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1,880 results for “Dimensionality”
Simulation data from the three-dimensional multifluid MHD model of Najib et al. (2011)
<p>Simulation data from the three-dimensional multifluid MHD model of Najib et al. (2011).</p> <p>We use the data to study the ion escape at Mars.</p> <p> </p>
Direct measurement of Tan's contact in a one-dimensional Lieb-Liniger gas
Open the record for dataset details and reuse information.
Upgrading and extending the lifecycle of soft robots by in-situ freeform liquid three-dimensional printing
<p>The data is associated with the research article titled '<em>Upgrading and extending the lifecycle of soft robots by in-situ freeform liquid three-dimensional printing</em>', published in <em>Science Robotics</em> journal.</p>
[Dataset] Intensity and Dimensionality-Dependent Dynamics of Laser-Proton Acceleration in 1D, 2D, and 3D Particle-in-Cell Simulations
<p>The included Jupyter Notebooks (.ipynb) were used to generate figures and make calculations for the work. The resulting image files are saved in Figure_Outputs.tar.gz. The other archives (1D.tar.gz, 2DS.tar.gz, 2DP.tar.gz, 3D.tar.gz, laser_focus.tar.gz, and EField.tar.gz) are compressed directories containing EPOCH PIC simulation outputs for the paper (relevant directories should be extracted to replicate analysis with provided files). </p> <p>Since the last version, a new figure (Figure 6, an ion phase space) has been added. Notebooks for later figures have been renumbered and the figure outputs have been updated. </p>
Data for "Boson-fermion pairing and condensation in two-dimensional Bose-Fermi mixtures"
<p>Data and Python scripts for reproducing figures of: Leonardo Pisani, Pietro Bovini, Fabrizio Pavan, and Pierbiagio Pieri, "Boson-fermion pairing and condensation in two-dimensional Bose-Fermi mixtures" (2024). <br><br>Arxiv: </p> <table> <tbody> <tr> <td><span><a href="https://arxiv.org/abs/2405.05029">arXiv:2405.05029</a></span></td> </tr> </tbody> </table> <p> </p>
Data for the publication "Predicting the three-dimensional stratigraphy of an ice rise".
<p><span>The input and output data for the simulations presented in "Predicting the three-dimensional stratigraphy of an ice rise" are organised in this repository as follows:</span></p> <p> </p> <p><span>Model Data:</span></p> <p> </p> <p><span>- Remesh: The code necessary for remeshing the domain for a higher resolution in the area encompassing the ice rise. </span></p> <p><span>- Init: Initialisation of the model.</span></p> <p><span>- Glen_n3: The code for running simulations with a Glen's flow law exponent of n=3 </span></p> <p><span>- Glen_n4: The code for running simulations with a Glen's flow law exponent of n=4 </span></p> <p><span>- SensitivitySimulations: The code for running further sensitivity simulations</span></p> <p> </p> <p><span>Figure Data:</span></p> <p> </p> <p><span>- The data used to produce each figure.</span></p> <p> </p> <p><span>Data Pre-Processing:</span></p> <p> </p> <p><span>- The data used in the pre-processing process. </span></p>
Four-dimensional phase space tomography from one-dimensional measurements of a hadron beam
<p>Data and analysis for "Four-dimensional phase space tomography from one-dimensional measurements of a hadron beam", published in Physical Review Accelerators and Beams.</p>
Code for "On the interplay of electronic and lattice screening on exciton binding in two-dimensional lead halide perovskites"
<p>The code and Adobe Illustrator file used to generate the figures for "On the interplay of electronic and lattice screening on exciton binding in two-dimensional lead halide perovskites" is provided. The renormalized exciton binding energies are stored in arrays in the Python script, and the P(r)'s are stored in the "bta" and "pea" folders. The source code for the path integral Monte Carlo calculations is provided in Github. </p>
Three-Dimensional Segmentation Assisted with Clustering Analysis for Surface and Volume Measurements of Equine Incisor in Multidetector Computed Tomography Data Sets
<p>The dataset contains computed tomography (CT) images of head horses with annotations of 12 segments corresponding to areas with teeth. Imaged animals: 49 horses. Measured animal features such as surface area, and volume are included. The study was supported by the National Science Centre, Poland as a part of the project<br>Miniatura 6 No 2022/06/X/ST6/00431. </p>
