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2,649 results for “optics”
OSBM: Optical sectioning of unlabeled samples using bright-field microscopy
<p>This includes the FIJI macro and the image stack dataset used in the manuscript "Optical sectioning of unlabeled samples using bright-field microscopy".</p>
Data for figures in "An ultra-stable microresonator-based electro-optic dual frequency comb"
<p>Data for figures in "An ultra-stable microresonator-based electro-optic dual frequency comb"</p> <p> </p> <p>"Contents.csv" summarises data files.</p>
Diffuse-optical data set measured with a smartphone-based sensor on Potato Hill, Oregon, USA
<p>This data set contains both raw data and derived data obtained on Potato Hill, Oregon, on December 17th 2021 using a diffuse-optical, smartphone-based sensor. The raw image files have been converted to an uncompressed Adobe-.dng file format, file names indicate whether the file contains data for the blue (405nm) or red (650nm) laser or spatial calibration data using a 9mm x 9mm calibration pattern. Spectral albedo measurements are contained in the subfilder ./Albedo, the raw images in ./Phone. The root directory contains the matlab code (Matlab R2021b) needed for analysis as well as the derived data.</p> <p>For analyzing the raw data set, use "CameraMatchPotatoHillFinal.m". It wraps around the function "CameraAnalysisFinal.m", which performs the image analysis and least-square fit to resorted and rescaled data, employing in turn the model function "theosurfGInf.m". It saves a derived data set (attenuation, absorption and scattering coefficients, albedos, absorption enhancement factor and snow density.</p> <p>The script "Albedo.m" analyzes the derived data set along with measured albedo and simulated albedo deposited in the file "snicar_120ppb.txt". The obtained albedo curves and black carbon mixing ratio are as shown in the below manuscript.</p> <p>If you wish to use this data set please contact Markus Allgaier at markusa@uoregon.edu with a description of the work and any questions so that we may offer guidance in regards to the best usage of our dataset. When using the data set within a publication, please cite:</p> <p>Markus Allgaier & Brian Smith, "A Smartphone-Based Sensor for Measuring the Optical Properties of Snow", in preparation, (2022)</p>
Dataset provided with "Artifacts in Optical Projection Tomography Due to Refractive-Index Mismatch: Model and Correction"
<p>Dataset and scripts used in "Artifacts in Optical Projection Tomography Due to Refractive-Index Mismatch: Model and Correction".</p>
Electromagnetic immune phosphor-tipped fibre-optic thermometers
<p>Calibration traceability can be broken where there are significant, unquantified uncertainties. Thermometry in harsh environments using electrical sensors, such as thermocouples or resistance thermometers, can be unpredictably affected by electro-magnetic sources. For this application, phosphor based temperature sensors with fibre-optic connection have been made following two different approaches – phosphor decay time changes and phosphor emission spectral changes – and tested by bombardment with high energy electrons and by measurements in large magnetic fields. Both sets of tests showed good immunity to exposure, and with magnetic field tests significantly better than a thermocouple. Phosphor thermometry therefore has potential to retain traceability in situations where conventional sensors might fail.</p>
Optics Continuum: F-Number and Focal Length of Light Field Systems
<p>Reference:</p> <p>Ivo Ihrke, <em>„F-Number and Focal Length of Light Field Systems: A Comparative Study of Field of View, Light Efficiency, Signal to Noise Ratio, and Depth of Field“</em>, OSA Continuum, 2022</p> <p>Contact:</p> <p>Ivo Ihrke [at] uni [minus] siegen [dot] de</p> <p>Content:</p> <p>The data set contains the raw images that have been used in generating the experimental Figures (4 and 6) in the article as well as a code snippet (see README.md) to extract the Lytro data.</p>
The influence of the Er3+ dopant concentration in LaPO4:Nd3+, Er3+ on thermometric properties of ratiometric and kinetic-based luminescent thermometers operating in NIR II and NIR III optical windows
