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9,674 results for “COVID-19”
Genomic data from a pharmacogenetic study based on the efficacy of Tocilizumab in severe COVID-19 patients
<p>These data correspond to a pharmacogenetic study aimed to explore genetic associations related to the efficacy of Tocilizumab in severe COVID-19 patients. The study comprised two sets of patients, an initial subset consisted of 425 patients to whom a in-dpeth sequencing of a thermofisher sequencing panel of immune-related genes was performed, as well as a second set of 245 patients with the information of the genotyping of three selected SNPs</p>
Dados de Excesso de Mortalidade durante a pandemia do COVID-19, Pernambuco.
<p>Banco de dados do artigo Excesso de óbitos em Pernambuco: estudo ecológico do impacto da pandemia do COVID-19</p>
Educational data collected from students - regarding the analysis of online activities in schools in Romania (during the Covid-19 pandemic, March 2020 - April 2020)
<p>The student questionnaire was designed with 20 questions. It was completed by 1,088 respondents and focuses on students' experiences related to online education. The collected data provides a broad perspective on various aspects of this, including access to technology, experiences with different platforms, perceptions of the advantages and disadvantages of this form of education, as well as direct feedback from students regarding their experiences. The full questionnaire can be accessed at: <a href="https://forms.gle/fhgzCUx1SDnxbCfZ6" target="_new" rel="noopener"><strong>https://forms.gle/fhgzCUx1SDnxbCfZ6</strong></a></p> <p>To protect the identity of the respondents and to obtain accurate responses, all data collected from teachers was anonymous. We did not collect any personal information whatsoever. This aspect was made clear to the respondents in the description of the questionnaire.</p>
Educational data collected from teachers - for the analysis of online activities in schools in Romania (during the Covid-19 pandemic, March 2020 - April 2020)
<p>The dataset comes from a questionnaire structured into 24 questions, which can be accessed at <a href="https://forms.gle/bUgYMfoNHh7r6ebs6" target="_new" rel="noopener">https://forms.gle/bUgYMfoNHh7r6ebs6</a>. This questionnaire was completed by 956 respondents and aims to analyze the online activities carried out during March - April 2020, being distributed to teachers.<br>Each question is designed to reveal different aspects of the experiences, skills, and perspectives of teaching staff regarding online teaching and learning.</p> <p>To protect the identity of the respondents and to obtain accurate responses, all data collected from teachers was anonymous. We did not collect any personal information whatsoever. This aspect was made clear to the respondents in the description of the questionnaire.</p>
Data underlying the manuscript: "Analysis of Research Data Sharing in Scientific Articles on Climate Change in the Covid-19 Year. The Spanish case 2020".
<p>This is the research data for the manuscript "Analysis of Research Data Sharing in Scientific Articles on Climate Change in the Covid-19 Year. The Spanish case 2020".<br>The following is the original abstract: Introduction: Sharing research data on climate change would facilitate the development of solutions to curb its impact, for this, data needs to be shared in an optimal way. General objective: To identify how many Spanish scientific articles on climate change published during 2020 share their research data in some way. Specific objectives: a) Identify the attributes of shared research data b) Describe the characteristics of the case studies found on how research data are shared. Methodology: Qualitative and descriptive study analyzing nine attributes: availability (1), accessibility (2), format (3), license (4), linkage (5), funding (6), editorial policy (7), content (8), statistics (9). Results: We analyzed 2212 articles were analyzed, 1867 (84%) articles had no associated research data. The remaining 16% have associated research data: 152 (7%) articles deposited their data in repositories, 42 (2%) submitted their data as supplementary material, 136 (6%) will share their data upon request to the author and 15 (1%) do not have publication permissions. Conclusions: Researchers are willing to share their research data, but under different conditions. Researchers who reused research data did not share the new data they generated. There is a lack of training among researchers on how to manage their research data. There is information on the web on this topic, but it is not just a matter of publishing manuals, but also of creating training spaces within universities, institutes and research centers to build a community of researchers committed to Open Science.</p>
SRAG 2020 a 2024 - Banco de Dados de Síndrome Respiratória Aguda Grave - incluindo dados de Vacinação contra COVID-19
