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130 results for “live imaging”

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zenodo40/100

(07)-Ratke2020A-DS0006 – Tribolium castaneum AGOC{Zen1'#O(LA)-mEmerald} #3 subline long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy

<p>(07)-Ratke2020A-DS0006 &ndash; <em>Tribolium castaneum</em> AGOC{Zen1'#O(LA)-mEmerald} #3 subline long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy</p>

opencc-by-4.0Jul 2020View details →
zenodo40/100

Trapalyzer: A computer program for quantitative analyses in fluorescent live-imaging studies of Neutrophil Extracellular Trap formation.

<p>This data set contains a set of fluorescent microscopy images of a co-culture of neutrophil cells and E. coli bacteria used to study the Neutrophil Extracellular Trap (NET) formation stimulated by bacteria.&nbsp;</p> <p>NETs and live cells were visualized with a double fluorescent staining of DNA using Hoechst 33342 and SYTOX Green.&nbsp;</p> <p><strong>Reagents.</strong></p> <p>Roswell Park Memorial Institute (RPMI) 1640 medium, HEPES, SYTOX<sup>TM</sup> Green, and Hoechst 33342 were purchased from Thermo Fisher Scientific (Waltham, USA). LB broth was purchased from Sigma Aldrich (St Louis, MO, USA).</p> <p><strong>Preparation of blood neutrophils.</strong></p> <p>Neutrophils were obtained from peripheral blood of one healthy blood donor. Blood sample was purchased at Local Blood Donation Centre and according to local regulations, the blood donor enabled blood donation center to sell their blood samples for scientific purposes and the consent of bioethical committee was not required. Blood was collected into a citrate tube and processed within 2 hours from collection. Neutrophils were isolated using density gradient centrifugation followed by polyvinyl alcohol sedimentation, exactly as described in [1]. Isolated neutrophils were suspended in RPMI 1640 medium with 10 mM HEPES (RH). &nbsp;</p> <p><strong>Preparation of bacteria.</strong></p> <p><em>Escherichia coli</em> (American Type Culture Collection(ATCC) 25922 strain) were grown overnight in LB broth with shaking. In the morning, an aliquot of bacterial culture was taken, diluted 100 x in a fresh LB medium and grown for subsequent 2-3 hours. Subsequently, bacterial cultures were washed and resuspended in RH medium.</p> <p><strong>Co-culture of neutrophils with bacteria</strong><br> Neutrophils were seeded into the wells of 48-well plates at the density of 2&nbsp;⨉ 10<sup>4</sup> cells/well and allowed to settle for 30 minutes at 37&deg;C, 5% CO2. Subsequently, <em>E. coli</em> was added into the appropriate wells at the multiplicity of infection of 4 or 1 (<em>E.coli</em>: neutrophil). Neutrophils incubated without bacteria were used as a control group. A technical duplicate for each condition was prepared. &nbsp;<br> For each intended timepoint (t=0, 60, 90, 120, 180 minutes), a separate 48 well plate was prepared. The plates were centrifuged for 5 minutes at 250 g to allow the contact of bacteria with neutrophils. The plates were incubated at 37&deg;C, 5\% CO2 for a specified time and then the samples were stained with SYTOX<sup>TM</sup> Green (100 nM) and Hoechst 33342 (1.25 &mu;M) for 10 minutes. Four images of each well were taken with Leica DMi8 fluorescent microscope equipped with a 10&times; magnification objective (Leica, Wetzlar, Germany). Overall, 120 images have been obtained.</p> <p>&nbsp;</p> <p><strong>2019_04_24--ecoli_neu_tiff_channel_merged.zip:</strong> Images in .tif format, each containing 5 channels: channel 1 for SYTOX Green fluorescent stain (green fluorescence), channel 2 for Hoechst 33342 fluorescent stain (blue fluorescence), and three channels for transmission light encoded in RGB values.&nbsp;</p> <p>&nbsp;</p> <p><strong>2019_04_24--ecoli_neu_tiff_raw_exported.zip:</strong> Images split by different light sources: transmission light (_ch00.tif), SYTOX Green fluorescence (_ch01.tif), Hoechst 33342 fluorescence (_ch02.tif).</p> <p>&nbsp;</p> <p>[1] Bystrzycka W, Moskalik A, Sieczkowska S, Manda-Handzlik A, Demkow U, Ciepiela O. The effect of clindamycin and amoxicillin on neutrophil extracellular trap (NET) release. <em>Cent Eur J Immunol</em>. 2016;41(1):1-5. doi:10.5114/ceji.2016.58811</p>

opencc-by-4.0Nov 2022View details →
zenodo40/100

Imaging data from "Live-cell 3D single-molecule tracking reveals modulation of enhancer dynamics by NuRD"

