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112 results for “DSM”
Digital Surface Model (DSM) shaded relief from 2005 LiDAR for the Green Lakes Valley, Colorado
This 1m Digital Surface Model (DSM) shaded relief 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 shaded relief 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. This 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 dataset 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: T
DSM_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria
<p>DSM_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria</p>
ICAERUS UC5 - RURAL LOGISTICS sample dataset from STRUMICA, NORTH MACEDONIA. Photogrammetry Orthomosaic and Digital Surface Model (DSM).
<table> <tbody> <tr> <td><strong>FOLDER NAME </strong></td> <td><strong>DESCRIPTION</strong></td> </tr> <tr> <td>3D_Point_cloud</td> <td>Photogrammetry 3D point cloud in LAS format</td> </tr> <tr> <td>Digital_Surface_Model</td> <td>2.5D Digital surface elevation model in GeoJPG resampled by x5</td> </tr> <tr> <td>Orthomosaic-GeoJPG</td> <td>Orthomosaic in GeoJPG format resampled by x5</td> </tr> <tr> <td>Orthomosaic-KMZ_tiles</td> <td>Orthomosaic in GeoJPG format in Google KMZ tiles</td> </tr> <tr> <td>Orthomosaic-OpenStreetMaps</td> <td>Orthomosaic in OpenStreetMasps format</td> </tr> <tr> <td>Raw_Images</td> <td>Initial Images captured by DJI Mavic 3E drone</td> </tr> </tbody> </table>
ICAERUS UC5 - RURAL LOGISTICS sample dataset from STRUMICA, NORTH MACEDONIA. Photogrammetry Orthomosaic and Digital Surface Model (DSM).
<table> <tbody> <tr> <td><strong>FOLDER NAME </strong></td> <td><strong>DESCRIPTION</strong></td> </tr> <tr> <td>3D_Point_cloud</td> <td>Photogrammetry 3D point cloud in LAS format</td> </tr> <tr> <td>Digital_Surface_Model</td> <td>2.5D Digital surface elevation model in GeoJPG resampled by x5</td> </tr> <tr> <td>Orthomosaic-GeoJPG</td> <td>Orthomosaic in GeoJPG format resampled by x5</td> </tr> <tr> <td>Orthomosaic-KMZ_tiles</td> <td>Orthomosaic in GeoJPG format in Google KMZ tiles</td> </tr> <tr> <td>Orthomosaic-OpenStreetMaps</td> <td>Orthomosaic in OpenStreetMasps format</td> </tr> <tr> <td>Raw_Images</td> <td>Initial Images captured by DJI Mavic 3E drone</td> </tr> </tbody> </table>
DSM Water Level: An UAV photogrammetry dataset for determination of river surface level using machine learning
<p>Orthophotos and digital surface models (DSMs) obtained using UAV photogrammetry can be used to determine the water surface level of a river. However, this task is difficult due to disturbances of the water surface on DSMs caused by limitations of photogrammetric algorithms. Machine Learning can be used to correct these disturbances as well as to extract a single water surface elevation value. The presented dataset contains raw photogrammetric orthophotos and DSMs of areas representing parts of a small river and the corresponding DSMs with corrected water surface disturbances. Also a single ground truth value of mean water surface level for each DSM sample is provided. This allows the dataset to be used for supervised training of a neural network performing a denoising or regression task.</p> <p>Acknowledgement: some of the samples were extracted from photogrammetric data acquired by Bandini et. al (https://doi.org/10.5281/zenodo.3519888)</p>
Lactobacillus Reuteri DSM 17938 Versus Placebo in the Treatment of Infantile Colic
ClinicalTrials.gov study NCT01541046. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Target Engagement of a Novel Dissonance-Based Treatment for DSM-5 Eating Disorders
ClinicalTrials.gov study NCT03261050. IPD Sharing: YES. Countries: 1. Publications: 4.
Immune Patterns in Pain Patients DSM-IV
ClinicalTrials.gov study NCT01106339. IPD Sharing: Not stated. Countries: 1. Publications: 1.
L. Reuteri ATCC PTA 5289 & L. Reuteri DSM 17938 for the Treatment of Children With Pharyngitis and/or Tonsillitis
ClinicalTrials.gov study NCT03377374. IPD Sharing: NO. Countries: 1. Publications: 5.
Safety Evaluation of Intranasal Use of DSM 32444 Postbiotic in Humans
ClinicalTrials.gov study NCT05984004. IPD Sharing: NO. Countries: 1. Publications: 4.
Efficacy of Lactobacillus Reuteri DSM 17938 for the Treatment of Acute Gastroenteritis in Children
ClinicalTrials.gov study NCT02989350. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
A Trial to Evaluate the Efficacy and Safety of Lactobacillus Plantarum DSM 33464 on Blood Lead Levels in Children
ClinicalTrials.gov study NCT04891666. IPD Sharing: NO. Countries: 1. Publications: 1.
Lactobacillus Reuteri DSM 17938 in Functional Constipation
ClinicalTrials.gov study NCT01244945. IPD Sharing: Not stated. Countries: 1. Publications: 5.
Metabolic Control Before and After Supplementation With Lactobacillus Reuteri DSM 17938 in Type 2 Diabetes Patients
ClinicalTrials.gov study NCT01620125. IPD Sharing: Not stated. Countries: 1. Publications: 1.
L. Reuteri ATCC PTA 5289 + L. Reuteri DSM 17938 in Pregnant Women
ClinicalTrials.gov study NCT03375125. IPD Sharing: NO. Countries: 2. Publications: 7.
Effect of Lactobacillus Reuteri DSM 17938 to Prevent Antibiotic-associated Diarrhea in Children
ClinicalTrials.gov study NCT02765217. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Lacticaseibacillus Rhamnosus CA15 (DSM 33960) Strain as a New Driver in Restoring the Normal Vaginal Microbiota
ClinicalTrials.gov study NCT05796921. IPD Sharing: NO. Countries: 1. Publications: 1.
L. Reuteri DSM 17938 and L. Reuteri ATCC PTA 6475 in Moderate to Severe Irritable Bowel in Adults
ClinicalTrials.gov study NCT04037826. IPD Sharing: UNDECIDED. Countries: 2. Publications: 5.
A Study to Investigate the Effect of Probiotics (L. Reuteri ATCC PTA 5289 and L. Reuteri DSM 17938) on Symptoms of Viral Upper Respiratory Tract Infections in Children
ClinicalTrials.gov study NCT06205966. IPD Sharing: NO. Countries: 1. Publications: 1.
Lactobacillus Reuteri DSM 17938 in the Prevention of Antibiotic-associated Diarrhea in Children: Protocol of a Randomized Controlled Trial
ClinicalTrials.gov study NCT02871908. IPD Sharing: Not stated. Countries: 1. Publications: 1.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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International Brain Laboratory public data
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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.