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.
10,244
datasets available to search
ShareScore release 0.9.0
Dataset results
10,244 results for “Vaccine”
Fig. 2 in Prairie dog responses to vector control and vaccination during an initial Yersinia pestis invasion
Fig. 2. Predicted flea parasitism (95% confidence intervals [CIs]) on blacktailed prairie dogs at the no dust and dusted plots, 2007–2008 at Conata Basin, South Dakota (prevalence on the left, intensity on the right). Prairie dog burrows on the dusted plots were treated annually with deltamethrin dust at ~4–6 g per burrow. Model predictions adjust (i.e., control) for year and Julian day (adjusted here as year 2008, and Julian day 212 for prevalence and 200 for intensity). Flea intensity data were log-transformed (log10) for analysis; hence, predicted flea intensity and 95% CIs could extend below 0. Sample sizes are depicted above the 95% CIs.
Fig. 4 in Prairie dog responses to vector control and vaccination during an initial Yersinia pestis invasion
Fig. 4. Predicted re-encounter rates (95% confidence intervals [CIs]) over two trapping intervals (2007–2008 and 2008–2009) for adult female and male black-tailed prairie dogs in Conata Basin, South Dakota inoculated in 2007 or 2008 with F1–V fusion protein vaccine or placebo on the dusted plots (with flea control). Sample sizes are depicted above or below the 95% CIs.
Pre-vaccination and early B cell signatures of the antibody response to SARS-CoV-2 mRNA vaccine
<p>The data presented in Code repository for Kardava, L., Rachmaninoff, N., Lau, W. W., Buckner, C. M., Trihemasava, K., Blazkova, J., ... & Moir, S. (2022). Early human B cell signatures of the primary antibody response to mRNA vaccination. Proceedings of the National Academy of Sciences, 119(28), e2204607119.<a href="https://www.pnas.org/doi/epdf/10.1073/pnas.2204607119">https://www.pnas.org/doi/epdf/10.1073/pnas.2204607119</a> are made available here.</p> <p>All code to reproduce the figures can be found here: https://github.com/niaid/COVID_Vaccine_Bcells</p> <p><a href="https://zenodo.org/api/files/f93859d0-b062-4def-8b17-c0f21ee36f09/all_subjects_cd19_positive_and_keys.zip">all_subjects_cd19_positive_and_keys.zip</a> contains a CSV file of all CD19+ cells with flowSOM clusters shown. Accompanying files allow for matching of timepoint and subject information.</p> <p><a href="https://zenodo.org/api/files/f93859d0-b062-4def-8b17-c0f21ee36f09/All_subjects_FCS_files_deidentified.zip">All_subjects_FCS_files_deidentified.zip</a> contains the raw fcs files and is organized by timepoint and subject.</p>
Associated code and data for "Multi-level computational modeling of anti-cancer dendritic cell vaccination utilized to select molecular targets for therapy optimization (doi: 10.3389/fcell.2021.74635)"
<p>This deposit contains the data, code, and analysis to reproduce the results in the manuscript - Lai X, Keller C, Santos-Rosales G, Schaft N, Dörrie J, Vera J. Multi-level computational modeling of anti-cancer dendritic cell vaccination utilized to select molecular targets for therapy optimization. Frontiers in Cell and Developmental Biolology. 2022; 9:746359; <a href="https://www.researchgate.net/publication/358461035_Multi-Level_Computational_Modeling_of_Anti-Cancer_Dendritic_Cell_Vaccination_Utilized_to_Select_Molecular_Targets_for_Therapy_Optimization">doi:10.3389/fcell.2021.746359</a>.</p> <p>If you have used the code for your research, please cite the original publication. Thank you very much.</p> <p> </p>
The knowledge, attitudes and perceptions towards the Covid-19 vaccine among dental staff at the University of the Western Cape, South Africa
