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29 results for “radiative transfer model”
Radiance data for "Systematic Comparison of Vectorial Spherical Radiative Transfer Models in Limb Scattering Geometry" by Zawada et al.
<p>Radiance data for "Systematic Comparison of Vectorial Spherical Radiative Transfer Models in Limb Scattering Geometry" by Zawada et al. which is to be submitted to Atmospheric Measurement Techniques. </p> <p>A comprehensive inter-comparison of seven radiative transfer models in the limb scattering geometry has been<br> performed. Every model is capable of accounting for polarisation within a fully spherical atmosphere. Three models (GSLS, SASKTRAN-HR, and SCIATRAN) are deterministic, and four models (MYSTIC, SASKTRAN-MC, Siro, and SMART-G)<br> are statistical using the Monte Carlo technique. This dataset consists of the raw radiance data used to perform the intercomparisons, atmospheric input data for the optical properties of the atmosphere, and data specifying the geometry of the test cases.</p> <p>Data is provided in NetCDF4 format with documentation present inside the variable attributes.</p> <p>More detail on the comparison scenarios can be found within the published article. (Link to be added when available).</p>
Radiative transfer modeling in structurally-complex stands: what aspects matter most?: Dataset
<p>This repository is linked to the paper "Radiative transfer modeling in structurally-complex stands: what aspects matter most?" submitted to Annals of Forest Science and written by Frédéric ANDRÉ (corresponding author), Louis DE WERGIFOSSE, François DE COLIGNY, Nicolas BEUDEZ, Gauthier LIGOT, Vincent GAUTHRAY-GUYÉNET, Benoit COURBAUD and Mathieu JONARD.</p> <p>The repository contains the three following files :</p> <ul> <li>CalibrationResults.csv: Bayes factors and summary statistics of parameter estimates for each calibration run</li> <li>ParameterPosteriorDistributions.csv: median values and 90% credible intervals for the parameter posterior distributions</li> <li>StatisticalComparison.csv: statistics (Fractional bias, Root mean square error, Paired Student test, Pearson correlation coefficient, Parameters of the Deming regression between observed and predicted values) used to compare the 'Best model configurations'</li> </ul> <p>For more information concerning this repository or the study, please do not hesitate to contact Frédéric ANDRÉ (frederic.andre@uclouvain.be) or Mathieu JONARD (mathieu.jonard@uclouvain.be).</p>
A dataset of global variations in directional solar radiation exposure for ocular research using the libRadtran radiative transfer model
<p>Directional solar photon flux density has particular relevance to eye disease research (keratitis, cataract formation, macula degeneration) because ocular components (cornea, lens, retina) experience different exposures dependent on global location, structural geometry of the eye and human behaviour (Sliney, 1997). The human macula has a field of view of ~17<strong>°</strong>, or 0.06901537 sr (Strasburger, Rentschler & Jüttner, 2011) and its cone of exposure can be modelled at a range of global locations using a radiation transfer model to estimate different directions of irradiation. This dataset provides examples of spectral radiance within the macula field of vision, calculated with the radiative transfer model libRadtran v2.0.3 (Mayer & Kylling, 2005). Three data sets are provided at different latitudes without correction for spectral ocular transmission. Unless otherwise specified, all simulations were parametrized according to local meteorological condition (altitude, pressure, temperature) and atmospheric conditions on the simulated day (aerosol optical density, water column, O<sub>3</sub> and NO<sub>2</sub> concentrations). The model was parametrized for a subject looking northward toward the ground (-15<strong>°</strong> from horizon), at a height of 170 cm above the ground.</p> <p>For each simulation, a separate file is available for each condition (latitude, time, date, see below) that includes radiance at each wavelength. Radiance values are in mW m<sup>-2</sup> nm<sup>-1</sup> sr<sup>-1</sup>.</p> <p>The technique provides future opportunity to model global exposures of different ocular components to spectral solar irradiance using information on ocular transmission, local terrain, albedo and human behaviour in order to explore their relevance in epidemiological studies of age-related eye disease.