Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
10,623
datasets available to search
ShareScore release 0.7.1
Dataset results
10,623 results for “COVID”
Dataset - paper: Child eating behaviors, parental feeding practices and food shopping motivations during the COVID-19 lockdown in France
<p>Dataset corresponding to a paper that has been published in Appetite (Philippe K, Chabanet C, Issanchou S, Monnery-Patris S. <em>Child eating behaviors, parental feeding practices and food shopping motivations during the COVID-19 lockdown in France: (How) did they change? </em>Appetite. 2021 Jun 1;161:105132. doi: <strong>10.1016/j.appet.2021.105132</strong>. Epub 2021 Jan 23. PMID: 33493611; PMCID: PMC7825985).</p> <p>The objective of the study was to evaluate possible changes in eating behaviors in children aged 3–12 years, in parental eating and cooking behaviors, in parental feeding practices, and also in parental motivations when shopping for food during the lockdown, compared to the period before the lockdown.</p> <p>Information about the dataset and the corresponding documents can be found in the document "Metadata-paper-COVID.docx".</p>
CT scans of COVID-19 patients
<p>Datasets contain CT scans of COVID-19 patients from Faculty hospital of Královké Vinohrady in DICOM (and TIFF) used in paper <em>Estimation of Covid-19 lungs damage based on computer tomography images analysis</em> presenting the tool is available on F1000reserach DOI: <a href="http://dx.doi.org/10.12688/f1000research.109020.1">10.12688/f1000research.109020.1</a>. The tool sued for the analysis of the dataset is published in Zenodo (<a href="https://doi.org/10.5281/zenodo.5805990">10.5281/zenodo.5805990</a>). Data were anonymized before exporting. Each patient has a folder with a unique ID, subfolder contains TIFF image for reach CT slice, and whenever possible DICOM files are added. All files contain ID and data format in the name. The CT data overview is in CSV for the whole dataset.</p> <p>Contributions:<br> Martin SCHÄTZ: Dataset preparation and couration<br> Olga RUBEŠOVÁ: Data selection and cleaning<br> David GIRSA: Data measuring and selection<br> Katarína NAĎOVA: Data measuring and selection</p> <p>The work was funded by the Ministry of Education, Youth and Sports by grant ‘Development of Advanced Computational Algorithms for evaluating post-surgery rehabilitation’ number LTAIN19007. The work was also supported from the grant of Specific university research – grant No FCHI 2022-001.</p>
Distancia-Covid Individual Contact Estimates for Spain
<p>Individual estimates of age-specific contact patterns in Spain during the Covid-19 pandemic. This data was generated from the CSIC Distancia-Covid survey (https://distancia-covid.csic.es/). It includes estimated numbers of coresidents and non-coresident contacts for each individual represented in the Spanish Labor Force Survey during 2020 and 2021. These estimates do not relate to any identifiable person; rather they provide information about the overall distribution of contacts across the population. These individual estimates have been used to calculate the mean age-specific contacts provided in <a href="https://doi.org/10.5281/zenodo.5983902">https://doi.org/10.5281/zenodo.5983902</a>. </p> <p>This dataset contains the following files:</p> <ul> <li>distancia_covid_individual_contact_estimates_metadata_dictionary.csv: Variable definitions</li> <li>distancia_covid_individual_contact_estimates_spain_cores_wave1.csv.gz: Estimates of coresidents during wave 1 of the Distancia-Covid survey (14 May 2020 through 10 June 2020)</li> <li>distancia_covid_individual_contact_estimates_spain_cores_wave2.csv.gz: Estimates of coresidents during wave 2 of the Distancia-Covid survey (24 July 2020 through 31 August 2020)</li> <li>distancia_covid_individual_contact_estimates_spain_cores_wave3.csv.gz: Estimates of coresidents during wave 3 of the Distancia-Covid survey (14 December 2020 through 10 January 2021)</li> <li>distancia_covid_individual_contact_estimates_spain_noncores_wave1.csv.gz: Estimates of non-coresident contacts during wave 1 of the Distancia-Covid survey (14 May 2020 through 10 June 2020)</li> <li>distancia_covid_individual_contact_estimates_spain_noncores_wave2.csv.gz: Estimates of non-coresident contacts during wave 2 of the Distancia-Covid survey (24 July 2020 through 31 August 2020)</li> <li>distancia_covid_individual_contact_estimates_spain_noncores_wave3.csv.gz: Estimates of non-coresident contacts during wave 3 of the Distancia-Covid survey (14 December 2020 through 10 January 2021)</li> <li>CITATION.cff: Citation file.</li> </ul> <p> </p>
