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datasets available to search
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Dataset results
155 results for “2D images”
TINKER_WP3_TU49 2D and 3D image dataset_071123
<p>Contains images taken during and after the pick-and-place assembly of the RADAR use case for project TINKER. </p><p>Machine assembly, before curing: 1) empty cavity snapshot, 2) Epoxy bright exposure, 3) Epoxy dark exposure, 4) Die placement </p><p>After curing: 5) Die after curing, 6) 3D topology data after curing</p>
nPSize LNE images as 2D arrays
<p>LNE1.zip LNE2.zip LNE3.zip are 2D arrays, to be extracted in the following directory: /dataset/images/LNE to build /dataset/images/LNE/[LNE_images.h5] Note: h5 is a compressed format, to uncompress.</p>
Dataset with results of "Joint 2D to 3D image registration workflow for comparing multiple slice photographs and CT scans of apple fruit with internal disorders"
<p><strong>Summary</strong></p> <p>This dataset contains all results from the paper "Joint 2D to 3D image registration workflow for comparing multiple slice photographs and CT scans of apple fruit with internal disorders". Most notably, this dataset contains the corresponding CT slices for slice photographs of 1347 'Kanzi' apples. This dataset also contains data of the results section, metadata required to make the registration code run, and segmentation masks of the apple slice photographs. The "raw" data that was used to produce these results can be found in another Zenodo dataset: <a href="../record/8167285">https://zenodo.org/record/8167285</a>.</p> <p><br><strong>Description</strong></p> <ul> <li><strong>registered ct photo side-by-side view.zip </strong>is the easiest way to explore the registered CT photo image pairs. For every apple slice it contains a .png image consisting of the slice photo, registered CT slice and a combined view (photo=green, CT=purple) side-by-side. The resolution was reduced to reduce the file size.</li> <li><strong>registered ct slices.zip </strong>contains the full resolution CT slices as .tiff files. The matching slice photos can be found in <strong>slice_photos_crop.zip</strong> in <a href="../record/8167285">https://zenodo.org/record/8167285</a>.</li> <li><strong>photo metadata.zip </strong>contains all metadata files required to run the code on <a href="https://github.com/D1rk123/apple_photo_ct_workflow">https://github.com/D1rk123/apple_photo_ct_workflow</a>.</li> <li><strong>results.zip</strong> contains the IPCED annotations and per apple metrics that were used to calculate all the average metrics and tables in the results section of the paper.</li> <li><strong>subset experiment registered annotation slice.zip </strong>contains the full resolution CT slices of the annotation slice in the subset experiment as .tiff files.</li> <li><strong>segmentation masks.zip </strong>contains slice photo segmentation masks as .png images. There are subfolders for the training set, the test set and the masks used for the workflow in the paper.</li> </ul> <p><br><strong>Research group</strong><br>This dataset was produced by the Computational Imaging group at Centrum Wiskunde & Informatica (CI-CWI) in Amsterdam, The Netherlands: <a href="https://www.cwi.nl/research/groups/computational-imaging">https://www.cwi.nl/research/groups/computational-imaging</a></p> <p><strong>Contact details</strong><br>dirk [dot] schut [at] cwi [dot] nl</p> <p><strong>Acknowledgments</strong><br>This work was funded by the Dutch Research Council (NWO) through the UTOPIA project (ENWSS.2018.003).</p>
A Pilot Study Evaluating the Effect of 2D Antiscatter Grids on CBCT Image Quality
ClinicalTrials.gov study NCT04565457. IPD Sharing: NO. Countries: 1. Publications: 0.
Fig. 8.1 in Handbook of best practice and standards for 2D+ and 3D imaging of natural history collections
Fig. 8.1. Sketchfab page of RMCA.
Fig. 8.2 in Handbook of best practice and standards for 2D+ and 3D imaging of natural history collections
Fig. 8.2. Annotated model of a grass snake skull on Sketchfab. https://skfb.ly/6tNwY
Fig. 6.3 in Handbook of best practice and standards for 2D+ and 3D imaging of natural history collections
Fig. 6.3. Back view of the skull digitised with different scanners.
Fig. 6.4 in Handbook of best practice and standards for 2D+ and 3D imaging of natural history collections
Fig. 6.4. Parietal view of the skull digitised with different scanners.
Fig. 6.5 in Handbook of best practice and standards for 2D+ and 3D imaging of natural history collections
Fig. 6.5. Sea urchin captured by 3 different structured light scanners and by SfM.
Fig. 5.5 in Handbook of best practice and standards for 2D+ and 3D imaging of natural history collections
Fig. 5.5. Corrected light settings (right image) for a too-dark texture (left image).
Fig. 8.3 in Handbook of best practice and standards for 2D+ and 3D imaging of natural history collections
Fig. 8.3. Virtual collections page of RBINS
Fig. 3.5 in Handbook of best practice and standards for 2D+ and 3D imaging of natural history collections
Fig. 3.5. Interface of the AGORA 3D Automated Photographer.
Fig. 3.23 in Handbook of best practice and standards for 2D+ and 3D imaging of natural history collections
Fig. 3.23. The HDI white light scanner.
Fig. 3.22 in Handbook of best practice and standards for 2D+ and 3D imaging of natural history collections
Fig. 3.22. Fringe projection.
Fig. 3.24 in Handbook of best practice and standards for 2D+ and 3D imaging of natural history collections
Fig. 3.24. The MechScan white light scanner.
Fig. 2.27 in Handbook of best practice and standards for 2D+ and 3D imaging of natural history collections
Fig. 2.27. Ammonite lighted with the RTI technique. Left normal view, right specular view.
Fig. 2.16 in Handbook of best practice and standards for 2D+ and 3D imaging of natural history collections
Fig. 2.16. Caloptilia galacotra, detail of the reproductive organs at 20× magnification.
Fig. 2.21 in Handbook of best practice and standards for 2D+ and 3D imaging of natural history collections
Fig. 2.21. Overview of the specimen page visible at Zoosphere.net. Image copyright MfN.
Multimodal 2D and 3D microscopic mapping of growth cartilage by computational imaging techniques – a short review including new research
<p>This dataset contains the phase and amplitude maps obtained with Fourier ptychographic microscopy and the X-ray diffraction tensor tomography dataset described in the journal article "Multimodal 2D and 3D microscopic mapping of growth cartilage by computational imaging techniques".</p>
Figure 2d from: Marek P (2017) Ultraviolet-induced fluorescent imaging for millipede taxonomy. Research Ideas and Outcomes 3: e14850. https://doi.org/10.3897/rio.3.e14850
Figure 2d - Posterior (left) and anterior (right) gonopods of Tylobolus uncigerus
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.