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9,674 results for “COVID-19”
Risk of bias assessments for the Cochrane review 'SARS-CoV-2-neutralising monoclonal antibodies for treatment of COVID-19'
<p>Risk of bias assessments and support for judgement with ROB 2 tool for the Cochrane Review: SARS-CoV-2-neutralising monoclonal antibodies for treatment of COVID-19.</p>
A suburban soundscape reveals altered acoustic dynamics during COVID-19 lockdown
<p>Abstract</p> <p>The 2020 COVID-19 pandemic and resulting national and international movement restrictions provide a unique opportunity to investigate the consequences of changing anthropogenic noise regimes on animal communities and soundscapes. Here I use this lockdown period as a natural experiment to investigate changes to soundscape intensity, structure, and dynamics during restricted human activity (lockdown) in suburban Nottingham, UK. Using 11 common acoustic indices, I tested for differences in the richness and evenness of the soundscape during COVID-19 lockdown, and I measured changes in soundscape dynamics by comparing the temporal variability of acoustic indices during versus after lockdown. Regardless of how the soundscape was summarised, there were significant differences in the intensity, evenness, and temporal variability of the soundscape during COVID-19 lockdown, principally driven by changes to anthropogenic noise. I recorded a shift away from a dominance of anthropophony towards more intense biological sounds during lockdown, and the lockdown soundscape was generally more even, particularly because of changes to the magnitude of the diurnal cycle. These preliminary results from a mass human confinement experiment provide an early glimpse into how suburban soundscapes are impacted by noise pollution. In time, globally distributed longer-term monitoring efforts will reveal the generality of these findings, facilitating a mechanistic understanding of the impacts of anthropogenic noise on the world’s natural and human-dominated soundscapes.<br> <br> Methods</p> <p>The dataset contains standardised acoustic index values for 11 commonly used acoustic indices, based on AudioMoth recordings taken during two periods around the COVID-19 lockdown (May 2020) and after restrictions had been lifted (Oct 2020) in suburban Nottingham, UK. I analysed the difference in acoustic index values during versus after the lockdown and compared their temporal variability using standardised effect sizes for the difference between these two time periods. I did this on the whole dataset and on several hourly subsets of the dataset (see the manuscript for further details). <br> <br> Usage notes</p> <p>See readme file for further details and main manuscript for descriptions of data.</p>
COVID19.BR: A Dataset of Misinformation about COVID-19 in Brazilian Portuguese WhatsApp Messages
<p>COVID19.BR is provided in a csv file where the columns are date, hour, phone number, international phone code, if the user is Brazilian its state, the text content of the message, word count, character count, and if the message contained media (audio, image, or video). Each row represents a WhatsApp message.</p>
Casos de COVID-19 en La Habana y Santiago de Cuba
<p>Basde de datos de la investigación: Comparación del comportamiento de la COVID-19 en La Habana y Santiago de Cuba.</p> <p>Comprende las variables: sexo, edad, lugar de residencia, fuente de infección y fecha de confirmación de la enfermedad.</p>
Human contact network analytics and COVID-19 hospital incidence in France
<p>This data set contains COVID-19 hospital incidence, temperature and human mobility and contact data recorded between 2020-03-24 and 2021-03-30 used in the paper:</p> <p>Selinger et al. 2021: Predicting COVID-19 incidence in French hospitals using human contact network analytics. 10.1016/j.ijid.2021.08.029</p> <p>See methods in the article for detailed descriptions and the data curation process.