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478 results for “3D data”
MCR LTER: Coral Reef: 3D photogrammetry improves measurement of growth and biodiversity patterns in branching corals; data for Curtis 2023, Coral Reefs
These data and code were generated in support of the manuscript: Curtis JS, Galvan JW, Primo A, Osenberg CW, and AC Stier, Coral Reefs. We collected manual and photogrammetry-based measurements of coral size and volume to examine which method best described short-term coral growth and links between coral habitat and biodiversity of CAFI (coral-associated fishes and invertebrates). This study was completed between August and December 2019 on an experimental array located in the back reef off the south shore of Moorea, French Polynesia. These data were published in Coral Reefs, analyses and full methods descriptions of this model can be found in the manuscript “3D photogrammetry improves measurement of growth and biodiversity patterns in branching corals”. This manuscript uses data collected by the U.S. National Science Foundation's (NSF) Moorea Coral Reef Long Term Ecological Research (MCR LTER) site under Grant No. OCE 2224354 (and earlier awards). Additional financial support to the MCR LTER site was provided through a generous gift from the Gordon and Betty Moore Foundation. Research was completed under permits issued by the French Polynesian Government (Délégation à la Recherche) and the Haut-commissariat de la République en Polynésie Francaise (DTRT) (Protocole d'Accueil 2005-2023).
iPlacenta: hIPSC placenta-on-a-chip RNAseq data from 3D vs 2D, day 0 vs day 4 differentiation
<p>RNAseq data from hIPSC dervived trophoblasts seeded in 3D (OrganoPlate) or 2D surface at day 0 or day 4 differentiation. </p> <p>Description of file names found below</p> <table> <tbody> <tr> <td> <p><strong>SampleID/File name</strong></p> </td> <td> <p><strong>Condition- Differentiation day</strong></p> </td> </tr> <tr> <td> <p>iPSC-THB-2D-D0-1</p> </td> <td> <p>2D-Day0</p> </td> </tr> <tr> <td> <p>iPSC-THB-2D-D0-2</p> </td> <td> <p>2D-Day0</p> </td> </tr> <tr> <td> <p>iPSC-THB-2D-D0-3</p> </td> <td> <p>2D-Day0</p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>iPSC-THB-2D-D4-4</p> </td> <td> <p>2D-Day4</p> </td> </tr> <tr> <td> <p>iPSC-THB-2D-D4-5</p> </td> <td> <p>2D-Day4</p> </td> </tr> <tr> <td> <p>iPSC-THB-2D-D4-6</p> </td> <td> <p>2D-Day4</p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>iPSC-THB-3D-D0-7</p> </td> <td> <p>3D-Day0</p> </td> </tr> <tr> <td> <p>iPSC-THB-3D-D0-8</p> </td> <td> <p>3D-Day0</p> </td> </tr> <tr> <td> <p>iPSC-THB-3D-D0-9</p> </td> <td> <p>3D-Day0</p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>iPSC-THB-3D-D4-10</p> </td> <td> <p>3D-Day4</p> </td> </tr> <tr> <td> <p>iPSC-THB-3D-D4-11</p> </td> <td> <p>3D-Day4</p> </td> </tr> <tr> <td> <p>iPSC-THB-3D-D4-12</p> </td> <td> <p>3D-Day4</p> </td> </tr> <tr> <td> <p>iPSC-THB-3D-D4-13</p> </td> <td> <p>3D-Day4</p> </td> </tr> </tbody> </table>
VoroCrack3d: An annotated data set of 3d CT concrete images with synthetic crack structures
<p>VoroCrack3d is an annotated data set of 3d CT images of concrete with synthetic crack structures. Its main purpose is the training and testing of machine learning models for 3d crack segmentation. The data set comprises 1344 images together with their corresponding ground truths. The concrete backgrounds are cropped out sections of size 400x400x400 voxels of CT images of concrete. To this end, several different concrete samples were scanned (normal concrete (NC), high-performance concrete (HPC), ultra-high-performance concrete (UHPC), air pore concrete; without and with reinforcements (straight steel fibers, crimped steel fibers, hooked-end steel fibers, polypropylene fibers, fibers made of glass fiber-reinforced polymer). The original concrete images have a resolution between 2.8 and 106 micrometers.