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2,649 results for “optics”
Inlist file for Optical color of Type Ib and Ic supernovae and implications for their progenitors
<p>Example inlist files for make He and CO progenitor models for the paper submitted to the AAS Journal entitled as "Optical color of Type Ib and Ic supernovae and implications for their progenitors"</p>
DATA REPOSITORY FOR: All-Optical Nuclear Quantum Sensing Using Nitrogen-Vacancy Centers in Diamond
<p><strong>DATA REPOSITORY:<br> ALL-OPTICAL NUCLEAR QUANTUM SENSING USING NITROGEN-VACANCY CENTERS IN DIAMOND</strong></p> <p>This data repository contains the raw data as measured on the experimental setup, the files required to do the data processing we performed on the raw data, the scripts to run the simulations described in the journal article, and the scripts to reproduce the plots shown in the article's figures.<br> <br> Use MatLab R2019b or later to run these files.<br> See ReadMe.txt for more information.</p>
Terahertz-induced anomalous currents following the optical excitation of excitons in semiconductor quantum wells
<p>Dataset of the publication “Terahertz-induced anomalous currents following the optical excitation of excitons in semiconductor quantum wells“, C. Ngo, S. Priyadarshi, H. T. Duc, M. Bieler, and T. Meier, Proc. SPIE 12419, Ultrafast Phenomena and Nanophotonics XXVII, 124190G (2023) ( <a href="https://doi.org/10.1117/12.2646022">https://doi.org/10.1117/12.2646022</a> ). The zip file includes the data on which the plots are based.<br> </p>
Global daily 0.5 deg coarse mode aerosol optical depth (cAOD) from 2001 to 2021
<p>Coarse mode aerosol optical depth (cAOD) is critical for understanding the impact of coarse mode particles on climate, such as dust. Currently, the limited data length and high uncertainty of satellite products impair the applicability of cAOD for climate research. Here, we propose a spatiotemporal co-action deep learning model (SCAM) for the retrieval of global land cAOD (500 nm) from 2001 to 2021 at daily temporal resolution and 0.5° spatial resolution.</p> <p>The cAOD products are uploaded in Geotiff format, stretched from -89.5° to 89.5° latitude and from -179.5° to 179.5° longitude. On certain days the products might be unavailable, due to the missing MODIS satellite data used for calculating cAOD. </p>
Single-Shot Optical Neural Network
<p>Data from classification of MNIST [1], Fashion-MNIST [2] and QuickDraw [3] images reported in "Single-Shot Optical Neural Network" by L. Bernstein et al. The networks of 784 -> N (-> N) -> 10 activations performed inference on the test sets through consecutive matrix products implemented on the optical hardware, with ReLU applied electronically between each layer (see main text for more details). Folders for each tested network contain the following text files:</p> <ul> <li>Inputs: 2D matrices of size B x 784 containing training (B = 50,000 or 100,000), validation (B = 10,000) and test (B = 10,000) sets. Each row is an input vector that can be reshaped into an input image of size 28 x 28.</li> <li>True labels: B-length vectors containing the true label of each input in the training, validation and test sets.</li> <li>Neural network weights: 2D matrices of size K x N used in inference experiments to classify the test sets. Weights were pre-trained on the training set using a digital electronic computer as described in Materials and Methods. Weight values were normalized such that all values fall between -255 and 255. In the optical neural network, the weighting SLM displays the absolute values of the weights (rounded to the nearest integer), and the negative weight signs are applied in post-processing. The 32-bit weight values were used for inference performed on the digital electronic computer for the ground truth comparison.</li> <li>Outputs (normalized): 2D output matrices of size 10,000 x 10 from the networks processed on a digital electronic computer (ground truth) and the optical neural network. Each row is an output vector where the position of the maximum value indicates the predicted label of the input in the same row of the test set.