Find research datasets worth reusing

opencc-by-4.0Apr 2020View details →

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>

opencc-by-4.0Apr 2020View details →

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>

opencc-by-4.0Jun 2014View details →

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>

opencc-by-4.0Aug 2024View details →

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>

opencc-by-nc-nd-4.0May 2024View details →

Measurement and simulation of optical properties of nanostructured silicon heavily implanted with selenium

<p><strong>Summary:</strong></p> <p>This is the collection of datasets used to plot the line art figures for the journal paper “Extended Infrared Absorption in Nanostructured Si Through Se Implantation and Flash Lamp Annealing”.</p> <p><strong>Methods:</strong></p> <p>The experimental and calculation methods for generating the datasets are described in the original paper and in the supplementary materials.</p> <p><strong>File Description:</strong></p> <ul> <li>The filenames for all files match the figure captions from the original paper and supplementary materials.</li> <li>Each file represents a specific plot, with all files provided in CSV format.</li> <li>Each column in the file represents a set of variable data.</li> <li>The datasets corresponding to each curve can be identified by comparing the first-row header information with the figure legend.</li> </ul> <p><strong>Credit:</strong></p> <p>When using the dataset/figures, please cite the original paper as: Radfar, B., Liu, X., Berencén, Y., Shaikh, M.S., Prucnal, S., Kentsch, U., Vähänissi, V., Zhou, S. and Savin, H. (2024), Extended Infrared Absorption in Nanostructured Si Through Se Implantation and Flash Lamp Annealing. Phys. Status Solidi A 2400133. <a title="https://doi.org/10.1002/pssa.202400133" href="https://doi.org/10.1002/pssa.202400133" target="_blank" rel="noreferrer noopener">https://doi.org/10.1002/pssa.202400133</a></p>

opencc-by-4.0Mar 2024View details →

Source code and simulation results: Efficient rational approximation of optical response functions with the AAA algorithm

<p>This publication provides data published in the article "Efficient rational approximation of optical response functions with the AAA algorithm" [1] in tabulated form along with the Matlab scripts that have been used to produce them. These scripts interface the finite element method solver JCMsuite [2,3]. The article presents rational approximations of optical response functions based on an extended version of the AAA algorithm [4] that allows to efficiently reconstruct sensitivty spectra and gives access to sensitivities of poles, residues, and zeros. Furthermore, the rational approximation of a scalar observalbe is used to construct solutions of the source free Maxwell's equation, i.e., a nonlinear eigenvalue problem. </p> <p><strong>The physical Structure</strong></p> <p>The example is based on the chiral metasurface introduced in [5]. For the sake of simplicity we added infinite layers of SiO$_2$ to the top and the bottom of the structure. The original structure has a SiO$_2$ substrate and a layer of PMMA polymethyl methacrylate (PMMA)  deposited on top. PMMA can be modelled with the same refractive index of 1.45 as SiO$_2$. Furthermore, our simulations include the 13 nm indium tin oxide (ITO) coating which drastically reduces the Q-factor as it is slightly absorbing. The accuracy of the discrete model is verified by assessing reflection, transmission, and absorption at 241 evenly spaced points within the specified range. Energy conservation requires that the discrepancy between their sum and the energy entering the system is zero. The numerical discretization is chosen such that the maximum relative error is less than $3\times10^{−5}$.</p> <p><strong>Dispersion</strong></p> <p>Tabulated data for ITO has been taken from the <a href="https://refractiveindex.info/?shelf=other&book=In2O3-SnO2&page=Konig">refractiveindex.info</a> database (T. A. F. König et al., 2014, https://doi.org/10.1021/nn501601e) and the data for TiO2 was kindly provided the authors of [5]. The permittivity $\varepsilon = (n+ik)^2$ is locally approximated as a rational function, i.e., only data in a vicinity of the frequency range of interest is considered. As we aim for a function with the symmetry $f^\ast(\omega) = f(-\omega^\ast)$ we add the complex conjugated data at negative frequencies and enforce the symmetry in a second step. The partial fraction decomposition of the required function is of the form: $\varepsilon(\omega) = \varepsilon_\infty + \sum_{j=1}^{4}a_j/(\omega-\omega_j) - a_j^\ast/(\omega+\omega_j^\ast)$ with the residues $a_j$ and the poles $\omega_j$. We expect 4 pairs of poles to sufficiently approximate the data within the range of interest (4 with positive and 4 with negative real parts).</p> <h4><strong>Requirements</strong></h4> <ul> <li>JCMsuite (at least 6.2.0)</li> <li>MATLAB (tested with version R2023b)</li> </ul> <p>In order to run the simulations with JCMsuite you must replace corresponding place holders with a path to your installation of JCMsuite. Free trial licenses are available, please refer to the homepage of <a href="https://jcmwave.com/">JCMwave</a>.</p> <p><strong>Usage</strong></p> <p>With the content of 'spectra.zip' you can reproduce results presented in the paper. Running the script 'plots.m' will not start any expensive simulation but use the provided data. With 'dispersion.m' the fits to the material data can be reproduced. Additionally, tabulated data is contained in 'data/ascii'. The archive 'eigenmodes.zip' must be extracted in the same directory as 'spectra.zip'.</p> <p><strong>References</strong></p> <p>[1] Fridtjof Betz, Martin Hammerschmidt, Lin Zschiedrich, Sven Burger, Felix Binkowski: Efficient rational approximation of optical response functions<br>with the AAA algorithm, https://doi.org/10.48550/arXiv.2403.19404.</p> <p>[2] Jan Pomplun, Sven Burger, Lin Zschiedrich, Frank Schmidt, Adaptive finite element method for simulation of optical nano structures, Physica Status Solidi B <strong>244</strong>, 3419 (2007), http://dx.doi.org/10.1002/pssb.200743192.</p> <p>[3] Fridtjof Betz, Felix Binkowski, Sven Burger, RPExpand: Software for Riesz projection expansion of resonance phenomena, SoftwareX <strong>15</strong>, 100763 (2021), https://doi.org/10.1016/j.softx.2021.100763.</p> <p>[4] Y. Nakatsukasa, O. Sète, and L. N. Trefethen, The AAA Algorithm for Rational Approximation, SIAM Journal on Scientific Computing <strong>40</strong>, A1494 (2018), http://dx.doi.org/10.1137/16M1106122.</p> <p>[5]  X. Zhang, Y. Liu, J. Han, Y. Kivshar, and Q. Song, Chiral emission from resonant metasurfaces, Science <strong>377</strong>, 1215 (2022), http://dx.doi.org/%2010.1126/science.abq7870.</p>

