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204 results for “shape model”
Training and test data, plus saved models for the upcoming paper `Top-down perceptual inference shaping the activity of early visual cortex'
<p>Each .pkl file contains a training or test dataset in the form of a Python dictionary (generated with Python 3.8.5) with the following fields:</p><ul><li>'train_images': 640,000 float32 images used for model training. These are 40px images that contain 1600 pixel intensities each.</li><li>'train_labels': float32 labels for each image in 'train_images'. All natural images are labeled with 0.0. Texture images are labeled with 0.0, 1,0, 2.0, 3.0, or 4.0, according to their texture family.</li><li>'test_images': 64,000 float32 images used for model testing. These are 40px images that contain 1600 pixel intensities each.</li><li>'test_labels': float32 labels for each image in 'test_images'. All natural images are labeled with 0.0. Texture images are labeled with 0.0, 1,0, 2.0, 3.0, or 4.0, according to their texture family.</li></ul><p>The .zip file contains a saved model snapshot and various intermediate evaluative data. Details on these are coming soon.</p>
Spherical harmonic models of the shape of (433) Eros
<p>This archive contains two spherical harmonic models of the shape of asteroid 433 Eros. One model is based on laser altimetry from the instrument NLR and the other is based on a stereo photoclinometry shape.</p> <p>The data used to generate the model based on laser altimetery were taken from the file <code>nlr125ar.img</code> on <a href="https://sbnarchive.psi.edu/pds3/near/NEAR_A_NLR_6_EROS_MAPS_MODELS_V1_0/data/img/">NASA's PDS website</a>. This image file was first converted to netcdf format using the <a href="https://www.generic-mapping-tools.org/">generic-mapping-tools</a> function <code>xyz2grd</code>, and the resulting pixel registed map was then converted to a gridline registration using the function <code>grdsample</code>. Following this, the resulting netcdf file was read into the <a href="https://shtools.github.io/SHTOOLS/index.html">pyshtools</a> software and expanded into spherical harmonics using the function <code>SHCoeffs.expand()</code>. The spherical harmonic functions were chosen to be "4pi" normalized and to exclude the Condon-Shortley phase factor of (-1)<sup>m</sup>. The units of the coefficients are meters.</p> <p>The data used to generate the model based on the stereo photoclinometric shape model were taken from the file <code>quad512q.tab</code> on <a href="https://sbnarchive.psi.edu/pds4/non_mission/gaskell.ast-eros.shape-model_V1_1/data/quad/">NASA's PDS website</a>. The vertices from the ICQ shape model with Q=512 were first converted from Cartesian to spherical coordinates, from which a regular gridline registered netcdf file was created using the <a href="https://www.generic-mapping-tools.org/">generic-mapping-tools</a> function <code>surface</code> with a tension of 0.6 and with a grid spacing of 0.17578125 degrees. This file was then read into the <a href="https://shtools.github.io/SHTOOLS/index.html">pyshtools</a> software and expanded into spherical harmonics in the same manner as the NLA based model.</p> <p>The two files in this archive are</p> <ul> <li>Eros_NLR_shape_719.bshc.gz</li> <li>Eros_SPC_shape_511.bshc.gz</li> </ul> <p>The numbers 719 and 511 in the filename refer to the maximum spherical harmonic degree of file, which corresponds to effective spatial resolutions of 8 and ~5.7 pixels per degree, respectively. The files are stored in the binary "bshc" format as described in the pyshtools documentation and are furthermore compressed using gzip.</p>
FIGURE 2 in Achieving kinematic identity across shape diversity in musculoskeletal modeling
FIGURE 2. The top images show an overlay of the reduced-asymmetry australopithecine pelvis (beige) with the ADL australopithecine pelvis (dark green). The bottom color-coded distance map pelvis show the distance between the reduced-asymmetry australopithecine pelvis with the ADL australopithecine pelvis.
FIGURE 4 in Achieving kinematic identity across shape diversity in musculoskeletal modeling
FIGURE 4. AnyBody australopithecine musculoskeletal model without (left) and with (right) muscle model visualization.
