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1,007 results for “three dimensional”
Characterizing multidimensional cellular physiological states with quantitative three-dimensional shape descriptors for cell membranes
<h3>Supplementary dataset and code for the article "<em>Characterizing Cellular Physiological States with Three-Dimensional Shape Descriptors for Cell Membranes</em>"</h3> <p>CShaper Dataset.zip: The 3D cell regions reused from the previously published article <a href="https://doi.org/10.1038/s41467-020-19863-x">https://doi.org/10.1038/s41467-020-19863-x</a>.</p> <p>Cell Shape Descriptors - Code & Data.zip: The code (exemplifed by embryo Sample04) and data (including embryo Sample04-Sample20) of 12 3D shape descriptors for all 3D cell regions in the <em>CShaper</em> dataset.</p> <p>GUI.zip: The user-friendly software <em>Shape Descriptor Tool</em> is a Graphical User Interface based on <em>Matlab</em> for calculating 12 shape descriptors for a 3D cell region (exemplified by embryo Sample20, time point 14, ABpl cell in the <em>CShaper</em> dataset). The instruction guidebook is included in the Supplementary Material of the article.</p>
Eulerian modelling of the three-dimensional distribution of seven popular microplastic types in the global ocean dataset
<p>Dataset for the paper "Eulerian modelling of the three-dimensional distribution of seven popular microplastic types in the global ocean" by A. S. Mountford and M. A. Morales Maqueda.</p> <p>ORCA2_5d_00010101_00011231_ptrc_T_con.nc<a href="https://zenodo.org/api/files/c6c9cbac-d5db-452a-aade-fd852db07351/ORCA2_5d_00010101_00011231_ptrc_T_con.nc"> </a> - control experiment (year 50)</p> <p>ORCA2_5d_00010101_00011231_ptrc_T_30m.nc - 30 m year<sup>-1</sup> piston velocity sensitivity experiment (year 50)</p> <p>ORCA2_5d_00010101_00011231_ptrc_T_90m.nc - 90 m year<sup>-1</sup> piston velocity sensitivity experiment (year 50)</p> <p>ORCA2_5d_00010101_00011231_ptrc_T_50.nc - neutrally buoyant sensitivity simulation (year 50)</p> <p>plastic_1_ts.nc - positively buoyant time series</p> <p>plastic_2_ts.nc - neutrally buoyant time series</p> <p>plastic_3_ts.nc - negatively buoyant time series</p> <p>plastic_input_ORCA2.nc - plastic input data file</p> <p>ORCA2_5d_00010101_00011231_grid_U.nc & ORCA2_5d_00010101_00011231_grid_V.nc - ocean velocity files</p>
Fine Three-Dimensional VHF Lightning Mapping Using Waveform Cross-Correlation TOA Method
<p>In a manuscript entitled “Fine Three-Dimensional VHF Lightning Mapping Using Waveform Cross-Correlation TOA Method”, the lightning data obtained by an improved VHF 3-D location system were analyzed. The data supports the aforementioned manuscript and can be used freely for scientific purposes with appropriate citation.</p>
Data for Three-dimensional X-line spreading in asymmetric magnetic reconnection
<p>The data and IDL scripts for all the figures of our 2019 JGR are organized in a self-explanatory way.</p>
Fig. 6 in Three-dimensional elasto-plastic soil modelling and analysis of sauropod tracks
Fig. 6. Detail of the calculation model: layer III of Miraflores I tracksite.
