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.
535
datasets available to search
ShareScore release 0.9.0
Dataset results
535 results for “dust”
Aeolian dust weights sampled by BSNE collectors quarterly from the CSIS study at Jornada Basin LTER, 2012-ongoing
This dataset contains weights of windblown dust collected by BSNE collectors at long-term observation plots that are part of the Jornada Basin LTER Cross-Scale Interaction Study (CSIS) located at the Jornada Experimental Range. There are 15 experimental blocks (or sites) in this study. Within each block, there are 4 plots with different experimental treatments: 1 control, 1 with mesquite herbicide applied, 1 with connectivity modifiers (Conmods) installed, and 1 with Conmods AND mesquite herbicide applied. The intent of Conmods is to decrease gap size between perennial vegetation. The plots are 8 x 8 meters and have an 8 x 8 meter buffer zone on both the upwind and downwind sides of the plot. There are two BSNE (aeolian dust collector) stands per experimental plot positioned at the edge of the upwind and downwind 8m x 8m buffers. Each stand has 3 collectors positioned at heights of 10 cm, 30 cm, and 50 cm, and all collector openings face the prevailing wind direction. Upwind BSNEs collected the amount of dust entering the plot, and the downwind BSNEs collected the amount of dust moving off the plot. These collectors estimate the effectiveness of the plot surface in obstructing wind blown dust. This study is ongoing with data collected quarterly each year.
Cirrus formation regimes - Data driven identification and quantification of mineral dust effect
<p>This repository contains the data for the paper: </p> <p>Authors: Kai Jeggle , David Neubauer , Hanin Binder and Ulrike Lohmann<br>Titel: Cirrus formation regimes - Data driven identification and quantification of mineral dust effect<br>Date: 2024</p> <p>Note that the scripts can be found in the accompanying code repository (https://github.com/tabularaza27/cloud_clustering)<br><br>Contents:<br><br>├── cirrus_cloud_trajectories.ftr<br>├── cluster_input_data.ftr<br>├── cluster_models<br>│ └── temperature_clustering_k4_12<br>│ ├── cloud_ids.npy<br>│ ├── model_params.json<br>│ └── trained_model.hdf5</p> <p>│ └── temperature_clustering_k4_24<br>│ ├── cloud_ids.npy<br>│ ├── model_params.json<br>│ └── trained_model.hdf5</p> <p>├── cluster_predictions.ftr<br>└── readme.txt<br><br>For more info, please have a look at the <em>readme.txt</em><br><br>This is an updated version of the data, containing updated models and predictions based on the Journal revisions</p>
Orbital- and Millennial-Scale Variability in Northwest African Dust Emissions Over the Past 67,000 years — Datasets
<p><strong>Title</strong>: Orbital- and Millennial-Scale Variability in Northwest African Dust Emissions Over the Past 67,000 years — Datasets</p> <p><strong>Version</strong>: 1.0</p> <p><strong>Date of Release</strong>: December 06, 2021</p> <p><strong>Last Update</strong>: December 06, 2021</p> <p><strong>Identifier</strong>: 10.5281/zenodo.5652189</p> <p><strong>Permalink</strong>: <a href="https://doi.org/10.5281/zenodo.5652188">https://doi.org/10.5281/zenodo.5652188</a></p> <p><strong>Associated publication</strong>: Kinsley, C.W.; Bradtmiller, L.I.; McGee, D.; Galgay, M.; Stuut, J.-B.; Tjallingii, R.; Winckler, G.; deMenocal, P.B. 2021. Orbital- and Millennial-Scale Variability in Northwest African Dust Emissions Over the Past 67,000 years. Paleoceanography and Paleoclimatology. doi: <a href="https://doi.org/10.1002/essoar.10506290.1">10.1002/essoar.10506290.1</a></p> <p><strong>Link to publication preprint</strong>: <a href="https://doi.org/10.1002/essoar.10506290.1">https://doi.org/10.1002/essoar.10506290.1</a></p> <p><strong>Suggested citation</strong>: Please reference the associated publication above when using any datasets or materials in this repository.</p> <p><strong>Contact information</strong>: Christopher W. Kinsley, ckinsley@mit.edu OR cwkinsley@gmail.com</p> <p><strong>Dates of data collection and generation</strong>: August 2013 to February 2016</p> <p>---------------</p> <p><strong>DESCRIPTION OF DATA</strong></p> <p>This data repository contains the following datasets. We refer the user to the original manuscript (see above) and the text of the Supporting Information published alongside this manuscript for additional general information regarding the collection and generation of these data.</p> <p>DATA TABLES FOR ALL CORE SITES</p> <ul> <li><strong>Kinsley et al. (2021) P&P - Data Tables for OC437-7-GC-37 core - v1</strong>: This Excel workbook contains all data used in the study for the OC437-7-GC-37 core site, taken by the R/V Oceanus during the 2007 Changing Holocene Environments of the Eastern Tropical Atlantic (CHEETA) cruise. This includes the age control and age model, biogenic %s, U-Th isotopic measurements, grain size distributions and endmember modeling, and <sup>230</sup>Th-normalized flux data. All previously published data is noted as such and referenced.</li> <li> <p><strong>Kinsley et al. (2021) P&P - Data Tables for OC437-7-GC-49 core - v1</strong>: This Excel workbook contains all data used in the study for the OC437-7-GC-49 core site, taken by the R/V Oceanus during the 2007 Changing Holocene Environments of the Eastern Tropical Atlantic (CHEETA) cruise. This includes the age control and age model, biogenic %s, U-Th isotopic measurements, grain size distributions and endmember modeling, and <sup>230</sup>Th-normalized flux data. All previously published data is noted as such and referenced.</p> </li> <li> <p><strong>Kinsley et al. (2021) P&P - Data Tables for OC437-7-GC-68 core - v1</strong>: This Excel workbook contains all data used in the study for the OC437-7-GC-68 core site, taken by the R/V Oceanus during the 2007 Changing Holocene Environments of the Eastern Tropical Atlantic (CHEETA) cruise. This includes the age control and age model, biogenic %s, U-Th isotopic measurements, grain size distributions and endmember modeling, and <sup>230</sup>Th-normalized flux data. All previously published data is noted as such and referenced.</p> </li> <li> <p><strong>Kinsley et al. (2021) P&P - Data Tables for ODP 108-658C</strong><strong> core - v1</strong>: This Excel workbook contains all data used in the study for the ODP 108-658C core site, taken by the R/V JOIDES Resolution off Cap Blanc, Mauritania during Ocean Drilling Program Leg 108. This includes the age control and age model, biogenic %s, U-Th isotopic measurements, grain size distributions and endmember modeling, and <sup>230</sup>Th-normalized flux data. All previously published data is noted as such and referenced.</p> </li> </ul>