</li> <li>Predicted labels: 10,000-element vectors that represent the labels predicted by the networks processed on a digital electronic computer (ground truth) and the optical neural network. These predicted labels were used to generate the confusion matrices and calculate the classification accuracies (versus the true labels).</li> </ul> <p>The classes for the Fashion-MNIST dataset are the following:</p> <ul> <li>0: T-shirt</li> <li>1: Trouser</li> <li>2: Pullover</li> <li>3: Dress</li> <li>4: Coat</li> <li>5: Sandal</li> <li>6: Shirt</li> <li>7: Sneaker</li> <li>8: Bag</li> <li>9: Ankle boot</li> </ul> <p>And the randomly selected classes for QuickDraw are:</p> <ul> <li>0: Hourglass</li> <li>1: Saw</li> <li>2: Golf club</li> <li>3: See saw</li> <li>4: Spoon</li> <li>5: Horse</li> <li>6: Onion</li> <li>7: Light bulb</li> <li>8: Harp</li> <li>9: Flip flops</li> </ul> <p>[1] Y. LeCun, L. Bottou, Y. Bengio, P. Haffner, Gradient-based learning applied to document recognition. <em>Proceedings of the IEEE</em> <strong>86</strong>, 2278–2324 (1998).</p> <p>[2] H. Xiao, K. Rasul, R. Vollgraf, Fashion-MNIST: a novel image dataset for benchmarking machine learning algorithms. Preprint at https://arxiv.org/abs/1708.07747 (2017).</p> <p>[3] J. Jongejan, H. Rowley, T. Kawashima, J. Kim, N. Fox-Gieg, The Quick, Draw! AI experiment, https://quickdraw.withgoogle.com/ (2016).</p>
FIGURE 2–5. Optical microscope view. 2 in Specificity of fleas associated with opossums in a landscape gradient in the Paranaense Rainforest Ecoregion
FIGURE 2–5. Optical microscope view. 2) Modified posterior segments of a male specimen of A. (A.) a. ronnai. 3) Detail of the metatibia of Polygenis (P.) r. roberti with seven dorsal notches, the subterminal one with three setae. 4–5) Genitalia and modified posterior segments of 4) female and 5) male specimens of Polygenis (P.) r. roberti.
THz optical solitons from dispersion-compensated antenna-coupled planarized ring quantum cascade lasers
Quantum Cascade Lasers (QCL) constitute an intriguing opportunity for the production of on-chip optical Dissipative Kerr Solitons (DKS): self-organized optical waves which can travel while preserving their shape thanks to the interplay between Kerr effect and dispersion. Originally demonstrated in passive microresonators, DKS were recently observed in mid-IR ring QCL paving the way for their achievement even at longer wavelengths. To this end we realized defect-less THz ring QCLs featuring anomalous dispersion leveraging on a technological platform based on waveguide planarization. A concentric coupled-waveguide approach is implemented for dispersion compensation whilst a passive broadband bullseye antenna improves the device power extraction and far field. In these devices, comb spectra featuring sech2 envelopes are presented for free-running operation. This first hint of the presence of solitons is further supported by the observation of highly hysteretic behaviour and by phase-sensitive measurements which show the presence of self-starting 12 ps-long pulses in the reconstructed time profile of the emission intensity. These observations are in very good agreement with our numeric simulations based on a Complex Ginzburg-Landau equation time-domain solver. Such devices constitute a new experimental platform for the study of soliton phenomena in the THz range, allowing as well on-chip, passive ultrashort THz pulse generation appealing for a variety of applications.
Maximally Error-Tolerant MZI Mesh-based Optical Neural Networks
<p>Codes.zip contains code that trains and tests maximally error-tolerant Mach-Zehnder mesh optical neural networks on MNIST, FashionMNIST, and KMNIST.</p> <p>models.zip contains all the trained model parameters.</p>
Deep Ensemble Learning and Transfer Learning Methods for Classification of Senescent Cells from Nonlinear Optical Microscopy Images
<p>This Dataset contains the train and test NLO images in pickle format used for the following publication: Deep Ensemble Learning and Transfer Learning Methods for Classification of Senescent Cells from Nonlinear Optical Microscopy Images</p>
A Fundus Image Dataset for Domain Generalization in Joint Segmentation of Optic Disc and Optic Cup