</p> <p> </p> <p><strong>1) cov_mob_tst_national.csv contains national-level data</strong><br> </p> <p>The columns comprise:</p> <p>incid_hosp: hospital admission incidence </p> <p>incid_rea: ICU admission incidence</p> <p>incid_dc: hospital death incidence </p> <p>incid_rad: incidence of those returned home</p> <p>within_departement_colocation_X%: X%-quantile of colocation probabilities with départements</p> <p>between_departement_colocation_X%: X%-quantile of colocation probabilities between départements</p> <p>fb_population_coverage_X%: X%-quantile of ratio of fb_population over census population in département</p> <p>null_links_X%: X%-quantile of null links across départements</p> <p>clustering_X%: X%-quantile of clustering coefficients across départements</p> <p>ricci_X%: X%-quantile of curvature across départements</p> <p>ricci_min_X%: X%-quantile of minimum curvature across départements</p> <p>ricci_mean_X%: X%-quantile of average curvature across départements</p> <p>ricci_max_X%: X%-quantile of maximum curvature across départements</p> <p>strength_X%: X%-quantile of network strengths across départements</p> <p>betweenness_centrality_X%: X%-quantile of betweenness_centrality scores across départements</p> <p>positive_test_ratio_weekly: ratio of weekly cumulated positive tested over weekly cumulated tests</p> <p>retail_and_recreation_percent_change_from_baseline: Google Mobility Reports</p> <p>grocery_and_pharmacy_percent_change_from_baseline: Google Mobility Reports</p> <p>parks_percent_change_from_baseline: Google Mobility Reports </p> <p>transit_stations_percent_change_from_baseline: Google Mobility Reports</p> <p>workplaces_percent_change_from_baseline: Google Mobility Reports</p> <p>residential_percent_change_from_baseline: Google Mobility Reports</p> <p>mean_temperature_X%: X% quantile of mean daily temperatures averaged over the week across départements</p> <p>min_temperature_X%: X% quantile of minimum daily temperatures averaged over the week across départements</p> <p>max_temperature_X%: X% quantile of maximum daily temperatures averaged over the week across départements</p> <p> </p> <p> </p> <p><strong>2) cov_mob_dep.csv contains département-level data</strong></p> <p>The columns comprise:</p> <p>dep: département code</p> <p>incid_hosp: hospital admission incidence </p> <p>incid_rea: ICU admission incidence</p> <p>incid_dc: hospital death incidence </p> <p>incid_rad: incidence of those returned home</p> <p>week: week (matched to colocation data recording usually on Tuesdays)</p> <p>dep_name: name of the département</p> <p>null_links: number of null links</p> <p>betweenness_centrality: betweenness centrality</p> <p>clustering: clustering coefficient</p> <p>strength: network strength</p> <p>ricci_mean: minimum curvature among all edges incident to a département</p> <p>ricci_min: mean curvature across all edges incident to a département </p> <p>ricci_X%: X%-quantile curvature among all edges incident to a département</p> <p>fb_population: number of facebook users </p> <p>facebook_colocation_within_dep: colocation probability within département</p> <p>fb_population_coverage: ratio of fb_population over census population in département</p> <p>facebook_colocation_between_dep_X%: X%-quantile of facebook colocation among all edges incident to the département</p> <p>min_temperature: minimum daily temperature averaged over the week</p> <p>max_temperature: maximum daily temperature averaged over the week</p> <p>mean_temperature: mean daily temperature averaged over the week</p> <p>incid_hosp_Y: incidence of hospital admission from Ynd most colocated département</p> <p>incid_rea_Y: incidence of ICU admission from Ynd most colocated département</p> <p>incid_dc_Y: incidence of hospital deaths from Ynd most colocated département</p> <p>incid_rad_Y: incidence of returned home from Ynd most colocated département</p> <p> </p>
Covid-19 Level Of Preparedness Data
<p>Pan India survey by the title of “Survey on General Indian population on the level of preparedness for COVID-19 pandemic” was launched and received around 1250 submissions.</p>
Risk of bias assessment for the Cochrane review "Colchicine for the treatment of COVID-19"
<p>Risk of bias assessment and support for judgement with the RoB 2 tool for the Cochrane review "Colchicine for the treatment of COVID-19"</p>
COVID-19++: A Citation-Aware Covid-19 Dataset for the Analysis of Research Dynamics
<p>COVID-19++ is a citation-aware COVID-19 dataset for the analysis of research dynamics. In addition to primary COVID-19 related articles and preprints from 2020, it includes citations and the metadata of first-order cited work. All publications are annotated with MeSH terms, either from the ground truth, or via ConceptMapper, if no ground truth was available. </p> <p>The data is organized in CSV files</p> <p>- Paper metadata (paper_id, publdate, title, data_source): paper.csv</p> <p>- Annotation data, mapping paper_id to MeSH terms: annotation.csv </p> <p>- Authorship data, mapping paper_id to author, optionally with ORCID: authorship.csv<br> - Paired DOIs of citing and cited papers: references.csv</p> <p>The column data source within the paper metadata has the value KE (for metadata from ZB MED KE), PP (for preprints) or CR (for cited resources from CrossRef)<br> </p> <p>This work was supported by BMBF within the programme ``Quantitative Wissenschaftsforschung'' under grant numbers 01PU17013A, 01PU17013B, 01PU17013C.<br> </p>
