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5,635 results for “3D”
3D Printed Antennas for mm-Wave Sensing Applicatins: Dataset
<p>This is the dataset related to the paper "3D Printed Antenass for mm-Wave Sensing Applications":</p> <p>This paper presents three low cost 3D printed antenna concepts for integration with a miniature mm-wave platform. The proposed solutions are optimized to operate in mm-wave ISM band (122GHz-123GHz). Different, inexpensive, detachable antennas can be used with the same platform for various RF sensing applications such as food safety, health and industrial.</p>
Lipschitz quaternions in the range [−10, 10]^4, which induce bijective 3D digitized rotations
<p>The file contains Lipschitz quaternions in the range [−10, 10]^4, such that they induce bijective 3D digitized rotations. It is a comma-separated values file format such that each line contains a different quaternion. This is an updated version which contains 576 more quaternions with respect to the previous version. These 576 quaternions where previously certified as ones which do not lead to bijective digitized rotations due to a bug in the used implementation of the algorithm described in:</p> <p>Pluta K., Romon P., Kenmochi Y., Passat N. (2016) Bijectivity Certification of 3D Digitized Rotations. In: Bac A., Mari JL. (eds) Computational Topology in Image Context. CTIC 2016. Lecture Notes in Computer Science, vol 9667. Springer, pp 30-41, doi:10.1007/978-3-319-39441-1_4</p> <p> </p> <p><strong>Acknowledgements:</strong><br> Special thanks for Victor Ostromoukhov and David Cœurjolly of University of Lyon 1, LIRIS, France, for finding the bug.</p>
3D reconstruction of a horse swimming
<p>3D reconstruction of a horse swimming and visualisation of the joint angles during two cycles of swimming.</p>
Dataset for Fisher et al. (2023). Motion stereo at sea: Dense 3D reconstruction from image sequences monitoring conveyor systems on board fishing vessels. IET Image Processing, 17(2), pp.349-361.
<p>This dataset contains the video clips used to produce the results presented in:</p><p>Fisher, M., French, G., Gorpincenko, A., Holah, H., Clayton, L., Skirrow, R. and Mackiewicz, M., 2023. Motion stereo at sea: Dense 3D reconstruction from image sequences monitoring conveyor systems on board fishing vessels. IET Image Processing, 17(2), pp.349-361.</p>
Rundum-Fotos eines marokkanischen rabābs zur Erstellung eines 3D-Modells mittels Photogrammetrie
<p>Instrument: <i>rabāb</i><strong> </strong><br>Herkunftsland: Marokko <br>Herkunftsort: Fès <br>Instrumentenbauer: Abdessalam Chiki <br>Herstellungsjahr: 2015 <br>Aufbewahrungsort: Basel, Privatbesitz von Thilo Hirsch </p><p>Maße: <br>Gesamtlänge: 513,2 mm <br>Max. Korpusbreite: 114,8 mm <br>Breite am Fellansatz: 96,2 mm <br>Breite am Obersattel: 31,6 mm <br>Korpustiefe am Fellansatz: ca. 80 mm </p><p>Schwingende Saitenlängen: <br>d-Saite: 410 mm <br>G-Saite: 403 mm </p><p>Material: <br>Korpus: Nussbaum <br>Wirbelkasten: Nussbaum <br>Griffbrett: Acajou (Mahagoni) <br>Dekoration: Perlmutt <br>Balken: Fichte <br>Obersattel/Saitenhalterknopf: Knochen <br>Steg: Bambus <br>Felldecke: Ziegenfell </p><p>Fotos: Thilo Hirsch, 29.–31.10.2019 und 7.12.2019 </p><p>Technische Angaben zu den Fotos: <br>Kamera: Nikon D7200 <br>Farbraum: RGB <br>Brennweite: 35 mm <br>Die Fotos wurden im RAW-Format (.nef) gemacht und müssen teilweise noch entsprechend aufgehellt werden, da für die Beleuchtung LED-Lampen verwendet wurden. Das erste Foto beinhaltet eine X-Rite Farbkarte für den Weissabgleich und die Farbkorrektur. Die Angaben zu Blendenzahl, Belichtungszeit und ISO-Empfindlichkeit finden sich in den jeweiligen Bildinformationen.</p>
A high-throughput 3D X-ray histology facility for biomedical research and preclinical applications - Supplementary Data
<p><strong>Videos</strong></p><ul><li><strong>Video 1</strong> A video going through the Z stack in single slices. This is a cross- sectional view of the XRH image stack along the XY plane. XRH datasets are normally oriented (resliced) in a way that a scroll through the stack along the XY plane emulates the physical histology slicing of the tissue.</li><li><strong>Video 2 </strong>A video going through the Y stack in single slices. This is a cross- sectional view of the XRH image stack along the XZ plane. XRH datasets are normally oriented (resliced) in a way that a scroll through the stack along the XY plane emulates the physical histology slicing of the tissue.