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.
48
datasets available to search
ShareScore release 0.9.0
Dataset results
48 results for “Live cell imaging”
Fast and long-term super-resolution imaging of ER nano-structural dynamics in living cells using a neural network
<p>Datasets acquired and generated for the manuscript "Fast and long-term super-resolution imaging of ER nano-structural dynamics in living cells using a neural network". The datasets include test, training and time series datasets each containing the raw data and the predicted data where it applies. </p>
Live-cell STED dataset of mitochondria containing ground truth and corresponding low intensity noisy images
<p>The dataset was acquired as part of the manuscript "Denoising diffusion models for high-resolution microscopy image restoration". The dataset contains ground truth and low intensity STED images of mitochondria acquired in live U2-OS cells stably expressing TOM20 coupled to the dead mutant of HaloTag7 which was made fluorescent by using the exchangeable ligand Hy4 bound to the fluorophore SiR. </p>
Subcellular behavior model enables highly precise temporal super-resolved live-cell imaging
<div> <div>This repository contains the preprocessed dataset for [SuB-VFI](https://github.com/sduzzx857/SuB-VFI), including the real datasets we collected and the simulated testing and training datasets. You can refer to the Github repository for details.</div> <div> </div> <div>The simulated testing datasets can be downloaded from [the 2014 ISBI Particle Tracking Challenge](http://bioimageanalysis.org/track/).</div> <div>The EB1 datasets can be downloaded from the paper [The dynamic behavior of the APC-binding protein EB1 on the distal ends of microtubules](https://www.cell.com/current-biology/fulltext/S0960-9822(00)00600-X?_returnURL=https%3A%2F%2Flinkinghub.elsevier.com%2Fretrieve%2Fpii%2FS096098220000600X%3Fshowall%3Dtrue). We used *Movie2* from the Supplementary data.</div> <br> <div>The CCR5 datasets can be downloaded from the paper [Tracking receptor motions at the plasma membrane reveals distinct effects of ligands on CCR5 dynamics depending on its dimerization status](https://elifesciences.org/articles/76281). We used *Video4* in the Results section. </div> <div> </div> <div>The Lysosome datasets can be downloaded from [Content-Aware Frame Interpolation Microscopy Datasets](https://zenodo.org/records/10076346). We used data from the `Zproject` folder within the compressed file `Source_Data_Lysosomes_z-proj_Fig_5.zip`</div> </div>
Long-term live imaging, cell identification and cell tracking in regenerating crustacean legs
<p>Supplementary data and videos for the manuscript 'Long-term live imaging, cell identification and cell tracking in regenerating crustacean legs', by Çevrim,<sup> </sup>Laplace-Builhé,<sup> </sup>Sugawara, Rusciano, Labert, Brocard, Almazán and Averof.</p> <p>The supplementary data include:</p> <p><strong>Supplementary Data 1 (.csv file); Live imaging of regenerating <em>Parhyale</em> legs: image acquisition settings</strong></p> <p>Table with information on the 22 time lapse recordings presented in Figure 3, including image acquisition settings, temperature and duration of the recordings.</p> <p><strong>Supplementary Data 2 (.zip file); Live imaging of regenerated <em>Parhyale</em> legs: maximum projections</strong></p> <p>Compressed folder including maximum projections for each of the 22 time lapse recordings presented in Figure 3. These files were generated by projecting all or a subset of the z slices acquired at each time point. A 20 micron scale bar was added on the first time point. These files serve as a quick way to examine the 22 time lapse recordings.</p> <p><strong>Supplementary Data 3 (22 .tif files); Live imaging of regenerated <em>Parhyale</em> legs: complete datasets</strong></p> <p>Complete image 3D+T hyperstacks for each of the 22 time lapse recordings presented in Figure 3. These files have been generated by concatenating the original image stacks and correcting any image shifts, as described in the Methods section of the paper.