<p><strong>Background:</strong> Despite the well-known increased risk of exposure to the Covid-19 virus in a dental setting, vaccination rates among staff members are low. This study was aimed at understanding the knowledge, attitudes and perceptions of staff towards the Covid-19 vaccine. This information, as well as the possible associations to demographic profiles, are necessary for authorities to adequately address specific concerns and uncertainties<strong>; (2) Methods:</strong> A descriptive cross-sectional study was conducted by means of an anonymous, online, validated questionnaire.; <strong>(3) Results:</strong> 105 staff members participated. Majority of staff have received the Covid-19 vaccine but stated that they would not take the booster vaccination. Significant associations between the level of education and the knowledge, attitudes and perceptions of staff were found.; <strong>(4) Conclusions:</strong> Majority of the staff members had a positive attitude towards the Covid-19 vaccine. However, specific concerns and uncertainties were identified and will need to be addressed in order to improve vaccination rates among staff members.</p>
Polio vaccine hesitancy in the networks and neighborhoods of Malegaon, India
<p>This dataset was created as part of our study on polio vaccine hesitancy in Malegaon, India. If using this dataset, please cite this data repository and the publication: Onnela JP, Landon BE, Kahn AL, Ahmed D, Verma H, O'Malley AJ, Bahl S, Sutter RW, Christakis NA. Polio vaccine hesitancy in the networks and neighborhoods of Malegaon, India. Soc Sci Med. 2016 Mar;153:99-106. doi: 10.1016/j.socscimed.2016.01.024. Epub 2016 Feb 4. PMID: 26889952.</p> <p> </p> <p><strong>A) Main data file data.csv</strong></p> <p>The files below can be constructed from this main file.</p> <p>Column: Variable (Value)<br> Col 0: Respondent ID (Integer)<br> Col 1: Data collection team ID (Integer)<br> Col 3: Vaccine status of household (1:Accepting, 2:Reluctant, 3:Refusing, 4:P0 House – no vaccine eligibles)<br> Col 12: Primary ID for Alter 1 – general questions (Integer)<br> Col 13: Duplicate ID for Alter 1 – general questions (Integer)<br> Col 14: Primary ID for Alter 2 – general questions (Integer)<br> Col 15: Duplicate ID for Alter 2 – general questions (Integer)<br> Col 16: Primary ID for Alter 3 – general questions (Integer)<br> Col 17: Duplicate ID for Alter 3 – general questions (Integer)<br> Col 18: Primary ID for Alter 4 – general questions (Integer)<br> Col 19: Duplicate ID for Alter 4 – general questions (Integer)<br> Col 48: Primary ID for Alter 1 if not household head – general questions (Integer)<br> Col 49: Duplicate ID for Alter 1 if not household head – general questions (Integer)<br> Col 50: Primary ID for Alter 2 if not household head – general questions (Integer)<br> Col 51: Duplicate ID for Alter 2 if not household head – general questions (Integer)<br> Col 52: Primary ID for Alter 3 if not household head – general questions (Integer)<br> Col 53: Duplicate ID for Alter 3 if not household head – general questions (Integer)<br> Col 54: Primary ID for Alter 4 if not household head – general questions (Integer)<br> Col 55: Duplicate ID for Alter 4 if not household head – general questions (Integer)<br> Col 56: Primary ID for Alter 1 – health questions (Integer)<br> Col 57: Duplicate ID for Alter 1 – health questions (Integer)<br> Col 58: Primary ID for Alter 2 – health questions (Integer)<br> Col 59: Duplicate ID for Alter 2 – health questions (Integer)<br> Col 60: Primary ID for Alter 3 – health questions (Integer)<br> Col 61: Duplicate ID for Alter 3 – health questions (Integer)<br> Col 62: Primary ID for Alter 4 – health questions (Integer)<br> Col 63: Duplicate ID for Alter 4 – health questions (Integer)<br> Col 92: Primary ID for Alter 1 if not household head – health questions (Integer)<br> Col 93: Duplicate ID for Alter 1 if not household head – health questions (Integer)<br> Col 94: Primary ID for Alter 2 if not household head – health questions (Integer)<br> Col 95: Duplicate ID for Alter 2 if not household head – health questions (Integer)<br> Col 96: Primary ID for Alter 3 if not household head – health questions (Integer)<br> Col 97: Duplicate ID for Alter 3 if not household head – health questions (Integer)<br> Col 98: Primary ID for Alter 4 if not household head – health questions (Integer)<br> Col 99: Duplicate ID for Alter 4 if not household head – health questions (Integer)<br> Col 101: Education (1:No school, 2:Primary school, 3:Middle school, 4:High school, 5:Intermediate diploma, 6:Graduate or post graduate, 7:Professional, 8:Islamic education)<br> Col 102: TV (1:Yes, 2:No)<br> Col 103: Phone (1:Yes, 2:No)<br> Col 104: Cooking cylinder (1:Yes, 2:No)<br> Col 105: Number of rooms (Integer)<br> Col 106: Toilet (1:Yes, 2:No)<br> Col 107: Number of people (Integer)</p> <p> </p> <p><strong>B) Derived network files (edge lists)</strong></p> <p>These directed network edge lists were derived from the main data file. Here N stands for number of nodes and L stands for number of edges.</p> <p>1) Nomination network (directed, general only): N=8161, L=7357; edgelist_dg.txt</p> <p>2) Nomination network (directed, health only): N=7223, L=5744; edgelist_dh.txt</p> <p>3) Nomination network (combined, directed): N=11828, L=11655; edgelist_d.txt</p> <p>4) Nomination network (combined, directed) LCC: N=6113, L=6647; edgelist_d_lcc.txt</p> <p>5) Vaccine network (directed): N=2428, L=1355; edgelist_g_vstatusok.txt</p> <p>6) Vaccine network (directed) LCC: N=710, L=813; edgelist_g_vstatusok_lcc.txt</p> <p> </p> <p><strong>C) Derived nodal attribute file final_node_data.csv</strong></p> <p>This nodal attribute file was derived from the main data file.</p> <p>Column: Variable (Value)<br> Col 0: Respondent ID (Integer)<br> Col 1: Data collection team ID (Integer)<br> Col 2: Vaccine status of household (1:Accepting, 2:Reluctant, 3:Refusing, 4:P0 House – no vaccine eligibles)<br> Col 3: In-degree – general questions (Integer)<br> Col 4: In-degree – health questions (Integer)<br> Col 5: In-degree – combined (Integer)<br> Col 6: Out-degree – general questions (Integer)<br> Col 7: Out-degree – health questions (Integer)<br> Col 8: Out-degree – combined (Integer)<br> Col 9: Education (1:No school, 2:Primary school, 3:Middle school, 4:High school, 5:Intermediate diploma, 6:Graduate or post graduate, 7:Professional, 8:Islamic education)<br> Col 10: TV (1:Yes, 2:No)<br> Col 11: Phone (1:Yes, 2:No)<br> Col 12: Cooking cylinder (1:Yes, 2:No)<br> Col 13: Number of rooms (Integer)<br> Col 14: Toilet (1:Yes, 2:No)<br> Col 15: Number of people (Integer)</p>
Dataset from "Merging Digital Humanities and Discourse Analysis in the Study of COVID-19 Vaccine Distribution in Norwegian Newspapers" (Sverdljuk et al. 2022)
<p>Contains URNs (identifiers) for the newspapers used in the corpus study "Merging Digital Humanities and Discourse Analysis in the Study of COVID-19 Vaccine Distribution in Norwegian Newspapers".</p> <p>For each subcorpus there is an Excel file containing references to the objects used, together with basic metadata.</p> <p>The corpus definitions can be used in various webapps of the DH-LAB at the National Library of Norway, e.g.:</p> <p><a href="https://beta.nb.no/dhlab/concordances/">https://beta.nb.no/dhlab/concordances/</a></p> <p><a href="https://beta.nb.no/dhlab/collocations/">https://beta.nb.no/dhlab/collocations/</a></p> <p>See more at <a href="https://www.nb.no/dh-lab/">https://www.nb.no/dh-lab/</a></p>
Twitter Dataset - Over 200,000 Tweets containing the word "Vaccine" for research porpuses
<p>This dataset contains 220,085 tweets containing the word vaccine between December 9th and December 18th 2021 at different times during each day, extracted using the Twitter API v2. Each tweet was extracted at least 3 days after its initial posting time in order to register 3 days of engagements, and it doesn't include retweets.</p> <p>Includes:</p> <ul> <li>Tweet ID</li> <li>Text</li> <li>Author ID</li> <li>Date</li> <li>Like count</li> <li>Retweet count</li> <li>Quote count</li> <li>Reply count</li> <li>User data (Followers, Following, Tweet count, Account creation date, Verified status)</li> </ul> <p>Usernames are hidden for privacy reasons</p>
