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10,244 results for “vaccination”
Long-term immunity against yellow fever in children vaccinated during infancy: a longitudinal cohort study
<p>The data represent the concentrations of specific neutralizing antibodies following infant immunization against yellow fever. We used a microneutralization assay to measure protective antibodies against yellow fever virus in 587 Malian and 436 Ghanaian children vaccinated around age 9 months, and followed for 4.5 years (Mali), or 2.5 and 6 years (Ghana). We standardized antibody concentrations with reference to the yellow fever WHO International Standard.</p> <p>The serum samples used in this study, and the sample metadata included in the present dataset originate from trials of the meningococcal group A conjugate vaccine, MenAfriVac, namely the PsATT-004 (phase II) and Pers-004 (phase IV) studies in Ghana, and the PsATT-007 (phase III) and Pers-007 (phase IV) studies in Mali (clinical trial registry numbers ISRCTN82484612, ISRCTN10763234, PACTR201110000328305, and ISRCTN37623829). MenAfriVac was developed by PATH and Serum Institute India Pvt. Ltd. (SIIPL).</p> <p>This dataset consists of three files:</p> <p>1. Ghana group data | Tab-delimited text file: Yellow_fever_nAb_Ghana.csv</p> <p>2. Mali group data | Tab-delimited text file: Yellow_fever_nAb_Mali.csv</p> <p>3. Data dictionary | PDF file: Yellow_fever_nAb_Data_Dictionary.pdf</p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p>
Antigen-specific CD4+ T cells exhibit distinct transcriptional phenotypes in the lymph node and blood following vaccination in humans
<p><strong>Abstract: </strong><br>SARS-CoV-2 infection and mRNA vaccination induce robust CD4+ T cell responses that are critical for the development of protective immunity. Here, we evaluated spike-specific CD4+ T cells in the blood and draining lymph node (dLN) of human subjects following BNT162b2 mRNA vaccination using single-cell transcriptomics. We analyze multiple spike-specific CD4+ T cell clonotypes, including novel clonotypes we define here using Trex, a new deep learning-based reverse epitope mapping method integrating single-cell T cell receptor (TCR) sequencing and transcriptomics to predict antigen-specificity. Human dLN spike-specific T follicular helper cells (TFH) exhibited distinct phenotypes, including germinal center (GC)-TFH and IL-10+ TFH, that varied over time during the GC response. Paired TCR clonotype analysis revealed tissue-specific segregation of circulating and dLN clonotypes, despite numerous spike-specific clonotypes in each compartment. Analysis of a separate SARS-CoV-2 infection cohort revealed circulating spike-specific CD4+ T cell profiles distinct from those found following BNT162b2 vaccination. Our findings provide an atlas of human antigen-specific CD4+ T cell transcriptional phenotypes in the dLN and blood following vaccination or infection.</p> <p><strong>More Information:</strong></p> <ul> <li><strong>Preprint:</strong> <a href="https://www.researchsquare.com/article/rs-3304466/v1">Research Square.</a></li> <li><strong>Sample information</strong>: data_inventory.csv file.</li> <li><strong>Code</strong> code_github_repo.zip or at the <a href="https://github.com/ncborcherding/COVID_TCR">original github repo</a></li> <li><strong>Interactive Portal</strong>: <a href="https://cellpilot.emed.wustl.edu/">CellPilot</a></li> </ul>
COVID-19 Vaccine Tweets in Turkish
<p>This dataset contains the Turkish tweets that are collected with the keyword vaccine, sinovac and biontech in Turkish by using Twitter Academic API. The new versions will be more up-to-date and will be classified monthly folders.</p> <p>You can visit the github repository of the project:</p> <p><a href="https://github.com/burakozturan/tria-covid19">https://github.com/burakozturan/Turkish-Vaccine-Tweets</a></p> <p> </p> <p> </p> <p> </p> <p> </p>
