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Dataset results
22 results for “Axioms”
Uncovering the Semantics of Wikipedia Categories - Axioms and Assertions
<p>Resulting axioms and assertions from applying the Cat2Ax approach to the DBpedia knowledge graph.<br> The methodology is described in the conference publication "N. Heist, H. Paulheim: Uncovering the Semantics of Wikipedia Categories, International Semantic Web Conference, 2019".</p>
Verifying Ontology Restrictions: Pizza Axioms
<p>Data created fot the experimental investigations performed in [1] and [2]. </p> <ul> <li><strong><em>PizzaExperiment-Dataset-HIT-Sources.xlsx</em></strong> contains 90 Examples (30 Ontology Axioms, represented in 3 formalism) [1] <ul> <li>For each axioms a correct and a corrupted version is provided with indication of the concrete defect type</li> </ul> </li> <li><em><strong>PizzaExperiment-Dataset-GPT Annottaions.xlsx </strong>-</em> an extension of the previuos dataset, inlcuding an additional TTL-representational formalism as well as GPT-4 annotations of each axiom [2].</li> </ul> <p>[1] <span>S. Tsaneva</span> and <span>M. Sabou</span>, “Enhancing Human-in-the-Loop Ontology Curation Results through Task Design,” Journal of Data and Information Quality, 2023.</p> <p>[2] <span>S. Tsaneva</span>, S. Vasic, and <span>M. Sabou</span>, “LLM-driven Ontology Evaluation: Verifying Ontology Restrictions with ChatGPT,” in The Semantic Web: ESWC Satellite Events, 2024.</p> <p> </p>
Data and scripts to reproduce the results shown in "Stability of attractor local dimension estimates in non-Axiom A dynamical systems"
<p>Here we make available all the codes and datasets to reproduce the results of the paper "Stability of attractor local dimension estimates in non-Axiom A dynamical systems" by Flavio Pons, Gabriele Messori and Davide Faranda.</p> <p>The pre-print of the article is available at https://hal.science/hal-04051659/document.</p> <p>Any question/comment can be sent to flavio.pons@gmail.com.</p> <p>License for the codes and simulation/analysis results (*.Rda files): the code is shared under the Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license, see https://creativecommons.org/licenses/by-nc-sa/4.0/</p> <p>License and terms of use for the ERA5 data (z500_daily_euro.nc): the ERA5 500 hPa geopotential was downloaded from https://climexp.knmi.nl/start.cgi</p>
AXIOM Face Database
<p>The main purpose of building the AXIOM Face Database was to gather challenging pictures for fine-tuning object recognition algorithms for facial analysis based on state-of-the-art machine learning techniques. Additionally, the database was also developed with the aim of enriching with supplementary human-annotated pictures existing datasets that are used for training demographic face estimation algorithms.<br> By taking into account these initial requirements, a team in the University of Siena collected pictures of faces of people who previously provided their written consent to participate in the experiment. The AXIOM Face Database includes both frontal and non-frontal images captured in multiple poses using different illumination conditions. More particularly, the database features 123 multiethnic individuals for whom 25 pictures were captured showing partially occluded faces coming from wearing glasses, sunglasses, caps, hats or scarves.</p>
Axiom voice recognition dataset
<p>The AXIOM Voice Dataset has the main purpose of gathering audio recordings from Italian natural language speakers. This voice data collection intended to obtain audio reconding sample for the training and testing of VIMAR algorithm implemented for the Smart Home scenario for the Axiom board. The final goal was to developing an efficient voice recognition system using machine learning algorithms. A team of UX researchers of the University of Siena collected data for five months and tested the voice recognition system on the AXIOM board [1]. The data acquisition process involved natural Italian speakers who provided their written consent to participate in the research project. The participants were selected in order to maintain a cluster with different characteristics in gender, age, region of origin and background. </p>
Axiom canine microarray data from Australian dingoes and domestic dogs for admixture and population structure analysis
