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Fig. 1. Medauromorpha gen. nov. spp., distribution maps. A, M in The new stick insect genus Medauromorpha gen. nov. with one new species from Vietnam and notes on Medauroidea Zompro, 2000 (Phasmida: Phasmatidae: Clitumninae)
Fig. 1. Medauromorpha gen. nov. spp., distribution maps. A, M. baviensis sp. nov. and M. regina (Brunner von Wattenwyl, 1907) comb. nov. B, M. foedata (Brunner von Wattenwyl, 1907) comb. nov.
Fig. 3. Free range M. robinsoni with interscapular wounds. A in Urban biodiversity: Cuterebriasis in free-ranging Robinson's mouse opossum (Marmosa robinsoni) in the suburbs of Barranquilla, Colombia
Fig. 3. Free range M. robinsoni with interscapular wounds. A. External appearance of bot fly larva wound. B. Extraction of Cuterebra sp. larvae.
FIGURE 16. M. costocoracoideus. Reconstruction for Neusticosaurus 1 in Soft-tissue anatomy of the Plesiosaur pectoral girdle inferred from basal Eosauropterygia taxa and the extant phylogenetic bracket
FIGURE 16. M. costocoracoideus. Reconstruction for Neusticosaurus 1 in anterior view, 2 in ventral view, 3 in lateral view, 4 fleshed-out muscle reconstruction and 5 complete skeletal reconstruction in lateral view. Reconstruction for Ceresiosaurus 6 in anterior view, 7 in ventral view, 8 in lateral view, 9 fleshed-out muscle reconstruction and 10 complete skeletal reconstruction in lateral view. Reconstruction for Rhomaleosaurus 11 in anterior view, 12 in ventral view, 13 in lateral view, 14 fleshed-out muscle reconstruction and 15 complete skeletal reconstruction in lateral view.
FIGURE 5. M. subcoracoscapularis. Reconstruction for Neusticosaurus 1 in Soft-tissue anatomy of the Plesiosaur pectoral girdle inferred from basal Eosauropterygia taxa and the extant phylogenetic bracket
FIGURE 5. M. subcoracoscapularis. Reconstruction for Neusticosaurus 1 in anterior view, 2 in ventral view, 3 in lateral view, 4 in fleshed-out muscle reconstruction and 5 complete skeletal reconstruction in lateral view. Reconstruction for Ceresiosaurus 6 in anterior view, 7 in ventral view, 8 in lateral view, 9 fleshed-out muscle reconstruction and 10 complete skeletal reconstruction in lateral view. Reconstruction for Rhomaleosaurus 11 in anterior view, 12 in ventral view, 13 in lateral view, 14 fleshed-out muscle reconstruction and 15 complete skeletal reconstruction in lateral view.
Abb. 16A-M in Zur Kenntnis der Arten der Tachinus fimbriatus-Gruppe des Himalaja-Gebietes (Coleoptera, Staphylinidae, Tachyporinae)
Abb. 16A-M: Tachinus haemorrhoidalis ULLRICH (Holotypus): 3-Sternit VIII (A); 3-Tergit VIII (B); 3-Sternit IX (C); Aedoeagus, lateral (D); Aedoeagus, ventral (E); Paramerenspitze, ventral (F); Tachinus holzschuhi nov.sp. (Holotypus): Aedoeagus, lateral (G); Aedoeagus, ventral (H); Paramerenspitze, ventral (I); Tachinus paracoriaceus nov.sp. (Paratypus): Aedoeagus, lateral (K); Aedoeagus, ventral (L); Parameren, lateral (M); Paramerenspitze, ventral (N); Innenstruktur des Aedoeagus (O).
Abb. 15A-M in Zur Kenntnis der Arten der Tachinus fimbriatus-Gruppe des Himalaja-Gebietes (Coleoptera, Staphylinidae, Tachyporinae)
Abb. 15A-M: Tachinus haemorrhoidalis ULLRICH (Holotypus): Habitus (A); Kopf (B); linke Elytre (C); Abdomen (D); Abdominalsegment VII, ventral (E); Mikroskulptur auf Kopf (F), Pronotum (G), Elytren (H) und Tergit VI (I); 3-Sternit VII (K); Borstenfeld auf 3-Sternit VII (L); Aedoeagus, lateral (M).
