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959 results for “GeoMetre”
Detecting cosmic voids via maps of geometric optics parameters
<ul> <li>lensing-ddbb4ac.pdf - research data in pdf format</li> <li>void_matches*.dat - plain text results files corresponding to Table 3 and Figures 2, 4, 6, 8.</li> <li>lensing-ddbb4ac-journal.tar.gz - source package for producing the article pdf, together with the reproducibility package, but without the git history; appropriate for ArXiv</li> <li>lensing-ddbb4ac-git.bundle - git source package that can be unbundled with 'git clone lensing-e4f7af0-git.bundle' and used for reproducibility: to download data, do calculations, analyse them, plot them and produce the research data pdf</li> <li>software-ddbb4ac.tar.gz - this should contain all the software, apart from a minimal POSIX-compatible system and LaTeX packages, needed for compiling and installing the software used in producing this work</li> <li>lensing-ddbb4ac-snapshot.tar.gz - source files of the project; these should be enough, provided that external software packages can be downloaded, to reproduce the full project</li> </ul> <p>The authors grant a perpetual, non-exclusive licence to distribute this pdf preprint.</p> <p>All the other materials here are free-licensed, as stated in the individual files and packages.</p>
Geometric Frustration Directs the Self-assembly of Nanoparticles with Crystallized Ligand Bundles
<p>This is the supporting dataset of the publication "Geometric Frustration Directs the Self-assembly of Nanoparticles with Crystallized Ligand Bundles".</p> <p><a href="https://doi.org/10.1021/acs.jpcb.4c04562">https://doi.org/10.1021/acs.jpcb.4c04562</a></p> <p>The description of the dataset can be found in the file README.txt</p>
ascii xyz files for all pure fullerene isomers from C20 to C80, and stable structures for C28Hn and C40Hn, n=1..5, geometrically optimised with xTB.
<p>ascii xyz files for all pure fullerene isomers from C20 to C80, and stable structures for C28Hn and C40Hn, n=1..5, geometrically optimised with xTB.</p> <p>Data refers to structures generated with the paper published in MDPI Crystals 2021 article "Methodological Investigation for Hydrogen Addition to Small Cage Carbon Fullerenes". Please cite this article if you use this data, many thanks. The article pre-print can be found here: https://www.preprints.org/manuscript/202109.0361/v1 but please cite the final published article.</p>
Efficient Geometric Algorithms Using Osculating Toroidal Patches
<p>The four objects used for Hausdorff distance computation have been created using tools and algorithms developed at the Technion, and are part of the IRIT geometric modeling kernel ( <a href="https://www.cs.technion.ac.il/~irit/">https://www.cs.technion.ac.il/~irit/ </a>).</p> <p>The algorithm can be applied to any objects, and the specific models that are used for computation in the thesis are given and created by IRIT.</p> <p>Model is provided in OBJ format.</p>
Replication Data for: Geometric Transformers for Protein Interface Contact Prediction
<p>This dataset contains replication data for the paper titled "Geometric Transformers for Protein Interface Contact Prediction". The dataset consists of pickled Python dictionaries containing pairs of DGLGraphs that can be used to train and validate protein interface contact prediction models. It also contains our best model checkpoints saved as PyTorch LightningModules. Our GitHub repository, DeepInteract, linked in the "Additional notes" metadata section below provides more details on how we use these files as examples for cross-validation.</p>
The Identification of Extinct Megafauna in Rock art Using Geometric Morphometrics: A Genyornis newtoni Painting in Arnhem Land, Northern Australia?
