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9,674 results for “COVID-19”
Covid19Kerala.info-Data: A collective open dataset of COVID-19 outbreak in the south Indian state of Kerala
<p>Covid19Kerala.info-Data is a consolidated multi-source open dataset of metadata from the COVID-19 outbreak in the Indian state of Kerala. It is created and maintained by volunteers of ‘Collective for Open Data Distribution-Keralam’ (CODD-K), a nonprofit consortium of individuals formed for the distribution and longevity of open-datasets. Covid19Kerala.info-Data covers a set of correlated temporal and spatial metadata of SARS-CoV-2 infections and prevention measures in Kerala. Static releases of this dataset snapshots are manually produced from a live database maintained as a set of publicly accessible Google sheets. This dataset is made available under the Open Data Commons Attribution License v1.0 (ODC-BY 1.0). <br> <br> <strong>Schema and data package</strong><br> Datapackage with schema definition is accessible at <a href="https://codd-k.github.io/covid19kerala.info-data/datapackage.json">https://codd-k.github.io/covid19kerala.info-data/datapackage.json</a>. Provided datapackage and schema are based on <a href="https://specs.frictionlessdata.io/data-package/">Frictionless data Data Package specification</a>.</p> <p><strong>Temporal and Spatial Coverage </strong></p> <p>This dataset covers COVID-19 outbreak and related data from the state of Kerala, India, from January 31, 2020 till the date of the publication of this snapshot. The dataset shall be maintained throughout the entirety of the COVID-19 outbreak. </p> <p>The spatial coverage of the data lies within the geographical boundaries of the Kerala state which includes its 14 administrative subdivisions. The state is further divided into Local Self Governing (LSG) Bodies. Reference to this spatial information is included on appropriate data facets. Available spatial information on regions outside Kerala was mentioned, but it is limited as a reference to the possible origins of the infection clusters or movement of the individuals. </p> <p><strong>Longevity and Provenance </strong></p> <p>The dataset snapshot releases are published and maintained in a designated GitHub repository maintained by CODD-K team. Periodic snapshots from the live database will be released at regular intervals. The GitHub commit logs for the repository will be maintained as a record of provenance, and archived repository will be maintained at the end of the project lifecycle for the longevity of the dataset.</p> <p><strong>Data Stewardship </strong></p> <p>CODD-K expects all administrators, managers, and users of its datasets to manage, access, and utilize them in a manner that is consistent with the consortium’s need for security and confidentiality and relevant legal frameworks within all geographies, especially Kerala and India. As a responsible steward to maintain and make this dataset accessible— CODD-K absolves from all liabilities of the damages, if any caused by inaccuracies in the dataset. </p> <p><strong>License </strong></p> <p>This dataset is made available by the CODD-K consortium under ODC-BY 1.0 license. The Open Data Commons Attribution License (ODC-By) v1.0 ensures that users of this dataset are free to copy, distribute and use the dataset to produce works and even to modify, transform and build upon the database, as long as they attribute the public use of the database or works produced from the same, as mentioned in the citation below. </p> <p><strong>Disclaimer </strong></p> <p>Covid19Kerala.info-Data is provided under the ODC-BY 1.0 license as-is. Though every attempt is taken to ensure that the data is error-free and up to date, the CODD-K consortium do not bear any responsibilities for inaccuracies in the dataset or any losses—monetary or otherwise—that users of this dataset may incur. </p>
The CoVidAffect dataset of mood variations following the COVID-19 outbreak in Spain
<p>Latest Update of the CoVidAffect dataset</p>
Effects of COVID-19 lockdown on heart rate variability