Three-dimensional soil organic carbon density by logarithmic function and coefficient scaling in Yangtze River Delta, China
<h3>Three-dimensional soil organic carbon density (SOCD) dataset with 90-m resolution generated by Lin, S., Zhu, Q., Yin, B., Yang, G., Liao, K., Lai, X., Guo, C., 2025. Generating three-dimensional soil organic carbon density dataset by soil depth function and correction methods in Yangtze River Delta, China. Environmental Modelling & Software, <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.envsoft.2025.106582" target="_blank" rel="noreferrer noopener"><span><span>https://doi.org/10.1016/j.envsoft.2025.106582.</span></span></a></h3> <h3>Here, based on the best performance, the three-dimensional SOCD generated by LF corrected with coefficient scaling method were provided. The accurate SOCD maps with the spatial resolution of 90-m at any specific depth interval can be generated by our method. This dataset includes:</h3> <ul> <li>Spatial distribution map of parameter 1 (p1) of LF (LF_p1.tif)</li> <li>Spatial distribution map of parameter 2 (p2) of LF (LF_p2.tif)</li> <li>The calculation code and fitted functions of scaling coefficient a, k of LF (fitted_fx_scalingcoff.m)</li> <li>Readme.docx</li> </ul> <p>Note: the unit of SOCD is kg m-2; the spatial distribution maps provided by this dataset does not mask any water bodies.</p> <p><strong>How to use our dataset? Please refer to our article and Readme.docx for more details.</strong></p> <p> </p> <p> </p>
Fisher zeroes and dynamical quantum phase transitions for two- and three-dimensional models
<p>Data for the publication <em>Fisher zeroes and dynamical quantum phase transitions for two- and three-dimensional models</em>. Included are data for return rates and Fisher zeroes. The names of the data files contain all metadata necessary for specifying the type of data, with a list of the paramters used. For more details on the models and paramters see the article.</p> <ul> <li>Firstly the three models are considered labelled by <em>Kit</em> for the 2D spinless px+ipy topological superconductor, <em>Sq</em> for the 2D spinfull topological superconductor, and <em>3D</em> for the 3D topological insulator.</li> <li>After <em>_N_</em> the system size is written, referring the number of sites along one direction (for finite size calcualtions only).</li> <li><em>RR</em> refers to the return rate. For <em>Kit</em> the columns are $t$, $l(t), $\dot{l}(t)$, $\ddot{l}(t)$. If only two columns exit then there is just $t$, $\dot{l}(t)$. For <em>Sq</em> the columns are $t$, $l(t), $\dot{l}(t)$, $|\lambda_0(t)|$. For <em>3D</em> the columns are $t$, $\dot{l}(t)$, $\ddot{l}(t)$.</li> <li><em>Density</em> refers to the density of Fisher zeroes along the real time axis, with the columns being simply time and density.</li> <li><em>Fisher</em> to Fisher zeroes in the complex plane with <em>kx</em>, etc meaning it is resolved along this momentum direction. The columns are the real and imaginary parts of the complex time argument.</li> <li><em>Critical_k</em> are the critical momenta.</li> <li><em>Critical_k_En</em> lists the energy for each critical momenta in the matching <em>Critical_k </em>file.</li> <li>For <em>Kit</em> the list of quench parameters are 100 times {$\mu_0$, $\Delta_0$, $\mu_1$, $\Delta_1$}.</li> <li>For <em>Sq</em> the list of quench parameters are 100 times {$\nu_0$, \nu_1$, $\mu_0$, $\Delta_0$, $\alpha_0$, $B_0$, $\mu_1$, $\Delta_1$, $\alpha_1$, $B_1$}.</li> <li>For <em>3D</em> the list of quench parameters are 100 times {$v_0$,$w_0$,$v_1$,$w_1$}.</li> <li>For <em>Fisher</em> data <em>branch</em> labels naturally the branch p.</li> <li><em>Zoom</em>, etc, label different zooms in differnet regions, as made explicit by the times inside the files.</li> </ul> <p>This work was supported by the National Science Centre (NCN, Poland) under the grant 2019/35/B/ST3/03625, by the German Research Council (DFG) via the Re- search Unit FOR 2316 and by the National Science and Engineering Resource Council (NSERC) of Canada via the Discovery Grant program.</p>
The reconstructed three-dimensional nitrate field dataset for the pan-European ocean using a continual learning-based multilayer perceptron
