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58 results for “stitching”
Numerical refractive index correction for the stitching procedure in tomographic quantitative phase imaging – dataset
<p>Raw volumetric data used in the work "Numerical refractive index correction for the stitching procedure in tomographic quantitative phase imaging" (<a href="http://doi.org/10.1364/BOE.466403">doi.org/10.1364/BOE.466403</a>). The data is packaged using the FIJI BigStitcher into HDF5 file. The file is split into 89 parts in ZIP format. Additionally we provide XML file needed for opening the data with BigStitcher and the TXT file with the nominal locations of the volumes based on the readings from the X-Y translation stage. The volumes inside the HDF5 file are already registered for stitching using the BigStitcher pairwise registration and global optimization procedure. Using the BigStitcher option "Resave to TIFF" one can access the raw data that we processed in the work. The processing code which operates on TIFF files is available here: <a href="https://github.com/biopto/QPI-stitching-2D-3D">https://github.com/biopto/QPI-stitching-2D-3D</a>.</p>
Reference Reflectance Transformation Imaging acquisitions for RTI stitching and acquisition optimization
<p>This dataset contains 1. RTI acquisitions a canvas painting and a metal print plate in parts, for development of RTI-stitching methods. 2. Dense RTI acquisitions of brushed metal and ruse coarse metal surfaces for development of methods for determining ideal light positions in a RTI acquisitions. </p>
A Stitch in Time: Combining More than Two Decades of Mooring Data from the Central Oregon Shelf
<p>The highly biologically productive northern California Current, which includes the Oregon continental shelf, is an archetypal eastern boundary region with summertime upwelling driven by prevailing equatorward winds and wintertime downwelling driven by prevailing poleward winds. Between 1960 and 1990, monitoring programs and process studies conducted off the central Oregon coast advanced the understanding of many oceanographic processes, including coastal trapped waves, seasonal upwelling and downwelling in eastern boundary upwelling systems, and seasonal variability of coastal currents. Starting in 1997, the U.S. Global Ocean Ecosystems Dynamics – Long Term Observational Program (GLOBEC-LTOP) continued those monitoring and process study efforts by conducting routine CTD (Conductivity, Temperature, and Depth) and biological sampling survey cruises along the Newport Hydrographic Line (NHL; 44.652°N, 124.1 – 124.65°W), located west of Newport, Oregon. Additionally, GLOBEC-LTOP maintained a mooring slightly south of the NHL, nominally at 44.64°N, 124.30°W, on the 81-meter isobath. This location is referred to as NH-10, as it is located 10 nautical miles or 18.5 km west of Newport. A mooring was first deployed at NH-10 in August 1997. This subsurface mooring collected water column velocity data using an upward-looking acoustic Doppler current profiler. A second mooring with a surface expression was deployed at NH-10 starting in April 1999. This mooring included velocity, temperature and conductivity measurements throughout the water column as well as meteorological measurements. GLOBEC-LTOP and the Oregon State University (OSU) National Oceanographic Partnership Program (NOPP) provided funding for the NH-10 moorings from August 1997 to December 2004. Since June 2006, the NH-10 site has been occupied by a series of moorings operated and maintained by OSU with funding from the Oregon Coastal Ocean Observing System (OrCOOS), the Northwest Association of Networked Ocean Observing Systems (NANOOS), the Center for Coastal Margin Observation & Prediction (CMOP), and most recently the Ocean Observatories Initiative (OOI). While the objectives of these programs differed, each program contributed to long-term observing efforts with moorings routinely measuring meteorological and physical oceanographic variables. This article provides a brief description of each of the six programs, their associated moorings at NH-10, and our efforts to combine over twenty years of temperature, practical salinity, and velocity data into one coherent, hourly averaged, quality-controlled data set. Additionally, the data set includes best-fit seasonal cycles calculated at a daily temporal resolution for each variable using harmonic analysis with a three-harmonic fit to the observations.</p>