<p><strong>Introduction: </strong>Strict lockdown rules were imposed to the French population from 17 March to 11 May 2020, which may result in limited possibilities of physical activity, modified psychological and health states. This report is focused on HRV parameters kinetics before, during and after this lockdown period.</p> <p><strong>Methods:</strong> 95 participants were included in this study (27 women, 68 men, 37 ± 11 years, 176 ± 8 cm, 71 ± 12 kg), who underwent regular orthostatic tests (a 5-minute supine followed by a 5-minute standing recording of heart rate (HR)) on a regular basis before (BSL), during (CFN) and after (RCV) the lockdown. HR, power in low- and high-frequency bands (LF, HF, respectively) and root mean square of the successive differences (RMSSD) were computed for each orthostatic test, and for each position. Subjective well-being was assessed on a 0-10 visual analogic scale (VAS). The participants were split in two groups, those who reported an improved well-being (WB+, increase >2 in VAS score) and those who did not (WB-) during CFN.</p> <p><strong>Results:</strong> Out of the 95 participants, 19 were classified WB+ and 76 WB-. There was an increase in HR and a decrease in RMSSD when measured supine in CFN and RCV, compared to BSL in WB-, whilst opposite results were found in WB+ (i.e. decrease in HR and increase in RMSSD in CFN and RCV; increase in LF and HF in RCV). When pooling data of the three phases, there was a moderate significant correlation between VAS and HR, RMSSD, HF, respectively, in the supine position; the higher the VAS score (i.e., subjective well-being), the higher the RMSSD and HF and the lower the HR. In standing position, HRV parameters were not modified during CFN.</p> <p><strong>Conclusion:</strong> Our results suggest that the strict COVID-19 lockdown likely had opposite effects on French population as 20% of participants improved parasympathetic activation (RMSSD, HF) and rated positively this period, whilst 80% showed altered responses and deteriorated well-being. The changes in HRV parameters during and after the lockdown period were in line with subjective well-being responses. The observed recordings may reflect a large variety of responses (anxiety, anticipatory stress, change on physical activity…) beyond the scope of the present study. However, these results confirmed the usefulness of HRV as a non-invasive means for monitoring well-being and health in the general population.</p>
The Psychological Burden of the COVID-19 Pandemic and Its Associated Factors among the Frontline Doctors of Bangladesh: A Cross-sectional Study-Extended Data
<p>Using this document, we tried to assess the mental health status of the frontline doctors of Bangladesh during Coronavirus 2019 pandemic.</p>
Synthetic COVID-19 Case Reporting Data Generated from an Agent-Based Simulation Model
<p>This is a synthetic case reporting data set for the SARS-CoV-2 epidemic in Austria. The data set statistically reproduces and synthetically augments data on reported cases and was generated with an agent-based simulation model. References to descriptions of the model and the parameterization used to generate the data set is included in the attached PDF file. The data format is described in the README file.</p>
ROB 2 assessments_Convalescent plasma or hyperimmune immunoglobulin for people with COVID-19_a living systematic review
<p>Risk of bias assessments and support for judgement with ROB 2 tool for the Cochrane Review: Convalescent plasma or hyperimmune immunoglobulin for people with COVID-19:a living systematic review (Version 3). </p>
Pre-Print; Polarización social en tiempos del Covid-19
<p>La presente figura forma parte del artículo científico en proceso de revisión por pares: "Polarización social en tiempos del Covid-19". El artículo utilizó una metodología combinada de análisis del barómetro mensual elaborado por el CIS, Centro de Investigaciones Sociológicas, español y el análisis del discurso vertido en redes sociales,en concreto Twitter, a través del hashtag #sesióndecontrol. Durante el 28 de abril del año 2020, se tuvo la oportunidad de analizar la polarización social vertida en redes durante la sesión de control al gobierno español con ocasión de la crisis sanitaria del covid-19. En este adelanto se puede visualizar un grafo de centralidad del discurso analizado. La centralidad, o popularidad, se midió través de la herramienta de twitter; retweet. Además se distribuyó el grafo por familias ideológicas donde pudo medirse el grado de implicación de cada familia en el discurso. </p>
Pandemia de COVID-19: Um Banco de Dados de Respostas Políticas Relacionadas ao Setor Financeiro
<p>Descrição do Calebe - Linha do tempo de decisões dos países com relação ao setor financeiro durante a pandemia.</p> <p>Descrição original:</p> <p>Visão geral das medidas de política adotadas nas jurisdições e por tipo de medida de apoio ao setor financeiro para enfrentar o impacto da pandemia COVID-19. Este conjunto de dados é atualizado regularmente e o trabalho continua em andamento.</p> <p>Ultima atualização para essa versão: </p> <p> </p> <p>2 de outubro de 2020</p> <p>Para perguntas, entre em contato com Erik Feyen ( <a href="mailto:efeijen@worldbank.org">efeijen@worldbank.org</a> ) e Tatiana Alonso Gispert ( <a href="mailto:talonsogispert@worldbank.org">talonsogispert@worldbank.org</a> ).</p> <p>https://datacatalog.worldbank.org/dataset/covid-19-finance-sector-related-policy-responses#</p>
