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2,649 results for “Optical”
Caloosahatchee River Estuary Optical Water Quality Data (May 2008 - May 2020)
This dataset is an aggregated dataset from various sources including the South Florida Water Management District and Lee County (Florida) Environmental Lab and includes parameters of optical water quality. Optical water quality samples were collected throughout the Caloosahatchee River Estuary from the head of the estuary to its outlet in San Carlos Bay and the Gulf of Mexico. Parameters within the dataset include light attenuation coefficient, Secchi disk depth, chlorophyll-a concentration, turbidity, total suspended solid concentration, color, water temperature, specific conductivity, and salinity. Samples were collected from various agencies from May 2008 to May 2020. Other water quality parameters were also collected and some of the monitoring continued to present but is not presented here.
Data on spatial distribution of tracers for optical sensing of stream surface flow
<p>Here, we present the numerical and field data used in the manuscript entitled <em>Spatial distribution of tracers for optical sensing of stream surface flow</em>. Numerical data were synthetically generated considering different values of seeding density and aggregation levels of tracers for image-velocimetry analyses. In total, 33,600 synthetic images were generated. Field data correspond with the Basento River case study located in southern Italy. The respective footage at 12 fps, pre-processed and stabilised frames, and reference velocity data are provided in this dataset.</p>
Dataset related to the publication "Transformation Optics: Large Multiphysics Simulation of Nonlinear Optomechanical Coupling in Microstructured Resonant Cavities", DOI: 10.1109/MMM.2018.2821086
<p>This folder contains the raw data from which the graphs in paper "Transformation Optics: Large Multiphysics Simulation of Nonlinear Optomechanical Coupling in Microstructured Resonant Cavities", DOI: 10.1109/MMM.2018.2821086, have been obtained.</p>
A quantum network node with crossed optical fibre cavities
<p>Data published in "<em> A quantum network node with crossed optical fibre cavities </em>"</p> <p>Nature Physics (2020)</p>
Measurement of Absolute Retinal Blood Flow Using a Laser Doppler Velocimeter Combined with Adaptive Optics
<p><strong>Purpose</strong>: Development and validation of an absolute laser Doppler velocimeter (LDV) based on an adaptive optical fundus camera which provides simultaneously high definition images of the fundus vessels and absolute maximal red blood cells (RBCs) velocity in order to calculate the absolute retinal blood flow.\newline<br> <strong>Methods</strong>: This new absolute laser Doppler velocimeter is combined with the adaptive optics fundus camera (rtx1, Imagine Eyes$^\copyright$,Orsay, France) outside its optical wavefront correction path. A 4 seconds recording includes 40 images, each synchronized with two Doppler shift power spectra. Image analysis provides the vessel diameter close to the probing beam and the velocity of the RBCs in the vessels are extracted from the Doppler spectral analysis. Combination of those values gives an average of the absolute retinal blood flow. An in vitro experiment consisting of latex microspheres flowing in water through a glass-capillary to simulate a blood vessel and in vivo measurements on six healthy humans were done to assess the device.\newline<br> <strong>Results</strong>: In the in vitro experiment, the calculated flow varied between 1.75µl/min and 25.9µl/min and was highly correlated (r<sup>2</sup>= 0.995) with the imposed flow by a syringe pump.<br> In the in vivo experiment, the error between the flow in the parent vessel and the sum of the flow in the daughter vessels was between -11% and 36% (mean±sd 5.7±18.5%). Retinal blood flow in the main temporal retinal veins of healthy subjects varied between 0.9 µL/min and 13.2µL/min.</p> <p><strong>Conclusion</strong>: This adaptive optics LDV prototype (aoLDV) allows the measurement of absolute retinal blood flow derived from the retinal vessel diameter and the maximum RBCs velocity in that vessel.</p>
