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278 results for “stereo”
Text-fig. 5. SMNK Ma1 Gips 17, left mandible and symphysis of Hippopotamodon erymanthius lacking the ascending ramus, from Mahmutgazi, Turkey. A) left lateral view, B) stereo occlusal view (note the d/2 between the p/2 and p/3 (scale 10 cm). in Hippopotamodon erymanthius (Suidae, Mammalia) from Mahmutgazi, Denizli-Çal basin, Turkey
Text-fig. 5. SMNK Ma1 Gips 17, left mandible and symphysis of Hippopotamodon erymanthius lacking the ascending ramus, from Mahmutgazi, Turkey. A) left lateral view, B) stereo occlusal view (note the d/2 between the p/2 and p/3 (scale 10 cm).
Text-fig. 7. Upper incisors of Hippopotamodon erymanthius from Mahmutgazi, Turkey. A) SMNK Ma1 Nr 176, right I1/ (A1 – distal, A2 – stereo lingual, A3 – mesial, A4 – labial views), B) SMNK Ma1 MP6, right I3/, stereo lingual view (scale 10 mm). in Hippopotamodon erymanthius (Suidae, Mammalia) from Mahmutgazi, Denizli-Çal basin, Turkey
Text-fig. 7. Upper incisors of Hippopotamodon erymanthius from Mahmutgazi, Turkey. A) SMNK Ma1 Nr 176, right I1/ (A1 – distal, A2 – stereo lingual, A3 – mesial, A4 – labial views), B) SMNK Ma1 MP6, right I3/, stereo lingual view (scale 10 mm).
Text-fig. 9. Stereo occlusal views of upper and lower molars of Hippopotamodon erymanthius from Mahmutgazi, Turkey. A) Ma1 Nr 181, deeply worn right M3/, B) Ma1 Nr 190, deeply worn left M2/–M3/, C) Ma1 Nr 131, left m/3 in fragment of mandible, D) Ma1 Nr 176, left m/3, E) Ma1 Nr 145, right m/2–m/3 in mandible fragment (scale 5 cm). in Hippopotamodon erymanthius (Suidae, Mammalia) from Mahmutgazi, Denizli-Çal basin, Turkey
Text-fig. 9. Stereo occlusal views of upper and lower molars of Hippopotamodon erymanthius from Mahmutgazi, Turkey. A) Ma1 Nr 181, deeply worn right M3/, B) Ma1 Nr 190, deeply worn left M2/–M3/, C) Ma1 Nr 131, left m/3 in fragment of mandible, D) Ma1 Nr 176, left m/3, E) Ma1 Nr 145, right m/2–m/3 in mandible fragment (scale 5 cm).
parameter set for ASP stereo : stereo.default
<p>This file comes directly from Willis et al. 2015, Recharge of a subglacial lake by surface meltwater in northeast Greenland (<em>Nature)</em>. Please cite this reference if necessary.</p>
Example Analysis Output of Stereo-seq Mouse Brain Data
<p>This is an example analysis output data from Stereo-seq Mouse Brain Data Analysis</p>
Leakage Position Estimation of Cooling Water Using a Stereo Camera for Fukushima Daiichi Nuclear Power Plant: Rendered Videos
<p>These are the rendered videos for a publication, "Leakage Position Estimation of Cooling Water Using a Stereo Camera for Fukushima Daiichi Nuclear Power Plant", accepted in Applied Sciences, MDPI.</p>
Data from: Application of Spatial-ID to large field mouse brain hemisphere dataset measured by Stereo-seq
<p>The application of Spatial-ID on the large field mouse brain hemisphere dataset measured by Stereo-seq technology. Briefly, three adjacent coronal sections along the anterior-posterior axis were cut from the brain 5-week-old male mouse. It fully proves the innovation and applicability of Spatial-ID.</p>
Processing steps to generate a Digital Surface Model based on SPOT-7 tri-stereo images published in the study "An assessment of the effects of DEM quality and spatial resolution on a model for mapping lahar inundation areas at volcan Copahue (Argentina & Chile)" in the Journal of South American Earth Sciences https://doi.org/10.1016/j.jsames.2022.104138
