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800 results for “Coronavirus”
Cruce de casos por Coronavirus con el PIB Per Cápita Mundial
<p>Esta práctica se ha realizado bajo el contexto de la asignatura Tipología y ciclo de vida de los datos, perteneciente al Máster en Ciencia de Datos de la Universitat Oberta de Catalunya. En ella, se aplican técnicas de web scraping mediante el lenguaje de programación Python para extraer información relevante al coronavirus a través de <a href="https://es.wikipedia.org/wiki/Pandemia_de_enfermedad_por_coronavirus_de_2019-2020">Wikipedia - Pandemia de enfermedad por coronavirus de 2019-2020</a> y del <a href="https://datosmacro.expansion.com/pib">PIB de cada país</a> con el periódico "Expansión". Se genera un dataset el cual sirve para buscar posibles correlaciones entre estos datos.</p>
Topic Labels of "Dynamic Topic Modelling for Exploring the Scientific Literature on Coronavirus: An Unsupervised Labelling Technique"
<p>These are the labels generated with the method proposed in the article <em>"Dynamic Topic Modelling for Exploring the Scientific Literature on Coronavirus: An Unsupervised Labelling Technique".</em> These labels are for the 100 and 200 DTM topic models, trained both with the whole corpus and with only the COVID-19 period data </p> <p> </p> <p>For the generation of these labels you can go to the original published work or to the linked Zenodo resource.</p>
Croatian Coronavirus News Comments Corpus News-CommHR
<p>A corpus of readers' news comments posted below news articles on the topic of the covid-19 pandemic, published in major Croatian daily newspapers and news portals in the six-month early pandemic period (March 2020 to September 2020).</p> <div>The corpus is designed to facilitate research on crisis discourses, crisis communication, as well as pandemic-time linguistic innovation. It is available in plain text version and XML with full metadata. The corpus complements a separate corpus of news articles Croatian Coronavirus Corpus NewsHR. Parallel versions from Slovenia and Serbia are also available.</div> <div> </div> <div>The project leading to this publication has received funding from the European Union’s Horizon 2020 research and innovation programme under the <a href="https://cordis.europa.eu/programme/id/H2020-EU.4./en">H2020-EU.4. - SPREADING EXCELLENCE AND WIDENING PARTICIPATION </a>programme Widening fellowships grant agreement No 101038047.</div>
Graphic Illustration of Molly McDonough's Talk: Exploring bat coronaviruses using the FMNH cryo collection
<p><a href="https://lib.ku.edu/people/courtney-foat" target="_blank" rel="noopener">Courtney Foat</a>, Advisor for Strategic Initiatives & Organizational Engagement at the University of Kansas, graphically recorded this invited talk by Molly McDonough at an NSF-supported Workshop: Digital Collections Data and Tracking Disease.</p>
Coronavirus COVID-19 (2019-nCoV) Data Repository for Africa
<p>The purpose of this repository is to collate data on the ongoing coronavirus pandemic in Africa. Our goal is to record detailed information on each reported case in every African country. We want to build a line list – a table summarizing information about people who are infected, dead, or recovered. The table for each African country would include demographic, location, and symptom (where available) information for each reported case. The data will be obtained from official sources (e.g., WHO, departments of health, CDC etc.) and unofficial sources (e.g., news). Such a dataset has many uses, including studying the spread of COVID-19 across Africa and assessing similarities and differences to what’s being observed in other regions of the world.</p> <p>See the repo here <a href="https://github.com/dsfsi/covid19africa">https://github.com/dsfsi/covid19africa</a></p>
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 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>
Dataset con datos de la evolución del coronavirus en España y las pruebas de test realizadas
<p>El dataset es un fichero csv que contiene los datos de coronavirus diarios desde Enero de 2020 hasta Enero de 2021, clasificados además de por fecha, por comunidad autónoma. Se indican además del volumen de nuevos casos, los positivos registrados en función de distintas pruebas diagnósticas.</p>
Sharing research data and findings relevant to the novel coronavirus (COVID-19) outbreak - Literature sources
<p>The spreadsheet in the present dataset (CSV format) includes the sources considered during the literature review stage for the report: From intent to impact: Investigating the effects of open sharing commitments. Please note that not all sources in this deposit have been referenced in the above-mentioned report and that the report may include additional sources</p>
Sharing research data and findings relevant to the novel coronavirus (COVID-19) outbreak - Survey responses