</li><li><strong>Video 3 </strong>A video going through the X stack in single slices. This is a cross- sectional view of the XRH image stack along the YZ plane. XRH datasets are normally oriented (resliced) in a way that a scroll through the stack along the XY plane emulates the physical histology slicing of the tissue.</li><li><strong>Video 4 </strong>3D X-ray histology (XRH) is a µCT -based workflow tailored to fit seamlessly into current histology workflows in biomedical and pre-clinical research, as well as clinical histopathology. Microanatomical detail can be captured from standard (non-stained) formalin-fixed and paraffin-embedded (FFPE) tissue blocks.</li><li><strong>Video 5</strong> Average Intensity Projection (AIP) of the sample through the Histologically relevant plane. This is a 2D visualisation rendering the Average Intensity of 20x single XY slices along the z-axis of the stack. XRH datasets are normally oriented (resliced) in a way that a scroll through the stack along the XY plane emulates the physical histology slicing of the tissue.</li><li><strong>Video 6 </strong>Maximum Intensity Projection (MIP) of the sample through the Histologically relevant plane. This is a 2D visualisation rendering the Maximum Intensity of 20x single XY slices along the z-axis of the stack. XRH datasets are normally oriented (resliced) in a way that a scroll through the stack along the XY plane emulates the physical histology slicing of the tissue.</li><li><strong>Video 7 </strong>Standard deviation projection of the sample going through the histologically relevant plane. This is a 2D visualisation rendering the Standard Deviation of 20x single XY slices along the z- axis of the stack. XRH datasets are normally oriented (resliced) in a way that a scroll through the stack along the XY plane emulates the physical histology slicing of the tissue.</li></ul><p><i>* <strong>Videos 5 -7</strong> are also referred to as "thick-slice rolls" </i>- <i>Thick-slice rolling is a 2D thick-slice viewing that allows rolling of a pre-selected number of slices (n) along the z-axis of the 3D data. A single thick-slice roll forwards is accomplished by translating the thick-slice by one single slice forwards; that is moving forward by one (+1) slice from the first and nth element and reapplying the criteria or operations to the new slice sub-stack.</i><br> </p><p><strong>The questionnaire used to collect feedback about the needs of the XRH community.</strong></p><ul><li>Survey.docx</li><li>Survey.pdf</li></ul><p><br><strong>Exemplar report of a semi-automatically generated augmented PDF file</strong> that contain sample information, imaging settings, still images with descriptive figure legends, and links to corresponding online videos</p><ul><li>DEMO02019-FFPE_report_99EbPXG.pdf</li></ul><p> </p><p>= = = = = = = = = = = = = = = = <br><strong>System performance data ZIP</strong><br>= = = = = = = = = = = = = = = = </p><p>This ZIP file contains imaging data collected through different systems and setups at the XRH facility at the μ-VIS X-ray Imaging Centre at the University of Southampton for the purpose of acceptance and/or system performance characterisation. Below is an overview of the folder structure and its contents</p><p>The following files are X-ray imaging data collected on September 28, 2017, using the Med-X system and a Jima phantom at 55 kV peak and 7 Watts. </p><ul><li>20170928_MEDX_1642_JIMA_55kVp7W-2.tif</li><li>20170928_MEDX_1642_JIMA_55kVp7W.tif</li><li>20170928_MEDX_1642_JIMA_55kVp7W.tif.profile.xml</li></ul><p>This PDF document is related to a QRM MicroCT bar pattern phantom, and its specifications</p><ul><li>QRM-MicroCT-Barpattern-Phantom.pdf</li></ul><p>Graphs showing the calculated focal-spot size as a function of the X-ray power (W) for the Molybdenum rotating target calculated using Edge Modulation function testing. The performance is then compared with the performance of the Reflection target across the same range of powers. Raw data can be found in XRH_QRM_Refl-vs-Rot-TargetComparison_SingleReconSlices_5umPixelSize folder. Test performed in July 2021. </p><ul><li>XRH_202107_MoRot-testing_EdgeModFunction-QRMrecons+RotReflCompar.png</li></ul><p> </p><p><i><strong>/ XRH-XT-H-225-ST_FocalSpots</strong></i><br>This directory contains radiographic data collected using the XRH system with a JIMA phantom and MoRt (Molybdenum rotating), TT (Transmission), and Reflection targets.</p><ul><li>20200113_XRH_Jima test MoRT 55kV 15W.tif, 20200113_XRH_Jima test MoRT 55kV 30W.tif, etc.: <br>These files represent radiographs taken on January 13, 2020, using the XRH system, Jima phantom, MoRT target at 55 kVp and varying wattages.