opencc-by-4.0Jan 2024View details →

Source Code and Simulation Results: Chiral and directional optical emission from a dipole source coupled to a helical plasmonic antenna

<h3>Summary</h3> <p>This publication supplements the article "Chiral and directional optical emission from a dipole source coupled to a helical plasmonic antenna" with tabulated data and Matlab code that allows the reproduction of the results. Within the article, the chiral behavior of single and double plasmonic nano antennas made from silver is numerically investigated with a focus on the coupling of a linear polarized dipole as an excitation source to the helix.</p> <h3>Simulation Setup - FEM Simulations</h3> <p>The script "run_wavlengthscan.m" allows to reproduce all simulations of the article. It can be chosen between the single and double helices, by specifying the keys parameter "keys.doppelhelix" where 0 gives a single and 1 a double helix. The number of turns can be specified by choosing "keys.case". The dipol is located within a 20nm thick hBN substrate layer, on glass (BK7). Results of the Purcell enhancement can be plotted using the scripts "display_results_single_helix.m" and "display_results_doublehelix.m" in the folder "results". The far-field plots can be reproduced using the scripts "display_farfiel_polarization_single_helix.m" and "display_farfiel_polarization_double_helix.m" of the folder "FunctionsAndScripts".</p> <p>The template for the mesh is contained in the folder "generate_grid_file", where the parameters of the helix (for example: radius, tube radius, and pitch height) can be modified.</p> <p>Within the folder "project3D" all required .jcm files are stored. Copy the "grid.jcm" file with the geometry of interest to this folder to perform simulations.</p> <p>All required keys parameters for the JCM template files (.jcmt, jcmpt) are set within the functions "set_numerical_parameter.m" and "set_physical_parameters.m", contained in the folder "FunctionsAndScripts". Therein, the function "set_sources.m" specifies the parameters for the dipole excitation, such as the position, and the strength (equivalent to the polarization).</p> <h3>Semi-Analytical Model</h3> <p>The Jupyter notebook "Semi_Analytical_Plasmonic_Helix.ipynb" contains the commented Python script for the semi-analytical design tool used to obtain far-field radiation patterns of the single helix. This semi-analytical design tool is based on an analytical model developed in [4]. The script can be divided into three parts. First, the single helix is defined, and a linear wavelength scaling law [5] is used to determine the illuminating wavelengths at which Fabry-Pérot resonances occur. Second, the overlap integral between the mode current on the helix and the incident electric field is evaluated for a given direction of incident light. Thirdly, the direction of incidence is varied to obtain the far-field radiation patterns. The script allows for the radiation patterns to be exported as a .csv file. Alternatively, the radiation patterns can be plotted directly using the provided single_plot functions.</p> <h3>Material</h3> <p>The material data has been taken from the <a href="https://refractiveindex.info/" target="_blank" rel="noopener">refractiveindex.info</a> database. For silver the data is taken from tabulated data from Johnson and Christy [1] . The dispersion relation for hBN comes from [2] and tabulated data for glass (BK7) from [3]. The MATLAB script "material_properties_plot.m" plots the material fits above the wavelengths of interest. The required tabulated data is given in the folder "material_data".</p> <p>With 'material_properties_plot.m' the fits to the material data can be reproduced and plotted.</p> <h3>Usage</h3> <p>The .zip folder Helix_FEM contains all data and scripts to reproduce the plots from the 3D FEM simulations.</p> <p>The Jupyter Notebook Semi_Analytical_Plasmonic_Helix reprouces the results from the semi-analytical model.</p> <h3>Requirements</h3> <ul> <li>JCMsuite (at least 5.4.0)</li> <li>MATLAB (tested with version R2023b)</li> <li>Python (tested with Version 3.10.9)</li> <li>Jupyter Notebook (tested with 6.5.2) </li> </ul> <p>To run the simulations with JCMsuite you must replace corresponding placeholders with a path to your installation of JCMsuite. Free trial licenses are available, please refer to the homepage of <a href="https://jcmwave.com/">JCMwave</a>.</p> <h3>References</h3> <p>[1] P. B. Johnson and R.-W. Christy, “Optical constants of the noble metals,” Phys. Rev. B 6, 4370 (1972).</p> <p>[2] S.-Y. Lee, T.-Y. Jeong, S. Jung, and K.-J. Yee, “Refractive index dispersion of hexagonal boron nitride in the visible and near-infrared,” Phys. Status Solidi B 256,  1800417 (2019).</p> <p>[3] “SCHOTT Zemax catalogue 2017-01-20b,” (2017).</p> <div>[4] K. Höflich et al., "Resonant behavior of a single plasmonic helix." Optica 6, 1098(2019).</div> <div> </div> <div>[5]L. Novotny, "Effective wavelength scaling for optical antennas", Phys. Rev. Lett. 98,266802 (2007).</div>

