Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
3,693
datasets available to search
ShareScore release 0.9.0
Dataset results
3,693 results for “Cardiovascular”
Prevalence and determinants of cardiovascular risk factors in Lesotho: a population-based survey
<p>These are pseudo-anonymised data from the ComBaCaL survey and belong to the manuscript "Prevalence and determinants of cardiovascular risk factors in Lesotho: a population-based survey" which can be found at <a href="https://doi.org/10.1093/inthealth/ihad058">https://doi.org/10.1093/inthealth/ihad058</a>. </p> <p>The data dictionary explains the critical data available in the dataset. Between November 2021 and August 2022 , 6061 participants over 18 years old were visited in their households in two districts of Lesotho. </p>
Shared genetic factors between stress-related disorders and cardiovascular disease
<p>The ultimate goal of this study is to advance our understanding of the biological mechanisms of stress-related disorders and CVD, through demonstrating pleiotropic genes and pathways underlying their comorbidity that are potentially testable as targets of future interventions in experimental investigations.</p>
Assessment of skin autofluorescence and its association with glycated hemoglobin, cardiovascular risk markers and concomitant chronic diseases in children with type 1 diabetes
<p>This is the dataset for the publication "Assessment of skin autofluorescence and its association with glycated hemoglobin, cardiovascular risk markers and concomitant chronic diseases in children with type 1 diabetes".</p>
Dataset for "Synchrotron-based phase contrast imaging of cardiovascular tissue in mice—grating interferometry or phase propagation?"
<p>This dataset contains images that were used in the analysis of the manuscript "Synchrotron-based phase contrast imaging of cardiovascular tissue in mice—grating interferometry or phase propagation?", that was published in Biomedical Physics and Engineering Express in 2018. Images are uploaded in .tif format. Three different synchrotron-based imaging techniques were compared on the same cardiovascular samples: grating interferometry (GI) and absorption-based phase propagation with and without phase retrieval according to Paganins method. An excel file is provided in which the nomenclature of the files is explained.</p>
Supplementary material of the article "OpenModelica-based virtual simulator for the cardiovascular and respiratory physiology of a neonate"
<p>This document contains the adjustment data for the parameters of the model presented in the article: "<strong>OpenModelica-based virtual simulator for the cardiovascular and respiratory physiology of a neonate</strong>"</p>
Sistema automatizado para determinar el Riesgo Cardiovascular Global.
<p><strong>El presente sistema automatizado facilita el cálculo del riesgo cardiovascular global y facilita guardar los datos de su investigación. Se recomienda utilizar la Versión 3.0, ya que se han rectificado errores de las dos versiones anteriores. Las tablas se utilizan a partir de los 30 años de edad y están destinadas a utilizar en Cuba con la selección de la Provincia, el municipio y el área de salud. </strong></p> <p><strong>Artículo Publicado en: </strong></p> <p><a href="http://www.revinformatica.sld.cu/index.php/rcim/article/view/406">http://www.revinformatica.sld.cu/index.php/rcim/article/view/406</a> </p> <p><strong>RESUMEN </strong></p> <p><strong>Introducción: </strong>El riesgo cardiovascular global es la probabilidad que tiene un individuo de contraer una enfermedad cardiovascular en un periodo de tiempo, basado en el número de factores de riesgo presentes en el mismo individuo o teniendo en cuenta la magnitud de cada uno de ellos. Las técnicas automatizadas en el procesamiento de la información logran una mayor eficacia en el trabajo.</p> <p><strong>Objetivos: </strong>Diseñar un sistema automatizado para determinar el riesgo cardiovascular global<strong>. </strong></p> <p><strong>Métodos: </strong>Se realizó una investigación de innovación tecnológica, consistente en el diseño de un sistema automatizado para determinar el Riesgo Cardiovascular Global<strong>.