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264 results for “3D Reconstruction”
Crinoline (1860, 6 hoops) - a 3D reconstruction
The 3D model presents a digital reconstruction of a historical crinoline - a special framework used to expand the fullness of the skirt in the mid 19th century. The crinoline is presented in a historical photograph. It has 6 hoops and 11 ribbons. A new method of parameterisation was applied to reproduce the shape and construction of the hoops, ribbons and belt (for further details see https://doi.org/10.1080/00405000.2019.1621042). The authors of the 3D model are Aleksei Moskvin https://independent.academia.edu/AlekseiMoskvin Mariia Moskvina https://independent.academia.edu/MariiaMoskvina (Saint Petersburg State University of Industrial Technologies and Design) DOI: http://dx.doi.org/10.13140/RG.2.2.31866.00967 The authors thank Prof. Victor Kuzmichev from Ivanovo State Polytechnic University for his important contribution to this reconstruction. Source: Objaverse 1.0 / Sketchfab
The belt form Illerup Ådal – 3D reconstruction 2
The reconstructed belt comes from a war booty sacrifice site, **Illerup Ådal**, in Denmark. The belt (inv. no. SAHR) dates to the beginning of the 3rd century AD. Location: The Moesgard Museum (https://www.moesgaardmuseum.dk). The belt was reconstructed in 3dsMax and Unreal Engine 4 by using original photographs by J. Ilkjær. The authors of the reconstruction are Aleksei Moskvin, Mariia Moskvina (Saint Petersburg State University of Industrial Technologies and Design) and Martijn A. Wijnhoven (VU University Amsterdam). https://independent.academia.edu/AlekseiMoskvin https://independent.academia.edu/MariiaMoskvina https://vu-nl.academia.edu/MartijnAWijnhoven Source: Objaverse 1.0 / Sketchfab
Armour from Vimose: 3D reconstruction
The 3D model presents a digital reconstruction of solid and riveted rings of archaeological mail armour. To create 3D models of the rings, we used polygonal modelling functions of Blender software. In order to record the relevant measurements of the mail rings, we used a list of parameters. This list includes 11 parameters for a riveted ring and four parameters for a solid ring. The accuracy of the digital replicas of the rings was 0.01 mm. The authors of the reconstructions are Martijn A. Wijnhoven (VU University Amsterdam) https://vu-nl.academia.edu/MartijnAWijnhoven Aleksei Moskvin (Saint Petersburg State University of Industrial Technologies and Design) https://independent.academia.edu/AlekseiMoskvin Mariia Moskvina (Saint Petersburg State University of Industrial Technologies and Design) https://independent.academia.edu/MariiaMoskvina DOI: https://doi.org/10.13140/RG.2.2.34104.88323 Source: Objaverse 1.0 / Sketchfab
Armour from Fluitenberg – a 3D reconstruction
The 3D model presents a digital reconstruction of solid and riveted rings of archaeological mail armour. To create 3D models of the rings, we used polygonal modelling functions of Blender software. In order to record the relevant measurements of the mail rings, we used a list of parameters. This list includes 11 parameters for a riveted ring and four parameters for a solid ring. The accuracy of the digital replicas of the rings was 0.01 mm. The authors of the reconstructions are Aleksei Moskvin (Saint Petersburg State University of Industrial Technologies and Design) https://independent.academia.edu/AlekseiMoskvin Mariia Moskvina (Saint Petersburg State University of Industrial Technologies and Design) https://independent.academia.edu/MariiaMoskvina and Martijn A. Wijnhoven (VU University Amsterdam) https://vu-nl.academia.edu/MartijnAWijnhoven DOI: https://doi.org/10.13140/RG.2.2.17327.66725 Source: Objaverse 1.0 / Sketchfab
The belt from Illerup Ådal – 3D reconstruction 1
