Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
53
datasets available to search
ShareScore release 0.9.0
Dataset results
53 results for “spectral model”
Open Soil Spectral Library (training data and calibration models)
<p><strong>Open Soil Spectral Library</strong> contains training MIR (91,631) and VisNIR (65,063) spectral scans + soil calibration data (>60,000 unique locations) and calibration models. Key data set:</p> <ul> <li>ossl_all_L1_v1.2.qs: soil laboratory, site and spectra information;</li> </ul> <p>Important note: The data set spatially over-represents USA and European Union, with little training data in Asia, South America and Australia, hence calibration models reflect primarily soils of USA and Europe.</p> <p>To use the models and data please install <a href="https://hub.docker.com/r/opengeohub/r-geo">R and required packages</a>. Read more about the <strong><a href="https://github.com/traversc/qs">QS data format</a></strong> and how to convert it to CSV or similar. Modeling steps are explained in detail in: <a href="https://github.com/soilspectroscopy/ossl-models">https://github.com/soilspectroscopy/ossl-models</a>. To visualize database please use: <a href="https://explorer.soilspectroscopy.org/">https://explorer.soilspectroscopy.org/</a></p> <p>Complete OSSL documentation can be found at: <a href="https://soilspectroscopy.github.io/ossl-manual/">https://soilspectroscopy.github.io/ossl-manual/</a></p> <p><a href="https://soilspectroscopy.org/"><strong>Soil Spectroscopy for the Global Good</strong></a> is a Coordinated Innovation Network funded by USDA NIFA Food and Agriculture Cyberinformatics Tools Program (<a href="https://nifa.usda.gov/press-release/nifa-invests-over-7-million-big-data-artificial-intelligence-and-other">Award #2020-67021-32467</a>).</p> <p>Input datasets are property of the <a href="https://www.nrcs.usda.gov/wps/portal/nrcs/main/soils/research">USDA NRCS National Soil Survey Center – Kellogg Soil Survey Laboratory</a>, <a href="https://www.worldagroforestry.org/">ICRAF-World Agroforestry</a>, <a href="https://www.isric.org/">ISRIC-World Soil Information</a>, the <a href="http://africasoils.net/services/data/soil-databases/">Africa Soil Information Service</a> funded by the Bill and Melinda Gates Foundation, the <a href="https://esdac.jrc.ec.europa.eu/">European Soil Data Centre</a>, the <a href="https://www.neonscience.org/">National Ecological Observatory Network</a>, and <a href="https://sae.ethz.ch/">ETH Zurich</a>. </p> <p>For more advanced uses of the soil spectral libraries <strong>we advise to contact the original data producers</strong> especially to get help with using, extending and improving the original SSL data.</p>
On effective spectral wideband models for clear sky atmospheric emissivity and transmissivity
<p><strong>Overview</strong></p> <p>The HDF5 file contains primary measurement data and secondary processing data that was used to assess clear sky effective emissivity and transmissivity estimates and generate the results in the associated manuscript (accepted and forthcoming).</p> <p>Data is indexed by solar time and provided per site for years 2010 through 2015. Sample Python code is provided to reconstruct training and validation sets by concatenating all 'tra' or 'val' samples across sites. Results can be explored by modifying choice of filters and constructing new training and validation sets.</p> <p><strong>Data usage</strong></p> <p>The usage of the data presented here is intended for research and development purposes only and implies explicit reference to the paper:<br><em>Matsunobu, L. M., & Coimbra, C. F. M. (2024). On effective spectral wideband models for clear sky atmospheric emissivity and transmissivity. Journal of Geophysical Research: Atmospheres, 129, e2023JD039798. https://doi.org/10.1029/2023JD039798</em></p> <p><strong>Data description</strong></p> <p>Column names and descriptions are as follows:<br>- dlw_m: measured downwelling longwave [W/m^2]<br>- ghi_m: measured global horizontal irradiance [W/m^2]<br>- dni_m: measured direct normal irradiance [W/m^2]<br>- dhi_m: measured diffuse horizontal irradiance [W/m^2]<br>- rh_m: measured relative humidity [%]<br>- pa_m: measured atmospheric pressure [hPa]<br>- t_m: measured temperature [K]<br>- sza: solar zenith angle [deg]<br>- ghi_c: clear sky global horizontal irradiance [W/m^2]<br>- dni_c: clear sky direct normal irradiance [W/m^2]<br>- dhi_c: clear sky diffuse horizontal irradiance [W/m^2]<br>- cs1: clear sky filter 1<br>- cs2: clear sky filter 2<br>- site_elev: station elevation [m]<br>- clr_pct: fraction of samples identified as clear for the given site and day<br>- clr_num: number of samples identified as clear for the given site and day<br>- pw_hpa: water vapor partial pressure [hPa]<br>- alt_correction: altitude correction<br>- tra: indicate if sample is included in training set<br>- val: indicate if sample is included in validation set<br>- sqrt_pw: square root of non-dimensional water vapor partial pressure<br>- e_sky: effective clear sky emissivity</p> <p>The last two columns, 'sqrt_pw' and 'e_sky' represent the input and target for linear regression, i.e. e_sky = c_1 + (c_2 * sqrt_pw).<br>Altitude corrected sky emissivity, or expected emissivity for a station at sea-level, is found by e_sky - alt_correction.</p> <p><strong>Sample code (Python v3.8)</strong></p> <pre>import pandas as pd site = "GWC" # or other station code df = pd.read_hdf("data.h5", key=site) # import single site</pre> <p>Training and validation sets can be reconstructed as below. Linear regression on 'sqrt_pw' to predict 'e_sky' - 'alt_correction' in the resultant training set will reproduce results in the associated manuscript.</p> <pre>training = [] validation = [] surfrad_sites = ['BON', 'DRA', 'FPK', 'GWC', 'PSU', 'SXF', 'TBL'] for site in surfrad_sites: # loop through sites df = pd.read_hdf("data.h5", key=site) df["site"] = site # add site name training.append(df.loc[df.tra]) # append samples marked as training validation.append(df.loc[df.val]) # append samples marked as validation # join respective set samples across sites training = pd.concat(training, ignore_index=False) validation = pd.concat(validation, ignore_index=False)</pre> <p>Reproduce regression results</p> <pre>from sklearn.linear_model import LinearRegression c1 = 0.6 # set intercept (c1 constant) x = training.sqrt_pw.to_numpy().reshape(-1, 1) y = training.e_sky - training.alt_correction - c1 # adjust for altitude and c1 y = y.to_numpy().reshape(-1, 1) model = LinearRegression(fit_intercept=False) model.fit(x, y) c2 = model.coef_[0][0] print(f"c1={c1:.3f}, c2={c2:.3f}") # output: c1=0.600, c2=1.652</pre>
Accurate modeling of plasma acceleration with arbitrary order pseudo-spectral particle-in-cell methods
<p>This is supplementary material to the publication "Accurate modeling of plasma acceleration with arbitrary order pseudo-spectral particle-in-cell methods" by S. Jalas et. al.</p> <p>Particle in Cell (PIC) simulations are a widely used tool for the investigation of both laser- and beam-driven plasma acceleration. It is a known issue that the beam quality can be artificially degraded by numerical Cherenkov radiation (NCR) resulting primarily from an incorrectly modeled dispersion relation. Pseudo- spectral solvers featuring infinite order stencils can strongly reduce NCR – or even suppress it – and are therefore well suited to correctly model the beam properties. For efficient parallelization of the PIC algorithm, however, localized solvers are inevitable. Arbitrary order pseudo-spectral methods provide this needed locality. Yet, these methods can again be prone to NCR. Here, we show that acceptably low solver orders are sufficient to correctly model the physics of interest, while allowing for parallel computation by domain decomposition.</p> <p>The given script can be run with the open source particle in cell codes FBPIC (https://github.com/fbpic/fbpic) and Warp (https://bitbucket.org/berkeleylab/warp)</p> <p>The produced data is conformant with the openPMD standard (openpmd.org) and can be analysed for example with the openPMD-viewer (https://github.com/openPMD/openPMD-viewer).</p>
Experimental absorption spectra used in "Spectral profile of ro-vibrational transitions of HCl broadened by He, Ar and SF6: testing the β-correction to the Hartmann-Tran profile and the speed dependent (complex) hard collision model"