</p> <p>For each simulation, a separate file is available for each condition (latitude, time, date, see below) that includes radiance at each wavelength. Radiance values are in mW m<sup>-2</sup> nm<sup>-1</sup> sr<sup>-1</sup>.</p> <p>The technique provides future opportunity to model global exposures of different ocular components to spectral solar irradiance using information on ocular transmission, local terrain, albedo and human behaviour in order to explore their relevance in epidemiological studies of age-related eye disease.</p> <p><em>Simulation 1: </em>This data set reports the spectral radiance from 250 - 500 nm at:</p> <ul> <li>3 latitudes (61.0: Southern Finland, 50.1 Northern France, 38.0: Central Spain).</li> <li>4 dates (April 17<sup>th</sup>, July 1<sup>st</sup>, September 1<sup>st</sup>, November 6<sup>th</sup> 2019).</li> <li>24 hours.</li> <li>8 cardinal directions (every 45<strong>° </strong>from North).</li> <li>2 aerosol optical densities (0.1 and 2.5).</li> </ul> <p><em>Simulation 2: </em>This data set reports the spectral radiance from 250 - 2,500 nm at:</p> <ul> <li>3 latitudes (61.0: Southern Finland, 50.1 Northern France, 38.0: Central Spain).</li> <li>4 dates (April 17<sup>th</sup>, July 1<sup>st</sup>, September 1<sup>st</sup>, November 6<sup>th</sup> 2019).</li> <li>24 hours.</li> <li>1 cardinal direction (North).</li> <li>2 aerosol optical densities (0.1 and 2.5).</li> </ul> <p><em>Simulation 3: </em>This data set reports the spectral radiance from 250 - 500 nm at:</p> <ul> <li>1 latitude (61.0: Southern Finland).</li> <li>4 dates (April 17<sup>th</sup>, July 1<sup>st</sup>, September 1<sup>st</sup>, November 6<sup>th</sup> 2019).</li> <li>24 hours.</li> <li>9 cardinal directions (every 40<strong>° </strong>from North).</li> <li>3 bidirectional reflectance distribution functions for the ground (forest, urban, snow).</li> <li>2 tilt angles for the eye direction (0<strong>° </strong> from horizon or -15<strong>°</strong> from horizon, toward the ground).</li> </ul> <p> </p>
An SI-traceable protocol for the validation of radiative transfer model-based reflectance simulation: datasets
<p>This data record contains datasets used in the study "An SI-traceable protocol for the validation of radiative transfer model-based reflectance simulation":</p> <ul> <li>The <code><span>final_design.ply</span></code> file contains the mesh corresponding to the final artefact design.</li> <li>The <code><span>material_measurements.nc</span></code> file contains goniophotometer records for the material reflectance.</li> <li>The <code><span>artefact_measurements.nc</span></code> file contains goniophotometer records for the artefact reflectance.</li> </ul>
Single-footprint retrievals for AIRS using a fast TwoSlab cloud-representation model and the all-sky infrared radiative transfer algorithm
<p>Dataset for AMT-2017-261 by DeSouza-Machado et. al.<br> <br> 1D-variational retrievals of temperature and moisture fields from<br> hyperspectral infrared satellite sounders use cloud-cleared radiances<br> as their observation. These derived observations allow the use of<br> clear-sky only radiative transfer in the inversion for geophysical<br> variables but at reduced spatial resolution compared to the native<br> sounder observations. Cloud-clearing can introduce various errors,<br> although scenes with large errors can be identified and<br> ignored. Information content studies show that when using multi-layer<br> cloud liquid and ice profiles in infrared hyperspectral radiative<br> transfer codes, there are typically only 2-4 degrees of freedom of<br> cloud signal. This implies a simplified cloud representation is<br> sufficient for some applications which need accurate radiative<br> transfer. Here we describe a single-footprint retrieval approach for<br> clear and cloudy conditions, which uses the thermodynamic and cloud<br> fields from Numerical Weather Prediction (NWP) models as a first<br> guess, together with a simple cloud representation model coupled to a<br> fast scattering radiative transfer algorithm (RTA). The NWP model<br> thermodynamic and cloud profiles are first co-located to the<br> observations, after which the N-level cloud profiles are<br> converted to two slab clouds (typically one for ice and one for water<br> clouds). From these, one run of our fast cloud representation model<br> allows an improvement of the \emph{a-priori} cloud state by comparing the<br> observed and model simulated radiances in the thermal window<br> channels. The retrieval yield is over 90\%, while the degrees of<br> freedom correlate with the observed window channel brightness<br> temperature which itself depends on the cloud optical depth. The cloud<br> representation/scattering package is bench-marked against radiances<br> computed using a Maximum Random Overlap cloud scheme. All-sky infrared<br> radiances measured by NASA’s Atmospheric Infrared Sounder (AIRS) and<br> NWP thermodynamic and cloud profiles from the European Center for<br> Medium Range Weather Forecasting (ECMWF) forecast model are used in<br> this paper.</p> <p> </p>
