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478 results for “3D data”
Underlying data for "Microscale 3D Liver Bioreactor for In Vitro Hepatotoxicity Testing under Perfusion Conditions"
<p>Underlying data for the paper "Microscale 3D Liver Bioreactor for In Vitro Hepatotoxicity Testing under Perfusion Conditions" published in the journal <em>Bioengineering</em>.</p>
Prototyping 3D Virtual Learning Environments with X3D-based Content and Visualization Tools-Figure 11. Online accessible repository of digital data on cultural heritage with X3D models (STARC Web Repository, 2017, © Copyright 2017, STARC, Cyprus Institute. Used with permission)
<p>Prototyping can also include the development of toolkits for automatic content generation simulator, but in the case of an architectural environment, the components are too complex to be automatically generated. Furniture elements or the learning artifacts (i.e. content created by learners) can be converted to be viewed in X3D compatible browsers or included in online galleries (Figure 11). After functional and 3D content prototyping, certain components of the virtual campus can be easily modified and adapted as needed.</p>
A Facial Motion Capture System Based on Neural Network Classifier Using RGB-D Data-Figure 8. 3D model of some facial expressions
<p>Face region is separated precisely from video frames by using a segmentation method based on skin color. The depth data corresponding to this separated area is taken for a 3D representation from depth data corresponding to each frame. At the end, a file is prepared for each frame consisting of face points with 6 features: X, Y, depth, red, green and blue color. These data are used for producing a 3D model and a graphical avatar for each frame (Figure 7). Figure 8 shows 3D model of some facial expressions.</p>
A Facial Motion Capture System Based on Neural Network Classifier Using RGB-D Data-Figure 7. Avatar 3D model generation
<p>Face region is separated precisely from video frames by using a segmentation method based on skin color. The depth data corresponding to this separated area is taken for a 3D representation from depth data corresponding to each frame. At the end, a file is prepared for each frame consisting of face points with 6 features: X, Y, depth, red, green and blue color. These data are used for producing a 3D model and a graphical avatar for each frame (Figure 7). Figure 8 shows 3D model of some facial expressions.</p>
Data for Matlab package locFISH to simulate realistic 3d smFISH images
<p>Different data-sets needed by the Matlab package locFISH. locFISH allows the simulation and analysis of realistic single molecule FISH (smFISH) images.</p> <p><strong>data_simulation.zip</strong><br> Contains all necessary data to simulated smFISH images. Specifically, the zip archive contains a library of 3D cell shapes, realistic imaging background, and a simulated PSF (Point Spread Function). </p> <p><strong>GAPDH.zip</strong><br> Contains the smFISH data of GAPDH and the corresponding analysis results, which were used to create the library of cell shapes provided in data_simulation.zip </p> <p>For more details on these data and how do to use them, please consult the detailed user-manual provided with <strong>locFISH</strong>, available at</p> <p>https://bitbucket.org/muellerflorian/fish_quant</p>
Data and results for manuscript "Small scale characterization of vine plant root water uptake via 3D electrical resistivity tomography and Mise-à-la-Masse method"
<p>This package contains measured raw ERT and MALM data used to generate the plots in the manuscript.</p> <p> </p>
Tomographic X-ray data of time-dependent 3D cross phantom
