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9,674 results for “COVID-19”
COVID-19 case fatality derived age-adjusted risk of death
<p><strong>This is an old version of the plot. For the newer version, please visit </strong><a href="https://zenodo.org/record/3829175">https://zenodo.org/record/3829175</a><br> doi:<a href="https://doi.org/10.5281/zenodo.3787930">10.5281/zenodo.3787930</a></p> <p>This is the plot of the COVID-19 risk of death adjusted by age based on the case fatality data from the Kaggle project Data Science for COVID-19 in South Korea (DS4C) hosted at https://www.kaggle.com/kimjihoo/coronavirusdataset</p> <p>The plot was created with the Python & C library published on April 3, 2020, (release 2.0 of April 21, 2020) on GitHub at https://github.com/yuryatin/covid19_age_adjusted_mortality</p>
COVID-19 case-fatality-derived age-adjusted risk of death
<p><strong>This is an old version of the plot. For the newer version, please visit </strong><a href="https://zenodo.org/record/3787931"><em>https://zenodo.org/record/3787931</em></a><br> doi:10.5281/zenodo.3787931</p> <p>This is the plot of the COVID-19 risk of death adjusted by age derived from the case fatality data from the Kaggle project Data Science for COVID-19 in South Korea (DS4C) hosted at https://www.kaggle.com/kimjihoo/coronavirusdataset</p> <p>The plot was created with the Python & C library initially published on April 3, 2020, (with the release 2.0 of April 21, 2020) on GitHub at https://github.com/yuryatin/covid19_age_adjusted_mortality</p>
COVID-19 age-adjusted risk of death derived from case fatality
<p><strong>This is an old version of the plot. For the newer version, please visit </strong><em>https://zenodo.org/record/3787931</em><br> doi:10.5281/zenodo.3787931</p> <p>This is a plot of the risk of death in COVID-19 adjusted by the age and derived from the case fatality data kindly collected by the team of data scientists and data engineers in the Kaggle project Data Science for COVID-19 in South Korea (DS4C), which is hosted at https://www.kaggle.com/kimjihoo/coronavirusdataset.</p> <p>The model for the plot was created with the release 2.0 of the Python & C library initially published on April 3, 2020 on GitHub at https://github.com/yuryatin/covid19_age_adjusted_mortality.</p> <p>Please, visit https://github.com/yuryatin/covid19_age_adjusted_mortality for more detailed description of the model and the source code.</p>
CanESM5 data for CCCma COVID-19 climate scenarios
<p>This data is associated with the publication:<br> <br> <strong> Quantifying the Influence of COVID-19 Emission Reductions on Climate</strong></p> <p><br> John C. Fyfe, Viatcheslav V. Kharin, Neil Swart, Gregory M. Flato, Michael<br> Sigmond and Nathan Gillett<br> <br> Canadian Centre for Climate Modelling and Analysis, Environment and Climate<br> Change Canada, Victoria, British Columbia, V8W 2Y2, Canada.<br> <br> The data includes the monthly CO2 emissions used to drive CanESM5, and also the<br> monthly CO2 concentrations, and Global Mean Screen Temperatures resulting from<br> the model simulations. Using this data, Figure 1 of the paper can be completely<br> reproduced.</p> <p> </p> <p>The organisation of the data is described in the readme.txt file. All contents are</p> <p>collected into a tar archive.<br> <br> </p>
COVID-19 risk of death by age
<p><strong>This is a version of the plot without split into gender-adjusted risks. For the newer version with gender-adjusted curves, please visit </strong><em>https://zenodo.org/record/3787931</em><br> doi:10.5281/zenodo.3787931</p> <p>This figure plots the risk of death in COVID-19 adjusted by the age and derived from the case fatality data kindly collected by the team of data scientists and data engineers in the Kaggle project Data Science for COVID-19 in South Korea (DS4C) hosted at https://www.kaggle.com/kimjihoo/coronavirusdataset.</p> <p>The model for the plot was created with the release 2.1 of the Python & C library initially published on April 3, 2020 on GitHub at https://github.com/yuryatin/covid19_age_adjusted_mortality.</p> <p>Please, visit https://github.com/yuryatin/covid19_age_adjusted_mortality for more detailed description of the model and the source code.</p>
