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628 results for “scattering”
Dataset of "Strain-Engineered Ir Shell Enhances Activity and Stability of Ir-Ru Catalysts for Water Electrolysis: An Operando Wide-Angle X-Ray Scattering Study"
<p>Ir-Ru alloys with high Ru content serve as stable and highly active catalysts for the oxygen evolution reaction (OER) in Proton Exchange Membrane Water Electrolyzers (PEM-WEs), enabling efficient operation with remarkably low Ir loadings (150 µg cm-²). Despite this, the mechanisms behind their enhanced stability remain unclear. In this study, we employ operando Wide-Angle X-ray Scattering (WAXS) and complementary ex-situ techniques to investigate the structural evolution of these magnetron-sputtered alloys within a PEM-WE cell. Our results reveal that, upon potential application, Ru is leached from the surface, leading to the formation of a bimetallic Ir-Ru@IrOx core-shell structure. The Ir shell, significantly strained by the underlying Ir-Ru core, exhibits substantially higher catalytic activity than pure Ir. Notably, the Ir-Ru 25:75 catalyst shows superior stability over Ir-Ru 50:50, despite its higher Ru content, due to a more robust Ir shell that protects subsurface Ir and Ru from oxidation and dissolution. This study not only clarifies the performance-enhancing mechanisms of Ir-Ru catalysts but also suggests that other, more economical materials such as Co, Os, or Ti could serve as effective cores in Ir-M systems, offering a pathway to more cost-effective catalysts for PEM-WE applications.</p>
Raw spectra measurements of scattered sunlight collected using a MAX-DOAS (Multi-Axis Differential Optical Absorption Spectroscopy) instrument in the austral summer of 2016/17 during the Antarctic Circumnavigation Expedition (ACE).
<p><strong>Dataset abstract</strong></p> <p>To achieve the objectives of the project, we installed a MAX-DOAS (Multi-AXis Differential Optical Absorption Spectroscopy) instrument on the vessel “Akademik Tryoshnikov”. This instrument is based on the DOAS technique, which is used to measure trace gas concentrations in the atmosphere. The method consists of the analysis of the spectral absorption lines that each trace gas produces in the solar spectra. The DOAS technique uses the narrowband features that every trace gas has in their spectral absorption coefficients. This differential cross section is unique and acts like a fingerprint for the trace gases, allowing to differentiate between them and to estimate their concentrations (for further details see Platt and Stutz, 2008).</p> <p>In the past decades, atmospheric chemists have come to realize that halogen species (like Cl, Br or I and their oxides ClO, BrO and IO) exert a powerful influence on the chemical composition of the troposphere and through that influence affect the evolution of pollutants, hence having a significant impact on climate. These reactive halogen species are potent oxidizers for organic and inorganic compounds throughout the troposphere. In particular, halogen cycles can act on several compounds (such as methane, ozone, particles…), all of which are climate forcing agents through direct and indirect radiative effects. Dynamic exchange of halogens between the ocean, sea ice, snowpack and atmosphere is the main driver for the frequent occurrence of Ozone Depletion Events (ODEs) and Atmospheric Mercury Depletion Events (AMDEs) (Saiz-Lopez and von Glasow, 2012).</p> <p>In this dataset we present the raw spectra measurements of scattered sunlight recorded by the MAX-DOAS onboard a research vessel in the Southern Ocean and Atlantic Ocean. Included are position and vessel inclination data. Data coverage is from December 2016 to April 2017.</p> <p><strong>Dataset contents</strong></p> <ul> <li>ace_maxdoas_gps.zip</li> <li>GPS_JDDD.txt, data file, ASCII text</li> <li>ace_maxdoas_inclination.zip</li> <li>Inclination_JDDD.txt, data file, ASCII text</li> <li>ace_maxdoas_spectra-YYYY-MM.zip</li> <li>- MAXDOAS<br> - - WWW<br> - - - JDDD<br> - - - - LiveInfo_DDDhhmmss.WWW, data file, ASCII text<br> - - - - Atmos<br> - - - - - DDDhhmmss_90.WWW, data file, ASCII text<br> - ZENITH<br> - - WWW<br> - - - JDDD<br> - - - - LiveInfo_DDDhhmmss.WWW, data file, ASCII text<br> - - - - Atmos<br> - - - - - DDDhhmmss_90.WWW, data file, ASCII text</li> <li>README.txt, metadata, text</li> <li>data_file_header_gps.txt, metadata, text</li> <li>data_file_header_inclination.txt, metadata, text</li> <li>data_file_header_spectra_atmos.txt, metadata, text</li> <li>data_file_header_spectra_liveinfo.txt, metadata, text</li> </ul> <p>where YYYY is the year and MM is the month. JDDD is the day of the year (Julian day) YYYY in which the file was recorded. hhmmss is the time. WWW is the central wavelength of the measured spectrum in the UV or VIS region.</p> <p><strong>Dataset license</strong></p> <p>This dataset of raw spectra of scattered sunlight measurements from ACE is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p>
Small Angle Neutron Scattering (SANS) virtual experiments at KWS-1
<p>Small Angle Neutron Scattering (SANS) virtual experiments at KWS-1, FRM-II dataset. Intended for Machine learning purposes. Data generated by performing simulations in <a href="https://www.mcstas.org/">McStas</a> with the <a href="https://www.sasview.org/docs/user/qtgui/Perspectives/Fitting/models/index.html">SasView small angle scattering form factor models</a> describing the sample interaction. Two parameter spaces are varied sistematically: form factor model parameters and instrument configuration parameters. For more detailed information, read the <code>README.md</code> file of this database.</p> <p>The database contains 46 SANS form factor models under different instrument configurations. All data is uploaded in <code>hdf5</code> files, and the corresponding metadata in <code>.csv</code> files. Description of what each instrument configuration means (sample-detector distance, collimation, incident wavelength) and which model is used is contained in the metadata file. </p> <p>Each array is the result of the position sensitive detector output in neutron intensity (float values). A Dataset loader for Pytorch may be found <a href="https://github.com/jorobledo/hdf_loader_pytorch" target="_blank" rel="noopener">in GitHub</a> and is intended for Machine Learning purposes.</p>
