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111,247 results for “cells”
Centripetal migration in Drosophila ovary VII: E-cadherin RNAi clones in follicle cells timelapse
<p>Data supporting Figs. 5, S7, S8, S9, S10, S11, S12, S13, S14, S15 of “Two phases for centripetal migration of Drosophila melanogaster follicle cells: initial ingression followed by epithelial migration”</p> <p>DOI: 10.1242/dev.200492</p> <ul> <li><strong>“FC RNAi flipout timelapse data complete” 43.53GB</strong></li> </ul> <p> Timelapse image data for clones of follicle cells with E-Cadherin knockdown</p> <ul> <li><strong>“Live FC RNAi Clonal Data prelim evaluation” 22.1 MB</strong></li> </ul> <p> Preliminary evaluation of follicle cell E-Cadherin knockdown samples</p> <ul> <li><strong>“RNAi clone M2-M3-M5 quant” 12 KB</strong></li> </ul> <p> Quantitative data from specific milestones for clones of follicle cells with E-Cadherin knockdown</p>
Differential response of α-synuclein expression to bacterial ligands and metabolites in mouse enteroendocrine cells
<p>Dataset for manuscript <em>"<strong> </strong></em><strong>α</strong><strong>-synuclein expression in response to bacterial ligands and metabolites in gut enteroendocrine cells</strong><em>". </em>Tabs in excel file are title with the figure number. </p>
Multiplexed histology of COVID-19 post-mortem lung samples - Single-cell Mean Fluorescence Intensities
<p>Data table containing single-cell mean fluorescence intensities (MFI) of all markers analyzed by multiplexed histology in all COVID-19 post-mortem lung samples and non-COVID-related pneumonia controls (14 lung samples, stratified based on disease duration into control, acute, chronic and prolonged). It contains information at the single-cell level about approx 50 proteins in around 40.000 lung cells.</p> <p>Data shown has been arcsin(h) transformed with a co-factor of 0.2. Additionally, cells expressing less than 0.15 MFI of all markers have been labeled as non-defined and excluded from the data set.</p> <p>Seurat package 4.0.0 was used in R to perform mean centering and scaling, followed by PCA, and reduced the dimensions of the data to the top 11 principal components. UMAP was initialized in this PCA space to visualize the data on reduced UMAP dimensions. The cells were clustered on PCA space using the SNN algorithm implemented as <em>FindNeighbors</em> and <em>FindClusters </em>with <em>n.epochs = 500</em> and default parameters (<em>res = 0.8</em>). We obtained 26 clusters that we merged to get relevant populations for our analysis based on canonical lineage markers. We ended up with 8 cell clusters that were manually annotated based on cell-type-specific markers found to be differentially expressed.</p> <p> </p> <p> </p>
Predicting patient treatment response and resistance via single-cell transcriptomics of their tumors
<p>Data required to reproduce the results/figures of the "<strong>Predicting patient treatment response and resistance via single-cell transcriptomics of their tumors</strong>" project. </p>
Data from: Maximizing the potential for living cell banks to contribute to global conservation priorities
<p><strong>Summary</strong></p> <p>This dataset accompanies the publication "<strong>Maximizing the potential for living cell banks to contribute to global conservation priorities</strong>" published in Zoo Biology. This study analyzed the representation of amphibian, bird, mammal and reptile species within the San Diego Zoo Wildlife Alliance (SDZWA) Frozen Zoo® living cell collection (as of April 2019) and implemented a qualitative framework for the prioritization of species for future sampling. We used global conservation assessment schemes (including the IUCN Red List of Threatened Species<sup>TM</sup>, CITES, the Alliance for Zero Extinction, the EDGE of Existence, and Climate Change Vulnerability), and opportunities for sample acquisition from the global zoo and aquarium community, to identify priority species for future cryobanking efforts.</p> <p>Two datasets accompany this publication. The first provides a complete list of all “<em>Threatened</em>” species not currently represented within the San Diego Zoo Wildlife Alliance (SDZWA) Frozen Zoo®, their representation under various conservation assessment schemes, and their presence in the global zoo and aquarium community, as represented by Species360 members (<strong>Supporting Information Data S1</strong>). The second provides a list of the 2,351 amphibian and bird species not currently represented in the SDZWA Frozen Zoo® or listed as “<em>Threatened</em>” under the IUCN Red List, but assessed by Foden <em>et al.