Dataset and images for "Instantaneous R calculation for COVID-19 epidemic in Brazil"
<p>This dataset was generated from raw data obtained at </p> <ul> <li>Ceará State - <a href="https://indicadores.integrasus.saude.ce.gov.br/api/casos-coronavirus/export-csv">https://indicadores.integrasus.saude.ce.gov.br/api/casos-coronavirus/export-csv</a></li> <li>São Paulo State - <a href="http://www.seade.gov.br/wp-content/uploads/2020/04/Dados-covid-19-estado.csv">http://www.seade.gov.br/wp-content/uploads/2020/04/Dados-covid-19-estado.csv</a></li> <li>Brazil - <a href="https://covid.saude.gov.br/">https://covid.saude.gov.br/</a></li> </ul> <p>Data was processed with R package EpiEstim (methodology in the associated preprint). Briefly, instantaneous R was estimated within a 5 day time window. Prior mean and standard deviation values for R were set at 3 and 1. Serial interval was estimated using a parametric distribution with uncertainty (offset gamma). We compared the results at two time points (day 7 and day 21 after the first case was registered at each region) from different brazillian states in order to make inferences about the epidemic dynamics.</p>
100 Bestselller books during COVID-19 in Spain
<p>Description of 100 Bestselller books in Amazon during COVID-19 in Spain. </p> <p>Data for the books: </p> <ul> <li>Name</li> <li>Author</li> <li>Value</li> <li>Num reviews</li> <li>Category</li> <li>Price</li> <li>Pages</li> <li>Editor</li> <li>Language</li> <li>ISBN-10</li> <li>ISBN-13</li> <li>Colection</li> <li>Edad recomendada</li> </ul> <p>Data for review: </p> <ul> <li>Name book</li> <li>User</li> <li>Punctuation</li> <li>Title</li> <li>Date</li> <li>Text</li> <li>Vots</li> </ul>
Prediction of repurposed drugs for treating lung injury in COVID-19
<p>These are output files of shared R scripts used in prediction of repurposed drugs for treating lung injury in COVID-19.</p> <p> </p> <p>R scripts are available here: https://doi.org/10.5281/zenodo.3822923</p> <p> </p> <p>Description of files:</p> <p>HCC515_6_data_for_drug.csv #Differential expression of genes in HCC515 cell at 6 h after treatment of ACE2 inhibitor</p> <p>HCC515_24_data_for_drug.csv #Differential expression of genes in HCC515 cell at 24 h after treatment of ACE2 inhibitor</p> <p>COVID19-Lung_data_for_drug.csv #Differential expression of genes in lung tissues with COVID-19</p> <p>HCC515_6_drug.csv #Drugs for HCC515 cell at 6 h after transfection of ACE2 inhibitor</p> <p>HCC515_24_drug.csv #Drugs for HCC515 cell at 24 h after transfection of ACE2 inhibitor</p> <p>COVID19-Lung_drug.csv #Drugs for lung tissuse from COVID-19 patients</p> <p>COL-3_single_treatment_response_data.csv #Differential expression of genes in HCC515 cell at 24h after treatment of COL-3</p> <p>CGP-60474_single_treatment_response_data.csv #Differential expression of genes in HCC515 cell at 24h after treatment of CGP-60474</p>
Digital Narratives of Covid-19: a Twitter Dataset
<p>We are releasing a Twitter dataset connected to our project <a href="https://covid.dh.miami.edu/"><em>Digital Narratives of Covid-19</em> </a>(DHCOVID) that -among other goals- aims to explore during one year (May 2020-2021) the narratives behind data about the coronavirus pandemic.</p> <p>In this first version, we deliver a Twitter dataset organized as follows:</p> <ul> <li>Each folder corresponds to daily data (one folder for each day): YEAR-MONTH-DAY</li> <li>In every folder there are 9 different plain text files named with "dhcovid", followed by date (YEAR-MONTH-DAY), language ("en" for English, and "es" for Spanish), and region abbreviation ("fl", "ar", "mx", "co", "pe", "ec", "es"): <ol> <li>dhcovid_YEAR-MONTH-DAY_es_fl.txt: Dataset containing tweets geolocalized in South Florida. The geo-localization is tracked by tweet coordinates, by place, or by user information.