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9,674 results for “COVID-19”
COVID-19 Twitter data, keyword stream 2020-01-13 to 2020-06-06
<p>Twitter data was collected through the Twitter API, specifically through the filter streaming endpoint, using the Crowdbreaks platform (<a href="http://crowdbreaks.org">crowdbreaks.org</a>) The data used in this work consists of a total of 353,993,900 tweets (thereof 267,026,740 retweets) posted by 26,262,332 users in a 146 day observation period, i.e. from January 13 to June 7, 2020. These tweets have been identified by Twitter to be in English language and match one or more of the keywords "wuhan", "ncov", "coronavirus", "covid" and "sars-cov-2".</p> <p>The data is complete with respect to these keywords, except during a period between mid-March to mid-April when volume exceeded the 1% threshold imposed by Twitter and was subsampled by an (unknown) degree.</p> <p>The following fields are published:</p> <ul> <li>id: Tweet ID</li> <li>is_retweet: Whether or not tweet is a retweet</li> <li>num_retweets: Number of retweets</li> <li>user.id: Id of tweeting user</li> <li>country_code: country code as predicted by local-geocode (https://github.com/mar-muel/local-geocode)</li> </ul>
I see how you feel: facial expressions' recognition and distancing in the time of COVID-19
<p>Data sets and R script for anlysis.</p>
Viral Validation: How the New Journal 'Rapid Reviews: COVID-19′ Accelerates Peer Review and Publishing
<p><strong>Episode Summary:</strong></p> <p>In this episode we talk to Professor Stefano Bertozzi, editor-in-chief Rapid Reviews: COVID-19 (RR:C19). This new open access overlay journal from the MIT Press is aiming to publish expert peer reviews of new COVID-19 research which will help validate and accelerate the discovery of high impact, useful studies. We discussed how the journal will work, the role human and AI input will play, and the importance of pan-disciplinary content. </p> <p><strong>Episode Links:</strong></p> <ul> <li><a href="https://rapidreviewscovid19.mitpress.mit.edu/">Rapid Reviews: COVID-19 Journal</a></li> <li><a href="https://mitpress.mit.edu/">MIT Press</a></li> <li><a href="https://publichealth.berkeley.edu/people/stefano-bertozzi/">Professor Stefano Bertozzi</a> <ul> <li><a href="https://twitter.com/StefBertozzi">Twitter</a></li> </ul> </li> </ul>
Blood test dynamics in hospitalized COVID-19 patients: potential utility of D-dimer for pulmonary embolism diagnosis
<p>SPSS dataset with metadata of the published article in PlosOne and MedRxIv</p>
Kuesioner, hasil uji statistik validitas & reablitias dan isian kuesioner Pengetahuan dan sikap ibu hamil trimester III terhadap pencegahan COVID-19
<p>Data pelengkap artikel pengetahuan dan sikap ibu hamil trimester III terhadap pencegahan COVID-19. Data berikut berisi:</p> <ol> <li>Kuesioner</li> <li>Hasil uji statistik validitas dan reabilitas</li> <li>Hasil uji statistik data isian kuesioner</li> </ol>
Reactome COVID-19: the literature curation strategy
<p>ABSTRACT</p> <p>In response to the deluge of COVID-19-related publications Reactome developed a computational triaging strategy to review and identify publications appropriate for manual curation (66,100 SARS-Cov-2 articles on PUBMED, tallied on 30/October/2020 https://www.ncbi.nlm.nih.gov/research/coronavirus/; Chen et al., 2020). </p> <p>The literature triaging approach consisted of 4 main steps: 1) Literature screening; 2) Literature selection; 3) Reference tagging; and 4) Reference database construction. Two primary reference databases were downloaded and automatically text-mined: CDC COVID-19 downloadable database and bioRxiv database. Other collections of SARS-Cov-2 literature were manually screened with a focus on molecular interactions. These included: a Zotero Library built and updated by members of COVID-19 Disease Map (Ostaszewski et al., 