<p>We provide a fundus image dataset for domain generalization, which includes 5 different medical centres.<br> This dataset is based on the REFUGE[1] dataset, Drishti-GS[2] dataset, ORIGA[3] dataset, and RIGA[4] dataset. We appreciate their efforts devoted by the authors of [1-4].</p> <table> <caption>Details of this dataset</caption> <tbody> <tr> <td>Domain</td> <td>Cases in Each Domain<br> (Training/Test)</td> </tr> <tr> <td>REFUGE</td> <td>320/80</td> </tr> <tr> <td>Drishti-GS</td> <td>50/51</td> </tr> <tr> <td>ORIGA</td> <td>500/150</td> </tr> <tr> <td>BinRushed (RIGA)</td> <td>156/39</td> </tr> <tr> <td>Magrabia (RIGA)</td> <td>76/19</td> </tr> </tbody> </table> <p>[1] Orlando J I, Fu H, Breda J B, et al. Refuge challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs[J]. Medical image analysis, 2020, 59: 101570.</p> <p>[2] Sivaswamy J, Krishnadas S R, Joshi G D, et al. Drishti-GS: Retinal image dataset for optic nerve head (onh) segmentation[C]//2014 IEEE 11th international symposium on biomedical imaging (ISBI). IEEE, 2014: 53-56.</p> <p>[3] Zhang Z, Yin F S, Liu J, et al. Origa-light: An online retinal fundus image database for glaucoma analysis and research[C]//2010 Annual international conference of the IEEE engineering in medicine and biology. IEEE, 2010: 3065-3068.</p> <p>[4] Almazroa A, Alodhayb S, Osman E, et al. Retinal fundus images for glaucoma analysis: the RIGA dataset[C]//Medical Imaging 2018: Imaging Informatics for Healthcare, Research, and Applications. SPIE, 2018, 10579: 55-62.</p> <p>If you find this dataset useful for your research, please consider citing the paper as follows:</p> <pre><code class="language-markdown">@article{chen2023treasure, title={Treasure in Distribution: A Domain Randomization based Multi-Source Domain Generalization for 2D Medical Image Segmentation}, author={Chen, Ziyang and Pan, Yongsheng and Ye, Yiwen and Cui, Hengfei and Xia, Yong}, booktitle={Medical Image Computing and Computer Assisted Intervention -- MICCAI 2023}, year={2023} }</code></pre> <p> </p>
Data for: Non-linear optical phonon spectroscopy revealing polaronic signatures of the LaAlO3/SrTiO3 interface
<p>We report the direct observation of lattice phonons confined at LaAlO3/SrTiO3 (LAO/STO) interfaces and STO surfaces using the sum-frequency phonon spectroscopy. This interface-specific nonlinear optical technique unveiled phonon modes localized within a few monolayers at the interface, with inherent sensitivity to the coupling between lattice and charge degrees of freedom. Spectral evolution across the insulator-to-metal transition at LAO/STO interface revealed an electronic reconstruction at the sub-critical LAO thickness, as well as strong polaronic signatures upon formation of the two-dimensional electron gas. We further discovered a characteristic lattice mode from interfacial oxygen vacancies, enabling us to probe such important structural defects in situ. Our study provides a new perspective on many-body interactions at the correlated oxide interfaces.</p>
Linear-to-Circular Polarization Conversion with Full-Silica Meta-Optics to Reduce Nonlinear Effects in High-Energy Lasers - Nature Com - Dataset
<p>Data from the Nature Communication paper entitled "Linear-to-Circular Polarization Conversion with Full-Silica Meta-Optics to Reduce<br> Nonlinear Effects in High-Energy Lasers"</p> <p>.OPJU files can be opened with ORIGINLab</p> <p>. MAT files can be opened with MATLAB/SCILAB</p>
Choroidal thickness measurement in central serous chorioretinopathy using swept source optical coherence tomography: an observational study
<p><strong>Choroidal thickness </strong><strong>measurement in central serous chorioretinopathy using swept source optical coherence tomography: an observational study </strong></p>
Time-Resolved Plasmon-Assisted Generation of Arbitrary Optical-Vortex Pulses - Supporting Information - Trajectories
<p>We provide videos of trajectories for a test charge, bound by Lennard-Jones potential. First video, titled "Plane Wave Trajectory " refers to the particle under the effect of a plane wave pulse. The other video , titled "Emitter Trajectory" 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>
Data and Code related to All-optical recreation of naturalistic neural activity with a multifunctional transgenic reporter mouse
