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63 results for “voxel”
Voxel-level summary statistics of hippocampus shape, white matter microstructure, and cortical surface curvature in UK Biobank (n=33,324)
<p>This deposit hosts GWAS summary statistics of hippocampus shape (n=33,324), white matter microstructure (n=33,324), and cortical surface curvature (n=15,752) using UKB unrelated white subjects. The data was generated by using the highly efficient imaging genetics (<a href="https://github.com/Zhiwen-Owen-Jiang/heig">HEIG v1.1.0</a>) framework where only the triplets - summary statistics of low-dimensional representations (LDRs), the functional bases, and the variance-covariance matrix LDRs - are shared, which is sufficient to recover all voxel-variant pairs as well as to conduct voxel-level heritability and (cross-trait) genetic correlation analysis. Check the <a href="https://github.com/Zhiwen-Owen-Jiang/heig/wiki">tutorial</a> and the <a href="../records/13770930">example data</a> used in the tutorial. </p> <p>The shared data includes:</p> <p>1. Triplets for hippocampus shape measured by the radial distance from the medial model for each vertex. The original images contain 30,000 vertices while the shared data contains 49 LDRs. Left and right hemispheres were analyzed separately, each with 15,000 vertices.</p> <p>2. Triplets for 21 white matter tracts measured by fractional anisotropy. The original images contain 32,217 voxels and each tract contains 88 ~ 3503 voxels while the shared data contains 1,034 LDRs. Tracts were analyzed separately.</p> <p>3. Triplets for cortical surface curvature. The original images contain 59,412 vertices while the shared data contains 1,750 LDRs. The entire brain was analyzed as a whole.</p> <p>4. LD matrix and its inverse for 22 chromosomes including 460k genotyped SNPs. LD matrix and its inverse were estimated by using two separate datasets each containing 8.4k white unrelated subjects in UKB. Two regularization levels are provided: {85%, 80%} for heritability and genetic correlations within images and {75%, 70%} for cross-trait genetic correlations.</p> <p>5. LD matrix and its inverse for 22 chromosomes including 1.2 million imputed HapMap3 SNPs. LD matrix and its inverse were estimated by using two separate datasets each containing 42k white unrelated subjects in UKB. Two regularization levels are provided: {98%, 95%} for heritability and genetic correlations within images and {90%, 85%} for cross-trait genetic correlations.</p>
Voxelized fragment dataset for machine learning
<p>One of the primary challenges inherent in utilizing deep learning models is the scarcity and accessibility hurdles associated with acquiring datasets of sufficient size to facilitate effective training of these networks. This is particularly significant in object detection, shape completion, and fracture assembly. Instead of scanning a large number of real-world fragments, it is possible to generate massive datasets with synthetic pieces. However, realistic fragmentation is computationally intensive in the preparation (e.g., pre-factured models) and generation. Otherwise, simpler algorithms such as Voronoi diagrams provide faster processing speeds at the expense of compromising realism. Hence, it is required to balance computational efficiency and realism for generating large datasets for marching learning.</p> <p>We proposed a GPU-based fragmentation method to improve the baseline Discrete Voronoi Chain aimed at completing this dataset generation task. The dataset in this repository includes voxelized fragments from high-resolution 3D models, curated to be used as training sets for machine learning models. More specifically, these models come from an archaeological dataset, which led to more than 1M fragments from 1,052 Iberian vessels. In this dataset, fragments are not stored individually; instead, the fragmented voxelizations are provided in a compressed binary file (.rle.zip). Once uncompressed, each fragment is represented by a different number in the grid. The class to which each vessel belongs is also included in <em>class.csv</em>. The GPU-based pipeline that generated this dataset is explained at <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.cag.2024.104104" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.cag.2024.104104</a>.</p> <p>Please, note that this dataset originally provided voxel data, point clouds and triangle meshes. However, we opted for including only voxel data because 1) the original dataset is too large to be uploaded to Zenodo and 2) the original intent of our paper is to generate implicit data in the form of voxels. If interested in the whole dataset (450GB), please visit the web page of our <a href="https://s5-ceatic.ujaen.es/fragment-dataset-uja/">research institute</a>.</p>
