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882 results for “3D Modeling”
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>
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>
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>
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>
3D model of a cairn grave near the Bear Trap in Northwest Greenland
<p>This dataset consists of a 3D dense point cloud and a textured mesh model (see the README file) of a cairn grave that is positioned near ‘The Bear Trap’. The Bear Trap is a Norse ruin at the western end of the Nuussuaq Peninsula. The 3D model was created from 639 digital photographs that were processed using Agisoft Metashape Pro v1.7; Linux Ubuntu). A 24 megapixel Sony a6000 APS-C mirrorless camera fitted with a 17 mm lens was used to acquire ground-level imagery of the structure.</p> <p>The image survey was conducted as part of the Vaigat Iceberg-Microbial Oil Degradation and Archaeological Heritage Investigation (VIMOA) project, which was funded by the Danish Centre for Marine Research and supported by the Arctic Research Centre at Aarhus University, the National Museum of Denmark, the Greenland Institute of Natural Resources, and The Greenland National Museum and Archives in Nuuk. Permits for the survey were obtained in advance from the Greenland National Museum and Archives in Nuuk. Walsh et al. (2020) provides an overview of the archaeological surveys conducted during the VIMOA project and Walsh et al. (in prep) provides further details specific to The Bear Trap and surrounding archaeological contexts. </p> <p>Walsh et al. (2020) The VIMOA project and archaeological heritage in the Nuussuaq Peninsula of north-west Greenland. <em>Antiquity</em> 94:e6 doi:10.15184/aqy.2019.230</p> <p>Walsh, Matthew J., Daniel F. Carlson, Pelle Tejsner, and Steffen Thomsen. The Bear Trap: Reinvestigating a unique stone structure on the northwest tip of the Nuussuaq Peninsula, Greenland. Submitted to <em>Arctic Anthropology</em>.</p>
Sogenannter Hexenturm von Schloss Ulmerfeld, NÖ – Datengrundlage des 3D-Modells des Innenraums
<p>Photos for the 2015's 3d model of the interior of the so-called witch tower (’Hexenturm’) at the south easteren corner of the outer fortifications of Ulmerfeld Castle. The model was made using 3d photogrammetry (image based modeling) and mast aerial photography.</p>
3D magnetotelluric modeling using high-order tetrahedral Nédélec elementson massively parallel computing platforms
<p>Accompanying data to journal article</p> <blockquote> <p>Castillo-Reyes, O., Modesto, D., Queralt, P., Marcuello, A., Ledo, J., Amor-Martin, A., de la Puente, J., García-Castillo, L.E. (2021) 3D magnetotelluric modeling using high-order tetrahedral Nédélec elements on massively parallel computing platforms. Computers & Geosciences, vol.(160): 105030 DOI: 10.1016/j.cageo.2021.105030. ISSN 0098-3004, Elsevier.</p> </blockquote>
3D Models of 6dof Motion
<p>This repository contains a 3d model (head_6dof.blend) visualizing motions with 6 degrees of freedom. For the head we use a 3D model created with <a href="http://www.makehumancommunity.org">make human</a> under the <a href="https://creativecommons.org/licenses/by-sa/3.0/">CC BY-SA 3.0</a> license. All body parts but the head were removed from the model used. The main model contains the head, all planes, axes, and labels. Examples of rendered images and an animation are included.The model was created with Blender.</p> <p><strong>Nomenclature</strong><br> Names of the axes and planes according to <a href="https://link.springer.com/content/pdf/10.1007/s00405-015-3835-y.pdf">Bremova et al., 2016</a>:</p> <table align="center"> <thead> <tr> <th scope="col">Abreviation</th> <th scope="col">Axis</th> </tr> </thead> <tbody> <tr> <td>IA</td> <td>inter-aural</td> </tr> <tr> <td>HV</td> <td>head-vertical</td> </tr> <tr> <td>NO</td> <td>naso-occipital</td> </tr> </tbody> </table> <p>Colors<br> Colors are taken from the <a href="https://www.nature.com/articles/nmeth.1618.pdf">Bang Wong color palette</a> which is designed to be accessible to people who are colorblind.