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2,194 results for “diffusion,”
Selective excitation enables encoding and measurement of multiple diffusion parameters in a single experiment
<p>NMR spectra data for diffusion experiments.</p> <p>NMR pulse sequence compatible with Bruker spectrometers for the selective diffusion measurement.</p>
Data for Aerosol Diffuse Fertilization Effect
<p>The simulated GPP and diffuse PAR caused by natural and anthropogenic aerosols under all and clear skies.</p>
Development of a diffuse reflectance probe for in situ measurement of inherent optical properties in sea ice
<p>Included are the data presented in the publication entitled: <em>Development of a diffuse reflectance probe for in situ measurement of inherent optical properties in sea ice</em> accepted for publication in The Cryosphere Journal (2021). The data set includes Data and codes:</p> <p>1. Data (duplicated in .xlsx and .mat):</p> <p> </p> <p>1.1 Sites coordinates- (figure 5) -Geolocalisation of both sea ice sampling sites visited for this study (1 and 4)</p> <p> </p> <p>1.2 cumu_sg- (figure 6)- cumulative signal vs depth vs source-detector distance vs scattering coefficient obtained with Monte Carlo simulations</p> <p> —cumu_sg- cumulative signal (%)</p> <p> — depth (mm)</p> <p> —standard deviation on depth where signal is cumulated</p> <p> —ddet (mm)- radial distance between source and detection point </p> <p> — b (m^-1)-scattering coefficient</p> <p> </p> <p>1.3 validation-(figure 7)- Error on IOPs vs IOP value estimated measuring on microspheres solutions </p> <p> </p> <p>—vf (-)- microspheres volume fraction (in water)</p> <p> —a_theo (m^-1) - theoretical value of the absorption coefficient</p> <p> — mean_error_a(%) - error between theoretical value and measured value</p> <p> —std_error_a_x (%) - standard deviation on theoretical value (based on the standard deviation on microspheres diameter)</p> <p> —std_error_a_y (%) -standard deviation on error_a </p> <p> —rb_theo (m^-1) - theoretical value of the reduced scattering coefficient</p> <p> —mean_error_rb(%) - error between theoretical value and measured value</p> <p> —std_error_rb_x (%) - standard deviation on theoretical value (based on the standard deviation on microspheres diameter)</p> <p> —std_error_rb_y (%)) -standard deviation on error_rb </p> <p> —gamma_theo (-) - theoretical value of gamma</p> <p> —mean_error_gamma (%) - standard deviation on theoretical value (based on the standard deviation on microspheres diameter)</p> <p> —std_error_gamma (%) - standard deviation on error_gamma</p> <p> </p> <p>-1.4 T-S-(figure 8)- Vertical profiles of temperature and bulk salinity of sampled sea ice available at both snow covered site 1 and bare ice site 4</p> <p> </p> <p> —T (celsius) - ice temperature</p> <p> —S_si (ppt) - ice bulk salinity</p> <p> —depth (cm)</p> <p> </p> <p>1.5 Rmes-(figure 9)-Vertical profiles of spatially resolved diffuse Reflectance in sea ice using different covers to shade available at both snow covered site 1 and bare ice site 4</p> <p> </p> <p> —Rmes (-) - spatially resolved diffuse Reflectance</p> <p> —Rmes_nbg (-) - spatially resolved diffuse Reflectance with no background sunlight subtraction in calculation of Rmes</p> <p> —dmes (mm) - distance between source and detecting fibre (named rho in the paper)</p> <p> —depth (cm)</p> <p> — cover - cover used to shade from the sun: te=tent,nc= no cover, ta=tarp</p> <p> </p> <p>1.6 IOPprofiles-(figure 9)-Vertical profiles of reduced scattering coefficient in sea ice using different covers to shade available at both snow covered site 1 (ice+snow) and bare ice site 4</p> <p> </p> <p> —infferedrb (m^-1) - reduced scattering coefficient</p> <p> —infferedrb_nbg (m^-1) - reduced scattering coefficient with no background sunlight subtraction in calculation of Rmes</p> <p> —cr1 (binary)— criteria determining if the measurement is kept or not</p> <p> —depth (cm)- depth from the surface . **watch out** at site 1 , the measurments start from the surface of the snow. Substract 24 cm to get measurement from surface of the ice.