<p>Based on a newly developed continual learning-based multilayer perceptron model and environmental features, we reconstructed the pan-European 3D ocean nitrate field from 2010 to 2023. The reconstructed field features a monthly temporal resolution, a horizontal spatial resolution of 0.25 degrees, and 63 depth levels, with vertical intervals ranging from 5 to 50 m.</p>
Data from: Three-dimensional trajectories and network analyses of group behaviour within chimney swift flocks during approaches to the roost
Chimney swifts (Chaetura pelagica) are highly manoeuvrable birds notable for roosting overnight in chimneys, in groups of hundreds or thousands of birds, before and during their autumn migration. At dusk, birds gather in large numbers from surrounding areas near a roost site. The whole flock then employs an orderly, but dynamic, circling approach pattern before rapidly entering a small aperture en masse. We recorded the three-dimensional trajectories of ≈1 800 individual birds during a 30 min period encompassing flock formation, circling, and landing, and used these trajectories to test several hypotheses relating to flock or group behaviour. Specifically, we investigated whether the swifts use local interaction rules based on topological distance (e.g. the n nearest neighbours, regardless of their distance) rather than physical distance (e.g. neighbours within x m, regardless of number) to guide interactions, whether the chimney entry zone is more or less cooperative than the surrounding flock, and whether the characteristic subgroup size is constant or varies with flock density. We found that the swift flock is structured around local rules based on physical distance, that subgroup size increases with density, and that there exist regions of the flock that are less cooperative than others, in particular the chimney entry zone.
The first Silurian trilobite with three-dimensionally preserved soft parts reveals novel appendage morphology
<p>The first Silurian trilobite known with soft parts preserved, a <i>Dalmanites</i> species, is described from the Herefordshire Lagerstätte. Biramous appendages and much of the alimentary system are evident. High-fidelity three-dimensional preservation reveals a novel, double arrangement of the exopod filaments, interconnected by a presumed membranous sheet. This morphology explains a misinterpretation of the exopod as supporting spiral structures, originally reported nearly 150 years ago. The new exopod morphology is considered primarily respiratory in function and comparison with other trilobite limbs suggests that it may be unique to Phacopida.</p>
Appendix 3 of "Analyses of three-dimensional species associations reveal departures from neutrality in a tropical forest"
<p>Temporal changes in three-dimensional structure of crowns in Luquillo, Puerto Rico. Each axis represents the overlap of one species over the other, measured as standardized effect sizes (SES). That is, deviations from a null model that randomizes crown three-dimensional positions. A high value of "SES of <em>Guarea guidonia</em> over <em>Psychotria brachiata</em>" means that <em>Guarea guidonia</em> shades <em>Psychotria brachiata</em> more than expected by chance, and so on.<br> <br> The coloured dots in the background of the figure represent all the observed relationships between species. Gray dots represent random relationships between pairs of species (i.e. similar to the expected by the null model). Black dots represent horizontal segregation between species. Blue dots represent horizontal aggregation but vertical segregation between species. Red dots represent three-dimensional aggregation between species.<br> <br> The larger green or red dots connected by lines represent the temporal changes observed for the relationship between the two target species in each figure. There are four censuses represented. Larger dots represent the more recent censuses. The green dots reflect the relationship between the two target species in the low-disturbance area within the Luquillo Forest Dynamics Plot. The red dots reflect the relationship between the two target species in the high-disturbance area within the Luquillo Forest Dynamics Plot.</p>
Determination of the spinel content in cycled Li1.2Ni0.13Mn0.54Co0.13O2 using three-dimensional electron diffraction and precession electron diffraction
<p>(hkl,I)-lists for the 150 times cycled NMC material</p>