Germinal centre-driven maturation of B cell response to SARS-CoV-2 mRNA vaccination
<p>These are the<strong> processed</strong> BCR repertoire and transcriptomics data described in <a href="https://doi.org/10.1038/s41586-022-04527-1">Kim & Zhou et al., <em>Nature</em>, 2022</a>. The <strong>raw</strong> sequencing data new to this study are available on SRA under BioProject <a href="https://www.ncbi.nlm.nih.gov/sra/?term=PRJNA777934">PRJNA777934</a>. This study also used BCR repertoire data from <a href="https://doi.org/10.1038/s41586-021-03738-2">Turner & O'Halloran et al., <em>Nature</em>, 2021</a> (<a href="https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA731610">PRJNA731610</a>) and <a href="https://doi.org/10.1016/j.immuni.2021.08.013">Schmitz, Turner & Liu et al., <em>Immunity</em>, 2021</a> (<a href="https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA741267">PRJNA741267</a>).</p> <p> </p> <p><strong>Code</strong></p> <p>Code along with Docker containers 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_2022">found on GitHub</a>.</p> <p> </p> <p><strong>Metadata</strong></p> <p>File: WU368_kim_et_al_nature_2022_meta.tsv</p> <p>Notes:</p> <ul> <li>Sample breakdown by `sequence_type` (132 total) <ul> <li>73 bulk BCR sequencing samples (`bulk`) <ul> <li>57 new</li> <li>5 from <a href="https://doi.org/10.1038/s41586-021-03738-2">Turner & O'Halloran et al., <em>Nature</em>, 2021</a></li> <li>11 from <a href="https://doi.org/10.1016/j.immuni.2021.08.013">Schmitz, Turner & Liu et al., <em>Immunity</em>, 2021</a>.</li> </ul> </li> <li>56 10x Genomics single-cell VDJ + 5' gene expression samples (`tgx`)</li> <li>3 samples from <a href="https://doi.org/10.1038/s41586-021-03738-2">Turner & O'Halloran et al., <em>Nature</em>, 2021</a> (`mab`) corresponding to a total of 37 S-binding mAbs previously reported. These are not the same as the 2099 recombinant mAbs generated in this study (see below).</li> </ul> </li> <li>Sample collection time was originally recorded in days in the `timepoint` column. Timepoints were referenced in weeks in the manuscript, as shown in the `timepoint_ms` column.</li> <li>`bio_rep` and `tech_rep` = biological replicate and technical replicate respectively.</li> </ul> <p>Abbreviations:</p> <ul> <li>LN = lymph node</li> <li>BM = bone marrow</li> <li>PB = plasmablast</li> <li>GC = germinal centre</li> <li>LLPC = long-lived plasma cell</li> <li>NS = no sorting</li> <li>mAb = monoclonal antibody</li> </ul> <p> </p> <p><strong>Information on the 2099 recombinant mAbs generated in this study</strong></p> <p>File: WU368_kim_et_al_nature_2022_mabs.tsv</p> <p>Notes on columns:</p> <ul> <li>`h_sequence_id` and `l_sequence_id`: Sequence IDs of the heavy and light chains respectively.</li> <li>`elisa`: ELISA results for binding to SARS-CoV-2 S (`TRUE` = positive).</li> </ul> <p> </p> <p><strong>Processed BCR data - heavy chains</strong></p> <p>File: WU368_kim_et_al_nature_2022_bcr_heavy.tsv</p> <p><em>Analysis was based on heavy chain-based clonal inference.</em></p> <p>Notes on columns:</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 were used, as opposed to IMGT-defined "junctions". Nonetheless, junction-related columns are included here as some repositories such as <a href="https://gateway.ireceptor.org/login"><em>iReceptor</em></a> use these. Non-standard columns are noted below.</p> <ul> </ul> <ul> <li>`cell_id`: Only sequences from single-cell samples and the 37 mAbs from Turner & O'Halloran et al., <em>Nature</em>, 2021 have cell IDs following the format `[donor]_[sample]@[id]`. `NA` for bulk sequences.