The reconstructed belt comes from a war booty sacrifice site, **Illerup Ådal**, in Denmark. The belt (inv. no. SAHR) dates to the beginning of the 3rd century AD. Location: The Moesgard Museum (https://www.moesgaardmuseum.dk). The belt was reconstructed in 3dsMax and Unreal Engine 4 by using original photographs by J. Ilkjær. The authors of the reconstruction are Aleksei Moskvin, Mariia Moskvina (Saint Petersburg State University of Industrial Technologies and Design) and Martijn A. Wijnhoven (VU University Amsterdam). https://independent.academia.edu/AlekseiMoskvin https://independent.academia.edu/MariiaMoskvina https://vu-nl.academia.edu/MartijnAWijnhoven Source: Objaverse 1.0 / Sketchfab
Armour from Fluitenberg – a 3D reconstruction
The 3D model presents a digital reconstruction of solid and riveted rings of archaeological mail armour. To create 3D models of the rings, we used polygonal modelling functions of Blender software. In order to record the relevant measurements of the mail rings, we used a list of parameters. This list includes 11 parameters for a riveted ring and four parameters for a solid ring. The accuracy of the digital replicas of the rings was 0.01 mm. The authors of the reconstructions are Martijn A. Wijnhoven (VU University Amsterdam) https://vu-nl.academia.edu/MartijnAWijnhoven Aleksei Moskvin (Saint Petersburg State University of Industrial Technologies and Design) https://independent.academia.edu/AlekseiMoskvin Mariia Moskvina (Saint Petersburg State University of Industrial Technologies and Design) https://independent.academia.edu/MariiaMoskvina DOI: https://doi.org/10.13140/RG.2.2.17327.66725 Source: Objaverse 1.0 / Sketchfab
Armour from Gammertingen: 3D reconstruction
The 3D model presents a digital reconstruction of solid and riveted rings of archaeological mail armour. Location: Landesmuseum Württemberg https://www.landesmuseum-stuttgart.de/ https://sketchfab.com/lmwstuttgart To create 3D models of the rings, we used polygonal modelling functions of Blender software. In order to record the relevant measurements of the mail rings, we used a list of parameters. This list includes 11 parameters for a riveted ring and four parameters for a solid ring. The accuracy of the digital replicas of the rings was 0.01 mm. The authors of the reconstructions are Aleksei Moskvin (Saint Petersburg State University of Industrial Technologies and Design) https://independent.academia.edu/AlekseiMoskvin Mariia Moskvina (Saint Petersburg State University of Industrial Technologies and Design) https://independent.academia.edu/MariiaMoskvina and Martijn A. Wijnhoven (VU University Amsterdam) https://vu-nl.academia.edu/MartijnAWijnhoven Original source: https://skfb.ly/o7Q8G Source: Objaverse 1.0 / Sketchfab
3D Reconstruction of Cold War Shelter: Aarhus, Denmark
<p>This collection showcases high-fidelity 3D models of a Cold War shelter located in Aarhus, Denmark. Leveraging advanced tools like Polycam, Metashape Agisoft Professional, Meshroom, Rhinoceros 3D 8.0, and V-Ray. These models offer detailed reconstructions of the shelter's interior and exterior. The dataset includes LiDAR scans and photogrammetry-derived models, meticulously refined and rendered for accuracy and realism. Shared under a Creative Commons license on Zenodo, these models serve as valuable resources for researchers, educators, and enthusiasts interested in Cold War history and urban resilience. The 3D models have also been uploaded and published in Sketchfab, which are accessible to everyone. </p> <p>For an overview of the models, visit the following Sketchfab URLs or my profil in Sketchfab:</p> <p>Model 1: <a title="Model 1" href="https://skfb.ly/oTV6P">https://skfb.ly/oTV6P</a><br>Model 2: <a title="Model 2" href="https://skfb.ly/oTVnQ">https://skfb.ly/oTVnQ</a><br>Model 3:<a title="Model 3" href="https://skfb.ly/oTVvx"> https://skfb.ly/oTVvx</a><br>Model 4: <a title="Model 4" href="https://skfb.ly/oTVwt">https://skfb.ly/oTVwt</a></p> <p><a href="https://sketchfab.com/kogka16">Konstantina Gkaraliakou (@kogka16) - Sketchfab</a></p> <p> </p> <p> </p> <p> </p>
Supplemental material for 'Characterization of structure and mixing in nanoparticle hetero-aggregates using convolutional neural networks: 3D-reconstruction versus 2D-projection'
