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478 results for “3D Data”
UK Industrial X-ray CT User-group Benchmark Data: 3D Al target 08
<p>X-ray tomography (CT) image data of a aluminium target as part of the 'UK Industrial X-ray CT User-group Benchmark Study'.<br> <br> The 3D image was generated with an X-ray tomography scan performed by Dr T Lowe at the Manchester X-ray Imaging Facility.<br> <br> The dataset includes: raw radiographs; scan & reconstruction parameter settings file; reconstructed 3D volume. To visualise the 3D volume use software such as ImageJ (https://imagej.net/Fiji/Downloads). The volume image data (.raw) is in binary format and has the following characteristics: 1596 x 1596 x 950; 16-bit Signed; little-endian byte order.</p>
UK Industrial X-ray CT User-group Benchmark Data: 3D Al target 02
<p>X-ray tomography (CT) image data of a aluminium target as part of the 'UK Industrial X-ray CT User-group Benchmark Study'.<br> <br> The 3D image was generated with an X-ray tomography scan performed by Dr T Lowe at the Manchester X-ray Imaging Facility.<br> <br> The dataset includes: raw radiographs; scan & reconstruction parameter settings file; reconstructed 3D volume. To visualise the 3D volume use software such as ImageJ (https://imagej.net/Fiji/Downloads). The volume image data (.raw) is in binary format and has the following characteristics: 1596 x 1596 x 950; 16-bit Signed; little-endian byte order.</p>
UK Industrial X-ray CT User-group Benchmark Data: 3D Al target 03
<p>X-ray tomography (CT) image data of a aluminium target as part of the 'UK Industrial X-ray CT User-group Benchmark Study'.<br> <br> The 3D image was generated with an X-ray tomography scan performed by Dr T Lowe at the Manchester X-ray Imaging Facility.<br> <br> The dataset includes: raw radiographs; scan & reconstruction parameter settings file; reconstructed 3D volume. To visualise the 3D volume use software such as ImageJ (https://imagej.net/Fiji/Downloads). The volume image data (.raw) is in binary format and has the following characteristics: 1596 x 1596 x 950; 16-bit Signed; little-endian byte order.</p>
UK Industrial X-ray CT User-group Benchmark Data: 3D Al target 07
<p>X-ray tomography (CT) image data of a aluminium target as part of the 'UK Industrial X-ray CT User-group Benchmark Study'.<br> <br> The 3D image was generated with an X-ray tomography scan performed by Dr T Lowe at the Manchester X-ray Imaging Facility.<br> <br> The dataset includes: raw radiographs; scan & reconstruction parameter settings file; reconstructed 3D volume. To visualise the 3D volume use software such as ImageJ (https://imagej.net/Fiji/Downloads). The volume image data (.raw) is in binary format and has the following characteristics: 1596 x 1596 x 950; 16-bit Signed; little-endian byte order.</p>
UK Industrial X-ray CT User-group Benchmark Data: 3D Al target 05
<p>X-ray tomography (CT) image data of a aluminium target as part of the 'UK Industrial X-ray CT User-group Benchmark Study'.<br> <br> The 3D image was generated with an X-ray tomography scan performed by Dr T Lowe at the Manchester X-ray Imaging Facility.<br> <br> The dataset includes: raw radiographs; scan & reconstruction parameter settings file; reconstructed 3D volume. To visualise the 3D volume use software such as ImageJ (https://imagej.net/Fiji/Downloads). The volume image data (.raw) is in binary format and has the following characteristics: 1596 x 1596 x 950; 16-bit Signed; little-endian byte order.</p>
UK Industrial X-ray CT User-group Benchmark Data: 3D Al target 04
<p>X-ray tomography (CT) image data of a aluminium target as part of the 'UK Industrial X-ray CT User-group Benchmark Study'.<br> <br> The 3D image was generated with an X-ray tomography scan performed by Dr T Lowe at the Manchester X-ray Imaging Facility.<br> <br> The dataset includes: raw radiographs; scan & reconstruction parameter settings file; reconstructed 3D volume. To visualise the 3D volume use software such as ImageJ (https://imagej.net/Fiji/Downloads). The volume image data (.raw) is in binary format and has the following characteristics: 1596 x 1596 x 950; 16-bit Signed; little-endian byte order.</p>
UK Industrial X-ray CT User-group Benchmark Data: 3D Al target 09
<p>X-ray tomography (CT) image data of a aluminium target as part of the 'UK Industrial X-ray CT User-group Benchmark Study'.<br> <br> The 3D image was generated with an X-ray tomography scan performed by Dr T Lowe at the Manchester X-ray Imaging Facility.<br> <br> The dataset includes: raw radiographs; scan & reconstruction parameter settings file; reconstructed 3D volume. To visualise the 3D volume use software such as ImageJ (https://imagej.net/Fiji/Downloads). The volume image data (.raw) is in binary format and has the following characteristics: 1596 x 1596 x 950; 16-bit Signed; little-endian byte order.</p>
Data set and 3d model from Emendi M, Sturla F, Ghosh RP, Bianchi M, Piatti F, Pluchinotta FR, Giese D, Lombardi M, Redaelli A, Bluestein D. Patient-Specific Bicuspid Aortic Valve Biomechanics: A Magnetic Resonance Imaging Integrated Fluid-Structure Interaction Approach. Ann Biomed Eng. 2020 Aug 17. doi: 10.1007/s10439-020-02571-4. Epub ahead of print. PMID: 32804291.
