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1,880 results for “Dimensionality”
Edelstein effect in an anisotropic KTaO3 two-dimensional electron gas
<p>This dataset contains the band-resolved Edelstein conversion efficiency versus the Fermi energy for a KTaO3 (001) 2DEG modeled by a tight-binding model with different on-site energies of the dzx and dyz orbitals leading to anisotropy of the model. The on-site difference is Delta E_yz-Delta E_zx=30meV. The spin ("s") and orbital ("l") Edelstein conversion efficiencies are given. Further, the iso-energy lines with spin and orbital textures are given for the isotropic ("iso", E=-15meV) and anisotropic ("aniso", E=5meV) tight-binding model, respectively.</p>
Spin and orbital textures of KTaO3 (001) two-dimensional electron gases from a tight-binding model
<p>The dataset contains the iso-energy lines with the expectation values of spin and orbital moments of KTaO3 (001) two-dimensional electron gases for selected energies. The data was generated using a tight-binding model.</p>
Edelstein effect in KTaO3 (001) two-dimensional electron gases
<p>This dataset contains the band-resolved Edelstein conversion efficiency versus the Fermi energy for a KTaO3 (001) 2DEG modeled by a tight-binding model. The spin ("s") and orbital ("l") Edelstein conversion efficiencies are given.</p>
Effect of Dimensionality on Photoluminescence and Dielectric Properties of Imidazolium Lead Bromides
<p>single-crystal, powder XRD data, Raman, absorption, emission spectra, DSC, dielectric measurements and SHG data</p>
The three-dimensional structure of Galactic molecular cloud complexes out to 2.5 kpc
<p>Data files for Dharmawardena et al., accepted MNRAS, 2022, <em>The three-dimensional structure of Galactic molecular cloud complexes out to 2.5 kpc.</em></p> <p>In this works we map the Interstellar Dust Extinction and Density in 3D using <em>Dustribution</em> code: Dharmawardena et al., 2022, Astronomy & Astrophysics, Volume 658, id.A166, 30 pp. <a href="https://www.aanda.org/articles/aa/full_html/2022/02/aa41298-21/aa41298-21.html">https://www.aanda.org/articles/aa/full_html/2022/02/aa41298-21/aa41298-21.html</a></p> <p>The <em>Dustribution</em> code can be accessed from <a href="https://github.com/Thavisha/Dustribution">https://github.com/Thavisha/Dustribution</a></p> <p>The latest results from Dustribution can be interactively viewed and downloaded from <a href="https://www2.mpia-hd.mpg.de/homes/tmueller/projects/ThavishaDustDensity/index.html">https://www2.mpia-hd.mpg.de/homes/tmueller/projects/ThavishaDustDensity/index.html</a></p> <p>Data available here:</p> <p>- Trained gpy functions for all sixteen mapped regions which can be used to predict on any selected spherical or cartesian grid. </p> <p>-predicted cubes of 3D dust extinction and corresponding lbd boundary grids</p> <p>- predicted cubes of dust densities on cartesian grids and corresponding xyz boundary grids. </p> <p>- <em>astrodendro</em> derived dendrograms for all sixteen molecular cloud regions.</p> <p>- Tables of <em>astrodendro</em> produced parameters and calculated error bars for each region's substructure as described in appendix C of paper. </p> <p>- Tables 1,2,3 in main body of paper in .csv format. </p> <p>- 3D rendering videos of the sixteen mapped regions.</p>
Dataset of "Giant gate-controlled odd-parity magnetoresistance in one-dimensional channels with a magnetic proximity effect"
<p>According to Onsager’s principle, electrical resistance <em>R</em> of general conductors behaves as an even function of external magnetic field <em>B</em>. Only in special circumstances, which involve time reversal symmetry (TRS) broken by ferromagnetism, the odd component of <em>R</em> against <em>B</em> is observed. This unusual phenomenon, called odd-parity magnetoresistance (OMR), was hitherto subtle (< 2%) and hard to control by external means. Here, we report a giant OMR as large as 27% in edge transport channels of an InAs quantum well, which is magnetized by a proximity effect from an underlying ferromagnetic semiconductor (Ga,Fe)Sb layer. Combining experimental results and theoretical analysis using the linearized Boltzmann’s equation, we found that simultaneous breaking of both the TRS by the magnetic proximity effect (MPE) and spatial inversion symmetry (SIS) in the one-dimensional (1D) InAs edge channels is the origin of this giant OMR. We also demonstrated the ability to turn on and off the OMR using electrical gating of either TRS or SIS in the edge channels. These findings provide a deep insight into the 1D semiconducting system with a strong magnetic coupling.</p>
Dimensionality and modularity of adaptive variation: Divergence in threespine stickleback from diverse environments