Distancia-Covid Contact Estimates for Spain
<p>Estimates of age-specific contact patterns in Spain during the Covid-19 pandemic. This data was generated from the CSIC Distancia-Covid survey (https://distancia-covid.csic.es/). It includes estimated mean numbers of coresidents and non-coresident contacts by age group during 2020 and 2021, for all of Spain and disaggregated by autonomous community. (See `data/distancia_covid_contact_estimates_spain_metadata_dictionary.csv` for variable descriptions.) This repository also includes the survey instrument used in each wave.</p> <p><em>File Descriptions</em></p> <p>- distancia_covid_contact_estimates_spain_metadata_dictionary: Data dictionary<br> - distancia_covid_contact_estimates_spain.csv: Contact estimates<br> - distancia_covid_instrument_wave_1.xlsx: Survey instrument used in Wave 1<br> - distancia_covid_instrument_wave_2.xlsx: Survey instrument used in Wave 2<br> - distancia_covid_instrument_wave_3_4.xlsx: Survey instrument used in Waves 3 and 4<br> - CITATION.cff: Citation information </p> <p>This data is also hosted in the <a href="https://github.com/Distancia-COVID/Distancia-Covid-Open-Data">Distancia-Covid-Open-Data</a> GitHub repository. This version corresponds with:</p> <p><a href="https://github.com/Distancia-COVID/Distancia-Covid-Open-Data/releases/tag/v1.2.0">https://github.com/Distancia-COVID/Distancia-Covid-Open-Data/releases/tag/v1.2.0</a></p>
A blood atlas of COVID-19 defines hallmarks of disease severity and specificity: Associated data
<p>This dataset contains raw and processed data from the COvid-19 Multi-omics Blood ATlas (COMBAT) consortium. Data are divided into 26 datasets representing anonymised raw and processed data from deep immune phenotyping of peripheral blood from COVID-19 patients. </p> <p>In addition to the data listed below, some datasets are available through other repositories: </p> <ul> <li> <p>Proteomics data (CBD-KEY-PROTEOMICS) is available at PRIDE</p> <ul> <li> <p>Accession number: PDX023175</p> </li> <li> <p>Contact: Roman Fischer</p> </li> </ul> </li> </ul> <ul> <li> <p>Genetic data and detailed clinical information are available via a data access agreement through EGA</p> <ul> <li> <p>Study accession: EGAS00001005493 </p> </li> </ul> </li> </ul> <p>For further information regarding specific datasets, please contact the individuals listed in Dataset_descriptions.pdf through <a href="mailto:contact@combat.ox.ac.uk">contact@combat.ox.ac.uk</a>. </p>
Data from: "Alteration of the gut microbiota's composition and metabolic output correlates with COVID-19-like severity in obese NASH hamsters"
<p>This dataset contains all data collected and used for the publication : "Alteration of the gut microbiota’s composition and metabolic output correlates with COVID-19-like severity in obese NASH hamsters". Besides the Readme, it contains 11 files.</p> <p><br> Excel files with classification (i.e. genes according to their fold induction or repression) are provided. Data include different conditions with varying number of samples per group. Data are structured according to employed methods and then stratify the data obtained within the individual work packages.</p>
LEARN-COVID: Dataset and documentation
<p>The LEARN-COVID pilot study collected data on infants and their parents during the COVID-19 pandemic. Assessments took place between April and July 2021. Predominantly Swiss parents answered a baseline questionnaire on their behaviour related to the pandemic, social support, infant nutrition, and infant regulation. Subsequently, parents answered a 10-day evening diary on daily nutrition, infant regulation, parental mood, and parental soothing behaviour.</p>
