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.
12,799
datasets available to search
ShareScore release 0.9.0
Dataset results
12,799 results for “immunity”
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>
Data set of the manuscript titled: Follicular Immune Landscaping Reveals a distinct profile of FOXP3hi CD4+ T cells in Treated compared to Untreated HIV
<p>Multiplex imaging data were collected using a scanning confocal system (STELARIS, Leica) and proccessed with the Imaris and Fiji imaging programs. csv files incuding the position identifiers and intensities for each fluorochrome used were generated and data were further analysed using the FlowJo10 program. Neighboring analysis was performed using the G function and mean of minimum distances of relevant cell type pairs. </p>
Supplemental Information - Allosteric activation of the co-receptor BAK1 by the EFR receptor kinase initiates immune signaling
<p>This folder contains</p> <p>1) maps of plasmids</p> <p>2) files of phylogenetic analysis </p> <p>3) Replication information</p> <p>4) Image cropping information</p> <p>5) Gene IDs and protein sequences</p> <p>that are part of the manuscript "Allosteric activation of the co-receptor BAK1 by the EFR receptor kinase initiates immune signaling"</p>
Dataset of 'HIV infection is associated with compromised tumor microenvironment adaptive immune reactivity in Hodgkin Lymphoma'
<p><span><span>§<span> </span></span></span><strong><span>:</span></strong><span>The data were generated using the i) GeoMx Digital Spatial Profiler (DSP) platform developed by Nanostring Technologies. GeoMx analysis utilizes <em>in situ </em>RNA hybridization with Whole Atlas Transcriptome probe (Nanostring) and ii) HTG platform (Immune Response kit) Our dataset comprises samples from donors categorized as HLposHIVnegEBVneg, HLposHIVposEBVpos, or HLposHIVnegEBVpos (HL: Hodgkin Lymphoma). Regions of interest (ROI) were spatially profiled to capture distinct molecular signatures associated with these donor categories.</span></p>
RNA datasets to derive predictors for immune checkpoint inhibitor therapy of non-small cell lung cancer
<p>Nanostring nCounter datasets and corresponding clinical data of tumor samples of patients with advanced NSCLC who received anti-PD-1 immuntherapy. Prospectively divided into a discovery and a validation cohort.</p> <p>Please cite the corresponding publication in Annals of Oncology (10.1093/annonc/mdz049)</p>
Dietary fibers boost gut microbiome-produced B vitamin pool and alter host immune landscape
<p>This dataset contains fcs files of lymphocytes from the colonic lamina propria, lungs, and spleens of specific-pathogen-free (SPF), gnotobiotic (14-member synthetic microbiota, 14SM) or germ-free (GF) mice fed five distinct rodent diets (Standard chow 1, SC1; Standard chow 2, SC2; Fiber-supplemented diet, FS; Inulin-supplemented diet, IN; or Fiber-free diet, FF), analysed by mass cytometry. Three million cells per organ per animal were transferred into 15 mL conical tubes. For live/dead staining, cells were incubated with 5 μM cisplatin for 5 minutes. Cells were washed, and cell surface staining mix was added containing pre-conjugated antibodies for 30 minutes at room temperature. Samples were washed twice with FACS buffer, then fixed using the FoxP3 Fix/Perm kit (eBiosciences) for 45 minutes at 4°C, followed by permeabilization wash. Samples were then incubated with the intracellular staining mix for 30 minutes at room temperature. Cells were washed with FACS buffer twice, and pellets were resuspended in Cell-ID™ Intercalator-Ir (Fluidigm) in MaxPar fixation solution (Fluidigm, catalogue no. 201192B) and refrigerated overnight, or for up to five days. Prior to acquisition, samples were washed twice with 1X PBS, and then washed twice with deionized water. Cell pellets were further resuspended in deionized water at 0.5 × 10^6 cells/mL and topped up with 10% calibration beads (EQ Four Element Calibration Beads, Fluidigm). All samples were acquired on the Helios Mass Cytometer (Fluidigm). Effector immune populations and activated T cells in the gut accumulate in a microbiota-dependent manner. Shifts in the microbiome according to dietary fiber source and content result in altered concentrations of B vitamins available to the host, which is tied to distinct alterations in innate and adaptive immune populations. </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>