opencc-by-4.0Nov 2024View details →

Dataset for "LOROS: Laboratory Simulations of the Optical RadiOmeter composed of CHromatic Imagers (OROCHI) Experiment of the Martian Moons eXploration (MMX) Mission"

<p>This dataset hosts the image and numerical data analysed and derived in the accompanying Stabbins & Kameda article for the special issue of Progress in Earth and Planetary Science on instrumentation and preparations for the JAXA Martian Moons eXploration (MMX) mission. The paper describes and validates the performance of the Laboratory OROCHI Simulator (LOROS).</p> <p>OROCHI (Optical RadiOmeter composed of CHromatic Imagers) is a multispectral multi-view imaging system for the JAXA MMX spacecraft, that will image Phobos and Deimos across 8 visible and near-infrared spectral channels with unprecedented spatial resolution, recording data that in synergy with the other instruments of the MMX spacecraft and rover will constrain hypotheses on the origin of the Martian moons.</p> <p>LOROS is a laboratory simulator of OROCHI, constructed from commercial off-the-shelf parts.</p> <p>The dataset for the characterisation and validation of LOROS is composed of the following sub-sets:</p> <p>A. Modulation Transfer Function<br>B. Expected Reflectance of Carbonaceous Chondrite & Dark Spectralon<br>C. Radiometric Calibration<br>D. Dark Spectralon Validation</p> <div> <h2>Dataset A: Modulation Transfer Function</h2> This dataset includes the table of results of MTF measurements of the slant-edge target at 5 different random orientations in the range of ~7--10°: <div>- <code>mtf_results_07122023.csv</code></div> <br> <div>and the region-of-interest images, for each orientation and each LOROS channel, used to perform the analysis via the <a href="https://sourceforge.net/p/mtfmapper/home/Home/" target="_blank" rel="noopener">MTF Mapper software</a>:</div> <div>- <code>mtf_measurements_07122023</code></div> <br> <div>The directory tree of measurements, for the <em>n</em>th orientation, is illustrated below. Region-of-interest images are stored under <code>img</code>, and are averaged over 25 repeat images to minimise random noise, have had dark frames subtracted, and have been converted from 12-bit to 8-bit grayscale images for compatibility with the MTF Mapper software. Modulation Transfer Function (MTF) and Spatial Frequency Response (SFR) diagnostics generated by MTF Mapper are stored in the <code>results</code> directory.</div> <div> </div> <div><code>mtf_measurements_07122023</code></div> <div><code>├── mtf_knifeedge_low_07122023_*n*</code></div> <div><code>│ ├── img</code></div> <div><code>│ │ ├── 0_850_img_ave.tif</code></div> <div><code>│ │ ├── 1_475_img_ave.tif</code></div> <div><code>│ │ ├── ...</code></div> <div><code>│ ├── results</code></div> <div><code>│ │ ├── 0_850_img_ave_annotated.jpg</code></div> <div><code>│ │ ├── 0_850_img_ave_edge_mtf_values.txt</code></div> <div><code>│ │ ├── 0_850_img_ave_edge_sfr_values.txt</code></div> <div><code>│ │ ├── 1_475_img_ave_annotated.jpg</code></div> <div><code>│ │ ├── ...</code></div> <div><code>├── mtf_knifeedge_low_07122023_*n+1*</code></div> <div><code>│ ├── img</code></div> <div><code>│ │ ├── ...</code></div> <div> </div> <div>This data constitutes part of <strong>Table 1</strong> and <strong>Figure 2</strong> of the manuscript.</div> <div> <h2>Dataset B: Expected Reflectance of Carbonaceous Chondrite & Dark Spectralon</h2> This dataset includes the high-resolution ($\delta\lambda$=1 nm) reference reflectance spectra of the representative Carbonaceous Chondrite meteorite (<a href="https://westernreflectancelab.com/visor/graph/?results-selection=16136&results-item=16136&results-item=15972&results-item=231&results-item=230&graph=&form-TOTAL_FORMS=1&form-INITIAL_FORMS=0&form-MIN_NUM_FORMS=0&form-MAX_NUM_FORMS=1000&form-0-sample_name=nogoya&form-0-any_field=meteorite&form-0-id=&sort_params=-sample_name&page_selected=1&jump-to-page=" target="_blank" rel="noopener">Nogoya)</a> and the 5% reflectance Spectralon calibration target (<a href="https://www.labsphere.com/wp-content/uploads/2021/09/SpectralonStandards.pdf" target="_blank" rel="noopener">SCT5</a>):<br> <div>- <code>highres_input.csv</code></div> <br> <div>and the resampled spectra of these materials expected for OROCHI and LOROS filter wavelengths:</div> <br> <div>- <code>loros_observation.csv</code></div> <div>- <code>orochi_observation.csv</code></div> <br> <div><code>B_expected_reflectance</code></div> <div><code>├── README.md</code></div> <div><code>├── highres_input.csv</code></div> <div><code>├── loros_observation.csv</code></div> <div><code>└── orochi_observation.csv</code></div> <br> <div>This data constitutes <strong>Table 1</strong> and <strong>Figure 10</strong> of the manuscript.