Survey on the Effects of COVID-19 on the Wellbeing of Mexico City Households (ENCOVID-19 CDMX – JULY 2021)
<p>Amid the COVID-19 outbreak, the ENCOVID-19 CDMX provides information on the well-being of Mexico City households in four main domains: labor, income, mental health, and food insecurity. It offers timely information to understand the social consequences of the pandemic and the lockdown measures. It is a cross-sectional telephone survey that, in addition to the four main domains and a set of COVID19-related questions, includes key indicators to capture the impact of the pandemic on issues like education, social programs, and crime. This is the third dataset of the project, corresponding to July 2021, collected 15 months after the lockdown began in Mexico. Data collection was performed from July 19 to 31, 2021.</p>
Large Dataset of Nigeria Covid-19 Tweets for Sentiment Analysis and Opinion Mining Tasks
<p><strong>Background</strong></p> <p>Information is essential for growth; without it, little can be accomplished. Data gathering has seen significant changes throughout the previous few centuries because of certain transitory medium. The look and style of information transference are affected by the employment of new and emerging technologies, some of which are efficient, others are reliable, and many more are quick and effective, but a few were disappointing for various reasons.</p> <p><strong>Aims</strong></p> <p>This study aims at using TextBlob and VADER analyser with historical tweets, to analyse emotional responses to the corona virus pandemic (covid-19). It shows us how much of a sociological, environmental, and economic impact it has in Nigeria, among other things. This study would be a tremendous step forward for students, researchers, and scholars who want to advance in fields like data science, machine learning, and deep learning.</p> <p><strong>Methodology</strong></p> <p>The hashtag ‘covid-19' was used to collect 1,048,575 tweets from Twitter. The tweets were pre-processed with a twitter tokenizer, and Valence Aware Dictionary for Sentiment Reasoning (VADER) and TextBlob were used for sentiment and text mining, respectively. Topic modelling was done with Latent Dirichlet Allocation (LDA). The simulated subjects, on the other hand, were visualized using Multidimensional scaling (MDS).</p> <p><strong>Results</strong></p> <p>The result of the VADER sentiment returned 39.8%, 31.3% and 28.9%, positive, neutral, and negative sentiment respectively while the result of the TextBlob sentiment returned 46.0%, 36.7% and 17.3%, neutral, positive, and negative sentiment, respectively.</p> <p><strong>Conclusion</strong></p> <p>With all of this, information from social media may be used to help organizations, governments, and nations around the world make smart and effective decisions about how to restrict and limit the negative effects of covid-19. Also know the opinion and challenges of people, then deal with problem of misinformation.</p> <p>It is concluded that with popular belief a significant number of the populace regards covid-19 as a virus that has come to stay, some believe it will eventually be conquered.</p>
FolkArtiNet: Folk music groups: their artistic practice and infrastructural needs in the COVID-19 era and beyond - survey data
<p> The online survey was one of three methods used for collecting information about the infrastructural needs of the folk music groups. It included a series of questions about different areas of artistic activity, such as working on repertoire, collaboration among group members, storage and sharing of data, and organization of artistic events. The survey data includes all questions and answers in csv format.</p>
COVID-19 German Student Well-being Study (C19 GSWS)