<p>This is the supplemental data for the manuscript titled ‘<em>Characterization of structure and mixing in nanoparticle hetero-aggregates using convolutional neural networks: 3D-reconstruction versus 2D-projection’</em> submitted to <em>Ultramicroscopy</em>.</p> <p><strong>Motivation:</strong></p> <p>Detection of nanoparticles and classification of the material type in scanning transmission electron microscopy (STEM) images can be a tedious task, if it has to be done manually. Therefore, a convolutional neural network (CNN) is trained to do this task for STEM-images of TiO<sub>2</sub>-WO<sub>3</sub> nanoparticle hetero-aggregates. In conventional STEM, only 2D-projection images of the samples can be measured. STEM tomography allows for a 3D-reconstruction but it is a time-consuming and hence expensive task. In the present work, evaluations of 2D-projections are compared quantitatively to evaluations of 3D-reconstructions. For both evaluations a CNN is trained to predict particle positions and classify the material. The present dataset contains training and evaluation data and some code scripts that can be used after installation of the MMDetection toolbox (<a href="https://github.com/open-mmlab/mmdetection">https://github.com/open-mmlab/mmdetection</a>) to train the CNN for 2D-projection data. For 3D-reconstruction a StarDist-3D network is trained (<a href="https://github.com/stardist/stardist">https://github.com/stardist/stardist</a>). Details are provided in the manuscript submitted to Ultramicroscopy and in the comments of the code scripts. For evaluation, we provide Python and MATLAB scripts.</p> <p><strong>Authors and funding:</strong></p> <p>The present dataset was created by the authors. The work was funded by the Deutsche Forschungsgemeinschaft within the priority program SPP2289 under contract numbers RO2057/17-1 and MA3333/25-1 and under contract number INST 144/462-1 FUGG.</p> <pre> </pre> <p><strong>Dataset description:</strong></p> <p>We provide several zip-archives. All of them contain two subfolders, one of them for 2D-projection data, the other one for 3D-reconstruction data.</p> <p><em>training_data.zip</em> contains the training data. In the 3D case, the subfolder <em>mask</em> contains the ground truth segmentation masks; the subfolder <em>reconstruction</em> contains the corresponding simulated 3D reconstructions. In the 2D case, the subfolder <em>HAADF</em> contains the 2D-projection images. In both cases, the subfolder <em>json </em>contains the annotation. Each file within the <em>json</em> folder provides for each image or reconstruction the following information:</p> <p>· aggregat_no: image id, the number of the corresponding image file</p> <p>· particle_position_x: list of particle position x-coordinates in nm</p> <p>· particle_position_y: list of particle position y-coordinates in nm</p> <p>· particle_position_z: list of particle position z-coordinates in nm</p> <p>· particle_radius: list of volume equivalent particle radii in nm</p> <p>· particle_type: list of particle types, 1: TiO<sub>2</sub>, 2: WO<sub>3</sub></p> <p>· particle_shape: list of particle shapes: 0: sphere, 1: box, 2: icosahedron</p> <p>· rotation: list of particle rotations in rad. Each particle is rotated twice by the listed angle (before and after deformation)</p> <p>· deformation: list of particle deformations. After the first rotation the particle x-coordinates of the particle’s surface mesh are scaled by the factor listed in deformation, y- and z-coordinates are scaled according to 1/sqrt(deformation).