</p> <p>The crack structures are modeled via minimum-weight surfaces in Voronoi diagrams according to the paper</p> <p>[1] C. Jung, C. Redenbach, Crack Modeling via Minimum-Weight Surfaces in 3d Voronoi Diagrams, Journal of Mathematics in Industry, 13, 10 (2023). https://doi.org/10.1186/s13362-023-00138-1.</p> <p>The surfaces are discretized, dilated and superimposed on the concrete backgrounds.</p> <p>The data set offers a high variety regarding concrete types, noise levels and crack widths, shapes, regularity and branching. This makes it suitable for studying the generalizability and robustness of 3d crack segmentation methods.</p> <p>______________________________________________________________________________________________</p> <p>The folder 'data' contains seven subfolders, each containing the data generated from a specific concrete type (NC, HPC, air pore concrete, polypropylene fiber-reinforced concrete, steel fiber-reinforced concrete (straight, crimped and hooked-end steel fibers)).</p> <p>Each subfolder again contains four subfolders according to the point process model that was used for generating the 3d Voronoi diagrams. The point processes and Voronoi diagrams are restricted to windows of size 400x150x400. </p> <p>- 'hc': Hard core point process with 60% volume density and intensity 0.000025 obtained from force-biased sphere packing.<br>- 'matclust': Matérn cluster process with parent intensity 0.0002/50, offspring intensity 50 and cluster radius 20.<br>- 'ppp': Poisson point process with intensity 0.0002.<br>- 'ppp-scaled': Poisson point process with intensity 0.0002 (but inside 200x150x200 window). The resulting Voronoi diagram is stretched in x- and z- direction by a factor of 2.</p> <p>Each of these contains five subfolders: one for the 3d input images, two for the corresponding labels (ground truths; one with and one without pores/fibers), one for the input and label previews (slice z=200 for each of the images) and a misc folder containing the concrete background without crack and, if applicable, the pore/fiber segmentation image.</p> <p>The data itself then contains 48 images:<br>1a-1d: crack with up to seven branches; fixed crack width (~1 voxel).<br>2a-2d: crack with up to four branches; fixed crack width (~1 voxel).<br>3a-3d: crack with up to one branch; fixed crack width (~1 voxel).<br>4a-4d: crack with no branches; fixed crack width (~1 voxel).<br>5a-5d: crack with no branches; fixed crack width (~3 voxels).<br>6a-6d: crack with no branches; fixed crack width (~5 voxels).<br>7a-7d: crack with no branches; fixed crack width (~7 voxels).<br>8a-8d: crack with up to seven branches; multiscale crack (bernoulli parameter 0.01);<br>9a-9d: crack with up to seven branches; multiscale crack (bernoulli parameter 0.02);<br>10a-10d: crack with up to seven branches; multiscale crack (bernoulli parameter 0.05);<br>11a-11d: crack with up to seven branches; multiscale crack (bernoulli parameter 0.1);<br>12a-12d: crack with up to seven branches; multiscale crack (bernoulli parameter 0.2);</p> <p>The names 'a'-'d' indicate level of added noise added to the image:<br>a: None.<br>b: Uniformly on [-sigma,sigma] <br>c: Uniformly on [-2*sigma,2*sigma] <br>d: Uniformly on [-4*sigma,4*sigma] <br>Negative values are mapped to 0. <br>For inputs of type int, noise values are rounded to the nearest integer.<br>(sigma = standard deviation of voxel greyvalues in image)</p> <p>Note that the grey values in the ground truths correspond to the local crack width. They can be thresholded to obtain binary masks.</p> <p>For more details, we refer to [1].</p>
3D Data Derivatives of Grotta di Fumane: GigaMesh-processed, Annotations and Segmentations