<p>This is the documentation of the tomographic X-ray data of a dynamic cross phantom made available at http://www.fips.fi/dataset.php. The data can be freely used for scientific purposes with appropriate references to the data and to this document in http://arxiv.org/. The data set consists of (1) the X-ray sinogram with 16 or 30 time frames (depending on resolution) of 2D slices of the cross phantom, made by crossing aluminum and graphite sticks in melted candle wax and (2) the corresponding static and dynamic measurement matrices modeling the linear operation of the X-ray transform. Each of these sinograms was obtained from a measured 360-projection fan-beam sinogram by down-sampling and taking logarithms. The original (measured) sinogram is also provided in its original form and resolution.</p> <p>This new version contains added file <a href="https://zenodo.org/api/files/5c00de09-bb57-4c9e-9beb-00c63358de3c/DataStatic_560x60.mat?versionId=1e53d43d-d999-435e-bb1c-8297cf32c984">DataStatic_560x60.mat </a> with 80 time frames and <a href="https://zenodo.org/api/files/5c00de09-bb57-4c9e-9beb-00c63358de3c/DataStatic_1120x60.mat?versionId=afded407-6c88-4880-b427-fe7fd09306f4">DataStatic_1120x60.mat </a> with 230 time frames. You can run these files with the code example 2, which computes a Tikhonov regularized reconstruction using conjugate gradient algorithm.</p>
3D-structured Supports create complete Data Sets for Electron Crystallography
<p>Each tar file contains the raw files in HDF5 format, together with the XDS.INP file used for data integration.</p> <p>NB: The meta-data in the HDF5 files have no meaning, please refer to the respective XDS.INP file for respective information.</p>
Geospatial data and 3D representation of Maungataketake, Auckland, New Zealand
<p><em>Context</em></p> <p>Maungataketake (Ellett’s Mountain) was a volcanic cone on the shore of the Manukau Harbour, Mangere, New Zealand. In the second half of the twentieth century the mountain was quarried away. Maungataketake was a terraced Māori Pā, and archaeological excavations (only now in the process of being published) were undertaken there between 1972 and 1975, and in 1982, prior to its complete destruction. There is aerial imagery of the mountain available that depicts the mountain prior to quarrying. With these data a 3D model of the site was made using photogrammetry, which was also used to create a digital surface model (DSM) and contour map of the mountain. The resulting data is provided here and is aimed for further geospatial applications. In addition, the resolution of the provided DSM has analogues for the wider region and therefore could be incorporated to represent the landscape pre-destruction. Further to this a representation of the 3D model may be found on <a href="https://sketchfab.com/3d-models/maungataketake-9a58745853154b88ac9bde1a74025cc4">SketchFab</a>.</p> <p> </p> <p><em>Method</em></p> <p>The photogrammetry model was created in Agisoft Metashape version 1.5.4. Ten aerial images were used of Maungataketake and the surrounding area, captured on 19<sup>th</sup> August 1960. These images were downloaded from http://retrolens.co.nz and are licensed by LINZ CC-BY 3.0. The model was aligned and the spare point cloud filtered by gradual selection with the following parameters: projection error = 0.2; reconstruction uncertainty = 10; projection accuracy = 2.5. The dense cloud was processed with depth maps of ultra high quality and aggressive filtering. The resulting points cloud was edited to remove outlying points and processed into a 3D model.</p> <p>The resulting 3D model was manually edited to remove faces representing trees on Maungataketake only, but not the surrounding area. This was done as to obtain representative surface contours of the mountain. The model was georeferenced by the identification of points on the landscape present on the 1960 composite image and contemporary satellite imagery. A 0.5 m DSM and contours at 1 m resolution were calculated of Maungataketake.</p> <p> </p> <p><em>Contents of dataset</em></p> <ul> <li>A geodatabase with: <ul> <li>Control points used for georectification</li> <li>1 m contours without elevation of Maungataketake</li> <li>1 m contours with elevation of Maungataketake</li> <li>0.5 m composite aerial image</li> <li>0.5 m DSM of area covering control points</li> <li>0.5 m DSM of Maungataketake</li> </ul> </li> <li>Aerial photographs Crown_583-1924_22-26, Crown_583_1925_22-26</li> <li>Licence for aerial photographs from http://retrolens.co.nz</li> <li>Attributes of aerial photographs</li> </ul>
