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91 results for “Fluorescence microscopy”
Example of Fluorescence Lifetime Imaging Microscopy (FLIM) image stack in .ptu format
<p>The dataset is a 3D stack of fluorescence lifetime imaging microscopy (FLIM) images in ptu format to be used as test and training data. It contains the original .lif file (1) with the stack and a single plane image (to be opened using LAS X and LAS X SMD FLIM), exported raw FLIM data in .ptu format of the stack (3) and the single plane (2a) (to be opened in software capable of reading .ptu files) as well as an intensity image in .tif format (2b) of the single plane for a quick sample overview.</p> <p>The sample is a cross-section of hazel (<em>Corylus avellana</em>) 'diclinous male flower t.s.' with Etzold staining provided by the company Zeiss (CZ 01/05). The dataset was generated using a Leica Stellaris 8 upright confocal laser scanning microscope using a 93x/1.4 glycerol immersion objective. Each image of the 65 slice stack with z step size of 0.287 µm contains 512 x 512 pixels with a pixel size of 0.078 µm x 0.078 µm. Excitation was done with a white-light laser at 491 nm and a laser pulse rate of 40 MHz and a pixel dwell time of 2.0875 µs. Images were acquired using a HyD X detector in counting mode in the spectral range of 496 to 739 nm using Leica Application Suite X (LAS X) version 4.4.0.24861 and LAS X SMD FLIM version 4.5.0 for FLIM image acquisition. 10 frames were accumulated per image. Metadata is available as text file (4a) and as metadata files from LAS X (4b).</p>
UniFMIR: Pre-training a Foundation Model for Universal Fluorescence Microscopy Image Restoration
<p>This repository contains the preprocessed dataset for [UniFMIR](https://github.com/cxm12/UNiFMIR/). All training and test data involved in the experiments are publicly available datasets. Licenses of the original dataset are applied. You can refer to the Github repository for details.</p> <p>* The 3D denoising/isotropic reconstruction/projection datasets can be downloaded from [Content Aware Image Restoration dataset](https://publications.mpi-cbg.de/publications-sites/7207/). `Projection_Flywing/train_data/my_training_data.npz` are generated according to the [CSBDeep](http://csbdeep.bioimagecomputing.com/doc/).</p> <p>* The SR dataset can be downloaded from [BioSR dataset](https://doi.org/10.6084/m9.figshare.13264793). The dataset is augmented according to the instructions in [DFCAN](https://github.com/qc17-THU/DL-SR/tree/main#train-a-new-model) and `my_training_data.npz` files are generated following [CSBDeep](http://csbdeep.bioimagecomputing.com/doc/datagen.html). </p> <p>* The Volumetric reconstruction dataset are from [VCD-LFM dataset](https://doi.org/10.5281/zenodo.4390067). The dataset is prepared according to the instructions in [VCD-Net](https://github.com/feilab-hust/VCD-Net).</p> <p>* DeepBacs dataset can be downloaded from [DeepBacs dataset](https://zenodo.org/record/6460867). We split the dataset into 5 folds for cross-validation. Shareloc dataset can be downloaded from [Shareloc dataset](https://zenodo.org/record/7234161).</p> <p> </p> <p>The data paths should be as follows:</p> <p>```</p> <p>VCD/vcdnet/</p> <p>CSB/DataSet/</p> <p> Denoising_Planaria/</p> <p> Denoising_Tribolium/</p> <p> Isotropic/Isotropic_Liver/</p> <p> Projection_Flywing/</p> <p> BioSR_WF_to_SIM/DL-SR-main/dataset/</p> <p> Synthetic_tubulin_gfp/</p> <p> Synthetic_tubulin_granules/</p> <p>DeepBacs/</p> <p>Shareloc/</p> <p>```</p>
Transcranial detection of amyloid-beta at single plaque resolution in vivo with large-field multifocal illumination fluorescence microscopy
<p>The abnormal deposition of beta-amyloid proteins in the brain is one of the major histopathological hallmarks of Alzheimer’s disease. Currently available intravital microscopy techniques can visualize plaques with high resolution, but are limited to a small field-of-view and with depth limitation. Here, we report the transcranial detection of amyloid-beta deposits at the whole brain scale with 20 mm resolution in APP/PS1 and arcAb mouse models of Alzheimer’s disease amyloidosis using a novel large-field multifocal illumination (LMI) fluorescence microscopy technique. High sensitive and specific detection of amyloid-beta deposits at single plaque level in APP/PS1 and arcAb mice was facilitated using luminescent conjugated oligothiophene HS-169. Immunohistochemical staining with HS-169, anti-Ab antibody 6E10, conformation antibodies OC (fibrillar) on brain tissue sections further showed that HS-169 resolved compact parenchymal and vessel-associated amyloid deposits. In conclusion, we demonstrate a new <em>in vivo </em>imaging platform for detection of amyloid-beta deposits at single plaque resolution in murine models of Alzheimer’s disease amyloidosis.</p>
Fluorescent microscopy images of larval zebrafish of either TraNac, Nacre or WT background
<p><strong>The images show larval zebrafish at 5 days post-fertilization using fluorescent microscopy. The fish are either of Nacre, TraNac, or WT background. The fluorescent channels are either GFP, YFP or mCherry. </strong></p>
Fluorescence Microscopy Data for Cellular Detection using Object Detection Networks.
