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5,635
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5,635 results for “3D”
York Archaeological Trust 1988.24.7669-7627 Archaeological 3D Use-wear Raw Measurements
<p>This dataset contains the 32 raw measurements/scans taken before the mesh creation step for object 1988.24.7669-7627 in the collections of York Archaeological Trust.</p> <p>1988.24.7669-7627 is a small campanulate bowl. Close analysis was not undertaken at time of data capture.</p> <p>Data was captured using a Zeiss Comet L3D 2 5M at 100 FOV to enable the analysis of use-wear on archaeological objects. The model is scaled in millimetres.</p> <p>This dataset consists of 31,429,144 points - additional metadata included in associated spreadsheet.</p>
York Archaeological Trust 1984.132.4148 3D Archaeological Use-wear (Incomplete Scan) Raw Measurements
<p>This dataset contains the 25 raw measurements/scans taken before the mesh creation step for object 1984.132.4148 in the collections of York Archaeological Trust.</p> <p>1984.132.4148 is a dish. Close analysis was not undertaken at time of data capture. Scanning was not complete. Holes in scan data: one small circle at the very centre of the base of the dish, and gaps in data capture under the rim. </p> <p>Data was captured using a Zeiss Comet L3D 2 5M at 100 FOV to enable the analysis of use-wear on archaeological objects. The model is scaled in millimetres.</p> <p>This dataset consists of 27,629,672 points - additional metadata included in associated spreadsheet.</p>
York Archaeological Trust 1975.6.7364.4244 Archaeological 3D Use-wear Raw Measurements
<p>This dataset contains the 78 raw measurements/scans taken before the mesh creation step for object 1975.6.7364.4244 in the collections of York Archaeological Trust.</p> <p>1975.6.7364.4244 is a large Ebor Ware carinated bowl (Monaghan 1993, 783, 875).</p> <p>Data was captured using a Zeiss Comet L3D 2 5M at 100 FOV to enable the analysis of use-wear on archaeological objects. The model is scaled in millimetres.</p> <p>This dataset consists of 278,742,689 points - additional metadata included in associated spreadsheet.</p>
Hunterian GLAHM:D.155 3D Archaeological Use-wear Raw Measurements
<p>This dataset contains the 65 raw measurements/scans taken before the mesh creation step for object GLAHM:D.155 in the Hunterian's Archaeology collection in Glasgow, Scotland.</p> <p>GLAHM:D.155 is an Bucchero Kantharos, likely produced in Cerveteri or Vulci: <a href="http://collections.gla.ac.uk/#/details/ecatalogue/117064">http://collections.gla.ac.uk/#/details/ecatalogue/117064</a></p> <p>Edited in CVA Glasgow, vol. 18, 47, pl. 57.7. Gran-Aymerich 2017, groups 3764-3765, pls. 58-88.</p> <p>Data was captured using a Zeiss Comet L3D 2 5M at 100 FOV to enable the analysis of use-wear on archaeological objects. The model is scaled in millimetres. </p> <p>This dataset consists of 108,061,041 points - additional metadata included in associated spreadsheet.</p> <p>CVA Glasgow: E. Moginard 1997, <em>Corpus Vasorum Antiquorum, Great Britain, Fascicule 18, Glasgow</em>, Oxford 1997.</p> <p>Gran-Aymerich 2017: J. Gran-Aymerich, <em>Les vases de bucchero. Le monde étrusque entre Orient et Occident,</em> Rome 2017.</p>
Hunterian GLAHM:D.1909.10 3D Archaeological Use-wear Raw Measurements
<p>This dataset contains the 43 raw measurements/scans taken before the mesh creation step for object GLAHM:D.1909.10 in the Hunterian's Archaeology collection in Glasgow, Scotland.</p> <p>GLAHM:D.1909.10 is a Black Gloss bowl from tombs at Cyrenaica, Libya: <a href="http://collections.gla.ac.uk/#/details/ecatalogue/118225">http://collections.gla.ac.uk/#/details/ecatalogue/118225</a> Similar to Morel Series 1735, (Morel 1981, 133, pl. 29).</p> <p>Data was captured using a Zeiss Comet L3D 2 5M at 100 FOV to enable the analysis of use-wear on archaeological objects. The model is scaled in millimetres.</p> <p>Final dataset consists of 44,681,096 points - additional metadata included in associated spreadsheet.</p> <p>Morel 1981: J.P. Morel, <em>Céramique campanienne: les formes</em>, Rome 1981.</p>
