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765 results for “pandemics covid-19”
Datasets used in the study "Trends in medication use after the onset of the COVID-19 pandemic in the Republic of Ireland: an interrupted time series study"
<p>This record contains datasets analysed as part of the study "Trends in medication use after the onset of the COVID-19 pandemic in the Republic of Ireland: an interrupted time series study".</p> <p>Two datasets were used, one relating to therapeutic subgroups defined by ATC codes (atc_wide_freq_avg.csv) and one relating to individual medications (drugs_wide_freq_avg.csv). Datasets were collated by combining monthly data reported by HSE Primary Care Reimbursement Services in Ireland relating to dispensing on the General Medical Services scheme at https://www.sspcrs.ie/portal/annual-reporting/</p> <p>Code used to collate datasets and for data management is included in Stata format (compile_data_export_for_analysis_final.do).</p> <p>The study protocol is available at https://doi.org/10.17605/OSF.IO/B4RTM</p>
Timeline of government interventions and events regarding the COVID-19 pandemic in Sweden December 31, 2019, to May 5, 2023.
<p>The Swedish approach to managing the COVID-19 pandemic has received significant attention in international scholarly work and the press. For this dataset, we have reviewed governmental and media archives to build a detailed timeline that chronicles significant policies, interventions, and events in the Swedish management of COVID-19. The dataset contains summary descriptions of what took place, when it happened, and who the principal actors involved were. Links to primary sources are provided for each entry. Because of the level of detail and saturation, the dataset offers a detailed account of Swedish pandemic governance and will benefit anyone working on Swedish pandemic management or doing comparative work between Sweden and other jurisdictions.</p> <p>The dataset contains details on the date an event took place (column 1), tags to facilitate navigation (column 2), details on the principal actors involved in the event (column 3), a summary description of what took place and who was involved (column 4), and links to primary materials (e.g., archival entries) (columns 4-12). Through a structured and detailed outline, the dataset provides a saturated account of policy interventions and events in Sweden during the COVID-19 pandemic for the period 2020-2023 until it was no longer considered a public health emergency of international concern by the WHO and complements existing and less detailed timelines published at earlier points in the period.</p>
3DNIV/3DNIV: A Novel Dual Non-Invasive Ventilator Continuous Positive Airway Pressure Non-Aerosolization Circuit for Emergency Use in the COVID-19 Pandemic
<p>The COVID19 pandemic is a public health emergency of unprecedented scale. The surge in clinical cases of patients with severe respiratory illness has overwhelmed the traditional capacity of healthcare systems worldwide. Continuous Positive Airway Pressure (CPAP) delivered through Non-Invasive Ventilation (NIV) has been shown to be useful in caring for patients with COVID19. In particular patients with early stage milder acute hypoxemic respiratory failure can benefit from NIV CPAP therapy, though there is an acknowledged risk of COVID19 aerosolization with traditional circuit use. Furthermore, given the surge in clinical care demand, there is an acute global shortage of ventilators, including NIV devices and therefore innovative methods are needed to increase NIV capacity and ameliorate infectious aerosolization. This work outlines an emergency use modified dual NIV CPAP Circuit that uses a 3D printed splitter designed to work with traditional international NIV CPAP tubing standards and a 3D printed respiratory face mask knuckle to allow for distal expiratory breath exhalation through a viral filter rather than through an open to air proximal valve, which is the traditional NIV CPAP configuration. We expect that this work will increase global NIV CPAP capacity and ameliorate aerosolization of COVID19 in patients undergoing therapy in an emergency scenario.</p>
TweetsCOV19 - A Semantically Annotated Corpus of Tweets About the COVID-19 Pandemic (Part 1, October 2019 - April 2020)
<p><strong><a href="https://data.gesis.org/tweetscov19/">TweetsCOV19</a></strong><strong> </strong>is a semantically annotated corpus of Tweets about the COVID-19 pandemic. It is a subset of <a href="https://data.gesis.org/tweetskb">TweetsKB</a> and aims at capturing online discourse about various aspects of the pandemic and its societal impact. <strong>Metadata</strong> information about the tweets as well as extracted <strong>entities</strong>, <strong>sentiments</strong>, <strong>hashtags</strong>, <strong>user mentions</strong>, and <strong>resolved URLs </strong>are exposed in RDF using established RDF/S vocabularies*.</p> <p>We also provide a <em><strong>tab-separated values (tsv)</strong></em> version of the dataset. Each line contains features of a tweet instance. Features are separated by tab character ("\t"). The following list indicate the feature indices:</p> <ol> <li>Tweet Id: Long.