FIGURE 1 in Achieving kinematic identity across shape diversity in musculoskeletal modeling
FIGURE 1. The flowchart shows the major steps required to build the ADL australopithecine model. In the blue boxes, the ADL human model is driven with the Schreiber and Moissenet (2019) human locomotion data. From these ADL human simulations, the dimension of the pelvis and femur can be extracted as well as model motion profiles used at later stages of the process (Figure 5). The gray boxes show the major steps in transforming (TPS-based morphing) the ADL human pelvis to match the australopithecine morphology (A.L. 288-1 reduced-asymmetry pelvis; Australopithecus afarensis), thus creating the ADL australopithecine pelvis. The green boxes show the steps necessary to create the ADL australopithecine (hybrid) femur from the ADL human femur.
FIGURE 5 in Achieving kinematic identity across shape diversity in musculoskeletal modeling
FIGURE 5. This flowchart shows the major steps required to generate the C3D motion file to drive the walking simulations with an australopithecine hip. Blue, light blue, and blue/grey and blue/green dashed boxes are the same boxes from Figure 1. The original ADL human model (blue box) is morphed based on the australopithecine pelvis (blue/grey dashed box) and femur (blue/green dashed box) to create the ADL australopithecine model (orange box). The results from the human walking simulation (blue box) are combined with the L5-sacral offset translation (light blue box) to generate new "experimental marker data" that are combined with the original ground reaction force data from Schreiber and Moissenet (2019) (purple box). The ADL australopithecine model and new motion data are then used to drive the simulations of walking with an australopithecine hip.
FIGURE 6 in Achieving kinematic identity across shape diversity in musculoskeletal modeling
FIGURE 6. Motion of the pelvis and lower limb joints for one individual walking simulation with both human (red lines) and australopithecine (black circles) shaped hips. A. Pelvic rotation (transverse plane), tilt (sagittal plane) and drop (coronal plane). B. Hip flexion-extension, abduction-adduction, and internal-external rotation. C. Knee flexion-extension, ankle dorsi-plantar flexion, subtalar eversion-inversion.
Scripts and data for: Integrating different facets of diversity into food web models: how adaptation among and within functional groups shape ecosystem functioning
<p>Adaptation of communities to environmental fluctuations can emerge from different facets of biodiversity, which may impact ecosystem functioning differently. Previous work examined how ecosystem functions can be influenced by two sources of adaptive potential: sorting (i.e., changes in community composition due to fitness differences) can occur when multiple species or groups are present (richness), and trait adaptability (i.e., trait adjustments within species or functional groups) can emerge from genetic or phenotypic diversity. However, their effect is typically studied separately, and often in the context of only one trophic level. Therefore, we used a bitrophic trait-based model varying in richness and in the presence of trait adaptability at each trophic level, to investigate how sorting and trait adaptability, at one or two trophic levels, separately or jointly shape ecosystem functions. We found that the adaptive potential emerging from any facet of diversity-induced changes in trophic interactions, in turn, affects biomass distributions within and across trophic levels, dynamical behaviour, and synchrony of biomass dynamics within a trophic level. Particularly, sorting and trait adaptability could contribute to a similar degree and at a similar time to temporal changes in ecosystem functions, but their respective contribution depended on the speed of trait adaptation, the trait range between similar functional groups, and trophic interactions. We thus suggest to consider multiple facets of diversity and their corresponding sources of adaptive potential to deepen our mechanistic understanding of ecosystem functioning, especially in a context of rapid biodiversity change.</p>
Finite strain continuum phenomenological model describing the shape-memory effects in multi-phase semi-crystalline networks
<p>This dataset comes from the following paper:</p> <p>Matteo Arricca, Nicoletta Inverardi, Stefano Pandini, Maurizio Toselli, Massimo Messori, Giulia Scalet, Finite strain continuum phenomenological model describing the shape-memory effects in multi-phase semi-crystalline networks, Journal of the Mechanics and Physics of Solids, 105955, 2024. <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.jmps.2024.105955" target="_blank" rel="noopener"><span><span>https://doi.org/10.1016/j.jmps.2024.105955</span></span></a></p> <p>It contains:</p> <ul> <li>"Notes.pdf" describing all the files uploaded</li> <li>. txt experimental data</li> </ul>
Spherical harmonic model of the shape of Mars: MarsTopo719
<p><strong><em>THIS MODEL IS SUPERSEDED BY </em><a href="../records/10794059"><em>Spherical harmonic models of the shape of Mars</em></a></strong></p> <p> </p> <p><strong>MarsTopo719.shape</strong> is a spherical harmonic model of the shape of the planet Mars. This model makes use of 4-pi normalized spherical harmonic functions that exclude the Condon-Shortley phase factor of (-1)<sup>m</sup>. The description of how this spherical harmonic model was constructed can be found in Wieczorek (2015). <strong>MarsTopo719.shape</strong> is a truncated version of <strong>MarsTopo2600.shape</strong>.</p>
