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535 results for “DUST”
FIGURE 4. Ombrothermic graphs prepared using 22 in Plant diversity of Khuzestan and dust sources in the southwest of Iran, with a checklist of vascular plants
FIGURE 4. Ombrothermic graphs prepared using 22-year (1996–2018) data from three synoptic stations close to the mountain area in the north of Khuz.
Data for "The Dust Extinction Curve: Beyond R(V)"
<p>24 million dust extinction curves, detemined from Gaia XP spectra, as described in <a href="https://ui.adsabs.harvard.edu/abs/2024arXiv241022537G/abstract">Green, Zhang & Zhang (2025)</a>.</p> <p>We represent the extinction curves using a set of 16 basis vectors. For each star, there are 16 coefficients, which can be used to reconstruct the extinction curves. After loading <code>A_zp</code> and <code>G_subspace</code> from the file <code>G_subspace.json</code>, and <code>coeffs</code> from the file <code>coeffs.h5</code>, the extinction curves can be reconstructed using:</p> <p> <code>A = A_zp + np.sum(coeffs[None,:] * G_subspace[:,:], axis=1)</code></p> <p>The output <code>A</code> will have shape (star, wavelength). The wavelengths at which <code>A</code> is sampled are stored in the field <code>wavelengths_nm</code> (in nanometers), in <code>G_subspace.json</code>.</p> <p>The covariance matrix of the coefficients for each star is stored in the files <code>coeffs_cov_?.h5</code>. We store the diagonals and the upper triangles of the covariances separately. They can be reconstructed using the function <code>reconstruct_symm_matrices</code> from <code>symm_matrix_utils.py</code>:</p> <p> <code>from symm_matrix_utils.py import reconstruct_symm_matrices</code><br> <code>cov = reconstruct_symm_matrices(cov_diag, cov_triu_wo_diag)</code></p> <p>Additionally, we store the inverse covariance matrices in the files <code>coeffs_icov_?.h5</code>, in the same manner as the covariance matrices.</p> <p>The file <code>source_info.h5</code> contains a few useful Gaia fields and parameter estimates (with corresponding uncertainties) from <a href="https://ui.adsabs.harvard.edu/abs/2024arXiv240714594Z/abstract">Zhang & Green (2025)</a>.</p> <p>The file <code>feature_EW.h5</code> contains the equivalent widths (in nanometers) of the VBS and the 770 and 850 nm extinction features.</p> <p>Every file contains the Gaia DR3 <code>source_id</code> of every star, labeled <code>gdr3_source_id</code>.</p>
Data for the paper: "Formation and new Transport pathways of the Global Tropopause Dust Layer"
<p>Model simulation output used for "Formation and new Transport pathways of the Global Tropopause Dust Layer" submitted to Geophysical Research Letters (GRL).</p>
Dust rings as a footprint of planet formation in a protoplanetary disk
<p>Simulation outputs of "Dust rings as a footprint of planet formation in a protoplanetary disk".</p> <p>average_output*.dat: azimuthally averaged surface density of gas</p> <p>average_output_dust0*.dat azimuthally averaged surface density of dust grains</p> <p>output*.dat: 2D data for gas</p> <p>output_dust0*.dat: 2D data for dust grains</p> <p>The number of the file indicates the time of output (10xnumber orbit)</p> <p>Regarding to planet0.dat and orbit0.dat, please see http://fargo.in2p3.fr/Output</p>
Video related to the study "Spatial variability of Saharan dust deposition revealed through a citizen science campaign"
<p>This video is related to the manuscript "Spatial variability of Saharan dust deposition revealed through a citizen science campaign", by Dumont et al., submitted in December 2022 to the journal "Earth System Science Data".</p> <p>It illustrates the timeline of dust deposition, simulated by the atmospheric transport model MOCAGE.</p> <p> </p>
Massive pre-main-sequence stars in M17 - Modelling hydrogen and dust in mYSO disks
<p>This is a basic reproduction package for the paper "Massive pre-main-sequence stars in M17 - Modelling hydrogen and dust in mYSO disks" by F. Backs, et al. 2023. It provides the data products required to reproduce the results.</p>
Experimental and analytical methods for thermal infrared spectroscopy of complex dust coatings in a simulated asteroid environment