</p> <p> — cover - cover used to shade from the sun: te=tent,nc= no cover, ta=tarp</p> <p> </p> <p>2. Code (written in .m with MATLAB_R2018b ®) :</p> <p> </p> <p>2.1 inversion algorithm—(figure 9 ) — used to find rb from Rmes (dmes) vertical profiles in sea ice</p> <p> </p> <p>— Main_vprofiles_Rtorb-qik2019_article.m - Main script of the inversion alorithm to get rb from Rmes (dmes)</p> <p>—importfiledata.m-subfunction to import data from .csv </p> <p>—importfiledatamay8.m-subfunction to import data from .csv (specific to may 8th because file was corrupted)</p> <p>—interp1lookup_HR_enlarged_bin10.mat - lookup table of Reflectance vs dmes vs a vs b’ vs gamma used in the inversion</p> <p>—calibjune6_ha_interp1_indcalib2.mat - calibration factor with microspheres as a reference</p> <p>—site1_c20-picture of the ice core taken at site 1</p> <p>—site4_c20-picture of the ice core taken at site 4</p> <p>—may8th+othertests_fixed.csv-raw data from may 8 (site1)</p> <p>—may9day3.csv-raw data from may 9 (site4)</p> <p> </p> <p> </p>
Dataset and structure database for an ML model to predict diffusivity in ZIF variants
<p>This dataset accompanies the publication titled "Data Mining for Predicting Gas Diffusivity in Zeolitic-imidazolate Frameworks (ZIFs)" (DOI: <a href="https://doi.org/10.1039/D2TA02624D">https://doi.org/10.1039/D2TA02624D</a>)</p> <p><a href="https://zenodo.org/api/files/b80f6d07-3bf4-484c-97ac-5d579fb0cc27/ESI_2_dataset.xlsx?versionId=dc4525d0-1c5c-478c-9bef-a156587ad69b">ESI_2_dataset.xlsx</a>: Descriptors for all ZIFs of the publication and simulations output, in the form of diffusivities of gas molecules (He up to iso-butane), in all ZIFs.</p> <p>ZIF_database.zip: ZIP file containing all ZIFs prepared by the authors (as discussed in the publication), through various units replacements, in the SOD topology, in .pdb format.</p>
Diffuse Emission of High-Energy Neutrinos from a Global Fit to Cosmic Rays
<p>Model of diffuse emission of high-energy neutrinos from a global fit of cosmic rays and model of high-energy neutrino emission from unresolved pulsar-powered sources.</p> <p>The maps presented in the form of <em>HEALPix </em>maps (Gorski et al 2005, ApJ, 622, 759) of per-flavor intensity in units of GeV<sup>-1</sup> cm<sup>-2</sup> s<sup>-1</sup> sr<sup>-1</sup> at 50 logarithmically spaced energies between 10 GeV and 10<sup>8</sup> GeV. We use a value of NSIDE=256 and the RING binning scheme.</p> <p>We here make available our fiducial model, which is calculated assuming the <em>Ferrière 2001</em> cosmic ray source distribution, the <em>AAfrag</em> hadronic production cross sections and the <em>GALPROP</em> gas maps. We calculated the emission from unresolved sources following Vecchiotti et al. 2022, ApJ, 928, 19.</p> <p>In Version 2 of this dataset, we also make available the local cosmic ray fluxes of our fiducial model obtained from a global fit to cosmic ray data together with the corresponding 68% and 95% uncertainty bands. These are shown in figure 6 of <a href="https://arxiv.org/abs/2211.15607">arXiv:2211.15607</a>. The nuclear fluxes are in (GeV/n)<sup>-1</sup> m<sup>-2</sup> s<sup>-1</sup> sr<sup>-1</sup>, the fluxes of electrons and positrons are in GeV<sup>-1</sup> m<sup>-2</sup> s<sup>-1</sup> sr<sup>-1</sup> . The fluxes are local interstellar fluxes without solar modulation.