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9,674 results for “COVID-19”
HCFMRP COVID-19 & LID (v1)
<p>Dataset designed to characterize lung interstitial diseases (LID) and COVID-19 on chest X-ray. For the composition of the dataset, frontal chest X-ray of patients from the Ribeirão Preto Medical School of University of São Paulo (Brazil) were classified into three groups: healthy (382 images), with LID (308 images), and with COVID-19 (189 images).</p> <p>All images were analyzed by a thoracic radiologist and COVID-19 diagnosis was confirmed by RT-PCR.</p> <p>For each of the groups, three types of files are available, all anonymized: the frontal view of the chest X-ray in DICOM format; the original images converted to PNG format; and the same images in 3-channel PNG format (RGB).</p> <p>The following directory and file structure is presented:</p> <p> <strong>1-Normal:</strong> for healthy cases</p> <ul> <li> <strong>normal-dcm-anonymized:</strong> anonymized DICOM files</li> <li> <strong>normal-png:</strong> image files converted to PNG</li> <li> <strong>normal-png-RGB:</strong> image files converted to 3-channel PNG</li> </ul> <p> <strong>2-LID:</strong> for LID cases</p> <ul> <li> <strong>lid-dcm-anonymized:</strong> anonymized DICOM files</li> <li> <strong> lid-png:</strong> image files converted to PNG</li> <li> <strong>lid-png-RGB:</strong> image files converted to 3-channel PNG</li> </ul> <p> <strong>3-COVID:</strong> for COVID-19 cases</p> <ul> <li> <strong>covid-dcm-anonymized:</strong> anonymized DICOM files</li> <li> <strong>covid-png:</strong> image files converted to PNG</li> <li> <strong>covid-png-RGB:</strong> image files converted to 3-channel PNG</li> </ul> <p>For more information, contact us: https://mainlab.fmrp.usp.br/</p>
Labeled data and models for COVID-19 vaccine related tweets with stance, location, and topics
<p>The dataset contains Tweet IDs along with the location and tweet timestamp. The tweets are labeled based on motivating/demotivating status, stance towards the COVID-19 vaccine, and topic in the tweet text. To comply with Twitter guidelines, we removed the tweet texts and author information. You can use Hydrator API to hydrate the tweets.</p> <p>The repository also contains the machine-learning models for topic modeling, de/motivation classifier, and stance detection from the tweets.</p>
Data and Software Archive for "Likely community transmission of COVID-19 infections between neighboring, persistent hotspots in Ontario, Canada"
<p>This is the Zenodo archive for the manuscript "Likely community transmission of COVID-19 infections between neighboring, persistent hotspots in Ontario, Canada" (Mucaki EJ, Shirley BC and Rogan PK. <em>F1000Research</em> 2021, <strong>10</strong>:1312, DOI: <a href="http://dx.doi.org/10.12688/f1000research.75891.1">10.12688/f1000research.75891.1</a>). This study aimed to produce community-level geo-spatial mapping of patterns and clusters of symptoms, and of confirmed COVID-19 cases, in near real-time in order to support decision-making. This was accomplished by area-to-area geostatistical analysis, space-time integration, and spatial interpolation of COVID-19 positive individuals. This archive will contain data and image files from this study, which were too numerous to be included in the manuscript for this study. It also provides all program files pertaining to the <em>Geostatistical Epidemiology Toolbox </em>(Geostatistical analysis software package to be used in ArcGIS), as well as all other scripts described in this manuscript and other software developed (cluster, outlier, streak identification and pairing)..</p> <p>We also provide a guide which provides a general description of the contents of the four sections in this archive (<em>Documentation_for_Sections_of_Zenodo_Archive.docx</em>). If you have any intent to utilize the data provided in Section 3, we greatly advise you to review this document as it describes the output of all geostatistical analyses performed in this study in detail.