<p>3D 20ms, 3D&nbsp;500ms and 2D dCas9 raw videos, localisation, tracking and trajectory analysis&nbsp;data</p> <p>From&nbsp;&#39;Live-cell 3D single-molecule tracking reveals modulation of enhancer dynamics by NuRD&quot; (2021). Biorxiv. https://doi.org/10.1101/2020.04.03.003178</p>

opencc-by-4.0May 2023View details →
dryad40/100

ThermoCyte: an inexpensive open-source temperature control system for in vitro live cell imaging

Open the record for dataset details and reuse information.

publicNov 2023View details →
dryad40/100

Data from: Live imaging of SARS-CoV-2 infected airway epithelium cultures

Open the record for dataset details and reuse information.

publicOct 2024View details →
dryad36/100

Data from: Live-cell single particle imaging reveals the role of RNA polymerase II in histone H2A.Z eviction

<p>The H2A.Z histone variant, a genome-wide hallmark of permissive chromatin, is enriched near transcription start sites in all eukaryotes. H2A.Z is deposited by the SWR1 chromatin remodeler and evicted by unclear mechanisms. We tracked H2A.Z in living yeast at single-molecule resolution, and found that H2A.Z eviction is dependent on RNA Polymerase II (Pol II) and the Kin28/Cdk7 kinase, which phosphorylates Serine 5 of heptapeptide repeats on the carboxy-terminal domain of the largest Pol II subunit Rpb1. These findings link H2A.Z eviction to transcription initiation, promoter escape and early elongation activities of Pol II. Because passage of Pol II through +1 nucleosomes genome-wide would obligate H2A.Z turnover, we propose that global transcription at yeast promoters is responsible for eviction of H2A.Z. Such usage of yeast Pol II suggests a general mechanism coupling eukaryotic transcription to erasure of the H2A.Z epigenetic signal.</p>

opencc-zeroMay 2020View details →
zenodo36/100

Super-resolved Reflectance Confocal Microscopy time-lapse imaging of a living MEF cell lamellipod

<p>This&nbsp;movies presents a time-lapse of a label-free living Mouse embryonic fibroblast cell observed with&nbsp;super-resolved rescanned reflectance confocal microscopy..&nbsp;</p>

opencc-by-4.0Jan 2021View details →
dryad36/100

BEHAV3D: A 3D live imaging platform for comprehensive analysis of engineered T cell behavior and tumor response

<p>The use of patient-derived material and immune cell co-cultures in modeling immune-oncology has gained significant interest for understanding and manipulating immune cell tumor targeting in a patient-specific context. However, current protocols have limitations in visualizing and analyzing the dynamic cellular features of these living culture systems. We recently developped a workflow names BEHAV3D,  that combines multi-color live 3D imaging and computational tools to analyze cell death dynamics, classify T cell behavior, and generate data-informed 3D images and videos. Here we provide some example pre-processed dataset of videos of two co-culture set ups: breast cancer Patient Derived Organoids with αβ T cells engineered to express a γδ TCR (TEGs) and Acute Lymphoblastic Leukemia cells with CD19 CART cells.</p>

opencc-zeroSep 2023View details →
zenodo36/100

[Dataset for] Whole-brain meso-vein imaging in living humans using fast 7 T MRI

<p>This dataset is associated with:</p> <ul> <li>Gulban, Stirnberg, Tse, Pizzuti, Koiso, Archila-Melendez, Huber, Bollmann, Goebel, Kay, Ivanov, 2025. Whole-brain meso-vein imaging in living humans using fast 7 T MRI (Preprint).</li> </ul> <p>This dataset is also used in:</p> <ul> <li>Pizzuti, Bazin, Ivanov, Dresbach, Peter, Goebel, Gulban, 2024.&nbsp; Multimodal laminar characterization of visual areas along the cortical hierarchy (Preprint).</li> </ul> <p>More data are going to be be added as we progress with our manuscripts though their publications or upon request (please contact Omer Faruk Gulban).</p>

opencc-by-4.0Nov 2024View details →
zenodo36/100

Live-Cell Imaging of MCF10A Cells Treated individually or in combination with EGF, OSM, or TGFB