</li> <li>`sequence_id`: Sequence IDs follow the format `[donor]_[sample]@[id]`.</li> <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 calls after corresponding clonal consensus sequence were reconstructed via <a href="https://changeo.readthedocs.io/en/stable/methods/germlines.html">`CreateGermlines.py --cloned` from 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`, `timepoint`, `tissue`, `sorting`, `seq_type`: Propagated as is from the metadata file. <ul> <li>In `seq_type`, `tgx` corresponds to 10x Genomics data; `mab` corresponds specifically to the 37 S-binding mAbs from Turner & O'Halloran et al., <em>Nature</em>, 2021.</li> </ul> </li> <li>`timepoint_2`: Same as `timepoint`, except that `d28+d35` and `d201+d208` were treated as `d28` (week 4) and `d201` (week 29) respectively as described in Materials & Methods.</li> <li>`gex_anno`: Cell type identity annotation based on transcriptomic profiles. Mapped from `anno_leiden_0.18` from WU368_kim_et_al_nature_2022_gex_b_cells.h5ad.</li> <li>`compartment`: B cell compartment. <ul> <li>ABC = activated B cell. LNPC = lymph node plasma cell. RMB = resting memory B cell.</li> <li>Minor differences in terminology <ul> <li>The manuscript refers to the memory compartment as MBCs, whereas the terminology used in the data is RMB. As described in Materials & Methods, analysis involving the memory compartment used specifically d201 bulk-sequenced memory sorts from blood. To get these sequences, subset `s_pos_clone`, `seq_type`, `compartment`, and `timepoint_2` to, respectively, `TRUE`, `bulk`, `RMB`, and `d201`. </li> <li>The manuscript uses the term BMPC (bone marrow plasma cell), whereas the data uses the term LLPC.</li> </ul> </li> </ul> </li> <li>`clone_id`: B cell clonal lineage IDs follow the format `[donor]@[id]`.</li> <li>`s_pos_clone`: `TRUE` if a sequence belonged to a B cell clone that was designated as S-binding by virtue of containing one of the recombinant mAbs that tested positive via ELISA or one of the S-binding mAbs from Turner & O'Halloran et al., <em>Nature</em>, 2021.</li> <li>`expressed_id`: mAb IDs for the 2099 recombinant mAbs generated in this study (mapped from `mab_id` from WU368_kim_et_al_nature_2022_mabs.tsv) and the 37 mAbs from Turner & O'Halloran et al., <em>Nature</em>, 2021. `NA` for everything else.</li> <li>`elisa`: ELISA results for binding of recombinant mAbs to SARS-CoV-2 S. `TRUE` if positive. `NA` if not tested.</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> <p> </p> <p><strong>Processed BCR data - light chains</strong></p> <p>File: WU368_kim_et_al_nature_2022_bcr_light.tsv</p> <p><em>Light chains were not used for heavy chain-based clonal inference or analysis.</em></p> <p> </p> <p><strong>Processed transcriptomics data</strong></p> <p>Files:</p> <ul> <li>WU368_kim_et_al_nature_2022_gex_all_cells.h5ad (clustering all cells)</li> <li>WU368_kim_et_al_nature_2022_gex_b_cells.h5ad (re-clustering only the B cells)</li> </ul> <p>Notes:</p> <ul> <li>The `h5ad` files can be imported into <a href="https://scanpy.readthedocs.io/en/stable/index.html">Scanpy</a> as an <a href="https://scanpy.readthedocs.io/en/stable/usage-principles.html#anndata">AnnData object</a>.</li> <li>Each `AnnData` object has 3 `.layers`, each representing a version of the count matrix. <ul> <li>`raw_counts`: Imported from `<a href="https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/6.0/using/aggregate">cellranger aggr</a>` output by `scanpy.read_10x_mtx`.</li> <li>`log_norm`: Log-noramlized expression values outputted by `scanpy.pp.normalize_total` followed by `scanpy.pp.log1p`.</li> <li>`scaled`: The `log_norm` layer scaled to unit variance and zero mean by `scanpy.pp.scale`. </li> </ul> </li> <li>The `gene_name` and `biotype` columns in `.var` were extracted from GENCODE v32 GTF.