</p> <p><strong>Supplementary Data 4 (.zip file); Analysis of trade-offs of imaging resolution and image quality</strong></p> <p>The data used for the analysis of trade-offs in imaging and the results shown in Table 1 are included in this compressed folder. Folders for the original recording (labelled 00), for each of the subsampled datasets (labelled 01 to 05), and for the denoised and deconvoluted datasets each include the corresponding image data and ground truth cell tracking files (.tif, .h5, .xml and .mastodon files) and three sets of cell track predictions (.mastodon files). There are also separate folders containing the Elephant detection and flow model parameters for each set of predictions.</p> <p dir="ltr"><strong>Supplementary Data 4 (.zip file); Analysis of trade-offs of imaging resolution and image quality</strong></p> <p dir="ltr">The data used for the analysis of trade-offs in imaging and the results shown in Table 1 are included in two folders. The folder named Image_and_tracking_data includes the image data (.tif, .h5, .xml), ground truth cell tracking files (.mastodon files) and three sets of cell track predictions (.mastodon files) for the original recording (labelled 00), for each of the subsampled datasets (labelled 01 to 05), and for the denoised and deconvoluted datasets. It also includes separate folders containing the Elephant detection and flow model parameters for each set of predictions. The folder named CTC_tracking_results includes the ground-truth data along with three sets of predictions for detection and tracking for each dataset, following the Cell Tracking Challenge format. For each dataset we include label image files (.tif) for every time point along with tracking results in .txt format, and each results directory (01_RES_*) also contains the evaluation results from the Cell Tracking Challenge Evaluation Software. For a detailed explanation of the folder structure, please refer to the Cell Tracking Challenge documentation.</p> <p><strong>Supplementary Data 5 (.zip file); Tracking the progenitors of spineless-expressing cells in the distal carpus</strong></p> <p>The data used to generate Figure 7 are included in this compressed folder, including the live imaging and cell tracking files (.h5, .xml and .mastodon files) and the image stack of the spineless and futsch HCR and DAPI stainings (.tif file). Channel 2 shows spineless expression (mostly nascent transcripts in nuclei), as well as background signal in epidermal nuclei (possibly due to photoconversion of DAPI, see Karg & Golic 2018, Chromosoma 127: 235-245) and strong autofluorescence in granular cells (also visible in channel 1, depicting futsch HCR).</p> <p><strong>Supplementary Data 6 (.txt file); Sequences of <em>Parhyale</em> genes targeted by the HCR probes</strong></p> <p>The sequences are provided in FASTA format.</p> <p dir="ltr"><strong>Supplementary Data 7 (.zip file); Apoptosis in legs that have not been subjected to live imaging</strong></p> <p dir="ltr">The data used to generate Figure 2 supplement 2 are contained in this compressed folder, including 9 image stacks of T4 and T5 legs fixed and stained with DAPI 3 days post amputation (with apoptotic nuclei marked) and a .txt file containing the apoptotic cell counts.</p> <p dir="ltr"><strong>Supplementary Data 8 (.zip file); Analysis of tracking performance in relation to imaging depth</strong></p> <p dir="ltr">The data used to generate Figure 5 are contained in this compressed folder, including separate folders for the data extracted from the analysis of datasets #1 to #5. Each folder includes data from three replicates (batches 001 to 003), with .csv files listing the z location of nucleus centroids (in µm) for the nuclei that were incorrectly detected by Elephant – either as false positives (FP) or as false negatives (FN) – and the ground truth data (GT). The folder also includes an .xlsx file gathering all the relevant data and the measurements of precision and recall.</p> <p dir="ltr"><strong>Supplementary Data 9 (.zip file); Detecting the temporal pattern of cell divisions in regenerating legs</strong></p> <p dir="ltr">The data used to generate Figure 4 are contained in this compressed folder, including the five image datasets (.tif, .h5, .xml), the detected cell divisions (.mastodon files), and an .xlxs file containing all the cell divisions counts and graphs.