A laser-plasma platform for photon-photon physics: the two photon Breit-Wheeler process, and Bounding elastic photon-photon scattering at $\sqrt s \approx 1$\,MeV using a laser-plasma platform
<p>The data contained in this repository was used in the production of the publication "A laser-plasma platform for photon-photon physics: the two photon Breit-Wheeler process" (<a href="https://doi.org/10.1088/1367-2630/ac3048">https://doi.org/10.1088/1367-2630/ac3048</a>) and "Bounding elastic photon-photon scattering at $\sqrt s \approx 1$\,MeV using a laser-plasma platform" (<a href="https://doi.org/10.1016/j.physletb.2025.139247">https://doi.org/10.1016/j.physletb.2025.139247</a>).</p>
Bolaform Surfactant-Induced Au Nanoparticle Assemblies for Reliable Solution-Based Surface-Enhanced Raman Scattering Detection
<p>Related publication: García-Lojo, D; Méndez-Merino, D; Pérez-Juste, I; Acuña, A; García-Río, L; Rodríguez-Patón, A; Pastoriza-Santos, I; Pérez-Juste, J. Bolaform surfactant-induced Au nanoparticle assemblies for reliable solution-based SERS detection. Adv.Mater. Technol. 2022, 2101726. <a href="https://doi.org/10.1002/admt.202101726">https://doi.org/10.1002/admt.202101726</a></p> <p> </p> <p> </p> <p>Abstract:</p> <p>Solution-based surface-enhanced Raman scattering (SERS) detection typically involves the aggregation of citrate-stabilized Au nanoparticles into colloidal assemblies. Although this sensing methodology offers excellent prospects for sensitivity, portability, and speed, it is still challenging to control the assembly process by a salting-out effect, which affects the reproducibility of the assemblies and, therefore, the reliability of the analysis. This work presents an alternative approach that uses a bolaform surfactant, B<sub>20</sub>, to induce the plasmonic assembly. The decrease of the surface charge and the bridging effect, both promoted by the adsorption of B<sub>20</sub>, are hypothesized as the key points governing the assembly. Furthermore, molecular dynamic simulations supported the bridging effect of the B<sub>20</sub> by showing the preferential bridging of surfactant monomers between two adjacent Au(111) slabs. The colloidal assemblies showed excellent SERS capabilities towards the rapid, on-site detection and quantification of beta-blockers and analgesic drugs in the nanomolar regime, with a portable Raman device. Interestingly, the application of state-of-the-art convolutional neural networks, such as ResNet, allows a 100% accuracy in classifying the concentration of different binary mixtures. Finally, the colloidal approach was successfully implemented in a millifluidic chip allowing the automation of the whole process, as well as improving the performance of the sensor in terms of speed, reliability, and reusability without affecting its sensitivity.</p>
X-ray scattering data from Norway spruce at different moisture conditions
<p>This data includes small and wide-angle X-ray scattering (SAXS, WAXS) intensities measured for Norway spruce (<em>Picea abies</em>) wood.</p> <p>The experiments were done in perpendicular transmission geometry, with the wood fiber axis roughly vertical and the radial direction of the wood tissue parallel to the X-ray beam, using a Xenocs Xeuss 3.0 C SAXS/WAXS device and Cu K-alpha radiation (wavelength 1.542 Å). The scattering patterns were recorded using an EIGER2 R 1M detector (pixel size 75 µm). The wood sample was measured first in wet state (saturated with water; "Wet"), and then equilibrated at different relative humidities (RH) in the following order: 95% ("RH95_1st"), 85% ("RH85"), 70% ("RH70"), 50% ("RH50_1st"), 20% ("RH20"), 10% ("RH10"), 50% ("RH50_2nd"), 95% ("RH95_2nd"). The sample-to-detector distance was 0.4139 m in SAXS, and 0.1528 m for the first 4 conditions (until "RH70") and 0.1525 m for the remaining 5 conditions in WAXS. Beam center (in detector pixels) was at x=540.6, y=667.0 (except y=635.0 in "RH70") in SAXS and x=1540, y=1521 in WAXS.</p> <p>For each of the 9 moisture conditions, files corresponding to 3 different processing steps are provided:</p> <ul> <li>"_bgsub_saxs.txt" and "_bgsub_waxs.txt" are ASCII files that contain the normalized and background-subtracted detector images (intensity in units mm^-1) corresponding to SAXS and WAXS, respectively. Pixels to be masked have the value "nan".</li> <li>"_bgsub_saxs_pol90.txt" and "_bgsub_waxs_pol90.txt" contain azimuthally regrouped images (90 bins in azimuthal angle) based on "_bgsub_saxs.txt" and "_bgsub_waxs.txt", respectively. PNG image files "_bgsub_saxs_pol.png" and "_bgsub_waxs_pol.png" are provided for reference.</li> <li>"_bgsub_pol90_ibg.txt" and "_bgsub_waxs_pol90_vert_ibg.txt" contain the equatorial and meridional anisotropic intensities, respectively, which were obtained from the azimuthally regrouped images by subtracting the isotropic scattering from the equatorial or meridional intensity (sector width 25°) at each value of the scattering vector <em>q</em>. The equatorial anisotropic intensities from SAXS and WAXS were merged by scaling the SAXS intensity, and the meridional anisotropic intensity is provided for the WAXS range only. The files contain columns for the magnitude of the scattering vector (q, unit Å<sup>-1</sup>), anisotropic intensity (I_ani, unit mm<sup>-1</sup>), error of anisotropic intensity (dI_ani, unit mm<sup>-1</sup>), and isotropic intensity (I_iso, unit mm<sup>-1</sup>).</li> </ul> <p>More detailed descriptions of the sample, the measurement, and the data processing can be found in the following reference:<br> Antti Paajanen, Aleksi Zitting, Lauri Rautkari, Jukka A. Ketoja, Paavo A. Penttilä. Nanoscale mechanism of moisture-induced swelling in wood microfibril bundles. <em>Nano Letters</em> 2022, 22(13): 5143–5150, DOI: 10.1021/acs.nanolett.2c00822</p>
Forward and backward Raman scattering photon counts in a FTTH topology.