<p>Raw data files used for the analysis of a contentiously identified rock-art image located in Arnhem Land, Northern Australia. The data were used to test a novel approach to quantifying species identification in rock art images to assess the extent to which an image resembles other rock art of sound identification or anatomical images of visually similar species.</p> <p>Included files are the raw coordinate data files ("[feature] PCA file", .txt format) for use in Morphologika2, and formatted files for use in CVAGen8 ("[feature]" x1y1 file for CVA", .x1y1 format; "[feature] group file", .txt format).</p> <p>Files produced using software by Rohlf (2015) and Sheets (2014)</p>
P2PXML Dataset: Deep Geometric Framework to Predict Antibody-Antigen Binding Affinity
<p>In drug development, the efficacy of an antibody depends on how the antibody interacts with the target antigen. The strength of these interactions indicates how successful an antibody is in neutralizing an antigen. Therefore, the strength, measured by “binding affinity”, is a critical aspect of antibody engineering. In theory, the higher the binding affinity, the higher the chances are that the antibody is successful against the target antigen. Currently, techniques such as molecular docking and molecular dynamics are utilized in quantifying the binding affinity. However, owing to the computational complexity of the aforementioned techniques, running simulations for large antibodies/antigens remains a daunting task. Despite the commendable improvements in deep learning-based binding affinity prediction, such approaches are highly dependent on the quality of the antibody-antigen structures and they tend to overlook the importance of capturing the evolutionary details of proteins upon mutation. Further, most of the existing datasets for the task only include antibody-antigen pairs related to one antigen variant and, thus, are not suitable for developing comprehensive data-driven approaches. To circumvent the said complexities, we first curate the largest and most generalized datasets for antibody-antigen binding affinity prediction, consisting of both protein sequences and structures. Subsequently, we propose a deep geometric neural network comprising a structure-based model and a sequence-based model that considers both atomistic and evolutionary details when predicting the binding affinity. The proposed framework exhibited a 10% improvement in mean absolute error compared to the state-of-the-art models while showing a strong correlation between the predictions and target values. We release the datasets and code publicly https://drug-discovery-entc.github.io/p2pxml/ to support the development of antibody-antigen binding affinity prediction frameworks for the benefit of science and society. </p>
Simple Geometric Shapes
<p>Dataset containing 10000 images of a geometric shape with varying sizes and gray shades and a uniform background. The set contains 5000 images of a circle and 5000 images of a triangle. All images have the same size of 64x64. A label is provided for every image. The images are split into train and test set.</p> <p>The dataset is intended as a toy dataset to explore machine learning with or, in our case specifically, to try out explainable AI (XAI) methods with.</p> <p>The dataset was created as part of the DIANNA project. See https://github.com/dianna-ai/.</p>
Impact of nonlinear hydrodynamic modelling on geometric optimisation of a spherical heaving point absorber
<p>Due to the amount of iterative computation involved, researchers involved in geometric optimisation of wave energy devices typically employ linear hydrodynamic models. However, the exaggerated motion of wave energy devices, aided by energy maximising control action, challenges the assumptions upon which linear hydrodynamic modelling relies. Furthermore, the optimal device geometry is also sensitive to the nature of the energy-maximisation controller employed, and to the set of wave conditions over which the optimisation is carried out.<br> <br> In order to focus on the essential issues, this study takes the simplest possible device for optimisation, a heaving sphere (with just one free parameter), but one which exhibits nonlinear hydrodynamic characteristics, due to the non-uniform cross-sectional area. The study examines the sensitivity to the inclusion of nonlinear Froude-Krylov forces. In addition, the sensitivity of the optimal device size to differences in the applied control algorithm is also studied, as are effects due to different representative sea state representations and performance evaluation criteria.</p>
Advanced open source data formats for geometrically and physically coupled systems - examples
<p>Some model files to describe a geometrically and physically coupled PDE and a ODE system, arising from discretization in space. The files correspond to:</p> <ul> <li>a simplified two component problem with coupling and an FMU in <strong>withFMU.json</strong></li> <li>the corresponding ODE in <strong>io_withFMU_FECoupled.json</strong></li> <li>a PDE model of a complete machine in <strong>ictimt_coupledModel.zip</strong>. This model belongs to https://doi.org/10.17973/MMSJ.2021_7_2021072</li> <li>the corresponding discrete model in <strong>ictimt_feCoupled.zip </strong>This model belongs to https://doi.org/10.17973/MMSJ.2021_7_2021072</li> </ul> <p> </p> <p> </p>
Biomechanical Study of the Eye with Keratoconus-Type Corneal Ectasia Using a 3D Geometric Model
<p>The aim is to analyze the effect of an increment of intraocular pressure applied to eyes with different severities of keratoconus disease. Finite element models of normal, keratoconus, and keratoglobus eyes were built. The load condition was equal, but the material was different. Besides, data about corneal curvature and thickness was contrasted too.</p>
Direct observation of geometric phase in dynamics around a conical intersection