</li> <li>Columns in `.obs` (each row corresponds to a cell) <ul> <li>`n_feature`: The `n_genes_by_counts` column produced by `scanpy.pp.calculate_qc_metrics`, renamed. The number of genes expressed. This is before subsetting the genes.</li> <li>`n_umi`: The `total_counts` column produced by `scanpy.pp.calculate_qc_metrics`, renamed. The total UMI counts in a cell.</li> <li>`pct_mt`: The `pct_counts_mt` column produced by `scanpy.pp.calculate_qc_metrics`, renamed. The percentage of counts in mitochondrial genes.</li> <li>`n_hkg`: The number of housekeeping genes for which expression was detected.</li> <li>`n_gene_expressed`: The total number of genes for which expression was detected. This is after subsetting the genes.</li> <li>`pre_qc_bcr`: `TRUE` if a cell also had paired BCR data available. Produced by cross-referencing the cellular barcodes in `cell_barcodes.json` outputted by `cellranger vdj`. At this point the BCR data had not gone through the QC process in the BCR processing pipeline (hence `pre_qc`). </li> <li>`leiden_[resolution]`: Cluster assignment by `scanpy.tl.leiden`.</li> <li>`anno_leiden_[resolution]`: Cell type identity annotations based on transcriptomic profiles. This was mapped onto the `gex_anno` column in the processed heavy chain BCR data.</li> </ul> </li> <li>UMAP coordinates can be found in `.obsm["X_umap"]`.</li> <li>`.X` has been set to `None` in order to reduce file size.</li> </ul> <p>In addition, the preprocessed count matrix outputted by `<a href="https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/6.0/using/aggregate">cellranger aggr</a>` is available from <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE195673">GEO under BioProject PRJNA777934</a>.</p>
Repeated information of benefits reduces COVID-19 vaccination hesitancy: Experimental evidence from Germany
<p>This replication package contains the raw data and code to replicate the findings reported in the paper. The data and code are licensed under a Creative Commons Attribution 4.0 International Public License. See <strong>LICENSE.txt</strong> for details.</p> <p><strong>Software requirements</strong></p> <p>All analysis were done in Stata version 16:</p> <ul> <li>Add-on packages are included in <strong>scripts/libraries/stata</strong> and do not need to be installed by user. The names, installation sources, and installation dates of these packages are available in <strong>scripts/libraries/stata/stata.trk</strong>.</li> </ul> <p><strong>Instructions</strong></p> <ol> <li>Save the folder <strong>‘replication_PLOS’</strong> to your local drive.</li> <li>Open the master script <strong>‘run.do’</strong> and change the global pointing to the working direction (line 20) to the location where you save the folder on your local drive</li> <li>Run the master script <strong>‘run.do’</strong> to replicate the analysis and generate all tables and figures reported in the paper and supplementary online materials</li> </ol> <p><strong>Datasets</strong></p> <ul> <li>Wave 1 – Survey experiment: <strong>‘wave1_survey_experiment_raw.dta’</strong></li> <li>Wave 2 – Follow-up Survey: <strong>‘wave2_follow_up_raw.dta'</strong></li> <li>Map: shape-files <strong>‘plz2stellig.shp’ ‘OSM_PLZ.shp’</strong>, area codes <em><em>‘Postleitzahlengebiete</em>-_OSM.csv’</em>_, (all links to the sources can be found in the script ‘04_figure2_germany_map.do’)</li> <li>Pretest: <strong>‘pre-test_corona_raw.dta’</strong></li> <li>For Appendix S7: <strong>‘alter_geschlecht_zensus_det.xlsx’, ‘vaccination_landkreis_raw.dta’, ‘census2020_age_gender.csv’</strong> (all links to the sources can be found in the script ‘06_AppendixS7.do’)</li> <li>For Appendix S10: ‘<strong>vaccination_landkreis_raw.dta’</strong> (all links to the sources can be found in the script ‘07_AppendixS10.do’)</li> </ul> <p><strong>Descriptions of scripts</strong></p> <p><strong>1_1_clean_wave1.do</strong><br> This script processes the raw data from wave 1, the survey