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.
1,506
datasets available to search
ShareScore release 0.9.0
Dataset results
1,506 results for “objects”
Figure 3 in Actionable, long-term stable and semantic web compatible identifiers for access to biological collection objects
Figure 3. CETAF stable HTTP URIs in the GBIF data portal. The Global Biodiversity Information Facility (GBIF) publishes CETAF stable HTTP URIs via their data portal.
Figure 2 in Actionable, long-term stable and semantic web compatible identifiers for access to biological collection objects
Figure 2. Basic redirection mechanisms. Human users are redirected to a human-readable web-representation of the specimens. Software systems are re-directed to a machine-readable metadata record.
Carolina Bay Object Detector Images and Labels
<p>Contained here (in CarolinaBayTrainingData.zip) are the images (jpegs) and labels (csv) that were used to train a bounding box object detector for Carolina Bays detailed in the following paper:</p> <p>Lundine, M., Trembanis, A., Using Convolutional Neural Networks for Detection and Morphometric Analysis of Carolina Bays from Publicly Available Digital Elevation Models, Remote Sensing, 2021, Volume 13(18), 3770, <a href="https://doi.org/10.3390/rs13183770">https://doi.org/10.3390/rs13183770</a>.</p> <p>Each image is a single channel jpeg. The channel corresponds to gridded LiDAR elevation values remapped to 256 values. Each image was normalized individually for maximum contrast.</p> <p>The labels csv contains the coordinates for each bounding box annotation of Carolina Bays in each available image. The columns are: filename, width, height, label, xmin, ymin, xmax, ymax, label_value. Each row is an annotation of a Carolina Bay. Filename corresponds to the image, width is the width in pixels of that image, height is the height in pixels of that image. xmin, ymin, xmax, ymax are the bounding box coordinates for the annotation. Label is 'bay' for each annotation, with a label_value of 1.</p> <p>Feel free to experiment with this dataset, add to it, and improve upon the results.</p> <p>Also contained here (in CarolinaBayDetections.zip) are the detection results (as unaggregated polygons, as aggregated polygons, as smooth polygons, and as points).</p>
Raw dataset for "Multi-Objective Bayesian Active Learning for MeV-ultrafast electron diffraction"
<p>this dataset contains raw data collected at the SLAC MeV-UED facility, the data was saved in .npy format. The name of each file starts with a number referring to the time stamp when it was recorded.</p> <p>“xxxxxxxxxx_Andor1.npy” contains the beam images recorded at the diffraction detector plane associated with the q-resolution</p> <p>“xxxxxxxxxx_qm.npy” contains the beam images recorded at the sample plane associated with the spot size</p> <p>“xxxxxxxxxx_scalars.npy” contains the machine settings and readouts from the EPICs system, scalar names are listed in “scalars.txt”</p> <p>“xxxxxxxxxx_vcc.npy” contains the images recorded at a virtual cathode camera</p> <p>“xxxxxxxxxx_THzon_img.npy” contains the THz streaked beam images associated with the temporal length</p> <p>“xxxxxxxxxx_THzoff_img.npy” contains the unstreaked beam images for subtracting intrinsic broadening without THz pulses</p>
Data used for analysis in "Calibrating tropical forest coexistence in ecosystem demography models using multi-objective optimization through population-based parallel surrogate search"
Open the record for dataset details and reuse information.