SASC: A Simple Approach to Synthetic Cohorts. Applying COVID-19 clinical data to generate longitudinal observational patient cohorts and comparison with alternative synthetic cohort approaches as well as real patient data
<p>Subset from COVID-19 Dataset from https://zenodo.org/record/3766350#.YVcfyTFBxgA. Used as reference for a publication dealing with synthetic patient cohort generation.</p>
Neutrophil Profiles of Pediatric COVID-19 and Multisystem Inflammatory Syndrome in Children
<p>Code and data for the manuscript "Neutrophil Profiles of Pediatric COVID-19 and Multisystem Inflammatory Syndrome in Children" to be published in Cell Reports Medicine.</p> <p>Contains all code located at <a href="https://github.com/lasalletj/COVID_Neutrophils">https://github.com/lasalletj/</a><a href="https://github.com/lasalletj/Pediatric_COVID_MISC_Neutrophils">Pediatric_COVID_MISC_Neutrophils</a> as well as additional data files needed to run the code and supplementary materials for the manuscript.</p> <p>Any additional information required to reanalyze the data reported in this work paper is available from the Lead Contact, Lael Yonker (lyonker@mgh.harvard.edu) upon request.</p>
COVID-19 Pandemic Stages Identification Using Machine Learning
<p>Here, we used python programming language as the Machine Learning process for identifying COVID-19 stages where we used the K-Means Clustering algorithm, Decision Tree, Naive Bayes Classifier, Random Forest, and AdaBoost algorithms. This repository also contains the dataset.</p> <p>GitHub Link:</p> <p>https://github.com/rayhanhemel/COVID-19-Pandemic-Stages-Identification-Using-Machine-Learning.git</p>
Persistent alveolar type 2 dysfunction and lung structural derangement in post-acute COVID-19
<p>SARS-CoV-2 infection can manifest as a wide range of respiratory and systemic symptoms well after the acute phase of infection in over 50% of patients. Key questions remain on the long-term effect of infection on tissue pathology and on recovered COVID-19 patients. Here we perform multiplexed imaging of post-mortem lung tissue from 12 individuals that died post-acute COVID-19 (PC) and compare them to patients who died during the acute phase of COVID-19, patients who died with idiopathic pulmonary fibrosis (IPF), and otherwise healthy lung. We find evidence of viral presence in the lung up to 359 days after the acute phase of disease, often in patients with negative nasopharyngeal swab test. Our analyses identify accumulation of senescent alveolar type 2 cells, fibrosis with hypervascularization of peribronchial areas and alveolar septa, as the most pronounced pathophysiological features seen in the lung of PC patients. At the cellular level, lung disease of PC patients is distinct from the chronic pulmonary disease of IPF but shares pathological features which may help rationalize interventions for PASC patients. Altogether, this study provides an important ground for the understanding of the long-term effects of SARS-CoV-2 infection at the microanatomical, cellular and molecular level.</p>
Ivercori Dataset and dictionary. Ivermectin impact in COVID-19 pneumonia mortality and need of respiratory support
<p>Dataset of IVERCORI and variables dictionary. IVERCORI is a propensity matched score retrospective study that analyses the impact of ivermectin in COVID-19 pneumonia in-hospital mortality and need of respiratory support:</p>
E-Learning Readiness In Higher Education Institutions In Nigeria during the COVID-19 Pandemic
<p>Data set for the paper " E-Learning Readiness In Higher Education Institutions In Nigeria during the COVID-19 Pandemic"</p>
Reporting behavior from WHO COVID-19 public data
