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9,674 results for “COVID-19”
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>
Why has the number of COVID-19 confirmed cases in Africa been insignificant compared to other regions? A descriptive analysis
<p>Method</p> <p>The dataset contains several confirmed COVID-19 cases, number of deaths, and death rate in six regions. The objective of the study is to compare the number of confirmed cases in Africa to other regions. </p> <p>Death rate = Total number of deaths from COVID-19 divided by the Total Number of infected patients.</p> <p>The study provides evidence for the country-level in six regions by the World Health Organisation's classification.</p> <p>Findings</p> <p>Based on the descriptive data provided above, we conclude that the lack of tourism is one of the key reasons why COVID-19 reported cases are low in Africa compared to other regions. We also justified this claim by providing evidence from the economic freedom index, which indicates that the vast majority of African countries recorded a low index for a business environment. On the other hand, we conclude that the death rate is higher in the African region compared to other regions. This points to issues concerning health-care expenditure, low capacity for testing for COVID-19, and poor infrastructure in the region.</p> <p>Apart from COVID-19, there are significant pre-existing diseases, namely; Malaria, Flu, HIV/AIDS, and Ebola in the continent. This study, therefore, invites the leaders to invest massively in the health-care system, infrastructure, and human capital in order to provide a sustainable environment for today and future generations. Lastly, policy uncertainty has been a major issue in determining a sustainable development goal on the continent. This uncertainty has differentiated Africa to other regions in terms of stepping up in the time of global crisis.</p> <p> </p>
Files and code for English dictionaries, gold and silver standard corpora for biomedical natural language processing related to SARS-CoV-2 and COVID-19
<p><span lang="EN-GB">Automated information extraction with natural language processing (NLP) tools is required to gain systematic insights from the large number of COVID-19 publications, reports and social media posts, which far exceed human processing capabilities. </span></p> <p><span lang="EN-GB">Here we present an NLP toolbox comprising COVID-19-related dictionaries and annotated corpora in English as well as useful code and workflows for their update and use. The dictionaries contain terms referring to the COVID-19 disease, the SARS-CoV-2 virus, its variants and common mutations, respectively. They were used together with the EasyNER NLP tool to extract and annotate all 764 398 abstracts in the CORD-19 dataset, creating a very large silver standard corpus (named Lund-Annotated-CORD-19 corpus). This was complemented with a small gold standard corpus consisting of PubMed abstracts manually annotated for key entity classes such as disease, virus, symptom, protein/gene, cell type, chemical and species terms. </span></p> <p><span lang="EN-GB">The toolbox can support various text analysis tasks related to COVID-19 such as named entity recognition and co-mention analysis. A preliminary version of the toolbox, which was released early in the pandemic, was</span><span lang="EN-GB"> for example already used to create a COVID-19 knowledge graph and study the evolution and variation of COVID-19-related terminology. In addition, the toolbox can be applied in the development of other NLP tools, for example to train and evaluate large language models.</span></p> <p><span lang="EN-GB">When using the toolbox, please cite this record and the associated article.</span></p> <p> </p> <p> </p>
TweetC19SR-Eng - Manually annotated dataset of English language COVID-19 tweets containing self-reports of symptoms
<p><strong>In this work, we release two expert curated, manually annotated datasets of COVID-19 self-reported symptoms. The first dataset contains tweets in English and the second contains tweets in Spanish, both containing around 36,500 tweets in total. These datasets were used for the Sixth and Seventh Workshop on Social Media Mining For Health (2021 and 2022)</strong></p>
TweetC19SR-Spa - Manually annotated dataset of Spanish language COVID-19 tweets containing self-reports of symptoms
<p><strong>In this work, we release two expert curated, manually annotated datasets of COVID-19 self-reported symptoms. The first dataset contains tweets in English and the second contains tweets in Spanish, both containing around 36,500 tweets in total. These datasets were used for the Sixth and Seventh Workshop on Social Media Mining For Health (2021 and 2022)</strong></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>
