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5,635 results for “3D”
Database of 3D Concrete Printed Buildings
<p>This dataset contains all 3D concrete printed buildings known to the authors built between 2013 and 2023. This dataset is part of a publication and was used to research different fabrication strategies. The Excel database developed for this purpose is divided into 22 categories and filled in as far as possible. The sources are also indicated in the database. For a more detailed description of the categories and the results of the study, please refer to the corresponding publication. We would be happy if the data are used and expanded for future research into 3D concrete printing.</p>
Helical dinuclear 3d metal complexes with bis(bidentate) [S,N] ligands: synthesis, structural and computational studies
<h1>Raw data for the publication entitled:</h1> <h2>Helical dinuclear 3d metal complexes with bis(bidentate)<br>[S,N] ligands: synthesis, structural and computational<br>studies</h2> <p><em>Dalton Transactions</em>, <strong>2024</strong>, DOI: 10.1039/D4DT02395A</p> <p>Authors:<br>Jamie Allen, Jörg Saßmannshausen, Kuldip Singh, Alexander F. R. Kilpatrick*</p> <p>These folders contain the raw data which were used to prepare the above publication.</p> <h1>Information regarding the raw files of the DFT calculations.</h1> <p>The zip-files in this section containing the raw-data of the DFT calculations leading to the Zn, Co and Fe calculated structures. As filenames are notoriously bad in handling special characters, the names of the folder appear different from what is being used in the final publication. We try to provide as much information as possible to facilitate the usage of these results.</p> <p>Thus:</p> <table> <tbody> <tr> <th>Abbreviation publication</th> <th>Abbreviation folder</th> <th>Abbreviation filename</th> </tr> </tbody> <tbody> <tr> <td>[Zn(<strong>3</strong>)<sub>2</sub>]</td> <td>Zn3-2</td> <td>SNdipp2Zn</td> </tr> <tr> <td>[Co(<strong>3</strong>) <sub>2</sub>]</td> <td>Co3-2</td> <td>SNdipp2Co</td> </tr> <tr> <td>[Fe(<strong>3</strong>) <sub>2</sub>]</td> <td>Fe3-2</td> <td>SNdipp2Fe</td> </tr> <tr> <td>[Zn<sub>2</sub>(μ-<strong>2</strong>)<sub>2</sub>]</td> <td>Zn2-2</td> <td>zn2</td> </tr> <tr> <td>[Co<sub>2</sub>(μ-<strong>2</strong>)<sub>2</sub>]</td> <td>Co2-2</td> <td>co2</td> </tr> <tr> <td>[Fe<sub>2</sub>(μ-<strong>2</strong>)<sub>2</sub>]</td> <td>Fe2-2</td> <td>fe2</td> </tr> </tbody> </table> <p>Some test calculations were performed as well utilizing Gaussian-09. They can be found in a folders with the suffix <em>-G09</em> or <em>-g09</em>.</p> <p>The closed shell compound [Zn<sub>2</sub>(μ-<strong>2</strong>)<sub>2</sub>] was investigated further. In order to look into the influence of the used Grimme dispersion correction, we re-calculated the final result without that correction. These files are in the Zn2-2-pbe0 folder. Furthermore, we used [Zn<sub>2</sub>(μ-<strong>2</strong>)<sub>2</sub>] and removed one of the Zn atoms and replaced the dangling bonds with H. We then fully optimized that structure. The results are in the Zn2-2-cut folder.</p> <h1> </h1> <h1>Information regarding the raw characterisation data</h1> <p>The raw characterisation data files for all nuclear magnetic resonance (NMR) spectroscopy, infrared (IR) spectroscopy, cyclic voltammetry (CV), single crystal X-ray diffraction (XRD) and solution magnetometry studies are enclosed in separate .zip files.</p>
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).
