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15 results for “Spectral simulations”
Thermal infrared emissivity spectral library of silicates measured under the Mercury simulated environment
<p>This is the thermal emissivity spectral library of silicates measured as a function of temperature under Mercury simulated environment. Data is measured at the Planetary Spectroscopy Laboratory (PSL), Institute of Planetary Research, German Aerospace Center (DLR), Berlin. The spectral library will be used for mineral identification of Mercury surface using MERTIS datasets. The manuscript related to this work is submitted to Icarus on the title "<strong>Thermal Infrared Spectroscopy (7-14 µm) of Silicates under Simulated Mercury Daytime Surface Conditions and their Detection: Supporting MERTIS onboard the BepiColombo Mission".</strong></p>
Quantification of 3D spatial correlations between state variables and distances to the grain boundary network in full-field crystal plasticity spectral method simulations
<p>This repository provides supplementary material to our paper: <a href="https://doi.org/10.1088/1361-651X/ab7f8c">https://doi.org/10.1088/1361-651X/ab7f8c</a></p> <p><strong>DAMASKPhenoPowerLaw75x75x75TestCase.zip</strong><br> An exemplary DAMASK simulation and corresponding output, generated from DAMASK v2.0.3. We used this to debug more productively the implementation of the post-processing tools. Furthermore we employed this simulation in the paper to identify why the graph clustering grain reconstruction method in many cases fuses neighboring grains in similar orientation.</p> <p><strong>DAMASKPhenoPowerLaw256x256x256ProductionRun.zip</strong><br> All input to run the DAMASK simulation that we discussed in the paper.</p> <p><strong>DAMASKPDTSettings256x256x256ProductionRun.zip</strong><br> All damaskpdt settings files to execute the individual post-processing studies of the paper.</p> <p><strong>DAMASKPDTSlurmSubmissionScripts256x256x256ProductionRun.zip</strong><br> All SLURM scripts we used to execute the compilation of damaskpdt and post-processing on TALOS.</p> <p><strong>DAMASKPDTSlurmLogs256x256x256ProductionRun.zip</strong><br> All logs from the SLURM job management system from the individual post-processing runs.</p> <p><strong>DAMASKPDTSourceCode_USedForAnalyticalDistanceToVoronoiCellFacets.zip</strong><br> The source code to the tool we developed during the revision process of our paper to verify the methods<br> via computing analytically exact distances to the facets of the Poisson-Voronoi tessellation from the<br> DAMASK microstructure instantiation.<br> <br> <strong>DAMASKPDTSourceCode_Production.zip</strong><br> The source code we used to post-process all results from the DAMASK simulations.</p> <p><strong>GitHub repository:</strong><br> https://github.com/mkuehbach/damaskpdt</p>
Simulated submerged aquatic vegetation spectral signatures under different water quality conditions using Hydrolight
<p>Reflectance spectra were simulated using the Hydrolight radiative transfer model (Sequoia Scientific, Bellevue, WA) for four different submerged macrophyte species under a range of water quality conditions at two different depths. We used the four-component case-2 model with spectral reflectance of four submerged species, Egeria densa, Ceratophyllum demersum, Cabomba caroliniana, and Stukenia pectinata. These four reflectance spectra were calculated from the median of 10 measurements of the canopies of the representative species placed in clear tap-water made with a handheld ASD FieldSpec Pro spectrometer. Total suspended solids concentration was varied from 1 to 40 g·m<sup>−3</sup>, chlorophyll-a concentration was varied from 0.5 to 50 mg·m<sup>−3</sup>, and colored dissolved organic matter (CDOM) was varied from 0.25 to 3.5 m<sup>−1</sup>. A total of 4,742 spectra were simulated for all four species and a mud substrate at two different depths, 1 m and 5 m, and for optically deep water.</p>
Reproduction packages for the paper "Spectral and Imaging properties of Sgr A∗ from High-Resolution 3 DGRMHD Simulations with Radiative Cooling"
<p>This is a basic reproduction package for the paper"Spectral and Imaging properties of Sgr A∗ from High-Resolution 3D GRMHD Simulations with Radiative Cooling" by Yoon et al. (2020). It aims to provide the most important data products to check and reproduce the main results of the paper.</p>
