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2,649 results for “optics”
Investigating the geometrical and optical properties of the persistent stratospheric aerosol layer observed over a Southern European lidar station during 2019
<p>Simulted aerosol extinction at 550nm profiles over Thessaloniki by the IFS-CB05-BASCOE-GLOMAP system.</p>
Optical polarimetric observations of the TDE AT 2020mot with RoboPol
<p>The dataset contains raw FITS images of the tidal disruption event <a href="https://www.wis-tns.org/object/2020mot">AT 2020mot</a> (Gaia20ead) and raw FITS images of a polarization standard stars that can be used for the instrumental polarization correction. The dataset was obtained with the <a href="http://robopol.org/">RoboPol</a> optical polarimeter in the R-band mounted at the 1.3 m telescope of the Skinakas Observatory, Greece. The data were collected between 7 September and 31 October 2020.</p>
Estimation of the Optical Feedback Factor C
<p>The dataset contains the estimated Optical Feedback factor value C for different experimental setups (i.e. laser-target distances, laser diode driving currents) as well as the estimated amplitude of the optical feedback interference signals. The calculations of C were performed using the non-uniform sampling approach [1].</p> <p>The raw data is provided in text format that is readable with Matlab for instance. The excel files summarize the estimated C and amplitude values for each set.</p> <p>[1] Bernal, O. D., Zabit, U., Jayat, F., & Bosch, T. (2021). <a href="https://www.mdpi.com/1424-8220/21/10/3528">Toward an Estimation of the Optical Feedback Factor C on the Fly for Displacement Sensing</a>. <em>Sensors</em>, <em>21</em>(10), 3528.</p>
Acoustic optical survey data for snapper survey in shark bay July 2020
<p>Dataset to accompany Scoulding et al. 2023. Estimating abundance of fish associated with structured habitats by combining<strong> </strong>acoustics and optics. Journal of Applied Ecology.</p> <p>The dataset includes:</p> <p>1. Acoustic integration outputs from Echoview</p> <p>2. Snapper lengths from RUV deployments</p> <p>3. Snapper lengths from commercial catch</p> <p>4. Fish species length-weight relationships</p> <p>5. Habitat validation determined from camera deployments</p> <p>6. Proportions of fish species determined per RUV deployment</p> <p>Raw data files (acoustic and optics) are too large for inclusion in this repository but can be provided on request. The Python code used to analysis the data is being packaged and will be added to the repository once complete.</p>
Tailoring optical properties of 2D semiconductors in van der Waals heterostructures
<p>Dataset for the publication 'Tailoring the dielectric screening in WS<sub>2</sub>-graphene heterostructures'</p>
Time-Resolved Plasmon-Assisted Generation of Arbitrary Optical-Vortex Pulses - Supporting Information for Trajectories
<p>We provide videos of trajectories for a test charge, bound by Lennard-Jones potential. First video, titled "PW Trajectory - Point 2 " refers to the particle under the effect of a plane wave pulse. The other video , titled "Emitter Trajectory - Point 2" refers to the particle affected by an orbital angular momentum carrying pulse.</p> <p>These videos are supplementary materials for the article titled "Time-Resolved Plasmon-Assisted Generation of Arbitrary Optical-Vortex Pulses".</p>
Rapid decline of aerosol absorption coefficient and aerosol optical properties effects on radiative forcing in urban areas of Beijing from 2018 to 2021
<p>data for Rapid decline of aerosol absorption coefficient and aerosol optical properties effects on radiative forcing in urban areas of Beijing from 2018 to 2021</p>
Dataset of "Challenging Point Scanning across Electron Microscopy and Optical Imaging using Computational Imaging"
<p>Dataset containing the jupyter notebook with codes for the simulation of the structured illumination patterns used for image reconstruction (the simulation parameters have been optimized to make sure that the patterns were almost identical to the experimental ones), the reconstruction algorithms. Moreover, there are three experimental dataset saved as txxt file, where each line contains the six biases applied to the electron modulator and the intensity measured by the single pixel detector that we used.</p>
Dataset of "Near-real-time diagnosis of electron optical phase aberrations in scanning transmission electron microscopy using an artificial neural network"