Dataset of preprint: Effects of dimensionality on the performance of hydrodynamic models
<p>The dataset supports the findings of the preprint entitled: Effects of dimensionality on the performance of hydrodynamic models. The manuscript presents a comparison of three models of distinct dimensionalities applied for the same reservoir. Therefore, the dataset is compound of:</p> <ul> <li>Boundary conditions for the hydrodynamic models (meteorological conditions, inflow discharges and water temperature, and outflow discharges), which were obtained through measurements and simulations (inflow discharges and water temperature).</li> <li>In situ measurements of temperature and flow velocities were used as a reference to evaluate the performance of each model.</li> <li>Results of Lake Analyzer (a tool to calculate several indexes commonly used for lake physical states), estimated with measurements and model results.</li> <li>Models set up and results.</li> <li>Relevant scripts used to perform the analysis, in MATLAB.</li> </ul> <p>Their respective sources are mentioned over the manuscript.</p>
Figure 4. Gyrostigma rhinocerontis, first instar larva. A in Three-dimensional characterization of first instar horse and rhinoceros stomach bot fly larvae (Oestridae: Gasterophilinae: Gasterophilus, Gyrostigma): novel morphology and evolutionary implications
Figure 4. Gyrostigma rhinocerontis, first instar larva. A, habitus, ventral view. B, pseudocephalon and thoracic segments, ventral view. C, magnification of pseudocephalon and thoracic segments in ventral view, with depth coding. D, anal division, ventral view. E, pseudocephalon and thoracic segments, right lateral view. Scale bars: A, D = 100 µm; B–C, E = 50 µm. Abbreviations: aI–aVII, abdominal segments I–VII; adI–adIII, subdivisions I, II and III of anal division; is, intermediate sclerite; lb, labrum; mh, mouthhook; pb, parastomal bar; pc, pseudocephalon; tI–tIII, thoracic segments I–III.
Figure 3 in Three-dimensional characterization of first instar horse and rhinoceros stomach bot fly larvae (Oestridae: Gasterophilinae: Gasterophilus, Gyrostigma): novel morphology and evolutionary implications
Figure 3. Gasterophilus pecorum, first instar larva, ultrastructural details on thoracic (A–F) and abdominal (G) segments and anal division (H–M). A, anterior end, dorsal view. B, trichoid sensillum I and II, and a pit. C, a trichoid sensillum I and a pit. D, Keilin's organ. E, a trichoid sensillum I and Keilin's organ. F, body spines on third thoracic segment. G, body spines on first abdominal segment. H, arrangement of coeloconic and trichoid sensilla on dorsal (top) and ventral (bottom) surface of anal subdivision II. I, a coeloconic sensillum IV on anal subdivision II. J, a trichoid sensillum I and a coeloconic sensillum IV on anal sudivision II. K, posterior spiracles, posterior view. L, left posterior spiracle. M, serrated margins of a posterior spiracular slit, with the the pores shown in the box. Scale bars: A = 20 µm; B, D, J = 2.5 µm; C, M = 2 µm, 0.5 µm in the box; E, L = 5 µm; F–G = 25 µm; H = 15 µm; I–J = 1.5 µm; K = 30 µm. Abbreviations: adII, subdivision II of anal division; Ko, Keilin's organ; scIV, sensillum coeloconica IV; p, pit; TrI–II, trichoid sensillum I–II.
Figure 1. Gasterophilus pecorum, first instar larva. A in Three-dimensional characterization of first instar horse and rhinoceros stomach bot fly larvae (Oestridae: Gasterophilinae: Gasterophilus, Gyrostigma): novel morphology and evolutionary implications
Figure 1. Gasterophilus pecorum, first instar larva. A, habitus, ventral view. B, pseudocephalon and thoracic segments, dorsal view. C, E, pseudocephalon and thoracic segments, ventral view, with cephaloskeleton emphasized in E. D, F, pseudocephalon and thoracic segments, right lateral view, with depth coding; mouthhooks and labrum exposed in F. G, anal division, dorsal view. Scale bars: A = 100 µm; B–G = 50 µm. Abbreviations: aI–aVII, abdominal segments I–VII; adI– adIII, subdivisions I, II and III of anal division; dc, dorsal cornua; is, intermediate sclerite; lb, labrum; mh, mouthhook; pb, parastomal bar; pc, pseudocephalon; tI–tIII, thoracic segments I–III.
