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1,880 results for “Dimensionality”
Data from: In vitro to in vivo extrapolation from three-dimensional hiPSC-derived cardiac microtissues and physiologically based pharmacokinetic modeling to inform next-generation arrythmia risk assessment
<p>Proarrhythmic cardiotoxicity remains a substantial barrier to drug development as well as a major global health challenge. <em>In vitro</em> human pluripotent stem cell-based new approach methodologies have been increasingly proposed and employed as alternatives to existing <em>in vitro</em> and <em>in vivo</em> models that do not accurately recapitulate human cardiac electrophysiology or cardiotoxicity risk. In this study, we expanded the capacity of our previously established three-dimensional human cardiac microtissue model to perform quantitative risk assessment by combining it with a physiologically based pharmacokinetic model, allowing a direct comparison of potentially harmful concentrations predicted <em>in vitro</em> to <em>in vivo</em> therapeutic levels. This approach enabled the measurement of concentration responses and margins of exposure for two physiologically relevant metrics of proarrhythmic risk (<em>i.e.</em>, action potential duration and triangulation assessed by optical mapping) across concentrations spanning three orders of magnitude. The combination of both metrics enabled accurate proarrhythmic risk assessment of four compounds with a range of known proarrhythmic risk profiles (<em>i.e., </em>quinidine, cisapride, ranolazine, and verapamil) and demonstrated close agreement with their known clinical effects. Action potential triangulation was found to be a more sensitive metric for predicting proarrhythmic risk associated with the primary mechanism of concern for pharmaceutical-induced fatal ventricular arrhythmias, delayed cardiac repolarization due to inhibition of the rapid delayed rectifier potassium channel, or hERG channel. This study advances human induced pluripotent stem cell-based three-dimensional cardiac tissue models as new approach methodologies that enable <em>in vitro</em> proarrhythmic risk assessment with high precision of quantitative metrics for understanding clinically relevant cardiotoxicity.</p>
Dataset for: Adapting Explainable Machine Learning to Study Mechanical Properties of Two-Dimensional Hybrid Halide Perovskites
<p>This archive contains the in plane and out of plane Young's moduli (complete with respective VASP in and outputs) for 154 n=1 and 30 n>1 2D hybrid organic and inorganic perovskites. The data was used in the publication "Adapting Explainable Machine Learning to Study Mechanical Properties of Two-Dimensional Hybrid Halide Perovskites".</p> <p>Computational settings for the calculations were:</p> <p>Perdew-Burke-Ernzerhof (PBE) exchange-correlation with Tkatchenko-Scheffler (TS) van der Waals (vdW) corrections<br>Projector augmented-wave (PAW) method for the description of interactions between core and valence electrons.<br>A plane wave cutoff energy of 520 eV<br>A Γ-centered Monkhorst-Pack k-point mesh with a grid spacing of 2π × 0.040 Å−1 <br>Geometry optimizations were performed until energy and residual forces fell below 10−6 eV and 0.001 eV/ Å, respectively. <br><br></p>
Dataset: Dimensional Global Credit ETF (DGCB) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Dimensional Global Ex US Core Fixed Income ETF (DFGX) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Dimensional Global Core Plus Fixed Income ETF (DFGP) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Two-dimensional Numerical Simulations of Mixing under Ice Keels Data
<p>This deposition contains time series of relevant outputs from the ice keel simulations. These include:</p> <ol> <li>The buoyancy frequency squared (Nstar_sq)</li> <li>The spatially-average irreversible mixing rate (phi_d)</li> <li>The spatially-averaged dimensionless diapycnal diffusivity (K)</li> <li>The mixing depth (95% of mixing occurs above this depth) in meters (z_mix)</li> <li>The relative mixing depth (z_mix_rel). This differs from the previous quantity as it is relative to the keel depth. That is, if the mixing depth was the keel height then the relative mixing depth would be 0.</li> </ol> <p>The files are formatted to be imported as a dictionary into a Python file via the Json package. The keys are the simulation names. The key values are a tuple with the first value being the time series and the second being an array of times at which the respective values were recorded. All values are separated into upstream and downstream files.</p>
A Large-Scale Sensitivity Analysis on Latent Embeddings and Dimensionality Reductions for Text Spatializations
<p>Result Files for the Paper "A Large-Scale Sensitivity Analysis on Latent Embeddings and Dimensionality Reductions for Text Spatializations" to be published at IEEE Vis 2024</p>
