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135 results for “anisotropy”
Model of Pn velocity and anisotropy in Hainan Island and surrounding areas
<p>This file is the model of Pn velocity and anisotropy in Hainan Island and the surrounding areas. It is only used for scientific research.</p>
AnisoVeg: Anisotropy and Nadir-normalized MODIS MAIAC datasets for satellite vegetation studies in South America
<p><strong>Dataset paper under review.</strong></p> <p><strong>Contact Ricardo Dalagnol (ricds@hotmail.com) for more information.</strong></p> <p> </p> <p><strong>Data:</strong> AnisoVeg: Anisotropy and Nadir-normalized MODIS MAIAC datasets for satellite vegetation studies in South America</p> <p><strong>Scale factor</strong>: 10000</p> <p><em>obs: the no_samples layer does not have scale.</em></p> <p><strong>Coverage:</strong> South America land</p> <p><strong>Time period:</strong> 2000 to 2021 (starting in March 2000)</p> <p><strong>Spatial resolution:</strong> 0.009107388 degree equivalent to ~1 km</p> <p><strong>Temporal resolution:</strong> Monthly</p> <p><strong>Coordinate reference system:</strong> geographic projection, datum WGS-84</p> <p><strong>Processing details summary (more detailed explanation in the paper):</strong></p> <ul> <li>The original MODIS (MAIAC) data were described by Lyasputin et al. 2011 (<a href="https://doi.org/10.1029/2010JD014986">https://doi.org/10.1029/2010JD014986</a>). The daily MODIS (MAIAC) surface reflectance data from collection 6, acquired from Terra and Aqua satellites, are available from the MCD19A1 product (<a href="https://ladsweb.modaps.eosdis.nasa.gov/archive/allData/6/MCD19A1">https://ladsweb.modaps.eosdis.nasa.gov/archive/allData/6/MCD19A1</a>). The Bidirectional Reflectance Distribution Function (BRDF) model parameters are available from MCD19A3 product (<a href="https://ladsweb.modaps.eosdis.nasa.gov/archive/allData/6/MCD19A3">https://ladsweb.modaps.eosdis.nasa.gov/archive/allData/6/MCD19A3</a>)</li> <li>The daily MCD19A1 data at 1 km spatial resolution were normalized using the BRDF parameters and Ross-Thick Li-Sparse (RTLS) model considering (1) a fixed nadir view and a 45 deg. solar zenith angle using the parameters from the MCD19A3 product; and (2) backward and forward scattering. For the nadir (NAD) product, the nadir normalization was taken. For the anisotropy (ANI) product, we calculated the backward minus forward surfaces for each layer, resulting in the ANI product.</li> <li>The daily data were aggregated into monthly composites by the pixel’s median.</li> <li>The tiles that cover the South America were mosaicked and re-projected from sinusoidal to geographic projection</li> <li>The Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) were calculated using standard formulas. The EVI parameters were: C1 = 6, C2 = 7.5, L = 1, G = 2.5</li> </ul> <p><strong>File(s) format:</strong></p> <ul> <li>Zip files - one per year. NAD are the nadir-normalized products and ANI the anisotropy. Inside zip files there are raster files with ".tif" format, one per month window.</li> <li>The filename syntax is "maiac_southamerica_month_PRODUCT_YYYY_MM_LAYER_latlon.tif", where YYYY is the year (e.g. 2000), MM is the month with two digits (e.g. 03 for March), PRODUCT is either nadir or anisotropy, and LAYER can be bands 1-8, EVI and NDVI.</li> <li>There is also the number of samples (no_samples) layers for each date, which can be used to filter composites with a minimum desirable number of daily observations. </li> </ul> <p> </p> <p><strong>Code:</strong> <a href="https://github.com/ricds/maiac_processing">https://github.com/ricds/maiac_processing</a></p> <p> </p> <p><strong>Acknowledgements:</strong> R.D. was supported by Sao Paulo Research Foundation (FAPESP) grants 2015/22987-7 and 2019/21662-8. FHW was supported by FAPESP grant 2015/50484-0. Part of this work was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration (NASA). The funders had no role in the study design, data collection and analysis, including the decision to publish or prepare the manuscript. We thank the MODIS MAIAC team from NASA for providing the freely available MODIS (MAIAC) daily dataset.