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9,674 results for “COVID-19”
Multiplexed histology of COVID-19 post-mortem lung samples - PROLONGED CASE 2 FOV1
<p><strong>Image-based data set of a post-mortem lung sample from a COVID-19 donor (PROLONGED 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>
Multiplexed histology of COVID-19 post-mortem lung samples - ACUTE CASE 1 FOV2
<p><strong>Image-based data set of a post-mortem lung sample from a COVID-19 donor (ACUTE 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 - CONROL CASE 2 FOV2
<p><strong>Image-based data set of a post-mortem lung sample from a COVID-19 donor (CONTROL CASE 2 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 3 FOV2
<p><strong>Image-based data set of a post-mortem lung sample from a COVID-19 donor (PROLONGED CASE 3 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>
Food aid in Europe in times of the COVID-19 crisis
<table> <tbody> <tr> <td><strong><span>Description (purpose, methodology, contents, file structure, etc.)</span></strong></td> <td> <p><span>The dataset is linked to a cross-sectional study on the organisation of food aid in different European countries before and during the COVID-19 crisis. The survey was conducted with local organisations providing food aid in Belgium, Germany, Hungary, Lithuania, the Netherlands, Poland, Portugal and Spain. We set the target population as the total number of local food aid organisations affiliated with the largest national food aid umbrella organisation(s) in each country. The English source questionnaire was developed based on many feedback loops with all project partners (see below) as well as a pilot study. For reasons of consistency, the translation process from the source questionnaire to the target languages followed the translation recommendations of the ‘Cross Cultural Survey Guidelines’ as much as feasible within the context of this project. The final questionnaire included multiple choice questions, dichotomous questions, matrix questions and open-ended questions. Survey participants were recruited by means of a self-administered questionnaire and the technique of computer-assisted web-interviewing as well as via email (in Belgium and partly in Lithuania and Portugal). </span></p> <p><span>Project website: </span><span><a href="https://food-aid-in-europe.eu/"><span>https://food-aid-in-europe.eu/</span></a></span></p> </td> </tr> <tr> <td><strong><span>Personal data yes/no</span></strong></td> <td>no</td> </tr> <tr> <td><strong><span>Type(s) of data and data format</span></strong></td> <td><span>Survey data, Excel file (1.8 MB)</span></td> </tr> <tr> <td><strong><span>Temporal and special coverage</span></strong></td> <td><span>Data was collected </span><span>from 22 March to 4 August 2021 in Belgium, Germany, Hungary, Lithuania, the Netherlands, Poland, Portugal and Spain.</span></td> </tr> <tr> <td><strong><span><span>Language of files</span></span></strong></td> <td><span>English</span></td> </tr> <tr> <td><strong><span><span>Subject</span></span></strong></td> <td><span>Food aid in Europe, COVID-19 crisis, survey data</span></td> </tr> <tr> <td><strong><span><span>Audience</span></span></strong></td> <td><span>Social Sciences</span></td> </tr> <tr> <td><strong><span><span>Rightsholder</span></span></strong></td> <td><span> <span>University of Antwerp, Johanna Greiss, phd student, ORCID </span><span><a href="https://orcid.org/0000-0001-6985-1792"><span>0000-0001-6985-1792</span></a></span></span></td> </tr> <tr> <td><strong><span><span>Access Rights</span></span></strong></td> <td><span>Open access</span></td> </tr> </tbody> </table>
Coronavirus disease (COVID-19) case data - South Africa
<p>COVID 19 Data for South Africa created, maintained and hosted by <a href="https://dsfsi.github.io/">DSFSI research group</a> at the University of Pretoria</p> <p><strong>Disclaimer:</strong> We have worked to keep the data as accurate as possible. We collate the COVID 19 reporting data from NICD and South Africa DoH. We only update that data once there is an official report or statement. For the other data, we work to keep the data as accurate as possible. If you find errors let us know. </p> <p>See original GitHub repo for detailed information <a href="https://github.com/dsfsi/covid19za">https://github.com/dsfsi/covid19za</a></p>
Complement activation induces excessive T cell cytotoxicity in severe COVID-19: Analysis of single cell data cohort 1 (Berlin).
