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9,674 results for “COVID-19”
Reductions in California's urban fossil fuel CO2 emissions during the COVID-19 pandemic
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Data from: physiological and emotional assessment of college students using wearable and mobile devices during the 2020 COVID-19 lockdown: an intensive, longitudinal dataset
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Open Source COVID-19
<p>Open Source COVID-19 collects open source projects during COVID-19. The projects are not necessarily hosted on GitHub, as long as it corporates in an open source way, that everyone can access, inspect and improve it.<br> The goal of this navigation site is to help people access data, contribute to the projects, and trigger new ideas.</p>
covid-19_global_spread
<p>The dataset contains information about the spread of the Covid-19 virus around the world. Each record represents the data associated with a country affected by the virus. At the time of this writing, there are a total of 162 affected countries, and therefore 162 records in the dataset.<br> <br> The data has been extracted from the following website: https://www.worldometers.info/coronavirus/. The team that supports this website is made up of developers and researchers from all over the world who work to collect and publish statistics on different topics. This website belongs to Dadax, an independent company. We can find more information on the website at the following link: https://www.worldometers.info/about/.<br> <br> Regarding the displayed propagation data of the Covid-19, this page has been collecting the information since the beginning of the propagation, so all the hitorical data is also available for future work.<br> <br> This website update the information daily. Between the main sources they emphasize official organisms like:<br> <br> 1. Novel Coronavirus (2019-nCoV) situation reports - World Health Organization (WHO)<br> 2. 2019 Novel Coronavirus (2019-nCoV) in the U.S. -. U.S. Centers for Disease Control and Prevention (CDC)<br> 3. Outbreak Notification - National Health Commission (NHC) of the People’s Republic of China<br> <br> More information regarding sources can be found on the website “Sources” section.</p>
Knowledge Graph about altmetrics of selected papers about COVID-19
<p>The knowledge graph (KG) contains data about altmetrics as well traditional indicators associated with 212 papers resulting from an early literature review. Publication dates encompass a time-window ranging from January 15th 2020 to February 24th 2020. The KG is represented as RDF and modelled by using the Indicators Ontology (I-Ont). I-Ont is an ontology for representing scholarly artefacts and their associated indicators, e.g. citation count or altmetrics such as the number of readers on Mendeley.</p>
Air contamination and covid-19 cases in Spain
<p>This data provides values of the Air Quality Index (AQI) for the most populated city in each Spanish autonomous community since 2019. The compounds selected to evaluate AQI are PM10, O3, and NO2. This dataset is oriented to people that want to evaluate quarantine effects on air pollution during a COVID-19 disease outbreak. For this reason, a table with notified cases of the disease to the Public Administration at an autonomous community-scale is also supplied with the aim of providing a framework of disease evolution. </p> <p>The air_contamination.csv dataset contains 9 variables:</p> <p> - timestamp: date in format: yyyy/mm/dd<br> - ca: autonomous community (text)<br> - ciudad: city (text)<br> - pm10: AQI value of pm10 particle (number)<br> - pm10_level: Air quality base on pm10 particles (text)<br> - o3: AQI value of o3 particle (number)<br> - o3_level: Air quality based on o3 particles (text)<br> - no2: AQI value of no2 particle (number)<br> - no2_level: Air quality based on no2 particles (text)</p> <p>The casos_covid19.csv dataset contains 4 variables:</p> <p>- comunidad: autonomous community (text)<br> - casos: Cases notified by Ministerio de Sanidad/Healthcare minister (text) <br> - casos_notificados: Cases notified by RENAVE (Red Nacional de Vigilancia Epidemiológica) a través de la plataforma SiVIES.<br> - datetime: datetime in format yyyy/mm/dd</p> <p> </p> <p>For more information about the project visit the link on [Github](<a href="https://github.com/shiny-data-scientist/webscrap_pract_1/">https://github.com/shiny-data-scientist/webscrap_pract_1/</a>)</p>
Measures to mitigate the spread of COVID-19 in Switzerland