</li><li>20200207_XRH_JIMA 80kV TT1a.tif, 20200207_XRH_JIMA 80kV TT1b.tif, etc.<br>Similar to the above, these files are from February 7, 2020, and use 80 kVp with a TT target.</li><li>20231115_XRH_reflW_80kVp6W.tif, 20231115_XRH_reflW_80kVp6W_02.tif, etc.<br>These files are from November 15, 2023, and collected using the XRH system with a Reflection target at 80 kVp and 6 Watts.</li></ul><p><i><strong>/ XRH_QRM_Refl-vs-Rot-TargetComparison_SingleRadioFromCTs_5umPixelSize</strong></i><br>This directory contains single radiographs taken with a pixel size of 5 micrometers using the Molybdenum rotating (MoRt), and the Reflection target using tungsten (W) and Molybdenum (Mo) metals.</p><p><i><strong>/ XRH_QRM_Refl-vs-Rot-TargetComparison_SingleReconSlices_5umPixelSize</strong></i><br>This directory contains sinlge reconstruction slices of the setups mentioned above. Slices are exported from CT volumes and were used for the Edge Modulation function study. </p><p>For interpretation of the filenames in the folders listed above please see below and refer to specific files and folders for detailed information and results related to each imaging session:</p><ul><li><i><xx>kVp or <xx>kV </i>:Imaging at a peak voltage of <xx> kVp.</li><li><i><y>W</i> :Imaging at <y> Watts;<i> </i>"." is represented with "-"; i.e. 20210705_XRH_2766_PJB_TEST03552-EQPMT_W_6-9W is acquired using a power of 6.9 W</li><li><i>MoRt, TT, Refl </i> :Molybdenum, Transmission, and Reflection targets, respectively.</li><li><i>_W_ and _Mo_ </i> :Tungsten and Molybdenum target materials.</li><li><i>_horiz</i> :Reconstruction slices in line with the X-ray beam's propagation direction.</li><li><i>_vert</i> :Reconstruction slices normal to the X-ray beam's propagation direction and parallel to the detector plane.</li></ul>
Deep and complex vascular anatomy in the rat brain described with Ultrasound Localization Microscopy in 3D
<p><strong>Abstract:</strong></p><p>Ultrasound Localization Microscopy (<strong>ULM</strong>) enables imaging microvessels in the brain with a resolution of a few tens of micrometers <i>in vivo</i>. The planar architecture of arterioles and venules was revealed with a 2D ultrasound scanner in the cortex of the rat brain. However, deeper in the brain, where the vascularization becomes tri-dimensional, 2D imaging remains limited by the elevation projection. In this study, volumetric ultrasound imaging was performed in the craniotomized rat brain to yield 3D ULM<i> in vivo</i> within 7.5 min of acquisition with a commercial system. For instance, it highlighted the thalamus or the circle of Willis with small vessels down to 21 µm. Microbubbles tracking also gave access to the 3D velocity vector of blood flow allowing to distinguish flow directions. Volumetric ULM resolved deep complex tri-dimensional vascular structures and was compared to 2D ULM. It is a safe, simple and repeatable system to image wide field of view in the brain.</p><p><strong>Data Description:</strong></p><p>Microbubbles have been detected, localized, and tracking with 3D ultrasound imaging <i>in vivo</i> in a rat brain with skull removal.</p><p>Individual microbubble trajectories are described in 4 columns vectores: <strong>[z, x, y, time]</strong> for each position of the path. Space positions are given in [mm], and times are given in [ms]. Trajectories data are stored in .mat files (<strong>tracks_0xx.mat </strong>and zipped inside <strong>tracks.zip</strong>) as cell arrays.</p><p>Tracks can be binned inside a volumetric grid with the sample code (<strong>ULM_rendering.m</strong>).</p><p><strong>Reference to be cited: </strong>Chavignon, Heiles, Hingot, Orset, Vivien and Couture.</p><p><i>Deep and complex vascular anatomy in the rat brain described with Ultrasound Localization Microscopy in 3D.</i><br> </p>
Analyzing marine biofilms developed on carbon nanotube-modified surfaces by 3D OCT approach
<p>Glass, epoxy resin, and carbon nanotubes (CNT) composite were analyzed regarding wettability by water contact angle measurement, and roughness by atomic force microscopy. Cyanobacterial biofilms formed by Nodosilinea cf. nodulosa LEGE 10377 were developed on these surfaces for seven weeks and under controlled hydrodynamic conditions. Biofilm wet weight and structural parameters such as biofilm thickness, contour coefficient, biovolume, porosity, and average size of non-connected pores obtained from Optical Coherence Tomography (OCT) were assessed.</p>
Additional steady-state simulations of Miocene Antarctic ice-sheet variability using 3D thermodynamical ice-sheet model IMAU-ICE