</li> <li>dhcovid_YEAR-MONTH-DAY_en_fl.txt: We are gathering only tweets in English that refer to the area of Miami and South Florida. The reason behind this choice is that there are multiple projects harvesting English data, and, our project is particularly interested in this area because of our home institution (University of Miami) and because we aim to study public conversations from a bilingual (EN/ES) point of view.</li> <li>dhcovid_YEAR-MONTH-DAY_es_ar.txt: Dataset containing tweets from Argentina.</li> <li>dhcovid_YEAR-MONTH-DAY_es_mx.txt: Dataset containing tweets from Mexico.</li> <li>dhcovid_YEAR-MONTH-DAY_es_co.txt: Dataset containing tweets from Colombia.</li> <li>dhcovid_YEAR-MONTH-DAY_es_pe.txt: Dataset containing tweets from Perú.</li> <li>dhcovid_YEAR-MONTH-DAY_es_ec.txt: Dataset containing tweets from Ecuador.</li> <li>dhcovid_YEAR-MONTH-DAY_es_es.txt: Dataset containing tweets from Spain.</li> <li>dhcovid_YEAR-MONTH-DAY_es.txt: This dataset contains all tweets in Spanish, regardless of its geolocation.</li> </ol> </li> </ul> <p>For English, we collect all tweets with the following keywords and hashtags: covid, coronavirus, pandemic, quarantine, stayathome, outbreak, lockdown, socialdistancing. For Spanish, we search for: covid, coronavirus, pandemia, quarentena, confinamiento, quedateencasa, desescalada, distanciamiento social.</p> <p>The corpus of tweets consists of a list of Tweet Ids; to obtain the original tweets, you can use "<a href="https://github.com/DocNow/hydrator">Twitter hydratator</a>" which takes the id and download for you all metadata in a csv file.</p> <p>We started collecting this Twitter dataset on April 24th, 2020 and we are adding daily data to our GitHub repository. There is a detected problem with file 2020-04-24/dhcovid_2020-04-24_es.txt, which we couldn't gather the data due to technical reasons.</p> <p>For more information about our project visit <a href="https://covid.dh.miami.edu/">https://covid.dh.miami.edu/</a></p> <p>For more updated datasets and detailed criteria, check our GitHub Repository: <a href="https://github.com/dh-miami/narratives_covid19/">https://github.com/dh-miami/narratives_covid19/</a></p>
COVID-19 case fatality-derived risk of death adjusted by age and gender
<p>This figure plots the risk of death in COVID-19 adjusted by the age and gender, which was modeled with the case fatality data kindly collected by the team of data scientists and data engineers in the Kaggle project <em>Data Science for COVID-19 in South Korea (DS4C)</em>, which is hosted at https://www.kaggle.com/kimjihoo/coronavirusdataset.</p> <p>The model for the plot was built with the release 2.2 of the open-sourced Python & C library for macOS and Linux initially published under <strong>GPLv3</strong> license on April 3, 2020, on GitHub at https://github.com/yuryatin/covid19_age_adjusted_mortality.</p> <p>Please, visit <a href="https://github.com/yuryatin/covid19_age_adjusted_mortality"><strong>https://github.com/yuryatin/covid19_age_adjusted_mortality</strong></a> for more detailed description of the model and for the source code.</p>
PRJNA638224 - BCR repertoire sequencing from COVID-19 patients
<p><strong>Description</strong></p> <p>These are the processed BCR repertoire sequence data that accompany the following manuscript: “Deep sequencing of B cell receptor repertoires from COVID-19 patients reveals strong convergent immune signatures”. The manuscript preprint is available at doi: <a href="https://doi.org/10.1101/2020.05.20.106294">https://doi.org/10.1101/2020.05.20.106294</a>. The raw sequence data are available on SRA under the BioProject PRJNA638224</p> <p> </p> <p><strong>Sequence processing</strong></p> <p>The Immcantation framework (docker container v3.0.0) was used for sequence processing. Briefly, paired-end reads were joined based on a minimum overlap of 20 nt, and a max error of 0.2, and reads with a mean phred score below 20 were removed. Primer regions, including