<p>Link Principal - https://dados.gov.br/dados/busca?termo=covid</p> <p>(3ª guia)</p> <p> </p> <p>Link dos bancos de dados (BD) - https://dados.gov.br/dados/conjuntos-dados/srag-2021-e-2022</p> <p>(guia "Recursos") </p> <p>Esta página tem como finalidade disponibilizar o legado dos bancos de dados (BD) epidemiológicos de SRAG, da rede de vigilância da Influenza e outros vírus respiratórios, desde o início da sua implantação (2009) até os dias atuais (2024), com a incorporação da vigilância da covid-19.</p> <p>O MS publicou o Guia de Vigilância Epidemiológica Emergência de Saúde Pública de Importância Nacional pela Doença pelo Coronavírus 2019 aonde estão disponíveis informações sobre definições de casos, critérios de confirmação e encerramento dos casos, dentre outros.</p> <p>Esclarece-se que as bases de dados de SRAG disponibilizadas neste portal passam por tratamento que envolve a anonimização, em cumprimento a Lei 13.709/2018.</p> <p>**Para mais informações, acessar: **</p> <p>Gripe/Influenza - https://www.gov.br/saude/pt-br/assuntos/saude-de-a-a-z/g/gripe-influenza</p> <p>COVID-19 - https://www.gov.br/saude/pt-br/coronavirus</p> <p>Guia Nacional de Vigilância da COVID-19 - https://www.gov.br/saude/pt-br/coronavirus/publicacoes-tecnicas/guias-e-planos/guia-de-vigilancia-epidemiologica-covid-19/view</p> <p>- Dicionário de Dados (Dicionario_de_Dados_SRAG_Hospitalizado_19.09.2022)</p> <p> </p>
Data and Code for "Comparing the Effects of Euclidean Distance Matching and Dynamic Time Warping in the Clustering of COVID-19 Evolution"
<p>This repository contains the datasets and data sources, analysis code, and workflow associated with the manuscript "<em>Comparing the Effects of Euclidean Distance Matching and Dynamic Time Warping in the Clustering of COVID-19 Evolution</em>". The following resources are provided:</p> <ul> <li> <p><strong>Data Files</strong>:</p> <ul> <li><code>time_series_data.csv</code>: A curated time series dataset with dates as rows and NUTS 2 regions as columns. Each column is labeled using a 4-letter abbreviation format "CC.RR", where "CC" represents the country code and "RR" represents the region code. This same abbreviation is also included in the accompanying GeoJSON file.</li> <li><code>geometry_data.geojson</code>: A GeoJSON file representing the spatial boundaries of the NUTS 2 regions, with the same 4-letter abbreviations used in the CSV file. EPSG:4326.</li> <li><code>COVID19_data_sources.xlsx</code>: This Excel file contains important metadata regarding the sources of COVID-19 data used in this study. It includes: <ul> <li>Source of the data for each country</li> <li>Official website(s)</li> <li>The agency responsible for the data</li> <li>Description of the processing steps used to curate the data into the final time series.</li> </ul> </li> </ul> </li> <li> <p><strong>Code</strong>:</p> <ul> <li><code>analysis.py</code>: A Python script used to process and analyze the data. This code can be run using Python 3.x. The libraries required to run this script are listed in the first lines of the code. The code is organized in different numbered sections (1), (2), ... and sub-sections (1a), (1b) ... Make sure to run the script one (sub-)section at a time, so that everything stays overviewable and you don't get all the output at once.</li> </ul> </li> <li> <p><strong>Workflow</strong>:</p> <ul> <li><code>workflow.png</code> : A detailed workflow according to the Knowledge Discovery in Databases (KDD) process, outlining the steps involved in processing and analyzing the data, including the methods used. This workflow provides a comprehensive guide to reproducing the analysis presented in the paper.</li> </ul> </li> </ul>
Italian Covid-19 Retweet Network (2020-2022)
<p><strong>Description</strong></p> <p>This repository contains directed retweet interactions between anonymized Twitter (now X) users.</p> <p>Data is stored in five csv files, each one relating to a different phase of the pandemic in Italy.</p> <ul> <li>early covid (01/01/2020 – 08/03/2020): from Covid’s first tracing in Wuhan,<br>China, up to the first lockdown in Italy;</li> <li>pre vaccine (09/03/2020 – 31/10/2020): from the first Italian lockdown to the start of the vaccination campaign;</li> <li>early vaccine (01/11/2020 – 16/04/2021): the first months of the vaccination campaign;</li> <li>vaccine drive (17/04/2021 - 31/07/2021): the main phase of intensive vaccination;</li> <li>late vaccine (01/08/2021 – 31/12/2021): phase in which a significant portion<br>of the Italian population was fully vaccinated;</li> </ul> <p>CSV files are structured as follows: </p> <p><em>from,to,weight</em></p> <p> </p> <p> </p> <p> </p> <p><strong>Acknowledgements</strong><br>This work is supported by (i) the European Union – Horizon 2020 Program under the scheme “INFRAIA-01-2018-2019 – Integrating Activities for Advanced Communities”, Grant Agreement n.871042, ”SoBigData++: European Integrated Infrastructure for Social Mining and Big Data Analytics” (http://www.sobigdata.eu); (ii) SoBigData.it which receives funding from the European Union –<br>NextGenerationEU – National Recovery and Resilience Plan (Piano Nazionale di Ripresa e Resilienza, PNRR) – Project: ”SoBigData.it – Strengthening the Italian RI for Social Mining and Big Data Analytics” – Prot. IR0000013 – Avviso n. 3264 del 28/12/2021; (iii) EU NextGenerationEU programme under the funding schemes PNRR-PE-AI FAIR (Future Artificial Intelligence Research).</p>
Dataset of Psychological Impact of the COVID-19 Pandemic on Families of People with Severe Mental Disorders in Brazil
<p>The objective of the study is to understand how the families of patients with severe mental disorders experienced the beginning of the pandemic in Brazil and how they fared during the period of restricted social contact.</p>
Base de datos- Población y percepción de riesgo y causas de la situación de la Pandemia de la COVID-19 en La Habana, Cuba.