Supplementary collection of preprocessed and analysed data objects for the paper: "Gene-level alignment of single cell trajectories"
<p>Supplementary collection of h5ad data files containing preprocessed and analysed single-cell reference and query datasets discussed in the case studies of the paper:<em><strong>"Gene-level alignment of single cell trajectories"</strong></em> (<a href="https://doi.org/10.1101/2023.03.08.531713" rel="nofollow">https://doi.org/10.1101/2023.03.08.531713</a>).</p> <p>Our artificial thymic organoid raw sequencing data are available from ArrayExpress (E-MTAB-12720).</p> <p>Below lists the original sources of the literature datasets we downloaded prior to our own preprocessing and trajectory analysis.</p> <ul> <li> <p>Original pan fetal reference data from: Suo, C. et al. (2022) "Mapping the developing human immune system across organs" (<a href="https://doi.org/10.1126/science.abo0510">https://doi.org/10.1126/science.abo0510</a><a href="http://paperpile.com/b/XTpgLG/TQtQ3">), </a>downloaded from <a href="https://developmental.cellatlas.io/fetal-immune">https://developmental.cellatlas.io/fetal-immune</a>.</p> </li> </ul> <ul> <li> <p>Original healthy/IPF data from: Adams, T. S. et al. (2020), "Single-cell RNA-seq reveals ectopic and aberrant lung-resident cell populations in idiopathic pulmonary fibrosis" (<a href="https://doi.org/10.1126/sciadv.aba1983">https://doi.org/10.1126/sciadv.aba1983</a>), downloaded from GEO (GSE136831).</p> </li> </ul> <p>We have run our scripts in <a href="https://github.com/Teichlab/G2G_notebooks">https://github.com/Teichlab/G2G_notebooks</a> to generate new latent embeddings and infer pseudotime trajectories for the above datasets, and used them to model trajectory alignments with our newly developed framework: ‘Genes2Genes’ <a href="https://github.com/Teichlab/Genes2Genes">https://github.com/Teichlab/Genes2Genes</a>. </p>
Population synthesis data of massive binaries: NS/BH-core mergers and double compact objects formation and mergers
<p><span>Binary population synthesis data generated using the rapid population synthesis code COMPAS.</span></p> <p><span>Contains:</span></p> <p><span>1. Full COMPAS output in (.csv format) for running massive binaries varying the common envelope efficiency parameter and the metallicity.</span></p> <p><span>2. Identifying seeds of systems that result in neutron star (NS)/black hole (BH)-core mergers, binary NSs, binary BHs and NS-BH binaries. </span></p>
Seurat objects for "Single-cell multi-omic analysis of the vestibular schwannoma ecosystem uncovers a nerve injury-like state" (https://doi.org/10.1038/s41467-023-42762-w)
Open the record for dataset details and reuse information.
Open Extensive 3D Dataset of Geometric Objects Placed in a Watertank for Electrical Impedance Tomography
<p>Electrical impedance tomography (EIT) is a non-invasive, radiation-free imaging method for visualizing the tomography of electrically conductive objects. The image reconstruction approach using data-driven methods or deep neural networks (DNN) has become very popular. To train such a reconstruction network, much data is required to reconstruct the conductivity distribution reliably, which is why simulation data are often used. This is an open-access experimental 3D EIT data set. The measurements were generated using the Sciospec EIT device, from which 64 electrodes were connected in two stages, with 32 electrodes, to a phantom tank filled with saline. Various ball objects of different sizes and materials were placed in this tank volume-wide. The provided dataset contains more than 124,000 samples.</p>
Certificates and Witnesses for Multi-Objective Queries in Markov Decision Processes - QEST 2024 Artefact