Hand-selective visual regions represent how to grasp 3D tools for use: brain decoding during real actions
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3D Mapping of Neurofibrillary Tangle Burden in the Human Medial Temporal Lobe
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P-S waves 3D velocity model of Los Humeros area from earthquake based travel-time tomography using CAT3D software (OGS)
<p>The dataset contains the 3D velocity model (VP (m/s), VS (m/s) and VP/VS) obtained from the tomographic inversion of seismological data in the area of Los Humeros (Mexico). The model was performed in the frame of the GEMex project (Mexico‐Europe Cooperation for research of enhanced geothermal systems and super-hot geothermal systems, WP5 ‘Detection of deep structures’, Jousset et al., D5.3, 2019).</p> <p>The inversion used 2661 P arrivals and 2272 S arrivals associated to 395 earthquakes recorded by 37 stations. The picking data was provided by Toledo et al., 2019.</p> <p>The inversion was performed by CAT3D software, a tomographic tool developed by OGS, which uses the SIRT method (Simultaneous Iterative Reconstruction Technique, Stewart, 1993) as inversion algorithm and the ray tracing procedure based on minimum time principle (Böhm et al., 1999). The velocities used as initial model for tomography were provided by the interpolated values obtained from the velocity analysis of four 2D seismic lines acquired inside the same investigated area by the tomographic inversion (See GEMex deliverable D5.3).</p> <p>The 3D velocity model is defined by a 3D grid of 61 nodes in X, 69 nodes in Y and 29 nodes in Z, equally spaced by 250 m in all directions. The total dimensions of the model is 15x17x7 km and the borders positions are (m) (WGS 84/UTM ZONE 14N):</p> <p>Xmin = 655000, Xmax = 670000</p> <p>Ymin = 2168000, Ymax = 2185000</p> <p>Zmin = -3000, Zmax = 4000</p>
Core collapse supernova yield from the post-processing of a long-term 3D simulation
<p>This dataset accompanies the publication<i> "Production of 44Ti and Iron-group Nuclei in the Ejecta of 3D Neutrino-driven Supernovae"</i> published in the <i>Astrophysical Journal Letters</i> Volume <strong>957</strong>, Issue 2, id.L25.</p><p>The dataset consists of an ACII text file that contains the isotopic yields from the post-processing of a 3D long-term supernova simulation for a 18.88 solar mass progenitor model. The yields are given in units of solar masses. </p><p><strong>Important: The dataset does not include the full stellar yield. </strong>It only represents the inner 0.142 solar masses. The total ejecta mass is expected to be larger. </p><p>The dataset is also available on the websites of the Max-Planck Institute for Astrophysics in Garching, Germany: https://wwwmpa.mpa-garching.mpg.de/ccsnarchive/data/Sieverding2023/</p><p>The results have been obtained using the open source nuclear reaction network code <a href="https://github.com/starkiller-astro/XNet">XNet.</a></p><p>Calculations have been performed on the supercomputing cluster Cobra the Max-Planck Computing and Data Facility (MPCDF) in Garching, Germany. </p>
Dataset for "Methodology for fast testing of carbon-based nanostructured 3D electrodes in vanadium redox flow battery"
<p>Here, we describe a technique for integrating carbon-based rod-like nanomaterials into a vanadium redox flow battery and a methodology for fast nanomaterial performance testing. The technique is based on creating a fixed nanomaterial bed sandwiched between two graphite felt electrodes, forming a 3D flow-through electrode in the battery. Performing various positive and negative control experiments, we show the beneficial effect of a nanostructured bed on the primary battery characteristics obtained from short-term electrochemical experiments. We then characterize carbon nanotubes exhibiting promising electrochemical behavior in vanadium electrolytes, as observed in our previous study. The load curves obtained from charge-discharge steps at various current densities and electrolyte flow rates revealed considerable differences in the performance of the tested materials, with few-walled carbon nanotubes reaching unsurpassable characteristics. Although developed for vanadium redox flow batteries, the method enables testing tube-like and rod-like (nano-)materials as electrodes for other flow battery systems. </p>
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>
Robust joint registration of multiple stains and MRI for multimodal 3D histology reconstruction: Application to the Allen human brain atlas
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3D resistivity structure of the Los Humeros geothermal field.