</p> <p>In Version 3 of this dataset, we add the fiducial diffuse gamma ray model calculated assuming the <em>Ferrière 2001</em> cosmic ray source distribution, the <em>AAfrag</em> hadronic production cross sections as well as the <em>GALPROP</em> gas maps and ISRF model. We separately make available 3 maps: The hadronic emission on neutral atomic gas, the hadronic emission on molecular gas and the leptonic emission from Inverse Compton Scattering. </p> <p>Similar to the dataset of the fiducial neutrino model, the maps are presented in the form of <em>HEALPix </em>maps (Gorski et al 2005, ApJ, 622, 759) in units of GeV<sup>-1</sup> cm<sup>-2</sup> s<sup>-1</sup> sr<sup>-1</sup>. We use a value of NSIDE=256 and the RING binning scheme. For the hadronic maps, the intensity is given at 50 logarithmically spaced energies between 10 GeV and 10<sup>8</sup> GeV. For the leptonic maps from Inverse Compton Scattering, the intensity is given at 48 logarithmically spaced energies between 1 GeV and 10<sup>6</sup> GeV.</p> <p>The structure of the files is somewhat different from the file containing the fiducial neutrino model. This is to allow for easy use of the gamma ray maps with the <em>gammapy</em> package (Deil et al. 2017, <a href="https://arxiv.org/abs/1709.01751"> arXiv:1709.01751</a>).</p> <p>Also available in Version 3 are the full spatio-spectral cosmic ray distributions in the Milky Way as predicted by our fiducial model. The nuclear fluxes are given for each species in (GeV/n)<sup>-1</sup> m<sup>-2</sup> s<sup>-1</sup> sr<sup>-1</sup> at 63 energies between 1 GeV and 10<sup>9</sup> GeV. The leptonic fluxes are given for each species in GeV<sup>-1</sup> m<sup>-2</sup> s<sup>-1</sup> sr<sup>-1</sup> at 36 energies between 1 GeV and 10<sup>5</sup> GeV. </p> <p>All fluxes are given on a spatial grid at 81 galactocentric radii from 0 kpc to 20 kpc and 61 distances perpendicular to the galactic plane between -6 kpc and 6 kpc.</p> <p>Finally, a word of caution about the extra component of cosmic ray leptons included in our model: This component is contained in the last <em>HDUnit</em> of the <em>fits</em> file containing the leptonic cosmic ray distributions. It is there denoted as a flux of electrons. It must, however, also be added to the flux of positrons to achieve correct results.</p> <p>Please refer to <a href="https://arxiv.org/abs/2211.15607">arXiv:2211.15607</a> for further details.</p> <p>When using these models in your research work, please refer to this Zenodo dataset and the publication.</p>
Supplementary data for the journal article "Quantifying the impact of 3D pore space morphology on diffusive mass transport in loam and sand"
<p>Binarized cutouts (black=pore, white=soil) of 3D CT images of soil samples from loam and sand together with geometrical descriptors and diffusive properties computed on these cutouts. The geometrical descriptors include, among others, porosity, specific surface area, geodesic tortuosity, geometric tortuosity, constrictivity, mean chord length and mean of spherical contact distribution. Diffusion is quantified by the so-called M-factor which equals the ratio of the effective and intrinsic diffusivity.</p> <p>This data supplements the journal article "Quantifying the impact of 3D pore space morphology on diffusive mass transport in loam and sand". Additional information can be found there.</p>
The Upper Bound of Information Diffusion in Code Review
<p>More details on <a href="https://github.com/michaeldorner/information-diffusion-boundaries-in-code-review">https://github.com/michaeldorner/information-diffusion-boundaries-in-code-review</a></p>
Data and code for gmd-2023-113 "Parameter estimation for ocean background vertical diffusivity coefficients in the Community Earth System Model (v1.2.1) and its impact on ENSO forecast"