</p> <table align="center"> <thead> <tr> <th scope="col">Axis</th> <th scope="col">Color</th> <th scope="col">RGB</th> </tr> </thead> <tbody> <tr> <td>IA</td> <td>bluish green</td> <td>000,158,115</td> </tr> <tr> <td>HV</td> <td>blue</td> <td>000,114,178</td> </tr> <tr> <td>NO</td> <td>vermillion</td> <td>213,094,000</td> </tr> <tr> <td>roll</td> <td>reddish purple</td> <td>204,121,167</td> </tr> <tr> <td>pitch</td> <td>orange</td> <td>230,159,000</td> </tr> <tr> <td>yaw</td> <td>sky blue</td> <td>086,180,233</td> </tr> </tbody> </table> <p> </p>
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>
Supplement for Drone-based magnetic and multispectral surveys to develop a 3D model for mineral exploration at Qullissat, Disko Island, Greenland
<p>Supplement to Jackisch et al., 2021: Drone-based magnetic and multispectral surveys to develop a 3D model for mineral exploration at Qullissat, Disko Island, Greenland.</p> <p><a href="https://se.copernicus.org/articles/13/793/2022/se-13-793-2022.html">https://se.copernicus.org/articles/13/793/2022/se-13-793-2022.html</a></p> <p>Data set contains 3D model in dxf file, additional images, selected handheld spectra.</p> <p>Publication summary:</p> <p>We integrate UAS-based magnetic and remote sensing mineral exploration data with legacy exploration data of a Ni-Cu-PGE prospect on Disko Island, West Greenland. The basalt unit has a complex magnetization, and we use a 3D magnetic vector inversion on the UAS magnetics to estimate magnetic properties and spatial dimensions of the mineralized unit. Our 3D modelling reveals a horizontal sheet and a strong remanent magnetization component. We highlight the advantage of UAS in rugged terrain.</p> <p> </p>
3D model of antenna system embedded into building envelope for improved cellular signal transmission through load-bearing walls
<p>The purpose of this dataset is to supplement the data presented in our journal publication "Electromagnetic–Thermal Analyses of Distributed Antennas Embedded Into a Load-Bearing Wall" (see <a href="https://ieeexplore.ieee.org/document/10151683">https://ieeexplore.ieee.org/document/10151683</a>).</p> <p>This dataset contains the 3-D discretized model, without the internal numerical mesh, of the unit cell of the spiral antenna system embedded in a load bearing wall. The 3D model is in .STP format (see ISO 10303-21:2016), which can be imported into most commercial computer-aided design (CAD) software. The wall's dielectric properties are calculated using the model described in ITU-R P.2040-2 (<a href="https://www.itu.int/rec/R-REC-P.2040/en">https://www.itu.int/rec/R-REC-P.2040/en</a>, material parameter and calculation model are on pages 22-23). Materials used in the antenna system and their electrical and thermal parameters are given in the file materials.txt</p>
Nabro 3D velocity model produced by the FMTOMO code
<p>These files relate to "Seismic tomography of Nabro caldera, Eritrea: insights into the magmatic and hydrothermal systems of a recently erupted volcano" by Gauntlett et al., 2023. </p> <p>Original seismic waveforms are from the Nabro Urgency Array (Hammond et al., 2011; <a href="https://doi.org/10.7914/SN/4H_2011">https://doi.org/10.7914/SN/4H_2011</a>), which is publicly available through IRIS Data Services (<a href="http://service.iris.edu/fdsnws/dataselect/1/">http://service.iris.edu/fdsnws/dataselect/1/</a>). See Hammond et al. (<a href="https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2021JB021910#jgrb55017-bib-0025">2011</a>) for further details on waveform