Figure 5 in Three-dimensional characterization of first instar horse and rhinoceros stomach bot fly larvae (Oestridae: Gasterophilinae: Gasterophilus, Gyrostigma): novel morphology and evolutionary implications
Figure 5. Gyrostigma rhinocerontis, first instar larva, ultrastructural details. A, habitus, ventral view. B, anterior end, ventral view. C, pseudocephalon, ventral view. D, pseudocephalon, anterior view. E, antennomaxillary sensory complex. F, mouthhooks, labrum and arrangement of the denticles. G, three thoracic segments, ventral view, showing arrangement of Keilin's organs and trichoid sensilla I. H, abdominal segments, ventral view. Scale bars: A = 0.3 mm; B = 68 µm; C, G–H = 20 µm; D = 19 µm; E–F = 10 µm. Abbreviations: aI–aVII, abdominal segments I–VII; adI–adIII, subdivisions I, II and III of anal division; and, antennal dome; ap, additional pit; asI–III, additional sensillum coeloconicum I–III; den, denticles; Ko, Keilin's organ; mp, maxillary palp; pc, pseudocephalon; sbI–II, sensilla basiconica I–II; scI–IV, sensilla coeloconica I–IV; tI–tIII, thoracic segments I–III; TrI, trichoid sensillum I.
Figure 7 in Three-dimensional characterization of first instar horse and rhinoceros stomach bot fly larvae (Oestridae: Gasterophilinae: Gasterophilus, Gyrostigma): novel morphology and evolutionary implications
Figure 7. Ancestral state reconstruction of the shape of first instar mouthhooks in Oestridae based on parsimony. Cladogram used for reconstruction follows Pape (2001, 2006).
Figure 6 in Three-dimensional characterization of first instar horse and rhinoceros stomach bot fly larvae (Oestridae: Gasterophilinae: Gasterophilus, Gyrostigma): novel morphology and evolutionary implications
Figure 6. Gyrostigma rhinocerontis, first instar larva, ultrastructural details of thoracic segments (A–C) and anal division (D–H). A, a trichoid sensillum I. B, a pair of Keilin's organs. C, a trichoid sensillum I and a Keilin's organ. D, posterior spiracles, dorsolateral view. E, coeloconic sensilla IV and a trichoid sensillum I on ventral side of anal subdivision II. F, posterior spiracles, ventral view. G, posterior spiracles, dorsal view. H, a coeloconic sensillum IV and a trichoid sensillum I. I, a coeloconic sensillum IV. Scale bars: A = 2 µm; B–C = 10 µm; D, G = 75 µm; E–F = 10 µm; H = 2.5 µm; I = 1 µm. Abbreviations: adI–adII, subdivisions I and II of anal division; Ko, Keilin's organ; scIV, sensilla coeloconica IV; TrI, trichoid sensillum I.
Dataset for Learning in Continuous Action Space for Developing High Dimensional Potential Energy Models
<p>The NN potentials developed in this study and the other available MLIP methods such as GAP, SNAP, qSNAP, and MEGNET used for benchmarking.</p>
Three dimensional microscale characterization of off-axis tunnelling cracks in non-crimp fabric based composites
<p>Video and x-ray data-sets behind the publications</p> <p>Bangaru, A.K., Mikkelsen, L.P. and, Sørensen, B.F. Three dimensional microscale characterization of off-axis tunnelling cracks in non-crimp fabric based composites, <em>Composites Science and Technology</em>, <strong>226</strong>, 109502, <a href="https://doi.org/10.1016/j.compscitech.2022.109502">https://doi.org/10.1016/j.compscitech.2022.109502</a>, 2022</p> <p>Videos: </p> <ul> <li>Figure 8: <a href="https://zenodo.org/api/files/cb486bb8-8183-4b67-bb03-4ed5e48d6d31/Fig08_CrackBranching_v1.mpg">Fig08_CrackBranching_v1.mpg</a>, <a href="https://youtu.be/rrp8Ax1er5I">Youtube-link</a></li> <li>Figure 10a: <a href="https://zenodo.org/api/files/cb486bb8-8183-4b67-bb03-4ed5e48d6d31/Fig10_a_MatrixPenetration.mpg">Fig10_a_MatrixPenetration.mpg</a>, <a href="https://video.dtu.dk/media/Fig10_a_MatrixPenetration.mpg/0_0ypbfkex">Video-link</a></li> <li>Figure 10b: <a