<p>This repository contains the R Markdown files with the analysis of CyTOF and scRNA-seq data corresponding to cohort 1 (Berlin) analysed in Georg et al. 2021 "Complement activation induces excessive T cell cytotoxicity in severe COVID-19". Additionally, here we include the necessary CyTOF data to reproduce this analysis.</p> <p>CyTOF data:</p> <ul> <li>The debarcoded fcs files (before batch-correction) can be found in <a href="https://flowrepository.org/id/FR-FCM-Z4P5">https://flowrepository.org/id/FR-FCM-Z4P5</a>. \</li> <li>Here you can find the necessary data to reproduce the analysis (cytof_analysis.Rmd, cytof_analysis.html): <ul> <li>data_norm_all.csv: single-cell protein expression data (after batch-normalization and in linear scale).</li> <li>data_Tcells_annotated.csv: single-cell protein expression of gated T cells with cluster annotation.</li> <li>phenograph_CD4_k30.csv, phenograph_CD8_k30.csv, phenograph_TCRgd_k30.csv: output from Louvain Clustering computed with PhenoGraph (<a href="https://github.com/jacoblevine/PhenoGraph">https://github.com/jacoblevine/PhenoGraph</a>) per T cell compartment.</li> <li>clusterannotation.csv: annotation for each cluster and metacluster</li> </ul> </li> </ul> <p>scRNA-seq data:</p> <ul> <li>The raw data can be found in <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE175450">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE175450</a></li> <li>Other files to reproduce the analysis (scRNAseq_analysis_1preprocessing.Rmd, scRNAseq_analysis_2clustering.Rmd, scRNAseq_analysis_3convalescent.Rmd): <ul> <li><a href="https://zenodo.org/api/files/76286c93-628d-4251-9118-52130d4a75c6/scRNAseq_Sawitzki_RECAST_09_2021.xlsx">scRNAseq_Sawitzki_RECAST_09_2021.xlsx</a>: Single-cell metadata.</li> <li>scRNAseq_samples.tsv: Samples metadata.</li> <li><a href="https://zenodo.org/api/files/76286c93-628d-4251-9118-52130d4a75c6/scRNAseq_genelist_annotation.xlsx">scRNAseq_genelist_annotation.xlsx</a>: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE175450">G</a>ene list for the annotation of T cells (Also in Mendeley, see Data and Code Availability).</li> <li><a href="https://zenodo.org/api/files/76286c93-628d-4251-9118-52130d4a75c6/scRNAseq_GO_RESPONSE_TO_TYPE_I_INTERFERON.txt">scRNAseq_GO_RESPONSE_TO_TYPE_I_INTERFERON.txt</a>, <a href="https://zenodo.org/api/files/76286c93-628d-4251-9118-52130d4a75c6/scRNAseq_GO_DEFENSE_RESPONSE_TO_VIRUS.txt">scRNAseq_GO_DEFENSE_RESPONSE_TO_VIRUS.txt</a>, , <a href="https://zenodo.org/api/files/76286c93-628d-4251-9118-52130d4a75c6/scRNAseq_GO_T_CELL_MEDIATED_CYTOTOXICITY.txt">scRNAseq_GO_T_CELL_MEDIATED_CYTOTOXICITY.txt</a>: Gene lists for the signatures “Response to Type I Interferon” , “Defense Response to virus” and “Cytotoxicity” used for GSEA. (Also in Table S2).</li> <li><a href="https://zenodo.org/api/files/76286c93-628d-4251-9118-52130d4a75c6/scRNAseq_traj18_trav10.txt">scRNAseq_traj18_trav10.txt</a>,<a href="https://zenodo.org/api/files/76286c93-628d-4251-9118-52130d4a75c6/scRNAseq_trbv25.txt">scRNAseq_trbv25.txt</a>: sequences to determine the proportion of TRAV10-TRAJ18-TRBV25 pairing T cell clones across all T cell clusters.</li> </ul> </li> </ul>
Changing social contact patterns among US workers during the COVID-19 pandemic: April 2020 to December 2021
<p>These are data from the CorporateMix US study rounds 1-4. Below is a description of the files:</p> <p>1. participants: this contains a list of all the study participants for each round. EaA unique participant identified by the participant_id and round.</p> <p>2. contacts: this contains the individuals with whom a participant had a contact.</p> <p>3.df_all: this is generated by merging the participant and contacts dataframes using the participant_id and round as primary keys.</p> <p>4. day_rd1: this is data for round 1 day of survey. The survey design for round 1 was different, so these data are important to distinguish between day 1 and day 2 contacts.</p>
