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766 results for “microscope”
Photographs under microscope of Santjordia pagesi holotype specimen HD84GS1 (DLSI063) tentacle squashes
<p>The holotype of <em>Santjordia pagesi</em>, captured by the ROV <em>Hyper-Dolphin</em> during Dive 84 on 10 March 2002 at a depth of 812 m (temperature 10.2˚C, salinity 34.30, dissolved oxygen 2.8 ml/L, Sigma T 26.37) within the Sumisu caldera (31˚28’N 140˚04’E) during a cruise by the R/V <em>Kaiyo</em> (KY02-03) to the Ogasawara Island Chain, south of the Japanese mainland. Photographs of nematocyst preparations of a formalin-preserved tentacle.</p>
IODP Expedition 369 Scanning electron microscope images
<p>Microscopic images of discrete samples were acquired using a scanning electron microscope (SEM) and captured as image files. These files were uploaded along with a brief description and a record of the microscopic conditions when the image was taken.</p>
Immobilized fluorescently stained zebrafish through the eXtended Field of view Light Field Microscope 2D-3D dataset
<p><strong>Immobilized fluorescently stained zebrafish through the eXtended Field of view Light Field Microscope 2D-3D dataset</strong></p> <p>This dataset comprises three immobilized fluorescently stained zebrafish imaged through the eXtended Field of view Light Field Microscope (XLFM, also known as Fourier Light Field Microscope). The images were preprocessed with the <a href="https://github.com/pvjosue/SLNet_XLFMNet">SLNet</a>, which extracts the sparse signals from the images (a.k.a. the neural activity).</p> <p>If you intend to use this with Pytorch, you can find a data loader and working source code to load and train networks <a href="https://github.com/pvjosue/CWFA">here</a>.</p> <p>This dataset is part of the publication: Fast light-field 3D microscopy with out-of-distribution detection and adaptation through Conditional Normalizing Flows.</p> <p> </p> <p>The fish present are:</p> <ul> <li>1x NLS GCaMP6s</li> <li>1x Pan-neuronal nuclear localized GCaMP6s Tg(HuC:H2B:GCaMP6s)</li> <li>1x Soma localized GCaMP7f Tg(HuC:somaGCaMP7f)</li> </ul> <p> </p> <p>The dataset is structured as follows::</p> <p>XLFM_dataset</p> <ul> <li><em>Dataset/</em> <ul> <li><em>GCaMP6s_NLS_1/</em> <ul> <li><em>SLNet_preprocessed/</em> <ul> <li><em>XLFM_image/</em> <ul> <li><em>XLFM_image_stack.tif</em>: tif stack of 600 preprocessed XLFM images.</li> </ul> </li> <li><em>XLFM_stack/</em> <ul> <li><em>XLFM_stack_nnn.tif</em>: 3D stack corresponding to frame nnn.</li> </ul> </li> <li><em>Neural_activity_coordinates.csv</em>: 3D coordinates of neurons found through the <a href="https://www.biorxiv.org/content/10.1101/061507v2">suite2p framework</a>.</li> </ul> </li> <li><em>Raw/</em> <ul> <li><em>XLFM_image/</em> <ul> <li><em>XLFM_image_stack.tif:</em> tif stack of 600 raw XLFM images.</li> </ul> </li> </ul> </li> </ul> </li> <li>(other samples)</li> </ul> </li> <li><em>lenslet_centers_python.txt</em>: 2D coordinates of the lenset in the XLFM images.</li> <li><em>PSF_241depths_16bit.tif: 3D PSF of the microscope can be used for 3D deconvolution. Spanning 734 × 734 × 550𝜇𝑚3 used to deconvolve this volumes. </em></li> </ul> <p> </p> <p>In this dataset, we provide a subset of the images and volumes.</p> <p>Due to space constraints, we provide the 3D volumes only for:</p> <ul> <li><em>SLNet_preprocessed/XLFM_stack/</em> <ul> <li>10 interleaved frames between frames 0-499 (can be used for training a network).</li> <li>20 consecutive frames, 500-520 (can be used for testing).</li> </ul> </li> <li><em>raw/</em> <ul> <li>No volumes are provided for raw data, but they can be reconstructed through 3D deconvolution.</li> </ul> </li> </ul> <p> </p> <p>Enjoy, and feel free to contact us for any information request, like the full PSF, 3 more samples or longer image sequences.</p> <p> </p> <p> </p> <p> </p>
Microscopic View of the iRGD Peptide Binding Mechanism
<p>Input and trajectory files used to obtain an atomistic picture of the folding and mechanism of binding of the iRGD peptide to RGD integrin receptors.</p>