</em> (2013) as highly vulnerable to climate change. This dataset also shows their presence in the global zoo and aquarium community, as represented by Species360 members (<strong>Supporting Information Data S2</strong>).</p> <p>Data provided by SDZWA are only available upon request. Data for each conservation assessment scheme were collected from each source individually (all freely available), and are accurate to April 2019. Zoo species holdings data were provided upon request by Species360 (https://www.species360.org/), which operates the real-time database ZIMS (Zoological Information Management System). ZIMS is the largest real-time database of comprehensive and standardized information spanning more than 1,200 zoological collections globally, and provides the number of institutions currently managing each species and their current population sizes. </p> <p> </p> <p><strong>Description of the Datasets</strong></p> <p> </p> <p><strong>Supporting Information Data S1 (accurate to April 2019):</strong> A complete list of all “<em>Threatened</em>” species not currently represented within the San Diego Zoo Wildlife Alliance (SDZWA) Frozen Zoo®, their representation under various conservation assessment schemes, and their presence in the global zoo and aquarium community, as represented by Species360 members. Provided in .csv format, variables include:</p> <ul> <li><strong>Class: </strong>The taxonomic class of the species</li> <li><strong>IUCN Name: </strong>The taxonomic name of the species (genus and epithet) according to the IUCN Red List</li> <li><strong>IUCN Status: </strong>The IUCN Red List status of the species (VU, EN, or CR)</li> <li><strong>ZIMS:</strong> The presence (1) or absence (0) of the species from Species360 member zoos and aquariums</li> <li><strong>AZE:</strong> The listing of the species on the Alliance for Zero Extinction assessment (listed = 1, not listed = 0)</li> <li><strong>AZA:</strong> The presence of an active <em>ex situ</em> management programme for the species in the Association of Zoos and Aquariums (active programme = 1, no programme = 0)</li> <li><strong>EAZA: </strong>The presence of an active <em>ex situ</em> management programme for the species in the European Association of Zoos and Aquaria (active programme = 1, no programme = 0)</li> <li><strong>CITES:</strong> The listing of the species on of the three CITES appendices (listed = 1, not listed = 0)</li> <li><strong>CC Vulnerability: </strong>The listing of the species as being highly vulnerable to climate change by Foden <em>et al</em>. 2013 (vulnerable = 1, not vulnerable = 0)</li> <li><strong>EDGE: </strong>The listing of the species on the Evolutionary Distinctiveness and Global Endangerment assessment (listed = 1, not listed = 0)</li> </ul> <p> </p> <p><strong>Supporting Information Data S2 (accurate to April 2019):</strong> A list of the 2,351 amphibian and bird species not currently represented in the SDZWA Frozen Zoo® or listed as “<em>Threatened</em>” under the IUCN Red List, but assessed by Foden <em>et al.</em> (2013) as highly vulnerable to climate change. This dataset also shows their presence in the global zoo and aquarium community, as represented by Species360 members. Provided in .csv format, variables include:</p> <ul> <li><strong>Class: </strong>The taxonomic class of the species</li> <li><strong>IUCN Name: </strong>The taxonomic name of the species (genus and epithet) according to the IUCN Red List</li> <li><strong>CC Vulnerability:</strong> The listing of the species as being highly vulnerable to climate change by Foden <em>et al</em>. 