<p>Dataset containing the jupyter notebook used to construct the database of image, to model and train ANN and to analyze the experimental data. Furthermore there are also a reduced database of 100 images that can be utilized to test the ANN, the h5 file containing the ANN weigths and other supporting files.</p>
Structural colour in the bacterial domain: the ecogenomics of an optical phenotype
<p>Structural colour is an optical phenomena resulting from light interacting with nanostructured materials. Although structural colour is widespread in the tree of life, the underlying genetics and genomics are not well understood. Here we collected and sequenced a set of 87 structurally coloured bacterial isolates, and 30 related strains lacking SC. Optical analysis of colonies indicated that diverse bacteria from at least two different phyla (Bacteroidetes and Proteobacteria) can create two dimensional packing capable of producing SC. Genome-wide association approaches were used to identify genes associated with structural colour. The biosynthesis of uroporphyrin and pterins, as well as carbohydrate utilisation and metabolism, were found to be involved. Using this information, we constructed a classifier to predict structural colour directly from bacterial genome sequences, validated it by scoring 100 strains that were not involved in creating the structural colour classifier, and predicted that photonic structures are widely distributed within Gram-negative bacteria. Analysis of over 13 thousand assembled metagenomes predicted that SC is nearly absent from most habitats associated with multicellular organisms except macroalgae and is abundant in marine waters and surface/air interfaces. This work provides the first large-scale ecogenomics view of structural colour in bacteria and identifies microbial pathways and evolutionary relationships that underlie this optical phenomenon.</p>
O2-O2, SO2, BrO, and IO differential slant column densities (dSCDs) measured by the University of Colorado Multi-AXis Differential Optical Absorption Spectroscopy (CU MAX-DOAS) instrument at Maido Observatory during April 29, 2018 and May 4, 2018
<p>Description: O<sub>2</sub>-O<sub>2</sub>, SO<sub>2</sub>, BrO, and IO differential slant column densities (dSCDs) measured by the University of Colorado Multi-AXis Differential Optical Absorption Spectroscopy (CU MAX-DOAS) instrument at Maido Observatory during April 29, 2018 and May 4, 2018.</p> <p>Instrument: University of Colorado Multi-AXis Differential Optical Absorption Spectroscopy (CU MAX-DOAS)<br> Instrument reference: Coburn et al. (2011); doi:10.5194/amt-4-2421-2011<br> Instrument contact: Christopher F. Lee (christopher.f.lee@colorado.edu)<br> Instrument PI: Rainer Volkamer (rainer.volkamer@colorado.edu)<br> <br> Measurement site: Maido Observatory, Reunion Island<br> Longitude: 55.384 degrees East<br> Latitude: 21.080 degrees South<br> Altitude: 2160 meters above sea level<br> Azimuth angle: Approximately 100 degrees clockwise from north<br> <br> The detection limit is defined as (2*Measured RMS) / (Maximum differential absorption cross section), where RMS = root-mean-square noise of spectral signal not accounted for by DOAS fit parameters [optical density units]. The maximum differential absorption cross sections used are 7.0e-21 [cm<sup>2</sup>] for SO<sub>2</sub>, 2.6e-17 [cm<sup>2</sup>] for BrO, and 3.5e-17 [cm<sup>2</sup>] for IO. Detection limits for SO<sub>2</sub> dSCDs, BrO dSCDs, and IO dSCDs are only reported during periods of significant SO<sub>2</sub> detection. BrO to SO<sub>2</sub> ratios are only reported during periods when both BrO dSCDs and SO<sub>2</sub> dSCDs are above the detection limit.</p> <p>Local time (RET) is UTC+4.<br> <br> Column 1: UTC start datetime (yyyy-mm-dd HH:MM:SS)<br> Column 2: UTC center datetime (yyyy-mm-dd HH:MM:SS)<br> Column 3: UTC stop datetime (yyyy-mm-dd HH:MM:SS)<br> Column 4: Elevation angle above the horizon (degrees)<br> Column 5: O<sub>2</sub>-O<sub>2</sub> dSCD [molec<sup>2</sup> cm<sup>-5</sup>]<br> Column 6: Spectral fit error for O<sub>2</sub>-O<sub>2</sub> dSCD [molec<sup>-2</sup> cm<sup>-5</sup>]<br> Column 7: SO<sub>2</sub> dSCD [molec cm<sup>-2</sup>]<br> Column 8: Spectral fit error for SO<sub>2</sub> dSCD [molec cm<sup>-2</sup>]<br> Column 9: Detection limit for SO<sub>2</sub> dSCD [molec cm<sup>-2</sup>]<br> Column 10: BrO dSCD [molec cm<sup>-2</sup>]<br> Column 11: Spectral fit error for BrO dSCD [molec cm<sup>-2</sup>]<br> Column 12: Detection limit for BrO dSCD [molec cm<sup>-2</sup>]<br> Column 13: IO dSCD [molec cm<sup>-2</sup>]<br> Column 14: Spectral fit error for IO dSCD [molec cm<sup>-2</sup>]<br> Column 15: Detection limit for IO dSCD [molec cm<sup>-2</sup>]<br> Column 16: Ratio of BrO dSCDs to SO<sub>2</sub> dSCDs<br> Column 17: Error in ratio of BrO dSCDs to SO<sub>2</sub> dSCDs</p>