<p>This set comprises a simulation tool in Mathematica for the generated Raman noise in a<br> GPON-based FTTH topology. This set takes into consideration various FTTH parameters,<br> such as the number of Optical Network Terminals (ONTs) and the splitting ratios, as well<br> as the drop and feeder fiber lengths, as it is depicted in Figure 1, providing in the output<br> the expected Raman noise counts calculated in counts per second (cps) detected in a<br> Single Photon Detector (SPD). The user of the code can manipulate various setup<br> parameters, such as the filtering passband and loss, as well as the specific SPAD<br> parameters which can be selected to be operating either in gated or free running mode.<br> The code provides as an output the expected noise count rates (cps) associated with the<br> forward and backward Raman scattering effect.<br> </p>
Dataset: Using light and X-ray scattering to untangle complex neuronal orientations and validate diffusion MRI
<p>This dataset supplements the research article <a href="https://doi.org/10.1101/2022.10.04.509781">"Using light and X-ray scattering to untangle complex neuronal orientations and validate diffusion MRI"</a>. It contains images and parameter maps obtained from measurements with Scattered Light Imaging (SLI), small-angle X-ray scattering (SAXS), and diffusion magnetic resonance imaging (dMRI) of a vervet monkey and a human brain sample (containing parts of the corona radiata, the cingulum, and the corpus callosum). Please refer to the research article for more information about the sample preparation, the measurement settings, and the generation of the different parameter maps - as well as for a more detailed analysis of the data.</p> <p>While SLI and SAXS were performed on two sections per sample (vervet monkey brain: sections no. 501 and 511; human brain: anterior section no. 20, posterior section no. 18), dMRI was performed on the entire human brain sample (3.5 x 3.5 x 1 cm³), and evaluated in the corresponding section plane of the anterior and posterior section, respectively. Pixel sizes in SLI are 3 µm, and in SAXS 100 µm (vervet) and 150 µm (human). Voxels in dMRI are 200 µm isotropic.</p> <p>All files are in tif-format and can be opened with standard image processing tools like ImageJ. The files labeled with "dMRI_ODF" contain a set of spherical harmonics for each voxel, describing the orientation distribution of the nerve fibers in the respective section plane obtained from the dMRI measurement, and can be visualized with MRtrix3, using the command 'mrview [filename] -odf.load_sh [filename]'.</p> <p>In addition to the ODFs, the dataset contains the b0-values and the dMRI-based metrics for the whole human brain sample in form of image stacks: fractional anisotropy (FA), axonal water fraction (AWF), axial/mean/radial diffusivity (AD/MD/RD), and axial/mean/radial kurtosis (AK/MK/RK).</p> <p>For the evaluated human brain sections (anterior/posterior), the 3D-orientations of the nerve fibers were derived from the dMRI and SAXS measurements, respectively: The files labeled with "3D-vectors" contain the unit vectors as X-Y-Z stack; the files labeled with "inclination" contain the (absolute) out-of-plane inclination of the fibers with respect to the section plane.</p> <p>All measurements were further evaluated with the software SLIX (https://github.com/3d-pli/SLIX) in order to derive the in-plane fiber directions (up to three fiber directions per pixel). The dataset contains the image stacks used as input (Stack) as well as the resulting parameter maps: average/maximum/minimum of the signal (avg/max/min), distance/prominence/width of peaks in the signal (peakdistance/peakprominence/peakwidth), the computed in-plane fiber directions (direction1,2,3), the fiber orientation map encoding the fiber directions in different colors (fom), as well as the vector maps (vectors) where fiber orientations of several pixels are displayed on top of each other. For the vervet brain section no. 511, the dataset also contains the parameter maps registered onto the SLI parameter maps.</p>
ParaTAXIS X-ray Scattering Input & Output
<p>This dataset describes the science case of the SIMEX platform tool chain for EUCALL WP4 Milestone M4.3.</p> <p>The file <em>opt_thick_1.6x1.6x3_micron_10fs_around_laser_max.h5</em> contains the ParaTAXIS density input data in openPMD format for the optically thick case (milestone 4.2.2.10). The data was obtained in a 2D PICLS simulation (milestone 4.2.2.7) which modeled the temporal evolution of a silicon grating irradiated by a <span class="math-tex">\(\tau_\mathrm{FWHM} = 83\,\mathrm{fs},\ \lambda = 800\,\mathrm{nm}\)</span> laser pulse of normalized amplitude <span class="math-tex">\(a_0 = 0.25\)</span>. Here 959 slices which correspond to subsequent PIC time steps of length <span class="math-tex">\(\Delta t_\mathrm{PIC} = 1.042 \cdot 10^{-17}\,\mathrm{s}\)</span> were stacked in propagation direction of the XFEL probe pulse thus taking time evolution of the target during X-ray pulse propagation into account. ParaTAXIS reads