2013 (all species are vulnerable = H)</li> <li><strong>IUCN Status: </strong>The IUCN Red List status of the species (excluding VU, EN, or CR)</li> <li><strong>ZIMS:</strong> The presence (1) or absence (0) of the species from Species360 member zoos and aquariums</li> </ul> <p> </p> <p><strong>Acknowledgements</strong></p> <p>We sincerely thank the San Diego Zoo Wildlife Alliance and Species360 member institutions for their support and data input. This research was funded by the Irish Fulbright Commission 2018/2019 to A.M., the Irish Research Council Laureate Awards 2017/2018 IRCLA/2017/60 to Y.M.B. D.A.C. and J.S. were funded by Species360, the University of Southern Denmark and the Species360 Conservation Science Alliance sponsors: the World Association of Zoos and Aquariums (WAZA), Mandai Wildlife Reserve, and Copenhagen Zoo. </p> <p> </p> <p><strong>Disclaimer</strong></p> <p>Despite our best efforts at screening the data for errors and inconsistencies, some information could be erroneous. Similarly, data collected from the various conservation assessment schemes, and those contained within ZIMS, could contain errors or reporting failures. For example, ZIMS data are are based on submitted records from individual institutions, and are not subject to editorial verification, potentially permitting errors or failure to update species holdings etc. Despite this, ZIMS represents the only global database of zoo collection composition records, and as a result, is used by the IUCN, Convention on International Trade in Endangered Species (CITES), the Wildlife Trade Monitoring Network (TRAFFIC), United States Fish and Wildlife Service (USFWS) and Department for Environment, Food and Rural Affairs (DEFRA). </p> <p> </p> <p><strong>Credit</strong></p> <p>If you use this dataset, please cite the corresponding publication:</p> <p>Mooney, A., Ryder, O. A., Houck, M. L., Staerk, J., Conde, D. A., & Buckley, Y. M. (2023). Maximizing the potential for living cell banks to contribute to global conservation priorities. <em>Zoo Biology</em>, 1– 12. <a href="https://doi.org/10.1002/zoo.21787">https://doi.org/10.1002/zoo.21787</a></p>
Single-Cell Profiling of CD8+ T Cells in Acute Myeloid Leukemia Reveals a Continuous Spectrum of Differentiation and Clonal Hyperexpansion
<p>Data for the publication <strong>Single-Cell Profiling of CD8<sup>+</sup> T Cells in Acute Myeloid Leukemia Reveals a Continuous Spectrum of Differentiation and Clonal Hyperexpansion</strong></p>
Single-cell atlases of two lophotrochozoan larvae highlight their complex evolutionary histories
<p>This archive contains all the code and data to reproduce the results of the associated manuscript: Piovani <em>et al</em>, "Single-cell atlases of two lophotrochozoan larvae highlight their complex evolutionary histories". We provide filtered scRNA-seq matrices, protein fasta files for each specie used to run SAMap and GenERA as well as the R-code used to generate the datasets and the jupyter notebook to generate SAMap results. In addition we provide the final Seurat objects and all analysis results which can be consulted without re-running the code.</p>
Benchmark FIB SEM and Airyscan data of HeLa cells
<p>Sample volume electron microscopy dataset cropped from EMPIAR-10819 and fluorescence dataset from BioImage Archive S-BSST707 for the CLEM-Reg paper.</p>
The First Transcriptomic Atlas of the Adult Lacrimal Gland Reveals Epithelial Complexity and Identifies Novel Progenitor Cells in Mice
<p>This project contains the R objects and code to reproduce the analyses and figures presented in the research article:</p> <p>'The First Transcriptomic Atlas of the Adult Lacrimal Gland Reveals Epithelial Complexity and Identifies Novel Progenitor Cells in Mice.' <em>Cells</em> <strong>2023</strong>, <em>12</em>, 1435. https://doi.org/10.3390/cells12101435</p> <p>Raw data (FASTQ files and CellRanger output files used for the preprocessing of individual datasets) can be found on Gene Expression Omnibus database (www.ncbi.nlm.nih.gov/geo/) under accession # GSE232146.</p>
Spatial transcriptomics of B and T cell receptors uncovers lymphocyte clonal dynamics.
<p>This dataset contains a single zipped folder containing:</p> <ul> <li> <p>data</p> </li> <li> <p>scripts</p> </li> </ul> <p>needed to reproduce the manuscript entitled "Spatial transcriptomics of B and T cell receptors uncovers lymphocyte clonal dynamics". Each folder is organized by tissue type, methodology, and analysis. A readme file accompanies each folder with details on the files/scripts within that folder. Alongside the paper and supplementary materials, it should be possible to reproduce all the figures in the manuscript.</p>
Supplementary datasets: sciCSR infers B cell state transition and predicts class-switch recombination dynamics using single-cell transcriptomic data (Ng et al.)