<p>Experimental absorption spectra used in the article entitled <strong>"Spectral profile of ro-vibrational transitions of HCl broadened by He, Ar and SF<sub>6</sub>: testing the β-</strong><strong>correction to the Hartmann-Tran profile and the speed dependent (complex) hard collision model", </strong>to be published in the Journal of Quantitative Spectroscopy and Radiative Transfer. Accepted 19 March 2024.</p> <div> <div> <div> <div> <h3><a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.jqsrt.2024.108977" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.jqsrt.2024.108977</a></h3> </div> </div> </div> </div> <p> </p> <p>Comma separated ASCII files with 1 header line.</p> <p>First column is wavenumber in cm-1</p> <p>The rest of the columns contain the napierian absorbance at the total pressure given in the header. </p> <p>Mind the units!: pressure of Ar-mixtures is expressed in Torr, pressure of He- and SF6-mixtures is expressed in mbar</p> <p> </p>
Spectral model fitting for all 4XMM-DR11 for all sources
<p>Fitting products for all sources: this deliverable includes fits with a simple model (absorbed power law) to the pipeline count rates from emldetect to all detections in the 4XMM DR11 catalogue.</p>
TimeSpec4LULC: A Smart-Global Dataset of Multi-Spectral Time Series of MODIS Terra-Aqua from 2000 to 2021 for Training Machine Learning models to perform LULC Mapping
<p>TimeSpec4LULC is a smart open-source global dataset of multi-spectral time series for 29 Land Use and Land Cover (LULC) classes ready to train machine learning models. It was built based on the seven spectral bands of the MODIS sensors at 500 m resolution from 2000 to 2021 (262 observations in each time series). Then, was annotated using spatial-temporal agreement across the 15 global LULC products available in Google Earth Engine (GEE).</p> <p>TimeSpec4LULC contains two datasets: the original dataset distributed over 6,076,531 pixels, and the balanced subset of the original dataset distributed over 29000 pixels.</p> <p>The original dataset contains 30 folders, namely "Metadata", and 29 folders corresponding to the 29 LULC classes. The folder "Metadata" holds 29 different CSV files describing the metadata of the 29 LULC classes. The remaining 29 folders contain the time series data for the 29 LULC classes. Each folder holds 262 CSV files corresponding to the 262 months. Inside each CSV file, we provide the seven values of the spectral bands as well as the coordinates for all the LULC class-related pixels.</p> <p>The balanced subset of the original dataset contains the metadata and the time series data for 1000 pixels per class representative of the globe. It holds 29 different JSON files following the names of the 29 LULC classes.</p> <p>The features of the dataset are:</p> <p>- ".geo": the geometry and coordinates (longitude and latitude) of the pixel center.</p> <p>- "ADM0_Code": the GAUL country code.</p> <p>- "ADM1_Code": the GAUL first-level administrative unit code.</p> <p>- GHM_Index": the average of the global human modification index.</p> <p>- "Products_Agreement_Percentage": the agreement percentage over the 15 global LULC products available in GEE.</p> <p>- "Temporal_Availability_Percentage": the percentage of non-missing values in each band.</p> <p>- "Pixel_TS": the time series values of the seven spectral bands.</p>
Supplementary data for "Collision-induced absorptions by pure CO2 in the infrared: New measurements in the 1150–4500 cm−1 spectral range and empirical modeling for applications" published in Icarus. https://doi.org/10.1016/j.icarus.2024.116265
<p>Supplementary data for the paper entitled ""Collision-induced absorptions by pure CO2 in the infrared: New measurements in the 1150–4500 cm−1 spectral range and empirical modeling for applications" published in <em>Icarus</em>. https://doi.org/10.1016/j.icarus.2024.116265</p> <p>These data files contain the coefficients needed to compute the shape of a pure CO2 CIA band at a given temperature (see Eq. (3)) of the paper https://doi.org/10.1016/j.icarus.2024.116265</p> <p>"CIA_shape_coeff.dat" involves a CIA band shape without dimer signatures </p> <p>"CIA_dimer_shape_coeff.dat" involves a CIA band shape with dimer signatures </p> <p>"CIA_Fermi_doublet_shape_coeff.dat" involves the Fermi doublet band shape</p> <p>First column is wavenumber in cm-1, the rest of the columns contain the coefficients.</p>
Synthetic urban gamma-ray spectra for training spectral detection and identification models
Open the record for dataset details and reuse information.