<p>The spreadsheets in the present dataset (CSV format) include the anonymised responses to our online survey of signatories of the Joint Statement on open research and data sharing. Responses have been split into quantitative responses (i.e., closed survey questions) and qualitative responses (i.e., free text survey questions).</p> <p>This data has been used to inform our final report, which is available in our <a href="https://zenodo.org/communities/data-sharing-in-public-health-emergencies">Zenodo Project Community</a>.</p>
Sharing research data and findings relevant to the novel coronavirus (COVID-19) outbreak - Thematic coding of qualitative research findings
<p>The spreadsheet in the present dataset (CSV format) includes the anonymised thematic coding that has been applied to our interview and literature review findings to inform the preparation of the report: From intent to impact: Investigating the effects of open sharing commitments.</p> <p>The thematic coding has been applied by using <a href="https://www.qsrinternational.com/nvivo-qualitative-data-analysis-software/home">NVivo</a>, a professional qualitative analysis software, and then exported in spreadsheet form for public sharing.</p> <p>Find out more about this project in our dedicated <a href="https://zenodo.org/communities/data-sharing-in-public-health-emergencies">Zenodo project community</a>.</p>
Croatian Coronavirus Corpus NewsHR
<p>A corpus of news articles on the topic of the covid-19 pandemic, published in major Croatian daily newspapers and news portals in the six-month early pandemic period (March 2020 to September 2020).<br>The corpus is designed to facilitate research on crisis discourses, crisis communication, as well as pandemic-time linguistic innovation. It is available in plain text version and XML with full metadata. <br>Covid-NEWS-HR is complemented with a separate corpus of citizen metalanguage comments, i.e. online comments to the news articles, available as Covid-NEWS-Comm-HR. Parallel versions from Slovenia and Serbia are also available.</p> <p>The project leading to this publication has received funding from the European Union’s Horizon 2020 research and innovation programme under the <a href="https://cordis.europa.eu/programme/id/H2020-EU.4./en">H2020-EU.4. - SPREADING EXCELLENCE AND WIDENING PARTICIPATION </a>programme Widening fellowships grant agreement No 101038047.</p>
Slovenian Coronavirus Corpus NewsSLO
<p>A corpus of news articles on the topic of the covid-19 pandemic, published in major Slovenian daily newspapers and news portals in the six-month early pandemic period (March 2020 to September 2020).<br>The corpus is designed to facilitate research on crisis discourses, crisis communication, as well as pandemic-time linguistic innovation. It is available in plain text version and XML with full metadata. <br>Covid-NEWS-SLO is complemented with a separate corpus of citizen metalanguage comments, i.e. online comments to the news articles, available as Covid-NEWS-Comm-SLO. Parallel versions from Croatia and Serbia are also available.</p> <p>The project leading to this publication has received funding from the European Union’s Horizon 2020 research and innovation programme under the <a href="https://cordis.europa.eu/programme/id/H2020-EU.4./en">H2020-EU.4. - SPREADING EXCELLENCE AND WIDENING PARTICIPATION </a>programme Widening fellowships grant agreement No 101038047.</p>
Coronavirus disease (COVID-19) case data - South Africa
<p>COVID 19 Data for South Africa created, maintained and hosted by <a href="https://dsfsi.github.io/">DSFSI research group</a> at the University of Pretoria</p> <p><strong>Disclaimer:</strong> We have worked to keep the data as accurate as possible. We collate the COVID 19 reporting data from NICD and South Africa DoH. We only update that data once there is an official report or statement. For the other data, we work to keep the data as accurate as possible. If you find errors let us know. </p> <p>See original GitHub repo for detailed information <a href="https://github.com/dsfsi/covid19za">https://github.com/dsfsi/covid19za</a></p>
Datasets Coronavirus (Cifras totales + Evolución de casos)
<p>Se presentan 3 datasets obtenidos mediante técnicas de web scraping.</p> <p>En los datasets 'data_countries.csv' y 'data_continents.csv' se presentan datos absolutos sobre muertes, casos, test realizadors, casos críticis, etc</p> <p>En el dataset 'data_series_cases_countries.csv' se encuentra el número de casos nuevos por día desde el 22 de enero al 13 de abril de 2020</p>
Genomic determinants of pathogenicity in SARS-CoV-2 and other human coronaviruses
<p><strong>Dataset S1.</strong>Complete nucleotide sequence alignment of all human CoV used for region identification. </p> <p><strong>Dataset S2.</strong>Complete nucleotide sequence alignment of all CoV (of human and non-human hosts).</p> <p><strong>Dataset S3.</strong>Distances between leaves (each CoV strain in Dataset S2 was considered), from every reference genome of each of the seven human CoV.</p> <p><strong>Dataset S4.</strong>Alignment of strains used for zoonotic jump analysis.</p>
STOP CORONAVIRUS | Por que lavar as mãos?