<p>This repository contains data files from the manuscript Ng et al. "sciCSR infers B cell state transition and predicts class-switch recombination dynamics using single-cell transcriptomic data".</p> <p><strong>Directories</strong></p> <p>Please untar the sciCSR-data-files.tar.gz archive.</p> <p><em><strong>Folder "Simulated_data"</strong></em></p> <ul> <li>'simulated_IGHC_reads' folder: containing list of simulated data (FASTQ sequence files and aligned BAM files) to test the accuracy of commonly used RNA-seq aligners (STAR, HISAT2) to distinguish sterile and productive heavy-chain transcripts. The code to generate these data is in the repository https://github.com/Fraternalilab/sciCSR-analysis.</li> <li>'simulated_transitions.RData': .RData file containing list of Seurat objects of simulated datasets of different number of cells, to test the robustness of sciCSR-inferred transitions across different dataset sizes.</li> </ul> <p><em><strong>Folder "Seurat_objects"</strong></em></p> <ul> <li>'human_Bcells_atlas_IGHC_NMF_rank.rds': Nonnegative matrix factorization (NMF) results to derive isotype signatures from the human B cell atlas (see below).</li> <li>'mouse_Bcells_atlas_IGHC_NMF_rank.rds': NMF results to derive isotype signatures from the mouse B cell atlas (see below)</li> <li>'Human_Bcells_atlas_IGHC.rds': Seurat object containing cells forming the 'human B cell atlas' (i.e. merging data from Stewart et al (https://doi.org/10.3389/fimmu.2021.602539) and King et al (https://doi.org/10.1101/2020.04.28.054775))</li> <li>'mouse_Bcells_atlas_IGHC.rds': Seurat object containing cells forming the 'mouse B cell atlas' (i.e. merging data from Mathew et al (https://doi.org/10.1016/j.celrep.2021.109286) and Luo et al (https://doi.org/10.1186/s13578-022-00795-6))</li> <li>'Stewart_HumanPeripheral_Bcells_IGHC.rds': Seurat object containing cells from the Stewart et al (https://doi.org/10.3389/fimmu.2021.602539) peripheral blood B cell atlas.</li> <li>* 'King_HumanTonsil_Bcells_IGHC.rds': Seurat object containing cells from the King et al. (https://doi.org/10.1101/2020.04.28.054775) human tonsilar B cell atlas.</li> <li>'Kim_Covid_Bcells_IGHC.rds': Seurat object containing cells from the Kim et al. (https://doi.org/10.1038/s41586-022-04527-1) time-course scRNA-seq data on human B cell response to SARS-CoV-2 vaccine.</li> <li>'Gomez_AID_VDJ_IGHC.rds': Seurat object containing cells from the Gómez-Escolar et al. (https://doi.org/10.15252/embr.202255000) Aicda mouse knockout scRNA-seq data.</li> <li>'Hong_IL23_Bcells_IGHC.rds': Seurat object containing cells from the Hong et al. (https://doi.org/10.4049/jimmunol.2000280) Il23 p19 mouse knockout scRNA-seq data.</li> <li>'scIFNg.rds': Seurat object containing scRNA-seq data of time-course in vitro culture of B cells stimulated with interferon gamma generated in this work.</li> </ul>
Ageing Data of Lithium-ion "Panasonic NCR18650B" Battery Cells
<p>A large-scale battery aging experiment dataset is presented in this dataset.<br> In total, 116 Lithium-ion cells were cycled in a controlled temperature ambient until they reached the end-of-life.<br> The cycling includes 37 different aging load profiles and more detailed reference tests throughout the whole lifespan of the cells.<br> The load profiles vary in seven parameters (i.e., temperature, charge current, average discharge current, peak discharge current, frequency, average state of charge, and state of charge swing) and were set up by a design of experiment.<br> Different approaches for the estimation of state-of-charge and state-of-health can be provided and analyzed by the offered data set.<br> Thus this dataset forms a broad basis for battery modeling or battery management algorithms for electric vehicles and hybrid electric vehicles.</p> <p>For further details, see the linked publication.</p>
Joint embedding of vertebrate brain single-cell RNA-Seq using sequence or structure