experiment<br> <strong>1_2_clean_wave2.do</strong><br> This script processes the raw data from wave 2, the follow-up survey<br> <strong>1_3_merge_generate.do</strong><br> This script creates the datasets used in the main analysis and for robustness checks by merging the cleaned data from wave 1 and 2, tests the exclusion criteria and creates additional variables<br> <strong>02_analysis.do</strong><br> This script estimates regression models in Stata, creates figures and tables, saving them to <strong>results/figures and results/tables</strong><br> <strong>03_robustness_checks_no_exclusion.do</strong><br> This script runs the main analysis using the dataset without applying the exclusion criteria. Results are saved in <strong>results/tables</strong><br> <strong>04_figure2_germany_map.do</strong><br> This script creates Figure 2 in the main manuscript using publicly available data on vaccination numbers in Germany.<br> <strong>05_figureS1_dogmatism_scale.do</strong><br> This script creates Figure S1 using data from a pretest to adjust the dogmatism scale.<br> <strong>06_AppendixS7.do</strong><br> This script creates the figures and tables provided in Appendix S7 on the representativity of our sample compared to the German average using publicly available data about the age distribution in Germany.<br> <strong>07_AppendixS10.do</strong><br> This script creates the figures and tables provided in Appendix S10 on the external validity of vaccination rates in our sample using publicly available data on vaccination numbers in Germany.</p>
Moral Values of Twitter COVID-19 Vaccine Data
<p>This data is part of our accepted paper "Learning to Adapt Domain Shifts of Moral Values via Instance Weighting" at the 33rd ACM Conference on Hypertext and Social Media (HT ’22). We annotate moral values of COVID-19 vaccine-related tweets. </p>
Waterloo, Ontario Federal and Provincial Voting Intention and Vaccine Hesitancy
<p>This it the initial release of federal and provincial voting intention in Waterloo Region in the spring of 2022, commissioned by the Laurier Institute for the Study of Public Opinion and Policy.</p>
Influence of conspiracy theories and distrust of community health volunteers on adherence to COVID-19 guidelines and vaccine uptake in Kenya
<p>This cross-sectional study collected data between 25 May –27 June 2021 n=447. It involved all registered community health volunteers (CHVs) who had participated in the COVID-19 vaccine hesitancy study. This data was collected as part of an Epidemic Ethics/WHO initiative that FCDO/Wellcome Grant 214711/Z/18/Z has supported. WHO’s specific grant number was 2020/1077878-0). The funders had no role in study design, data collection and analysis, decision to publish, or manuscript preparation. No authors received a salary from the funders.</p>
Data for Analyses presented in associated publication: Investigating trends in those who experience menstrual bleeding changes after SARS CoV-2 vaccination
<p>This is the data used for analyses presented in our associated publication (details to be provided later). There are three datasets, because we analyzed by specific, biologically relevant subgroups (pre-menopausal people with predictable menstrual cycles, pre-menopausal people who usually do not menstruate, and post-menopausal people). The data provided here has been cleaned and aggregated as described in the associated publication. Specifically,</p> <ul> <li>here we have only included the variables that are included in our analyses</li> <li>we collapsed specific identities (gender, race, ethnicity) into binarized categories (as used in the publication) in order to reduce the likelihood someone could be re-identified from this de-identified data</li> <li>this dataset also does not include unique identifiers for individuals, but instead has row numbers for each dataset (starting at 1 for each data subset). There are no repeated individuals between datasets, as these are mutually exclusive subgroups</li> <li>Additional information regarding the survey and data processes can be found at https://osf.io/6rvxk/?view_only=f91f1247658f49e3bbf59b2f6cfd3898</li> </ul>
