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9,674 results for “COVID-19”
Replication Package: Pandemic Startup Software Engineering: An Experience Report on the Development of a COVID-19 Certificate Verification System
<p><strong>Welcome to the public repository for the additional content of the paper "Pandemic Startup Software Engineering: An Experience Report on the Development of a COVID-19 Certificate Verification System" (Journal of Systems and Software)<br></strong></p> <p>This repository provides additional information to the experience report, including the following files:</p> <ul> <li>survey_questions_de.txt: sheet containing the online questionnaire in German (original language)</li> <li>survey_questions_en.txt: sheet containing the online questionnaire translated into English</li> <li>survey_answers_original.csv: sheet containing the extracted questionnaire data of the participants in German (original language)</li> <li>survey_analysis.csv: sheet containing the analysis of the extracted questionnaire data in English</li> </ul>
Prediction of COVID-19 case numbers using state-space modeling and wastewater virus datasets
Open the record for dataset details and reuse information.
Data and code from "COVID-19 lockdown effects on adolescent brain structure suggest accelerated maturation that is more pronounced in females than in males"
<p>Data and code used to perform analyses published in the research article "COVID-19 lockdown effects on adolescent brain structure suggest accelerated maturation that is more pronounced in females than in males" which is currently in press in the Proceedings of the National Academy of Sciences, USA. (https://doi.org/10.1073/pnas.2403200121).</p>
APNet, an explainable sparse deep learning model to discover differentially active drivers of severe COVID-19
<p><i><strong>Motivation:</strong></i> Computational analyses of plasma proteomics provide translational insights into complex diseases such as COVID-19 by revealing molecules, cellular phenotypes, and signaling patterns that contribute to unfavorable clinical outcomes. Current in silico approaches dovetail differential expression, biostatistics, and machine learning, but often overlook nonlinear proteomic dynamics, like post-translational modifications, and provide limited biological interpretability beyond feature ranking.</p><p><i><strong>Results:</strong></i> We introduce APNet, a novel computational pipeline that combines differential activity analysis based on SJARACNe co-expression networks with PASNet, a biologically-informed sparse deep learning model to perform explainable predictions for COVID-19 severity. Co-expression and classification weights are ingested by the APNet driver-pathway network to aid result interpretation and hypothesis generation. APNet outperforms alternative models in patient classification across three COVID-19 proteomic datasets, identifying predictive drivers and pathways, including some confirmed by single-cell omics and highlighting under-explored biomarker circuitries in COVID-19.</p><p><i><strong>Availability and Implementation:</strong></i></p><p> APNet's R, Python scripts and Cytoscape methodologies are available at </p><p><a href="https://github.com/BiodataAnalysisGroup/APNet">https://github.com/BiodataAnalysisGroup/APNet</a></p>
COVID-19 evidence syntheses with artificial intelligence: an empirical study of systematic reviews
<p><strong>Objectives</strong>: A rapidly developing scenario like a pandemic requires the prompt production of high-quality systematic reviews, which can be automated using artificial intelligence (AI) techniques. We evaluated the application of AI tools in COVID-19 evidence syntheses.</p> <p><strong>Study design</strong>: After prospective registration of the review protocol, we automated the download of all open-access COVID-19 systematic reviews in the COVID-19 Living Overview of Evidence database, indexed them for AI-related keywords, and located those that used AI tools. We compared their journals' JCR Impact Factor, citations per month, screening workloads, completion times (from pre-registration to preprint or submission to a journal) and AMSTAR-2 methodology assessments (maximum score 13 points) with a set of publication date matched control reviews without AI.</p> <p><strong>Results</strong>: Of the 3999 COVID-19 reviews, 28 (0.7%, 95% CI 0.47-1.03%) made use of AI. On average, compared to controls (n=64), AI reviews were published in journals with higher Impact Factors (median 8.9 vs 3.5, P<0.001), and screened more abstracts per author (302.2 vs 140.3, P=0.009) and per included study (189.0 vs 365.8, P<0.001) while inspecting less full texts per author (5.3 vs 14.0, P=0.005). No differences were found in citation counts (0.5 vs 0.6, P=0.600), inspected full texts per included study (3.8 vs 3.4, P=0.481), completion times (74.0 vs 123.0, P=0.205) or AMSTAR-2 (7.5 vs 6.3, P=0.119).</p> <p><strong>Conclusion</strong>: AI was an underutilized tool in COVID-19 systematic reviews. Its usage, compared to reviews without AI, was associated with more efficient screening of literature and higher publication impact. There is scope for the application of AI in automating systematic reviews.</p>