Dataset used in "Marine Sediment Characterized by Ocean-Bottom Fiber-Optic Seismology" by Spica et al., 2020 in Geophysical Research Letters
<p>3000fullhisy: raw data to reproduce Fig. 2<br> ppsdspec.npz: all spectrogram as shown in Fig. 3a<br> AllVelMods: All velocity model shown in Fig. 3b<br> ac.out.final.npz: auto-correlation image in Fig. 3c<br> DAS11_lpf5.stack51.grd: Earthquake wavefield as shown in Fig. 3d<br> </p> <p> </p>
Experimental data to the publication "Genetic-optimised aperiodic code for distributed optical fibre sensors"
<p>The source data underlying Figs. 3-5 and Supplementary Figs. 6, 8-14 are provided as a Source Data file.</p>
Heat Dissipation Test with single Fiber Optic cable
<p> A Heat Dissipation Test implies heating a conducting element within the saturated soil until its temperature increase reaches steady state while monitoring the temperature development of the heating element during heating and cooling phases. In this case, we used a single Fiber Optic (FO) cable to perform a Heat Dissipation Test, aiming to quantify groundwater flow. The FO cable is installed along the outer casing of a piezometer located in an unconsolidated shallow aquifer.</p> <p>The data presented here are the maximum temperature reached each depth, the filtered temperature increment for the most representative depths, and the resulting values of thermal conductivity and groundwater flow based on the interpretation of the recorded data.</p> <p>Additionally, we included all the raw data obtained from the heated cable installed in the N325 borehole which was calibrated externally. And finally, we added two more files were we included the smooth heating curves and log-derivative resulting from filtering all data obtained from the heat dissipation test.</p>
ModIs Dust AeroSol (MIDAS): A global fine resolution dust optical depth dataset
<p>Monitoring and describing the spatiotemporal variability of dust aerosols is crucial to understand their multiple effects, related feedbacks and impacts within the Earth system. This study describes the development of the MIDAS (ModIs Dust AeroSol) dataset. MIDAS provides columnar daily dust optical depth (DOD) at 550 nm at global scale and fine spatial resolution (0.1° x 0.1°) over a 15-year period (2003-2017). This new dataset combines quality filtered satellite aerosol optical depth (AOD) retrievals from MODIS-Aqua at swath level (Collection 6.1, Level 2), along with DOD-to-AOD ratios provided by MERRA-2 reanalysis to derive DOD on the MODIS native grid. The uncertainties of MODIS AOD and MERRA-2 dust fraction with respect to AERONET and LIVAS, respectively, are taken into account for the estimation of the total DOD uncertainty. MERRA-2 dust fractions are in very good agreement with those of LIVAS across the “dust belt”, in the Tropical Atlantic Ocean and the Arabian Sea; the agreement degrades in North America and the Southern Hemisphere where dust sources are smaller. MIDAS, MERRA-2 and LIVAS DODs strongly agree when it comes to annual and seasonal spatial patterns, with collocated global DOD averages of 0.033, 0.031 and 0.029, respectively; however, deviations in dust loading are evident and regionally dependent. Overall, MIDAS is well correlated with AERONET-derived DODs (R=0.89), only showing a small positive bias (0.004 or 2.7%). Among the major dust areas of the planet, the highest R values (> 0.9) are found at sites of N. Africa, Middle East and Asia. MIDAS expands, complements and upgrades existing observational capabilities of dust aerosols and it is suitable for dust climatological studies, model evaluation and data assimilation.</p>
Quantification of plant morphology and leaf thickness with optical coherence tomography