<p><strong>COVID-19 German Student Well-being Study (C19 GSWS)</strong></p> <p>Following the COVID-19 International Student Well-being Study (C19 ISWS; survey phase: May 13<sup>th</sup>, 2020 to May 29<sup>th</sup>, 2020 in 27 European countries coordinated by the University of Antwerp), well-being of university students during the COVID-19 pandemic continued to be the focus of the collaborative COVID-19 German Student Well-being Study (C19 GSWS) which was conducted at five universities in Germany.</p> <p> </p> <p>The five German universities taking part in the study were the Charité – Universitätsmedizin Berlin (PI: Prof. Christiane Stock), the University of Bremen (PI: Dr. Heide Busse), Heinrich-Heine-University Duesseldorf (PI: Prof. Claudia Pischke), University of Siegen (PI: Prof. Claus Wendt) and Martin-Luther University Halle-Wittenberg (PI: Prof. Rafael Mikolajczyk).</p> <p> </p> <p>The following research questions were addressed:</p> <p> </p> <p>- How did university students' (physical and socioeconomic) living conditions and academic workload change during the COVID-19 pandemic?</p> <p>- How were living and study conditions associated with mental health outcomes among university students during the COVID-19 pandemic?</p> <p>- How were living conditions and academic workload associated with health behaviours (e.g., substance use) among university students during the pandemic?</p> <p>- Which attitudes towards COVID-19 vaccination and determinants of vaccination behavior were prevalent r among university students?</p> <p> </p> <p>To answer the research questions, an online survey among university students was conducted at all participating universities from October 27<sup>th</sup>, 2021 to November 14<sup>th</sup>, 2021. The resulting data allow for a description of living conditions, as well as well-being, during the ongoing COVID-19 pandemic in German university student populations.</p> <p> </p> <p>Information about C19 ISWS on Zenodo:</p> <p>https://zenodo.org/communities/c19-isws/?page=1&size=20</p>
Replication data for: Online Media Use and COVID-19 Vaccination in Real-World Personal Networks: Quantitative Study
<p>This is the replication data for the scientific paper titled "Online Media Use and COVID-19 Vaccination in Real-World Personal Networks: Quantitative Study" accepted for publication in the Journal of Medical Internet Research (JMIR). For details on how to use the data files, please consider the "supplementary_material.R" file or the "supplementary_material.pdf" where the variables of interest and R code are presented.</p> <p>For the code to run correctly, have the files "multilevel_labels.R" and "glm_labels.R" in the same working directory as the .R or .Rmd script. They are executed in the background, applying modifications to labels inside the regression tables. </p> <p> </p>
Dataset for the manuscript Marmet, Wicki, Gmel, Gachoud, Daeppen, Bertholet, Studer (2021). The psychological impact of the COVID-19 crisis is higher among young Swiss men with a lower socioeconomic status: evidence from a cohort study. Plos One. DOI:10.1371/journal.pone.0255050
<p>Dataset for the manuscript Marmet, Wicki, Gmel, Gachoud, Daeppen, Bertholet, Studer (2021). The psychological impact of the COVID-19 crisis is higher among young Swiss men with a lower socioeconomic status: evidence from a cohort study. Plos One DOI:10.1371/journal.pone.0255050</p> <p>The dataset contains all data needed to reproduce the results in the above cited manuscript. Variable description and labels can be found in the codebook. For further information on the instruments used please refer to the manuscript.</p> <p> </p>
Weekly CoMix contact matrices for UKHSA COVID-19 dashboard and ONS COVID-19 infection survey age-groups
<p>Weekly contact matrices calculated from data collected as part of the UK arm of the CoMix survey. All contact matrices were calculated over two survey rounds (SR) of data to account for alternating panels (the indicated SR and the previous SR). Full details of composition can be found in Munday et. al. [1]. Contact matrices are provided for age-groups consistent with publicly available case data from the UKHSA COVID-19 dashboard <strong> (0-9, 10-19, 20-29, 30-39, 40-49, 50-59, 60-69, 70+) </strong>and publicly available aggregates of infection and antibody prevalence from the ONS COVID-19 infection survey <strong>(2-10, 11-15, 16-24, 25-34, 35-49, 49-69 and 70+)</strong>. The data is provided in qs files as 1000 bootstrapped samples of each contact matrix for weekly 'survey rounds' between 19 and 94 (see directory "survey_round_dates.csv"). The files that begin with UKHSA contain the contact matrices for the age stratification of the UKHSA COVID-19 dashboard case data. The files that begin with ONS contain the contact matrices for the age stratification of the ONS COVID-19 infection survey. </p> <p>Ethics: The study and method of informed consent were approved by the ethics committee of the London School of Hygiene & Tropical Medicine (LSHTM; reference number 21795).