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.
111,247
datasets available to search
ShareScore release 0.9.0
Dataset results
111,247 results for “cells”
Drosophila Cell Tracking
<p>This 3D+t dataset shows an excerpt of a developing fruit fly (Drosophila melanogaster) embryo over 100 time steps during gastrulation. The movie has been recorded using a light sheet microscope. We provide dense manual annotations for cell lineages for all ~800 cells per frame.</p> <p><a href="../api/records/8032735/draft/files/benchmark-dataset-abstract-2.png/content" target="_blank" rel="noopener">Preview Image</a></p> <p>The movie of this developing <em>Drosophila melanogaster</em> embryo has been recorded by the <a href="https://www.embl.de/research/units/cbb/hufnagel/">Hufnagel group, EMBL Heidelberg, Germany</a>, with the light sheet microscope described in (<a href="http://www.nature.com/nmeth/journal/v9/n7/abs/nmeth.2064.html">Krzic et al., 2012</a>).</p> <p>The challenging task is to automatically segment and track all cells over all time steps to reconstruct full cell lineages.</p> <p>To acquire manual cell tracking annotations, we first segmented this dataset using <a href="http://ilastik.org/">ilastik</a> and refined the result with a seeded watershed. On average, this yielded ~800 cells per frame, which we then tracked manually over all 100 time steps using the <a href="http://ilastik.org/documentation/tracking/tracking.html#sec_manual">Manual Tracking workflow in ilastik</a>. We here provide the raw data, our segmentation and our manual annotations for benchmarking purposes.</p> <p> </p>
Single-Cell Mapping Reveals Several Immune Subsets Associated with Liver Metastasis of Pancreatic Ductal Adenocarcinoma
<p>Identifying a metastasis-correlated immune cell composition within the tumor microenvironment (TME) of pancreatic ductal adenocarcinoma (PDAC) will help to develop promising and innovative therapeutic strategies. Twenty-six samples from 11 patients (including 11 primary tumor tissues, 10 blood, and 5 lymph nodes) with different stages were used to develop a multiscale immune profile. High-dimensional single-cell analysis with mass cytometry was performed to search for metastasis-correlated immune changes in the microenvironment.</p> <p>The details about the files uploaded are as follows:</p> <p>1. panelA_Blood.zip includes 10 .fcs files from blood samples in Panel A;</p> <p>2. panelA_LN.zip includes 5 .fcs files from lymph node samples in Panel A;</p> <p>3. panelA_Tumor.zip includes 11 .fcs files from tumor tissue samples in Panel A;</p> <p>4. panelB_Tumor.zip includes 11 .fcs files from tumor tissue samples in Panel B;</p> <p>5. panel_metadata.xlsx describes marker used in Panel A and B;</p> <p>6. sample_metadata.xlsx describes detailed sample information.</p>
Cell behaviors underlying Myxococcus xanthus aggregate dispersal
<p>The soil bacterium Myxococcus <em> xanthus</em> is a model organism with a set of diverse behaviors. These behaviors include the starvation-induced multicellular development program, in which cells move collectively to assemble multicellular aggregates. After initial aggregates have formed, some will disperse, with smaller aggregates having a higher chance of dispersal. Initial aggregation is driven by two changes in cell behavior: cells slow down inside of aggregates and bias their motion by reversing direction less frequently when moving towards aggregates. </p> <p>However, the cell behaviors that drive dispersal are unknown. Here we use fluorescent microscopy to quantify changes in cell behavior after initial aggregates have formed. We observe that after initial aggregate formation, cells adjust the bias in reversal timings by initiating reversals more rapidly when approaching unstable aggregates. Using agent-based modeling, we then show dispersal is predominantly generated by this change in bias, which is strong enough to overcome slowdown inside aggregates. Notably, the change in reversal bias is correlated with the nearest aggregate’s size, connecting cellular activity to previously observed correlations between aggregate size and fate. To determine if this connection is consistent across strains, we analyze a second M. <em>xanthus</em> strain with reduced levels of dispersal. We find that far fewer cells near smaller aggregates modified their bias. This implies that aggregate dispersal is under genetic control, providing a foundation for further investigations into the role it plays in the life cycle of M. <em> xanthus</em>.</p> <p> </p>
Simulation data for the office cell building energy model with the attached overhang
<p>Simulation data for 729,000 variants of the office cell building model with the overhang attached over the window. The variants are determined by the overhang depth and height, location, presence of obstacles, orientation and cooling and heating set points. The office cell model is described in the manuscript "Predicting the shape of loads for an office cell with an overhang from a small number of building energy simulations".</p>
