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9,674 results for “COVID-19”
Replication data for: Occupations and their impact on the spreading of COVID-19 in urban communities
<p>This dataset contains real-world COVID-19 human-to-human transmission network data. The data mimics how COVID-19 infections may have spread from one individual to another in Bucharest (Romania) during August 1<sup>st</sup> and October 31<sup>st</sup>, 2020. The information refers to COVID-19 patients (referees) and their contacts (referrals), i.e., the people they interacted with before being tested COVID-19 positive. The dataset is structured as an edge-list file (referee - referral ties). For each referee (referral), we provide the following attributes: sex (male/female), age, sector (public/private), a job in the medical sector (yes/no), ISCO-08 one-digit code, ISCO-08 two-digit code, ISCO-08 three-digit code, employability (active/non-active), age class (minor, adult, pensioner), confirmation month (when a patient was tested positive for COVID-19 infection), confirmation day (when a patient was tested positive for COVID-19 infection). The data were analyzed using relational hyperevent modeling (<a href="https://github.com/juergenlerner/eventnet">https://github.com/juergenlerner/eventnet</a>). </p> <p>This dataset allows replication of the analysis reported in the manuscript entitled: Occupations and their impact on the spreading of COVID-19 in urban communities (Hâncean M-G, Lerner J, Perc M, Oană I, Bunaciu D-A, Stoica AA & Ghiță M-C). </p>
COVID-19 Plane Infection Risks
<p>Cross-sectional survey results from a COVID19 plane infection risk survey conducted between the 22<sup>nd</sup> to 23<sup>rd</sup> October, 2020. Participants (<em>n</em> = 2103) were aged 18 years or older, were living in the UK and had undertaken foreign air travel. The survey consisted of 18 closed-ended questions, with seventeen of the questions addressing issues associated with travelling by air and 11 questions addressing specific demographic topics. The questionnaire was designed by the research team, consisting of environmental microbiologists, public health specialists and social scientists, based on the study objectives and incorporating information from previous studies on same topic. The draft questionnaire was then tested on an expert panel, a panel of non-experts, a local ethics committee. First, perceived risks, concerns, and subjective knowledge of COVID-19 symptoms were measured using 16 options that included 14 actual symptoms and 2 which were not. Other questions about perception and risk were measured by statements with a 5-point Likert scale (e.g. strongly disagree to strongly agree).</p>
Datasets corresponding to "Real-time intelligent classification of COVID-19 and thrombosis via massive image-based analysis of platelet aggregates"
<p>Datasets corresponding to "Real-time intelligent classification of COVID-19 and thrombosis via massive image-based analysis of platelet aggregates"</p> <p> </p> <p>Please find below an explanation for the <strong>files </strong>in this repository:</p> <p><br> <br> <strong>DiseaseClassifPaper_Dataset_01.7z, DiseaseClassifPaper_Dataset_02.7z</strong></p> <p>Experimental data. To reproduce the analyses, unzip both files and put the content into a folder called "Dataset"</p> <p><strong>02_CNN_PhenotypeClassif.7z</strong></p> <p>CNN Phenotype classification. Model was trained using AIDeveloper. using manually labelled data. Labelled Data is contained in folder "03_GatedData". The AIDeveloper session file in "02_Model\M10_Nitta6l_32pix_8class_meta.xlsx" shows, which files correspond to which subpopulation. The final model "M10_Nitta6l_32pix_8class_448.model" and corresponding .pb files are also located in that folder.</p> <p><strong>03_ExampleMeasurement.zip</strong></p> <p>One measurement file and a corresponding scatterplot</p> <p><strong>04_Dataset_load.zip</strong></p> <p>The python script "03_ExtractFeatures.py" loads the list of available experiment files (01_Dataset_Table_v02.csv). The experiment files are contained in DiseaseClassifPaper_Dataset_01.7z, DiseaseClassifPaper_Dataset_02.7z. The scrip then evaluates each experiment file to obtain distribution parameters for Area and Solidity. These values are written to new "01_Dataset_Table_v03.csv".</p> <p><strong>05_RF_training</strong></p> <p>Scripts to train and evaluate the Random Forest model (using features contained in "01_Dataset_Table_v03.csv").</p> <p><strong>07_pytranskit</strong></p> <p>Scripts for training and evaluating CDT-PLDA classifier</p> <p> </p> <p> </p>