2020); CORD-19 (Wang et al., 2020); LitCOVID (Chen et al., 2020); Johns Hopkins literature summary; Cell Press Coronavirus Resource Hub; Nature Coronavirus and COVID-19 updates; and Science’s Latest Coronavirus research. </p> <p>About 5% of the articles made it through reference screening focused on Reactome SARS-CoV-2 map construction to the literature selection step. If relevance was confirmed, the reference was then tagged in step 3. SARS-CoV-2 selected references were tagged regarding multiple features: (i) type of publication (e.g., article, review, pre-print, comment); (ii) Virus and host species (e.g., SARS-CoV-2, SARS-CoV-1; MERS, ACE2); (iii) Entity (specific molecules studied); (iv) Methods (e.g., Cryo-EM, ELISA, IC50); (v) Cell line and/or Tissue (e.g., vero-E6, lung tissue); (vi) subcellular localization (e.g., plasma membrane, ER); (vii) Molecular event (e.g., virus cycle step, pathway, host response); and (viii) Phenotype (e.g., immune, coagulation). Features i, ii, iii, vii and viii were mandatory. This Reference Database is stored in a shared spreadsheet, in which Reactome team members can edit and refine the Library (e.g., inclusion of tags). </p> <p> As Reactome is an evidence-based database built on reliable experimental published data, the process of SARS-CoV-2 reference selection is stringent and prioritizes peer-reviewed references. Nevertheless, the final decision on the reliability of the scientific evidence to support a molecular interaction was made by Reactome curators. In this case the focused literature triaging provides curators with a deeply researched trove of articles.</p> <p> The Reactome strategy of first creating an individual map for SARS-CoV-1 supported and guided the construction of a refined SARS-CoV-2 map. The SARS-CoV-2 Reactome map is an ongoing task, built on the foundation of a strong literature curation strategy. Together the literature triage and curatorial groups have built open-source COVID-19 viral infection pathways incorporating rapid scientific development and literature availability.</p> <p> </p> <p>References.</p> <p> </p> <p>Chen, Q., Allot A., Lu Z. Keep up with the latest coronavirus research. Nature 579, 193 (2020). doi: 10.1038/d41586-020-00694-1 </p> <p> </p> <p>Ostaszewski, M., Mazein, A., Gillespie, M.E. et al. COVID-19 Disease Map, building a computational repository of SARS-CoV-2 virus-host interaction mechanisms. Sci Data 7, 136 (2020).<a href="https://doi.org/10.1038/s41597-020-0477-8"> https://doi.org/10.1038/s41597-020-0477-8</a></p> <p> </p> <p>Wang, L., Lo K, Chandrasekhar, Y., et al. CORD-19: The Covid-19 Open Research Dataset. Preprint. ArXiv. 2020;arXiv:2004.10706v2. Published 2020 Apr 22.CORD-19.<a href="https://covidsearch.sinequa.com/app/covid-search/#/home"> https://covidsearch.sinequa.com/app/covid-search/#/home</a></p> <p> </p> <p>Reference Databases: CDC Covid-19 Database (<a href="https://www.cdc.gov/library/researchguides/2019novelcoronavirus/researcharticles.html">https://www.cdc.gov/library/researchguides/2019novelcoronavirus/researcharticles.html</a>); bioRxiv database (<a href="https://www.biorxiv.org/about-biorxiv">https://www.biorxiv.org/about-biorxiv</a>); Johns Hopkins literature summary (<a href="https://ncrc.jhsph.edu/topics/">https://ncrc.jhsph.edu/topics/</a>); Cell Press Coronavirus Resource Hub (<a href="https://www.cell.com/COVID-19">https://www.cell.com/COVID-19</a>); Nature Coronavirus and COVID-19 updates (<a href="https://www.nature.com/collections/aijdgieecb">https://www.nature.com/collections/aijdgieecb</a>); Science’s Latest Coronavirus research (<a href="https://www.sciencemag.org/collections/coronavirus?IntCmp=coronavirussiderail-128">https://www.sciencemag.org/collections/coronavirus?IntCmp=coronavirussiderail-128</a>).</p> <p> </p>
Bits x la Marató: Looking for similar patients: the AI Doctor House conquers severe COVID-19!
