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2,649 results for “optics”
Optical Flow FLDAS Climate Velocity for 2001-2021
<p><span>Climate velocity estimated using an optical flow method using global surface temperature data of the NASA FLDAS model at 0.1</span><span>×</span><span>0.1-degree grid for 2000-2021</span></p>
Reduced optical data for Bayesian model trial
<p>The dataset is a subset of a few selected optical indices from the column experiment. </p> <p>The dataset contains all the observations after the column reversal, with day00 indicating the day before the reversal where all the columns are samplead and day0 indicating the day of the reversal. It does not contain any of the control measurements before day00. </p> <p>From the absorbance and fluoresence measurements, we have calculated several indices among which bix (biological index), fi (fluoresence index), hix, (humification index), a254 (decadal absorption coefficient at 254 nm) ,E2_E3 (ratio of absorbance E2 to E3) , SR (slope ratio). All of the indices are calcuated using the StaRdom package from the raw data. </p> <p>Sample name is in the samplingDay_replicate_columnNo format. </p>
Dataset for "Year-long optical time scale with sub-nanosecond capabilities"
<p>Dataset of the realization of an optical time scale (OTS) based on an hydrogen maser and an ytterbium optical lattice clock. Results discussed in Formichella et al., Year-long optical time scale with sub-nanosecond capabilities, <a href="https://doi.org/10.1364/OPTICA.509706">Optica 11, 4, 523 (2024)</a>.</p> <p> </p>
Deciphering the impacts of main inflowing rivers on dissolved organic matter in Lake Daye using isotopes, optical spectroscopy, and FT-ICR-MS during non-flood season
<p>The uploaded data include water quality data, isotopes, DOM fluorescence index and FT ICR MS data of Daye Lake and its inflowing rivers.</p>
Data for "Measuring mean radiant temperature for indoor comfort assessment using low-resolution optical sensors"
<p>Data for "Measuring mean radiant temperature for indoor comfort assessment using low-resolution optical sensors".</p>
Magic running and standing wave optical traps for Rydberg atoms - Data and code for analysis
<p>Data, theory calculation and plotting scripts for the publication titled "Magic running and standing wave optical traps for Rydberg atoms" (<a href="https://arxiv.org/abs/2410.20901" target="_blank" rel="noopener">arXiv:2410.20901</a>).</p> <p> </p> <p><strong>File legend</strong></p> <ul> <li> <code>data_FIGx_yyy.mat</code> contains the calculated or measured data used in Figure x</li> <li> <code>calc_FIGx_yyy.py</code> is the script to calculate the theoretical data used in Figure x</li> <li> <code>plot_FIGx_yy.py</code> is the script to create the Figure x of the paper</li> <li> <code>simulation_class.py</code> is a class with theory functions</li> <li> <code>paperstyle.mplstyle</code> is a matplotlib style file</li> <li> <code>requirements.txt</code> lists all the required python packages</li> </ul> <p> </p> <p><strong>Abstract</strong></p> <p>Magic trapping of ground and Rydberg states, which equalizes the AC Stark shifts of these two levels, enables increased ground-to-Rydberg state coherence times. We measure via photon storage and retrieval how the ground-to-Rydberg state coherence depends on trap wavelength for two different traps and find different optimal wavelengths for a 1D optical lattice trap and a running wave optical dipole trap. Comparison to theory reveals that this is caused by the Rydberg electron sampling different potential landscapes. The observed difference increases for higher principal quantum numbers, where the extent of the Rydberg electron wave function becomes larger than the optical lattice period. Our analysis shows that optimal magic trapping conditions depend on the trap geometry, in particular for optical lattices and tweezers.</p> <p> </p> <p><strong>Theory calculation</strong></p> <p>We implemented the potential arising from the Hamiltonians described in the paper. The functions are shared here in the python class <code>simulation_class.py</code>. This class is used in the calculation scripts named <code>calc_FIGx_yyy.py</code> and saves the data as <code>data_FIGx_yyy.mat</code> for the respective Figure x.