Barley as a production platform for oral vaccines in sustainable fish aquaculture
<p>Experimental data for the study "Barley as a production platform for oral vaccines in sustainable fish aquaculture"</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>
Invasive pneumococcal diseases in children and adults before and after introduction of the 10-valent pneumococcal conjugate vaccine into the Austrian national immunization program
<p>The dataset contains case-based data on invasive pneumococcal disease in Austria, 2009/01 to 2017/02, by year and month of diagnosis, serotype and clinical presentation. Cases are anonymised by using a random ID.</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>
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>
Analysis of human humoral responses in a typhoid vaccine efficacy trial used for SIMON analysis
<p>The VAST dataset contains data from 72 individuals enrolled in the clinical study to evaluate humoral responses in a typhoid vaccine efficacy trial in a controlled human <em>Salmonella </em>Typhi infection model (see original publication: <a href="https://doi.org/10.3389/fimmu.2019.02582">https://doi.org/10.3389/fimmu.2019.02582</a>). Only day 0 (day of the challenge) log-transformed data were used in the SIMON analysis, as described in the publication (<a href="https://doi.org/10.1101/2020.08.16.252767">https://doi.org/10.1101/2020.08.16.252767</a>). Individuals were vaccinated with either a purified Vi polysaccharide (Vi-PS) vaccine (35 individuals) or the Vi tetanus toxoid conjugate (Vi-TT) vaccine (37 individuals) one month prior to oral challenge with live <em>Salmonella </em>Typhi. Out of 72 individuals, 26 developed an acute typhoid infection following the challenge.</p>
MAVIS Twitter dataset: A collection of tweets and sentiment analysis in Spanish about vaccines and diseases during the period 2015-2018
<p>MAVIS dataset comprises a full knowledge base regarding Twitter messages published in Spanish during the period 2015-2018, in the context of sentiment analysis of specific vaccines and their related diseases. Such diseases and vaccines are summarized as follows:</p> <ul> <li>Invasive meningococcal disease (“EMI” in Spanish): Bexsero, Trumenba, Nimenrix</li> <li>Invasive pneumococcal disease (“ENI” in Spanish)</li> <li>Influenza</li> <li>Hepatitis</li> <li>Rotavirus: Rotarix, Rotateq</li> <li>Measles (“Sarampión” in Spanish) and MMR (“Triple vírica” in Spanish)</li> <li>Sepsis</li> <li>Whooping cough (“Tosferina” in Spanish)</li> <li>Chickenpox (“Varicela” in Spanish): Varivax, Varilrix; and Shingles (“Zoster” in Spanish)</li> <li>Human papillomavirus infection (“VPH” in Spanish): Cervarix, Gardasil</li> </ul> <p>Tweets have been manually classified as having a negative or non-negative sentiment by 5 experts. Moreover, an automatic classification has been performed by 3 different tools: IBM Watson (now Watson Tone Analyzer, <a href="https://www.ibm.com/watson/services/tone-analyzer/">https://www.ibm.com/watson/services/tone-analyzer/</a>), Google Cloud Natural Language (<a href="https://cloud.google.com/natural-language">https://cloud.google.com/natural-language</a>), and Meaning Cloud (<a href="https://www.meaningcloud.com/">https://www.meaningcloud.com/</a>). IBM Watson and Google Cloud Natural Language returned a numerical sentiment score ranging from -1 to 1, while Meaning Cloud returned a categorical variable with the values ‘P+’, ‘P’, ‘NEU’, ‘N’ and ‘N+’, which were converted to 1, 2, 3, 4 and 5 respectively.</p> <p>With these variables (IBM Watson, Google Cloud Natural Language, and Meaning Cloud annotations and the experts’ classification as the target label), a machine learning metamodel was developed. Tweets were also annotated with the sentiment output given by this classifier. </p> <p>The provided data includes intrinsic tweets information, intrinsic information regarding the users that posted the tweets, the keywords mentioned in each tweet, and the annotations that the experts, the tools, and the model gave to each tweet.