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.
123
datasets available to search
ShareScore release 0.9.0
Dataset results
123 results for “Light microscopy”
(07)-Ratke2020A-DS0002 – Drosophila melanogaster w[*]; P{w[+mC]=Tub84B-EGFP.NLS}3 long-term live imaging dataset acquired with light sheet fluorescence microscopy
<p>(07)-Ratke2020A-DS0002 <em>–</em> <em>Drosophila melanogaste</em>r y[1] w[67c23]; P{w[+mC]=Ubi-GFP.nls}ID-2; P{Ubi-GFP.nls}ID-3 (Bloomington <em>Drosophila</em> Stock Center #29724) long-term live imaging dataset acquired with light sheet fluorescence microscopy</p>
(08)-Strobl2021A-DS0003 – Tribolium castaneum Gruul #1 hybrid line long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy
<p>(08)-Strobl2021A-DS0003 – <em>Tribolium castaneum</em> Gruul #1 hybrid line long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy</p>
(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 – <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>
Data from: Spectroscopic approach to correction and visualisation of bright-field light transmission microscopy biological data
<p>The most realistic information about the transparent sample such as a live cell can be obtained only using bright-field light microscopy. At high-intensity pulsing LED illumination, we captured a primary 12-bit-per-channel (bpc) response from an observed sample using a bright-field wide-field microscope equipped with a high-resolution (4872x3248) image sensor. In order to suppress data distortions originating from the light interactions with undesirable elements in the optical path, poor sensor reproduction (geometrical defects of the camera sensor and some peculiarities of sensor sensitivity), this uncompressed 12-bpc data underwent a kind of correction after simultaneous calibration of all the parts of the experimental arrangement. Moreover, the final intensities of the corrected images are proportional to the photon fluxes detected by a camera sensor. It can be visualized in 8-bpc intensity depth after the Least Information Loss compression [Lect. Notes Bioinform. 9656, 527 (2016)].</p>
Dataset for Adaptive Light-Sheet Fluorescence Microscopy with a Deformable Mirror for Video-Rate Volumetric Imaging
<p>1. Underlying data of figures in the paper </p> <p>2. Background images used to process the experimental data</p> <p>3. image stack of 250 nm beads</p> <p>4. image stack of sunflower pollen grains</p> <p>5. image stacks and videos of Fluo-4 labelled cells</p> <p>6. image stacks and videos of CMO-labelled cells</p> <p>The data is organised according to the figures they are related to in the following publication:</p> <p> </p> <p><a href="https://aip.scitation.org/author/Hong%2C+Wenzhi">Wenzhi Hong</a><em>, </em><a href="https://aip.scitation.org/author/Wright%2C+Terry">Terry Wright</a><em>, </em><a href="https://aip.scitation.org/author/Sparks%2C+Hugh">Hugh Sparks</a><em>, </em><a href="https://aip.scitation.org/author/Dvinskikh%2C+Liuba">Liuba Dvinskikh</a><em>, </em><a href="https://aip.scitation.org/author/MacLeod%2C+Ken">Ken MacLeod</a><em>, </em><a href="https://aip.scitation.org/author/Paterson%2C+Carl">Carl Paterson</a><em>, and </em><a href="https://aip.scitation.org/author/Dunsby%2C+Chris">Chris Dunsby</a> </p> <p>, "Adaptive light-sheet fluorescence microscopy with a deformable mirror for video-rate volumetric imaging", Appl. Phys. Lett. 121, 193703 (2022) <a href="https://doi.org/10.1063/5.0125946">https://doi.org/10.1063/5.0125946</a></p>
Supplementary videos for the "Remote-refocusing light-sheet fluorescence microscopy enables 3D imaging of electromechanical coupling of hiPSC-derived and adult cardiomyocytes in co-culture" manuscript