</p> <p><strong>Data Files:</strong></p> <p><strong>Section 1. "Section_1.Tables_S1_S7.Figures_S1_S11.zip"</strong></p> <p>This section contains all additional tables and figures described in the manuscript "Likely community transmission of COVID-19 infections between neighboring, persistent hotspots in Ontario, Canada". Additional tables S1 to S7 are presented in an Excel document. These 7 tables provide summary statistics of various geostatistical tests described in the study (“Section 1 – Tables S1-S4”) and lists all identified single and paired high-case cluster streaks (“Section 1 – Tables S5-S7”). This section also contains 11 additional figures referred to in the manuscript (“Section 1 – Figures S1-S11”) both individually and within a Word document which describes them.</p> <p><strong>Section 2. "Section_2.Localized_Hotspot_Lists.zip"</strong></p> <p>All localized hotspots (identified through kriging analysis) were catalogued for each municipality evaluated (Hamilton, Kitchener/Waterloo, London, Ottawa, Toronto, Windsor/Essex). These files indicate the FSA in which the hotspot was identified, the date in which it was identified (utilizing 3-day case data at the postal code level), the amount of cases which occurred within the FSA within these 3 dates, the range of cases interpolated by kriging analysis (between 5-10, 10-15, 15-20, 20-25, 25-30, 30-35, 35-40, 40-50, >50), and whether or not the FSA was deemed a hotspot by Gi* relative to the rest of Ontario on any of the three dates evaluated. Please see Section 4 for map images of these localized hotspots.</p> <p><strong>Section 3. "Section_3.All-Data_Files.Kriging_GiStar_Local_and_GlobalMorans.2020_2021"</strong></p> <p>Section 3 – All output files from the geostatistical tests performed in this study are provided in this section. This includes the output from Ontario-wide FSA-level Gi* and Cluster and Outlier analyses, and PC-level Cluster and Outlier, Spatial Autocorrelation, and kriging analysis of 6 municipal regions. It also includes kriging analysis of 7 other municipal regions adjacent to Toronto (Ajax, Brampton, Markham, Mississauga, Pickering, Richmond Hill and Vaughan). This section also provides data files from our analyses of stratified case data (by age, gender, and at-risk condition). All coordinates presented in these data files are given in “PCS_Lambert_Conformal_Conic” format. Case values between 1-5 were masked (appear as “NA”).</p> <p><strong>Section 4. "Section_4.All_Map_Images_of_Geostat_Analyses.zip"</strong></p> <p>Sets of image files which map the results of our geostatistical analyses onto a map of Ontario or within the municipalities evaluated (Hamilton, Kitchener/Waterloo, London, Ottawa, Toronto, Windsor/Essex) are provided. This includes: Kriging analysis (PC-level), Local Moran's I cluster and outlier analysis (FSA and PC-level), normal and space-time Gi* analysis, and all images for all analyses performed on stratified data (by age, gender and at-risk condition). Kriging contour maps are also included for 7 other municipal regions adjacent to Toronto (Ajax, Brampton, Markham, Mississauga, Pickering, Richmond Hill and Vaughan). </p> <p><strong>Software:</strong></p> <p>This Zenodo archive also provides all program files pertaining to the <em>Geostatistical Epidemiology Toolbox </em>(Geostatistical analysis software package to be used in ArcGIS), as well as all other scripts described in this manuscript. This geostatistical toolbox was developed by CytoGnomix Inc., London ON, Canada and is distributed freely under the terms of the GNU General Public License v3.0. It can be easily modified to accommodate other Canadian provinces and, with some additional effort, other countries. </p> <p>This distribution of the <em>Geostatistical Epidemiology Toolbox </em>does not include postal code (PC) boundary files (which are required for some of the tools included in the toolbox). The PC boundary shapefiles used to test the toolbox were obtained from <a href="https://www.dmtispatial.com/">DMTI</a> (<a href="https://www.google.com/url?q=https://www.dmtispatial.com/canmap/&sa=D&source=hangouts&ust=1637875735980000&usg=AOvVaw2wG3iVnyGyrkTIkN5FQ4NS">https://www.dmtispatial.com/canmap/</a>) through the Scholar's Geoportal at the University of Western Ontario (<a href="http://geo2.scholarsportal.info/">http://geo2.scholarsportal.info/</a>). The distribution of these files (through sharing, sale, donation, transfer, or exchange) is strictly prohibited. However, any equivalent PC boundary shape file should suffice, provided it contains polygon boundaries representing postal code regions (see guide for more details).