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10,244 results for “Vaccine”
COVID-19 Vaccines Database: 2020-2022
<p>The attached databases were generated and used in for the analysis of the research article <em>'Which roads lead to access? A global landscape of six COVID-19 vaccine innovation models'. </em>They contain data related to COVID-19 vaccines' registration status, prices, production, purchases, deliveries, and investments between 2020 and 2022. <em><br></em></p> <p>These databases were compiled by collecting and revising data from two sources: UNICEF's COVID Market Dasboard, and the COVID-19 vaccine R&D investments tracker from the Geneva Graduate Institute's Global Health Centre: https://www.knowledgeportalia.org/covid-19-vaccine-r-d-funding.<em><br></em></p>
Supplementary dataset to the publication "Ultraviolet C inactivation of Coxiella burnetii for production of a structurally preserved whole cell vaccine antigen"
<p>The dataset supplements the journal article "Ultraviolet C inactivation of <em>Coxiella burnetii </em>for production of a structurally preserved whole cell vaccine antigen" published by Katja Mertens-Scholz, Amira A. Moawad, Elisabeth M. Liebler-Tenorio, Andrea Helming, Jennifer Andrack, Peter Miethe, Heinrich Neubauer, Mathias W. Pletz and Ina-Gabriele Richter in the journal BMC Microbiology (https://doi.org/10.1186/s12866-024-03246-z). The file "NMII 100µW" contains all data regarding inactivation of <em>C. burnetii</em> Nine Mile phase II with 100µW in a time dependent manner. The file "NMI 100 and 250µW" contains all data regarding inactivation of <em>C. burnetii</em> Nine Mile phase I with 100µW and 250µW in a time dependent manner. The file "surviving fraction" contains all data regarding inactivation of <em>C. burnetii </em>Nine Mile phase I and II after UVC treatment. The file "serology" contains all data obtained from ELISA experiments. The file "diameter" contains all data regarding the bacterial diameter after UVC treatment.</p>
Oral cholera vaccine coverage survey, Goma, August 2022
<p>Dataset collected during Vaccination Coverage Survey for Oral Cholera Vaccine in Goma, August 2022</p>
Supporting transcriptomic data and code for: "Rapid and dose-dependent Natural Killer (NK) cell modulation and cytokine correlations after human rVSV-ZEBOV Ebolavirus vaccination"
<p>The counts_NK.csv file contains gene expression data (counts) for genes in the Ion Ampliseq human Gene expression kit panel. Data were obtained from whole blood RNA. Subjects were vaccinated with a high dose of the rVSV-ZEBOV vaccine against Ebola virus disease in the Geneva clinical trial.</p> <p>The Descriptive_Table_NK_2.csv contains descriptive data of the subjects for differential expression analysis.</p> <p>The NK_analysis_code.R file contains the code used for analysis.</p>
Interviews for New Business Models for Pharmaceutical Innovation and Access to Medicines - Case Study of the Oral Cholera Vaccine Development
<p>These supplementary materials represent the partial dataset in the form of semi-structured interviews, collected and analyzed in the research article "The 30-year evolution of oral cholera vaccines: A case study of a collaborative network alternative innovation model". This article is one of the outcomes of the "New Business Models for Pharmaceutical Innovation and Global Access to Medicines" research project, conducted at the Global Health Center, within the Geneva Graduate Institute. The dataset contains 8/16 interviews collected and used in this article, which are published with the informed consent of the interviewees.</p>
Vaccine Attitudes Examination (VAX) scale dataset in Spain
<p>This dataset contains data collected between November 15, 2021, and March 15, 2022. Demographic variables, data on vaccinated people, reasons for not getting vaccinated, and responses to items on the Vaccination Attitudes Examination (VAX) scale are included. Although the language of the open answers is Spanish, the name of the variables and the value labels have been written in English to facilitate their understanding.<br>Data and codebooks are provided in csv format, following the FAIR principles.<br>Three files are provided:<br>1. VAX data, with the data related to sample characteristics and the answers to the questionnaire items.<br>2. Database codebook of variables, with information of the labels of the variables of the VAX data file.<br>3. Variables values codebook, with the labels of the values of the variables in the VAX data file.</p>
SARS-CoV-2 mRNA vaccines induce persistent human germinal centre responses