<p><strong>Overview:</strong></p> <p>This repository contains derivatives of the Open Access publication by Falcucci & Peresani [FP22]. Our derived dataset (n = 62) is used to demonstrate our segmentation algorithm [BHM23], as shown in [BLM22], [BLM23], [LBM23], and will serve as a benchmark dataset for future analyses. To date, and to the best of our knowledge, our dataset is the first dataset of annotated lithic artifacts. In addition to the annotated dataset, we will also provide the segmented [BLM23] and GigaMesh preprocessed datasets [Mar+10; MK13] (n = 732) in separate folders. </p> <p><strong>Repository description: </strong></p> <p>A detailed description of the data can be found in 3D_Data_Derivatives_of_GdF_overview.pdf.</p> <p>For information on the archaeological interpretation of the artifacts, please refer to the original data publication by Falcucci and Peresani (2022). In our publications, we have expanded the CSV file from Falcucci and Peresani (2022) to document the use of the extended dataset:</p> <ul> <li> <p>Annotated: All artifacts that are annotated are marked with a 1.</p> </li> <li> <p>GT_PLY: All artifacts that are annotated and included in this publication are referenced by their respective file, such as 31_gt_labels.ply.</p> </li> <li> <p>Bullenkamp_et_al_2022: Artifacts utilized in [BLM22] are marked with a 1 .</p> </li> <li> <p>Bullenkamp_et_al_2023: Artifacts utilized in [BLM23] are marked with a 1.</p> </li> <li> <p>Linsel_et_al_2023: Artifacts utilized in [LBM23] are marked with a 1.</p> </li> </ul>
Data for: 3D in vitro modeling of the exocrine pancreatic unit using tomographic volumetric bioprinting
<p><strong>Abstract</strong></p> <div> <div> <p><span><span>Pancreatic ductal adenocarcinoma (PDAC) is the most frequent type of pancreatic cancer, one of the leading causes of cancer-related deaths worldwide. The first lesions associated with PDAC occur within the functional units of exocrine pancreas</span><span>. T</span><span>he crosstalk between PDAC cells and stromal cells plays a key role in tumor progression.</span><span> Thus,</span> <span>i</span></span><span><span>n vitro</span></span><span><span>, fully human models of the pancreatic cancer microenvironment are needed to foster the development of new, more effective therapies</span><span>.</span> <span>However,</span><span> it is challenging to make these models anatomically and functionally relevant. Here, we used tomographic volumetric bioprinting, a novel method to fabricate </span><span>three-dimensional </span><span>cell-laden constructs</span><span>,</span><span> to produce a </span><span>portion</span><span> of the </span><span>complex convoluted </span><span>exocrine pancreas</span> </span><span><span>in vitro</span></span><span><span>.</span><span> Human fibroblast-laden gelatin methacrylate-based pancreatic models were processed to reassemble the </span><span>tubuloacinar</span><span> structures of the exocrine pancreas and, then human pancreatic ductal epithelial (HPDE) cells overexpressing the KRAS oncogene (HPDE-KRAS) were seeded in the acinar lumen to reproduce the pathological exocrine pancreatic tissue. The growth and organization of HPDE cells within the structure was evaluated and the formation of a thin epithelium which covered the acini inner surfaces in a physiological way inside the 3D model was</span> <span>successfully</span> <span>demonstrated</span><span>. Interestingly, immunofluorescence assays revealed a significantly higher expressions of alpha smooth muscle </span><span>actin</span><span> (α-SMA) vs. </span><span>actin</span><span> in the fibroblasts co-cultured with cancerous than with wild-type HPDE cells. Moreover, α-SMA expression increased with time, and it was found to be higher in fibroblasts that laid closer to HPDE cells than in those </span><span>laying </span><span>deeper into the model. Increased levels of interleukin (IL)-6 were also quantified in supernatants from co-cultures of stromal and HPDE-KRAS cells. These findings