UMIs and sample barcodes, were then identified within each read, and trimmed. Together, the sample barcode, UMI, and constant region primer were used to assign molecular groupings for each read. Within each grouping, usearch, was used to subdivide the grouping, with a cutoff of 80% nucleotide identity, to account for randomly overlapping UMIs. Each of the resulting groupings is assumed to represent reads arising from a single RNA. Reads within each grouping were then aligned, and a consensus sequence determined. For each processed sequence, IgBlast was used to determine V, D and J gene segments, and locations of the CDRs and FWRs. Isotype was determined based on comparison to germline constant region sequences. Sequences annotated as unproductive by IgBlast were removed.</p> <p> </p> <p><strong>Sequence data column description</strong></p> <ul> <li><strong>sample_id </strong>Unique identifier for each sequencing library</li> <li><strong>sequence_id </strong>Unique identifier for a sequence within a sample_id</li> <li><strong>sequence_alignment </strong>IMGT gapped nucleotide sequence</li> <li><strong>germline_alignment </strong>IMGT gapped germline sequence</li> <li><strong>v_call </strong>IGHV gene segment(s) and allele</li> <li><strong>d_call </strong>IGHD gene segment(s) and allele</li> <li><strong>j_call </strong>IGHJ gene segment(s) and allele</li> <li><strong>c_call </strong>Isotype subclass</li> <li><strong>junction </strong>Junction nucleotide sequence</li> <li><strong>junction_aa </strong>Junction amino acid sequence</li> <li><strong>duplicate_count </strong>UMI count for the given unique sequence</li> <li><strong>consensus_count </strong>Raw read count for the given unique sequence</li> </ul> <p> </p> <p><strong>Sequence metadata column description</strong></p> <ul> <li><strong>sample_id </strong>Unique identifier for each sequencing library</li> <li><strong>bioproject_accession </strong>NCBI BioProject accession number</li> <li><strong>biosample_accession </strong>NCBI BioSample accession number</li> <li><strong>sra_accession </strong>NCBI SRA accession number</li> <li><strong>sex </strong>Sex of patient</li> <li><strong>age </strong>Age of patient at time of sampling</li> <li><strong>ethnicity </strong>Ethnicity of patient</li> <li><strong>health_state </strong>One of worsening, stable, or improving</li> </ul>
Number of cases of coronavirus disease (COVID-19) in Ireland
<p>Datasets in this publication report the number of diagnoses with coronavirus disease (COVID-19) as reported by the Department of Health in Ireland. This includes new cases diagnosed per day and cumulative cases, hospitalisations, ICU admissions, deaths, number of healthcare workers, number of clusters, gender of cases, age groups of cases, mode of transmission, age groups of those hospitalised, and cases per county. To aid standardisation of age groups and cases per county, the population estimates by age group for 2019 and the actual county population in the 2016 Census from Ireland's Central Statistics Office are also included as separate datasets, to allow expression of cases per million population.</p> <p>These are </p> <ol> <li><em>doh_covid_ie_cases_analysis.csv</em>, where data from Ireland's Health Protection Surveillance Centre is included up to midnight on each included date (currently up to 16-Jun-2020). </li> <li><em>age_population_cso_2019.csv</em></li> <li><em>counties_population_cso_2016.csv</em></li> </ol> <p><em>age_population_cso_2019.csv </em>has been updated to include separate population estimates for those aged 65-74 years, 75-84 years, and 85 years and over. This is in response to the HSPC releasing case and hospitalisation data for these groups rather than a combined 65 years and over group.</p> <p><em>counties_population_cso_2016.csv </em>has been updated to remove trailing spaces in the 'county' column.</p> <p><em>doh_covid_ie_cases_analysis.csv </em>is regularly updated at <a href="https://github.com/frankmoriarty/covid_ie/blob/master/doh_covid_ie_cases_analysis.csv">https://github.com/frankmoriarty/covid_ie/blob/master/doh_covid_ie_cases_analysis.csv</a></p>