</div> <div> </div> <div> <div> <h2>Dataset C: Radiometric Calibration</h2> This dataset contains the image and derived data for 4 experiments with different illumination conditions for characterising the radiometric response of each of the 8 channels of LOROS.</div> <div><br> <div>This dataset contributes to <strong>Tables 2 - 4</strong> and <strong>Figures 3 - 9</strong> of the manuscript.</div> <br> <div>The final derived metrics are hosted in the spreadsheet:</div> <br> <div>- <code>measured_sensor_properties.csv</code></div> <br> <div>and image data and intermediary derived properties for each experiment are stored in the</div> <br> <div>- <code>experiments</code></div> <br> <div>directory.</div> <br> <div><code>C_radiometric_calibration</code></div> <div><code>├── README.md</code></div> <div><code>├── experiments</code></div> <div><code>│ ├── F*S5L10</code></div> <div><code>│ ├── F*S99L10</code></div> <div><code>│ ├── FGS99L2</code></div> <div><code>│ └── FGS99L10</code></div> <div><code>└── measured_sensor_properties.csv</code></div> <br> <h3><code>experiments</code> Directories</h3> In the directory of each experiment are sub-directories hosting Photon Transfer and Dark Transfer datasets, and a spreadsheet of derived metrics of these.<br> <div> </div> <div><code>C_radiometric_calibration</code></div> <div><code>├── README.md</code></div> <div><code>├── experiments</code></div> <div><code>│ ├── F*S5L10</code></div> <div><code>│ │ ├── dark_transfer_curve</code></div> <div><code>│ │ ├── photo_transfer_curve</code></div> <div><code>│ │ └── F*S5L10_derived_properties.csv</code></div> <div><code>│ └── ...</code></div> <div><code>└── measured_sensor_properties.csv</code></div> <div> </div> </div> <div> </div> <div><strong>Derived Properties</strong><br> <div> </div> <div>The spreadsheet (<code>[experiment]_derived_properties.csv</code>) collecting the properties derived from each experiment holds the following information, that has been extracted from the Photon Transfer and Dark Transfer curves as described in §4.2 of the manuscript:</div> <br> <div><code>camera # The camera number and wavelength</code></div> <div><code>k_adc # Sensitivity (e-/DN)</code></div> <div><code>full_well_e # Saturation Capacity (electrons)</code></div> <div><code>full_well_dn # Saturation Capacity (Digital Numbers)</code></div> <div><code>read_noise_e # Read Noise (electrons)</code></div> <div><code>read_noise_dn # Read Noise (Digital Numbers)</code></div> <div><code>bias_e # Offset (electrons)</code></div> <div><code>bias_dn # Offset (Digital Numbers)</code></div> <div><code>dark_current_e # Dark Current (electrons/second)</code></div> <div><code>dark_current_dn # Dark Current (Digital Numbers/second)</code></div> <div><code>DR # Dynamic Range</code></div> <div><code>lin_min # Minimum Linearity Error</code></div> <div><code>lin_max # Maximum Linearity Error</code></div> <div><code>linearity # Average Linearity Error</code></div> <div><code>snr_max # Maximum Signal-to-Noise Ratio</code></div> <div><code>t_exp_min # Minimum Exposure used in experiment (seconds)</code></div> <div><code>t_exp_max # Maximum Exposure used in experiment (seconds)</code></div> <div><code>expected_response # Expected Response (or 'Digital Flux') for OROCHI^12 at Phobos (Digital Numbers/second)</code></div> <div><code>response # Fitted Response (or 'Digital Flux') (Digital Numbers/second)</code></div> <br> <div>These values are given for each channel of LOROS, as well as the expected values for LOROS in off-the-shelf configuration (with no gain adjustment), LOROS with the gain adjustment, and OROCHI if downsampled to 12-bit resolution digital numbers.</div> <br> <div>This data constitutes <strong>Table 2</strong> of the manuscript.</div> <br> <div><strong>Dark Transfer Curve</strong></div> <br> <div>The <code>dark_transfer_curve</code> directory hosts the derived Dark Transfer Curve data (<code>derived_data</code>) and the source region-of-interest dark image pair data (<code>raw_data</code>) for each LOROS channel.