<p>These are the<strong> processed</strong> BCR repertoire bulk sequencing data described in <a href="https://doi.org/10.1038/s41586-021-03738-2">Turner & O'Halloran et al., Nature, 2021</a> (Fig 3b-d; Extended Data Fig 3; Extended Data Table 6). The corresponding <strong>raw</strong> sequencing reads are available on SRA under <a href="https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA731610">BioProject PRJNA731610</a>.</p> <p><strong>Summary</strong>: Bulk-sorted total plasmablasts from PBMCs and germinal centre B cells at 4 weeks after primary immunization from 3 vaccinees who had no prior history of infection with SARS-CoV-2. </p> <p><strong>Code: </strong>Code along with Docker container for reproducing the NGS data-based figures and analyses in the published paper can be <a href="https://github.com/julianqz/wustl_published/tree/main/nature_2021">found on GitHub</a>.</p> <p><strong>Metadata file</strong>: WU368_turner_et_al_nature_2021_meta.tsv</p> <p>Abbreviations:</p> <ul> <li>LN = lymph node</li> <li>PB = plasmablast</li> <li>GC = germinal centre</li> <li>mAb = monoclonal antibody</li> </ul> <p><strong>BCR data file</strong>: WU368_turner_et_al_nature_2021_bcr.tsv.gz</p> <p>In addition to the processed bulk sequences, also included are the heavy chains of 37 mAbs that had been validated to be spike-binding and that were used together with the bulk sequences for clonal lineage inference. The mAbs are annotated as "mab" in the "seq_type" column.</p> <p><strong>BCR data column descriptions</strong></p> <p>The columns largely follow the <a href="https://changeo.readthedocs.io/en/stable/standard.html">AIRR-C Rearrangement format</a>. The main deviation is that CDR3s are used, as opposed to IMGT-defined "junctions". Non-standard columns are noted below.</p> <ul> <li>v_call_genotyped: V gene annotation reassigned after individualized genotyping by <a href="https://tigger.readthedocs.io/en/stable/">TIgGER</a></li> <li>germline_[vdj]_call: clonal consensus germline sequence reconstructed via <a href="https://changeo.readthedocs.io/en/stable/methods/germlines.html">`CreateGermlines.py --cloned` using Change-O</a></li> <li>isotype: IGH[ADEGM]</li> <li>cdr3: CDR3 nucleotide sequence</li> <li>cdr3_length: CDR3 nucleotide sequence length</li> <li>cdr3_aa: CDR3 amino acid sequence</li> <li>collapse_count: number of duplicate IMGT-aligned V(D)J sequences that were collapsed by <a href="https://alakazam.readthedocs.io/en/stable/topics/collapseDuplicates/">`alakazam::collapseDuplicates`</a></li> <li>donor: vaccinee</li> <li>sample: sample ID (arbitrary)</li> <li>timepoint: time point at which sample was collected</li> <li>tissue: tissue from which sample was collected</li> <li>sorting: FACS sorting</li> <li>seq_type: sequence type (mAb or bulk)</li> <li>nuc_RS_19_312: number of replacement and silent mutations between IMGT-numbered nucleotide positions 19-312 along IGHV sequences, calculated by <a href="https://shazam.readthedocs.io/en/stable/topics/calcObservedMutations/">`shazam::calcObservedMutations`</a></li> <li>nuc_denom_19_312: number of informative nucleotide positions for counting mutations, excluding non-A/T/G/C positions (such as "N", "-", ".")</li> <li>nuc_RS_freq_19_312: nucleotide-level mutation frequency (= nuc_RS_19_312 / nuc_denom_19_312)</li> </ul>
Datasets Cured and Enriched with Provenance from the National Vaccination Campaign Against COVID-19
<p>The COVID-19 pandemic is a global threat. If, on the one hand, weaccount for many losses, on the other hand, the generation of datasets and ur-gent analytical demands has accelerated. Among the combat strategies, vacci-nation and data-centered epidemiological investigations stand out. This datasetpaper presents the process of building cured and annotated datasets with prove-nance metadata. The main dataset is based on the registration data of the Vacci-nation Campaign against COVID-19 in Brazil. The dataset contains thousandsof records processed up to March 2021. The data were analyzed, investigated,treated and cross-checked with other sources, in order to correct and comple-ment them, resulting in cured datasets and aligned to the FAIR principles.</p>
A vaccine-induced public antibody protects against SARS-CoV-2 and emerging variants
<p>These are the<strong> processed</strong> BCR repertoire bulk sequencing data described in <a href="https://doi.org/10.1016/j.immuni.2021.08.013">Schmitz, Turner & Liu et al., Immunity, 2021</a>. The <strong>raw</strong> sequence data are available on SRA under BioProjects <a href="https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA731610">PRJNA731610</a> and <a href="https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA741267">PRJNA741267</a>. </p> <p><strong>Summary</strong>: Bulk-sorted total plasmablasts and IgDlo enriched B cells from PBMCs and germinal centre B cells from lymph nodes from various timepoints after primary immunization from 22 BNT162b2 vaccinees who had no prior history of infection with SARS-CoV-2. </p> <p><strong>Metadata file</strong>: WU368_schmitz_et_al_immunity_2021_meta.tsv</p> <p>Abbreviations:</p> <ul> <li>LN = lymph node</li> <li>PB = plasmablast</li> <li>GC = germinal center</li> <li>mAb = monoclonal antibody</li> </ul> <p><strong>BCR data file</strong>: WU368_schmitz_et_al_immunity_2021_bcr.tsv.gz</p> <p>In addition to the processed bulk sequences, also included are the heavy chains of 37 mAbs (including 2C08) first reported in <a href="https://doi.org/10.1038/s41586-021-03738-2">Turner & O'Halloran et al., Nature, 2021</a> that had been validated to be spike-binding. The mAbs are annotated as "mab" in the "seq_type" column.