<p>Admixture between species is a cause for concern in wildlife management. Canids are particularly vulnerable to inter-specific hybridisation, and genetic admixture has shaped their evolutionary history. Microsatellite DNA testing, relying on a small number of genetic markers and geographically restricted reference populations, has identified extensive domestic dog admixture in Australian dingoes and driven conservation management policy. There has been concern that geographic variation in dingo genotypes could confound ancestry analyses that use a small number of genetic markers. Here we apply genome-wide single nucleotide polymorphism (SNP) genotyping to a set of 385 wild and captive dingoes from across Australia and then carry out comparisons to domestic dogs, and perform ancestry modelling and biogeographic analyses to characterize population structure in dingoes and investigate the extent of admixture between dingoes and dogs in different regions of the continent. We show that there are at least five distinct dingo populations across Australia. We observed limited evidence of dog admixture in wild dingoes, challenging previous reports regarding the occurrence and extent of dog admixture in dingoes, as our ancestry analyses show that previous assessments severely overestimate the degree of domestic dog admixture in dingo populations, particularly in southeastern Australia. These findings strongly support the use of genome-wide SNP genotyping as a refined method for wildlife managers and policy makers to assess and inform dingo management policy and legislation moving forwards.</p>
Axiom canine microarray data from Australian dingoes and domestic dogs for admixture and population structure analysis
Open the record for dataset details and reuse information.
An Observational Study of Xeloda (Capecitabine) in Patients With Metastatic Colorectal Cancer (AXIOM)
ClinicalTrials.gov study NCT01697462. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Pilot Study to Evaluate The Effectiveness And Safety Of Axiom Worldwide Drx9000™ Spinal Decompression System
ClinicalTrials.gov study NCT00414596. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Axiom Sugarcane100K SNP array data for sugarcane mapping population
GEO Series GSE128450. Saccharum officinarum. 471 samples. Type: Genome variation profiling by SNP array.
Axiom Pinpointing for Lightweight Description Logics through Incremental SAT
<p>This repository contains results of empirical evaluation of the procedure described in article SATPin: Axiom Pinpointing for Lightweight Description Logics through Incremental SAT, and input for this evaluation.</p>
Clinical Efficacy and Safety of Axiom Worldwide DRX9000 Axial Decompression System for Treatment of Low Back Pain
ClinicalTrials.gov study NCT00469118. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Multicentric Observational Study Confirming the Performance and Safety of the Axiom® BL X3 Implants
ClinicalTrials.gov study NCT05768295. IPD Sharing: NO. Countries: 1. Publications: 0.
Testing Atezolizumab With Selinexor in People ≥ 12 Years Old With Alveolar Soft Part Sarcoma, The AXIOM Trial
ClinicalTrials.gov study NCT05333458. IPD Sharing: YES. Countries: 1. Publications: 0.
Affymetrix axiom EUR array data for MDS fibroblasts and iPSCs
GEO Series GSE52691. Homo sapiens. 5 samples. Type: Genome variation profiling by SNP array.
Axiom Genome-Wide Chicken Array of Broiler Chickens
GEO Series GSE131764. Gallus gallus. 96 samples. Type: Genome variation profiling by SNP array.
Affymetrix SNP array data for Cryptomeria japonica samples [Axiom_Cj70K_v2]
GEO Series GSE95618. Cryptomeria japonica. 478 samples. Type: Genome variation profiling by SNP array; SNP genotyping by SNP array.
Axiom® Exome 319 Array data to identify susceptible genetic variations of gastric cancer
GEO Series GSE58356. Homo sapiens. 1012 samples. Type: SNP genotyping by SNP array; Genome variation profiling by SNP array.
Affymetrix SNP array data for Cryptomeria japonica samples [Axiom_Cj70K_v1]
GEO Series GSE95616. Cryptomeria japonica. 473 samples. Type: Genome variation profiling by SNP array; SNP genotyping by SNP array.
Affymetrix Axiom® Peanut 47K Genotyping Array
GEO Series GSE289195. Arachis hypogaea. 499 samples. Type: SNP genotyping by SNP array; Genome variation profiling by SNP array.
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International Brain Laboratory public data
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OpenNeuro
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