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9,674 results for “COVID-19”
Replication package for "Does Communist Nostalgia Lead to COVID-19 Conspiracy Beliefs?"
<p>Replication package for the paper “Does Communist Nostalgia Lead to COVID-19 Conspiracy Beliefs?” accepted for publication at European Societies. The package contains the survey data and the R code.</p>
Laboratory risk factors for mortality in severe and critical COVID-19 patients admitted to the ICU
<p>raw data sheet</p>
Laboratory risk factors for mortality in severe and critical COVID-19 patients admitted to the ICU
<p>Data legends</p>
Detection of SARS-CoV-2 in conjunctival secretion and tears in patients with COVID-19 in a tertiary care centre, South India
<p><strong>Aims and objectives</strong>: The purpose of this study is to detect the presence of SAR-CoV-2 viral RNA in conjunctival secretions of COVID-19 patients and to compare the RT-PCR positivity rate for SARS-CoV-2 in conjunctival and nasopharyngeal swabs.</p> <p><strong>Materials and method</strong>: Eighty hospitalised COVID-19 patients whose nasopharyngeal swab tested positive for SARS-CoV-2 by RT-PCR were included in the study. Conjunctival swab was collected from the eyes of these patients and sent for detection of SARS-CoV-2 by RT-PCR method.</p> <p><strong>Results</strong>: <span>Among the eighty patients, 51 (63.7%) were males and 29 (36.3%) were females. The mean age of the patients was 55.93 ± 16.59. Six patients had ocular manifestations. Eleven (13.75%) patients tested positive on conjunctival swab for SARS-CoV-2 viral RNA, and only one of them had ocular manifestations out of the eleven.</span></p> <p><strong><span>Conclusion</span></strong><span>: In our study, the presence of SARS-CoV-2 in conjunctival secretions of COVID-19 patients was detected and this was not dependent on the presence of ocular manifestations or duration of disease. Though the conjunctival positivity is lower compared to the nasopharyngeal swab sampling, ocular surface and secretions can be a potential route of viral transmission.</span></p>
Effectiveness of COVID-19 vaccination on reduction of hospitalizations and deaths in elderly patients in Rio Grande do Norte, Brazil
<p><strong>Data Repository </strong></p> <p><strong>Dataset name:</strong> covid19_rn-br.csv </p> <p><strong>Version: </strong>1.0 </p> <p><strong>Data collection period:</strong> 04/2020 - 08/2021 </p> <p><strong>Dataset Characteristics: </strong>Multivalued </p> <p><strong>Number of Instances:</strong> 12,635</p> <p><strong>Number of Attributes: </strong>16</p> <p><strong>Missing Values:</strong> Yes </p> <p><strong>Area(s): </strong>Health</p> <p><strong>Sources:</strong> </p> <p>- <em>Primary</em>: </p> <ul> <li>RegulaRN (<a href="https://regulacao.saude.rn.gov.br/sala-situacao/sala_publica/">https://regulacao.saude.rn.gov.br/sala-situacao/sala_publica/</a>).</li> </ul> <p>- <em>Secondary</em>: </p> <ul> <li>RN Mais Vacina (<a href="https://rnmaisvacina.lais.ufrn.br/cidadao/">https://rnmaisvacina.lais.ufrn.br/cidadao/</a>).</li> </ul> <p><strong>Description:</strong> The covid19_rn-br.csv dataset is composed of data from individuals who were hospitalized due to the Sars-CoV-2 virus. The data comes from the ecosystem of services that includes the regulatory system for clinical and critical beds related to Covid-19 (RegulaRN) and the vaccination system against Covid-19 that records the data of the general population (RN Mais Vacina) from Rio Grande do Norte state, Brazil. This dataset provides elementary data to analyze the impact of vaccination on patients hospitalized in the state. Table 1 presents the dictionary used during the data analysis.</p> <p> </p> <p><strong>Table 1: </strong>Description of Dataset Features. </p> <table> <tbody> <tr> <td> <p><strong>Attributes </strong></p> </td> <td> <p><strong>Description </strong></p> </td> <td> <p><strong>datatype </strong></p> </td> <td> <p><strong>Value</strong></p> </td> </tr> <tr> <td> <p><strong>usp</strong></p> </td> <td> <p>Unified Score for Prioritization scale, which combines the parameters described in the quick Sequential Organ Failure Assessment (qSOFA), the Charlson Comorbidity Index (CCI), the Clinical Frailty Scale (CFS) and The Karnofsky Performance Status scores</p> </td> <td> <p>Numerical</p> </td> <td> <p>2.0. 