</p> <p>In case of questions to the code or calculations, please contact Chris Nill or Lukas Ahlheit.</p> <p> </p> <p><strong>Experimental data</strong></p> <p>The experimental data published here are photon storage and retrieval traces of 780 nm probe photons as function of storage duration. We recorded photon traces for different trap laser detunings and Rydberg states.</p> <p>In case of questions to the data, please contact Lukas Ahlheit or Sebastian Hofferberth.</p> <p> </p> <p><strong>Inkscape modification to specific figures</strong></p> <ul> <li>Figure 1: The plotted data is joined in Inkscape with schematic drawings</li> <li>Figure 2: The plot created by the python file is edited in Inkscape for readability</li> <li>Figure 5: We add two schematics into the figure created by the python file</li> </ul>
FAIRmat Tutorial 15: Use of pynxtools with Examples from Optical Spectroscopy
<p>The FAIRmat Tutorial 15 will address the necessity of FAIR research data management when working with experimental data in materials science. FAIRmat provides NOMAD (https://nomad-lab.eu/nomad-lab/) to the scientific community as a platform specifically developed for this purpose. </p> <p>NOMAD integrates the NeXus Ontology based on the NeXus community standard (https://www.nexusformat.org/). The NeXus standard has been significantly expanded over the years and now includes a comprehensive range of metadata definitions, making it applicable to various experimental techniques used in materials science. </p> <p>FAIRmat, in collaboration with the scientific community and technology partners, has developed pynxtools. These software tools simplify the conversion of experimental data and metadata according to the community standard, making it easy to integrate experimental data into NOMAD. </p> <p>This tutorial will cover using the pynxtools and how such datasets are managed within NOMAD. To demonstrate the functionality of pynxtools in combination with NOMAD, we will use ellipsometry and Raman spectroscopy data as examples.</p> <p>The main topics to be covered are: <br>• FAIR research data management <br>• NeXus data modelling <br>• Data conversion and verification using pynxtools<br>• Data management with NOMAD </p> <p>Disclaimer: NOMAD is being continuously developed based on input and feedback from the scientific community. Hence the features, services or interface may have changed since the time of recording of this video. For up-to-date information please consult our latest tutorials and the NOMAD documentation https://nomad-lab.eu/prod/v1/docs/</p>
Electronic and optical properties of computationally Predicted Na-K-Sb crystals
<p>This is the dataset for the identically-named paper "Electronic and optical properties of computationally predicted Na-K-Sb crystals".</p> <p>This dataset contains output files from exciting for the two considered polymorphs: hexagonal Na2KSb (hNa2KSb.zip) and cubic NaK2Sb (cNaK2Sb.zip). Inside each zip file contains:</p> <ul> <li>INFO.OUT, INFOXS.OUT, GW_INFO.OUT, INFO_SCR.OUT</li> <li>Lattice-optimised input.xml files for both crystals</li> <li>BAND_Sxx_Ayyyy.OUT files for projected bandstructures</li> <li>PDOS_Sxx_Ayyyy.OUT files for projected density of states</li> <li>BAND.OUT, BANDLINES.OUT, BAND-QP.OUT, IDOS.OUT and TDOS/TDOS-QP.OUT for DOS</li> <li>EFERMI.OUT and EIGVAL.OUT</li> <li>EPSILON files from the dielectric tensor with/without the TDA and IQPA</li> <li>EXCITON files from the dielectric tensor with/without the TDA and IQPA</li> <li>KPATH files for considered excitons with the TDA</li> </ul>
Measuring age-dependent viscoelasticity of organelles, cells and organisms with Time-Shared Optical Tweezer Microrheology
<p>Source data for Nature Nanotechnology, <span>DOI: 10.1038/s41565-024-01830-y</span></p>
Label-free multiplexed detection of diabetic retinopathy biomarkers using fiber optic biosensors: towards lab-in-the-tear
<p>Raw experimental data on label-free detection of diabetic retinopathy biomarkers using fiber optic biosensors. This data contains information on the multiplexed and separate detection of LCN1 and VEGF diabetic retinopathy biomarkers in artificial tears. </p>
Data from: Structured Detection for Simultaneous Super-Resolution and Optical Sectioning in Laser Scanning Microscopy