A vertically-resolved atmospheric dust reanalysis for Mars Years 28-29 using Analysis Correction
<p>This is a dataset of meteorological variables for the atmosphere of Mars, obtained by assimilating measurements (retrievals) of atmospheric temperature and dust opacity into a 3-dimensional, time-dependent numerical model of the Martian atmospheric circulation (known as a “reanalysis”).</p> <p>The observations come from two spacecraft - the Mars Climate Sounder (MCS) instrument on board NASA’s Mars Reconnaissance Orbiter (e.g. Kleinboehl et al. 2009) and the Thermal Emission Imaging Spectrometer (THEMIS) on board NASA’s Mars Odyssey spacecraft, and cover the period from 21 September 2006 until 5 November 2009 (Mars Years 28:Ls=109.98 - 30:Ls=4.78). MCS observations include profiles of temperature and dust opacity from near the surface up to altitudes of around 80 km obtained from infrared limb-sounding (MCS version 3 retrievals, based on opacities at around 21.6 micron wavelengths), while THEMIS measurements are of column dust opacity in the infrared (centred around 9.3 micron wavelength). Further details can be found on the websites</p> <p>https://pds-geosciences.wustl.edu/missions/odyssey/themis.html,<br> https://atmos.nmsu.edu/data and services/atmospheres data/MARS/aerosols.html</p> <p>The model into which the observations are assimilated is the UK version of Laboratoire de Météorologie Dynamique Mars Global Circulation Model (LMDMGCM), a 3-dimensional, time-dependent numerical circulation model of the Martian atmosphere and near-surface environment, simulating the changing winds, temperature, pressure and dust content of the atmosphere across the whole planet. The model solves the equations of motion, mass and energy conservation using a spherical harmonic representation in the horizontal and finite difference formulation in the vertical direction, but outputs the data here on a regular longitude-latitude grid with 72 points in longitude, 36 points in latitude and 25 terrain-following sigma levels in the vertical direction (where sigma = pressure/surface pressure) on a stretched vertical grid that extends from the surface to an altitude of approximately 100 km. More details can be found in publications by Forget et al. (1999), Newman et al. (2001), Mulholland et al. (2013).</p> <p>The observations and model are linked by an assimilation scheme, based on the Analysis Correction (AC) algorithm developed by Lorenc et al. (1991) and adapted for Mars by Lewis et al. (2007). Previous reanalyses of Mars observations using this scheme include the MACDA dataset (Montabone et al. 2014) and OPENMars (Holmes et al. 2020). This new dataset, however, makes use of an extension of the AC scheme to enable assimilation of both column integrated dust opacity measurements and dust opacity profiles in the vertical direction (see Ruan et al. 2021). This new dataset therefore provides a more realistic representation of the distribution of dust loading in the Martian atmosphere than previous work, which may also result in improved representation of other meteorological variables, notably temperature.</p> <p>Data are provided as 2D and 3D fields of variables in netCDF format as generated by the numerical model on the (longitude, latitude, sigma) grid at 2-hourly intervals. Each file contains 360 time steps covering 30 Martian days or sols. The variables contained in each file are as follows:</p> <p> Variables and attributes<br> 0 lon: FLOAT(72) = FLOAT(lon)<br> 0 long_name: longitude<br> 1 units: degrees_east<br> 1 lat: FLOAT(36) = FLOAT(lat)<br> 0 long_name: latitude<br> 1 units: degrees_north<br> 2 sigma: FLOAT(25) = FLOAT(sigma)<br> 0 long_name: sigma<br> 1 units: sigma_level = p/ps<br> 3 soil: FLOAT(18) = FLOAT(soil)<br> 0 long_name: soil levels (i.e. levels below the surface to represent thermal variations)<br> 1 units: none<br> 4 time: FLOAT(360) = FLOAT(time)<br> 0 long_name: model time<br> 1 units: days since 00:00:00 (the beginning of the file)<br> 5 controle: FLOAT(100) = FLOAT(lentable)<br> 0 long_name: Table of run parameters<br> 1 description: MGCM run 5.000<br> 6 Ls: FLOAT(360) = FLOAT(time)<br> 0 Physics_diagnostic: Solar longitude (such that Ls=0 is northern Spring equinox)<br> 1 units: deg<br> 7 tsurf: FLOAT(72,36,360) = FLOAT(lon,lat,time)<br> 0 Physics_diagnostic: Surface temperature<br> 1 units: K<br> 8 ps: FLOAT(72,36,360) = FLOAT(lon,lat,time)<br> 0 Physics_diagnostic: surface pressure<br> 1 units: Pa<br> 9 co2ice: FLOAT(72,36,360) = FLOAT(lon,lat,time)<br> 0 Physics_diagnostic: co2 ice thickness (column mass density)<br> 1 units: kg.m-2<br> 10 fluxsurf_lw: FLOAT(72,36,360) = FLOAT(lon,lat,time)<br> 0 Physics_diagnostic: fluxsurf_lw (surface infrared radiative flux)<br> 1 units: W.m-2<br> 11 fluxsurf_sw: FLOAT(72,36,360) = FLOAT(lon,lat,time)<br> 0 Physics_diagnostic: fluxsurf_sw (surface visible radiative flux)<br> 1 units: W.m-2<br> 12 temp: FLOAT(72,36,25,360) = FLOAT(lon,lat,sigma,time)<br> 0 Physics_diagnostic: temperature<br> 1 units: K<br> 13 u: FLOAT(72,36,25,360) = FLOAT(lon,lat,sigma,time)<br> 0 Physics_diagnostic: Zonal (east-west) wind<br> 1 units: m.s-1<br> 14 v: FLOAT(72,36,25,360) = FLOAT(lon,lat,sigma,time)<br> 0 Physics_diagnostic: Meridional (north-south) wind<br> 1 units: m.s-1<br> 15 rho: FLOAT(72,36,25,360) = FLOAT(lon,lat,sigma,time)<br> 0 Physics_diagnostic: density<br> 1 units: kg.m-3<br> 16 udrag: FLOAT(72,36,360) = FLOAT(lon,lat,time)<br> 0 Physics_diagnostic: Drag velocity<br> 1 units: m/s<br> 17 udragt: FLOAT(72,36,360) = FLOAT(lon,lat,time)<br> 0 Physics_diagnostic: Threshold velocity for dust lifting<br> 1 units: m/s<br> 18 aerosol: FLOAT(72,36,25,360) = FLOAT(lon,lat,sigma,time)<br> 0 Physics_diagnostic: dust opacity considering layer thickness<br> 1 units: SI (opacity/m)<br> 19 taudustvis: FLOAT(72,36,360) = FLOAT(lon,lat,time)<br> 0 Physics_diagnostic: Dust optical depth<br> 1 units: SI<br> 20 q01: FLOAT(72,36,25,360) = FLOAT(lon,lat,sigma,time)<br> 0 Physics_diagnostic: mix. ratio<br> 1 units: kg/kg<br> 21 dqsdevtot: FLOAT(72,36,360) = FLOAT(lon,lat,time)<br> 0 Physics_diagnostic: dust devil lift rate<br> 1 units: kg.m-2.s-1<br> 22 dqsstrtot: FLOAT(72,36,360) = FLOAT(lon,lat,time)<br> 0 Physics_diagnostic: near surface wind stress dust lifting rate<br> 1 units: kg.m-2.s-1<br> 23 dqssedtot: FLOAT(72,36,360) = FLOAT(lon,lat,time)<br> 0 Physics_diagnostic: dust sedimentation rate<br> 1 units: kg.m-2.s-1</p> <p> </p>
Geochemical Characterizations for Identifying Fugitive Dust Deposition and Enrichment of Surface and Subsurface Subalpine Soils from Phosphorus Mining, Eastern Ashley National Forest, Utah, 2022-2023.