</div> <br> <div><code>dark_transfer_curve</code></div> <div><code>├── derived_data</code></div> <div><code>│ ├── F*S5L10_0_850_dtc.csv</code></div> <div><code>│ ├── F*S5L10_1_475_dtc.csv</code></div> <div><code>│ ├── F*S5L10_2_400_dtc.csv</code></div> <div><code>│ ├── F*S5L10_3_550_dtc.csv</code></div> <div><code>│ ├── F*S5L10_4_725_dtc.csv</code></div> <div><code>│ ├── F*S5L10_5_950_dtc.csv</code></div> <div><code>│ ├── F*S5L10_6_650_dtc.csv</code></div> <div><code>│ └── F*S5L10_7_550_dtc.csv</code></div> <div><code>└── raw_data</code></div> <div><code>├── 0_850</code></div> <div><code>│ ├── 850_10095570us_1_calibration.tif</code></div> <div><code>│ ├── 850_10095570us_2_calibration.tif</code></div> <div><code>│ ├── 850_104us_1_calibration.tif</code></div> <div><code>│ ├── 850_104us_2_calibration.tif</code></div> <div><code>│ ├── ...</code></div> <div><code>├── 1_475</code></div> <div><code>├── 2_400</code></div> <div><code>├── 3_550</code></div> <div><code>├── 4_725</code></div> <div><code>├── 5_950</code></div> <div><code>├── 6_650</code></div> <div><code>├── 7_550</code></div> <div><code>└── camera_config.csv</code></div> <br> <div>The <code>raw_data</code> directory hosts a dark image pair for each exposure time used, and the <code>camera_config.csv</code> spreadsheet gives metadata for the system configuration, including the coordinates and dimensions of the region-of-interest for each channel.</div> <br> <div>The dark transfer curve for each experiment and each channel (<code>[experiment]_[channel]_[wavelength]_dtc</code>) gives the data derived from each raw image data, with the following values:</div> <br> <div><code>exposure # exposure duration (seconds)</code></div> <div><code>n_pix # number of pixels in the region of interest</code></div> <div><code>mean # average value of the region of interest</code></div> <div><code>std_t # total standard deviation of the region of interest</code></div> <div><code>std_rs # read+shot-noise standard deviation, copmuted from the difference of the image pair</code></div> <br> <div>This data constitutes <strong>Figures 5 and 8</strong> of the manuscript.</div> <br> <div><strong>Photon Transfer</strong></div> <br> <div>The <code>photon_transfer_curve</code> directory hosts the derived Photon Transfer Curve data (<code>derived_data</code>) and the source region-of-interest illuminated image pairs and associated dark frame image data (<code>raw_data</code>) for each LOROS channel.</div> <br> <div><code>photo_transfer_curve</code></div> <div><code>├── derived_data</code></div> <div><code>│ ├── F*S5L10_0_850_ptc.csv</code></div> <div><code>│ ├── F*S5L10_1_475_ptc.csv</code></div> <div><code>│ ├── F*S5L10_2_400_ptc.csv</code></div> <div><code>│ ├── F*S5L10_3_550_ptc.csv</code></div> <div><code>│ ├── F*S5L10_4_725_ptc.csv</code></div> <div><code>│ ├── F*S5L10_5_950_ptc.csv</code></div> <div><code>│ ├── F*S5L10_6_650_ptc.csv</code></div> <div><code>│ └── F*S5L10_7_550_ptc.csv</code></div> <div><code>└── raw_data</code></div> <div><code>├── 0_850</code></div> <div><code>│ ├── 850_104us_1_calibration.tif</code></div> <div><code>│ ├── 850_104us_2_calibration.tif</code></div> <div><code>│ ├── 850_104us_d_drk.tif</code></div> <div><code>│ ├── 850_105828us_1_calibration.tif</code></div> <div><code>│ ├── ...</code></div> <div><code>├── 1_475</code></div> <div><code>├── 2_400</code></div> <div><code>├── 3_550</code></div> <div><code>├── 4_725</code></div> <div><code>├── 5_950</code></div> <div><code>├── 6_650</code></div> <div><code>├── 7_550</code></div> <div><code>└── camera_config.csv</code></div> <br> <div>The <code>raw_data</code> directory hosts an image pair and dark frame for each exposure time used, and the <code>camera_config.csv</code> spreadsheet gives metadata for the system configuration, including the coordinates and dimensions of the region-of-interest for each channel.</div> <br> <div>The photon transfer curve for each experiment and each channel (<code>[experiment]_[channel]_[wavelength]_ptc</code>) gives the data derived from each raw image data, with the following values across the region-of-interest:</div> <br> <div><code>exposure # exposure duration (seconds)</code></div> <div><code>n_pix # number of pixels in the region of interest</code></div> <div><code>mean # average value (Digital Numbers)</code></div> <div><code>std_t # total standard deviation (Digital Numbers)</code></div> <div><code>std_rs # read+shot-noise standard deviation (Digital Numbers), computed from the difference of the image pair</code></div> <div><code>d_mean # average value of the dark (Digital Numbers)</code></div> <div><code>d_dsnu # Dark Signal Nonuniformity (Digital Numbers)</code></div> <div><code>std_s # Shot Noise (read noise removed) (Digital Numbers)</code></div> <div><code>k_adc # Sensitivity (note this the point-wise sensitivity, rather than fitted) (electrons/Digital Number)</code></div> <div><code>linearity # Linearity Error (point-wise distance to least-squares linear fit) (%)</code></div> <div><code>snr # Signal-to-Noise Ratio, derived from shot-noise (point-wise)</code></div> <div><code>snr_t # Signal-to-Noise Ratio, derived from total noise (point-wise)</code></div> <div><code>e- # Electron count, derived from sensitivity</code></div> <div><code>e-_noise # Electron shot-noise, derived from sensitivity</code></div> <br> <div>This data constitutes <strong>Figures 3, 4, 6, 7 & 9</strong> of the manuscript.