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Dataset results
289 results for “10x”
Soil bulk density [10x kg/m3] for continental Europe at 30 m spatial resolution for period 2000-2020: Open Soil Data Cube for Europe
<p>Predictions are based on the 3D Ensemble Machine Learning framework, as implemented in the R environment for statistical computing (Hengl & MacMillan, 2019; Hengl, et al., 2021). For each pixel we provide prediction errors as 1 standard deviation in either log or the original variable scale.</p> <p>The short description of currently available soil properties: db_od = bulk density over dry [kg/m3 ⨉ 10];</p> <p>Soil properties were predicted at fixed depths:</p> <p> Surface soil = s0..0cm,<br> Subsoil 1 = s30..30cm,<br> Subsoil 2 = s60..60cm,<br> Subsoil 3 = s100..100cm.</p> <p>To produce estimates for depth intervals e.g. 0–30 cm, 0–100 cm best use the trapezoidal rule formula.</p> <p>Periods: 2000 (2000–2003), 2004 (2004–2007), 2008 (2008–2011), 2012 (2012–2015), 2016 (2016–2019), 2020;</p> <p>The bulk density maps are also provided in 10 kg / m-cubic to reduce total data size; to convert values to kg / m-cubic multiply by 10 e.g. 120 = 1200 kg / m-cubic = 1.2 t / m-cubic.</p>
PBMC CITE-seq, 10x Multiome, and TEA-seq multiomic datasets from Swanson, et al. eLife (2021).
<p>Assembled multiomic datasets from Swanson, et al. <em>Simultaneous trimodal single-cell measurement of transcripts, epitopes, and chromatin accessibility using TEA-seq</em>. eLife 2021;10:e63632 DOI: <a href="https://doi.org/10.7554/eLife.63632">10.7554/eLife.63632</a> </p> <p>Datasets were assembled from the GEO repository: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE158013">GSE158013</a> </p> <p>Data are provided as SeuratObjects stored in separate .rds files using the saveRDS() function in R.</p> <p>MuData files for use with Python tools (muon, scanpy, scvitools, et al.) will be added soon.</p> <p>Contact Lucas Graybuck (lucasg at alleninstitute dot org) if there are problems with these datasets.</p>
Integrative spatial omics reveals distinct tumor-promoting multicellular niches and immunosuppressive mechanisms in African American and European American patients with TNBC (Spatial Transcriptomic 10X Visium portion)
<p>Racial disparities in triple-negative breast cancer (TNBC) outcomes have been reported. However, the biological mechanisms underlying these disparities remain unclear. We integrated imaging mass cytometry and spatial transcriptomics, to characterize the tumor microenvironment (TME) of African American (AA) and European American (EA) patients with TNBC. The TME in AA patients was characterized by interactions between endothelial cells, macrophages, and mesenchymal-like cells, which were associated with poor patient survival. In contrast, the EA TNBC-associated niche is enriched in T-cells and neutrophils suggestive of an exhaustion and suppression of otherwise active T cell responses. Ligand-receptor and pathway analyses of race-associated niches found AA TNBC to be “immune cold” and hence immunotherapy resistant tumors, and EA TNBC as ‘inflamed’ tumors that evolved a distinctive immunosuppressive mechanism. Our study revealed the presence of racially distinct tumor-promoting and immunosuppressive microenvironments in AA and EA patients with TNBC, which may explain the poor clinical outcomes.</p> <p> </p> <p>This dataset contains the 10X Visium Spatial Transcriptomic data of TNBC patients. There are two cohorts.</p> <p> </p> <p><strong>Baylor Scott and White (BSW) cohort</strong>: <strong>10x.visium.tar.gz</strong>, containing 10 patients with TNBC from Baylor Scott and White affiliated Hospital. </p> <p>Each sample is made of Space Ranger processed spot-separated gene expression data (processed to HDF5 AnnData file). There are also H&E images, and spot coordinate files available. </p> <p> </p> <p>For <strong>Georgia validation cohort</strong>, 400 genes used for validation of ESG signatures (associated with BA-Community 1 and WA-Community-1) were obtained and provided by Ritu Aneja's lab. These 400 genes' spot-based expression data across Black and White TNBC patients are provided. See file <strong>georgia.validation.visium.tar.gz</strong>. Expression was normalized by total counts per spot, followed by log-normalization by Giotto.</p> <p> </p> <p>As well in our paper, we integrated a published racial TNBC cohort for deriving some of initial results in the paper. This refers to the Bassiouni et al (Cancer Research) paper in Carpten's group. <strong>GSM_giotto_processed.tar.gz</strong> refers to this dataset, which we deposit here. The data were normalized by Giotto using standard procedure.</p>
Dataset: 10x Genomics, Inc. (TXG) 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.