</p> <p>1. Munday, J.D., Jarvis, C.I., Gimma, A. <em>et al.</em> Estimating the impact of reopening schools on the reproduction number of SARS-CoV-2 in England, using weekly contact survey data. <em>BMC Med</em> <strong>19</strong>, 233 (2021). https://doi.org/10.1186/s12916-021-02107-0</p>
Multi-omics identify LRRC15 as a COVID-19 severity predictor and persistent pro-thrombotic signals in convalescence
<p>RNA sequencing, SomaLogic proteomics and flow cytometry data were generated for two cohorts of end-stage kidney disease patients with COVID-19. The Wave 1 cohort consists of samples collected from patients during the first wave of COVID-19 in early 2020, while samples were collected for the Wave 2 cohort in the following year.</p> <p>This data deposition includes the RNA-seq counts, SomaScan proteomics, flow cytometry and clinical metadata associated with the study. For further information about the study and data, see the associated GitHub repository (https://github.com/jackgisby/covid-longitudinal-multi-omics) or our pre-print (https://doi.org/10.1101/2022.04.29.22274267). The repository also contains code to replicate our analysis of the data.</p> <p>The raw RNA-seq reads were processed using the nf-core RNA-seq v3.2 pipeline before htseq-count was used to generate a raw counts matrix, which is included in this deposition (<code>htseq_counts.csv</code>). Three files make up the proteomics data: <code>sample_technical_meta.csv</code>, <code>feature_meta.csv</code> and <code>soma_abundance.csv</code>. The first two files contain metadata columns for the samples and protein features, respectively. The final file includes the unprocessed protein abundance data. The files <code>general_panel.csv</code> and <code>t_cell_panel.csv</code> contain the flow cytometry data, split into the general and T-cell panels, respectively. Finally, clinical metadata is available for the two cohorts described in this study (<code>w1_metadata.csv</code>, <code>w2_metadata.csv</code>).</p> <p>The features in the clinical metadata include:</p> <table> <thead> <tr> <th>Column Name</th> <th>Data Type</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td>sample_id</td> <td>Character</td> <td>Unique identifier for samples</td> </tr> <tr> <td>individual_id</td> <td>Character</td> <td>Unique identifier for individuals</td> </tr> <tr> <td>ethnicity</td> <td>Character</td> <td>The individual's ethnicity (asian, white, black or other)</td> </tr> <tr> <td>sex</td> <td>Character</td> <td>The individual's sex (M or F)</td> </tr> <tr> <td>calc_age</td> <td>Integer</td> <td>Age in years</td> </tr> <tr> <td>ihd</td> <td>Character</td> <td>Information on coronary heart disease</td> </tr> <tr> <td>previous_vte</td> <td>Character</td> <td>Whether individuals have had venous thromboembolism</td> </tr> <tr> <td>copd</td> <td>Character</td> <td>Whether individuals have chronic obstructive pulmonary disease</td> </tr> <tr> <td>diabetes</td> <td>Character</td> <td>Whether individuals have diabetes, and, if so, the type of diabetes</td> </tr> <tr> <td>smoking</td> <td>Character</td> <td>Smoking status</td> </tr> <tr> <td>cause_eskd</td> <td>Character</td> <td>Cause of ESKD</td> </tr> <tr> <td>WHO_severity</td> <td>Character</td> <td>The peak (WHO) severity for the patient over the disease course</td> </tr> <tr> <td>WHO_temp_severity</td> <td>Character</td> <td>The (WHO) severity at time of sampling</td> </tr> <tr> <td>fatal_disease</td> <td>Logical</td> <td>Whether the disease was fatal</td> </tr> <tr> <td>case_control</td> <td>Character</td> <td>Whether the individual was COVID-19 <code>POSITIVE</code> or <code>NEGATIVE</code> at time of sampling. Convalescent patients are denoted by the label <code>RECOVERY</code></td> </tr> <tr> <td>radiology_evidence_covid</td> <td>Character</td> <td>Evidence of COVID-19 from radiology</td> </tr> <tr> <td>time_from_first_symptoms</td> <td>Integer</td> <td>The number of days since the individual first experienced COVID symptoms at time of sampling</td> </tr> <tr> <td>time_from_first_positive_swab</td> <td>Integer</td> <td>The number of days since the individual's first positive swab was taken at time of sampling</td> </tr> </tbody> </table>