</p> <p><strong>Sequence data column description</strong></p> <p>The columns largely follow the <a href="https://changeo.readthedocs.io/en/stable/standard.html">AIRR-C Rearrangement format</a>. The main deviation is that CDR3s are used, as opposed to IMGT-defined "junctions". Non-standard columns are noted below.</p> <ul> <li>v_call_genotyped: V gene annotation reassigned after individualized genotyping by <a href="https://tigger.readthedocs.io/en/stable/">TIgGER</a></li> <li>isotype: IGH[ADEGM]</li> <li>cdr3: CDR3 nucleotide sequence</li> <li>cdr3_length: CDR3 nucleotide sequence length</li> <li>cdr3_aa: CDR3 amino acid sequence</li> <li>donor: vaccinee ID</li> <li>sample: sample ID (arbitrary)</li> <li>timepoint: time point at which sample was collected</li> <li>tissue: tissue from which sample was collected</li> <li>sorting: FACS sorting</li> <li>seq_type: sequence type (mAb or bulk)</li> </ul>
Proactive COVID-19 testing in a partially vaccinated population.
<p>Complete simulation-generated datasets analyzed in McGee et al. (2021) Proactive COVID-19 testing in a partially vaccinated population. medRxiv 2021.08.15.21262095.</p> <p>Data is uploaded in comma-separated .csv files which have been compressed using gzip. Descriptions of data columns can be found in the column_descriptions.csv file.</p>
Vaximap's home vaccination dataset (October 2022)
<p>Dataset used in preparation for the publication "Vaximap: route optimisation for housebound vaccination", Nature Digital Health, 2022. DOI: 10.1038/s41746-022-00726-2. <strong>Please reference this publication when using this dataset. </strong></p> <p>This dataset comprises requests made to the Vaximap service between Jan 2021 and Oct 2022 for planning vaccinations of housebound patients. Patient locations have been anonymised at the cost of some geographic precision. </p> <p>Data is stored in HDF5 format generated via python and pandas.to_hdf(). </p>
Population-level health and economic impacts of introducing Vaccae vaccination in China: A modeling study
<p>Supplementary to "Population-level health and economic impacts of introducing Vaccae vaccination in China: A modeling study"</p>
Database Fear of COVID-19 and Vaccine Attitudes Examination Scale (VAX) in Spain
<p>This dataset contains data collected between November 15, 2021 and March 7, 2022, and between December 1, 2022 and February 6, 2023. It contains demographic variables, data on vaccinated people, responses to the items on the Fear of COVID-19 Scale, and to the items on the Vaccination Attitudes Examination (VAX) scale are included. Although the language of the open answers is Spanish, the name of the variables and the value labels are written in English to facilitate their understanding.<br> Data and codebooks are provided in csv format, following the FAIR principles.<br> Three files are provided:<br> 1. Database Fear of COVID-19 and VAX, with the data related to sample characteristics and the answers to the items of the questionnaires.<br> 2. Database codebook of variables, with information of the labels of the variables of the Database file.<br> 3. Variable values codebook, with the labels of the values of the variables in the Database file.</p>
Let's talk about COVID-19 vaccination: relevance of conversations about COVID-19 vaccination and information sources on vaccination intention in Switzerland
<p>Data to replicate the publication "Let's talk about COVID-19 vaccination: relevance of conversations about COVID-19 vaccination and information sources on vaccination intention in Switzerland?". This publication examines how public information sources and conversations about COVID-19 are associated with COVID-19 vaccination intention. Multivariable logistic regression and mediation analysis using generalized structural equation modeling were applied.</p>
Covid-19 Vaccine Monitoring project (CVM)-Electronic Health Record data sources Codelist