</div> <br> <div><strong>Measured Sensor Properties</strong></div> <br> <div>The <code>measured_sensor_properties.csv</code> spreadsheet collects and averages the following metrics over the 4 experiments performed, to give the values for each channel, along with the expected values for LOROS in off-the-shelf configuration, gain-adjusted LOROS, and OROCHI downsampled to 12-bit resolution.</div> <br> <div><code>SNR Max</code></div> <div><code>Dynamic Range (dB)</code></div> <div><code>Dynamic Range (bits)</code></div> <div><code>Sensitivity (e-/DN)</code></div> <div><code>Saturation Capacity (e-)</code></div> <div><code>Saturation Capacity (DN)</code></div> <div><code>Read Noise (e-)</code></div> <div><code>Read Noise (DN)</code></div> <div><code>Nonlinearity (%)</code></div> <div><code>Dark Signal@30°C (e-/s)</code></div> <div><code>Dark Signal@30°C (DN/s)</code></div> <div><code>Bias (e-)</code></div> <div><code>Bias (DN)</code></div> <div><code>DSNU1288 (DN)</code></div> <div><code>DSNU1288 (e-)</code></div> <div><code>PRNU1288 (%)</code></div> <br> <div>This data constitutes <strong>Table 3</strong> of the manuscript.</div> <div> </div> <div> <h2>Dataset D: Dark Spectralon Validation</h2> This dataset contains the raw image and derived data used to demonstrate the ability of LOROS to measure the spectral reflectance of the 5% reflectance Spectralon calibration target (<a href="https://www.labsphere.com/wp-content/uploads/2021/09/SpectralonStandards.pdf" target="_blank" rel="noopener">SCT5</a>).<br> <div>The image data is hosted in the directory:</div> <br> <div>- <code>raw_data</code></div> <br> <div>and the processed data (e.g. reflectance products) are hosted in the directory:</div> <br> <div>- <code>processed_data</code></div> <br> <div><code>D_dark_spectralon_validation</code></div> <div><code>├── processed_data</code></div> <div><code>│ ├── SCT5</code></div> <div><code>│ └── SCT99</code></div> <div><code>├── raw_data</code></div> <div><code>│ ├── SCT5</code></div> <div><code>│ ├── SCT5_dark</code></div> <div><code>│ ├── SCT99</code></div> <div><code>│ └── SCT99_dark</code></div> <div><code>└── README.md</code></div> <br> <div><strong>Raw Data</strong></div> <br> <div>The raw data directory contains images captured of <code>SCT5</code> and <code>SCT99</code> (99% reflectance white Spectralon), and accompanying dark frames, hosted in the <code>SCT5_dark</code> and <code>SCT99_dark</code> frames respectively.</div> <br> <div>For each channel, 25 repeat images have been captured for the illuminated and dark frames.</div> <br> <div><strong>Processed Data</strong></div> <br> <div>The processed SCT99 and SCT5 datasets differ slightly. Both include:</div> <br> <div><code>├── img</code></div> <div><code>├── rfl</code></div> <div><code>└── rois</code></div> <br> <div>directories, with the SCT99 scene also including a <code>cal</code> directory.</div> <br> <div><code>img</code> hosts a set of <code>context</code> figures, showing the regions of interest selected, <code>fits</code> hosts the floating point mean (<code>ave</code>), standard error (<code>err</code>), standard deviation (<code>std</code>) and single-frame (<code>one</code>), all in units of Digital Number, after dark frame subtraction, flat-fielding and linearity correction. <code>uint8</code> hosts the same data rescaled to 8-bit resolution, for quick-view.</div> <br> <div><code>rfl</code> hosts the same set as <code>img</code>, after conversion to units of reflectance against the results of the SCT99 calibration (see §3.5 of the manuscript).</div> <br> <div><code>rois</code> gives plots of the mean and error of the reflectance spectrum of the region of interest, as well as the Signal-to-Noise Ratio, as well as the data for each region-of-interest (<code>roi_data</code>).</div> <br> <div><code>cal</code> also gives context figures for each channel region-of-interest, as converted to units of reflectance coefficients (1/DN/s).</div> </div> </div> </div> </div> </div>