TomoBreast randomized clinical trial's lung-heart outcomes and mortality through the 2020 COVID-19 pandemic: data and software
<p>Dataset and R script to reproduce the analyses of the manuscript:</p> <p>Vinh-Hung V, Gorobets O, Adriaenssens N, Van Parijs H, Storme G, Verellen D, Nguyen NP, Magne N, De Ridder M.</p> <p><strong>Lung-heart outcomes and mortality through the 2020 COVID-19 pandemic in a prospective cohort of breast cancer radiotherapy patients.</strong></p> <p>Cancers 2022; 14(24):6241. https:// doi.org/10.3390/cancers14246241</p> <p>https://www.mdpi.com/2072-6694/14/24/6241</p> <p>PubMed: PMID: 36551726</p> <p>PMCID: PMC9777311</p> <p>Info on the variables in file "aelq6_public.R"</p> <p>reproduced in "aelq_2_3_readme.txt":</p> <p>"aelq2_base2.txt" = baseline characteristics.</p> <p>"aelq3.txt" = longitudinal maesurements.</p> <p>Variables in "aelq2_base2.txt":</p> <p>"<strong>aelq2_base2.txt</strong>" = baseline characteristics. <br># Age at randomization, years. <br># RTdose: cf TomoBreast papers. <br># 51 Gy = hypofractionated, simultaneous integrated boost<br># 42 Gy = hypofractionated, no boost, mastectomy cases only<br># 50 Gy = conventional, no boost, mastectomy cases only<br># 66 Gy = conventional, sequential boost<br># Weight kg, Height cm, <br># Detection 1=found by screening (senology follow-up/controle)<br># 2=found by symptoms (pain, palpable)<br># 9=unknown<br># Smoker 0= Not smoker<br># 1= Smoker<br># 2=ex-smoker<br># Mastectomy (and other binary coded) 1= yes<br># chemosched 0=none<br># 1= planned after RT (sequential)<br># 2= prior to RT and is finished (sequential)<br># 3= chemo is on-going or is planned to start with RT (concomitant)<br># hormonetherapy 0=no<br># 1=tamoxifen (nolvadex)<br># 2=Femara (Letrozole)<br># 3=zoladex<br># 4=tamoxifen + zoladex<br># Laterality 1,=Right, 2=Left, 3=Bilateral<br># LengthFU: length of follow-up, days from randomization</p> <p>"<strong>aelq3.txt</strong>" = longitudinal maesurements.<br># "Nr" = Case ID<br># "Time" in days from origin (origin =date of randomization), <br># if negative =before randomization<br># "KPS" "Weight" <br># "Died" "LocalRec" "Metast" "NewPrim" = binary code, 0=no, 1=yes<br># "fAEBreast" "fAEHeart" "fAELung" "fAEOther" <br># fAE = freedom from breast, heart, lung, other adverse event score<br># "LVEF2" = ejection fraction, %<br># "MacIver" = estimated cardiac strain</p> <p># the following are pulmonary function tests, untransformed units<br># "FVC", "FEV1", "PEF", "VC", "TLC", "RV", "FRC", "Raw", "sRaw", "DLCO",<br># "VA", "PF"</p> <p># "fDY", "fFA", "fPA" = freedom from dyspnea, from fatigue, from pain<br># range 0 to 100 (best)<br># see papers:</p> <p># Van Parijs, H.; Vinh-Hung, V.; Fontaine, C.; Storme, G.; Verschraegen, C.;<br># Nguyen, D.M.; Adriaenssens, N.; Nguyen, N.P.; Gorobets, O.; De Ridder, M.<br># Cardiopulmonary-related patient-reported outcomes in a randomized clinical<br># trial of radiation therapy for breast cancer. BMC Cancer 2021, 21, 1177,<br># doi:10.1186/s12885-021-08916-z.</p> <p># preprint:<br># Van Parijs, H.; Cecilia-Joseph, E.; Gorobets, O.; Storme, G.; <br># Adriaenssens, N.; Heyndrickx, B.; Verschraegen, C.; Nguyen, N.P.;<br># De Ridder, M.; Vinh-Hung, V. Lung-heart toxicity in a randomized <br># clinical trial of hypofractionated image guided radiation therapy for<br># breast cancer. Preprints 2022, 202212, 0214.<br># https://doi.org/10.20944/preprints202212.0214.v1</p> <p># <br># "Year" = year of the observation<br># example: randomized 1/1/2011, measurement done 1/31/2011, time = 30 days,<br># Year =2011<br>#<br> </p>
The Impact of the COVID-19 Pandemic On Cities. A Scoping Review Protocol