<div> </div> <div> <div> <div>We supplement our previous dataset (<a href="https://doi.pangaea.de/10.1594/PANGAEA.939114">doi:10.1594/PANGAEA.939114</a>), with six additional steady-state simulations of the Miocene Antarctic ice sheet using the reference Miocene settings.</div> <div> </div> <div>IMAU-ICE was run using a 40x40km grid covering the Antarctic continent. Initial conditions were obtained from reconstructions of the Antarctic bathymetry and bedrock topography pertaining to 23 to 24 million years (Myr) ago (dataset <a href="https://doi.pangaea.de/10.1594/PANGAEA.923109" target="_self">doi:10.1594/PANGAEA.923109</a>). The simulations were forced by climate input data obtained from GENESIS simulations with varying CO2 levels (280 to 840 ppm) and Antarctic ice sheet cover (no ice to a large East-Antarctic ice sheet), and with present-day insolation. We utilized a matrix interpolation method to construct the time-varying climate forcing, based on the prescribed CO2 levels and ice cover simulated by IMAU-ICE.</div> <div> </div> <div>For each simulation, we provide the run script, 1D output variables including CO2 level and the sea level contribution of the Antarctic ice sheet, and 3D output variables including ice thickness, bedrock and surface height, surface mass balance, basal mass balance, ice velocities, and ice temperatures. For more information, please contact L.B. Stap at l.b.stap@uu.nl.</div> </div> </div>
Feasibility of 3D Body Tracking from Monocular 2D Video Feeds in Musculoskeletal Telerehabilitation
<p>Musculoskeletal conditions affect millions of people globally, however, conventional treatments pose challenges concerning price, accessibility, and convenience. Many telerehabilitation solutions offer an engaging alternative but rely on complex hardware for body tracking. This work explores the feasibility of models for 3D Human Pose Estimation (HPE) from monocular 2D videos (MediaPipe Pose) in a physiotherapy context, by comparing its performance to ground truth measurements. MediaPipe Pose was investigated in eight exercises typically performed in musculoskeletal physiotherapy sessions, where the Range of Motion (ROM) of the human joints was the evaluated parameter. This model showed the best performance for shoulder abduction, shoulder press, elbow flexion, and squat exercises (MAPE ranging between 14.9% and 25.0%, Pearson’s coefficient ranging between 0.963 and 0.996, and cosine similarity ranging between 0.987 and 0.999). Some exercises (e.g. seated knee extension and shoulder flexion) posed challenges due to unusual poses, occlusions and depth ambiguities, possibly related to a lack of training data. This study demonstrates the potential of HPE from monocular 2D videos, as a markerless, affordable and accessible solution for musculoskeletal telerehabilitation approaches. Future work should focus on exploring variations of the 3D HPE models trained on physiotherapy-related datasets, such as the Fit3D dataset, and post-preprocessing techniques to enhance the model's performance.</p>
[MedMNIST+] 18x Standardized Datasets for 2D and 3D Biomedical Image Classification with Multiple Size Options: 28 (MNIST-Like), 64, 128, and 224
<h2><strong>Code</strong> [<a href="https://github.com/MedMNIST/MedMNIST" target="_blank" rel="noopener">GitHub</a>] | <strong>Publication</strong> [<a href="https://doi.org/10.1038/s41597-022-01721-8" target="_blank" rel="noopener">Nature Scientific Data'23</a> / <a href="https://doi.org/10.1109/ISBI48211.2021.9434062" target="_blank" rel="noopener">ISBI'21</a>] | <strong>Preprint</strong> [<a href="https://arxiv.org/abs/2110.14795" target="_blank" rel="noopener">arXiv</a>]</h2> <p> </p> <p><strong>Abstract</strong></p> <p>We introduce MedMNIST, a large-scale MNIST-like collection of standardized biomedical images, including 12 datasets for 2D and 6 datasets for 3D. All images are pre-processed into 28x28 (2D) or 28x28x28 (3D) with the corresponding classification labels, so that no background knowledge is required for users. Covering primary data modalities in biomedical images, MedMNIST is designed to perform classification on lightweight 2D and 3D images with various data scales (from 100 to 100,000) and diverse tasks (binary/multi-class, ordinal regression and multi-label). The resulting dataset, consisting of approximately 708K 2D images and 10K 3D images in total, could support numerous research and educational purposes in biomedical image analysis, computer vision and machine learning. We benchmark several baseline methods on MedMNIST, including 2D / 3D neural networks and open-source / commercial AutoML tools. The data and code are publicly available at <a href="https://medmnist.com/">https://medmnist.com/</a>.