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2,649 results for “optics”
Determining intrinsic potentials and validating optical binding forces between colloidal particles using optical tweezers - Part I
<p>Dataset Part I for publication "Determining intrinsic potentials and validating optical binding forces between colloidal particles using optical tweezers", in Nature Communications.</p>
Dataset for Popov, Strokov, and Surdyaev 2021: Black hole shadows against an optically thick, geometrically thin disk
<p>The datasets contain images of an optically thick, geometrically thin accretion disk distorted by the presence of a Kerr black hole (i.e., black hole shadows). The images are in a compressed text format (.gz) as well as in a graphic format. The training dataset comprises 2,301 images for various values of the disk's outer radius, the spin and sense of rotation of the black hole, and for different viewing angles (w.r.t. the equatorial plane where the disk resides). The test set contains 89 images with parameters which were randomly sampled from the same ranges as in the training set. For details, see the works associated with this record.</p>
Public data for nonlinear optical computational histology
<p>Experimental datasets for training and testing nonlinear optical computational histology (NOCH), which include label-free nonlinear optical data: stimulated Raman scattering (SRS) images of human brain tumors and multiphoton (MP) images of human ovarian cancers, and the corresponding H&E slices. The <a href="https://github.com/shenblin/NOCH">contrastive deep learning framework</a> can generate diagnostic quality H&E slides comparable to conventional histopathology. </p> <p>If you find this work useful in your research, please consider citing the paper:</p> <p><a href="https://doi.org/10.1002/advs.202308630">B. Shen, Z. Li, Y. Pan, Y. Guo, Z. Yin, R. Hu, J. Qu, L. Liu, Noninvasive Nonlinear Optical Computational Histology. Adv. Sci. 2023, 2308630.</a></p>
Absorbing Aerosol Optical Central Height (AOCH) retrieved from TROPOMI with UIowa's AOCH-O2AB algorithm
<p>Absorbing Aerosol Optical Centroid Height (AOCH) retrieved from TROPOMI with UIowa’s AOCH-O<sub>2</sub>AB algorithm. Dataset for analyzing dust and smoke cases over Asia during 2021-2023.</p> <p>More information about this dataset can be found in: </p> <p>Chen, X., Wang, J., Xu, X. G., Zhou, M., Zhang, H. X., Garcia, L. C., Colarco, P. R., Janz, S. J., Yorks, J., McGill, M., Reid, J. S., de Graaf, M., and Kondragunta, S.: First retrieval of absorbing aerosol height over dark target using TROPOMI oxygen B band: Algorithm development and application for surface particulate matter estimates, Remote Sensing of Environment, 265, 18, <a href="https://doi.org/10.1016/j.rse.2021.112674">https://doi.org/10.1016/j.rse.2021.112674</a>, 2021.</p>
Supplementary Material: The Importance of Optical Wavelength Data on Atmospheric Retrievals of Exoplanet Transmission Spectra
<p>Supplementary material for "The Importance of Optical Wavelength Data on Atmospheric Retrievals of Exoplanet Transmission Spectra" DOI: <a href="https://ui.adsabs.harvard.edu/link_gateway/2024arXiv240307801F/doi:10.48550/arXiv.2403.07801" target="_blank" rel="noreferrer noopener">10.48550/arXiv.2403.07801</a></p> <p>Contents of this record:</p> <ul> <li>The retrieved atmospheric parameters for the population (see supplementary_material.pdf).</li> <li>The retrieval statistics per planet for the wavelength ranges 0.3-4.5, 0.6-4.5, and 1.1-4.5 microns (see supplementary_material.pdf).</li> <li>Planet specific retrieved spectra for the wavelength ranges 0.3-4.5, 0.6-4.5, and 1.1-4.5 microns.</li> <li>Retrieved parameter cornerplots per planet for the wavelength ranges 0.3-4.5, 0.6-4.5, and 1.1-4.5 microns.</li> </ul> <p>(NOTE: the retrieval model and priors for the results displayed in this record are specified in tables 2 and 3 of the paper.)</p>
Phytoplankton optical fingerprint libraries for development of phytoplankton ocean color satellite products