<p>Data and code related to the publication "All-optical recreation of naturalistic neural activity with a multifunctional transgenic reporter mouse"</p> <p>Additional code for running online analysis can be found <a href="https://zenodo.org/record/8139926">here</a>, and code for analysis of all-optical calibration and activity recreation experiments can be found <a href="http://doi.org/10.5281/zenodo.8139926">here.</a></p>
Synchronization of spin-driven limit cycle oscillators optically levitated in vacuum
<p>Trajectories of optically levitated particles in vacuum. Trajectories are recorded using quadrant photodiode and ultra-fast CMOS camera. The readme file with more detailed description is added.</p>
Dataset for the paper: Linking the Dynamic PicoProbe Analytical Electron-Optical Beam Line / Microscope to Supercomputers
<p><strong>Dataset</strong></p> <p>This dataset contains the input and output examples used in the publication described below. The hyperspectral folder contains the first example and use case. The spatiotemporal folder contains the second example use case.</p> <p><strong>Contents</strong></p> <p>data/<br> ├── hyperspectral<br> │ ├── VeloxTest-Membranes.emd<br> │ └── example_output<br> │ ├── simulator-0-VeloxTest-Membranes.emd<br> │ ├── simulator-0-VeloxTest-Membranes.json<br> │ └── simulator-0-VeloxTest-Membranes.png<br> └── spatiotemporal<br> ├── 2023081040-700kx MultiFrams 991ms 600F Counting & FFI mode Falcon 700 kx 1819.emd<br> ├── best-yolo.pt<br> └── example_output<br> ├── prediction-simulator-17-2023081040-700kx MultiFrams 991ms 600F Counting & FFI mode Falcon 700 kx 1819.mp4<br> ├── simulator-17-2023081040-700kx MultiFrams 991ms 600F Counting & FFI mode Falcon 700 kx 1819.emd<br> ├── simulator-17-2023081040-700kx MultiFrams 991ms 600F Counting & FFI mode Falcon 700 kx 1819.json<br> └── simulator-17-2023081040-700kx MultiFrams 991ms 600F Counting & FFI mode Falcon 700 kx 1819.mp4<br> </p> <p><strong>Source Code</strong><br> Our code is hosted on GitHub at the following link: <a href="https://github.com/ramanathanlab/PicoProbeDataFlow/tree/main">https://github.com/ramanathanlab/PicoProbeDataFlow/tree/main</a><br> <br> <strong>Paper Name</strong><br> Linking the Dynamic PicoProbe Analytical Electron-Optical Beam Line / Microscope to Supercomputers<br> <br> <strong>Paper Abstract</strong><br> The Dynamic PicoProbe at Argonne National Laboratory is undergoing upgrades that will enable it to produce up to 100s of GB of data per day. While this data is highly important for both fundamental science and industrial applications, there is currently limited on-site infrastructure to handle these high-volume data streams. We address this problem by providing a software architecture capable of supporting large-scale data transfers to the neighboring supercomputers at the Argonne Leadership Computing Facility. To prepare for future scientific workflows, we implement two instructive use cases for hyperspectral and spatiotemporal datasets, which include: (i) off-site data transfer, (ii) machine learning/artificial intelligence and traditional data analysis approaches, and (iii) automatic metadata extraction and cataloging of experimental results. This infrastructure supports expected workloads and also provides domain scientists the ability to reinterrogate data from past experiments to yield additional scientific value and derive new insights.</p>
Optical measurement of the work function and the field reduction factor of metallic needle tips
<p>The dataset belongs to a technique to measure the work function, as well as the factor (k*r_tip), of metallic needle tips in situ by electron emission triggered by continuous laser beams with 405 nm and 488 nm.</p>
Datasets for "Irradiance and cloud optical properties from solar photovoltaic systems" (final version)