Ionisation of Atoms Determined by Kappa Refinement against 3D Electron Diffraction Data
<p>The following submission contains the data reduction and processing files, dynamical refinement files, refinement files for theoretical structure factors, and CIF files of five inorganic compounds: quartz, natrolite, borane, caesium lead bromide, and lutetium aluminium garnet collected by 3D electron diffraction (3D ED) for studying ionisation of atoms by kappa refinement against 3D ED data.</p> <p>The data set for quartz was collected using the precession-assisted 3D ED method and for borane, caesium lead bromide, and lutetium aluminium garnet was collected using the continuous-rotation 3D ED method. Two data sets were collected from the same crystal for natrolite using continuous-rotation and precession-assisted 3D ED method. The data reduction and processing were done using PETS2 (<em>1</em>) software and the dynamical refinements were performed using the JANA2020 (<em>2</em>) software. The refinements were performed in two primary stages: IAM refinements (without taking into consideration the effects of charge transfer between the atoms) and kappa refinements (by taking into consideration the effects of charge transfer between the atoms).</p> <p>The submission also contains JANA2020 files of refinements against theoretical structure factors obtained using periodic DFT calculations and on the structure model obtained after IAM refinements of each of the experimental data sets.</p> <p>The folders are divided according to the compounds. Each folder contains the relevant data reduction and processing files (PETS2 files), dynamical refinement files (JANA2020 files for IAM and kappa refinements), refinement files for theoretical structure factors (JANA2020 files for IAM and kappa refinements) and final CIF files (for IAM and kappa refinements).</p> <p> </p> <p>References</p> <p>1. L. Palatinus, P. Brázda, M. Jelínek, J. Hrdá, G. Steciuk, M. Klementová, Specifics of the data processing of precession electron diffraction tomography data and their implementation in the program PETS2.0. <em>Acta Cryst B</em> <strong>75</strong>, 512–522 (2019).</p> <p>2. V. Petříček, L. Palatinus, J. Plášil, M. Dušek, Jana2020 – a new version of the crystallographic computing system Jana. <em>Zeitschrift für Kristallographie - Crystalline Materials</em> <strong>238</strong>, 271–282 (2023).</p> <p> </p> <p>The following table summarises the crystallographic information and data collection parameters for the data sets.</p> <table> <tbody> <tr> <td> <p><strong>Crystal data</strong></p> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> <p>Sample</p> </td> <td> <p>Quartz</p> </td> <td> <p>Natrolite</p> </td> <td> <p>Natrolite</p> </td> <td> <p>Borane</p> </td> <td> <p>Caesium lead bromide</p> </td> <td> <p>Lutetium Aluminium Garnet</p> </td> </tr> <tr> <td> <p>Chemical formula</p> </td> <td> <p>SiO<sub>2</sub></p> </td> <td> <p>Na<sub>2</sub>Al<sub>2</sub>Si<sub>3</sub>O<sub>12</sub>H<sub>4</sub></p> </td> <td> <p>Na<sub>2</sub>Al<sub>2</sub>Si<sub>3</sub>O<sub>12</sub>H<sub>4</sub></p> </td> <td> <p>B<sub>18</sub>H<sub>22</sub></p> </td> <td> <p>CsPbBr<sub>3</sub></p> </td> <td> <p>Lu<sub>3</sub>Al<sub>5</sub>O<sub>12</sub></p> </td> </tr> <tr> <td> <p>M<sub>r</sub></p> </td> <td> <p>60.1</p> </td> <td>380.2</td> <td> <p>380.2</p> </td> <td> <p>108.4</p> </td> <td> <p>579.8</p> </td> <td> <p>851.8</p> </td> </tr> <tr> <td> <p>Crystal system, space group</p> </td> <td> <p>Trigonal, P3<sub>2</sub>21</p> </td> <td> <p>Orthorhombic, Fdd2</p> </td> <td> <p>Orthorhombic, Fdd2</p> </td> <td> <p>Orthorhombic, Pccn</p> </td> <td> <p>Orthorhombic, Pbnm</p> </td> <td> <p>Cubic, Ia3 ̅d</p> </td> </tr> <tr> <td> <p>a, b, c (Å)</p> </td> <td> <p>4.9012(24), 4.9012, 5.4068(26)</p> </td> <td> <p>18.3885(1), 18.7183(32), 6.6569(11)</p> </td> <td> <p>18.4125(9), 18.7073(7), 6.6306(2)</p> </td> <td> <p>10.7789(17), 11.9869(16), 10.7338(17)</p> </td> <td> <p>8.1189(4), 8.359(4), 11.7593(5)</p> </td> <td> <p>11.9105(4), 11.9105(4), 11.9105(4)</p> </td> </tr> <tr> <td> <p>α, β, γ (°)</p> </td> <td> <p>90, 90, 120</p> </td> <td>90, 90, 90</td> <td> <p>90, 90, 90</p> </td> <td> <p>90, 90, 90</p> </td> <td> <p>90, 90, 90</p> </td> <td> <p>90, 90, 90</p> </td> </tr> <tr> <td> <p>V (Å<sup>3</sup>)</p> </td> <td> <p>112.48(8)</p> </td> <td> <p>2291.31(54)</p> </td> <td> <p>2283.90(16)</p> </td> <td> <p>1386.87(36)</p> </td> <td> <p>798.1(1)</p> </td> <td> <p>1689.6(1)</p> </td> </tr> <tr> <td> <p>Z</p> </td> <td> <p>3</p> </td> <td> <p>8</p> </td> <td> <p>8</p> </td> <td> <p>4</p> </td> <td> <p>4</p> </td> <td> <p>8</p> </td> </tr> <tr> <td> <p>Crystal size (mm)</p> </td> <td> <p>0.0004</p> </td> <td> <p>0.0005</p> </td> <td> <p>0.0005</p> </td> <td> <p>0.0015</p> </td> <td> <p>0.0004</p> </td> <td> <p>0.0003</p> </td> </tr> <tr> <td> <p> </p> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> <p><strong>Data collection</strong></p> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> <p>Diffractometer</p> </td> <td> <p>TEM FEI Technei G2 20</p> </td> <td> <p>TEM FEI Technei G2 20</p> </td> <td> <p>TEM FEI Technei G2 20</p> </td> <td> <p>TEM FEI Technei G2 20</p> </td> <td> <p>TEM FEI Technei G2 20</p> </td> <td> <p>TEM FEI Technei G2 20</p> </td> </tr> <tr> <td> <p>3D ED method</p> </td> <td> <p>Precession</p> </td> <td> <p>Precession</p> </td> <td> <p>Continuous Rotation</p> </td> <td> <p>Continuous Rotation</p> </td> <td> <p>Continuous Rotation</p> </td> <td> <p>Continuous Rotation</p> </td> </tr> <tr> <td> <p>Detector</p> </td> <td> <p>Medipix 3 ASI Cheetah</p> </td> <td> <p>Medipix 3 ASI Cheetah</p> </td> <td> <p>Medipix 3 ASI Cheetah</p> </td> <td> <p>Medipix 3 ASI Cheetah</p> </td> <td> <p>Medipix 3 ASI Cheetah</p> </td> <td> <p>Medipix 3 ASI Cheetah</p> </td> </tr> <tr> <td> <p>Radiation source</p> </td> <td> <p>LaB<sub>6</sub></p> </td> <td> <p>LaB<sub>6</sub></p> </td> <td> <p>LaB<sub>6</sub></p> </td> <td> <p>LaB<sub>6</sub></p> </td> <td> <p>LaB<sub>6</sub></p> </td> <td> <p>LaB<sub>6</sub></p> </td> </tr> <tr> <td> <p>Radiation type</p> </td> <td> <p>Electron, λ = 0.0251 Å</p> </td> <td> <p>Electron, λ = 0.0251 Å</p> </td> <td> <p>Electron, λ = 0.0251 Å</p> </td> <td> <p>Electron, λ = 0.0251 Å</p> </td> <td> <p>Electron, λ = 0.0251 Å</p> </td> <td> <p>Electron, λ = 0.0251 Å</p> </td> </tr> <tr> <td> <p>Temperature (K)</p> </td> <td> <p>293</p> </td> <td> <p>95</p> </td> <td> <p>95</p> </td> <td> <p>100</p> </td> <td> <p>153</p> </td> <td> <p>153</p> </td> </tr> <tr> <td> <p>(sin θ/λ)<sub>max</sub> (Å<sup>−1</sup>)</p> </td> <td> <p>1.25</p> </td> <td> <p>1.1</p> </td> <td> <p>1.00</p> </td> <td> <p>0.85</p> </td> <td> <p>1.00</p> </td> <td> <p>1.4</p> </td> </tr> <tr> <td> <p>No. of measured, independent and<br>observed [I > 3σ(I)] reflections</p> </td> <td> <p>3631, 1076, 1004 </p> </td> <td> <p>15767, 6018, 4419 </p> </td> <td> <p>12368, 4546, 4422 </p> </td> <td> <p>30304, 13809, 4779</p> </td> <td> <p>16736, 422, 363</p> </td> <td> <p>23256, 1562, 1363</p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> <td> <p> </p> </td> <td> <p> </p> </td> <td> <p> </p> </td> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><strong>Software used</strong></p> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> <p>Data collection</p> </td> <td> <p>RATS software</p> </td> <td> <p>RATS software</p> </td> <td> <p>RATS software</p> </td> <td> <p>RATS software</p> </td> <td> <p>RATS software</p> </td> <td> <p>RATS software</p> </td> </tr> <tr> <td> <p>Data reduction and