<p>This artifact accompanies the QEST+FORMATS 2024 paper "Certificates and Witnesses for Multi-Objective Queries in Markov Decision Processes" (<a href="https://arxiv.org/abs/2406.08175">arXiv</a>). It contains the implementation (<code>switss-multi</code>) of the presented techniques, that is, the computation of certificates, witnessing subsystems and schedulers for multi-objective queries in MDPs. Further, the artifact contains the PRISM models, PRISM properties and scripts bundled in a Docker image for completely reproducing the results presented in Section 5 and Appendix D. Additionally, it also contains the original raw experimental data presented in Section 5 and Appendix D and the corresponding analysis scripts. Lastly, we provide a documentation of our implementation <code>switss-multi</code> and describe how to use our tool via its command-line and programmatically via its Python interface.</p> <p><strong>Relation to paper</strong><br>This artifact can be used to reproduce all the experimental results (including examples) presented in the paper, that is:<br>- The toy examples presented in Example 1 and Example 2<br>- Table 1 in Section 5<br>- Table 3 in Appendix D<br>- Figure 5 in Appendix D<br>- Figure 6 in Appendix D<br>- Table 4 in Appendix D</p> <p><strong>Aritfact structure</strong><br>This artifact consists of the following files and folders:<br>- <code>data</code>: Contains the PRISM models, PRISM properties (queries) and original raw experimental data presented in Section 5 and Appendix D. Additionally, the log files and scripts for summarizing the raw experimental data are provided.<br>- <code>switss-multi</code>: The source code of the implementation of our presented techniques.<br>- <code>switss-multi-docs</code>: A documentation of the Python API of <code>switss-multi</code>.<br>- <code>qest-docker-image.tar.gz</code>: The compressed Docker image, with the installed implementation (<code>switss-multi</code>), PRISM models, PRISM properties and the scripts for running the experiments and analysing the raw experimental data. Moreover, it contains a copy of the <code>data</code> folder, in case you want to run the analysis scripts on the original data.<br>- <code>docker-results</code>: An empty folder that will be populated with results when running the experiments and analysis with the provided Docker image.<br>- <code>LICENSE</code>: The license of this artifact (MIT license).<br>- <code>GUROBI-EULA</code>: The end-user license agreement of Gurobi (also see https://pypi.org/project/gurobipy/).<br>- <code>GPL-3.0</code>: The GPL 3.0 license. It is included because our dependency Storm (https://www.stormchecker.org) is licensed under it.</p> <p><strong>Note on the versions:</strong> The first version (v1) contains the data and implementation at the point of the paper submission. The second version (v2) contains a Docker image and more detailed documentation and was evaluated by the QEST+FORMATS 2024 Artifact Evaluation Comittee and awarded the artifact evaluation badge. This version (v3) incorporates the feedback of the QEST+FORMATS 2024 artifact evaluation and contains improvements on the second version (v2).</p> <p><strong>Acknowledgments:</strong> We would like to thank the anonymous reviewers in the QEST+FORMATS Artifact Evaluation Committee for their valuable feedback. The authors were supported by the German Federal Ministry of Education and Research (BMBF) within the project SEMECO Q1 (03ZU1210AG) and by the German Research Foundation (DFG) through the Cluster of Excellence EXC 2050/1 (CeTI, project ID 390696704, as part of Germany’s Excellence Strategy) and the DFG Grant 389792660 as part of TRR 248 (Foundations of Perspicuous Software System).</p>
Processed Seurat objects from scRNA-seq data of the aging subventricular zone (SVZ) neurogenic niche with partial reprogramming