data access and availability. Sources were originally located by Lapins et al. (<a href="https://doi.org/10.1029/2021JB021910">2021</a>), and the full catalogue is archived <a href="https://zenodo.org/record/7669717#.ZAXtfuymP0s">here</a> (Lapins, 2022). </p> <p>The FMTOMO package is freely available to download at<a href="http://rses.anu.edu.au/~nick/fmtomo.html"> http://rses.anu.edu.au/~nick/fmtomo.html</a>. </p> <p>This repository contains three 3D velocity models:</p> <ol> <li>vp_model.out</li> <li>vs_model.out</li> <li>vpvs_model.out</li> </ol> <p>These plain text files comprise the output of the FMTOMO tomography algorithm, which inverts for 3D P-wave velocity and S-wave velocity Vp and Vs) structure and Vp/Vs ratio for a grid centred around Nabro volcano. The velocity is given in km/s for Vp and Vs, and the values are dimensionless for Vp/Vs ratio.</p> <p>The grid is specified in the first four lines of the file:</p> <ul> <li>Line 1: for these data, this line will always hold the values `1' and `1'. </li> <li>Line 2: the number of grid nodes in radius (depth), latitude and longitude. </li> <li>Line 3: the radial (depth) node spacing in km, latitude spacing in radians, longitude spacing in radians.</li> <li>Line 4: the grid origin radius (km), latitude (radians) and longitude (radians).</li> </ul> <p>Each node of the grid has a P-wave, S-wave and Vp/Vs ratio associated with it, which is specified in lines 5 onward.</p> <ul> <li>Line 5 - 55229: Value of Vp, Vs or Vp/Vs (given by file name) at each node.</li> </ul> <p>The values loop over the grid, with longitude varying first, and radius last. The first node is at the grid origin. The second node is at the grid origin, plus the grid spacing in longitude. This continues for the `nlon` longitude nodes. The `nlon+1`the point is then at the grid origin, plus the latitude spacing; and so on.</p> <p>If there are `nr` radial nodes, `nlat` latitude nodes and `nlon` longitude nodes, then the following pseudocode shows how to read lines 5 forwards using the imaginary function `readline`, which reads a single real value from a plain text file and moves to the next line:</p> <p>```</p> <p># Comment: have already read the first four lines</p> <p>for ir in 1:nr:</p> <p>for ilat in 1:nlat:</p> <p>for ilon in 1:nlon:</p> <p>grid[ir,ilat,ilon] = readline(file_handle)</p> <p>```</p> <p>The repository also contains the event catalogue, with hypocenter locations after relocation by the FMTOMO code:</p> <p>4. event_catalogue.csv</p> <p>The columns are depth in km (negative values indicate depths below sea level, positive values indicate depths above sea level), latitude in degrees, longitude in degrees, depth error (km), latitude error (km), longitude error (km). </p>
SARS-CoV-2 main protease 3D print model
<p>A 3D model for printing SARS-CoV-2 main protease from our paper on FAIR sharing molecular visualization experiences.</p>
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>
3D mesh model and raw images of a drifting iceberg in Dickson Fjord (NE Greenland) on 21 August 2018 at 17:09 UTC
<p>This dataset consists of low-altitude aerial imagery that was acquired by a DJI Phantom 3 Standard unoccupied aerial vehicle (UAV) in Dickson Fjord in northeast Greenland on 21 August 2018. The UAV survey commenced at 17:09 UTC. These images were processed in Agisoft PhotoScan Pro (v1.4; Linux Ubuntu). During the image alignment step in PhotoScan, the ‘High’ accuracy setting and key point and tie point limits of 60000 and 0 were used. Generic and reference preselection were disabled. Gradual selection was used to remove tie points that exceeded thresholds for the projection accuracy, reconstruction uncertainty, and reprojection error and the lens parameters were computed. Reference data from images DJI_417-419 were used to scale the sparse point cloud. The dense point cloud was then computed using the ‘High’ setting, followed by the textured mesh. The mesh model was exported in .obj and .pdf formats. </p> <p>A complete file list is provided in the README file that accompanies this dataset.</p> <p>This dataset is discussed in:</p> <p>Carlson et al. Quantifying iceberg deterioration using UAV imagery and Structure from Motion photogrammetry software. Submitted to <em>Remote Sensing.</em></p>