<p>The dataset is the final three-dimensional resistivity model of the high temperature geothermal field Los Humeros, in Mexico.</p> <p>The model is described in deliverable 5.2 of the GEMex Project, funded by the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 727550, and by the Mexican Energy Sustainability Fund<br> CONACYT-SENER, Project 2015-04-268074.</p>
3D Tomography Images of wheat grains for several development stages
<p>Images of wheat grains acquired by 3D tomography at various stages of the early development of the grain. This data set serves as companion for the article "Use of X-ray micro computed tomography imaging to analyze the morphology of wheat grain through its development" submitted to the "Plant Methods" journal.</p> <p><strong>Grain samples</strong></p> <p>A total of 41grains obtained at height different stages was imaged. The files correspond to the collections of grains at each stage:</p> <ul> <li>060 degree-days: 5 grains</li> <li>080 degree-days: 5 grains</li> <li>100 degree-days: 5 grains</li> <li>120 degree-days: 5 grains</li> <li>180 degree-days: 6 grains</li> <li>210 degree-days: 5 grains</li> <li>270 degree-days: 5 grains</li> <li>310 degree-days: 5 grains</li> </ul> <p>A more detailed description is provided in the file "<a href="https://zenodo.org/api/files/923a7508-b325-4264-8553-6a62092bb84d/wheatGrainTomoDataset.pdf">wheatGrainTomoDataset.pdf</a>".</p> <p><strong>Image format</strong></p> <p>All images are in TIFF format.</p> <p>Two kinds of images are provided: the volumes of the whole grains after conversion tu 256 gray levels, and the results of the segmentation of the grains as described in the manuscript. </p>
3D density models of the Los Humeros and Acoculco geothermal fields, Mexico.
<p>The GEMex project addresses different challenges in the development of Enhanced Geothermal Systems (EGS) and Superhot Geothermal Systems (SHGS) in the Trans-Mexican Volcanic Belt. Although they are located in similar tectonic settings, the geothermal conditions in Acoculco and Los Humeros differ and they can be categorized as an EGS and a SHGS system, respectively. The Los Humeros field is currently under conventional exploitation. North of the current production area, temperatures higher than 380°C are expected. The Acoculco site presents temperatures >300°C at a depth of 2 km, but a reservoir has not been identified. The main goal of this work is to visualize and characterize the reservoir conditions using gravity data. To accomplish this, we processed data from a total of 344 gravity stations at Los Humeros and 84 stations at Acoculco. The datasets contain the 3D density model of the Los Humeros and Acoculco geothermal fields as density contrasts values in g/cm³. The background density is 2.67 g/cm³.</p>
3D-COSI ~ 3D Collection of Surgical Instruments
<h2><strong>COSI - 3D STL Collection of Surgical Instruments</strong></h2><p><i><strong>Due to large file names, we have chosen to use </strong></i><a href="https://www.7-zip.org/"><i><strong>https://www.7-zip.org/</strong></i></a><i><strong> which is 100% free and compatible with WinZip. If you encounter an error using WinZip, it's likely due to large file names, please use 7zip.</strong></i></p><p>Inside the repository, you will find an information overview "<i>Overview.docx", </i>a showcase video "<i>Example video 3D instruments.mp4"</i>, STL files of 103 surgical instruments "<i>Surgical Instruments.7z"</i>, examples of variations of the surgical instruments using Blender add-on or Python script build on the Trimesh library "<i>Blender Part x of 8 ... 7z"</i>, or "<i>Trimesh part x of 9 ... .7z". </i>You will also find the used Blender Add On <i>"MultiMesh.zip", </i>measurements of virtual instruments and settings for the add-on (.xlsx), and the script that was used to perform these measurements inside a single folder, "<i>Scripts, measurements and used Blender settings.7z</i>".</p><p>The proposed data collection consists of 103 3D-scanned medical instruments from the clinical routine, scanned with structured light scanners. The collection consists, for example, of instruments like retractors, forceps, and clamps. The collection is augmented by generating likewise models using 3D software, resulting in an inflated dataset for analysis. The collection can be used for general instrument detection and tracking in operating room settings or a freeform marker-less instrument registration for tool tracking in augmented reality. Furthermore, for medical simulation or training scenarios in virtual reality or mixed reality.<br><br><strong>Related article:</strong><br>Luijten, G., Gsaxner, C., Li, J. <i>et al.</i> 3D surgical instrument collection for computer vision and extended reality. <i>Sci Data</i> <strong>10</strong>, 796 (2023). https://doi.org/10.1038/s41597-023-02684-0</p>
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>