<p>This repository contains the processed Seurat objects from the publication "Restoration of neuronal progenitors by partial reprogramming in the aged neurogenic niche" (https://doi.org/10.1038/s43587-024-00594-3).</p> <p>Raw sequencing data is available at the Gene Expression Omnibus (GEO) under accession number GSE224438. Code used to process and analyze the data is available on GitHub (https://github.com/gitlucyxu/SVZreprogramming). </p> <p>These Seurat objects are filtered to high-quality singlets for samples included in the publication. Descriptions and notable metadata:</p> <ul> <li>svz_iOSKM_cohort1_toshare.rds - SVZ after whole-body partial reprogramming, cohort 1 <ul> <li>Celltype - cell type annotation</li> <li>Treatment - condition <ul> <li>untr: old control</li> <li>2Dox0: old+OSKM</li> </ul> </li> <li>hash.ID - mouse ID (biological replicate)</li> </ul> </li> <li>svz_iOSKM_cohort2_toshare.rds - SVZ after whole-body partial reprogramming, cohort 2 <ul> <li>Celltype - cell type annotation</li> <li>Age_Treatment - condition <ul> <li>young_untr: young control</li> <li>old_untr: old control</li> <li>old_2Dox0: old+OSKM</li> </ul> </li> <li>hash.ID - mouse ID (biological replicate)</li> </ul> </li> <li>svz_ciOSKM_toshare.rds - SVZ after SVZ-targeted partial reprogramming <ul> <li>Celltype - cell type annotation</li> <li>Age_Treatment - condition <ul> <li>young_untr: young control</li> <li>old_untr: old control</li> <li>old_Dox: old+OSKM(SVZ)</li> </ul> </li> <li>MULTI_classification_rescued - mouse ID (biological replicate)</li> </ul> </li> </ul> <p> </p> <p><em>Updated 2024/07/01 (v2): replaced a corrupted file. </em></p>
Count matrices, conos objects and annotation files
<p>Early timepoint samples (E18.5 and preterm P0): </p> <ul> <li>conos object: con1_m5</li> <li>list of count matrices: cms1_m5</li> <li>cell type annotations: anno1_final4_rough, anno1_final3</li> </ul> <p>Late timepoint samples (full-term P2 and preterm P3):</p> <ul> <li>conos object: con2_m5_2</li> <li>list of count matrices: cms2_m5_2</li> <li>cell type annotations: anno2_final6_rough, anno2_final5</li> </ul> <p>Fullterm samples (E18.5 and fullterm P2):</p> <ul> <li>conos object: con3</li> <li>cell type annotations: anno3_final2, anno3_final3_rough</li> </ul> <p>All samples:</p> <ul> <li>conos object: con_all</li> <li>cell type annotations: anno_all_6_generic, anno_all7_rough</li> </ul>
RNA velocity objects of FACS-sorted (CD31+/CD45-) endothelial cells of individual pathological entities
<p><strong>RNA velocity objects (zip files of .h5ad) of FACS-sorted (CD31+/CD45-) endothelial cells of individual pathological entities<br></strong></p> <p><em><span>-> part of the manuscript: Single-cell atlas of the human brain vasculature across development, adulthood and disease</span></em><span><br><em><span>https://www.nature.com/articles/s41586-024-07493-y</span></em></span></p> <p> </p> <p>-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p><em>-i) AVM sorted endothelial cells_RNA velocity.zip: </em><br> -> this is a zip file of an RNA velocity object (.h5ad) of FACS-sorted (CD31+/CD45-) endothelial cells isolated from brain arteriovenous malformations (AVM) (a brain vascular malformation).<br><br><em>- ii) LGG sorted endothelial cells_RNA velocity.zip: </em><br> -> this is a zip file of an RNA velocity object (.h5ad) of FACS-sorted (CD31+/CD45-) endothelial cells isolated from lower-grade gliomas (LGG) (a brain tumor)<br><br><em>- iii) GBM sorted endothelial cells_RNA velocity.zip: </em><br> -> this is a zip file of an RNA velocity object (.h5ad) of FACS-sorted (CD31+/CD45-) endothelial cells isolated from high-grade gliomas (glioblastoma (GBM)) (a brain tumor).<br><br><em>- iv) MET sorted endothelial cells_RNA velocity.zip: </em><br> -> this is a zip file of an RNA velocity object (.h5ad) of FACS-sorted (CD31+/CD45-) endothelial cells isolated from brain metastasis (MET) (a brain tumor).<br><br><em>- v) MEN sorted endothelial cells_RNA velocity.zip: </em><br> -> this is a zip file of an RNA velocity object (.h5ad) of FACS-sorted (CD31+/CD45-) endothelial cells isolated from brain meningioma (MEN) (a brain tumor).</p>
seurat objects of unsorted endothelial and perivascular cells - overall merges