<p><strong>Objective</strong></p> <p>Daily COVID-19 data reported by the World Health Organization (WHO) may provide the basis for political ad hoc decisions including travel restrictions. Data reported by countries, however, is heterogeneous and metrics to evaluate its quality are scarce. In this work, we analyzed COVID-19 case counts provided by WHO and developed tools to evaluate country-specific reporting behaviors.</p> <p><strong>Methods</strong></p> <p>In this retrospective cross-sectional study, COVID-19 data reported daily to WHO from 3rd January 2020 until 14th June 2021 were analyzed. We proposed the concepts of binary reporting rate and relative reporting behavior and performed descriptive analyses for all countries with these metrics. We developed a score to evaluate the consistency of incidence and binary reporting rates. Further, we performed spectral clustering of the binary reporting rate and relative reporting behavior to identify salient patterns in these metrics.</p> <p><strong>Results</strong></p> <p>Our final analysis included 222 countries and regions. Reporting scores varied between -0.17, indicating discrepancies between incidence and binary reporting rate, and 1.0 suggesting high consistency of these two metrics. Median reporting score for all countries was 0.71 (IQR 0.55 to 0.87). Descriptive analyses of the binary reporting rate and relative reporting behavior showed constant reporting with a slight "weekend effect" for most countries, while spectral clustering demonstrated that some countries had even more complex reporting patterns.</p> <p><strong>Conclusion</strong></p> <p>The majority of countries reported COVID-19 cases when they did have cases to report. The identification of a slight "weekend effect" suggests that COVID-19 case counts reported in the middle of the week may represent the best data basis for political ad hoc decisions. A few countries, however, showed unusual or highly irregular reporting that might require more careful interpretation. Our score system and cluster analyses might be applied by epidemiologists advising policymakers to consider country-specific reporting behaviors in political ad hoc decisions.</p>
Multiplexed histology of COVID-19 post-mortem lung samples - PROLONGED CASE 1 FOV2
<p><strong>Image-based data set of a post-mortem lung sample from a COVID-19 donor (PROLONGED CASE 1 FOV2)</strong></p> <p>Each image shows the same field of view (FOV), sequentially stained with the depicted fluorescence-labelled antibodies, including surface proteins, intracellular proteins and transcription factors. Images contain 2024 x 2024 pixels and are generated using an inverted wide-field fluorescence microscope with a 20x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Images have been normalized and intensities adjusted.</p>
Multiplexed histology of COVID-19 post-mortem lung samples - PROLONGED CASE 1 FOV1
<p><strong>Image-based data set of a post-mortem lung sample from a COVID-19 donor (PROLONGED CASE 1 FOV1)</strong></p> <p>Each image shows the same field of view (FOV), sequentially stained with the depicted fluorescence-labelled antibodies, including surface proteins, intracellular proteins and transcription factors. Images contain 2024 x 2024 pixels and are generated using an inverted wide-field fluorescence microscope with a 20x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Images have been normalized and intensities adjusted.</p>
Multiplexed histology of COVID-19 post-mortem lung samples - CONTROL CASE 1 FOV2
<p><strong>Image-based data set of a post-mortem lung sample from a non-COVID-related pneumonia donor (CONTROL CASE 1, FOV2)</strong></p> <p>Each image shows the same field of view (FOV), sequentially stained with the depicted fluorescence-labelled antibodies, including surface proteins, intracellular proteins and transcription factors. Images contain 2024 x 2024 pixels and are generated using an inverted wide-field fluorescence microscope with a 20x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Images have been normalized and intensities adjusted.</p>
Multiplexed histology of COVID-19 post-mortem lung samples - CHRONIC CASE 2 FOV1
<p><strong>Image-based data set of a post-mortem lung sample from a COVID-19 donor (CHRONIC CASE 2 FOV1)</strong></p> <p>Each image shows the same field of view (FOV), sequentially stained with the depicted fluorescence-labelled antibodies, including surface proteins, intracellular proteins and transcription factors. Images contain 2024 x 2024 pixels and are generated using an inverted wide-field fluorescence microscope with a 20x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Images have been normalized and intensities adjusted.</p>
Datasets for ``Quadratic growth during the COVID-19 pandemic: merging hotspots and reinfections''
<pre>This directory contains an index.html file with links to the run directories for Figs.8-11 and idl plotting routines with secondary data for the other figures for the paper "Quadratic growth during the COVID-19 pandemic: merging hotspots and reinfections" by Axel Brandenburg (Nordita); see https://arxiv.org/abs/2206.15459. </pre>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.