correlate with inflamed tumor-associated fibroblast behavior, thus being relevant biomarkers to </span><span>monitor</span><span> the early progression of the disease and to target drug efficacy. </span></span><span> </span></p> </div> <div> <p><span><span>To our knowledge, this is the first</span> <span>demonstration of a </span><span>3D </span><span>bioprinted</span> <span>portion</span><span> of </span><span>pancreas that</span> <span>rec</span><span>apit</span><span>ulates</span> <span>its</span> <span>true 3-dimensional </span><span>microanatomy</span><span>,</span><span> and which shows </span><span>tumor triggered </span><span>inflammation</span><span>. </span></span><span> </span></p> </div> </div> <p> </p> <p><strong>Contents</strong></p> <p>This repository contains the raw data, materials list, protocols, and code necessary to reproduce the work in the namesake preprint.</p> <p> </p>
3D-data Runstenar i Medelpad
<p>3D-scans of runestones in Medelpad. This dataset includes 3D-models of 11th century runestones 3D-scanned for a study within the research project Evighetsrunor: en forskningsplattform för Sveriges runinskrifter (Everlasting Runes: a research platform for Sweden's runic inscriptions) and as a preparation for a corpus publication about runic inscriptions in Medelpad.</p>
Data for: 3D bioprinting patents
<p>This dataset contains information regarding 3D bioprinting patent/patent applications. </p> <p><a href="https://www.orbit.com/">Orbit</a> (a fee-based patent database provided by Questel), accessed on Jan. 2, 2022, was used for data mining.</p> <p>The files titled “<em>3D bioprinting patents</em>” and “<em>Bioink patents</em>” contain information related to Priority, Application and Publication numbers, Priority Application and Publication dates, Title, Abstract, and Current assignees.</p> <p>The patent searches were carried out by keywords and classification codes. </p> <p>Both IPC (International Patent Classification) and CPC (Cooperative Patent Classification) codes were used. </p> <p>Instead of 309 patents (see reference 1), a total number of 3,681 documents were retrieved (of which 3,027 are still alive and 2,461 filed in the period 2016 – 2020).</p> <p>Most of the published patent applications were generated in China and the USA (1,360 vs 1,063 priority applications). China is effectively the leading country (37%), followed by the USA with 29% of priority patent applications. </p> <p><strong>Value of the dataset</strong>: prior art searches, technological trends </p> <p><strong>Steps to reproduce data</strong>: </p> <p>The search strategy is reported in the table below:</p> <table> <tbody> <tr> <td> <p>1</p> </td> <td> <p>857</p> </td> <td> <p>(BIOPRINT+ OR BIOINK? OR ORGAN_ON_A_CHIP)/TI/AB/CLMS/ICLM</p> </td> </tr> <tr> <td> <p>2</p> </td> <td> <p>3096</p> </td> <td> <p>((A61L-027+ OR A61F-002+ OR A61L2430/00)</p> <p>AND (B33Y+ OR B29C-064+))/IPC/CPC</p> </td> </tr> <tr> <td> <p>3</p> </td> <td> <p>3681</p> </td> <td> <p> 1 OR 2</p> </td> </tr> <tr> <td> <p>4</p> </td> <td> <p>71912</p> </td> <td> <p>(C09D-011+)/IPC/CPC</p> </td> </tr> <tr> <td> <p>5</p> </td> <td> <p>104</p> </td> <td> <p> 3 AND 4</p> </td> </tr> </tbody> </table> <p><strong>Definition of the classification codes used</strong></p> <p><strong>A61L 27</strong>: Materials for grafts or prostheses or for coating grafts or prostheses</p> <p><strong>A61F 2</strong>: Filters implantable into blood vessels; Prostheses, i.e., artificial substitutes or replacements for parts of the body; Appliances for connecting them with the body; Devices providing patency to, or preventing collapsing of, tubular structures of the body, e.g., stents</p> <p><strong>A61L2430/00</strong>: Materials or treatment for tissue regeneration</p> <p><strong>B33Y</strong>: Additive manufacturing, i.e., manufacturing of three-dimensional [3-d] objects by additive deposition, additive agglomeration, or additive layering, e.g., by 3-d printing, stereolithography, or selective laser sintering</p> <p><strong>B29C 64/00</strong>: Additive manufacturing, i.e., manufacturing of three-dimensional [3D] objects by additive deposition, additive agglomeration, or additive layering, e.g., by 3D printing, stereolithography, or selective laser sintering</p> <p><strong>C09D 11</strong>: inks </p>