<p>Embeddings of single-cell RNA-Seq data from three adult vertebrate brain datasets into Orthogroup feature space or Structural cluster feature space. Orthogroups were generated using OrthoFinder v5.5.0; Structural clusters were assigned by using FoldSeek to cluster AlphaFold-v4 structural predictions.<br> <br> The three datasets used as the basis for these embeddings were:</p> <ul> <li>sample <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM3768152">"Brain8"</a> from the <a href="https://www.frontiersin.org/articles/10.3389/fcell.2021.743421/full">Jiang et al. 2021</a> zebrafish cell atlas (files beginning with GSM3768152)</li> <li>sample <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM2906405">"Brain1"</a> from the <a href="https://www.sciencedirect.com/science/article/pii/S0092867418301168#sec4">Han et al. 2018</a> mouse cell atlas (files beginning with GSM2906405)</li> <li>sample <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM6214268">"Xenopus_brain_COL65"</a> from the <a href="https://www.nature.com/articles/s41467-022-31949-2">Liao et al. 2022</a> Xenopus laevis adult cell atlas (files beginning with GSM6214268)</li> </ul> <p>For each dataset, we also generated a standardized cell type annotation file based on the author's originally provided cell type annotation data. The first column is the cell barcode for that species and the second column is the original study's cell type annotation for that cell.</p> <p>For the Xenopus brain data, we removed around ~18k cells that were not annotated in the original data to simplify data analyses - these are reflected in the files with the "subsampled" suffix. Subsampled versions of the data are also available for the joint embedding space (prefixed with "DrerMmusXlae").</p> <p>For the final datasets used in our analyses, we also provide features x cell matrices as .h5ad files for smaller file sizes and faster loading using Scanpy. </p> <p>For visualizing our UMAP plots of our top200 embedding space, we provide ".tsv" files with a variety of metrics and the x and y positions of each cell in the UMAP. See "DrerMmusXlae_adultbrain_FoldSeek_plotlydata.tsv" and "DrerMmusXlae_adultbrain_OrthoFinder_plotlydata.tsv"</p> <p>These data are part of the Arcadia Science Pub titled <a href="https://doi.org/10.57844/arcadia-vw5e-2670">"Comparing gene expression across species based on protein structure instead of sequence"</a>.</p>
Cortical cell assemblies and their underlying connectivity: an in silico study
<p>Dataset linked to the article with the same title</p> <ul> <li>simulation_config.zip: contains SONATA config files needed to re-run an exemplary simulation (after downloading the circuit from <a href="https://zenodo.org/record/7930275">10.5281/zenodo.7930275</a>). In order to run it, paths in circuit_config.json, and simulation_config.json have to be updated!</li> <li>assemblies.h5 is a dataset produced (and can be easily opened) by: <a href="https://zenodo.org/record/8112725">assemblyfire</a> (see GitHub README for more documentation) and serves as a basis for the manuscript. As the assemblies are the results of an unsupervised clustering (of high activity time bins) the resulting labels are not necessary meaningful. In the manuscript we have ordered the assemblies (from early to late responding ones, and from pattern A to J responsive ones) but the HDF5 file still stores the original labels. The mapping from the "random" labels to the ones presented in our article is stored in the config files on GitHub.</li> </ul> <p><em>The development of this dataset was supported by funding to the Blue Brain Project, a research center of the École polytechnique fédérale de Lausanne (EPFL), from the Swiss government’s ETH Board of the Swiss Federal Institutes of Technology.</em></p>
NewSOC, supplementary information to WT2.5.4 "Cells with honeycomb structured oxygen electrodes": electrochemical and post-mortem data sets.
<p>These are data set, related to participation of the IEN in project NewSOC. It includes results of the SEM-EDS analysis of the cells with hexagonal current collecting net and electrochemical performance data, both EIS and C-V characteristics. Results are grouped in zip archives, named according to cell design, “infill-net”.</p> <p>Compositions:</p> <p>LNF – LaNi<sub>0.6</sub>Fe<sub>0.4</sub>O<sub>3</sub> (net)</p> <p>LSC – La<sub>0.6</sub>Sr<sub>0.4</sub>CoO<sub>3-</sub><sub>d</sub> (infill)</p> <p>LSF – La<sub>0.5</sub>Sr<sub>0.5</sub>FeO<sub>3-</sub><sub>d</sub> (infill)</p> <p>LSCCF – La<sub>0.6</sub>Sr<sub>0.4</sub>Co<sub>0.15</sub>Cu<sub>0.05</sub>Fe<sub>0.8</sub>O<sub>3-</sub><sub>d </sub>(net)</p> <p>PCM – Pr<sub>0.5</sub>Ca<sub>0.5</sub>MnO<sub>3 </sub>(net)</p> <p>BSCFM – Ba<sub>0.5</sub>Sr<sub>0.5</sub>Co<sub>0.725</sub>Fe<sub>0.2</sub>Mo<sub>0.075</sub>O<sub>3-</sub><sub>d</sub> (infill)</p> <p> </p> <p><strong>Data presentation. </strong></p> <p><em>C-V characteristics:</em></p> <p>This is text files, generated by Zahner galvanostat, with self-decriptional titles.