A Single-cell Perturbation Landscape of Colonic Stem Cell Polarisation
<p>Cancer cells are regulated by oncogenic mutations and microenvironmental signals, yet these processes are often studied separately. To functionally map how cell-intrinsic and cell-extrinsic cues co-regulate cell-fate in colorectal cancer (CRC), we performed a systematic single-cell analysis of 1,107 colonic organoid cultures regulated by 1) CRC oncogenic mutations, 2) microenvironmental fibroblasts and macrophages, 3) stromal ligands, and 4) signalling inhibitors. Multiplexed single-cell analysis revealed a stepwise epithelial differentiation landscape dictated by combinations of oncogenes and stromal ligands, spanning from fibroblast-induced Clusterin (CLU)<sup>+</sup> revival colonic stem cells (revCSC) to oncogene-driven LRIG1<sup>+</sup> hyper-proliferative CSC (proCSC). The transition from revCSC to proCSC is regulated by decreasing WNT3A and TGF-β-driven YAP signalling and increasing KRAS<sup>G12D</sup> or stromal EGF/Epiregulin-activated MAPK/PI3K flux. We find APC-loss and KRAS<sup>G12D</sup> collaboratively limit access to revCSC and disrupt stromal-epithelial communication -- trapping epithelia in the proCSC fate. These results reveal that oncogenic mutations dominate homeostatic differentiation by obstructing cell-extrinsic regulation of cell-fate plasticity.</p>
Generating VISA scores on glioma bulk RNAseq and single cell RNAseq glioma
<p>Tissue stiffness is collectively determined by the stiffness of ECM and cells. Gliomas are characterized by dysregulated expression of ECM proteins, ECM crosslinking enzymes, and aberrant cellular contractility.While The RNA sequencing datasets have been comprehensively analyzed to interrogate tumor genomes, epigenomes, and transcriptomes, we realized an untapped potential: using transcriptomic data coupled with analysis of gene expression associated with ECM and actomyosin contractility to establish a bioinformatic tool, which we named VIrtual Stiffness Algorithm (VISA), capable of inferring tissue stiffness.</p> <p>This data repository is for reproducing VISA scores on glioma bulk RNAseq and single cell RNAseq glioma.</p>
Causal identification of single-cell experimental perturbation effects with CINEMA-OT
<p>Recent advancements in single-cell technologies allow characterization of experimental perturbations at single-cell resolution. While methods have been developed to analyze such experiments, the application of a strict causal framework has not yet been explored for the inference of treatment effects at the single-cell level. In this work, we present a causal inference-based approach to single-cell perturbation analysis, termed CINEMA-OT (Causal INdependent Effect Module Attribution + Optimal Transport). CINEMA-OT separates confounding sources of variation from perturbation effects to obtain an optimal transport matching that reflects counterfactual cell pairs. These cell pairs represent causal perturbation responses permitting a number of novel analyses, such as individual treatment effect analysis, response clustering, attribution analysis, and synergy analysis. We benchmark CINEMA-OT on an array of treatment effect estimation tasks for several simulated and real datasets and show that it outperforms other single-cell perturbation analysis methods. Finally, we perform CINEMA-OT analysis of two newly-generated datasets: (1) rhinovirus and cigarette smoke-exposed airway organoids, and (2) combinatorial cytokine stimulation of immune cells. In these experiments, CINEMA-OT reveals potential mechanisms by which cigarette smoke exposure dulls the airway antiviral response, as well as the logic that governs chemokine secretion and peripheral immune cell recruitment.</p>
Trellis Single-Cell Screening Reveals Stromal Regulation of Patient-Derived Organoid Drug Responses
<p>Patient-derived organoids (PDOs) can model personalized therapy responses, however current screening technologies cannot reveal drug response mechanisms or how tumor microenvironment cells alter therapeutic performance. To address this, we developed a highly-multiplexed mass cytometry platform to measure post translational modification (PTM) signaling, DNA-damage, cell-cycle activity, and apoptosis in >2,500 colorectal cancer (CRC) PDOs and cancer associated fibroblasts (CAFs) in response to clinical therapies at single-cell resolution. To compare patient- and microenvironment-specific drug responses in thousands of single-cell datasets, we developed <em>Trellis</em> — a highly-scalable, hierarchical tree-based treatment effect analysis method. Trellis single-cell screening revealed that on-target cell-cycle blockage and DNA-damage drug effects are common, even in chemorefractory PDOs. However, drug-induced apoptosis is rare, patient-specific, and aligns with cancer cell PTM signaling. We find that CAFs can regulate cancer cell plasticity — shifting proliferative stem cells to slow-cycling revival stem cells via YAP to protect cancer cells from chemotherapy.</p> <p> </p> <p>This repo contains the processed scRNA-seq Scanpy AnnData objects generated from the study. More information describing the data can be found at: https://github.com/TAPE-Lab/Ramos-et-al-Trellis</p>