Phosphorus is a non-renewable resource essential for all life. Anthropogenic alterations to the phosphorus cycle have led to widespread phosphorus pollution, and the unsustainable management of P has led to the threat of global depletion of phosphorus resources. Thus, accounting for the natural and anthropogenic flow paths of phosphorus is essential for its conservation and pollution reduction. One such source of human alteration to the phosphorus-cycle is phosphate rock mining. Mining, however, has many adverse environmental effects, including widespread fugitive dust emissions. Dust collection in the Ashley National Forest of northeastern Utah, proximate to a surface phosphorus mine, has shown phosphorus concentrations in dust more than four times that of other regional samples. Elevated phosphorus in dust near active surface mining suggests that mining emissions may alter the natural phosphorus loading of the soils in the National Forest through dust deposition; however, no research has been done to identify the abundance and range of mine-attributable phosphorus enrichment in the soils surrounding phosphate mining activities. The combined geospatial and geochemical approach of this study shows that surface soil phosphorus concentrations were found to be enriched above naturally occurring levels up to 6.5 km from mining activity (enrichment factor > 1.5), with the most significant enrichment occurring within the first 3 km (enrichment factor > 2). On average, surface phosphorus concentrations were significantly enriched by 25% within 6.5 km of phosphorus mining activity. Observed phosphorus enrichment was positively correlated with the presence of fluorapatite in the soil, which is the primary phosphorus-mineral extracted from the nearby mine. Further, bioavailable phosphorus concentrations were also higher for the soils that were enriched in phosphorus. This study shows that fugitive emissions associated with the surface mining of phosphate rock are a significan
Aeolian dust weights sampled by BSNE collectors before and after the windy season from the NEAT study at Jornada Basin LTER, 2008-ongoing
This data package contains weights of windblown dust collected by BSNE collectors at long-term vegetation-removal plots that are part of the Jornada Basin LTER Nutrient and Ecosystem impacts of Aeolian Transport (NEAT) study located at the Jornada Experimental Range. The dataset can be used to estimate horizontal dust flux in vegetation removal treatment plots (different percentage vegetation removed) and contiguous downwind plots. Year 2008 was the initial collection and collections in subsequent years occur before and after the windy season. The experiment was designed to test the effects of increases in wind erosion on soil and vegetation properties on the sand sheet geomorphic unit for different levels of herbaceous cover. In order to increase wind erosion rates, vegetation was removed each spring to increase bare surface area and stimulate erosion (the less vegetation present the greater the wind erosion). The experimental design includes three blocks located in one pasture, each with four treatment plots that are maintained at one of four levels of herbaceous vegetation and small shrubs removed (25, 50, 75, 100%) and a control. Treatment plots are 25x50m with 25m buffers between. The vegetation removal includes grasses and small shrubs (like Gutierrezia sarothrae and Zinnia grandiflora), but not mesquite or yucca or any of the larger shrubs. Also, contiguous downwind plots are monitored for soil and vegetation properties, but no removal treatments are performed in these areas. A control treatment where no vegetation was removed that was not downwind of any treatment is also included. This study is ongoing and is updated twice per year - before and after the spring windy season.
Aeolian dust weights sampled by BSNE collectors in 18 locations at the Jornada Basin LTER site, 1998-ongoing
This data package contains aeolian dust weights from BSNE collectors at 18 locations at the Jornada Basin LTER. Collections are obtained at the 15 NPP study locations, the Geomet location, Scrape study location (now known as GROWES study), and Pasture 13 Burn study location. The collectors are turned into the wind with wind vanes. The amount of material collected corresponds to the horizontal flux at the height of the collector and the opening area of the collector and the duration of the sampling time. The five heights of the BSNE collectors above the soil surface are 5, 10, 20, 50, and 100 centimeters for every location where samples are taken. The vertical flux of the particles smaller than 10 micrometers is assumed to be a constant ratio of the horizontal sand flux. The objectives of the study are to find patterns of sand flux rates as related to soil and vegetation. Site info: The NPP sites were established to estimate patterns of aboveground primary production. The Geomet site is within a mesquite-dune area that has had long-term protection from cattle grazing. The scrape site (now known as the GROWES site) was originally designed to measure the abrasion of surface crust. The Pasture 13 Burn site is located in a pasture that was originally burned in 1998. Contact the data manager for additional information and site locations. This data collection is ongoing with new data added quarterly.