<p>The uploaded scripts and data are used to obtain the figures 2, 4, 5, 6 and 7 in the publication. </p> <p>The code has been run with Python 3.7 in Spyder (Anaconda).</p> <p>There are three scripts, each needing specific datasets to run the code.</p> <p>1. The core is the segmentation of the leaf surface and this is subsequently used to calculate leaf thickness and obtain en-face images.</p> <p><a href="https://zenodo.org/api/files/89412f06-4c84-4516-9e7d-113796b42834/3D_segmentation_thickness_enface.py">3D_segmentation_thickness_enface.py</a>: This file loads the 3D processed OCT data, does the leaf surface segmentation and calculates the en face images. It needs the files processed_3Ddata.npy and videoim.npy</p> <p><a href="https://zenodo.org/api/files/89412f06-4c84-4516-9e7d-113796b42834/processed_3Ddata.npy">processed_3Ddata.npy</a>: This file contains the processed 3D OCT dataset (linear amplitude data), with respectively dimensions z,x,y. The data is saved as uint16 to save memory, and should be converted to double before further processing, as done in the script.</p> <p><a href="https://zenodo.org/api/files/89412f06-4c84-4516-9e7d-113796b42834/videoim.npy">videoim.npy</a>: This file contains the RGB image of Fig. 6(a) as image matrix.</p> <p>2. The non-infiltrated and infiltrated image (Figure 4)</p> <p><a href="https://zenodo.org/api/files/89412f06-4c84-4516-9e7d-113796b42834/2D_fig4.py">2D_fig4.py</a>: This script produces Figure 4 of the paper and also shows the two RGB images that indicate the scan location on the leaf. It needs the files OCTdata_figure4.npy (containing OCT data) and videoimages_figure4.npy</p> <p><a href="https://zenodo.org/api/files/89412f06-4c84-4516-9e7d-113796b42834/OCTdata_figure4.npy">OCTdata_figure4.npy</a>: This file contains the processed 2D OCT dataset (linear amplitude data), with respectively dimensions (a/b),z,x. The data is saved as uint16 to save memory, and should be converted to double before further processing, as done in the script.</p> <p><a href="https://zenodo.org/api/files/89412f06-4c84-4516-9e7d-113796b42834/videoimages_figure4.npy">videoimages_figure4.npy</a> This file contains the two RGB images that show the scan area of the data in Figure 4.</p> <p>3. The calculation of the refractive index and making Figure 5</p> <p><a href="https://zenodo.org/api/files/89412f06-4c84-4516-9e7d-113796b42834/refractiveindex_fig5.py">refractiveindex_fig5.py</a>: this script segments the cuvette wall and leaf surface on 2D images and calculates the refractive index by evaluating equation 1 of the publication. It needs the file images_refractiveindex.npy</p> <p><a href="https://zenodo.org/api/files/89412f06-4c84-4516-9e7d-113796b42834/images_refractiveindex.npy">images_refractiveindex.npy</a>: This file contains the processed 2D OCT dataset (linear amplitude data), with respectively dimensions (leaf/empty),z,x. The data is saved as uint16 to save memory, and should be converted to double before further processing, as done in the script.</p>
NEXT GENERATION OPTICAL ENCODER
<p>Linear encoders provide direct position feedback to various machine tool and automation systems. Working in a linear format allows extreme length position measurement and control. José Luis de la Peña from <a href="https://www.fagorautomation.com/en/">Fagor Automation</a> explains how <a href="https://www.laser4surf.eu">Laser4Surf</a> technology will make linear encoders even more precise with the help of lasered nano strucures on the tape.</p>
Raw data accompanying the manuscript "Multiscale and multimodal optical imaging of the human liver"
<p>These are the raw datasets used to generate the figures for the manuscript entitled "Multiscale and multimodal optical imaging of the human liver". The file CARS_SRS.zip contains folders with all raw CARS and SRS data (TIFF format). The file CLSM.zip contains confocal laser scanning microscopy data using the manufacturers data format (Zeiss). The file LSFM.zip contains light sheet fluorescence microscopy data files using the manufacturers data format (LaVision Biotec). The file OPT.zip contains raw optical projection tomography data at different excitation wavelengths (TIFF format). The file SRSIM.zip contains reconstructed structured illumination microscopy data files (TIFF format).</p>