<p>Supplementary videos for preprint manuscript: </p> <p><em>Remote-refocusing light-sheet fluorescence microscopy enables 3D imaging of electromechanical coupling of hiPSC-derived and adult cardiomyocytes in co-culture</em><br> Liuba Dvinskikh, Hugh Sparks, Liliana Brito, Kenneth T MacLeod, Sian E Harding, Christopher Dunsby<br> bioRxiv 2023.01.28.526043; doi: https://doi.org/10.1101/2023.01.28.526043</p> <p>All videos have been rendered with JPEG compression.</p> <p>Shortened video captions (Please see supplementary information document for full caption)<br> <strong>Video 1:</strong> 3D LSFM timelapse of hiPSC-CM undergoing spontaneous calcium transients. <br> <strong>Video 2:</strong> Widefield transillumination timelapse of hiPSC-CM and adult-CM <br> <strong>Video 3:</strong> Widefield fluorescence timelapse of hiPSC-CM and adult CM with synchronized spontaneous calcium transients. <br> <strong>Video 4a:</strong> 3D LSFM timelapse of hiPSC-CM and adult-CM day 1 co-culture undergoing synchronized spontaneous transients. <br> <strong>Video 4b</strong>: Depth-encoded MIPs of the 3D LSFM timelapse of hiPSC-CM and adult-CM day 1 co-culture undergoing synchronized spontaneous transients. <br> <strong>Video 5a:</strong> 3D LSFM timelapse of hiPSC-CM and adult-CM day 1 co-culture undergoing synchronized spontaneous transients in a sample without NBleb. <br> <strong>Video 5b</strong>: Depth-encoded MIPs of the 3D LSFM timelapse of hiPSC-CM and adult-CM day 1 co-culture without NBleb undergoing synchronized spontaneous transients. <br> <strong>Video 6a</strong>: 3D LSFM timelapse of hiPSC-CM and adult-CM co-culture undergoing synchronized spontaneous transients in a sample treated with NBleb. <br> <strong>Video 6b:</strong> Depth-encoded MIPs of the 3D LSFM timelapse of hiPSC-CM and adult-CM day 1 co-culture with NBleb undergoing synchronized spontaneous transients. <br> <strong>Video 7a:</strong> 3D LSFM timelapse of hiPSC-CM and adult-CM day 0 co-culture undergoing synchronized spontaneous transients in a sample without NBleb. <br> <strong>Video 7b: </strong>Depth-encoded MIPs of the 3D LSFM timelapse of hiPSC-CM and adult-CM day 0 co-culture without NBleb. </p> <p> </p>
Supplementary material - Full-spectrum CARS Microscopy Of Cells And Tissues With Ultrashort White-light Continuum Pulses
<p>Supplementary material containing the raw Broadband CARS data presented in the manuscript entitled "Full-spectrum CARS Microscopy Of Cells And Tissues With Ultrashort White-light Continuum Pulses".</p>
Data from: Spectroscopic approach to correction and visualisation of bright-field light transmission microscopy biological data
Open the record for dataset details and reuse information.
Data for: Image processing tools for petabyte-scale light sheet microscopy data (Part 2/2)
Open the record for dataset details and reuse information.
Data for: Image processing tools for petabyte-scale light sheet microscopy data (Part 1/2)
Open the record for dataset details and reuse information.
Data from: Open-top Bessel beam two-photon light sheet microscopy for three-dimensional pathology
Open the record for dataset details and reuse information.
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>
Dataset of the European Light Microscopy Initiative 2019 Core Facility Large Survey
<p>In occasion of the 2019 ELMI meeting held in Brno, Czech Republic the organizers of the Core Facility day (Laurent Gelman, Marco Marcello, Roland Nitschke, Laure Plantard, Stefan Terjung) launched a Large Core Facility Survey.<br> This repository contains the screenshots of the original survey, a formatted list of questions, and the anonymised data from this survey, a total of 226 responses from 25 countries distributed over 5 continents. Results are subdivided in 5 Areas, Staff, Finances, Equipment, Quality control, IT and Data Management.</p>
Fig. 8 in Stereoscopic light-microscopy in biology - A review
Fig. 8: Spiraliform tests of a recent foraminifer (diameter: 300 µm).