Data from: The shape of aroma: Measuring and modeling citrus oil gland distribution
<p>From preventing scurvy to being part of religious rituals, citrus are intrinsically connected to human health and perception. From tiny mandarins to head-sized pummelos, citrus capability of hybridization provides a vastly diverse array of fruit sizes and shapes, which in turn corresponds to a diversity of flavors and aromas. These sensory qualities are tightly linked to oil glands in the citrus skin. The oil glands are also key to understanding fruit development, and the essential oils contained by them are fundamental in the food and perfume industries. We study the shape of citrus based on 3D X-ray CT scan reconstruction of 163 different citrus samples comprising 58 different species and cultivars, including samples of all fundamental citrus species. First, using the power of X-rays and image processing, we are able to compare and contrast size ratios between different tissues, such as the size of the skin compared to the rind or the flesh. Second, we model the fruit shape as an ellipsoidal surface, and later we study and infer possible oil gland distributions on this surface using principles of directional statistics. We finally compare and contrast these overall fruit shape models along their gland distributions across different citrus species. This morphological modeling will allow us later to link genotype with phenotype, furthering our insight on how the physical shape is genetically specified in DNA.</p>
Data supporting 'Controls on Greenland moulin geometry and evolution from the Moulin Shape model'
<p>The data included here are MouSh model results generated as part of the following publication: Andrews, L. C., Poinar., K, Trunz, C. (2022). Controls on Greenland moulin geometry and evolution from the Moulin Shape model. The Cryosphere. </p> <p>Nearly all meltwater from glaciers and ice sheets is routed englacially through moulins. Therefore, the geometry and evolution of moulins has the potential to influence subglacial water pressure variations, ice motion, and the runoff hydrograph delivered to the ocean. We develop the Moulin Shape (MouSh) model, a time-evolving model of moulin geometry. MouSh models ice deformation around a moulin using both viscous and elastic rheologies and melting within the moulin through heat dissipation from turbulent water flow, both above and below the water line. We force MouSh with idealized and realistic surface melt inputs. Our results show that variations in surface melt change the geometry of a moulin by approximately 10% daily and over 100% seasonally. These size variations cause observable differences in moulin water storage capacity and moulin water levels compared to a static, cylindrical moulin. Our results suggest that moulins are storage reservoirs for meltwater, with storage capacity and water levels varying over multiple timescales. Representing moulin geometry within subglacial hydrologic models may therefore improve the representation of subglacial pressures, especially over seasonal periods or in regions where overburden pressures are high.</p> <p>These data are also accessible at: https://ubir.buffalo.edu/xmlui/handle/10477/82587</p> <p>The development of the MouSh model was funded by NASA Cryosphere grant 80NSSC19K0054 to Lauren Andrews and Kristin Poinar.</p>
Spherical harmonic models of the gravitational field and shape of (101955) Bennu
<p>Provided are (i) three types of spherical harmonic models of the gravitational field of Bennu, (ii) reference gravitational data and (iii) a spherical harmonic model of Bennu's shape. The gravitational field models and the reference data were obtained by gravity forward modelling using a degree-15 spherical harmonic expansion of Bennu's shape and a constant mass density.</p> <p>The datasets have been published in Bucha, B., Sanso, F., 2021. <em>Gravitational field modelling near irregularly shaped bodies using spherical harmonics: a case study for the asteroid (101955) Bennu</em>. Journal of Geodesy, 95, 56, <a href="https://link.springer.com/article/10.1007/s00190-021-01493-w">https://link.springer.com/article/10.1007/s00190-021-01493-w</a></p>
BRAIN Journal-Artificial Neuron Modelling Based on Wave Shape-Figure 1. Typical feedforward artificial neural network processing unit
<p>In Figure 1, each input X is weighted by a separate weight value w. These are then summed together to give a total input value for the neuron. This total can then be passed through a function, to transform it into the desired output value. This is then compared to the actual output value d; where the error or differences between the two sets is measured and used to correct the weight<br> values, to bring the two sets of values closer. One way to update the weights is after each individual pattern is presented and processed. Another option is to process the error after the presentation of the whole dataset, as part of a batch update.</p>
BRAIN Journal-Artificial Neuron Modelling Based on Wave Shape-Figure 2. New neuronal model, based on matching a wave-like representation of the output.