</p> <p> </p> <p><strong>Auxiliary data of AnisoVeg:</strong></p> <ul> <li>Backscattering data of AnisoVeg: <a href="https://doi.org/10.5281/zenodo.6040299">https://doi.org/10.5281/zenodo.6040299</a> and <a href="https://doi.org/10.5281/zenodo.6040790">https://doi.org/10.5281/zenodo.6040790</a></li> <li>Forward scattering data of AnisoVeg: <a href="https://doi.org/10.5281/zenodo.6048784">https://doi.org/10.5281/zenodo.6048784</a> and <a href="https://doi.org/10.5281/zenodo.6048793">https://doi.org/10.5281/zenodo.6048793</a></li> </ul> <p> </p> <p><strong>Layers available at Google Earth Engine (GEE):</strong></p> <ul> <li>EVI Anisotropy: <a href="https://code.earthengine.google.com/?asset=projects/anisoveg/assets/evi_anisotropy">https://code.earthengine.google.com/?asset=projects/anisoveg/assets/evi_anisotropy</a></li> <li>EVI Nadir: <a href="https://code.earthengine.google.com/?asset=projects/anisoveg/assets/evi_nadir">https://code.earthengine.google.com/?asset=projects/anisoveg/assets/evi_nadir</a></li> </ul> <p>Obs: these require a (free) google earth engine account.</p> <p> </p> <p><strong>Dataset usage</strong>: This dataset is a product of hundreds of hours of coding starting in 2016 with the first author PhD work and then into his Postdoc, and many more hundreds hours of data processing. Data is free to use, but if you use this dataset, please cite the dataset paper or this repository (while paper is under review). Invitation for collaboration are welcomed.</p> <ul> </ul> <p> </p> <p><strong>While the paper is under review, for use of this dataset please cite:</strong></p> <p>Dalagnol, Ricardo; Galvão, Lênio Soares; Wagner, Fabien Hubert; Moura, Yhasmin Mendes; Gonçalves, Nathan; Wang, Yujie; Lyapustin, Alexei; Yang, Yan; Saatchi, Sassan; Aragão, Luiz Eduardo Oliveira e Cruz. (2022). "AnisoVeg: Anisotropy and Nadir-normalized MODIS MAIAC datasets for satellite vegetation studies in South America". (Version v1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.3878879</p>
Spintronic terahertz emitters exploiting uniaxial magnetic anisotropy for field-free emission and polarization control
<p>Data for Figures 1-5</p>
Event Data used in Seismic anisotropy along the Haida Gwaii margin from receiver function analysis
<p>This CSV file contains metadata for earthquake events used in the study: Seismic anisotropy along the Haida Gwaii margin from receiver function analysis</p> <p>Event start time (UTC), latitude, longitude, depth, magnitude and the seismic station at which the event is recorded are included.</p>
Dataset for "Spreading rate dependent anisotropy and thermal structure of the oceanic plate"
<p>This is the dataset for "Spreading rate dependent anisotropy and thermal structure of the oceanic plate" by M. Morishige.</p>
Data for the article "Stabilizing perpendicular magnetic anisotropy with strong exchange bias in PtMn/Co by magneto-ionics"
<p>Dataset for the article:</p> <p>B. Bednarz et al., “Stabilizing perpendicular magnetic anisotropy with strong exchange bias in PtMn/Co by magneto-ionics,” <em>Appl. Phys. Lett.</em> 124, 232403 (2024).</p> <div> <div><a href="https://doi.org/10.1063/5.0213731" target="_blank" rel="noopener">https://doi.org/10.1063/5.0213731</a></div> </div>
Crystallographic Preferred Orientation of Phase D at High Pressure and Temperature: Implications for Seismic Anisotropy in the Mid-mantle
<p> The dataset includes the data of Crystallographic Preferred Orientation of phase D aggregates in this study. It include electron backscatter diffraction (EBSD) mapping data in .cpr and .crc file and transmission two-dimensional X-ray diffraction (2D-XRD) patterns acquired at BL04B1 beamline of synchrotron facility of SPring-8, Hyogo, Japan. </p>
A Mid-crustal Channel of Positive Radial Anisotropy Beneath the Eastern South China Block From F-J Multimodal Ambient Noise Tomography