Latvia: Behaviours and attitudes in response to the Covid-19 pandemics
<p>Web-based survey which was conducted in Latvia in 2020. The survey assesses the sociodemographic effects of the COVID-19 outbreak in Latvia. This survey is titled “Behaviours and attitudes in response to the Covid-19 pandemics”.</p>
Survey on the Effects of COVID-19 on the Wellbeing of Mexico City Households (ENCOVID- 19 CDMX – DECEMBER 2020)
<p>Amid the COVID-19 outbreak, the ENCOVID-19 CDMX provides information on the well-being of Mexico City households in four main domains: labor, income, mental health, and food insecurity. It offers timely information to understand the social consequences of the pandemic and the lockdown measures. It is a cross-sectional telephone survey that, in addition to the four main domains and a set of COVID19-related questions, includes key indicators to capture the impact of the pandemic on issues like education, social programs, and crime. This is the second dataset of the project, corresponding to December 2020, collected eight months after the lockdown began in Mexico. Data collection was performed from November 29 to December 10, 2020.</p>
The effects of Covid-19 lockdown on health, lifestyle, and wellbeing of children with Type 1 diabetes and their parents in Kuwait
<p>This is a full data set to accompany our study, presently in submission.</p> <p>Abstract</p> <p><strong><em>Objective.</em></strong> The restrictions brought about by Covid-19 pandemic have substantially affected people’s health and rapidly changed their daily routines. This is a prospective study that investigated the impact of the pandemic on primary school children with Type 1 diabetes and their parents during the first lockdown in Kuwait.</p> <p><strong><em>Methods.</em></strong> A questionnaire battery related to mental health, well-being, and lifestyle was administered at baseline in Summer 2019 (face-to-face, at a diabetes outpatient clinic) and at follow-up during lockdown in Summer 2020 (via telephone, in adherence with Covid-19 restrictions). Data were collected for 70 dyads with children aged 9-12 years.</p> <p><strong><em>Results.</em></strong> Significant differences were found in most scores for both children and parents. Their mental health worsened to a higher level of depression, anxiety, stress, and a poor level of wellbeing. The average scores on the follow-up tests fell within a clinical range on these measures. Significant differences in their lifestyle, compared to before the lockdown, included decreased levels of physical activity and lower healthy core nutritional intake.</p> <p><strong><em>Conclusions.</em></strong> Our findings indicate that the Covid-19 lockdown has had a significant psychological and possibly physiological impact on children with Type 1 diabetes and their parents. We conclude that there is a need for mental health support services focusing on these groups. Although full lockdown restrictions will have stopped in the past year, post-pandemic stressors may be expected to continue to adversely affect this cohort. </p> <p> </p>
COVID-19-related fatigue: cognitive profiles and multidomain complaints at 12 months follow-up
<p>Cognitive deficits were often reported during the acute and post-acute stages of severe COVID-19 and were often, but not always, associated with signs of brain damage. Population-wide surveys highlighted the need for investigations into the nature, the severity and the evolution of cognitive, behavioural and psychiatric deficits. In this observational study, we describe the outcome at 12 months after severe COVID-19 involving intensive care unit stay and critical chronic illness in six patients, who had no history of prior brain dysfunction. A pervading mental and physical fatigue was consistently reported as well as numerous multidomain complaints, which affect to a certain extent everyday life, and for some of the patients a certain degree of neurobehavioural (apathy) and/or psychiatric (anxiety) dysfunction. Standardized neuropsychological tests revealed for 4 of the 6 patients the occurrence relatively isolated of cognitive dysfunction or performance at the lower limit of the norm in some, but not all attentional, executive and/or working memory tests. Somatic scales highlighted the presence of dyspnoea, muscle weakness, an olfactory disorder and/or minor sleep problems in some but not all patients.</p>