The redox mediated – scanning droplet cell system for evaluation of the solid electrolyte interphase in Li-ion batteries
<p>Dataset of articles "The redox mediated – scanning droplet cell system for evaluation of the solid electrolyte interphase in i-ion batteries"</p>
In silico spatial transcriptomic editing at single-cell resolution
<p>The data for training the GAN (Inversion) model and reproduce the results reported in the paper </p>
Deep model predictive control of gene expression in thousands of single cells
<p>Experimental data, training datasets, and trained models for our study on deep model predictive control of gene expression in bacteria. This data can be used to reproduce our results and figures.</p> <p>See our preprint here: <a href="https://www.biorxiv.org/content/10.1101/2022.10.28.514305">biorxiv.org/content/10.1101/2022.10.28.514305</a></p> <p>And our code repository here: <a href="https://gitlab.com/dunloplab/deepcellcontrol">gitlab.com/dunloplab/deepcellcontrol</a></p> <p><strong>Contents:</strong></p> <ul> <li><em>datasets.zip</em>: Formatted experimental data used to train and validate fluorescence forecasting models.</li> <li><em>experiments.zip:</em> Processed data for all control experiments.</li> <li><em>misc.zip</em>: Files necessary to reproduce some figures or to exactly reproduce some of our results.</li> <li><em>models.zip</em>: Trained neural network models and associated files.</li> </ul>
TCGA Kidney Renal Clear Cell Carcinoma (KIRC) Clinical Data
<p><strong>Abstract:</strong></p> <p>The Cancer Genome Atlas (TCGA) was a large-scale collaborative project initiated by the National Cancer Institute (NCI) and the National Human Genome Research Institute (NHGRI). It aimed to comprehensively characterize the genomic and molecular landscape of various cancer types. This dataset includes curated survival data from the Pan-cancer Atlas paper titled <a href="http://www.cell.com/cell/fulltext/S0092-8674(18)30229-0">"An Integrated TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR) to drive high quality survival outcome analytics"</a>. The paper highlights four types of carefully curated survival endpoints, and <a href="http://www.cell.com/action/showFullTableImage?isHtml=true&tableId=tbl3&pii=S0092867418302290">recommends the use of the endpoints of OS, PFI, DFI, and DSS for each TCGA cancer type</a>. The dataset also includes phenotypic information about KIRC. The Sample IDs are unique identifiers, which can be paired with the gene expression dataset. </p> <p><strong>Inspiration:</strong></p> <p>This dataset was uploaded to UBRITE for GTKB project. </p> <p><strong>Instruction:</strong></p> <p>The survival and phenotype data were merged into one file. Empty columns were removed. Columns with the same value for every sample were also removed. </p> <p><strong>Acknowledgments:</strong></p> <p>Goldman, M.J., Craft, B., Hastie, M. et al. Visualizing and interpreting cancer genomics data via the Xena platform. Nat Biotechnol (2020). https://doi.org/10.1038/s41587-020-0546-8</p> <p>Liu, Jianfang, Caesar-Johnson, Samantha J. et al. An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics. Cell, Volume 173, Issue 2, 400 - 416.e11. <a href="https://doi.org/10.1016/j.cell.2018.02.052">https://doi.org/10.1016/j.cell.2018.02.052</a></p> <p>The Cancer Genome Atlas Research Network., Weinstein, J., Collisson, E. et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet 45, 1113–1120 (2013). https://doi.org/10.1038/ng.2764</p> <p><strong>U-BRITE last update: </strong>07/13/2023</p>
TCGA Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma (CESC) Clinical Data
<p><strong>Abstract:</strong></p> <p>The Cancer Genome Atlas (TCGA) was a large-scale collaborative project initiated by the National Cancer Institute (NCI) and the National Human Genome Research Institute (NHGRI). It aimed to comprehensively characterize the genomic and molecular landscape of various cancer types. This dataset includes curated survival data from the Pan-cancer Atlas paper titled <a href="http://www.cell.com/cell/fulltext/S0092-8674(18)30229-0">"An Integrated TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR) to drive high quality survival outcome analytics"</a>. The paper highlights four types of carefully curated survival endpoints, and <a href="http://www.cell.com/action/showFullTableImage?isHtml=true&tableId=tbl3&pii=S0092867418302290">recommends the use of the endpoints of OS, PFI, DFI, and DSS for each TCGA cancer type</a>. The dataset also includes phenotypic information about CESC. The