Dataset of "Single-Pixel Imaging in Space and Time with Optically Modulated Free Electrons"
<p>This dataset contains the simulated spatial images and temporal profiles reconstructed using the Electron Single-Pixel Imaging where free-electrons are shaped by light pulses. The reconstruction is performed using two different basis (Hadamard and Fourier) and for three different light frequency cutoffs. Some of these data and images are published in https://doi.org/10.1021/acsphotonics.3c00047. </p>
Data set from "A free-space interferometer design for optical frequency dissemination and out-of-loop characterization below the 10^{-21}-level"
<p>The data set contains the data underlying the improved out-of-loop interferometer layout performance evaluation published in Photonics Research (<a href="https://doi.org/10.1364/PRJ.485899">https://doi.org/10.1364/PRJ.485899</a>). The experimental setup and the methodology used is explained in that publication.</p> <p>The data is stored in the Matlab(R)-native file format. This proprietary file format is also readable by other numerical computing environments.</p> <p><br> The files 'data_i_*_S*.mat' contain the timeseries of the analysed continuous measurement runs in configuration S*. Each of these files includes the following variables:</p> <p>year, month, day, hour, minute, second: date at which the measurment point was acquired<br> rem_float, rem: observed 1s Lambda-averaged out-of-loop frequency deviation in Hz as float and string, respectively<br> p: measured air pressure in hPa<br> T: measured laboratory temperature inside the cover close to the interferometer in °C<br> pressure_phase_OOL: phase variations of the out-of-loop signal estimated from the measured pressure variations<br> temp_phase_OOL: phase variations of the out-of-loop signal estimated from the measured temperature variations</p> <p> </p> <p>Different barometers have been used for the pressure mesaurements. In the measurement runs for the S1 and S2PM configurations, a barometer placed in a neighboring laboratory in the same building at PTB was used to characterize pressure fluctuations. For the measurement in the S2 configuration, we used air pressure data from the climate station of the department of Hydrology and River Basin Management of the Technical University Braunschweig, which is ≈6km apart. At times of overlapping operation, we have observed matching pressure instabilities of both barometers for averaging times 𝜏>1000s, which shows that on these averaging times the exact placement of the barometer is of lesser importance.</p>
The Role Of The Intraoperative Optical Coherence Tomography For Vitreoretinal Surgery In A Real-Life Setting
<p>Intraoperative coherence tomography represents a useful tool for managing several eye surgical challenges. Indeed, it provides surgeons with a previously unreachable source of information. The use of OCT revolutionized the diagnosis and treatment of vitreoretinal diseases and is currently an indispensable tool in this field. In this paper, we report our experience using Intraoperative coherence tomography in dealing with vitreoretinal diseases in a real-life setting.</p> <p> </p> <p> </p>
On-demand entanglement of molecules in a reconfigurable optical tweezer array
<div> <div> <div> <div> <div> <div> <p>Entanglement is crucial to many quantum applications including quantum information processing, quantum simulation, and quantum-enhanced sens- ing. Because of their rich internal structure and interactions, molecules have been proposed as a promising platform for quantum science. Determinis- tic entanglement of individually controlled molecules has nevertheless been a long-standing experimental challenge. Here we demonstrate on-demand en- tanglement of individually prepared molecules. We deterministically create Bell pairs of molecules by using the electric dipolar interaction between polar molecules prepared using a reconfigurable array of optical tweezer traps. Our results demonstrate the key building blocks needed for quantum applications and may advance quantum-enhanced fundamental physics tests using trapped molecules.</p> </div> </div> </div> </div> </div> </div>
Dataset associated with publication "Direct observation of coherence transfer and rotational-to-vibrational energy exchange in optically centrifuged CO2 super-rotors" to be published in Nature Communications
<p>This dataset contains all data to compose figures in the associated manuscript. Some of the images are presented in MatLab .mat files. If a different format is needed, please contact the corresponding author. </p>