</p> <p>· cluster_index: list of cluster indices for each particle</p> <p>· initial_cluster_index: list of initial cluster indices for each particle, before primary clusters of the same material were merged</p> <p>· fractal_dimension: the intended fractal dimension of the aggregate</p> <p>· fractal_dimension_true: the realized geometric fractal dimension of the aggregate (neglecting particle densities)</p> <p>· fractal_dimension_weight_true: the realized fractal dimension of the aggregate (including particle densities)</p> <p>· fractal_prefactor: fractal prefactor</p> <p>· mixing_ratio_intended: the intended mixing ratio (fraction of WO<sub>3</sub> particles)</p> <p>· mixing_ratio_true: the realised mixing ratio (fraction of WO<sub>3</sub> particles)</p> <p>· mixing_ratio_volume: the realised mixing ratio (fraction of WO<sub>3</sub> volume)</p> <p>· mixing_ratio_weight: the realised mixing ratio (fraction of WO<sub>3</sub> weight)</p> <p>· particle_1_rho: density of TiO<sub>2</sub> used for the calculations</p> <p>· particle_1_size_mean: mean TiO<sub>2</sub> radius</p> <p>· particle_1_size_min: smallest TiO<sub>2</sub> radius</p> <p>· particle_1_size_max: largest TiO<sub>2</sub> radius</p> <p>· particle_1_size_std: standard deviation of TiO<sub>2</sub> radii</p> <p>· particle_1_clustersize: average TiO<sub>2</sub> cluster size</p> <p>· particle_1_clustersize_init: average TiO<sub>2</sub> cluster size of primary clusters (before merging into larger clusters)</p> <p>· particle_1_clustersize_init_intended: intended TiO<sub>2</sub> cluster size of primary clusters</p> <p>· particle_2_rho: density of WO<sub>3 </sub>used for the calculations</p> <p>· particle_2_size_mean: mean WO<sub>3</sub> radius</p> <p>· particle_2_size_min: smallest WO<sub>3</sub> radius</p> <p>· particle_2_size_max: largest WO<sub>3</sub> radius</p> <p>· particle_2_size_std: standard deviation of WO<sub>3</sub> radii</p> <p>· particle_2_clustersize: average WO<sub>3</sub> cluster size</p> <p>· particle_2_clustersize_init: average WO<sub>3</sub> cluster size of primary clusters (before merging into larger clusters)</p> <p>· particle_2_clustersize_init_intended: intended WO<sub>3</sub> cluster size of primary clusters</p> <p>· number_of_primary_particles: number of particles within the aggregate</p> <p>· gyration_radius_geometric: gyration radius of the aggregate (neglecting particle densities)</p> <p>· gyration_radius_weighted: gyration radius of the aggregate (including particle densities)</p> <p>· mean_coordination: mean total coordination number (particle contacts)</p> <p>· mean_coordination_heterogen: mean heterogeneous coordination number (contacts with particles of the different material)</p> <p>· mean_coordination_homogen: mean homogeneous coordination number (contacts with particles of the same material)</p> <p>· material_1: the name of the first material (TiO2)</p> <p>· material_2: the name of the second material (WO3)</p> <p>· radius_equiv: list of area equivalent particle radii (in projection) in nm</p> <p>· k_proj: projection direction of the aggregate: 0: z-direction (axis = 2), 1: x-direction (axis = 1), 2: y-direction (axis = 0)</p> <p>· polygons: list of polygons that surround the particle (COCO annotation)</p> <p>· bboxes: list of particle bounding boxes</p> <p>· aggregate_size: projected area of the aggregate translated into the radius of a circle in nm</p> <p>· n_pix: number of pixel per image in horizontal and vertical direction (squared images)</p> <p>· pixel_size: pixel size in nm</p> <p>· image_size: image size in nm</p> <p>· add_poisson_noise: 1 if poisson noise was added, 0 otherwise</p> <p>· frame_time: simulated frame time (required for poisson noise)</p> <p>· dwell_time: dwell time per pixel (required for poisson noise)</p> <p>· beam_current: beam current (required for poisson noise)</p> <p>· electrons_per_pixel: number of electrons per pixel</p> <p>· dose: electron dose in electrons per Å<sup>2</sup></p> <p>· add_scan_noise: 1 if scan noise was added, 0 otherwise</p> <p>· beam_misposition: parameter that describes how far the beam can be misplaced in pm (required for scan noise)</p> <p>· scan_noise: parameter that describes how far the beam can be misplaced in pix (required for scan noise)</p> <p>· add_focus_dependence: 1 if a focus effect is included, 0 otherwise</p> <p>· add_partial_coating: 1 if some TiO<sub>2</sub> particles were coated by a thin WO<sub>3</sub> film, 0 otherwise.