COVID-19@Semantics
<p>A semantic software framework in the context of the COVID-19 cases worldwide, named the COVID-19@Semantics, in which we have developed a specific ontology, COVID-Ont, an open and linked dataset and different services in the mentioned scope.</p>
COVID-19 Mobility Data Aggregator
<p><strong>Description</strong></p> <p>This repository includes:<br> 1) Data scraper of Google, Apple and Waze Mobility data<br> 2) Preprocessed mobility reports in different formats<br> 3) Merged mobility reports in summary files</p> <p><strong>About data</strong></p> <p>About <a href="https://www.google.com/covid19/mobility/">Google COVID-19 Community Mobility Reports</a></p> <p>About <a href="https://www.apple.com/covid19/mobility">Apple COVID-19 Mobility Trends Reports</a></p> <p>About <a href="https://www.waze.com/covid19">Waze COVID-19 local driving trends</a></p> <p><strong>Description of data files</strong></p> <p><em><strong>Google reports (located in google_reports directory):</strong></em></p> <p>The raw report in ZIP format: Global_Mobility_Report.zip<br> Data for the worldwide (only 1st level of subregions): mobility_report_countries (CSV and Excel formats available)<br> Data for Brazil: mobility_report_brazil (CSV and Excel formats available)<br> Data for Europe: mobility_report_europe (CSV and Excel formats available)<br> Data for Asia + Africa: mobility_report_asia_africa (CSV and Excel formats available)<br> Data for North and South America + Oceania (Brazil and US excluded): mobility_report_america_oceania (CSV and Excel formats available)</p> <p><em><strong>Apple reports (located in apple_reports directory):</strong></em></p> <p>Raw report: applemobilitytrends.csv<br> Data for the worldwide: apple_mobility_report (Google Sheets, CSV and Excel formats available)<br> Data for the US: apple_mobility_report_US (CSV and Excel formats available)</p> <p><em><strong>Waze reports (located in waze_reports directory):</strong></em></p> <p>Raw CSV files: Waze_Country-Level_Data.csv, Waze_City-Level_Data.csv<br> Preprocessed report: waze_mobility (Google Sheets, CSV and Excel formats available)</p> <p><em><strong>Summary reports (located in summary_reports directory)</strong></em></p> <p>These are merged Apple and Google reports.</p> <p>Report by regions: summary_report_regions (CSV and Excel formats available)<br> Report by countries: summary_report_countries (Google Sheets, CSV and Excel formats available)<br> Report for the US: summary_report_US (CSV and Excel formats available)</p> <p><strong>License</strong></p> <p>See LICENSE.txt</p> <p><strong>Credits</strong></p> <p>If you use this dataset, please also cite the original data sources:</p> <p>1. Google LLC <em>"Google COVID-19 Community Mobility Reports"</em>. https://www.google.com/covid19/mobility/ Accessed: <date></p> <p>2. Apple Inc. "<em>Apple COVID-19 Mobility Trends Reports"</em>. https://www.apple.com/covid19/mobility Accessed: <date></p> <p>3. Waze Ltd "<em>Waze COVID-19 Impact Dashboard". </em>https://www.waze.com/covid19 Accessed: <date></p>
What is the clinical course of patients hospitalised for COVID-19 treatment Ireland: a retrospective cohort study in Dublin's North Inner City (the 'Mater 100')
<p><strong>Background: </strong>Since March 2020, Ireland has experienced an outbreak of coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). While several cohorts from China have been described, there is little data describing the epidemiological and clinical characteristics of patients with COVID-19 in Ireland. <strong>To improve our understanding of this emerging infection we carried out </strong>a retrospective review of patient data to<strong> examine the clinical characteristics </strong>of patients admitted for COVID-19 hospital treatment.