<p>MCF10A cell culture and experimental procedures were conducted based on established methodologies (10.1038/s42003-022-03975-9). For routine maintenance and passaging, cells were cultured in a growth medium composed of DMEM/F12 (Invitrogen, #11330-032) supplemented with 5% horse serum (Sigma, #H1138), 20 ng/ml EGF (R&amp;D Systems, #236-EG), 0.5 &micro;g/ml hydrocortisone (Sigma, #H-4001), 100 ng/ml cholera toxin (Sigma, #C8052), 10 &micro;g/ml insulin (Sigma, #I9278), and 1% Penicillin/Streptomycin (Invitrogen, #15070-063). For experiments involving EGF perturbation, a growth factor-free medium was prepared using DMEM/F12, 5% horse serum, 0.5 &micro;g/ml hydrocortisone, 100 ng/ml cholera toxin, and 1% Pen/Strep.</p> <p>Cells were cultured to 50&ndash;80% confluency before being detached with 0.05% trypsin-EDTA (Thermo Fisher Scientific, #25300-054). Subsequently, 20,000 cells were seeded into 24-well plates (Thermo Fisher Scientific, #267062) coated with collagen-1 (Cultrex, #3442-050-01) in growth medium.<span>After an 18-hour incubation in the new media, cells were treated with </span><span>single </span><span>ligand</span><span> or </span><span>combinations of ligands</span><span> in fresh growth factor-free media: 10 ng/ml EGF (R&amp;D Systems #236-EG), 10 ng/ml OSM (R&amp;D Systems #8475-OM), and 10 ng/ml TGF&beta; (R&amp;D Systems #240-B).</span></p> <p>Phenotypic responses to individual and combination treatment with EGF, OSM, and TGFB treatment were assessed through live-cell imaging using the Incucyte S3 microscope (Essen BioScience, #4647), which captured images every 30 minutes over a 24-hour period. The dataset includes an Excel spreadsheet that documents the experimental conditions for each imaged well.</p> <p>Companion RNAseq <span><span>can be accessed from the Gene Expression Omnibus</span><span>: </span><span>GSE282654</span><span>.</span></span><span>&nbsp;</span></p>

opencc-by-4.0Dec 2024View details →
zenodo36/100

(12)-Pereyra2024A-DS0001--0009 – Nine Tribolium castaneum long-term live imaging datasets of embryonic development acquired with light sheet fluorescence microscopy

<p>(12)-Pereyra2024A-DS0001--0009 &ndash; Nine <em>Tribolium castaneum</em> long-term live imaging datasets of embryonic development acquired with light sheet fluorescence microscopy</p>

opencc-by-4.0Sep 2024View details →
zenodo36/100

Long Live The Image: Container-Native Data Persistence in Production

<p>The (Docker) source files for creating&nbsp;a read-only database container.</p> <p><strong>Note:</strong></p> <p>&quot;tail -F /var/log/mysql/error.log&quot; (after the demo of selecting all data) makes the container keep&nbsp;alive as a MySQL server.</p> <p><strong>To be Updated:</strong></p> <p>The database user and password are still hardcoded.</p>

opencc-by-4.0Mar 2021View details →
zenodo36/100

Live-cell imaging of LLC-PK1 cells microtubule dynamics

<p>Stable LLC-PK1 cell line (ATCC:CL-101) was generated and provided by Michael W. Davidson [1,2]. Using a Zeiss Celldiscoverer 7 microscope with a 100X/1.47 NA oil-immersion objective (Plan-Apochromat, Zeiss) and a sCMOS sensor (Hamamatsu, ORCA-Fusion, C15440-20UP), cultured LLC-PK1 cells expressing mEmerald-EB3 were imaged with a 43 nm pixel size. Exposure time was 100 ms, with each frame being captured every two seconds. During acquisition, temperature and CO2 control was set to 37&deg;C and 5%, respectively. mEmerald-EB3 was excited using a 488 nm laser at 1% power, with a FITC filter for fluorescence collection. Acquisition software ZEN 3.2 (blue edition) was used for the imaging protocol.</p> <p>1.&nbsp;Rizzo, M. A., Davidson, M. W., &amp; Piston, D. W. (2009). Fluorescent protein tracking and detection: fluorescent protein structure and color variants. Cold Spring Harbor Protocols, 2009(12), pdb-top63.</p> <p>2.&nbsp;Huang, F., Hartwich, T., Rivera-Molina, F. et al. Video-rate nanoscopy using sCMOS camera&ndash;specific single-molecule localization algorithms. Nat Methods 10, 653&ndash;658 (2013).</p>

opencc-by-4.0Jul 2022View details →
dryad36/100

A method to evaluate body length of live aquatic vertebrates using digital images