the density into a simulation volume of 1024 x 512 x 512 cells. The cell sizes of the PIC and the ParaTAXIS simulations are equally <span class="math-tex">\(3.125\,\mathrm{nm}\)</span> in every spatial direction. Density data is given in units of critical densities with respect to the <span class="math-tex">\(800\,\mathrm{nm}\)</span> laser. One critical density corresponds to <span class="math-tex">\(n_\mathrm{c} = 1.7422 \cdot 10^{27}\,\mathrm{m}^{-3}\)</span>. The time window chosen is situated from <span class="math-tex">\(5\,\mathrm{fs}\)</span> before until <span class="math-tex">\(5\,\mathrm{fs}\)</span> after the optical laser main pulse maximum hits the foil indicating a delay of <span class="math-tex">\(\Delta t = 0\)</span>. The optical laser incidence is in z-direction (ParaTAXIS coordinates) under 0°.</p> <p>The total illuminated area for both the optically thick and thin cases was <span class="math-tex">\(1.6 \times 1.6\, \mathrm{\mu m}\)</span>. We assume a target thickness of <span class="math-tex">\(3\,\mathrm{\mu m}\)</span>. The detector distance was <span class="math-tex">\(d = 1.4\,\mathrm{m}\)</span> and the detector pixel size was <span class="math-tex">\(a_\mathrm{D} = 13.5\,\mathrm{\mu m}\)</span>. For Thomson scattering most photons are scattered in forward direction. We therefore assumed a maximum polar scattering angle of <span class="math-tex">\(0.01\,\mathrm{rad}\)</span> in order to increase statistics on the detector.</p> <p>Via 16 simulations we obtained the detector outputs in the optically thick case which can be found in opt_thick_run_<run-number>_detector_<number-of-simulated-photons>_photons.h5 in openPMD format.</p> <p>The file <em>opt_thin_integrated_1.6x1.6x3_micron_10fs_around_laser_max</em> contains the total electron density data for the optically thin case (milestone 4.2.2.9) integrated over 959 slices in the propagation direction of the probe laser beam. The density is only non-zero in the 6th cell of the simulation volume thus enforcing single-scattering in the ParaTAXIS simulation as can be assumed for an optically thin medium. This data was read by a ParaTAXIS into a simulation volume of 12 x 512 x 512 cells.</p> <p>We launched 10 parallel simulations, each arriving at detector images for <span class="math-tex">\(10^{12}\)</span> simulated photons. The detector output of these simulations can be found in <em>opt_thin_detector_1e12_photons_run<run-number>.h5</em> also in openPMD format.</p> <p> </p>
Radar and Lidar scattering lookup tables for atmospheric hydrometeors using a T-Matrix method and a Mie theory
<h2>Overview</h2> <p>The database includes text files containing the scattering amplitude matrices for single spherical/nonspherical particles for radar and lidar. They are the lookup tables used for calculating radar and lidar observables in the Cloud-Resolving Radar Simulator (Oue et al. 2020). The radar scattering properties were calculated for several hydrometeor categories using a T-matrix method proposed by Mishchenko (2000) accounting for incident angles, scattering direction (forward and backward), polarimetry (horizontally (H) and vertically (V) polarized waves), particle aspect ratio, phase (liquid or ice), bulk density, temperature, particle size, and radar frequency. The lidar scattering properties at a vertical incidence were calculated for spherical liquid or ice particles using the BHMIE Mie code (Bohrean and Hyffman,1998) accounting for lidar wavelength, temperature, and bulk density. The hydrometeor categories are commonly used for cloud resolving models employing bulk microphysical schemes (e.g., cloud, rain, ice cloud, snow aggregates, and graupel). Detailed descriptions are also available in the CR-SIM user guide (https://github.com/marikooue/CR-SIM/releases/tag/crsim-v3.34).</p> <h2>Data structure</h2> <p>The data files are arranged and zipped every hydrometeor types. The names of the tar-zipped directories under the top directory LLUT3 represents the hydrometer type.<br>For lidar scattering, the following directories are included:<br>ceilo: Ceilometer lidar backscatter properties at a wavelength of 905 nm<br>mpl: Micropulse lidar (MPL) backscatter properties at wavelengths of 353 and 532 nm</p> <p>For radar scattering, the following hydrometer types are included:<br>cloud: Radar scattering for liquid cloud droplets (spherical shape)<br>raina: Radar scattering for raindrops with the aspect ratio model proposed by Andsager et al. (1999)<br>rainb: Radar scattering for raindrops with the aspect ratio model proposed by Brandes et al (2002)<br>ice_ar0.90: Radar scattering for cloud ice with an aspect ratio of 0.9<br>ice_ar0.20: Radar scattering for cloud ice with an aspect ratio of 0.2<br>smallice: Radar scattering for spherical cloud ice particles<br>snow_ar0.60: Radar scattering for snowflakes with an aspect ratio of 0.6<br>graupel_ar0.60: Radar scattering for graupel particles with an aspect ratio of 0.6<br>graupel_ar0.80: Radar scattering for graupel particles with an aspect ratio of 0.8<br>graupel: Radar scattering for spherical