</p> <p><strong>Methods<strong>:</strong></strong> Demographic, clinical and laboratory data on the first 100 adult patients admitted to Mater Misericordiae University Hospital (MMUH) for in-patient COVID-19 treatment after onset of the outbreak in March 2020 was extracted from clinical and administrative records.</p> <p><strong>R<strong>esults:</strong></strong> Fifty-eight per cent were male, 63% were Irish nationals, and median age was 45 years (interquartile range [IQR] =34-64 years). Patients had symptoms for a median of five days before diagnosis (IQR=2.5-7 days), most commonly cough (72%), fever (65%), dyspnoea (37%), fatigue (28%), myalgia (27%) and headache (24%). Of all cases, 54 had at least one pre-existing chronic illness (most commonly hypertension, diabetes mellitus or asthma). At initial assessment, the most common abnormal findings were: C-reactive protein >7.0mg/L (74%), ferritin >247μg/L (women) or >275μg/L (men) (62%), D-dimer >0.5μg/dL (62%), chest imaging (59%), NEWS Score (modified) of ≥3 (55%) and heart rate >90/min (51%). Twenty-seven required supplemental oxygen, of which 17 were admitted to the intensive care unit - 14 requiring ventilation. Forty received antiviral treatment (most commonly hydroxychloroquine or lopinavir/ritonavir). Four died, 17 were admitted to intensive care, and 74 were discharged home, with nine days the median hospital stay (IQR=6-11).</p> <p>C<strong>onclusion:</strong> Our findings reinforce the emerging consensus of COVID-19 as an acute life-threatening disease and highlights, the importance of laboratory (ferritin, C-reactive protein, D-dimer) and radiological parameters, in addition to clinical parameters. Further cohort studies involving larger samples followed longitudinally are a priority.</p>
COVID-19 Tweets : A dataset contaning more than 600k tweets on the novel CoronaVirus
<p>This dataset contains 653 996 tweets related to the Coronavirus topic and highlighted by hashtags such as: #COVID-19, #COVID19, #COVID, #Coronavirus, #NCoV and #Corona. The tweets' crawling period started on the 27<sup>th</sup> of February and ended on the 25<sup>th</sup> of March 2020, which is spread over four weeks. </p> <p>The tweets were generated by 390 458 users from 133 different countries and were written in 61 languages. English being the most used language with almost 400k tweets, followed by Spanish with around 80k tweets. </p> <p>The data is stored in as a CSV file, where each line represents a tweet. The CSV file provides information on the following fields:</p> <ul> <li>Author: the user who posted the tweet</li> <li>Recipient: contains the name of the user in case of a reply, otherwise it would have the same value as the previous field</li> <li>Tweet: the full content of the tweet</li> <li>Hashtags: the list of hashtags present in the tweet</li> <li>Language: the language of the tweet</li> <li>Relationship: gives information on the type of the tweet, whether it is a retweet, a reply, a tweet with a mention, etc. </li> <li>Location: the country of the author of the tweet, which is unfortunately not always available</li> <li>Date: the publication date of the tweet</li> <li>Source: the device or platform used to send the tweet</li> </ul> <p>The dataset can as well be used to construct a social graph since it includes the relations "Replies to", "Retweet", "MentionsInRetweet" and "Mentions".</p>
STROBE checklist for a set of scientific works about COVID-19
<p>STROBE checklist for a set of scientific works about COVID-19. This dataset is the result of the expert-based assessment carried out in <a href="https://arxiv.org/abs/2004.06179">arXiv:2004.06179</a>.</p>
CMU-MisCov19: A Novel Twitter Dataset for Characterizing COVID-19 Misinformation