<p>Since February 25, 2020 Switzerland has been affected by COVID-19. Modelling predictions show that this pandemic will not stop on its own and that stringent migitation strategies are needed. Switzerland has implemented a series of measures both at cantonal and federal level. On March 16, 2020 the Federal Council of Switzerland declared “extraordinary situation” and introduced a series of stringent measures. This includes the closure of schools, restaurants, bars, businesses with close contact (e.g. hair dressers), entertainment or leisure facilities. Incoming cross-border mobility from specific countries is also restricted to Swiss citizens, residency holders or work commuters. As of March 20, 2020 mass gatherings of more than five people are also banned. Already in early March various cantons had started to ban events of various sizes and have restricted or banned access to short- and long-term care facilites and day care centers. </p> <p>The aim of this project is to collect and categorize these control measures implemented and provide a continously updated data set, which can be used for modelling or visualization purposes. Please use the newest version available. </p> <p>We collect the date/duration and level of the most important measures taken in response to COVID-19 from official cantonal and federal press releases. A description of the measures, the levels as well as the newest version of data dataset can be found <a href="https://github.com/baffelli/covid-2019-measures">here</a>. </p>
Data and Code to support COVID-19 - exploring the implications of long-term condition type and extent of multimorbidity on years of life lost: a modelling study
<p>Data and code to support paper published in Wellcome Open research on years of life lost among people who died with COVID-19.</p>
HUB-TIC contra el COVID-19 (Enfoque de nación)
<p>Mapa mental para la creación de un HUB-TIC para combatir los efectos del COVID-19 en Panamá (versión 01 del 22/03/20).</p>
COVID-19 CT Lung and Infection Segmentation Dataset
<p>This dataset contains 20 labeled COVID-19 CT scans. Left lung, right lung, and infections are labeled by two radiologists and verified by an experienced radiologist. <br> To promote the studies of annotation-efficient deep learning methods, we set up three segmentation benchmark tasks based on this dataset <a href="https://gitee.com/junma11/COVID-19-CT-Seg-Benchmark">https://gitee.com/junma11/COVID-19-CT-Seg-Benchmark</a>.</p> <p>In particular, we focus on learning to segment left lung, right lung, and infections using</p> <ul> <li>pure but limited COVID-19 CT scans;</li> <li>existing labeled lung CT dataset from other non-COVID-19 lung diseases;</li> <li>heterogeneous datasets include both COVID-19 and non-COVID-19 CT scans.</li> </ul>
COVID-19 impact on the concentration and composition of submicron particulate matter in a typical city of Northwest China
<p>This dataset include the data used in the study of "COVID-19 impact on the concentration and composition of submicron particulate matter in a typical city of Northwest China" submitted to GRL.</p>
Covid-19 Seafood Impacts
<p>Reported impacts and responses on the seafood sector were compiled from April 3 to May 30, 2020, which includes (n = 175) articles published from January 28 to May 27, 2020. News articles were collected by monitoring Google News alerts for ("seafood" OR "fish") AND ("coronavirus" OR "covid"), daily data scraping of Twitter posts for "seafood" and "coronavirus" OR "covid," website searches of primary seafood industry news outlets (e.g., SeafoodSource,Undercurrent, and IntraFish), and compiling information shared through the authors’ professional networks. All news articles containing information about an impact on or response to the COVID-19 pandemic relating to any stage of the seafood supply chain were considered relevant. For each article, we extracted information the: article title, article date, article link, date of the impact, the type of impact, and the countries, sector(s), supply chain stage(s), species, and product form(s) involved, as well as whether the production is recreational, small-scale, and/or industrial.</p>
Data from: Initial experiences of US neurologists in practice during the COVID-19 pandemic via survey
<p><strong>Background</strong> The current coronavirus disease of 2019 (COVID-19) pandemic has caused widespread disease and death. Rapid increases in patient volumes have exposed weaknesses in healthcare systems and challenged our ability to provide optimal patient care and adequate safety measures to healthcare workers (HCWs).</p> <p><strong>Objective</strong> To test the hypothesis that US neurologists were experiencing significant challenges with lack of personal protective equipment (PPE), rapid changes in practice and varying institutional protocols, we conducted this survey study.</p> <p><strong>Methods</strong> A 36-item survey was distributed to neurologists around the US through various media platforms.</p> <p><strong>Results</strong> Over a one-week period, 567 responses were received. Of these, 56% practiced in academia. A total of 87% had access to PPE with 45% being asked to reuse PPE due to shortages. The pandemic caused rapid changes in practice, most notably a shift towards providing care by teleneurology, although a third experienced challenges in transitioning to this model. Wide variations were noted both in testing and in the guidance provided for the exposed, sick or vulnerable HCWs. Notably, 59% of respondents felt that their practices were doing what they could, although 56% did not feel safe taking care of patients.</p> <p><strong>Conclusions</strong> Results from our survey demonstrate significant variability in preparedness and responsiveness to the COVID-19 pandemic in neurology, impacted by region, health care setting and practice model. Practice guidelines from professional societies and other national entities are needed to improve protection for physicians and their patients, promote recommended practice changes during a pandemic, and optimize future preparedness for public health emergencies.</p> <p></p><p></p><p></p>