<p>Data set and 3d model from Emendi M, Sturla F, Ghosh RP, Bianchi M, Piatti F, Pluchinotta FR, Giese D, Lombardi M, Redaelli A, Bluestein D. Patient-Specific Bicuspid Aortic Valve Biomechanics: A Magnetic Resonance Imaging Integrated Fluid-Structure Interaction Approach. Ann Biomed Eng. 2020 Aug 17. doi: 10.1007/s10439-020-02571-4. Epub ahead of print. PMID: 32804291.</p> <p> </p> <p>This is the abstract:</p> <p>Congenital bicuspid aortic valve (BAV) consists of two fused cusps and represents a major risk factor for calcific valvular stenosis. Herein, a fully coupled fluid-structure interaction (FSI) BAV model was developed from patient-specific magnetic resonance imaging (MRI) and compared against in vivo 4-dimensional flow MRI (4D Flow). FSI simulation compared well with 4D Flow, confirming direction and magnitude of the flow jet impinging onto the aortic wall as well as location and extension of secondary flows and vortices developing at systole: the systolic flow jet originating from an elliptical 1.6 cm<sup>2</sup> orifice reached a peak velocity of 252.2 cm/s, 0.6% lower than 4D Flow, progressively impinging on the ascending aorta convexity. The FSI model predicted a peak flow rate of 22.4 L/min, 6.7% higher than 4D Flow, and provided BAV leaflets mechanical and flow-induced shear stresses, not directly attainable from MRI. At systole, the ventricular side of the non-fused leaflet revealed the highest wall shear stress (WSS) average magnitude, up to 14.6 Pa along the free margin, with WSS progressively decreasing towards the belly. During diastole, the aortic side of the fused leaflet exhibited the highest diastolic maximum principal stress, up to 322 kPa within the attachment region. Systematic comparison with ground-truth non-invasive MRI can improve the computational model ability to reproduce native BAV hemodynamics and biomechanical response more realistically, and shed light on their role in BAV patients' risk for developing complications; this approach may further contribute to the validation of advanced FSI simulations designed to assess BAV biomechanics.</p> <p> </p>
Data and Code for 3D flightpaths reveal the development of spatial memory in wild hummingbirds
Open the record for dataset details and reuse information.