Randomly sampled coefficients for synchrotron radiative transfer in the Stokes basis, power law model, computed by rimphony, for consumption by neurosynchro
<p>This directory contains a training set of 22 million randomly-sampled radiative transfer coefficients generated by <a href="https://github.com/pkgw/rimphony/">rimphony</a>, suitable for use with the <a href="https://github.com/pkgw/neurosynchro/">neurosynchro</a> package. These coefficients can be used for numerical radiative transfer of synchrotron emission in the Stokes basis with a package such as <a href="https://github.com/jadexter/grtrans/">grtrans</a>.</p> <p>In this particular dataset, coefficients were computed using a model of a power law electron distribution isotropic in pitch angle. The input parameters, which were sampled randomly in a three-dimensional space, are:</p> <ul> <li><em>s</em>, the harmonic number, dimensionless, sampled logarithmically between 5 and 50,000,000.</li> <li><em>theta</em>, the angle between the ray path and the local magnetic field, measured in radians, sampled linearly between 0.001 and π/2 (namely, 1.5707963267948966).</li> <li><em>p</em>, the power-law index of the energetic electrons, dimensionless, sampled linearly between 1.5 and 7.</li> </ul> <p>The coefficients were computed on Harvard’s Odyssey cluster using Git commit <a href="https://github.com/pkgw/rimphony/commit/772161ebda0217b8c1ccb8ce3801ad9dc3701a4f">772161</a> of rimphony. A total of about 5,000 CPU hours were used, with 500 processes running for about 10 hours each. There are 2,748,835 data rows in total. The data are provided in their original format, split among 500 files, so that smaller subsamples of the data may be loaded easily. A README.md file provides more detailed information.</p>
Trained neural network data for synchrotron radiative transfer in the Stokes basis, power law model, computed by rimphony, for consumption by neurosynchro
<p>This archive contains data representing a trained-up neural network suitable for use with the <a href="https://github.com/pkgw/neurosynchro/">neurosynchro</a> package. The network generates coefficients that can be used for numerical radiative transfer of synchrotron emission in the Stokes basis with a package such as <a href="https://github.com/jadexter/grtrans/">grtrans</a>.</p> <p>In this particular dataset, networks were trained on a training set of coefficients generated by <a href="https://github.com/pkgw/rimphony/">rimphony</a> that is available as <a href="https://doi.org/10.5281/zenodo.1341154">DOI:10.5281/zenodo.1341154</a>. The data were generated using a model of a power law electron distribution isotropic in pitch angle. The input parameters, which were sampled randomly in a three-dimensional space, were:</p> <ul> <li><em>s</em>, the harmonic number, dimensionless, sampled logarithmically between 5 and 50,000,000.</li> <li><em>theta</em>, the angle between the ray path and the local magnetic field, measured in radians, sampled linearly between 0.001 and π/2 (namely, 1.5707963267948966).</li> <li><em>p</em>, the power-law index of the energetic electrons, dimensionless, sampled linearly between 1.5 and 7.</li> </ul> <p>The training set was computed on Harvard’s Odyssey cluster using Git commit <a href="https://github.com/pkgw/rimphony/commit/772161ebda0217b8c1ccb8ce3801ad9dc3701a4f">772161</a> of rimphony. A total of about 5,000 CPU hours were used, with 500 processes running for about 10 hours each, yielding about 22 million numbers. Training the networks took about 3 hours on an 8-core laptop.</p> <p>For the purposes of <em>neurosynchro</em>, the formats of the files in this package should be regarded as internal implementation details. The <a href="https://pypi.org/project/neurosynchro/">neurosynchro</a> Python package will load up the files in this archive and use them to predict synchrotron coefficients. For specifics, see <a href="https://neurosynchro.readthedocs.io/en/stable/">the neurosynchro documentation</a>.</p>