differential dust extinction towards SNR RX J1713.7-3946
<p>The dataset contains 6 approximate posterior sample of differential dust extinction towards the supernova remnant RX J1713.7-3946 .</p>
Dataset envolved in "Evaluation of the single-component thermal dust emission model in CMB experiments"
<h1>Dataset envolved in "Evaluation of the single-component thermal dust emission model in CMB experiments"</h1> <p>See http://arxiv.org/abs/2411.04543.</p> <p>This data set contains the .fits files envolved in our work, from <a href="https://irsa.ipac.caltech.edu/data/Planck/" target="_blank" rel="noopener">Planck release</a> and <a href="https://cdsarc.cds.unistra.fr/ftp/J/A+A/623/A21/" target="_blank" rel="noopener">Irfan et. al., 2019</a>: </p> <p>In order to use these data files, </p> <p>please follow: (github readme)</p> <h2>Data from <em>Planck</em> release</h2> <h3><em>Planck</em> Release 1, 2013</h3> <p><strong>Format: .FITS file</strong></p> <p><strong>Type: map for thermal dust model (optical depth, spectral index, and temperature)<br></strong></p> <p><strong>Relation to this work: provide parameters of model Planck 2013</strong></p> <p>HFI_CompMap_ThermalDustModel_2048_R1.20.fits</p> <p><a title="HFI_CompMap_ThermalDustModel_2048_R1.20.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_1/all-sky-maps/maps/HFI_CompMap_ThermalDustModel_2048_R1.20.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_1/all-sky-maps/maps/HFI_CompMap_ThermalDustModel_2048_R1.20.fits</a></p> <p><strong>Format: .FITS file</strong></p> <p><strong>Type: full-sky maps at 217 GHz with zodiacal light and without zodiacal light</strong></p> <p><strong>Relation to this work: used to filter out regions with strong zodiacal emission</strong></p> <p>HFI_SkyMap_217_2048_R1.10_nominal.fits<br><a title="HFI_SkyMap_217_2048_R1.10_nominal.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_1/all-sky-maps/maps/HFI_SkyMap_217_2048_R1.10_nominal.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_1/all-sky-maps/maps/HFI_SkyMap_217_2048_R1.10_nominal.fits</a></p> <p>HFI_SkyMap_217_2048_R1.10_nominal_ZodiCorrected.fits<br><a title="HFI_SkyMap_217_2048_R1.10_nominal_ZodiCorrected.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_1/all-sky-maps/maps/HFI_SkyMap_217_2048_R1.10_nominal_ZodiCorrected.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_1/all-sky-maps/maps/HFI_SkyMap_217_2048_R1.10_nominal_ZodiCorrected.fits</a></p> <h3> </h3> <h3><em>Planck</em> Release 2, 2015</h3> <p><strong>Format: .FITS file</strong></p> <p><strong>Type: map of synchrotron emission</strong></p> <p><strong>Relation to this work: used to remove synchrotron emission from full-sky maps</strong></p> <p>COM_CompMap_Synchrotron-commander_0256_R2.00.fits<br><a title="COM_CompMap_Synchrotron-commander_0256_R2.00.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/all-sky-maps/maps/component-maps/foregrounds/COM_CompMap_Synchrotron-commander_0256_R2.00.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/all-sky-maps/maps/component-maps/foregrounds/COM_CompMap_Synchrotron-commander_0256_R2.00.fits</a></p> <p><strong>Format: .FITS file</strong></p> <p><strong>Type: map of free-free emission</strong></p> <p><strong>Relation to this work: used to remove free-free emission from full-sky maps</strong></p> <p>COM_CompMap_freefree-commander_0256_R2.00.fits<br><a title="COM_CompMap_freefree-commander_0256_R2.00.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/all-sky-maps/maps/component-maps/foregrounds/COM_CompMap_freefree-commander_0256_R2.00.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/all-sky-maps/maps/component-maps/foregrounds/COM_CompMap_freefree-commander_0256_R2.00.fits</a></p> <p><strong>Format: .FITS file</strong></p> <p><strong>Type: map of carbon monoxide</strong></p> <p><strong>Relation to this work: used to remove carbon monoxide emission from full-sky maps</strong></p> <p>COM_CompMap_CO21-commander_2048_R2.00.fits<br><a title="COM_CompMap_CO21-commander_2048_R2.00.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/all-sky-maps/maps/component-maps/foregrounds/COM_CompMap_CO21-commander_2048_R2.00.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/all-sky-maps/maps/component-maps/foregrounds/COM_CompMap_CO21-commander_2048_R2.00.fits</a></p> <p><strong>Format: .FITS file</strong></p> <p><strong>Type: map of 94/100 GHz molecular emission lines</strong></p> <p><strong>Relation to this work: used to remove 94/100 GHz emission lines from full-sky maps</strong></p> <p>COM_CompMap_xline-commander_0256_R2.00.fits<br><a title="COM_CompMap_xline-commander_0256_R2.00.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/all-sky-maps/maps/component-maps/foregrounds/COM_CompMap_xline-commander_0256_R2.00.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/all-sky-maps/maps/component-maps/foregrounds/COM_CompMap_xline-commander_0256_R2.00.fits</a></p> <p><strong>Format: .FITS file</strong></p> <p><strong>Type: Galactic plane masks with no apodization</strong></p> <p><strong>Relation to this work: used to mask Galactic plane</strong></p> <p>HFI_Mask_GalPlane-apo0_2048_R2.00.fits<br><a title="COM_CompMap_xline-commander_0256_R2.00.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/all-sky-maps/maps/component-maps/foregrounds/COM_CompMap_xline-commander_0256_R2.00.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/ancillary-data/masks/HFI_Mask_GalPlane-apo0_2048_R2.00.fits</a></p> <p><strong>Format: .FITS file</strong></p> <p><strong>Type: point source masks</strong></p> <p><strong>Relation to this work: used to mask point sources in full-sky maps and inpaint them </strong></p> <p>HFI_Mask_PointSrc_2048_R2.00.fits<br><a title="HFI_Mask_PointSrc_2048_R2.00.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/ancillary-data/masks/HFI_Mask_PointSrc_2048_R2.00.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/ancillary-data/masks/HFI_Mask_PointSrc_2048_R2.00.fits</a></p> <p><strong>Format: .FITS file</strong></p> <p><strong>Type: maps for thermal dust model (optical depth, spectral index, and temperature)<br></strong></p> <p><strong>Relation to this work: provide parameters of model Planck 2015 (GNILC pipeline, without CIB contamination)</strong></p> <p>COM_CompMap_Dust-GNILC-Model-Opacity_2048_R2.01.fits<br><a title="COM_CompMap_Dust-GNILC-Model-Opacity_2048_R2.01.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/all-sky-maps/maps/component-maps/foregrounds/COM_CompMap_Dust-GNILC-Model-Opacity_2048_R2.01.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/all-sky-maps/maps/component-maps/foregrounds/COM_CompMap_Dust-GNILC-Model-Opacity_2048_R2.01.fits</a></p> <p>COM_CompMap_Dust-GNILC-Model-Spectral-Index_2048_R2.01.fits<br><a title="COM_CompMap_Dust-GNILC-Model-Spectral-Index_2048_R2.01.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/all-sky-maps/maps/component-maps/foregrounds/COM_CompMap_Dust-GNILC-Model-Spectral-Index_2048_R2.01.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/all-sky-maps/maps/component-maps/foregrounds/COM_CompMap_Dust-GNILC-Model-Spectral-Index_2048_R2.01.fits</a></p> <p>COM_CompMap_Dust-GNILC-Model-Temperature_2048_R2.01.fits<br><a title="COM_CompMap_Dust-GNILC-Model-Temperature_2048_R2.01.