</p> <p><strong>Software File 1. "Software.GeostatisticalEpidemiologyToolbox.zip"</strong></p> <p>The Geostatistical Epidemiology Toolbox is a set of custom Python-based geoprocessing tools which function as any built-in tool in the ArcGIS system. This toolbox implements data preprocessing, geostatistical analysis and post-processing software developed to evaluate the distribution and progression of COVID-19 cases in Canada. The purpose of developing this toolbox is to allow external users without programming knowledge to utilize the software scripts which generated our analyses and was intended to be used to evaluate Canadian datasets. While the toolbox was developed for evaluating the distribution of COVID-19, it could be utilized for other purposes. </p> <p>The toolbox was developed to evaluate statistically significant distributions of COVID-19 case data at Canadian Forward Sortation Area (FSA) and Postal Code-level in the province of Ontario utilizing geostatistical tools available through the ArcGIS system. These tools include: 1) Standard Gi* analysis (finds areas where cases are significantly spatially clustered), 2) spacetime based Gi* analysis (finds areas where cases are both spatially and temporally clustered), 3) cluster and outlier analysis (determines if high case regions are an regional outlier or part of a case cluster), 4) spatial autocorrelation (determines the cases in a region are clustered overall) and, 5) Empirical Bayesian Kriging analysis (creates contour maps which define the interpolation of COVID-19 cases in measured and unmeasured areas). Post-processing tools are included that import these all of the preceding results into the ArcGIS system and automatically generate PNG images. </p> <p>This archive also includes a guide ("UserManual_GeostatisticalEpidemiologyToolbox_CytoGnomix.pdf") which describes in detail how to set up the toolbox, how to format input case data, and how to use each tool (describing both the relevant input parameters and the structure of the resultant output files).</p> <p><strong>Software File 2: “Software.Additional_Programs_for_Cluster_Outlier_Streak_Idendification_and_Pairing.zip"</strong></p> <p>In the manuscript associated with this archive, Perl scripts were utilized to evaluate postal code-level Cluster and Outlier analysis to identify significantly, highly clustered postal codes over consecutive periods (i.e., high-case cluster “streaks”). The identified streaks are then paired to those in close proximity, based on the neighbors of each postal code from PC centroid data ("paired streaks"). Multinomial logistic regression models were then derived in the R programming language to measure the correlation between the number of cases reported in each paired streak, the interval of time separating each streak, and the physical distance between the two postal codes. Here, we provide the 3 Perl scripts and the R markdown file which perform these tasks:</p> <p><em>“Ontario_City_Closest_Postal_Code_Identification.pl”</em></p> <p>Using an input file with postal code coordinates (by centroid), this program identifies the nearest neighbors to all postal codes for a given municipal region (the name of this region is entered on the command line). Postal code centroids were calculated in ArcGIS using the “Calculate Geometry” function against DMTI postal code boundary files (not provided). Input from other sources could be used, however, as long as the input includes a list of coordinates with a unique label associated with a particular municipality.