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10,623 results for “COVID”
Short-lived air pollutants and climate forcers through the lens of the COVID-19 pandemic
<p>The data in this repository is part of the paper titled "Short-lived air pollutants and climate forcers through the lens of the COVID-19 pandemic". The data is required to obtain a detrended lockdown effects on air quality. The raw data was downloaded from the European Centre for Medium-Range Weather Forecasts Atmospheric Composition Reanalysis 4 (EAC4) product portal. More details of the data are listed below:</p> <p>"ozone_data.nc": Global mixing ratio of ozone (monthly)</p> <p>"pm_data.nc": Global mass concentration of fine particulate matters, and aerosol optical depth (AOD) at 550 nm (monthly)</p> <p>"BAU_clean_latest.csv": The pollution level under a business-as-usual (BAU) scenario, inferred from the historical pollution data by Theil-Sen linear regression (monthly)</p>
Protests Ukraine Covid 2020-22: YouTube Videos
The collection "YTV Protests Ukraine Covid 2020-22" contains 146 videos (mp4) on protests relating to government measures due to Covid-19. We have downloaded all data in October 2024 and made screenshots (pdf) of websites so that the discussion and comments on the single video posts can be followed. All data is processed in an MS Excel database with metadata. We collect all videos that are 1) event related, 2) show actions of this event, 3) we can find with our search words during a particular period. We strictly aim at a systematic and objective selection and organized storage of protest-related videos. The collection is based on extensive research into Covid-related protest events in Ukraine, which made it possible to identify relevant search words. According to the snowball principle, we then start the collection of videos with the help of these search words and try to download as much relevant content as possible. However, we cannot guarantee the completeness of protest videos on the particular event. We search the videos and include them into the collection until a particular degree of saturation has been reached. Due to copyright restrictions, we are only allowed to give access to the database of the collected video files including the hyperlinks with its metadata and not to the videos themselves. The videos have been posted mainly by TV channels and news outlets. Therefore, the material is only an extract and biased by the perspective of the single creator/creating institution. The collection is part of a larger and ongoing collection of videos on protest events in the post-Soviet region.
The complete corpus of #COVID-19 Twitter dataset
<p><br> COVID-19 pandemic initiated over a year ago continues to spread around the globe and the ongoing research regarding COVID-19 is on a continues growth as well. The online discourse on social media regarding COVID-19 has been growing along with the timeline of the pandemic.</p> <p>Open data on Twitter have been released and offer the research community the opportunity for new findings and resolving this new threat. In this dataset, we open a corpus of Twitter's data from March 2020 till today, that is being updated every day based on the two most important hashtags regarding COVID-19. This dataset will offer the research community the opportunity to explore the social extensions of this pandemic including topic analysis, hate speech sentiment analysis, regarding either the opinion of the users on the pandemic, the comments on the public discourse, or the vaccination releases. The dataset has been collected by retrieving all the tweets that contain the hashtags: #coronavirus and #COVID19 including approximately 208M tweets for hashtags #coronavirus and 392M tweets for hashtag #COVID-19, resulting in a total of 600M tweets. </p>
Coffee Consumption per Capita and Covid-19 Mortality Rate
<p>There is a correlation between average of "Coffee Consumption per capita" and average of "Covid-19 Mortality Rate" for countries with high coffee consumption per capita (the countries that has more than 5.4 kg per capita per year consumption).</p> <p>The Details of computations and data are provided in an attached supplementary file (Excel File Format).</p> <p>Data gathered on 10 Aug 2021</p> <p> </p>
Datasets Cured and Enriched with Provenance from the National Vaccination Campaign Against COVID-19
<p>The COVID-19 pandemic is a global threat. If, on the one hand, weaccount for many losses, on the other hand, the generation of datasets and ur-gent analytical demands has accelerated. Among the combat strategies, vacci-nation and data-centered epidemiological investigations stand out. This datasetpaper presents the process of building cured and annotated datasets with prove-nance metadata. The main dataset is based on the registration data of the Vacci-nation Campaign against COVID-19 in Brazil. The dataset contains thousandsof records processed up to March 2021. The data were analyzed, investigated,treated and cross-checked with other sources, in order to correct and comple-ment them, resulting in cured datasets and aligned to the FAIR principles.</p>
Proactive COVID-19 testing in a partially vaccinated population.
<p>Complete simulation-generated datasets analyzed in McGee et al. (2021) Proactive COVID-19 testing in a partially vaccinated population. medRxiv 2021.08.15.21262095.</p> <p>Data is uploaded in comma-separated .csv files which have been compressed using gzip. Descriptions of data columns can be found in the column_descriptions.csv file.</p>
Model-driven mitigation measures for reopening schools during the COVID-19 pandemic.
