary drugs in foods by liquid chromatography tandem mass-spectrometry (LC-MS/MS) is presented. Its scope encompasses raw materials of animal origin (milk, meat, fish, egg and fat) but also related processed ingredients and finished products commonly used and manufactured by food business operators. Due to the complexity of the matrices considered and to efficiently deal with losses during extraction and matrix effects during MS source ionisation, each sample was analysed twice, that is ‘unspiked’ and ‘spiked at the screening target concentration’ using a QuEChERS-like extraction. The entire procedure was validated according to the European Community Reference Laboratories Residues Guidelines. False-negative and false-positive rates were below 5% for all veterinary drugs whatever the food matrix. Effectiveness of the procedure was further demonstrated through participation to five proficiency tests and its ruggedness demonstrated in quality control operations by a second laboratory.
DOI: 10.6084/m9.figshare.5906461.v1
title: Determination of 105 antibiotic, anti-inflammatory, antiparasitic agents and tranquilizers by LC-MS/MS based on an acidic QuEChERS-like extraction

description: A procedure for screening 105 veterinary drugs in foods by liquid chromatography tandem mass-spectrometry (LC-MS/MS) is presented. Its scope encompasses raw materials of animal origin (milk, meat, fish, egg and fat) but also related processed ingredients and finished products commonly used and manufactured by food business operators. Due to the complexity of the matrices considered and to efficiently deal with losses during extraction and matrix effects during MS source ionisation, each sample was analysed twice, that is ‘unspiked’ and ‘spiked at the screening target concentration’ using a QuEChERS-like extraction. The entire procedure was validated according to the European Community Reference Laboratories Residues Guidelines. False-negative and false-positive rates were below 5% for all veterinary drugs whatever the food matrix. Effectiveness of the procedure was further demonstrated through participation to five proficiency tests and its ruggedness demonstrated in quality control operations by a second laboratory.
DOI: 10.6084/m9.figshare.5906086
title: A novel method for automated assessment of megakaryocyte differentiation and proplatelet formation

description: Transfusion of platelet concentrates represents an important treatment for various bleeding complications. However, the short half-life and frequent contaminations with bacteria restrict the availability of platelet concentrates and raise a clear demand for platelets generated <i>ex vivo</i>. Therefore, <i>in vitro</i> platelet generation from megakaryocytes represents an important research topic. A vital step for this process represents accurate analysis of thrombopoiesis and proplatelet formation, which is usually conducted manually. We aimed to develop a novel method for automated classification and analysis of proplatelet-forming megakaryocytes <i>in vitro</i>. After fluorescent labelling of surface and nucleus, MKs were automatically categorized and analysed with a novel pipeline of the open source software <i>CellProfiler</i>. Our new workflow is able to detect and quantify four subtypes of megakaryocytes undergoing thrombopoiesis: proplatelet-forming, spreading, pseudopodia-forming and terminally differentiated, anucleated megakaryocytes. Furthermore, we were able to characterize the inhibitory effect of dasatinib on thrombopoiesis in more detail. Our new workflow enabled rapid, unbiased, quantitative and qualitative in-depth analysis of proplatelet formation based on morphological characteristics. Clinicians and basic researchers alike will benefit from this novel technique that allows reliable and unbiased quantification of proplatelet formation. It thereby provides a valuable tool for the development of methods to generate platelets <i>ex vivo</i> and to detect effects of drugs on megakaryocyte differentiation.
DOI: 10.6084/m9.figshare.5906086.v2
title: A novel method for automated assessment of megakaryocyte differentiation and proplatelet formation

description: Transfusion of platelet concentrates represents an important treatment for various bleeding complications. However, the short half-life and frequent contaminations with bacteria restrict the availability of platelet concentrates and raise a clear demand for platelets generated <i>ex vivo</i>. Therefore, <i>in vitro</i> platelet generation from megakaryocytes represents an important research topic. A vital step for this process represents accurate analysis of thrombopoiesis and proplatelet formation, which is usually conducted manually. We aimed to develop a novel method for automated classification and analysis of proplatelet-forming megakaryocytes <i>in vitro</i>. After fluorescent labelling of surface and nucleus, MKs were automatically categorized and analysed with a novel pipeline of the open source software <i>CellProfiler</i>. Our new workflow is able to detect and quantify four subtypes of megakaryocytes undergoing thrombopoiesis: proplatelet-forming, spreading, pseudopodia-forming and terminally differentiated, anucleated megakaryocytes. Furthermore, we were able to characterize the inhibitory effect of dasatinib on thrombopoiesis in more detail. Our new workflow enabled rapid, unbiased, quantitative and qualitative in-depth analysis of proplatelet formation based on morphological characteristics. Clinicians and basic researchers alike will benefit from this novel technique that allows reliable and unbiased quantification of proplatelet formation. It thereby provides a valuable tool for the development of methods to generate platelets <i>ex vivo</i> and to detect effects of drugs on megakaryocyte differentiation.
DOI: 10.6084/m9.figshare.5906074
title: Value-Added and Student Growth Percentile Models: What Drives Differences in Estimated Classroom Effects?

description: This study shows value-added models (VAM) and student growth percentile (SGP) models fundamentally disagree regarding estimated teacher effectiveness when the classroom distribution of test scores conditional on prior achievement is skewed (i.e., when a teacher serves a disproportionate number of high- or low-growth students). While conceptually similar, the two models differ in estimation method which can lead to sizable differences in estimated teacher effects. Moreover, the magnitude of conditional skewness needed to drive VAM and SGP models apart often by three and up to 6 deciles is within the ranges observed in actual data. The same teacher may appear weak using one model and strong with the other. Using a simulation, I evaluate the relationship under controllable conditions. I then verify that the results persist in observed student–teacher data from North Carolina.
DOI: 10.6084/m9.figshare.5906074.v2
title: Value-Added and Student Growth Percentile Models: What Drives Differences in Estimated Classroom Effects?

description: This study shows value-added models (VAM) and student growth percentile (SGP) models fundamentally disagree regarding estimated teacher effectiveness when the classroom distribution of test scores conditional on prior achievement is skewed (i.e., when a teacher serves a disproportionate number of high- or low-growth students). While conceptually similar, the two models differ in estimation method which can lead to sizable differences in estimated teacher effects. Moreover, the magnitude of conditional skewness needed to drive VAM and SGP models apart often by three and up to 6 deciles is within the ranges observed in actual data. The same teacher may appear weak using one model and strong with the other. Using a simulation, I evaluate the relationship under controllable conditions. I then verify that the results persist in observed student–teacher data from North Carolina.