<p>This dataset contains all the relevant data for the algorithms described in the paper "<a href="https://amt.copernicus.org/articles/16/4975/2023/">Irradiance and cloud optical properties from solar photovoltaic systems</a>", which were developed within the framework of the <a href="https://www.h-brs.de/de/satelliten-und-meteorologie-unterstuetzte-vorhersage-der-energieerzeugung-von-pv-anlagen-auf">MetPVNet</a> project.</p> <p><strong>Input data:</strong></p> <ol> <li><a href="http://www.cosmo-model.org/">COSMO</a> weather model data (DWD) as NetCDF files (cosmo_d2_2018(9).tar.gz) <ol> <li>COSMO atmospheres for <a href="http://www.libradtran.org/">libRadtran</a> (cosmo_atmosphere_libradtran_input.tar.gz)</li> <li>COSMO surface data for calibration (cosmo_pvcal_output.tar.gz)</li> </ol> </li> <li><a href="https://aeronet.gsfc.nasa.gov/">Aeronet</a> data as text files (MetPVNet_Aeronet_Input_Data.zip)</li> <li>Measured data from the <a href="https://www.h-brs.de/de/satelliten-und-meteorologie-unterstuetzte-vorhersage-der-energieerzeugung-von-pv-anlagen-auf">MetPVNet</a> measurement campaigns as text files (MetPVNet_Messkampagne_2018(9).tar.gz) <ol> <li>PV power data</li> <li>Horizontal and tilted irradiance from pyranometers</li> <li>Longwave irradiance from pyrgeometer</li> </ol> </li> <li>MYSTIC-based lookup table for translated tilted to horizontal irradiance (gti2ghi_lut_v1.nc)</li> </ol> <p><strong>Output data:</strong></p> <ol> <li>Global tilted irradiance (GTI) inferred from PV power plants (with calibration parameters in comments) <ol> <li>Linear temperature model: MetPVNet_gti_cf_inversion_results_linear.tar.gz</li> <li>Faiman non-linear temperature model: MetPVNet_gti_cf_inversion_results_faiman.tar.gz</li> </ol> </li> <li>Global horizontal irradiance (GHI) inferred from PV power plants <ol> <li>Linear temperature model: MetPVNet_ghi_inversion_results_linear.tar.gz</li> <li>Faiman non-linear temperature model: MetPVNet_ghi_inversion_results_faiman.tar.gz</li> </ol> </li> <li>Combined GHI averaged to 60 minutes and compared with COSMO data <ol> <li>Linear temperature model: MetPVNet_ghi_inversion_combo_60min_results_linear.tar.gz</li> <li>Faiman non-linear temperature model: MetPVNet_ghi_inversion_combo_60min_results_faiman.tar.gz</li> </ol> </li> <li>Cloud optical depth inferred from PV power plants <ol> <li>Linear temperature model: MetPVNet_cod_cf_inversion_results_linear.tar.gz</li> <li>Faiman non-linear temperature model: MetPVNet_cod_cf_inversion_results_faiman.tar.gz</li> </ol> </li> <li>Combined COD averaged to 60 minutes and compared with COSMO and APOLLO_NG data <ol> <li>Linear temperature model: MetPVNet_cod_inversion_combo_60min_results_linear.tar.gz</li> <li>Faiman non-linear temperature model: MetPVNet_cod_inversion_combo_60min_results_faiman.tar.gz</li> </ol> </li> </ol> <p><strong>Validation data:</strong></p> <ol> <li>COSMO cloud optical depth (cosmo_cod_output.tar.gz)</li> <li>APOLLO_NG cloud optical depth (MetPVNet_apng_extract_all_stations_2018(9).tar.gz)</li> <li>COSMO irradiance data for validation (cosmo_irradiance_output.tar.gz)</li> <li><a href="https://www.soda-pro.com/web-services/radiation/cams-radiation-service">CAMS</a> irradiance data for validation (CAMS_irradiation_detailed_MetPVNet_MK_2018(9).zip)</li> </ol> <p><strong>How to import results:</strong></p> <p>The results files are stored as text files ".dat", using Python multi-index columns. In order to import the data into a Pandas dataframe, use the following lines of code (replace [filename] with the relevant file name):</p> <p>import pandas as pd<br>data = pd.read_csv("[filename].dat",comment='#',header=[0,1],delimiter=';',index_col=0,parse_dates=True)</p> <p>This gives a multi-index Dataframe with the index column the timestamp, the first column label corresponds to the measured variable and the second column to the relevant sensor</p> <p><strong>Note:</strong></p> <p>The output data has been updated to match the latest version of the paper, whereas the input and validation data remains the same as in Version 1.0.0</p>
Infrared Optical Constants of Crystalline Cyanogen at 100 K
<p>The optical constants of crystalline cyanogen (C<sub>2</sub>N<sub>2</sub>) at 100 K in the wavenumber range from 3100 to 550 cm<sup>-1</sup> are given here. These data are discussed in a manuscript submitted to the Planetary Science Journal (Hudson & Gerakines 2023). </p> <p>The same data have been uploaded in two different formats: plain ASCII (C2N2_100K_n_k.txt) and Microsoft Excel (C2N2_100K_n_k.xlsx). </p>
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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.