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2,649 results for “optics”
Clear optically matched panoramic access channel technique (COMPACT) for large-volume deep brain imaging
<p>Source data of paper "Clear optically matched panoramic access channel technique (COMPACT) for large-volume deep brain imaging" published on Nature Methods.</p>
Optic flow and odometry data from intelrealsense camera
<p>Insects rely on the perception of image motion, or optic flow, to estimate their velocity relative to nearby objects. This information provides important sensory input for avoiding obstacles. However, certain behaviors, such as estimating the absolute distance to a landing target, accurately measuring absolute distance travelled, and estimating the ambient wind speed require decoupling optic flow into its component parts: absolute ground velocity and distance to nearby objects. Behavioral experiments suggest that insects perform these calculations, but their mechanism for doing so remains unknown. Here we present a novel algorithm that combines the geometry of dynamic forward motion with known features of insect visual processing to provide a hypothesis for how insects might \textit{directly} estimate absolute ground velocity from a combination of optic flow and acceleration information. Our robotics-inspired-biology approach reveals three critical requirements. First, absolute ground velocity can only be directly estimated from optic flow during times of active acceleration and deceleration. Second, spatial pooling of optic flow across a receptive field helps to alleviate the effects of noise and/or low resolution visual systems. Third, averaging velocity estimates from multiple receptive fields further helps to reject noise. Our algorithm provides a hypothesis for how insects might estimate absolute velocity from vision during active maneuvers, and also provides a theoretical framework for designing fast analog circuitry for efficient state estimation that can be applied to insect-sized robots. </p>
Accurate photon echo timing by optical freezing of exciton dephasing and rephasing in quantum dots
<p>Dataset of the publication “Accurate photon echo timing by optical freezing of exciton dephasing and rephasing in quantum dots“, ( <a href="https://doi.org/10.1038/s42005-020-00491-2">https://doi.org/10.1038/s42005-020-00491-2</a> ). The zip file includes the data on which the plots shown in figures 2-5 of the main text, and supplementary figures S1-S5 are based.</p>
Controlling the emission time of photon echoes by optical freezing of exciton dephasing and rephasing in quantum-dot ensembles
<p>Dataset of the publication “Controlling the emission time of photon echoes by optical freezing of exciton dephasing and rephasing in quantum-dot ensembles“, <a href="https://www.spiedigitallibrary.org/conference-proceedings-of-spie/11684/2576887/Controlling-the-emission-time-of-photon-echoes-by-optical-freezing/10.1117/12.2576887.short?SSO=1">Proc. SPIE 11684,116840X (2021)</a> ( <a href="https://doi.org/10.1117/12.2576887">https://doi.org/10.1117/12.2576887</a> ). The zip file includes the data on which the figures are based, the gnuplot files for the figures, and an explaining readme.txt.</p>
Animation of Sentinel-3 aerosol optical depth over Europe in 2019.