</p> <p><strong>Video 1. Time lapse recording of regeneration in a Parhyale T5 leg (dataset li48-t5)</strong></p> <p>Live imaging of nuclei labelled with H2B-mREFruby (maximum projection of z slices 3-10). Proximal parts of the leg are to the left and the amputation site is at the right of the frame. For annotations of different features please refer to Figure 2. Shortly after leg amputation (0 hpa) hemocytes adhere to the wound. By 16 hpa the wound has melanized. Up to ~32 hpa epithelial cells can be seen migrating and accumulating at the wound, below the melanized scab (Figure 2A,B). Around 31 hpa, the leg tissues become detached from the scab (Figure 2C). At 43 hpa, the carpus-propodus boundary first becomes visible, and thereafter many cells can be observed dividing at the distal part of the leg stump (Figure 2D). At 56 hpa, the propodus-dactylus boundary first becomes visible (Figure 2E). At later stages, tissues in more proximal parts of the leg retract, making space for the regenerating leg to grow (Figure 2F,G). After ~90 hpa cell proliferation there is less cell proliferation and cell movements, and the nuclear positions within the tissue become fixed. Scale bars, 20 µm.</p> <p><strong>Video 2. Time lapse recording of regeneration in a Parhyale T5 leg (dataset li36-t5)</strong></p> <p>Live imaging of nuclei labelled with H2B-mREFruby (maximum projection of z slices 3-15). Proximal parts of the leg are to the left and the amputation site is at the right of the frame. The sequence of events is similar to that described in Video 1, but the progression is slower: epithelial migration towards the wound is observed up to 40 hpa, tissues detach from the scab at 65 hpa, and the carpus-propodus and propodus-dactylus boundaries first become visible at 78 and 91 hpa. The tissues making up the carpus and propodus can be seen pulsating from 105 to 145 hpa. Scale bars, 20 µm.</p>
Dynamics of CTCF and cohesin mediated chromatin looping revealed by live-cell imaging
<p><strong>Overview</strong></p> <p>This repository contains all the raw and processed trajectory data associated with “paper title”. In this ReadMe file we provide the following information:</p> <ul> <li>The cell lines and conditions used in this study</li> <li>A summary of how the data was collected</li> <li>The structure of the chromosome locus tracking data</li> </ul> <p><strong>Cell lines and conditions</strong></p> <p>In total, the dataset covers 12 experimental conditions representing the following cell lines and treatment conditions:</p> <ul> <li>C36</li> <li>C65</li> <li>C27</li> <li>CTCF-AID (untreated)</li> <li>CTCF-AID (2 hours AID)</li> <li>CTCF-AID (4 hours AID)</li> <li>RAD21-AID (untreated)</li> <li>RAD21-AID (2 hours AID)</li> <li>RAD21-AID (4 hours AID)</li> <li>WAPL-AID (untreated)</li> <li>WAPL-AID (4 hours AID)</li> <li>WAPL-AID (6 hours AID)</li> </ul> <p> </p> <p><strong>Data and data processing</strong></p> <p>Trajectories were obtained from 3D timeseries of mouse embryonic stem cell colonies in the conditions listed above using a LSM900 Airyscan 2 Zeiss microscope. For each movie we recorded 365 frames of 49.69 µm x 49.69 µm (584 x 584 pixels, pixel size: 0.085 µm by 0.085 µm), separated by an interval of 20 seconds for a total of just over 2 hours. 3D images were composed of 30 z-stacks separated by 0.25 µm, for a total height of 7.25 µm. Imaging was performed in two colors allowing the tracking of two arrays of fluorophores on Chromosome 18 near the <em>Fbn2</em> gene. In all conditions, the fluorophore arrays were separated by 515 kb (except the C27 clone where separation was 10 kb).</p> <p>The 3D image time series were processed using ConnectTheDots: <a href="https://github.com/ahansenlab/connect_the_dots">https://github.com/ahansenlab/connect_the_dots</a> to obtain paired trajectories of chromosome loci over time. The trajectories have been corrected for chromatic shifts and aberrations.</p> <p>Data are provided in an “unfiltered” format (meaning that individual dot localizations were not quality control filtered) , or a filtered format (the same data set, but having undergone quality control). The filtered (quality controlled) trajectory data was used for all the quantitative analyses in the article “”.</p> <p>File names are formatted follows.