<p>Complete simulation-generated datasets analyzed in McGee et al. (2021) Model-driven mitigation measures for reopening schools during the COVID-19 pandemic. PNAS. In press at time of upload. (medRxiv 2021.01.22.21250282).</p> <p>Data is uploaded in tab-separated .csv files which have been compressed using gzip. Descriptions of data columns can be found in the column_descriptions.csv file.</p>
Fighting COVID-19 with computational tools: an AI guided review of 17,000 studies - The CSCoV database.
<p>CSCoV (Computational Studies about COVID-19) is a dataset containing COVID-19 related studies extracted from PubMed, bioRxiv, medRxiv, and arXiv, together with article and author related metrics obtained from Semantic Scholar (plus page views from bioRxiv and medRxiv). Using machine learning, the articles are categorized in six topics (Pharmacology, Genomics, Epidemiology, Healthcare, Clinical Medicine, Clinical Imaging) and prioritized. The database is periodically updated.</p> <ul> <li>Publication: TBA</li> <li>Files included in this release: <ul> <li>cscov_09_2021.png: dataset statistics for the current CSCoV release.</li> <li>cscov_09_2021.tsv: CSCoV database.</li> <li>schema.json: metadata.</li> <li>cscov_09_2021.tar.gz: Doc2Vec and DeepWalk features used for the DL model</li> </ul> </li> <li> <p>Source code: <a href="https://github.com/SFB-KAUST/covid-review">https://github.com/SFB-KAUST/covid-review</a></p> </li> </ul>
Protein tagged from Covid-19 trial records in ClinicalTrials.gov
<p>Top 200 proteins appeared in the Covid-19 trial records in https://clinicaltrials.gov/, grouped by CATH classification, with URL linking back to the trial record at https://clinicaltrials.gov/.</p>
Extended data for "TeenCovidLife: A resource to understand the impact of the Covid-19 pandemic on adolescents in Scotland"
<p>Extended data for "TeenCovidLife: A resource to understand the impact of the Covid-19 pandemic on adolescents in Scotland" Wellcome Open Research submission</p>
Predicting COVID-19 Incidence Through Spatiotemporal Human Interactions
<p>This repository contains data (features) necessary to run STXGB model. STXGB is a spatiotemporal autoregressive model that predicts county-level new cases of COVID-19 in the coterminous US in 1- to 4-week prediction horizons using spatiotemporal lags of infection rates, human interactions, human mobility, and socioeconomic composition of counties as predictive features.</p>
COVRIN D0.3.1: Database of COVID-19 research activities
<p>OHEJP project: COVRIN "One Health research integration on SARS-CoV-2 emergence, risk assessment and preparedness".</p> <p>Since the start of the pandemic in early 2020, a huge number of research projects have been initiated on SARS-CoV-2/COVID-19; additionally, many pre-existing networks and infrastructures have turned their attention to the virus, setting up SARS-CoV-2-specific services. To avoid overlaps and ensure optimal use of resources, a scoping review was performed of European Union-supported SARS-CoV-2 research activities that overlap with COVRIN in terms of focus.</p> <p>This database is associated with OHEJP Deliverable report "D0.3.1: Scoping review of European Union-supported COVID-19 research activities" available at https://doi.org/10.5281/zenodo.5537781</p>
Spatiotemporal Prediction of COVID-19 Cases using Inter- and Intra-County Proxies of Human Interactions (dataset)
<p>This repository contains data (features) necessary to run STXGB model and accompanies the paper titled "Spatiotemporal Prediction of COVID-19 Cases using Inter- and Intra-County Proxies of Human Interactions".</p> <p> </p> <p>STXGB is a spatiotemporal autoregressive model that predicts county-level new cases of COVID-19 in the coterminous US in 1- to 4-week prediction horizons using spatiotemporal lags of infection rates, human interactions, human mobility, and socioeconomic composition of counties as predictive features.</p>
A Competing Compound For Remdesivir and Molnupiravir In Inhibiting RdRp Protein of COVID-19
