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112 results for “DSM”
RGB orthophoto mosaic, DSM, 3d point cloud and LIDAR LAZ of the flash flood damages in Karavelovo and Bogdan vilages, Bulgaria- September 2, 2022
<p>The present dataset contains geospatial resources aimed at investigating and assessing the consequences of a flash flood of debris flow character, relatively significant in extent and magnitude of damage, in the area of two villages in the Municipality of Karlovo, located in central Bulgaria, which happened on September 2, 2022. For this purpose, an integrated approach based on the combination of digital photogrammetry with high spatial resolution and spatial accuracy, based on a fixed wing unmanned aerial system, and laser altimetry (LIDAR), based on a multirotor unmanned platform, was used. The data collection was carried out 2 days after the occurrence of the disaster, resulting in the generation of valuable information resources that allow not only to spatially and quantitatively determine the damage of the disaster, but also to reveal the mechanism of occurrence of the phenomenon: 1) orthophoto mosaic, Digital surface model-DSM and 3D point cloud (from photogrammetry) 2) Classified 3D point cloud- from LIDAR survey.</p>
Photogrammetric Point Cloud and DSM from UAV campaign at Niwot Ridge, 2017.
Elevation data from 14 August 2017 collected as part of unmanned aerial vehicle (UAV)/drone campaign during Summer 2017. Investigating snow depth varaibility and spatiotemporal variations and controls on vegetation productivity within the Niwot Ridge LTER Saddle Catchment.
Orthophoto and DSM Rutor Glacier 2021
<p>Orthophoto and Digital Surface Model (DSM) obtained from the photogrammetric flight over Rutor Glacier in September 2021. Ground Sample Distance (GSD) = 0.5 m (resampled form the 0.2 m original GSD)</p>
Orthophoto and DSM Rutor Glacier 2020
<p>Orthophoto and Digital Surface Model (DSM) obtained from the photogrammetric flight over Rutor Glacier in September 2020. Ground Sample Distance (GSD) = 0.5 m (resampled form the 0.2 m original GSD)</p>
V2V Comms Utilizing DSM
<p>Utilization of Dynamic Spectrum Management (DSM) in V2V communications by selecting an appropriate frequency band through the selection of available licensed and unlicensed frequency bands for vehicles</p>
Supplementary material - Eggerthella lenta DSM 2243 alleviates bile acid stress response in Clostridium ramosum and Anaerostipes caccae by transformation of bile acids
<p>The word document is a collection of supplementary figures and tables. <br> The excel file is a collection of raw data from experiments.</p>
Figure 1: Venn diagram of a DSm hybrid model for a 3D frame.
<p>As another simple example of hybrid DSm model, let's consider the 3D case with the frame £ = fµ1; µ2; µ3g with the model M 6= Mf in which we force all possible conjunctions to be empty, but µ1 \ µ2. This hybrid DSm model is then represented with the Venn diagram on Fig. 1 (where boundaries of intersection of µ1 and µ2 are not precisely de¯ned if µ1 and µ2 represent only fuzzy concepts like smallness and tallness by example).</p>
Figure 5 in Escherichia coli expression and characterization of -amylase from Geobacillus thermodenitrificans DSM-465
Figure 5. The interaction of amylopectin (PubChem ID - 439207) and alpha-amylase enzyme active site. The left figure is built by Chimera and the right by MOE software.
Figure 4 in Escherichia coli expression and characterization of -amylase from Geobacillus thermodenitrificans DSM-465
Figure 4. Ramachandran plot for validation of alpha-amylase enzyme 3D structure generated by Raptor-X server.
Figure 3. 3 in Escherichia coli expression and characterization of -amylase from Geobacillus thermodenitrificans DSM-465
Figure 3. 3-dimentionsl protein structure of alpha-amylase built by Raptor-X software.α-helices are indicated by red, β-sheet by yellow and random coils by white. The protein structure consists of a single monomer.
Figure 2 in Escherichia coli expression and characterization of -amylase from Geobacillus thermodenitrificans DSM-465
Figure 2. Enzyme kinetics. (A) The effect of temperature on the enzyme stability; (B) determination of KM and Vmax values of purified alpha-amylase using Lineweaver-Burk plot; (C) The effect of reaction mixture temperature on the enzyme activity indicating an optimum temperature of 70°C; (D) The effect of pH on the enzyme activity, showing maximum activity at pH 8.
Figure 1 in Escherichia coli expression and characterization of -amylase from Geobacillus thermodenitrificans DSM-465
Figure 1. SDS-PAGE photograph indicating the expression and purification of alpha amylase. Lane C- control experiment (without gene), Lane-M. Protein marker (ThermoFisher Scientific PageRulerTM Prestained Protein Ladder, 10 kDa to 180 kDa), Lane E, experimental with expression of gene, Lane P, partially purified enzyme alpha amylase. The molecular weight of the purified enzyme was found as 63 kDa on SDS-PAGE.