<p>In this work, properties of the near infrared absorbing and near infrared emitting Er<sup>3+</sup> and Nd<sup>3+</sup> co-doped LaPO<sub>4</sub> nanocrystals luminescent thermometer were investigated by exploiting luminescence spectra ratiometric and luminescence lifetime. The unique configuration of the energy levels of Nd<sup>3+</sup> and Er<sup>3+</sup> ions and the energy transfer between them requires temperature dependent phonon assistance. Since, the probability of this process is dependent on the distance between interacting ions, the thermometric parameters of luminescent thermometers were investigated as a function of Er<sup>3+</sup> dopant concentration. Strong susceptibility of its probability to temperature changes enables to quantify temperature with relative sensitivity as high as S<sub>R</sub> = 1.15%/K for LaPO<sub>4</sub>:1%Nd<sup>3+</sup>, 20%Er<sup>3+</sup> nanocrystals in the ratiometric approach and S<sub>R</sub> = 2.3%/K at 600 K for LaPO<sub>4</sub>:1%Nd<sup>3+</sup>, 5%Er<sup>3+</sup> in the luminescence lifetime based mode. The obtained results confirm the high applicative potential of this phosphor for remote temperature measurements.</p> <ul> </ul>
Stanford fiber-optic DAS array: Earthquake detection dataset
<p>Repurposing the fiber-optic cables from the existing telecommunication infrastructure makes it possible to record dense continuous seismic data in urban areas at low cost. From 2016 to 2019, we connected a disctributed acoustic sensing (DAS) interrogator unit to the fiber-optic cables in telecommunication conduits under Stanford University campus, recording years of continuous seismic data.</p> <p>This repository contains processed TensorFlow Record data files containing examples of earthquake and background noise signals recorded by the Stanford fiber-optic DAS array. These data were used for training, evaluation, and testing of a convolutional neural network for earthquake detection. </p> <p> </p> <p> </p>
Dataset: Two-dimensional wavefront characterization of adaptable corrective optics and Kirkpatrick–Baez mirror system using ptychography
<p>The ptychography datasets and processed wavefront data in support of the publication "Two-dimensional wavefront characterization of adaptable corrective optics and Kirkpatrick–Baez mirror system using ptychography". File are in the HDF format and contain a number of datasets detailed below. If you require more information, please contact the corresponding author of the publication or thomas.moxham@eng.ox.ac.uk</p>
RGB and Thermal Integral Image dataset for Search and Rescue with Airborne Optical Sectioning.
<p>The `Integral Images` folder contains labels and augmented AOS integral images (both RGB and Thermal) used for training, validation and testing (`data`).</p> <p>The integral images are computed using the complete data that were recorded during 18 flights at 6 different sites over 10 different days.</p> <p> </p> <p>The dataset mirrors [YOLO (8GB)](https://zenodo.org/record/3894774/files/YOLO.zip?download=1) (`data`) for integral (`SARAOS/AOS`) images, however, now additionally contain corresponding RGB integral images in addition to corresponding thermal integral images.</p>
Optical and X-ray data on V496 UMa
<p>The optical light curves and X-ray spectra and light curves presented in Kennedy et al. 2022 are given here. The code to reproduce the MCMCs and plots from the paper is given in a github repo.</p>
Data accompanying "Super-broadband on-chip continuous spectral translation unlocking coherent optical communications beyond conventional telecom bands"
<p>This dataset contains measurement data and processing scripts (Matlab) for the results presented in "Super-broadband on-chip continuous spectral translation unlocking coherent optical communications beyond conventional telecom bands". </p> <p>The paper can be found here:</p> <p><a href="https://www.nature.com/articles/s41467-022-31884-2">Super-broadband on-chip continuous spectral translation unlocking coherent optical communications beyond conventional telecom bands | Nature Communications</a></p> <p><a href="https://www.researchsquare.com/article/rs-1086400/v1">Activating Unconventional Wavelength Bands for Coherent Optical Communication by On-chip Continuous Spectral Translation | Research Square</a></p>
Accurate lattice parameters from 3D electron diffraction data I: Optical distortions
<p>3D ED data were measured with an FEI Tecnai G2 20 transmission electron microscope equipped with an Olympus SIS Veleta camera (CCD, 14 bit, 2048 x 2048 px) and a NanoMEGAS Digistar precession unit.</p> <p>Supporting information for article submitted to a scientific journal. Examples 1 and 2 including manuals and command files for optical distortions refinement in 3D ED data using PETS2 software.</p> <p>Manuals for the examples are available as the supporting information of the submitted article.</p>