<p><strong>Seurat objects of overall merges of unsorted endothelial and perivascular cells<br></strong></p> <p><em><span>-> part of the manuscript: Single-cell atlas of the human brain vasculature across development, adulthood and disease</span></em><span><br><em><span>https://www.nature.com/articles/s41586-024-07493-y</span></em></span></p> <p><strong> </strong></p> <p>-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p> </p> <p><em>-i) Overall merge of all unsorted endothelial and perivascular cells_seurat object.rds: </em><br> -> this seurat object is the overall merge of unsorted endothelial and perivascular cells isolated from fetal brain, adult/control brains (temporal lobes), brain tumors (lower-grade glioma, high-grade glioma (glioblastoma), brain metastasis, meningiomas) and brain vascular malformations (brain arteriovenous malformations). <br><br><em>-ii) Overall merge of pathological unsorted endothelial and perivascular cells_seurat object.rds: </em><br> -> this seurat object is the overall merge of unsorted endothelial and perivascular cells isolated from brain tumors (lower-grade glioma, high-grade glioma (glioblastoma), brain metastasis, meningiomas) and brain vascular malformations (brain arteriovenous malformations). </p>
RNA velocity objects of FACS-sorted (CD31+/CD45-) endothelial cells of fetal and adult/control brains
<p><strong>RNA velocity objects (zip files of .h5ad) of FACS-sorted (CD31+/CD45-) endothelial cells of individual fetal and adult/control brains entities<br></strong></p> <p><em><span>-> part of the manuscript: Single-cell atlas of the human brain vasculature across development, adulthood and disease</span></em><span><br><em><span>https://www.nature.com/articles/s41586-024-07493-y</span></em></span></p> <p><strong> </strong></p> <p>-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p> </p> <p><em>-i) Fetal CNS sorted endothelial cells_RNA velocity.zip:</em> <br> -> this is a zip file of an RNA velocity object (.h5ad) of FACS-sorted (CD31+/CD45-) endothelial cells isolated from fetal brain (Fetal CNS).</p> <p><em>- ii) Adult control brain (temporal lobe) sorted endothelial cells_RNA velocity.zip:</em> <br> -> this is a zip file of an RNA velocity object (.h5ad) of FACS-sorted (CD31+/CD45-) endothelial cells isolated from adult/control brains (temporal lobes).</p>
Diffusion map objects of FACS-sorted (CD31+/CD45-) endothelial cells
<p><strong>Diffusion map objects of FACS-sorted (CD31+/CD45-) endothelial cells<br></strong></p> <p><em><span>-> part of the manuscript: Single-cell atlas of the human brain vasculature across development, adulthood and disease</span></em><span><br><em><span>https://www.nature.com/articles/s41586-024-07493-y</span></em></span></p> <p><strong> </strong></p> <p>-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p> </p> <p><em>-i) Fetal CNS sorted endothelial cells_diffusionmap.rds: </em><br> -> this is a diffusion map object of FACS-sorted (CD31+/CD45-) endothelial cells isolated from fetal brain (Fetal CNS).<br><br><em>- ii) Adult control brain (temporal lobe) sorted endothelial cells_diffusionmap.rds: </em><br> -> this is a diffusion map object of FACS-sorted (CD31+/CD45-) endothelial cells isolated from adult/control brains (temporal lobes).<br><br><em>- iii) AVM sorted endothelial cells_diffusionmap.rds: </em><br> -> this is a diffusion map object of FACS-sorted (CD31+/CD45-) endothelial cells isolated from brain arteriovenous malformations (AVM) (a brain vascular malformation).<br><br><em>- iv) LGG sorted endothelial cells_diffusionmap.rds: </em><br> -> this is a diffusion map object of FACS-sorted (CD31+/CD45-) endothelial cells isolated from lower-grade gliomas (LGG) (a brain tumor)<br><br><em>- v) GBM sorted endothelial cells_diffusionmap.rds:</em> <br> -> this is a diffusion map object of FACS-sorted (CD31+/CD45-) endothelial cells isolated from high-grade gliomas (glioblastoma (GBM)) (a brain tumor).<br><br><em>- vi) MET sorted endothelial cells_diffusionmap.rds: </em><br> -> this is a diffusion map object of FACS-sorted (CD31+/CD45-) endothelial cells isolated from brain metastasis (MET) (a brain tumor).<br><br><em>- vii) MEN sorted endothelial cells_diffusionmap.rds: </em><br> -> this is a diffusion map object of FACS-sorted (CD31+/CD45-) endothelial cells isolated from brain meningioma (MEN) (a brain tumor).</p>