<p>The CSV files contain experimental and theoretical data corresponding to figures 3 and A1 of the paper "Direct observation of geometric phase in dynamics around a conical intersection", available at <a href="http://arxiv.org/abs/2211.07320">arxiv:2211.07320</a>. </p> <p>The contents of the files are described in README.txt.</p>
water vole m1 geometric morphometrics
<p>Code and data for the geometric morphometric analysis of water vole (<em>A amphibius</em>) lower first molars. The files include:</p> <p>1) the complete information on the specimens included in the geometric morphometric analysis (“./redig_specinfo_20230222.csv”)</p> <p>2) the landmarks file (“./lmrks_20230222.TPS”)</p> <p>3) R code (“./gmm_pipeline_vole_20230727.R”)</p> <p>For any questions, please contact nimrod.arch@gmail.com</p>
Seed morphology in the Vitaceae based on geometric models
<p>Images of the seeds in the Vitaceae used in the experiments corresponding to the article entitled: Seed morphology in the Vitaceae based on geometric models, by José Javier Martín-Gómez et al. (Agronomy, 2020 submitted)</p>
Replication R code and data for "Geometric morphometric investigation of craniofacial morphological change in domesticated silver foxes"
<p>This repository holds various files and R code used in the publication of the manuscript entitled "Geometric morphometric investigation of craniofacial morphological change in domesticated silver foxes".</p> <p>Data files include: The 3D landmark coordinates of each individual specimen (Fox_data_Morphologika.txt), the linear measurement data associated with those foxes (fox_linear_volume_data.csv), and replication data measurements. </p> <p>The following files include the R code used to perform the analyses contained within the paper:</p> <p>1_Procrustes_analysis - details the Geometric morphometrics analyses performed</p> <p>2_linear_models - details the model specification for the GLS models employed in the paper</p> <p>3_graph_code - contains R script for the creation of the graphs displayed in the paper</p> <p>4_repeatability_script - contains R code that details the statistical calculations made with the repeatability measurements indicated above. </p> <p> </p> <p> </p> <p> </p>
Data for: Geometrical parameters for musculoskeletal modeling of hand
<p>Dataset to be linked with not yet published manuscript "Geometrical parameters for<br> musculoskeletal modeling of hand"</p> <p>In musculoskeletal modelling, parameters identification, such as the exact position and trajectories of muscle attachments, is a crucial issue. The main goal of this study was to calculate the position, attachment dimensions and cross section areas of twenty-five extrinsic and intrinsic hand muscle complexes. We integrated measurements taken from cadaveric preparations, magnetic resonance imaging and mathematical theory. Sixteen cadaveric preparations were dissected to draw up the anatomical maps including the position of muscle attachments, dimensions, shapes, cross section areas and variations. The magnetic resonance imaging of cadaveric upper extremity was performed to reconstruct the geometry of all bones and hand muscles. Using these outcomes, the muscle attachments and cross section areas were extracted and verified using the obtained morphological and morphometric analysis. The exact trajectories of muscle lines of action were computed using the modified weighted k-means method and Hungary algorithm. This work introduces a new approach to acquiring musculoskeletal modelling data in general and contributes extensive dataset to the hand musculature modelling in particular.</p> <p><strong>Acknowledgments</strong></p> <p>This work was supported by the project n. 182 “Obstetrics 2.0 - Virtual models for the prevention of injuries during childbirth” realised within the frame of the Program INTERREG V-A: Cross- border cooperation between the Czech Republic and the Federal State of Germany Bavaria, Aim European Cross-border cooperation 2014-2020. The realisation is supported by financial means of the European Regional Development Fund (85 % of the costs) and the state budget of the Czech Republic (5 %). KI is part-funded by project No. CZ.02.1.01/0.0/0.0/16_019/0000787 “Fighting INfectious Diseases“, awarded by the Ministry of Education, Youth and Sports of the Czech Republic, financed from The European Regional Development Fund.</p>
FIGURE 3 in Sexual dimorphism in a freshwater atyid shrimp (Decapoda: Caridea) with direct development: a geometric morphometrics approach
FIGURE 3. Relative deformations grids illustrating the variation in the mean shape of the carapace for (a) females and (b) males.
FIGURE 2 in Sexual dimorphism in a freshwater atyid shrimp (Decapoda: Caridea) with direct development: a geometric morphometrics approach
FIGURE 2. Scatter plot of first versus second principal component axes for the total variation of the carapace shape for females, juvenile females and males of Neocaridina davidi.
Figure 6. from Detecting taxonomic signal in an under-utilised character system: geometric morphometrics of the forcipular coxae of Scutigeromorpha (Chilopoda) - ZooKeys 156: 49-66 (20 December 2011) https://doi.org/10.3897/zookeys.156.1997
Figure 6. - Strobe models of five positions along the canonical variates indicated in Fig. 5. CV-1, CV-2, and CV-3 axes account for 79.5% of the observed between-species shape variation. Landmarks and semi-landmarks are superimposed in the figure to the right of each sequence to express the magnitudes and directions (arrows) of shape trends. In all models, the mesial margin of the coxa is depicted to the left, the lateral margin to the right.
Supplementary material 1: Appendix from Detecting taxonomic signal in an under-utilised character system: geometric morphometrics of the forcipular coxae of Scutigeromorpha (Chilopoda) - ZooKeys 156: 49-66 (20 December 2011) https://doi.org/10.3897/zookeys.156.1997
Voucher data for specimens use din morphometric analyses and supplementary figures of Canonical Variates scatterplots
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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
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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.