</p> <p>The output of this program (for the same municipal region being evaluated) is required for the following two Perl scripts:</p> <p><em>“Local_Morans_Analysis.Recurrent_Clustered_PC_Identifier.pl”</em></p> <p>This program uses the output of postal code-level Cluster and Outlier analysis for a municipality (these files are available in a second Zenodo archive: <a href="http://doi.org/10.5281/zenodo.5585812">doi.org/10.5281/zenodo.5585812</a>) and the output from <em>“Ontario_City_Closest_Postal_Code_Identification.pl” </em>(for the same municipal region) as input to identify high-case clustered postal codes that occur consecutively over a course of several dates (referred to as high-case cluster “streaks”). The script allows for a single day in which the PC was either not clustered or did not meet the minimum case count threshold of ≥ 6 cases within the 3-day sliding window (i.e. if clustered for 3 days, then not significant for one, then clustered for 3 more days, it will considered a 7 day streak). This script also lists any neighbors that are also identified to have streaks during these same dates.</p> <p><em>“Local_Morans_Analysis.Clustered_Streak_Pairing_Program.pl”</em></p> <p>This program uses the output from “<em>Local_Morans_Analysis.Recurrent_Clustered_PC_Identifier.pl</em>” to pair streaks that were identified in two closely situated postal codes spatially (requires output from <em>“Ontario_City_Closest_Postal_Code_Identification.pl”)</em>. The output of this script provides the postal codes of the streaks which are paired, describe the interval of each streak (and whether they occur concurrently), the number of cases which occurred during these streaks, and how these streaks are separated (both distance [in meters] and temporally [in days]).</p> <p>"<em>Streak_Analysis_using_Multinomial_Logistic_Regression_Models.Rmd</em>"</p> <p>This R Markdown file contains the code which derived multinomial logistic regression models to describe the relation between the number of COVID-19 case counts, physical distance (in meters), and the time interval between paired streaks (in days). The script then performs a Wald two-tailed z-test to identify which factors are significantly correlated (relative to total case counts between streaks [i.e., the response variable]). The p-values computed from the Wald test are then reported. This script requires the 'multinom' function of the 'nnet' package in R.</p> <p>Two data files in which these models were derived (a list of all consecutive Toronto paired streaks for COVID-19 wave 2 and wave 3) are also included. </p>
ICU post- discharge persistent symptoms, self-reported health and quality of life of COVID-19 survivors: A cohort study.
<p> </p> <p>Understanding the consequences and health impact of COVID-19 survivors discharged from the ICU is still unclear. The aim of this study was to investigated persistent symptoms, health satisfaction and health related quality of life (HRQoL) of patients that were hospitalized due COVID-19 infection after 30, 90 and 180 days from ICU discharge. This is a multicentric prospective cohort study of COVID-19 survivors discharged from 8 hospitals of Curitiba – Paraná (Brazil), between September 2020 and January 2022. Eligible COVID 19 survivors were contacted by phone and invited to answer telephone survey at 30, 90 and 180 days after ICU discharge. They responded a phone questionnaire to collect post-discharge clinical symptoms, and we also asked about health satisfaction and HRQoL. 62 COVID-19 survivors (51,6% males, mean age 50,3 years, median length of ICU stay of 13 days) responded to the telephone survey at 30, 90 and 180 days. The most persistent symptoms were fatigue (65,9%, 51,3%, 44,7%, respectively), mild dyspnea (42%, 31%, 29,8%, respectively) and myalgia (29%, 22,1%, 17%, respectively). Myalgia showed a significant reduction from 30 days to 180 days (p=0,034), and the number of symptoms also reduced significantly (30 to 90 days, <em>p=0.018</em>, 30 to 180 days<em>, p=0.001 </em>). At 30, 90 and 180 follow up days the most patients had reported “good” quality of life (59,7%, 62,9%, 51,6%, respectively), and “satisfied” with health (43,5%, 48,4%, 46,8%, respectively). We found that COVID-19 symptoms persist to 180 days, fatigue more commonly. Nevertheless, in this cohort study, most COVID-19 survivors reported good quality of life and were satisfied with health.</p>
COVID-19 mortality correlation with cloudiness, sunlight, latitude in European countries