Data and Results for: Cost-effective, high-throughput fruit phenotyping system for three-dimensional reconstruction of fruit form
<p>The dataset contains the raw data used for generating the results in the paper, and the reconstructed results at 1mm and 2mm voxel resolution.</p> <p><strong>Raw data</strong>: Samples are gathered into groups of five, DataObjects1-5.zip, ... , DataObjects56-59.zip.</p> <p>Once uncompressed, each directory holds the images and configuration information used for each sample. For instance, '1_obj' is for the first sample, and the 'data' folder has the image files by camera. In this case one camera was used, 'cam2', and there are 62 images. 'pattern_square_mm_external.txt', 'pattern_square_mm_internal.txt', and 'rotate_specification_file.txt' are all configuration files needed for the calibration step.</p> <p> </p> <p><strong>Results</strong>: Reconstructions results are in compressed folders 'reconstruction-results-1mm.zip' and 'reconstruction-results-2mm.zip' for 1 mm and 2 mm voxel resolutions, respectively. These results are three-dimensional model files than can be viewed with a variety of software, we have used the free MeshLab: https://www.meshlab.net/ .</p> <p> </p> <p> </p> <p> </p>
Three dimensional localization refinement and motion model parameter estimation for confined single particle tracking under low-light conditions: Simulation datasets
<p><span>The datasets store both motion and observation information of a single fluorescent sub-diffraction limit-sized particle moving in a three-dimensional confined environment. The confined motion is following a nonlinear model driven by non-Gaussian noise, the observation is formed by engineered Double-helix (DH) point spread function (PSF) and captured by scientific complementary metal-oxide semiconductor (sCMOS) camera. Based on our prior computationally efficient application of Sequential Monte Carlo - Expectation Maximization (SMC-EM), we extended it to handle the DH-PSF for encoding the three-dimensional position of the particle in two-dimensional image plane of the camera. We focus on studying the datasets at low signal and low signal-to-background ratio (SBR). Based on the datasets across different SBR and confinement lengths, a quantitative comparison is conducted to show that in the low signal regime, the SMC-EM approach outperforms the other methods while at higher signal-to-background levels, SMC-EM and the MLE-based methods perform equally well and both are significantly better than fitting to the MSD. In addition, our results indicate that at smaller confinement lengths where the nonlinearities dominate the motion model, the SMC-EM approach is superior to the alternative approaches. </span></p>
Three-dimensional simulations of rapidly rotating core-collapse supernovae: finding a neutrino-powered explosion aided by non-axisymmetric flows
<p>Those are movies of numerical supernova models, which appear in Takiwaki, Kotake, and Suwa 2016 Monthly Notices of the Royal Astronomical Society: Letters, Volume 461, Issue 1, p.L112-L116.</p> <p> </p> <p> </p>
Three-dimensional dataset of hydrating cement paste (CEM I Ladce, 273 m^2/kg Blaine, w/c=0.50) in TIFF format
<p>A detailed description of this dataset can be found in <a href="https://doi.org/10.1016/j.dib.2023.108903">https://doi.org/10.1016/j.dib.2023.108903</a><a href="https://doi.org/10.1016/j.dib.2023.108903">.</a></p> <p>This dataset contains a collection of digitized three-dimensional hardened cement paste microstructures obtained from X-ray micro-computed tomography, screened after approx. 1, 2, 3, 4, 7, 14, and 28 days of elapsed hydration at 20˚C in saturated conditions. Each paste specimen had a cylindrical shape (with a diameter of ~1 mm) and was screened at a designated time (as specified in the file name, e.g. “t23hrs”=23 hours of elapsed hydration) and finally saved as an uncompressed and unprocessed *.tif greyscale image data file in 16-bit image depth (as unsigned integers) using a little-endian byte sequence.