Validation of Spectral Light Simulation Tools: Dataset of Simulated and Measured Indoor Light Exposure
<p>Since the discovery of a new photoreceptor in our eye, and with the growing awareness about the related ipRGC-influenced light (IIL) responses, design applications related to these responses are flourishing. Optimizing our ocular light exposure in buildings can have beneficial effects on our health, well-being, and performance through the action of this photoreceptor. To compare different design options and optimize the lighting conditions for building occupants, lighting simulations are typically used. However, as our IIL responses depend on various aspects of the light exposure including its spectral characteristics, spectral simulations are required. The dataset shared here was originally collected to validate two spectral simulation tools, <em>ALFA</em> and <em>Lark</em>, for the study of building design in relation to occupants’ IIL responses. The validation was done by comparing the simulation outputs against actual measurements, and assessing how reliable these tools were in predicting spectral irradiance under different indoor light conditions. Data were collected in two different experimental setups, one under daylight conditions only and the other one under electric light conditions only. The experimental protocol and README files contain detailed information on how the data was collected and what data was collected.</p>
Data Set : Seismic Wave Propagation Simulations in Indo Gangetic Basin using Spectral Element Method
<p>Indo Gangetic (IG) basin is one of the largest alluvial basins in the world. The surrounding Himalayan topography and the geometry of the basin make the IG basin unique. The analysis of seismic response of the basin is important as the region is seismically active with more than 40% of Indian population residing in it. This online database consists of the input files for performing the spectral finite element simulation for IG basin by incorporating the 3D variation of material properties and basin geometry. The input files consists of mesher, solver and CMTSOLUTION files for SPECFEM3D Cartesian (Version-3) simulation.</p>
Derived data supporting the analysis of surface albedo changes from Mars 2020 observations: Probabilistic distribution of the Amplitude Spectral Densities of Supercam microphone recordings and Monte-Carlo dust devil simulations.
<p>These files contain derived data used in the analysis submitted for publication in Journal of Geophysical Research: Planets, entitled "Dust Lifting Through Surface Albedo Changes at Jezero Crater, Mars" by Vicente-Retortillo et al. The article was initially submitted on November 14, 2022, and the revised version on March 1, 2023.</p> <p>Files include the derived data and information needed to generate Figures 4 (Microphone_Data.mat and Plot_ASD_from_Microphone_Data) and 6 (remaining files) of the article.</p>
Using spectral reflectance and random forest method for modeling soil surface changes induced by simulated rainfall - datasets
<p>Using spectral reflectance and random forest method for modeling soil surface changes induced by simulated rainfall - datasets</p> <p>The impact of simulated rainfall on the soil surface roughness of different soil types with various initial surface states and the differences between their spectral characteristics were studied under laboratory conditions. The soil samples were collected from a horizon of fields near Poznań, western Poland. The physical and physicochemical properties of each soil sample were determined. Then, the part of the soil materials, consisting of natural aggregates, were used to form three soil surface roughness. </p> <p>An explanation of the table column names in the “soils properties.csv” file:</p> <p> </p> <ul> <li> <p>“textural classification” - Name of the granulometric group. Soil texture was determined by the hydrometer method according to standard PN-R-04032.</p> </li> <li> <p>“sand” - Sand content in the soil sample in %.</p> </li> <li> <p>“silt” – Silt content in the soil sample in %.</p> </li> <li> <p>“clay” – Clay content in the soil sample in %.</p> </li> <li> <p>pHH2O” - The pH of the soil sample determined in water. The soil pH was determined by the potentiometry method.</p> </li> <li> <p>“pHKCl” – The pH of the soil sample determined in KCl. The soil pH was determined by the potentiometry method.