Neutrophil and emergency granulopoietic drivers of sepsis immune suppression and an extreme response to infection
<p>The dysregulated host response to infection leading to organ dysfunction is highly heterogeneous. It is currently poorly delineated by sepsis as a clinical syndromic classification, thus confounding immunotherapy trials. Here we establish the pathophysiology and potential therapeutic targets of a specific extreme response to infection state (sepsis response signature SRS1), characterised by immune compromise and poor outcome. We first derive a whole blood single-cell multi-omic atlas of the sepsis response (2727,993 cells, n=39), finding an increase in IL1R2+ immature neutrophils in SRS1, which we confirmed by CyTOF and RNA-sequencing (n=53). We next uncovered high activity of neutrophil STAT3 gene expression programs in SRS1, which were shared across multiple infectioius disease settings (n=1044) irrespective of the clinical definition of the patient cohorts. We observed elevated plasma G-CSF and IL-6 in SRS1, suggesting heightened emergency granulopoiesis (EG). We therefore characterised patient and healthy control hematopoietic stem cells (HSCs) using single-cell RNA/chromatin accessibility multi-omics (29,366 cells, n=27), identifying SRS1-specific EG transcriptional skewing, together with STAT3 and EG master regulator CEBPB epigenetic signatures. Our findings establish a common cellular axis present across extreme responses to infection, reveal its hematopoietic origin, and nominate G-CSF and IL-6 as potential therapeutic targets for the SRS1 state.</p> <p> </p> <p>The present data deposit includes processed and quality-controlled data tables for:</p> <p>1. Whole blood leukocytes profiled with the BD Rhapsody platform in a cohort of 39 sepsis patients (RNA and protein count matrices, as well as their accompanying metadata table)</p> <p>2. Circulating HSCs in blood profiled with the 10X multiomics platform in a cohort of 27 sepsis patients (RNA and ATAC-seq count matrices, as well as their accompanying metadata tables)</p>
scRNA-seq data for article: Kupffer cell and recruited macrophage heterogeneity orchestrate granuloma maturation and hepatic immunity in visceral leishmaniasis
<p>Single-cell RNA-seq dataset from sorted CD11bInt, F4/80Hi, CD64+ mouse liver cells in naive or Leishmania infantum-infected animals at 42 d.p.i.. Data analyses and results are described in manuscript: "Kupffer cell and recruited macrophage heterogeneity orchestrate granuloma maturation and hepatic immunity in visceral leishmaniasis". Data files are Seurat objects in RDS format. Filtered-out potential doublets, low quality cells and dying cells (excluded cells with <1000 genes detected, cells with >6000 genes detected, cells with mitochondrial gene expression > 10% and cells with <5000 transcript molecules). Data normalization, scaling and integration performed using Seurat.</p> <p>Filtered dataset containing all KCs and macrophages is in the "pessenda_KC_Macro_seurat" file.</p> <p>Our data were then mapped onto a reference dataset published by Remmerie et al. (DOI: 10.1016/j.immuni.2020.08.004) for annotation consistent with the literature. The reference mapped object can be found in the "pessenda_refmap_KC_Macro_seurat" file.</p> <p>Dataset containing the additional analysis of CLEC4F-TIM4+ FACS-sorted KCs can be found in the "pessenda_refmap_KCTimPos_seurat" file.</p>
A Single-Cell Tumor Immune Atlas for Precision Oncology