<p>Data and code for the paper "Parameter estimation for ocean background vertical diffusivity coefficients in the Community Earth System Model (v1.2.1) and its impact on ENSO forecast"</p> <p>includes: </p> <p>The model is Community Earth System Model (v1.2.1) (provided by www.cesm.ucar.edu)</p> <p>Data assimilation code is initially provided by Data Assimilation Research Testbed (DART) (https://dart.ucar.edu/), some modifications are made to enable parameter estimation function of ocean background vertical diffusivity coefficients. And the programs and scripts for deal with OISST and EN4 profiles are also developed.</p> <p>The parameter sensitivity experiment results are saved as <a href="https://zenodo.org/api/files/6e5b4bfa-61cf-44bc-8e15-e62fab467ea6/sensitive2008-2012.nc">sensitive2008-2012.nc</a> and <a href="https://zenodo.org/api/files/6e5b4bfa-61cf-44bc-8e15-e62fab467ea6/sensitive2008-2012salt.nc">sensitive2008-2012salt.nc</a> for temperature and salinity, respectively. And the python script to draw the results is </p> <p>The state estimation results are provided as <a href="https://zenodo.org/api/files/6e5b4bfa-61cf-44bc-8e15-e62fab467ea6/Temp_05-17.nc">Temp_05-17.nc</a> and <a href="https://zenodo.org/api/files/6e5b4bfa-61cf-44bc-8e15-e62fab467ea6/Temp_05-17.nc">Salt_05-17.nc</a> for temperature and salinity, respectively.</p> <p>The parameter estimation results are provided as <a href="https://zenodo.org/api/files/6e5b4bfa-61cf-44bc-8e15-e62fab467ea6/PE_Temp_05-17.nc">PE_Temp_05-17.nc</a> and <a href="https://zenodo.org/api/files/6e5b4bfa-61cf-44bc-8e15-e62fab467ea6/PE_Temp_05-17.nc">PE_Salt_05-17.nc</a> for temperature and salinity, respectively.</p> <p>the estimated paremeter ensemble is saved in <a href="https://zenodo.org/api/files/6e5b4bfa-61cf-44bc-8e15-e62fab467ea6/parameters.nc">parameters.nc</a></p> <p>the python script for comparing the SE and PE results is <a href="https://zenodo.org/api/files/6e5b4bfa-61cf-44bc-8e15-e62fab467ea6/plot_analysis.py">plot_analysis.py</a></p> <p>the nino3.4 indices computed by the forecast experiment is saved in <a href="https://zenodo.org/api/files/6e5b4bfa-61cf-44bc-8e15-e62fab467ea6/fcst_correlation.nc">fcst_correlation.nc</a></p> <p> </p>
Datasets and trained diffusion models for "Diffusion Models for Interferometric Satellite Aperture Radar"
<p>A set of trained Probabilistic Diffusion Models (PDMs) and corresponding training datasets for the paper "<a href="https://doi.org/10.48550/arXiv.2308.16847">Diffusion Models for Interferometric Satellite Aperture Radar</a>", by Tuel, Kerdreux et al. The code for this paper can be found at <a href="https://github.com/thomaskerdreux/PDM_SAR_InSAR_generation">this link</a>.</p> <p><strong>Training datasets</strong></p> <p>- "InSAR_noise_32x32.zip": a dataset of 32x32 ground deformation scenes obtained from InSAR interferograms over New Mexico with the small baseline subset (SBAS) algorithm. Images were normalised to [0, 1].</p> <p>- "insar_unwrapped_phase_normalised.zip": a dataset of 128x128 InSAR interferograms obtained from Sentinel-1 acquisitions over Nex Mexico. Images were normalised to [0, 1].</p> <p><strong>Trained Models</strong></p> <p>We provide 6 trained PDMs in separate .zip files. Each .zip file contains the model weights (in *.pt format) and the model metadata file (in *.json format).</p> <p>- "mnist_32_cond_sigma_100.zip": a class-conditional model trained with 100 diffusion time steps on 32x32 MNIST images;</p> <p>- "mnist_32_no_cond_sigma_100.zip": an unconditional model trained with 100 diffusion time steps on 32x32 MNIST images;</p> <p>- "SAR_lowres_128_cond_sigma_2000.zip": a low-resolution (256 to 128) model trained with 2000 diffusion time steps on 128x128 TenGeoP-SARwv images;</p> <p>- "SAR_superres_128_to_256_cond_sigma_2000.zip": a super-resolution (128 to 256) model trained with 2000 diffusion time steps on TenGeoP-SARwv images;</p> <p>- "insar_phase_128_sigma_2000.zip": an unconditional model trained with 2000 diffusion time steps on 128x128 Sentinel-1 InSAR interferograms over New Mexico;</p> <p>- "insar_noise_32_sigma_1000.zip": an unconditional model trained with 1000 diffusion time steps on 32x32 Sentinel-1 InSAR ground deformation scenes over New Mexico.</p>