Datasets used in 'Streambed hydraulic conductivity estimated by spectral induced polarization imaging can help to improve groundwater modeling'
<p>These datasets pertain to the manuscript entitled 'Streambed hydraulic conductivity estimated by spectral induced polarization imaging can help to improve groundwater modeling', which is currently submitted for revision in Water Resources Research. They comprise raw data from measurements taken in the field at 2 sites in terms of (1) pressure time series during the performed slug tests, (2) impedance from spectral induced polarization, (3) submersion levels of the used electrodes below the top of the water column, and (4) three-dimensional Cartesian coordinates relating the measurements spatially. The coordinates have been projected to a local coordinate system for each site, to comply with a non-disclosure agreement of the measurement locations. The projection of the coordinates still allows to fully reproduce the presented results, if using the methods described in the manuscript. The naming convention throughout the datasets is consistent with site labels used in the manuscript. All data is given as comma-separated values with intuitive file names and self-explanatory headers containing a list of field names. The slug test data includes multiple repetitions of the same measurement, and the impedance measurements contain normal as well as reciprocal readings – as described in the manuscript.</p> <p>The data is separated into two compressed file archives, named according to the site names given in the manuscript. Each file pertaining to slug tests at a certain location, in the subfolder “slugTestRecordings” has the following naming convention: “<locationTag>_<finalDepth>.csv”, where <locationTag> corresponds to the local coordinates given in “coordinates.txt”, and where <finalDepth> is an integer describing the largest depth in cm at which a slug test was performed according to the protocol described in the manuscript. Each file pertaining to impedance measurements at a certain profile, in the subfolder “SIPRecordings”, has the following naming convention: “<locationTag>_<frequency>.dat”, where <locationTag> corresponds to the local coordinates given in “coordinates.txt”, and where <frequency> is a zero-padded integer describing the measurement frequency in Hz at which the measurement was performed according to the protocol described in the manuscript. Submersion levels of the electrodes below the top of the stream’s water column are given in m in the file “submersionLevels.txt”, corresponding to the local coordinates given in “coordinates.txt”. The local coordinates given in m in “coordinates.txt” have the following convention for the column “locationTag”: “<profileTag>-<electrodeNumber>, where <profileTag> pertains to a name of the electrical array and <electrodeNumber”> is a continuous number for the electrode. Slug tests were exclusively perfomed at the location of electrodes and files are, thus, as described above, named accordingly.</p>
Spectral data and R modeling code from: Polarized light sensitivity in Pieris rapae is dependent on both color and intensity
<p>This dataset provides supplementary spectral data and the R code underlying the spectral sensitivy moding of female <em>Pieris rapae</em> photoreceptors used in the manuscript "Polarized light sensitivity in <em>Pieris rapae</em> is dependent on both color and intensity".</p>
Molodensky's truncation coefficients for cap integration in spectral gravity forward modelling
<p>Provided are Molodensky's truncation coefficients for cap-modified spectral gravity forward modelling from the <a href="https://doi.org/10.1007/s00190-019-01277-3">Bucha et al. (2019)</a> study. The coefficients are evaluated for</p> <ul> <li>the spherical distance of <em><span>\(\psi_0 = 100000\ \mathrm{m} / 6378137\ \mathrm{m}\)</span> </em>(100 km integration radius from the evaluation point),</li> <li>the reference sphere having the radius <span>\(R = 6378137\ \mathrm{m}\)</span>,</li> </ul> <ul> <li>the radius of the evaluation point<em> <span>\(r = 6378137\ \mathrm{m} + 7000\ \mathrm{m}\)</span></em>,</li> <li>harmonic degrees <span>\(n=0,\dots,21600\)</span>,</li> <li>topography powers <span>\(p=1,\dots,30\)</span>,</li> <li>radial derivatives <span>\(k=0,\dots,40\)</span>, and</li> <li>the first- and second-order horizontal derivatives.</li> </ul> <p>The coefficients were computed using 256 significant digits, ensuring 24-digit accuracy or better. After the evaluation, the coefficients were converted to double precision with 16 significant digits. Importantly, in some cases, the loss of significance errors may be encountered during the spherical harmonic synthesis when using the coefficients (see the reference below).</p> <p>Bucha, B., Hirt, C., Kuhn, M., 2019. <em>Cap integration in spectral gravity forward modelling up to the full gravity tensor</em>. Journal of Geodesy, <a href="https://doi.org/10.1007/s00190-019-01277-3">https://doi.org/10.1007/s00190-019-01277-3</a>.</p>