graupel particles<br>gh_ryzh: Radar scattering for graupel particles with the graupel aspect ratio model proposed by Ryzhkov et al (2011)<br>unrimedice_ar0.40: Radar scattering for unrimed ice particles with an aspect ratio of 0.4<br>unrimedice_ar0.60: Radar scattering for unrimed ice particles with an aspect ratio of 0.6<br>unrimedice_ar0.80: Radar scattering for unrimed ice particles with an aspect ratio of 0.8<br>unrimedice: Radar scattering for spherical unrimed ice particles<br>partrimedice_ar0.40: Radar scattering for partially rimed ice particles with an aspect ratio of 0.4<br>partrimedice_ar0.60: Radar scattering for partially rimed ice particles with an aspect ratio of 0.6<br>partrimedice_ar0.80: Radar scattering for partially rimed ice particles with an aspect ratio of 0.8<br>partrimedice: Radar scattering for partially rimed spherical ice particles </p> <h2>The file name convention </h2> <p>For lidar scattering data, each file name has the following format:<br>[hydrometeor type]_[instrument name]_ [wavelength in nm]_[phase ID]_d[bulk density in kg m-3].dat<br>The hydrometeor type shows: 1) ‘cld’ for liquid cloud droplets, and 2) ‘ice’ for ice particles. The phase ID shows: 1) ‘p25’ for ceilometer liquid cloud, 2) ‘p20’ for MPL lidar liquid cloud, and 3) ‘m30’ for MPL lidar ice. </p> <p>For radar scattering data, each file name has the following format.<br>[hydrometeor type]_fr[frequency in GHz]GHz_t[temperature in K]_rho[bulk density in kg m-3]_el[elevation angle in degree].dat<br>The hydrometeor type follows the directory name presented above.</p> <h2>Format of the data files</h2> <p>Line 1: Wavelength in mm<br>Line 2: Temperature in K<br>Line 3: Refractive index (real and imaginary)<br>Line 4: Number of radii calculated and number of elevation angles<br>Line 6: Incident angle and scattered angle in degrees<br>Line 7: Radius in mm and aspect ratio<br>Line 8: Forward scattering amplitude for co-polarization VV and HH (complex number)<br>Line 9: Backward scattering amplitude for co- and cross polarizations VV, VH, HV, HH (complex number) <br>Line 10 to the end of file: Repeat Line 7 to Line 9 with different radii until the maximum radius.</p>
Dataset for Enhancing stimulated Brillouin scattering in suspended silicon waveguides through subwavelength nanostructuration [Invited]
<p>This dataset contains the raw data for the figures (Fig. 2, Fig. 5, and Fig. 7) in the publication entitled "Enhancing stimulated Brillouin scattering in suspended silicon waveguides through subwavelength nanostructuration" published by Optical Materials Express (DOI: 10.1364/OME.534474). Datafiles are in .txt format.</p> <p>All relevant information regarding the dataset, how it was obtained and its context is contained in the manuscript.</p>
The Outer Stellar Mass of Massive Galaxies: A SimpleTracer of Halo Mass with Scatter Comparable to Richness and Reduced Projection Effects
<p>These are the data for reproducing the results of the publication titled "The Outer Stellar Mass of Massive Galaxies: A Simple Tracer of Halo Mass with Scatter Comparable to Richness and Reduced Projection Effects" by Song Huang et al.</p> <p>Please see the Python scripts and Jupyter notebooks provided in the <a href="https://github.com/dr-guangtou/jianbing">jianbing</a> repo for examples about how to use these data files. And please contact dr.guangtou@gmail.com if you have any questions about these data.</p> <p>-------------------------------------------------------------------------------------------------</p> <p>Here is a brief description of all the files:</p> <p><strong>Data from N-body simulation:</strong></p> <ul> <li><a href="https://zenodo.org/api/files/f10135d5-64ea-47c1-b292-bea86bbcdf08/mdpl2_halos_0.7333_reduced_logmvir_13.npy?versionId=1648006b-a91a-4300-aadf-c4746d6f3ef2">mdpl2_halos_0.7333_reduced_logmvir_13.npy</a> <ul> <li>Basic information about the dark matter halos from MDPL2 simulation</li> <li>For scale factor = 0.7333 (or z~0.4).</li> <li>Only for halos with logMvir > 13.0.</li> </ul> </li> <li><a href="https://zenodo.org/api/files/f10135d5-64ea-47c1-b292-bea86bbcdf08/mdpl2_particles_0.7333_72m.npy?versionId=ff7d5847-df44-46f5-9bcc-d8a7f3cc040d">mdpl2_particles_0.7333_72m.npy</a> <ul> <li>Particle catalog of the a=0.7333 snapshot from MDPL2</li> <li>This is a down-sampled version with 72 million particles.</li> </ul> </li> <li><a href="https://zenodo.org/api/files/f10135d5-64ea-47c1-b292-bea86bbcdf08/topn_theory_demo.pkl?versionId=7ed87c28-7adc-4987-9e00-b6223c744d42">topn_theory_demo.pkl</a> <ul> <li>These are the data used to create the theoretical demo of the TopN test.</li> <li>It is used for making the figures in <a href="https://github.com/dr-guangtou/jianbing/blob/master/notebooks/figure/fig1.ipynb">this notebook</a>.</li> </ul> </li> </ul> <p><strong>Catalogs of Galaxies or Galaxy Clusters:</strong></p> <ul> <li><a href="https://zenodo.org/api/files/f10135d5-64ea-47c1-b292-bea86bbcdf08/camira_s16a_cluster_use_bsm.fits?versionId=ca274c83-4025-41c4-b993-3cc9074f08b2">camira_s16a_cluster_use_bsm.fits</a> <ul> <li>The HSC S16A CAMIRA cluster catalog.