The Impact of Oxygen Surface Coverage and Carbidic Carbon on the Activity and Selectivity of Two-Dimensional Molybdenum Carbide (2D-Mo2C) in Fischer–Tropsch Synthesis
<p>Datasets categorized per figure and contain data in x,y format.</p> <p>for the DFT part:</p> <p>35 elementary steps were studied. Each step is marked RX_NEB_InitialState_FinalState, and corresponds to the neb calculation for the identification of the transition state. In each file, POSCAR_00 corresponds to the initial structure and POSCAR_09 to the final structure, in both cases after geometry optimization. In each state, a file with the vibration calculation for the calculation of the Gibbs Energy is included. </p> <p>The Gibbs energies of the initial, transition and final states in table format are provided in <em>Figure 4 - panel b - Gibbs Energies_Initial_Transition_Final_states_35_elementary_reactions</em></p> <p>The calculation of Gibbs energies of the gas phase molecules in the empty unit cell, used as reference states are provided in <em>Figure 4 - Reference_state_Gases_empty_unit_cell</em></p> <p>The computations for the comparison of the different sites (HMo, Hc, atop and bridge) are provided in <em>Table S6 - Comparison_adsorption_sites</em></p> <p>The computations on the model with a partial oxygen coverage (O.67 O ML) are provided in:</p> <p><em>Figure S29 - mo2c-ctx-3x3_067OML_C_CH_CCH</em></p> <p><em>Figure S29 - mo2c-ctx-3x3_067OML_CH3_H_CH4</em></p> <p><em>Figure S29 - mo2c-ctx-3x3_067OML_CO_C_O</em></p> <p><em>Figure S29 - mo2c-ctx-3x3_067OML_CO_O_CO2</em></p>
Instantaneous, three-dimensional velocity fields past a bio-prosthetic aortic valve measured in-vitro with tomographic particle image velocimetry.
<p>Each folder contains instantaneous, three-dimensional velocity vector data obtained in a simplified model of the aortic root with a distinct size and geometry (small, medium, large, and sinus-less). The specific geometry of each aortic root model is contained in the corresponding folder.</p> <p>The velocity data is structured in the following way: Two separate folders for velocity data in the "ascending aorta" domain (AAo) and in the "sinus of Valsalva" domain (SOV). Each domain contains velocity datasets for instances t=0.00, 0.03, 0.06, ..., 0.39 s (t000, t003, t006, ..., t039). Each velocity dataset contains N=16 phase-locked instantaneous 3D velocity fields.</p> <p>The data was acquired using tomographic particle image velocimetry and a custom built hydraulic setup capable of replicating normal physiological flow conditions in the human aorta (heart rate = 72 bpm, cardiac output = 4.8 l)</p> <p>Data format:</p> <p>- aortic root geometry: STL (the geometry is provided with respect to the reference frame of the velocity data)</p> <p>- velocity data: NPY (NumPy), shape= (N_nodes, 6), columns contain X, Y, Z, U, V, W data, where U, V, W are the X, Y, Z components of the instantaneous vector field</p>
Three dimensional characterization of nickel coarsening in solid oxide cells via ex-situ ptychographic nano-tomography
<p>Three-dimensional dataset of a solid oxide cell (SOC) fuel electrode microstructure. Data were acquired using ptychographic X-ray computed tomography (PXCT). </p>
BRAIN Journal-ANNSVM: A Novel Method for Graph-Type Classification by Utilization of Fourier Transformation, Wavelet Transformation, and Hough Transformation-Figure 8. Simulation of Coiflet 1 (PyWavelets discussion group, 2008), analyzing as one-dimensional images
<p>Using only the wavelet coefficients was inadequate for classification. For example, for the pie chart, we obtained large wavelet coefficients located in the low-frequency domain; however, if we changed a circle in the pie chart to other shapes, such as a radar chart, the wavelet transformation gave results that were similar to those of the original pie chart. The Hough transformation can solve this problem since it detects the shapes of objects</p>
BRAIN Journal-ANNSVM: A Novel Method for Graph-Type Classification by Utilization of Fourier Transformation, Wavelet Transformation, and Hough Transformation-Figure 2. Illustrating the core process of one-dimensional image construction by applying a DFT
<p>First, we collect graph images as raw data, which contain different scales and sizes, and therefore need to be normalized. We clean the images by omitting irrelevant areas. For example, we omit unnecessary text that has nothing to do with our classification procedure. Moreover, to standardize the sizes and shapes of the images, we resize and reshape them to be 64 x 64 squares. Second, we examine each image pixel, each of which contains one color value. After each pixel is projected along the x- and y-axes, we count the number of projected pixels with a color value greater than zero to reduce image dimensionality. We, therefore, obtain two one-dimensional images from the x- and y-axes. </p>