<p> </p> <p>Curta-metragem vinculado à série STOP CORONAVÍRUS, aborda medias preventivas frente a disseminação do novo Coronavírus (Sars-CoV-2), agente da COVID-19. Produto Fauna Brasil - UFF / Laboratório de Registro Audiovisual da Fauna Brasileira.</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>
Studied disinfectant substances against SARS-CoV-2 and other coronaviruses
<p>This data-sheet covers those disinfectants tested against SARS-CoV-2 or other coronaviruses. Data were extracted from several research articles indicated in the reference row. The data-sheet comprises a total of 11 fields with info regarding the virus (virus and strain/isolate names), formulation (substance(s) and its concentration in percentage) and test characteristics (suspension or surface tested, kind of surface, use dilution before testing, disinfectant and inoculum volumes, organic load type and concentrations and contact time) as well as their results, normalized in terms of Log<sub>10 </sub>viral infectivity reduction. Data is included and comented in the following journal article: <a href="https://doi.org/10.3390/foods10020283">https://doi.org/10.3390/foods10020283</a> Please reference also to this publication if using the data-sheet.</p>
Research Survey on 'Effects of the Coronavirus Pandemic on Scientific Research'
<p><strong>1. The aim of the research</strong></p><p>The survey aimed to collect opinions on how the pandemic has changed scientific research, especially in regards to the usage of different forms of digital tools. The onset of COVID-19 has had an immediate impact on how scientists were able to use their time and resources to do their work. However, there was a lack of real-time data on how they are responding to this event and how it has had differential impacts across scientific fields.</p><p><strong>2. Research instrument and subject</strong></p><p>A survey questionnaire prepared in English was developed for collecting empirical data from academics in Poland and abroad. We used the same version of the survey questionnaire both in Poland and in other countries in the world. The survey questionnaire consisted of several parts. Its first part included questions on demographic information. The second part of the survey questionnaire contained questions about how respondents' work hours were allocated for different activities during, and after the coronavirus pandemic outbreak and the predicted changes in future publication and funding related to spending on researching, writing during and after the coronavirus pandemic. The last part of the survey questionnaire included questions about scientific works, their quality, and support by ICTs during the coronavirus pandemic, as well as the implications of the pandemic for these issues in the future. At the end of the survey questionnaire, we asked respondents about their opinions with regard to the forecast situation in research and education after the coronavirus pandemic. Selecting a sample is a fundamental element of a quantitative study. Stratified sampling was used to obtain the sample, which can be taken to be true for the whole population. The strata were identified based on country, age, gender, position type, and research discipline. To gather a substantial number of respondents, snowball sampling was pursued, which involved daily and routine distribution (social media and e-mail posting) of an introductory e-letter and survey-link requesting participation in the research. To increase response rates, the following methods were used: involving academics (encouraging colleagues), pushing the survey (providing respondents with the survey URL in e-mails sent directly to them), publishing the project with a link for the basic questionnaire on the ResearchGate website and Facebook fan pages and providing frequent reminders.1</p><p><strong>3. Data collection</strong></p><p>Based on several analyses showing that surveys conducted over the internet provide results that are as valid as more "traditional" methods and due to social distancing caused by the coronavirus pandemic, we used the Computer Assisted Web Interview (CAWI) method for recruiting respondents and collecting data. The LimeSurvey tool was employed for recruiting reliable samples. The data were collected during a two-month period of work, between June 11, 2020, and August 18, 2020. This led to 982 responses. After screening the responses and excluding outliers, 476 usable, correct, and complete responses were collected. This uploded dataset is a subset of a larger dataset that the authors collected for their research project titled "The Effects of the Coronavirus Pandemic on Scientific Research and University Teaching". It includes the questionnaire responses related to research during and after the COVID-19 pandemic. The questions were designed to gather information about the impact of the pandemic on academic scientists' research.1</p><p><strong>4. Results</strong></p><p>Our results may suggest targeted policies to alleviate the disruptions experienced by specific scientific fields. The findings of the past research were documented in three articles, which can be found below: </p><p>1. Ziemba, E. W., & Eisenbardt, M. (2022). The effect of the Covid-19 pandemic on ICT usage by academics. Journal of Computer Information Systems, 62(6), 1154-1168. DOI: 10.1080/08874417.2021.1992806 </p><p>2. Wartini-Twardowska, J., Grabara, D., & Ziemba, E. W. (2021). The Influence of the COVID-19 Pandemic on the Use of Digital Technologies by Scientists: A Comparison Between Poland and Abroad. Problemy Zarządzania, 19(3/2021 (93), 12-31. DOI:10.7172/1644-9584.93.1 </p><p>3. Maruszewska, E. W., Eisenbardt, M., & Tuszkiewicz, M. (2022). COVID Pandemic as a disruptive factor enhancing ICT USE in social sciences' teaching practices. Scientific Papers of Silesian University of Technology. Organization & Management/Zeszyty Naukowe Politechniki Slaskiej. Seria Organizacji i Zarzadzanie, (160). DOI: 10.29119/1641-3466.2022.160.25 </p>
ScienceDex guides
Understand access before you commit
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.