<p>Traditional methods to measure body lengths of aquatic vertebrates rely on anesthetics, and extended handling times. These procedures can increase stress, potentially affecting the animal's welfare after its release. We developed a simple procedure using digital images to estimate body lengths of coastal cutthroat trout (<i>Oncorhynchus clarkii clarkii</i>) and larval coastal giant salamander (<i>Dicamptodon tenebrosus</i>). Images were post-processed using ImageJ2. We measured more than 1,800 individuals of these two species from 200 pool habitats along 9.6 river kilometers. The percent error (mean ± SE) of our approach compared to the use of a traditional graded measuring board was relatively small for all metrics of the two species. Total length of trout was -2.2% ± 1.0. Snout-vent length and total length of larval salamanders was 3.5% ± 3.3 and -0.6% ± 1.7, respectively. We cross-validated our results by two independent observers that followed our protocol to measure the same animals and found no significant differences (<i>p</i> &gt; 0.7) in body size distributions for all metrics of the two species. Our procedure provides reliable information of body size reducing stress and handling time in the field. The method is transferable across taxa and the inclusion of multiple animals per image increases sampling efficiency with stored images that can be reviewed multiple times. This practical tool can improve data collection of animal size over large sampling efforts and broad spatiotemporal contexts.</p>

opencc-zeroAug 2022View details →
zenodo36/100

Demo Live Cell Imaging Dataset for PetaKit5D

<p>Demo dataset for PetaKit5D (<a href="https://github.com/abcucberkeley/LLSM5DTools">https://github.com/abcucberkeley/PetaKit5D</a>).&nbsp;</p> <p>The dataset is for the large field of view live cell imaging of LLC-PK1 cells of nuclei and endoplasmic reticulum (ER). It contains two time points with two channels. Each time point and channel contains 4 volumetric tiles. Some demos in PetaKit5D use this dataset to demonstrate the usage.</p> <p>Please cite our paper (<a href="https://doi.org/10.1101/2023.12.31.573734">https://doi.org/10.1101/2023.12.31.573734</a>) if you use this dataset in your research:</p> <p><code>Xiongtao&nbsp;Ruan,&nbsp;Matthew&nbsp;Mueller,&nbsp;Gaoxiang&nbsp;Liu,&nbsp;Frederik&nbsp;G&ouml;rlitz,&nbsp;Tian-Ming&nbsp;Fu,&nbsp;Daniel E.&nbsp;Milkie,&nbsp;Joshua L.&nbsp;Lillvis,&nbsp;Alexander&nbsp;Kuhn,&nbsp;Chu Yi Aaron&nbsp;Herr,&nbsp;Wilmene&nbsp;Hercule,&nbsp;Marc&nbsp;Nienhaus,&nbsp;Alison N.&nbsp;Killilea,&nbsp;Eric&nbsp;Betzig,&nbsp;Srigokul&nbsp;Upadhyayula. Image processing tools for petabyte-scale light sheet microscopy data. bioRxiv 2023.12.31.573734; doi: <a href="https://doi.org/10.1101/2023.12.31.573734">https://doi.org/10.1101/2023.12.31.573734</a></code></p>

opencc-by-nc-4.0Jun 2024View details →
zenodo36/100

Sample data for "Live Cell Fluorescence Microscopy – An End-to-End Workflow for High-Throughput Image and Data Analysis"

<p>This repository contains:</p> <ul> <li> <p>Sample data for the "Live Cell Fluorescence Microscopy &ndash; From Sample Preparation to Numbers and Plots" methodology paper by Zahumensky &amp; Malinsky. The paper describes the preparation of live yeast cell samples for microscopy, the subsequent semi-automatic analysis of the microscopy images using our custom-written Fiji macros, and automatic processing of the output (Results table) from the image analys using custom-written R scripts.&nbsp;The data provided here are real experimental data from two publications of our group: Zahumensky et al., 2022 and Vesela et al., 2023</p> </li> <li> <p>"Results tables" from the Fiji based analysis</p> </li> <li> <p>Outputs of the processing of these Results tables using our R scripts, in the form of summary tables, graphs, and statistical analyses</p> </li> </ul>

opencc-by-4.0Jun 2024View details →
zenodo36/100

Differential interference contrast (DIC) image of unstained living HepG2 human liver cancer cells