<p>The spectral and thermophysical effects of thin-continuous, macro-discontinuous, and micro-discontinuous dust cover are not understood and require relevant laboratory analyses to be deconvolved in an orbital setting. We have constructed a custom environment chamber that enables the controlled deposition of size-regulated dust particles in coatings with varying continuity and thickness. TIR spectra of coated substrates acquired in a simulated asteroid environment (SAE) are used to investigate the extent to which dust coatings of different thicknesses and arrangements contribute to orbital spectral signatures of airless body surfaces. </p> <p>TIR (5-50 𝜇m) spectra of each sample are acquired under SAE conditions using the Planetary and Asteroid Regolith Spectroscopy Environmental Chamber (PARSEC) at Stony Brook University. PARSEC is designed to measure samples under environmental conditions typical of airless bodies. The chamber houses a sample wheel with six sample cups and a calibration target coated with Nextel black. There is also a black body target under the wheel. All sample cup and black body targets can be individually heated and rotated into position from outside of the chamber. Temperature is controlled through two Eurotherm Mini8 Loop Controllers and managed on an in-lab computer with the Eurotherm iTools interface. Samples are illuminated at 55° incidence by a quartz halogen lamp connected to a Bentham 610 power source. Surrounding the sample wheel is a cold shield actively cooled by the input of liquid nitrogen into an internal dewar to reach temperatures < 150 K. Pressure in the chamber is controlled by a Pfeiffer HiCube turbo vacuum pump to reach 10<sup>-6</sup> mbar. In line with the vacuum chamber is a pressure regulated tank of N<sub>2</sub> used for purging and ambient pressure measurements. The PARSEC chamber is connected to a Nicolet 6700 FTIR spectrometer equipped with a Cesium Iodide (CsI) beamsplitter and a deuterated L-alanine doped triglycine sulfate (DLaTGS) detector with a CsI window. The spectrometer is actively purged with air scrubbed of CO<sub>2</sub> and water vapor and sealed at the interface with PARSEC. A total of 256 scans from 2,200 to 400 cm<sup>-1</sup> are integrated for a 10-minute measurement period, using a spectral sampling of 2 cm<sup>-1</sup>. During each experimental session, all samples and the black body are measured under SAE conditions. Calibration measurements of the blackbody target at 70 and 100°C are acquired, then the integrated sample cup heaters and solar lamp are adjusted to achieve the desired sample brightness temperature of 80°C. Samples are allowed to reach temperature under the lamp for more than 45 minutes until the spectral maximum stabilizes and the calculated brightness temperature at the CF is within ~10 K of the target 353 K. This procedure is repeated for all samples while ensuring the chamber temperature remains stable under 150 K through continued addition of liquid nitrogen. The Radiance-to-emissivity conversion method used determines the maximum brightness temperature between 500 and 1700 cm-1 and divides the radiance by a Planck function of the same temperature. This assures the maximum brightness temperature is the kinetic temperature of the same, and its emissivity is unity at the frequency of this maximum. This dataset includes 15 different samples acquired under SAE and ambient pressure/temperature conditions. These samples range in layer thickness and continuity and are intended to test and demonstrate the range of the coating process.</p>
Supporting data for the Eastern equatorial Pacific dust fertilization and source weathering influences on the Eocene to Miocene CO2 decline
<p>This is supporting data for Equatorial Pacific dust fertilization and source weathering influences on Eocene to Miocene global CO<sub>2 </sub>decline.</p>
dataset for "Anthropogenic dust as a significant source of ice-nucleating particles in the urban environment"
<p>These are data that correspond to the publication of "anthropogenic dust ice nucleating particles"</p>
The shrinking Great Salt Lake contributes to record high dust-on-snow deposition in the Wasatch Mountains during the 2022 snowmelt season