Monocle 3 CDS pseudotime objects of FACS-sorted (CD31+/CD45-) endothelial cells
<p><strong>Monocle 3 CDS pseudotime objects of FACS-sorted (CD31+/CD45-) endothelial cells<br></strong></p> <p><em><span>-> part of the manuscript: Single-cell atlas of the human brain vasculature across development, adulthood and disease</span></em><span><br><em><span>https://www.nature.com/articles/s41586-024-07493-y</span></em></span></p> <p><strong> </strong></p> <p>-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p> </p> <p><em>-i) Adult control brain (temporal lobe) sorted endothelial cells_cds monocle object.rds: </em><br> -> this is a Monocle 3 CDS pseudotime object of FACS-sorted (CD31+/CD45-) endothelial cells isolated from adult/control brains (temporal lobes).<br><br><em>-ii) Fetal CNS sorted endothelial cells_cds monocle object.rds: </em><br> -> this is a Monocle 3 CDS pseudotime object of FACS-sorted (CD31+/CD45-) endothelial cells isolated from fetal brain (Fetal CNS).<br><br><em>- iii) AVM sorted endothelial cells_cds monocle object.rds: </em><br> -> this is a Monocle 3 CDS pseudotime object of FACS-sorted (CD31+/CD45-) endothelial cells isolated from brain arteriovenous malformations (AVM) (a brain vascular malformation).<br><br><em>- iv) LGG sorted endothelial cells_cds monocle object.rds: </em><br> -> this is a Monocle 3 CDS pseudotime object of FACS-sorted (CD31+/CD45-) endothelial cells isolated from lower-grade gliomas (LGG) (a brain tumor).<br><br><em>- v) GBM sorted endothelial cells_cds monocle object.rds: </em><br> -> this is a Monocle 3 CDS pseudotime object of FACS-sorted (CD31+/CD45-) endothelial cells isolated from high-grade gliomas (glioblastoma (GBM)) (a brain tumor).<br><br><em>- vi) MET sorted endothelial cells_cds monocle object.rds: </em><br> -> this is a Monocle 3 CDS pseudotime object of FACS-sorted (CD31+/CD45-) endothelial cells isolated from brain metastasis (MET) (a brain tumor).<br><br><em>- vii) MEN sorted endothelial cells_cds monocle object.rds: </em><br> -> this is a Monocle 3 CDS pseudotime object of FACS-sorted (CD31+/CD45-) endothelial cells isolated from brain meningioma (MEN) (a brain tumor).<br><br><em>-viii) Overall merge of tumor sorted endothelial cells_cds monocle object.rds: </em><br> -> this is a Monocle 3 CDS pseudotime object of FACS-sorted (CD31+/CD45-) endothelial cells isolated from brain tumors (lower-grade glioma, high-grade glioma (glioblastoma), brain metastasis, meningiomas). <br><br><em>-ix) Overall merge of pathological sorted endothelial cells_cds monocle object.rds: </em><br> -> this is a Monocle 3 CDS pseudotime object of FACS-sorted (CD31+/CD45-) endothelial cells isolated from brain tumors (lower-grade glioma, high-grade glioma (glioblastoma), brain metastasis, meningiomas) and brain vascular malformations (brain arteriovenous malformations). </p>
RNA velocity objects of FACS-sorted (CD31+/CD45-) endothelial cells of overall merge of brain tumors
<p><strong>RNA velocity objects (zip files of .h5ad) of FACS-sorted (CD31+/CD45-) endothelial cells of overall merge of brain tumors:<br></strong></p> <p><em><span>-> part of the manuscript: Single-cell atlas of the human brain vasculature across development, adulthood and disease</span></em><span><br><em><span>https://www.nature.com/articles/s41586-024-07493-y</span></em></span></p> <p><br>--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p><em>- Overall merge of tumor sorted endothelial cells_RNA velocity.zip:</em> <br> -> this is a zip file of an RNA velocity object (.h5ad) of the overall merge of FACS-sorted (CD31+/CD45-) endothelial cells isolated from brain tumors (lower-grade glioma, high-grade glioma (glioblastoma), brain metastasis, meningiomas). </p>
seurat objects of FACS-sorted (CD31+/CD45-) endothelial cells - individual entities