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/all-sky-maps/maps/component-maps/foregrounds/COM_CompMap_Dust-GNILC-Model-Temperature_2048_R2.01.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/all-sky-maps/maps/component-maps/foregrounds/COM_CompMap_Dust-GNILC-Model-Temperature_2048_R2.01.fits</a></p> <p><strong>Format: .FITS file (table)</strong></p> <p><strong>Type: <em>Planck</em> catalogue of compact sources at 30, 44, 70, 100, 143, 217, 353, 545, and 857 GHz</strong></p> <p><strong>Relation to this work: to mask compact sources</strong></p> <p>COM_PCCS_030_R2.04.fits<br><a title="COM_PCCS_030_R2.04.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_030_R2.04.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_030_R2.04.fits</a></p> <p>COM_PCCS_044_R2.04.fits<br><a title="COM_PCCS_044_R2.04.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_044_R2.04.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_044_R2.04.fits</a></p> <p>COM_PCCS_070_R2.04.fits<br><a title="COM_PCCS_070_R2.04.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_070_R2.04.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_070_R2.04.fits</a></p> <p>COM_PCCS_100-excluded_R2.01.fits<br><a title="COM_PCCS_100-excluded_R2.01.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_100-excluded_R2.01.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_100-excluded_R2.01.fits</a></p> <p>COM_PCCS_100_R2.01.fits<br><a title="COM_PCCS_100_R2.01.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_100_R2.01.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_100_R2.01.fits</a></p> <p>COM_PCCS_143-excluded_R2.01.fits<br><a title="COM_PCCS_143-excluded_R2.01.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_143-excluded_R2.01.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_143-excluded_R2.01.fits</a></p> <p>COM_PCCS_143_R2.01.fits<br><a title="COM_PCCS_143_R2.01.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_143_R2.01.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_143_R2.01.fits</a></p> <p>COM_PCCS_217-excluded_R2.01.fits<br><a title="COM_PCCS_217-excluded_R2.01.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_217-excluded_R2.01.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_217-excluded_R2.01.fits</a></p> <p>COM_PCCS_217_R2.01.fits<br><a title="COM_PCCS_217_R2.01.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_217_R2.01.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_217_R2.01.fits</a></p> <p>COM_PCCS_353-excluded_R2.01.fits<br><a title="COM_PCCS_353-excluded_R2.01.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_353-excluded_R2.01.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_353-excluded_R2.01.fits</a></p> <p>COM_PCCS_353_R2.01.fits<br><a title="COM_PCCS_353_R2.01.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_353_R2.01.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_353_R2.01.fits</a></p> <p>COM_PCCS_545-excluded_R2.01.fits<br><a title="COM_PCCS_545-excluded_R2.01.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_545-excluded_R2.01.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_545-excluded_R2.01.fits</a></p> <p>COM_PCCS_545_R2.01.fits<br><a title="COM_PCCS_545_R2.01.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_545_R2.01.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_545_R2.01.fits</a></p> <p>COM_PCCS_857-excluded_R2.01.fits<br><a title="COM_PCCS_857-excluded_R2.01.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_857-excluded_R2.01.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_857-excluded_R2.01.fits</a></p> <p>COM_PCCS_857_R2.01.fits<br><a title="COM_PCCS_857_R2.01.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_857_R2.01.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_857_R2.01.fits</a></p> <h3> </h3> <h3><em>Planck</em> Release 3, 2018</h3> <p><strong>Format: .FITS file</strong></p> <p><strong>Type: map of CMB anisotropies (SMICA from <em>Planck</em> 2018)</strong></p> <p><strong>Relation to this work: used to remove CMB anisotropies from full-sky maps</strong></p> <p>COM_CMB_IQU-smica_2048_R3.00_full.fits<br><a title="COM_CMB_IQU-smica_2048_R3.00_full.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_3/all-sky-maps/maps/component-maps/cmb/COM_CMB_IQU-smica_2048_R3.00_full.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_3/all-sky-maps/maps/component-maps/cmb/COM_CMB_IQU-smica_2048_R3.00_full.fits</a></p> <p><strong>Format: .FITS file</strong></p> <p><strong>Type: <em>Planck</em> 2018 full-sky maps at 100, 143, 217, 353, 545, and 857 GHz</strong></p> <p><strong>Relation to this work: used to obtain dust data maps at these bands</strong></p> <p>HFI_SkyMap_100_2048_R3.01_full.fits<br><a title="HFI_SkyMap_100_2048_R3.01_full.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_3/all-sky-maps/maps/HFI_SkyMap_100_2048_R3.01_full.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_3/all-sky-maps/maps/HFI_SkyMap_100_2048_R3.01_full.fits</a></p> <p>HFI_SkyMap_143_2048_R3.01_full.fits<br><a title="HFI_SkyMap_143_2048_R3.01_full.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_3/all-sky-maps/maps/HFI_SkyMap_143_2048_R3.01_full.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_3/all-sky-maps/maps/HFI_SkyMap_143_2048_R3.01_full.fits</a></p> <p>HFI_SkyMap_217_2048_R3.01_full.fits<br><a title="HFI_SkyMap_217_2048_R3.01_full.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_3/all-sky-maps/maps/HFI_SkyMap_217_2048_R3.01_full.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_3/all-sky-maps/maps/HFI_SkyMap_217_2048_R3.01_full.fits</a></p> <p>HFI_SkyMap_353_2048_R3.01_full.fits<br><a title="HFI_SkyMap_353_2048_R3.01_full.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_3/all-sky-maps/maps/HFI_SkyMap_353_2048_R3.01_full.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_3/all-sky-maps/maps/HFI_SkyMap_353_2048_R3.01_full.fits</a></p> <p>HFI_SkyMap_545_2048_R3.01_full.fits<br><a title="HFI_SkyMap_545_2048_R3.01_full.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_3/all-sky-maps/maps/HFI_SkyMap_545_2048_R3.01_full.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_3/all-sky-maps/maps/HFI_SkyMap_545_2048_R3.01_full.fits</a></p> <p>HFI_SkyMap_857_2048_R3.01_full.fits<br><a title="HFI_SkyMap_857_2048_R3.01_full.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_3/all-sky-maps/maps/HFI_SkyMap_857_2048_R3.01_full.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_3/all-sky-maps/maps/HFI_SkyMap_857_2048_R3.01_full.fits</a></p> <p>HFI_RIMO_R3.00.fits<br><a title="HFI_RIMO_R3.00.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_3/ancillary-data/HFI_RIMO_R3.00.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_3/ancillary-data/HFI_RIMO_R3.00.fits</a></p> <h2> </h2> <h2>Thermal dust model from Melis O. Irfan et al. <a href="https://www.aanda.org/articles/aa/abs/2019/03/aa34394-18/aa34394-18.html" target="_blank" rel="noopener">A&A 623, A21 (2019)</a></h2> <p><strong>Format: .FITS file</strong></p> <p><strong>Type: maps for thermal dust model (optical depth, spectral index, and temperature)<br></strong></p> <p><strong>Relation to this work: provide parameters of model Melis O. Irfan et al. 2019</strong></p> <p>beta.fits<br><a title="beta.fits" href="https://cdsarc.cds.unistra.fr/ftp/J/A+A/623/A21/fits/beta.fits" target="_blank" rel="noopener">https://cdsarc.cds.unistra.fr/ftp/J/A+A/623/A21/fits/beta.fits</a></p> <p>tau.fits<br><a title="tau.fits" href="https://cdsarc.cds.unistra.fr/ftp/J/A+A/623/A21/fits/tau.fits" target="_blank" rel="noopener">https://cdsarc.cds.unistra.fr/ftp/J/A+A/623/A21/fits/tau.fits</a></p> <p>temp.fits<br><a title="temp.fits" href="https://cdsarc.cds.unistra.fr/ftp/J/A+A/623/A21/fits/temp.fits" target="_blank" rel="noopener">https://cdsarc.cds.unistra.fr/ftp/J/A+A/623/A21/fits/temp.fits</a></p> <p> </p>
