Find research datasets worth reusing

opencc-by-4.0Jul 2023View details →

Elasticity tensors of 10276 crystals from DFT computations

<h2>Paper introducing this dataset</h2> Wen, M., Horton, M., Munro, J., Huck, P., & Persson, K. (2024). An equivariant graph neural network for the elasticity tensors of all seven crystal systems. Digital Discovery. DOI:<a title="Link to landing page via DOI" href="https://doi.org/10.1039/D3DD00233K">https://doi.org/10.1039/D3DD00233K</a>   This dataset consists of three data files in the json format. Each file is explained below. <h2>crystal_elasticity_tensor.json</h2> DFT computed elastic tensors of 10276 crystals used for developing the MatTen model. structure: crystal structure of the material formula_pretty: chemical formula crystal_system: crystal system elastic_tensor: full fourth-rank elastic tensor elastic_tensor_voigt: 6x6 Voigt matrix of the elastic tensor split: split of the data into train, validation, and test subsets for model development <h2> max_directional_E.json</h2> New crystals with large maximum directional Young's modulus. material_id: Materials Project identifier formula_pretty: chemical formula structure_original: crystal structure from the Materials Project database elastic_tensor_matten_original: MatTen predicted elastic tensor using `structure_original` max_directional_E_matten_original: MatTen predicted maximum directional Young's modulus using `structure_original` structure: further DFT optimized structure with a tigher criterion elastic_tensor: DFT elastic tensor corresponding to `structure` max_directional_E: DFT maximum directional Young's modulus using `structure` elastic_tensor_matten: MatTen predicted elastic tensor using `structure` max_directional_E_matten: MatTen predicted maximum directional Young's modulus using `structure` <h2> elemental_cubic_metal_max_E_along_100_direction.json</h2> New crystals with its maximum directional Young's modulus along the [100] direction. material_id: Materials Project identifier formula_pretty: chemical formula structure_original: crystal structure from the Materials Project database elastic_tensor_matten: MatTen predicted elastic tensor using `structure_original` Delta_S_matten: value of $S_{1111} - S_{1122} - 2*S_{2323}$ using `elastic_tensor_matten` structure: further DFT optimized structure with a tigher criterion elastic_tensor: DFT elastic tensor corresponding to `structure` Delta_S: value of $S_{1111} - S_{1122} - 2*S_{2323}$ using `elastic_tensor`

opencc-by-4.0Mar 2024View details →

Seismic moment tensor solutions of Mw > 3.4 earthquakes occurred between 2002 and 2023 in the Southeastern Alps

Seismic moment tensor solutions of 63 earthquakes with 3.4≤ Mw≤ 5.1 occurring from 2002 to 2023 in the Southeastern Alps and strict surroundings (latitude 45°N-47.5°N and longitude 10°E-15°E). The seismograms have been recorded and acquired by the OGS - North-Eastern Italy Seismic and Deformation Network (<a href="https://doi.org/10.7914/SN/OX">https://doi.org/10.7914/SN/OX</a>).  For more details: Saraò A., Sugan M., Bressan G., Renner G., and Restivo A.: A focal mechanism catalogue of earthquakes that occurred in the southeastern Alps and surrounding areas from 1928–2019, Earth Syst. Sci. Data, 13, 2245–2258, https://doi.org/10.5194/essd-13-2245-2021, 2021.

opencc-by-4.0Nov 2024View details →

Code and Data for "Anticoncentration and state design of random tensor networks"

We investigate quantum random tensor network states where the bond dimensions scale polynomially with the system size, N. Specifically, we examine the delocalization properties of random Matrix Product States (RMPS) in the computational basis by deriving an exact analytical expression for the Inverse Participation Ratio (IPR) of any degree, applicable to both open and closed boundary conditions. For bond dimensions χ&sim;γN, we determine the leading order of the associated overlaps probability distribution and demonstrate its convergence to the Porter-Thomas distribution, characteristic of Haar-random states, as γ increases. Additionally, we provide numerical evidence for the frame potential, measuring the 2-distance from the Haar ensemble, which confirms the convergence of random MPS to Haar-like behavior for χ≫\sqrt{N}. We extend this analysis to two-dimensional systems using random Projected Entangled Pair States (PEPS), where we similarly observe the convergence of IPRs to their Haar values for χ≫\sqrt{N}. These findings demonstrate that random tensor networks with bond dimensions scaling polynomially in the system size are fully Haar-anticoncentrated and approximate unitary designs, regardless of the spatial dimension.