<p><span>It contains the CCAB-FJpy, the Python code package for calculating the multi-component cross-correlation functions (CCFs) and extracting multimodal Love dispersions with multi-component frequency-Bessel (F-J) transform method. . Also, it contains multi-component CCFs (i.e., RR and TT), Vsh model and 3-D crustal radial anisotropy model of the eastern South China Block (ESCB).</span></p>
Burial impact on the Tatra Mts from a rock magnetic and magnetic fabric perspective: anisotropy of anhysteretic remanent magnetization, part 2
<p>The dataset contains the second part of the anisotropy of anhysteretic remanent magnetization analyses of Cretaceous and Paleogene rocks from the Tatra Mts.</p>
Burial impact on the Tatra Mts from a rock magnetic and magnetic fabric perspective: anisotropy of anhysteretic remanent magnetization, part 1
<p>The dataset contains the first part of the anisotropy of anhysteretic remanent magnetization analyses of Cretaceous rocks from the Tatra Mts.</p>
Burial impact on the Tatra Mts from a rock magnetic and magnetic fabric perspective: anisotropy of anhysteretic remanent magnetization, part 3
<p>The dataset contains the third part of the anisotropy of anhysteretic remanent magnetization analyses of Paleogene rocks from the Tatra Mts.</p>
Permeability Anisotropy Data of Carbonate Fault Rocks
<p>Permeability and porosity data from core plugs of faulted and unfaulted carbonate lithofacies. 2D and 3D image analysis results of samples used in the publication also included.</p>
Data for manuscript: The orbital anisotropy profiles of nearby globular clusters from Gaia Data Release 2
<p>We upload the data used in our paper here so that our results may be reproduced. We include the dataset of stars that survive our cuts, the profiles we plot, and the manual points selected as part of our CMD cut. See the paper for details. The first version of this paper is published on the arXiv with ID: arXiv:1903.11070. </p>
Strain Anisotropy and Magnetic Domains in Embedded Nanomagnets
<p>Transmission electron microscopy data and processing scripts related to the journal article "Strain Anisotropy and Magnetic Domains in Embedded Nanomagnets": <a href="https://doi.org/10.1002/smll.201904738">https://doi.org/10.1002/smll.201904738</a></p> <p><strong>Data files</strong></p> <p>The data is contained within the 00N_....hdf5 files, which can be accessed using an HDF5 reader. Note that these datasets are very large, and trying to load one of them directly will most likely lead to your computer crashing.</p> <p>Loading the data in python with h5py:</p> <pre><code class="language-python">import h5py f = h5py.File('003_stripe1.hdf5', mode='r') data = f['fpd_expt/fpd_data/data'] data_subset = data[0:16, 0:16, :, :]</code></pre> <p>Exploring the datasets lazily, i.e. without loading the whole dataset into memory at the same time. Using pixStem:</p> <pre><code class="language-python">import pixstem.api as ps s = ps.load_ps_signal("003_stripe1.hdf5", lazy=True) s.plot()</code></pre> <p><br> <strong>Processing files</strong></p> <p>All the TEM data has been processed using python scripts, which is named based on the type of processing:</p> <ul> <li>d00N_...: STEM-DPC processing</li> <li>l00N_...: lattice size processing</li> <li>s00N_...: rotation "simulations" to find the relation between the scan and detector rotation</li> </ul> <p>Several of the scripts generate intermediate files, which are saved in folders with the same prefix as the scripts. So the d001_... script makes a folder named d001_... . These intermediate files are included here as zip-files, since Zenodo doesn't support folder structures.</p> <p>The python libraries required to run the scripts are listed in requirements.txt. Newer versions of the libraries will most likely also work.</p> <p>To setup the python environment with the required libraries, and run all the scripts:</p> <pre><code class="language-bash">pip3 install -r requirements.txt python3 run_all_scripts.py</code></pre> <p>This will most likely take several hours to complete.</p>
Data for "Integrated ab initio modelling of atomic order and magnetic anisotropy for rare-earth-free magnet design: effects of alloying additions in L1$_0$ FeNi."