</p> <p>“05iv_650c_100h2+100h2o_500air.txt” – measurement at 650°C, 100 mL/min H<sub>2</sub> and 100 mL/min H<sub>2</sub>O on fuel side, 500 mL/min of air on air side.</p> <p><em>EIS data:</em></p> <p>EIS results were extracted from proprietary binary files, generated by Zahner galvanostat, and raw data is generally meaningless except the owners of such hardware. So, extracted EIS can be found in Excel files, used in analysis, in sheet “Experimental”. Other sheets in xlsx include some metadata (“info”), results of the equivalent circuit fit (“fit”) and some plots. Fit results might not be relevant. </p> <p><em>SEM</em></p> <p>Post-mortem results presented as SEM images (tif or jpg files) and pdf files with results of the EDS analysis.</p> <p><strong>LSC-LNF </strong></p> <p><em>(air flow 1 L/min, current density 0.25 A/cm<sup>2</sup>)</em></p> <p>test_1_07: </p> <p>03_eis20200917142808.xlsx – EIS, SOFC, 700°C, Flows L/min: F:0.2 H<sub>2</sub>;</p> <p>05_eis20200917123105.xlsx – EIS, SOFC, 700°C, flows L/min: F:0.1 H<sub>2</sub>+ 0.1 H<sub>2</sub>O;</p> <p>08_eis20200917123857.xlsx – EIS, SOFC, 700°C, flows L/min: F:0.1 H<sub>2</sub>+ 0.1 H<sub>2</sub>O;</p> <p>10_eis20200917131537.xlsx – EIS, SOEC, 700°C, flows L/min: F:0.1 H<sub>2</sub>+ 0.1 H<sub>2</sub>O;</p> <p>test_1_08:</p> <p>03_eis20200917125707.xlsx – EIS, SOFC, 700°C, Flows L/min: F:0.2 H<sub>2</sub>;</p> <p>09_eis20200917130019.xlsx – EIS, SOEC, 700°C, flows L/min: F:0.09 H<sub>2</sub>+ 0.21 H<sub>2</sub>O;</p> <p>10_eis20200917130600.xlsx – EIS, SOFC, 700°C, flows L/min: F:0.06 H<sub>2</sub>+ 0.14 H<sub>2</sub>O;</p> <p>12_eis20200917130737.xlsx – EIS, SOEC, 700°C, flows L/min: F:0.12 H<sub>2</sub>+ 0.28 H<sub>2</sub>O;</p> <p>test_3_14:</p> <p>01_eis20210517102042.xlsx– EIS, SOFC, 700°C, Flows L/min: F:0.1 H<sub>2</sub>+0.1 N<sub>2</sub>; 05_eis20210517102625.xlsx– EIS, SOFC, 700°C, flows L/min: F:0.1 H<sub>2</sub>+ 0.1 H<sub>2</sub>O;</p> <p>07_eis20210517102305.xlsx– EIS, SOEC, 700°C, flows L/min: F:0.06 H<sub>2</sub>+ 0.14 H<sub>2</sub>O;</p> <p>SEM</p> <p><em>(post-mortem after test_1_07)</em></p> <p>ogniwo_310_2020 ****.jpg - surface</p> <p> </p> <p><strong>BSCMF-PCM</strong></p> <p><em>(SOFC, air flow 0.5 L/min)</em></p> <p>test_2_14</p> <p>01eis_700c_cc4a_100h2+100n2_500air_eqc20220110112836.xlsx –700°C, 0.25 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 N<sub>2</sub></p> <p>02eis_700c_cc4a_100h2+100h2o_500ai_eqc20220110112724.xlsx –700°C, 0.25 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 H<sub>2</sub>O;</p> <p>04eis_650c_cc3a_100h2+100h2o_500ai_eqc20220110112503.xlsx–650°C, 0.1875 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 H<sub>2</sub>O;</p> <p>06eis_650c_cc3a_100h2+100n2_500air_eqc20220110112332.xlsx –650°C, 0.1875 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 N<sub>2</sub>;</p> <p>07eis_625c_cc3a_100h2+100n2_500air_eqc20220110112214.xlsx –625°C, 0.125 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 N<sub>2</sub>;</p> <p>08eis_625c_cc3a_100h2+100h2o_500ai_eqc20220110111834.xlsx–625°C, 0.125 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 H<sub>2</sub>O;</p> <p>test_1_67</p> <p>01_eqc20220831134628.xlsx–700°C, 0.125 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 N<sub>2</sub></p> <p>02_eqc20220831134724.xlsx - 700°C, 0.25 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 H<sub>2</sub>O;</p> <p>04_eqc20220831135442.xlsx–650°C, 0.0625 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 N<sub>2</sub>;</p> <p>06_eqc20220831135622.xlsx - 650°C, 0.0625 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 H<sub>2</sub>O;</p> <p>08_eqc20220831135801.xlsx - 625°C, 0.0625 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 H<sub>2</sub>O;</p> <p>09_eqc20220831135933.xlsx –650°C, 0.0625 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 N<sub>2</sub>;</p> <p>SEM</p> <p><em>(post-mortem of the test_2_14)</em></p> <p>493-2021-1.pdf, 493-2021-1i.pdf, 493-2021-2.pdf, 493-2021-2-2.pdf, 493-2021-2-3.pdf –cross-sections with