</li> <li>Username: String. Encrypted for privacy issues*.</li> <li>Timestamp: Format ( "EEE MMM dd HH:mm:ss Z yyyy" ).</li> <li>#Followers: Integer.</li> <li>#Friends: Integer.</li> <li>#Retweets: Integer.</li> <li>#Favorites: Integer.</li> <li>Entities: String. For each entity, we aggregated the original text, the annotated entity and the produced score from <a href="https://github.com/yahoo/FEL">FEL</a> library. Each entity is separated from another entity by char ";". Also, each entity is separated by char ":" in order to store "original_text:annotated_entity:score;". If FEL did not find any entities, we have stored "null;".</li> <li>Sentiment: String. <a href="http://sentistrength.wlv.ac.uk/">SentiStrength</a> produces a score for positive (1 to 5) and negative (-1 to -5) sentiment. We splitted these two numbers by whitespace char " ". Positive sentiment was stored first and then negative sentiment (i.e. "2 -1").</li> <li>Mentions: String. If the tweet contains mentions, we remove the char "@" and concatenate the mentions with whitespace char " ". If no mentions appear, we have stored "null;".</li> <li>Hashtags: String. If the tweet contains hashtags, we remove the char "#" and concatenate the hashtags with whitespace char " ". If no hashtags appear, we have stored "null;".</li> <li>URLs: String: If the tweet contains URLs, we concatenate the URLs using ":-: ". If no URLs appear, we have stored "null;"</li> </ol> <p>This dataset consists of <strong>8,151,524 tweets</strong> in total, posted by <strong>3,664,518 users</strong> and reflects the societal discourse about COVID-19 on Twitter in the period of October 2019 until April 2020.</p> <p>To extract the dataset from <a href="https://data.gesis.org/tweetskb">TweetsKB</a>, we compiled a seed list of 268 COVID-19-related <a href="https://data.gesis.org/tweetscov19/keywords.txt">keywords</a>.</p> <p><em>* For the sake of privacy, we anonymize user IDs and we do not provide the text of the tweets.</em></p> <p> </p>
Global Macroeconomic Scenarios of the COVID-19 Pandemic: Epidemiological Assumptions
<p>Epidemiological Assumptions used for modelling the Global Macroeconomic Scenarios of the COVID-19 Pandemic</p>
The Psychological Burden of the COVID-19 Pandemic and Its Associated Factors among the Frontline Doctors of Bangladesh: A Cross-sectional Study-Extended Data
<p>Using this document, we tried to assess the mental health status of the frontline doctors of Bangladesh during Coronavirus 2019 pandemic.</p>
Vector sequences in early WIV SRA sequencing data of SARS-CoV-2 inform on a potential large-scale security breach at the beginning of the COVID-19 pandemic
<p>DESCRIPTION</p> <p>Sequences identified as Influenza A virus, Spodoptera frugiperda rhabdovirus and Nipah henipavirus have been previously identified within the early HiSeq 1000 and HiSeq 3000 sequencing data of SARS-CoV-2, SRR11092059,SRR11092060,SRR11092061 and SRR11092062, and were being used to support the hypothesis that a "simultaneous outbreak of multiple zoonotic viruses" have happened in the Huanan Seafood market. https://doi.org/10.31219/osf.io/s4td6</p> <p>However, a closer examination of these sequences revealed that they were not sequences of actual wild viruses, but were in stead fragments left behind from PCR products and cloning vectors harboring both cDNA clones and infectious clones of such viruses, with evidence of viral sequences being joined directly to DNA sequences of vector and non-human origin within the same short reads.</p> <p>Here are the vector sequences and PCR product-like sequences recovered from the earliest WIV SRA sequencing data of Human SARS-CoV-2 from dataset SRR11092059,SRR11092060,SRR11092061,SRR11092062.</p> <p>Sequences associated with Vectors and PCR products from 3 distinct viral species have been obtained: The 3'-end of a Nipah Henipahvirus with fusion to a Hepatitis D virus Ribozyme, a T7 terminator and a Tetracycline resistance gene, The 5'-end of the same Nipah Henipahvirus with fusion to sequences found in diverse vectors, A complete vector genome encoding the HA gene of Influenza A virus subtype H7N9 under a CMV promoter and a bgH polyA terminator, and 221 Contiguous sequences corresponding to the Spodoptera frugiperda rhabdovirus reference genome fused to sequences that were homologous to multiple Plastid sequences and Notably Mitochondrial sequences of Rodents.</p> <p>As sequences corresponding to a rescued infectious clone of a BSL-4 organism (Nipah Henipahvirus) were found in sample sequences that supposedy represents patient samples that were obtained from Hospital ICU and sequenced in a pathogen diagnosis laboratory (which is separate from the Virology Research laboratory which is implied by the context of an Infectious Clone of such an organism, evident by the 3'-HDV ribozyme and T7 terminator fused directly to the 3'-terminus of the Nipah Henipahvirus reads), The discovery of artifact-containing sequences of at least 3 different pathogen species that are phylogenetically and methodologically distinct from each other in samples that were supposedly submitted by a laboratory that is Separate from the virological research laboratories that could have hosted such clone sequences imply extensive crosstalk and cross-contamination between the various laboratories within the Wuhan Institute of Virology, which includes at least one BSL-4 laboratory with evidence of containment breach of a BSL-4 organism and it's subsequent introduction into RNA-seq samples that were processed by a laboratory of distinct and separate purposes than the basic virological research evidenced by the Infectious Clone of the Hipah Henipahvirus.