<p><span>Quantifying changes in phytoplankton communities using ocean color is essential for predicting ocean food resources, occurrences of harmful algal blooms, and carbon and other elemental cycles, among other predictions. Here we present a dataset of greater than fifty strains of phytoplankton, from a range of taxonomic lineages, geographic locations, and time in culture, alone and in mixtures, grown to exponential and/or stationary phase for determination of hyperspectral UV-VIS absorption coefficients, multi-angle and multi-spectral backscatter coefficients, volume scattering functions, particle size distributions, fluorescence, and hyperspectral remote sensing reflectance. The measurements obtained from these experiments are valuable to facilitate development of new global and/or regional ocean color models by the broader scientific community. </span></p>
Genetic variants affecting NQO1 protein levels impact on efficacy of idebenone treatment in Leber hereditary optic neuropathy
<p>Fastq files with complete sequencing of NQO1 gene after PCR amplification for cell lines harboring m.11778G>A/MT-ND4 or m.3460A>G/MT-ND1 and controls.</p>
Figure data for: Free-electron interaction with nonlinear optical states in microresonators
<p>This dataset contains the figure data and code for the paper "Free-electron interaction with nonlinear optical states in microresonators".</p>
Quantum Material-Based Self-Propelled Microrobots for the Optical "On-the-Fly" Monitoring of DNA
<p>Quantum dot-based materials have been found to be excellent platforms for biosensing and bioimaging applications. Herein, self-propelled microrobots made of graphene quantum dots (GQD–MRs) have been synthesized and explored as unconventional dynamic biocarriers toward the optical “on-the-fly” monitoring of DNA. As a first demonstration of applicability, GQD–MRs have been first biofunctionalized with a DNA biomarker (i.e., fluorescein amidite-labeled, FAM-L) via hydrophobic π-stacking interactions and subsequently exposed toward different concentrations of a DNA target. The biomarker–target hybridization process leads to a biomarker release from the GQD–MR surface, resulting in a linear alteration in the fluorescence intensity of the dynamic biocarrier at the nM range (1–100 nM, <em>R</em><sup>2</sup> = 0.99), also demonstrating excellent selectivity and sensitivity, with a detection limit as low as 0.05 nM. Consequently, the developed dynamic biocarriers, which combine the appealing features of GQDs (e.g., water solubility, fluorescent activity, and supramolecular π-stacking interactions) with the autonomous mobility of MRs, present themselves as potential autonomous micromachines to be exploited as highly efficient and sensitive “on-the-fly” biosensing systems. This method is general and can be simply customized by tailoring the biomarker anchored to the GQD–MR’s surface.</p>
Propagating Gottesman-Kitaev-Preskill states encoded in an optical oscillator
<p>Gottesman-Kitaev-Preskill (GKP) qubit in a single Bosonic harmonic oscillator is an efficient logical qubit for mitigating errors in a quantum computer. The entangling gates and syndrome measurements for quantum error correction only require noise-robust linear operations, a toolbox that is naturally available and scalable in optical system. To date, however, GKP qubits have been only demonstrated at mechanical and microwave frequency in a highly nonlinear stationary system. In this work, we realize a GKP state in propagating light at the telecommunication wavelength and demonstrate homodyne measurements on the GKP states without loss corrections. Our states do not only show nonclassicality and non-Gaussianity at room temperature and atmospheric pressure, but the propagating wave property also permits large-scale quantum computation with strong compatibility to telecommunication technology.</p>
collected PTR-ToF-MS data, optical-based data, food frequency questionairre results, adipose tissue measurements, and anthroprometrics
<p><span>This file contains the collected PTR-ToF-MS data, optical-based data, food frequency questionairre results, adipose tissue measurements, and anthroprometrics. These data were used in the Periodic Technical Report Part B covering M37-M54. Graph generated from these data are Figure 16 and Figure 17 under Taks 7.5.</span></p> <p><span> </span></p> <p><span>Abbreviation<span> </span>Explanation</span></p> <p><span>BMI<span> </span>Body Mass Index</span></p> <p><span>SAT<span> </span>Subcutaneous adipose tissue</span></p> <p><span>VAT<span> </span>Visceral adipose tissue</span></p> <p><span>DHD1<span> </span>Vegetable intake</span></p> <p><span>DHD2<span> </span>Fruit intake</span></p> <p><span>DHD3<span> </span>Whole wheat products</span></p> <p><span>DHD4<span> </span>Legumes</span></p> <p><span>DHD5<span> </span>Nuts/seeds</span></p> <p><span>DHD6<span> </span>Dairy</span></p> <p><span>DHD7<span> </span>Fish</span></p> <p><span>DHD8<span> </span>Fat/oils</span></p> <p><span>DHD9<span> </span>High-fat and processed meat</span></p> <p><span>DHD10<span> </span>Sugar-containing beverages</span></p> <p><span>DHD11<span> </span>Unhealthy choices</span></p>
