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9,674 results for “COVID-19”
OpenAIRE Covid-19 publications, datasets, software and projects metadata.
<p>This dataset provides access to the metadata records of publications, research data, software and projects that may be relevant to the Corona Virus Disease (COVID-19) fight. The dataset contains the OpenAIRE COVID-19 Gateway records, identified via full-text mining and inference techniques applied to the <a href="https://explore.openaire.eu">OpenAIRE Graph</a>. The OpenAIRE Graph is one of the largest Open Access collections of metadata records and links between publications, datasets, software, projects, funders, and organizations, aggregating 12,000+ scientific data sources world-wide, among which the Covid-19 data sources Zenodo COVID-19 Community, WHO (World Health Organization), BIP! FInder for COVID-19, Protein Data Bank, Dimensions, scienceOpen, and RSNA.</p> <p>The dataset consists of a tar archive containing gzip files with one json per line. Each json is compliant to the schema available at <a href="https://doi.org/10.5281/zenodo.3974226">https://doi.org/</a><a href="https://doi.org/10.5281/zenodo.8238913">10.5281/zenodo.8238913</a>.</p> <p> </p>
Characterization of the anti-spike IgG immune response to COVID-19 vaccines in people with a wide variety of immunodeficiencies
<p>Participants submitted saliva using the OME-505 collection device (OMNIgene Oral, Ottawa, Canada) every two weeks from vaccination through six months post-dose 3 to detect breakthrough SARS-CoV-2 infections. Viral RNA was extracted using the NucliSENS easyMag automated extraction system from 200ul of saliva in stabilizing solution and eluted in a total volume of 50ul. First strand cDNA synthesis was performed from 5ul of eluted RNA using SuperScript IV VILO Master Mix (Thermo Fisher). Positive specimens were then sequenced. Multiplex tiled amplicon libraries were prepared using the Midnight panel and Rapid barcoding kit RBK-004 (Oxford Nanopore technologies) using previously published protocol. Twelve sample pooled libraries were sequenced on a GridION X5 nanopore sequencer using Flongle adapters. After sequencing, raw data were processed using interARTIC to generate consensus sequences and variant calls. SARS-CoV-2<strong> </strong>lineages were determined using these consensus sequences and the NextClade and Pangolin platforms.</p>
Evaluation of COVID-19 antigen rapid diagnostic tests for self-testing in Lesotho and Zambia - Zambia data
<p>Dataset with Zambia data belonging to the publication: Evaluation of COVID-19 antigen rapid diagnostic tests for self-testing in Lesotho and Zambia - Zambia data</p>
Molecular docking: Hydroxychloroquine alternative to inhibit the COVID-19 main protease (MPro)
<p>Docking study shows best binding affinity against the main protease of COVID-19. As per the docking results top twelve compounds as a MPro inhibitor, Coumermycin A1 (-10.2), Irinotecan (-9.4), Suramin (-9.4), Trovafloxacin (-9.3), Aclarubicin (-9.0), Dactinomycin (-9.0), TG-100801 (-9.0), Raltegravir (-8.9), Digoxin (-8.9), Etoposide (-8.9), Doxorubicin (-8.8), and Venetoclax (-8.8) from the tested compounds.</p> <p>ARULANANDAM, CHARLI DEEPAK (2020): Molecular docking: Hydroxychloroquine alternative to inhibit the COVID-19 main protease (MPro). figshare. Dataset. https://doi.org/10.6084/m9.figshare.12032745.v26</p>
Covid-19 - impact on evolution of energy demand
<p><strong>Consumo_elect_COVID_2020 & Consumo elect_COVID_2019</strong></p> <p>Dataset with register of energy demand in Spain. Timeframe: January to March (2019 & 2020)</p> <p> </p> <p><strong>Casos_COVID_ESPAÑA</strong></p> <p>Dataset with register of the evolution of the spread of Covid-19 in Spain. Break-down of data per region (CCAA). Timeframe: January to March 2020</p> <p> </p> <p><strong>Casos_COVID_mundo</strong></p> <p>Dataset with register of the evolution of the spread of Covid-19 in Spain. Break-down of data per country. Timeframe: January to March 2020</p>
COVID-19, evolución y repercusión.