opencc-by-4.0Jul 2023View details →

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>

opencc-by-4.0Jul 2020View details →

The simulated dataset associated with the paper "Mesoscale modelling of optical turbulence in the atmosphere: The need for ultrahigh vertical grid resolution"

<p>The WRF model-generated meteorological profiles are available in netcdf format. More information will be provided shortly. </p>

opencc-zeroDec 2016View details →

Data from: Modelling and simulation of a thermally induced optical transparency in a dual micro-ring resonator

This paper introduces the simulation and modelling of a novel dual micro-ring resonator. The geometric configuration of the resonators, and the implementation of a simulated broadband excitation source, results in the realization of optical transparencies in the combined through port output spectrum. The 130 nm silicon on insulator rib fabrication process is adopted for the simulation of the dual-ring configuration. Two titanium nitride heaters are positioned over the coupling regions of the resonators, which can be operated independently, to control the spectral position of the optical transparency. A third heater, centrally located above the dual resonator rings, can be used to red shift the entire spectrum to a required reference resonant wavelength. The free spectral range with no heater currents applied is 4.29 nm. For a simulated heater current of 7 mA (55.7 mW heater power) applied to one of the through coupling heaters, the optical transparency exhibits a red shift of 1.79 nm from the reference resonant wavelength. The ring-to-ring separation of approximately 900 nm means that it can be assumed that there is a zero ring-to-ring coupling field in this model. This novel arrangement has potential applications as a gas mass airflow sensor or a gas species identification sensor.

opencc-zeroNov 2023View details →

Wavelength-division multiplexing optical Ising simulator enabling fully programmable spin couplings and external magnetic fields

<p>Recently various physical systems have been proposed for modeling Ising spin Hamiltonians appealing to solve combinatorial optimization problems with remarkable performance. However, how to implement arbitrary spin-spin interactions is a critical and challenging problem in various kinds of unconventional Ising machines. Here we propose a general gauge transformation scheme to enable arbitrary spin-spin interactions and external magnetic fields as well, by decomposing an Ising Hamiltonian into multiple Mattis-type interactions. Based on this scheme, a wavelength-division multiplexing spatial photonic Ising machine (SPIM) is developed to show the programmable capability of general spin coupling interactions. We exploit the wavelength-division multiplexing SPIM to simulate three spin systems: pm J models, Sherrington-Kirkpatrick models, and only locally connected J1/J2 models and observe the phase transitions among the spin-glass, the ferromagnetic, the paramagnetic and the stripe-antiferromagnetic phases. We also demonstrate the ground state search for solving the Max-Cut problem with the wavelength-division multiplexing SPIM. These results promise the realization of ultrafast-speed and high-power-efficiency Boltzmann sampling of a generalized large-scale Ising model.</p>

opencc-by-4.0Jun 2022View details →

Monte Carlo ray tracing code and simulations for photons scattered by an optically thin slab

<p>This directory contains code and data used to simulate photon path length distributions in an optically thin slab.<br> This material was used to prepare the manuscript "Photon Path Distributions in Optically Thin Slabs" by Quentin Libois and Anthony B. Davis, submitted to Optics Express</p>

opencc-by-4.0Jul 2022View details →

Optical motion capturing of change of direction motions reconstructed with inverse kinematics and dynamics and optimal control simulation

<p>This is the data belonging to the publication "Change the direction: 3D optimal control simulation by directly tracking marker and ground reaction force data".</p> <p>This study investigated the feasibility and accuracy of reconstructing, especially change of direction motions with a 3D full-body musculoskeletal model by tracking marker and ground reaction force (GRF) data in optimal control simulations. We recorded in total 30 trials with optical motion capture. Using this data, we compared inverse methods (inverse kinematics and dynamics) to coordinate tracking simulations and marker tracking simulations.</p> <p>Please see the README and the publication for further details.</p>

opencc-by-4.0Feb 2023View details →

Main text figure data and scripts for "Simulating optical linear absorption for mesoscale molecular aggregates: an adaptive hierarchy of pure states approach"