Linked COVID-19 Data: Geometries
<p>Linked COVID-19 <strong>Geometries</strong></p> <p>maintained at</p> <p><a href="https://github.com/Research-Squirrel-Engineers/COVID-19">https://github.com/Research-Squirrel-Engineers/COVID-19</a></p> <p>data origin by</p> <p>countries:</p> <p>data by <a href="https://github.com/AshKyd/geojson-regions">https://github.com/AshKyd/geojson-regions</a> extracted by <a href="https://geojson-maps.ash.ms/">https://geojson-maps.ash.ms/</a></p> <p>federal states:</p> <p><a href="https://gdz.bkg.bund.de/index.php/default/open-data/verwaltungsgebiete-1-2-500-000-stand-01-01-vg2500.html">https://gdz.bkg.bund.de/index.php/default/open-data/verwaltungsgebiete-1-2-500-000-stand-01-01-vg2500.html</a></p> <p>Verwaltungsgebiete 1:2 500 000, Stand 01.01. (VG2500)</p>
Data from: From trial to implementation, bringing team-based learning online – Duke-NUS Medical School's response to the COVID-19 pandemic
<p>The restrictions imposed by the COVID-19 pandemic resulted in Duke-NUS Medical School moving all their lessons online. Duke-NUS employs a team-based learning (TBL) pedagogy, which depends heavily on student discussion. In 2015, our university had implemented an eLearning week where lessons were conducted online. Using the already present online assessment processes, the data, insights and student feedback allowed for swift implementation of an online TBL module for home-based learning in response to the pandemic in 2020. These protocols were modified over the weeks, guided by feedback from students and faculty. An analysis of this online TBL module is presented herein.</p>
Japanese COVID-19 Tweets from 2020-01-17 to 2020-04-30 (40,720,545 tweets and 105,317,606 retweets)
<p><strong>Abstract</strong> (our paper)</p> <p>The spread of COVID-19, the so-called new coronavirus, is currently having an enormous social and economic impact on the entire world. Under such a circumstance, the spread of information about the new coronavirus on SNS is having a significant impact on economic losses and social decision-making. In this study, we investigated how the new type of coronavirus has become a social topic in Japan, and how it has been discussed. In order to determine what kind of impact it had on people, we collected and analyzed Japanese tweets containing words related to the new corona on Twitter. First, we analyzed the bias of users who tweeted. As a result, it is clear that the bias of users who tweeted about the new coronavirus almost disappeared after February 28, 2020, when the new coronavirus landed in Japan and a state of emergency was declared in Hokkaido, and the new corona became a popular topic. Second, we analyzed the emotional words included in tweets to analyze how people feel about the new coronavirus. The results show that the occurrence of a particular social event can change the emotions expressed on social media.</p> <p><strong>Data</strong></p> <p>Tweets_YYYY-MM-DD.tsv.gz:<br> The first column is the tweet id, the second column is the date and time (JST) when the tweet was posted, the third column is the flag as to whether the tweet was used for emotion analysis or not, and the fourth column is the tweet id of the retweet source.<br> This data was collected by giving the query "新型肺炎 OR 武漢 OR コロナ OR ウイルス OR ウィルス" to the Twitter Search API. Therefore, most of the tweets are Japanese tweets.<br> We conducted emotion analysis on tweets, excluding retweets and tweets containing links. The fourth column is empty if the tweet is not a retweet.</p> <p>KL-Divergence.tsv.gz:<br> The first column is the date (JST), and the second column is the value of KL-Divergence that calculated the bias of the users who posted tweets related to COVID-19.<br> The value of KL-Divergence was calculated with all users appearing in Tweets_YYYY-MM-DD.tsv.gz. Based on the sampling stream data, we determined that if the value is below 0.6, there is no bias.</p> <p>Emotions_by_ML-Ask.tsv.gz:<br> The first column is the date (JST), the second and subsequent columns are the number of tweets for each emotion, and the last column is the number of tweets analyzed for the day.<br> For this analysis, we only used tweets with a value of 1 in the third column of Tweets_YYYY-MM-DD.tsv.gz. We used <a href="https://github.com/ikegami-yukino/pymlask">pymlask</a> (Python implementation of <a href="http://doi.org/10.5334/jors.149">ML-Ask</a>) to estimate the emotion of the tweet.</p> <p><strong>Publication</strong></p> <p>This data set was created for our study. If you make use of this data set, please cite:<br> Fujio Toriumi, Takeshi Sakaki, Mitsuo Yoshida. Social Emotions Under the Spread of COVID-19 Using Social Media. <em>Transactions of the Japanese Society for Artificial Intelligence (in Japanese)</em>. vol.35, no.4, pp.F-K45_1-7, 2020.<br> 鳥海不二夫, 榊剛史, 吉田光男. ソーシャルメディアを用いた新型コロナ禍における感情変化の分析. <em>人工知能学会論文誌</em>. vol.35, no.4, pp.F-K45_1-7, 2020.<br> <a href="https://doi.org/10.1527/tjsai.F-K45">https://doi.org/10.1527/tjsai.F-K45</a></p>
Data from: Clinical management and mortality among COVID-19 cases in sub-Saharan Africa: a retrospective study from Burkina Faso and simulated case analysis.