3D-data Runstenar i Södermanland / Runestones in Södermanland
<p>3D-scans of runestones in Södermanland. This dataset includes 3D-models of 11th century AD runestones 3D-scanned for a study within the research project Runristandets dynamik (2009-2014). The project focussed on analysis of the runic inscriptions and ornament, therefor only the inscription surfaces have been scanned. For some stones, this is the only option as they are inserted into church walls.</p>
Cup-marked stone, Zermatt-Hubelwäng, Switzerland - imagery and photogrammetrically derived 2.5D data, 3D data and orthophoto of stone slab no. 3920-01
<p>Imagery and derived 2.5D data, 3D data and orthophoto of cup-marked stone slab No. 3920-01 (http://www.ssdi.ch/), Zermatt-Hubelwäng, Switzerland.</p> <p>Supplemental data for: J. Reinhard, Was in den Rucksack passt… In: Chr. Rinne et al. (ed.), Vom Bodenfund zum Buch - Archäologie durch die Zeiten. Festschrift für Andreas Heege. Historische Archäologie Sonderband 1 (Bonn 2017), 503-520. URL: <a href="http://www.histarch.uni-kiel.de/sonderband01.htm">http://www.histarch.uni-kiel.de/sonderband01.htm</a>, DOI:<a href="https://doi.org/10.18440/ha.2017.101"> https://doi.org/10.18440/ha.2017.101</a> (original paper and additional poster contained in the upload). See <a href="http://skfb.ly/6sxJT">https://skfb.ly/6sxJT</a> for an online visualization of the data on Sketchfab.</p> <p> </p> <p>Contents:</p> <p><a href="https://zenodo.org/api/files/166f08c2-3b28-4c6d-8fca-dcd6fadd8ef5/HASB2017_130_503.pdf?versionId=2c4ebd68-da57-42da-b49a-b95d10a9f4f8">HASB2017_130_503.pdf</a>: PDF of Reinhard 2017 (cited above).</p> <p><a href="https://zenodo.org/api/files/166f08c2-3b28-4c6d-8fca-dcd6fadd8ef5/HASB2017_130_sup1.zip?versionId=87498b06-3e60-4dc1-a182-0157df73ad80">HASB2017_130_sup1.zip</a>: dense point cloud (full resolution, .ply)</p> <p><a href="https://zenodo.org/api/files/166f08c2-3b28-4c6d-8fca-dcd6fadd8ef5/HASB2017_130_sup2.zip?versionId=951d3131-b09b-4efb-b768-adbd29e55e91">HASB2017_130_sup2.zip</a>: orthophoto (5 mm resolution, GeoTIFF)</p> <p><a href="https://zenodo.org/api/files/166f08c2-3b28-4c6d-8fca-dcd6fadd8ef5/HASB2017_130_sup3.zip?versionId=8f5ac851-e596-4a31-b724-5f056a4940eb">HASB2017_130_sup3.zip</a>: DEM (1 mm resolution, GeoTIFF)</p> <p><a href="https://zenodo.org/api/files/166f08c2-3b28-4c6d-8fca-dcd6fadd8ef5/HASB2017_130_sup4.zip?versionId=29043ecd-1bea-4fbd-8264-38e99c583691">HASB2017_130_sup4.zip</a>: orthophoto (1 mm resolution, GeoTIFF)</p> <p><a href="https://zenodo.org/api/files/166f08c2-3b28-4c6d-8fca-dcd6fadd8ef5/HASB2017_130_sup5.zip?versionId=ae05c230-c25a-4a29-801f-202823648ade">HASB2017_130_sup5.zip</a>: 3D model (full resolution, .obj/.mtl/.jpg)</p> <p><a href="https://zenodo.org/record/3373713/files/Image-based_modeling_report.pdf?download=1">Image-based_modeling_report.pdf</a>: Image-based modeling report generated by Agisoft PhotoScan</p> <p><a href="https://zenodo.org/api/files/166f08c2-3b28-4c6d-8fca-dcd6fadd8ef5/In_Rock_We_Trust_Poster_EAA_Bern_2019-09-07.pdf?versionId=7c003048-b261-45a6-803f-6affc3c48721">In_Rock_We_Trust_Poster_EAA_Bern_2019-09-07.pdf</a>: poster presented at the EAA annual conference 2019 in Bern</p> <p><a href="https://zenodo.org/record/3373713/files/Notes_on_image-based_modeling.pdf?download=1">Notes_on_image-based_modeling.pdf</a>: Notes on the image-based modeling process including scaling information</p> <p><a href="https://zenodo.org/api/files/166f08c2-3b28-4c6d-8fca-dcd6fadd8ef5/Photos.zip?versionId=833aaf74-8c0e-48a5-8459-28d47af02d2e">Photos.zip</a>: complete set of images used in this project, taken in april 2016</p> <p> </p>