</p> <p><em><strong>Disclaimer</strong></em>: The only official distribution link for the MedMNIST dataset is <a href="https://doi.org/10.5281/zenodo.10519652">Zenodo</a>. We kindly request users to refer to this original dataset link for accurate and up-to-date data.</p> <p><strong><em>Update</em>:</strong> We are thrilled to release <a href="https://github.com/MedMNIST/MedMNIST/blob/main/on_medmnist_plus.md">MedMNIST+</a> with larger sizes: 64x64, 128x128, and 224x224 for 2D, and 64x64x64 for 3D. As a complement to the previous 28-size MedMNIST, the large-size version could serve as a standardized benchmark for medical foundation models. Install the latest API to try it out!</p> <p> </p> <p><strong>Python Usage</strong></p> <p>We recommend our official <a href="https://github.com/MedMNIST/MedMNIST">code</a> to download, parse and use the MedMNIST dataset:</p> <blockquote> <pre>% pip install medmnist<br>% python</pre> <div> <div>To use the standard 28-size (MNIST-like) version utilizing the downloaded files:</div> <br> <div>>>> from medmnist import PathMNIST</div> <div>>>> train_dataset = PathMNIST(split="train")</div> <br> <div>To enable automatic downloading by setting `download=True`:</div> <br> <div>>>> from medmnist import NoduleMNIST3D</div> <div>>>> val_dataset = NoduleMNIST3D(split="val", download=True)</div> <br> <div>Alternatively, you can access MedMNIST+ with larger image sizes by specifying the `size` parameter:</div> <br> <div>>>> from medmnist import ChestMNIST</div> <div>>>> test_dataset = ChestMNIST(split="test", download=True, size=224)</div> </div> </blockquote> <p> </p> <p><strong>Citation</strong></p> <p>If you find this project useful, please cite both v1 and v2 paper as:</p> <blockquote> <p>Jiancheng Yang, Rui Shi, Donglai Wei, Zequan Liu, Lin Zhao, Bilian Ke, Hanspeter Pfister, Bingbing Ni. Yang, Jiancheng, et al. "MedMNIST v2-A large-scale lightweight benchmark for 2D and 3D biomedical image classification." Scientific Data, 2023.</p> <p>Jiancheng Yang, Rui Shi, Bingbing Ni. "MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image Analysis". IEEE 18th International Symposium on Biomedical Imaging (ISBI), 2021.</p> </blockquote> <p>or using bibtex:</p> <blockquote> <pre>@article{medmnistv2, title={MedMNIST v2-A large-scale lightweight benchmark for 2D and 3D biomedical image classification}, author={Yang, Jiancheng and Shi, Rui and Wei, Donglai and Liu, Zequan and Zhao, Lin and Ke, Bilian and Pfister, Hanspeter and Ni, Bingbing}, journal={Scientific Data}, volume={10}, number={1}, pages={41}, year={2023}, publisher={Nature Publishing Group UK London} } @inproceedings{medmnistv1, title={MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image Analysis}, author={Yang, Jiancheng and Shi, Rui and Ni, Bingbing}, booktitle={IEEE 18th International Symposium on Biomedical Imaging (ISBI)}, pages={191--195}, year={2021} }</pre> </blockquote> <p>Please also cite the corresponding paper(s) of source data if you use any subset of MedMNIST as per the description on the <a href="https://medmnist.github.io/">project website</a>.</p> <p> </p> <p><strong>License</strong></p> <p>The MedMNIST dataset is licensed under <em>Creative Commons Attribution 4.0 International</em> (<a href="https://creativecommons.org/licenses/by/4.0/">CC BY 4.0</a>), except DermaMNIST under <em>Creative Commons Attribution-NonCommercial 4.0 International</em> (<a href="https://creativecommons.org/licenses/by-nc/4.0/">CC BY-NC 4.0</a>).</p> <p>The code is under <a href="https://github.com/MedMNIST/MedMNIST/blob/main/LICENSE">Apache-2.0 License</a>.</p> <p> </p> <p><strong>Changelog</strong></p> <p><a href="https://doi.org/10.5281/zenodo.10519652">v3.0</a> (this repository): Released MedMNIST+ featuring larger sizes: 64x64, 128x128, and 224x224 for 2D, and 64x64x64 for 3D.</p> <p><a href="https://doi.org/10.5281/zenodo.10519195">v2.2</a>: Removed a small number of mistakenly included blank samples in OrganAMNIST, OrganCMNIST, OrganSMNIST, OrganMNIST3D, and VesselMNIST3D. </p> <p><a href="https://doi.org/10.5281/zenodo.6496656">v2.1</a>: Addressed an issue in the NoduleMNIST3D file (i.e., nodulemnist3d.npz). Further details can be found in this <a href="https://github.com/MedMNIST/MedMNIST/issues/22#issuecomment-1103438191">issue</a>.</p> <p><a href="https://doi.org/10.5281/zenodo.5208230">v2.0</a>: Launched the initial repository of MedMNIST v2, adding 6 datasets for 3D and 2 for 2D.</p> <p><a href="https://doi.org/10.5281/zenodo.4269852">v1.0</a>: Established the initial repository (in a separate repository) of MedMNIST v1, featuring 10 datasets for 2D.</p> <p> </p> <p><strong>Note</strong>: This dataset is <strong>NOT</strong> intended for clinical use.</p>