Age-adjusted Covid-19 mortality rates for Brazilian municipalities
<p>This dataset present Covid-19 crude and age-adjusted mortality rates for Brazilian municipalities from 2020 to 2022 per epidemiological week, on 100,000 inhabitants base. </p><p>The mortality data source is the "Sistema de Informações de Mortalidade -- SIM", available at https://opendatasus.saude.gov.br/dataset/sim .</p><p>The population reference is the Brazilian age-structure at 2020.</p><p>Notebook with method and code: https://rfsaldanha.github.io/posts/std_br_covid_rates.html</p>
Transformation of social relationships in COVID-19 America: Remote communication may amplify political echo chambers
<p>The COVID-19 pandemic, with millions of Americans compelled to stay home and work remotely, presented an opportunity to explore the dynamics of social relationships in a predominantly remote world. Using the 1972-2022 General Social Surveys, we found that the pandemic significantly disrupted the patterns of social gatherings with family, friends, and neighbors, but only momentarily. Drawing from the nationwide ego-network surveys of 41,033 Americans from 2020 to 2022, we found that the size and composition of core networks remained stable, though political homophily increased among non-kin relationships compared to previous surveys between 1985 and 2016. Critically, heightened remote communication during the initial phase of the pandemic was associated with increased interaction with the same partisans, though political homophily decreased during the later phase of the pandemic when in-person contacts increased. These results underscore the crucial role of social institutions and social gatherings in promoting spontaneous encounters with diverse political backgrounds.</p>
Short and long term impacts of Covid-19 on Older childreN's healTh-Related behAviours, learning and wellbeing STudy (CONTRAST) dataset
<p>The CONTRAST study explored how the Covid-19 (lockdown) restrictions affected lives of older children in the UK, particularly how they have influenced learning, eating, physical and other activities and wellbeing.</p>
Data and code for: The centrality of the Huanan market among early COVID-19 cases is robust to fundamental misconceptions about epidemiology and misrepresentations of Worobey et al. (2022)
<p>Data and R code for <br>The centrality of the Huanan market among early COVID-19 cases is robust to fundamental misconceptions about epidemiology and misrepresentations of Worobey et al. (2022)</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>
BioPropaPhenKG Towards Monkeypox and COVID-19 Case Tracing and Analysing
<p>This repository contains:</p> <ul> <li>The BioPropaPhen ontology created from PropaPhen, being specialized with UMLS and World Knowledge Graph ontologies;</li> <li>A neo4j 4.4.3 dump file of the BioPropaPhenKG knowledge graph with WHO ground truth data about COVID-19 and Monkeypox, and enhanced presence edges between UMLS entities to World KG entities for evaluating the<a href="https://github.com/Gabriel382/DDPF-Health-Risks"> Description-Detection-Prediction Framework </a></li> </ul> <p>The datasets used for enhancing the KG are:</p> <table> <tbody> <tr> <td>Phenomenon</td> <td>Dataset</td> <td>Period</td> <td>Documents</td> <td>Source</td> <td>Link</td> </tr> <tr> <td>COVID-19</td> <td>Aylien</td> <td>Nov-2019</td> <td>8</td> <td>Online News</td> <td>ttps://aylien.com/resources/datasets/coronavirus-dataset</td> </tr> <tr> <td>COVID-19</td> <td>CORD-19</td> <td>Dec-2019</td> <td>720</td> <td>Medical Articles</td> <td>https://allenai.org/data/cord-19</td> </tr> <tr> <td>COVID-19</td> <td>RedditCOVID</td> <td>Feb-2020</td> <td>4,980</td> <td>Social Media</td> <td>https://paperswithcode.com/dataset/the-reddit-covid-dataset</td> </tr> <tr> <td>Monkeypox</td> <td>Mined from BBC</td> <td>May-2022</td> <td>27</td> <td>Online News</td> <td> </td> </tr> <tr> <td>Monkeypox</td> <td>Mined from Pubmed</td> <td>June-2022</td> <td>36</td> <td>Medical Articles</td> <td> </td> </tr> <tr> <td>Monkeypox</td> <td>MonkeyPox2022</td> <td>May-2022</td> <td>33,826</td> <td>Social Media</td> <td>https://doi.org/10.3390/idr14060087</td> </tr> </tbody> </table>
THE ROLE OF HOMOCYSTEINE AS A BIOMARKER OF CYTOKINE STORM IN IMMUNOONCOLOGY AND THERAPY OF COVID-19