opencc-by-4.0Oct 2019View details →

EDEN2020 Ovine Diffusion Tensor Magnetic Resonance Tractography Atlas

This dataset has been created to share the first in vivo, population-averaged Diffusion Tensor Magnetic Resonance Imaging (DTI) Ovine Tractography Atlas (OTA), where the course of the main white matter fiber bundles of the ovine brain has been reconstructed. The OTA has been described in the related paper ‘In vivo Diffusion Tensor Magnetic Resonance Tractography of the Sheep Brain: An Atlas of the Ovine White Matter Fiber Bundles’ by Pieri et al. (2019) <a href="https://doi.org/10.3389/fvets.2019.00345">https://doi.org/10.3389/fvets.2019.00345</a> In the context of the EU’s Horizon EDEN2020 project, in vivo brain MRI protocol for ovine animal models was optimized on a 1.5T scanner. High resolution conventional MRI scans and DTI sequences (b-value = 1,000 s/mm2, 15 directions) were acquired on ten anesthetized sheep ovis aries, to define the diffusion features of normal adult ovine brain tissue. Topography of the ovine cortex was studied, and DTI maps were derived, to perform DTI deterministic tractography reconstruction of the corticospinal tract (CST), corpus callosum (CC), fornix (FX), visual pathway (VP), and occipitofrontal fascicle (OF), bilaterally for all the animals. Binary masks of the tracts were then coregistered and reported in the space of a standard stereotaxic ovine reference system ('ovine_model_05.nii', Nitzsche B. et al., Front. Neuroanat. 9:69. <a href="https://doi.org/10.3389/fnana.2015.00069">https://doi.org/10.3389/fnana.2015.00069</a>). Finally, these were combined across animals to obtain population probability masks for each tract, representing voxel- by-voxel probability of the presence of the tract in the 10 animals, thus ranged between 0 and 10.  Please don't forget to cite this publication when using the Ovine Tractography Atlas:                  Pieri V., Trovatelli M., Cadioli M., Zani D.D., Brizzola S., Ravasio G., Acocella F., Di Giancamillo M., Malfassi L., Dolera M., Riva M., Bello L., Falini A., & Castellano A. (2019). In vivo Diffusion Tensor Magnetic Resonance Tractography of the Sheep Brain: An Atlas of the Ovine White Matter Fiber Bundles. Front Vet Sci, 6(345), 345 <a href="https://doi.org/10.3389/fvets.2019.00345">https://doi.org/10.3389/fvets.2019.00345</a>     This work has been carried out in the context of the EDEN2020 (Enhanced Delivery Ecosystem for Neurosurgery in 2020, www.eden2020.eu) project, that received funding from the European Union’s EU Research and Innovation programme Horizon 2020 under Grant Agreement No. 688279.

opencc-by-4.0Feb 2022View details →

Centroid Moment Tensor solutions for the earthquake dataset of the project IMAGINE_IT

The project IMAGINE_IT (PI Dr. Dimitri Komatitsch) received 40 million CPU-hours on the Tier-0 GENCI/TGCC CURIE supercomputer as a winner of the 9th PRACE consortium call (2014).  The awarded computational resources allowed us to construct a new 3D tomographic model for the Italian lithosphere, Im25, by combining spectral-element three-dimensional wavefield simulations and an adjoint-state method. To obtain the final model Im25, we performed 25 adjoint tomography iterations. Moreover, two additional source inversion iterations have been performed in order to improve the earthquake source parameter estimates and reduce the misfit between observed and synthetic seismograms: one inversion using the 3D wavespeed model considered as starting model of the tomographic procedure, and one inversion for the improved wavespeed model at iteration 12 (Im12).  The presented table contains the Centroid Moment Tensor parameters of the163 earthquakes considered in the IMAGINE_IT project for: the initial (Time Domain Moment Tensor; http://terremoti.ingv.it/) source solution based on a 1D wavespeed model (iter=0), the source inversion solution with the starting 3D wavespeed model (iter=1), and the source inversion solution with model Im12 (iter=2).

opencc-by-4.0Sep 2022View details →

Waveform data for centroid moment tensor solutions presented in publication "Bayesian seismic source inversion with a 3-D Earth model of the Japanese islands"

The dataset includes waveform data for centroid moment tensor solutions inferred using Hamiltonian Monte Carlo and a 3-D Earth model in the Japanese islands. The data are provided as Green's strains at the maximum-likelihood location (indicated in the title of each text file) for all study events inverted at different periods. Inversion period is also indicated in the title. All the data are filtered between 15 s and 80 s. Additionally we provide a Python code to obtain displacement from strains given a moment tensor.

opencc-by-4.0Aug 2024View details →

Processed data from SnoHATS and METCRAX II: anisotropic turbulence and geometry of the Reynolds stress tensor in a streamline coordinate system