<p>La temática elegida para este dataset ha sido la actual sobre el Coronavirus o COVID-19. Este virus ha entrado en la vida de todas las personas del planeta desde hace unos pocos meses, llevando su letalidad a parar las actividades normales de todo tipo (trabajos, rutinas…). En la actualidad, este es un tema recurrente y sobre el que se tiene información casi las 24 horas del día, debido a su notoriedad y magnitud.</p> <p>En cuanto a las fuentes elegidas, se ha seleccionado la web oficial del gobierno de España y la web Worldometers como fuentes estadísticas. Ambas webs proporcionan datos actualizados del virus, la primera de España y la segunda a nivel mundial. De esta forma, se consideran fuentes fiables debido a que, en la primera, el gobierno es el que proporcionaría los datos; y la segunda debido a que, tras realizar un proceso de investigación, se ha visto que los datos que utilizan se corresponden con los de los gobiernos correspondientes.</p> <p>Además, hemos utilizado Twitter como fuente textual, una red sociales en la que se puede recolectar datos de forma más sencilla y, sobre todo, inmediata, necesario para establecer correlaciones temporales entre la evolución del virus y la opinión del mismo de la sociedad.</p> <p>Esta plataforma permite él envió de mensajes en texto plano de corta longitud por parte de los usuarios, con un máximo de 280 caracteres. Estos mensajes, llamados tweets, se muestran en la página principal del usuario y pueden ser capturados a través de una API proporcionada por la propia red social.</p>
Lung ultrasonography features and risk stratification in 80 patients with COVID-19: a prospective observational cohort study
<p><strong>Background</strong></p> <p>Point-of-care lung ultrasound (LUS) is a promising and pragmatic risk stratification tool in COVID-19. This study describes and compares early LUS characteristics across of range of clinical outcomes.</p> <p><strong>Method</strong></p> <p>Prospective observational study of PCR-confirmed COVID-19 patients in the emergency department (ED) of Lausanne University Hospital. A trained physician recorded LUS images using a standardized protocol. Two experts retrospectively reviewed images blinded to patient outcome. We describe and compare early LUS findings (acquired within 24hours of presentation at the ED) between patient groups based on their outcome at 7-days after inclusion: 1) self-resolving outpatients, 2) hospitalised and 3) intubated/death. The LUS score was used to discriminate between groups.</p> <p><strong>Findings</strong></p> <p>Between March 6 and April 3 2020, we included 80 patients (18 outpatients, 41 hospitalized and 21 intubated/dead). 73 patients (91%) had abnormal LUS (72% outpatients, 95% hospitalised and 100% intubated/death; p=0.004). The proportion of involved zones was lower in outpatients compared with other groups (median 30% [IQR 0-40%], 44% [33-70%] and 70% [50-88%], p<0.001). Predominant abnormal patterns were bilateral and multifocal spread thickening of the pleura with pleural line irregularities (77%), confluent B lines (66%) and pathologic B lines (55%). Posterior inferior zones were more often affected. Median LUS score had a good level of discrimination between outpatients and others with area under the ROC of 0.80 (95% CI 0.66-0.95).</p> <p><strong>Interpretation</strong></p> <p>Systematic LUS is a reliable, cheap and easy-to-use triage tool for the early stratification of risk in COVID-19 patients presenting at emergency departments.</p> <p><strong>Funding</strong></p> <p>Leenaards Foundation</p>
The virus and socioeconomic inequality: An agent-based model to simulate and assess the impact of interventions to reduce the spread of COVID-19 in Rio de Janeiro, Brazil
<p>This video shows de simulation of scenarios presented in the article "The virus and socioeconomic inequality: An agent-based model to simulate and assess the impact of interventions to reduce the spread of COVID-19 in Rio de Janeiro, Brazil"</p>
3DNIV/3DNIV: A Novel Dual Non-Invasive Ventilator Continuous Positive Airway Pressure Non-Aerosolization Circuit for Emergency Use in the COVID-19 Pandemic