S101 | MTMDUST | List of chemicals characterised in indoor dust samples
<p>This is the collection associated with list S101 MTMDUST List of chemicals characterised in indoor dust samples on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a></p> <p>A list of 300 chemicals characterised with identification confidence level of ≥ 3 found in retrospective analysis of 30 dust samples from 4 different indoor settings (offices, households, preschools and various occupational settings) described in Dubocq et al. (2022) DOI: <a href="https://www.oaepublish.com/jeea/article/view/5192">10.20517/jeea.2022.23</a> which can help to better estimate the exposure risks of organic contaminants to humans in the indoor environment. The categories of the main detected chemical groups were plant natural products (n = 57), personal care products (n = 44), pharmaceuticals (n = 44), food additives (n = 43), plasticisers (n = 43), flame retardants (n = 43), colourants (n = 42) and pesticides (n = 31).</p> <p>Additional notes: For entries existing in multiple isomeric forms, an additional row has been added (ID347-352) with corresponding identifiers while the metadata for each isomer exist as per original entries<br> </p>
Bitcoin dust transactions
<p><strong>General information</strong></p> <p>This repository contains data regarding Bitcoin <em>dust</em> transactions. In the Bitcoin protocol, dust refers to the small amounts of currency that are lower than the fee required to spend them in a transaction. </p> <p>The repository comprises all transactions with at least one dust output or input. According to our definition, a dust output (or input) is considered dust if the associated amount is between 1 and 545 satoshis (where 1 satoshi = 10<sup>-8</sup> bitcoin). For more details about the definition of dust, see [1].</p> <ul> </ul> <p>All dust transactions have been extracted from the first 479,970 blocks of the Bitcoin blockchain, thus covering the time period between January 3rd, 2009 18:15 GMT and August 10th, 2017 18:03 GMT.</p> <p><strong>Data set description</strong></p> <table> <tbody> <tr> <td>File</td> <td>Description</td> </tr> <tr> <td>txs</td> <td>A text file containing a representation of all Bitcoin transactions that create and consume dust. See the description below for more information about the structure of this file.</td> </tr> <tr> <td>txs_addr_map.csv</td> <td>A CSV file that maps numeric address identifiers to real Bitcoin addresses. This file comprises all addresses appearing in the txs data set.</td> </tr> <tr> <td>labels.csv</td> <td>A CSV file containing categorical entity labels for Bitcoin addresses appeared in transactions between 2010 and 2018. This file has been derived from the <em>Entity-Address data set</em> [2, 3] (see also: <a href="https://github.com/Maru92/EntityAddressBitcoin">https://github.com/Maru92/EntityAddressBitcoin</a>).</td> </tr> <tr> <td>outputs_spent_stats.csv </td> <td>A CSV file containing statistics about all spent outputs in the first 479970 blocks of the Bitcoin blockchain. The file describes the durations of dust and non-dust outputs. The duration is defined as the difference between the height of the block where the output is spent and the height of the block where it was created. </td> </tr> <tr> <td>cluster_sizes_*.csv</td> <td>These CSV files contain information about clusters of addresses induced by Bitcoin transactions. They have been used for the clustering analysis presented in [4]. See <a href="https://github.com/mloporchio/DustAnalysis">this GitHub repository</a> for more information.</td> </tr> </tbody> </table> <p><strong>Transaction representation</strong></p> <p>The <em>txs</em> file contains a textual representation of dust transactions in the Bitcoin blockchain. Each row of the file corresponds to a transaction and is represented as a sequence of fields</p> <p><code>info:inputs:outputs</code></p> <p>with the following meaning.</p> <ol> <li> <p>The <code>info</code> section contains general information about the transaction. It is represented as a list of comma-separated fields, namely: <code>timestamp,blockId,txId,isCoinbase,fee,approxSize</code>.</p> <p>The meaning of the fields is the following:</p> <ol> <li><code>timestamp</code> represents the Unix timestamp of the block containing the transaction.</li> <li><code>blockId</code> represents the height of the block containing the transaction.</li> <li><code>txId</code> is a numeric value that univocally identifies the transaction.</li> <li><code>isCoinbase</code> is equal to 1 if the transaction is a coinbase transaction, 0 otherwise.</li> <li><code>fee</code> denotes the transaction fee, expressed in satoshis (i.e., the smallest bitcoin denomination).</li> <li><code>approximateSize</code> denotes the approximate size of the transaction (expressed in bytes).</li> </ol> </li> <li> <p>The <code>inputs</code> section contains a sequence of (0 or more) transaction inputs separated by a semicolon. Each input, in turn, is represented as a comma-separated string <code>addrId,amount,prevTxId,offset</code> where:</p> <ol> <li><code>addrId</code> represents the numeric identifier of the spending address;</li> <li><code>amount</code> is the amount of value associated with the input (expressed in satoshis);</li> <li><code>prevTxId</code> represents the numeric identifier of the transaction that created the output that is currently being spent;</li> <li><code>offset</code> represents the position, among all outputs of <code>prevTxId</code>, of the output that is currently being spent.<br> </li> </ol> </li> <li>The <code>outputs</code> section contains a sequence of (1 or more) transaction outputs separated by a semicolon. Each output, in turn, is represented as a comma-separated string <code>addrId,amount,scriptType</code> where:<br> <ol> <li><code>addrId</code> represents the numeric identifier of the receiving address;</li> <li><code>amount</code> is the amount of value associated with the output (expressed in satoshis);</li> <li><code>scriptType</code> is a numeric identifier representing the type of the script associated with the output (i.e., 0=UNKNOWN; 1=P2PK; 2=P2PKH; 3=P2SH; 4=RETURN; 5=EMPTY).</li> </ol> </li> </ol> <p><strong>Data analysis</strong></p> <p>Data included in this repository have been employed for the analyses presented in [4, 5]. <a href="https://github.com/mloporchio/DustAnalysis">This GitHub repository</a> contains several tools, written in Java and Python, for analyzing the data.