<p>Data for "Integrated ab initio modelling of atomic order and magnetic anisotropy for rare-earth-free magnet design: effects of alloying additions in L1$_0$ FeNi", published in npj Comput. Mater. <strong>10</strong> 272 (2024).</p> <p>Version 2 contains additional results relating to Ni-rich systems.</p> <p>Version 3 contains data relating to vibrational considerations for the A1-L1$_0$ transition in equiatomic FeNi.</p> <p>Version 4 contains date pertaining to the Curie temperatures of the disordered, partially ordered, and fully ordered alloys considered in this work.</p>
Supplementary Data for: Benchmark of density functional theory in the prediction of chemical shielding anisotropies for anisotropic NMR based structural elucidation
<p>Additional Data for the research paper titled: Benchmark of density functional theory in the prediction of chemical shielding anisotropies for anisotropic NMR based structural elucidation.</p> <p>Anisotropy Benchmark for Carbon NS372:</p> <ul> <li>Chemical Shielding Tensor for the molecules in the NS372 test set for carbon (XLSX)</li> <li>Coordinate files for the molcules of the NS372 test set that contained carbon (in NS372-Carbon-COORD-Files.zip)</li> </ul> <p>DFT Benchmark for RCSA for Natural Products:</p> <ul> <li>Chemical Shielding Tensor used for the RCSA analysis of 6 Natural Products (CSV)</li> <li>Turbomole Input and Ouput files for the DFT calculation of the natural products (in RAW_DATA_for_RCSA_Analysis.zip)</li> <li>ConArch+ Input and Ouput files for the RCSA analysis using (in RAW_DATA_for_RCSA_Analysis.zip)</li> <li>Coordinate files used for the RCSA analysis (in RAW_DATA_for_RCSA_Analysis.zip)</li> </ul> <p> </p>
Seismic Azimuthal Anisotropy Model for the Juan de Fuca ‐ Gorda Plate System
<p>This dataset is supplementary to:</p> <p>Liu, C., et al. (2024) Seismic Azimuthal Anisotropy Within the Juan de Fuca ‐ Gorda Plate System, GRL</p> <div> <p>DOI: 10.1029/2024GL111835</p> <p>Azimuthal anisotropy model from 10-100 km</p> <p> </p> <p>Model 1: <code>JdFG_Azi_anisotropy_model.nc</code>: contains</p> <ul> <li>Anisotropic lithospheric layer: base of sediments to 20km below the Moho</li> <li>Anisotropic asthenospheric zone: 50 km thick layer beneath the lithosphere layer</li> <li>A complementary deeper asthenosphere layer </li> </ul> <p>Model 2:<code>JdFG_Azi_anisotropy_model_ios_crust.nc</code>: </p> <ul> <li>Anisotropic lithospheric layer: the Moho to 30km below the Moho</li> <li>Anisotropic asthenospheric zone: 50 km thick layer beneath the lithosphere layer</li> <li>A complementary deeper asthenosphere layer </li> </ul> </div> <p>The uploaded file uses NetCDF4 format.</p> <p>File format:</p> <p><code>Longitude</code>, <code>Latitude</code>, <code>Depth</code>, <code>fa</code>,<code>unc_fa</code>,<code>amp</code>,<code>unc_amp</code></p> <ul> <li> <p>Dimensions:</p> <ul> <li><code>Longitude</code>: -130.2° to -125.0° with 0.4° interval.</li> <li><code>Latitude</code>: 40.6° to 49.0° with 0.4° interval.</li> <li><code>Depth</code>: 10 to 100 with 10 km interval</li> </ul> </li> <li>Model variables: <ul> <li><code>fa</code>: depth-dependent fast azimuth (deg)</li> <li><code>unc_fa</code>: uncertainty for depth-dependent fast azimuth (deg)</li> <li><code>amp</code>: depth-dependent anisotropy amplitude (%)</li> <li><code>unc_amp</code>: uncertainty for depth-dependent anisotropy amplitude (%)</li> </ul> </li> </ul>
Seismic radial and azimuthal anisotropy tomography beneath Greenland and surrounding regions
<p>Dear readers,</p> <p>These four files contain P-wave velocity model beneath Greenland and surrounding regions obtained by regional anisotropy tomography (Toyokuni & Zhao, 2021, ESS). </p> <p>P-wave radial anisotropy (RAN) model<br> RAN_DV0.DAT: Isotropic component (DV0) of RAN tomography<br> -FORMAT: Latitude(deg), Longitude(deg), Depth(km), dVp0(%)</p> <p>RAN_AI.DAT: Anisotropic component RAN tomography<br> -FORMAT: Longitude(deg), Latitude(deg), Depth(km), alpha(%)</p> <p>P-wave azimuthal anisotropy (AAN) model<br> AAN_DV0.DAT: Isotropic component (DV0) of AAN tomography<br> -FORMAT: Latitude(deg), Longitude(deg), Depth(km), dVp0(%)</p> <p>AAN_AI.DAT: Anisotropic component AAN tomography<br> -FORMAT: Longitude(deg), Latitude(deg), Depth(km), FVD(deg), beta(%)</p> <p>We note that the tomography was conducted in the transformed coordinates. The details of the coordinate transformation are described in the above paper. We also note that the fast velocity direction (FVD) of the AAN model is in the transformed coordinates. Please contact us if you want any program to convert it back to the geographical coordinates.</p> <p>We hope it is useful to you.</p> <p>Kind wishes,</p> <p>Genti Toyokuni & Dapeng Zhao<br> Tohoku University, Japan<br> E-mail: toyokuni@tohoku.ac.jp</p> <p>Reference:<br> Toyokuni, G. & Zhao, D. (2021).<br> P wave tomography for 3-D radial and azimuthal anisotropy beneath Greenland and surrounding regions.<br> Earth and Space Science, under review.</p>