<p>For the new model, shown in Figure 2, the ‘differences’ between the outputs is measured and combined, to produce a kind of wave-like shape. If output point 1 has a value of 10, and output point 2 has a value of 5, for example, then this results in the shape moving down 5 points on the wave shape. This shape will, of course, change depending on the order that the dataset values are presented in. Also, if new data is presented, then that will produce a different shape. However, there is still an association between the input values and the output values and it is still this relation that is being learnt. The neural network needs to be able to learn a function that can generalise over the presented datasets, so that it can recognise the relation in previously unseen data as well. Generalising over trying to learn a wave-like shape, or trying to match the output values exactly looks quite similar, suggesting that the process is at least valid. In Figure 2, note that a transposition to move the learned shape up or down first is possible, before a weight value would try to scale it. If the initial match of the combined inputs can be as close as possible, then these adjustments will become less and will be more for fine tuning.</p>
Digital Elevation Models from Planetary Flyby Images of Mercury and the Moon with Shape and Albedo from Shading
<p>Supplemantary material to Krüll, I., Wohlfarth, K., Tenthoff, M., Wöhler, C., Galluzzi, V., Wright, J., Benkhoff, J., and Zender, J.: Shape and Albedo from Shading with Planetary Flyby Images of Mercury and the Moon, Europlanet Science Congress 2024, Berlin, Germany, 8–13 Sep 2024, EPSC2024-247, https://doi.org/10.5194/epsc2024-247, 2024.</p> <p><strong>Abstract</strong></p> <p>Surface reconstruction of planetary bodies such as the Moon and Mercury is crucial for geomorphological analysis, reflectance normalization, thermal modeling, rover landing site planning, and outreach activities. Stereo algorithms and Shape-and-Albedo-from-Shading (SAfS) are well-established methods for planetary 3D reconstruction. SAfS refines the surface slopes of a stereo Digital Elevation Model (DEM) and typically yields 3D models at image resolution. This approach is well-validated for scientifically calibrated instruments that observe the planetary body under favorable conditions. This work applied the SAfS algorithm to more challenging planetary flyby images acquired with uncalibrated off-the-shelf cameras. We investigated three scenarios: a fly-by image of the Moon captured by a GoPro during the Artemis I mission, a fly-by image of Mercury which was obtained with a monitoring camera during BepiColombo’s third flyby, and a telescope image taken in Wetter, Germany. We qualitatively and quantitatively assessed the algorithm's performance. The results of the two flyby images indicate that, despite the challenging conditions, the SAfS algorithm could reconstruct the surface up to image resolution and increase the level of detail of the input DEM. The reconstructed DEM of the telescope image is the one with the lowest resolution. All in all, our flyby-derived DEMs are accurate. They provide excellent outreach products, as demonstrated by ESA's BepiColombo flyby movie: https://www.esa.int/Science_Exploration/Space_Science/BepiColombo/BepiColombo_s_third_Mercury_flyby_the_movie</p> <p><strong>Dataset<br></strong></p> <p>We applied the SAfS algorithm to different Regions of Interest (ROIs) in the flyby and telescope images. The ROIs are marked in Artemis_Flyby_ROIs.png, Bepicolombo_Flyby3_ROIs.png and Moon_Telescope_ROIs.png, respectively. For each ROI a DEM is provided centered on the latitude and longitude (0-360, positive east) in the filename. Furthermore a Red/ Blue Stereo anaglyph of the original image was created with the SAfS DEM (for this purpose the height has been exaggerated).</p> <p> </p>
Data for: Determining Young's modulus of arbitrarily-shaped granite samples using accurate grain-based modelling with Micro-RME
<p>HaH 346 meteorite samples with shock melt veins are tested using the nanoindentation experiment with Berkovich indenter. The data includes Young's modulus of different rock-forming minerals measured by nanoindentation test and Raman spectrum data for jadeite and wadsleyite in HaH346 meteorite. </p>
Ecological signal in the size and shape of marine amniote teeth – 3D models and landmarks