href="https://zenodo.org/api/files/cb486bb8-8183-4b67-bb03-4ed5e48d6d31/Fig10_b_MatrixPenetration_HighResolution.mpg">Fig10_b_MatrixPenetration_HighResolution.mpg</a>, <a href="https://video.dtu.dk/media/Fig10_b_MatrixPenetration_HighResolution/0_1bexfv16">Video-link</a></li> <li>Figure 10c: <a href="https://zenodo.org/api/files/cb486bb8-8183-4b67-bb03-4ed5e48d6d31/Fig10_c_CrackTwisting_HighResolution.mpg">Fig10_c_CrackTwisting_HighResolution.mpg</a>, <a href="https://video.dtu.dk/media/Fig10_c_CrackTwisting_HighResolution/0_yphlldlh">Video-link</a></li> <li>Figure 14: <a href="https://zenodo.org/api/files/cb486bb8-8183-4b67-bb03-4ed5e48d6d31/Fig14_CrackPenetration_HighResolution.mpg">Fig14_CrackPenetration_HighResolution.mpg</a>, <a href="https://youtu.be/MO0VBdyYlAw">Youtube-link</a></li> <li>Figure 15: <a href="https://zenodo.org/api/files/cb486bb8-8183-4b67-bb03-4ed5e48d6d31/Fig15_CrackDeflection_HighResolution.mpg">Fig15_CrackDeflection_HighResolution.mpg</a>, <a href="https://video.dtu.dk/media/Fig15_CrackDeflection_HighResolution/0_cqw7xgyg">Video-link</a></li> </ul> <p>Scan data:</p> <ul> <li>Fig08_SpecimenS1_DataSet.txm: Data behind figure 8</li> <li>Fig10_a_SpecimenS2_DataSet_LowResolution_Stitch22_FoV6.5.txm: Data behind figure 10a</li> <li>Fig10_b_c_SpecimenS2_DataSet_HighResolution_FoV1.5.txm: Data behind figures 10b, 10c, 11, and 13</li> <li>Fig14_SpecimenS2_DataSet_HighResolution_FoV1.5.txm: Data behind figure 14</li> <li>Fig15_SpecimenS3_DataSet_HIghResolution_FoV1.5.txm: Data behind figure 15</li> </ul> <p> </p> <p> </p>
Three‐dimensional soil heterogeneity modulates responses of grassland mesocosms to an experimentally imposed drought extreme
<p>Heterogeneity is an intrinsic characteristic of soils, which regulates plant diversity and ecosystem functioning. However, whether soil heterogeneity also modulates responses of plant communities to climate change, including climate extremes, remains largely an open question. Here, we explore responses of plant communities to drought extremes across four levels of spatial soil heterogeneity, with cell sizes varying from very small to very large, i.e. 0, 12, 24 and 48 cm. These were created in mesocosms by alternating nutrient-rich and nutrient-poor substrate in three dimensions. A seed mixture of 24 grassland species was evenly sown on each mesocosm in spring. In late summer, a three-week drought was imposed with a rainout shelter. During the drought, soil water content at the mesocosm scale decreased more at intermediate (12 and 24 cm) than at small or large (0 and 48 cm) cell sizes, which was reflected in increased senescence and drought-induced heat stress. These responses could be traced to greater plant biomass coupled with higher water demand at those intermediate cell sizes, likely related to between-cell access to nutrients and effects of diversity and community composition. Our results indicate that soil heterogeneity can modulate the impact of drought extremes on plant communities, though more research is needed on the transition between intermediate and extreme cell sizes, where heterogeneity effects seem to change most. We propose that soil heterogeneity be considered more explicitly in studies of changing precipitation regimes.</p>
A 3-dimensional animation of a Kompolti root section revealing the aerenchyma 9-days post waterlogging.
<p>A three-dimensional animation of a root section from cultivar ‘Kompolti’ revealing the aerenchyma 9 d post-waterlogging. Aerenchyma are shown in blue and the whole root section in white. The images were obtained by X-ray Computed Tomography scanning of the root section. The resolution was 11 µm, and the animation was created using the program Volume Graphics Studio Max® 3.01.</p>
ScienceDex guides
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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.