<p><strong>Publication version of the Single-Cell Tumor Immune Atlas</strong></p> <p>This upload contains:</p> <ul> <li><strong>TICAtlas.rds:</strong> an rds file containing a Seurat object with the whole Atlas</li> <li><strong>TICAtlas.h5ad:</strong> an h5ad file with the whole Atlas</li> <li><strong>TICAtlas_downsampled.rds:</strong> an rds file containing a downsampled version of the Seurat object of the whole Atlas</li> <li><strong>TICAtlas_downsampled.h5ad:</strong> an rds file containing a downsampled version of the Seurat object of the whole Atlas</li> <li><strong>TICAtlas_metadata.csv: </strong>a comma-separated text file with the metadata for each of the cells</li> </ul> <p>All the files contain the following patient/sample metadata variables:</p> <ul> <li>patient: assigned patient identifiers</li> <li>nCountRNA and nFeatureRNA: number of UMIs and genes per cell</li> <li>percent.mt: percentage of mitochondrial genes</li> <li>gender: the patient's gender (male/female/unknown)</li> <li>source: dataset of origin</li> <li>subtype: cancer type (abbreviations as indicated in the preprint)</li> <li>kmeans_cluster: patients clusters, NA if filtered out before clustering</li> <li>lv1 and lv2: annotated cell type for each of the cells, two level annotation (lv2 has more cell types)</li> </ul> <pre> </pre> <p>If you have any issues with the metadata (i.e. unexpected factors, NA values...) you can use the <strong>TICAtlas_metadata.csv </strong>file.</p> <p>For more information, <a href="https://genome.cshlp.org/content/early/2021/09/21/gr.273300.120.">read our paper</a>, <a href="https://github.com/Single-Cell-Genomics-Group-CNAG-CRG/Tumor-Immune-Cell-Atlas">check our GitHub</a> and our <a href="https://singlecellgenomics-cnag-crg.shinyapps.io/TICA/">ShinyApp</a>.</p> <p>h5ad files can be read with Python using <a href="https://scanpy.readthedocs.io/en/stable/">Scanpy</a>, rds files can be read in R using <a href="https://satijalab.org/seurat/">Seurat</a>. For format conversion between AnnData and Seurat we recommend <a href="https://mojaveazure.github.io/seurat-disk/">SeuratDisk</a>. For other single-cell data formats you can use <a href="https://github.com/cellgeni/sceasy">sceasy</a>.</p>
NanoString dataset for study: Impairment of cancer-associated fibroblasts promotes CD8+ T cell infiltration and enhances sensitivity to immune checkpoint blockade
<p>Pre-processed NanoString mRNA abundance data and associated sample sheet for study:</p> <p>Impairment of cancer-associated fibroblasts promotes CD8+ T cell infiltration and enhances sensitivity to immune checkpoint blockade</p>
Cellular and Humoral Immune Responses after Immunisation with Low Virulent African Swine Fever Virus in the Large White Inbred Babraham Line and Outbred Domestic Pigs
<p>Raw data for manuscript. Contains temperature, clinical scores, qPCR, blood cell numbers and immune responses over time for two groups of pigs immunised with low virulent African swine fever virus and challenged with highly virulent virus. Data for each panel or figure is displayed on a separate worksheet in the file. The readme worksheet contains a brief description of each figure. The majority of data is displayed in an XY table format, with the number of days post immunisation with low virulent virus indicated.</p>
Genome-wide identification of cell-surface and intracellular immune receptors in 350 plant species
<p>Here we identified cell-surface (LRR-RLKs, LRR-RLPs, LysM-RLKs and LysM-RLPs) and intracellular immune receptors (NB-ARCs) from the genomes of 350 plant species. </p> <p> </p> <p>Zip file contains:</p> <p>Folder 'Immune_receptor_sequences' - FASTA files of the identified LRR-RLPs, Lys-RLKs, LysM-RLPs and NB-ARCs.</p> <p>Folder 'RLK_sequences' - FASTA files of the identified LRR-RLKs (all and 20 individual subgroups).</p> <p>Folder 'RLK_trees' - Phylogenetic TREE files of the identified LRR-RLKs (all and 20 individual subgroups); classified according to their kinase domains.</p> <p>238.species - Phylogenetic tree of the 238 plant species used in the analyses (taken from <a href="https://doi.org/10.1093/jpe/rtv047">https://doi.org/10.1093/jpe/rtv047</a>).</p> <p>350.species - Phylogenetic tree of the 350 plant species used in the analyses.</p> <p>simple.to.original.ids- Translator file for the original ID of each gene. </p> <p> </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>
Development of ferret immune repertoire reference resources and single-cell-based high- throughput profiling assays
<p>We performed long read transcriptome sequencing of ferret splenocyte and lymph node samples full-length, non-chimeric circular consensus sequencing (CCS) reads to obtain over 120,000 high-quality immunoglobin (Ig) and T cell receptor (TCR) transcripts.</p>
Robust estimation of cancer and immune cell-type proportions from bulk tumor ATAC-Seq data.