<p>Animation of Sentinel-3 aerosol optical depth over Europe in 2019. 14 day averages. Left: Sentinel-3 Synergy land product. Middle: Machine learning based retrieval. Right: POPCORN post-process corrected aerosol optical depth.</p>
Global daily Aerosol Optical Depth measurements from Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA's Aqua and Terra satellites
<p>This repository contains input MODIS AOD data prepared for “Subways and Urban Air Polution” by Gendron-Carrier, Gonzalez-Navarro, Polloni and Turner (American Economic Journal: Applied Economics, <a href="https://doi.org/10.1257/app.20180168">https://doi.org/10.1257/app.20180168</a>). The main replication archive is available at <a href="https://doi.org/10.3886/E126401V1">https://doi.org/10.3886/E126401V1</a> .</p> <p>Description of input MODIS AOD data</p> <p>The Moderate Resolution Imaging Spectroradiometers aboard the Terra and Aqua earth-observing satellites provide daily measures of the aerosol optical depth of the atmosphere at a 3km spatial resolution everywhere in the world. Data is available in ‘granules’ which describe five minutes of satellite time. These granules are available, more or less continuously, from February 24, 2000 for the Terra satellite and from July 4, 2002 for Aqua. During September of 2018, we downloaded all available granules for Terra and Aqua until August 31, 2018 and subsequently consolidated them into daily rasters describing global AOD. In August 2020, we processed additional Terra data. This archive therefore contains daily rasters for Aqua (from 2002-07-04 to 2018-08-31) and Terra (from 2000-02-24 to 2020-07-31). We note that February 2005 data are missing for the Aqua satellite.</p> <p> </p> <p>We use source products MOD04_3K (<a href="https://doi.org/10.5067/MODIS/MOD04_L2.006">https://doi.org/10.5067/MODIS/MOD04_L2.006</a>) and MYD04_3K (<a href="https://doi.org/10.5067/MODIS/MYD04_L2.006">https://doi.org/10.5067/MODIS/MYD04_L2.006</a>). The product files are stored in Hierarchical Data Format (HDF) and we use the "Optical Depth Land And Ocean" layer, which is stored as a Scientific Data Set (SDS) within the HDF file, as our measure of aerosol optical depth. The "Optical Depth Land And Ocean" dataset contains only the AOD retrievals of high quality. We convert all HDF formatted granules to GIS compatible formats using the HDF-EOS To GeoTIFF Conversion Tool (HEG) provided by NASA’s Earth Observing System Program. We consolidate GeoTIFF granules into a global raster for each day using ArcGIS. First, we keep only AOD values that do contain information. The missing value is -9999 in AOD retrievals. Second, we create a raster catalog with all the granules for a given day and calculate the average AOD value using the Raster Catalog to Raster Dataset tool. The code used to accomplish this is included for reference purposes in “dofiles/old_work” of the main replication archive at <a href="https://doi.org/10.3886/E126401V1">https://doi.org/10.3886/E126401V1</a>.</p>
Data for "Advanced Structural Health Monitoring Method by Integrated Isogeometric Analysis and Distributed Fiber Optic Sensing"
<p>This dataset includes the experiment and simulation data of a new structural health monitoring system using distributed fiber optic sensing (DFOS) and Isogeometric Analysis (IGA).</p> <p>The experiment setup was a 5mm thick PVC pipe with a fiber optic cable wrapped around the outer surface of the pipe. The PVC pipe was subjected to an applied deformation and the distributed strains along the optical fiber was measured with a Neubrescope (NBX7031) instrument using Rayleigh backscattering technology.</p> <p>The simulation was performed using the in-house code JWRIAN-IGA developed in Joining and Welding Research Institute, Osaka University. The simulated data includes deformation, stress and strain distributions of the pipe, and projected one-dimensional fiber strains. The visualization files are post-processed with ParaView software.</p>
Optical properties of water-coated sea salt aerosol
<p>Optical properties of water-coated and partially dissolved sea salt aerosol particles have been computed with</p> <ul> <li>non-concentrical core-shell models (using MSTM)</li> <li>cube-sphere hybrids (using Tsym)</li> <li>homogeneous superellipsoids (using Tsym)</li> <li>inhomogeneous superellipsoids (using ADDA)</li> <li>coated convex polyhedra (using ADDA)</li> </ul> <p>Model raw output data as specified in the respective code documentations is provided. In addition input files for the Tsym calculations and the geometry files for the coated convex polyhedra (in DDSCAT input file format) are included.</p> <p>The manuscript describing this research was submitted to Optics Express in July 2021.</p> <p>Version 2.0 contains results and the respective geometry files for additional calculations with the coated convex polyhedra model.</p>