Dataset for "The visual appearances of disordered optical metasurfaces"
<p>DATASET for "The visual appearances of disordered optical metasurfaces" by Kevin Vynck, Romain Pacanowski, Adrian Agreda, Arthur Dufay, Xavier Granier and Philippe Lalanne</p>
Polarization Mode Dispersion Measurements on 76Km of wrapped Aerial Optical Fibre
<p>This dataset consists of Polarisation Mode Dispersion measurements taken on 76Km of aerial fibre. The fibre is wrapped on a high voltage line. </p> <p>The results with the Nexus teser where taken in April 2009 and the second set of measurements were taken using a more accurate Adaptif PMD tester in August 2009. </p> <p>Hopefully this data will be useful to others. </p> <p> </p>
Dataset of paper "Critical assessment of optical sensor parameters for the measurement of ultraviolet LED lamps"
<p>Dataset of paper "Critical assessment of optical sensor parameters for the measurement of ultraviolet LED lamps"</p> <ul> <li>Sensor specifications</li> <li>Peak wavelength of light sources studied. </li> <li>Relative spectral intensity of each light source.</li> <li>Angle of Acceptance of sensors.</li> <li>Summary of angular response of detectors.</li> <li>Change of acceptance angle of detectors with wavelength.</li> <li>Raw reference counts as measured by a saturated and unsaturated sensor.</li> <li>Change in measured intensity with integration time.</li> <li>Effect of temperature on intensity measured by the sensor. </li> <li>Comparison between sensor measurements.</li> </ul>
Active optical switching evaluation in lab settings
<p>Transfer function of a MZI with PZT actuators on top by tuning the voltage in both arms</p>
Dataset: Impact of GST thickness on GST-loaded silicon waveguides for optimal optical switching
<p>The following files provide the dataset of the work "Impact of GST thickness on GST-loaded silicon waveguides for optimal optical switching". The description and organization of the files are explained in the file README.txt.</p>
Dataset - Generalization of deep recurrent optical flow estimation for particle-image velocimetry data
<p>This is the official test datasets of "Generalization of deep recurrent optical flow estimation for particle-image velocimetry data" published in Measurement Science and Technology. Particle-Image Velocimetry (PIV) is one of the key techniques in modern experimental fluid mechanics to determine the velocity components of flow fields in a wide range of complex engineering problems. Current PIV processing tools are mainly handcrafted models based on cross-correlations computed across interrogation windows. Although widely used, these existing tools have a number of well-known shortcomings, including limited spatial output resolution and peak-locking biases. Recently, new approaches for PIV processing leveraging a novel neural network architecture for optical flow estimation called Recurrent All-Pairs Field Transforms (RAFT) have been developed. These have matched or exceeded the performance of classical, handcrafted models. While the RAFT-PIV method is a promising approach, it is important for the broader fluids community to more completely understand its empirical behavior and performance. To this end, in this study, we thoroughly investigate the performance of RAFT-PIV under varying image and lighting conditions. IWe consider applications spanning synthetic and experimental data, with a breadth and depth going far beyond currently available empirical results. The results for the wide variation of experiments included in this dataset shed new light on the capabilities of deep learning for PIV processing. This dataset is given as binary TFRECORD format.</p>
Multishell Diffusion MR Tractography Yields Morphological and Microstructural Information of the Anterior Optic Pathway: A Proof-of-Concept Study in Patients with Leber's Hereditary Optic Neuropathy. RAW DATA
<p>Raw data used to prepare Figures and Tables in article "Multishell Diffusion MR Tractography Yields Morphological and Microstructural Information of the Anterior Optic Pathway: A Proof-of-Concept Study in Patients with Leber’s Hereditary Optic Neuropathy", accepted for publication at International Journal of Environmental Research and Public Health, 27 May 2022.</p>
ScienceDex guides
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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.