<p>Clinical case reports for the task Looking for similar patients: the AI Doctor House conquers severe COVID-19! at the event Bits x la Marató: https://www.fib.upc.edu/en/la-marato</p> <p> </p> <p>There is a pressing need by healthcare professionals to access information relevant to clinical practice in a more effective way. Over 80% of clinically relevant data is essentially unstructured, mainly images like MRI and clinical texts.</p> <p>One of the challenges faced by doctors is finding patients and clinical cases that show particular similarities to a given case (similar symptoms, diagnosis, treatments, or other characteristics) amongst the rapidly growing amount of clinical records and medical publications and the complexity of the data. Detection of similarities among patients or groups of patients is key for evidence-based clinical practice, the selection of patients for clinical trials, prioritizing patients for vaccination and for understanding the variability in clinical outcomes.</p> <p>From a COVID-19 point of view, AI tools should distinguish between patients with and with no risk of a severe outcome, so that clinicians could intervene promptly. <strong>Specifically, this task aims to promote the development of systems able to detect similarities among a collection of clinical case texts.</strong></p> <p> </p> <p><strong>Technology point of view:</strong></p> <p>The objective is to be able to compute and measure similarity between patients represented by their clinical case, that is, the text describing their medical condition, previous morbidities, medical tests and treatments performed, diagnosis or outcome. This very complex scenario can in principle be approached by a diversity of methodologies ranging from text similarity techniques used to detect plagiarism, clinical concept detection, or even more advanced semantic textual similarity strategies dealing with the meaning of natural language through AI.</p> <p> </p> <p><strong>Healthcare point of view:</strong></p> <p>Access to medically relevant information hidden in clinical texts is one of the principal challenges for healthcare professionals in the AI digital age. Questions such as which are the symptoms of patients with a worse outcome, given similar comorbidities, medications or procedures are very difficult to answer without systematically processing clinical texts. Even simpler, epidemiological questions like how many days have passed before COVID-19 symptoms started or if patients had travelled to certain geographical areas can only be answered efficiently by means of computational tools. Similarities between patients can aid prognosis, diagnosis and decision making, saving vital time to healthcare practitioners.</p> <p> </p> <p>If you need some help, <a href="https://medium.com/@adriensieg/text-similarities-da019229c894">here</a> is a helpful resource that will help you get started.</p> <p> </p> <p><a href="https://www.youtube.com/playlist?list=PL5uSCzf1azhBeVCHyswazImBNpIW8gYTD">YouTube playlist with our session at BITSXLAMARATÓ</a></p>
Data from: Disparate patterns of movements and visits to points of interest located in urban hotspots across U.S. metropolitan cities during COVID-19
<p>We examined the effect of social distancing on changes in visits to urban hotspot points of interest. In a pandemic situation, urban hotspots could be potential superspreader areas as visits to urban hotspots can increase the risk of contact and transmission of a disease among a population. We mapped origin-destination networks from census block groups to points of interest (POIs), such as restaurants, museums, and schools, in sixteen cities in the United States. We adopted a coarse-grain approach to examine patterns of visits to POIs among hotspots and non-hotspots from January to May 2020. Also, we conducted chi-square tests to identify POIs with significant flux-in changes during the analysis period. The results showed disparate patterns across cities in terms of reduction in hotspot POI visits. Sixteen cities are divided into two categories. In one category, which includes the cities of, San Francisco, Seattle, and Chicago, we observe a considerable decrease in hotspot POI visits, while in another category, including the cites of, Austin, Houston, and San Diego, the visits to hotspots did not greatly decrease. While all the cities exhibited overall decreasing visits to POIs, one category maintained the proportion of visits to hotspot POIs. The proportion of visits to some POIs (e.g., Restaurants) remained stable during the social distancing period, while some POIs had an increased proportion of visits (e.g., Grocery Stores). We also identified POIs with significant flux-in changes, showing that related businesses were greatly affected by social distancing.</p>
Mental health status of health care professionals during the Covid-19 outbreak The initial study in Qazvin province, Iran
<p>This study was conducted to examine the mental health status of health care workers fighting COVID-19 in Qazvin province, Iran.</p>
Dataset for "Chemistry of Atmospheric Fine Particles during the COVID-19 Pandemic in a Megacity of Eastern China"
<p>Original data for the air pollutants during the COVID-19 pandemic in Hangzhou, China</p>
Diffusion segregation and the disproportionate incidence of COVID-19 in African American communities