Sample IDs are unique identifiers, which can be paired with the gene expression dataset. </p> <p><strong>Inspiration:</strong></p> <p>This dataset was uploaded to UBRITE for GTKB project. </p> <p><strong>Instruction:</strong></p> <p>The survival and phenotype data were merged into one file. </p> <p><strong>Acknowledgments:</strong></p> <p>Goldman, M.J., Craft, B., Hastie, M. et al. Visualizing and interpreting cancer genomics data via the Xena platform. Nat Biotechnol (2020). https://doi.org/10.1038/s41587-020-0546-8</p> <p>Liu, Jianfang, Caesar-Johnson, Samantha J. et al. An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics. Cell, Volume 173, Issue 2, 400 - 416.e11. <a href="https://doi.org/10.1016/j.cell.2018.02.052">https://doi.org/10.1016/j.cell.2018.02.052</a></p> <p>The Cancer Genome Atlas Research Network., Weinstein, J., Collisson, E. et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet 45, 1113–1120 (2013). https://doi.org/10.1038/ng.2764</p> <p><strong>U-BRITE last update: </strong>07/13/2023</p>
MetForTC-Phase transition plateau measurements of the new Al slim cell
<p>The main goal was to test whether the new cells improve the reliability of measurements for in-situ conditions, and to work on characterisation of one thermocouple using new designed Al cell.The measurement in the Three-zone furnace was made in a quartz tube that was ordered according to the dimensions of the fixed point cell produced in ČMI and according to the dimensions of the openings in the three zones of the furnace.</p> <p>Equipment used: Aluminium Fixed Point Cell (designed and produced by ČMI), 3 Zone High Temperature Furnace – 465, ISOTECH Oberon Furnace, ISOTECH, Nanovoltmeter, 2182A, Keithley, Rotameter, FT-052-08-ST-VN, Omega S-typeThermocouple, ISOTECH, 30474/6.</p>
MetForTC-Examples of phase transition plateau measurements of the new Sn miniature cells- 2023-07-27
<p>The data presents the characterization of the prototype of the Sn fixed-point mini-cell and the associated reference thermocouple type SThe cells was utilised for the experimental characterisation for drift under different thermal conditions in three zone furnaces and dry-block calibrators., which was made by TUBITAK and measured by Lab1,Lab2 and Lab3.</p>
MetForTC-Design ver2 for the new miniature cells
<p>TUBITAK and FSB drafted designs of the miniature fixed‑point cells developed, one design having immersion depth of approx. 100 mm. The related technical documentation is given below in this document Figures 1 for assembling the performance of the mini-cells.</p>
Kupffer cell-like syncytia replenish resident macrophage function in fibrotic liver
<p class="AbstractSummary"><span>Kupffer cells (KCs) are localized in liver sinusoids but extend pseudopods to parenchymal cells to maintain their identity and serve as the body's central bacterial filter. Liver cirrhosis dramatically alters vascular architecture, but how KCs adapt is unclear. We used a mouse model of liver fibrosis and human tissue to examine immune adaptation. Fibrosis forced KCs to lose contact with parenchymal cells, downregulating "KC identity", which rendered them incapable of clearing bacteria. Commensals stimulated the recruitment of monocytes via CD44 to a spatially distinct vascular compartment. There, they formed large aggregates of multinucleated cells (syncytia) that expressed phenotypical KC markers and displayed enhanced bacterial catching ability. Syncytia formed via CD36 and were observed in human cirrhosis as a possible antimicrobial defense that evolved with fibrosis.</span></p>
CT Data of Battery Pouch Cell with Defects
<p>These are CT slices as TIF files.</p> <p>Data: cubic voxels of (22 µm)³</p> <p>You may also open the included project file in VGStudio or the free myVGL viewer (https://www.volumegraphics.com/en/download-viewer.html) or other compatible software.</p> <p> </p> <p>The sample was prepared by Johannes Münch. For further details see the following paper:</p> <p><a href="https://doi.org/10.1002/ente.202300323">https://doi.org/10.1002/ente.202300323</a></p>
Sarcopenia index predicts short-term prognosis of head and neck squamous cell carcinoma
<p>Dataset of "Sarcopenia index predicts short-term prognosis of head and neck squamous cell carcinoma"</p>
PANDA cell-type-specific networks for S858R and WT mouse cerebral cortex
<p>The below files are from the data/results directory of this associated project and include the following:</p> <ul> <li><strong>PANDA networks </strong>: cell-type-specific TF-gene regulatory networks constructed using multi-omic inputs (snRNA-seq, TF-motif, and PPI) for all cell types in S858R and WT mouse cerebral cortex tissues (n = 16). All the files included for PANDA networks indicate the condition (heterozygous or control), tissue (cerebral cortex) followed by _PANDA.Rdata. (example: astrocytes_heterozygouscortexexpression_PANDA.Rdata)</li> </ul>
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.