<p>This repository contains the raw data of the experimental ISM dataset used to make the figures and supplementary figures for the paper entitled <em>Structured Detection for Simultaneous Super-Resolution and Optical Sectioning in Laser Scanning Microscopy.<br></em></p>
A global daily seamless 9-km Vegetation Optical Depth (VOD) product from 2010 to 2021
<p> </p> <p><strong>(I) DESCRIPTION</strong>:</p> <p>· A <strong>global daily seamless 9-km Vegetation Optical Depth (VOD)</strong> product is generated through gap-filling and spatiotemporal fusion model. This daily products start <strong>from Jan 01, 2010 to Jul 31, 2021 </strong>(about 20GB memory after uncompressing all zip files).</p> <p>· To further validate the effectiveness of these products, three verification ways are employed as follow: 1) Time series validation; 2) Simulated missing-region validation; And 3) Data comparison validation.</p> <p>· It is important to note that the original data contain missing dates, and these corresponding gaps are also present in our dataset.</p> <p><strong>(II) DATA FORMATTING AND FILE NAMES </strong></p> <p>For the convenience of our readers, we have two formats of data available for download.</p> <p><strong>1) MAT file (Version v1)</strong></p> <p>Data from 2010 to 2021 are stored separately into folders for the corresponding years, with each folder containing daily `.mat` files. The naming convention for the data is “YYYYXXZZ,” where YYYY is the 4-digit year, XX is the 2-digit month, and ZZ is the 2-digit date. The geographic scope is global and the grid size is 4000*2000.</p> <p>MATFILES (.mat): The folders with matfiles contain individual files for:</p> <p>1. Vegetation Optical Depth: VOD_seamless_9km_ YYYYXXZZ.mat</p> <p>2. Latitude/Longitude: VOD_9km_Coordinates.mat</p> <p><strong>2) NetCDF file (Version v2)</strong></p> <p>The year-by-year daily data from 2010 to 2021 are stored in the ‘.nc’ files for the corresponding years. The daily data within each year into one NetCDF file. <span>The variable names are named as VOD_xxxxyydd, where xxxx represents the year, yy represents the month, and dd represents the day. The longitude variable is named “lon” with a dimension of 4000×1, and the latitude variable is named “lat” with a dimension of 2000×1. </span></p> <p><span>It should be noted that these NetCDF files are saved using the netCDF4 library in Python, with the dimension order being (lat, lon). When reading these NetCDF files in MATLAB, the default data dimension order is (lon, lat). Therefore, it is necessary to transpose the variables to match the correct dimension order.</span></p>
A new, fluorescence-based method for visualizing the pseudopupil and assessing optical acuity in the dark compound eyes of honeybees and other insects
<p>Images reported here are the raw Data set acquired to obtain Figure 6 in the manuscript Rigosi et al, “A new, fluorescence-based method for visualizing the pseudopupil and assessing optical acuity in the dark compound eyes of honeybees and other insects” accepted in Scientific Reports (DOI: 10.1038/s41598-021-00407-2).</p> <p>Please check the Methods section for a description of the analysis of the eye map obtained with these images.<br> Note that all the images are 16-bit.</p>
Data for "Measurement report: Comparison of airborne in-situ measured, lidar-based, and modeled aerosol optical properties in the Central European background – identifying sources of deviations"
<p>A unique set of data is presented, derived from measurements conducted at the rural central European observatory at Melpitz, Germany. Data derived from remote sensing (lidar), airborne platforms (helicopter, balloon), and ground-based in-situ methods is included. Measured and Mie-modeled optical aerosol parameters are presented in the dry- and ambient state. Modeled optical parameters are based on Mie-theory. For ambient state hygroscopic growth simulations are utilized.</p>
Optical properties of mineral dust aerosols with non-absorptive coating: a numerical investigation