muBrain - a 3D volumetric reconstruction of the mid-fetal brain
<h2><strong>File descriptions</strong></h2> <h3><strong>Volumes:</strong></h3> <table> <tbody> <tr> <td><strong>uBrain-volume.nii.gz</strong></td> <td>microBrain template volume. A 3D reconstruction of the right hemisphere of a mid-fetal brain. Voxel size: 0.15mm.</td> </tr> <tr> <td><strong>uBrain-atlas-labels.nii.gz</strong></td> <td>microBrain brain tissue labels. Brain tissue labels (n=20) for the microBrain volume.</td> </tr> <tr> <td><strong>brain-tissue-labels.txt</strong></td> <td>LUT for brain tissue labels</td> </tr> </tbody> </table> <h3><strong>Surfaces:</strong></h3> <table> <tbody> <tr> <td><strong>uBrain.R.outer.surf.gii</strong></td> <td>outer (pial) cortical surface of the microBrain volume</td> </tr> <tr> <td><strong>uBrain.R.inner.surf.gii</strong></td> <td>inner (white) cortical surface of the microBrain volume</td> </tr> <tr> <td><strong>uBrain.cortical-atlas.fetal36w-template.label.gii</strong></td> <td>microBrain cortical atlas labels projected onto the 36w timepoint of the <a href="https://gin.g-node.org/kcl_cdb/dhcp_fetal_brain_surface_atlas">dHCP fetal surface template</a></td> </tr> <tr> <td><strong>cortical-labels.txt</strong></td> <td>LUT for cortical atlas labels.</td> </tr> </tbody> </table> <h3><strong>Microarray data:</strong></h3> <table> <tbody> <tr> <td><strong>uBrain-processed-lmd-data.csv</strong></td> <td>LMD microarray data from the <a href="https://www.brainspan.org/lcm/search/index.html">BrainSpan</a> atlas aligned to the microBrain cortical labels. </td> </tr> </tbody> </table>
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>
LigPCDS: Labeled Dataset of X-ray Protein Ligand Images in 3D Point Cloud and Validated Deep Learning Models
<p>The difference electron density from X-ray protein crystallography was used to create the first dataset of labeled ligand images in 3D point clouds, named <strong>LigPCDS</strong>. The dataset contain 244,226 entries of free organic ligands containing 3D representations labeled with two major labeling approaches: SP-based and AtomSymbol-based.</p> <p> </p> <p>The data from free organic molecules (non-covalent ligands) was retrieved from the Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB) in december 2019 with resolutions ranging from 1.5 to 2.2 Å. The ligand images (blobs) were interpolated from their calculated difference electron density map in a 3D grid-like bounding box, around their atomic positions, and stored in point clouds. These ligand grid representations were further processed to retrive the final ligands representation in 3D point clouds using a mask of the shape of the ligand. A grid spacing of 0.5 Å gave the best results. The density value of the grid points was used as feature. The labeling approach used the structure of the ligands to propose vocabularies of chemical classes based on the chemical atoms themselves and their cyclic substructures. These structure annotations were applied pointwise to the ligand 3D representations using an atomic sphere model. Four proposed vocabularies were validated by successfully training good performance deep learning models for the semantic segmentation of a stratified dataset from LigPCDS, using 78902 entries.</p> <p>The four validated deep learning models are: (i) the LigandRegion, composed by generic atoms of any type; (ii) the AtomCycle, composed by generic atoms outside cycles and generic cycles; (iii) the AtomC347CA56, composed by generic atoms outside cycles, not aromatic cycles of size 3 to 7 and aromatic cycles of size 5 and 6; and (iv) the AtomSymbolGroups, composed by the atoms symbols with groupings. The mean accuracy of these models in their cross-validation was between 49.7% <span lang="EN-GB">[-19.4,20.</span><span lang="EN-GB">2]</span> and 77.4% <span lang="EN-GB">[-11.7,12.1]</span> in terms of Intersection over Union (mIoU) metric and between 62.4% <span lang="EN-GB">[-18.8,19.</span><span lang="EN-GB">7]</span> and 87.0% <span lang="EN-GB">[-8.4,8.8]</span> in F1-score (mF1), confidence interval between squared brackets. The models i, ii and iii and the used labeled representations in 3D point cloud are contained in the SP-based record; and model iv and its used labeled representations are contained in the AtomSymbol-based record.</p> <p>The dataset and validated models may be used to tackle problems regarding known and unknown ligand building to drug discovery and fragment screening pipelines. </p> <p>The code used to create and validated the LigPCDS is available at the following repository: https://github.com/danielatrivella/np3_ligand</p> <p>This repository also contains the NP³ Blob Label application for ligand building using the validated deep learning models from LigPCDS.</p>
ScienceDex guides
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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.