<p>(as README.txt):</p> <p>Main text figure data and scripts for “Simulating optical linear absorption for mesoscale molecular aggregates: an adaptive hierarchy of pure states approach”, by Tarun Gera, Lipeng Chen, Alex Eisfeld, Jeffrey R. Reimers, Elliot J. Taffet and Doran I. G. B. Raccah.</p> <p>Each directory is dedicated to a particular figure published in the paper. In each directory there are sub-directories which contains the data plotted in each panel. Each data file is a 2-D list in the format of (x,y) for each plot. There are python scripts (Fig_X.py) in each directory to plot the data.</p> <p>Table of contents:</p> <p>Figure_2:</p> <p>    - 4_site_edge_contri.npy: Calculated edge sites contribution to the total absorption spectrum for a 4-site chain system v/s energy. <br>     - 4_site_inner_contri.npy: Calculated inner sites contribution to the total absorption spectrum for a 4-site chain system v/s energy. <br>     - 4_site_total_spectra.npy: Calculated total absorption spectrum for a 4-site chain system v/s energy.  </p> <p><br> Figure_3:</p> <p>Panel A:<br>     <br>     - Mean_Error_Edge.npy: Mean error for the edge case v/s number of trajectories.<br>     - Mean_Error_Inner.npy: Mean error for the inner case v/s number of trajectories.<br>     - Mean_Error_SS.npy: Mean error for a single site initial condition v/s number of trajectories.<br>     - Mean_Error_GD.npy: Mean error for a 4-site chain system with Gaussian distributed site energies v/s number of trajectories.</p> <p>Panel B: </p> <p>    - Scaled_error_SS.npy:  Mean error for a single site initial condition normalized by the square-root of one v/s number of trajectories.<br>     - Scaled_error_PS.npy:  Mean error for a pair site initial condition normalized by the square-root of two v/s number of trajectories.<br>     - Scaled_error_AS.npy:  Mean error for an all site initial condition normalized by the square-root of four v/s number of trajectories.</p> <p>Figure_4: </p> <p>Panel_A:</p> <p>    - List_Error.npy: Calculated mean error for a 4-site chain for a set of auxiliary error bounds.</p> <p>Panel_B:</p> <p>    - Cw_4S_HOPS.npy: Absorption spectrum for a 4-site chain calculated using dyadic HOPS v/s energy.<br>     - Cw_4S_DadHOPS.npy: Absorption spectrum for a 4-site chain calculated using DadHOPS v/s energy.</p> <p>Panel_C: </p> <p>    - Cw_12S_DadHOPS.npy: Absorption spectrum for a 12-site chain calculated using DadHOPS without including state adaptivity v/s energy.<br>     - Cw_12S_DadHOPS_SA.npy: Absorption spectrum for a 12-site chain calculated using DadHOPS with state adaptivity v/s energy.</p> <p>Panel_D:</p> <p>    - Aux_states_DadHOPS.npy: Number of auxiliary states required to run a DadHOPS calculation for each N-pigment system.<br>     - Aux_states_HOPS.npy: Number of auxiliary states required to run a dyadic HOPS calculation for each N-pigment system.<br>     - N_states_DadHOPS.npy: Number of site states required to run a DadHOPS calculation for each N-pigment system.<br>     - N_states_HOPS.npy: Number of site states required to run a dyadic HOPS calculation for each N-pigment system.<br>     </p> <p>Figure_5:<br>     <br> Panel_C: </p> <p>    - PSI_Cw_HEOM.npy: PSI absorption spectrum calculated using HEOM v/s energy.<br>     - PSI_Cw_HOPS.npy: PSI absorption spectrum calculated using dyadic HOPS v/s energy.</p> <p>Panel_D:</p> <p>    - PSI_Error_Random.npy: Calculated mean error, where clusters of 4 were assigned randomly v/s number of trajectories.<br>     - PSI_Error_Coupling.npy: Calculated mean error, where clusters of 4 were assigned based on electronic coupling values v/s number of trajectories.</p> <p><br> Figure_6:</p> <p>Panel_A:</p> <p>    - PBI_Exp_data_dil.npy: Experimental data for a dilute solution of PBI v/s energy.<br>     - PBI_Cw_DadHOPS_300.npy: Calculated spectrum for a PBI monomer with the spread in static disorder of value 300 cm^{-1} v/s energy.<br>     - PBI_Cw_DadHOPS_400.npy:: Calculated spectrum for a PBI monomer with the spread in static disorder of value 400 cm^{-1} v/s energy.</p> <p>Panel_B:</p> <p>    - PBI_Exp_data_conc.npy: Experimental data for a concentrated solution of PBI v/s energy.<br>     - PBI_trimer_Cw_DadHOPS.npy: Calculated spectrum for a PBI trimer using DadHOPS v/s energy.</p> <p>Panel_C: </p> <p>    - Cw_PBI_monomer.npy: Calculated spectrum for a PBI monomer using DadHOPS v/s energy.<br>     - Cw_PBI_dimer.npy: Calculated spectrum for a PBI dimer using DadHOPS v/s energy.<br>     - Cw_PBI_trimer.npy: Calculated spectrum for a PBI trimer using DadHOPS v/s energy.<br>     - Cw_PBI_heptamer.npy: Calculated spectrum for a PBI heptamer using DadHOPS v/s energy.<br>     - Cw_PBI_1000mer.npy: Calculated spectrum for a PBI 1000mer using DadHOPS v/s energy.</p> <p>Panel_D:</p> <p>    - peak_00_position.npy: relative position of the 00 peak for different number of pigments.<br>     - peak_00_position_1000.npy: relative position of the 0,0 peak for a system with 1000 pigments. (Single value file)<br>     - peak_I_ratio.npy: ratio of intensities of peak 0,1 w.r.t peak 0,0 for different number of pigments.<br>     - peak_I_ratio_1000.npy: ratio of intensities of peak 0,1 w.r.t peak 0,0 for a system with 1000 pigments. (Single value file)</p> <p><br> Figure_7:</p> <p>    - PBI_N_states_DadHOPS.npy: Number of states required to run a DadHOPS calculation for each N-PBI molecules system.  <br>     - PBI_Aux_states_HOPS.npy: Number of auxiliary states required to run a dyadic HOPS calculation for each N-PBI molecules system.  <br>     - PBI_Aux_states_DadHOPS.npy: Number of auxiliary states required to run a DadHOPS calculation for each N-PBI molecules system.  </p> <p>The packaged scripts may be run with Python 3.10 and the associated versions of the os, numpy, and matplotlib packages. <br>  </p>

publicJul 2017View details →

Data from: Modelling and simulation of a thermally induced optical transparency in a dual micro-ring resonator

Open the record for dataset details and reuse information.