10X Single Cell RNA-Seq On WT and Mutation mouse models
<p>Four four-week-old mice, consisting of wild type, Pten mutation, MAP3K3 mutation, and MAP3K3+Pten mutation, were sampled at 4 weeks of age. Endothelial cells were enriched using CD31 magnetic beads, followed by 10X single-cell RNA sequencing.</p>
Training material for the mapping and quantification of single-cell ATAC-seq 10X Datasets
<p>The data provided here is part of the Galaxy Training Network tutorial that analyses 10x genomics single-cell ATAC-seq data from the 10x platform. The original data is from 1k Peripheral Blood Mononuclear Cells (PBMCs) from a Healthy Donor.</p> <p>Due to time constraints during training, the datasets were subsampled to reads that map to chromosome 21 only.</p> <p>The 10x Genomics Datasets follow the <a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution</a> license.</p> <p>There is an additional count matrix in Anndata format created from full datasets.</p>
Immcantation 10x Tutorial Data
<p>Necessary datasets to run the Immcantation 10x Tutorial. Below is the description of the files in the data set. </p> <ul> <li>BCR_data_sample1.tsv: data corresponding to the first sample (sample 1) of the two samples analyzed in the 10x tutorial. This is the sample used to show the Change-O steps.</li> <li>filtered_contig_annotations.csv: filtered contig annotations file for sample 1, output of cellranger vdj.</li> <li>filtered_contig.fasta: sequence fasta file for sample 1, output of cellranger vdj.</li> <li>BCR_data.tsv: AIRR rearrangement file containing the data for both samples 1 and 2 used in the 10x tutorial.</li> <li>BCR.data_08112023.rds: R dataframe object containing the single-cell BCR sequencing data for both samples 1 and 2 used in the 10x tutorial.</li> <li>GEX.data_08112023.rds: Seurat object containing the single-cell gene expression data used in the 10x tutorial.</li> </ul>
moFluMemB - Dataset : scRNA-seq from Lymph node, Spleen and Lung - 10x_191105_m_moFluMemB
<p><strong>Title</strong></p> <p>Viral infection engenders bona fide and bystander subsets of lung-resident memory B cells through a permissive mechanism<br><br><strong>Authors</strong><br>Claude Gregoire,1 Lionel Spinelli,1 Sergio Villazala-Merino,1 Laurine Gil,1 María Pía Holgado,1 Myriam Moussa,1 Chuang Dong,1 Ana Zarubica,2 Mathieu Fallet,1 Jean-Marc Navarro,1 Bernard Malissen,1,2 Pierre Milpied,1,* and Mauro Gaya1,*<br><br><strong>Affiliations</strong><br>1 Centre d'Immunologie de Marseille-Luminy (CIML), Aix Marseille Université, INSERM, CNRS, Marseille, France<br>2 Centre d'Immunophénomique (CIPHE), Aix Marseille Université, INSERM, CNRS, Marseille, France<br>* Correspondence: milpied@ciml.univ-mrs.fr (P.M.), gaya@ciml.univ-mrs.fr (M.G.)<br><br><strong>Summary</strong><br>Lung-resident memory B cells (MBCs) provide localized protection against reinfection in the respiratory airways. Currently, the biology of these cells remains largely unexplored. Here, we combined influenza and SARS-CoV-2 infection with fluorescent-reporter mice to identify MBCs regardless of antigen specificity. We found that two main transcriptionally distinct subsets of MBCs colonized the lung peribronchial niche after infection. These subsets arose from different progenitors and were both class-switched, somatically mutated and intrinsically biased in their differentiation fate towards plasma cells. Combined analysis of antigen-specificity and B cell receptor repertoire segregated these subsets into “bona fide” virus-specific MBCs and “bystander” MBCs with no apparent specificity for eliciting viruses and generated through an alternative permissive mechanism. Thus, diverse transcriptional programs in MBCs are not linked to specific effector fates but rather to divergent strategies of the immune system to simultaneously provide rapid protection from reinfection while diversifying the initial B cell repertoire.