Abb. 8A-M in Zur Kenntnis der Arten der Tachinus fimbriatus-Gruppe des Himalaja-Gebietes (Coleoptera, Staphylinidae, Tachyporinae)
Abb. 8A-M: Tachinus paramalaisei LI & OHBAYASHI (Holotypus und Paratypen von T. nepalensis SCHEERPELTZ): Habitus (A-C); Kopf (D); linke Elytre (E); Etikettierung (F, G); Mikroskulptur auf Kopf (H), Pronotum (I), Scutellum (K), Elytren (L) und Tergit III (M). Holotypus von T. nepalensis (A, D-F, H-M), ♀♀-Paratypen von T. nepalensis (B, C, G).
Abb. 2A-M in Zur Kenntnis der Arten der Tachinus fimbriatus-Gruppe des Himalaja-Gebietes (Coleoptera, Staphylinidae, Tachyporinae)
Abb. 2A-M: Tachinus orientalis ULLRICH (Holotypus): Habitus (A, B); Kopf (C); linke Elytre (D); Mikroskulptur auf Kopf (E), Pronotum (F), Elytren (G) und Tergit III (H); 3-Sternit VII (I); 3- Sternit VIII (K); 3-Sternit IX (L); 3-Tergit VIII (M).
Áramo - LiDAR 2023 (DSM 0.5m)
<h2>Abstract</h2> <p>This depository contains data generated within the European S34 project. </p> <h2>Metadata Information</h2> <table> <tbody> <tr> <td> <p><strong>Identification</strong></p> </td> </tr> <tr> <td> <p>Full Title</p> </td> <td> <p>Áramo - LiDAR 2023 (DSM 0.5m)</p> </td> </tr> <tr> <td> <p>Abstract</p> </td> <td> <p>DSM, 0.5m grid size, from LiDAR data 30/09+01/10/23</p> </td> </tr> <tr> <td> <p>Keywords</p> </td> <td> <p>DSM</p> </td> </tr> <tr> <td> <p>Pilot area</p> </td> <td> <p>Áramo</p> </td> </tr> <tr> <td> <p>Associated resources</p> </td> <td> <p>AOI, Tile definition (SHP)</p> <p>Processing report (PDF)</p> </td> </tr> <tr> <td> <p>Language</p> </td> <td> <p>English</p> </td> </tr> <tr> <td> <p>URL</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Categories</p> </td> <td> <p>Elevation</p> </td> </tr> <tr> <td> <p><strong>Temporal reference</strong></p> </td> </tr> <tr> <td> <p>Creation date (dd.mm.yyyy)</p> </td> <td> <p>15.12.2023</p> </td> </tr> <tr> <td> <p>Revision date (dd.mm.yyyy)</p> </td> <td> <p>15.12.2023</p> </td> </tr> <tr> <td> <p><strong>Quality and validity</strong></p> </td> </tr> <tr> <td> <p>Representation type</p> </td> <td> <p>Raster</p> </td> </tr> <tr> <td> <p>Format</p> </td> <td> <p>GeoTIFF</p> </td> </tr> <tr> <td> <p>Lineage</p> </td> <td> <p>DSM – based on highest Z value of all the points falling in the cell.</p> <p>Blocks 1x1 km</p> </td> </tr> <tr> <td> <p>Spatial resolution</p> </td> <td> <p>0,5m</p> </td> </tr> <tr> <td> <p>Positional accuracy</p> </td> <td> <p>0,15m</p> </td> </tr> <tr> <td> <p>Maintenance information</p> </td> <td> <p>no updates planned</p> </td> </tr> <tr> <td> <p>Coordinate system</p> </td> <td> <p>EPSG 4326</p> </td> </tr> <tr> <td> <p><strong>Constranits related to access and use</strong></p> </td> </tr> <tr> <td> <p>Use limitation</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Access constraint</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Public/Private</p> </td> <td> <p>Public</p> </td> </tr> <tr> <td> <p><strong>Responsible organisation</strong></p> </td> </tr> <tr> <td> <p>Responsible Contact</p> </td> <td> <p>Rolf Wilting/ Victoria Jadot</p> </td> </tr> <tr> <td> <p>Responsible Party</p> </td> <td> <p>Eurosense</p> </td> </tr> <tr> <td> <p><strong>Metadata on metadata</strong></p> </td> </tr> <tr> <td> <p>Contact</p> </td> <td> <p>Rolf Wilting, rolfwilting@eurosense.com (until 01/24), Victoria Jadot, victoria.jadot@eurosense.com (from 02/24)</p> </td> </tr> <tr> <td> <p>Metadata language</p> </td> <td> <p>English</p> </td> </tr> </tbody> </table>