Results of Improved SMAP Soil Moisture Retrieval Using a Deep Neural Network-based Replacement of Radiative Transfer and Roughness Model
<p>This repository contains:</p> <ol> <li>A deep neural network (DNN) based soil moisture (SM) estimates (NN) based on the SMAP TB (Descending, 6 AM) and SMAP SCA-V ancillary data as the input variables. (<a href="../api/records/13309165/draft/files/SMAP_NN_36km_20150331_20220326.nc/content" target="_blank" rel="noopener noreferrer">SMAP_NN_36km_20150331_20220326.nc</a>)</li> <li>Temporally averaged roughness parameter (hNN) and scattering albedo (omegaNN) which are retrieved by inversely tracking the DNN model. (<a href="../api/records/13309165/draft/files/SMAP_hNN_omegaNN_36km_temporal_average_201503_202103.nc/content" target="_blank" rel="noopener noreferrer">SMAP_hNN_omegaNN_36km_temporal_average_201503_202103.nc</a>)</li> </ol> <p>Summary:</p> <p>The DNN model has been developed by relating SMAP TB and SMAP SCA-V ancillary data with in-situ SM data from the international soil moisture network (ISMN) using DNN. To minimize scale mismatch between gridded SMAP data and point in-situ data, the triple collocation analysis was conducted.</p> <p>The SM estimated from the DNN algorithm (NN) showed a good agreement with the ISMN data that was not used in the model training. Moreover, for a densely vegetated region located in the Amazon (Tambopata site) the NN showed less bias compared to available SM retrievals. </p> <p>Two parameters hNN and omegaNN are retrieved by ingesting NN to the modified dual channel algorithm. When the SM retrieval was conducted using the hNN and omegaNN, the result showed good agreement with the NN (DNN-based SM) with R of 0.986, ubRMSD of 0.015 m3/m3, and bias of -0.001 m3/m3.</p> <p>The paper "Improved SMAP Soil Moisture Retrieval Using a Deep Neural Network-based Replacement of Radiative Transfer and Roughness Model" published in the Transactions on Geoscience and Remote Sensing.</p> <p>For more details, please contact me (wotp12@unist.ac.kr)</p>
Multi-fidelity Gaussian Process Emulation for Atmospheric Radiative Transfer Models
<p>This repository contains several datasets of spectral atmospheric transfer functions (i.e. path radiance, transmittances, spherical albedo) simulated with MODTRAN6 atmospheric radiative transfer model. The simulations are stored in hdf5 files using the Atmospheric Look-up table Generator (ALG) toolbox (<a href="https://doi.org/10.5194/gmd-13-1945-2020">https://doi.org/10.5194/gmd-13-1945-2020</a>). Each dataset has an associated .xml file that includes the configuration of ALG/MODTRAN6 executions. All datasets include the input atmospheric/geometric variables that are summarized in the following table. Each dataset file has a random distribution (based on latin hypercube sampling) these input variables with varying number of points (e.g. train500.h5 contains 500 samples). The <em>reference </em>dataset contains 10000 samples and was used as reference for evaluating Gaussian Processes emulators.</p> <table> <tbody><tr> <th>Input Variables</th> <th>Units</th> <th>Min</th> <th>Max</th> </tr> </tbody><tbody> <tr> <td>O3 column concentration</td> <td>atm-cm</td> <td>0.25</td> <td>0.45</td> </tr> <tr> <td>Columnar Water Vapor</td> <td>g/cm2</td> <td>0.2</td> <td>4</td> </tr> <tr> <td>Aerosol Optical Thickness</td> <td>-</td> <td>0.04</td> <td>0.6</td> </tr> <tr> <td>Asymmetry parameter</td> <td>-</td> <td>0.5</td> <td>0.85</td> </tr> <tr> <td>Angstrom exponent</td> <td>-</td> <td>0.1</td> <td>2</td> </tr> <tr> <td>Single Scattering Albedo</td> <td>-</td> <td>0.8</td> <td>1</td> </tr> <tr> <td>Surface elevation</td> <td>km</td> <td>0</td> <td>2.5</td> </tr> <tr> <td>Solar Zenith Angle</td> <td>deg</td> <td>0</td> <td>70</td> </tr> <tr> <td>Relative Zenith Angle</td> <td>deg</td> <td>0</td> <td>180</td> </tr> </tbody> </table> <p> </p>
Input and Output simulation data of the THOR GCM for the paper Dynamical and radiative effects resulting from the deep non-hydrostatic vs deep quasi-hydrostatic equations in the global circulation model THOR with an added non-grey radiative transfer scheme