</p> <p><strong>Data:</strong> 10x_191105_m_moFluMemB_processedData.tar.gz : pre-processed data of 10x 5’ scRNA-Seq on memory B cells sorted from single-cell suspensions of spleen, lymph nodes and lungs with mechanical dissociation of lung tissue at 4°C.</p> <p>See the three other Zenodo deposit for the rest of the data:</p> <p><strong>10.5281/zenodo.5566674</strong></p> <p><strong>10.5281/zenodo.5564624</strong></p> <p><strong>10.5281/zenodo.10559312</strong></p>
moFluMemB - Dataset : scRNA-seq from Lymph node, Spleen and Lung - 10x_190712_m_moFluMemB
<p><strong>Title</strong></p> <p>Viral infection engenders bona fide and bystander subsets of lung-resident memory B cells through a permissive mechanism<br><br><strong>Authors</strong><br>Claude Gregoire,1 Lionel Spinelli,1 Sergio Villazala-Merino,1 Laurine Gil,1 María Pía Holgado,1 Myriam Moussa,1 Chuang Dong,1 Ana Zarubica,2 Mathieu Fallet,1 Jean-Marc Navarro,1 Bernard Malissen,1,2 Pierre Milpied,1,* and Mauro Gaya1,*<br><br><strong>Affiliations</strong><br>1 Centre d'Immunologie de Marseille-Luminy (CIML), Aix Marseille Université, INSERM, CNRS, Marseille, France<br>2 Centre d'Immunophénomique (CIPHE), Aix Marseille Université, INSERM, CNRS, Marseille, France<br>* Correspondence: milpied@ciml.univ-mrs.fr (P.M.), gaya@ciml.univ-mrs.fr (M.G.)<br><br><strong>Summary</strong><br>Lung-resident memory B cells (MBCs) provide localized protection against reinfection in the respiratory airways. Currently, the biology of these cells remains largely unexplored. Here, we combined influenza and SARS-CoV-2 infection with fluorescent-reporter mice to identify MBCs regardless of antigen specificity. We found that two main transcriptionally distinct subsets of MBCs colonized the lung peribronchial niche after infection. These subsets arose from different progenitors and were both class-switched, somatically mutated and intrinsically biased in their differentiation fate towards plasma cells. Combined analysis of antigen-specificity and B cell receptor repertoire segregated these subsets into “bona fide” virus-specific MBCs and “bystander” MBCs with no apparent specificity for eliciting viruses and generated through an alternative permissive mechanism. Thus, diverse transcriptional programs in MBCs are not linked to specific effector fates but rather to divergent strategies of the immune system to simultaneously provide rapid protection from reinfection while diversifying the initial B cell repertoire.</p> <p><strong>Data: 1</strong>0x_190712_m_moFluMemB_processedData.tar.gz : pre-processed data of 10x 5’ scRNA-Seq on memory B cells sorted from single-cell suspensions of spleen, lymph nodes and lungs with enzymatic digestion of lung tissue at 37°C.</p> <p>See the three other Zenodo deposit for the rest of the data:</p> <p><strong>10.5281/zenodo.5566674</strong></p> <p><strong>10.5281/zenodo.5565863</strong></p> <p><strong>10.5281/zenodo.10559312</strong></p>
Processing of 10X 500 PBMC single cell ATAC-seq data with SnapATAC2
<p>Input files for SnapATAC2 tutorial on Galaxy</p>
Fig. 2.- Glycaspis brimblecombei Moore. Genitalia del macho 10x in Primera cita de Glycaspis brimblecombei Moore 1964 y situación actual de otros psílidos del eucalipto en Galicia (NW Península Ibérica) (Hemiptera: Psylloidea: Psyllidae).
Fig. 2.- Glycaspis brimblecombei Moore. Genitalia del macho 10x.