Outcome of severe COVID-19: spotlight on fatigue, fatiguability, multidomain complaints and pattern of cognitive deficits in a case series without prior brain dysfunction and without COVID-19-related stroke and/or cardiac arrest
<p>Outcome of severe COVID-19: spotlight on fatigue, fatiguability, multidomain complaints and pattern of cognitive deficits in a case series without prior brain dysfunction and without COVID-19-related stroke and/or cardiac arrest<br> Valérie Beaud1, Sonia Crottaz-Herbette1, Vincent Dunet2, Jean-François Knebel1, Pierre-Alexandre Bart3, Stephanie Clarke1<br> 1 Service of Neuropsychology and Neurorehabilitation, Lausanne University Hospital and University of Lausanne, 1011 Lausanne, Switzerland<br> 2 Service of Diagnostic and Interventional Radiology, Lausanne University Hospital and University of Lausanne, 1011 Lausanne, Switzerland<br> 3 Service of Internal Medicine, Lausanne University Hospital and University of Lausanne, 1011 Lausanne, Switzerland<br> ABSTRACT<br> Background: Cognitive deficits were often reported during the acute and post-acute stages of severe COVID-19 and were often, but not always, associated with signs of brain damage. Population-wide surveys highlighted the need for investigations into the nature, the severity and the evolution of cognitive, behavioural and psychiatric deficits.<br> Case presentation: We report the outcome at 12 months after severe COVID-19 involving intensive care unit stay and mechanical ventilation in six patients. None of the patients had history of prior brain dysfunction and none sustained stroke and/or cardiac arrest during COVID-19. A pervading mental and physical fatigue was consistently reported as well as numerous multidomain complaints, which affect to some extend everyday life, and for some of the patients a mental fatiguability, a certain degree of neurobehavioural (apathy) and/or psychiatric (anxiety) dysfunction. Standardized neuropsychological tests revealed for 4 of the 6 patients the occurrence relatively isolated of cognitive dysfunction or performance at the lower limit of the norm in some, but not all attentional, executive and/or working memory tests. Somatic scales highlighted the presence of dyspnoea, muscle weakness, olfactory disorder and/or minor sleep problems in some but not all patients.<br> Conclusion: Fatigue, fatiguability, multidomain complaints, which affect to some extend everyday life, cognitive dysfunction or performance at the lower limit of the norm and a certain degree of neurobehavioural and/or psychiatric and/or somatic dysfunction can occur in the aftermath of severe COVID-19 even in the absence of neurological antecedents or of COVID-19-related stroke and/or cardiac arrest.</p>
Integrated plasma proteomic and single-cell immune signaling network signatures demarcate mild, moderate, and severe COVID-19
<p>The biological determinants underlying the range of COVID-19 clinical manifestations are not fully understood. Here, over 1400 plasma proteins and 2600 single-cell immune features comprising cell phenotype, endogenous signaling activity, and signaling responses to inflammatory ligands are cross-sectionally assessed in peripheral blood from 97 patients with mild, moderate, and severe COVID-19 and 40 uninfected patients. Using an integrated computational approach to analyze the combined plasma and single-cell proteomic data, we identify and independently validate a multivariate model classifying COVID-19 severity (multi-class AUC<sub>training</sub> = 0.799, p-value = 4.2e-6; multi-class AUC<sub>validation</sub> = 0.773, p-value = 7.7e-6). Examination of informative model features reveals novel biological signatures of COVID-19 severity, including the dysregulation of JAK/STAT, MAPK/mTOR, and NF-κB immune signaling networks in addition to recapitulating known hallmarks of COVID-19. These results provide a set of early determinants of COVID-19 severity that may point to therapeutic targets for prevention and/or treatment of COVID-19 progression.</p>
Data and R script: Ecotourism impacts on reef fishes in a marine reserve during the COVID-19 era
<p>Raw data and R code necessary to reproduce the results of the paper entitled "Ecotourism impacts on reef fishes in a marine reserve during the COVID-19 era" published in Frontiers in Ecology and the Environment.</p> <p> </p>
NH3 levels over Europe during COVID-19 were modulated by changes in atmospheric chemistry