Data: Non-utopian optical properties computed of a tomographically reconstructed real photonic band gap crystal
<p>This repository contains the scripts and data used for the manuscript "Non-utopian optical properties computed of a tomographically reconstructed real photonic band gap crystal'' by LJ Corbijn van Willenswaard, S Smeets, N Renaud, M Schlottbom, JJW van der Vegt and WL Vos</p> <p>This dataset contains the following:</p> <ul> <li>The starting slice of the X-ray holotomagraphy dataset</li> <li> All data generated that is used in the paper including: <ul> <li>X-ray processing results</li> <li>Raw results from the computations</li> <li>Post processed results that are the basis of the manuscript</li> </ul> </li> <li>All scripts that were used to automate this process</li> <li>A copy of: <ul> <li>Nanomesh repository (for x-ray processing)</li> <li>hpgem & DGMax source code (for the computations)</li> </ul> </li> </ul> <p>A more detailed readme is included with the data.</p>
Data for: A coupled optical waveguides system in a fluidic medium that elucidates different parity-time-symmetric phases
<p>This research introduces a novel methodology of harnessing liquids to facilitate the realization of parity-time (<em>PT</em>)- symmetric optical waveguides on highly integrated microscale platforms. Additionally, we propose a realistic and detailed fabrication process flow, demonstrating the practical feasibility of fabricating our optofluidic system, thereby bridging the gap between theoretical design and actual implementation. Extensive research has been conducted over the past two decades on <em>PT</em>-symmetric systems across various fields, given their potential to foster a new generation of compact, power-efficient sensors and signal processors with enhanced performance. Passive <em>PT</em>-symmetry in optics can be achieved by evanescently coupling two optical waveguides and incorporating an optically lossy material into one of the waveguides. The essential coupling distance between two optical waveguides in air is usually less than 500 nm for nearinfrared wavelengths and under 100 nm for ultraviolet wavelengths. This necessitates the construction of the coupling region via expensive and time-consuming electron beam lithography, posing a significant manufacturing challenge for the mass production of <em>PT</em>-symmetric optical systems. We propose a solution to this fabrication challenge by introducing liquids capable of dynamic flow between optical waveguides. This technique allows the attainment of evanescent wave coupling with coupling gap dimensions compatible with standard photolithography processes. Consequently, this paves the way for the cost-effective, rapid and large-scale production of <em>PT</em>-symmetric optofluidic systems, applicable across a wide range of fields.</p>
Data for "Heat treatment and fiber drawing effect on the matrix structure and fluorescence lifetime of Er- and Tm-doped silica optical fibers"
<p>Includes data for absorption and attenuation measurements and calculations, profiles of refractive index and concentrations, TEM images, XRD patters, and data for fluorescence decay curves presented in the graphs.</p>
Precision and bias in dynamic light scattering optical coherence tomography measurements of diffusion and flow