Data from: Crustacean photoreceptor damage and recovery: Applying a novel scanning electronic microscopy protocol in artificial light at night studies
<p>As sources of artificial light at night (ALAN) expand worldwide, research on their impacts has also increased. Most of these studies, including those in coastal habitats, have focused on behavioral and ecological responses to ALAN, overlooking impacts on the photoreceptor, the basic functional structure of animals to absorb light. Examining structural changes in the photoreceptor is essential to understand the mechanisms by which ALAN may be impacting species, particularly those adapted to different light backgrounds. This study examined the photoreceptor (rhabdom) of two sandy beach crustaceans exhibiting different light tolerances at night: the amphipod <em>Orchestoidea tuberculata</em> and the isopod <em>Tylos spinulosus</em>. We developed a novel protocol to measure these species' photoreceptor areas and quantify the damage caused by ALAN using histological sections and scanning electron microscopy (SEM). Our results showed that in the isopod, a species naturally adapted to lower light intensities at night than the amphipod, the rhabdom surface was 20-times larger, and presented a tapetum, an adaptive feature found in species living in low light conditions. This confirmed that this species is potentially more sensitive to ALAN than the amphipod. Consistently, a brief period of exposure to ALAN (1 h, 20 lux) caused 3-6 times more damage in the isopod' rhabdom. In fact, ALAN caused structural damage in the isopod' but not in the amphipod' rhabdom, a damage that did not show signs of recovery from ALAN after 1 and 24 h. Thus, the damage caused by ALAN on an organism's photoreceptors is likely to be more severe and persistent in species naturally adapted to lower light levels at night. Installation of permanent ALAN sources nearby the burrowing area of these light sensitive species may have differential effects on their activity and interactions at night. ALAN may also become a new selection pressure on these species, a concern with wide implications given the ubiquity among animals of the photoreceptor structure and its response to light.</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>
Dataset: Towards a representative reference for MRI-based human axon radius assessment using light microscopy
<p>Dataset for: Towards a representative reference for MRI-based human axon radius assessment using light microscopy</p>
Training Data for "DeepCLEM: automated registration for correlative light and electron microscopy using deep learning"
<p><strong>This folder contains the training dataset used for the paper</strong></p> <p>"DeepCLEM: automated registration for correlative light and electron microscopy using deep learning"</p> <p><em>Rick Seifert, Sebastian M. Markert, Sebastian Britz, Veronika Perschin, Christoph Erbacher, Christian Stigloher and Philip Kollmannsberger</em></p> <p>F1000Research 9:1275 (2020), https://f1000research.com/articles/9-1275</p> <p>------------------------------------------------------------</p> <p>These are 117+4 manually aligned CLEM images of C.elegans acquired by Sebastian M. Markert, Sebastian Britz and Rick Seifert in the Electron Microscopy Facility of the Biocenter of University of Wuerzburg, Germany. For details and experimental protocols, please see the paper linked above.</p> <p>Contents:</p> <ul> <li>"fluo_training": Fluorescence microscopic channel of the 117 training images </li> <li>"sem_training": Scanning electron microscopic channel of the 117 training images</li> <li>"fluo_validation": Fluorescence microscopic channel of the 4 validation images </li> <li>"sem_validation": Scanning electron microscopic channel of the 4 validation images</li> </ul> <p>License: CC-BY 4.0</p>
Octopus vulgaris, Sepia officinalis, Loligo vulgaris and Illex coindetii early life phases Light Sheet Fluerescence Microscopy (LSFM) 3D scans.
<p>Acronyms: OV: <em>Octopus vulgaris</em>, SO: <em>Sepia officinalis</em>, LV: <em>Loligo vulgaris</em>, IC: <em>Illex coindetii</em>, DPH: Days Post-Hatching.</p> <p>Two detection objectives were used, depending on sample size, a 4x/0.28 NA Olympus XLFLUOR4x/340 objective (0, 5, 10, 19 DPH <em>Octopus vulgaris</em> individuals,<em> Loligo vulgaris</em> and<em> Illex coindetii</em>) and a Nikon 10x/0.5 NA CFI Plan Apochromat 10xC Glyc (Rest of the samples). For illumination, two 4x/0.95 NA Nikon CFI Plan Apo Lambda 4x were used when using the 10x detection objective and two 4x/0.13 NA Nikon Plan Fluor illumination objectives were used when using the 4x detection objective. </p> <p>Microscope: MuVi SPIM (Luxendo), LCS SPIM (Luxendo, only <em>Sepia officinalis</em> and 60 DPH <em>Octopus vulgaris</em> individuals).</p> <p>All the data has been scaled in order to reduce file sizes. Full size stacks can be requested to dgvilar@gmail.com.</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.