</p> <ul> <li>Quality controlled data have the structure: {Clone_and_condition_name}.tagged_set.tsv</li> <li>Unfiltered data have the structure: {Clone_and_condition_name}.unfiltered.tagged_set.tsv</li> </ul> <p>For example, for RAD21-AID tagged clone, for imaging performed after two hours of protein degradation, the quality-controlled file name is: RAD21_2_hr.tagged_set.tsv. Please note that for all no-treatment conditions, we used “0 hours” as the tag. Thus, the RAD21 (untreated) becomes RAD21_0_hr.tagged_set.tsv.</p> <p> </p> <p><strong>Structure of Data</strong></p> <p>The trajectory data are provided as tab-separated text files consisting of 10 columns. The column headers are:</p> <ul> <li>id: a unique dot pair index</li> <li>t: the frame in which the dots were localized</li> <li>x: x-coordinate of the dot in the EGFP channel (units in µm)</li> <li>y: y-coordinate of the dot in the EGFP channel (units in µm)</li> <li>z: z-coordinate of the dot in the EGFP channel (units in µm)</li> <li>x2: x-coordinate of the dot in the mScarlet channel (units in µm)</li> <li>y2: y-coordinate of the dot in the mScarlet channel (units in µm)</li> <li>z2: z-coordinate of the dot in the mScarlet channel (units in µm)</li> <li>dist: 3D distance between the dots across channels (units in µm)</li> <li>movie_index: an identifier used to link the dot pair back to the raw image timeseries.</li> </ul>
Spatiotemporal multiplexed immunofluorescence imaging of living cells and tissues with bioorthogonal cycling of fluorescent probes
<p>Raw multichannel and/or Z-stack source data from time series images in TIF format to accompany publication of:</p> <p><strong>Spatiotemporal multiplexed immunofluorescence imaging of living cells and tissues with bioorthogonal cycling of fluorescent probes</strong></p> <p>Jina Ko<sup>1</sup>, Martin Wilkovitsch<sup>2</sup>, Juhyun Oh<sup>1</sup>, Rainer Kohler<sup>1</sup>, Evangelia Bolli<sup>1,3</sup>, Mikael J. Pittet<sup>1,3,4,5</sup>, Claudio Vinegoni<sup>1</sup>, David B. Sykes<sup>6,7</sup>, Hannes Mikula<sup>2</sup>, Ralph Weissleder<sup>1,8</sup>*, Jonathan C. T. Carlson<sup>1,7</sup>*</p> <p><sup>1 </sup>Center for Systems Biology, Massachusetts General Hospital, 185 Cambridge St, CPZN 5206, Boston, MA 02114 </p> <p><sup>2</sup> Institute of Applied Synthetic Chemistry, TU Wien, 1060 Vienna, Austria </p> <p><sup>3</sup> Department of Pathology and Immunology, University of Geneva, Geneva, Switzerland</p> <p><sup>4</sup> Ludwig Institute for Cancer Research, Lausanne Branch, Switzerland</p> <p><sup>5</sup> AGORA Cancer Center, Lausanne, Switzerland</p> <p><sup>6</sup> Center for Regenerative Medicine, Massachusetts General Hospital, Boston, MA, USA</p> <p><sup>7 </sup>Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA</p> <p><sup>8 </sup>Department of Systems Biology, Harvard Medical School, 200 Longwood Ave, Boston, MA 02115</p>
ThermoCyte: an inexpensive open-source temperature control system for in vitro live cell imaging
<p>Live-cell imaging is a common technique in microscopy to investigate dynamic cellular behaviour and permits the accurate and relevant analysis of a wide range of cellular and tissue parameters, such as motility, cell division, wound healing responses, and calcium (Ca2+) signalling in cell lines, primary cell cultures, and ex vivo preparations. Furthermore, this can take place under many experimental conditions, making live-cell imaging indispensable for biological research. Systems which maintain cells at physiological conditions outside of a CO<sub>2</sub> incubator are often bulky, expensive, and use proprietary components. Here we present an inexpensive, open-source temperature control system for in vitro live cell imaging. Our system 'ThermoCyte', which is constructed from standard electronic components, enables precise tuning, control, and logging of a temperature 'set point' for imaging cells at physiological temperature. We achieved stable thermal dynamics, with reliable temperature cycling and a standard deviation of 0.42°C over 1 hour. Furthermore, the device is modular in nature, and is adaptable to the researcher's specific needs. This represents simple, inexpensive, and reliable tool for laboratories to carry out custom live-cell imaging protocols, on a standard lab bench, at physiological temperature.</p>
Live-Cell Imaging of MCF10A Cells Treated with EGF or PBS