3D-data Runstenar signerade av Åsmund Kåresson / Runestones signed by Asmund Karasun
<p>3D-scans of runestones signed by Asmund Karasun (Åsmund Kåresson). This dataset includes 3D-models of 11th century runestones 3D-scanned for a study within the research project Runristandets dynamik (2009-2014). The project focussed on analysis of the runic inscriptions and ornament, therefor only the inscription surfaces have been scanned. For some stones, this is the only option as they are leaning against, or inserted into, church walls. Results of analysis have been published in the article "Åsmund Kåresson - en sällskaplig runristare" (English summary) in the journal Situne Dei (Situne Dei 2016, p. 26-39; see related publications).</p>
Tailored Sticky Solutions: 3D-Printed Miconazole Buccal Films for Pediatric Oral Candidiasis - Underlying CT data
<p>Underlying CT data of "<strong>Tailored Sticky Solutions: 3D-Printed Miconazole Buccal Films for Pediatric Oral Candidiasis</strong>"<br><strong>DOI: <a href="https://doi.org/10.1208/s12249-024-02908-5">https://doi.org/10.1208/s12249-024-02908-5</a></strong></p> <p>by </p> <p>Konstantina Chachlioutaki, Anastasia Iordanopoulou, Orestis L. Katsamenis, Anestis Tsitsos, Savvas Koltsakidis, Pinelopi Anastasiadou, Dimitrios Andreadis, Vangelis Economou, Christos Ritzoulis, Dimitrios Tzetzis, Nikolaos Bouropoulos, Iakovos Xenikakis & Dimitrios Fatouros </p> <p> </p> <div> <h3>Authors and Affiliations</h3> <ol> <li> <p>Department of Pharmacy Division of Pharmaceutical Technology, Aristotle University of Thessaloniki, Thessaloniki, Greece</p> <p>Konstantina Chachlioutaki, Anastasia Iordanopoulou, Iakovos Xenikakis & Dimitrios Fatouros</p> </li> <li> <p>Center for Interdisciplinary Research and Innovation (CIRI-AUTH), Thessaloniki, Greece</p> <p>Konstantina Chachlioutaki & Dimitrios Fatouros</p> </li> <li> <p>μ-VIS X-Ray Imaging Centre, Faculty of Engineering and Physical Sciences, University of Southampton, Southampton, SO17 1BJ, UK</p> <p>Orestis L. Katsamenis</p> </li> <li> <p>Institute for Life Sciences, University of Southampton, Southampton, SO17 1BJ, UK</p> <p>Orestis L. Katsamenis</p> </li> <li> <p>Laboratory of Animal Food Products Hygiene - Veterinary Public Health, School of Veterinary Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54124, Thessaloniki, Greece</p> <p>Anestis Tsitsos & Vangelis Economou</p> </li> <li> <p>Digital Manufacturing and Materials Characterization Laboratory, School of Science and Technology, International Hellenic University, 14km Thessaloniki–N. Moudania, 57001, Thermi, Greece</p> <p>Savvas Koltsakidis & Dimitrios Tzetzis</p> </li> <li> <p>Department of Oral Medicine/Pathology, School of Dentistry, Aristotle University of Thessaloniki, 54124, Thessaloniki, Greece</p> <p>Pinelopi Anastasiadou & Dimitrios Andreadis</p> </li> <li> <p>Department of Food Science and Technology, International Hellenic University, Sindos Campus, 57400, Thessaloniki, Greece</p> <p>Christos Ritzoulis</p> </li> <li> <p>Department of Materials Science, University of Patras, Rio, 26504, Patras, Greece</p> <p>Nikolaos Bouropoulos</p> </li> <li> <p>Foundation for Research and Technology Hellas, Institute of Chemical Engineering and High Temperature Chemical Processes, 26504, Patras, Greece</p> <p>Nikolaos Bouropoulos</p> </li> </ol> </div>
3D and assay data published in "XRF and 3D modelling on a composite Etruscan helmet"