Data from: Spectral wear modelling of rubber friction on a hard substrate with large surface roughness
<p>Soft-hard matter friction is a long-standing tribology problem that remains unclarified, requiring engineers to empirically predict the wear life. To clarify this issue, this study examines the transient running-in regime of rubber friction on a hard rough substrate and models the temporal wear progression using the spectrum curves of surface roughness for both materials. Performing a series of friction tests and three-dimensional surface-height measurements, the time-dependent behaviours of the power spectral densities (PSDs) are divided into two phases, namely the initial non-steady and long-term steady phases. The detailed spectral analyses of worn rubber surfaces in the initial phase lead to a blended PSD function between self-affine and K-correlation surface models, consisting of one variable (the Hurst exponent) that is saturated by the substrate self-affinity. Supported by the Greenwood–Williamson theory concerning rough contact mechanics, the volumetric estimate with the blended PSD function is used to assess the volume rate of wear debris in the steady phase, which is validated experimentally. These findings not only improve the wear predictions of soft materials from previous measurements of worn surfaces but also help clarify the constrained multiscale mechanism of wear.</p>
Spectral reconstruction using iteratively reweighted-regulated model from two illumination camera responses
<p>This document shows spectral reflectance data and RGB values of 380 samples for spectral estimation, which includes 5 databases: 1) Spectral reflectance of ColorChecker semigloss chart (CCSG, 140 patches), 2) Spectral reflectance of ColorChecker DC matte chart (CCDC, 240 patches including 232 mattes, and 8 glossy patches), 3) RGB values of ColorChecker semigloss chart (CCSG, 140 patches) , 4) RGB values of ColorChecker DC matte chart (CCDC, 240 patches including 232 mattes, and 8 glossy patches), 5) Spectral Power Distribution value of 3500K & 6500K illumination conditions.</p>
Storyline data used in the paper "The July 2019 European heatwave in a warmer climate: Storyline scenarios with a coupled model using spectral nudging"
<p>We provide the storyline data (in NetCDF format) used in the paper: “The July 2019 European heatwave in a warmer climate: Storyline scenarios with a coupled model using spectral nudging” published in Journal of Climate. The data is structured in four .tar.gz files (Preindustrial, Present, 2 and 4 K warmer climates) containing all variables used in this each climate. The data from the five ensemble members (E1 to E5) have been included separately in 3-months files.</p> <p>Atmospheric variables (Files are named as: {variable}_E{ensemble member}_{starting month}{year}.nc:</p> <ul> <li> <p>Latent heat flux (ahfl)</p> </li> <li> <p>Sensible heat flux (ahfs)</p> </li> <li> <p>Monthly Global Mean 2m Temperature (GMTT2mMonthly)</p> </li> <li> <p>Maximum 2m Temperature (t2max)</p> </li> <li> <p>Mean 2m Temperature (t2mean)</p> </li> <li> <p>Minimum 2m Temperature (t2max)</p> </li> <li> <p>Soil Wetness (ws)</p> </li> </ul> <p> Only for present climate:</p> <ul> <li> <p>850 hPa Temperature (T850)</p> </li> <li> <p>Total Cloud Cover (TCC)</p> </li> <li> <p>500 hPa Geopotential Height (Z500)</p> </li> </ul> <p>Five layers soil moisture (Only for present climate, Files are named as: From20172019in2017Climatessp370{ensemble member}_{year}{starting month}.01_jsbid.nc) </p> <p>Oceanic variables (from FESOM, Files are named as: {variable}_E{ensemble member}_{year}{starting month}01.nc:</p> <ul> <li> <p>Sea Ice Concentration (SIC)</p> </li> <li> <p>Sea Surface Temperature (SST)</p> </li> </ul> <p><strong>Please, note that FESOM uses an unstructured mesh.</strong></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>
The SPHINX M-dwarf Spectral Grid. I. Benchmarking New Model Atmospheres to Derive Fundamental M-Dwarf Properties