IODP Expedition 382 Scanning electron microscope images
<p>Microscopic images of discrete samples were acquired using a scanning electron microscope (SEM) and captured as image files. These files were uploaded along with a brief description and a record of the microscopic conditions when the image was taken.</p>
IODP Expedition 392 Scanning electron microscope images
<p>Microscopic images of discrete samples were acquired using a scanning electron microscope (SEM) and captured as image files. These files were uploaded along with a brief description and a record of the microscopic conditions when the image was taken.</p>
Microscopic images of Dictyosphaeria ocellata (Howe 5090 and Howe 5585), New York Botanical Garden (NY)
<p>Microscopic images of</p> <p>Valonia ocellata M.Howe - M. A. Howe 5090, 1907-11-25, Bahamas, Watling Island, in the lagoon, 24.063924 -74.532563, New York Botanical Garden (NY) 00937651 (Holotype)</p> <p>Valonia ocellata M.Howe – M.A. Howe 5585, 1907-12-16, Turks and Caicos Islands, Cockburn Harbor, South Caicos, B.W.I., 21.500434 -71.532698, New York Botanical Garden (NY) 02140870</p>
Microscopic defect dynamics during a brittle-to-ductile transition
<p>Description of Data employed in “<em>Microscopic defect dynamics during a brittle-to-ductile transition</em>”-</p> <p>The data reported in Table S.1 of PNAS paper is as follows :</p> <ol> <li>All raw data [mechanical synchronized with ultrasonic probes ] are in .mat files.</li> <li>The data includes both strain, stress, P-velocityand waveforms of passive sensor in the point of strain-stress also are provided . All data are in 2ns time-resolution –</li> <li>AE_CAT_xxx includes all information recorded during test in Paterson rig. The data are synchronized with pulsing (active) and passive excitations based on simple off-line synchronized matching ;</li> <li>The index the AE_CAT_xxx table doe have a corresponding waveforms due to an AE in M_xxx</li> <li>UM_XXX are based on table S1 and does have full strain-stress data set.</li> <li>Dist_xx_yy is distance measurement calculated based on DTW algorithm between each pair of tests</li> </ol> <p>Feel free to request [ hoghaff@mit.edu or <a href="mailto:mpec@mit.edu">mpec@mit.edu</a>] further information regarding our methods of analysis the data or obtaining the waves in Paterson rig. </p> <p>**Apart of the presented data in the paper , we repeated each test at least twice to be confident about our results in particular ultra-high frequency AEs and the strain-stress history. Feel free to request data independently from corresponding authors.</p>
Revealing the microscopic mechanism of elementary vortex pinning in superconductors
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A three-photon head-mounted microscope for imaging all layers of visual cortex in freely moving mice
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Data from: The effect of external flow on 3D orientation of a microscopic sessile suspension feeder, Vorticella convallaria
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Microscopic images and schematics illustrating processes of microenvironment sensing and cortical actomyosin partitioning in T cells
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Microscope-based phytoplankton community composition for Green Lake 4, 2000 - 2007.
These data represent phytoplankton community composition in Green Lake 4 observed during bi-weekly sampling events following ice-off during the 2000-2007 field seasons. Samples were collected from the deepest part of the lake using a Van Dorn sampler from depths of 0, 3, and 9 m. Samples from the inlet and outlet are grab samples. Phytoplankton samples were preserved in 1% Lugol’s solution. Cell counts for each taxonomic group were conducted using an inverted microscope with 1000x magnification. Taxonomic resolution was based on the imaging capabilities of the instrument and varies based on the size and distinguishing characteristics of the cells in a given taxonomic group. Please see details in Methods.
