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1,007 results for “three dimensional”
Regularized quantum periods for three-dimensional Fano manifolds
<p><strong>The database smooth_fano_3</strong></p> <p>This is a database of regularized quantum periods for three-dimensional Fano manifolds. There are 105 entries in the database.</p> <p>Each entry in the database is a key-value record with keys and values as described in the paper:</p> <p><em>Databases of Quantum Periods for Fano Manifolds</em>, Tom Coates and Alexander M. Kasprzyk, 2021.</p> <p>If you make use of this data, please cite the above paper and the DOI for this data:</p> <p>doi:10.5281/zenodo.5708272</p>
ZEO-1, A Stable Zeolite Catalyst with Intersecting Three-Dimensional extralarge Plus Large Pores
<p>ZEO-1 is an aluminosilicate zeolite with a multidimensional system of interconnected extra-large pores. This aluminosilicate has a high silica content and a zeolitic, non-interrupted framework, with high thermal and hydrothermal stability. The pore system of ZEO-1 contains both 3D 16MR and large 3D 12MR channels with high interconnectivity that results in three types of supercages with four windows of 16MR and/or 12MR. </p>
The Three-Dimensional Collapse Of A Rapidly Rotating 16 $M_{\odot}$ Star
<p>Data products from ApJL article The Three-Dimensional Collapse Of A Rapidly Rotating 16 $M_{\odot}$ Star, 2022. 3D rapidly rotating core-collapse supernova progenitor model at two different times. MESA inlist and model at time of mapping also included. All 3D data are in FLASH4 format using the HDF5 data structure. </p>
Leaf economics in a three‐dimensional environment: Testing leaf trait responses in vascular epiphytes to land use, climate, and tree zone
<p>1. The study of functional traits offers predictive power for community ecology. Particularly in cases where individual species are difficult to study, known properties of trait spectra, such as the leaf economics spectrum (LES), and trait-environment relationships can provide crucial generalizable information that can contribute to forecasts of distributional shifts in response to the abiotic effects of climate and land use changes.</p> <p>2. Vascular epiphytes have been proposed as indicators of environmental change, but we know little about the ecology of most species. Key functional trait assumptions are based on terrestrial plants; testing these in epiphytes verifies their universality and will inform applying functional traits in predicting epiphyte responses to climate and land use change.</p> <p>3. In this study, we use functional traits from 37 vascular epiphyte species from forests and shade coffee farms at two sites in northern Nicaragua. We compare correlations among traits and intraspecific trait variances with those of terrestrial plants and among epiphyte taxonomic groups. We also test trait responses to environmental differences between sites and land use types, and within zones of the tree.</p> <p>4. We find that epiphyte leaf traits fall toward the slower end of the LES, but with about one-third lower leaf nitrogen per change in specific leaf area (SLA) relative to terrestrial herbaceous plants. Bromeliads show less relationship between these traits than other epiphyte groups and also deviate in other trait interrelationships, suggesting unique leaf construction.</p> <p>5. Trait-environment relationships varied most strongly along vertical gradients within trees, but some traits, including SLA and carbon isotope ratio, also responded to land use and site differences.</p> <p>6. We present evidence that trait relationships established for terrestrial plants appear to translate to epiphytes, but identify some possible caveats. Strong trait response to vertical gradients within trees suggests that within-canopy shifts could provide resilience to climate and land use changes for some epiphyte species.</p>
Wake vortices and dissipation in a tidally modulated flow past a three-dimensional topography
<p>LES simulation data of tidally modulated flow past an abyssal hill. Data used in the figures are available</p>
Shallow three-dimensional shear wave velocity model of Volcán de Colima
<p><strong>Velocity model presented in the paper:</strong></p> <p><strong>De Plaen, R.S.M., Mordret, A., Arámbula-Mendoza, R., Vargas-Bracamontes, D., Márquez-Ramírez, V.H., Lecocq, T., Ramírez Vázquez, C.A., González Amezcua, M. The shallow three-dimensional structure of Volcán de Colima revealed by ambient seismic noise tomography</strong></p> <p>Please cite the paper above when using this VS model.</p> <p><em>Description:</em></p> <ul> <li><strong>Colima_ANT_VSmodel.mat:</strong> Shallow 3D VS model of Volcán de Colima along with the corresponding velocity anomaly, error, and radial anisotropy. This version of the model has no vertical smoothing and no topographic correction.</li> <li><strong>Colima_AverageVSmodel.csv:</strong> Simple 1D VS model averaging over the entire study area.</li> <li><strong>Colima_CraterVSmodel.csv:</strong> Simple 1D VS model under the crater of Volcán de Colima.</li> <li><strong>3d plot.ipynb:</strong> Small jupyter notebook (in python) to plot, and understand the structure of the VS model.</li> </ul>