<p>"COVID-19 mortality correlation with cloudiness, sunlight, latitude in European countries"</p> <p>Dataset for preprint titled <br> "COVID-19 mortality: positive correlation with cloudiness but no correlation with sunlight and latitude in Europe"<br> https://doi.org/10.1101/2021.01.27.21250658 </p> <p>by SECIL OMER, ADRIAN IFTIME, VICTOR BURCEA</p> <p>Corresponding author: A. Iftime, University of Medicine and Pharmacy "Carol Davila", Biophysics Department, 8 Blvd. Eroii Sanitari, 050474 Bucharest, Romania. Email address: adrian.iftime [at] umfcd.ro.</p> <p> </p> <p>===========<br> Dataset file: <br> 2.0.0.COVID-19_Mortality_Cloudiness_Insolation_EUROPE_March_December_2020.csv</p> <p><br> Dataset graphical preview: <br> 2.0.0.INFOGRAPHIC_CloudFraction_vs_COVID-19_mortality_Europe_March-December_2020.png</p> <p>DATASET:<br> 444 rows (records), with the following fields:</p> <p>"Country" :<br> Country name; 37 European countries included.</p> <p>"Date": <br> Date stamp at the collection time.<br> Data collection was performed in the last day of every month. <br> Date format: YYYY-MM-DD</p> <p>"Month_Key" : <br> Date stamp at the collection time, formatted for easier monthly time series analysis.<br> Date format: YYYY-MM</p> <p>"Month_Fct2020"<br> Date stamp at the collection time,formatted for easier graphing, as a string with names of the months<br> (in English). </p> <p>"Deaths_per_1Mpop" :<br> Monthly mortality from COVID-19 raported in the country, <br> reported as number of COVID-19 deaths per 1 million population of the country, <br> in that particular month / country. <br> NB: it is reported as million population, not patients. </p> <p>"LogDeaths_per_1Mpop" :<br> Log10 transformation of "Deaths_per_1Mpop"</p> <p>"Insolation_Average" :<br> Insolation average (solar irradiance at ground level),<br> in that particular month / country. <br> It is expressed in Watt / square meter of the ground surface. <br> Data derived from data avaialble at NASA Langley Research Center, NASA’s Earth Observatory, <br> CERES / FLASHFlux team, 2020, <br> https://neo.gsfc.nasa.gov/view.php?datasetId=CERES_INSOL_M<br> (old link: https://neo.sci.gsfc.nasa.gov/view.php?datasetId=CERES_INSOL_M )</p> <p>"Cloud_Fraction" :<br> Cloudiness (also known as cloud fraction, cloud cover, cloud amount or sky cover),<br> as decimal fraction of the sky obscured by clouds, <br> in that particular month / country. <br> Data derived from NASA Goddard Space Flight Center, NASA’s Earth Observatory,<br> MODIS Atmosphere Science Team, 2020, <br> https://neo.gsfc.nasa.gov/view.php?datasetId=MODAL2_M_CLD_FR<br> (old link: https://neo.sci.gsfc.nasa.gov/view.php?datasetId=MODAL2_M_CLD_FR )</p> <p>"CENTR_latitude" and<br> "CENTR_longitude" :<br> Latitude and Longitude of the country centroid, for each country. <br> Data derived from Google LLC, "Dataset publishing language: country centroids",<br> https://developers.google.com/public-data/docs/canonical/countries_csv <br> NOTE: This is identical in every month (obviuously); <br> it is redundantly included for easier monthly sectional analysis of the data. </p> <p>===========</p> <p>Versioning of the dataset: <br> MAJOR: changes yearly; 1 = 2020<br> MINOR: changes if new monthly data is added in that particular year. <br> PATCH: Changes only if errors or minor edits were performed. </p> <p><br> ===========<br> CHANGELOG: </p> <p>Version 2.0.0.COVID-19_Mortality_Cloudiness_Insolation_EUROPE_March_December_2020.csv<br> - CERES/FLASHFLUX data for August-December 2020 became available at new links at nasa.gov<br> - These data were gathered, analyzed and introduced in this dataset (2.0.0). <br> - updated links for CERES/FLASHFLUX and MODIS dataset<br> - added DOI link for preprint<br> - minor edits on text. <br> -Dataset file source for this version (internal analysis source file):<br> db_covid_all-ANALYSIS.2020-all-year_versiunea18d.csv</p> <p><br> Version 1.0.0.COVID-19_Mortality_Cloudiness_Insolation_EUROPE_March_August_2020.csv <br> First version<br> Dataset file source for this version (internal analysis source file):<br> db_covid_all-ANALYSIS.2020-09-22_r10.csv</p>
Política, Twitter y Covid-19. La percepción de los turistas extranjeros durante la desescalada en España en la primavera de 2021