Data and Code for "3D flightpaths reveal the development of spatial memory in wild hummingbirds"
<p>Data and Code for "3D flightpaths reveal the development of spatial memory in wild hummingbirds"</p>
Data_Indirect 3D Printed Electrode Mixers
<p>The folder contains the processed data of figures 4, 5, 6, S2 and S3 that appear in:</p> <p>J. Hereijgers, J. Schalck, J. Lölsberg, M. Wessling, T. Breugelmans, Indirect 3D Printed Electrode Mixers, ChemElectroChem. 6 (2019) 378–382. <a href="https://doi.org/10.1002/celc.201801436">https://doi.org/10.1002/celc.201801436</a>.</p>
Data for Matlab package FISH-sim 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> </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/locfish</p> <p> </p>
Data_Mass transfer and hydrodynamic characterization of structured 3D electrodes for electrochemistry
<p>The folder contains the processed data of figures 3, 4A, 4B, 4C, 4D, 4E, 4F, 5, 6A, 6B, 6C, 6D, 7 that appear in:</p> <p>J. Hereijgers, J. Schalck, T. Breugelmans, Mass transfer and hydrodynamic characterization of structured 3D electrodes for electrochemistyr, Chem Eng. J. (2020) 123283.</p>
Data_ Effects of Structured 3D Electrodes on the Performance of Redox Flow Batteries
<p>The folder contains the processed data of figures 3, 5, 7, 8, 9, S4, S5, S6, S7, S8, S9, S10, S11 and S12 that appear in:</p> <p>R. De Wolf, M. De Rop, and J. Hereijgers, “Effects of Structured 3D Electrodes on the Performance of Redox Flow Batteries,” <em>ChemElectroChem</em>, vol. 202200640, 2022, doi: 10.1002/celc.202200640.</p>
Expression data from 3D cell-printed human bone marrow derived mesenchymal stem cells (hBMMSCs) with liver, heart, skin and cornea decellularized extracellular matrix (dECM) bioinks
GEO Series GSE126877. Homo sapiens. 8 samples. Type: Expression profiling by array.
Data set from Spinelli D, Marconi S, Caruso R, Conti M, Benedetto F, De Beaufort HW, Auricchio F, Trimarchi S. 3D printing of aortic models as a teaching tool for improving understanding of aortic disease. J Cardiovasc Surg (Torino). 2019 Oct;60(5):582-588. doi: 10.23736/S0021-9509.19.10841-5. Epub 2019 Jun 26. PMID: 31256581.
<p>Data set from Spinelli D, Marconi S, Caruso R, Conti M, Benedetto F, De Beaufort HW, Auricchio F, Trimarchi S. 3D printing of aortic models as a teaching tool for improving understanding of aortic disease. J Cardiovasc Surg (Torino). 2019 Oct;60(5):582-588. doi: 10.23736/S0021-9509.19.10841-5. Epub 2019 Jun 26. PMID: 31256581.</p> <p> </p> <p>This is the abstract:</p> <p><strong>Background: </strong>A geometrical understanding of the individual patient's disease morphology is crucial in aortic surgery. The aim of our study was to validate a questionnaire addressing understanding of aortic disease and use this questionnaire to investigate the value of 3D printing as a teaching tool for surgical trainees.</p> <p><strong>Methods: </strong>Anonymized CT-angiography images of six different patients were selected as didactic cases of aortic disease and made into 3D models of transparent rigid resin with the Vat-photopolymerization technique. The 3D aortic models, which could be disassembled and reassembled, were displayed to 37 surgical trainees, immediately after a seminar on aortic disease. A questionnaire was developed to compare the trainees' understanding before (T0) and after (T1) demonstration of the 3D printed models.</p> <p><strong>Results: </strong>A panel of 15 experts participated in evaluating face and content validity of the questionnaire. The questionnaire validity was established and therefore the information investigated by the questionnaire could be synthetized using the mean of the items to indicate the understanding. The participants (mean age 28 years, range 26-34, male 59%) showed a significant improvement in understanding from T0 (median=7.25; IQR=1.50) to T1 (median=8.00; IQR=1.50; P=0.002).</p> <p><strong>Conclusions: </strong>Preliminary data suggest that the use of 3D-printed aortic models as a teaching tool was feasible and improved the understanding of aortic disease among surgical trainees.</p>
Modification of polyurethane materials with fluorine and siloxane groups for use as biomedical materials in 3D printing - experimental data
<p>Experimental data collected as part of the research project "Modification of polyurethane materials with fluorine and siloxane groups for use as biomedical materials in 3D printing" (grant no. 2022/06/X/ST4/00452) financed by the National Science Center (NCN) as part of the MINIATURA 6 program.</p>
DATA ON: Stochastic dynamic quantitative and 3D structural matrix assisted laser desorption/ionization mass spectrometric analyses of muxture of nucleosides
<p>Experimental MALDI-MS spectra in solid-state associated with the entitled publication.</p> <p> </p>
ScienceDex guides
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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)
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.