<p>The data presented here are published as part of the publication Emmitt, J.J., McAlister, A., Bawden, N., and J. Armstrong "XRF and 3D modelling on a composite Etruscan helmet" <em>Applied Sciences</em>. <em>11</em>(17): 8026. DOI: 10.3390/app11178026. The methodology for the creation of the photogrammetry model is presented Emmitt et al. (2021a), and further information about the methods used to collect the pXRF data can be found in Emmitt et al. (2021b). The interpolation analysis is done using PyVista by Sullivan and Kaszynski (2019)</p> <p>The model is are published as a .ply file, the assay data is in a csv file with the corresponding location on the model, and a Juypter notebook for running the analysis. The PyVista Python package will be required (Sullivan and Kaszynski 2019). Contained here are:</p> <ul> <li>Negau Helmet, Doug Gold Collection - 1x .ply</li> <li>Helmet assay points and data - 1x .csv</li> <li>Juypter Notebook - 1x .ipynb</li> </ul> <p>Data are published with permission of Museo Nazionale Etrusco di Villa Giulia e Villa Poniatowski di Roma (Director Valentino Nizzo).</p>
Mechanism for the Uplift of Gongga Shan in the Southeastern Tibetan Plateau Constrained by 3D Magnetotelluric Data
<p>The *.data, *.rho, and *.zip files are associated with a paper titled 'Mechanism for the Uplift of Gongga Shan in the Southeastern Tibetan Plateau Constrained by 3D Magnetotelluric Data' in Geophysical Research Letters published in 2022. On the basis of this data and inversion model, we addressed that the rapid uplift of the Gongga Shan massif likely occurred by the underthrusting of the Yangtze Craton. More details about the electrical resistivity model and its interpretations can be found in our journal paper. </p> <p>All the resulting files from ModEM are included in the 'ModEM_Inversion_Results.zip'. All the figures in the paper and supplementary are included in the 'GRL_All_Figures.zip' and 'Figure_S5_All_Responses.zip'.</p> <p>The resulting model and data output in ModEM format can be found in .rho and .data files. The ModEM is an open-source code package for MT 3D inversion, which is provided by Gary Egbert, Anna Kelbert, and Naser Meqbel and can be found on this website: <a href="https://sites.google.com/site/modularem/download">https://sites.google.com/site/modularem/download</a>. </p> <p>Please note that the 3D resistivity model files in general format includes four columns -- longitude, latitude, depth, and resistivity, the one who wants to plot the model via GMT, MATLAB, Surface, etc., can find these files in 'Gongga_3D_Resistivity_Model_Files.zip'. In this zip, you will find the resistivity model of each horizontal slice of different depths and a file including all the slices. A MATLAB script called 'see_slice.m' is included in the folder which can help to quickly view these resistivity slices.</p>
Sila National Park - 3D Point cloud data
<p>This dataset contains 3 types of data.</p> <ul> <li>GPS data (the ones starting with <em>"GPS"</em>) of sampling plot centers collected with a Trimble GPS and post processed to ensure positioning errors lower than 2 meters.</li> <li>TLS data, (the ones starting with <em>"ID_"</em>): such data were collected in the end of August 2019 with a mobile terrestrial laser scanner (mobile ZEB TLS) in a squared area of approximatively 30x30m. Data have been normalized using TreeLS package in R.</li> <li>ALS data collected in the end of July 2019. For the entire study area, we upload 2 different ALS data: "<em>merged.las</em>" is the original point cloud; "<em>myLas_norm_lt22.las</em>" is the normalised point cloud, cut at 22 meters from the ground in order to perform specific analysis (i.e. paper under submission).</li> </ul> <p>Data collection was founded by the <em>AGRIDIGIT Selvicoltura</em> project.</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>
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>
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>
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>
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.