<p>The input and ouput simulation data of the THOR GCM for Dynamical and radiative effects resulting from the deep non-hydrostatic vs deep quasi-hydrostatic equations in the global circulation model THOR with an added non-grey radiative transfer scheme</p> <p>Global circulation models (GCMs) play an important role in contemporary investigations of exoplanet atmospheres. Different GCMs evolve various sets of dynamical equations which can result in obtaining different atmospheric properties between models. In this study, we investigate the effect of different dynamical equation sets on the atmospheres of hot Jupiter exoplanets. We compare GCM simulations using the quasi-primitive dynamical equations (QHD) and the deep Navier-Stokes equations (NHD) in the GCM THOR. We utilise a two-stream non-grey "picket-fence" scheme to increase the realism of the radiative transfer scheme. We perform GCM simulations covering a wide parameter range grid of system parameters in the population of exoplanets. Our results show significant differences between simulations with the NHD and QHD equation sets at lower gravity, higher rotation rates or at higher irradiation temperatures. The parameter exploration shows the relevance of choosing dynamical equation sets dependent on system and planetary properties.Climate states of hot Jupiters seemed to be more diverse than previously thought. There are exceptions to prograde superrotation. Overall, our study shows the evolution of different climate states which arise just due to different selection of Navier-Stokes equations and approximations. We show the shortcomings of approximations in GCMs made for Earth, but used for non Earth-like planets.</p>
Data in "Impact of microstructure on solar radiation transfer within sea ice during summer in the Arctic: A model sensitivity study"
<p>The files contain the data of results in the paper "Impact of microstructure on solar radiation transfer within sea ice during summer in the Arctic: A model sensitivity study".</p>
Fine-scale Quantification of Absorbed Photosynthetically Active Radiation (APAR) in Plantation Forests with 3D Radiative Transfer Modeling and LiDAR Data
<p>In recent years, LiDAR technology has gained widespread attention for its ability to provide precise 3D vertical structural data for various objects, particularly forests. In our dataset, we utilized LiDAR data to reconstruct intricately detailed three-dimensional representations of specific larch forest landscapes. These detailed forest structural models enable us to drive three-dimensional radiative transfer models, analyze the radiation budget of the forest canopy, and gain valuable insights into fine-scale forest management strategies.</p> <p>This is the research work we conducted by combining the aforementioned 3D forest scenes with the 3D RTM LESS. If you use our data, please cite our article. You can access our publication via DOI: 10.34133/plantphenomics.0166.</p> <p>We welcome researchers interested in a wide range of fields, such as vegetation ecological applications, to communicate with us by combining 3D vegetation modeling.</p> <p><br><br></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>
RT Dataset -- Updated radiative transfer model for Titan in the near-infrared wavelength range: Validation against Huygens atmospheric and surface measurements and application to the Cassini/VIMS observations of the Dragonfly landing area
<p>This dataset contains all Radiative Transfer (RT) results made for the paper.</p> <p>The data are stored in 5 zipped-folders names with the Cassini/VIMS cube flyby and id, or explicitly for Huygens/ULIS calibrated observations:</p> <ul> <li>TB_C1481624349_1</li> <li>T40_C1578266417_1</li> <li>T38_C1575509158_1</li> <li>T40_C1578263500_1</li> <li>T40_C1578263152_1</li> <li>ULIS_observations</li> </ul> <p>The TB_C1481624349_1 folder contains the Cassini/VIMS cube over HLS, the HLS end-member (End_member.txt), the surface albedo retrieved by Karkoschka et al. (2016) corrected for the photometry (HLS_Karkoschka_2016_spectrum.txt), and the inverted surface albedo (Surface_albedo.txt).</p> <p>In these folders, each VIMS pixel is stored in a .txt file with the following pattern:</p> <p><CUBE_ID>_<PIXEL_SAMPLE>_<PIXEL_LINE> .txt</p> <p>It starts with a header describing the observation: </p> <ul> <li>CUBE_ID: the VIMS cube id (`C1234567890_1` format)</li> <li>SAMPLE: the pixel sample number.</li> <li>LINE: the pixel line number.</li> <li>LONG: the pixel longitude (in degree).</li> <li>LAT: the pixel latitude (in degree).</li> <li>INC: the surface incident angle (in degree).</li> <li>EMI: the surface emergent angle (in degree).</li> <li>PHASE: the surface phase angle (in degree).