</li> </ul> </li> <li><a href="https://zenodo.org/api/files/f10135d5-64ea-47c1-b292-bea86bbcdf08/redmapper_hsc_s16a_cluster_bsm.fits?versionId=11608e41-2427-4808-9060-a06139de165c">redmapper_hsc_s16a_cluster_bsm.fits</a> <ul> <li>The HSC S16A redMaPPer cluster catalog.</li> </ul> </li> <li><a href="https://zenodo.org/api/files/f10135d5-64ea-47c1-b292-bea86bbcdf08/redmapper_sdss_cluster_bsm.fits?versionId=b977c4ed-11c9-4751-b32f-60883d2e81b0">redmapper_sdss_cluster_bsm.fits</a> <ul> <li>The SDSS DR8 redMaPPer clusters in the HSC S16A footprint.</li> </ul> </li> <li><a href="https://zenodo.org/api/files/f10135d5-64ea-47c1-b292-bea86bbcdf08/s16a_massive_logm_11.2.fits?versionId=603cb17c-bb64-4aa7-ae05-5ec61c7ee861">s16a_massive_logm_11.2.fits</a> <ul> <li>0.2 <z < 0.5 massive galaxies in the HSC S16A footprint.</li> </ul> </li> </ul> <p><strong>Galaxy-Galaxy Lensing Data:</strong></p> <ul> <li><a href="https://zenodo.org/api/files/f10135d5-64ea-47c1-b292-bea86bbcdf08/s16a_weak_lensing_medium.hdf5?versionId=593c4ba0-6d7d-4b83-b8d9-01740a351fcd">s16a_weak_lensing_medium.hdf5</a> <ul> <li>A compilation of the weak lensing data to calculate the DeltaSigma profiles.</li> <li>This includes the weak lensing source catalog, photometric redshift calibration file, and the random catalog.</li> <li>"medium" here means we applied the medium criteria for selecting source galaxies. Please refer to <a href="https://ui.adsabs.harvard.edu/abs/2019MNRAS.490.5658S/abstract">Speagle et al. (2019)</a> for the exact meaning of these criteria.</li> <li>We also have a "basic" and "strict" version. Please send your request if you need them.</li> </ul> </li> <li><a href="https://zenodo.org/api/files/f10135d5-64ea-47c1-b292-bea86bbcdf08/topn_public_s16a_medium_precompute.hdf5?versionId=2216ecf9-b836-4dd5-a9dd-7e070e4977bf">topn_public_s16a_medium_precompute.hdf5</a> <ul> <li>A compilation of pre-computed lensing profiles for each individual object in a different galaxy or cluster samples for the TopN test.</li> <li>These are the data used to create the stacked DeltaSigma profiles.</li> <li>We also provide the "strict" and the "basic" versions if you want to test the robustness of the TopN tests against the different selections of source galaxies in weak lensing measurements. You just need these files to generate the stacked DeltaSigma profiles.</li> </ul> </li> </ul>
COHERENT Collaboration data release from the first detection of coherent elastic neutrino-nucleus scattering on argon
<p>Release of COHERENT collaboration data from the first detection of coherent elastic neutrino-nucleus scattering (CEvNS) on argon. This data release corresponds with the results of "Analysis A" published in arXiv:2003.10630[nucl-ex]. The data release enables further studies of CEvNS.</p> <p>Use of the data release is presented in the accompanying pdf document within this submission. Example code is included within the release as part of this submission. The materials here are also available at http://coherent.ornl.gov/data/, which preserves the directory structure used within the accompanying document. Note the use of the example code in this release expects the directory structure written within the accompanying pdf document.</p>
Dataset: Mapping intrinsic and scattering attenuation in the southern Aegean crust using S-wave envelope inversion and sensitivity kernels derived from perturbation theory
<p><strong>Data Set S1: </strong>File “ds01.csv” contains the catalogue of relocated events used in this study. The columns in the file represent origin time (in year-month-day’H’hour’M’minute’S’seconds format), event longitude, event latitude, event depth in a sequential manner.</p> <p><strong>Data Set S2: </strong>File “ds02.zip” contains four ASCII data files (ray_prmtrs12.txt, ray_prmtrs24.txt, ray_prmtrs48.txt and ray_prmtrs816.txt). The data files contain scattering coefficient (<em>g<sup>*</sup></em>) and intrinsic coefficient (<em>b</em>) values in 1-2, 2-4 Hz, 4-8 Hz and 8-16 Hz bands respectively. The columns in the text files represent event latitude, event longitude, event depth, station latitude, station longitude, station velocity, envelope duration, <em>g<sup>*</sup></em>, <em>b</em>, early-S window length, percentage error in early-S window, percentage error for full envelope, and event origin time in a sequential manner.</p> <p><strong>Data Set S3: </strong>File “ds03.zip” contains four data files (envnodes15g_3_3_1-2.txt, envnodes15g_3_3_2-4.txt, envnodes15g_3_3_4-8.txt, and envnodes15g_3_3_8-16.txt), one BASH script containing GMT and Octave commands (Qs_envg.gmt), and a lat-long coordinate file (SAegean_poly_coord_extnd.txt) to mask the area outside the seismic network. The data files contain log<sub>10</sub>(<em>Q<sub>sc</sub></em><sup>-1</sup>) values in 1-2, 2-4, 4-8 and 8-16 Hz bands respectively. The columns in the text files represent node latitude, node longitude and log<sub>10</sub>(<em>Q<sub>sc</sub></em><sup>-1</sup>) value of the node sequentially. This GMT script also uses GSHHG coastline data whose path can be added to the script by changing the value of variable GDIR at the beginning of the script. The BASH script file can be run to see the spatial distribution of log<sub>10</sub>(<em>Q<sub>sc</sub></em><sup>-1</sup>) using GMT-6 (Wessel et al., 2019) and Octave version 5.1 and above.