<p>The Digital Surface Model (DSM) was created from SPOT-7 tri-stereo images for the Copahue volcano between the border of Argentina and Chile. Two versions of the DSM are provided: an unfiltered product and a final, filtered product. The final product has a spatial resolution of 5-m and was used for lahar inundation modeling for the Copahue volcano (Viotto, Toyos, and Bookhagen 2022, <a href="https://doi.org/10.1016/j.jsames.2022.104138">https://doi.org/10.1016/j.jsames.2022.104138</a> : An assessment of the effects of DEM quality and spatial resolution on a model for mapping lahar hazard inundation at Volcán Copahue (Argentina & Chile). <em>Journal of South American Earth Sciences</em> ). The dataset provided should be cited together with the article. </p> <p><strong>DSM processing </strong></p> <p>The source images were given by a SPOT-7 snow- and cloud-free triplet (Nadir, Backward and Forward) of 1.5 m spatial resolution from 19 April 2018 (SPOT Image, Airbus Defence and Space GmbH, distributed by CONAE; Dataset ID: <em>SEN_SPOT7_20180419_142955500_000</em>, delivered by CONAE as <em>DS_SPOT7_20180419</em>).</p> <p>The data were processed with the suite of digital photogrammetry tools AMES Stereo Pipeline ASP (Beyer et al., 2018). The procedure for the generation of the DSM is summarized by following steps: </p> <ol> <li> <p>The orbital parameters (RCP models) were adjusted using the bundle adjustment tool with no ground control points, since they were unavailable.</p> </li> <li> <p>The scenes were map-projected onto the NASADEM (spatial resolution of 30 m) elevation dataset, assisted by the results of the orbital adjustment in Step 1.</p> </li> <li>The stereo correlation of the map-projected scenes including the results of the adjusted orbital parameters, was performed three times, using as first scene (i.e., primary image) the nadir (N), backward (B), and forward (F) images . In each run, the order of images to perform the stereo correlation was: N-F-B, F-N-B, and B-N-F. Thus, three point clouds were generated. Specific ASP correlator settings (other than defaults parameters; for details see the provided stereo-default file) were set in the following way: <em>Correlation Kernel</em>: 15 x 15 pixels; <em>Sub-pixel Refinement Kernel</em>: 21 x 21 pixels; <em>Subpixel Refinement Mode</em>: 2 (Weighted Affine Adaptive Window Correlator EM)</li> <li> <p>The three point clouds were merged into one point cloud with a regular grid of 5 m (unfiltered product, known as <em>DSM_Copahue_UTM19S_WGS84_5m_raw.tif</em>).</p> </li> </ol> <p>The quality of the final point cloud was assessed by comparing the unfiltered DSM with a spatial resolution of 12-m against the WorldDEM<sup>TM</sup> elevation dataset (Collins et al., 2015). The WorldDEM was provided by Airbus Defence and Space GmbH under license for the scope of the Viotto et al., 2022 study. The comparison of the pixel-to-pixel heights above the ellipsoid (WGS84) between the two datasets resulted in a mean difference of 0.67 m and a standard deviation of +/- 4.82 m. </p> <p>Comprehensive details on the methodologies evaluated to create the dataset with ASP, can be found in the corresponding master's thesis “Topografía digital y modelado de lahares en el Volcán Copahue, Argentina-Chile” from S. Viotto (link: https://rdu.unc.edu.ar/handle/11086/15384). Recommended literature about processing DEMs from SPOT imagery is given by Mueting et al., 2021 (<a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021JF006330">https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021JF006330</a>). </p> <p><strong>Creation of the Final, Filtered DSM product</strong></p> <p>The corrections and improvements applied to the unfiltered product to create the final, filtered DSM (named DSM_Copahue_UTM19S_WGS84_5m_VoidFilled.tif) are summarized by following steps. </p> <p> </p> <ol> <li> <p><em>Water Bodies Delineation</em></p> </li> </ol> <p>The delineation of the water bodies was based on a mask created from the free access water bodies datasets provided by the Instituto Geográfico Nacional of Argentina (<a href="https://www.ign.gob.ar/NuestrasActividades/InformacionGeoespacial/CapasSIG">https://www.ign.gob.ar/ NuestrasActividades/InformacionGeoespacia l/CapasSIG</a>) and by the Ministerio de Bienes Nacionales in Chile ( <a href="https://www.ide.cl/index.php/aguas-continentales/item/1508-catastro-de-lagos">https://www.ide.cl/index.php /aguas-continentales/item/1508-catastro-de-lagos</a>). A total of 45 lakes within the area of interest were considered. Lakes with areas below or equal to 25 m2 were smoothed with a median filter in the last step. Lakes with areas above this threshold were filled in with a constant value and their borders were smoothed with a median filter to provide smooth shorelines.</p> <p><em>2 . Void Filling</em></p> <p>Voids (other than water bodies) were filled with the tool “Close Gaps” from Saga GIS software. </p> <p><em>3. Smoothing</em></p> <p>Finally, the elevation dataset was smoothed with a median filter using a 3 x 3 pixel window, excluding water bodies filled in the step 1. </p> <p><strong>Final Remarks and Suggestion</strong></p> <p>The quality assessment of the final version by visual inspection of the hillshades suggested an improvement of the signal to noise ratio. However, the void filling process may be improved.</p> <p><br> </p> <p><strong>Dataset Description</strong></p> <table align="center"> <caption> </caption> <tbody> <tr> <td>Digital Surface Models</td> <td> <p>No Data Value = -9999</p> <p>Format = float 32 bit</p> <p>File Format = GeoTiff</p> <p>Vertical Datum: WGS84</p> <p>Projection information: EPSG 32719 (UTM19S)</p> <p>Spatial Resolution: 5m (subfix: <em>_5m</em>) </p> <p>Versions: </p> <ul> <li> <p>Unfiltered product: without corrections <em>DSM_Copahue_UTM19S_WGS84_5m_raw.tif</em></p> </li> <li> <p>Final, filtered product: smoothed and void filled <em>DSM_Copahue_UTM19S_WGS84_5m_VoidFilled.tif</em></p> </li> </ul> </td> </tr> <tr> <td>Water Bodies Mask</td> <td> <p>No Lake Value = 0</p> <p>Lakes Values = 1 to 45</p> <p>File Format= GeoTiff</p> <p>Spatial Resolution: 5m (subfix: <em>_5m</em>)</p> <p>Projection information : EPSG 32719 (UTM19S)</p> <p><em>WB_mask_5m_UTM19S.tif</em></p> </td> </tr> </tbody> </table> <p> </p> <p> </p> <p><strong>Repository structure</strong></p> <p>|__ 01_Scripts</p> <p> |+ run21_CopahueDSM_AMES_sviotto.sh</p> <p> |+ stereo.default</p> <p>|__ 02_DSMs</p> <p> |+ DSM_Copahue_UTM19S_WGS84_5m_raw.tif</p> <p> |+ DSM_Copahue_UTM19S_WGS84_5m_VoidFilled.tif</p> <p> |+ WB_mask_5m_UTM19S.tif</p> <p><strong>References</strong></p> <p>Beyer, R. A., Alexandrov, O., & McMichael, S. (2018). The Ames Stereo Pipeline: NASA's open source software for deriving and processing terrain data. <em>Earth and Space Science</em>, 5, 537– 548. <a href="https://doi.org/10.1029/2018EA000409">https://doi.org/10.1029/2018EA000409</a></p> <p>Collins, J., Riegler, G., Schrader, H., Tinz, M., 2015. Applying terrain and hydrological editing to TanDEM-X data to create a consumer-ready worlddem product. Int. Arch. Photogram. Rem. Sens. Spatial Inf. Sci. 40 (7), 1149. https://doi.org/10.5194/isprsarchives-XL-7-W3-1149-2015.</p> <p>Mueting, A., Bookhagen, B., & Strecker, M. R. (2021). Identification of debris-flow channels using high-resolution topographic data: A case study in the Quebrada del Toro, NW Argentina. <em>Journal of Geophysical Research: Earth Surface</em>, 126, e2021JF006330. <a href="https://doi.org/10.1029/2021JF006330">https://doi.org/10.1029/2021JF006330</a></p> <p>Viotto, S., Toyos, G., & Bookhagen, B. (2022). An assessment of the effects of DEM quality and spatial resolution on a model for mapping lahar hazard inundation at volcán copahue (Argentina & Chile). Journal of South American Earth Sciences, 104138. https://doi.org/10.1016/j.jsames.2022.104138</p> <p> </p> <p> </p>
Eurex-LUNa Abisko Trials Stereo Camera Calibration