TelePi: Stitching Dataset
<p>TelePi is a camera system based on the Raspberry Pi Zero v1.3 with its High Quality camera module. TelePi functions as a telepathology camera system as well as a standard color microscope camera. To capture a larger field of view, overlapping single images of a hematoxylin and eosin-stained, formalin-fixed, paraffin-embedded ureter specimen were taken (with approximately a 10% overlap) in 20X using an Olympus BX41 microscope (Olympus, Tokyo, Japan). Before each image was taken, any necessary adjustment in the focus was performed, so that the image stayed sharp. Afterwards, the images were stitched together using the bundled Fiji plugin Stitching v1.2 (Preibisch, Saalfeld et al. 2009). The original single images were saved as JPEGs as captured by the camera without editing. The stitched final output is included in the dataset.</p>
Example SDCM Stitching Dataset
<p>Example dataset for use with the following Fiji macro: https://github.com/RippeLab/MBEN/tree/main/stitching used in the following publication: https://doi.org/10.1101/2024.04.03.586404</p>
Dataset: Stitch Fix, Inc. (SFIX) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Stitched Image files - adenoma quantification using Featurecounter
<p>Hugin-merged & stitched image files from Apc<sup>min</sup> mice</p>
ICS/IDS stitched file
<p>Hi <a href="https://forum.image.sc/groups/ome">@ome</a> team !</p> <p>We usually use ICS/IDS file formats as an output to our stitching pipeline as the reading and writing is pretty fast. However, it seems that since Bio-Formats 7.x opening the files is not working anymore.</p> <p>I tried with a Fiji with Bio-Formats 6.10.1 and the files open, but more recent versions give an issue.</p> <div> </div> <pre><code>java.lang.NullPointerException at loci.formats.in.ICSReader.initFile(ICSReader.java:1481) at loci.formats.FormatReader.setId(FormatReader.java:1480) at loci.plugins.in.ImportProcess.initializeFile(ImportProcess.java:498) at loci.plugins.in.ImportProcess.execute(ImportProcess.java:141) at loci.plugins.in.Importer.showDialogs(Importer.java:156) at loci.plugins.in.Importer.run(Importer.java:77) at loci.plugins.LociImporter.run(LociImporter.java:78) at ij.IJ.runUserPlugIn(IJ.java:244) at ij.IJ.runPlugIn(IJ.java:210) at ij.Executer.runCommand(Executer.java:152) at ij.Executer.run(Executer.java:70) at ij.IJ.run(IJ.java:326) at ij.IJ.run(IJ.java:337) at ij.macro.Functions.doRun(Functions.java:703) at ij.macro.Functions.doFunction(Functions.java:99) at ij.macro.Interpreter.doStatement(Interpreter.java:281) at ij.macro.Interpreter.doStatements(Interpreter.java:267) at ij.macro.Interpreter.run(Interpreter.java:163) at ij.macro.Interpreter.run(Interpreter.java:93) at ij.macro.MacroRunner.run(MacroRunner.java:146) at java.lang.Thread.run(Thread.java:750) </code></pre> <p>You can find one example file at <a href="https://filesender.switch.ch/filesender2/?s=download&token=72e8cd81-5ec7-466a-b46c-57d9230112ae" rel="noopener nofollow ugc">this link <span>1</span></a>.</p> <p>Thanks for your help !<br>Best,<br>Laurent</p>
Organoid tiny dataset for stitching (SearchFirst, ome-zarr)
<p>Ome-zarr dataset that is the output of raw tifs (doi <a href="../doi/10.5281/zenodo.12795728">10.5281/zenodo.12795728</a>) processed by Fractal task (see .json file). </p> <p>Dataset acquired on Yokogawa CV8000 on 2024.06.07 by Nicole Repina, Friedrich Miescher Institute for Biomedical Research</p> <p>Dataset contains 11 fields of view (each 1000 x 1000 pix) acquired with SearchFirst*. Touching fields have 50 pix overlap. </p> <p>60x water objective, 2x2 binning, zyx spacing (6, 0.21667, 0.21667) um per pixel. Each field of view has 6 z-slices.</p> <p>Mouse small intestinal organoids (day 4) immunostained with the following dyes (2 fluorescence channels):</p> <p>Channel 1 (C01, 405nm) = DAPI nuclear stain<br>Channel 2 (C02, 488nm) = B-catenin membrane stain</p> <p>*In SearchFirst, the sample is first imaged with a first-pass low-magnification objective (e.g. 4x). An object detection algorithm is then used to identify object-containing regions. Next, the second-pass acquisition is performed with a higher magnification objective that only acquires object-containing regions. The objects are thus imaged not in a tiled grid. Further, some fields may be standalone, while others may have one or more touching fields that are acquired with the specified pixel overlap.</p>