</p> <p>· data_format: data format of the images, e.g. uint8</p> <p>For 3D reconstructions, the following information is added:</p> <p>· add_image_shifts, 1 if all images of the tilt series were shifted randomly by some pixel to account for misaligned images, 0 otherwise.</p> <p>· add_projection_noise: 1 if noise was added to projection angles, 0 otherwise.</p> <p>· N_SIRT: number of SIRT iterations for the 3D-reconstruction.</p> <p>· proj_angles: list of angles used for the projection directions of the tilt series in rad.</p> <p>For the 2D case, there are 24000 training images, 5500 validation images, 5500 test images, and their corresponding annotations. Aggregates and STEM images were obtained with the algorithm explained in the main work. The important data for CNN training is extracted from the files of individual aggregates and concluded in the subfolder <em>COCO</em>. For training, validation and test data there is a file <em>annotation_COCO.json</em> that includes all information required for the CNN training.</p> <p>For the 3D case, there are 100 simulated reconstructions that are divided into 80 training and 20 validation images within the training script.</p> <p>The zip archive <em>models.zip</em> includes the two networks that were trained, evaluated and used for the investigation in the manuscript. In the 2D case, network weights are stored in the file <em>2D_projection/logs/fit/20240209-095952/iter_60000.pth</em>. These weights can be loaded with the jupyter-notebook <em>2D_projection_prediction.ipynb</em>. Furthermore, a configuration file, which is required by the notebooks, is stored as <em>2D_projection/logs/fit</em> <em>20240209-095952/config_file.py</em>. In the 3D case, network weights are stored in <em>3D_reconstruction/model/weights_best.h5</em>. Also for this case, a configuration file is provided. The network can be loaded with the jupyter-notebook <em>3D_reconstruction_prediction.ipynb</em>.</p> <p>The zip archive <em>experiment_measurement.zip</em> includes the experimental 2D-projection images and the experimental 3D-reconstructions investigated in the manuscript. In the 3D case, we provide measured reconstructions as obtained by MATLAB and as transformed for the prediction with the CNN.</p> <p>The zip archive<em> experiment_prediction.zip</em> includes predictions and visualizations obtained by the 2D and 3D CNNs.</p> <p>The zip archive <em>simulation_measurement.zip</em> includes simulated 2D-projection images and simulated 3D-reconstructions that were not used for the network training. These simulations were used for evaluation of trained networks.</p> <p>The zip archive<em> simulation_prediction.zip</em> includes predictions and visualizations obtained by the 2D and 3D CNNs for the simulated data that was not used during the training process.</p> <p>In the zip archive <em>code.zip</em>, we provide several files with Python and MATLAB code that can be used for training, prediction and evaluation of the CNNs. A lot of information is provided within the comments and markdowns. For the application, it is required that the MMDetection toolbox and the StarDist3D framework are installed.</p> <p>· <em>2D_projection_training.py:</em> This Python script can be used for network training of the 2D Mask R-CNN after installation of the MMDetection toolbox.</p> <p>· <em>3D_reconstruction_training.py</em>: This Python script can be used for network training of the StarDist-3D network after installation of the StarDist package.</p> <p>· <em>2D_projection_prediction.ipynb</em>: This jupyter-notebook can be used for the application of a trained Mask R-CNN to experimental and simulated 2D-projection data.</p> <p>· <em>3D_reconstruction_prediction.ipynb</em>: This jupyter-notebook can be used for the application of a trained StarDist-3D network to experimental and simulated 3D-reconstruction data.</p> <p>· <em>Evaluation_experiment.m</em>: This MATLAB script is for the visualization and quantitative comparison of 2D and 3D experimental evaluations.