</p> <p>Such a discovery therefore likely imply a major security breach happening within the Wuhan institute of Virology at the time when the first sequences of SARS-CoV-2 was sampled and sequenced, which have important implications on the origins of the SARS-CoV-2 virus itself.</p> <p>METHODS</p> <p>The metagenomic sequencing datasets, SRR11092059,SRR11092060,SRR11092061 and SRR11092062 were first analyzed using the NCBI phylogenetic analysis tool, which identified viral sequences that is not related to SARS-CoV-2 itself. These include Influenza A virus (IAV, subtype H7N9), Spodoptera frugiperda rhabdovirus and Nipah Henipahvirus.</p> <p>The datasets were then subjected to BLAST search using MEGABLAST against the reference sequences of such viruses to verify the existence of the viral sequences and determine the exact sybtype of such viruses and the closest sequences on GenBank that corresponds to the reads. There seuqences are MH926031.1 for the Spodoptera frugiperda rhabdovirus, KY199425.1 for the Influenza A virus and AY988601.1 for the Nipah Henipahvirus.</p> <p>A second round BLAST analysis with these identified sequences were then performed, which unexpectedly revealed numerous reads corresponding to Cloning vectors and non-human Mitochondrial and Plastid sequences being fused directly to the sequences of the identified viral species. Reads were then downloaded and subjected to assembly using the CAP3 sequence assembly program and the EGASSEMBLER tool. Contig sequences were then queried against the NCBI nr/nt database which unanimously identified the original sample sequences as viral sequences inserted into cloning vectors.</p> <p>The complete sequence of the Influenza A virus Haemagluttinin (HA) gene clone was obtained from SRR11092061,SRR11092062 using multiple rounds of BLAST search and sequence assembly expansion on the existing vector-virus junction contigs, and a partial sequence corresponding the 3'-end of Nipah Henipahvirus AY988601.1 fused to a 3'-HDV ribozyme, T7 terminator and a Tet resistance gene was obtained from SRR11092059. In addition, 221 Contig sequences corresponding to the Rhabdovirus MH926031.1 fused to Chloroplast sequence MN524635.1 and Rodent Mitochondrial sequence MT241668.1 have been recovered from SRR11092061.</p> <p>We then performed a BLAST search using the identified vector sequences on SRR11092059,SRR11092060,SRR11092061 and SRR11092062, which confirms the existence of these two vetor sequences in all 4 datasets.</p>
Population disruption: estimating changes in population distribution in the UK during the COVID-19 pandemic - Estimates for Local Authority Districts
<p><strong>Overview:</strong></p> <p>Population estimates from the publication: <em>Population disruption: estimating changes in population distribution in the UK during the COVID-19 pandemic.</em> </p> <p>Population estimates were aggregated to Local Authority Districts (LADs). </p> <p><strong>Methodology: </strong></p> <p>Population estimates were extracted from Bing Tiles (Zoom Level 12) to 2019 LADs by assigning tiles to LADs by their percent areal overlap. This method assumes constant population distribution across a single Bing Tile.</p> <p>2019 LAD boundaries are available from the <a href="https://geoportal.statistics.gov.uk/datasets/local-authority-districts-december-2019-boundaries-uk-bfc/explore">UK Government Open Geography Portal</a>.</p> <p> </p>
Factors influencing the likelihood of accessing healthcare during the COVID-19 pandemic in Ireland: lessons for the future
<p>This is an adapted version of the original National Household Survey - Wave 1 whereby existing variables were recoded to create new variables for the purpose of a new analysis.</p>
Replication Package of Understanding Developers Well-Being and Productivity: a 2-year Longitudinal Analysis during the COVID-19 Pandemic
<p>The COVID-19 pandemic has brought significant and enduring shifts in various aspects of life, including increased flexibility in work arrangements. In a longitudinal study, spanning 24 months with six measurement points from April 2020 to April 2022, we explore changes in well-being, productivity, social contacts, and needs of software engineers during this time. Our findings indicate systematic changes in various variables. For example, well-being and quality of social contacts increased while emotional loneliness decreased as lockdown measures were relaxed. Conversely, people's boredom and productivity, remained stable. Furthermore, a preliminary investigation into the future of work at the end of the pandemic revealed a consensus among developers for a preference of hybrid work arrangements. We also discovered that prior job changes and low job satisfaction were consistently linked to intentions to change jobs if current work conditions do not meet developers' needs. This highlights the need for software organizations to adapt to various work arrangements to remain competitive employers. Building upon our findings and the existing literature, we introduce the Integrated Job Demands-Resources and Self-Determination (IJARS) Model as a comprehensive framework to explain the well-being and productivity of software engineers during the COVID-19 pandemic.</p>