SRR606249 spiked with fungal and viral Illumina reads at 1x, 10X, and 100X
<p>SRR606249 spiked with fungal and viral Illumina reads at 1x, 10X, and 100X using the ART simulator.</p> <p>See also: https://osf.io/dm938/ and https://dahak-metagenomics.github.io/</p> <p>Fungal and viral genomes used for computational spike-ins.<br> Kingdom Organism Accession Link<br> Fungus Candida albicans SC5314 NC_032089.1 https://www.ncbi.nlm.nih.gov/genome/?term=Candida+albicans<br> Fungus Cryptococcus neoformans var. grubii H99 NC_026745.1 https://www.ncbi.nlm.nih.gov/genome/?term=Cryptococcus+neoformans<br> Fungus Aspergillus fumigatus Af293 NC_007194.1 https://www.ncbi.nlm.nih.gov/genome/?term=Aspergillus+fumigatus<br> Fungus Histoplasma capsulatum NAm1 NW_001813913.1 https://www.ncbi.nlm.nih.gov/genome/?term=Histoplasma+capsulatum<br> Virus Human immunodeficiency virus 1 NC_001802.1 https://www.ncbi.nlm.nih.gov/genome/10319<br> Virus Influenza A virus (A/New York/392/2004(H3N2)) NC_007373.1 https://www.ncbi.nlm.nih.gov/genome/10290<br> Virus Marburg virus - Musoke, Kenya, 1980 NC_001608.3 https://www.ncbi.nlm.nih.gov/genome/5314<br> Virus Ebola virus - Mayinga, Zaire, 1976 NC_002549.1 https://www.ncbi.nlm.nih.gov/assembly/GCF_000848505.1/</p>
EBAII n1 scRNAseq : 10X PBMC10K Cell Ranger v3 MEX matrices
<ul> <li>A public dataset created and provided by <strong>10X Genomics</strong>, that consists in ~ <strong>10,000 PBMC</strong> (peripheral bone marrow cells) from a <strong>human donor</strong></li> <li>The experiment was performed with the <strong>3’ capture kit v3</strong></li> <li>The analysis was performed with <strong>Cell Ranger v3</strong></li> <li>Mapping was performed on the <strong>GRCh38-2020-A</strong> manufacturer reference</li> <li>The structure of these Cell Ranger outputs are described <a href="https://www.10xgenomics.com/support/software/cell-ranger/latest/analysis/outputs/cr-outputs-mex-matrices" target="_blank" rel="noopener">here</a></li> <li>The sparse matrix format corresponds to the <a href="https://math.nist.gov/MatrixMarket/formats.html" target="_blank" rel="noopener">Market Exchange Format (MEX)</a></li> <li>For educational purpose (speed), the dataset columns were reduced to 1/5th (from 737,280 to 147,456 barcodes)</li> </ul>
HG006_Father (GIAB_ChineseTrio_10X)
<p>This is a sub-sampled version (10X) of ChineseTrio HG006(father) fastq files published by NIST's Genome in a Bottle project.<br> We created them for practicing variant-call processes.<br> These files are 10X fastq files. Though 30X fastq files are preferable for WGS analysis, we prepared 10X fastq files for reducing the burden of handling the large files.</p> <p>The original data is available under:<a href="https://github.com/genome-in-a-bottle/giab_data_indexes">https://github.com/genome-in-a-bottle/giab_data_indexes</a><br> The original article is below;<br> Wang, YC., Olson, N.D., Deikus, G. et al. High-coverage, long-read sequencing of Han Chinese trio reference samples. Sci Data 6, 91 (2019). <a href="https://doi.org/10.1038/s41597-019-0098-2">https://doi.org/10.1038/s41597-019-0098-2</a><br> We are permitted to published sub-sampled versions by Genome in a Bottle project.</p>
HG007_Mother (GIAB_ChineseTrio_10X)
<p>This is a sub-sampled version (10X) of ChineseTrio HG007(mother) fastq files published by NIST's Genome in a Bottle project.<br> We created them for practicing variant-call processes.<br> These files are 10X fastq files. Though 30X fastq files are preferable for WGS analysis, we prepared 10X fastq files for reducing the burden of handling the large files.</p> <p>The original data is available under:<a href="https://github.com/genome-in-a-bottle/giab_data_indexes">https://github.com/genome-in-a-bottle/giab_data_indexes</a><br> The original article is below;<br> Wang, YC., Olson, N.D., Deikus, G. et al. High-coverage, long-read sequencing of Han Chinese trio reference samples. Sci Data 6, 91 (2019). <a href="https://doi.org/10.1038/s41597-019-0098-2">https://doi.org/10.1038/s41597-019-0098-2</a><br> We are permitted to published sub-sampled versions by Genome in a Bottle project.</p>