Áramo - LiDAR 2023 (DTM 0.5m)
<h2>Abstract</h2> <p>This depositry contains data generated within the European S34 project.</p> <h2>Metadata Information</h2> <table> <tbody> <tr> <td> <p><strong>Identification</strong></p> </td> </tr> <tr> <td> <p>Full Title</p> </td> <td> <p>DTM 2023</p> </td> </tr> <tr> <td> <p>Abstract</p> </td> <td> <p>DTM 0.5m grid size, from LiDAR data 30/09+01/10/23</p> </td> </tr> <tr> <td> <p>Keywords</p> </td> <td> <p>DTM</p> </td> </tr> <tr> <td> <p>Pilot area</p> </td> <td> <p>Áramo</p> </td> </tr> <tr> <td> <p>Associated resources</p> </td> <td> <p>AOI, Tile definition (SHP)</p> <p>Processing report (PDF)</p> </td> </tr> <tr> <td> <p>Language</p> </td> <td> <p>English</p> </td> </tr> <tr> <td> <p>URL</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Categories</p> </td> <td> <p>Elevation</p> </td> </tr> <tr> <td> <p><strong>Temporal reference</strong></p> </td> </tr> <tr> <td> <p>Creation date (dd.mm.yyyy)</p> </td> <td> <p>15.12.2023</p> </td> </tr> <tr> <td> <p>Revision date (dd.mm.yyyy)</p> </td> <td> <p>15.12.2023</p> </td> </tr> <tr> <td> <p><strong>Quality and validity</strong></p> </td> </tr> <tr> <td> <p>Representation type</p> </td> <td> <p>Raster</p> </td> </tr> <tr> <td> <p>Fromat</p> </td> <td> <p>GeoTIFF</p> </td> </tr> <tr> <td> <p>Lineage</p> </td> <td> <p>DTM – based on average Z values of the ground points falling in the cell. </p> <p>Blocks 1x1 km</p> </td> </tr> <tr> <td> <p>Spatial resolution</p> </td> <td> <p>0.5m</p> </td> </tr> <tr> <td> <p>Positional accuracy</p> </td> <td> <p>0.15m</p> </td> </tr> <tr> <td> <p>Maintenance information</p> </td> <td> <p>no updates planned</p> </td> </tr> <tr> <td> <p>Coordinate system</p> </td> <td> <p>EPSG 4326</p> </td> </tr> <tr> <td> <p><strong>Constranits related to access and use</strong></p> </td> </tr> <tr> <td> <p>Use limitation</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Access constraint</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Public/Private</p> </td> <td> <p>Public</p> </td> </tr> <tr> <td> <p><strong>Responsible organisation</strong></p> </td> </tr> <tr> <td> <p>Responsible Contact</p> </td> <td> <p>Rolf Wilting/ Victoria Jadot</p> </td> </tr> <tr> <td> <p>Responsible Party</p> </td> <td> <p>Eurosense</p> </td> </tr> <tr> <td> <p><strong>Metadata on metadata</strong></p> </td> </tr> <tr> <td> <p>Contact</p> </td> <td> <p>Rolf Wilting, rolfwilting@eurosense.com (until 01/24), Victoria Jadot, victoria.jadot@eurosense.com (from 02/24)</p> </td> </tr> <tr> <td> <p>Metadata language</p> </td> <td> <p>English</p> </td> </tr> </tbody> </table>
Figure 1. Professor M in Maria Guiomar Nates-Parra: Biographical sketch and summary of contributions to the melittological knowledge of Colombia
Figure 1. Professor M. Guiomar Nates-Parra in 2006 showing a collection of orchid bees in her lab (Photograph courtesy of M.L. Bueno).
Fig.. 3. Simocyon batalleri, MNCN B−3458, Batallones−1, Madrid Province, Spain, cranium in left lateral view. A. Photograph. B. Schematic drawing showing the main anatomical features of this view (artwork by M. Antón). in Ailurid carnivoran mammal Simocyon from the late Miocene of Spain and the systematics of the genus
Fig.. 3. Simocyon batalleri, MNCN B−3458, Batallones−1, Madrid Province, Spain, cranium in left lateral view. A. Photograph. B. Schematic drawing showing the main anatomical features of this view (artwork by M. Antón).