processing</p> </td> <td> <p>PETS2</p> </td> <td> <p>PETS2</p> </td> <td> <p>PETS2</p> </td> <td> <p>PETS2</p> </td> <td> <p>PETS2</p> </td> <td> <p>PETS2</p> </td> </tr> <tr> <td> <p>Refinement</p> </td> <td> <p>JANA2020</p> </td> <td> <p>JANA2020</p> </td> <td> <p>JANA2020</p> </td> <td> <p>JANA2020</p> </td> <td> <p>JANA2020</p> </td> <td> <p>JANA2020</p> </td> </tr> <tr> <td> <p>DFT calculation</p> </td> <td> <p>WIEN2k and Crystal23</p> </td> <td> <p>WIEN2k</p> </td> <td> <p>Crystal23</p> </td> <td> <p>Crystal23</p> </td> <td> <p>WIEN2k</p> </td> <td> <p>WIEN2k</p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> <td> </td> <td> <p> </p> </td> <td> <p> </p> </td> <td> <p> </p> </td> <td> </td> </tr> </tbody> </table>
Compilation of data collected in surveys on the WissKI-based 3D Repository with DFG 3D-Viewer project partners and architecture students from the Warsaw University of Technology and the Technical University of Łódź
<p>The dataset contain the compiliation of responses from users of WissKI-based 3D Repository (https://3d-repository.hs-mainz.de/)., which is the open platform for deposit of 3D models of cultural heritage. The beta version of the WissKI 3D Repository, initiated in June 2022, has been subjected to evaluation by two primary target groups since its launch. The initial group, composed of students in the cultural heritage domain, was tasked with showcasing the importance of documenting and publishing 3D models of digital reconstructions. The survey with sutdents was conducted for three different classes: </p> <p>1) In summer 2022 with bachelor architectrue students at Warsaw University of Technology during seminar of choice regarding digital reconstruction of wooden synagogues;</p> <p>2) In summer 2023 with bachelor architectrue students at Warsaw University of Technology, and master students from Technology University of Łódź during seminar of choice regarding digital reconstruction of wooden synagogues;</p> <p>3) In autumn 2023 during international workshop about digital 3D heritage of CoVHer project with studnets of architecture from Warsaw Univeristy of Technology, Alma Mater Studiorum – Universita di Bologna, Facoltà di Architettura di Porto and Hochschule Mainz - University of Applied Sciences, as well as archaeology studnets from Universitat Autònoma de Barcelona.</p> <p>The second group, comprising digital 3D cultural heritage professionals, predominantly focused on archiving digital assets. Participants were project partners of DFG 3D Viewer project, which were professionals from the Institute of Archaeology at University Cologne, the Institute of Art History at the Ludwig-Maximilians-Universität Munich, the Architecture, Civil Engineering and Urban Planning Department of BTU Cottbus Senftenberg, and the Detushce Museum. They were asked for evaluaton of system after three differetn stages of work: at the begging wihtout any introduction to the system, after proivision of intorudctionary materilas and finally at the end of work.</p> <p>All participants were requested to report their experiences across four categories: metadata form, 3D viewer, provided guidelines, and overall experience. A 5-point rating scale was employed to assess specific issues, with 1 being the most negative and 5 being the most positive. The form length question was an exception, where a median value of 3 was considered ideal, and extreme values indicated either excessive length or brevity.</p>
Supplementary data for study on "Superplastic 3D printed nitinol woven metamaterials lead to dramatic variations of mechanical properties by design"
<p>Raw and processed data from experimental compression testing of 3D printed nitinol lattices and wovens are provided as supplementary materials for the mentioned study, submitted for evaluation to the journal of Virtual and Physical Prototyping.</p>
Data products for "3D modeling of long-term slow slip events along the flat-slab segment in the Guerrero Seismic Gap, Mexico"