</p> <p>The dataset contains two sets of images:</p> <p>- “full-sized” digital images stored in a three-dimensional voxel-based matrix with a fixed size of 1100x1100x1100 voxels, denoted as “CEM_I_Ladce_*” in the file name; each file size amounts to ~2.5 GB and contains the whole screened specimen with a variable voxel size in the range 1.0913 − 1.1174 µm depending on the particular specimen (as specified in the file name, e.g. “1d1174um”=1.1174 µm/voxel)</p> <p>- smaller image subvolumes, denoted as Region Of Interest (ROI), extracted from the interior of the full-sized specimen from an arbitrary location, and denoted as “filteredROI_*” in the file name; this cropped ROI has a cubic shape and stores a three-dimensional voxel-based matrix with a fixed size of 500x500x500 µm<sup>3</sup> constituted by a variable voxel count (given the fluctuating voxel size for each specimen, see above). Both the exact voxel count (i.e. three-dimensional matrix dimensions) and voxel size are further specified in each file name. A sequence of imaging filters was sequentially applied to this ROI to further enhance the contrast among the different microstructural phases, see <a href="https://doi.org/10.1016/j.cemconcomp.2022.104798">10.1016/j.cemconcomp.2022.104798</a> for details.</p> <p>Note that the same dataset stored in *raw format is available from <a href="https://doi.org/10.5281/zenodo.7193819">https://doi.org/10.5281/zenodo.7193819</a></p>
Three-dimensional dataset of hydrating cement paste (CEM I Ladce, 273 m^2/kg Blaine, w/c=0.35) in TIFF format
<p>A detailed description of this dataset can be found in <a href="https://doi.org/10.1016/j.dib.2023.108903">https://doi.org/10.1016/j.dib.2023.108903</a><a href="https://doi.org/10.1016/j.dib.2023.108903">.</a></p> <p>This dataset contains a collection of digitized three-dimensional hardened cement paste microstructures obtained from X-ray micro-computed tomography, screened after approx. 1, 2, 3, 4, 7, 14, and 28 days of elapsed hydration at 20˚C in saturated conditions. Each paste specimen had a cylindrical shape (with a diameter of ~1 mm) and was screened at a designated time (as specified in the file name, e.g. “t23hrs”=23 hours of elapsed hydration) and finally saved as an uncompressed and unprocessed *.tif greyscale image data file in 16-bit image depth (as unsigned integers) using a little-endian byte sequence.</p> <p>The dataset contains two sets of images:</p> <p>- “full-sized” digital images stored in a three-dimensional voxel-based matrix with a fixed size of 1100x1100x1100 voxels, denoted as “CEM_I_Ladce_*” in the file name; each file size amounts to ~2.5 GB and contains the whole screened specimen with a variable voxel size in the range 1.0913 − 1.1174 µm depending on the particular specimen (as specified in the file name, e.g. “1d1174um”=1.1174 µm/voxel)</p> <p>- smaller image subvolumes, denoted as Region Of Interest (ROI), extracted from the interior of the full-sized specimen from an arbitrary location, and denoted as “filteredROI_*” in the file name; this cropped ROI has a cubic shape and stores a three-dimensional voxel-based matrix with a fixed size of 500x500x500 µm<sup>3</sup> constituted by a variable voxel count (given the fluctuating voxel size for each specimen, see above). Both the exact voxel count (i.e. three-dimensional matrix dimensions) and voxel size are further specified in each file name. A sequence of imaging filters was sequentially applied to this ROI to further enhance the contrast among the different microstructural phases, see <a href="https://doi.org/10.1016/j.cemconcomp.2022.104798">10.1016/j.cemconcomp.2022.104798</a> for details.</p> <p>Note that the same dataset stored in *raw format is available from <a href="https://doi.org/10.5281/zenodo.7193808">https://doi.org/10.5281/zenodo.7193808</a></p>
Three-dimensional dataset of hydrating cement paste (CEM I Ladce, 391 m^2/kg Blaine, w/c=0.50) in TIFF format