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>
Dataset of Russian multiword expressions for a DSM
<p>The dataset consists of Russian multiword expressions selected according to the presence or absence of a 'categorical shift' between the meaning of a noun phrase and that of its head. The items with the categorical shift are intended for inclusion in the vocabulary of a distributional-semantic model (DSM) as single items. The dataset consists of 2 parts, 173 positive and 173 negative examples. The entries are additionally labeled for further categorization.</p>
Dataset frontal sinuses fossil and extant Canidae for DSM analyses
<p>Dataset of 3D files of the frontal sinuses of fossil (<em>Eucyon</em>) and extant Canidae (<em>Canis</em>, <em>Vulpes</em>, <em>Lupulella</em>, <em>Lycaon</em>), for Diffeomorphic surface matching analyses of the article Frosali et al. "First digital study of the frontal sinus of stem-Canini (Canidae, Carnivora): evolutionary and ecological insights" published in Frontiers in Ecology and Evolution.</p>
KLF2 maintains lineage fidelity and suppresses CD8 T cell exhaustion during acute LCMV infection (LCMV DSM scRNA data and ATAC-seq)
Open the record for dataset details and reuse information.
DSM: Deep Sequential Model for Complete Neuronal Morphology Representation and Feature Extraction
<p>This is an open-source repository for hosting codes and data files from research, "DSM: Deep Sequential Model for Complete Neuronal Morphology Representation and Feature Extraction". We also provided a web service based on our methods, please go to http://114.117.165.134:8501/.</p><p>(1)raw_dataset.zip: </p><ol><li>1,282 neuron reconstructions from SEU-Allen dataset;</li><li>1,002 neuron reconstructions from Janelia dataset;</li><li>1,100 neuron reconstructions from ION dataset.</li></ol><p>(2)Supplementary.zip: Supplementary information, including tables and figures;</p><p>(3)DSM-tools.zip: A python package for converting neuron morphology into sequences and implementing DSM models.</p><ol><li>NeuronSequenceDataset class: to transform SWC files to sequence dataframes by binary tree traversals, and prepare for the input of DSM networks.</li><li>DSMDataConverter class: a helper to convert the sequence dataframes for classification and clustering.</li><li>DSMHierarchicalAttentionNetwork class: classification model, giving a pre-trained DSM-HAN model by default.</li><li>DSMAutoencoder class: clustering model, giving a pre-trained DSM-AE model by default.</li></ol><p>(4)neuron2seq_for_developer.zip: A repository for further development of the models, including source codes and all data files.</p>
1M DSM LIDAR Cockfield Fell, Cockfield.
1M DSM LIDAR Cockfield Fell, Cockfield, County Durham, England. A 19C and 20C industrial landscape on the top of Medieval and Prehistoric settlements. A protected scheduled area by Historic England, but very little information about any of the archaeology present. Source: Objaverse 1.0 / Sketchfab
The Port of Dover - Environment Agency LiDAR DSM
A 3-dmensional visualisation of the port of Dover, including Dover Castle, the Roman lighthouse, Fort Burgoyne, North Centre Bastion, and Drop Redoubt. Data from the Environment Agency (1 m DSM LiDAR data), processed using QGIS and Planlauf Terrain. Source: Objaverse 1.0 / Sketchfab
Digital Surface Model (DSM) from 2005 LiDAR for the Green Lakes Valley, Colorado
This 1m Digital Surface Model (DSM) is derived from first-stop Light Detection and Ranging (LiDAR) point cloud data from September 2005 for the Green Lakes Valley, near Boulder Colorado. The DSM was created from LiDAR point cloud tiles subsampled to 1-meter postings, acquired by the National Center for Airborne Laser Mapping (NCALM) project. This data was collected in collaboration between the University of Colorado, Institute of Arctic and Alpine Research (INSTAAR) and NCALM, which is funded by the National Science Foundation (NSF). The DSM has the functionality of a map layer for use in Geographic Information Systems (GIS) or remote sensing software. Total area imaged is 35 km^2. The LiDAR point cloud data was acquired with an Optech 1233 Airborne Laser Terrain Mapper (ALTM) and mounted in a twin engine Piper Chieftain (N931SA) with Inertial Measurement Unit (IMU) at a flying height of 600 m. Data from two GPS (Global Positioning System) ground stations were used for aircraft trajectory determination. The continuous DSM surface was created by mosaicing and then kriging 1 km2 LiDAR point cloud LAS-formated tiles using Golden Software's Surfer 8 Kriging algorithm. Horizontal accuracy and vertical accuracy is unknown. cm RMSE at 1 sigma. The layer is available in GEOTIF format approx. 265 MB of data. It has a UTM zone 13 projection, with a NAD83 horizonal datum and a NAVD88 vertical datum computed using NGS GEOID03 model, with FGDC-compliant metadata. A shaded relief model was also generated. A similar layer, the Digital Terrain Model (DTM), is a ground-surface elevation dataset better suited for derived layers such as slope angle, aspect, and contours. A processing report and readme file are included with this data release. The DSM is available through an unrestricted public license. The LiDAR DEMs will be of interest to land managers, scientists, and others for study of topography, ecosystems, and environmental change. NOTE: This EML metadata file does not contain
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