<p>According to a docking study conducted with the online tool [1][2], the compound Nicotinate mononucleotide with the formula C11H15NO9P+ could inhibit RNA-dependent RNA polymerase (RdRp (RTP site)) protein in COVID-19 coronavirus, with a Score Value of -9.4 (kcal / mol). This is better than the amount for the Remdesivir molecule (-9.2 (kcal / mol)) and after MW correction, it is also better than Molnupiravir, to inhibit the same protein. Since inhibition of this protein plays a key role in the inhibitory function of Remdesivir against COVID-19 virus [3], we can see the Nicotinate mononucleotide compound as an alternative to Remdesivir in inhibiting this coronavirus. Since the molecular weight of Nicotinate mononucleotide (336 g / mol) is approximately half that of Remdesivir (603 g / mol) and Molnupiravir-active-form (499.16 g / mol), it is better in terms of both protein adhesion and absorption capacity. Since the main precursor of Nicotinate mononucleotide, Trigonelline alkaloid, is a naturally occurring plant secondary metabolite, and Nicotinate mononucleotide itself is present in mammalian biomolecular pathways, it is likely to be more available, more cost-effective, and more non-toxic and be better than Remdesivir and Molnupiravir. Please see added data.</p>
speaker populations of the languages targeted by translations of COVID 19 preventive measures
<p>The data are based on the list of the languages listed on the repository of the Endangered Languages Project (https://endangeredlanguagesproject.github.io/COVID-19) and on the figures found on ethnologue.org</p> <p> </p> <p> </p>
Brazilian Portuguese COVID-19 Tweets
<p><strong>Brazilian Portuguese symptoms about COVID-19:</strong></p> <ul> <li><strong>Source</strong>: Twitter</li> <li><strong>Start</strong>: 2019-01-01 (January 1st)</li> <li><strong>End</strong>: 2021-09-30 (September 30th)</li> <li><strong>Tweets</strong>: 13,859,059 <ul> <li>Year 2019 [full year]: 4,043,958 obs. of 26 variables (Brazil_Portuguese_COVID19_Tweets2019.csv)</li> <li>Year 2020 [full year]: 6,155,844 obs. of 26 variables (Brazil_Portuguese_COVID19_Tweets2020.csv)</li> <li>Year 2021 [Q1 - Q3]: 3,659,257 obs. of 26 variables (Brazil_Portuguese_COVID19_Tweets2021.csv)</li> </ul> </li> </ul> <p><strong>Search terms (56 symptoms keywords about COVID-19):</strong></p> <p><strong>(1)</strong> adinamia, <strong>(2)</strong> ageusia, <strong>(3)</strong> anosmia, <strong>(4)</strong> boca azulada, <strong>(5)</strong> calafrio, <strong>(6)</strong> cansaço, <strong>(7) </strong>cefaleia, <strong>(8)</strong> cianose, <strong>(9)</strong> coloração azulada no rosto, <strong>(10)</strong> congestão nasal, <strong>(11)</strong> conjuntivite, <strong>(12) </strong>coriza, <strong>(13)</strong> desconforto respiratório, <strong>(14)</strong> diarreia, <strong>(15)</strong> dificuldade para respirar, <strong>(16)</strong> diminuição do apetite, <strong>(17)</strong> dispneia, <strong>(18)</strong> distúrbio gustativo, <strong>(19)</strong> distúrbio olfativo, <strong>(20)</strong> dor abdominal, <strong>(21)</strong> dor de cabeça, <strong>(22)</strong> dor de garganta, <strong>(23)</strong> dor no corpo, <strong>(24)</strong> dor no peito, <strong>(25) </strong>dor persistente no tórax, <strong>(26) </strong>erupção cutânea na pele, <strong>(27)</strong> fadiga, <strong>(28)</strong> falta de ar, <strong>(29)</strong> febre, <strong>(30)</strong> gripe, <strong>(31)</strong> hiporexia, <strong>(32)</strong> inapetência, <strong>(33)</strong> infecção respiratória, <strong>(34)</strong> lábio azulado, <strong>(35)</strong> mialgia, <strong>(36)</strong> nariz entupido, <strong>(37) </strong>náusea, <strong>(38)</strong> obstrução nasal, <strong>(39)</strong> perda de apetite, <strong>(40)</strong> perda do olfato, <strong>(41)</strong> perda do paladar, <strong>(42)</strong> pneumonia, <strong>(43)</strong> pressão no peito, <strong>(44)</strong> pressão no tórax, <strong>(45)</strong> prostração, <strong>(46)</strong> quadro gripal, <strong>(47)</strong> quadro respiratório, <strong>(48)</strong> queda da saturação, <strong>(49)</strong> resfriado, <strong>(50)</strong> rosto azulado, <strong>(51)</strong> saturação baixa, <strong>(52)</strong> saturação de o2 menor que 95%, <strong>(53)</strong> síndrome respiratória aguda grave, <strong>(54) </strong>srag, <strong>(55)</strong> tosse, <strong>(56)</strong> vômito.