<p>This data accompanies work from the paper entitled: </p> <p><strong>Object Detection Networks and Augmented Reality for Cellular Detection in Fluorescence Microscopy Acquisition and Analysis. </strong></p> <p>Waithe D1*,2,, Brown JM3, Reglinski K4,6,7, Diez-Sevilla I<sup>5</sup>, Roberts D<sup>5</sup>, Christian Eggeling1,4,6,8</p> <p>1 Wolfson Imaging Centre Oxford and 2 MRC WIMM Centre for Computational Biology and 3 MRC Molecular Haematology Unit and 4 MRC Human Immunology Unit, Weatherall Institute of Molecular Medicine, University of Oxford, OX3 9DS, Oxford, United Kingdom. 5 Nuffield Division of Clinical Laboratory Sciences, Radcliffe Department of Medicine, John Radcliffe Hospital, University of Oxford, Headley Way, Oxford, OX3 9DU.<br> 6 Institute of Applied Optics and Biophysics, Friedrich-Schiller-University Jena, Max-Wien Platz 4, 07743 Jena, Germany.<br> 7 University Hospital Jena (UKJ), Bachstraße 18, 07743 Jena, Germany.<br> 8 Leibniz Institute of Photonic Technology e.V., Albert-Einstein-Straße 9, 07745 Jena, Germany.</p> <p>Further details of these datasets can be found in the methods section of the above paper.</p> <p><strong>Erythroblast DAPI (+glycophorin A):</strong> erythroblast cells were stained with DAPI and for glycophorin A protein (CD235a antibody, JC159 clone, Dako) and with Alexa Fluor 488 secondary antibody (Invitrogen). DAPI staining was performed through using VectaShield Hard Set mounting solution with DAPI (Vector Lab). Num. of images used for training: 80 and testing: 80. Average number of cells per image: 4.5.</p> <p><strong>Neuroblastoma phalloidin (+DAPI): </strong>images of neuroblastoma cells (N1E115) stained with phalloidin and DAPI were acquired from the Cell Image Library [26]. Cell images in the original dataset were acquired with a larger field of view than our system and so we divided each image into four sub-images and also created ROI bounding boxes for each of the cells in the image. The images were stained for FITC-phalloidin and DAPI. Num. of images used for training: 180, testing: 180. Average number of cells per image: 11.7.</p> <p><strong>Fibroblast nucleopore</strong>: fibroblast (GM5756T) cells were stained for a nucleopore protein (anti-Nup153 mouse antibody, Abcam) and detected with anti-mouse Alexa Fluor 488. Num. of images for training: 26 and testing: 20. Average number of cells per image: 4.8.</p> <p><strong>Eukaryote DAPI:</strong> eukaryote cells were stained with DAPI and fixed and mounted in Vectashield (Vector Lab). Num. of images for training: 40 and testing: 40. Average number of cells per image: 8.9.</p> <p><strong>C127 DAPI:</strong> C127 cells were initially treated with a technique called RASER-FISH[27], stained with DAPI and fixed and mounted in Vectashield (Vector Lab). Num. of images for training: 30 and testing: 30. Average number of cells per image: 7.1.</p> <p><strong>HEK peroxisome All</strong>: HEK-293 cells expressing peroxisome-localized GFP-SCP2 protein. Cells were transfected with GFP-SCP2 protein, which contains the PTS-1 localization signal, which redirects the fluorescently tagged protein to the actively importing peroxisomes[28]. Cells were fixed and mounted. Num. of images for training: 55 and testing: 55. Additionally we sub-categorised the cells as ‘punctuate’ and ‘non-punctuate’, where ‘punctuate’ would represent cells that have staining where the peroxisomes are discretely visible and ‘non-punctuate’ would be diffuse staining within the cell. The ‘HEK peroxisome All’ dataset contains ROI for all the cells: average number of cells per image: 7.9. The ‘HEK peroxisome’ dataset contains only those cells with punctuate fluorescence: average number of punctuate cells per image: 3.9.