Data from: Maps made with smartphones highlight lower noise pollution during COVID-19 pandemic lockdown at four locations in Boston
<p>Noise pollution in cities has major negative effects on the health of both humans and wildlife. Using iPhones, we collected sound-level data at hundreds of locations in four areas of Boston, Massachusetts (USA) before, during, and after the fall 2020 pandemic lockdown, during which most people were required to remain at home. These spatially dispersed measurements allowed us to make detailed maps of noise pollution that are not possible when using standard fixed sound equipment. The four sites were: the Boston University campus (which sits between two highways), the Fenway/Longwood area (which includes an urban park and several hospitals), Harvard Square (home of Harvard University), and East Boston (a residential area near Logan Airport). Across all four sites, sound levels averaged 6.4 dB lower during the pandemic lockdown than after. Fewer high noise measurements occurred during lockdown as well. The resulting sound maps highlight noisy locations such as traffic intersections and quiet locations such as parks. This project demonstrates that changes in human activity can reduce noise pollution and that simple smartphone technology can be used to make highly detailed maps of noise pollution that identify sources of high sound levels potentially harmful to humans in urban environments.</p>
Mental health, physical health, training load and subjective performance during the COVID-19 pandemic – a Swiss elite athletes' cohort study
<p>Dataset of Swiss elite athletes (n=203) participating in a repeated online survey evaluating mental and physical health factors, as well as training and performance related metrics. After the first survey during the first lockdown between April and May 2020, there were monthly follow-up surveys over a 6-month period.</p>
Viral Communication: Longitudinal Survey Data on the Social Dimensions of the COVID-19 Pandemic
<p>This dataset represents the anonymised data collected as part of the Viral Communication (Understand-ELSED) project, which focussed on the social and ethical dimensions of the COVID-19 pandemic in Germany. It includes the three measurements; Phase I (30 October 2020 and 14 December 2020), Phase II (2 March 2021 and 22 March 2021) and Phase III.</p> <p>The first phase built the foundation for the wider suite of data collection approaches and research methods used in the Viral Communication project by allowing respondents to opt-in to multiple research pathways.</p> <p>Overall sample frame (Phase I): <em>N </em>= 1480</p> <p>Phase II sample frame: <em>N </em>= 482</p> <p>Phase III sample frame: <em>N </em>= 426</p> <p>Computed variables such as weights, groupings (experimental set-ups), and composite scores are included in the dataset.</p>
Supplementary Data for "Sequencing the Pandemic: Rapid and High-Throughput Processing and Analysis of COVID-19 Clinical Samples for 21st Century Public Health"
<p>Supplementary material for F1000 methods manuscript. Includes raw sequencing metrics for two COVID sequencing methodologies, as well as a complete cost breakdown for each methodology.</p>
Datasets for "Effects of the COVID-19 Pandemic on Authors and Reviewers of American Geophysical Union Journals"
<p>These files include summary data on demographics of people submitting journal articles and reviewing manuscripts submitted to American Geophysical Union (AGU) journals. The date range is January 2018 through February 2021, divided by two years before the pandemic and during the pandemic ("year grouping"): March 2018- February 2019; March 2019- February 2020, and March 2020- February 2021 ("year of the COVID-19 pandemic"). Author-related files only include demographics of the submitting, or "corresponding" author. These datasets supplement the under-review manuscript and pre-print submission to ESSOAr (Earth and Space Science Open Archive) "Effects of the COVID-19 Pandemic on Authors and Reviewers of American Geophysical Union Journals."</p> <p> </p>
Entrepreneurial Leadership toward Global Management of COVID-19, Is it always being used during Pandemic? A Bibliometric Study
<p>Entrepreneurial Leadership toward Global Management of COVID-19, Is it always being used during Pandemic? A Bibliometric Study entitled manuscript all of the metadata had taken from Scopus. </p>
The impact of stress and its influencing factors among dentists during the COVID-19 pandemic in Kingdom of Bahrain