<p>Each network corresponds to a metropolitan area, where nodes represent census tracts. For each tract we report information about the number of people belonging to each of the seven high-level ethnic groups defined in the US Census. Physical adjacency networks are undirected and unweighted, and an edge between two tracts A and B indicates that A and B are bordering each other. Commuting flow graphs are undirected and weighted, and the weight of an the edge between A and B corresponds to average of the total number of work commuting trips from A to B and from B to A.</p>
Increased sCD163 and sCD14 plasmatic levels and depletion of peripheral blood pro-inflammatory monocytes, myeloid and plasmacytoid dendritic cells in patients with severe COVID-19 pneumonia
<p><strong>Background: </strong>Emerging evidence argues that monocytes, circulating innate immune cells, are principal players in COVID-19 pneumonia. The study aimed to investigate the role of soluble (s)CD163 and sCD14 plasmatic levels in predicting disease severity and characterize peripheral blood monocytes and dendritic cells (DCs), in patients with COVID-19 pneumonia (COVID-19 subjects). </p> <p><strong>Methods:</strong> On admission, in COVID-19 subjects sCD163 and sCD14 plasmatic levels, and peripheral blood monocyte and DC subsets were compared to healthy donors (HDs). According to clinical outcome, COVID-19 subjects were divided into ARDS and non-ARDS groups. </p> <p><strong>Results:</strong> Compared to HDs, COVID-19 subjects showed higher sCD163 (p<0.0001) and sCD14 (p<0.0001) plasmatic levels. We observed higher sCD163 plasmatic levels in the ARDS group compared to the non-ARDS one (p=0.002). The cut-off for sCD163 plasmatic level greater than 2032 ng/ml was predictive of disease severity (AUC: 0.6786, p=0.0022; sensitivity 56.7% [CI: 44.1-68.4] specificity 73.8% [CI: 58.9-84.7]). Positive correlation between plasmatic levels of sCD163, LDH and IL-6 and between plasmatic levels of sCD14, D-dimer and ferritin were found. Compared to HDs, COVID-19 subjects showed lower percentages of non-classical (p=0.0012) and intermediate monocytes (p=0.0447), slanDCs (p<0.0001), myeloid DCs (mDCs, p<0.0001) and plasmacytoid DCs (pDCs, p=0.0014). Compared to the non-ARDS group, the ARDS group showed lower percentages of non-classical monocytes (p=0.0006), mDCs (p=0.0346) and pDCs (p=0.0492). </p> <p><strong>Conclusions:</strong> The increase in sCD163 and sCD14 plasmatic levels, observed on hospital admission in COVID-19 subjects, especially in those who developed ARDS, and the correlations of these monocyte/macrophage activation markers with typical inflammatory markers of COVID-19 pneumonia, underline their potential use to assess the risk of progression of the disease. In an early stage of the disease, the assessment of sCD163 plasmatic levels could have clinical utility in predicting the severity of COVID-19 pneumonia. </p>
Data for National interest may require distributing COVID-19 vaccines to other countries
<p>Countries Groups contains information on which countries are considered as part of COVAX. </p> <p>Population contains information on population. </p>
Data set for study '"Optimal intervention strategies to mitigate the COVID-19 pandemic effects"
<p>Data associated with the findings presented in the study '"Optimal intervention strategies to mitigate the COVID-19 pandemic effects"</p>
The Early Childhood Education and Care COVID-19 Impact Study
<p>This survey was designed to address the gap in knowledge around the impact that the COVID-19 global pandemic has had on Early Childhood Education and Care (ECEC) policy, practice and provision across the UK. </p> <p>The survey explores the extent to which changes have been made in early years education and care settings and the perceptions of these changes among practitioners. The survey asked for perceptions about the impact of COVID-19 on staff, the children in their care (aged 0-8) and on their setting as a whole. </p>
Climatic effects of flight reduction during the COVID-19 pandemic
<p>Raw data, processed data, all scripts used for processing figures.</p>
Aviation contrail cirrus and radiative forcing over Europe for six months in 2020 during COVID-19 compared with 2019: Observations and model results
<p>This file contains supporting information for a manuscript submitted for publication.</p> <p>Aviation contrail cirrus and radiative forcing over Europe for six months in 2020 during COVID-19 compared with 2019: Observations and model results </p> <p>U. Schumann, L. Bugliaro, and C. Voigt</p> <p>Corresponding author: Ulrich Schumann (Ulrich.schumann@dlr.de)</p> <p>For details see the README.txt</p> <p> </p> <p> </p>
Data of CP in COVID-19 patients
<p>data source </p>
Written news coverage by CNN and FOX on China's COVID-19 epidemic from January 1, 2020, to May 31, 2021.
<p><span><span> </span>The researchers applied Python software to FOX's health section, CNN's health section, with "Coronavirus + China" </span><span>、</span><span>"Covid-19 + China" as keywords, to capture 4272 and 4167 articles on CNN and FOX from January 1, 2020 to May 31, 2021, respectively.</span></p>
Distress and rewards of nurses with experience in COVID-19 wards
<p>The data were obtained from interviews with nurses who had worked in COVID-19 wards regarding their distresses and rewards.<br>The study spanned from January 2022 to March 2023</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.