</p> <p><strong>Erythroid DAPI All: </strong>Murine embryoid body-derived erythroid cells, differentiated from mES cells. Stained with DAPI and fixed and mounted in Vectashield (Vector Lab). Num. of images for training: 51 and testing: 50. Multinucleate cells are seen with this differentiation procedure. There is a variation in size of the nuclei (nuclei become smaller as differentiation proceeds). The smaller, 'late erythroid' nuclei contain heavily condensed DNA and often have heavy ‘blobs’ of heterochromatin visible. Apoptopic cells are also present, with apoptotic bodies clearly present. The ‘Erythroid DAPI All’ dataset contains ROI for all the cells in the image. Average number of cells per image: 21.5. The subset ‘Erythroid DAPI’ contains non-apoptotic cells only: average number of cells per image: 11.9</p> <p><strong>COS-7 nucleopore. </strong>Slides were acquired from GATTAquant. GATTA-Cells 1C are single color COS-7 cells stained for Nuclear pore complexes (Anti-Nup) and with Alexa Fluor 555 Fab(ab’)2 secondary stain. GATTA-Cells are embedded in ProLong Diamond. Num. of images for training: 50 and testing: 50. Average number of cells per image: 13.2</p> <p><strong>COS-7 nucleopore 40x</strong>. Same GATTA-Cells 1C slides (GATTAquant) as above but imaged on Nikon microscope, with 40x NA 0.6 objective. Num. of images for testing: 11. Average number of cells per image: 31.6.</p> <p><strong>COS-7 nucleopore 10x.</strong> Same GATTA-Cells 1C slides (GATTAquant) as above but imaged on Nikon microscope, with 10x NA 0.25 objective. Num. of images for testing: 20. Average number of cells per image: 24.6</p> <p><strong>Dataset Annotation</strong></p> <p>Datasets were annotated by a skilled user. These annotations represent the ground-truth of each image with bounding boxes (regions) drawn around each cell present within the staining. Annotations were produced using Fiji/ImageJ [29] ROI Manager and also through using the OMERO [30] ROI drawing interface (<a href="https://www.openmicroscopy.org/omero/">https://www.openmicroscopy.org/omero/</a>). The dataset labels were then converted into a format compatible with Faster-RCNN (Pascal), YOLOv2, YOLOv3 and also RetinaNet. The scripts used to perform this conversion are documented in the repository (<a href="https://github.com/dwaithe/amca">https://github.com/dwaithe/amca</a>/scripts/).</p>
I2K2020 Data for "Quantification of the 3D brain vasculature in zebrafish light sheet fluorescence microscopy data"
<p>Example data for the I2K2020 tutorial "Quantification of the 3D brain vasculature in zebrafish light sheet fluorescence microscopy data" (https://www.janelia.org/you-janelia/conferences/from-images-to-knowledge-with-imagej-friends/virtual-workshop-program)</p> <p>"Readme" file for data description included in folder.</p> <p><strong>Background:</strong> Zebrafish transgenic lines and light sheet fluorescence microscopy (LSFM) allow unrivalled insights into vascular development <em>in vivo</em> and 3D. The vascular architecture can be used to describe physiological status. However, assessment of the vasculature still relies on individual visual assessment rather than objective quantification. Thus, an image analysis pipeline is required to allow data assessment in 3D robustly and sensitively, while being able to handle LSFM data.</p> <p>Kugler et al have produced an image analysis workflow to quantify the zebrafish brain vasculature in 3D (https://www.biorxiv.org/content/10.1101/2020.08.06.239905v2).</p> <p><strong>Aim</strong>: In this tutorial we will use the analysis workflow produced by Kugler et al to examine and quantify the zebrafish brain vasculature in 3D with a hands-on practical (https://github.com/ElisabethKugler/ZFVascularQuantification).</p>
Corrected super-resolution microscopy enables nanoscale imaging of auto-fluorescent lung macrophages
<p>Observing the cell surface and underlying cytoskeleton at nanoscale resolution using super-resolution microscopy has enabled many insights into cell signalling and function. However, the nanoscale dynamics of tissue-specific immune cells have been relatively little studied. Tissue macrophages, for example, are highly auto-fluorescent, severely limiting the utility of light microscopy. Here, we report a correction technique to remove auto-fluorescent noise from Stochastic Optical Reconstruction Microscopy (STORM) datasets. Simulations identified a moving median filter as an accurate and robust correction technique. Using