<p>Bulk ATAC-seq data of tumour samples result in an averaged signal across different cell-types (cancer, stromal, vascular and immune cells). We propose a deconvolution framework called EPIC-ATAC (<a href="https://doi.org/10.7554/eLife.94833.1">https://doi.org/10.7554/eLife.94833.1</a>), which relies on newly identified cell-type specific ATAC-Seq marker peaks and reference profiles for all major cancer-relevant cell-types to predict the proportions of each cell-type.</p> <p>To evaluate EPIC-ATAC, we generated a bulk ATAC-Seq dataset from peripheral blood mononuclear cells (PBMCs) samples, from which the number of cells in each cell-type has been estimated using flow cytometry, as ground truth for cell proportions. The data provided in this Zenodo deposit correspond to:</p> <p>- The raw counts matrix for each peak called in this ATAC-Seq dataset: PBMC_counts.txt</p> <p>- The normalized (TPM-like) counts matrix for each peak called in this ATAC-Seq dataset: PBMC_counts_norm.txt</p> <p>- The cell fractions of each cell type in each sample: PBMC_cell_fractions.txt</p> <p>- The peaks called in each sample using MACS2 (*narrow.peaks): *_normalized.narrowPeak</p> <p>- Bed files listing ATAC-Seq fragments for each sample: *.bed</p> <p>We also evaluated EPIC-ATAC on multiple pseudobulks generated from single-cell ATAC-Seq data. We provide rds files containing the pseudobulks data used in our work for the evaluation of EPIC-ATAC. The rds files are located in the zip file "pseudobulks.zip".</p> <p>The file "additional_data.zip" contains additional files used to generate the reference profiles in EPIC-ATAC and to reproduce the main analyses performed in the manuscript: <a href="https://doi.org/10.7554/eLife.94833.1">https://doi.org/10.7554/eLife.94833.1</a>. These files are required to run the code available on the following GitHub repository: GfellerLab/EPIC-ATAC_manuscript. </p>
Conventional therapy induces tumor immunoediting and modulates the immune contexture in colorectal cancer
<p>Cancer immunotherapies for patients with colorectal cancer (CRC) continue to lag behind other solid cancer types with the exception of 4% of patients with microsatellite-instable tumors. Thus, there is an urgent need to broaden the clinical benefit of checkpoint blockers to CRC by combining conventional therapies to sensitize tumors to immunotherapy. However, the impact of conventional drugs on immunoediting and hence, imposing positive selection towards less immunogenic variants, and on the tumor immune contexture in CRC remains elusive.</p> <p>In this study, we performed comprehensive multimodal profiling using longitudinal samples from metastatic CRC patients undergoing neoadjuvant therapy with mFOLFOX6 and Bevacizumab. Exome-sequencing, RNA-sequencing and multiplexed immunofluorescence imaging was carried out on tumor samples obtained before and after therapy and the data was analyzed using established methods. The results of the analysis were extrapolated to publicly available datasets (TCGA and CPTAC). In order to identify a surrogate marker, an explainable artificial intelligence method was developed using a transformer-based analytical pipeline for the identification of features in H&E images associated with specific biological processes, followed by manual evaluation of highly informative tiles by a pathologist.</p> <p>We expect that the results of this project will provide a deeper understanding of the tumor-immune interactions and will allow the development of more robust combinatorial therapeutic strategies for MSS CRC.</p>
Association of GDF-15 expression with immune parameter in a pan-cancer analysis
<p>Immunotherapy with checkpoint blockers has significantly revolutionized the treatment landscape for many cancer patients. However, despite their success, checkpoint inhibitors have limitations that affect their effectiveness across a broader patient population. Soluble and cell-bound factors in the tumor microenvironment negatively impact cancer immunity. GDF15, a member of the TGF-β superfamily is associated with various physiological and pathological conditions, including cancer. Its overexpression in certain cancers has been linked to immune evasion. In this study we investigated the relationship between high GDF15 expression with various immune parameter in an in-silico analysis of 11,000 tumors from the TCGA database. Patients with non-small cell lung cancer and urothelial cancer was identified frequently GDF-15 immunosuppressed. Processed RNA sequencing data (TPM) obtained from firebrowse.org and the corresponding immune parameters were included in this dataset. </p>
Data from: Polymorphic tandem repeats shape single-cell gene expression across the immune landscape
<p>This dataset contains the association summary statistics (v0.1) for genome-wide tandem repeat (TR) expression quantitative trait (eQTL) analysis of TenK10K Phase 1 (https://doi.org/10.1101/2024.11.02.621562). </p> <p>Please access the README for a detailed description of file contents. </p> <p> </p>
Macroevolutionary foundations of a recently-evolved innate immune defense
<p>There are two datasets along with accompanying statistical code used to generate the results of our article, the first is a database of published articles that from the basis of the literature review and then the other file contains data from an experimental study of phylogenetic conservation of peritoneal fibrosis in 17 species of ray-finned fish. Detailed description of the methodology can be found in the article (https://doi.org/10.1101/2020.07.08.191601).</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.