3.0, 4.0, 5.0, 6.0+</p> </td> </tr> <tr> <td> <p><strong>age</strong></p> </td> <td> <p>Informs the patient's age</p> </td> <td> <p>Numerical.</p> </td> <td> <p>integer value for age</p> </td> </tr> <tr> <td> <p><strong>outcome</strong></p> </td> <td> <p>Informs the outcome of the hospitalized patient after leaving the hospital</p> </td> <td> <p>Categorical</p> </td> <td> <p>“Discharge” or “Death"</p> </td> </tr> <tr> <td> <p><strong>comorbidities</strong></p> </td> <td> <p>Informs if the patient has comorbidities</p> </td> <td> <p>Categorical.</p> </td> <td> <p>“Yes” or “No”</p> </td> </tr> <tr> <td> <p><strong>vaccine</strong></p> </td> <td> <p>Informs which type of vaccine was applied to the patient</p> </td> <td> <p>Categorical</p> </td> <td> <p>“Vaccine #1”, “Vaccine #2” or NaN</p> </td> </tr> <tr> <td> <p><strong>bed_date_admission</strong> </p> </td> <td> <p>Informs the date the patient was hospitalized</p> </td> <td> <p>Date</p> </td> <td> <p>Date</p> </td> </tr> <tr> <td> <p><strong>bed_date_outcome</strong></p> </td> <td> <p>Informs the date that the patient left the hospital bed</p> </td> <td> <p>Date</p> </td> <td> <p>Date</p> </td> </tr> <tr> <td> <p><strong>length_hospitalization</strong></p> </td> <td> <p>Informs the number of days that the patient was hospitalized</p> </td> <td> <p>Numerical</p> </td> <td> <p>An integer value for days</p> </td> </tr> <tr> <td> <p><strong>interval_d1_hospitalization</strong></p> </td> <td> <p>Informs the interval (in days) that the patient had between the first dose and admission</p> </td> <td> <p>Numerical</p> </td> <td> <p>An integer value for days or NaN</p> </td> </tr> <tr> <td> <p><strong>interval_d2_hospitalization</strong></p> </td> <td> <p>Informs the interval (in days) that the patient had between the second dose and admission</p> </td> <td> <p>Numerical</p> </td> <td> <p>An integer value for days or NaN</p> </td> </tr> <tr> <td> <p><strong>dt_d1</strong></p> </td> <td> <p>Informs the date of application of the patient's first dose</p> </td> <td> <p>Date</p> </td> <td> <p>Date or NaN</p> </td> </tr> <tr> <td> <p><strong>dt_d2</strong></p> </td> <td> <p>Informs the patient's second dose application date</p> </td> <td> <p>Date</p> </td> <td> <p>Date or NaN</p> </td> </tr> <tr> <td> <p><strong>comorbidities_txt</strong></p> </td> <td> <p>Informs patients' comorbidities</p> </td> <td> <p>Categorical</p> </td> <td> <p>Free text or NaN</p> </td> </tr> <tr> <td> <p><strong>immunization</strong></p> </td> <td> <p>It informs the patient's immunization level according to the number of doses received and the interval (in days) of application of these doses</p> </td> <td> <p>Categorical</p> </td> <td> <p>“Partially”, “Fully” or “Not vaccinated”</p> </td> </tr> <tr> <td> <p><strong>health_professionals</strong></p> </td> <td> <p>Informs if the patient is a health professional</p> </td> <td> <p>Boolean</p> </td> <td> <p>0 or 1</p> </td> </tr> <tr> <td> <p><strong>age_group</strong></p> </td> <td> <p>Informs the age group of the hospitalized patient according to their age</p> </td> <td> <p>Categorical</p> </td> <td> <p>0-19, 20-49, 50-59, 60-69, 70-79, 80-89, 90+</p> </td> </tr> </tbody> </table> <p> </p> <p><strong>Article</strong>: Effectiveness of COVID-19 vaccination on reduction of hospitalizations and deaths in elderly patients in Rio Grande do Norte, Brazil</p> <p><br> <strong>Authors</strong>: Ana Isabela L. Sales-Moioli, Leonardo J. Galvão-Lima, Talita K. B. Pinto, Pablo H. Cardoso, Rodrigo D. Silva, Felipe Fernandes, Ingridy M. P. Barbalho, Fernando L. O. Farias, Nicolas V. R. Veras, Gustavo F. Souza, Agnaldo S. Cruz, Ion G. M. Andrade, Lúcio Gama, Ricardo A. M. Valentim</p>
Figure S1: Two-tailed intergroup Pearson analysis between patient age, COVID-19 detection, de-livery time, gravida, and termination week (Y-axis) with maternal and neonatal anti-COVID-19 immunoglobulins (IgG and IgM) (X-axis), depicted as a three-point color map (lowest = blue, 0 = white, highest = red) showed no significant correlation (at p>0.05).