</p> <p><strong>Funding</strong>: This dataset was obtained with funding from MSD, Spain under MAVIS Study (VEAP ID: 7789).</p> <p><strong>Current studies using this dataset at the moment of the publication</strong>:</p> <ul> <li>Rodríguez-González et al., “Creating a metamodel based on machine learning to identify the sentiment of vaccine and disease-related messages in Twitter: the MAVIS study” in 2020 IEEE 33st International Symposium on Computer-Based Medical Systems (CBMS), Jul. 2020, p. 6. DOI: 10.1109/CBMS49503.2020.00053</li> <li>Rodríguez-González et al., "Identifying Polarity in Tweets from an Imbalanced Dataset about Diseases and Vaccines Using a Meta-Model Based on Machine Learning Techniques" in Applied Sciences, 2020, 10. DOI: 10.3390/app10249019</li> </ul>
Kotliarov 2020 Vaccine Responsiveness PBMC dataset for Besca
<p>Kotliarov, Y., Sparks, R., Martins, A.J. <em>et al.</em> Broad immune activation underlies shared set point signatures for vaccine responsiveness in healthy individuals and disease activity in patients with lupus. <em>Nat Med</em> <strong>26, </strong>618–629 (2020). https://doi.org/10.1038/s41591-020-0769-8. We reprocessed the dataset using the Besca package (<a href="https://github.com/bedapub/besca">https://github.com/bedapub/besca</a>). The original gene expression data are available from <a href="https://doi.org/10.35092/yhjc.c.4753772">https://doi.org/10.35092/yhjc.c.4753772</a>.</p>
In vivo treatment with insulin-like growth factor 1 reduces CCR5 expression on vaccine-induced activated CD4+ T-cells
<p>Dataset of the publication "In vivo treatment with insulin-like growth factor 1 reduces CCR5 expression on vaccine-induced activated CD4+ T-cells" by Bissa et al. on the journal Vaccines. </p><p>Each folder contains the original files reporting the data used to generate the manuscript.</p><p>For flowcytometry based assays the Flow panel is included in the folders. </p><p>For ELISA based assays the schemes of the plates are included in the folders. </p><p>The excel table "Bissa et al._Vaccines_2023_Animal IDs and viral acquisition" reports the IDs and grouping of the animals together with their viral acquisition</p><p>The excel table "Bissa et al._Vaccines_2023_Master table" reports each data used to generate the figures and supplemental materials included in the publication </p>
Impact of vaccinations, boosters and lockdowns on COVID-19 waves in French Polynesia
<p>COVID-19 case, hospitalisation, death, seroprevalence, vaccination and population data, and age-dependent contact rate, severe burden risk and vaccine effectiveness parameter estimates, required to fit model and run simulations in article "Impact of vaccinations, boosters and lockdowns on COVID-19 waves in French Polynesia"</p>
CanVaxKB: A Web-based Cancer Vaccine Knowledgebase
<p>CanVaxKB is a web-based cancer vaccine knowledgebase. CanVaxKB collects, annotates and analyzes various types of cancer vaccines around the world. Currently it contains all cancer vaccines stored in the VIOLIN vaccine database. CanVaxKB also provides a user-friendly web interface for users to interactively search, compare, and analyze different cancer vaccines. The CanVaxKB website is here: https://violinet.org/canvaxkb. </p> <p>The Vaccine Ontology (VO) also includes the CanVaxKB stored cancer vaccine information, which is accessible at: https://github.com/vaccineontology/VO. </p> <p>The four supplemental files provided here are for the NCI Cancer paper about CanVaxKB. The citation for the CanVaxKB NCI Cancer is here:</p> <p>Eliyas Asfaw*, Asiyah Yu Lin*, Anthony Huffman*, Siqi Li*, Madison George*, Chloe Darancou, Madison Kalter, Nader Wehbi, Davis Bartels, Elyse Fleck, Nancy Tran, Daniel Faghihnia, Kimberly Berke, Ronak Sutariya, Farah Reyal, Youssef Tammam, Bin Zhao, Edison Ong, Zuoshuang Xiang, Virginia He, Justin Song, Andrey I. Seleznev, Jinjing Guo, Yuanyi Pan, Jie Zhang, Yongqun He. CanVaxKB: A Web-based Cancer Vaccine Knowledgebase. NCI Cancer. In press. </p>
