Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
60
datasets available to search
ShareScore release 0.9.0
Dataset results
60 results for “Depth Image”
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).
Effects of External Testosteron Intake on the Choroid: Enhance-depth Imaging Optical Coherence Tomography Study
ClinicalTrials.gov study NCT04342247. IPD Sharing: NO. Countries: 1. Publications: 0.
Evaluation of a Novel Optical Microscope With a Deep Depth of Field (DeepDOF) to Provide Histologic-quality Images on Cervical Biopsies and Loop Electrosurgical Excision Procedure (LEEP) Specimens at
ClinicalTrials.gov study NCT06850402. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Study on the Diagnostic Efficacy of ICL Selection and Prediction Depth Model Based on Eye Images
ClinicalTrials.gov study NCT06669728. IPD Sharing: NO. Countries: 1. Publications: 0.
A Clinical Trial to Evaluate the Sensitivity and Specificity of "WAYMED Endo" Compared to the Endoscopists in Classifying Early Gastric Cancer (EGC) Based on the Depth of Invasion in Endoscopic Images
ClinicalTrials.gov study NCT06317103. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Evaluation of Choroidal Thickness During Pregnancy Using Enhanced Depth Imaging Optical Coherence Tomography
ClinicalTrials.gov study NCT02024048. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Data from: The (in)effectiveness of simulated blur for depth perception in naturalistic images
Open the record for dataset details and reuse information.
GHRSST Level 2P NAVO 1 m Depth Global Sea Surface Temperature version 3.0 from the Visible Infrared Imaging Radiometer Suite (VIIRS) on the NOAA-21 satellite
The VIIRS_N21-NAVO-L2P-v3.0 dataset produced by the Naval Oceanographic Office (NAVO) derives the 1-meter depth Sea Surface Temperature (SST) from the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Joint Polar Satellite System (JPSS)-2 satellite, renamed as NOAA-21 (N21). N21 was launched on November 10, 2022, the 3rd satellite in the US NOAA JPSS series. <br><br> VIIRS L2P SST products are derived at the native sensor resolution (~0.75 km at nadir, ~1.5 km at swath edge) using NAVO's Level-2 SST processor version 3.0 (v3.0). Data contains the global near daily-coverage Sea Surface Temperature at 1-meter depth with 750 m (along) x 750 m (cross) spatial resolution in swath coordinates. Each netCDF file has 768 x 3200 pixels in size, in compliance with the Group for High Resolution Sea Surface Temperature (GHRSST) Data Specification version 2 (GDS2). The data record is available back to Feb. 21 2024. The L2P SST v3.0 is the first release at PO.DAAC derived from the L2P SST processor v3.0, which was upgraded from the v2.0 with several significant improvements in processing algorithms, including contamination detection, cloud detection, and data format upgrades.<br><br>The product is comparable with the NPP VIIRS L2P (https://podaac.jpl.nasa.gov/dataset/VIIRS_NPP-NAVO-L2P-v3.0) and the N20 VIIRS L2P (https://podaac.jpl.nasa.gov/dataset/VIIRS_N20-NAVO-L2P-v3.0). It also has similar coverage and quality as the NOAA ACSPO VIIRS L2P SST (https://podaac.jpl.nasa.gov/dataset/N21-VIIRS-L2P-ACSPO-v2.80).