<p><strong>Seurat objects of individual entities of FACS-sorted (CD31+/CD45-) endothelial cells<br></strong></p> <p><em><span>-> part of the manuscript: Single-cell atlas of the human brain vasculature across development, adulthood and disease</span></em><span><br><em><span>https://www.nature.com/articles/s41586-024-07493-y</span></em></span></p> <p><strong> </strong></p> <p>-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p> </p> <p><em>-i) Fetal CNS sorted endothelial cells_seurat object.rds: </em><br> -> this seurat object is of FACS-sorted (CD31+/CD45-) endothelial cells isolated from fetal brain (Fetal CNS).<br><br><em>-ii) Fetal periphery sorted endothelial cells_seurat object.rds: </em><br> -> this seurat object is of FACS-sorted (CD31+/CD45-) endothelial cells isolated from fetal peripheral organs (Fetal periphery).<br><br><em>- iii) Adult control brain (temporal lobe) sorted endothelial cells_seurat object.rds: </em><br> -> this seurat object is of FACS-sorted (CD31+/CD45-) endothelial cells isolated from adult/control brains (temporal lobes).<br><br><em>- iv) AVM sorted endothelial cells_seurat object.rds: </em><br> -> this seurat object is of FACS-sorted (CD31+/CD45-) endothelial cells isolated from brain arteriovenous malformations (AVM) (a brain vascular malformation).<br><br><em>- v) LGG sorted endothelial cells_seurat object.rds: </em><br> -> this seurat object is of FACS-sorted (CD31+/CD45-) endothelial cells isolated from lower-grade gliomas (LGG) (a brain tumor).<br><br><em>- vi) GBM sorted endothelial cells_seurat object.rds: </em><br> -> this seurat object is of FACS-sorted (CD31+/CD45-) endothelial cells isolated from high-grade gliomas (glioblastoma (GBM)) (a brain tumor).<br><br><em>- vii) MET sorted endothelial cells_seurat object.rds:</em><br> -> this seurat object is of FACS-sorted (CD31+/CD45-) endothelial cells isolated from brain metastasis (MET) (a brain tumor).<br><br><em>- viii) MEN sorted endothelial cells_seurat object.rds: </em><br> -> this seurat object is of FACS-sorted (CD31+/CD45-) endothelial cells isolated from brain meningioma (MEN) (a brain tumor).</p>
seurat objects of FACS-sorted (CD31+/CD45-) endothelial cells - overall merges
<p><strong>Seurat objects of overall merges of FACS-sorted (CD31+/CD45-) endothelial cells<br></strong><em>-> part of the manuscript: Single-cell atlas of the human brain vasculature across development, adulthood and disease</em><br><em>https://www.nature.com/articles/s41586-024-07493-y</em><strong><br></strong></p> <p>-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p><em>-i) Overall merge of all brain sorted endothelial cells_seurat object.rds: </em><br> -> this seurat object is the overall merge of FACS-sorted (CD31+/CD45-) endothelial cells isolated from fetal brain, adult/control brains (temporal lobes), brain tumors (lower-grade glioma, high-grade glioma (glioblastoma), brain metastasis, meningiomas) and brain vascular malformations (brain arteriovenous malformations). <br><br><em>-ii) Overall merge of all sorted brain and peripehry endothelial cells_seurat object.rds: </em><br> -> this seurat object is the overall merge of FACS-sorted (CD31+/CD45-) endothelial cells isolated from fetal brain, fetal peripheral organs, adult/control brains (temporal lobes), brain tumors (lower-grade glioma, high-grade glioma (glioblastoma), brain metastasis, meningiomas) and brain vascular malformations (brain arteriovenous malformations). <br><br><em>-iii) Overall merge of pathological sorted endothelial cells_seurat object.rds: </em><br> -> this seurat object is the overall merge of FACS-sorted (CD31+/CD45-) endothelial cells isolated from brain tumors (lower-grade glioma, high-grade glioma (glioblastoma), brain metastasis, meningiomas) and brain vascular malformations (brain arteriovenous malformations). <br><br><em>-iv) Overall merge of tumor sorted endothelial cells_seurat object.rds: </em><br> -> this seurat object is the overall merge of FACS-sorted (CD31+/CD45-) endothelial cells isolated from brain tumors (lower-grade glioma, high-grade glioma (glioblastoma), brain metastasis, meningiomas). </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.