<p>Populations are subjected to diverse environmental conditions that affect fitness and induce evolutionary or plastic responses, resulting in phenotypic divergence among populations. Some authors contend such divergence is concentrated along a single major axis of trait covariance, even if that axis does not lead populations directly toward fitness optimum. Other authors argue that divergence can occur readily along many phenotype axes at the same time. We use populations of threespine stickleback (<em>Gasterosteus aculeatus</em>) from 14 lakes with contrasting ecological conditions to find some resolution along the continuum between these two extremes. Unlike many previous studies, we included several functional suites of traits (defensive, swimming, trophic) potentially subject to different sources of selection. We find that populations exhibit dimensionality of divergence that is high enough to preclude a history of constraint along a single axis – both for divergence in multivariate mean trait values and for the structure of trait covariances. Dimensionality varied among trait suites and were strongly influenced by the inclusion of specific traits, and integration of trait suites varied between populations. We leverage this variation into new insights about the process of divergence and suggest that similar analyses could increase understanding of other adaptive radiations. </p>
Virtual cohort of fetal cases with suspected coarctation of the aorta from fetal three-dimensional magnetic resonance images
<p>The present dataset is a supplementary dataset to the paper: Hermida, U., van Poppel, M.P.M., Lloyd, D.F.A. <em>et al.</em> Learning the Hidden Signature of Fetal Arch Anatomy: a Three-Dimensional Shape Analysis in Suspected Coarctation of the Aorta. <em>J. of Cardiovasc. Trans. Res.</em> (2022). https://doi.org/10.1007/s12265-022-10335-9</p> <p><strong>Dataset Description: </strong></p> <p>We present a database of fetal great arch anatomical models derived from a statistical shape model (SSM) of fetuses with suspected coarctation of the aorta (CoA). The dataset consists of 1000 meshes including the fetal ascending aorta, arterial duct and descending aorta. For each synthetic case, the corresponding PCA coefficients for the 10 first PCA modes, as well as their CoA risk score, are shared. PCA scores are provided both as raw scores and Z-scores. </p> <p>To build the SSM, a dataset of 112 motion-corrected 3D fetal cardiovascular magnetic resonance (CMR) images and their corresponding segmentations from cases with suspected CoA was used. Data included confirmed CoA cases as well as false positive (FP) cases. Centerlines were extracted and aligned to build the SSM of the fetal great arches. </p> <p><strong>Source of funding</strong></p> <p>This work was supported by the Wellcome Trust IEH Award [102431] (iFIND project), by core funding from the Wellcome/EPSRC Centre for Medical Engineering [WT203148/Z/16/Z] and by the Rosetrees Trust [A2725]. The research was funded by the National Institute for Health Research (NIHR) Biomedical Research Centre based at Guy’s and St Thomas’ NHS Foundation Trust and King’s College London and supported by the NIHR Clinical Research Facility. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health. D.F.A. Lloyd acknowledges support from the Rothschild Foundation (2020/2017). P. Lamata holds a Wellcome Trust Senior Research Fellowship (209450/Z/17/Z).</p>
Two-dimensional Energy Histograms as Features for Machine Learning to Predict Adsorption in Diverse Nanoporous Materials
<p>This repo contains the supplementary data sets for the to-be-published paper entitled "Two-dimensional Energy Histograms as Features for Machine Learning to Predict Adsorption in Diverse Nanoporous Materials".</p> <p> </p> <p>This repo contains the following data sets:</p> <p>1. CIF files for amorphous porous materials (activated carbon, hyper-cross-linked polymers, Kerogen, PIMs).</p> <p>2. Grand canonical Monte Carlo (GCMC) simulation results for single-component adsorption isotherms in ToBaCCo1.0 MOFs and in amorphous porous materials. Gas molecules include Kr, Xe, ethane, propane, butane, n-hexane, and 2,2-dimethylbutane.</p> <p>3. Textural properties of ToBaCCo1.0 MOFs and amorphous porous materials.</p> <p>4. Trained machine learning models. R code that can work with these ML models is hosted on <a href="https://github.com/snurr-group/2D-energy-histogram">GitHub</a>. </p>
CS2900 - Multi-dimensional Data Processing Slidecasts Royal Holloway, University of London
<p>Slidecasts of CS2900 (Multi-dimensional Data Processing - but essentially Linear Algebra for Computer Scientists) 2018/2019. For undergraduate CS students.</p>
Estimation of curvature from volume fractions using parabolic reconstruction on two-dimensional unstructured meshes (Supporting data)
<p>This data accompanies the paper "Estimation of curvature from volume fractions using parabolic reconstruction on two-dimensional unstructured meshes", published in Journal of Computational Physics (2017). The document "supportingdata.pdf" gives a description of the data provided in the txt-files.</p>
Supplementary material for: Sparse Arrays for Four-Dimensional Scanning Transmission Electron Microscopy
<p>Supplementary material for: Sparse Arrays for Four-Dimensional Scanning Transmission Electron Microscopy</p> <p>The link to the main publication with detailed information will be added later.</p> <p>The three videos show liv eand offline processing with the CheeTah T3, ASI Serval, ASI Accos, LiberTEM and CEOS Panta Rhei. They are also available at https://www.youtube.com/playlist?list=PLZCH_qD2RkB5oFdBc12xcYUcbmWZpJQHh</p> <p>benchmark.ipynb is a Jupyter notebook that was used to test the performance, as reported in the paper.</p> <p>raw_csr.zip is a test dataset recorded on a gold grid in the CSR format that can be opened by LiberTEM</p> <p>benchmark.ipynb is a Jupyter notebook that shows how the test dataset can be opened and processed with common Python packages and with LiberTEM.</p>