GHRSST Level 2P NAVO 1 m Depth Global Sea Surface Temperature version 3.0 from the Visible Infrared Imaging Radiometer Suite (VIIRS) on the NOAA-20 satellite
The VIIRS_N20-NAVO-L2P-v3.0 dataset produced by the Naval Oceanographic Office (NAVO) derives the 1-meter depth Sea Surface Temperature (SST) from the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Joint Polar Satellite System (JPSS)-1 satellite, renamed as NOAA-20 (N20). N20 was launched on November 18, 2017, the 2nd satellite in the US NOAA JPSS series. <br><br> VIIRS L2P SST products are derived at the native sensor resolution (~0.75 km at nadir, ~1.5 km at swath edge) using NAVO's Level-2 SST processor version 3.0 (v3.0). Data contains the global near daily-coverage Sea Surface Temperature at 1-meter depth with 750 m (along) x 750 m (cross) spatial resolution in swath coordinates. Each netCDF file has 768 x 3200 pixels in size, in compliance with the Group for High Resolution Sea Surface Temperature (GHRSST) Data Specification version 2 (GDS2). The data record is available back to Feb. 20 2024. The L2P SST v3.0 is the first release at PO.DAAC derived from the L2P SST processor v3.0, which was upgraded from the v2.0 with several significant improvements in processing algorithms, including contamination detection, cloud detection, and data format upgrades.<br><br>The product is comparable with the NPP VIIRS L2P (https://podaac.jpl.nasa.gov/dataset/VIIRS_NPP-NAVO-L2P-v3.0) and the N21 VIIRS L2P (https://podaac.jpl.nasa.gov/dataset/VIIRS_N21-NAVO-L2P-v3.0) datasets. It also has similar coverage and quality as the NOAA ACSPO VIIRS L2P SST (https://podaac.jpl.nasa.gov/dataset/N20-VIIRS-L2P-ACSPO-v2.80).
Limitations of Using a Sheimpflug Image-Based Device for Measuring Anterior Chamber Depth in Pseudophakic Eyes
ClinicalTrials.gov study NCT00712231. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Swept Source Enhanced Depth Imaging Optical Coherence Tomography
ClinicalTrials.gov study NCT02443129. IPD Sharing: Not stated. Countries: 0. Publications: 0.
SnowEx Colorado 3M Snow Depth Time Series and DEMs from High-Resolution Satellite Image Pairs V001
This data set contains a time series of snow depth maps and related intermediary snow-on and snow-off DEMs for Grand Mesa and the Banded Peak Ranch areas of Colorado derived from very-high-resolution (VHR) satellite stereo images and lidar point cloud data. Two of the snow depth maps coincide temporally with the 2017 NASA SnowEx Grand Mesa field campaign, providing a comparison between the satellite derived snow depth and in-situ snow depth measurements. The VHR stereo images were acquired each year between 2016 and 2022 during the approximate timing of peak snow depth by the Maxar WorldView-2, WorldView-3, and CNES/Airbus Pléiades-HR 1A and 1B satellites, while lidar data was sourced from the USGS 3D Elevation Program.
DIH: Depth Images with Humans
<p><strong>Description</strong></p> <p>The DIH dataset has been created for human body landmark detection and human pose estimation from depth images. Training and deploying good models for these tasks require large amounts of data with high quality annotations. Unfortunately, obtaining precise manual annotation of depth images with body parts is hampered by the fact that people appear roughly as blobs and the annotation task is very time consuming. Synthesizing images provides an easy way to introduce variability in body pose, view perspective and high quality annotations can be easily generated.</p> <p>However, synthetic depth images does not match real depth data in several aspects: visual characteristics that arise from the depth image generation, i.e. measurement noise, the most problematically depth discontinuity and missing measurements. Hence, real data with annotations is required to fill the detection performance gap this data mismatch can provoke in real data. With this is mind, the DIH dataset also provides real data with annotations for Kinect 2. This real data can be used for both finetuning and testing.</p> <p> </p> <p><strong>References</strong></p> <p>Angel Martínez-González, Michael Villamizar, Olivier Canévet and Jean-Marc Odobez, "Real-time Convolutional Networks for Depth-based Human Pose Estimation", in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2018</p> <p>Angel Martínez-González, Michael Villamizar, Olivier Canévet and Jean-Marc Odobez, "Investigating Depth Domain Adaptation for Efficient Human Pose Estimation", in European Conference of Computer Vision - Workshops (ECCV), 2018</p>
Garment depth images - Personal Robotics Lab
<p>Real garment depth images</p>
Garment depth images_3 - Personal Robotics Lab
<p>Garment depth images_2 - Personal Robotics Lab</p>
Garment depth images_2 - Personal Robotics Lab
<p>Garment depth images_2 - Personal Robotics Lab</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.