<p>The COVID19 pandemic is a public health emergency of unprecedented scale. The surge in clinical cases of patients with severe respiratory illness has overwhelmed the traditional capacity of healthcare systems worldwide. Continuous Positive Airway Pressure (CPAP) delivered through Non-Invasive Ventilation (NIV) has been shown to be useful in caring for patients with COVID19. In particular patients with early stage milder acute hypoxemic respiratory failure can benefit from NIV CPAP therapy, though there is an acknowledged risk of COVID19 aerosolization with traditional circuit use. Furthermore, given the surge in clinical care demand, there is an acute global shortage of ventilators, including NIV devices and therefore innovative methods are needed to increase NIV capacity and ameliorate infectious aerosolization. This work outlines an emergency use modified dual NIV CPAP Circuit that uses a 3D printed splitter designed to work with traditional international NIV CPAP tubing standards and a 3D printed respiratory face mask knuckle to allow for distal expiratory breath exhalation through a viral filter rather than through an open to air proximal valve, which is the traditional NIV CPAP configuration. We expect that this work will increase global NIV CPAP capacity and ameliorate aerosolization of COVID19 in patients undergoing therapy in an emergency scenario.</p>
COVID-19 Press Briefings Corpus
<p>The Coronavirus (COVID-19) Press Briefings Corpus is a work in progress to collect and present in a machine readable text dataset of the daily briefings from around the world by government authorities. During the peak of the pandemic, most countries around the world informed their citizens of the status of the pandemic (usually involving an update on the number of infection cases, number of deaths) and other policy-oriented decisions about dealing with the health crisis, such as advice about what to do to reduce the spread of the epidemic.</p> <p>Usually daily briefings did not occur on a Sunday.</p> <p>At the moment the dataset includes:</p> <ul> <li>UK/England: Daily Press Briefings by UK Government between 12 March 2020 - 01 June 2020 (70 briefings in total)</li> <li>Scotland: Daily Press Briefings by Scottish Government between 3 March 2020 - 01 June 2020 (76 briefings in total)</li> <li>Wales: Daily Press Briefings by Welsh Government between 23 March 2020 - 01 June 2020 (56 briefings in total)</li> <li>Northern Ireland: Daily Press Briefings by N. Ireland Assembly between 23 March 2020 - 01 June 2020 (56 briefings in total)</li> <li>World Health Organisation: Press Briefings occuring usually every 2 days between 22 January 2020 - 01 June 2020 (63 briefings in total)</li> </ul> <p>More countries will be added in due course, and we will be keeping this updated to cover the latest daily briefings available.</p> <p>The corpus is compiled to allow for further automated political discourse analysis (classification).</p>
TweetsCOV19 - A Semantically Annotated Corpus of Tweets About the COVID-19 Pandemic (Part 1, October 2019 - April 2020)
<p><strong><a href="https://data.gesis.org/tweetscov19/">TweetsCOV19</a></strong><strong> </strong>is a semantically annotated corpus of Tweets about the COVID-19 pandemic. It is a subset of <a href="https://data.gesis.org/tweetskb">TweetsKB</a> and aims at capturing online discourse about various aspects of the pandemic and its societal impact. <strong>Metadata</strong> information about the tweets as well as extracted <strong>entities</strong>, <strong>sentiments</strong>, <strong>hashtags</strong>, <strong>user mentions</strong>, and <strong>resolved URLs </strong>are exposed in RDF using established RDF/S vocabularies*.</p> <p>We also provide a <em><strong>tab-separated values (tsv)</strong></em> version of the dataset. Each line contains features of a tweet instance. Features are separated by tab character ("\t"). The following list indicate the feature indices:</p> <ol> <li>Tweet Id: Long.</li> <li>Username: String. Encrypted for privacy issues*.</li> <li>Timestamp: Format ( "EEE MMM dd HH:mm:ss Z yyyy" ).</li> <li>#Followers: Integer.</li> <li>#Friends: Integer.</li> <li>#Retweets: Integer.</li> <li>#Favorites: Integer.</li> <li>Entities: String. For each entity, we aggregated the original text, the annotated entity and the produced score from <a href="https://github.com/yahoo/FEL">FEL</a> library. Each entity is separated from another entity by char ";". Also, each entity is separated by char ":" in order to store "original_text:annotated_entity:score;". If FEL did not find any entities, we have stored "null;".</li> <li>Sentiment: String. <a href="http://sentistrength.wlv.ac.uk/">SentiStrength</a> produces a score for positive (1 to 5) and negative (-1 to -5) sentiment. We splitted these two numbers by whitespace char " ". Positive sentiment was stored first and then negative sentiment (i.e. "2 -1").</li> <li>Mentions: String. If the tweet contains mentions, we remove the char "@" and concatenate the mentions with whitespace char " ". If no mentions appear, we have stored "null;".</li> <li>Hashtags: String. If the tweet contains hashtags, we remove the char "#" and concatenate the hashtags with whitespace char " ". If no hashtags appear, we have stored "null;".