Electrochemical data plotted in A. Fasano, C. Guendon, A. Jacq-Bailly, A. Kpebe, J. Wozniak, C. Baffert, M. del Barrio, V. Fourmond, M. Brugna, C. Léger , « A chimeric NiFe hydrogenase heterodimer to assess the role of the electron transfer chain in tuning the enzyme's catalytic bias and oxygen tolerance », J. Am. Chem. Soc. 145, 36, 20021–20030 (2023). doi: 10.1021/jacs.3c06895
<p>Text file of all the electrochemical data shown in the following paper: A. Fasano, C. Guendon, A. Jacq-Bailly, A. Kpebe, J. Wozniak, C. Baffert, M. del Barrio, V. Fourmond, M. Brugna, C. Léger , « A chimeric NiFe hydrogenase heterodimer to assess the role of the electron transfer chain in tuning the enzyme's catalytic bias and oxygen tolerance », J. Am. Chem. Soc. 145, 36, 20021–20030 (2023). <a href="dx.doi.org/10.1021/jacs.3c06895" target="_blank" rel="noopener">doi: 10.1021/jacs.3c06895</a></p>
30 m annual soybean area map in China from 2000 to 2022
<p><span>China is the world’s largest consumer and importer of soybeans, with rising domestic demand driving the expansion of production and</span><span> </span><span>cultivation areas since</span><span> 2000</span><span>. Knowing the spatial distribution of soybean, including the trends and interannual variability, is essential for yield estimation, agricultural planning, and </span><span>ensuring </span><span>national food security. However, a high-precision, long-term, national-scale spatial dataset for soybean cultivation area</span><span>s</span><span> </span><span>of China remain</span><span>s</span><span> </span><span>unavailable. To address this gap, we developed the China Soybean Area (ChinaSoyA30m) dataset at a 30-m resolution—covering the years 2000–2022 at the national scale, using Landsat imagery and a combined phenology-based and machine learning approach. We analyzed time-series phenological characteristics of major crops across nine major agricultural regions in China and automatically generated annual training samples for supervised classifiers through a GWCCI-derived unsupervised method. To enhance the accuracy of these samples, we applied gap statistics, <em>K</em>-means clustering, and spectral angle mapping techniques to minimize noise and improve classification reliability. The supervised classification was conducted using a multi-random forest fusion strategy on the Google Earth Engine (GEE) platform, leveraging dense Landsat-5/7/8/9 data to generate yearly soybean maps. Our ChinaSoyA30m dataset showed strong correlation with official statistics at provincial, prefectural, and county levels, with R<sup>2 </sup>values of 0.95, 0.89, and 0.80, respectively.</span></p>
Database from Large-Eddy Simulations of a Supersonic Jet Flow (Re= 1.6x10E6 , M=1.4) - Database 1 of 6
<p>Numerical Database from Large-Eddy Simulations of a Supersonic Jet Flow (Re= 1.6x10E6 , M=1.4) - Database 1 / 6<br>Authors: Diego F. Abreu, João Luiz F. Azevedo, Carlos Junqueira-Junior</p> <p>This database presents unsteady calculated data derived from large-eddy simulations (LES) of a supersonic jet flow utilizing the FLEXI solver (https://numericsresearchgroup.org/codes.html#codes_flexi). <br>The operational parameters for the jet flow include a Mach number of 1.4 and a Reynolds number of 1.58E6 referenced to the nozzle exit diameter, corresponding to a perfectly expanded supersonic condition. The pressure and temperature of the jet flow match those of the surrounding ambient conditions.</p> <p>The dataset originates from six numerical simulations employing various mesh resolutions and polynomial orders, along with different boundary conditions. These calculations were performed to investigate the impact of mesh resolution, polynomial order, and boundary conditions on LES of the supersonic jet flow in the absence of nozzle effects. The database encompasses a collection of probes and planes extracted from the 3-D domain as outlined in the attached README.md file. </p> <p>For further details regarding these probes and planes, as well as information on the numerical simulations, please refer to the supplemental-material-database.pdf file. <br>The database is divided into six parts. The present set of data is number one.</p> <p>This database is associated with the manuscript entitled "Assessment of Jet Inflow Condition on the Development of Supersonic Jet Flows". The numerical data presented herein were previously published in the work entitled "Accuracy Assessment of Discontinuous Galerkin Spectral Element Method in Simulating Supersonic Free Jets" (https://doi.org/10.1007/s40430-024-04788-z) and the Ph.D. Thesis "Study of Turbulent Supersonic Jet Flows and the Influence of Nozzle-Exit Boundary Conditions on the Jet Initial Development". </p>
Database from Large-Eddy Simulations of a Supersonic Jet Flow (Re= 1.6x10E6 , M=1.4) - Database 6 of 6