JRC COVID-19 In Vitro Diagnostic Devices and Test Methods Database
<p>SUMMARY</p> <p>The <em>JRC COVID-19 In Vitro Diagnostic Devices and Test Methods Database</em>, aimed to collect in a single place all publicly available information on performance of CE-marked <em>in vitro</em> diagnostic medical devices (IVDs) as well as <em>in house</em> laboratory-developed devices and related test methods for COVID-19, is here presented. The database, manually curated and regularly updated, has been developed as a follow-up to the Communication from the European Commission “Guidelines on <em>in vitro</em> diagnostic tests and their performance” of 15 April 2020 and is freely accessible at <a href="https://covid-19-diagnostics.jrc.ec.europa.eu/">https://covid-19-diagnostics.jrc.ec.europa.eu/</a>.</p>
The spatial landscape of lung pathology during COVID-19 progression - raw IMC data
<p>Recent studies have provided insights into the pathology and immune response to coronavirus disease 2019 (COVID-19). However thorough interrogation of the interplay between infected cells and the immune system at sites of infection is lacking. We use high parameter imaging mass cytometry9 targeting the expression of 36 proteins, to investigate at single cell resolution, the cellular composition and spatial architecture of human acute lung injury including SARS-CoV-2. This spatially resolved, single-cell data unravels the disordered structure of the infected and injured lung alongside the distribution of extensive immune infiltration. Neutrophil and macrophage infiltration are hallmarks of bacterial pneumonia and COVID-19, respectively. We provide evidence that SARS-CoV-2 infects predominantly alveolar epithelial cells and induces a localized hyper-inflammatory cell state associated with lung damage. By leveraging the temporal range of COVID-19 severe fatal disease in relation to the time of symptom onset, we observe increased macrophage extravasation, mesenchymal cells, and fibroblasts abundance concomitant with increased proximity between these cell types as the disease progresses, possibly as an attempt to repair the damaged lung tissue. This spatially resolved single-cell data allowed us to develop a biologically interpretable landscape of lung pathology from a structural, immunological and clinical standpoint. This spatial single-cell landscape enabled the pathophysiological characterization of the human lung from its macroscopic presentation to the single-cell, providing an important basis for the understanding of COVID-19, and lung pathology in general.</p>
The Corona Connection: How LabHive and Open Science is Helping Scientists Solve COVID-19
<p><strong>Episode Summary: </strong></p> <p>The Coronavirus pandemic has led to many new initiatives to help scientists share resources and data. We talked to, Tobias Opialla and Lisa Rieble who have created a new platform called LabHive. We discussed what LabHive is and how it got started, as well as how Open Science principles and practices relate to the new normal and how communication is key. </p> <p><strong>Episode Links: </strong></p> <p><a href="https://labhive.de/#/">LabHive</a></p> <p><a href="https://wirvsvirus.org/">WirVsVirus</a></p> <p><a href="https://berlin.impacthub.net/">Impact Hub Berlin</a></p> <p><strong>Bonus links regarding Drosten, the Teachers and Kindergarteners and the BILD:</strong></p> <p>Original tweet from Drosten:<br> <a href="https://twitter.com/c_drosten/status/1264934434756755456">https://twitter.com/c_drosten/status/1264934434756755456</a> </p> <p>Replies from improperly quoted reviewers:<br> <a href="https://twitter.com/jdoeschner/status/1264948078790029313">https://twitter.com/jdoeschner/status/1264948078790029313</a> <br> <a href="https://twitter.com/domliebl/status/1264935266185293826">https://twitter.com/domliebl/status/1264935266185293826</a> <br> <a href="https://twitter.com/polenz_r/status/1264946109719379970">https://twitter.com/polenz_r/status/1264946109719379970</a> <br> <a href="https://twitter.com/christoph_rothe/status/1265344225979314177">https://twitter.com/christoph_rothe/status/1265344225979314177</a> <br> <a href="https://twitter.com/christoph_rothe/status/1264930677306413058">https://twitter.com/christoph_rothe/status/1264930677306413058</a> </p> <p>The xkcd regarding preprints:<br> <a href="https://xkcd.com/2304/">https://xkcd.com/2304/</a> </p> <p>Regarding renewed interest in Testing:<br> Drosten wanting to test schools and Kindergartens<br> <a href="https://www.ndr.de/nachrichten/info/39-Welche-Chancen-neue-Tests-bieten,podcastcoronavirus206.html">https://www.ndr.de/nachrichten/info/39-Welche-Chancen-neue-Tests-bieten,podcastcoronavirus206.html</a> </p> <p>Press Release from Berlin Senate regarding test strategy:<br> <a href="https://www.berlin.de/rbmskzl/aktuelles/pressemitteilungen/2020/pressemitteilung.935676.php">https://www.berlin.de/rbmskzl/aktuelles/pressemitteilungen/2020/pressemitteilung.935676.p</a>df </p>