EDS</p> <p>493_2021_*_**.tif - cross-sections</p> <p> </p> <p><strong>BSCMF-LSCCF</strong></p> <p><em>(SOFC, air flow 0.5 L/min)</em></p> <p> test_2_08</p> <p>01_eis20211104144342.xlsx - 700°C, 0.25 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 N<sub>2 </sub></p> <p>03eis_700c__eis20211108102241.xlsx - 700°C, 0.25 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 H<sub>2</sub>O;</p> <p>04eis_700c__eis20211108102401.xlsx - 700°C, 0.25 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 H<sub>2</sub>O;</p> <p>08eis__eis20211110094642.xlsx - 650°C, 0.1875 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 N<sub>2 </sub></p> <p>10eis__eis20211110094945.xlsx - 650°C, 0.1875 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 H<sub>2</sub>O;</p> <p>11eis__eis20211110101358.xlsx - 625°C, 0.125 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 H<sub>2</sub>O;</p> <p>13eis__eis20211110100837.xlsx- 625°C, 0.125 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 N<sub>2</sub>;</p> <p> test_2_26</p> <p>04_eqc20220817114103.xlsx - 700°C, 0.25 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 H<sub>2</sub>O;</p> <p>06_eqc20220817121529.xlsx - 700°C, 0.25 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 N<sub>2 </sub></p> <p>07_eqc20220802084119.xlsx - 650°C, 0.1875 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1</p> <p>08_eqc20220802084616.xlsx - 650°C, 0.1875 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 H<sub>2</sub>O;</p> <p>10_eqc20220802150705.xlsx - 625°C, 0.125 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 N<sub>2</sub>;</p> <p>11_eqc20220802150108.xlsx - 625°C, 0.125 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 H<sub>2</sub>O;</p> <p>SEM</p> <p><em>(post-mortem of the test_2_26)</em></p> <p>661_BSCMF_LSCCF_p.pdf – cross-section with EDS</p> <p>661_BSCMF_LSCCF_PM.pdf - surface with EDS</p> <p>661_BSCMF_LSCCF_p_01.tif, 661_BSCMF_LSCCF_p_02.tif, 661_BSCMF_LSCCF_p_03.tif, 661_BSCMF_LSCCF_p_04.tif - cross-section, infill zone</p> <p>661_BSCMF_LSCCF_p_05.tif, 661_BSCMF_LSCCF_p_06.tif - cross-section, net zone zone</p> <p>661_BSCMF_LSCCF_PM_**.tif - surface</p> <p> </p> <p><strong>LSF-LSCCF</strong></p> <p><em>(SOFC, air flow 0.5 L/min)</em></p> <p>test_1_65</p> <p>01_eqc20220816105347.xlsx - 700°C, 0. 1875 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 N<sub>2</sub>;</p> <p>02_eqc20220816105941.xlsx - 700°C, 0. 1875 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 H<sub>2</sub>O;</p> <p>04_eqc20220816112311.xlsx - 650°C, 0. 125 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 H<sub>2</sub>O;</p> <p>06_eqc20220816111640.xlsx - 650°C, 0.125 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 N<sub>2</sub>;</p> <p>07_eqc20220816142436.xlsx- 625°C, 0.0625 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 N<sub>2</sub>;</p> <p>08_eqc20220816142842.xlsx-625°C, 0.625 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 H<sub>2</sub>O;</p> <p>SEM</p> <p><em>(post-mortem) </em></p> <p>663_LSF_LSCCF_p.pdf – cross-section with EDS</p> <p>663_LSF_LSCCF_PM.pdf - surface with EDS</p> <p>663_LSF_LSCCF_P_GR_**.tif – cross-section of the cell</p> <p>663_LSF_LSCCF_P_LSCCF_**.tif – surface of the LSCCF grid</p> <p>663_LSF_LSCCF_PM_**.tif - surface of the LSF infill</p> <p> </p> <p> </p> <p><strong>LSF-PCM</strong></p> <p><em>(SOFC, air flow 0.5 L/min)</em></p> <p>tests_1_66</p> <p>01_eqc20220831133210.xlsx - 700°C, 0. 125 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 N<sub>2</sub></p> <p>02_eqc20220831133409.xlsx - 700°C, 0. 125 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 N<sub>2</sub></p> <p>03_eqc20220831133928.xlsx - 700°C, 0. 125 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 H<sub>2</sub>O;</p> <p>05_eqc20220831134048.xlsx - 650°C, 0.0625 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 N<sub>2</sub>;</p> <p>06_eqc20220831134142.xlsx - 650°C, 0.0625 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 H<sub>2</sub>O;</p> <p>08_eqc20220831134237.xlsx - 625°C, 0.0625 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 H<sub>2</sub>O;</p> <p>10_eqc20220831134412.xlsx - 625°C, 0.0625 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 N<sub>2</sub>;</p> <p>SEM</p> <p><em>(post-mortem)</em></p> <p>658_LSF_PCM_p.pdf – cross-section with EDS</p> <p>658_LSF_PCM_PM.pdf - surface with EDS</p> <p>658_LSF_PCM_P_LSF_**.tif – cross-section, infill zone</p> <p>658_LSF_PCM_P_pcm_**.tif - cross-section, net zone</p> <p>658_LSF_PCM_PM_**.tif - surface</p> <p><strong>description.pdf </strong>- pdf version of this information.</p>