Hunterian GLAHM:D.163 3D Archaeological Use-wear Raw Measurements
<p>This dataset contains the 58 raw measurements/scans taken before the mesh creation step for object GLAHM:D.163 in the Hunterian's Archaeology collection in Glasgow, Scotland.</p> <p>GLAHM:D.163 is a Bucchero cup: <a href="http://collections.gla.ac.uk/#/details/ecatalogue/117079">http://collections.gla.ac.uk/#/details/ecatalogue/117079</a></p> <p>This is a possible reproduction because the type is unusual and cannot find comparisons. Edited in CVA Glasgow, vol. 18, 49, pl. 60.5-6.</p> <p>Data was captured using a Zeiss Comet L3D 2 5M at 100 FOV to enable the analysis of use-wear on archaeological objects. The model is scaled in millimetres.</p> <p>This dataset consists of 91,703,605 points - additional metadata included in associated spreadsheet.</p> <p>CVA Glasgow: E. Moginard 1997, <em>Corpus Vasorum Antiquorum, Great Britain, Fascicule 18, Glasgow</em>, Oxford 1997.</p>
SARS-CoV-2 main protease 3D print model
<p>A 3D model for printing SARS-CoV-2 main protease from our paper on FAIR sharing molecular visualization experiences.</p>
Dynamic Contrast Enhanced MRI Raw Data Acquired with 3D Cones Trajectory
<p>This repository contains the raw data for the second dynamic contrast enhanced (DCE) MRI in <a href="https://arxiv.org/abs/1909.13482">Extreme MRI: Large-Scale Volumetric Dynamic Imaging from Continuous Non-Gated Acquisitions</a>. The data is stored as numpy arrays, containing k-space data (ksp.npy), coordinates (coord.npy), and density compensation factors (dcf.npy). Code to process and reconstruct the data is available here: <a href="https://github.com/mikgroup/extreme_mri">https://github.com/mikgroup/extreme_mri</a></p> <p>For more information about how the data is acquired, please see the linked paper.</p>
VR-Together Pilot 3: 3D Character Models and Animation Data
<p>VR-Together Pilot 3 Character and Animation Dataset.</p> <p>This dataset contains the 3D characters and animations as used in <a href="https://vrtogether.eu/about-vr-together/pilots/pilot3/">Pilot 3 of the VR-Together project</a>. It contains the 4 characters of the associated experience and their post-processed motion capture animation data in the FBX format, as well as the texture data in the PNG format. </p> <p>The data contained in this dataset was prepared for the Unity game engine, but should be usable in other content creation systems without issue. </p> <p>VR-Together has been funded by the European Commission as part of the H2020 program, under the grant agreement 762111.</p>
Adele 3D seismic survey segy format used in the FORCE 2020 machine learning competition for fault identification
<p>Adele seismic 3D survey segy format used in the FORCE 2020 machine learning competition for fault identification.</p> <p>Dataset is courtesy of GEOSCIENCE Australia who need to be acknowledged in each publication</p> <p> </p>
3D mesh model and raw images of a drifting iceberg in Dickson Fjord (NE Greenland) on 21 August 2018 at 17:09 UTC
<p>This dataset consists of low-altitude aerial imagery that was acquired by a DJI Phantom 3 Standard unoccupied aerial vehicle (UAV) in Dickson Fjord in northeast Greenland on 21 August 2018. The UAV survey commenced at 17:09 UTC. These images were processed in Agisoft PhotoScan Pro (v1.4; Linux Ubuntu). During the image alignment step in PhotoScan, the ‘High’ accuracy setting and key point and tie point limits of 60000 and 0 were used. Generic and reference preselection were disabled. Gradual selection was used to remove tie points that exceeded thresholds for the projection accuracy, reconstruction uncertainty, and reprojection error and the lens parameters were computed. Reference data from images DJI_417-419 were used to scale the sparse point cloud. The dense point cloud was then computed using the ‘High’ setting, followed by the textured mesh. The mesh model was exported in .obj and .pdf formats. </p> <p>A complete file list is provided in the README file that accompanies this dataset.</p> <p>This dataset is discussed in:</p> <p>Carlson et al. Quantifying iceberg deterioration using UAV imagery and Structure from Motion photogrammetry software. Submitted to <em>Remote Sensing.</em></p>