<p>Figure S1: Two-tailed intergroup Pearson analysis between patient age, COVID-19 detection, de-livery time, gravida, and termination week (Y-axis) with maternal and neonatal anti-COVID-19 immunoglobulins (IgG and IgM) (X-axis), depicted as a three-point color map (lowest = blue, 0 = white, highest = red) showed no significant correlation (at p>0.05).</p>
Data for: COVID-19 lockdowns cause global air pollution declines
<p>Data in support of published manuscript: <a href="https://www.pnas.org/doi/full/10.1073/pnas.2006853117" rel="nofollow">https://www.pnas.org/doi/full/10.1073/pnas.2006853117</a></p> <p>For details of how the data are used in analysis please refer to the GitHub repository: https://github.com/NINAnor/covid19-air-pollution</p> <p>Authors of the paper acknowledge support from the <span>EU H2020 EXHAUSTION project.</span></p>
Parents' participation in care during neonatal intensive care unit stay in COVID-19 era
<p>During the stay in the neonatal intensive care unit, parents play a crucial role in the care of their infants. Recent studies reported a decrease in parental participation due to Coronavirus Disease (COVID-19) pandemic that determined restricted access policies in hospitals. The aim of this study is to describe the barriers to a good parents’ participation during the stay in the neonatal intensive care unit in the COVID-19 era. Methods: A quantitative observational study was carried out. Results: 270 parents participated in this study. Mothers’ participation in care seems to be higher as compared to fathers (p = 0.017). Parents who lived the birth of their first child reported a better level of participation in care when compared to those who lived the birth of their second born (p = 0.005). Parents of extremely preterm neonates reported a lower interaction with their infant if compared to parents of term newborns (p < 0.001). Conclusions: Some more disadvantaged categories have reported lower scores: cultural and linguistic minorities, parents of multiple children and fathers. COVID-19 pandemic made several Family Centred Care activities not possible with a higher impact on those who benefited the most of these facilities. This study was prospectively registered by the IRB-CRRM of the University “G. d’Annunzio” Chieti-Pescara on the 23/01/2024 with registration number CRRM;2023_12_07_01.</p>
A mathematical model of COVID-19 with multiple variants of the virus under optimal control in Ghana
Open the record for dataset details and reuse information.
Stratified analyses refine association between TLR7 rare variants and severe COVID-19 - Datasets
<p>This dataset comprises single variant summary statistics (MAF>0.01%) and the results of the burden analyses for the publication <em>Stratified analyses refine association between TLR7 rare variants and severe COVID-19 </em>by Boos et al. 2024 in HGG Advances.</p> <p>Please refer to the respective README-Files for a description of the file contents.</p>
Dataset of "Lesson learned from the COVID-19 pandemic: toddlers learn earlier to read emotions with face masks"
<p>Dataset used for statistical analyeses.</p> <p> </p> <p>Subject_ID: individual code</p> <p>Age: Age (years old)</p> <p>Group: Toddlers. Adults</p> <p>Emotion: S = Sadness, A = Anger, H = Happiness, F = fear, N = Neutral</p> <p>Mask: yes = presence of mask, no = lack ok mask</p> <p>Correct_Answer: 1 = correct, 0 = wrong </p>
How did COVID-19 Impact Software Design Activities in Global Software Engineering — Systematic Review
<p>This dataset is part of research publication <a href="https://doi.org/10.1142/S0218194024500098" target="_blank" rel="noopener">How did COVID-19 Impact Software Design Activities in Global Software Engineering — Systematic Review</a> published in <em>International Journal of Software Engineering and Knowledge Engineering</em>. This study systematically analyzes the evolution of research emphasis in the field of Global Software Engineering (GSE), particularly focusing on the software design phase during the COVID-19 pandemic.</p> <h3>Key contribution</h3> <ol> <li>Systematic analysis of GSE research <ul> <li>Phase 1 - Mapped GSE research over the two decades leading to the pandemic (2000–2020)</li> <li>Phase 2 - Used the forward snowballing approach to examine literature on the software design phase published between 2020 and 2022.</li> </ul> </li> <li>Analyzed trends in GSE research through 592 research studies across both phases.</li> </ol> <h3>Dataset content</h3> <p>The dataset consis of following files:</p> <ol> <li>Readme file - Overview and instructions for the dataset.</li> <li>Search string and snowballing - Contains the search strings used in the first phase and details of the forward snowballing approach used in the second phase.</li> <li>Data extraction template and Inclusion/exclusion criteria - Includes the template for organizing key features of papers relevant to the research questions and lists the inclusion and exclusion criteria applied.</li> <li>Screening process - Describes the three-level screening process applied in both phases (title, abstract, full text). A separate tab analyzes primary studies used in the second phase.</li> <li>RQs analysis - Provides detailed analysis of research questions (RQs) from the final set of 592 papers.</li> </ol>