Multiplexed histology of COVID-19 post-mortem lung samples - Single-cell Mean Fluorescence Intensities
<p>Data table containing single-cell mean fluorescence intensities (MFI) of all markers analyzed by multiplexed histology in all COVID-19 post-mortem lung samples and non-COVID-related pneumonia controls (14 lung samples, stratified based on disease duration into control, acute, chronic and prolonged). It contains information at the single-cell level about approx 50 proteins in around 40.000 lung cells.</p> <p>Data shown has been arcsin(h) transformed with a co-factor of 0.2. Additionally, cells expressing less than 0.15 MFI of all markers have been labeled as non-defined and excluded from the data set.</p> <p>Seurat package 4.0.0 was used in R to perform mean centering and scaling, followed by PCA, and reduced the dimensions of the data to the top 11 principal components. UMAP was initialized in this PCA space to visualize the data on reduced UMAP dimensions. The cells were clustered on PCA space using the SNN algorithm implemented as <em>FindNeighbors</em> and <em>FindClusters </em>with <em>n.epochs = 500</em> and default parameters (<em>res = 0.8</em>). We obtained 26 clusters that we merged to get relevant populations for our analysis based on canonical lineage markers. We ended up with 8 cell clusters that were manually annotated based on cell-type-specific markers found to be differentially expressed.</p> <p> </p> <p> </p>
NHS England COVID-19 Exposure Notification App and Test Availability Data
<p>This dataset was scraped from the API serving the NHS COVID-19 App for England and Wales, and the NHS COVID-19 test availability service.</p> <p>It contains the following files:</p> <p><strong>exposure_keys.csv</strong><br> Metadata associated with the published exposure keys for the Bluetooth Google/Apple Exposure Notification (GAEN) system. The actual broadcast keys were not collected, only the metadata attached to them. The columns in the table match those in the <a href="https://developers.google.com/android/exposure-notifications/exposure-key-file-format">exposure key export format</a>, with the exception of the "export_date" field which is the "end_timestamp" of the key export in which that key was first seen. This file is believed to be complete between 2020-09-13 and 2023-04-29 when the NHS COVID-19 app was retired.</p> <p><strong>exposure_configuration.csv</strong><br> This table contains the NHS COVID-19 App's exposure configuration JSON file fetched from the API, with a new record inserted whenever this changed. History of this file is also available in the <a href="https://github.com/ukhsa-collaboration/covid19-app-system-public">app's git repository</a>, and entries in this file from before 2021-07-11 were imported from there. The timestamps for entries dated since that point will match the time that the configuration was published to the API, which may not be the case for the git repository as this was normally updated after a delay.</p> <p><strong>risky_venues.csv</strong><br> "Risky venue" notification data for the COVID-19 App. This was an NHS App-specific feature, not part of the GAEN specification, which allowed users to "check in" to a venue and receive a notification if they were present at the same time as someone who subsequently tested positive for COVID-19. This file is believed to be complete between 2020-09-24 and 2022-02-22 (when it appears the "risky venue" feature was retired), with a known data collection gap between 2021-08-03 and 2021-08-06.