</li> <li>URLs: String: If the tweet contains URLs, we concatenate the URLs using ":-: ". If no URLs appear, we have stored "null;"</li> </ol> <p>This dataset consists of <strong>8,151,524 tweets</strong> in total, posted by <strong>3,664,518 users</strong> and reflects the societal discourse about COVID-19 on Twitter in the period of October 2019 until April 2020.</p> <p>To extract the dataset from <a href="https://data.gesis.org/tweetskb">TweetsKB</a>, we compiled a seed list of 268 COVID-19-related <a href="https://data.gesis.org/tweetscov19/keywords.txt">keywords</a>.</p> <p><em>* For the sake of privacy, we anonymize user IDs and we do not provide the text of the tweets.</em></p> <p> </p>
GeoCoV19: A Dataset of Hundreds of Millions of Multilingual COVID-19 Tweets with Location Information
<p>We present GeoCoV19, a large-scale Twitter dataset related to the ongoing COVID-19 pandemic. The dataset has been collected over a period of 90 days from February 1 to May 1, 2020 and consists of more than 524 million multilingual tweets. As the geolocation information is essential for many tasks such as disease tracking and surveillance, we employed a gazetteer-based approach to extract toponyms from user location and tweet content to derive their geolocation information using the Nominatim (Open Street Maps) data at different geolocation granularity levels. In terms of geographical coverage, the dataset spans over 218 countries and 47K cities in the world. The tweets in the dataset are from more than 43 million Twitter users, including around 209K verified accounts. These users posted tweets in 62 different languages.</p>
Symptoms in health care workers during the COVID-19 epidemic. A cross-sectional survey.
<p>data collected during the COVID-19 epidemics on workers of the Health Care Unit Roma4, Civitavecchia. Paper submitted.</p>
Global Macroeconomic Scenarios of the COVID-19 Pandemic: Epidemiological Assumptions
<p>Epidemiological Assumptions used for modelling the Global Macroeconomic Scenarios of the COVID-19 Pandemic</p>
Covid-19 Twitter Database: Turkish Sample
<p>This dataset is a comprehensive dataset that includes Turkish tweet ids one month before and after of pandemic outbreak. You can see the tweets classified across COVID, economy, politics, religion, disinformation, international relations themes. To see research design and analyze the code for the text analysis please visit this link: <a href="https://github.com/burakozturan/tria-covid19">https://github.com/burakozturan/css_covid19</a></p>
Bibliographic data and analysis of COVID-19 research outputs from Imperial College London 16.01.2020-02.04.2020
<p>Bibliographic data and analysis of 41 research outputs, including reports/preprints/published articles/code, identified as having Imperial authorship and being relevant to COVID-19, published between 16.01.2020 - 02.04.2020. </p> <p>Related report can be found at: Price RC and Ozkan YA. 13 weeks in a pandemic: a descriptive study of Imperial College London’s COVID-19 publications. Imperial College London (April 2020), https://doi.org/10.25561/77970</p>
Datasets for The Effect of COVID-19 on AGU Journal Authors by Gender and Geographical Location
<p>These files provide anonymized source data and tabular data on gender, age, and country of corresponding authors (submitting author) of American Geophysical Union (AGU) journals from January 2018 through June 2020. These datasets supplement an iposter presented at Japan Geosciences Union- American Geophysical Union joint 2020 meeting and supplement the corresponding preprint submission to ESSOAR.</p>
Monocyte class switch and hyperinflammation characterise severe COVID-19 in type 2 diabetes
<p>raw and source data for manuscript EMM-2020-13038 under revision and preprint doi: https://doi.org/10.1101/2020.06.02.20119909</p>
Data for "Incidence, clinical course and risk factor for recurrent PCR positivity in discharged COVID-19 patients in Guangzhou, China: a prospective cohort study"
<p>Data for "Incidence, clinical course and risk factor for recurrent PCR positivity in discharged COVID-19 patients in Guangzhou, China: a prospective cohort study"</p>
Drug molecules binding to Covid-19 main protease
<p>A comparative look at where drug molecules bind to the main protease of Covid-19 depicted by interactive raytracing in the UnityMol software. The visualization is inspired by the animation of small molecules in 92 protein databank structures (<a href="https://www.rbvi.ucsf.edu/chimerax/data/sars-protease-may2020/">https://www.rbvi.ucsf.edu/chimerax/data/sars-protease-may2020/</a>) prepared by the ChimeraX team.</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.