this, we were able to visualise lung macrophages activated through Fc receptors by antibody-coated glass slides. Accurate, nanoscale quantification of macrophage morphology revealed that activation induced the formation of cellular protrusions tipped with MHC class I protein. These data are consistent with a role for lung macrophage protrusions in antigen presentation. We further show that the tetraspanin and extracellular vesicle (EV) marker CD81 appears in ring-shaped structures (mean diameter 93 ± 50 nm) at the surface of activated lung macrophages, likely marking the secretion of extracellular vesicles. Moreover, this correction method for super-resolution microscopy is widely applicable to other challenging biological samples.</p>
Dataset related to article "Evaluation of cell metabolic adaptation in wound and tumour by fluorescence Lifetime imaging Microscopy"
<p>This record contains data related to article "Evaluation of cell metabolic adaptation in wound and tumour by fluorescence Lifetime imaging Microscopy"</p> <p>Abstract</p> <p>Acidic pH occurs in acute wounds progressing to healing as consequence of a cell metabolic adaptation in response to injury-induced tissue hypoperfusion. In tumours, high metabolic rate leads to acidosis affecting cancer progression. Acidic pH affects activities of remodelling cells in vitro. The pH measurement predicts healing in pathological wounds and success of surgical treatment of burns and chronic ulcers. However, current methods are limited to skin surface or based on detection of fluorescence intensity of specific sensitive probes that suffer of microenvironment factors. Herein, we ascertained relevance in vivo of cell metabolic adaptation in skin repair by interfering with anaerobic glycolysis. Moreover, a custom-designed skin imaging chamber, 2-Photon microscopy (2PM), fluorescence lifetime imaging (FLIM) and data mapping analyses were used to correlate maps of glycolytic activity in vivo as measurement of NADH intrinsic lifetime with areas of hypoxia and acidification in models of skin injury and cancer. The method was challenged by measuring the NADH profile by interfering with anaerobic glycolysis and oxidative phosphorylation in the mitochondrial respiratory chain. Therefore, intravital NADH FLIM represents a tool for investigating cell metabolic adaptation occurring in wounds, as well as the relationship between cell metabolism and cancer.</p>
Fluorescence Microscopy Images of Hela Cell infected with Plasmodium Berghei parasite expressing mCherry in cytoplasm
<p>The purpose of our experiments was to delve into the liver stage development of the P. berghei parasite and examine the host-parasite interactions using HeLa cells. This research is primarily focused on in vitro analysis and does not extend to in vivo applications. Our study investigated the integration of fluorescent microscopy with artificial intelligence to <br>track and predict the developmental milestones of Plasmodium liver stage development. </p> <p>This is the dataset used in our study.</p>
(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 – Nine <em>Tribolium castaneum</em> long-term live imaging datasets of embryonic development acquired with light sheet fluorescence microscopy</p>
Live-cell fluorescence microscopy data for: Structural Dynamics of the Functional Nonameric Type III Translocase Export Gate
<p><strong>yuan_t3ss_raw_data.zip:</strong></p> <p>Microscopy data types:<br> <strong>- </strong>Brightfield images<br> - Fluorescence images (514 nm excitation)</p> <p>Microscope:<br> Olympus IX-83 with 1.49NA oil-immersion objective (Olympus UAPON 100x)<br> ET442/514/561 Laser triple band set filtercube (69904, Chroma)</p> <p>Camera:<br> Hamamatsu C9100-13 EM-CCD Camera<br> 80 nm pixel size</p> <p><strong>yuan_t3ss_cell_objects.zip:</strong></p> <p>EPEC and C41 ColiCoords cell objects derived from the raw data. The set contains a total of 24979 individual cells (brightfield, binary, fluorescence and localization datasets for each cell) with corresponding coordinate systems.</p> <p>Code to generate these cell objects from the raw data: https://github.com/Jhsmit/T3SS-paper</p> <p>The data format is .hdf5 and can be read with any HDF5 reader or directly with ColiCoords: https://github.com/Jhsmit/ColiCoords</p> <p><strong>Contact:</strong><br> Jochem Smit</p>