Training and validation datasets for "Three-Dimensional Implicit Structural Modeling Using Convolutional Neural Network"
<p>This is training and validation datasets used in manuscript "Three-Dimensional Implicit Structural Modeling Using Convolutional Neural Network". In this manuscript, we propose an efficient deep learning method using a Convolutional Neural Network (CNN) to predict a scalar field from sparse structural data associated with multiple distinct stratigraphic layers and faults. The CNN architecture is beneficial for the flexible incorporation of empirical geological knowledge when trained with numerous and realistic structural models that are automatically generated from a data simulation workflow. It also presents an expressive characteristic of integrating various types of structural constraints by optimally minimizing a hybrid loss function to compare predicted and reference structural models, opening new opportunities for further improving geological modeling. </p>
Accounting for the three-dimensional nature of mountain waves: parametrizing partial critical level filtering
<p>This data is used in the production of figures and results presented in the paper 'Accounting for the three-dimensional nature of mountain waves: parametrizing partial critical level filtering', submitted to the Quarterly Journal of the Royal Meteorological Society.</p> <p> </p> <p>See README file for details.</p> <p> </p> <p> </p>
Three-dimensional stratification pattern in an old-growth lowland forest: how does height in canopy and season influence temperate bat activity?
<p class="PaperLauftext">The study of animal-habitat interactions is of primary importance for the formulation of conservation recommendations. Flying, gliding and climbing animals have the ability to exploit their habitat in a three-dimensional way and the vertical canopy structure in forests plays an essential role for habitat suitability. Forest bats as flying mammals may seasonally shift their microhabitat use due to differing energy demands or changing prey availability, but the patterns are not well understood.</p> <p>We investigated three-dimensional and seasonal habitat use by insectivorous bats in a temperate lowland old-growth forest, the <i>Belovezhskaya Pushcha</i> in Belarus. We acoustically sampled broadleaved and mixed coniferous plots in the forest interior and in gaps in three heights during two reproductive periods (pregnancy/lactation versus post-lactation). In canopy gaps, vertical stratification in bat activity was less pronounced than in the forest interior. Vertical activity patterns differed among species. The upper canopy levels were important foraging habitats for the open-space forager guild and for some edge-space foragers like the Barbastelle bat <i>Barbastella barbastellus</i> and the soprano pipistrelle <i>Pipistrellus pygmaeus</i>. <i>Myotis</i> species had highest activity levels near the ground in forest gaps. Moreover, we found species-dependent seasonal microhabitat shifts. Generally, all species and species groups considered except <i>Myotis</i> species showed higher activity levels during post-lactation. <i>Myotis</i> species tended towards higher activity in the forest interior during post-lactation. <i>P. pygmaeus</i> switched from high activity levels in the upper canopy during pregnancy and lactation to high activity levels near the ground during post-lactation. We conclude that a full comprehension of forest bat habitat use is only possible when height in canopy and seasonal patterns are considered.</p>
Training and validation data for artificial neural networks using three-dimensional partial convolutions to fill gaps in satellite image time series