<p>The aim of the scoping review is to map out evidence based research on the Covid-19 pandemic impact on the European cities. The review questions touch three broad areas of interest:</p> <ol> <li>the aspects of urban life described and analysed in publications on the impact of the pandemic on cities</li> <li>the aspects of urban life that are described in terms of crisis, breakdown, turnaround, etc. (crisis, disruption, slump, shift…) in such studies</li> <li>theoretical and methodological approaches applied in such studies</li> </ol> <p>The search was conducted in June 2022, with the final body of literature consisting of 3,994 publication references from EBSCOhost, APA Psyc, Scopus, Web of Science, Proquest, Wiley, Sage, JSTOR, Tailor&Francis, Oxford Journals databases (Fig. 1). The following English words were searched for in titles, abstracts and keywords in the databases: (pandemic OR ‘Covid-19’) AND (city OR cities OR urban*). We used the following criteria for articles to be included in the study: 1) peer and non-peer-reviewed empirical papers in journals published in English from January 2019 to June 2022; 2) included studies where the impact of COVID-19 pandemic on European city/cities was an explicit variable of interest; 3) contained analysis of empirical data on cities or urban life retrieved or collected within and explicitly addressing the COVID-19 pandemic; 4) addressed the social, cultural, economic, political and socio-geographical aspects of a city. We excluded from our sample papers that were: 1) theoretical and opinion literature, media press releases, reports, MA dissertations and PhD theses; 2) secondary research papers (reviews, meta-analyses); 3) papers not in English; 4) studies about non-European cities; 5) studies which do not explicitly address the impact of the COVID-19 pandemic on cities; 6) studies addressing a city as a variable of secondary importance; 7) studies outside the scope of the COVID-19 pandemic, published before December 2019; 8) studies not addressing the social or human aspects of urban life.</p> <p>The final database of coded documents consisted of 138 empirical articles presenting findings on the impact of the COVID-19 pandemic on European cities. </p>
VaxxHesitancy: A Dataset for Studying Hesitancy Towards COVID-19 Vaccination on Twitter
<p>We create a publicly available dataset of over 3,100 COVID-19 vaccine-related tweets labeled as one of four stance categories: <em>pro-vaxx, anti-vaxx</em>, <em>vaxx-hesitant</em>,<em> or irrelevant</em>.</p> <p><strong>***</strong></p> <p><strong>Please use the V2 version.</strong></p> <p><strong>***</strong></p> <p>We split our dataset into two separate files:</p> <p>(1) VaccineHesitancy_train_v2.csv (Single + Double annotated)</p> <p>(2) VaccineHesitancy_test.csv (Double annotated)</p> <p>We present the details of this dataset here:</p> <p>VaxxHesitancy: A Dataset for Studying Hesitancy Towards COVID-19 Vaccination on Twitter (ICWSM 2023)</p> <p><strong>Our Pre-trained model</strong> (GateNLP/covid-vaccine-twitter-bert) : https://huggingface.co/GateNLP/covid-vaccine-twitter-bert</p> <p><strong>Paper</strong>: https://ojs.aaai.org/index.php/ICWSM/article/view/22213/21992</p> <p> </p> <pre>@inproceedings{mu2023vaxxhesitancy, title={VaxxHesitancy: A Dataset for Studying Hesitancy Towards COVID-19 Vaccination on Twitter}, author={Mu, Yida and Jin, Mali and Grimshaw, Charlie and Scarton, Carolina and Bontcheva, Kalina and Song, Xingyi}, booktitle={Proceedings of the International AAAI Conference on Web and Social Media}, volume={17}, pages={1052--1062}, year={2023} } </pre> <p> </p> <p> </p> <p> </p>
BY-COVID Impact Framework
<p>Presentation of the project’s pathways towards impact (left to right). Starting with BY-COVID’s Objectives (blue boxes), pathways move from left to right, with Expected Outcomes (green boxes) and Expected Impacts (light purple boxes) given for the topic in the Horizon Europe Work Programme 2021-2022 for Research infrastructures. Other impacts considered to be relevant to the project’s work are shown (dark purple boxes).</p>