<p><span>The coronavirus outbreak in 2020 had a devastating impact on human life, albeit a positive effect for the environment, reducing primary atmospheric constituents and improving air quality. Here we present, for the first time, inverse modelling estimates of ammonia emissions during the European lockdowns of 2020 based on satellite observations. Ammonia has a strong seasonal cycle; it mainly originates from agriculture, which was influenced insignificantly by the lockdowns, as practically agricultural activity never ceased. The key result is a -0.7% decrease in emissions in the first half of 2020 compared to the same period in 2016–2019 attributed to restrictions related to the global pandemic or an abrupt -9.8% decrease due to reductions in the traffic-related precursors of atmospheric acids, with which ammonia reacts to form secondary aerosols. When comparing emissions before, during and after lockdowns, the typical seasonal trends of ammonia prevail. However, when reductions in the precursors of atmospheric acids are considered, a delay of 11% was found in the evolution of the emissions. Thus, changes in atmospheric conditions such as those of the ammonia's reactant precursor species induce extra bias in top-down calculations and, hence, emissions should be interpreted carefully. Despite the small drop in emissions, satellite levels of ammonia increased. On one hand, this was due to the reduction of atmospheric acids that caused binding and thus removing less ammonia; on the other, the reduction of traffic-related emissions in Europe increased the oxidative capacity of the atmosphere resulting in nitrate abatement that favored accumulation of free ammonia.</span></p> <p>Update March 2023:</p> <p>- 4deg_avgEENV.tar.gz file was added containing the inversion results using the avgEENV dataset as a priori information. This prior creates a better fit of the posterior modelled concentrations to ground-based independent observations of NH3 over Europe in the first half of 2020.</p>
Survey among self-employed persons in Germany during the COVID-19 pandemic. wave 2020.
<p>In spring 2020, DIW Berlin, ZEW Mannheim, and the University of Trier conducted an online survey among self-employed persons to collect data on the situation of the self-employed in Germany at the onset of the COVID-19 pandemic. The online survey was disseminated in cooperation with the Verband der Gründer und Selbstständigen Deutschland (VGSD) e.V. and other professional associations.</p> <p>The questionnaire included 51 questions and focused on the following topics:<br> - the self-employed's affectedness by the pandemic and by government containment measures<br> - financial situation during the pandemic<br> - application to government support programs<br> - business strategies to overcome the crisis<br> - assessments of the future<br> - sociodemographic characteristics<br> - business-related characteristics</p> <p>In total, 27,262 interviews were collected.</p>
COVID-19 Vaccine Hesitancy in the Pandemic's Third Year
<p>Dataset and code for June 2022 study, "<strong>COVID-19 Vaccine Hesitancy in the Pandemic's Third Year"</strong></p>
Cloud computing is one of the most popular and sophisticated technologies adopted by organizations worldwide. Some world-leading organizations enhance their efficiency and effectiveness by using cloud computing technology. Working from home (WFH) has been a popular trend among organizations during the coronavirus (COVID-19) pandemic. The COVID-19 saw a breakthrough in work cultures and environments where working from home was a remarkable success in remote working environments, despite being a rare phenomenon in Sri Lanka. Yet, it is argued that the deployment of work from home has not been effective among Sri Lankan business organizations due to a lack of IT infrastructure, facilities, and knowledge. The purpose of the study is to investigate the impact of cloud computing, embracing the service models (Infrastructure as a Service, Platform as a Service, and Software as a Service) as theoretical lenses and testing the COVID-19 as the moderator. The study has been conducted based on a deductive approach and adopted a stratified random sampling method. The sample consisted of 384 IT employees among those who had experienced working from home. The study utilized multiple regression and found that cloud computing service models significantly impact work from home with the moderating effect of COVID-19.