Dataset for the Manuscript: Surfactants Control Optical Trapping Near a Glass Wall
<p>This repository includes datasets supporting our manuscript that will be transferred to <em>the Journal of Physical Chemistry C</em>. This Version 2 includes extensive new results conducted during the revision process. </p> <ul> <li><strong>'Videos.zip'</strong>: Recordings of trapped particles, estimated trajectories, and calculated MSDs<strong>.</strong></li> <li><strong>'Dynamic Light Scattering.zip'</strong>: Measurement data using dynamic light scattering (DLS). It includes the conductivity, zeta potentials, and hydrodynamic size measurements.</li> <li><strong>'Raw data manual.html'</strong>: A data manual explaining the data details and visualizing the results.</li> </ul> <p>Notes:</p> <p>For finding particle trajectories, we used a Python package, <a href="http://soft-matter.github.io/trackpy/v0.4.2/index.html">Trackpy</a>, developed by Allan et al. We simply followed <a href="http://soft-matter.github.io/trackpy/v0.4.2/tutorial/walkthrough.html">their walkthrough</a> to find particle locations in a video recording and link them to a particle trajectory. The details of our trajectory outputs (_traj.csv files) can be found in <a href="http://soft-matter.github.io/trackpy/v0.4.2/generated/trackpy.locate.html#trackpy.locate">their API reference</a>. </p>
OCTAVA: an open-source toolbox for quantitative analysis of optical coherence tomography angiography images
<p>This is a dataset of OCTA images used in the development of the manuscript <em>OCTAVA: an open-source toolbox for quantitative analysis of optical coherence tomography angiography images</em></p>
Data presented in "Laser cooling and magneto-optical trapping of molecules analyzed using optical Bloch equations and the Fokker-Planck-Kramers equation"
<p>Results of simulations presented in our paper "Laser cooling and magneto-optical trapping of molecules analyzed using optical Bloch equations and the Fokker-Planck-Kramers equation"</p>
Miniature optical fiber curvature sensor via integration with GaN optoelectronics
<p>Raw data of publication "<strong>Miniature optical fiber curvature sensor via integration with GaN optoelectronics</strong>"</p>
MAVIS adaptive optics system matrices dataset
<p>MAVIS adaptive optics system matrices dataset.</p>
Code and extensive data for training neural networks for radiation, used in "Implementation of a machine-learned gas optics parameterization in the ECMWF Integrated Forecasting System: RRTMGP-NN 2.0""
<p>Data and code used in a paper submitted to JAMES titled :<em> Implementation of a machine-learned gas optics parameterization in the ECMWF Integrated Forecasting System</em></p> <p>1) The files <strong>ml_training_*.7z</strong> contain extensive datasets (in NetCDF format) for training neural network versions of the RRTMGP gas optics scheme as described in the paper. The datasets are read by <a href="https://github.com/peterukk/rte-rrtmgp-nn/blob/main/examples/rrtmgp-nn-training/ml_train.py">ml_train.py.</a></p> <p>2) The ML datasets were in turn generated using the input profiles (in NetCDF format) inside <strong>inputs_to_RRTMGP.zip </strong>by running the Fortran programs <code>rrtmgp_sw_gendata_rfmipstyle.F90 and rrtmgp_lw_gendata_rfmipstyle.F90 </code>in <em>rte-rrtmgp-nn/examples/rrtmgp-nn-training</em>, which call the RRTMGP gas optics scheme, The input profiles contain <strong>millions of columns, hundreds of perturbation experiments (including hypercube-sampled gas concentrations), are derived from several different data sources (including CAMS reanalysis, GCM, and CKDMIP-MMM), and span present-day, preindustrial, and future atmospheric conditions.</strong> They could be used to generate training data for developing emulators of the full RTE+RRTMGP radiation scheme, not just gas optics (see nn_dev on the <a href="https://github.com/peterukk/rte-rrtmgp-nn">RTE+RRTMGP-NN repository on Github</a>, used in a previous paper where different emulation methods were compared)</p> <p>3) The Fortran and Python code used for data generation and NN training are found in<a href="https://github.com/peterukk/rte-rrtmgp-nn/tree/main/examples/rrtmgp-nn-training"> <em>rte-rrtmgp-nn/examples/rrtmgp-nn-training</em> </a>on the main branch on Github; <strong>an archived version is also included here </strong>(<strong>rte-rrtmgp-nn-2.0.zip</strong>). See the readme in the above sub-directory for further information.</p> <p> </p>
