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438 results for “3D imaging”
Figure 4 from: Ströbel B, Schmelzle S, Blüthgen N, Heethoff M (2018) An automated device for the digitization and 3D modelling of insects, combining extended-depth-of-field and all-side multi-view imaging. ZooKeys 759: 1-27. https://doi.org/10.3897/zookeys.759.24584
Figure 4 Workflow of EDOF-calculation. For a detailed description, see Suppl. material 1: S2.
Figure 18 from: Ströbel B, Schmelzle S, Blüthgen N, Heethoff M (2018) An automated device for the digitization and 3D modelling of insects, combining extended-depth-of-field and all-side multi-view imaging. ZooKeys 759: 1-27. https://doi.org/10.3897/zookeys.759.24584
Figure 18 Interactive, textured 3D-model of Anoplotrupes stercorosus (21 mm body size).
Figure 16 from: Ströbel B, Schmelzle S, Blüthgen N, Heethoff M (2018) An automated device for the digitization and 3D modelling of insects, combining extended-depth-of-field and all-side multi-view imaging. ZooKeys 759: 1-27. https://doi.org/10.3897/zookeys.759.24584
Figure 16 Interactive 3D-model of Helicodonta obvoluta (9 mm shell diameter).
Figure 15 from: Ströbel B, Schmelzle S, Blüthgen N, Heethoff M (2018) An automated device for the digitization and 3D modelling of insects, combining extended-depth-of-field and all-side multi-view imaging. ZooKeys 759: 1-27. https://doi.org/10.3897/zookeys.759.24584
Figure 15 Interactive 3D-model of Prosopocoilus savagei (23 mm body size).
Figure 14 from: Ströbel B, Schmelzle S, Blüthgen N, Heethoff M (2018) An automated device for the digitization and 3D modelling of insects, combining extended-depth-of-field and all-side multi-view imaging. ZooKeys 759: 1-27. https://doi.org/10.3897/zookeys.759.24584
Figure 14 Interactive 3D-model of Pogonocherus hispidus (6 mm body size).
Figure 13 from: Ströbel B, Schmelzle S, Blüthgen N, Heethoff M (2018) An automated device for the digitization and 3D modelling of insects, combining extended-depth-of-field and all-side multi-view imaging. ZooKeys 759: 1-27. https://doi.org/10.3897/zookeys.759.24584
Figure 13 Interactive 3D-model of Oscinella frit (1.5 mm body size).
Figure 1 from: Ströbel B, Schmelzle S, Blüthgen N, Heethoff M (2018) An automated device for the digitization and 3D modelling of insects, combining extended-depth-of-field and all-side multi-view imaging. ZooKeys 759: 1-27. https://doi.org/10.3897/zookeys.759.24584
Figure 1 Schematic setup (A) and image (B) of the Darmstadt Insect Scanner DISC3D.
Figure 6 from: Ströbel B, Schmelzle S, Blüthgen N, Heethoff M (2018) An automated device for the digitization and 3D modelling of insects, combining extended-depth-of-field and all-side multi-view imaging. ZooKeys 759: 1-27. https://doi.org/10.3897/zookeys.759.24584
Figure 6 Calibration sphere (A) and camera positions estimated in PhotoScan Pro (B).
Dataset and 3D Vs Model for "Crustal velocity images of north-western Türkiye along the North Anatolian Fault Zone from transdimensional Bayesian ambient seismic noise tomography"
<p>Final 3D Vs model and dispersion data for the paper entitled "Crustal velocity images of north-western Türkiye along the North Anatolian Fault Zone from transdimensional Bayesian ambient seismic noise tomography".</p> <p>In the vel_files folder, there are 10 files for each depth for 1-15 km. The format of each velocity file is as follows:</p> <p>Column Value<br> 1 Lattitude (°)<br> 2 Longitude (°)<br> 3 Vs (km/s)</p> <p>The format of the dispersion data is as follows (See <a href="https://www.eas.slu.edu/eqc/eqc_cps/TUTORIAL/EMPIRICAL_GREEN/example1.html">Computer Programs in Seismology Tutorials - do_mft</a> for more information on the format):</p> <p>Column Value<br> 1 Type of file, MFT96<br> 2 Wave type: R for Rayleigh <br> 3 Dispersion type: U for group velocity<br> 4 Mode: 0 represents the fundamental mode<br> 5 Filter period, T, in seconds<br> 6 Dispersion value, either group or phase<br> 7 Error in dispersion. This is just a place holder since there is no way to estimate an error from a single trace. The group velocity error is determined from the ratio of the filter period to travel time<br> 8 Distance in km<br> 9 Azimuth from the source to the receiver<br> 10 Spectral amplitude. <br> 11 Epicenter latitude <br> 12 Epicenter longitude<br> 13 Station latitude<br> 14 Station longitude<br> 15 control flag<br> 16 control flag<br> 17 Instantaneous period if this is preferred. This differs from the ilter period because the signal spectram is not flat.<br> 18 Comment: keyword<br> 19 Station <br> 20 Component<br> 21 Year<br> 22 Day of year<br> 23 Hour<br> 24 Minute - these identify the event origin time </p>
3D Imaging of Hard and Soft Tissue in Orthognathic Surgery
ClinicalTrials.gov study NCT00285714. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Is Any Additional Information Gained Regarding Margins Using 3D Tomosynthesis Vs 2D Conventional Digital Imaging When Imaging Operative Breast Specimens?
ClinicalTrials.gov study NCT02096185. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Evaluation of 3D Translabial Ultrasound Imaging for Pessary Size Estimation
ClinicalTrials.gov study NCT04099121. IPD Sharing: NO. Countries: 1. Publications: 0.
Objective Evaluation of the Effect of Oculoplastic Operations Using the Vectra M 3D Imaging System
ClinicalTrials.gov study NCT06040671. IPD Sharing: NO. Countries: 1. Publications: 0.
Quantitative 3D UltraSound Brain Imaging: Development of New Bedside Biomarkers to Better Predict Neurodevelopmental Outcomes in Preterm Infants
ClinicalTrials.gov study NCT07358819. IPD Sharing: NO. Countries: 1. Publications: 0.
Artificial Intelligence to Detect Missed Root Canals in Previously Treated Lower Back Teeth Using 3D Dental Imaging (CBCT)
ClinicalTrials.gov study NCT07077694. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Development of an Aid to Melanoma Detection Using Artificial Intelligence Algorithms Based on Images From the VECTRA 3D System.
ClinicalTrials.gov study NCT06999499. IPD Sharing: Not stated. Countries: 1. Publications: 0.
3D Reconstructed Image Processin by Computer Proram From MRI Data
ClinicalTrials.gov study NCT01249729. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Retinal Therapy Guided by 3D OCT Image Analysis
ClinicalTrials.gov study NCT02308215. IPD Sharing: NO. Countries: 1. Publications: 0.
3D Imaging of the Hip Using DXA
ClinicalTrials.gov study NCT00666640. IPD Sharing: Not stated. Countries: 1. Publications: 0.
MRI 3D UTE Hyper-Cones & ZTE for PET/MR Lung Attenuation Correction & for Lung Diagnostic Imaging
ClinicalTrials.gov study NCT03562806. IPD Sharing: NO. Countries: 1. Publications: 0.
ScienceDex guides
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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.