</p> <p><strong>Data Set S4: </strong>File “ds04.zip” contains four data files (envnodes15b_3_3_1-2.txt, envnodes15b_3_3_2-4.txt, envnodes15b_3_3_4-8.txt, and envnodes15b_3_3_8-16.txt), one BASH script containing GMT and Octave commands (Qi_env.gmt), and a lat-long coordinate file (SAegean_poly_coord_extnd.txt) to mask the area outside the seismic network. The data files contain log<sub>10</sub>(<em>Q<sub>i</sub></em><sup>-1</sup>) values in 1-2, 2-4, 4-8 and 8-16 Hz bands respectively. The columns in the text files represent node latitude, node longitude and log<sub>10</sub>(<em>Q<sub>i</sub></em><sup>-1</sup>) value of the node sequentially. This GMT script also uses GSHHG coastline data whose path can be added to the script by changing the value of variable GDIR at the beginning of the script. The BASH script file can be run to see the spatial distribution of log<sub>10</sub>(<em>Q<sub>i</sub></em><sup>-1</sup>) using GMT-6 (Wessel et al., 2019) and Octave version 5.1 and above.</p> <p><strong>Data Set S5: </strong>File “ds05.zip” contains four data files (envnodes15a_3_3_1-2.txt, envnodes15a_3_3_2-4.txt, envnodes15a_3_3_4-8.txt, and envnodes15a_3_3_8-16.txt), one BASH script containing GMT and Octave commands (albd_env.gmt), and a lat-long coordinate file (SAegean_poly_coord_extnd.txt) to mask the area outside the seismic network. The data files contain Albedo (<em>B<sub>o</sub></em>) as % values in 1-2, 2-4, 4-8 and 8-16 Hz bands respectively. The columns in the text files represent node latitude, node longitude and <em>B<sub>o</sub></em> value of the node sequentially. This GMT script also uses GSHHG coastline data whose path can be added to the script by changing the value of variable GDIR at the beginning of the script. The BASH script file can be run to see the spatial distribution of <em>B<sub>o</sub></em> using GMT-6 (Wessel et al., 2019) and Octave version 5.1 and above. </p>
The Effects of Asymmetric Dark Matter on Stellar Evolution I: Spin-Dependent Scattering - Supporting Data
<p>Supporting code and data for the paper: </p> <p><em>The Effects of Asymmetric Dark Matter on Stellar Evolution I: Spin-Dependent Scattering</em></p> <p>Raen (2020)</p> <p><strong>Supporting code</strong> includes `run_star_extras.f`, inlist templates, and our dark matter module (to be used in conjunction with MESA: <a href="http://mesa.sourceforge.net/index.html">Modules for Experiments in Stellar Astrophysics</a>). The full source code used in the production of this paper is available at <a href="https://github.com/troyraen/DM-in-Stars/">github.com/troyraen/DM-in-Stars</a> in the Raen2020 branch. (The master branch is intended for use by those wishing to use our module to explore DM effects beyond the scope of this paper.) We used MESA version 12115, and MESA SDK version 20190830.</p> <p><strong>Model data</strong> includes MESA history and profile data for the models highlighted in the paper (<span class="math-tex">\(1.0\ \mathrm{M}_\odot\)</span> and <span class="math-tex">\(3.5\ \mathrm{M}_\odot\)</span> models with <span class="math-tex">\(\Gamma_B = 0\)</span> (no dark matter), <span class="math-tex">\(\Gamma_B = 10^4\)</span>, and <span class="math-tex">\(\Gamma_B = 10^6\)</span>). The specific inlists used to generate the models are also included. Additional data will be shared on reasonable request to the paper's corresponding author.</p>
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>
Microwave single scattering properties of non-spheroidal rain drops
<p>The database contains single scattering properties (SSP) of non-spheroidal droplets. They were modeled using the parameterization by Chuang and Beard (1990) which make use of Chebyshev polynomials. Spherical and spheroidal drop are also included for reference. The spheroidal drops are set to have the same aspect ratio as the Chebyshev drops.</p> <p>The frequency and temperature grid is identical to the one used for the SSP database presented in Eriksson et al. (2018). Frequencies range from 1 to 886.4 GHz and 5 temperatures from 230 to 310 K are included. Sizes range from 10 μm to 5.75 mm, with logarithmic spacing up to 1 mm and linear spacing above 1 mm in steps of 0.25 mm. Note that below 788 μm the Chebyshev drops are essentially spherical, and are therefore not included. At sizes below 788 μm the spheroidal drop SSP can be used instead.</p> <p>A manuscript (Ekelund et al., 2020) has been submitted to Atmospheric Measurement Techniques, which will serve as the main documentation of the data.</p> <p>Database Specifications:</p> <p>Format:<br> NetCDF4</p> <p>Version:<br> 1.0.0</p> <p>Shapes:<br> Chebyshev (non-spheroidal), spheroidal, sphere.