Socioeconomic disparities in subway use and COVID-19 outcomes in New York City
<p>Using data from New York City, we found that there was an estimated 28-day lag between the onset of reduced subway use and the end of the exponential growth period of SARS-CoV-2 within New York City boroughs. We also conducted a cross-sectional analysis of the associations between human mobility (i.e., subway ridership), sociodemographic factors, and COVID-19 incidence as of April 26, 2020. Areas with lower median income, a greater percentage of individuals who identify as non-white and/or Hispanic/Latino, a greater percentage of essential workers, and a greater percentage of healthcare essential workers had greater mobility during the pandemic. When adjusted for the percent of essential workers, these associations do not remain, suggesting essential work drives human movement in these areas. Increased mobility and all sociodemographic variables (except percent older than 75 years old and percent of healthcare essential workers) was associated with a higher rate of COVID-19 cases per 100k, when adjusted for testing effort. Our study demonstrates that the most socially disadvantaged are not only at an increased risk for COVID-19 infection, but lack the privilege to fully engage in social distancing interventions.</p>
Novel Coronavirus (COVID-19) Cases in The Netherlands
<p>On 27 February 2020, the first case of COVID-19 disease was confirmed in The Netherlands by RIVM (National Institute for Public Health and the Environment). In the weeks after, thousands of people were diagnosed with the infectious disease. Data on COVID-19 case counts are important for research and applications on various topics like epidemiology and statistics.</p> <p>This dataset contains reported case counts derived from official sources like RIVM (National Institute for Public Health and the Environment), LCPS (National Coordination Center for Patient Distribution), and NICE (National Intensive Care Evaluation). Data from these sources are collected, standardized, and published in various formats on a daily basis.</p> <p>The README document in this repository provides an overview of the available datasets, their file location(s), and codebooks. Copies of the original data are stored in the folder named 'raw_data'. Scripts to process the raw data into standardized files can be found in the folder workflows.</p>
Covid-19 automated diagnosis and risk assessment through Metabolomics and Machine Learning
<p>COVID-19 plasma samples spectrometry datasets for machine learning input. Used in the work of article Covid-19 automated diagnosis and risk assessment through Metabolomics and Machine Learning, currently under submittion.</p> <p>Abstract:</p> <p>COVID-19 is still placing a heavy health and financial burden worldwide. Impairments in patient screening and risk management play a fundamental role on how governments and authorities are directing resources, planning reopening, as well as sanitary countermeasures, especially in regions where poverty is a major component in the equation. An efficient diagnostic method must be highly accurate, while having a cost-effective profile. We combined a machine learning-based algorithm with mass spectrometry to create an expeditious platform that discriminate COVID-19 in plasma samples within minutes, while also providing tools for risk assessment, to assist healthcare professionals in patient management and decision-making. A cross-sectional study with 815 patients (442 COVID-19, 350 controls and 23 COVID-19 suspicious) was enrolled from three Brazilian epicenters from April to July 2020. We were able to elect and identify 19 molecules that are related to the disease’s pathophysiology and several discriminating features to patient’s health-related outcomes. The method applied for COVID-19 diagnosis showed specificity >96% and sensitivity >83%, and specificity >80% and sensitivity >85% during risk assessment, both from blinded data. Our method introduced a new approach for COVID-19 screening, providing the indirect detection of infection through metabolites and contextualizing the findings the disease’s pathophysiology. The pairwise analysis of biomarkers brought robustness to the model developed using Machine Learning algorithms, transforming this screening approach in a tool with great potential for real-world application. </p>
SIR Model Fitting for COVID-19 Dataset
<p>SIR Model for COVID-19 Dataset. The dataset is described in "Analytical parameter estimation of the SIR epidemic model. Applications to the COVID-19 pandemic" https://arxiv.org/abs/2010.07000</p> <p>The primary data source is ECDC.</p>