</strong> Se realizó además un análisis de las herramientas computacionales disponibles para la realización del sistema y determinar cuál o cuáles eran las más apropiadas de acuerdo al objetivo del sistema y a las características del personal encargado de su implementación y uso. Se utilizaron las herramientas para tabulación de información y fórmulas que ofrece el programa Excel del paquete Microsoft Office 2013. Para el diseño se utilizó una interface muy simple, de fácil manejo por el posible usuario.</p> <p><strong>Resultados:</strong> Se propone el presente sistema cuya característica principal es la simplicidad de su uso. El mismo determina el Riesgo Cardiovascular Global de forma automatizada<strong>.</strong> Según las tablas de riesgo de Framingham por categorías (Wilson), en su versión de 1998 y las tablas de predicción del riesgo cardiovascular global de la OMS. Para la región AMR A del 2008.</p> <p><strong>Conclusiones: </strong>Se realizó el diseño del sistema automatizado para facilitar la determinación del Riesgo Cardiovascular Global.</p> <p> </p>
Dataset for the comparison of accelerometry-based and self-reported physical activity and their association with cardiovascular risk markers in children from South Africa
<p>Dataset used to evaluate and compare self-reported with accelerometry-based physical activity measurements as well as their associations with cardiovascular risk markers among South African school-aged children from disadvantaged communities.</p> <p>It encompasses anonymized, unique, identification numbers, demographic and body-mass-index, blood pressure, lipid panel and blood glucose measures.</p>
Brazilian Cohort for Predicting Cardiovascular Events Using Machine Learning (PRE-CARE ML project)
<p>The project PRE-CARE ML addresses the development and internal and external validation of predictive models for the assessment of risks of major adverse cardiovascular events. </p> <p>Global and local interpretability analyses of predictions were conducted towards improving model reliability and tailoring preventive interventions. </p> <p>The models were trained and validated in a retrospective cohort with the use of data from Hospital das Clínicas da Faculdade de Medicina de Ribeirao Preto, Brazil</p> <p>The International Classification of Diseases (ICD-10) classified patients with MACE (case group). (see Table I for the ICD-10 codes that defined MACE).</p> <p>Table 1: ICD-10 Codees for MACE definition</p> <table> <tbody> <tr> <td><strong>ICD-10</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>I20</td> <td>Angina pectoris</td> </tr> <tr> <td>I21</td> <td>Acute myocardial infarction</td> </tr> <tr> <td>I24</td> <td>Other acute ischaemic heart diseases</td> </tr> <tr> <td>I46</td> <td>Cardiac arrest</td> </tr> <tr> <td>I63</td> <td>Cerebral infarction</td> </tr> <tr> <td>I64</td> <td>Stroke, not specified as hemorrhage or infarction</td> </tr> <tr> <td>I71</td> <td>Aortic aneurysm and dissection</td> </tr> <tr> <td>I74</td> <td>Arterial embolism and thrombosis</td> </tr> </tbody> </table> <p>Only a patient’s first MACE was considered, and all previous hospitalizations within a 5-year window were considered MACE. The control group (non-MACE) involved hospitalizations with no MACE and no death within a 5-year window (between 2017 and 2022). A sample of 6,000 MACE (labeled as 1) cases and 12,000 non-MACE (labeled as 0) ones was constructed with data from HCFMRP for training and internal validation purposes. Another balanced MIMIC IV sample of 8,000 MACE cases and 8,000 nonMACE cases was used for external validation.</p>
Risk Factors that modify the familial aggregation for ischemic cardiovascular illness
<p><strong>Introduction:</strong> Ischemic cardiovascular disease is a global health problem. <strong>Objective:</strong> To determine the risk factors that modified the familial aggregation for ischemic cardiovascular disease in affected individuals. <strong>Material and Methods:</strong> An observational, analytical, longitudinal and retrospective case/control study was carried out from the population of the Banes municipality, Holguín province during May 2020-May 2022. The universe included all the diagnosed individuals and their families. By simple random sampling, the sample (149 cases) was obtained and the control group was formed