</p> <p><strong>walk_in_pcr_availability.csv</strong><br> Walk-in PCR test availability for the entire UK, used by the NHS PCR test booking service. This contains JSON objects provided by the API, which are broken down by region. A new row was inserted whenever this JSON object changed. This file is believed to be complete between 2021-12-27 and 2022-03-30 (after which it appears the online test booking service was retired).</p> <p><strong>home_test_availability.csv</strong><br> Home test (PCR or Lateral Flow Device) availability, for the public and for "key workers" who had priority ordering PCR tests. A new row was inserted whenever the availability changed. This file is believed to be complete between 2021-12-27 and 2022-08-22 when the data ended.</p> <p> </p> <p>The final version of the source code used to fetch this data is <a href="https://doi.org/10.5281/zenodo.7883754">available here</a>.</p>
Covid-19 - Symptoms - Impact on quality of life and needs of affected people
<p>Dataset « Covid-19 - Symptoms - Impact on quality of life and needs of affected people ». The data came from an online study involving a sample of 639 participants resident in France affected by COVID-19 symptoms several days, weeks or months after infection. It was collected to provide characterization of a wide range of symptoms of COVID-19, their effects on quality of life and the needs of those affected.</p>
Database Fear of COVID-19 and Vaccine Attitudes Examination Scale (VAX) in Spain
<p>This dataset contains data collected between November 15, 2021 and March 7, 2022, and between December 1, 2022 and February 6, 2023. It contains demographic variables, data on vaccinated people, responses to the items on the Fear of COVID-19 Scale, and to the items on the Vaccination Attitudes Examination (VAX) scale are included. Although the language of the open answers is Spanish, the name of the variables and the value labels are written in English to facilitate their understanding.<br> Data and codebooks are provided in csv format, following the FAIR principles.<br> Three files are provided:<br> 1. Database Fear of COVID-19 and VAX, with the data related to sample characteristics and the answers to the items of the questionnaires.<br> 2. Database codebook of variables, with information of the labels of the variables of the Database file.<br> 3. Variable values codebook, with the labels of the values of the variables in the Database file.</p>
Multiplexed histology of COVID-19 post-mortem lung samples - CHRONIC CASE 3 FOV1
<p><strong>Image-based data set of a post-mortem lung sample from a COVID-19 donor (CHRONIC CASE 3 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 - CHRONIC CASE 3 FOV2
<p><strong>Image-based data set of a post-mortem lung sample from a COVID-19 donor (CHRONIC CASE 3 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>
Datasets used in the study "Trends in medication use after the onset of the COVID-19 pandemic in the Republic of Ireland: an interrupted time series study"
<p>This record contains datasets analysed as part of the study "Trends in medication use after the onset of the COVID-19 pandemic in the Republic of Ireland: an interrupted time series study".</p> <p>Two datasets were used, one relating to therapeutic subgroups defined by ATC codes (atc_wide_freq_avg.csv) and one relating to individual medications (drugs_wide_freq_avg.csv). Datasets were collated by combining monthly data reported by HSE Primary Care Reimbursement Services in Ireland relating to dispensing on the General Medical Services scheme at https://www.sspcrs.ie/portal/annual-reporting/</p> <p>Code used to collate datasets and for data management is included in Stata format (compile_data_export_for_analysis_final.do).</p> <p>The study protocol is available at https://doi.org/10.17605/OSF.IO/B4RTM</p>
Timeline of government interventions and events regarding the COVID-19 pandemic in Sweden December 31, 2019, to May 5, 2023.