3D mesh model and raw images of a drifting iceberg in Dickson Fjord (NE Greenland) on 20 August 2018 at 12:41 UTC
<p>This dataset consists of low-altitude aerial imagery that was acquired by a DJI Phantom 3 Standard unoccupied aerial vehicle (UAV) in Dickson Fjord in northeast Greenland on 20 August 2018. The UAV survey commenced at 12:41 UTC. These images were processed in Agisoft PhotoScan Pro (v1.4; Linux Ubuntu). During the image alignment step in PhotoScan, the ‘High’ accuracy setting and key point and tie point limits of 60000 and 0 were used. Generic and reference preselection were disabled. Gradual selection was used to remove tie points that exceeded thresholds for the projection accuracy, reconstruction uncertainty, and reprojection error and the lens parameters were computed. Reference data from images DJI_493-497 were used to scale the sparse point cloud. The dense point cloud was then computed using the ‘High’ setting, followed by the textured mesh. The mesh model was exported in .obj and .pdf formats. </p> <p>A complete file list is provided in the README file that accompanies this dataset.</p> <p>This dataset is discussed in:</p> <p>Carlson et al. Quantifying iceberg deterioration using UAV imagery and Structure from Motion photogrammetry software. Submitted to <em>Remote Sensing.</em></p>
Testing 3D modelling software. Modelling charging pads for WPT of electric vehicles for EM emissions simulation.
<p>Even for the experienced 3D FEM modelers it may not be obvious which geometry discretization is the most appropriate and suitable for this type of problem. It may be a conservative approach to test the computation tool on a simplified geometry, on which the magnetic field distribution is known. As part of the “Metrology for inductive charging of electric vehicles” (MICEV) project (www.micev.eu), an axisymmetric geometry was used, with the results reported.</p>
Towards an open-source landscape for 3D CSEM modelling
<p>Accompanying data to journal article</p> <blockquote> <p>Werthmüller, D., R. Rochlitz, O. Castillo-Reyes, and L. Heagy, 2021, Towards an open-source landscape for 3D CSEM modelling: Geophysical Journal International; ggab238, DOI: <a href="https://doi.org/10.1093/gji/ggab238">10.1093/gji/ggab238</a>.</p> </blockquote> <ul> <li>Official article: <a href="https://doi.org/10.1093/gji/ggab238">https://doi.org/10.1093/gji/ggab238</a></li> <li>GitHub repo: <a href="https://github.com/swung-research/3d-csem-open-source-landscape">https://github.com/swung-research/3d-csem-open-source-landscape</a></li> <li>arXiv.org: <a href="https://arxiv.org/abs/2010.12926">https://arxiv.org/abs/2010.12926</a></li> </ul> <p>The Marlim R3D model can be found at:</p> <ul> <li>Original, fine resistivity model: <a href="https://doi.org/10.5281/zenodo.400233">https://doi.org/10.5281/zenodo.400233</a></li> <li>Upscaled computational model: <a href="https://doi.org/10.5281/zenodo.3748491">https://doi.org/10.5281/zenodo.3748491</a></li> <li>CSEM data set: <a href="https://doi.org/10.5281/zenodo.1256786">https://doi.org/10.5281/zenodo.1256786</a></li> <li>Noise-free CSEM data set: <a href="https://doi.org/10.5281/zenodo.1807134">https://doi.org/10.5281/zenodo.1807134</a></li> </ul>
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>
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>
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.