at a ratio of 3:1 (447 individuals with no history of disease). The bioethical requirements were met. Inclusion/exclusion criteria were applied. The statistics were used: Chi square, Odd Ratio (OR), including p and confidence interval. The variables were operationalized: age, degree of consanguinity and risk factors. The family tree was obtained. <strong>Results:</strong> The relatives of first and second degree of consanguinity showed the highest incidence of disease. The age group 60-69 years was more affected. Familial aggregation for the disease was demonstrated (X<sup>2</sup>=45.93 OR=5.85 99.9% CI (3.29; 10.41)). Ischemic cardiomyopathy (43.3%) and arrhythmias (20.7%) were notable forms of presentation. The risk factors showed an association for the disease (X<sup>2</sup>=67.11 p ≤0.001). Arterial hypertension (X<sup>2</sup>=57.9 OR=3.04 99.9% CI (2.27; 4.06)) and smoking (X<sup>2</sup>=45.7 OR=2.66 99.9% CI (2.3 ; 5,6)) expressed a highly significant association for ischemic cardiovascular disease. <strong>Conclusions:</strong> The risk factors: arterial hypertension, smoking and family history modified the family aggregation for ischemic cardiovascular disease.</p>
Screening for side effects of COVID-19 drug candidates on cardiovascular development -RAW DATA qPCR RESULTS
<p>Raw Data relating to Figures 4 and Supplemental Figures S7 and S8 of the article </p> <p><strong>Screening for side effects of COVID-19 drug candidates on cardiovascular development </strong></p> <p>Alexander Ernst<sup>1#</sup>, Indre Piragyte<sup>1,2#</sup>, Ayisha Marwa MP<sup>1,2</sup>, Ngoc Dung Le<sup>3</sup>, Denis Grandgirard<sup>3</sup>, Stephen L. Leib<sup>3</sup>, Andrew Oates<sup>4</sup>, Nadia Mercader<sup>1,2,5</sup></p> <p> </p> <p> </p> <p> </p> <p><sup>1</sup> Institute of Anatomy, University of Bern, Switzerland</p> <p><sup>2</sup> Department for Biomedical Research DBMR, University of Bern, Switzerland</p> <p><sup>3</sup> Institute for Infectious Diseases, University of Bern, Switzerland</p> <p><sup>4 </sup>School of Life Sciences, École polytechnique fédérale de Lausanne, Switzerland</p> <p><sup>5</sup> Centro Nacional de Investigaciones Cardiovasculares, CNIC, Madrid, Spain</p> <p># shared first-authorship</p>
Artritis reumatoide y riesgo cardiovascular
<p>Rheumatoid arthritis patient series database. Data on cardiovascular comorbidity and subclinical carotid atheromatosis.</p>
E-Cigarette Effects on Markers of Cardiovascular and Pulmonary Disease
ClinicalTrials.gov study NCT03863509. IPD Sharing: YES. Countries: 1. Publications: 1.
Study of Semaglutide for Non-Alcoholic Fatty Liver Disease (NAFLD), a Metabolic Syndrome With Insulin Resistance, Increased Hepatic Lipids, and Increased Cardiovascular Disease Risk (The SLIM LIVER St
ClinicalTrials.gov study NCT04216589. IPD Sharing: YES. Countries: 2. Publications: 2.
Telemedicine in High-Risk Cardiovascular Patients Post-ACS
ClinicalTrials.gov study NCT05015634. IPD Sharing: NO. Countries: 1. Publications: 9.
Reducing Cardiovascular Disease Risk in Perimenopausal Latinas
ClinicalTrials.gov study NCT04313751. IPD Sharing: YES. Countries: 1. Publications: 3.
Effect of Sotagliflozin on Cardiovascular Events in Participants With Type 2 Diabetes Post Worsening Heart Failure (SOLOIST-WHF Trial)
ClinicalTrials.gov study NCT03521934. IPD Sharing: YES. Countries: 32. Publications: 6.
Personalized Patient Data and Behavioral Nudges to Improve Adherence to Chronic Cardiovascular Medications
ClinicalTrials.gov study NCT03973931. IPD Sharing: YES. Countries: 1. Publications: 18.
An Extension Trial of Inclisiran in Participants With Cardiovascular Disease and High Cholesterol
ClinicalTrials.gov study NCT03060577. IPD Sharing: YES. Countries: 5. Publications: 2.
Effect of Sotagliflozin on Cardiovascular and Renal Events in Participants With Type 2 Diabetes and Moderate Renal Impairment Who Are at Cardiovascular Risk
ClinicalTrials.gov study NCT03315143. IPD Sharing: YES. Countries: 45. Publications: 5.
Study of Efficacy and Safety of Inclisiran in Japanese Participants With High Cardiovascular Risk and Elevated LDL-C
ClinicalTrials.gov study NCT04666298. IPD Sharing: YES. Countries: 1. Publications: 1.
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.