Data and Code for "Genetic tracing of market wildlife and viruses at the epicenter of the COVID-19 pandemic"
<p>Please see the README.md file for a detailed description of each of the code and data files in this dataset.</p>
Plasma metabolomic signatures for copy number variants and COVID-19 risk loci in Northern Finland Populations
<p>A MySQL database for metabolomic signatures for The Northern Finland Birth Cohorts, <strong>NFBC1966 </strong>and <strong>NFBC1986</strong>, a longitudinal research program based at the Medical Faculty, University of Oulu, Finland. This dataset is a supplement to the manuscript titlted "Plasma metabolomic signatures for copy number variants and COVID-19 risk loci in Northern Finland Populations"</p>
Data for constructing China's co-invention network across 371 cities and depicting the regional context of 19 city-regions under COVID-19
<p>co-inventions.xlsx contains the application records of China's invention patents with more than one applicants in 371 cities from 2005 to 2021.</p> <p>determinants.xlsx contains statistical data depicting the socioeconomic background, innovation conditions, and COVID-19 pandemic severity of 19 city-regions in China.</p>
Food Safety and Online Food Delivery Apps Post Covid-19
<p>The data collection method that will be used will be questionnaires that will be made available online and written in Indonesian. Questionnaires were distributed during the post outbreak of COVID-19</p>
Datasets for COVID-19 data in Ukraine for period 2020-2022
<p>Datasets were collected by <a href="https://www.nas.gov.ua/EN/Colegial/Pages/Default.aspx?CID=000000188">Working group on mathematical modeling of the SARS-CoV-2 coronavirus epidemic in Ukraine</a> of National Academy of Science of Ukraine. Data is based on daily reports of the Ministry of Health of Ukraine and raw data from the Public Health Center of Ukraine. Data were collected for the 25 Ukrainian oblasts and contain information about the total amount of registered cases, deathes and recoveries.</p> <p>File 'Ukraine_Covid_data_reported. xlsx' contains data for the total amount cases, deaths and recoveries from the daily oficial reports from the Ministry of Health, dates are the dates of reporting about the new cases. The reporting date may significantly differ from the date of actual event.</p> <p>File 'Ukraine_Covid_data_actualdate. xlsx' contains daily data for new cases, deaths and recoveries but based on detailed information of the Public Health Center. Date of new cases is the date of the positive testing. Date of death is actual date of death. Date of recovery is date of negative testing or date of discharge from the hospital.</p> <p>Dataset may be used for analysing reporting delays and its affect on the prognoses and decision making</p>
Psychological inflexibility, avoidance coping, psychological trauma, and adherence to COVID-19 preventive measures
<p>The data focuses on the effects of psychological inflexibility, avoidance coping and psychological trauma on perceptions of threat of COVID-19 and adherence to preventive measures among refugees. The data refers to refugees in Uganda, collected during the COVID-19 pandemic.</p>
Dataset for article "Prevalence of Depressive Symptoms Among the US Population During COVID-19 (2021-2022): A Study Using the Goodville App"
<p>This dataset contains PHQ-8 data obtained using Goodville app. The dataset was used for preparing research article "Prevalence of Depressive Symptoms Among the US Population During COVID-19 (2021-2022): A Study Using the Goodville App". The first page of excel table includes all US PHQ-8 data. The second page includes only valid PHQ-8 patterns after Rasch analysis. </p>
Figure of Recovery ASEAN Economy through a Sustainable Tourism Sector in the Post COVID-19
<p>The figure in this article demonstrates the significant impact of the sustainable development theory. It shows that among international and regional cooperation, tourism-promoting communities, and government regulations, the government's budgeting role has the most influence on enhancing the ASEAN economy, especially in improving the environment in tourist destinations.</p> <p>This figure results from thorough data processing by our committed research team, It will pave the way for future investigations into the pandemic and its effects on sustainable development.</p>
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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)
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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.