<p>Bottom Stereo Camera Calibration Images for AUV Deepleng. They should be used for both determining intrinsic as well as extrinsic calibration parameters for the bottom-looking stereo camera. This is necessary for the image data published with DOI 10.5281/zenodo.7035119 .</p> <p>Contact: tom.creutz@dfki.de, bilal.wehbe@dfki.de</p> <p> </p> <p>Funded by BMWi (Kennziffer 50 NA 2002)</p>
List of SHARAD type-III burst candidate based on cross-detections with STEREO and Wind
<p>Complete list of SHARAD type-III burst candidate based on cross-detections with STEREO and Wind, including products in which a burst was not seen.</p>
PSMNet-FusionX3: LiDAR-Guided Deep Learning Stereo Dense Matching on Aerial Images
<p>We release two datasets we used in the paper "PSMNet-FusionX3: LiDAR-Guided Deep Learning Stereo Dense Matching on Aerial Images". These two datasets are from aerial images and LiDAR, the detailed information can be found on Github : https://github.com/whuwuteng/PSMNet-FusionX3. These datasets can be also used for Deep learning stereo-dense matching.</p>
Recording of the STEREO-A space weather beacon with the Allen Telescope Array
<p>This dataset contains an IQ recording of the X-band 8443.5 MHz space weather beacon from STEREO-A, done with one of the antennas of the Allen Telescope Array. The recording was made as part of a demo for some GNU Radio tutorials for the Breakthrough Listen REU program.</p> <p>The recording was done with the spacecraft at around 9 million km from Earth, so the SNR is very good. Only one linear polarization (X, or horizontal) was recorded, using antenna 4g from the array.</p> <p>The hardware configuration was as follows: the antenna was connected to RFCB LO d, which was tuned to a frequency of 8443.54 MHz and used an output IF of 512 MHz. A USRP N321 digitized the X polarization. The USRP used an external 10 MHz reference and PPS coming from the observatory distribution system. The IQ sample rate of the USRP was 480 ksps, and a GNU Radio flowgraph was used to record IQ data to disk. The GNU Radio flowgraph stored the IQ data for each channel as 16-bin integers in the <a href="https://wiki.gnuradio.org/index.php/Metadata_Information">GNU Radio metatata file format</a>, with detached headers.</p> <p>After recording, the data was formatted using <a href="https://github.com/gnuradio/SigMF">SigMF</a>.</p>
Processed Stereo-seq data of mouse olfactory bulb data analyzed in STAMarker
<p>Processed Stereo-seq data of mouse olfactory bulb data analyzed in STAMarker</p>
Stereo Photogrammetry Imaging in Normal Volunteers and Patients With Head and Facial Malformations
ClinicalTrials.gov study NCT00100529. IPD Sharing: Not stated. Countries: 3. Publications: 3.
Quantifying Bone and Skin Movement in the Residual Limb of Individuals With Transtibial Amputation Using Dynamic Stereo X-Ray
ClinicalTrials.gov study NCT05287646. IPD Sharing: YES. Countries: 1. Publications: 1.
STEREO-POSTOP 01 (PostOperative Hypofractionated Stereotactic Body Radiotherapy ) - GORTEC 2017-03
ClinicalTrials.gov study NCT03401840. IPD Sharing: NO. Countries: 1. Publications: 2.
Recording "Fast Ripples" Using Microelectrodes During Stereo-encephalography in Patients With Drug-resistant Partial Epilepsy
ClinicalTrials.gov study NCT02491476. IPD Sharing: Not stated. Countries: 1. Publications: 9.
Osimertinib and Locally Ablative Radiotherapy in Patients With Synchronous Oligo-metastatic EGFR Mutant NSCLC (STEREO)
ClinicalTrials.gov study NCT04908956. IPD Sharing: NO. Countries: 8. Publications: 6.
Data from: Calibration of pelagic stereo-BRUVs and scientific longline surveys for sampling sharks
Open the record for dataset details and reuse information.
Stereo video files used for 3d tracking horsefly trajectories
Open the record for dataset details and reuse information.
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.