</p> <p>· <em>Evaluation_simulation.m</em>: This MATLAB script is for the visualization and quantitative comparison of 2D and 3D evaluations of simulations.</p> <p>· <em>particle_detection_functions.py:</em> This Python script contains functions required by the jupyter-notebooks. Details can be found within the comments.</p> <p>· <em>ASTRA_CM_plot_results.m</em>: This MATLAB script provides functions for the visualization of experimental and simulated 3D-reconstructions.</p> <p>· <em>ASTRA_CM_particle_detection.m</em>: This MATLAB script is used for the quantitative evaluation of segmentations of 3D-reconstructions.</p> <p> </p> <p>There is no confidential data in this dataset. It is neither offensive, nor insulting or threatening.</p> <p>The dataset was generated to discriminate between TiO<sub>2 </sub>and WO<sub>3</sub> nanoparticles in STEM-images and STEM tomography reconstructions. It might be possible that it can discriminate between different materials if the STEM contrast is similar to the contrast of TiO<sub>2 </sub>and WO<sub>3</sub> but there is no guarantee.</p>
3D Dataset "Computation of Exact g-Factor Maps in 3D GRAPPA Reconstructions"
<p>Datasets used in the paper entitled "", containing the following acquisitions:</p> <p> <strong>1) Simulated abdomen data set</strong>: we have synthetized a 3D volume using the simulation environment XCAT based on the extended cardio-torso phantom. We simulated a T1-weighted acquisition using the following acquisition parameters: TE/TR=1.5/3ms, flip angle=60º, acquisition matrix size=60x60x32. A 32-coil acquisition was simulated by modulating the image using artificial sensitivity maps coded for each coil. The noise-free coil images were transformed into the \bk--space and corrupted with synthetic Gaussian noise characterized by the matrices <span class="math-tex">\(\Gamma_k\)</span>and <span class="math-tex">\(C_k\)</span> with SNR=25 for each coil, and the correlation coefficient between coils was set to <span class="math-tex">\(\rho\)</span>=0.1$. For statistical purposes, 4000 realizations of each image were used.</p> <p><br> <strong>2) Water phantom acquisition</strong>: A MR phantom sphere with solution (GE Medical Systems, Milwaukee, WI) was scanned in a 32-channel head coil on a 3.0T scanner (MR750, GE Healthcare, Waukesha, WI). A spoiled gradient-echo acquisition with 100 realizations of the same fully-encoded k-space sampling was used. Acquisition parameters included: coronal view, TE/TR=0.96/3.69ms, flip angle=12º, field of view=22x22$x30.7<span class="math-tex">\(cm³\)</span>, acquisition matrix size=60x60x32, bandwidth=62.5KHz. We corrected for <span class="math-tex">\(B_0\)</span> field drift related phase variations and magnitude decay by a pre-processing step. First we estimated the phase-shift between realizations from the center of the k-space as a cubic function of time and removed it afterwards. And, second, we estimated the magnitude-decay in the k-space as a linear function and substracted it in order not to affect the noise.</p> <p><br> <strong>3) In vivo acquisition</strong>: in order to assess the feasibility of the proposed method, after obtaining the approval fo the local institutional review board (IRB), a volunteer was scanned in a 32-channel head coil on a 3.0T scanner (MR750, GE Healthcare, Waukesha, WI). A spoiled gradient-echo acquisition of a fully-encoded \bk--space sampling was used. Acquisition parameters included: coronal view, TE/TR=2.2/5.7ms, flip angle=12º,field of view=22x22x22<span class="math-tex">\(cm³\)</span>, matrix size=220x220x220, bandwidth=62.5$KHz.</p>
PLayer: A Plug-and-Play Embedded Neural System to Boost Neural Organoid 3D Reconstruction