Thermally Averaged Magnetic Anisotropy Tensors via Machine Learning Based on Gaussian Moments
<ul> <li><strong>D-Tensor Data</strong></li> </ul> <p>The reference data for 3500 configurations of [Co(N<sub>2</sub>S<sub>2</sub>O<sub>4</sub>C<sub>8</sub>H<sub>10</sub>)<sub>2</sub>]<sup>2−</sup> (CoSar), [Fe(tpa)<sup>Ph</sup>]<sup>−</sup> (FeTPAPh), and [Ni(HIM<sub>2</sub>−py)<sub>2</sub>NO<sub>3</sub>]<sup>+</sup> (NiComplex) is generated employing Molpro package [1]. For more details see the original publication [2]. The data is stored in python compressed array format (.npz) with the D-Tensor in cm<sup>-1</sup>. The data set contains four <span class="math-tex">\(np.ndarray\)</span></p> <pre><code class="language-python">import numpy as np data = np.load('CoSar.npz') R = data['R'] # Cartesian coordinates of nuclei in Ang. D = data['MAT'] # D-Tensor values in cm-1, D = (D11, D12, D13, D22, D23, D33) N = data['N'] # Number of atoms in each structure Z = data['Z'] # Nuclear charges</code></pre> <ul> <li><strong>AIMD Data</strong></li> </ul> <p>To propagate the periodic cell containing four CoSar molecules, for which D-Tensor was computed above, a data set containing total energies as well as atomic forces of 3500 structures was generated employing VASP package [3-6]. For more details see the original publication [2]. The data is stored in python compressed array format (.npz) with the total energy in kcal/mol and atomic forces in kcal/mol/Ang. The data set contains six <span class="math-tex">\(np.ndarray\)</span></p> <pre><code class="language-python">import numpy as np data = np.load('CoSar_bulk.npz') R = data['R'] # Cartesian coordinates of nuclei in Ang. C = data['C'] # Cell vectors in Ang. E = data['E'] # Total energy in kcal/mol F = data['F'] # Atomic forces in kcal/mol/Ang. N = data['N'] # Number of atoms in each structure Z = data['Z'] # Nuclear charges</code></pre> <ul> <li><strong>References</strong></li> </ul> <p>[1] H.-J. Werner, P. J. Knowles, G. Knizia, F. R. Manby, M. Schütz,et al.,“Molpro, version 2020.0, a package of ab initio programs,” (2020), see https://www.molpro.net.</p> <p>[2] V. Zaverkin, J. Netz, F. Zills, A. Köhn, and J. Kästner, “Thermally Averaged Magnetic Anisotropy Tensors via Machine Learning Based on Gaussian Moments,”<strong> submitted</strong> (2021).</p> <p>[3] P. E. Blöchl, “Projector augmented-wave method,” Phys. Rev. B 50, 17953 (1994).</p> <p>[4] G. Kresse and J. Hafner, “Ab initio molecular dynamics for liquid metals,” Phys. Rev. B 47, 558 (1993).</p> <p>[5] G. Kresse and J. Furthmüller, “Efficiency of ab-initio total energy calculations for metals and semiconductors using a plane-wave basis set,” Comput. Mater. Sci. 6, 15 – 50 (1996).</p> <p>[6] G. Kresse and J. Furthmüller, “Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set,” Phys. Rev. B 54, 11169 (1996).</p> <p> </p>
CPCP-1: Anisotropy of magnetic susceptibility (AMS) data
<p>The Colorado Plateau Coring Project Phase 1 (CPCP-1) acquired three continuous drill cores from Petrified Forest National Park (PFNP), Arizona, U.S.A. Two cores, CPCP-PFNP13-1A and CPCP-PFNP13-2B, hereafter CPCP-1A and CPCP-2B; respectively, intersected the Upper Triassic Chinle Formation, Lower(?)-Middle Triassic Moenkopi Formation (MF) and Permian Coconino Sandstone. We examined CPCP-1A and CPCP-2B cores to construct a high-resolution magnetostratigraphy of Moenkopi Formation strata. These data files contain anisotropy of magnetic susceptibility (AMS) data collected from the specimens from core CPCP-2B.</p>
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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)
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DANDI Archive for NWB datasets
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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.