Visualizing the Impact of COVID-19 and the Vaccination Data in 2021
<p>COVID-19 has been a hot topic in recent years. While numerous visualizations have showcased the distribution of COVID-19 cases and deaths, few demonstrate the temporal relationships between cases, deaths, and COVID-19 vaccinations. Our visualization aims to fill this gap by showcasing the temporal evolution of COVID-19 cases, deaths, and vaccinations in the U.S. while also comparing them geographically by U.S. states.</p> <p>Our dataset was obtained from two different organizations: the New York Times and Our World in Data. The New York Times dataset focuses on COVID-19 cases and deaths within each county/state of the US in 2021, while the dataset from Our World in Data contains information on the various vaccination data of each state throughout the year. We chose to focus on 2021 since that is when the first data was collected for the us_state_vaccinations.csv, in addition to the reason that the majority of vaccination data from 2022 are not as consistent and missing a lot.</p> <p>Our visualizations are targeted towards individuals who want to learn more about the timeline of COVID-19 cases, deaths, and vaccinations data and the complex relationships among them. This includes public health officials who need to make informed decisions regarding interventions to mitigate the spread of COVID-19, journalists and media organizations who want to report accurate information about the pandemic to the public, and the general public who are interested in understanding the impact of COVID-19 on their local communities.</p> <p>We implemented our visualizations using Python's Altair and Streamlit libraries, using drop-down selection bars, time/date sliders, multi-select widgets, and various types of linked views. These interactive features are implemented using built-in functions from the Streamlit and Altair libraries, including st.selectbox, st.multiselect, st.slider, alt.selection_interval, and alt.selection_single. The details of the code that we wrote to implement these visualizations can be found on our project's GitHub page (https://github.com/Tony-Xiayi-Ding/COVID-19-Visualizations).</p> <p>Our visualizations showed that the temporal evolution of COVID-19 cases and deaths exhibited a striking similarity, with a rather consistent trend over time. Additionally, the cases and deaths count generally remained at much slower increasing rates during seasons with higher temperatures and at much higher increasing rates during colder months, highlighting the complex interplay of demographic and seasonal factors in shaping the pandemic in the U.S. Moreover, the overall trend for case fatality rate was decreasing for most states, and states that were close to each other shared similar trends of case fatality rate. Furthermore, states with higher average temperatures shared similar trends in case fatality rates that were quite different from those states that were relatively colder. Lastly, as total vaccinations per hundred increased over time, the relative case fatality rate dropped, and coastal states were found to have slightly higher total vaccinations per hundred values, potentially due to their higher population densities and that the residents in those states are more aware of the importance of getting vaccinated due to their elevated chances of contracting COVID-19.</p> <p>The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.</p> <p>References:</p> <p>1. New York Times. (2022). Covid-19-data/US-counties-2021.csv. GitHub. Retrieved February 12, 2023, from https://github.com/nytimes/covid-19-data/blob/master/us-counties-2021.csv</p> <p>2. Our World in Data. (2023). Covid-19-data/US_state_vaccinations.CSV. GitHub. Retrieved February 12, 2023, from https://github.com/owid/covid-19-data/blob/master/public/data/vaccinations/us_state_vaccinations.csv</p> <p>3. U.S. Department of Health and Human Services. (2023). What is a FIPS code and why do I need one? National Institutes of Health. Retrieved February 12, 2023, from https://nitaac.nih.gov/resources/frequently-asked-questions/what-fips-code-and-why-do-i-need-one</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.