<p>This file contains the job database described below. The traditional journalism of the written press of the day and the linear generalist television news have lost their monopoly as generators of public opinion. The communicative and social relevance of social networks is indisputable, even more so in circumstances such as those provoked by the Covid-19 election period for the Community of Madrid. We focus on two issues of great relevance in networks in this period: the image of tourism and policy makers on Twitter and in Spain between March and May 2021. The results of the analyses carried out with computational and corpus tools reveal a very heterogeneous constellation of participants, as well as that the pandemic and its management was, in general, used as an electoral weapon through the arrival of tourism in the middle of the pandemic in the capital. The analyses of key words and their associations show a vision of tourism with a clear electoral intention, where all the general interest was subordinated to the battle for Madrid.</p>
Self-medication for anxiety symptoms in the context of COVID-19, in users who go to a drugstore in Los Olivos, Lima-2021
<p><strong>Background:</strong> To determine the relationship between self-medication and anxiety symptoms in the context of COVID-19, in users who go to a drugstore in Los Olivos, Lima 2021.</p> <p><strong>Methods:</strong> The research method was deductive, basic and with a quantitative approach; the design used was non-experimental, descriptive, correlational, cross-sectional, and prospective. Spearman's Rho analysis was performed to validate the hypothesis.</p> <p><strong>Results:</strong> 384 users were evaluated, finding 93.5% aged 18-59 years, of whom 53.4% were female, 42.7% had completed high school, 57.8% were single and 51.6% presented physical symptoms, preferably muscular tension accompanied by pain, 60.7% presented behavioral symptoms, highlighting unusual sadness in the face of COVID-19 and 70.1% presented cognitive symptoms with greater frequency of concern about contracting COVID-19. In addition, the greater the symptoms of anxiety, the higher the self-medication increased from 9.0% to 21.1%, a similar case was evidenced in self-medication on their own initiative where the increase was from 7.5% to 33.3%; likewise, self-medication without medical prescription increased from 15.8% to 47.7%, the consumption of anxiolytics or antidepressants increased from 0.8% to 26.3% caused by the symptoms of anxiety.</p> <p><strong>Conclusion:</strong> It was determined that there is a moderate relationship between self-medication and anxiety symptoms in the context of COVID-19, in users who go to a drugstore in Los Olivos, Lima 2021.</p> <p><strong>Keywords:</strong> Self-medication, prescription, anxiety, depression, COVID-19.</p> <p><strong>Background:</strong> To determine the relationship between self-medication and anxiety symptoms in the context of COVID-19, in users who go to a drugstore in Los Olivos, Lima 2021.</p> <p><strong>Methods:</strong> The research method was deductive, basic and with a quantitative approach; the design used was non-experimental, descriptive, correlational, cross-sectional, and prospective. Spearman's Rho analysis was performed to validate the hypothesis.</p> <p><strong>Results:</strong> 384 users were evaluated, finding 93.5% aged 18-59 years, of whom 53.4% were female, 42.7% had completed high school, 57.8% were single and 51.6% presented physical symptoms, preferably muscular tension accompanied by pain, 60.7% presented behavioral symptoms, highlighting unusual sadness in the face of COVID-19 and 70.1% presented cognitive symptoms with greater frequency of concern about contracting COVID-19. In addition, the greater the symptoms of anxiety, the higher the self-medication increased from 9.0% to 21.1%, a similar case was evidenced in self-medication on their own initiative where the increase was from 7.5% to 33.3%; likewise, self-medication without medical prescription increased from 15.8% to 47.7%, the consumption of anxiolytics or antidepressants increased from 0.8% to 26.3% caused by the symptoms of anxiety.</p> <p><strong>Conclusion:</strong> It was determined that there is a moderate relationship between self-medication and anxiety symptoms in the context of COVID-19, in users who go to a drugstore in Los Olivos, Lima 2021</p> <p> </p>