Precision viticulture dataset for detailed vineyard mapping composed of geotagged smartphone ground images, phytosanitary status, UAV orthomosaics, 3D point clouds, and RTK GNSS data - Northern Spain, July 2022
<p>This dataset offers a rich multimodal collection of data from vineyards, designed to enhance agricultural research with a focus on vineyard management and disease monitoring. It includes geotagged smartphone ground images in ".7z" format for detailed plant-level analysis, a ".csv" file detailing plants' phytosanitary status for health assessment, UAV-derived 3D Point Clouds and orthomosaics in ".las" and ".tiff" formats for aerial landscape views, and RTK GNSS data in ".shp" format for precise plant geolocations.</p> <p>This dataset can be combined with other datasets to enable a comprehensive view of the vineyards and improve its value:</p> <div> <ul> <li>Ariza-Sentís, Mar, Sergio Vélez, and João Valente. ‘Dataset on UAV RGB Videos Acquired over a Vineyard Including Bunch Labels for Object Detection and Tracking’. <em>Data in Brief</em> 46 (February 2023): 108848. <a href="https://doi.org/10.1016/j.dib.2022.108848">https://doi.org/10.1016/j.dib.2022.108848</a>.</li> <li>Vélez, Sergio, Mar Ariza-Sentís, and João Valente. ‘VineLiDAR: High-Resolution UAV-LiDAR Vineyard Dataset Acquired over Two Years in Northern Spain.’ <em>Data in Brief</em>, October 2023, 109686. <a href="https://doi.org/10.1016/j.dib.2023.109686">https://doi.org/10.1016/j.dib.2023.109686</a>.</li> <li> <div> <div>Vélez, Sergio, Mar Ariza-Sentís, and João Valente. ‘Dataset on Unmanned Aerial Vehicle Multispectral Images Acquired over a Vineyard Affected by Botrytis Cinerea in Northern Spain’. <em>Data in Brief</em> 46 (February 2023): 108876. <a href="https://doi.org/10.1016/j.dib.2022.108876">https://doi.org/10.1016/j.dib.2022.108876</a>.</div> <div> </div> </div> </li> </ul> </div>
Data for Paper "Scalable Semantic 3D Mapping of Coral Reefs with Deep Learning"
<p><strong>Example Data for DeepReefMap</strong></p> <p>This dataset contains input videos in MP4 format taken with GoPro Hero 10 Cameras in Reefs in the Red Sea to demonstrate the DeepReefMap tool, which is described in the paper "Scalable Semantic 3D Mapping of Coral Reefs with Deep Learning" by Sauder et al.</p> <p>It contains a directory for model checkpoints for semantic segmentation, and for the 3D SLAM component:</p> <p>```<br>checkpoints/<br> segmentation_net.pth<br> sfm_net.pth<br>```</p> <p>It also contains videos to run the reconstruction with. See the detailed instructions for running reconstructions in https://github.com/josauder/mee-deepreefmap</p> <p>```<br>input_videos/<br> GX_SINGLE_VIDEO.MP4<br> GX_VIDEO_1_OF_2.MP4<br> GX_VIDEO_2_OF_2.MP4<br>```</p>
MRI Neonatal Lung Segmentation and 3D Morphologic Features
<p>We developed an ensemble of deep convolutional neural networks (2D-UNets) to perform automated neonatal lung segmentation from MRI sequences. A three-dimensional reconstruction is used to calculate MRI features for lung volume, shape, pixel intensity, and surface.</p> <p>In addition, ML Models for severity prediction of Bronchopulmonary Dysplasia (BPD) are implemented as an applied example of the use of MRI lung volumetric features for disease prognosis.</p> <p>This dataset comprises:</p> <ul> <li>Three pretrained 2D-UNet Models for Neonatal MRI Lung Segmentation.</li> <li>Resulting performances and features per MRI-sequence.</li> </ul> <p>See Publication:</p> <p>Automated MRI Lung Segmentation and 3D Morphologic Features for Quantification of Neonatal Lung Disease (2023)</p> <p><a href="https://doi.org/10.1148/ryai.220239">https://doi.org/10.1148/ryai.220239</a></p>
3D Models of the yellow coffins in the Museo Egizio, Torino (Italy)
<p>3D Models of yellow coffin lids in the <a href="https://www.museoegizio.it/" target="_blank" rel="noopener"><strong>Museo Egizio, Torino</strong></a>.</p> <p>The 3D models consider only the external upper part of coffin lids as far down as the lower part of the crossed forearms. </p> <p>The dataset contains:</p> <ol> <li>zip files with the 3D models generated with the software Agisoft Metashape 1.8.3 (.jpg; .mtl; .obj);</li> <li>.tif files with the orthophtgraphs of the coffins textured and not textured</li> <li>Exported Report of 3D models (.pdf)</li> <li>.pdn file with the overlapped layers (orthophotographs textured and not textured, drawings and points) generated with the open source paint. net</li> </ol> <p>The dataset is part of the results of the <a href="https://facesrevealed.museoegizio.it/"><strong>Faces Revealed</strong> <strong>Project</strong></a>. The project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 895130</p> <p><strong>If you publish material based on datasets contained in this archive, then, in your acknowledgements, please cite the original source, referring to it through the following DOI: 10.5281/zenodo.10589491</strong></p>