Preprocessed rat brain voxel time series
<p>Preprocessed version of voxel time-series for three rats, originally described in Becq et al., Functional connectivity is preserved but reorganized across several anesthetic regimes, NeuroImage, 2020. Used in Achard et al., Inter-regional correlation estimators for functional magnetic resonance imaging, arXiv, 2022, arXiv:2011.08269.</p> <p>The files named "coord_ROI_x.txt" contain the coordinates of the voxels inside region x (each line corresponds to one voxel).</p> <p>The files named "ts_ROI_x.txt" contain the BOLD signal time series of the voxels inside region x (each line corresponds to one voxel, each column to one timepoint). The voxels with time series equal to zero have been removed</p> <p>The files named "weight_ROI_x.txt" contain the weights associated with the voxels inside region x (each line corresponds to one voxel). Indeed, when assigning voxels to regions, some voxels end up at the border of several regions. These weights characterize the proportion of a given voxel present inside a given region. Hence, some voxels are included in several different regions. So when we compute the voxel-to-voxel inter-correlation between two regions we sometimes end up with inter-correlations equal to 1. In the current dataset this issue has been resolved and each voxel has been assigned to a single region.</p>
Voxel Carving Based 3D Reconstruction of Sorghum
<p><strong>Dataset generated in the following paper:</strong></p> <p>Gaillard, M., Miao, C., Schnable, J. C., & Benes, B. (2020). Voxel carving-based 3D reconstruction of sorghum identifies genetic determinants of light interception efficiency. Plant Direct, 4(10), e00255. https://doi.org/10.1002/pld3.255</p>
3D reconstruction of plants using probabilistic voxel carving
<p>This is a companion dataset for the paper titled "3D reconstruction of plants using probabilistic voxel carving." by Feng, J., Saadati, M., Jubery, T., Jignasu, A., Balu, A., Li, Y., Attigala, L., Schnable, P.S., Sarkar, S., Ganapathysubramanian, B. and Krishnamurthy, A., published in <em>Computers and Electronics in Agriculture</em>, <em>213</em>, p.108248, 2023.</p> <p> </p>
BRAIN Journal-Electrophysiological Neuroimaging using sLORETA Comparing 12 Anorexia Nervosa Patients to 12 Controls-Figure 2: All axial slices of sLORETA imaging results of Resting State EEG Supra-Threshold Voxels in both the Parahippocampal (limbic) and Fusiform (temporal) Gyri illustrating decreased neuronal activity in the Anorexia Nervosa patients.
<p>Results from the sLORETA imaging indicates decreased neuronal activation within the Left<br> Fusiform Gyrus located in the Temporal lobe and Parrahippocampal gyrus, which is located in the<br> Limbic Lobe (Table 1). This correlates with other fMRI findings where patients with early onset<br> AN have exhibited reduced unilateral blood flow in the temporal lobe. The Parahippocampal and<br> Fusiform Gyri are centers that process emotions. Previous studies in which these regions have<br> shown activation involve women that have distorted perceptions of their bodies from a cognitive<br> perspective (Santel et al., 2006). It is apparent from our findings that the Fusiform Gyrus may play<br> a vital role in the processing of visual appearance of the human body. There is also a correlation<br> with the somatosensory limbic pathway in the limbic lobe, due to the similarity of function.</p>
BRAIN Journal-Electrophysiological Neuroimaging using sLORETA Comparing 12 Anorexia Nervosa Patients to 12 Controls-Figure 1: sLORETA imaging results of Resting State EEG Supra-Threshold Voxels in both the Parahippocampal (limbic) and Fusiform (temporal) Gyri illustrating decreased neuronal activity in the Anorexia Nervosa patients relative to Control participants