COVID-19 vaccination data in Israel by age over time until August 2021
<p>COVID-19 vaccination data in Israel processed to show vaccination by age over time. These datasets are derived from publicly available Ministry of Health data, but processed for analytics about uptake in different age groups over time. They cover the mass vaccination campaign for COVID-19 until August 2021. The campaign consisted of the administration of multiple doses of the Pfizer vaccine.</p>
Covid-19 Worldwide Data 2021 Cases and Vaccination
<p>This dataset includes information about active cases, accumulative cases, accumulative deaths, daily information, vaccination classified by country of year 2021. </p>
A Multilingual Dataset of COVID-19 Vaccination Attitudes on Twitter
<p>This dataset consists of the IDs of 2,198,090 tweets collected from Western Europe, of which 17,934 are annotated with labels indicating the originators' affective vaccination stances, including Positive (PO), Negative (NG), Positive but dissatisfaction (PD), Neutral (NE) and Off-topic (OT).</p> <p>all_tweets.txt contains all the ids of the collected tweets, annotated_tweets.txt contains the ids of the annotated tweets and the categories they are annotated to.</p> <p> </p>
A Novel Dataset of Misinformation Tweets Regarding the CoronaVac Vaccine in Brazil
<p>This dataset was built to analyze the spread of misinformation about CoronaVac in Brazil by using data from Twitter for two specific events: the approval for emergency use in adults over 18 years old (January 17, 2021) and the approval for use in children aged 6 to 17 years (January 20, 2022).</p> <p>We choose to label the original tweets with at least one retweet in the analyzed period. The manual labeling of such tweets was initially performed by two annotators with high knowledge about the dataset and the considered context. In cases in which there was no agreement between the two annotators, a third annotator was considered to define the class of the tweet. </p> <p>The final dataset contains <strong>1,010 tweets from January 17, 2021</strong>, and <strong>816 tweets from January 20, 2022</strong>.</p> <p>This dataset was originally built for a conference paper accepted at BraSNAM 2022. If you make use of the dataset, please also cite the following paper:</p> <p><em>Gabriel P. Oliveira, Beatriz F. Paiva, Ana Paula Couto da Silva, and Mirella M. Moro. Characterizing the Diffusion of Misinformation Regarding the CoronaVac Vaccine in Brazil. In Proceedings of the XI Brazilian Workshop on Social Network Analysis and Mining </em><em>(BraSNAM 2022), 2022.</em></p> <pre><code>@inproceedings{brasnam/OliveiraPSM22, title = {Characterizing the Diffusion of Misinformation Regarding the CoronaVac Vaccine in Brazil}, author = {Gabriel P. Oliveira and Beatriz F. Paiva and Ana Paula Couto da Silva and Mirella M. Moro}, booktitle = {Proceedings of the XI Brazilian Workshop on Social Network Analysis and Mining (BraSNAM)} year = {2022} }</code></pre>
Municipal-level vaccination data (Sweden)
<p>This is a dataset created for a project investigating variation in COVID-19 vaccination rates in Swedish municipalities (aggregated). The dataset compiles data from several publicly available data sources, namely: the Swedish Public Health Agency, the Swedish Election Authority, Statistics Sweden, Frikyrkoundersökning, and the Swedish Association of Local Authorities and Region’s database (KOLADA).</p>
Risk and symptoms of COVID-19 in health professionals according to baseline immune status and booster vaccination during the Delta and Omicron waves in Switzerland – a multicentre cohort study
<p>For details, see publication</p>
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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.