Organoid tiny dataset for stitching (tiled, tif input)
<p>Dataset acquired on Yokogawa CV8000 on 2023.11.29 by Nicole Repina, Friedrich Miescher Institute for Biomedical Research</p> <p>Dataset contains 3 x 4 (12) tiled fields of view (each 1000 x 1000 pix) acquired with 50 pix overlap. Partial tile (gridded) acquisition.</p> <p>60x water objective, 2x2 binning, zyx spacing (10, 0.21667, 0.21667) um per pixel. Each field of view has 5 z-slices.</p> <p>Mouse small intestinal organoids immunostained with the following dyes (2 fluorescence channels):</p> <p>Channel 1 (C01, 405nm) = DAPI nuclear stain<br>Channel 3 (C03, 568nm) = B-catenin membrane stain</p>
Organoid tiny dataset for stitching (tiled, ome-zarr)
<p>Ome-zarr dataset that is the output of raw tifs (doi <a href="../doi/10.5281/zenodo.12794818">10.5281/zenodo.12794818</a>) processed by Fractal task (see .json file). </p> <p>Dataset acquired on Yokogawa CV8000 on 2023.11.29 by Nicole Repina, Friedrich Miescher Institute for Biomedical Research</p> <p>Dataset contains 3 x 4 (12) tiled fields of view (each 1000 x 1000 pix) acquired with 50 pix overlap. Partial tile (gridded) acquisition.</p> <p>60x water objective, 2x2 binning, zyx spacing (10, 0.21667, 0.21667) um per pixel. Each field of view has 5 z-slices.</p> <p>Mouse small intestinal organoids immunostained with the following dyes (2 fluorescence channels):</p> <p>Channel 1 (C01, 405nm) = DAPI nuclear stain<br>Channel 3 (C03, 568nm) = B-catenin membrane stain</p>
stitches package data
<p>This dataset contains the package data for the "stitches" model developed by the GCIMS project. More information about stitches can be found at https://github.com/jgcri/stitches</p>
Trial Comparing Cosmetic Outcomes of Pediatric Laceration Closure Using Skin Glue, Medical Tape Versus Stitches
ClinicalTrials.gov study NCT03280628. IPD Sharing: NO. Countries: 1. Publications: 1.
Automated Stitching of Noisy Scanning Electron Microscopy Images for Integrated Circuit Reverse Engineering
<p>A scanning electron microscope (<strong>SEM</strong>) usually creates images in the range of megapixel resolutions, but analyzing an IC layer requires resolutions in the gigapixel range. To create such large images, many individual images must be taken and then fused into one large image, which poses unique challenges: <strong>SEM</strong> images are affected by distortion due to charging effects and often exhibit high levels of noise and low contrast. One way of reducing the entry barrier to IC reverse engineering is to develop algorithms that can provide good results even in the case of suboptimal image quality, as can be produced by older, more readily available <strong>SEMs</strong>.</p> <p>This dataset contains images and additional information that has been cut from the PAINE paper due to length constraints. It also links to the thesis which served as base for the PAINE paper.</p>
Exploring the Search Space of Neural Network Combinations obtained with Efficient Model Stitching - Results Data
Open the record for dataset details and reuse information.
OpenFlexure Stitching - example data
<p>Example dataset for stitching in OpenFlexure Stitching</p>
BEnefit of HYpnosis on Pain During Stitches in Emergency Room
ClinicalTrials.gov study NCT06497712. IPD Sharing: Not stated. Countries: 1. Publications: 0.
When Closing Midline Incisions, do Small Stitches Reduce the Risk for Incisional Hernia, Wound Infection or Dehiscence?
ClinicalTrials.gov study NCT00508053. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Effect of Stitch Technique on the Occurrence of Incisional Hernia After Abdominal Wall Closure
ClinicalTrials.gov study NCT01965249. IPD Sharing: Not stated. Countries: 2. Publications: 7.
Cervical Occlusion Double-level Stitch Application
ClinicalTrials.gov study NCT05268640. IPD Sharing: YES. Countries: 1. Publications: 10.
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.