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882 results for “3d model”
CT data and 3D models associated with: Palaeoneurology of the Early Cretaceous iguanodont Proa valdearinnoensis and its bearing on the parallel developments of cognitive abilities in theropod and ornithopod dinosaurs
<p><i>Proa valdearinnoensis </i>is a relatively large-headed and stocky iguanodontian dinosaur from the latest Early Cretaceous of Spain. Its braincase is known from three specimens. Similar to that of other dinosaurs, it shows a mosaic ossification pattern in which most of the bones seem to have fused together indistinguishably while a few bones (frontoparietal, basioccipital) might have remained loosely attached. The endocasts of the three specimens are described based on CT data and digital reconstructions. They show unmistakable morphological similarities with the endocast of closely related taxa, such as <i>Sirindhorna khoratensis </i>(which is close in age but from Thailand). This supports a high conservatism of the endocranial cavity. The issue of volumetric correspondence between endocranial cavity and brain in dinosaurs is analysed. Although a brain-to-endocranial cavity (BEC) index of 0.50 has been traditionally used, we employ instead 0.73. This is indeed the mid-value between the situation in adults of <i>Alligator mississippiensis</i> and <i>Gallus gallus</i>, which are members of the extant bracketing taxa of dinosaurs (Crocodilia and Aves). We thence gauge the level of encephalisation of <i>Proa valdearinnoensis</i> by the calculation of the Encephalisation Quotient (EQ), which remains valuable as a metric for assessing the degree of cognitive function in extinct taxa, especially those with fully ossified braincases like dinosaurs and other archosaurs. The EQ obtained for <i>Proa valdearinnoensis</i> (3.611) suggests that this species was significantly more encephalised than most if not all extant non-avian, non-mammalian amniotes. Our work adds to the growing body of data concerning theoretical cognitive capabilities in dinosaurs and supports the idea that increasing encephalisations were fostered not only once in theropods but also in parallel in the shorter-lived lineage of ornithopods. <i>Proa valdearinnoensis</i> was ill-equipped to respond to theropod dinosaurs and possibly lived in groups as a strategy to mitigate the risk of being predated upon. We hypothesize that group-living and protracted caring of juveniles in this and possibly many other iguanodontian ornithopods favoured a degree of encephalisation that was outstanding by reptile standards.</p>
Ground truth 3d tetrahedra models
<p>This dataset contains three 3D model files of a tetrahedron, each at a different scale, in .stl format.</p> <p>These models were generated from a 3D model file authored by Anenome at Thingiverse: <a href="https://www.thingiverse.com/thing:942120">https://www.thingiverse.com/thing:942120</a> .</p> <p> </p> <p> </p>
FIGURES 8–9 in A sketch-based system for modeling 3D objects: Applications to taxonomy
FIGURES 8–9. Examples of usages of our system. Fig. 8. Sketching, visualization and reconstruction of an open contour. (a) Haken's gordian knot (adapted from Fish & Lisitsa 2014); (b) our sketch traced over the original drawing (adapted from Burton
FIGURE 14 in A sketch-based system for modeling 3D objects: Applications to taxonomy
FIGURE 14. Male terminalia of Chrysopilus (Diptera, Rhagionidae): (a) Epandrium, dorsal view, of C. phaeopterus (from Santos & Amorim 2007); (b) our sketch traced over the original illustration; and (c) The 3D resulting model.
FIGURE 1 in A sketch-based system for modeling 3D objects: Applications to taxonomy
FIGURE 1. Categories supported as inputs for the reconstruction of a chromosome. Input strokes belong to three categories: (a) open contours and their respective 3D reconstruction; (b) closed contours and 3D surface reconstruction; (c) stripes sketched using our system and interactions and their 3D reconstruction; and (d) final 3D model combining the three categories.
FIGURE 16 in A sketch-based system for modeling 3D objects: Applications to taxonomy
FIGURE 16. Head of Austroleptis papaveroi (Diptera, Austroleptidae): (a) photograph using a microscope (from Fachin et al. 2018); (b) our closed contour selection based on the photograph; (c) colored layers; and (d)–(f) resulting 3D model.