</p> </li> <li> <p>“SOC” – Organic matter content in soil was determined by oxidation titration using K2Cr2O7 with H2SO4 on the block mineralization.</p> </li> </ul> <p> </p> <p>An explanation of the table column names in the “rainfall doses.csv” file:</p> <p> </p> <ul> <li> <p>“rainfall simulation” - Rainfall simulation number.</p> </li> <li> <p>“rainfall dose” - One-time amount of rainfall dose expressed in millimeters.</p> </li> <li> <p>“accumulated rainfall” – Summation of rainfall after each successive dose expressed in millimeters.</p> </li> </ul> <p> </p> <p>An explanation of the table column names in the “soil measurements” file:</p> <p> </p> <ul> <li> <p>“textural classification” - Name of the granulometric group. Soil texture was determined by the hydrometer method according to standard PN-R-04032.</p> </li> <li> <p>“rainfall simulation” - Rainfall simulation number.</p> </li> <li> <p> “reflectance” - The amount of radiation reflected from the soil surface under the influence of successive rainfalls and expressed in nanometres. </p> </li> <li> <p>“roughness state” - The size of the roughness: R1 is the lowest soil roughness state, R2 represents medium soil roughness, and R3 represents the greatest roughness.</p> </li> <li> <p>“T3D” - Tortuosity index is a surface roughness index. It was calculated from DEM (Digital Elevation Model). It expresses the ratio between the true surface of DEM and its flat horizontal area.</p> </li> <li> <p>“HSD” - Height Standard Deviation is the second surface roughness index. It was calculated from DEM and expressed in millimeters. </p> </li> </ul> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p><br> </p> <p> </p>
Implementation of a reconstructed spectral sky definition in a light simulation tool and comparison to measurements
<p>Lark is a spectral light simulation tool that emerged to optimize indoor light exposure for ipRGC-influenced light (IIL) responses which play a significant role in our physiological and psychological health. Lark provides two sky spectrum models – D65 Standard Illuminant and measurements of sky spectral power distribution, both of which present limitations. D65 Standard Illuminant is an average sky spectrum based on measurements taken in locations in 45°N–55°N latitude range and may result in inaccuracies for locations outside of this range and for certain times of the day and year. Measurements of sky spectral power distribution, on the other hand, are very accurate, however, inaccessible to most users due to cost of equipment and lack of publicly available data. These limitations can be addressed by Occupant Wellbeing through Lighting (OWL), another spectral light simulation tool. OWL reconstructs a spectral power distribution for a given time and location, and its components can be implemented in Lark interface. The dataset presented here shows the results of the study that compared the impact of these three sky spectrum models on accuracy of Lark simulation tools for prediction of IIL responses.</p>
Spectral data associated to the publication: "Reflectance study of ice and Mars soil simulant associations—II. CO2 and H2O ice" by Z. Yoldi et al. (Icarus 386, 2022)
<p>This is the complete set of experimental VIS-NIR reflectance data collected by Z. Yoldi and co-authors for the article "Reflectance study of ice and Mars soil simulant associations—II. CO2 and H2O ice" published in Icarus 386 (2022). doi: https://doi.org/10.1016/j.icarus.2022.115116.</p> <p>A pre-print of the article is also freely available on ArXiv:</p> <p>https://arxiv.org/abs/2207.13905</p> <p>The article provides the methodology for the spectral aquisitions, discussion of the errors and uncertainties, analysis of the spectra and implications for the composition of Solar System surfaces.</p> <p>The spectral data are organised in folders corresponding to the different types of experiments detailed in the article. In case both hyperspectral and multispectral data were acquired, they are organised in subfolders.</p> <p>The spectral files inside these folders and subfolders have the following naming convention:</p> <p>spectrum_YYYYMMDD_experiment_name_TYPE_XX_YYY.csv</p> <p>Where TYPE is either multi (multispectral) or hyper (hyperspectral), XX indicates different samples within the experiment (see paper, figures and tables) and YYY is a sequential number in case of a temporal evolution (sublimation experiment in "20180207_ternary_mixture" with 12 timesteps).</p> <p> The files are in csv format (columns separated by comma) and the content of each column is indicated in the first line (header).</p>