</p> <p>Diameter grid (um):<br> 1.00, 1.27, 1.61, 2.04, 2.59, 3.29, 4.18, 5.30, 6.72, 8.53, 10.83, 13.74, 17.43, 22.12, 28.07, 35.62, 45.20, 57.36, 72.79, 92.37, 117.21, 148.74, 188.74, 239.50, 303.92, 385.66, 489.39, 621.02, 788.05, 1000.00, 1250.00, 1500.00, 1750.00, 2000.00, 2250.00, 2500.00, 2750.00, 3000.00, 3250.00, 3500.00, 3750.00, 4000.00, 4250.00, 4500.00, 4750.00, 5000.00, 5250.00, 5500.00, 5750.00.</p> <p>Frequency grid (GHz):<br> 1.00, 1.40, 3.00, 5.00, 7.00, 9.00, 10.00, 13.40, 15.00, 18.60, 24.00, 31.30, 31.50, 35.60, 50.10, 57.60, 88.80, 94.10, 115.30, 122.20, 164.10, 166.90, 175.30, 191.30, 228.00, 247.20, 314.20, 336.10, 439.30, 456.70, 657.30, 670.70, 862.40, 886.40.</p> <p>Temperature grid (K):<br> 230, 250, 270, 290, 310.</p> <p>References:</p> <p>Ekelund, R., Eriksson, P., and Kahnert, M.: Microwave single scattering properties of non-spheroidal rain drops, Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2020-85, in review, 2020.</p> <p>Eriksson, P., Ekelund, R., Mendrok, J., Brath, M., Lemke, O., and Buehler, S. A.: A general database of hydrometeor single scattering properties at microwave and sub-millimetre wavelengths, Earth Syst. Sci. Data, 10, 1301–1326, https://doi.org/10.5194/essd-10-1301-2018, 2018.</p>
Terahertz Spin-to-Charge Conversion by Interfacial Skew Scattering in Metallic Bilayers
<p>Data of the publication "Terahertz Spin-to-Charge Conversion by Interfacial Skew Scattering in Metallic Bilayers" published in Advanced Materials, 33, 2006281 (2021). THz waveforms for a subset and RMS data - corrected for pump incoupling and THz outcoupling - for various F and N metallic bilayers and interface modifications as well as the calculated spin Hall angles for different interfacial impurities are provided.</p>
Spatially-localized X-ray scattering and X-ray microtomography measurements on Moso bamboo
<p><strong>Spatially-localized X-ray scattering and X-ray microtomography measurements on Moso bamboo</strong></p> <p> </p> <p>This data set is originally used in:</p> <p>Ahvenainen, P., Dixon, P. G., Kallonen, A., Suhonen, H., Gibson, L. J., & Svedström, K. (2017). Spatially-localized bench-top X-ray scattering reveals tissue-specific microfibril orientation in Moso bamboo. <em>Plant Methods</em>. <strong>13</strong>:5 DOI: 10.1186/s13007-016-0155-1</p> <p>This data set includes measurements on Moso bamboo (<em>Phyllostachys edulis</em>) performed with two separate set-ups at the Department of Physics, University of Helsinki as described in the above open-access publication. The X-ray microtomography (XMT) measurements, X-ray diffraction tomography (XDT) and localized X-ray scattering (LXS) are done with set-up 1. In LXS, the region-of-interest is selected from a tomographic reconstruction slice based on the XMT measurement using a small X-ray beam (diameter: 200 µm). Additional wide-angle X-ray scattering (WAXS) measurements are conducted with set-up 2 using a larger X-ray beam (diameter approx. 1 mm). </p> <p>The two-dimensional scattering patterns (Pilatus 1M hybrid pixel array detector) and tomographic reconstruction slices obtained with set-up 2 are stored as TIFF-images (.tif). The two-dimensional scattering patterns (MAR345 image plate detector) obtained with set-up 2 are stored as 32-bit RAW files (unsigned integers, 2300 columns, 2300 rows). </p> <p>The novel combined WAXS/XMT set up (set-up 1) is first presented in: Suuronen, J.-P., Kallonen, A., Hänninen, V., Blomberg, M., Hämäläinen, K., & Serimaa, R. (2014). Bench-top X-ray microtomography complemented with spatially localized X-ray scattering experiments. <em>Journal of Applied Crystallography</em>, <strong>47</strong>(1), 471–475. doi:10.1107/S1600576713031105</p> <p>Any queries related to the data set or the related Plant Methods article may be directed to the first author by email:</p> <p>Patrik Ahvenainen, PhD; patrik.ahvenainen@alumni.helsinki.fi</p>
O(3P)+CO2 scattering cross sections at superthermal collision energies for planetary aeronomy: Raw data release
<p>Raw data and codes used in M. Gacesa, R. J. Lillis, and K. J. Zahnle, "O(3P)+CO2 scattering cross sections at superthermal collision energies for planetary aeronomy", MNRAS 491, 5650-5659 (2020).</p> <ul> <li>v1.1 includes <strong>differential cross section</strong> data for inelastic scattering: O(3P)+CO2(v=0,j=ji) -> O(3P)+CO2(v=0,jf) and energy transfer to the internal degrees of freedom calculated as in Gacesa & Kharchenko, Geophys. Res. Lett. 39, L10203 (2012).</li> </ul> <p>These files are distributed under GNU General Public License v3.0 and include NO liability or warranty of any kind. No support is provided by the authors. We cannot promise to answer any questions related to this dataset nor to prepare different products for you.</p> <p>Please cite this work as: Marko Gacesa, Lillis, Robert J., & Zahnle, Kevin J. (2019). O(3P)+CO_2 scattering cross sections at superthermal collision energies for planetary aeronomy: Raw data pre-release (Version v0.9-beta) [Data set]. Zenodo. <a href="http://doi.org/10.5281/zenodo.3256699">http://doi.org/10.5281/zenodo.3256699</a></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.