Bipolar device fabrication using a scanning tunnelling microscope
<p>Hydrogen resist lithography with the tip of a scanning tunneling microscope (STM) can be used to fabricate atomic-scale dopant devices in silicon substrates and could potentially be used to build a dopant-based quantum computer. However, all devices fabricated so far have been based on the n-type dopant precursor phosphine. Here, we show that diborane can be used as p-type dopant precursor, allowing p-type and bipolar dopant devices to be created. Characterisation of diborane <span class="math-tex">\(\delta\)</span>-layers reveals that similar mobilities and densities can be achieved as for phosphine, with sheet resistivities as low as 300 <span class="math-tex">\(\Omega\)</span>. STM imaging and transport measurements of a 5.5 nm-wide p-type dopant nanowire give an estimated upper bound of 2 nm for the lithographic resolution of the p-type dopant profiles.<br> By combining our p-type doping approach with established phosphine-based n-type doping, we fabricate a 100 nm wide pn-junction and show that its electrical behaviour is similar to that of an Esaki diode.</p>
Darwin travelling microscope
3D model of small portable brass microscope owned by Charles Darwin and dating from period when he sailed on HMS Beagle as geologist and naturalist. Manufactured by Cary of London, 1826-30. Source: Objaverse 1.0 / Sketchfab
Compound microscope
Creator: Ernst Gundlach (1834–1908) Time of creation: 1869–1871 Jagiellonian University Museum Collegium Maius Inventory number: 5172; 1369/V https://muzea.malopolska.pl/en/objects-list/2746 Source: Objaverse 1.0 / Sketchfab
Dataset of Axonal Synapses, Acquired using a Two-photon Microscope in the Live Mouse Cortex
<p>This Dataset consists of TIFF 100 images, split in 20 test and 80 training images, of axons with their synapses (boutons) labelled. The labels are in form of ground-truth binary images of the same size, in which the corresponding synapses have been labelled as boxes.</p> <p>This data was collected in the live mouse cortex, using a two-photon microscope, with a 40x objective, at zoom 4, with a resolution of 512 x 512 x 0.147 microns per pixel, and a Point Spread Function characterised by a Full Width at Half Maximum (FWHM) values of 0.45 x 0.45 x 2.5 microns (x, y, z). </p> <p> </p> <p><strong>Please cite the following paper when using this dataset:</strong></p> <p>Bass C, Helkkula P, De Paola V, Clopath C, Bharath AA. Detection of axonal synapses in 3D two-photon images. Giniger E, ed. <em>PLoS ONE</em>. 2017;12(9):e0183309. doi:10.1371/journal.pone.0183309.</p> <p> </p>
Research data supporting: "Non-trivial stimuli-responsive collective behaviours emerging from microscopic dynamic complexity in supramolecular polymer systems"
<p>Contains the relevant simulation data and input files. See "readme.txt" for information.</p>
Dataset package for the manuscript "A microscopic Kondo lattice model for the heavy fermion antiferromagnet CeIn3"
<p><strong>Figure 2:</strong> Derivation of the Kondo-Heisenberg model.</p><p><strong>Description:</strong></p><p>The electronic structure of CeIn3 on the reciprocal-space path RGXMG (G=Gamma) is stored in "fbands.dat" and in "allotherbands.dat". The file "fbands.dat" has 15 columns, where the first column provides the wave-vector projection onto the path RGXMG and columns 2-15 the energies of the 14 <i>f</i>-bands. In the file "allotherbands.dat" the first column corresponds to the wave-vector values and the other columns contain all remaining bands.</p><p>The wave-vector dependent interactions are stored in "InteractionSpectrum.dat". Here, the first column provides the wave-vector projection onto the path RGXMG, the second column the RKKY-interaction, the third column the superexchange, the fourth column the particle-particle interaction and the last column the sum of all interactions.</p><p> </p><p><strong>Figure 3:</strong> Calculated and measured magnon dispersion and dynamic magnetic susceptibility in the antiferromagnetic state of CeIn3.