<p>The findings of the sLORETA analysis indicated that, the difference is statistically<br> significant (p=0.03) using a one-tailed t-test: Anorexia > Controls. The brains of the patients with<br> Anorexia Nervosa illustrated decreased neuronal activity in the Left Fusiform Gyrus and the Left<br> Parahippocampal Gyrus (p=0.03) in the resting-state brains when Anorexia Patients were sitting for<br> 3min, as compared to the Controls sitting for 3minutes.</p>
The ECOLOPES Voxel Model: Multi-domain data integration for ontology-aided generative computational design of ecological building envelopes
<p>The research portrayed in this article is part of the research project ‘ECOlogical building enveLOPES: a game-changing design approach for regenerative ecosystems’ funded by Horizon 2020 Future and Emerging Technologies. The overall research project focuses on developing a multi-domain data-driven computational design framework for the design of ecological building enclosures that addresses humans, plants, animals and microbiota. This article focuses on the development of a key component of the computational workflow in which initial designs are computationally initiated generated and analyzed, namely the ECOLOPES Voxel Model that contains and correlates multi-domain spatialised data for the design process, and its interactions with other components of the ontology-aided generative computational design process for ecological building envelopes.</p> <p>This repository contains all relevant data produced in this paper. Extended technical description is available in the Appendix A to the published paper, containing listing and description of individual voxel data layers. Data were exported from the RDB server (PostgreSQL) in text-based, future-proof format (csv).</p>
Musketeer (Voxel)
Voxel Musketeer made in Magica Voxel by Alex White, mobile friendly Source: Objaverse 1.0 / Sketchfab
Voxel-wise maps for the paper: Longitudinal associations of magnetic susceptibility with clinical severity in Parkinson's disease
<p>This upload contains voxel-wise group level QSM data, and statistical maps for group level results associated with the paper: Longitudinal associations of magnetic susceptibility with clinical severity in Parkinson's disease.</p> <p>The assocaited code for reproducing these statistical maps can be found here: <a href="https://github.com/gecthomas/QSM_PD_longitudinal">gecthomas/QSM_PD_longitudinal (github.com)</a></p>
1940' nobel swimming pool (voxel)
The nobel swiming pool was made in 1940',along with a park and a football stadium, first as only summer swimming pool, later in the eightees the roof was added. This building is located in Bratislava, Slovakia. Its part of "Istrochem" (or Dynamit Nobel chemical plant in 1940') it works from 1873 to 2004, the swiming pool was probably left abandoned from 1989 because the fall of communism. it was used as a storage to 2004, somewhere then the roof was burned down and replaced with plastic one. In 2005 it was bought by my family and i live there now. in 2019 the roof fell to disrepair and the pool itself was filled with dirt, i didnt put the dirt into the model because i like it like this. Source: Objaverse 1.0 / Sketchfab
Vabank - Voxel Movie Scene
Voxel model of a scene from a polish movie Vabank(1981). Made in Magicavoxel for Multimedia Graphics class at Polish-Japanese Academy of information technology. Source: Objaverse 1.0 / Sketchfab
Cryo-EM and X-ray crystallography ligands represented as 3D voxel grids for training deep learning models
<p>Ligand datasets used to train and evaluate the models studied in <em>"Ligand Identification using Deep Learning</em><em>"</em> by Karolczak, J. <em>et al.</em></p> <p>The blobs_full.tar.gz and cryoem_blobs.zip files contain compressed 3D numpy arrays (*.npz) of all the ligand blobs extracted from X-ray and cryo-EM PDB deposits prior to quality filtering. The npz file names correspond to the PDB ID, chain, residue number, and ligand name of the extracted blob. The cmb_data.csv file contains the tabular data used to train the CheckMyBlob model. The X-ray data were later divided into training and testing subsets according to the xray_train.csv and xray_holdout.csv files, respectively. The ligand_mapping.csv file contains the mapping from ligand IDs to ligand group names. Finally, the cryoem_qscores.csv file contains Q-scores that were used to filter cryo-EM ligands.</p>