Datasets used for the paper 'Interpreting turbulence anisotropy in a streamline coordinate system'. Data from SnoHATS and METCRAX II field campaigns. Datasets include turbulent quantities calculated on 30- and 1-min averaging windows for unstable and stable conditions, with prior linear detrending. Planar fit was used in METCRAX II and double rotation in SnoHATS to rotate the flow into the mean wind direction. Datasets include quantities to characterize the anisotropy of the Reynolds stress tensor, such as eigenvalues, eigenvectors, and the angles between the eigenvectors and the streamline coordinate system, defined in the direction of the mean wind vector. 1c: one-component Reynolds stress tensor 2c: two-component axisymmetric Reynolds stress tensor 3c: isotropic Reynolds stress tensor

opencc-by-4.0May 2021View details →

Constraining scalar-tensor theories by neutron star-balck hole gravitational wave events

This data release corresponds to the paper "Constraining scalar-tensor theories by neutron star-balck hole gravitational wave events" (<a href="https://arxiv.org/abs/2105.13644">arXiv:2105.13644</a>). In this paper, we consider three specific models of scalar-tensor theories, including the Brans-Dicke theory (BD), the theory with scalarization phenomena proposed by Damour and Esposito-Far\`{e}se (DEF), and Screened Modified Gravity (SMG). From all 4 possible NSBH events so far, we use two of them to place the constraints. The other two are excluded in this work due to the possible unphysical deviations. Four equations of state (EoSs), sly, alf2, H4 and mpa1, are used to derive the scalar charges of neutron stars for BD and DEF. The constraints are obtained by performing the full Bayesian inference with the help of the open source software <a href="https://git.ligo.org/lscsoft/bilby">Bilby</a>. This dataset contains all posterior samples of the runs discussed in the paper. The models and EoSs can be read form the filenames for the runs of BD and DEF. The files of "*_half_dipole.json" correspond to the runs for constraining the dipole radiation without considering specific model parameters. All files are JSON format which is the default output format of <a href="https://git.ligo.org/lscsoft/bilby">Bilby</a>. They are human readable and also can be processed or visualized by <a href="https://git.ligo.org/lscsoft/bilby">Bilby</a> or <a href="https://git.ligo.org/lscsoft/pesummary">PESummary</a> conveniently.

openmpl-2.0Feb 2023View details →

Simulated data for "Small-angle scattering tensor tomography algorithm for robust reconstruction of complex textures"

These are HDF5 files with the 15 simulated data sets which are used in the work "Small-angle scattering tensor tomography algorithm for robust reconstruction of complex textures", intended for use with the software MUMOTT.   MUMOTT is <a href="https://pypi.org/project/mumott/">obtainable via PyPI</a>.

opencc-by-4.0Jun 2023View details →

Dataset: Swarms in Central Utah – event detection lists, arrival picks, relocations & moment tensor solutions

Dataset containing results of moment tensor inversions, relocations and event detections for our study "Petersen & Pankow (2023): Small-magnitude seismic swarms in Central Utah: Interactions of regional tectonics, local structures and hydrothermal systems" (<a href="https://doi.org/10.1029/2023GC010867">https://doi.org/10.1029/2023GC010867</a>). Please read the pdf-README file for more information on the dataset.

opencc-by-4.0Jul 2023View details →

Dataset for the article "Reduced order model approaches for predicting the magnetic polarizability tensor for multiple parameters of interest"

Datasets to accompany the article "Reduced order model approaches for predicting the magnetic polarizability tensor for multiple parameters of interest". Written by J. Elgy and P. D. Ledger (Keele University, 2023). The datasets include data files, meshes, and source code for generating figures from the paper. This requires the open source MPT-Calculator software available at <a href="https://github.com/MPT-Calculator/MPT-Calculator%7D">https://github.com/MPT-Calculator/MPT-Calculator</a> (InitialRelease branch). The authors gratefully acknowledge the financial support received from EPSRC in the form of grant EP/V009028/1

opencc-by-4.0Jul 2023View details →

Dataset for the article "Computations and Measurements of the Magnetic Polarizability Tensor Characterisation of Highly Conducting and Magnetic Objects"

Datasets to accompany the article "Computations and Measurements of the Magnetic Polarizability Tensor Characterisation of Highly Conducting and Magnetic Objects". Written by J. Elgy, P. D. Ledger, J. L. Davidson, T. Özdeğer and A. J. Peyton. The article has been submitted to "Engineering Computations" (2023). The datasets include data files, meshes, and code for recreating the results from the paper. This requires the open source MPT-Calculator software available at <a href="http://github.com/MPT-Calculator/MPT-Calculator">https://github.com/MPT-Calculator/MPT-Calculator</a> (InitialRelease branch). The datasets also include measurement data for real world objects courtesy of The University of Manchester. J. Elgy and P. D. Ledger gratefully acknowledge the financial support received from EPSRC in the form of grant EP/V009028/1. J. L. Davidson and A. J. Peyton are grateful for the financial support received from an Innovate UK Grant (reference number 39814). T. Özdeğer and A. J. Peyton are grateful for the financial support received from EPSRC, U.K. through the research grant EP/R002177/1.