Triple-resolution of spectral phases via semi-relativistic ab-initio RABBITT simulations DATA & WORKFLOW
<p>This dataset contains the necessary atomic structure and input files to use the <a href="https://gitlab.com/Uk-amor/RMT/rmt">R-Matrix with Time-dependence code suite</a> (open source and freely available) to replicate the results presented in "Triple-resolution of spectral phases via semi-relativistic ab-initio RABBITT simulations".</p> <p>Additionally, the output photoelectron momentum spectra data output from the RMT simulations are provided, to allow replication of the post-processing and spectral phase extraction processes in the absence of access to a large HPC cluster.</p> <p>Finally, a link is provided to a <a href="https://gitlab.com/lukeroantree/argon_rabbitt_scripts">git repository</a> hosted on gitlab.com where post-processing, spectral phase extraction, and visualisation tools are available to operate on these momentum spectra, and an interactive example is provided via a webhosted (via mybinder) Python Jupyter notebook.</p>
Evaluation of Spectral Light Simulation Tools For Prediction of ipRGC-influenced Light Responses in Field Conditions
<p><span>Spectral light simulation tools can be used to optimize lighting design in built environments for occupants’ ipRGC-influenced light (IIL) responses. The accuracy of these tools has been previously tested against measurements in controlled, neutral-colored spaces. This study aims to evaluate the accuracy of ALFA and Lark spectral light simulation tools in spectrally and geometrically complex environments. The methods consist of comparison of spectral irradiance, three IIL response metrics, and photopic illuminance in two office rooms in a university building. Measurements and simulations are performed in daylight, electric light, and combination of daylight and electric light. The dataset presented here documents this research.</span></p>
Cloud-resolving simulations with radiative transfer at reduced spectral resolution
<p>Data and scripts belonging to the paper titled "The impact of radiative transfer at reduced spectral resolution in cloud-resolving models", submitted to the Journal of Advances in Modeling Earth Systems.</p>
Rotamer distributions and spectral densities for 'Fitting side-chain NMR relaxation data using molecular simulations'
<p>Rotamer distributions and spectral density functions of methyl-bearing side chains of T4-Lysozyme from all-atom molecular dynamics simulations.</p> <p>3 sets of all-atom MD simulations:</p> <ul> <li>3 x 5 µs a99*-ILDN + modified methyl rotation barriers<sup>1</sup> & TIP4P/2005 water</li> <li>5 x 1 µs a99*-ILDN + modified methyl rotation barriers<sup>1</sup> & TIP4P/2005 water</li> <li>3 x 1 µs a15ipq + modified methyl rotation barriers<sup>2</sup> & SPC/Eb water</li> </ul> <p><sup>1</sup> Hoffmann, F., Mulder, F. A. A., & Schäfer, L. V. (2018). Accurate Methyl Group Dynamics in Protein Simulations with AMBER Force Fields. <em>The Journal of Physical Chemistry B</em>, <em>122</em>(19), 5038–5048. https://doi.org/10.1021/acs.jpcb.8b02769<br> <sup>2</sup> Hoffmann, F., Mulder, F. A. A., & Schäfer, L. V. (2020). Predicting NMR relaxation of proteins from molecular dynamics simulations with accurate methyl rotation barriers. <em>Journal of Chemical Physics</em>, <em>152</em>(8). https://doi.org/10.1063/1.5135379</p>
Time series of fluxes, biochemical and spectral variables simulated SCOPE model
<p>The dataset contains:</p> <ul> <li>Time series of fluxes, biochemical and spectral variables simulated with Soil Canopy Observation of Photochemistry and Energy fluxes (SCOPE) model, parameterized using structural vegetation parameters as well as meteorological data from the research station of Majadas de Tiétar (39°56′24.68″N, 5°45′50.27″W) (Cáceres, Spain)</li> <li>Time series of decomposed Photochemical Reflectance Index, far-red solar-induced chlorophyll fluorescence and far-red fluorescence yield into seasonal, diurnal and sub-diurnal components with Singular Spectrum Analysis</li> </ul>
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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)
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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.