Soil temperature profiles, measured using a coil-shaped fiber-optic distributed temperature sensor
<p>Measurements of soil temperature temperature profile, by reference sensors and a coil-shaped fiber optic distributed temperature sensor.</p> <p>Retrieved at the Speulderbos measurement site, 52.251048 N, 5.690061 E.</p> <p> </p> <p>A full description can be found in:</p> <p>Schilperoort, B. (2022). <em>Heat Exchange in a Conifer Canopy: A Deep Look using Fiber Optic Sensors</em> [Delft University of Technology]. https://doi.org/10.4233/uuid:6d18abba-a418-4870-ab19-c195364b654b</p>
Characterization of two electronic subsystems in cuprates through optical conductivity
<p>Data sets for original figures in the article titled 'Characterization of two electronic subsystems in cuprates through optical conductivity' published in <a href="https://doi.org/10.1103/PhysRevB.107.144515">Physical Review B <strong>107</strong>, 144515 (2023)</a>. The file name of each xls file corresponds to the figure number in the published article. The files can be opened using the Excel program. If there are sub-figures, or multiple frames in each figure, the data of each sub-figure is stored in separate sheets within one xls file.</p>
A Multimodal Dataset on Stainless Steel for Electrochemical Corrosion Studies: Optical Microscopy and Linear Sweep Voltammetry
<p>The upload includes optical and electrochemical data for corrosion experiments.</p> <p>This dataset presents the results of an experimental study conducted to investigate the electrochemical behavior of electropolished Stainless Steel 316L (SS316L) samples immersed in NaCl solutions. The combination of Linear Sweep Voltammetry (LSV) and optical microscopy techniques was employed to gather comprehensive insights into the electrochemical processes occurring on the surface of the stainless steel samples.</p> <p>The samples used in the experiment were electropolished SS316L, chosen for its widely recognized corrosion resistance properties and frequent application in various industrial sectors. LSV was performed on the samples in a potential range of -0.5V to 1.35V, (vs 3.4M KCl Ag/AgCl). NaCl solutions with concentrations of 5mM, 10mM, and 50mM were prepared to simulate different electrolyte conditions.</p> <p>Two different scan rates, 50mV/s and 100mV/s, were applied during the LSV experiments to observe the effect of varying scan rates on the electrochemical behavior of the SS316L samples. The scan rates were chosen to cover a range commonly encountered in electrochemical studies.</p> <p>List of experiments:</p> <ul> <li> 5 mM solution, 100mV/s scan rate</li> <li> 10 mM solution, 50mV/s scan rate</li> <li> 10 mM solution, 100mV/s scan rate</li> <li> 50 mM solution, 50mV/s scan rate</li> <li> 50 mM solution, 100mV/s scan rate</li> </ul> <p>The dataset is accompanied by animated plots. The top left plot shows electrochemistry data, bottom left - average normalized intensity and derivative of intensity. Top right - original optical images, bottom right - normalized images.</p> <p>The scale for optical images: 1px = 480 nm. Axes on images are in pixels</p> <p>Jupyter notebook with the code, used to create videos included. We recommend opening the Jupyter notebook file in a Python 3 environment.<br> </p>
Simulated top-of-atmosphere (120 km) downward and upward solar and thermal-infrared irradiances and ice cloud optical thickness; calculated solar, TIR and net cloud radiative effect. Simulated with ice crystal properties for aggregates, droxtals, and plates based on Yang (2013).
<p>This dataset consists of three .nc files for ice crystal shapes of aggregates, plates, and droxtals. The files include ice cloud optical thickness <span class="math-tex">\(\tau\)</span> (550nm), the simulated upward and downward irradiances <span class="math-tex">\(F\)</span> at the top-of-atmosphere (with and without the presence of the ice cloud), and the calculated ice cloud radiative effect <span class="math-tex">\(\Delta F\)</span> (solar [0.3-3.5 <span class="math-tex">\(\mu\)</span>m], thermal-infrared [3.5-75 <span class="math-tex">\(\mu\)</span>m], and net). The data set allows the user to extract <span class="math-tex">\(\Delta F\)</span> values for their parameter combinations. The available cloudy and cloud-free irradiances further allow to calculate the cirrus radiative effect (RE) by scaling the 'cloudy' RE with the required cloud cover. This serves as a first-approximation because, as 3D effects are neglected.</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.