<p>Numerical Database from Large-Eddy Simulations of a Supersonic Jet Flow - Database 6 / 6<br>Authors: Diego F. Abreu, João Luiz F. Azevedo, Carlos Junqueira-Junior</p> <p>This database presents unsteady calculated data derived from large-eddy simulations (LES) of a supersonic jet flow utilizing the FLEXI solver (https://numericsresearchgroup.org/codes.html#codes_flexi). <br>The operational parameters for the jet flow include a Mach number of 1.4 and a Reynolds number of 1.58E6 referenced to the nozzle exit diameter, corresponding to a perfectly expanded supersonic condition. <br>The pressure and temperature of the jet flow match those of the surrounding ambient conditions.</p> <p>The dataset originates from six numerical simulations employing various mesh resolutions and polynomial orders, along with different boundary conditions. <br>These calculations were performed to investigate the impact of mesh resolution, polynomial order, and boundary conditions on LES of the supersonic jet flow in the absence of nozzle effects. <br>The database encompasses a collection of probes and planes extracted from the 3-D domain as outlined in the attached README.md file. <br>For further details regarding these probes and planes, as well as information on the numerical simulations, please refer to the supplemental-material-database.pdf file. <br>The database is divided into six parts. The present set of data is number six.</p> <p>This database is associated with the manuscript entitled "Assessment of Jet Inflow Condition on the Development of Supersonic Jet Flows". <br>The numerical data presented herein were previously published in the Ph.D. Thesis "Study of Turbulent Supersonic Jet flows and the Influence of Nozzle-Exit Boundary Conditions on the Jet Initial Development".</p>
Database from Large-Eddy Simulations of a Supersonic Jet Flow (Re= 1.6x10E6 , M=1.4) - Database 5 of 6
<p>Numerical Database from Large-Eddy Simulations of a Supersonic Jet Flow - Database 5 / 6 (Continuation of https://doi.org/10.5281/zenodo.13902381 database)<br>Authors: Diego F. Abreu, João Luiz F. Azevedo, Carlos Junqueira-Junior</p> <p>This database presents unsteady calculated data derived from large-eddy simulations (LES) of a supersonic jet flow utilizing the FLEXI solver (https://numericsresearchgroup.org/codes.html#codes_flexi). <br>The operational parameters for the jet flow include a Mach number of 1.4 and a Reynolds number of 1.58E6 referenced to the nozzle exit diameter, corresponding to a perfectly expanded supersonic condition. <br>The pressure and temperature of the jet flow match those of the surrounding ambient conditions.</p> <p>The dataset originates from six numerical simulations employing various mesh resolutions and polynomial orders, along with different boundary conditions. <br>These calculations were performed to investigate the impact of mesh resolution, polynomial order, and boundary conditions on LES of the supersonic jet flow in the absence of nozzle effects. <br>The database encompasses a collection of probes and planes extracted from the 3-D domain as outlined in the attached README.md file. <br>For further details regarding these probes and planes, as well as information on the numerical simulations, please refer to the supplemental-material-database.pdf file. <br>The database is divided into six parts. The present set of data is number five.</p> <p>This database is associated with the manuscript entitled "Assessment of Jet Inflow Condition on the Development of Supersonic Jet Flows". <br>The numerical data presented herein were previously published in the Ph.D. Thesis "Study of Turbulent Supersonic Jet flows and the Influence of Nozzle-Exit Boundary Conditions on the Jet Initial Development".</p>
Database from Large-Eddy Simulations of a Supersonic Jet Flow (Re= 1.6x10E6 , M=1.4) - Database 2 of 6
<p>Numerical Database from Large-Eddy Simulations of a Supersonic Jet Flow (Re= 1.6x10E6 , M=1.4) - Database 2 / 6 (Continuation of https://doi.org/10.5281/zenodo.13902381 database)<br>Authors: Diego F. Abreu, João Luiz F. Azevedo, Carlos Junqueira-Junior</p> <p>The operational parameters for the jet flow include a Mach number of 1.4 and a Reynolds number of 1.58E6 referenced to the nozzle exit diameter, corresponding to a perfectly expanded supersonic condition. The pressure and temperature of the jet flow match those of the surrounding ambient conditions.</p> <p>The dataset originates from six numerical simulations employing various mesh resolutions and polynomial orders, along with different boundary conditions. These calculations were performed to investigate the impact of mesh resolution, polynomial order, and boundary conditions on LES of the supersonic jet flow in the absence of nozzle effects. The database encompasses a collection of probes and planes extracted from the 3-D domain as outlined in the attached README.md file.