List of Electron Diffusion Regions (EDR) observed by NASA/MMS
<p><strong>List of Electron Diffusion Regions (EDR) during the phases 1a & 1b of the NASA/MMS mission.</strong><br> EDR from Lenouvel_et_al._[2021] and Lenouvel_(unpublished) were detected with two distinct machine learning algorithms. The first algorithm (MLP) is detailed in Lenouvel_et_al._[2021]. Both algorithms (MLP and CNN) are described in Q. Lenouvel's PhD manuscript and in "Advanced Methods for Analyzing In-Situ Observations of Magnetic Reconnection" submitted to Space Science Reviews by Hasegawa et al..<br> For completeness, other EDR events reported in the literature are added to the list. All references are given below. Note that some events were observed by other MMS spacecraft at slightly different times.</p> <p>Burch, J. L., and T. D. Phan, Magnetic reconnection at the dayside magnetopause: Advances with MMS, Geophysical Research Letters, 43 (16), 8327–8338, https://doi.org/10.1002/2016GL069787, 2016.<br> Burch, J. L., et al., Electron-scale measurements of magnetic reconnection in space, Science, 352 (6290), aaf2939, https://doi.org/10.1126/science.aaf2939, 2016.<br> Chen, L.-J., et al., Electron energization and mixing observed by mms in the vicinity of an electron diffusion region during magnetopause reconnection, Geophysical Research Letters, 43 (12), 6036–6043, https://doi.org/10.1002/2016GL069215, 2016.<br> Chen, L.-J., et al., Electron diffusion region during magnetopause reconnection with an intermediate guide field: Magnetospheric multiscale observations, Journal of Geophysical Research: Space Physics, 122 (5), 5235–5246, https://doi.org/10.1002/2017JA024004, 2017.<br> Cozzani, G., et al., In situ spacecraft observations of a structured electron diffusion region during magnetopause reconnection, Phys. Rev. E 99, 043204 (2019); https://doi.org/10.1103/PhysRevE.99.043204<br> Dong, X.-C., et al., Observation of nonuniform energy dissipation in the electron diffusion region of magnetopause reconnection, Geophysical Research Letters, 48 (13), e2020GL091928, https://doi.org/10.1029/2020GL091928, 2021.<br> Ergun, R. E., et al., Drift waves, intense parallel electric fields, and turbulence associated with asymmetric magnetic reconnection at the magnetopause, Geophysical Research Letters, 44 (7), 2978–2986, https://doi.org/10.1002/2016GL072493, 2017.<br> Eriksson, S., et al., Magnetospheric multiscale observations of the electron diffusion region of large guide field magnetic reconnection, Phys. Rev. Lett., 117, 015,001, https://doi.org/10.1103/PhysRevLett.117.015001, 2016.<br> Fuselier, S. A., et al., Large-scale characteristics of reconnection diffusion regions and associated magnetopause crossings observed by mms, Journal of Geophysical Research: Space Physics, 122 (5), 5466–5486, https://doi.org/10.1002/2017JA024024, 2017.<br> Genestreti, K. J., et al., Mms observation of asymmetric reconnection supported by 3-d electron pressure divergence, Journal of Geophysical Research: Space Physics, 123 (3), 1806–1821, https://doi.org/10.1002/2017JA025019, 2018.<br> Graham, D. B., et al., Instability of agyrotropic electron beams near the electron diffusion region, Phys. Rev. Lett., 119, 025,101, https://doi.org/10.1103/PhysRevLett.119.025101, 2017.