</p> <p><strong>Cite this work</strong></p> <p>If the data included in this repository have been useful, please cite the following article in your work.</p> <pre>@article{loporchio2023bitcoin,   title={Is Bitcoin gathering dust? An analysis of low-amount Bitcoin transactions},   author={Loporchio, Matteo and Bernasconi, Anna and Di Francesco Maesa, Damiano and Ricci, Laura},   journal={Applied Network Science},   volume={8},   number={1},   pages={1--28},   year={2023},   publisher={SpringerOpen} } </pre> <p><strong>References</strong></p> <ol> <li>Pérez-Solà, Cristina, et al. "Another coin bites the dust: an analysis of dust in UTXO-based cryptocurrencies." <em>Royal Society open science</em> 6.1 (2019): 180817.</li> <li>Jourdan, Marc, et al. "Characterizing entities in the bitcoin blockchain." <em>2018 IEEE international conference on data mining workshops (ICDMW)</em>. IEEE, 2018.</li> <li>Jourdan, Marc, et al. "A probabilistic model of the bitcoin blockchain." <em>Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops</em>. 2019.</li> <li>Loporchio, Matteo, et al. "Is Bitcoin gathering dust? An analysis of low-amount Bitcoin transactions." <em>Applied Network Science</em> 8.1 (2023): 1-28.</li> <li>Loporchio, Matteo, et al. "An Analysis of Bitcoin Dust Through Authenticated Queries." <em>Complex Networks and Their Applications XI: Proceedings of The Eleventh International Conference on Complex Networks and their Applications: COMPLEX NETWORKS 2022—Volume 2</em>. Cham: Springer International Publishing, 2023.</li> </ol>
Vibration-based smart sensor for high flow dust measurement
<p><strong>Abstract:</strong> Drying process of aggregates needed for asphalt manufacturing involves a high quantity of dust or filler that needs to be heated and extracted with the aid of a baghouse. A sensor that is able to measure the amount of filler aspirated will be a relevant innovation as the current state of the art for drying of aggregates involves a high amount of energy to heat all the aggregates so the highest amount of dust or filler is extracted. The final step of asphalt production is to mix all the components like bitumen, aggregates and cold filler itself. In the context of European project CAPRI [1,2], it is presented a prototype for measurement of filler flow based on vibration analysis, inside the pipe with an accelerometer in the insulator of an existing thermocouple subjected to the hard conditions of temperature and pressure. The paper shows the laboratory prototype results together with preliminary onsite evaluation previously to final demonstration. The paper provides also open access to all the data and results used as part of the commitment of CAPRI project with open science.</p> <p><strong>Keywords:</strong> Sensors, Innovation, Process Industry, Automation, Industry 4.0, Digital Transformation, Industrial Plants, Filler, Dust, Vibration, Signal processing, Smart sensing.</p>
Dataset for manuscript "Thermal infrared dust optical depth and coarse-mode effective diameter over oceans retrieved from collocated MODIS and CALIOP observations"
<p>This is the long-term satellite retrieval dataset of dust aerosol optical depth at 10 μm (DAOD<sub>10μm</sub>) and dust coarse mode effective diameter (D<sub>eff</sub>) based on collocated MODIS and CALIOP observations from July 2006 to August 2018. The full description is in the manuscript "<strong>Thermal infrared dust optical depth and coarse-mode effective diameter over oceans retrieved from collocated MODIS and CALIOP observations" </strong>by Zheng, Jianyu, et al. The readme file for the data is in "readme_dust_aod_size_product.txt". The variable list of Level-2 data is in "variable_list_L2.txt". The variable list of Level-3 data is in "variable_list_L3.txt".</p>
MarsWRF imposed dust simulations for investigation of dust storm trajectories
<p>This dataset contains representative MarsWRF simulation outputs used to conduct the analysis discussed in Wang et al. [2023]. The model uses a staggered "C" computational grid, with 52 vertical eta layers and a 2º longitude × 2° latitude horizontal resolution. The model grid structure is summarized in a data object structure that is saved in the IDL software (NV5 Geospatial, Broomfield, CO) SAV file format as “wrfgrid.sav”. It can be read using the IDL software command “restore,'wrfgrid.sav'” or using the Python “scipy” library module that can interpret the IDL SAV file format, scipy.io.readsav.</p> <p>Files for each simulation are collected using the “tar” archive tool and compressed using the “gzip” tool to minimize storage requirements, and can be extracted similarly, (e.g., tar -xvzf *.tar.gz). Each file is written in NetCDF format and contains 30 sols of 2-hourly output (i.e., 360 output timesteps per file) for U (zonal wind), V (meridional wind), T (perturbation potential temperature with respect to 300 K, i.e., potential temperature – 300., which is a native WRF output field), PSFC (surface pressure), L_S (solar longitude), and UST (surface friction velocity). </p> <p>The no-storm control run simulation employs the dust optical depth scenario saved in dustscenario_nostorm.nc. This optical depth scenario is derived from the observationally-derived multiannual dust climatology [Montabone et al., 2015] by reducing the climatology to a single year and removing the influence of large dust storm episodes in the contributing years. The other MarsWRF simulations included in this archive impose additional dust optical depth over the base no-storm dust scenario in different latitudinal bands (i.e., spanning all longitudes) and for different L<sub>S</sub> time periods, as indicated by the archive file names. For example, ls200n230_45N75N_tau1.732.tar is the archive file for the simulation with additional imposed dust between 45ºN and 75ºN from Ls = 200º to Ls = 230º with the imposed optical depth amplitude of 1.732. Due to the large data volume, the archived files for each simulation can only cover the corresponding Ls period of interest for a representative Mars year. For details of the simulations, please refer to Wang et al. [2023 submitted] listed in the References section.</p>
Dust geochemistry and lead isotopes along an urban-rural transect in central Ohio, 2021
This data package contains geochemical concentrations and stable lead isotope ratios for dust samples collected along an urban-rural land use gradient in central Ohio during 2021. The purpose of the study was to characterize the geochemical and isotopic signatures of dust in relation to different land uses, to see how much dust varies across land use and by season. At four sites along an urban-rural transect in central Ohio, we collected weekly bulk deposition samples and analyzed the geochemical composition (47 elements including major elements, trace metals, and rare earth elements) and stable lead isotopes (208Pb, 207Pb, 206Pb, and 204Pb) of the particulate matter. This study demonstrates the tight connection between land use and anthropogenic dust composition in a region where land use is changing rapidly as development encroaches into farmland. This dataset is complete and will not be updated.