</p> <p>For further details regarding these probes and planes, as well as information on the numerical simulations, please refer to the supplemental-material-database.pdf file.<br>The database is divided into six parts. The present set of data is number one.</p> <p>This database is associated with the manuscript entitled "Assessment of Jet Inflow Condition on the Development of Supersonic Jet Flows". The numerical data presented herein were previously published in the work entitled "Accuracy Assessment of Discontinuous Galerkin Spectral Element Method in Simulating Supersonic Free Jets" (https://doi.org/10.1007/s40430-024-04788-z) and the Ph.D. Thesis "Study of Turbulent Supersonic Jet Flows and the Influence of Nozzle-Exit Boundary Conditions on the Jet Initial Development".</p>
Database from Large-Eddy Simulations of a Supersonic Jet Flow (Re= 1.6x10E6 , M=1.4) - Database 4 of 6
<div> <p>Numerical Database from Large-Eddy Simulations of a Supersonic Jet Flow (Re= 1.6x10E6 , M=1.4) - Database 4 / 6 (Continuation of https://doi.org/10.5281/zenodo.13902381 database)<br>Authors: Diego F. Abreu, João Luiz F. Azevedo, Carlos Junqueira-Junior</p> <p>The operational parameters for the jet flow include a Mach number of 1.4 and a Reynolds number of 1.58E6 referenced to the nozzle exit diameter, corresponding to a perfectly expanded supersonic condition. The pressure and temperature of the jet flow match those of the surrounding ambient conditions.</p> <p>The dataset originates from six numerical simulations employing various mesh resolutions and polynomial orders, along with different boundary conditions. These calculations were performed to investigate the impact of mesh resolution, polynomial order, and boundary conditions on LES of the supersonic jet flow in the absence of nozzle effects. The database encompasses a collection of probes and planes extracted from the 3-D domain as outlined in the attached README.md file.</p> <p>For further details regarding these probes and planes, as well as information on the numerical simulations, please refer to the supplemental-material-database.pdf file.<br>The database is divided into six parts. The present set of data is number one.</p> <p>This database is associated with the manuscript entitled "Assessment of Jet Inflow Condition on the Development of Supersonic Jet Flows". The numerical data presented herein were previously published in the work entitled "Accuracy Assessment of Discontinuous Galerkin Spectral Element Method in Simulating Supersonic Free Jets" (https://doi.org/10.1007/s40430-024-04788-z) and the Ph.D. Thesis "Study of Turbulent Supersonic Jet Flows and the Influence of Nozzle-Exit Boundary Conditions on the Jet Initial Development".</p> <p> </p> </div>
Database from Large-Eddy Simulations of a Supersonic Jet Flow (Re= 1.6x10E6 , M=1.4) - Database 3 of 6
<p>Numerical Database from Large-Eddy Simulations of a Supersonic Jet Flow (Re= 1.6x10E6 , M=1.4) - Database 3 / 6 (Continuation of https://doi.org/10.5281/zenodo.13902381 database)<br>Authors: Diego F. Abreu, João Luiz F. Azevedo, Carlos Junqueira-Junior</p> <p>The operational parameters for the jet flow include a Mach number of 1.4 and a Reynolds number of 1.58E6 referenced to the nozzle exit diameter, corresponding to a perfectly expanded supersonic condition. The pressure and temperature of the jet flow match those of the surrounding ambient conditions.</p> <p>The dataset originates from six numerical simulations employing various mesh resolutions and polynomial orders, along with different boundary conditions. These calculations were performed to investigate the impact of mesh resolution, polynomial order, and boundary conditions on LES of the supersonic jet flow in the absence of nozzle effects. The database encompasses a collection of probes and planes extracted from the 3-D domain as outlined in the attached README.md file.</p> <p>For further details regarding these probes and planes, as well as information on the numerical simulations, please refer to the supplemental-material-database.pdf file.<br>The database is divided into six parts. The present set of data is number one.</p> <p>This database is associated with the manuscript entitled "Assessment of Jet Inflow Condition on the Development of Supersonic Jet Flows". The numerical data presented herein were previously published in the work entitled "Accuracy Assessment of Discontinuous Galerkin Spectral Element Method in Simulating Supersonic Free Jets" (https://doi.org/10.1007/s40430-024-04788-z) and the Ph.D. Thesis "Study of Turbulent Supersonic Jet Flows and the Influence of Nozzle-Exit Boundary Conditions on the Jet Initial Development".</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.