<p>Homocysteine is a sulfur-containing amino acid that can act as an important biomarker of inflammatory processes, including cytokine storm, observed in various pathological conditions such as cancer and COVID-19. In the conditions of the cytokine storm characteristic of severe forms of COVID-19 and progressive stages of cancer, elevated homocysteine levels can increase oxidative stress and inflammatory reactions, which, in turn, exacerbates tissue and organ damage. Studies show that homocysteine can play a significant role in the pathogenesis of these conditions, affecting the vascular and immune systems. Thus, monitoring of homocysteine levels is important for the diagnosis, prognosis and development of new therapeutic strategies in the field of immuno-oncology and treatment of COVID-19.</p>
Lex-Atlas:Covid-19 Emergency Powers Dataset
<p>Data on the use of emergency powers used to handle the Covid-19 pandemic mined from country reports published by the Lex-Atlas: Covid-19 project and the Oxford University Press. For more information see https://lexatlas-c19.org. </p> <p><span>The coding proceeded in two phases. A first phase was conducted between 2020 and 2023 on a set of 38 reports (37 Countries plus the European Union) that were produced in the initial stages of the project. A second phase was conducted in 2024, on an additional set of 13 reports that were published in the meantime. Both datasets were published but the final ones supecede the earlier ones and have made some amendments to previous codings. </span></p>
Dataset Saminbot un asistente virtual para recolección de datos en tiempos de pandemia del COVID-19 de la ciudad de Cusco-Perú
<p>El presente <em>dataset </em>fue recopilado a través de un asistente virtual denominado SaminBot. SaminBot es un Chatbot para recolectar datos en las áreas de salud, economía y educación en la región del Cusco-Perú durante la pandemia ocasionada por el COVID-19. Los archivos publicados son datos brutos, la recolección de datos fue llevada a cabo por medio de cuestionarios (los cuestionarios también se encuentran adjuntos) validados por especialistas y personalizados para los usuarios en cada área respetando la privacidad de los usuarios. Éstos datos fueron recopilados mediante diferentes servicios de mensajería instantánea, como Whatsapp, Facebook Messenger y página web. Fueron recolectados 1586 registros para las tres áreas desde Enero hasta Junio del 2021 mediante una campaña orgánica es decir campaña realizada a través de redes sociales por los autores y una campaña pagada al servicio de red social Facebook de dos semanas. La campaña organica no tuvo éxito ya que llegó a muy pocos usuarios mientras la campaña pagada pudo llegar a una mayor cantidad de usuarios.</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>
Assessment of Potential Risk Factors for COVID-19 among Health Care Workers in a Health Care Setting in Delhi, India
<p><strong><em>Executive summary</em></strong></p> <p>The novel coronavirus SARS-CoV2 (COVID-19), first detected by Wuhan Municipal Health Commission, China, in Wuhan, Hubei Province in December 2020 and eventually the disease became pandemic. It was declared as Public Health Emergency of International Concern (PHEIC) by WHO in January 2020. The COVID-19 disease primarily spreads through droplets of saliva or discharge from the nose when an infected person coughs or sneezes. People infected with the COVID-19 virus experiences mild, moderate or serious respiratory illness. </p> <p>Health workers play a critical role in the clinical management of patients with COVID-19 and hence are likely to be the most vulnerable for contracting the disease. Therefore, investigating the extent of infection in health care settings and identifying risk factors for infection among health workers along with follow-up within a facility in which a confirmed case of COVID-19 infection is receiving care can provide useful information on virus transmissibility and routes of transmission, and will bear important step in limiting amplification events in health care facilities. </p> <p> </p> <p><strong>Objectives:</strong></p> <p>1. To find out the extent of human-to-human transmission of the SARS-CoV-2 infection among health workers<br> 2. To study the clinical presentations of COVID-19 infection and the risk factors for infection among health workers.<br> 3 To evaluate the effectiveness of infection prevention and control measures among health workers in protecting against COVID-19.<br> 4. To evaluate the effectiveness of infection prevention and control programmes at health facility level <br> 5. To determine the serological response of health workers with symptomatic and possibly asymptomatic COVID-19 infection.