3D mesh model and raw images of a drifting iceberg in Dickson Fjord (NE Greenland) on 20 August 2018 at 12:41 UTC
<p>This dataset consists of low-altitude aerial imagery that was acquired by a DJI Phantom 3 Standard unoccupied aerial vehicle (UAV) in Dickson Fjord in northeast Greenland on 20 August 2018. The UAV survey commenced at 12:41 UTC. These images were processed in Agisoft PhotoScan Pro (v1.4; Linux Ubuntu). During the image alignment step in PhotoScan, the ‘High’ accuracy setting and key point and tie point limits of 60000 and 0 were used. Generic and reference preselection were disabled. Gradual selection was used to remove tie points that exceeded thresholds for the projection accuracy, reconstruction uncertainty, and reprojection error and the lens parameters were computed. Reference data from images DJI_493-497 were used to scale the sparse point cloud. The dense point cloud was then computed using the ‘High’ setting, followed by the textured mesh. The mesh model was exported in .obj and .pdf formats. </p> <p>A complete file list is provided in the README file that accompanies this dataset.</p> <p>This dataset is discussed in:</p> <p>Carlson et al. Quantifying iceberg deterioration using UAV imagery and Structure from Motion photogrammetry software. Submitted to <em>Remote Sensing.</em></p>
3D CMT catalogue of moderate size offshore earthquakes along the Nankai Trough
<p>3D CMT inversion solutions of moderate-size earthquakes along the Nankai Trough, <strong>version 3.1. </strong> </p> <ul> <li>Analyzed periods: <strong>January 2003 to December 2020</strong> <ul> <li>The catalog version 3, containing CMT solutions from January 2003 to April 2020.</li> <li>The catalog version 2.2, which is containing CMT solutions from April 2004 to August 2019, has been published in GJI (Takemura, Okuwaki et al. 2020 <a href="https://doi.org/10.1093/gji/ggaa238">doi:10.1093/gji/ggaa238</a>).</li> </ul> </li> <li>The method is described in Takemura, Okuwaki, et al., 2020, GJI, <a href="https://doi.org/10.1093/gji/ggaa238">doi:10.1093/gji/ggaa238</a> <a href="https://doi.org/10.31223/osf.io/nbd79">the submitted preprint</a>. </li> </ul> <p>If you use this version, you should cite the appropriate DOI and Takemura, Okuwaki, et al. 2020 GJI.</p> <p><strong>Included files</strong></p> <ul> <li>YYYYMMDDHHMM_25-100s__CMT.dat<br> CMT solutions at all selected source grids for an earthquake that occurred at HH:MM on DDth MM YYYY (JST). Latitude, longitude, depth, VR [%], M<sub>rr</sub>, M<sub>tt</sub>, M<sub>ff</sub>, M<sub>rt</sub>, M<sub>rf</sub>, M<sub>tf</sub>, exponent (dyne-cm), Mo [Nm], strike1, dip1, rake1, strike2, dip2, rake2, Mw, index of source grid (internal parameter), and centroid time are listed. </li> <li>YYYYMMDDHHMM_25-100s__CMTparam.dat<br> Input directory (internal parameter), Green's function directory (internal parameter), the number of source grids, the number of used stations, station names used in CMT inversion, frequency range, initial epicenter and distance range are listed.</li> <li>3DCMTcatalog_v3.csv<br> CSV format file of the 3D CMT catalog for earthquakes with Mw of 4.3-6.5</li> <li>catalog3DCMT_Takemura2019_Mw7.2_7.5SEKii.csv<br> CSV format file of the 3D CMT catalog for the Mw 7.2 and 7.5 southeast off the Kii Peninsula earthquake occurred on 19:07 and 23:57 5th September 2004 (JST), respectively.</li> </ul>
Testing 3D modelling software. Modelling charging pads for WPT of electric vehicles for EM emissions simulation.