<p>Cloud computing is one of the most popular and sophisticated technologies adopted by organizations worldwide. Some world-leading organizations enhance their efficiency and effectiveness by using cloud computing technology. Working from home (WFH) has been a popular trend among organizations during the coronavirus (COVID-19) pandemic. The COVID-19 saw a breakthrough in work cultures and environments where working from home was a remarkable success in remote working environments, despite being a rare phenomenon in Sri Lanka. Yet, it is argued that the deployment of work from home has not been effective among Sri Lankan business organizations due to a lack of IT infrastructure, facilities, and knowledge. The purpose of the study is to investigate the impact of cloud computing, embracing the service models (Infrastructure as a Service, Platform as a Service, and Software as a Service) as theoretical lenses and testing the COVID-19 as the moderator. The study has been conducted based on a deductive approach and adopted a stratified random sampling method. The sample consisted of 384 IT employees among those who had experienced working from home. The study utilized multiple regression and found that cloud computing service models significantly impact work from home with the moderating effect of COVID-19.</p>
Forecasting the publication and citation outcomes of Covid-19 preprints
<p>The scientific community reacted quickly to the <em>Covid-19</em> pandemic in 2020, generating an unprecedented increase in publications. Many of these publications were released on preprint servers such as <em>medRxiv</em> and <em>bioRxiv</em>. It is unknown however how reliable these preprints are, and if they will eventually be published in scientific journals. In this study, we use crowdsourced human forecasts to predict publication outcomes and future citation counts for a sample of 400 preprints with high <em>Altmetric</em> scores. Most of these preprints were published within one year of upload on a preprint server (70%), and 46% of the published preprints appeared in a high-impact journal with a Journal Impact Factor of at least 10. On average, the preprints received 162 citations within the first year. We found that forecasters can predict if preprints will be published after one year and if the publishing journal has high impact. Forecasts are also informative with respect to preprints' rankings in terms of <em>Google</em> <em>Scholar</em> citations within one year of upload on a preprint server. For both types of assessment, we found statistically significant positive correlations between forecasts and observed outcomes. While the forecasts can help to provide a preliminary assessment of preprints at a faster pace than the traditional peer-review process, it remains to be investigated if such an assessment is suited to identify methodological problems in pre-prints. </p>
Analysis of shared research data in Spanish scientific papers about COVID-19: a first approach
<p><strong>Introduction:</strong> During the coronavirus pandemic, changes in the way science is done and shared occurred, which motivates meta-research to help understand science communication in crises and improve its effectiveness. <strong>Objective: </strong>To study how many Spanish scientific papers on COVID-19 published during 2020 share their research data. <strong>Methodology:</strong> Qualitative and descriptive study applying nine attributes: (1) availability, (2) accessibility, (3) format, (4) licensing, (5) linkage, (6) funding, (7) editorial policy, (8) content and (9) statistics. <strong>Results:</strong> We analyzed 1340 papers, 1173 (87.5%) did not have research data. 12.5% share their research data of which 2.1% share their data in repositories, 5% share their data through a simple request, 0.2% do not have permission to share their data and 5.2% share their data as supplementary material. <strong>Conclusions:</strong> There is a small percentage that shares their research data, however it demonstrates the researchers' poor knowledge on how to properly share their research data and their lack of knowledge on what is research data.</p>
anonymized author dataset from the publication "Impact of the COVID-19 pandemic on publishing in astronomy in the initial two years"