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>
Pre- and post-intervention responses to a knowledge, attitudes, and practices survey for the study, "Disseminating vaccination information in baby soap products increases knowledge and vaccine uptake in central Uganda: A non-randomized controlled trial"
<p>This dataset contains responses to the pre- and post-intervention knowledge, attitudes, and practices surveys utilized for the study, "Disseminating vaccination information in baby soap products increases knowledge and vaccine uptake in central Uganda: A non-randomized controlled trial."</p>
Data presented in Multi-trial analysis of HIV-1 envelope gp41-reactive antibodies among global recipients of candidate HIV-1 vaccines.
<p>This folder contains datasets analyzed in the manuscript:</p> <p>Multi-trial analysis of HIV-1 envelope gp41-reactive antibodies among global recipients of candidate HIV-1 vaccines.</p> <p>Frontiers in Immunology<br> Sec. Vaccines and Molecular Therapeutics<br> doi: 10.3389/fimmu.2022.983313</p>
Optimal vaccination at high reproductive numbers: sharp transitions and counterintuitive allocations
Optimization of vaccine allocations among different segments of a heterogeneous population is important for enhancing the effectiveness of vaccination campaigns in reducing the burden of epidemics. Intuitively, it would seem that allocations designed to minimize infections should prioritize those with the highest risk of being infected and infecting others. This prescription is well supported by vaccination theory, e.g. when the vaccination campaign aims to reach herd immunity. In this work, we show, however, that for vaccines providing partial protection (leaky vaccines) and for sufficiently high values of the basic reproduction number, intuition is overturned: the optimal allocation minimizing the number of infections prioritizes the vaccination of those who are least likely to be infected. The work combines numerical investigations, asymptotic analysis for a general model, and complete mathematical analysis in a two-group model. The results point to important considerations in managing vaccination campaigns for infections with high transmissibility.
Alternative splicing and genetic variation of MHC-E: Implications for rhesus cytomegalovirus-based vaccines
<p>We used long-read sequencing to interrogate rhesus macaque (RM) MHC-E (Mamu-E) alternative splicing and genetic variations. Full-length Mamu-E RNA isoforms were recovered using the PacBio Iso-Seq method. Incomplete 5' ends of Mamu-E isoforms were confirmed using Sanger sequencing, where we identified three additional isoforms. Full-length human MHC-E (HLA-E) isoforms were also recovered using the PacBio Iso-Seq method. Isoform sequences and annotations are provided for both Mamu-E and HLA-E in addition to Mamu-E Sanger sequencing data. HLA-E annotations are reported using the hg38 reference, while Mamu-E annotations are shown using rhesus MHC Class I and II assemblies previously generated using Bacterial Artificial Cloning (BAC) technology (https://www.ncbi.nlm.nih.gov/nuccore/AC148696.1).</p> <p>Using PacBio Long Amplicon Analysis, we sequenced complete Mamu-E coding regions of 59 RMs and additionally captured 3' UTR polymorphism using mRNA-seq haplotype phasing analysis. The complete genotyping data for these animals are provided as well as animal metadata. Genotyping data is shown using the Mamu-E canonical isoform (Mamu-E1 from Iso-Seq analysis) as reference.</p>
Dataset: VBI Vaccines Inc. (VBIV) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
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.