<p>This dataset contains training and validation data for artificial neural networks using three-dimensional partial convolutions to fill gaps in satellite image time series. The data have been derived from Sentinel-5P total column carbon monoxide observations, using the offline processing stream.</p> <p><strong>Preprocessing</strong></p> <p>The following operations have been applied on the original S5P imagery:</p> <ol> <li>Images have been resampled to 0.1 by 0.1 degree spatial resolution</li> <li>Pixels with quality assessment value less than or equal to 0.5 have been set to NA</li> <li>Images have been aggregated by day of observation</li> <li>Images have been cropped to -60 to 60 degrees latitude</li> <li>Images have been devided into spatiotemporal blocks of size 128 x 128 pixels and 16 days</li> </ol> <p>Imagery has been recorded between 2021-01-01 and 2021-11-25. Notice that both the training and the validation blocks have been randomly sampled from all available blocks.</p> <p><br> <strong>Data Format and Naming Conventions</strong></p> <p>Input and output data blocks are stored as GeoTIFF files, where bands represent time. Notice the following file naming conventions:</p> <ul> <li>Files starting with <em>X</em> represent input measurements for training, where artificial gaps have been added.</li> <li>Files starting with <em>Y</em> represent true measurements without artificially added gaps (but still containing gaps in many cases).</li> <li>Binary masks of input data where all pixels with valid measurements are 1 and others 0 are stored in files whose name starts with <em>MASK</em></li> <li>Files starting with <em>VALMASK</em> contain a binary mask where only pixels that are available in Y but not in X are 1. The latter is used for validation on artificially removed pixels only.</li> </ul> <p>Numbers in filenames encode spatial and temporal block indexes.</p> <p>In addition, the dataset contains prediction of the validation blocks from different models in the `predictions` directory. The subfolders contain output from different models:</p> <ul> <li>mean refers to simple block-wise mean predictions.</li> <li>timeseries refers to simple linear time series interpolation.</li> <li>gapfill refers to the method proposed in [1].</li> <li>stmra refers to the method proposed in [2].</li> <li>STpconv refers to predictions passed on an artificial neural netowork with three-dimensional partial convolutions.</li> </ul> <p><strong>References</strong></p> <p>[1] Gerber, F., de Jong, R., Schaepman, M. E., Schaepman-Strub, G., & Furrer, R. (2018). Predicting missing values in spatio-temporal remote sensing data. IEEE Transactions on Geoscience and Remote Sensing, 56(5), 2841-2853.</p> <p>[2] Appel, M., & Pebesma, E. (2020). Spatiotemporal multi-resolution approximations for analyzing global environmental data. Spatial Statistics, 38, 100465.</p>
THREE-DIMENSIONAL VOLUMETRIC EVALUATION OF ROOT RESORPTION IN MAXILLARY ANTERIORS FOLLOWING EN-MASSE RETRACTION WITH VARYING FORCE VECTORS - A RANDOMIZED CONTROL TRIAL
<p>To assess the severity of root resorption (RR) during retraction of maxillary anteriors with and without skeletal anchorage (three different force systems); and analyze it comprehensively using cone-beam computed tomography (CBCT) superimpositions.</p>
Three-dimensional mapping of carbon, nitrogen, and phosphorus in soil microbial biomass and their stoichiometry at the global scale
<p>R code, raw datasets, and predicted global maps of soil microbial biomass C, N, and P and their stoichiometric ratios at 0-30 cm depth.</p> <p>When using any of these layers, please cite: Gao et al., Three-dimensional mapping of carbon, nitrogen, and phosphorus in soil microbial biomass and their stoichiometry at the global scale (2022). Global Change Biology. DOI: 10.1111/gcb.16374</p>
Three-dimensional surface scans of tooth surfaces of Japanese wild boars.
<p>Three-dimensional surface scans of tooth surfaces of Japanese wild boars are saved in .sur format and surface analyses files in .mnt format, which are analyzed in:</p> <p>Miyamoto, K., Kubo, M. O. and Yokohata, Y. "Dental microwear texture of wild boars from Japan reflects inter- and intra-populational feeding preferences" submitted to Frontiers in Ecology and Evolution.</p> <p>Please see information in the paper for details.</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>
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>
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>
All-sky three-dimensional dust density and extinction Maps of the Milky Way out to 2.8 kpc
<p>Three-dimensional dust density maps are crucial for understanding the structure of the interstellar medium of the Milky Way and the processes that shape it. However, constructing these maps requires large datasets and the methods used to analyse them are computationally expensive and difficult to scale up. As a result it is has only recently become possible to map kiloparsec-scale regions of our Galaxy at parsec-scale grid sampling. We present all-sky three-dimensional dust density and extinction maps of the Milky Way out to 2.8~kpc in distance from the Sun using the fast and scalable Gaussian Process algorithm Dustribution. The sampling of the three-dimensional map is $l,b,d = 1^{\circ} \times1^{\circ} \times 1.7$~pc. The input extinction and distance catalogue contains 120 million stars with photometry and astrometry from Gaia DR2, 2MASS and AllWISE. This combines the strengths of optical and infrared data to probe deeper into the dusty regions of the Milky Way. We compare our maps with other published 3D dust maps. All maps quantitatively agree at the $0.001$~mag~pc$^{-1}$ scale with many qualitatively similar features, although each map also has its own features. We recover Galactic features previously identified in the literature. Moreover, we also see a large under-density that may correspond to an inter-arm or -spur gap towards the Galactic Centre.