Input and Output dataset for "Robustness tests for an optical time scale"
<p>The uploaded dataset includes the data reported in the figures of the pubblication Robustness tests for an optical time scale (V Formichella et al 2022 Metrologia 59 015002, DOI 10.1088/1681-7575/ac3801, Euramet Pubblication REPRef-2989)</p>
A supramolecular cucurbit[8]uril-based rotaxane chemosensor for the optical tryptophan detection in human serum and urine
<p>Dataset of the publication data. Raw and analyzed data included in each excel file.</p>
Optical trapping of micro-particles and bacterial cells in single channel and flow-focusing microfluidic devices
<p><strong>Video 1</strong> - The video shows the flow-focusing and trapping of 1.84 μm bacteria-sized particles flowing at a sample flow rate of 0.1 μL/min. The horizontal sheath flow rate 1 μL/min and the vertical sheath flow rate is 0.5 μL/min. Trapping is achieved using a maximum laser power of 250mW. </p> <p><strong>Video 2</strong> - The video shows the flow and fluorescence trapping of 1.84 μm bacteria-sized particles flowing at a flow rate of 0.013 μL/min. The channel surface is treated with pluronic F-127 to prevent cell adhesion. </p> <p><strong>Video 3</strong> - The video shows the flow and trapping of 1.84 μm bacteria-sized particles flowing at a flow rate of 1 μL/min. Increased flow rate results in continuous transient trapping of the cells is achieved at a trapping power of 250mW. The microchannel surface is not treated with pluronic F-127, therefore lot of particles stick to the channel surface. </p> <p><strong>Video 4</strong> - The video shows the flow and trapping of 1.84 μm bacteria-sized particles flowing at a flow rate of 0.013 μL/min. Trapping is achieved at a laser power of 250mW. The channel surface is treated with pluronic F-127 to prevent cell adhesion. </p> <p><strong>Video 5</strong> - The video shows the flow and trapping of <em>E. coli</em> MG1655 flowing at a flow rate of 0.013 μL/min. Trapping is achieved at a laser power of 250mW. The channel surface is treated with pluronic F-127 to prevent cell adhesion. </p> <p><strong>Video 6</strong> - The video shows the flow and trapping of <em>S. aureus</em> 6538 flowing at a flow rate of 0.013 μL/min. Trapping is achieved at a laser power of 250mW. The channel surface is treated with pluronic F-127 to prevent cell adhesion. </p>
Dataset for Optical Axion induction of antiferromagnetic order
<p>This file contains the data for the main figures of the publication "Optical Axion induction of antiferromagnetic order". </p>
Sub-diffusion flow velocimetry with number fluctuation optical coherence tomography
<p>This repository contains raw data and analysis routines of the publication <strong>“<em>Sub-diffusion flow velocimetry with number fluctuation optical coherence tomography</em>”</strong> in Optics Express (doi.org/10.1364/OE.474279<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. Keep in mind that running files with larger time series length may take up to 10 minutes and 2D flow profile analysis may take up to one hour.</p> <p>For 1D depth-resolved measurements each dataset includes diffusion, focus (beam shape) calibration, and flow measurements for different discharge rates, <em>Q</em>. For all measurements time series length is 31000 points and the sampling rate is 5.5 kHz. Diffusion measurements are performed on a static sample with a stationary beam. Focus (waist) calibration measurements are performed by moving the OCT beam over the static sample with a known velocity. Flow measurements are performed on the flowing sample with the stationary beam. Each measurement is averaged 6 times. The analysis process is as follows: Firstly, the beam waist (focus) calibration is performed using the script ‘Beam Shape.py’. For improved accuracy it is preferable to perform several measurements and average beam waist values at every depth. Secondly, the Doppler angle is determined using a flow measurement with the largest discharge rate using the script ‘Doppler Angle.py’. Thirdly, the flow profiles are obtained with predetermined calibration parameters using the script ‘Flow Profile.py’. Finally, the particle number density is calculated using the script ‘Number Density.py’. This requires knowledge of particle size for calculating the theoretical number density values. The particle size can be determined using the script ‘Diffusion.py’. All file names are sufficiently descriptive, showing whether it is diffusion, focus (waist) calibration or flow measurement.