<p><strong>NEW UPDATE:::::::::::::VERSION 4</strong></p> <p>MODEL GRID AND SUPPLEMENTARY FIGURES for <strong>The SPHINX M-dwarf Spectral Grid. I. Benchmarking New Model Atmospheres to Derive Fundamental M-Dwarf Properties:</strong></p> <p><strong>(1) MODEL GRID:</strong> Zip file titled 'SPHINX_MODELS_MLT_1.zip' contains a directory (376MB on disk) that is divided into three folders: ATMS, SPECTRA, and ABUNDANCES. All models here assume mixing length parameter of 1. For more info, we direct the reader to the paper.</p> <p>The ATMS directory contains thermal profiles/atmospheres of all models. (Temperature in K, Pressure in bars)</p> <p>The SPECTRA directory contains synthetic spectra. (Wavelength--0.1 to 20 microns, Flux in W/m2/m, R~250)</p> <p>The ABUNDANCES directory contains mixing ratios of all atomic and molecular species included in these models.</p> <p><strong>(2) SUPPLEMENTARY FIGURES:</strong></p> <p>The plot files are named as 'target name' _ corner.</p> <p>Each file is a corner plot of posterior probability distributions from grid-model fit of low-resolution spectra of benchmark M dwarfs using the SPHINX model grid. We also include posterior distributions of Starfish (Czekala et al.2015) hyperparameters to properly constrain model and data systematics. The vertical blue lines in the plots indicate values from observations (empirically derived [M/H] from Mann et al. 2013 and interferometrically measured radii from Boyajian et al. 2012b).</p> <p>File named Gl436_mixinglength shows difference in grid model fit assuming mixing length parameter 1 vs 0.5.</p> <p>File named Gl725B_spot shows difference in grid model fit assuming stellar photospheric heterogeneity vs without.</p> <p><strong>If you use our stellar atmosphere models, please remember to cite both the zenodo doi for the open source data as well as the paper. Thank you!</strong></p> <p> </p> <p>--> link to <a href="https://iopscience.iop.org/article/10.3847/1538-4357/acabc2/meta">published paper</a></p>
DirtyGrid: 3D dust radiative transfer modeling of spectral energy distributions of dusty stellar populations
<p>Output global SEDs of a large grid of 3D stellar+dust radiative transfer models spanning the range of star formation and dust contents of regions of galaxies.</p> <p>Paper describing the DirtyGrid is Law, Gordo, & Misset (2018, ApJ, submitted)</p> <p>Code to make to access this data at: https://github.com/karllark/pydirtygrid</p>
Spectral models for binary products: Unifying subdwarfs and Wolf-Rayet stars as a sequence of stripped-envelope stars
<p>MESA inlists associated with <a href="https://ui.adsabs.harvard.edu/#abs/2018A&A...615A..78G/abstract">Götberg et al. (2018)</a>. MESA version 8118.</p> <p>Publication DOI: <a href="https://doi.org/10.1051/0004-6361/201732274">10.1051/0004-6361/201732274</a></p>
A Novel Hybrid Finite Element-Spectral Boundary Integral Scheme for Modeling Earthquake Cycles: Application to Rate and State Faults with Low-Velocity Zones
<p>We present a novel hybrid finite element (FE) - spectral boundary integral (SBI) scheme that enables efficient simulation of earthquake cycles. This combined FE-SBI approach captures the benefits of finite elements in modelling problems with nonlinearities, as well as the computational superiority of SBI. The domain truncation enabled by this scheme allows us to utilize high-resolution finite elements discretization to capture inhomogeneities or complexities that may exist in a narrow region surrounding the fault. Combined with an adaptive time stepping algorithm, this framework opens new opportunities for modeling earthquake cycles with high-resolution fault zone physics. In this initial study, we consider a two dimensional (2-D) anti-plane model with a vertical strike-slip fault governed by rate and state friction in the quasi-dynamic limit under the radiation damping approximation. The proposed approach is first verified using the benchmark problem BP-1 from the Southern California Earthquake Center (SCEC) sequence of earthquake and aseismic slip (SEAS) community verification effort. The computational framework is then utilized to model the earthquake sequence and aseismic slip of a fault embedded within a low-velocity fault zone (LVFZ) with different widths and compliance levels. Our results indicate that sufficiently compliant LVFZs contribute to the emergence of sub-surface events that fail to penetrate to the free surface and may experience earthquake clusters with nonuniform inter-seismic time. Furthermore, the LVFZ leads to slip rate amplification relative to the homogeneous elastic case. We discuss the implications of our results for understanding earthquake complexity as an interplay of fault friction and bulk heterogeneities. The complete work consists of all files listed below. </p>
Model Fit Figures for: The 100 pc White Dwarf Sample in the SDSS Footprint II. A New Look at the Spectral Evolution of White Dwarfs
<p>Figures for the model atmosphere fits to the spectroscopically confirmed white dwarfs in the 100 pc sample and the SDSS footprint (<span>arXiv:2412.04611). </span></p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.