<p><strong><span>Background:</span></strong><span> It is well known that all medical professions are linked to work stress, including dentistry, which is seen as facing higher risk due to the nature of the job, especially the working conditions. </span></p> <p><span><strong>Objective:</strong> This study aimed to assess the impact of stress and its influencing factors among dentists during the COVID-19 pandemic in Bahrain.</span></p> <p><strong><span>Methods</span></strong><span><strong>:</strong> A cross-sectional survey was designed to assess the impact of stress and its influencing factors among Bahraini dentists. A total of 306 participants were randomly selected from 1489 registered professionals in the NHRA (National Health Regulatory Authority Bahrain). In addition, an online survey was used to minimise face-to-face communication as well as to accommodate dental practitioners who work in private and government hospitals in Bahrain and a convenient sample of dentists was requested to participate in this study.</span></p> <p><strong><span>Results</span></strong><span><strong>:</strong> Out of 306 participants invited in the survey, only 253 responded, which was adequate for the study. Overall, the participants have reported moderate stress. All the variables of the study showed different effects, but the highest stressor with a strong correlation was "fear of social isolation "(FI) at the significance level of 0.01 (β= 0.393, t= 5.090, p < 0.05= (0.000) with </span> <span>= 0.201 above 0.15 and less than 0.35 which was considered as a moderate effect size approximately (20%), which strongly supported the hypothesis that researchers have proposed. Overall, the total effect for all stressors were (30 %) which was considered as a moderate effect size. All hypotheses were supported except BCP -> OUTCOME due to insufficient evidence at the insignificant level of 0.01 (β= -0.184, t=1.560, p > 0.05 = (0.060). whereas the R² values of independent variables were above 95% for the variance of dentists' outcome, which is considered an excellent fit to the data as evidenced by the squared multiple correlations (</span> <span>) values for the dependent variables.</span></p> <p><span><strong>Conclusions:</strong> </span><span>The study is unique based on its findings that reveal the impact of stress among dentists. Moreover, the results of this study may serve as guidance for future monitoring of dental practitioners' burnout, anxiety, and workload. Furthermore, it may provide supports in different aspects.</span></p>
Spatiotemporal variations of air pollution during the COVID-19 pandemic across Tehran, Iran: Commonalities with and differ-ences from global trends
<p>Figure S1: Green space and green area per capita across Tehran; Figure S2: Temporal distribution of CO content at each station, gray rectangular shows strict social distancing time. Figure S3: Temporal distribution of NO2 content in all investigated stations, gray rectangular shows strict social distancing time; Figure S4: Temporal distribution of PM10 content in all investigated stations gray rectangular shows strict social distancing time; Figure S5: Temporal distribution of O3 content in all investigated stations, gray rectangular shows strict social distancing time; Figure S6: Temporal distribution of SO2 content in all investigated stations, gray rectangular shows strict social distancing time; Figure S7: Temporal distribution of AQI indices in all investigated stations, gray rectangular shows strict social distancing time. </p>
Replication Archive for "Teen Social Interactions and Well-being during the COVID-19 Pandemic"
<p><span>This archive includes the Stata code to replicate all results in the referenced paper.</span></p>
COVID-19 pandemic impact on student achievements
<div>The COVID-19 pandemic created a natural experiment for comparisons in performance during in-person versus synchronous online and hybrid learning mode. We tracked changes in student achievements across the first two years of their engineering studies. The inquiry was conducted on 787 students.</div> <div> </div> <div><strong>Variable information</strong></div> <ol> <li>group (year of study commencement): 0 - 2019/2020, 1 - 2020/2021, 2 - 2021/2022</li> <li>nation: 1 - native (Poland), 0 - non-native</li> <li>gender: 1 - male, 0 - female</li> <li>studyoption: 1 - full-time studies, 0 - part-time studies</li> <li> <div>ICS - Introduction to Computer Science test results, test included 20 questions worth 20 points </div> <div>ICS is the first semester course</div> </li> <li> <div>NAA - Numerical Analysis Algorithms test results, test included 20 questions worth 20 points</div> <div>NAA is the third semester course</div> </li> <li>firstyear: the year in which the data were collected from the first semester course (ICS)</li> <li>secondyear: the year in which the data were collected from the third semester course (NAA)</li> </ol>
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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)
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DANDI Archive for NWB datasets
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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.