<p>A detailed description of this dataset can be found in <a href="https://doi.org/10.1016/j.dib.2023.108903">https://doi.org/10.1016/j.dib.2023.108903</a><a href="https://doi.org/10.1016/j.dib.2023.108903">.</a></p> <p>This dataset contains a collection of digitized three-dimensional hardened cement paste microstructures obtained from X-ray micro-computed tomography, screened after approx. 1, 2, 3, 4, 7, 14, and 28 days of elapsed hydration at 20˚C in saturated conditions. Each paste specimen had a cylindrical shape (with a diameter of ~1 mm) and was screened at a designated time (as specified in the file name, e.g. “t23hrs”=23 hours of elapsed hydration) and finally saved as an uncompressed and unprocessed *.tif greyscale image data file in 16-bit image depth (as unsigned integers) using a little-endian byte sequence.</p> <p>The dataset contains two sets of images:</p> <p>- “full-sized” digital images stored in a three-dimensional voxel-based matrix with a fixed size of 1100x1100x1100 voxels, denoted as “CEM_I_Ladce_*” in the file name; each file size amounts to ~2.5 GB and contains the whole screened specimen with a variable voxel size in the range 1.0913 − 1.1174 µm depending on the particular specimen (as specified in the file name, e.g. “1d1174um”=1.1174 µm/voxel)</p> <p>- smaller image subvolumes, denoted as Region Of Interest (ROI), extracted from the interior of the full-sized specimen from an arbitrary location, and denoted as “filteredROI_*” in the file name; this cropped ROI has a cubic shape and stores a three-dimensional voxel-based matrix with a fixed size of 500x500x500 µm<sup>3</sup> constituted by a variable voxel count (given the fluctuating voxel size for each specimen, see above). Both the exact voxel count (i.e. three-dimensional matrix dimensions) and voxel size are further specified in each file name. A sequence of imaging filters was sequentially applied to this ROI to further enhance the contrast among the different microstructural phases, see <a href="https://doi.org/10.1016/j.cemconcomp.2022.104798">10.1016/j.cemconcomp.2022.104798</a> for details.</p> <p>Note that the same dataset stored in *raw format is available from <a href="https://doi.org/10.5281/zenodo.7193798">https://doi.org/10.5281/zenodo.7193798</a></p>
Three-dimensional anisotropic phase velocity and S-wave velocity models in Northeastern Tibet
<p>This supplementary material contains three parts.</p> <p>The file named "COR_XM2_all.tar.gz" contains the ambient noise cross-correlation waveforms of the Rayleigh wave.</p> <p>The file named "phase_velocity.rar" contains the anisotropic phase velocity using double beamforming tomography (DBF) method.</p> <p>The file named "Anisotropic_Vs_model.rar" contains the anisotropic S-wave velocity models.</p>
Anticipating fluctuations of bigeye tuna in the Pacific Ocean from three-dimensional ocean biogeochemistry
<p>1. Subseasonal to decadal ocean forecasting can make significant contributions to achieving effective management of living marine resources in a changing ocean. Most applications rely on indirect proxies, however, often measured at the ocean surface and lacking a direct mechanistic link to the dynamics of marine populations.</p> <p>2. Here we take advantage of three-dimensional, dynamical reconstructions and forecasts of ocean biogeochemistry based on a global Earth System Model to hindcast and assess the capacity to anticipate fluctuations in the dynamics of bigeye tuna (<em>Thunnus obesus</em> Lowe) in the Pacific Ocean during the last six decades. We reconstructed spatial patterns in catch per unit effort (CPUE) through the combination of physiological indices capturing both habitat preferences and physiological tolerance limits in bigeye tuna.</p> <p>3. Our analyses revealed a sequence of four distinct regimes characterized by changes in the zonal distribution and average CPUE of bigeye tuna in the Pacific Ocean. Habitat models accounting for basin-wide fluctuations in the thermal structure and oxygen concentration throughout the water column captured interannual fluctuations in CPUE and regime switches that models based solely on surface information were unable to reproduce. Decade-long forecast experiments further suggested that forecasts of three-dimensional biogeochemical information might enable anticipation of fluctuations in bigeye tuna several years ahead.</p> <p>4. <em>Synthesis and applications.</em> Together, our results reveal the impact of variability of biogeochemical conditions in the ocean interior on the dynamics of bigeye tuna in the Pacific Ocean, raising concerns about the future impact of ocean warming and deoxygenation. The results also lend support to incorporating subsurface biogeochemical information into ecological forecasts to implement efficient dynamic management strategies and promote the sustainable use of marine living resources.</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.