</p> <p> </p> <p>Here we provide the full dataset generated from Dustribution covering the full sky out to 2.8 kpc. We provide the 3D dust extinction and density files as well the l,b,d coordinate grid that the data is presented in. </p> <p> </p> <p>The data in each file is as follows:</p> <ul> <li>lbdGrid_l_bounds.pkl.npy: The boundaries of the grid cells of the merged map in l (degrees). Numpy array of size 881.</li> <li>lbdGrid_b_bounds.pkl.npy: The boundaries of the grid cells of the merged map in b (degrees). Numpy array of size 273.</li> <li>lbdGrid_d_bounds.pkl.npy: The boundaries of the grid cells of the merged map in d (pc). Numpy array of size 1644.</li> <li>Density_Median_lbd.pkl.npy: The median of 100 density samples of the merged map (mag pc^-1). Treat this as the estimate of the density. Numpy array of size (880, 272, 1644)</li> <li>Density_16P_lbd.pkl.npy: The 16th percentile of the density of the merged map (mag pc^-1). Treat median-16p as the estimate of the lower uncertainty on the density. Numpy array of size (880, 272, 1644)</li> <li>Density_84P_lbd.pkl.npy: The 84th percentile of the density of the merged map (mag pc^-1). Treat 84p-median as the estimate of the upper uncertainty on the density. Numpy array of size (880, 272, 1644) </li> <li>Ext_Median_lbd.pkl.npy: The median of the integral of 100 sample maps at each cell in the merged map (mag). Treat this as the estimate of the extinction in each cell. Numpy array of size (880, 272, 1644). </li> <li>Ext_16P_lbd.pkl.npy: The 16th percentile of the integral of 100 sample maps at each cell in the merged map (mag). Treat median-16p as the estimate of the lower uncertainty on the extinction in each cell. Numpy array of size (880, 272, 1644)</li> <li>Ext_84P_lbd.pkl.npy: The 84th percentile of the integral of 100 sample maps at each cell in the merged map (mag). Treat 84p-median as the estimate of the upper uncertainty on the extinction in each cell. Numpy array of size (880, 272, 1644)</li> <li>read_and_plot.ipynb: a short jupyter notebook demonstrating how to read in the data files, with some quick examples of plotting them.</li> </ul>
An innovative method of three-dimensional bone reconstruction of the anterior mandible with preserved dentition using an allogeneic bone block- 6-month follow-up- supplementary materials
<p>Table S1. The Shapiro-Wilk’s normality test (N=32 for each variable and each tooth) for the differences between before and after the treatment. W – test values, p – statistical significance.</p> <p>Table S2. Descriptive statistics of the studied bone growth parameters.</p> <p>Table S3 . Relative Treatment Effects for the studied teeth and the statistical significance of the differences between before and after treatment.</p> <p>Table S4. Spearman’s correlations of the bone growth parameters (averaged data for all teeth). N – sample size, R – correlation coefficient, t(N-2) – test value, p – statistical significance.</p> <p>Table S5. The differences in bone growth parameters (averaged data for all teeth) between sexes. N – sample size, U – test value, p – statistical significance.</p> <p>Table S6. The differences in bone growth parameters (averaged data for all teeth) between patients who underwent orthodontic treatment with a passive wire (Wire) and patients without ongoing treatment (Without wire). N – sample size, U – test value, p – statistical significance.</p> <p>Table S7. The differences in bone growth parameters (averaged data for all teeth) between patients with recessions (Yes) and patients without recession (No). N – sample size, U – test value, p – statistical significance.</p> <p>Table S8. The differences in bone growth parameters (averaged data for all teeth) between patients with thin biotype (Thin) and patients with thick biotype (Thick). N – sample size, U – test value, p – statistical significance.</p> <p>Table S9. The differences in bone growth parameters (averaged data for all teeth) between patients with excessive mentalis muscle tension (Yes) and patients without such the feature (No). N – sample size, U – test value, p – statistical significance.</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.