<br> Khotyaintsev, Y. V., et al., Electron jet of asymmetric reconnection, Geophysical Research Letters, 43 (11), 5571–5580, https://doi.org/10.1002/2016GL069064, 2016.<br> Lenouvel, Q., Identification par apprentissage machine et analyse de régions de diffusion électronique à la magnétopause terrestre observées par MMS, Thèse de doctorat en Astrophysique, Sciences de l'Espace, Planétologie, Université de Toulouse, http://thesesups.ups-tlse.fr/5558/, 2022.<br> Lenouvel, Q., et al., Identification of electron diffusion regions with a machine learning approach on mms data at the earth’s magnetopause, Earth and Space Science, 8 (5), e2020EA001530, https://doi.org/10.1029/2020EA001530, 2021.<br> Li, W. Y., et al., Electron Bernstein waves driven by electron crescents near the electron diffusion region, Nature Communications, 11 (1), 141, https://doi.org/10.1038/s41467-019-13920-w, 2020.<br> Norgren, C., et al., Finite gyroradius effects in the electron outflow of asymmetric magnetic reconnection, Geophysical Research Letters, 43 (13), 6724–6733, https://doi.org/10.1002/2016GL069205, 2016.<br> Phan, T. D., et al., Mms observations of electron-scale filamentary currents in the reconnection exhaust and near the x line, Geophysical Research Letters, 43 (12), 6060–6069, https://doi.org/10.1002/2016GL069212, 2016.<br> Webster, J. M., et al., Magnetospheric Multiscale Dayside Reconnection Electron Diffusion Region Events, Journal of Geophysical Research (Space Physics), 123 (6), 4858–4878, https://doi.org/10.1029/2018JA025245, 2018.</p> <p> </p>
Scripts, models, and data for manuscript "On the Role of Stern- and Diffuse-Layer Polarization Mechanisms in Porous Media"
<p>This repository contains Matlab scripts, Comsol Multiphysics models, and numerical simulation data used to generate the plots in the manuscript</p> <p>Bücker, M., Flores Orozco, A., Undorf, S., and Kemna, A., 2019, <em>On the Role of Stern- and Diffuse-Layer Polarization Mechanisms in Porous Media</em>, submitted to JGR: Solid Earth.</p> <p>If you find this data useful in your own research, please cite this manuscript.</p>
Methane ebullition and diffusion rates, turbulence, water temperature, and water depth data from Falling Creek Reservoir (Virginia, USA) in the ice-free period during 2016-2019
This dataset includes weekly and subweekly methane ebullition and diffusion rates collected from March through November in 2016, 2017, 2018, and 2019 in Falling Creek Reservoir (FCR), a drinking water reservoir owned and managed by the Western Virginia Water Authority and located in Vinton, Virginia, USA. In 2016, ebullition rates were measured at four near-shore sites along a longitudinal gradient in FCR that included four replicate locations within each site, for a total of 16 sites in the reservoir. In 2017, methane emission rates and five different potential environmental driver variables (water temperature, wind speed, pressure, turbulence, primary production) were measured at five longitudinal transects that included four replicate sites per transect, for a total of 20 sites in the reservoir. In 2018, ebullition was measured at the same five longitudinal transects but with two replicate sites per transect, for a total of 10 sites. In 2019, ebullition was only measured at the furthest upstream transect monitored in 2017-2018, with four replicate sites within that transect. The dataset consists of four tables: 1) weekly methane ebullition and diffusion rates and the water depths at each of the 24 unique monitoring sites; 2) 10-minute resolution sediment-water interface and surface water temperatures from 16 sites in 2017; 3) weekly sediment-water interface and surface turbulence at the four transects in 2017; and 4) geographical coordinates of each of the 24 sites in the reservoir.