<p>We provide the anonymized author dataset that was prepared for the research presented in <a href="https://arxiv.org/pdf/2203.15621.pdf">https://arxiv.org/pdf/2203.15621.pdf</a> . The data is provided as two pickled pandas data frames. The first data frame contains the number of publications each author has written in each year per author position (as 1st author etc.) and their assigned gender. The second file contains the corresponding country of affiliation for each author and year. The two data frames can be matched by author_id.</p> <p>Columns in "matched_author_dataset_publication_counts_zenodo.pkl":</p> <pre>['author_id', 'gender', 'P(gender)', 'pub1_tot', 'first_auth_pub_year', 'pub2_tot', 'auth_2_pub_year', 'pub3_tot', 'auth_3_pub_year', 'pub4_tot', 'auth_4_pub_year', 'pub5_tot', 'auth_5_pub_year', 'pub6_tot', 'auth_6_pub_year', 'pub7_tot', 'auth_7_pub_year', 'pub8_tot', 'auth_8_pub_year', 'pub9_tot', 'auth_9_pub_year', 'pub10_tot', 'auth_10_pub_year', 'pub11_tot', 'auth_11_pub_year', 'pub12_tot', 'auth_12_pub_year', 'pub13_tot', 'auth_13_pub_year', 'pub14_tot', 'auth_14_pub_year', 'pub15_tot', 'auth_15_pub_year', 'pub16_tot', 'auth_16_pub_year', 'tot_pub', 'tot_pub_year', 'last_pub', 'first_pub']</pre> <p>'author_id' is a unique id assigned to the author. 'gender' contains the author's most likely gender and 'P('gender')' the likelihood of correct assignment. 'pubX_tot' contains the total number of publications the author has written as Xth author, 'auth_X_pub_year' contains a list with one entry per year from 1950 to 2022 counting the number of papers the author has published as Xth author in that year. 'tot_pub' counts the total number of publications (summed over all years) and 'tot_pub_year' for each year/ 'last_pub' and 'first pub' contain the list indices of the years when the author last/first published.</p> <p>Columns in author_dataframe_country.pkl:</p> <pre>['author_id', 'aff_year', 'aff_country_author']</pre> <p>'aff_year' is a list of years for which country information could be inferred for this author. 'aff_country_author' is a list of countries for each entry in 'aff_year'.</p> <p> </p>
The impact of COVID-19 on the everyday life of blind and sighted individuals
<p>The datasets contain: </p> <p>- raw data of the open-ended question with the percentage of participants declaring a specific difficulty for COVID-19.</p> <p>- raw data of Questionnaire 1 divided by: Daily routine, Social life and Sleep habits. In each file the question number is reported in the header of each column. </p> <p>- raw data of Questionnaire 2 with the individual sum of responses for each personality category.</p>
COVID-19: data and indicators to measure the return to work in Area Science Park after the emergency phase
<p>Data to measure the impact of SARS-CoV-2 virus on work, activities and services of Area Science Park and the return to work after the emergency epidemiological phase.</p> <p>Files available:</p> <ul> <li>Report Restart Area</li> <li>Stima_Presenze_Area_Science_Park</li> </ul> <p> </p> <p> </p>
Pesquisa da UFSCar analisa a evolução epidemiológica da COVID-19 em crianças de São Carlos
<p>Cristina Ortiz Sobrinho Valete, docente do Departamento de Medicina da Universidade Federal de São Carlos (DMed - UFSCar), fala da pesquisa que analisou a evolução epidemiológica da COVID-19 em crianças no município de São Carlos em 2020 e 2021, além de características dos casos graves que necessitaram de internação.</p> <p>Lattes: http://lattes.cnpq.br/3246791895201559</p> <p>CLICK CIÊNCIA Projeto de divulgação e popularização da Ciência produzido pelo Laboratório Aberto de Interatividade para Disseminação do Conhecimento Científico e Tecnológico da Universidade Federal de São Carlos (LAbI - UFSCar). http://www.labi.ufscar.br</p> <p>Pesquisa da UFSCar analisa a evolução epidemiológica da COVID-19 em crianças de São Carlos de <a href="https://youtu.be/L4eUF2BdasM">https://youtu.be/L4eUF2BdasM</a> está licenciado com uma Licença <a href="http://creativecommons.org/licenses/by-nc-nd/4.0/">Creative Commons - Atribuição-NãoComercial-SemDerivações 4.0 Internacional</a>. Podem estar disponíveis autorizações adicionais às concedidas no âmbito desta licença em <a href="https://www.labi.ufscar.br/">https://www.labi.ufscar.br/</a>.</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.