BSNE aeolian dust collector sample weights from the three ConMod Pilot study locations at Jornada Basin LTER, 2008-2016
This data package contains measurements of dust collected by BSNE collectors for the Connectivity Modifier (ConMod) Pilot study plots from 2008-2016 on the Jornada Experimental Range. There were 3 sites for this study: Gravelly Ridges, Aeolian, and Dona Ana. Within each site, there were 8 plots. The plots are 8 x 8 meters and have an 8 x 8 buffer zone on both sides of the plot (up and down). There are four BSNE (aeolian dust collector) stands for each plot, 2 in each of the 2 buffer zones (8 collectors per plot). Each stand contains 2 BSNE collectors at a 30cm height with the collection opening at 10cm x 2 cm wide x 5 cm height. These BSNE collectors are in a fixed position pointing into the direction of the prevailing wind, which corresponds to the plot alignment. The collectors in the upwind buffer are facing away from the plot and the collectors in the downwind buffer are facing into the plot. Upwind BSNEs collect the amount of dust entering the plot, and the downwind BSNEs collect the amount of dust moving off the plot. These collectors estimate the effectiveness of the plot surface in obstructing wind blown dust. This study is complete (finished in 2016) and was the pilot study to the newer Cross Scale Interactions Study.
Far-infrared to millimeter data of protoplanetary disks: dust growth in the Taurus, Ophiuchus, and Chamaeleon I star-forming regions
<p>This repository contains the data set presented in the manuscript "Far-infrared to millimeter data of protoplanetary disks: dust growth in the Taurus, Ophiuchus, and Chamaeleon I star-forming regions" (Ribas et al. 2017), and includes a table with several sample properties (e.g. stellar properties, Herschel photometry, different spectral indices), spectral energy distributions, Spitzer/IRS and Herschel/SPIRE spectra, the median SEDs of Taurus, Ophiuchus and Chamaeleon I, and the Herschel maps used.</p> <p>ERRATUM: three Chamaeleon I sources (Hn 11, T45a, and WY Cha) were mislabeled in the original version of the manuscript, which resulted in their names, stellar parameters, extinction values, infrared slopes, and silicate feature properties being assigned to incorrect coordinates. Because the photometry and spectroscopy presented in the original article is coordinate- based, the provided SEDs and spectra were also missmatched: the data files labeled Hn 11 in the original manuscript correspond to T45a, those labeled T45a correspond to WY Cha, and those labeled WY Cha correspond to Hn 11. Additionally, due to a mislabeling issue in Manoj et al. 2011, the source formerly labeled UX Cha is actually CHSM 8284. Therefore, stellar parameters and photometry labeled UX Cha in our original manuscript correspond to CHSM 8284 The updated version of the repository fixes the issue both in the sample.csv file and in the individual SED and Spitzer/IRS spectra files. The published erratum is available here: <a href="https://iopscience.iop.org/article/10.3847/1538-4357/abb66e">https://iopscience.iop.org/article/10.3847/1538-4357/abb66e</a>.</p>
Dataset for the project "Evaluation of the effects of trace elements from street dust under urban – industrial conditions on the ecophysiology of Acer platanoides L. and Tilia cordata Mill.
<p>Description of the project: The rapid growth of cities, industry and transport has significantly deteriorated environmental quality, especially in areas with the highest population densities. It applies to water, soil, and air, especially in urban areas. Air pollutants include particulate matter (PM), which harms human health. According to WHO reports (2021), PM pollution is the cause of cardiovascular and respiratory diseases, leading to 4.2 million premature deaths worldwide in 2016. Although improving every year, the situation in Poland is still worse than in many European countries. The particulate matter also includes heavy metals, which have a toxic effect on plants. Plants in urban areas are particularly vulnerable, especially trees, which perform several vital functions, including mitigating climate change, filtering pollutants, and improving air quality. The aim of the project was to determine and compare the morphological and physiological responses of selected tree species to particulate pollution stress under urban conditions. Tree leaves are an essential barrier to atmospheric dust by trapping it on their surface. However, this may come at the cost of reduced light absorption, increased leaf temperature, damage to leaf blades and consequently impaired photosynthesis and plant productivity. However, the ability to absorb dust varies between tree species. It depends on the leaf surface structure, and the response may be due to the species' sensitivity to pollutants. Investigations were conducted in the Upper Silesian Industrial Area around various emission sources, such as heavy metal smelters, combined heat and power plants, and busy streets. The research focused on two tree species common in urban areas, the Norway maple (<i>Acer platanoides</i>) and the small-leaved lime (<i>Tilia cordata</i>). It included measurement of heavy metal concentrations in leaf blades and dust collected on their surface, analysis of concentrations of selected pigments and ascorbic acid as markers of environmental stress. The study provided a preliminary assessment of the impact of particulate pollution on tree function under harsh urban conditions and determined the potential of the studied species to reduce atmospheric dust.</p>
A subset of the EMARS dataset in MY24 and MY26 converted from the sigma-p hybrid coordinate to the pressure coordinate and a list of local dust storms detected during the MYs in western Arcadia Planitia
<p>This dataset includes a subset of EMARS' background mean data (Greybush et al., 2019) converted from the sigma-p hybrid coordinate to the pressure coordinate. Only MY24 and MY26 were used to generate the figures shown in Ogohara (submitted to JGR Planets). <br>Updates from the original EMARS are:</p> <ul> <li>The vertical coordinate has been converted from the sigma-p hybrid coordinate to the pressure coordinate.</li> <li>The variables expressing the Earth date (e.g., year, month, day, etc.) have been combined into one variable, earth_date.</li> <li>A new variable, emars_date, has been created from emars_sol and mars_hour.</li> </ul> <p>In addition, this dataset provides two lists of local dust storms events during MY24 and MY26 which were detected in western Arcadia Planitia using a deep learning-based method proposed by Ogohara and Gichu (2022). The lists are:</p> <ul> <li>[Data Set S1] List of global image swath files examined. Only file names of MGS/MOC red band images are listed. The list consists of 5 columns indicating image ID, observation date, orbit number, solar longitude, and filter name (RED).</li> <li>[Data Set S2] List of global image swath files containing identified dust storms, as well as some attributes of the detected dust storms. Only file names of red band images are listed. The list consists of 7 columns indicating image ID, observation date, orbit number, solar longitude, center longitude and latitude, and area (km2.)</li> </ul>
Dataset associated with Banks et al. (2022): "Impacts of the desiccation of the Aral Sea on the Central Asian dust life-cycle"
<p>This dataset contains the COSMO-MUSCAT simulation output for the 'Dustbelt' (DUBLT) scenarios of Central Asian dust aerosol described by the paper "Impacts of the desiccation of the Aral Sea on the Central Asian dust life-cycle", written by Banks et al. and published in JGR in 2022 (<a href="https://doi.org/10.1029/2022JD036618">https://doi.org/10.1029/2022JD036618</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.