<p>This dataset supports the study titled "PLayer: A Plug-and-Play Embedded Neural System to Boost Neural Organoid 3D Reconstruction." It comprises a total of 539 high-resolution images, each with dimensions of 2048x2048 pixels. The image collection took place roughly over one month. We cultured the neural organoids ourselves based on the STEMdiff™ Cerebral Organoid Kit (STEMCELL Technologies Catalog #08570), aged 19, 34, 71, and 112 days, and the STEMdiff™ Dorsal Forebrain Organoid Differentiation Kit (STEMCELL Technologies Catalog #08620), aged 82 days. All organoids were collected on the same day, rinsed twice with PBS to remove any residual medium, and subsequently fixed with 4.0% (w/v) PFA at 4°C overnight before processing.</p>
Respiratory-triggered MRCP acquisition at 3T using a T2-weighted TSE (3D SPACE) sequence for Deep Learning-based reconstruction of MRCP
<h1>Description</h1> <p>This dataset is the sample data for MRCP_DLRecon (<a href="https://github.com/JinhoKim46/MRCP_DLRecon">GitHub</a>). <br>Place this dataset in the "Sample_data/" directory along with the "dataset.csv" file. </p> <h1>Data</h1> <p>The provided 3D MRCP data were acquired at 3T (Skyra, Siemens Healthineers AG, Erlangen) using the 3D-SPACE (3D T2w TSE) sequence for a single healthy volunteer. We provide two 2x and one 6x 3D MRCP data to ensure various training and testing scenarios. Each data in the HDF5 format contains the following structures:</p> <ul> <li>Datasets <ul> <li><strong>grappa</strong>: target data (y * x *<em> </em>slice)</li> <li><strong>kdata_raw</strong>: Raw <em>k</em>-space data (x2 or x6) (nCoil * PE * RO * slice)</li> <li><strong>kdata_fs</strong>: Fully-sampled k-space data from <strong>kdata_raw</strong> using GRAPPA (nCoil ×× PE ×× RO ×× Slice)</li> <li><strong>sm_espirit</strong>: ESPIRiT-based sensitivity maps (nCoil * y * x * slice)</li> </ul> </li> </ul> <h1>Citation</h1> <p>Please cite the following <a href="https://analyticalsciencejournals.onlinelibrary.wiley.com/doi/10.1002/nbm.70002" target="_blank" rel="noopener">paper</a> if this dataset is helpful for your research :)</p> <blockquote> <p>Kim, J., Nickel, M. and Knoll, F. (2025), Deep Learning-Based Accelerated MR Cholangiopancreatography Without Fully-Sampled Data. NMR in Biomedicine, 38: e70002. https://doi.org/10.1002/nbm.70002 </p> </blockquote>
Geochemistry and 3D reconstruction dataset for the Reyðarártindur Pluton, Iceland
<p>-Whole rock major and trace element geochemistry</p> <p>-3D reconstruction of pluton</p> <p>-Feldspar EMP profiles</p> <p>-Sample GPS locations</p>
Palaeontological reconstruction (3D model) of Dolichoderus jonasi Dubovikoff et Zharkov, 2022 (worker)
<p>Supplementary file 1 from Dubovikoff, D. A., Zharkov, D. M. 2022. A new species of the genus <em>Dolichoderus</em> 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, <em>Dolichoderus jonasi</em> 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> <p> </p>
Automatic 3D CAD models reconstruction from 2D orthographic drawings
<p>This dataset is built to reconstruct 3D CAD models from 2D drawings and is based on the public dataset <a href="https://github.com/AutodeskAILab/Fusion360GalleryDataset">Fusion 360 gallery</a>. The dataset includes two parts that are the original data and the reconstructed data (in folders '/original_data" and '/reconstructed'). The part of the original data contains the '.svg' files of 2D drawings and the '.step' files of CAD models. Our reconstruction results are shown in the second part (folder '/reconstructed'), which includes the reconstructed 3D wireframes, 3D shapes with faces (storage in FreeCAD files '.FCStd'), and the images of reconstructed models (screenshot). We also test some cases from the <a href="https://deep-geometry.github.io/abc-dataset/">ABC dataset</a>, shown in the 2_ABC folder.</p> <p>Please cite our paper if you use the dataset.</p> <pre>@article{zhang2023automatic, title={Automatic 3D CAD models reconstruction from 2D orthographic drawings}, author={Zhang, Chao and Pinqui{\'e}, Romain and Polette, Arnaud and Carasi, Gregorio and De Charnace, Henri and Pernot, Jean-Philippe}, journal={Computers \& Graphics}, year={2023}, publisher={Elsevier} }</pre>
CT reconstructions and 3D surface models of preserved impressions found on a Rijksmuseum terracotta sculpture