WRF-Chem simulation results of Aerosol Optical Depth and PM2.5 concentrations
<p>This is the data for the manuscript submitted to JGR-Atmosphere: Simulations and characteristics of extreme aerosol events over eastern North America. </p>
Raw Data related to Research Article: Scaling of metal-clad InP nanodisk lasers: optical performance and thermal effects, Optics Express, volume 29, issue 3, 2021
<p>These are the plotted and raw data used to obtain figures shown in:</p> <p>P. Tiwari, P. Wen, D. Caimi, S. Mauthe, N. Vico Triviño, M. Sousa, and K. E. Moselund, Scaling of metal-clad InP nanodisk lasers: optical performance and thermal effects., Optics Express, volume 29, issue 3, 2021</p> <p>Please comply with copyright rules of the Optical Society of America under the terms of the OSA Open Access Publishing Agreement.:</p> <p>https://www.osapublishing.org/library/license_v1.cfm#VOR-OA</p> <p> </p>
Diffuse Optical Tomography and Fluorescence Simulation
<p>Diffusion of a source of light (Dirac) in a turbid medium. The object owns two inclusions, one more absorbant and one more diffusive than the background. These inclusions can be seen as tumours that have different optical and fluorescence properties compared to the "homogeneous" background.</p> <p>This simulation shows the forward problem solutions for choosen optical and fluorescence parameters and computed with FEEL++, a C++ library for Generalized Garlerkin methods (FEM, HP-FEM, ...).</p>
Tutorial Photonics Explorer Module 3 part 2: lenses, imaging rules, optical setups and telescopes
<p>Photonics Austria (PhAu) has conducted Teacher Training Programmes about Phoronics - the Photonics Explorer- in order to promote the potential of photonics to enliven physics lessons. This video is concerned with the topic polarisation and optical activity.</p> <p> </p>
Tutorial Photonics Explorer Module 3 part 1: lenses, imaging rules, optical setups and telescopes
<p>Photonics Austria (PhAu) has conducted Teacher Training Programmes about Photonics - the Photonics Explorer- in order to promote the potential of photonics to enliven physics lessons. This video tutorial contains several experiments designed to illustrate imaging equation and the laws of lenses.</p>
Data of the publication: Optical Line Width Broadening Mechanisms at the 10 kHz Level in Eu3+:Y2O3 Nanoparticles by J.G. Bartholomew et al.
<p>Data corresponding to the figures of the publication "Optical Line Width Broadening Mechanisms at the 10 kHz Level in Eu3+:Y2O3 Nanoparticles" by J.G. Bartholomew et al. (https://doi.org/10.1021/acs.nanolett.6b03949). A text file describes data in each compressed folder, please refer to the caption in the publication for more details. </p>
Research data supporting "Online quantitative monitoring of live cell engineered cartilage growth using diffuse fiber-optic Raman spectroscopy"
<p>Research data supporting the publication:</p> <p>M. Bergholt, 2017, Online quantitative monitoring of live cell engineered cartilage growth using diffuse fiber-optic Raman spectroscopy, Biomaterials, Volume 140, September 2017, Pages 128–137, DOI: 10.1016/j.biomaterials.2017.06.015</p>
Global scale leaf broadband optical properties derived from CliMA Land and associated CESM simulations
<p>Leaf level broadband reflectance and transmittance computed from leaf traits.</p> <ul> <li>clm_refl_tran_1m_weighted.nc: monthly data (144*96 pixels)</li> <li>surfdata_CMIP6_fluspect_v3.nc: surface data to run CESM (144*96 pixels)</li> </ul> <p>Global scale simulation results</p> <ul> <li>research_data_coupled_future_v2.nc: CESM coupled future simulations</li> <li>research_data_coupled_history_v2.nc: CESM coupled historical simulations</li> <li>research_data_uncoupled_history_v2.nc: CESM uncoupled future simulations</li> <li>research_data_uncoupled_ssp_v2.nc: CESM uncoupled SSP245 and SSP585 simulations</li> </ul> <p>Code changes</p> <ul> <li>SurfaceAlbedoMod.F90: modified CLM module</li> <li>Julia-and-Python-Code.tar.gz: code used for processing the data and plot the figures</li> </ul>
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.