Dataset: Ambient BTEX concentrations during the COVID-19 lockdown in a peri-urban environment (Orléans, France)
<p>The dataset of the manuscript "Ambient BTEX concentrations during the COVID-19 lockdown in a peri-urban environment (Orléans, France)" are presented here</p>
The Role of Ethnic Enclaves in the Labour Market During the Covid-19 Crisis: An Instrumental Variable Approach
<p>Replication package for "The Role of Ethnic Enclaves in the Labour Market During the Covid-19 Crisis: An Instrumental Variable Approach"</p>
The Influence of cultural and psychological factors on mental health status during COVID-19 in Saudi Arabia
<p>The data supporting the findings of the article is available here to be published in the open psychology journal.</p> <p> </p> <p>the data set is excel file generated from google form</p>
Population-based age-stratified seroepidemiological investigation protocol for coronavirus 2019 (COVID-19) infection in the Federation of Bosnia and Herzegovina
<p>Results of population-based age stratified seroepidemiological investigation in the Federation of Bosnia and Herzegovina</p>
A NATIONAL STUDY OF SEROPREVALENCE OF COVID-19 INFECTION IN THE POPULATION OF THE REPUBLIKA SRPSKA
<p>Results of population-based age stratified seroepidemiological investigation in the Republika Srpska.</p>
Work Life Balance dan Produktivitas Kerja Dosen PTKIN Selama Work From Home di Era Pandemik Covid-19
<p>Naskah ini merupakan hasil penelitian tentang work life balance dan produktifitas kerja dosen pada Perguruan Tinggi Keagamaan Islam Negeri (PTKIN) di Indonesia, terutama di masa pandemi covid-19. Jenis penelitian yang digunakan adalah kualitatif dengan pendekatan deskriptif. Proses pengumpulan data dilakukan melalui wawancara terstruktur dengan menggunakan google form.</p>
COVID-19 contact rates between mobile devices in Connecticut
<p>Close contact between people is the primary route for transmission of SARS-CoV-2, the virus that causes coronavirus disease 2019 (COVID-19). We sought to quantify interpersonal contact at the population-level by using mobile device geolocation data. We computed the frequency of contact (within six feet) between people in Connecticut during February 2020 - January 2021 and aggregated counts of contact events by area of residence. When incorporated into a SEIR-type model of COVID-19 transmission, the contact rate accurately predicted COVID-19 cases in Connecticut towns. Contact in Connecticut explains the large initial wave of infections during March–April, the drop in cases during June–August, local outbreaks during August–September, broad statewide resurgence during September–December, and decline in January 2021. The transmission model exhibits a better fit to COVID-19 transmission dynamics using the contact rate than other mobility metrics. Contact rate data can help guide social distancing and testing resource allocation.</p>
A detailed ultrastructural examination of lung cryobiopsy samples from a COVID-19 patient case series – Data set 12
<p>We investigated six cryobiopsy samples from six deceased patients (patients C03 to C08 from Barisione et al. 2020 <a href="https://doi.org/10.1007/s00428-020-02934-1">doi.org/10.1007/s00428-020-02934-1</a>) by using thin section electron microscopy (Cortese et al. 2022 <a href="http://doi.org/10.1007/s00428-022-03308-5">doi.org/10.1007/s00428-022-03308-5</a>). A detailed description of the methods and the data set is provided in the download container.</p> <p>Data set 12 contains stitched image montages of thin sections (selected areas) through the lung of patients C04 and C07 which were acquired by scanning electron microscopy. The images show alveolae with various degree of epithelial damage.</p>
A detailed ultrastructural examination of lung cryobiopsy samples from a COVID-19 patient case series – Data set 11
<p>We investigated six cryobiopsy samples from six deceased patients (patients C03 to C08 from Barisione et al. 2020 <a href="https://doi.org/10.1007/s00428-020-02934-1">doi.org/10.1007/s00428-020-02934-1</a>) by using thin section electron microscopy (Cortese et al. 2022 <a href="http://doi.org/10.1007/s00428-022-03308-5">doi.org/10.1007/s00428-022-03308-5</a>). A detailed description of the methods and the data set is provided in the download container.</p> <p>Data set 11 contains a stitched image montage of a thin section (selected area) through the lung of patient C05 which was acquired by scanning electron microscopy. The image shows a lung area with a dissolved alveolar architecture and a massive type-2-cell hyperplasia.</p>
A detailed ultrastructural examination of lung cryobiopsy samples from a COVID-19 patient case series – Data set 10