<p>Model data for optical calculations of non-absorptive coated mineral dust aerosol</p> <p>This repository provides data used for plots in a manuscript submitted to Optics Express. The folder contains optical data from calculations performed with the T-matrix code. The subdirectory aspect ratio contains results for eleven aspect ratios with the coating ratio equal to 0.5 stored in respective directories. The directory coating ratio contains results for eleven coating ratios with an aspect ratio of 1.37. The directory naming of folders within the subdirectories derives from the parameter choice as indicated in the manuscript.</p>
Computer-aided Veress needle guidance using endoscopic optical coherence tomography and convolutional neural networks
<p>During laparoscopic surgery, the Veress needle is commonly used in pneumoperitoneum establishment. Precise placement of the Veress needle is still a challenge for the surgeon. In this study, a computer-aided endoscopic optical coherence tomography (OCT) system was developed to effectively and safely guide Veress needle insertion. This endoscopic system was tested by imaging subcutaneous fat, muscle, abdominal space, and the small intestine from swine samples to simulate the surgical process, including the situation with small intestine injury. Each tissue layer was visualized in OCT images with unique features and subsequently used to develop a system for automatic localization of the Veress needle tip by identifying tissue layers (or spaces) and estimating the needle-to-tissue distance. We used convolutional neural networks (CNNs) in automatic tissue classification and distance estimation. The average testing accuracy in tissue classification was 98.53±0.39%, and the average testing relative error in distance estimation reached 4.42±0.56% (36.09±4.92 μm).</p> <p>The dataset is split into two parts:<br> (1) <strong>Classification</strong>. The zip file <em>veress_classification_raw_images.zip</em> contains 40K images from 8 swine samples where there are 1K images per layer (skin, fat, muscle, abdominal space, and small intestine)<br> (2) <strong>Regression</strong>. The zip file <em>veress_regression_raw_images.zip</em><strong> </strong>contains 8K images of the abdominal space from the same 8 swine samples, and the ground truth distance labels for each sample are found in the Excel files <em>S[1-8]_distance_measurement_20210803.xlsx.</em></p>
Monthly mean optical depth at 550 nm derived from AERONET data used for model evaluation in GMD-2021-357
<p>Climatological monthly means over 2000-2014 derived from Aerosol Robotic Network version 3 level 2.0 direct sun retrievals (monthly data) used for model evaluation in Myriokefalitakis et al. (2021), doi: 10.5194/gmd-2021-357</p> <p>The netCDF file includes the AOD at the 4 native AERONET wavelengths (440 nm, 670 nm, 870 nm and 1020 nm), as well as the interpolated values at 550 nm used for the evaluation. The statistics stored are calculated over the monthly values for the 15 year period and include: monthly mean, 5th, 50th, and 95th percentile, standard deviation, standard error, number of days available per station and month and number of days where coarse AOD dominates (used as proxy for dusty days). </p> <p>The AERONET retrievals of optical depth were downloaded through the AERONET data download tool (available at: https://aeronet.gsfc.nasa.gov/, last accessed March 28, 2020). We thank the principal investigators and their collaborators for their effort in establishing and maintaining all AERONET sites used in this compilation.</p> <p>The use of this dataset must follow the guidelines of the original data providers at AERONET, explained here: https://aeronet.gsfc.nasa.gov/new_web/data_usage.html</p>
Plasmonic Copper Sulfide Nanoparticles Enable Dark Contrast in Optical Coherence Tomography
<p>Dataset of https://onlinelibrary.wiley.com/doi/10.1002/adhm.201901627</p>
Optical parametric amplification seeded by four-wave mixing in photonic crystal fibres
<p>Open access dataset for figures in 'Optical parametric amplification seeded by four-wave mixing in photonic crystal fibres', accepted for publication in Nonlinear Frequency Generation and Conversion: Materials and Devices XXI, paper 11985-4, SPIE LASE Photonics West, 2022.</p>
Deep tissue localization and sensing using optical microcavity probes
<p>Data and code for publication Deep tissue localization and sensing using optical microcavity probes.</p>
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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.