Hyperspectral X-ray CT Voxelized TV reconstruction of a single, iodine-stained lizard head sample
<p><strong>Dataset description</strong></p> <p>These datasets are voxel based reconstructions of hyperspectral CT data using the Core Imaging Library (CIL). They are stored as NeXus files (derived from hdf5) which can be read in, visualised and manipulated using CIL.</p> <p> - PDHG_TV_1000_Sp_alpha_0.004.nxs</p> <p>Is the solution after 1000 iterations of PDHG with TV applied in the spatial domain. </p> <p> - PDHG_TV_1000_SpCh_alpha_0.003_beta_0.5.nxs</p> <p>Is the solution after 1000 iterations of PDHG with TV applied both in the spatial domain, and in the energy (channel) domain. </p> <p> </p> <p><strong>Dataset intended use</strong></p> <p>These datasets are used in the CIL training notebook:</p> <p>https://github.com/TomographicImaging/CIL-Demos/blob/main/examples/3_Multichannel/03_Hyperspectral_reconstruction.ipynb</p> <p>They can be imported using CIL, with the following code snippet:</p> <pre><code class="language-python">from cil.io import NEXUSDataReader reader = NEXUSDataReader(file_name='path/to/data/PDHG_TV_1000_Sp_alpha_0.004.nxs') data = reader.read()</code></pre> <p> </p>
Voxel Viaduct in Telgárt summer
https://www.ciernediery.sk/telgartska-slucka/ The Telgárt loop is part of the Červená Skala - Margecany railway line and is one of our most demanding railway technical works in Slovakia. Looking at the beautiful countryside around the track today, it is easy to forget what complications have accompanied its construction in times of deep economic crisis. build in 1931-1936 Source: Objaverse 1.0 / Sketchfab
Sami Swoi - Voxel Movie Scene
Voxel model of a scene from a polish 1967 movie *Sami Swoi*. Made in Magicavoxel for Multimedia Graphics class at PJAIT Warsaw. Source: Objaverse 1.0 / Sketchfab
Supplementary data to the paper "Large scale voxel-based FEM formulation for NMR relaxation in porous media"
<p>This repository contains supplementary data to the paper "Large scale voxel-based FEM formulation for NMR relaxation in porous media", which is being considered for publication. The files are:<br><br>**_data.zip - Binary image files;</p> <p>FEM_T2_**.csv - T2 simulation results using finite elements;</p> <p>FEM_**_inv.csv - T2 inversion for the finite element simulations;</p> <p>RW_T2_**.csv - T2 simulation results using random walk;</p> <p>RW_**_inv.csv - T2 inversion for the random walk simulations;</p> <p>local_digital_correction.c - Vector containing the local geometry correction factors, accessed integers formed by the 8-bit binary key of the node neighborhood;</p> <p>Where ** is the rock type:</p> <p>AC - Austin Chalk</p> <p>BS - Berea Stripe</p> <p>DP - Desert Pink</p> <p>IB - Idaho Brown</p> <p>Obs.: in order to obtain the relaxation curves the following parameters were utilized in the simulations:</p> <p>Bulk diffusivity: 2500 micro-m2/s (all rock samples) </p> <p>Bulk relaxation: 2.6 s (all rock samples)</p> <p>Voxel size: {1.0; 2,0; 0.9; 2.0} micro-m (for AC, BS, DP, IB respectively)</p> <p>Surface relaxivity: {23.3; 12.1; 12.3; 8.3} micro-m/s (for AC, BS, DP, IB respectively)</p>
Par04 Lead Tungstate Voxel Shower with 10000 Events at 1GeV, ZxRxP = 35x22x16 PbWO4
<p>The dataset contains 10,000 Geant4 voxelized events generated with Par04 [1] with modifications to the original detector geometry. The dataset is made with a single bloc of 30cm of Lead Tungstate (PbWO4) and a cylindrical scoring mesh of Z = 35 , Rho = 22 , Phi = 16. Cylinder dimensions were targeted to conserve 98% of incident energy, intially from a 90° pion collision at 1GeV. </p> <p> </p> <pre>-[1]: https://gitlab.cern.ch/fastsim/par04 </pre>
Demonstrating voxel-by-voxel (v×v) single-point calibration in liver tissue by IR-MALDESI quantitative MSI
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Data from: Application of a 1H brain MRS benchmark dataset to deep learning for out-of-voxel artifacts
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