Single-cell information extracted from IMC example data
<p>If you are working with these files, please cite them as follows:<br><br>Windhager, J., Zanotelli, V.R.T., Schulz, D. et al. An end-to-end workflow for multiplexed image processing and analysis. Nat Protoc (2023). <a href="https://doi.org/10.1038/s41596-023-00881-0">https://doi.org/10.1038/s41596-023-00881-0</a></p><p>This repository contains additional information related to IMC example data available at <a href="https://zenodo.org/record/5949116">zenodo.org/record/5949116</a>. The following files are available and are part of the <a href="https://bodenmillergroup.github.io/IMCDataAnalysis/">IMC Data Analysis workflow</a></p><ul><li><strong>gated_cells.zip:</strong> contains SpatialExperiment objects storing cells that were manually gated based on their expression values to derive ground truth cell phenotype labels.</li><li><strong>spe.rds:</strong> SpatialExperiment object containing the single-cell information (mean intensity per cell and per channel; cellular metadata; channel metadata) of the processed data.</li><li><strong>images.rds:</strong> CytoImageList object containing the spillover-corrected images.</li><li><strong>masks.rds:</strong> CytoImageList object containing the segmentation masks.</li></ul>
Supplements for "Understanding the interaction between a human transferrin receptor aptamer-short double stranded RNA conjugate and its cell membrane target by in silico methods"
<p>Supplements for "Understanding the interaction between a human transferrin receptor aptamer-short double stranded RNA conjugate and its cell membrane target by in silico methods". </p> <p>This supplement includes the following files:</p> <p> </p> <p>1. Structures of the most stable Protein-Aptamer complexes predicted from HADDOCK</p> <p>Haddock_Cluster1.pdb <br> Haddock_Cluster2.pdb <br> Haddock_Cluster3.pdb </p> <p>2. Structure of the most stable conformation aligned with Protein-transferring complex PDB</p> <p>cluster1_aligned.pdb <br> transferrin_aligned.pdb </p> <p>3. MM-GBSA decomposition analysis of the three replicas for Protein-Aptamer</p> <p>aptamer_new_rep01_Decomp.dat <br> aptamer_new_rep02_Decomp.dat <br> aptamer_new_rep03_Decomp.dat </p> <p>4. MM-GBSA decomposition analysis of the three replicas for Protein-Aptamer-Conjugate<br> conjugate_new_rep01_Decomp.dat <br> conjugate_new_rep02_Decomp.dat <br> conjugate_new_rep03_Decomp.dat <br> </p> <p><br> <br> </p>
Supplementary Movies and Source Data for: Quantitative real-time in-cell imaging reveals heterogeneous clusters of proteins prior to condensation
<p>Supplementary Movies and raw data for the manuscript: "Quantitative real-time in-cell imaging reveals heterogeneous clusters of proteins prior to condensation":</p> <p>Source_Data.zip: Supplementary Code, Supplementary Data and Weka Analysis</p> <p>Lan_supplementary_movies_AVI.zip: Supplementary movies as AVI</p> <p>Lan_supplementary_movies_MP4.zip: Supplementary movies as MP4</p> <p>Lan_raw_movies.zip: Raw TIFF stacks of the movies.</p> <p>Lan_supplementary_movies.zip: Old version of the movies.</p>
Supplementary Tables for Expression of cell-wall related genes is highly variable and correlates with sepal morphology
<p>Supplementary Information and script for "Expression of cell-wall related genes is highly variable and correlates with sepal morphology"</p> <p>R scripts for analysis</p> <p>Data necessary to run analyses</p> <p>Generated data</p> <p>Supplementary Tables</p> <p> </p>
Dataset for "Blue-shift photoconversion of near-infrared fluorescent proteins for labeling and tracking in living cells and organisms"
<p>Dataset that supports the observation, characterization and application of the blue-shift photoconversion of the near infrared proteins, miRFPs, reported in the manuscript: "Blue-shift photoconversion of near-infrared fluorescent proteins for labeling and tracking in living cells and organisms". The data references to the specific figures and graphs in the manuscript.</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.