opencc-by-4.0Jul 2020View details →

List of VLFEs obtained in the paper "Influence of a subducted oceanic ridge on the distribution of shallow VLFEs in the Nankai Trough as revealed by moment tensor inversion and cluster analysis"

List of VLFEs obtained in Toh et al., (2020, GRL). "Influence of a subducted oceanic ridge on the distribution of shallow VLFEs in the Nankai Trough as revealed by moment tensor inversion and cluster analysis" by Akiko Toh, Wan-Jou Chen, Nozomu Takeuchi, Douglas Dreger, Wu-Cheng Chi, and Satoshi Ide.

opencc-by-4.0Dec 2020View details →

Data set for the article "Efficient Computation of the Magnetic Polarizability Tensor Spectral Signature using POD"

Data set to accompany the article  "Efficient Computation of the Magnetic Polarizability Tensor Spectral Signature using POD" written by B.A. Wilson (Swansea University) and P.D. Ledger (Keele University)

opencc-by-4.0Dec 2018View details →

Trabecular bone datasets for SAXS Tensor Tomography

Experimental data from SAXS tensor tomography measurement. Results from these data were first published in Liebi et al. (2015, https://doi.org/10.1038/nature16056, 2018, https://doi.org/10.1107/S205327331701614X). They have been repackaged for use with the software MUMOTT (https://doi.org/10.5281/zenodo.7919490) The data from the set called trabecular_bone_9.h5 was previously published on Zenodo (https://doi.org/10.5281/zenodo.1480589) as a supplement to Gao et al. (2019, https://doi.org/10.1107/S2053273318017394). In addition, the data from trabecular_bone_10.h5 was used in Guizar-Sicairos et al. (2020, https://doi.org/10.1107/S1600577520003860).

openmpl-2.0Dec 2023View details →

X-ray scattering tensor-tomography dataset for a steel wire using the austenitic {220}-peak.

Experimental data from a scanning-probe wide angle scattering experiment performed at the cSAXS beamline at teh Swiss Light Source at the Paul Scherrer Institure in Villigen, Switzerland. The file-format is that used in by the software package mumott (mumott.org). The sample is a tangled knot of hard-tempered steel. The detector images have been azimuthally re-grouped and only the intensity of the austeinte {220} peak is included in 48 separrate azimuthal bins.

opencc-by-4.0Feb 2022View details →

Dataset: Tensor-network study of correlation-spreading dynamics in the two-dimensional Bose-Hubbard model

Dataset Tensor-network study of correlation-spreading dynamics in the two-dimensional Bose-Hubbard model Ryui Kaneko, Ippei Danshita

opencc-by-4.0Aug 2016View details →

A suite of 2-arcsec surface gravitational maps of the Slovak Republic up to the full gravitational tensor

grav-sr-2arcsec is a suite of 2-arcsec surface gravitational maps of the Slovak Republic in terms of <ul> <li>the gravitational potential (V),</li> <li>the full gravitational vector (Vx, Vy, Vz) in the local north-oriented reference frame (LNOF), and</li> <li>the full gravitational tensor in LNOF (Vxx, Vxy, …, Vzz).</li> </ul> The suite was computed from a high-resolution gravity field model of Slovakia developed by <a href="http://doi.org/10.1093/gji/ggw311">Bucha et al. (2016)</a>. The maps rely on three sources of gravitational information: <ul> <li>the Zero-Tide version of the global geopotential model EIGEN-6C4 (Forste, C. et al., 2014) up to degree 2190 (~5 arcmin resolution, ~9 km),</li> <li>the expansion of the residual gravitational signal up to degree 21600 in terms of spherical radial basis functions (<a href="http://doi.org/10.1093/gji/ggw311">Bucha et al. 2016</a>) (~30 arcsec, ~950 m), and</li> <li>residual terrain modelling (<a href="http://doi.org/10.1093/gji/ggw311">Bucha et al. 2016</a>) (2 arcsec, ~60 m).</li> </ul> An independent validation of grav-sr-2arcsec revealed the standard deviation of 0.789 mGal in terms of the gravity. Bucha, B., Janak, J., Papco, J., Bezdek, A., 2016. High-resolution regional gravity field modelling in a mountainous area from terrestrial gravity data. Geophysical Journal International 207, 949-966, <a href="http://doi.org/10.1093/gji/ggw311">http://doi.org/10.1093/gji/ggw311</a>

opengpl-3.0-or-laterMay 2024View details →

Bolivia_Moment_Tensor_Figure_Results

This is the supplementary data for the research "New contributions to enhance the knowledge of stresses in the Central Andes, through moment tensor inversion for shallow seismic earthquakes in Bolivia."