Visualizing histopathologic deep learning classification and anomaly detection using nonlinear feature space dimensionality reduction
<p>Representative Testing/Validation WSIs used in the manuscript "Visualizing histopathologic deep learning classification and anomaly detection using nonlinear feature space dimensionality reduction"</p>
Visualizing histopathologic deep learning classification and anomaly detection using nonlinear feature space dimensionality reduction
<p>Training image dataset used in the manuscript "Visualizing histopathologic deep learning classification and anomaly detection using nonlinear feature space dimensionality reduction"</p>
Simulated Neutral Landscape Models and Scaling Results for Testing of Multi-Dimensional Grid-Point Scaling Algorithm
<p>The data package contains (1) simulations of neutral landscape models of categorical data for benchmark testing of scaling algorithms, and (2) scaling results for testing consistency and sensitivity of the newly developed Multi-Dimensional Grid-Point (MDGP) scaling algorithm.</p> <p>Neutral landscapes were generated using the "nlmpy" python module. The MDGP scaling algorithm and the test framework were implemented in R (https://github.com/gannd/landscapeScaling). </p>
Giant vortex clusters in a two-dimensional quantum fluid: Data sets
<p>This data set collates the experimental and simulation data for the paper "Giant vortex clusters in a two-dimensional quantum fluid."</p> <p><strong>Database S1: Data_Excel_Sheet.xlsx </strong>contains the data shown in Figs. 1, 3, and 4 of the paper.</p> <p><strong>Database S2: Exp_Vortex_Location_Data.zip</strong> contains the experimental vortex positions.</p> <p><strong>Database S3: 2DGPE.zip</strong> contains the outputs of the 2D GPE simulations, along with the generating scripts.</p> <p><strong>Database S4: MonteCarlo_raw.zip</strong> contains the Monte Carlo outputs used to generate the shown in Fig. 1. </p> <p>Additional scripts to reproduce the MC data, GPE data, and reproduce the figures may be found at <a href="https://github.com/UQBEC/GiantVortices">https://github.com/UQBEC/GiantVortices</a>.</p> <p> </p>
Micro-CT scans, whole-test meshes, and internal chamber segments of planktonic foraminifera for three-dimensional analysis of inter- and intra-specific variation in ontogenetic growth trajectories
<p> </p> <p>Here, we release tomographic reconstructions of 42 planktonic foraminifera from plankton tows and sediment traps, along with meshes and shrinkwrap meshes the whole tests and internal meshes of segmented chambers. Shrinkwrap meshes are test meshes that have been modified to close all pores and apertures in the test. Additionally, we have provided sample metadata for each specimen and volumetric measurements for the tests and chambers. This dataset was used in a study of ontogenetic growth in planktonic foraminifera and its variation within and among species.</p> <p> The CT-scans and reconstructions were obtained at Naturalis Biodiversity Center in Leiden, the Netherlands with a Zeiss Xradia 520 Versa micro-CT scanner. The meshes and segments were created at Yale University.</p> <ol> <li>Sample_Metadata.csv: Spreadsheet containing information on the sampling localities and dates for all specimens.</li> <li>Scan_data.csv: Spreadsheet containing metadata for all micro-CT scans including current strength, pixel size, voltage, image height, image width, and the number of images taken.</li> <li>Whole_Test_Measurements.csv: Spreadsheet containing measurements of linear dimensions (axis1, axis2, axis 3), total number of chambers, calcite test volume, calcite test surface area, shrinkwrap volumes, and and shrinkwrap surface areas for all specimens.</li> <li>Chamber_Measurements.csv: Spreadsheet containing measurements of individual internal chamber segments, including position from the final chamber (F-chamber), position from the first chamber (Chamber), volume, and surface area.</li> <li>CT_Scan_Stacks.zip: reconstructed micro-CT image stacks (.tif files) for each specimen.</li> <li>Meshes.zip: Meshes of the test calcite, the shrinkwrap, and the internal chamber segments for each specimen (.stl 3D mesh files). Regular test meshes are named with the format “SampleID.stl”, and shrinkwrap meshes are named “SampleID-WRAP.stl”. Chamber meshes are named “SampleID-CH#.stl” and “SampleID-CH#-Wrap.stl”. Chambers are numbered in relation to their position from the final chamber, with “CH1” being the final chamber and “CH2” being the penultimate chamber.</li> </ol> <p>This data is described and analyzed in the manuscript “Three-Dimensional Analysis of Inter- and Intraspecific Variation in Ontogenetic Growth Trajectories of Planktonic Foraminifera” submitted to the journal <em>Marine Micropaleontology.</em></p>
Video supplement: A three-dimensional palaeo-reconstruction of the groundwater salinity distribution in the Nile Delta Aquifer