<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&D Systems, #236-EG), 0.5 µg/ml hydrocortisone (Sigma, #H-4001), 100 ng/ml cholera toxin (Sigma, #C8052), 10 µg/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 µg/ml hydrocortisone, 100 ng/ml cholera toxin, and 1% Pen/Strep.</p> <p>Cells were cultured to 50–80% 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. After six hours, the cells were rinsed with PBS, and the medium was replaced with growth factor-free medium. Following an 18-hour period of growth factor deprivation, cells were treated with either PBS or 10 ng/ml EGF (R&D Systems, #236-EG).</p> <p>Phenotypic responses to EGF 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>
Live cell microscopy: From image to insight - raw data & analysis
<p>Accompanying raw and processed data as well as analysis scripts for the publication Biophysics Rev. 3, (2022); <a href="https://doi.org/10.1063/5.0082799">10.1063/5.0082799</a> "Live cell microscopy: From image to insight".</p>
Long-Term, Single-Molecule Imaging of Proteins in Live Cells with Photoregulated Fluxional Fluorophores
<p>Supporting data for paper "Long-Term, Single-Molecule Imaging of Proteins in Live Cells with Photoregulated Fluxional Fluorophores"</p>
Raw data accompanying the manuscript "Cost-effective high-speed, three-dimensional live-cell imaging of HIV-1 transfer at the T cell virological synapse"
<p>These are the raw datasets used to generate the figures for the manuscript entitled "Cost-effective high-speed, three-dimensional live-cell imaging of HIV-1 transfer at the T cell virological synapse". The data files are 3D image stacks of a custom-built wide field deconvolution fluorescence microscope (.tif) and super-resolution structured illumination microscopy data (.dv) of Jurkat T cells transferring HIV-1 virus particles to previously uninfected primary T cells.</p>
Imaging data from "Live-cell 3D single-molecule tracking reveals modulation of enhancer dynamics by NuRD"
<p>3D 20ms, 3D 500ms and 2D dCas9 raw videos, localisation, tracking and trajectory analysis data</p> <p>From 'Live-cell 3D single-molecule tracking reveals modulation of enhancer dynamics by NuRD" (2021). Biorxiv. https://doi.org/10.1101/2020.04.03.003178</p>
ThermoCyte: an inexpensive open-source temperature control system for in vitro live cell imaging
Open the record for dataset details and reuse information.
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>
Super-resolved Reflectance Confocal Microscopy time-lapse imaging of a living MEF cell lamellipod
<p>This movies presents a time-lapse of a label-free living Mouse embryonic fibroblast cell observed with super-resolved rescanned reflectance confocal microscopy.. </p>
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>
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&D Systems, #236-EG), 0.5 µg/ml hydrocortisone (Sigma, #H-4001), 100 ng/ml cholera toxin (Sigma, #C8052), 10 µg/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 µg/ml hydrocortisone, 100 ng/ml cholera toxin, and 1% Pen/Strep.</p> <p>Cells were cultured to 50–80% 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&D Systems #236-EG), 10 ng/ml OSM (R&D Systems #8475-OM), and 10 ng/ml TGFβ (R&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> </span></p>
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°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. Rizzo, M. A., Davidson, M. W., & 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. Huang, F., Hartwich, T., Rivera-Molina, F. et al. Video-rate nanoscopy using sCMOS camera–specific single-molecule localization algorithms. Nat Methods 10, 653–658 (2013).</p>
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>). </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 Ruan, Matthew Mueller, Gaoxiang Liu, Frederik Görlitz, Tian-Ming Fu, Daniel E. Milkie, Joshua L. Lillvis, Alexander Kuhn, Chu Yi Aaron Herr, Wilmene Hercule, Marc Nienhaus, Alison N. Killilea, Eric Betzig, Srigokul 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>
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 – From Sample Preparation to Numbers and Plots" methodology paper by Zahumensky & 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. 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>
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.