<p>This site contains inputs/outputs used for atmospheric backward trajectory analyses (.zip files) and snowmelt mass and energy balance modeling (all other files) in this study. The abstract of the study is below: </p> <p>Seasonal snowmelt from the Wasatch Mountains of northern Utah, USA is a primary control on water availability for the metropolitan Wasatch Front, surrounding agricultural valleys, and the Great Salt Lake (GSL). Prolonged drought, increased evaporation due to warming temperatures, and sustained agricultural and domestic water consumption have caused GSL water levels to reach record low stands in 2021 and 2022, resulting in increased exposure of dry lakebed sediment. When dust emitted from the GSL dry lakebed is deposited on the adjacent Wasatch snowpack, the snow is darkened, and snowmelt is accelerated. Regular observations of dust-on-snow (DOS) began in the Wasatch Mountains in 2009, and the 2022 season was notable for both having the most dust deposition events and the highest snowpack dust concentrations. To understand if record high DOS concentrations were linked to record low GSL levels, dust source regions for each dust event were identified through a backward trajectory model analysis combined with aerosol measurements and field observations. Backward trajectories indicated that the exposed lakebed of the GSL likely contributed 23% of total dust deposition and had the highest dust emissions per surface area. The other potential primary contributors were the Great Salt Lake Desert (45%) and the Sevier + Tule dry lakebeds (17%), both with lower per-area emissions. The impact on snowmelt, quantified by mass and energy balance modeling in the presence and absence of snow darkening by dust, was over two weeks (17 days) earlier. The impact of dust on snowmelt could have been more dramatic if the spring had been drier, but frequent snowfall buried dust layers, delaying dust-accelerated snowmelt later into the melt season.</p>
Dataset related to the study "Spatial variability of Saharan dust deposition revealed through a citizen science campaign"
<p>This dataset contains the data and the measurements related to the manuscript "Spatial variability of Saharan dust deposition revealed through a citizen science campaign", by Dumont et al., submitted in December 2022 to the journal "Earth System Science Data".</p> <p> </p>
ICON-D2-ART output for "Aerosol-cloud-radiation interaction during Saharan dust episodes: the dusty cirrus puzzle"
<p>ICON-D2-ART output for the three dusty cirrus episodes that are discussed in section 3 of the paper.</p> <p>Some grib and netcdf data of ICON-D2-ART is provided for 16 March 2022 (Figure 4), 3 March 2021 (Figure 12) and 6 May 2022 (Figure 16). In addition, the sounding data of Figures 7 and 8 is included as well as the CERES statistics of Figures 6, 14, 17 and 18.</p>
Supplemental Data Set for "Constraining Changes in Surface Dust Thickness on Mars Using Diurnal Surface Temperature Observations from EMIRS"
<p>Supporting data set for manuscript "Constraining Changes in Surface Dust Thickness on Mars Using Diurnal Surface Temperature Observations from EMIRS". Provides the binned and averaged EMIRS surface temperatures acquired before and after the January 2022 dust storm for each region of interest to derive changes in surface dust thickness.</p>
The export of African mineral dust across the Atlantic and its impact over the Amazon Basin
<p>the codes used to analyze the model simulations in the paper</p>
Dose Finding Study With CYT003-QbG10 in Patients With House Dust Mite Allergy
ClinicalTrials.gov study NCT00800332. IPD Sharing: Not stated. Countries: 6. Publications: 1.
Dust Mite Allergen Reduction Study
ClinicalTrials.gov study NCT00339690. IPD Sharing: Not stated. Countries: 1. Publications: 4.
Efficacy and Safety Trial of the ALK HDM Tablet in House Dust Mite Allergic Subjects
ClinicalTrials.gov study NCT00389363. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Impact of a Novel Immune Modulating Dietary Supplement on House Dust Mite Induced Allergic Rhinoconjunctivitis
ClinicalTrials.gov study NCT04477382. IPD Sharing: NO. Countries: 1. Publications: 0.
Evaluation of Immunological Changes and Clinical Efficacy of Specific Immunotherapy With Der p House Dust Mite Allergen in Polysensitized and Monosensitized Patients With Allergic Rhinitis and/or Asth
ClinicalTrials.gov study NCT01795846. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Intralymphatic Immunotherapy for House Dust Mite, Dog, and Cat Allergy Using Tyrosine S® in Allergic Rhinitis
ClinicalTrials.gov study NCT02665754. IPD Sharing: NO. Countries: 1. Publications: 14.
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OpenNeuro
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