FIGURES 2–7 in A sketch-based system for modeling 3D objects: Applications to taxonomy
FIGURES 2–7. Creation, visual enhancement and 3D reconstruction. Fig. 2. Strokes: (a) raw stroke; (b) default smoothing applied three times; (c) smoothing applied nine times; (d) oversketching through drawing a new curve near the original one; and (e) replacing the current sketch with the new curve. Fig. 3. Selection operation: (a) cross selection (green) for a closed contour; (b) brush selection (red) by painting the segments belonging to the same contour. Fig. 4. Reconstruction process of a 3D stripe: (a) lines drawn with twist annotation using inverted colors for stripe sides; (b) automatic creation of stripe twists; (c) 3D parametric reconstruction of the stripe. Fig. 5. Sketch inference: (a) triangles created combining points; (b) "headlight beam" effect created by painting triangles according to their angle. Fig. 6. Different depth effects: (a) contours using gray-scale to depict different layers; (b) halo effect; (c) hatching effect; (d) layer coloring from blue to yellow. Fig. 7. Smoothing operations of an open contour. (a) one time; (b) ten times; (c) fifty times; (d) one hundred times; and (e) two hundred times.
FIGURE 15 in A sketch-based system for modeling 3D objects: Applications to taxonomy
FIGURE 15. Male terminalia of Chrysopilus (Diptera, Rhagionidae): (a) hypopygium, ventral view, of C. balbii (from Santos & Amorim 2017); (b)–(c) our sketch traced over the original illustration; and (d)–(f) The resulting 3D model.
FIGURE 13 in A sketch-based system for modeling 3D objects: Applications to taxonomy
FIGURE 13. Female terminalia of Austroleptis papaveroi (Diptera, Austroleptidae): (a) photograph using a microscope; (b) drawing used as input for our 3D reconstruction (both from Fachin et al. 2018); (c)–(d) our closed contour selection traced from the adapted illustration; and (e)–(f) the resulting 3D model.
Paleontological reconstruction (3D model) of Dolichoderus jonasi Dubovikoff et Zharkov, 2022 (male).
<p>Supplementary file 2 from Dubovikoff, D. A., Zharkov, D. M. 2022. A new species of the genus Dolichoderus Lund, 1831 (Hymenoptera: Formicidae) from a Late Eocene European amber. Caucasian Entomological Bulletin 181, 147–152 (doi:10.23885/181433262022181-147152).</p> <p>Abstract. A new species of ants, Dolichoderus jonasi sp. n., from a Late Eocene amber (Rovno and presumably Baltic ambers) of Europe is described from three workers and one male. The new species differs from all known fossil and recent species of the genus by the following set of characters: the presence of thorns on the pronotum, a head tapering to the back with pronounced occipital angles, a dimpled (with numerous pits) sculpture on the head and thorax, the presence of a ridge on the posterior edge of the main surface of the propodeum with a row of large setae, the presence of large straight setae on the body arranged in rows, high and somewhat narrowed to the apex petiole scale. The described species cannot be assigned to any of species groups (complexes) in the genus. The phylogenetic relationships of the new species with other species of the genus are discussed. Based on the studied morphological features, the species is closest to representatives of the debilis complex, widespread in South and Central America. However, it has significant differences and should be considered as the separate jonasi complex. We used computer microtomography methods to study structures inaccessible for optical microscopes and accurate measurements, which made it possible to characterize all diagnostic characters of the new species. Reconstructions of a worker and a male using 3D modeling are presented. The discovery of D. jonasi sp. n. in European Late Eocene amber is another possible evidence of relations between the faunas of Europe and the Americas in the past.</p>
Last glacial cycle simulations forced by PMIP3 climate with a matrix and index method using a 3D thermodynamical ice-sheet model IMAU-ICE
<p>IMAU-ICE 2.0 model output of the ice evolution during the last glacial cycle at a 10 ka temporal resolution, as described in Scherrenberg at al., 2023.</p>
Synthetic plant modelling: creating plants in 3D to train neural networks
<p><strong>The following video describes how virtual plant modeling can be used to create synthetic datasets that inform machine learning and computer vision. funded by EU Grant 773875</strong></p> <p><em>Videos are available in:</em></p> <ul> <li>Hi-res (1080p Apple ProRes)</li> <li>Mid-res (1080p H265)</li> </ul> <p><strong>Video script:</strong></p> <p>(MINCHIN) At INRIA in Lyon in France we were also looking at virtual modelling of plants. This creates synthetic data sets that can then help us train machine learning algorithms to identify plants in the field or particular traits in real life.