</p><p><strong>Description:</strong></p><p>The imaginary part of the dynamic magnetic susceptibility, as inferred from high-energy inelastic neutron scattering intensity, on the reciprocal space-path RG is stored in the file "HighEnergy_Int_RG.dat" in terms of a 59 x 68 matrix. Here, the first index enumerates the wavevector-projection onto the path RG and the second index the energy transfer. The respective wave-vector and energy values are stored in "HighEnergy_Q_RG.dat" and "HighEnergy_E_RG.dat", respectively. Similarly, inelastic neutron scattering intensity, wave-vector values, and energy-transfer values for the paths GX, XM, and MG are stored in the files "HighEnergy_Int_GX.dat", "HighEnergy_Q_GX.dat", "HighEnergy_E_GX.dat", "HighEnergy_Int_XM.dat", "HighEnergy_Q_XM.dat", "HighEnergy_E_XM.dat", "HighEnergy_Int_MG.dat", "HighEnergy_Q_MG.dat", and "HighEnergy_E_MG.dat".</p><p>The imaginary part of the dynamic magnetic susceptibility on the path RGXMG, as inferred from theory, is stored in the files "Theory_Q_RGXMG.dat", "Theory_E_RGXMG.dat", and "Theory_Int_RGXMG.dat", where the first, second, and third file provide the wave-vector projections, the energy transfers, and the values of the dynamic magnetic susceptibility, respectively.</p><p>The dispersion on the path RGXMG inferred from MOPAM calculations is stored in "MOPAM-Dispersion_RGXMG.dat" and the dispersion of the J1-model in "J1Model-Dispersion_RGXMG.dat". In both files, the first column corresponds to the wave-vector projection onto the path RGXMG and the second column to the energy.</p><p>Cuts at the constant wave-vectors Q1 and Q2, as inferred from experiments, are stored in "Experiment_ConstQ1.dat" and "Experiment_ConstQ2.dat", respectively. The cuts from theory are stored in "Theory_ConstQ1.dat" and "Theory_ConstQ2.dat".</p><p>The integral over the dynamic magnetic susceptibility on the path RGXMG inferred from theory is stored in "Theory_IntegralOverchi.dat", where the first and second columns provide the wave-vector projection and the integrated values, respectively. The values inferred from experiment are stored in "Experiment_IntegralOverchi.dat". The first and second columns provide the wave-vector projection and the integrated values, respectively. The last column provides the error bars.</p><p> </p><p><strong>Figure 4: </strong>Signature of long-range RKKY interactions in CeIn3,</p><p><strong>Description:</strong></p><p>High-resolution inelastic neutron scattering data on the path RG are presented in "HighResolution_Int_RG.dat". The first and second index enumerates the wave-vector projection onto the path RG and energy transfer, respectively. The respective values are stored in "HighResolution_Q_RG.dat" and "HighResolution_E_RG.dat".</p><p>Similarly, data for the paths RX and RM are stored in the files "HighResolution_Int_RX.dat", "HighResolution_Q_RX.dat", "HighResolution_E_RX.dat", "HighResolution_Int_RM.dat", "HighResolution_Q_RM.dat", and "HighResolution_E_RM.dat".</p><p>The dispersion inferred from MOPAM calculations on the paths RG, RX, and RM, is stored in "MOPAM-Dispersion_RG.dat", "MOPAM-Dispersion_RX.dat", and "MOPAM-Dispersion_RM.dat", respectively. Similarly, the dispersions of the J1 model on the paths RG, RX, and RM, are stored in "J1Model-Dispersion_RG.dat", "J1Model-Dispersion_RX.dat", and "J1Model-Dispersion_RM.dat", respectively.</p><p>Cuts at constant energies 0.6 meV and 1.4 meV along RG are stored in "HighResolution_RG_ConstEcut_0p6meV.dat" and "HighResolution_RG_ConstEcut_1p4meV.dat", respectively. Similarly, cuts along RX are stored in "HighResolution_RX_ConstEcut_0p6meV.dat" "and HighResolution_RX_ConstEcut_1p4meV.dat" and along RM in "HighResolution_RM_ConstEcut_0p4meV.dat" "and HighResolution_RM_ConstEcut_1p4meV.dat".</p><p> </p>
EnderScope: A low-cost 3D printer based scanning microscope for microplastic detection
<p>The EnderScope is a novel, low-cost microscope for automated scanning and detection of microplastics in filtered seawater samples. This microscope is based on the mechanics of a low-cost 3D printer (Creality Ender 3). The hotend of the printer is replaced with an optics module, allowing for the reliable and calibrated motion system of the 3D printer to be used for automated scanning over a large area (>20x20 cm). </p> <p>Here we present the optical and mechanical validtion of the EnderScope. Along with data from some proof of concept experiments that show the EnderScope is capable of detecting plastics in environmental seawater samples.</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.