Polytopic autoencoders for very low-dimensional parametrizations of fluid flow models [source code]
<p>J. Heiland & Y. Kim, 'Polytopic autoencoders for very low-dimensional parametrizations of fluid flow models', GAMM 2024</p>
Data for "Impact of Surface Adsorbates and Dimensionality on Templating of Halide Perovskites"
<p>This record contains databases with data from density functional theory calculations used for training of a neuroevolution potential (NEP) for 2D halide perovskites. </p>
Dimensional coding of native and non-native speech categories: data and code
<p><strong>Data</strong>: (i) fMRI nii files, (ii) categorization responses , and (iii) stimuli</p> <p><strong>Code</strong>: (i) Dimensionality analyses, (ii) statistical analyses, and (iii) MVPC analyses, and (iv) data visualization</p>
Dimensiones de Carga de Transporte en Jalisco
<p>Este dataset contiene las dimensiones detalladas de una variedad de objetos frecuentemente transportados en camiones, recopilados por una empresa líder de logística en México. Incluye muebles grandes como sofás y escritorios, electrodomésticos pesados, cajas de cartón llenas de diversos artículos, colchones, equipos de gimnasio, instrumentos musicales voluminosos, artículos de jardinería, grandes obras de arte, equipos electrónicos y muebles de oficina.</p>
Data for coherence measurements of polaritons in thermal equilibrium reveal a power law for two-dimensional condensates
<p>All the raw data sets collected for this project are included in this submission. The code for the numerics is also included. 'Readme.text' files are included with the data sets explaining what the data sets are and how to read them. </p>
Toward three-dimensional DNA industrial nanorobots
<p>Nanoscale industrial robots have potential as manufacturing platforms, capable of automatically performing repetitive tasks to handle and produce nanomaterials with consistent precision and accuracy. We demonstrate a DNA industrial nanorobot, that fabricates a three-dimensional (3D) optically active, chiral structure from optically inactive parts. By making use of externally controlled temperature and ultraviolet (UV) light, our programmable robot, ~ 100 nanometers in size, grabs different parts, positions and aligns them so that they can be "welded", releases the construct and returns to its original configuration ready for its next operation. Our robot can also self-replicate its 3D structure and functions, surpassing single-step templating (restricted to two-dimensional (2D)) by using folding to access the third dimension and more degrees of freedom. Our introduction of multiple-axis precise folding and positioning as a tool/technology for nanomanufacturing will open the door to more complex and useful nano-micro devices.</p>
Front Data for Scalable GPU-Enabled Creation of Three Dimensional Weather Fronts
<p>Frontal Polylines for 2016 created with the network described in:</p> <p>https://doi.org/10.5194/wcd-3-113-2022</p> <p> </p> <p>The work is supported by the project ``Big Data in Atmospheric Physics<br>(BINARY)'', funded by the Carl Zeiss Foundation (grant P2018-02-003).</p>
Data from: Three-dimensional infrared scanning: An enhanced approach for spatial registration of probes for neuroimaging
<p>Significance: Accurate spatial registration of probes (e.g., optodes and electrodes) for measurement of brain activity is a crucial aspect in many neuroimaging modalities. It may increase measurement precision and enable the transition from channel-based calculations to volumetric representations.</p> <p>Aim: This technical note evaluates the efficacy of a commercially available infrared three-dimensional (3D) scanner under actual experimental (or clinical) conditions and provides guidelines for its use.</p> <p>Method: We registered probe positions using an infrared 3D scanner and validated them against magnetic resonance imaging (MRI) scans on five volunteer participants.</p> <p>Results: Our analysis showed that with standard cap fixation, the average Euclidean distance of probe position among subjects could reach up to 43 mm, with an average distance of 15.25 mm [standard deviation (SD) = 8.0]. By contrast, the average distance between the infrared 3D scanner and the MRI-acquired positions was 5.69 mm (SD = 1.73), while the average difference between consecutive infrared 3D scans was 3.43 mm (SD = 1.62). The inter-optode distance, which was fixed at 30 mm, was measured as 29.28 mm (SD = 1.12) on the MRI and 29.43 mm (SD = 1.96) on infrared 3D scans. Our results demonstrate the high accuracy and reproducibility of the proposed spatial registration method, making it suitable for both functional near-infrared spectroscopy and electroencephalogram studies.</p> <p>Conclusions: The 3D infrared scanning technique for spatial registration of probes provides economic efficiency, simplicity, practicality, repeatability, and high accuracy, with potential benefits for a range of neuroimaging applications. We provide practical guidance on anonymization, labeling, and post-processing of acquired scans.</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.