<p>Absolute numbers of COVID-19 cases and deaths reported to date in the sub-Saharan Africa (SSA) region have been relatively low. As a result, there has been limited investigation into deceased cases in the region, as well as the impacts of different case management strategies. We detail demographic, epidemiological, and clinical information derived from publicly available information on deceased cases in SSA and, for cases in Burkina Faso, from aggregate records at the Center Hospitalier Universitaire de Tengandogo. Logistic regression was conducted on a synthetic case population to evaluate the adjusted odds of survival for patients receiving oxygen therapy or convalescent plasma, based on therapeutic effectiveness observed for other respiratory illnesses. Across SSA, deceased cases have been predominantly male and over 50 years of age. After adjustment for sex, age, and underlying conditions, the odds of mortality among cases in the synthetic population not receiving oxygen therapy was significantly higher than those receiving oxygen (OR: 2.07; 95%CI: 1.56-2.75). Cases receiving convalescent plasma had 50% reduced odds of mortality (95%CI: 0.24-0.93).<b> </b>Investment in sustainable oxygen therapy could reduce COVID-19 deaths in SSA. Ongoing investigation into convalescent plasma is warranted, as data on its effectiveness specifically in treating COVID-19 becomes available.</p>
Use of wearable sensors to assess compliance of asthmatic children in response to lockdown measures for the COVID-19 epidemic
<p>Dataset from LIFE-MEDEA participants (asthmatic children) from Cyprus and Greece, including Study ID, gender, age, study year, ambient temperature, ambient humidity, recording day, percentage of time staying at home, steps per day, callendar day, calendar week, date, lockdown status (phase 1, 2, or 3) due to COVID-19 pandemic, and if the date was during the weekend (binary variable).</p>
Encuesta Nacional sobre los Efectos del COVID-19 en el Bienestar de los Hogares Mexicanos (ENCOVID-19-ABRIL)
<p>Amid the COVID-19 outbreak, the ENCOVID-19 provides information on the well-being of Mexican households in four main domains: labor, income, mental health, and food insecurity. It offers timely information to understand the social consequences of the pandemic and the lockdown measures. It is a project consisting of a series of cross-sectional telephone surveys collected once a month for one year. In addition to the four main domains and a set of COVID19-related questions, the survey includes new key indicators every month to capture the impact of the pandemic on issues like education, social programs, and crime. This is the first dataset of the project, corresponding to April 2020, collected one month after the lockdown began in Mexico.</p>
LG-covid19-HOTP: Literature Graph of Scholarly Articles Relevant to COVID-19 Study
<p>Parallel to the dataset <a href="http://dx.doi.org/10.5281/zenodo.3727291">CORD-19</a> of scholarly articles, we provide the literature graph <a href="https://lg-covid-19-hotp.cs.duke.edu"> LG-covid19-HOTP</a> composed of not only articles (graph nodes) that are relevant to the study of coronavirus, but also in and out citation links (directed graph edges) to base navigation and search among the articles. The article records are related and connected, not isolated. The graph has been updated weekly since March 26, 2020. The current graph includes <strong>42,279</strong> hot-off-the-press (HOTP) articles since January 2020. It contains <strong>485,097</strong> articles and <strong>4,259,944</strong> links. The link-to-node ratio is remarkably higher than some other existing literature graphs. In addition to the dataset we provide more functionalities at <a href="https://lg-covid-19-hotp.cs.duke.edu">lg-covid-19-hotp.cs.duke.edu</a> such as new articles, weekly meta-data analysis in terms of publication growth over time, ranking by citation, and statistical near-neighbor embedding maps by similarity in co-citation, and similarity in co-reference. Since April 11, we have enabled a novel functionality - self-navigated surf-search over the maps. At the site we also take courtesy input of COVID-19 articles that are missing from the current collection.</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.