<p class="MsoNormal"><span>Amniotes have been a major component of marine trophic chains from the beginning of the Triassic to present day, with hundreds of species. However, inferences of their (palaeo)ecology have mostly been qualitative, making it difficult to track how dietary niches have changed through time and across clades. Here, we tackle this issue by applying a novel geometric morphometric protocol to 3D models of tooth crowns across a wide range of raptorial marine amniotes. Our </span><span>results highlight the phenomenon of dental simplification and widespread convergence in marine amniotes, implying strong functional constraints which limit the range of tooth crown morphologies. </span><span>Importantly, we quantitatively demonstrate that tooth </span><span>crown form (shape plus size) is strongly associated with diet, whereas crown surface complexity is not. The maximal range of tooth shapes in both mammals and reptiles is seen in medium-sized taxa; large crowns are simple and restricted to a fraction of the morphospace. </span><span>We recognise four principle raptorial guilds within toothed marine amniotes (durophages, generalists, meat cutters, and flesh piercers). Moreover, even though all these feeding guilds have been convergently colonised over the last 200 million years, a series of dental morphologies are unique to the Mesozoic period, probably reflecting a distinct ecosystem structure.</span></p>
Whole genome demographic models indicate divergent effective population size histories shape contemporary genetic diversity gradients in a montane bumble bee
<p>Understanding historical range shifts and population size variation provides important context for interpreting contemporary genetic diversity. Methods to predict changes in species distributions and model changes in effective population size (N<sub>e</sub>) using whole genomes make it feasible to examine how temporal dynamics influence diversity across populations. We investigate N<sub>e</sub> variation and climate-associated range shifts to examine the origins of a previously observed latitudinal heterozygosity gradient in the bumble bee <em>Bombus</em> <em>vancouverensis</em> Cresson (Hymenoptera: Apidae: <em>Bombus</em> Latreille) in western North America. We analyze whole genomes from a latitude-elevation cline using sequentially Markovian coalescent models of N<sub>e</sub> through time to test whether relatively low diversity in southern high-elevation populations is a result of long-term differences in N<sub>e</sub>. We use Maxent models of the species range over the last 130,000 years to evaluate range shifts and stability. N<sub>e</sub> fluctuates with climate across populations, but more genetically diverse northern populations have maintained greater Ne over the late Pleistocene and experienced larger expansions with climatically favorable time periods. Northern populations also experienced larger bottlenecks during the last glacial period which matched the loss of range area near these sites, however, bottlenecks were not sufficient to erode diversity maintained during periods of large N<sub>e</sub>. A genome sampled from an island population indicated a severe postglacial bottleneck, indicating that large recent post-glacial declines are detectable if they have occurred. Genetic diversity was not related to niche stability or glacial-period bottleneck size. Instead, spatial expansions and increased connectivity during favorable climates likely maintain diversity in the north while restriction to high elevations maintains relatively low diversity despite greater stability in southern regions. Results suggest genetic diversity gradients reflect long-term differences in N<sub>e</sub> dynamics and also emphasize the unique effects of isolation on insular habitats for bumble bees. Patterns are discussed in the context of conservation under climate change.</p>
Fig. 9 in Capybaras, size, shape, and time: A model kit
Fig. 9. Cardiatheriines from the Ituzaingó Formation. A. Anchimys marshi, MLP 73−I−10−7, holotype. A1. Right mandible in lateral view. A2. Right p4–m3 in occlusal view. B–F. Occlusal view of right lower cheek teeth. B. MLP 61−VI−8−6, m1 (inverted). C. Procardiatherium simplicidens, MLP 73−I−10−8, p4–m2 (inverted). D. Kiyutherium scillatoyanei, MLP 78−II−27−1, p4–m3. E. Cardiatherium paranense, MLP 40−XI−15−1, neotype, p4–m3. F. Cardiatherium sp. A, MLP 61−VI−8−2, m3.
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.