3D nuclei instance segmentation dataset of fluorescence microscopy volumes of C. elegans
<p>The dataset consists of 28 confocal microscopy volumes of C. elegans worms at the L1 stage and corresponding stacks of densely annotated nuclei instance segmentation masks.</p> <p>* 28 raw images and corresponding masks of average dimension (xyz) 1050 x 140 x 140<br> * Pixelsize (xyz): 0.116 x 0.116 x 0.122μm<br> * Microscope: Leica confocal microscopy, 63x oil objective</p> <p><br> The original raw data and preliminary annotations were part of the following publication (please cite if you use the dataset):<br> <br> <em>Long, F., Peng, H., Liu, X., Kim, S. K., & Myers, E. (2009). A 3D digital atlas of C. elegans and its application to single-cell analyses. Nature methods, 6(9), 667-672.</em></p> <p>The nuclei annotation masks were further manually curated by Dagmar Kainmueller (MDC Berlin) for the following publication:</p> <p><em>Hirsch, P., & Kainmueller, D. (2020). An auxiliary task for learning nuclei segmentation in 3d microscopy images. In Medical Imaging with Deep Learning (pp. 304-321). PMLR.</em></p> <p>We provide the dataset already structured into the train/validation/test split as used by the above as well as the following publications: </p> <p><em>Weigert, M., Schmidt, U., Haase, R., Sugawara, K., & Myers, G. (2020). Star-convex polyhedra for 3d object detection and segmentation in microscopy. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (pp. 3666-3673).</em><br> </p> <p> </p>
100 Hz ROCS microscopy correlated with fluorescence reveals cellular dynamics on different spatiotemporal scales
<p>Image Datasets to 100 Hz ROCS microscopy correlated with fluorescence reveals cellular dynamics on different spatiotemporal scales</p>
Gold Nanoparticles Synthesized in the Presence of Peptides - UV-Vis Spectra, Fluorescence, USAXS, Electron Microscopy; pre-publication version
<p>Content Summary:</p> <ul> <li>Data from experiments in which gold nanoparticles were synthesized in the presence of peptides using a liquid-handling robot. Samples were analyzed using UV-Vis spectroscopy, fluorescence emission, USAXS, TEM, and SEM. </li> <li>Notebooks for loading and plotting data</li> <li>Code for synthesizing samples using an OT2 Opentrons liquid-handling robot.</li> </ul> <p>README:</p> <blockquote> <p><strong>/Data</strong></p> <p>Contains all UV-Vis, electron microscopy, fluorescence, and SAXS data for gold nanoparticles synthesized in the presence of peptides and HEPES.</p> <p><strong>/Data/2021_12_30_Prepared_UV_Vis_Data</strong></p> <p>The primary portion of the experimental dataset. UV-Vis spectroscopy data collected on a Biotek Epoch 2 microplate spectrophotometer 24 hours after samples were synthesized using a liquid handling robot (Opentrons OT2). The <strong>4x4x4_SI.csv </strong>file is the compilation of all sample information:</p> <ul> <li>Concentrations (M) of peptide, HAuCl4, and HEPES</li> <li>UID – unique ID based on date of synthesis, sample position, and peptide which was used to synthesize the sample.</li> <li>Peptide names: Z2: RMRMKMK; MZ2: myristoylated - RMRMKMK; MZ2R: myristoylated - KMKMRMR; PZ2: palmitoylated – RMRMKMK; Z2M6I: RMRMKIK; Z2M246I: RIRIKIK; AG3: AYSSGAPPMPPF.</li> </ul> <p>Each sample’s UID is a key to match with UV-Vis measurement result stored in the {<strong>UID}.txt </strong>files. Each of these files contains the wavelength, absorbance, and absorbance after subtraction of a water measurement.</p> <p><strong>/Data/ElectronMicroscopy</strong></p> <p>Scanning electron microscopy and transmission electron microscopy results of gold nanoparticles formed from the reduction of HAuCl4 in the presence or absence of different peptides.</p> <p>Fig A, B, C, D, E/F were prepared in the presence of Z2, Z2M6I, Z2M246I, no peptide, and MZ2R, respectively.