<p>Data products for '3D modeling of long-term slow slip events along the flat-slab segment in the Guerrero Seismic Gap, Mexico' by A. Perez-Silva, D. Li, A.-A. Gabriel and Y. Kaneko</p>
Assessment of 3D MINFLUX data for quantitative structural biology in cells
<p>Reanalysed data for "Assessment of 3D MINFLUX data for quantitative structural biology in cells"</p> <p>https://www.biorxiv.org/content/10.1101/2021.08.10.455294v1</p> <p>Contact Hell lab for the raw data</p> <p>https://www.mpibpc.mpg.de/hell</p>
Replication Data for: Geometry-Complete Perceptron Networks for 3D Molecular Graphs
<p>Included are preprocessed data files for the Newtonian many-body systems modeling task described in our accompanying manuscript.</p>
3D Data from "New look at Concavicaris woodfordi (Euarthropoda: Pancrustacea?) using micro-computed tomography"
<p>It includes the tomograms (CT-scan), the segmentation project (Mimics) the 3D rendered data (STL) of the holotype of <em>Concavicaris woodfordi</em> (USNM PAL 112025).</p> <p><strong>Tomograms</strong>. The specimen was micro-CT scanned using the North Star Imaging µCT scanner housed at Vanderbilt University (Tennessee, USA). 1377 two-dimensional images were obtained with a voxel size of 46 µm at a voltage of 115 kV and current of 10 µA; the volume was reconstructed using EFX-CT (North Star Imaging, Minnesota, USA).</p> <p><strong>Segmentation. </strong>Rotation (178°), cropping and conversion to 8-bit were applied to every slice prior to segmentation. Manual and semi-automatic segmentation were done using Mimics 24.0 Research Edition (Materialise). The results of the segmentation were exported as STL files. 3D rendering and processing was done using Meshlab 2021.05 (GNU GPL 3.0)</p>
Data set for publication Sikora P., Techman M., Federowicz K., El-Khayatt A.M., Saudi H.A., Abd Elrahman M., Hoffmann M., Stephan D., Chung S.-Y. Insight into the microstructural and durability characteristics of 3D printed concrete: Cast versus printed specimens. Case Studies in Construction Materials (2022), 17, e01320.
<p>Open dataset for publication Sikora P., Techman M., Federowicz K., El-Khayatt A.M., Saudi H.A., Abd Elrahman M., Hoffmann M., Stephan D., Chung S.-Y. Insight into the microstructural and durability characteristics of 3D printed concrete: Cast versus printed specimens. <strong>Case Studies in Construction Materials (2022)</strong>, 17, e01320. <a href="https://doi.org/10.1016/j.cscm.2022.e01320">https://doi.org/10.1016/j.cscm.2022.e01320</a></p> <p>File 1 - Mechanical characteristics - *.opju (Origin)</p> <p>File 2 - Particle size distributions of used materials - *.opju (Origin)</p> <p>File 3 - Sorptivity measurement data - *.opju (Origin)</p> <p>File 4 - G-code for printing of 1 layered specimen - *txt</p> <p>File 5 - G-code for printing of 3 layered specimens - *txt</p>
Supplemental Data for Architector for high-throughput cross-periodic table 3D complex building
<p>This repository contains all of the data presented in either the main text or the SI for the manuscript "<strong><em>Architector</em> for high-throughput cross-periodic table 3D complex building</strong>".</p>
3D motion corrupted PET/MRI phantom raw data
<p>The measurements were all performed on a PET/MRI system from Siemens Healthineers AG (Biograph mMR). An MR-compatible robotic system was used to generate rigid movements of a head-like phantom (translational and more comlpex motion patterns). </p> <p> </p> <p><strong>A README.md is available for further information regarding the dataset.</strong></p>
3D Golden radial phase encoding MR raw data
<p>MR raw dataset in ISMRMRD format acquired with a 3D Golden radial phase encoding trajectory. One data set is of a static phantom, the other data set is of a moving phantom. For details about how to reconstruct this data sets please have a look at: <a href="https://github.com/SyneRBI/SIRF-Exercises/tree/master/notebooks/MR">SyneRBI/SIRF-Exercises</a></p>
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.