<br> <br> (GODIN) So this is the current bottleneck where we are, we need absolutely massive ground truth data if we want to train this algorithm, this new algorithm, these new families of algorithms to produce this 3d structures and segment it in the the correct botanical way. So how to do this? There are two options basically; the first option would be to acquire a real plant data by photograph, by scanner laser, and then the expert segment by hand different parts of the plant, saying that this is a leaf, this part of the point cloud is a leaf, this part of the point cloud is a stem et cetera. And you can imagine that this is extremely time consuming, this is extremely heavy task and we are blocked at this point because of the of the ability of humans to do such complicated tasks.<br> <br> And there is another option that is to create artificial plants and then say whether with this segmentation of the virtual plant. Whether we are right or not because as we designed the virtual plant we know that this part of the point cloud corresponds to a leaf, this part of the point cloud corresponds to a stem and because of this it is possible to automatise the training of the system.<br> <br> So what we want to do in the context of such a phenotyping approach is to use a virtual pipeline where we would produce the virtual plant in the computer. Then we would create point clouds out of these virtual plants, so this would be virtual point clouds. Then we would use training algorithms in the context of this machine learning construction process, and then we would get as an output the trained machine learning system. Then once we have this, it is possible to get back to the original pipeline and use here this trained machine learning system in order to recognise identify segment the different organs on the plant.<br> <br> So L-py is the programming language to simulate plants and from this it is possible to create full databases of plants by a stochastic simulation of this, of plant populations, to produce massive data, and then this massive data, give them to machine learning systems in order to train this machine learning based on this large amount of input data.<br> <br> The first thing to know is that how plants are growing. You have leaves like this and then you have a stem like this. This is growing due to this small part of the tip that is called the apex, and the apex is producing all the organs that are being built on the plant. So for the leaves and also the fruits here or the flowers. So if you look at this in a more detailed manner you you make a close-up on this. What you observe is that it is like a small dome like this, that is producing lateral organs and this is the stem, and these are the young organs. Like this can be a leaf or a flower or anything else that is produced by the by the meristem. And this part here, it is the place where all the stem cells are living and they are dividing and they are producing the small organs here one after the other at the tip of the plant. So if we want to model this we need to model how this small part of the plant which is built, which is made up of a small amount of stem cells, undifferentiated cells, how this is growing. an apex let's call it ‘a producing a piece of stem’, (that I call for the internet) and literally produces an apex that will in turn be able to grow on it on its own. We formalise this rule of growth by this, let's say, a mathematical expression.<br> <br> (BESNARD) In the case of ROMI, so this European project we are involved in, we are using virtual plants for a precise objective which is to use virtual plants to create a data set, virtual dataset that we can use for a training deep learning algorithm very efficiently and costless, and as I told you. Then, once you have an objective you have to question yourself whether realism and the realistic rendering of the plant is useful or not for your objective. In the program. We use pipelines that are able to detect automatically to segment the plant and to detect automatically the organs, okay so here basically it will be the branching point that will be discovered and this branching point can be either branches that are cut here or the helix here. And once it is detected we use a representation where we highlight these branching points so this or branches, and you can see that here you have another layer and this highlight overlays perfectly with the branches. So you can say wow it's really good, but when you dig into these plants sometimes algorithms, machine learning algorithms are not working well and here is when you proceed with the same algorithm, the leaf is recognised partly as a leaf at the beginning okay, but the tip of the leaf is recognised as a fruit or a branch. So it shows you here, that you have some issues so those machine learning algorithms are not perfect and we need to improve them to increase our performances in terms of fragmentation.<br> <br> So I will proceed in the natural plant, and you can see that definitely the the leaves here are in the current model with which we trained this network, they are like quiet simple structure so it's a flat very simple shape that are not like really the shape of the current leaf. So here's an important question if we make a new plant modified here leaf shapes, would this help in this the algorithm to make a better plan segmentation?