Total and diffuse photosynthetically active radiation (PAR) recorded by a beam fraction (BF3) sensor during the summer of 2012 in vicinity of Toolik Lake, Alaska.
This file contains irradiance (PAR) and diffuse light data logged from a beam fraction (BF3) sensor near Toolik Lake, Alaska during the summer of 2012. The data comes from a compilation of automated datalogger readings as well as measurements taken during the field season in conjunction with the Delta-T SunScan wand to measure PAR in tall shrub canopies dominated by Betula nana or Salix pulchra species. The sensor was leveled and mounted to a 2m tripod in each location, and programmed to record instantaneous readings in 30 second to 5 minute intervals.
Summary of measured and modeled light curve parameters for diffuse, direct, and intermediate light curves for 14 whole-canopy 1mx1m plots sampled near the shrub LTER sites at Toolik Field Station, Alaska, summer 2012.
14 1m x 1m shrub plots were sampled the summer of 2012 under direct and diffuse light conditions. Light response curves were measured under each light condition for each plot using a Li-Cor 6400 to measure net ecosystem exchange (NEP); these measurements were modelled using a saturatingMichaelis-Menton formula. The best fit parameters for those models are contained here (Pmax, K, RE, Eo, and light compensation point) for each individual NEP light response curve (direct, diffuse, and intermediate light conditions) measured with corresponding NDVI , LAI, diffuse light fraction, and average temperature. Sorting variables and curve ID numbers for each curve match the corresponding data in the flux data file.
datasets for "VLF Transmitters and Lightning Generated Whistlers 2: Diffusion of Radiation Belt Electrons"
<p>Supporting information for "VLF Transmitters and Lightning Generated Whistlers 2: Diffusion of Radiation Belt Electrons," submitted to Journal of Geophysical Research Space Physics. Diffusion coefficients for selected values of L and energy due to Very Low Frequency (VLF) transmitters and lightning generated whistlers (LGW), as well as Da0a0 and energy drag rates |dE/dt|/E from Coulomb collisions. Also provided are precipitation lifetimes, which include Da0a0 from plasmaspheric hiss but do not account for energy drag. Calculations are presented for high and low-density plasmasphere models, for all four combinations of ducted or nonducted VLF and LGW waves.</p>
Datasets to "Turbulent radiative diffusion and turbulent Newtonian cooling"
<pre>This directory contains an index.html file with links to the run directories for Figs.1, 2-6 and idl plotting routines with secondary data for the other figures for the paper "Turbulent radiative diffusion and turbulent Newtonian cooling".</pre>
SPAD-FCS data of freely diffusing Alexa 488 AB
<p>Confocal laser-scanning microscopy based fluorescence fluctuation spectroscopy (FFS) data set of freely diffusing Alexa 488 linked to an antibody. Excitation at 485 nm, 100x/1.4 Leica objective, detection with a 5x5 pixel SPAD array detector. The file "05_Alexa488AB_20ugmL_P60percP_SPAD.7z" contains a binary data file with the raw data of a 200 s FFS measurement. For each time bin of 500 ns, the fluorescence photon counts in each of the 25 pixels are stored. The text file "05_Alexa488AB_20ugmL_P60percP_SPAD_info.txt" contains the metadata. The file "05_Alexa488AB_20ugmL_P60percP_SPAD_xcorrs.7z" contains all 625 cross-correlations between I_1(t) and I_2(t), with I_1 and I_2 the intensity traces of any of the 25 pixels.</p>
Genomic analyses of phenotypic differences between native and invasive populations of diffuse knapweed (Centaurea diffusa)