<p>This is the code list that was used to identify outcomes and covariates (those tagged as in narrow) in electronic health records of participating data sources in the the CVM study which was addressing the following questions</p> <p> </p> <p>1)<strong> To create and assess readiness of electronic health record data sources for rapid evaluation of safety signals by </strong></p> <ul> <li> <p>Providing an overview of the methods for identification of COVID-19 vaccine exposure in the data sources </p> </li> <li> <p>Monitoring the number of individuals exposed to any COVID-19 vaccine and to compare this to COVID-19 vaccine exposure (benchmark: ECDC vaccine tracker)1 </p> </li> <li> <p>Generation of updated background rates for AESIs </p> </li> </ul> <p><strong>2) To conduct rapid safety assessment studies using electronic healthcare records and support EMA safety assessments. </strong></p> <p>The protocol for this study is publicly available www.encepp.eu/encepp/viewResource.htm?id=42637. The report with results using the code list is publicly available on Zenodo as well. </p> <p> </p> <p> </p> <p> </p>
Characterization of the anti-spike IgG immune response to COVID-19 vaccines in people with a wide variety of immunodeficiencies
<p>Participants submitted saliva using the OME-505 collection device (OMNIgene Oral, Ottawa, Canada) every two weeks from vaccination through six months post-dose 3 to detect breakthrough SARS-CoV-2 infections. Viral RNA was extracted using the NucliSENS easyMag automated extraction system from 200ul of saliva in stabilizing solution and eluted in a total volume of 50ul. First strand cDNA synthesis was performed from 5ul of eluted RNA using SuperScript IV VILO Master Mix (Thermo Fisher). Positive specimens were then sequenced. Multiplex tiled amplicon libraries were prepared using the Midnight panel and Rapid barcoding kit RBK-004 (Oxford Nanopore technologies) using previously published protocol. Twelve sample pooled libraries were sequenced on a GridION X5 nanopore sequencer using Flongle adapters. After sequencing, raw data were processed using interARTIC to generate consensus sequences and variant calls. SARS-CoV-2<strong> </strong>lineages were determined using these consensus sequences and the NextClade and Pangolin platforms.</p>
Data from: Fluorescent biomarkers demonstrate prospects for spreadable vaccines to control disease transmission in wild bats
Vaccines that autonomously transfer among individuals have been proposed as a strategy to control infectious diseases within wildlife populations. However, understanding rates of spread and epidemiological efficacy in real world systems remain elusive. Here, we investigated whether topical vaccines that transfer among bats through social contacts can control vampire bat rabies, a medically and economically important zoonosis in Latin America. Field experiments in 3 Peruvian bat colonies which used fluorescent biomarkers as a proxy for the bat-to-bat transfer and ingestion of an oral vaccine revealed that vaccine transfer would increase population-level immunity up to 2.6 times beyond the same effort using conventional, non-spreadable vaccines. Mathematical models demonstrated that observed levels of vaccine transfer would reduce the probability, size, and duration of rabies outbreaks, even at low, but realistically achievable levels of vaccine application. Models further predicted that existing vaccines provide substantial advantages over culling bats, the policy currently implemented in North, Central, and South America. Linking field studies with biomarkers to mathematical models can inform how spreadable vaccines may combat pathogens of health and conservation concern prior to costly investments in vaccine design and testing.
Fig. 3 in Prairie dog responses to vector control and vaccination during an initial Yersinia pestis invasion
Fig. 3. Predicted re-encounter rates (95% confidence intervals [CIs]) over a single trapping interval (2007–2008) for adult female and male black-tailed prairie dogs inoculated at Conata Basin, South Dakota in 2007 with F1–V fusion protein vaccine or placebo on the no dust and dusted plots (the latter with flea control). Sample sizes are depicted above the 95% CIs.
Fig. 1 in Prairie dog responses to vector control and vaccination during an initial Yersinia pestis invasion
Fig. 1. Categories of flea vector control (deltamethrin dust) and F1–V fusion protein plague vaccination (V = vaccine, P = placebo, N = no inoculation) used for analyses of black-tailed prairie dog annual re-encounter rates (2007–2008 and 2008–2009) at Conata Basin, South Dakota. Annual re-encounter rates were compared for subsets of animals, here each enclosed by unique rectangles. Sample sizes are depicted in subsequent figures with results from multivariate analyses.
Fig. 5 in Prairie dog responses to vector control and vaccination during an initial Yersinia pestis invasion
Fig. 5. Predicted re-encounter rates (95% confidence intervals [CIs]) over a single trapping interval 2007–2008 for non-inoculated adult and juvenile blacktailed prairie dogs on the no dust and dusted plots (the latter with flea control) at Conata Basin, South Dakota. Sample sizes are depicted above the 95% CIs.
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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.