<p>The Swedish approach to managing the COVID-19 pandemic has received significant attention in international scholarly work and the press. For this dataset, we have reviewed governmental and media archives to build a detailed timeline that chronicles significant policies, interventions, and events in the Swedish management of COVID-19. The dataset contains summary descriptions of what took place, when it happened, and who the principal actors involved were. Links to primary sources are provided for each entry. Because of the level of detail and saturation, the dataset offers a detailed account of Swedish pandemic governance and will benefit anyone working on Swedish pandemic management or doing comparative work between Sweden and other jurisdictions.</p> <p>The dataset contains details on the date an event took place (column 1), tags to facilitate navigation (column 2), details on the principal actors involved in the event (column 3), a summary description of what took place and who was involved (column 4), and links to primary materials (e.g., archival entries) (columns 4-12). Through a structured and detailed outline, the dataset provides a saturated account of policy interventions and events in Sweden during the COVID-19 pandemic for the period 2020-2023 until it was no longer considered a public health emergency of international concern by the WHO and complements existing and less detailed timelines published at earlier points in the period.</p>
Let's talk about COVID-19 vaccination: relevance of conversations about COVID-19 vaccination and information sources on vaccination intention in Switzerland
<p>Data to replicate the publication "Let's talk about COVID-19 vaccination: relevance of conversations about COVID-19 vaccination and information sources on vaccination intention in Switzerland?". This publication examines how public information sources and conversations about COVID-19 are associated with COVID-19 vaccination intention. Multivariable logistic regression and mediation analysis using generalized structural equation modeling were applied.</p>
A Complement Atlas identifies interleukin 6 dependent alternative pathway dysregulation as a key druggable feature of COVID-19.
<p>Improvements in COVID-19 treatments, especially for the critically ill, require deeper understanding of the mechanisms driving disease pathology. The complement system is a crucial component of innate host defense, but can also contribute to tissue injury. Although all complement pathways have been implicated in COVID-19 pathogenesis, the upstream drivers and downstream effects on tissue injury remain poorly defined. We demonstrate that complement activation is primarily mediated by the alternative pathway, and we provide a comprehensive atlas of the complement alterations around the time of respiratory deterioration. Proteomic and single-cell sequencing mapping across cell types and tissues reveals a division of labor between lung epithelial, stromal, and myeloid cells in complement production, in addition to liver-derived factors. We identify IL-6 and STAT1/3 signaling as an upstream driver of complement responses, linking complement dysregulation to approved COVID-19 therapies. Furthermore, an exploratory proteomic study indicates that inhibition of complement C5 decreases epithelial damage and markers of disease severity. Collectively, these results support complement dysregulation as a key druggable feature of COVID-19.</p>
Dataset:Biomarker-based diagnosis of Post COVID-19 Condition
<p>The persistence or development of new symptoms three months after the initial acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) infection is referred to as post-coronavirus disease (COVID) condition (PCC). The identification of new biomarkers specific for the occurrence of PCC is vital to proceed in the future towards prediction of its evolution.</p> <p>This study was registered with ISRCTN Registry during recruitment (ISRCTN27312680), and it was conducted comparing two parallel groups: individuals diagnosed with PCC versus individuals who completely recovered within 3 months after acute COVID-19.</p> <p>All participants were enrolled between the first semester of 2022 in Primary Health Care Centers (PHCCs) of Zaragoza (Spain). The two parallel groups were matched by age, gender, and date of acute COVID-19 diagnosis. The diagnosis of PCC was determined by a general practitioner, following the WHO criteria [7], before or at the time of inclusion in the study. Recovered individuals were required to have passed acute COVID-19, confirmed by RT-qPCR, antigen test, or SARS-CoV-2 serology.<strong> </strong></p>
Covid-19 Vaccine Monitoring project (CVM)-Electronic Health Record data sources Codelist
<p>This is the code list that was used to identify outcomes and covariates (those tagged as in narrow) in electronic health records of participating data sources in the the CVM study which was addressing the following questions</p> <p> </p> <p>1)<strong> To create and assess readiness of electronic health record data sources for rapid evaluation of safety signals by </strong></p> <ul> <li> <p>Providing an overview of the methods for identification of COVID-19 vaccine exposure in the data sources </p> </li> <li> <p>Monitoring the number of individuals exposed to any COVID-19 vaccine and to compare this to COVID-19 vaccine exposure (benchmark: ECDC vaccine tracker)1 </p> </li> <li> <p>Generation of updated background rates for AESIs </p> </li> </ul> <p><strong>2) To conduct rapid safety assessment studies using electronic healthcare records and support EMA safety assessments. </strong></p> <p>The protocol for this study is publicly available www.encepp.eu/encepp/viewResource.htm?id=42637. The report with results using the code list is publicly available on Zenodo as well. </p> <p> </p> <p> </p> <p> </p>
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