Dataset Challenges and Opportunities for Academic Parents during COVID-19
<p>Anonimized dataset of the survey on the impact of COVID-19 on academic parents. Participants who did not give consent were filtered out as well.</p>
Social determinants of Covid-19 infection and death in a rural Indonesia: A rapid healthcare assessment
<p>Understanding the social determinants of Covid-19 infection and death is vital for effective Covid-19 early detection and mitigation strategies. This study aims to examine social determinants of Covid-19 infection and death in the context of rural Indonesia. We used Malang district government Covid-19 contact tracing data from 14,264 individuals, spanning the period from March 1, 2020 to July 29, 2020. The contact tracing data was merged with administrative data from 390 villages to determine whether village characteristics (i.e., the number of health workers, number of community-based healthcare interventions, access to Covid-19 referred hospitals, number of indigenous socio-cultural activities, poverty level and distance to a Covid-19 epicentre city) are associated with Covid-19 infection and death. We used multilevel logistic regression to take advantage of the nested structure of data at the village level. We found among the 14,264 samples, 551 individuals were confirmed infected with Covid-19, and 62 died of Covid-19. Individuals aged 18 and older, civil servants (non-health workers), and those having close contact with people with confirmed cases had a higher likelihood of infection with Covid-19. Greater numbers of community-based healthcare interventions and a lesser distance to a pandemic epicentre reduced the likelihood of infection with the virus. Males, older people, individuals with hypertension, individuals diagnosed with pneumonia, and those diagnosed with respiratory failure had a higher likelihood of death due to Covid-19. A greater number of community-based healthcare interventions seems to reduce the likelihood of Covid-19 infection, while better access to a Covid-19 referred hospital seems to reduce the risk of death among Covid-19 patients. The findings suggest the government to prioritise strategies to control the pandemic in rural area through empowering rural community in health education to prevent Covid-19 and in monitoring people mobility, while providing Covid-19 emergency services for rural areas for reducing mortality.</p>
Dataset for English Health-Related Advice Directed to the General Public on Twitter During the Early Spread of COVID-19 [Dataset]
<p>Health-related advice directed to the public on twitterprovides insight into the use of social media duringa pandemic. This paper describes our data collection, sampling, and analysis of 44 million tweets in English in March 2020. We make reference to a parallel dataset and analysis of tweets in Arabic during thesame period. The contribution of this paper is a description of our dataset, our coding process to indicate tweets with health related advice, and our analysis and comparisons of the characteristics of the tweets with and without health-related advice. These contributions providethe basis for future research on semi-automated classifiers for health-related advice and efforts to reduce thespread of harmful health advice.</p>
AraHealth: A Dataset for Arabic Health-Related Advice Directed to the General Public on Twitter During the Early Spread of COVID-19 [Dataset]
<p>Health-related advice directed to the general public on Twitter provides insight into the use of social media during health emergencies. This paper describes our data collection, sampling, and analysis of 24 million tweets in Arabic in March and early April 2020. We make reference to a parallel dataset and analysis of tweets in English during the same period. The contribution of this paper is a description of our dataset, our coding process to indiciate tweets with health related advice, and our analysis and comparisons of the characteristics of the tweets with and without health-related advice. These contributions provide the basis for future research on semi-automated classifiers for health-related advice and efforts to reduce the spread of harmful health advice.</p>
A Geo-Tagged COVID-19 Twitter Dataset for 10 North American Metropolitan Areas
<p>The dataset comprises of 10 JSON files, each containing geographic metadata and a sentiment score collected from tweets between March 20, 2020 and December 1, 2020 pertaining to the COVID-19 global pandemic for ten of the most populous cities in the United States and Canada. </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
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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.