COVID-19, physical activity, and health
<p>Dataset related to the project on COVID-19, physical activity, and health</p> <p>This dataset includes:</p> <p><strong>1) A codebook (including the name of the main variables)</strong></p> <p>--> "code_book_covid.xlsx"</p> <p><strong>2) The anonymous data set</strong></p>
Transforming the UK's diagnostics agenda after COVID-19 and grand challenges – Future Blood Testing Landscape report - Prof Dimitris Grammatopoulos (University Hospitals Coventry & Warwickshire, University of Warwick)
<p>This video is the second talk from our two day Future Blood Testing: Challenges & Opportunities Event that took place on the 13/09/2022.</p> <p>Transforming the UK’s diagnostics agenda after COVID-19 and grand challenges – Future Blood Testing Landscape report - Prof Dimitris Grammatopoulos (University Hospitals Coventry & Warwickshire, University of Warwick)</p> <p>Bio: Dimitris Grammatopoulos, PhD, FRCPath, is Professor of Molecular Medicine at Warwick Medical School and Consultant in Clinical Biochemistry and Molecular Diagnostics at the University Hospitals of Coventry and Warwickshire, NHS Trust, United Kingdom. He also leads the Novel Biomarkers theme of the Institute of Precision Diagnostics and Translational Medicine, Pathology-UHCW NHS Trust. where he combines clinical expertise in diagnostic laboratory medicine with a research track-record in application of cutting edge multidiscipline methodologies in routine clinical diagnostics. He received academic and clinical training in Newcastle, Bristol, Johns Hopkins-Baltimore and Warwick. He has expertise in biochemical/molecular diagnosis of many endocrine and metabolic disorders. His translational research interest is focused on stress hormones and homeostatic adaptations of fetal development to maternal disease as well as development of novel -omics based biomarker approaches suitable for precision medicine and better characterisation of patient phenotype. He has experience around use of AI and ML for development and refinement of clinical and diagnostic pathways for complex chronic conditions that are considered as national priorities. Dimitris is the Lead in Diagnostics, Global Health Priorities in Health, University of Warwick.</p> <p>Further details on this event can be found at: https://futurebloodtesting.org/event/13-14-09-2022/</p> <p>This video is an output from the Future Blood Testing Network which is funded by EPSRC under Grant Number EP/W000652/1</p> <p>YouTube Link: https://youtu.be/HiOlRzJPR7Q</p>
SSHOC Training Material Video 3 - Contributing metadata to the COVID-19 collection of the Ethnic and Migrant Minorities (EMM) Survey Registry as a data producer - training video in English, French and Spanish
<p>Contributing metadata to the COVID-19 collection of the Ethnic and Migrant Minorities (EMM) Survey Registry as a data producer</p> <p>A training video targeting COVID-19 survey producers to entice contributions to the COVID-19 collection of the EMM Survey Registry<br> <strong>Target Audience for the video</strong>: Survey producers (academic and non-academic) of COVID-19 surveys with EMM respondents</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>
Gajderowicz, B., Fisher, A., Mago, V.: (preperation) "Graph pruning for identifying COVID-19 misinformation dissemination patterns and indicators on Twitter/X"
<p>This dataset is for the repository <a href="https://github.com/bgajdero/social-graph-analysis-2024">https://github.com/bgajdero/social-graph-analysis-2024</a>.</p>
STATA code to reproduce results in the manuscript "Low birth weight risk during COVID-19: Evidence from a nationwide study in India"
<p>This STATA code will reproduce results in the manuscript "Low birth weight risk during COVID-19: Evidence from a nationwide study in India" The users will have to register and access the data from www.dhsprogram.com to run the analysis code. </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>
FIV model for COVID-19 in People with HIV