XRECO 3D Buildings and Monuments v1
<p>The dataset consists of 201 textured 3D models created with photogrammetry of monuments and buildings mainly across Europe. The 3D models depict various buildings and monuments mainly across Europe. They were cleaned manually by removing all background information from the scene and keeping only the main building. The data are annotated into 12 building classes including: castle, cathedral, church, city hall, factory, hotel, house, mosque, office, palace, school, villa.</p>
Proposal of a domain model for 3D representation of buildings for the 3D cadastre in Ecuador
<p><span>The accelerated urban sprawl of cities around the world presents major challenges for urban planning and land resource management. In this context, it is crucial to have a detailed 3D representation of buildings enriched with accurate alphanumeric information. A distinctive aspect of this proposal is its specific focus on the spatial unit corresponding to buildings. In order to propose a domain model for the 3D representation of buildings, the national standard of Ecuador and the international standard (ISO 19152) were considered. The proposal includes a detailed specification of attributes, both for the general subclass of buildings and for their infrastructure. The application of the domain model proposal was crucial in a study area located in the Riobamba canton, due to the characteristics of the buildings in that area. For this purpose, a geodatabase was created in pgAdmin4 with official information, taking into account the structure of the proposed model and linking it with geospatial data for an adequate management and 3D representation of the buildings in an open-source Geographic Information System. This application improves cadastral management in the study region and has wider implications. This model is intended to serve as a benchmark for other countries facing similar challenges in cadastral management and 3D representation of buildings, promote efficient urban development and contribute to global sustainable development.</span></p>
A floating 3D printed formulation for the coadministration and sustained release of antihypertensive drugs - Underlying CT data
<p>Underlying CT data of <strong>"A floating 3D printed formulation for the coadministration and sustained release of antihypertensive drugs"</strong></p> <p>Paola Zgouro1, Orestis L. Katsamenis3,4, Thomas Moschakis5, Georgios K. Eleftheriadis6, Athanasios S. Kyriakidis6, Konstantina Chachlioutaki1,2, Paraskevi Kyriaki Monou1,2, Marianna Ntorkou7, Constantinos K. Zacharis7, Nikolaos Bouropoulos8,9, Dimitrios G. Fatouros1,2, Christina Karavasili1, Christos I. Gioumouxouzis1</p> <p><em>1 Laboratory of Pharmaceutical Technology, Department of Pharmaceutical Sciences, Aristotle University of Thessaloniki, GR-54124, Thessaloniki, Greece</em><br><em>2 Center for Interdisciplinary Research and Innovation (CIRI-AUTH), 57001 Thessaloniki, Greece</em><br><em>3 μ-VIS X-Ray Imaging Centre, Faculty of Engineering and Physical Sciences, University of Southampton, Southampton, SO17 1BJ, UK</em><br><em>4 Institute for Life Sciences, University of Southampton, University Rd, Highfield, Southampton, SO17 1BJ, UK</em><br><em>5 Department of Food Science and Technology, School of Agriculture, Aristotle University of Thessaloniki, GR-541 24 Thessaloniki, Greece</em><br><em>6 Pharmacare Premium Limited, R&D Department, HHF003 Hal Far Industrial Estate, Birzebbugia BBG3000, Malta</em><br><em>7 Laboratory of Pharmaceutical Analysis, Department of Pharmacy, Aristotle University of Thessaloniki, GR-54124, Greece</em><br><em>8 Department of Materials Science, University of Patras, 26504 Rio, Patras, Greece</em><br><em>9 Foundation for Research and Technology Hellas, Institute of Chemical Engineering and High Temperature Chemical Processes, Patras, Greece</em></p> <p><strong>Microfocus Computed Tomography (μCT)</strong></p> <p>X-ray microfocus computed tomography (μCT) was employed for the characterization of the