</p> <p>For 2D depth and laterally resolved measurements each dataset includes diffusion, focus (beam shape) calibration, M-scan and B-scan flow measurements for different discharge rates, <em>Q</em>. Diffusion and focus calibration measurements are same as in 1D. M-scan flow measurements are performed on a flowing sample with a stationary beam. They are same as flow measurements in 1D and are only used for determining the Doppler angle. B-scan flow measurements are performed by moving the OCT beam over the flowing sample with a known velocity. 2D flow profiles can be determined using the script ‘2D Flow Profile.py’. The table below summarizes all datasets and Python scripts uploaded to this repository.</p> <table align="center"> <tbody> <tr> <td> <p><strong>Name</strong></p> </td> <td> <p><strong>Usability</strong></p> </td> <td> <p><strong>Description</strong></p> </td> </tr> <tr> <td> <p>Dataset, 15-03-2022.zip</p> </td> <td> <p>1D measurements</p> </td> <td> <p>Dataset for Doppler angle of 0.34 deg and alignment angle of 0 deg.</p> </td> </tr> <tr> <td> <p>Dataset, 16-03-2022.zip</p> </td> <td> <p>1D measurements</p> </td> <td> <p>Dataset for Doppler angle of 1.74 deg and alignment angle of 2.3 deg.</p> </td> </tr> <tr> <td> <p>Dataset, 22-03-2022.zip</p> </td> <td> <p>1D measurements</p> </td> <td> <p>Dataset for Doppler angle of 1.00 deg and alignment angle of 1.15 deg.</p> </td> </tr> <tr> <td> <p>Dataset, 08-07-2022.zip</p> </td> <td> <p>2D measurements</p> </td> <td> <p>Dataset for Doppler angle of 1.84 deg and alignment angle of 0 deg.</p> </td> </tr> <tr> <td> <p>Chirp.data</p> </td> <td> <p>All measurements</p> </td> <td> <p>File containing k-interpolation data</p> </td> </tr> <tr> <td> <p>ReadOCTFile.py</p> </td> <td> <p>All measurements</p> </td> <td> <p>Written by Jos de Wit, this module reads and imports spectra from raw OCT files.</p> </td> </tr> <tr> <td> <p>Processing.py</p> </td> <td> <p>All measurements</p> </td> <td> <p>This module contains all analysis and processing routines.</p> </td> </tr> <tr> <td> <p>Diffusion.py</p> </td> <td> <p>All measurements</p> </td> <td> <p>This script determines particle size from raw OCT spectra.</p> </td> </tr> <tr> <td> <p>Beam Shape.py</p> </td> <td> <p>All measurements</p> </td> <td> <p>This script determines axial beam shape from raw OCT spectra.</p> </td> </tr> <tr> <td> <p>Doppler Angle.py</p> </td> <td> <p>All measurements</p> </td> <td> <p>This script determines Doppler angle from raw OCT spectra.</p> </td> </tr> <tr> <td> <p>Flow Profile.py</p> </td> <td> <p>1D measurements</p> </td> <td> <p>This script determines flow profiles from raw OCT spectra.</p> </td> </tr> <tr> <td> <p>Number Density.py</p> </td> <td> <p>1D measurements</p> </td> <td> <p>This script determines particle number density raw OCT spectra.</p> </td> </tr> <tr> <td> <p>2D Flow Profile.py</p> </td> <td> <p>2D measurements</p> </td> <td> <p>This script determines 2D flow profiles from raw OCT spectra.</p> </td> </tr> </tbody> </table> <p> </p>
Dataset related to the publication "Optical realization of the pascal - Characterization of two gas modulated refractometers"
<p>The data set consists of; The published paper, all figures that present measurement or simulation data in .png and .fig format and the underlying data plotted in the figures in text format. The published plots were generated from the fig files. The text files were generated by reading the plotted data from the fig files. The files are named Fig_XX were XX corresponds to the figure number in the publication. The format of the text file is as follows. Before every data set there is a header consisting of; The number of the subplot where the data is plotted (Plot: XX), the number of the data set in the sub plot (DataSet: XX), and the color of the line or marker in the plot (Color: XX). The description of what each color represents can be found in the publication.</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.