<p>We investigated six cryobiopsy samples from six deceased patients (patients C03 to C08 from Barisione et al. 2020 <a href="https://doi.org/10.1007/s00428-020-02934-1">doi.org/10.1007/s00428-020-02934-1</a>) by using thin section electron microscopy (Cortese et al. 2022 <a href="http://doi.org/10.1007/s00428-022-03308-5">doi.org/10.1007/s00428-022-03308-5</a>). A detailed description of the methods and the data set is provided in the download container.</p> <p>Data set 10 contains stitched image montages of thin sections (selected areas) through the lung of patient C08 which were acquired by transmission electron microscopy. Cells, infected with SARS-CoV-2 particles, are shown in overview (A, C) and detail (B, C).</p>
A detailed ultrastructural examination of lung cryobiopsy samples from a COVID-19 patient case series – Data set 14
<p>We investigated six cryobiopsy samples from six deceased patients (patients C03 to C08 from Barisione et al. 2020 <a href="https://doi.org/10.1007/s00428-020-02934-1">doi.org/10.1007/s00428-020-02934-1</a>) by using thin section electron microscopy (Cortese et al. 2022 <a href="http://doi.org/10.1007/s00428-022-03308-5">doi.org/10.1007/s00428-022-03308-5</a>). A detailed description of the methods and the data set is provided in the download container.</p> <p>Data set 14 contains stitched image montages of a thin section through the lung of patient C04 which were acquired by scanning electron microscopy. The file “Data_set_14.tif” contains a montage of the entire thin section while the other files contain selected areas of the section recorded at higher resolution.</p>
A detailed ultrastructural examination of lung cryobiopsy samples from a COVID-19 patient case series – Data set 09
<p>We investigated six cryobiopsy samples from six deceased patients (patients C03 to C08 from Barisione et al. 2020 <a href="https://doi.org/10.1007/s00428-020-02934-1">doi.org/10.1007/s00428-020-02934-1</a>) by using thin section electron microscopy (Cortese et al. 2022 <a href="http://doi.org/10.1007/s00428-022-03308-5">doi.org/10.1007/s00428-022-03308-5</a>). A detailed description of the methods and the data set is provided in the download container.</p> <p>Data set 09 contains stitched image montages of thin sections (selected areas) through the lung of patient C03 which were acquired by scanning electron microscopy (C03_A & C) or transmission electron microscopy (C03_B). The images show accumulation of cells and debris in the alveolar cavity.</p>
A detailed ultrastructural examination of lung cryobiopsy samples from a COVID-19 patient case series – Data set 08
<p>We investigated six cryobiopsy samples from six deceased patients (patients C03 to C08 from Barisione et al. 2020 <a href="https://doi.org/10.1007/s00428-020-02934-1">doi.org/10.1007/s00428-020-02934-1</a>) by using thin section electron microscopy (Cortese et al. 2022 <a href="http://doi.org/10.1007/s00428-022-03308-5">doi.org/10.1007/s00428-022-03308-5</a>). A detailed description of the methods and the data set is provided in the download container.</p> <p>Data set 08 contains stitched image montages of thin sections (selected areas) through the lung of patients C04 and C08 which were acquired by scanning electron microscopy (C04) or transmission electron microscopy (C08_A & B). The images show the pathological changes of the alveolar epithelium: Type-1-cells detachment from the basal membrane (C08_A & B) and type-2-cell hyperplasia (C04).</p>
A detailed ultrastructural examination of lung cryobiopsy samples from a COVID-19 patient case series – Data set 07
<p>We investigated six cryobiopsy samples from six deceased patients (patients C03 to C08 from Barisione et al. 2020 <a href="https://doi.org/10.1007/s00428-020-02934-1">doi.org/10.1007/s00428-020-02934-1</a>) by using thin section electron microscopy (Cortese et al. 2022 <a href="http://doi.org/10.1007/s00428-022-03308-5">doi.org/10.1007/s00428-022-03308-5</a>). A detailed description of the methods and the data set is provided in the download container.</p> <p>Data set 07 contains stitched image montages of thin sections (selected areas) through the lung of patients C04 to C06 which were acquired by scanning electron microscopy. The images show alveolae with different degree of structural modification: Intact alveolar septum (C06); alveolar septum with detached alveolar epithelium (C04); dissolved alveolar organization (C05).</p>
A detailed ultrastructural examination of lung cryobiopsy samples from a COVID-19 patient case series – Data set 06
<p>We investigated six cryobiopsy samples from six deceased patients (patients C03 to C08 from Barisione et al. 2020 <a href="https://doi.org/10.1007/s00428-020-02934-1">doi.org/10.1007/s00428-020-02934-1</a>) by using thin section electron microscopy (Cortese et al. 2022 <a href="http://doi.org/10.1007/s00428-022-03308-5">doi.org/10.1007/s00428-022-03308-5</a>). A detailed description of the methods and the data set is provided in the download container.</p> <p>Data set 06 contains a stitched image montage of the first and of the last semithin section from the analysis of patient C08, which was acquired by bright-field light microscopy.</p>
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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)
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.