</li> </ul> <p>For the Selk crater cubes (T40_C1578266417_1, T38_C1575509158_1, T40_C1578263500_1, T40_C1578263152_1), the header also contains the spatial sampling and the radiative transfer model outputs: </p> <ul> <li>Spatial sampling (km/pix).</li> <li>Fh: the haze scaling factor.</li> <li>Fm: the mist scaling factor.</li> <li>1-sigma (Fh): the 1-sigma uncertainty on Fh.</li> <li>1-sigma (Fm): the 1-sigma uncertainty on Fm.</li> <li>Reduced chi2: the reduced chi2. </li> </ul> <p>Then contains the observed spectra:</p> <ul> <li>Column 1: the VIMS channel central wavelength (in micrometers).</li> <li>Column 2: the VIMS pixel I/F.</li> <li>Column 3: the VIMS pixel I/F 1-sigma uncertainty. </li> </ul> <p>For the Selk crater cubes (T40_C1578266417_1, T38_C1575509158_1, T40_C1578263500_1, T40_C1578263152_1), 3 columns are added for: </p> <ul> <li>Column 4: the surface albedo.</li> <li>Column 5: the upper 1-sigma uncertainty on the surface albedo.</li> <li>Column 6 : the lower 1-sigma uncertainty on the surface albedo.</li> </ul> <p>The ULIS folder contains the Huygens/ULIS calibrated observations (in I/F) and the simulations with 1-sigma uncertainties as a function of the altitude (in km):</p> <ul> <li>Column 1: the VIMS channel central wavelength (in micrometers), stopped at the end of the Huygens/ULIS wavelength range.</li> <li>Column 2: the ULIS I/F.</li> <li>Column 3 : the simulated I/F.</li> <li>Column 4: the lower 1-sigma uncertainty on the simulation.</li> <li>Column 5 : the upper 1-sigma uncertainty on the simulation.</li> </ul>
Data - A Functionalized Monte Carlo 3D Radiative Transfer Model: Radiative Effects of Clouds over Reflecting Surfaces
<p>Data and scripts associated with the article "A Functionalized Monte Carlo 3D Radiative Transfer Model: Radiative Effects of Clouds over Reflecting Surfaces"</p>
Radiative transfer model and datasets for Li et al. (2023), 'Wintertime low-level clouds over sea ice cool the Arctic climate system'
<p>Source code for the radiative transfer model (RAPRAD) and cloud radiative flux data used in the study Li et al. (2022).</p>
Modeling Data from "The VLA/ALMA Nascent Disk and Multiplicity (VANDAM) Survey of Orion Protostars. Insights from Radiative Transfer Modeling"
<p>This dataset includes the results from the radiative transfer modeling done in the paper "The VLA/ALMA Nascent Disk and Multiplicity (VANDAM) Survey of Orion Protostars: Insights from Radiative Transfer Modeling" by Sheehan et al. Included are the full posteriors from the model fitting for each source as a Python pickle file that contains a dictionary with keys given by the source names, e.g. "HOPS-2", that point to numpy arrays containing the posterior distributions. The information can be loaded like so:</p> <pre><code class="language-python">import pickle data, keys = pickle.load(open("posteriors.p","rb"))</code></pre> <p>Here "keys" is a list containing the names of the parameters from the model fit that are a part of the posterior distribution for each source.</p> <p>Also included are the configuration files and datasets used in the modeling for each source, as well as the results from the fit so that anyone can work with these models for their own purposes. An example script that shows how to use these files is included, and further information can be found at <a href="http://pdspy.readthedocs.io">http://pdspy.readthedocs.io</a>.</p>
Data for "Optical properties of sea ice doped with black carbon – an experimental and radiative-transfer modelling comparison"
<p>All data recorded for reflectance and e-folding depth measurements associated with "Optical properties of sea ice doped with black carbon – an experimental and radiative-transfer modelling comparison"</p>
Model dataset for the journal publication titled "Improved snow albedo evolution in Noah-MP land surface model coupled with a physical snowpack radiative transfer scheme"
<div> <p>This is the Noah-MP model simulation dataset for the journal publication titled "Improved snow albedo evolution in Noah-MP land surface model coupled with a physical snowpack radiative transfer scheme"</p> <p> </p> </div>
Critical ingredients of Type Ia supernova radiative-transfer modelling
<p>Model spectra published in <a href="https://ui.adsabs.harvard.edu/abs/2014MNRAS.441.3249D">Dessart et al. 2014, MNRAS, 441, 3249</a>. This also includes the input hydrodynamical model at 0.98 d past explosion.</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.