<p>Base de datos de más de 2000 ciudadanos cubanos que respondieron una encuesta autogestionada por los autores sobre la percepción de riesgo y causas de la situación de la Pandemia de la COVID-19 en La Habana.</p>
Epidemiology, Biodiversity, and Technological Trajectories in the Brazilian Amazon: From Malaria to COVID-19
<p>Dataset of the publication: Codeço CT, Dal’Asta AP, Rorato AC, Lana RM, Neves TC, Andreazzi CS,<br> Barbosa M, Escada MIS, Fernandes DA, Rodrigues DL, Reis IC, Silva-Nunes M, Gontijo AB,<br> Coelho FC and Monteiro AMV (2021) Epidemiology, Biodiversity, and Technological Trajectories in the<br> Brazilian Amazon: From Malaria to COVID-19. Front. Public Health 9:647754. doi: 10.3389/fpubh.2021.647754</p> <p> </p>
Co2 concentration, temperature and humidity in primary classrooms during the Covid-19 Safety Measures in Spain
<p>Co2 concentration, temperature and humidity in primary classrooms during the Covid-19 Safety Measures in Spain. The data presented were collected between 1 May 2020 and 23 June 2021 using a low-cost CO<sub>2</sub> sensor called SCD30 (https://bit.ly/3dDWXu1). This sensor can messure CO<sub>2</sub>, temperature and air humidity. Six nodes were built and deployed in six classrooms in two different schools in two different periods. In the first school, located in Vilafamés (Castellón, Spain), a total of 38,891 observations were carried out. Altogether 34,570 measurements were captured in the second school located in Vall d’Alba (Castellón, Spain).</p>
Simulation results for "COVID-19 vaccination in Sindh Province, Pakistan: a modelling study of health impact and cost-effectiveness"
<p>The high-performance computing results for raw epidemiological simulations and quantiled scenarios associated with https://doi.org/10.1101/2021.02.24.21252338.</p> <p>All results stored as compressed rds files of data.table objects (use-able as data.frame objects) for use with R programming language.</p>
Supplemental Material: Page Length Calculations for BMJ EBM Manuscript on COVID-19 Vaccine Transparency
<p>Supplemental Material: Page Length Calculations for COVID-19 Vaccine Transparency Manuscript</p>
Physical phenotype of blood cells is altered in COVID-19
<p>Clinical syndrome coronavirus disease 2019 (COVID-19) induced by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is characterized by rapid spreading and high mortality worldwide. While the pathology is not yet fully understood, hyper-inflammatory response and coagulation disorders leading to congestions of microvessels are considered to be key drivers of the still increasing death toll. Until now, physical changes of blood cells have not been considered to play a role in COVID-19 related vascular occlusion and organ damage. Here we report an evaluation of multiple physical parameters including the mechanical features of five frequent blood cell types, namely erythrocytes, lymphocytes, monocytes, neutrophils, and eosinophils. More than 4 million blood cells of 17 COVID-19 patients at different levels of severity, 24 volunteers free from infectious or inflammatory diseases, and 14 recovered COVID-19 patients were analyzed. We found significant changes in lymphocyte stiffness, monocyte size, neutrophil size and deformability, and heterogeneity of erythrocyte deformation and size. While some of these changes recovered to normal values after hospitalization, others persisted for months after hospital discharge, evidencing the long-term imprint of COVID-19 on the body.</p>
Looking in the medicine cabinet: methods for using real-world data to assess the impact of measles, mumps and rubella (MMR) and recombinant adjuvanted varicella-zoster vaccines on coronavirus disease 2019 (COVID-19) prevention and case fatality