<p>Even for the experienced 3D FEM modelers it may not be obvious which geometry discretization is the most appropriate and suitable for this type of problem. It may be a conservative approach to test the computation tool on a simplified geometry, on which the magnetic field distribution is known. As part of the “Metrology for inductive charging of electric vehicles” (MICEV) project (www.micev.eu), an axisymmetric geometry was used, with the results reported.</p>
3D-HPE_CERTH_dataset
<p>CERTH dataset is a single-view dataset, which comprises 2 actors performing a collaborative task. Specifically, it shows 2 actors assembling an LCD-TV. An Orbbec Astra (0.6m-8m) was used in order to record both RGB (1280 x 720) and Depth (640 x 480) data at 30 fps. The final dataset includes 700 frames and can be utilized as a testing dataset, while 1 frame every 10 frames has been manually annotated for evaluation purposes. Given that CERTH dataset is single-view, we managed to manually annotate 70% of the total number of joints, whereas the rest of them are deemed as occluded.</p>
Towards an open-source landscape for 3D CSEM modelling
<p>Accompanying data to journal article</p> <blockquote> <p>Werthmüller, D., R. Rochlitz, O. Castillo-Reyes, and L. Heagy, 2021, Towards an open-source landscape for 3D CSEM modelling: Geophysical Journal International; ggab238, DOI: <a href="https://doi.org/10.1093/gji/ggab238">10.1093/gji/ggab238</a>.</p> </blockquote> <ul> <li>Official article: <a href="https://doi.org/10.1093/gji/ggab238">https://doi.org/10.1093/gji/ggab238</a></li> <li>GitHub repo: <a href="https://github.com/swung-research/3d-csem-open-source-landscape">https://github.com/swung-research/3d-csem-open-source-landscape</a></li> <li>arXiv.org: <a href="https://arxiv.org/abs/2010.12926">https://arxiv.org/abs/2010.12926</a></li> </ul> <p>The Marlim R3D model can be found at:</p> <ul> <li>Original, fine resistivity model: <a href="https://doi.org/10.5281/zenodo.400233">https://doi.org/10.5281/zenodo.400233</a></li> <li>Upscaled computational model: <a href="https://doi.org/10.5281/zenodo.3748491">https://doi.org/10.5281/zenodo.3748491</a></li> <li>CSEM data set: <a href="https://doi.org/10.5281/zenodo.1256786">https://doi.org/10.5281/zenodo.1256786</a></li> <li>Noise-free CSEM data set: <a href="https://doi.org/10.5281/zenodo.1807134">https://doi.org/10.5281/zenodo.1807134</a></li> </ul>
Photonics4All Bookmark 3D Printing (German)
<p>The purpose of the bookmarks for the project Photonics4all is to increase the public awareness of photonics and especially of the technological advances of photonics which have changed and improved everyday life (basic technology introduction).<br> <br> How is Light revolutionizing manufacturing?<br> <br> During the last 5 years, a huge improvement has been made in laser sources, which has enabled the growth of 3D printing.<br> 3D Printing is such a new type of process, offering so many applications that this technology opens new doors to manufacturing industries. The Aerospace and Automotive industries are already making parts for the International Space station in space, aeroplanes and cars. And in medicine, implants and prosthetics are being made, and smart objects and new optical components are emerging. Home 3D printers are also just starting to hit the market. Who knows what will be made next!<br> All thanks to the progress in Photonics!</p> <p> </p>
Photonics 4 All Bookmark 3D Printing (Swedish)
<p>The purpose of the bookmarks for the project Photonics4all is to increase the public awareness of photonics and especially of the technological advances of photonics which have changed and improved everyday life (basic technology introduction).<br> <br> How is Light revolutionizing manufacturing?<br> <br> During the last 5 years, a huge improvement has been made in laser sources, which has enabled the growth of 3D printing.<br> 3D Printing is such a new type of process, offering so many applications that this technology opens new doors to manufacturing industries. The Aerospace and Automotive industries are already making parts for the International Space station in space, aeroplanes and cars. And in medicine, implants and prosthetics are being made, and smart objects and new optical components are emerging. Home 3D printers are also just starting to hit the market. Who knows what will be made next!<br> All thanks to the progress in Photonics!</p> <p> </p>
Photonics4All Bookmark 3D Printing (French)
<p>The purpose of the bookmarks for the project Photonics4all is to increase the public awareness of photonics and especially of the technological advances of photonics which have changed and improved everyday life (basic technology introduction).<br> <br> How is Light revolutionizing manufacturing?<br> <br> During the last 5 years, a huge improvement has been made in laser sources, which has enabled the growth of 3D printing.<br> 3D Printing is such a new type of process, offering so many applications that this technology opens new doors to manufacturing industries. The Aerospace and Automotive industries are already making parts for the International Space station in space, aeroplanes and cars. And in medicine, implants and prosthetics are being made, and smart objects and new optical components are emerging. Home 3D printers are also just starting to hit the market. Who knows what will be made next!<br> All thanks to the progress in Photonics!</p> <p> </p>
Photonics4All Bookmark 3D Printing (English)
<p>The purpose of the bookmarks for the project Photonics4all is to increase the public awareness of photonics and especially of the technological advances of photonics which have changed and improved everyday life (basic technology introduction).<br> <br> How is Light revolutionizing manufacturing?<br> <br> During the last 5 years, a huge improvement has been made in laser sources, which has enabled the growth of 3D printing.<br> 3D Printing is such a new type of process, offering so many applications that this technology opens new doors to manufacturing industries. The Aerospace and Automotive industries are already making parts for the International Space station in space, aeroplanes and cars. And in medicine, implants and prosthetics are being made, and smart objects and new optical components are emerging. Home 3D printers are also just starting to hit the market. Who knows what will be made next!<br> All thanks to the progress in Photonics!</p> <p> </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.