<br> <br> (GODIN) To make more realistic plants we decided to grow real Arabidopsis plants in growth chamber and to measure them in a systematic manner. We then used these detailed measurements to refine our virtual Arabidopsis plant in different ways. First we refined the modelling of the different organs for example the coiling of leaves. With time the leaf would be produced laterally by the stem in a sort of straight way like this, or like this, and then with time it would curl and change the shape. Instead of having one single curve. Now I will have a set of curves, I would just put the curve, it would be straight in the beginning then i would fold a bit the curve fall a bit the curve fold a bit like this, even I can go in this way. So I would define the curve, zero curve, one curve, six. Okay actually this is what I did here. And then I say all this bunch, create me an object that is able, given a time . So the Curve, Function, Object, ‘curve function object’, it is able to take a time too ‘Tao’ (T) and then to return a curve object as at the time term. And then so this curve object is provided by L-pi. You just call it with a series of functions, then once you have this you can call curve of ’T’ and then if I know this, I can compute the current curve at time, and then display it and and actually while it is doing this, it is able to compute all the intermediate curves by interpolation. Like this.<br> <br> Detailed models of organs were then assembled to model groups of organs, such as flowers or at an even more integrated level the dynamics of inferences development. The difficulty here is to synchronise the growth dynamics of each part, for this we use the new strategy that we developed in the course of the project and that is called ‘hierarchical timeline warping’. This strategy makes it possible to synchronise the growth dynamics of the different plant parts onto each other in a hierarchical and non-linear manner similarly still based on real plant measurements. We modelled also the observed variability in the dimensions and orientation of the different organs. The gravitropism and the mechano-perception of the different axes resulting in complex and dynamic bending of their parts. The final model provides a realistic rendering of the plant growth that can be used as a faithful reference to train ROMI’s machine learning algorithms.<br> <br> Then the technology to construct virtual plants developed for Arabidopsis was used to generate other virtual plants with different levels of complexity and accuracy, we first updated our tomato model by introducing stochasticity in the development of the plant here you can observe two tomato plants that were generated using the same stochastic model. The model varies the number of organs their size their dimensions their orientation with respect to their parent stem, their bending and so on. Another virtual model was made of a Canopodium this plant is a weed that can be easily found in crop fields similarly to Arabidopsis although with less details, we grew several plants in growth chamber and observed how it grows. We used these observations to construct a virtual model model of a Canopodium, the model is very different from that of a tomato, it grows with a main axis that dominates over secondary axis that grows in turn with slight delay with respect to the leader. Here also stochasticity was introduced in the model so that one can generate different individuals with a simple click.<br> <br> Finally based on the training course participants could start to produce their own virtual plants. Here is the first version of a model of a pepper plant showing the step-by-step approach of the student based on real plant observations. Here is a 3d model of a carrot plant with fractal leaves and stochasticity. Several instances of this stochastic model can be generated to create a virtual field of carrots. So by the way we will learn on Friday, Ayan will make a presentation showing how we can use these systems and to scan them make a sample of points, a 3d cloud sample of points, so that we can train systems, machine learning systems, that take as an input sample points, in order to produce the right segmentation of the point cloud.<br> <br> Parts of this video were extracted from a week-long course given in April 2022 and covering every aspect of how to virtually model plants in three dimensions, with L systems in Python. If you are interested don't hesitate to go and check it out on the ROMI youtube channel.</p>
Preliminary DOI/Repository of ALPINE3D and SNOWPACK data of the submitted paper "Towards a fully physical representation of snow on Arctic sea ice using a 3D snow-atmosphere model"