</p> <p> </p> <p><strong>Materials and Methods:</strong></p> <p>This was a prospective cohort study conducted over a period of seven months, from December 2020 to June 2021, the period covering India’s deadly second wave of COVID-19 pandemic. This was done among the health care workers working in HIMSR & HAHC hospital, a tertiary health care setting (Dedicated COVID-19 Hospital) providing care to patients with a laboratory-confirmed COVID-19 infection. This hospital located in South East Delhi has 200 bedded COVID-19 Care Hospital and 1050 registered healthcare workers who come in contact with COVID-19-infected persons. The study population (sampling frame) included all the health personnel like doctors, nurses, paramedical staff, housekeeping staff, security staff, students of medical, nursing and paramedical sciences and other front office staff who come in contact with the patients. In this study, the first visit / interview (Baseline) was done when the staff came in contact with a confirmed COVID-19 case. The second visit / interview (Endline) was done between 22-28 days. During each of these two visits, biological sample in the form of serum was collected to check the presence of anti-COVID-19 antibodies</p> <p> </p> <p><strong>Results:</strong></p> <p>A total of 192 HCW were recruited in this study. All of them were interviewed and blood was collected for serology at the baseline visit as well as at endline. Out of 192 participants, 119 (61.97%) were detected with SARS-CoV2 antibodies at baseline whereas 73 (38.02%) were seronegative. Again, on22-28 days of follow-up, the seropositivity was 77.7% at the endline. We found that seropositivity was significantly and negatively associated with doctor as profession [OR:0.353, CI:0.176-0.710], COVID-19 symptoms [OR:0.210, CI:0.054-0.820], comorbidities [OR:0.139 , CI: 0.029 - 0.674], recent IPC Training [OR:0.250, CI:0.072 -0.864] , while positively associated with Partially [OR:3.303,CI: 1.256-8.685], as well as fully Vaccinated for COVID-19 [OR:2.428, CI:1.118-5.271]. We also observed seroconversion among 36.7% while 64.0% had increase in titre of antibodies during our follow-up period. The seroconversion was 63.2% in doctors, 42.9% in nurses and 13.0% in paramedics staff. Seroconversion was positively associated with doctor as profession [OR:11.43, CI:2.47 - 52.79] and with partially, as well as fully vaccinated for COVID-19 [OR: 32.63, CI: 5.11 - 208.49]. None of the HCW who were smokers and with any comorbidity did not found to have been seroconversion. We observe a negative and significant relationship of increase in titre of antibodies with recent any ILI symptoms [OR:0.17, 0.13 - 0.94], smokers[OR: 0.35, 95%CI: 0.13 - 0.94], HCW with comorbidities [OR:0.08,95CI: 0.01 - 0.71],, recent full IPC Training [OR:0.07, CI:0.01 -0.63] , while positively associated with partially [OR: 7.87, 95CI: 2.18 - 28.40)], as well as fully Vaccinated for COVID-19 [OR: 3.59, 95CI: 1.46 - 8.87]. Majority of the health care worker enrolled in our study had close contact exposure with COVID-19 patients while 5 had indirect exposure. It was observed that almost all (100% in both) doctors and nurses as well as almost all paramedical staff (99%) were wearing some kind of personal protective equipment (PPE) when they were exposed to a COVID-19 patient. We did not found adherences to any of the infection prevention measure adopted by the enrolled HCW during the recent contact with COVID-19 patients to be significantly associated with seroconversion. </p> <p><strong>Conclusion:</strong></p> <p>Majority of the health care worker (67% doctor, 80% nurses & 55% paramedics) enrolled in our study had close contact exposure with COVID-19 patient. The results show that among 192 HCW enrolled, 62% were seropositive at the baseline. At end line the seropositivity was increased to 77.7%. The seroconversion rate was also studied. It was found to be 36.7% in our study population (63.2% in doctors, 42.9% in nurses and 13.0% in paramedic’s staff.). Adherence to the recommended IPC measures was reported by most participants. About two third (63%) of the HCW in our study were not vaccinated against COVID-19; nurses and paramedics were higher in proportion among those who were unvaccinated. Fifteen percentage were partially vaccinated and 22% were fully vaccinated against COVID-19, with doctors comprising majority among them. We also found that vaccination had the strongest association with seropositivity, seroconversion as well as serial rise of titre.</p>
Italian IT Initiatives against COVID-19 (Dataset)
<p>This dataset contains the descriptions of 128 initiatives of the Italian computer science and engineer community against the COVID-19 pandemic, obtained from a survey carried out in May 2020, during the first pandemic wave in Italy, by the Covid19-IT Task Force established by CINI (National Interuniversity Consortium for Informatics).</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.