<p>From conspiracy theories to fake cures and fake treatments, COVID-19 has become a hot-bed for the spread of misinformation online. It is more important than ever to identify methods to debunk and correct false information online. Detection and characterization of misinformation requires an availability of annotated datasets. Most of the published COVID-19 Twitter datasets are generic, lack annotations or labels, employ automated annotations using transfer learning or semi-supervised methods, or are not specifically designed for misinformation. Annotated datasets are either only focused on "fake news", are small in size, or have less diversity in terms of classes.</p> <p>Here, we present a novel Twitter misinformation dataset called <strong>"CMU-MisCov19"</strong> with 4573 annotated tweets over 17 themes around the COVID-19 discourse. We also present our annotation codebook for the different COVID-19 themes on Twitter, along with their descriptions and examples, for the community to use for collecting further annotations. Further details related to the dataset, and our analysis based on this dataset can be found at <a href="https://arxiv.org/abs/2008.00791">https://arxiv.org/abs/2008.00791</a>. In adherence to the Twitter’s terms and conditions, we do not provide the full tweet JSONs but provide a ".csv" file with the tweet IDs so that the tweets can be rehydrated. We also provide the annotations, and the date of creation for each tweet for the reproduction of the results of our analyses.</p> <p><strong>Note: If for any reason, you are not able to rehydrate all the tweets, reach out to Shahan Ali Memon at (shahan@nyu.edu).</strong></p> <p>If you use this data, please cite our paper as follows: </p> <p><em>"Shahan Ali Memon and Kathleen M. Carley. Characterizing COVID-19 Misinformation Communities Using a Novel Twitter Dataset, In Proceedings of The 5th International Workshop on Mining Actionable Insights from Social Networks (MAISoN 2020), co-located with CIKM, virtual event due to COVID-19, 2020."</em></p>
Longitudinal high-throughput TCR repertoire profiling reveals the dynamics of T cell memory formation after mild COVID-19 infection
<p>Processed TCRbeta and TCRalpha repertoires after mild COVID-19 (Version 2.0: day 85 timepoints added) infection, see preprint: <a href="https://www.biorxiv.org/content/10.1101/2020.05.18.100545v3">https://www.biorxiv.org/content/10.1101/2020.05.18.100545v3</a></p> <p>and GitHub repository: <a href="https://github.com/pogorely/Minervina_COVID">https://github.com/pogorely/Minervina_COVID</a></p> <p>Two donors (M and W), two biological replicates of PBMC (F1 and F2), CD4+, CD8+, and Memory subpopulations for each post-infection time points (day 15, 30, 37, 45, 85 post-infection), and pre-infection PBMC repertoires sampled in 2019 and 2018. </p>
Data from: The impact of human mobility networks on the global spread of COVID-19
<p>This is empirical dataset from the paper "The impact of human mobility networks on the global spread of COVID-19". Specifically, the dataset includes several files: (a) the COVID-19 network - an origin/destination matrix (i.e., "covid_network.csv"); (b) the common language network - edgelist format (i.e. "edge_list_comlang.csv"); (c) the same continent network - edgelist format (i.e., "edge_list_continent.csv"; (d) the contiguity network (i.e., "edge_list_contig.csv"); (e) the migration network - edgelist format (i.e., "edge_list_migration_in.csv"; (f) the tourism network - edgelist format (i.e., edge_list_tourism_in.csv"); (g) the list of nodes (countries) corresponding to files (b)-(e) (i.e., "nodes.csv"). Additionally, we uploaded the Rcode used in the paper (i.e. "code"), as a .pdf file format, the data source for the figures included in the paper (i.e., "covid_network_matrix.csv", "matrix_migration_out.csv", "matrix_tourism.csv" - Figure 1; "Fig_2_a_matrix_comlang.csv", Fig_2_b_matrix_contig.csv", "Fig_2_c_matrix_continent.csv" - Figure 2; "Fig_3.graphmlz - Figure 3; Fig_4.graphmlz - Figure 4) and the "global network of COVID-19 onset" (an individual-level data) (i.e., "global_covid_network.csv"). </p> <p>For details, please, see the Methods section of the paper: The impact of human mobility networks on the global spread of COVID-19 (Hancean, M.-G., Slavinec, M., Perc, M). </p> <p> </p> <p> </p> <p> </p>
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.