<p>There are 2 zip folders in this repository.</p> <p>"a3d_jgr.zip" contains a folder structure that must be kept as it is in order to run the simulation in the current configuration.<br> The setup contains both input and output data as well as the model configuration as used in the submitted manuscript <br> "Towards a fully physical representation of snow on Arctic sea ice using a 3D snow-atmosphere model".</p> <p>The zip file contains 3 main folders:</p> <ul> <li>base_setup_files</li> <li>a3d_jgr_alpha1</li> <li> a3d_jgr_alpha3</li> </ul> <p>The "base_setup_files" contains all input files that are necessary to run the reference (R) simulation ("a3d_jgr_alpha1" folder) and the comparison "C" scenario ("a3d_jgr_alpha3") folder. In the a3d_jgr_alpha1 and a3d_jgr_alpha3 folders you find the corresponding outputs as used in the paper, as well as the settings used - which only differ by the changed "SCHMIDT_DRIFT_FUDGE" value that is found in each a3d_jgr_alphax/setup/io.ini file. The input data is already linked accordingly in each io.ini file.</p> <p>a3d_jgr_alpha1 also contains the detailed snow profiles for each point along the transects.</p> <p>To reproduce the results, download and compile the source code for the adjusted ALPINE3D model first, which can be obtained from https://gitlabext.wsl.ch/snow-models/alpine3d.git under the "alpine3d_mosaic" branch. After installing, you can run the provided model setup uploaded here.</p> <p>_________________________________________________________________________________________________________<br> <br> "SNOWPACK_JGR.zip" contains both input and output data for SNOWPACK as well as the model configuration as used in the submitted manuscript "Towards a fully physical representation of snow on Arctic sea ice using a 3D snow-atmosphere model".</p> <p>The zip file contains 2 main folders: </p> <ul> <li>SNOWPACK_JGR_ALPHA1</li> <li>SNOWPACK_JGR_ALPHA3</li> </ul> <p>In the SNOWPACK_JGR_ALPHA1 (reference "SP_R" simulation) and SNOWPACK_JGR_ALPHA3 (comparison "SP_C" scenario) folders you find the corresponding inputs, outputs and configuration as used in the paper, as well as the settings used - which only differ by the changed "SCHMIDT_DRIFT_FUDGE" value that is found in each SNOWPACK_JGR_ALPHA/setup/io.ini file. The input data is already linked accordingly in each io.ini file.</p> <p>To reproduce the results, download and compile the source code for the adjusted SNOWPACK model first, which can be obtained from https://gitlabext.wsl.ch/snow-models/snowpack.git under the "snowpack_mosaic" branch. After installing, you can run the provided model setup uploaded here.</p>
3D brain model with DBS targets and electrodes
<p>No description provided.</p>
Dataset comparing 2D and 3D culture for the calibration/validation of computational models
<p>Dataset comparing invasion adhesion and response to cisplatin and paclitaxel measured in PEO4 cells maintained either in 2D or 3D.</p> <p>Paper submitted to PLOS Computational Biology</p>
Architectural Sketch to 3D Model
<p>This is the dataset that used in Architectural Sketch to 3D Model, the final project of Visual Intelligence: Machine and Minds.</p> <p> </p>
Magnetotelluric data in Subei area, northern Tibet and the 3D isotropic/anisotropic models
<p>This dataset contains four folders. They are ‘aniinv’, ‘isoinv’, ‘sensitivity_test’, ‘syn_mod_test’, respectively. In the ‘aniinv’ folder, there are three sub-folders include ‘azimu_ani_inv’, ‘gener_ani_inv’, ‘verti_ani_inv’, indicating the inversion results for azimuthal, general, and vertical anisotropic inversions, respectively. The ‘isoinv’ folder contains results for isotropic inversion. The ‘sensitivity_test’ folders contains modeified models and their responses for sensitivity tests of anomalies. The ‘syn_mod_test’ folder contains a synthetic model constructed according the final model, the responses of this model, and the recovering for this model. In each folder, there is a ‘readme.txt’ file describing the details of individual files.</p>
Supplemental 3D Model Data - Hapalosiphonacean cyanobacteria (Nostocales) thrived amid emerging embryophytes in a 407-million-year-old landscape
<p>Three-dimensional reconstruction models of cyanobacteria from a 407 million year old fossil from the Lower Devonian Rhynie chert, UK. Thin sections SU.PB. 2023.0.1.2.8 from the Palaeobotany Collection in the Pôle Collections scientifiques et patrimoniales. Bibliothèque de Sorbonne Université, Paris (France).</p> <p>Imaris files (.IMS) can be viewed using the Imaris Viewer software, freely available in both Windows and Mac versions from https://imaris.oxinst.com/imaris-viewer. </p>
Data for: Contributions of deep learning to automated numerical modelling of the interaction of electric fields and cartilage tissue based on 3D images
<p>Replication data for: Contributions of deep learning to automated numerical modelling of the interaction of electric fields and cartilage tissue based on 3D images</p> <p> </p> <p> </p>
Blast Furnace Raw Material Granularity Recognition Model Based on Deep Learning and Multimodal Fusion of 3D Point Cloud
<p>Provide data code</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)
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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.