<p>This repository contains raw data and analysis routines of the publication <strong>“<em>Precision and bias in dynamic light scattering optical coherence tomography measurements of diffusion and flow</em>”</strong> in Biomedical Optics Express (doi.org/10.1364/BOE.505847<em>). </em>The reader is free to use the scripts and data in this depository if the manuscript is correctly cited in their work. For further questions, feel free to contact the corresponding author. Python 3.7 was used for programming. Kindly note that simulating autocorrelation functions from extensive time series data, especially with a high repetition rate, can be time-consuming, often requiring more than 5-10 minutes. Despite parallelized processing routines for the measurement data, the full analysis may still take up to an hour. Please restart the kernel and run the code again if the parallelization fails.</p> <p>For the diffusion measurement under static conditions, there is only one file. However, for experiments involving both flowing and diffusing particles, the dataset comprises diffusion calibration, focus (beam shape) calibration, and flow measurement files. Due to the upload size limitations of the Zenodo repository, only the flow measurements corresponding to one discharge rate have been uploaded. Furthermore, only the non-dilute flow dataset has been uploaded for the same reason. However, for the dilute flow, the analysis logic remains the same, but users will need to utilize the complete g2 formula outlined in Section 2.2 of our article. All file names are sufficiently descriptive, showing whether it is diffusion, focus (waist) calibration or flow measurement. To conduct the analysis, it's essential to have information regarding the time series length (number of A-scans), the number of repeats (B-scans), and the acquisition rate.</p> <p>The results are plotted at the end of our analysis routines. The parameters are displayed as a function of depth. Users can readily compute the Signal-to-Noise Ratio (SNR) at each depth by utilizing the fitted autocorrelation amplitudes. Occasionally, the fitted amplitudes may surpass unity. In such instances, users can assume an extremely high (even infinite) SNR.</p> <div> <table> <tbody> <tr> <td> <p><strong>Name</strong></p> </td> <td> <p><strong>Description</strong></p> </td> <td> <p><strong>Parameters</strong></p> </td> </tr> <tr> <td> <p>Diffusion_03032023.oct</p> </td> <td> <p>Diffusion measurement file.</p> </td> <td> <p>Na=4096, Nb=1100, 5.5 kHz</p> </td> </tr> <tr> <td> <p>Diffusion_07032023.oct</p> </td> <td> <p>Diffusion calibration file for flow measurement.</p> </td> <td> <p>Na=4096, Nb=10, 36 kHz</p> </td> </tr> <tr> <td> <p>Waist_07032023.oct</p> </td> <td> <p>Beam waist calibration file for flow measurement.</p> </td> <td> <p>Na=4096, Nb=40, 36 kHz</p> </td> </tr> <tr> <td> <p>Q=2_07032023.oct</p> </td> <td> <p>Flow measurement file for a discharge rate of 2 ml/min.</p> </td> <td> <p>Na=4096, Nb=1000, 36 kHz</p> </td> </tr> <tr> <td> <p>Chirp.data</p> </td> <td> <p>File containing k-interpolation data.</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>ReadOCTFile.py</p> </td> <td> <p>Written by Jos de Wit, this module reads and imports spectra from raw OCT files.</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Data_processing.py</p> </td> <td> <p>This module contains all analysis, simulation and processing routines.</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Simulation_diffusion.py</p> </td> <td> <p>This script is for simulating and fitting g1 and g2 from diffusive particles.</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Simulation_flow.py</p> </td> <td> <p>This script is for simulating and fitting g1 and g2 from flowing and diffusive particles.</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Diffusion_parallel.py</p> </td> <td> <p>This script is for analyzing static diffusion measurements performed using Thorlabs Ganymede OCT system.</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Flow_parallel.py</p> </td> <td> <p>This script is for analyzing flow measurements performed using Thorlabs Ganymede OCT system.</p> </td> <td> <p> </p> </td> </tr> </tbody> </table> </div> <p> </p>
Dataset of "Optical Signatures of Thermal Damage on ex-vivo Brain, Lung and Heart Tissues using Time-Domain Diffuse Optical Spectroscopy"