<p>Supplementary File S1. 20210712_vx_off_target_pubdraft_S1 (Tables, Graphs and scripts associated with publication)</p> <p>Data file 1. Basic_Analysis.R (descriptive analysis script in R, for use with cleaned data files 3, 4 and 6)<br> Data file 2. Cleaning (Script demonstrating how Cerner data was cleaned upon download)<br> Data file 3. COVID_all_cleaned (CSV file with all COVID+ subjects in Cerner institutions)<br> Data file 4. COVID_mmr_data_cleaned (CSV file with COVID+ patients between 25-64 years old, including institution id, age category, gender, whether patient is in emergency department or inpatient, flu vaccine history, MMR vaccine history and mortality outcomes)<br> Data file 5. COVID_mmr_data_matched (CSV file matching MMR vaccine-exposed cases to controls based on propensity scores)<br> Data file 6. COVID_zoster_data_cleaned (CSV file with COVID+ patients above 50 years old, including institution id, age category, gender, whether patient is in emergency department or inpatient, flu vaccine history, zoster vaccine history and mortality outcomes)<br> Data file 7. COVID_zoster_data_matched (CSV file matching zoster vaccine-exposed cases to controls based on propensity scores)<br> Data file 8. General_25_64_data_cleaned (CSV file, all patients in Cerner institutions between 25 – 64 years old, including institution id, age category, gender, whether patient is in emergency department or inpatient, flu vaccine history, MMR vaccine history, SARS-CoV-2 infection and COVID-19 mortality outcomes ).<br> Data file 9. General_over50_data_cleaned (CSV file, all patients in Cerner institutions above 50 years old, including institution id, age category, gender, whether patient is in emergency department or inpatient, flu vaccine history, zoster vaccine history, SARS-CoV-2 infection and COVID-19 mortality outcomes).<br> Data file 10. Included_tenants (CSV file, institution IDs whose contributed cases comprise at least 0.5% of the aggregate sample size).<br> Data file 11. MMR_ps (R script to run for MMR-related files analysis)<br> Data file 12. Zoster_ps (R script to run for zoster-related files analysis)</p>
COVID-19 Taxonomy Graph
<p>This is data for COVID-19 Texonomy Graph</p>
synthetic dataset covid-19 PER
<p>Synthetic dataset for outbreaks col-per 2021</p>
Epidemiological geography at work. An exploratory review about the overall findings of spatial analysis applied to the study of CoViD-19 propagation along the first pandemic year (DATASET)
<p><strong>Literature review dataset</strong></p> <p>This table lists the surveyed papers concerning the application of spatial analysis, GIS (Geographic Information Systems) as well as general geographic approaches and geostatistics, to the assessment of CoViD-19 dynamics. The period of survey is from January 1<sup>st</sup>, 2020 to December 15<sup>th</sup>, 2020. The first column lists the reference. The second lists the date of publication (preferably, the date of online publication). The third column lists the Country or the Countries and/or the subnational entities investigated. The fourth column lists the epidemiological data utilized in each paper. The fifth column lists other types of data utilized for the analysis. The sixth column lists the more traditionally statistically-based methods, if utilized. The seventh column lists the geo-statistical, GIS or geographic methods, if utilized. The eight column sums up the findings of each paper. The papers are also classified within seven thematic categories. The full references are available at the end of the table in alphabetical order.</p> <p>This table was the basis for the realization of a comprehensive geographic literature review. It aims to be a useful tool to ease the "due-diligence" activity of all the researchers interested in the spatial analysis of the pandemic.</p> <p>The reference to cite the related paper is the following:</p> <p><strong>Pranzo, A.M.R., Dai Prà, E. & Besana, A. Epidemiological geography at work: An exploratory review about the overall findings of spatial analysis applied to the study of CoViD-19 propagation along the first pandemic year. GeoJournal (2022). https://doi.org/10.1007/s10708-022-10601-y</strong></p> <p>To read the manuscript please follow this link: <strong>https://doi.org/10.1007/s10708-022-10601-y</strong></p> <p> </p>
Multiply improved positive matrix factorization for source apportionment of volatile organic compounds during the COVID-19 shutdown in Tianjin, China
<p>This dataset contains the input and output data of the multiple PMF for volatile organic compounds (VOCs) source apportionment in the suburbs of Tianjin, China, during the outbreak of COVID-19 period (November 2019 to March 2020).</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)
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.