</p> <p><strong>Variables:</strong></p> <pre><code>Variable str Description ---------------------------------------------------------------------------------- id (integer64) - Tweet identifier conversation_id (integer64) - Tweet conversation identifier date (POSIXct) - Tweet created date (format: YYYY-MM-DD hh:mm:ss) tweet (chr) - Symptoms mention about COVID-19 language (chr) - Tweet language: Portuguese hashtags (chr) - Sign (#) used to identify specific topic user_id (integer64) - User identifier username (chr) - Twitter user name link (chr) - Tweet url urls (chr) - External urls from tweet photos (chr) - Photos posted in message (link) video (int) - Video posted in message (1=True;0=False) thumbnail (chr) - Thumbnail posted in message retweet (logi) - Message reposted by another user nlikes (int) - Number of tweet likes nreplies (in) - Number of tweet replies nretweets (int) - Number of tweet retweets Near (logi) - Near a certain City (Example: London) geo (logi) - Geo coordinates (lat,lon,km/mi.) user_rt_id (logi) - User retweet identifier user_rt (logi) - Retweet user retweet_id (logi) - Retweet identifier reply_to (chr) - Answer to someone retweet_date (logi) - Retweet created date (format: YYYY-MM-DD hh:mm:ss) symptoms (chr) - Symptoms mentioned nsymptoms (int) - Number of symptons mentioned </code></pre> <p><em>str: Compactly Display the Structure of an Arbitrary R Object</em></p>
Base de datos - Bibliométria científica de salud bucal en pandemia de COVID 19 de América Latina-El Caribe
<p>Base de datos de la investigación y proceso de selección de los artículos. </p>
Italian COVID-19 Integrated Surveillance Dataset (v42.0.0)
<p><strong>Abstract</strong></p> <p>COVID-19 integrated surveillance data provided by the <a href="http://www.iss.it/">Italian National Institute of Health</a> and processed via <a href="https://github.com/InPhyT/UnrollingAverages.jl">UnrollingAverages.jl</a> to deconvolve the weekly simple moving averages.</p> <p><strong>Overview</strong> </p> <p>Every week the National Institute for Nuclear Physics (<a href="https://home.infn.it/it/">INFN</a>) imports an anonymous individual-level dataset from the Italian National Institute of Health (<a href="https://www.iss.it/">ISS</a>) and converts it into an incidence time series data organized by date of event and disaggregated by sex, age and administrative level with a consolidation period of approximately two weeks. The information available to the <a href="https://home.infn.it/it/">INFN</a> is summarised in the following <a href="https://covid19.infn.it/iss/campi-iss.pdf">meta-table</a>.</p> <p><strong>Output Data </strong></p> <p>The output data has been stored <a href="https://github.com/InPhyT/COVID19-Italy-Integrated-Surveillance-Data/tree/main/3_output/data">here</a> and contain the following information:</p> <ul> <li>Reconstructed daily time series of <strong>confirmed cases by date of diagnosis</strong> stratified by sex and age at the regional level;</li> <li>Reconstructed daily time series of <strong>symptomatic cases by date of symptoms onset</strong> stratified by sex and age at the regional level;</li> <li>Reconstructed daily time series of <strong>ordinary hospital admissions</strong> by date of admission stratified by sex and age at the regional level;</li> <li>Reconstructed daily time series of <strong>intensive hospital admissions</strong> by date of admission stratified by sex and age at the regional level;</li> <li>Reconstructed daily time series of <strong>deceased cases by date of death</strong> stratified by sex and age at the regional level.</li> </ul>
Experience of COVID-19 disease and fear of the SARS-CoV-2 virus among Polish students
<p>The deposited files contain a database related to the study of the fear of COVID-19 among Polish students and a code book. It is connected with the article titled <em>Experience of COVID-19 disease and fear of the SARS-CoV-2 virus among Polish students</em></p>
Social contact data before and during COVID-19 in China
<p>Social contact data for Wuhan and Shanghai, China before and during the COVID-19 outbreak.<br> Changelog for Version 2:<br> - Contact data from outbreak and baseline merged. A variable to distinguish the two ("collection_period") is added to "contact_extra".<br> <br> For problems with the dataset, please contact:</p> <table> <tbody> <tr> <td> </td> <td>socialcontactdata@gmail.com</td> </tr> </tbody> </table>
ScienceDex guides
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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.