</p> <p><strong>/Data/Fluorescence</strong></p> <p>Pyrene fluorescence data collected in the presence of different concentrations of lipidated peptides (MZ2, MZ2R, and PZ2) for estimation of the peptide critical micelle concentration.</p> <p><strong>/Data/SAXS</strong></p> <p>SAXS data of a high concentration of MZ2 which was fit using a cylindrical model form factor. The evaluated model is also shared in this directory.</p> <p><strong>/Data/USAXS</strong></p> <p>Similarly to the UV-Vis data directory, the <strong>USAXS_SI.csv</strong> file contains sample information for all of the USAXS measurements. The <strong>dsm_rg.csv</strong> file contains the output of AUTORG evaluated on the desmeared data after subtraction of a flat background at high-q. <strong>/DSM_Nexus, DSM_sub_AUTORG, </strong>and <strong>SMR_Nexus</strong> contain the desmeared, desmeared with background subtraction, and smeared versions of the USAXS data, respectively.</p> <p><strong>/Notebooks</strong></p> <p>Notebooks for plotting the shared data and estimating the CMC from the fluorescence data. See <strong>/Notebooks/environment.yml</strong> for packages necessary to execute the notebooks here and in <strong>/Synthesis_Protocol</strong>. Refer to <a href="https://github.com/SasView/sasmodels">https://github.com/SasView/sasmodels</a> for specific instructions on how to install the sasmodels module.</p> <p><strong>/Figures</strong></p> <p>Figures generated from <strong>/Notebooks</strong>.</p> <p><strong>/Synthesis_Protocol</strong></p> <p>Please read the instructions within <strong>/Synthesis_Procol/Example.ipynb</strong>. In short, this folder contains the code used to synthesize the samples in this dataset using an OT2 Opentrons liquid handling robot.</p> <p> </p> </blockquote>
Fluorescence microscopy image of invitrogen FluoCells #2 Slide
<p>Image recorded by Lennart Hilbert at Institute of Biological and Chemical Systems, Karlsruhe Institute of Technology Images were acquired using a fluorescence confocal microscope based on the VT-iSIM high-speed super-resolution scanner. Channels show DNA, Actin, and microtubuli, and a blank camera image recorded for technical reasons.</p>
3D+time nuclei tracking dataset of confocal fluorescence microscopy time series of C. elegans embryos
<p>The dataset consists of 3 confocal microscopy time series of <em>C. elegans</em> embryos, fully tracked with StarryNite followed by manual curation</p> <ul> <li>3 raw time-series and the corresponding tracks/lineage trees</li> <li>temporal resolution; 75s</li> <li>temporal extent: 400 frames, tracked for at least 370 frames</li> <li>spatial resolution (zyx): 0.75 x 0.15 x 0.15 μm</li> <li>spatial extent (zyx):/ 41 x 512 x 512px</li> <li>Microscope: Zeiss Axio Observer.Z1</li> </ul> <p>The annotations were created using the method described in:</p> <p><em> Santella, A., Du, Z. & Bao, Z. A semi-local neighborhood-based framework for probabilistic cell lineage tracing. BMC Bioinformatics 15, 217 (2014). <a href="https://doi.org/10.1186/1471-2105-15-217">https://doi.org/10.1186/1471-2105-15-217</a></em></p> <p>Additionally the data was extended and used for the development of a new tracking method in the following publication:</p> <p><em> Hirsch, P., Malin-Mayor C., Santella, A., Preibisch, S., Kainmueller, D., Funke, J. Tracking by weakly-supervised learning and graph optimization for whole-embryo C. elegans lineages. MICCAI 2022.</em></p> <p>For questions please contact Peter Hirsch (<a href="mailto:peter.hirsch@mdc-berlin.de">peterhirsch@posteo.de</a>).</p>
Fluorescence Microscopy Images
<p>Fluorescent microscopy images (orthogonal projections) labelling several protein targets in live, Saccharomyces cerevisiae cells</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>
Data for: ColiCoords: A Python package for the analysis of bacterial fluorescence microscopy data
<p>Data associated with the ColiCoords software paper</p>
(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 – <em>Tribolium castaneum</em> foxQ2-5' line long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy</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.