<p>This dataset contains 3D micro Computed Tomography (CT) reconstructions (*.tiff files) and 3D surface models (Wavefront OBJ files) of preserved impressions found on the terracotta sculpture "Study for a Hovering Putto", dated between 1735 and 1750, and attributed to Laurent Delvaux (Gent, 17 January 1696 – Nivelles, 24 February 1778, Rijksmuseum, BK-NM-9352). The preserved impressions are fingermarks and toolmarks, which we analyse in the manuscript "Artist profiling using micro-CT scanning of a Rijksmuseum terracotta sculpture" by Sero et al. (<a href="https://doi.org/10.1126/sciadv.adg6073">DOI: 10.1126/sciadv.adg6073</a>).</p> <p>Folder names correspond to the names assigned to each impression in the manuscript. Each folder contains a stack of *.tiff files and a "Segmentation.obj", which is the 3D model obtained from Otsu's segmentation method in Slicer3D. The stack of *tiff files and the 3D models can be visualized together in Slicer3D.</p>
3D kinematics and kinetics of change of direction motions reconstructed from virtual inertial sensor data through optimal control simulation
<p>This is the data belonging to the publication "Estimating 3D kinematics and kinetics from inertial sensor data through musculoskeletal movement simulations".</p> <p>This study investigated the feasibility and accuracy of reconstructing, especially change of direction motions, with a 3D full-body musculoskeletal model by tracking virtual inertial sensor data in optimal control simulations. We used the recordings of 90 trials with optical motion capture to generate marker tracking simulations from which we computed virtual inertial sensor data. Using this data, we compared inertial tracking simulations and marker tracking simulations.</p> <p>Please see the README and the publication for further details.</p>
Roots tips 3D trajectory reconstructed from stereovision cameras
<p>Dataset of 3D trajectories of root tips of maize plants in the first week of growth. 3D reconstruction obtained using a system based on stereovision with 2 cameras having IR (infra-red) sensors to capture motion in dark conditions. </p> <p>Plant Species: <em>Zea Mays (B72) L.</em></p>
The belt form Illerup Ådal – 3D reconstruction 3
The reconstructed belt comes from a war booty sacrifice site, **Illerup Ådal**, in Denmark. The belt (inv. no. SAHR) dates to the beginning of the 3rd century AD. Location: The Moesgard Museum (https://www.moesgaardmuseum.dk). The belt was reconstructed in 3dsMax and Unreal Engine 4 by using original photographs by J. Ilkjær. The authors of the reconstruction are Aleksei Moskvin, Mariia Moskvina (Saint Petersburg State University of Industrial Technologies and Design) and Martijn A. Wijnhoven (VU University Amsterdam). https://independent.academia.edu/AlekseiMoskvin https://independent.academia.edu/MariiaMoskvina https://vu-nl.academia.edu/MartijnAWijnhoven Source: Objaverse 1.0 / Sketchfab
The Thorsberg tunic – 3D reconstruction
The tunic (Archäologisches Landesmuseum Schleswig, inv. no.F.S. 3683) is made from high quality woollen diamond twill cloth. Four panels make up the garment: one for the back and one for the front, and two for the sleeves. The reconstruction was made by using the original cutting pattern. The panels were sewn together and put on the mannequin in Clo3D software, textured in Substance Painter and post-processed in 3dsMax. For further details see https://doi.org/10.1016/j.culher.2021.03.003 The authors of the reconstruction are Martijn A. Wijnhoven (VU University Amsterdam), Aleksei Moskvin & Mariia Moskvina (Saint Petersburg State University of Industrial Technologies and Design). Please follow us on "Academia" to receive updates on our latest reconstructions and surprising discoveries. https://vu-nl.academia.edu/MartijnAWijnhoven https://independent.academia.edu/AlekseiMoskvin https://independent.academia.edu/MariiaMoskvina http://doi.org/10.13140/RG.2.2.19139.73760 Source: Objaverse 1.0 / Sketchfab
ScienceDex guides
Understand access before you commit
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.