<p>Videos of validated models. The filenames indicate simulation code, for explanation see accompanying paper, submitted to HESS.</p> <p><strong>Abstract</strong></p> <p>The Nile Delta is an important agricultural area with a fast-growing population. Though traditionally irrigated with surface water, the delta increasingly relies on groundwater. However, saline groundwater extends far land inward, rendering groundwater close to the coastal zone useless for consumption or agriculture. To aid groundwater management decisions, hydrogeologists reconstructed this saline and brackish groundwater zone using variable-density groundwater models with very large dispersivities. However, this approach cannot explain the observed freshening of this zone as observed by hydrogeochemists, who hypothesize that the coastal saline zone is the effect of the Holocene transgression. Here, we investigated physical plausibility of this hypothesis by conducting a palaeo-reconstruction of groundwater salinity for the last 32 ka with a complex 3D variable-density groundwater flow model, using state-of-the-art model code that allows for parallel computation. Several scenarios with different lithologies and hypersaline groundwater provenances were simulated, of which five were selected that showed the best match with the observations. Amongst these selections, total fresh water volumes varied strongly, ranging from 1526 to 2659 km3, mainly due to uncertainties in the lithology offshore and at larger depths. This range is smaller (1511-1989 km3) when we consider the volumes of onshore fresh groundwater within 300 m depth. Regardless of the variance, in all cases the total volume of hypersaline groundwater exceeded that of sea water. We also show that during the last 32 ka, the total fresh groundwater volumes significantly declined, with a factor ranging from 1.9 to 5.4, due to the rising sea-level. Compared to a steady-state solution with present-day boundary conditions, the palaeo-reconstruction improved our validation for the saline zone (5 g/L – 35 g/L TDS). Also, under highly permeable conditions the marine transgression simulated with the palaeo-reconstruction led to a steeper fresh-salt interface compared to its steady-state equivalent, while low permeable clay layers allowed for the preservation of volumes of fresh groundwater. This shows that long-term transient simulations are needed when estimating present-day fresh-salt groundwater distribution in large deltas. The insights of this study are also applicable to other major deltaic areas, given the wide-range of lithological model scenarios used in this study and since many deltas also experienced a Holocene marine transgression.</p>
One-dimensional hydrodynamic solution data of wavelet-based adaptive finite volume and discontinuous Galerkin shallow water solvers
<p>Raw data for a series of idealised, one-dimensional hydrodynamic test cases:</p> <ul> <li>dambreakwet (SWASHES 4.1.1)</li> <li>dambreakdry (SWASHES 4.1.2)</li> <li>dambreakmanning (SWASHES 4.1.3)</li> <li>dambreakupslope, dambreakdownslope (<a href="http://doi.org/10.1061/(ASCE)HY.1943-7900.0000494">Kesserwani and Liang 2011</a>)</li> <li>dambreakonehump (<a href="https://doi.org/10.1080/19942060.2011.11015393">Ozmen-Cagatay and Kocaman 2011</a>)</li> <li>lakeatrest (<a href="https://doi.org/10.29007/vm3q">Kesserwani et al. 2018</a>)</li> <li>parabolicbowlswashes (SWASHES 4.2.1)</li> <li>parabolicbowlliangmarche (<a href="https://doi.org/10.1016/j.advwatres.2009.02.010">Liang and Marche 2009</a>)</li> <li>steadysubcritical (SWASHES 3.1.3)</li> <li>steadysupercritical (<a href="https://doi.org/10.2166/hydro.2015.039">Haleem et al. 2015</a>)</li> <li>steadytranscriticalshockless (SWASHES 4.1.4)</li> <li>steadytranscriticalshock (SWASHES 4.1.5) </li> </ul> <p>SWASHES refers to <a href="https://doi.org/10.1002/fld.3741">Delestre et al. 2013</a> </p>
Data two dimensional Fermi polaron
<p>Here we report the supporting data for the manuscript "Two-dimensional repulsive Fermi polarons with short and long-range interactions", by Bombín, R., Comparin, T., Bertaina, G., Mazzanti, F., Giorgini, S., & Boronat, J. (2019) (http://arxiv.org/abs/1905.10125). If you use these data in a scientific work, please cite the corresponding article</p> <p>For additional details, please contact raul.bombin@upc.edu.</p> <p>The two folders "Dipoles" and "Hard_disk" refer to the two models studied in this work. Different subfolders correspond to different figures in the manuscript. Also subfolders that include the study of systematic errors are included.</p> <p>Each subfolder contains a README.txt file with a description of the data files and details on the simulations.</p> <p>New in v1.0.1: Updated data and descriptions in the Hard_disk folder.</p> <p>New in v1.0.2: bug-fixes in README files and folder structure. Also the data files in folder "Hard_disk/Volume_deficit" have been updated.</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.