<p>Dataset for the article entitled "Optical Signatures of Thermal Damage on ex-vivo Brain, Lung and Heart Tissues using Time-Domain Diffuse Optical Spectroscopy" </p> <p> </p> <p>Abstract:</p> <p> </p> <p>Thermal Therapies treat tumors by means of heat, greatly reducing pain, post-operation complications, and cost as compared to traditional methods. Yet, effective tools to avoid under- or over-treatment are mostly needed, to guide surgeons in laparoscopic interventions.<br>In this work, we investigated the temperature-dependent optical signatures of ex-vivo calf brain, lung, and heart tissues, based on the reduced scattering and absorption coefficients in the near-infrared spectral range (657 to 1107 nm). These spectra were measured by time domain diffuse optics, applying a step-like spatially homogeneous thermal treatment at 43 °C, 60 °C, and 80 °C.<br>We found three main increases in scattering spectra, possibly due to the denaturation of collagen, myosin, and proteins secondary structure.<br>After 75 °C, we found the rise of two new peaks at 770 and 830 nm in the absorption spectra due to the formation of a new chromophore, possibly related to hemoglobin or myoglobin.<br>This research marks a significant step forward in controlling thermal therapies with diffuse optical techniques by identifying several key markers of thermal damage. This could enhance the ability to monitor and adjust treatment in real-time, promising improved outcomes in tumor therapy.</p> <p> </p> <p> </p> <p>Authors:</p> <p>ALESSANDRO BOSSI , LEONARDO BIANCHI , PAOLA SACCOMANDI , AND ANTONIO PIFFERI<br>Politecnico di Milano</p> <p> </p> <p> </p> <p><a href="https://opg.optica.org/boe/fulltext.cfm?uri=boe-15-4-2481&id=548108" target="_blank" rel="noopener">Link to the article</a></p>
Data for "From Kitchen Garden to Multifunctionality: Leek-inspired Surface Structures Introduce Optical and Self-cleaning Properties to Cellulose-based Films"
<p>UV-Vis:<br>The optical properties were measured from 300 nm to 800 nm. The transmittance and haze were calculated using the following equations, and the results were reported for three sets of measurements:<br>Transmittance (%) = T2/T1 × 100 <br>Haze (%) = (T4/T2 - T3/T1) × 100 <br>where T1 is the reference transmitted light without the sample, T2 is the total light transmitted with the presence of the sample, T3 is light beam scattering by the UV-Vis device, and T4 is the diffusive transmittance, referring to the light transmitted by both the sample and the device. <br>The zip files are named by noting 'UV-Vis' followed by the type of sample.</p> <p>Current density-Voltage:<br>Seven sets of measurements were conducted on perovskite solar cell (PSC) devices, comparing the performance of uncoated (noted as pristine) devices to those coated with the replica. Additionally, another seven sets of measurements compared the performance of devices with and without the replica+2%CW coating. The data are recorded in .txt files noted by 'Current density-Voltage spectra.'</p> <p>Light scattering with halogen light beam:<br>The intensity of the illuminated light is determined for the length of a horizontal line passing through the center by using image analysis. The intensities are provided for all the cases, i,e,. with no film, with plain CA film, and with the replica. The .txt file is named 'Light scattering with halogen light beam.'</p> <p> </p>
The existence of optimal (v,4,1) optical orthogonal codes achieving the Johnson bound
<p>This is a program for checking Lemma 3.1 of the paper ``The existence of optimal (v,4,1) optical orthogonal codes achieving the Johnson bound'' .</p>
Optical Bias and Cryogenic Laser Readout of a Multipixel Superconducting Nanowire Single Photon Detector
<p>The complete datasets of the Publication are updloaded in this repository.</p> <p>Have a look into the ReadMe files for further information.</p> <p>Info about the Spice Model: ReadME LTSPICE</p> <p>Info about the Detector Efficiency Analysis: ReadMe Countrate</p> <p>Info about the PNR Extraction: ReadMe PNR Extraction</p> <p>Info about the SNSPD traces: ReadMe Traces </p> <p>Dataset of the Laser Diode Characterisation: IVP_Laserdiode.txt</p> <p> </p> <p> </p> <p> </p>
Dataset for Tunable on-chip electro-optic frequency-comb generation at 8 µm wavelength
<p>This dataset contains the information contained in Figures 2, 3, 4, 6, 7, 8, 9 of the related manuscript. This research dataset should be interpreted and understood in the context of the corresponding manuscript, which has been published in Laser & Photonics Reviews with DOI: 10.1002/lpor.202300961. All relevant information regarding the dataset, how it was obtained and its context is contained in the manuscript. The data correspond to the information shown in the figures of the manuscript. </p> <p>Each file is in .txt format, the decimal separator is a point '.' and the column separator is a tab '\t'.</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.