<p>Invasive species represent excellent opportunities to study the evolutionary potential of traits important to success in novel environments. Although some ecologically-important traits have been identified in invasive species, little is typically known about the genetic mechanisms that underlie invasion success in non-model species. Here, we use a genome-wide association (GWAS) approach to identify the genetic basis of trait variation in the non-model, invasive, diffuse knapweed (<i>Centaurea diffusa </i>Lam.<i> </i>[Asteraceae]). To assist with this analysis, we have assembled the first draft genome reference and fully annotated plastome assembly for this species, and the one of the first from this large, weedy, genus, which is of major ecological and economic importance. We collected phenotype data from 372 individuals from four native and four invasive populations of <i>C. diffusa </i>grown in a common environment. Using these individuals, we produced reduced-representation genotype-by-sequencing (GBS) libraries and identified 7058 SNPs. We identify two SNPs associated with leaf width in these populations, a trait which significantly varies between native and invasive populations. In this rosette forming species, increased leaf width is a major component of increased biomass, a common trait in invasive plants correlated with increased fitness. Finally, we use annotations from <i>Arabidopsis thaliana</i> to identify 98 candidate genes that are near the associated SNPs and highlight several good candidates for leaf width variation.</p>
Examples of ALD saturation profiles in rectangular channel LHAR structures simulated with a diffusion-reaction model
<p>Examples of atomic layer deposition ALD saturation profiles in rectangular channel LHAR structures simulated with a diffusion-reaction model by Ylilammi et al. (Journal of Applied Physics <strong>123</strong>, 205301 (2018); <a href="https://doi.org/10.1063/1.5028178">https://doi.org/10.1063/1.5028178</a>) as function of (a) Reactant A pulse time <em>t</em>, (b) Reactant A partial pressure <em>p</em>, and (c) sticking coefficient <em>c</em>. Parameters used in the simulation, if not otherwise stated: channel height <em>H</em> 500 nm, temperature 250°C, 250 cycles, inert carrier gas partial pressure 500 Pa, Reactant A molar mass 100 g/mol, inert carrier gas molar mass 28 g/mol, Reactant A diameter 0.600 nm, inert gas diameter 0.374 nm, adsorption capacity 4 metal atoms per nm<sup>2</sup>, density of the material grown 3.5 g/cm<sup>3</sup>, pulse time 0.1 s, Reactant A partial pressure 100 Pa, sticking coefficient 0.01. </p> <p>Abbreviations: ALD = atomic layer deposition, LHAR = lateral high aspect ratio</p>
Supplementary data for calcium-vesicles perform active diffusion in the sea urchin embryo during larval biomineralization
<p><strong>Supplementary datasets for the paper Calcium-vesicles perform active diffusion in the sea urchin embryo during larval biomineralization.</strong></p> <p>Two deskewed and deconvolved lattice light-sheet datasets (100 frames each) from the live-cell experiments are available, a control embryo dataset (01-07-2016_TimeLapse4_DMSO_21hrs_Calcein_FM464) and a VEGFR inhibited dataset (24-06-2016_Timelapse1_Axtinib_150_19hrs_Calcein_FM464). These datasets were used for collecting size and motion statistics. The control embryo dataset is available in raw microscope output without deskew or deconvolution applied (Raw_01-07-2016_TimeLapse4_DMSO_21hrs_Calcein_FM464).</p> <p>Four confocal datasets from the cytoskeletal remodeling experiments are also included, phalloidin stained images, control (Phalloidin PMC DMSO 5 zoom4s) and VEGFR inhibited (Phalloidin PMC Axt 18 zoom4); and myosinIIP stained images, control (Myosin PMC 30h DMSO new slid 4a zoom4) and VEGFR inhibited (Phalloidin PMC Axt 18 zoom4).</p> <p>Source code and instructions for the analysis tools used for both the lattice light-sheet and confocal data is available at: <a href="https://git-bioimage.coe.drexel.edu/opensource/llsm-calcium-vesicles-lever">https://git-bioimage.coe.drexel.edu/opensource/llsm-calcium-vesicles-lever</a></p> <p>Code for the deconvolution and deskew algorithms is available from the Janelia research center at: <a href="https://www.janelia.org/open-science/lattice-light-deconvolution-software-cudadeconv">https://www.janelia.org/open-science/lattice-light-deconvolution-software-cudadeconv</a></p> <p> </p> <p> </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.