<div> <p>Baseline characteristic data for animal groups</p> <p> </p> <p> </p> </div> <div>Output tables from viral_variant_caller (https://github.com/stenglein-lab/viral_variant_caller) and SNPGenie (https://github.com/chasewnelson/SNPGenie) pipeline.</div> <div> </div> <div> </div> <div>Next-generation sequencing reads are available from the NCBI SRA under Bioproject<a href="https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.ncbi.nlm.nih.gov%2Fbioproject%2FPRJNA1129543&data=05%7C02%7Cshoroq.shatnawi%40okstate.edu%7C0ccb2c0f42b847d049c808dc9acaa999%7C2a69c91de8494e34a230cdf8b27e1964%7C0%7C0%7C638555443277564291%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=MDrv0ysURQ6YvOBdyX7kSeTcLp7Hh50BKU6rE%2FvXpfw%3D&reserved=0" rel="noopener noreferrer"> </a>PRJNA1129543: <a href="https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.ncbi.nlm.nih.gov%2Fbioproject%2FPRJNA1129543&data=05%7C02%7Cshoroq.shatnawi%40okstate.edu%7C0ccb2c0f42b847d049c808dc9acaa999%7C2a69c91de8494e34a230cdf8b27e1964%7C0%7C0%7C638555443277573779%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=k8T6f22oMhX66jaQSDKyOzK9khFTyROZNa7JlLdG%2BNY%3D&reserved=0" rel="noopener noreferrer">https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1129543</a></div>
Covid-19 apps in India
<p>Dataset of Covid-19 mobile apps released in India in 2020. I first published this compilation in June 2020. </p> <p>Some are documented further in the article titled "Tracking quarantine, tracing cases, sharing info Can these govt-issued apps help fight Covid-19?" available at <a href="../records/12633530" target="_blank" rel="noopener">https://zenodo.org/records/12633530</a> as published on CitizenMatters.in</p> <p><a href="https://citizenmatters.in/tracking-quarantine-tracing-cases-sharing-info-can-these-govt-issued-apps-help-fight-covid-19/" target="_blank" rel="noopener">https://citizenmatters.in/tracking-quarantine-tracing-cases-sharing-info-can-these-govt-issued-apps-help-fight-covid-19/</a></p> <p>Details of my recorded talk on the topic "Usability and privacy issues in government-issued Covid-19 apps in India" hosted by Hasgeek and Thus Critique, June 2020: <a href="https://hasgeek.com/thus/usability-and-privacy-issues-in-government-issued-covid-19-apps-in-india2/">https://hasgeek.com/thus/usability-and-privacy-issues-in-government-issued-covid-19-apps-in-india2/</a></p> <p>Video of the talk: <a href="https://youtu.be/z2fjZIIbALc">https://youtu.be/z2fjZIIbALc</a></p>
COVID-19 daily situation reports - partial data extracted to .csv
<p><span>Government of Nepal Ministry of Health and Population (MoHP) and World Health Organization’s Country Office in Nepal published daily situation reports monitoring the pandemic activity on a national level. Daily situation reports were published in a PDF format including up-to-date figures on the number of COVID-19 cases, the number of PCR-tests performed at each laboratory as well as the respective number of positive test results. The provided data contains parts of the data from these .pdf reports extracted to a .csv file. The data was extracted during a joint project between MoHP, WHO Country Office Nepal, WHO South East Asia Regional Office, Polytechnique Montréal, University of Amsterdam, and Karlsruhe Institute of Technology. </span></p>
Figura 1 in Mudanças no processo de compra e consumo de alimentos orgânicos durante a pandemia do COVID-19
Figura 1. Resumo dos resultados obtidos no Estudo. Mudanças que ocorreram no processo de compra e consumo dos consumidores de alimentos orgânicos, e nas influências desse processo durante a pandemia do COVID-19.
Comparison of Drug Prescribing Before and During the COVID-19 Pandemic - A Cross-national European Study. Pharmaceuticals Consumption data
<p>Data and code supporting the article:</p> <p><strong>Comparison of Drug Prescribing Before and During the COVID-19 Pandemic - A Cross-national European Study </strong></p> <p>Prescription data from January 2017 to March 2021 for the Czech Republic, Germany, Lithuania, Slovenia, Spain (Catalonia), Sweden, and the United Kingdom (Scotland).<br>Data include the codes of the Anatomical-Therapeutic-Chemical classification (ATC) and corresponding numbers of dispensed defined daily doses (DDDs) and packs, aggregated by country, month and ATC.</p> <p>For more information, see the accompanying document ReadMe.txt.</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.