<p><strong>Introduction</strong></p> <p>This dataset is associated with our submission to Computers in Biology and Medicine, titled "Accurate Detection and Instance Segmentation of Unstained Living Adherent Cells in Differential Interference Contrast Images". The submission number for this manuscript is CIBM-D-23-09623R1.</p> <p><strong>Authors</strong>: Fei Pan, Yutong Wu, Kangning Cui, Shuxun Chen, Yanfang Li, Yaofang Liu, Adnan Shakoor, Han Zhao, Beijia Lu, Shaohua Zhi, Raymond Hon-Fu Chan, Dong Sun</p> <p><strong>Dataset Description</strong></p> <p>Our dataset comprises 520 differential interference contrast (DIC) images of 12,198 unstained HepG2 human liver cancer cells, each with a corresponding fluorescence image stained with calcein acetoxymethyl (AM), ensuring high-quality ground-truth annotations. Unique in addressing the multi-state nature of adherent cells commonly seen in wet labs, it includes both healthy and unhealthy cells in a single image, providing a valuable resource for studying multi-state cell detection and instance segmentation.<br>Citation</p> <p>We kindly request that researchers who use this dataset cite both our paper and this dataset. This will help acknowledge the work and facilitate further advancements in the field.</p> <p><br><strong>Please cite as follows:</strong></p> <p><strong>Paper:</strong><br>Pan, F., Wu, Y., Cui, K., Chen, S., Li, Y., Liu, Y., Shakoor, A., Zhao, H., Lu, B., Zhi, S., Chan, R. H.-F., &amp; Sun, D. "Accurate detection and instance segmentation of unstained living adherent cells in differential interference contrast images,&rdquo; <em>Computers in Biology and Medicine</em>, vol. 182, p. 109151, Nov. 2024, doi: 10/g5p9d8.</p> <p><strong>Dataset:</strong><br>Pan, F., Chen, S., Li, Y., Shakoor, A., Zhao, H., &amp; Sun, D. (2024). Differential interference contrast (DIC) image of unstained living HepG2 human liver cancer cells. Zenodo.&nbsp;</p> <p>Thank you for your interest and support in our work. We look forward to seeing the innovative research that this dataset will enable.</p> <p>&nbsp;</p> <p>&nbsp;</p>

opencc-by-sa-4.0Jul 2024View details →
zenodo36/100

Microscopy images and movies supporting the publication: "tRNA tracking for direct measurements of protein synthesis kinetics in live cells"

<p>This repository contains experimental and simulated microscopy movies and images supporting the&nbsp;publication: Volkov et al. (2018) tRNA tracking for direct measurements of protein synthesis kinetics in live cells. <em>Nat Chem Biol, </em>DOI: 10.1038/s41589-018-0063-y</p> <p>A detailed list of files and file&nbsp;organisation&nbsp;can be found in Repository_content.pdf.</p>

opencc-by-4.0Apr 2018View details →
zenodo36/100

(06)-He2019A-DS0001 – Tribolium castaneum foxQ2-5' line long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy

<p>(06)-He2019A-DS0001 &ndash; <em>Tribolium castaneum</em> foxQ2-5' line long-term live imaging dataset&nbsp;of embryonic development acquired with light sheet fluorescence microscopy</p>

opencc-by-4.0May 2019View details →
zenodo36/100

Raw images, video, and data file for the manuscript "Fabrication of Low-Cost, High-Resolution Open Capillary Microfluidics towards Self-Sustaining, Long-Term Hydration of Engineered Living Materials"

<p>This dataset includes the raw images and data file in the manuscript "Fabrication of Low-Cost, High-Resolution Open Capillary Microfluidics towards Self-Sustaining, Long-Term Hydration of Engineered Living Materials", specifically:</p> <ul> <li>Raw images for the optimized print with the PEGDA-glycerol-water resin (Figure 2 &amp; Figure S2)</li> <li>Raw images for the optimized print with the PEGDA-glycerol-LB resin (Figure 2)</li> <li>Raw images for the optimized print with the BSA-PEGDA-water resin (Figure 3)</li> <li>Raw images and video for the spontaneous capillary flow of LB media in a PEGDA-glycerol-LB microfluidic chip (Figure 4)</li> <li>Raw data for the UV-vis spectrum of LB media (Figure S4)</li> </ul>

opencc-by-4.0Oct 2024View details →

ScienceDex guides

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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

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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.

allen-brain-atlas
neuroscienceopenDocumentation, web resources, and API references are available online.
Last verified 2026-04-30Open record

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.

abode-home-cage
behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.
Last verified 2026-04-30Open record

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.

dandi-nwb
electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.
Last verified 2026-04-30Open record

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.

ibl
behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.
Last verified 2026-04-29Open record

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.

openneuro
neuroscienceopenPublished datasets are available on demand over the internet.
Last verified 2026-04-29Open record