HG005_Son (GIAB_ChineseTrio_10X)
<p>This is a sub-sampled version (10X) of ChineseTrio HG005(son) fastq files published by NIST's Genome in a Bottle project.<br> We created them for practicing variant-call processes.<br> These files are 10X fastq files. Though 30X fastq files are preferable for WGS analysis, we prepared 10X fastq files for reducing the burden of handling the large files.</p> <p>The original data is available under:<a href="https://github.com/genome-in-a-bottle/giab_data_indexes">https://github.com/genome-in-a-bottle/giab_data_indexes</a><br> The original article is below;<br> Wang, YC., Olson, N.D., Deikus, G. et al. High-coverage, long-read sequencing of Han Chinese trio reference samples. Sci Data 6, 91 (2019). <a href="https://doi.org/10.1038/s41597-019-0098-2">https://doi.org/10.1038/s41597-019-0098-2</a><br> We are permitted to published sub-sampled versions by Genome in a Bottle project.</p>
Anndata object of 10x Mouse Brain 5k data set for scDGD training
<p>This data from 10x (5k Adult Mouse Brain Nuclei Isolated with Chromium Nuclei Isolation Kit, Single Cell Gene Expression Dataset by Cell Ranger 7.0.0, 2022) is comprised of 7377 cells from the adult mouse brain with 32285 features. Cell type annotations were approximated using CellTpyist with the `Developing_Mouse_Brain` reference model and majority voting. This resulted in 7 distinct cell types.</p>
microcat_10x_example
<p>The 10x test dataset consists of all reads from 12 cells sequenced by Ben-Moshe2019, sample id as GSM3454529</p>
10x sequencing
<p>Powerful approaches have become more accessible and allow the profiling of rare or heterogeneous populations of cells. The averaging that occurs in Conventional 'bulk' methods of sequencing does not allow the direct assessment of the fundamental biological unit—the cell—or the individual nuclei that package the genome. CD genomics provides access to the genomic and genetic analysis at single-cell level using <a href="https://www.cd-genomics.com/10x-sequencing.html">10x sequencing</a> system and its engineered reagent delivery method. The droplet-based platforms Chromium 10x System, powered by GemCode Technology, enables the biomedical researchers and clinicians to make important new discoveries using this powerful approach as the technologies and tools needed for conducting single-cell studies. Moreover, the droplet-based platforms, maximizing the throughput of cells and meanwhile downscaling the reactions to nanoliter volumes, have been shown to improve detection sensitivity and quantitative accuracy. The droplet-based methods hold advantages, allowing direct analysis of rare cell types or primary cells for which there may be insufficient material for conventional bulk sequencing protocols. It is also ideal to profile interesting subpopulations of cells from a larger heterogeneous population, for example, malignant tumor cells within a tumor mass, or hyper-responsive immune cells within a seemingly homogeneous group. It can even reveal entirely new cell types. 10X genomics also permits to study cellular states in scenarios such as embryonal development, cancer, myoblast and lung epithelium differentiation, and lymphocyte fate diversification.</p>
Figs. 1–10X in Studies on Stiliderus Motschulsky and Stilicoderus Sharp: biogeographical notes and descriptions of new species (Coleoptera: Staphylinidae: Paederinae)
Figs. 1–10X. Stilicoderus spp., aedoeagi in lateral (a) and ventral (b) views. – 1. S. laticeps n. sp. 2. S. riedeli n. sp. 3. S. cephalicus n. sp. 4. S. obesus n. sp. 5. S. kolaensis n. sp. 6. S. sepikensis n. sp. 7. S. riedelianus n. sp. 8. S. lobatus n. sp. 9. S. maritimoides n. sp. 10. S. meraukeanus n. sp. 10X. S. batantaensis n. sp. – Scales: 0.5 mm.
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.