microstructure of the printed object, assessing the overall volume, porosity, local thickness and other printing defects. The imaging took place at the University of Southampton’s μ-VIS X-ray Imaging Centre (<a title="&mu;-VIS X-ray Imaging Centre at the University of Southampton" href="https://www.muvis.org" target="_blank" rel="noopener">www.muvis.org</a>) / 3D X-ray Histology facility using a customized μCT scanner optimized for 3D X-ray histology (<a title="3D X-ray Histology facility at University of Southampton" href="https://www.xrayhistology.org" target="_blank" rel="noopener">www.xrayhistology.org</a>) (<a title="A high-throughput 3D X-ray histology facility for biomedical research and preclinical applications" href="https://doi.org/10.12688/wellcomeopenres.19666.2" target="_blank" rel="noopener">Katsamenis et al., 2023</a>) based on Nikon’s XTH225ST system (Nikon Metrology, Castle Donington, UK). The scanner was operated at 110 kVp / 90 μA (9.9 W), with the X-ray beam prefiltered using 0.04 mm of aluminum. The source-to-object and source-to-detector distances were 28.4 mm and 1136.7 mm, respectively, resulting in a magnification factor of 40x. Acquisition parameters included 2201 projections, averaging 4 frames per projection, with an exposure time of 177 ms per projection. The 2850 x 2850 dexels detector was binned 2x (virtual detector: 1425 × 1425 dexels), resulting in an isotropic voxel edge of 7.5 μm. The reconstructed data underwent visualization and analysis using Dragonfly software (Comet Technologies Canada Inc.; software available at http://www.theobjects.com/dragonfly).</p>
Hypersonic Transport: 3D Emission Inventory of STRATOFLY-MR3 Fleet Operated on Brussels to Sydney Route in 2075
<p>High-resolution 3D inventories of future hypersonic transport (HST) are compiled for the year 2075, integrating the gaseous engine emissions of a fleet of 200 hydrogen-powered Mach 8 passenger aircraft*. These aircraft are operated once a day for 360 days on a reference route from Brussels (BRU) to Sydney (MYA) with either NO<sub>x</sub>-optimized (ICA**: 114 000 ft; 34.75 km) or H<sub>2</sub>O-optimized (ICA**: 107 500 ft; 32.77 km) flight profiles, derived to minimize environmental impacts in terms of total emissions. The emissions are spatially gridded at a horizontal resolution of 1° in longitude and latitude, with a vertical resolution of 1000 ft, and are temporally accumulated on an annual basis. Note that the 3D emission inventories encompass detailed data on species-specific HST emissions***, fuel burn, and total distance traveled: </p> <ul> <li>Species: NO, H<sub>2</sub>O; H<sub>2</sub></li> <li>Temporal information: 2075; annually</li> <li>Spatial information: 1° x 1° x 1000 ft</li> <li>Data Format: NetCDF</li> </ul> <p>-----------------------------------------------------------------------------------------------------------------------------------------<br>* The hypersonic aircraft concept under consideration is the <a href="https://arc.aiaa.org/doi/abs/10.2514/6.2021-1877">STRATOFLY-MR3</a> vehicle, which was conceptually developed in <br> the framework of the <a href="https://cordis.europa.eu/project/id/769246">H2020 STRATOFLY project</a>.<br>** Initial Cruise Altitude<br>*** with a unit of kg/km<sup>3 </sup>(corrected in v0.2)</p> <p> </p>
3d Transition Metal K-edge XANES Dataset for Machine Learning Models
<p><strong>Data</strong><br><br>This dataset contains machine learning data for K-edge X-ray Absorption Near-Edge Structure (XANES) prediction models for eight 3d transition metals (Ti -Cu).</p> <ul> <li><strong>features_and_spectra:</strong> Material features (X) and corresponding XAS spectra (y) for each dataset split: training (train), validation (val), and test.</li> <li><strong> material_id_and_site:</strong> Material identifiers and site indices (according to <a href="https://github.com/AI-multimodal/Lightshow">Lightshow</a>) for each dataset split. </li> </ul> <p><strong>Funding</strong><br><br>This research is based upon work supported by the U.S. Department of Energy, Office of Science, Office Basic Energy Sciences, under Award Number FWP PS-030. This research also used theory and computational resources of the Center for Functional Nanomaterials, which is a U.S. Department of Energy Office of Science User Facility, and the Scientific Data and Computing Center, at Brookhaven National Laboratory under Contract No. DE-SC0012704 and by Brookhaven National Laboratory (BNL), Laboratory Directed Research and Development (LDRD) grant no. 24-004.</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.