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183 results for “Laser Scanning”
Figure 4 in Confocal laser scanning microscopy analysis of the phalloidin-labelled musculature in nemerteans
Figure 4. Confocal projections of the longitudinal (A–H, J) and transversal (I) sections of anterior proboscis region. (A, B) Inner circular musculature of Carinoma mutabilis (A) and Lineus alborostratus (B); (C) longitudinal, diagonal and outer circular musculature of Carinina sp.; (D) anterior proboscis bulb of Carinomella sp.; (E, F) interweaving of the longitudinal musculature of Micrura kulikovae (E) and Malacobdella grossa (F); (G, H, J) longitudinal and diagonal musculature of Cephalothrix sp. (G), Carinoma mutabilis (H), and Micrura kulikovae (J); (I) everted proboscis of Carinoma mutabilis (muscle crosses indicated by arrows; high magnification image shows muscle cross). Abbreviations: dm, diagonal muscles; icm, inner circular musculature; lm, longitudinal musculature; ocm, outer circular musculature. Scales: 50 µm.
Figure 1 in Confocal laser scanning microscopy analysis of the phalloidin-labelled musculature in nemerteans
Figure 1. Confocal projections of the longitudinal (A–G) and transversal (H, I) sections of the body wall musculature. (A) Epidermal muscle meshwork immediately posterior to the mouth in Carinoma mutabilis, ventral view; (B) diagonal epidermal muscle fibres (arrows), pigment ring region of Callinera sp., dorsal view; (C) radial epidermal muscles (arrows) of Carinina sp., anterior region, ventral view of body margin; (D–F) intraganglionic radial muscles (arrows) in the brain of Carinoma mutabilis (D), in the lateral nerve cords of Cephalothrix cf. simula (E), and in the head of Cephalothrix sp., larva (F), all dorsal view; (G–I) Cerebratulus marginatus, radial epidermal muscles, just behind mouth, ventral view (G), fragment of transversal section through the intestine region showing epidermal muscles (arrows) (H), fragment of transversal section through the foregut region showing radial muscles (arrows) (I). Abbreviations: br, brain; dm, dermal muscles; dvm, dorsoventral muscles; fg, foregut; ep, epidermis; ilm, inner longitudinal musculature; lm, longitudinal musculature; ln, longitudinal nerve cord; mcm, middle circular musculature; pr, proboscis. Scales: 50 µm.
Supplementary Data for Manuscript : "Application of OSL surface exposure dating with the use of two-dimensional OSL laser scanning instruments and energy-dispersive x-ray spectroscopy'
<p>Contains all supplementary works mentioned in the manuscript.</p>
Supplementary Documents for Manuscript 'Investigating the use of two-dimensional OSL laser scanning instruments and energy-dispersive x-ray spectroscopy for OSL exposure dating'
<p>Appendix Data for Manuscript 'Investigating the use of two-dimensional OSL laser scanning instruments and energy-dispersive x-ray spectroscopy for OSL exposure dating' - with added "Readme's" for relevant databases.</p>
Supplementary Documents for Manuscript 'Investigating the use of two-dimensional OSL laser scanning instruments and energy-dispersive x-ray spectroscopy for OSL exposure dating'
<p>This file contains all the supplementary material for the manuscript 'Investigating the use of two-dimensional OSL laser scanning instruments and energy-dispersive x-ray spectroscopy for OSL exposure dating' sent to Radiation Measurements on 8/20/2023.</p>
Fundus Autofluorescence Imaging in Age-related Macular Degeneration Using Confocal Scanning Laser Ophthalmoscopy
ClinicalTrials.gov study NCT00393692. IPD Sharing: UNDECIDED. Countries: 1. Publications: 25.
Measuring Cyclotorsion on Scanning Laser Ophthalmoscopy (SLO)-Fundus Photographs
ClinicalTrials.gov study NCT02631369. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Changes in Macular Thickness After Patterns Scan Laser
ClinicalTrials.gov study NCT00563628. IPD Sharing: Not stated. Countries: 1. Publications: 7.
Terrestrial laser scanning (TLS) data on tree crown morphology and neighbourhood competition from both Cuellar and Alto Tajo, Spain
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Data from: Using terrestrial laser scanning data to estimate large tropical trees biomass and calibrate allometric models: a comparison with traditional destructive approach
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Data from: Effects of deer on woodland structure revealed through terrestrial laser scanning
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Data from: Tree-centric mapping of forest carbon density from airborne laser scanning and hyperspectral data
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Data from: Ancient lowland Maya complexity as revealed by airborne laser scanning of northern Guatemala
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Plot-level wood-leaf separation for terrestrial laser scanning point clouds
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Terrestrial Laser Scans of surface deformation associated with the 11/11/2019 Mw 4.7 Le Teil earthquake (SE France)
<p>Terrestrial Laser Scans of surface deformation produced by the 11/11/2019 Mw 4.7 Le Teil earthquake in SE France.</p> <p>All scans were produced with a Faro X330 equipment at 1/2 resolution, low laser power, with in-field filters (lost points). Processing includes import and registration with Faro Scene software, export as LAS files, manual editing of noise, vegetation and scattered points with CloudCompare software and rasterization with universal kriging with Golden Software Surfer software.</p>
Original point cloud from laser scanning of the Kladno railway station
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NIBIO_MLS: a forest point cloud panoptic segmentation dataset from mobile laser scanning (Geoslam Horizon)
<h1>General description</h1> <p>This dataset consists of a ML-ready labelled mobile laser scanning (MLS) point cloud dataset including 16 manually labelled forest plots (approx. 250 m2) for forest panoptic segmentation and thus including both semantic and instance labels. The data was collected using a Geoslam Horizon RT and processed using Geoslam Hub.</p> <h1>Labels</h1> <p>The data were then labelled into the following semantic classes (<em>label</em>):</p> <ul> <li>1= ground</li> <li>2= vegetation: these include both branches, leaves, and low vegetation</li> <li>3= lying deadwood</li> <li>4= stems</li> </ul> <p>In addition for each tree, a unique tree identifier (<em>treeID</em>) was also assigned to each point.</p> <h1>Data split</h1> <p>Each plot was split into train (50%), validation (25%), and test (25%) sets by dividing the circular plot into four slices, out of which the first two were used for training, the third for validation, and the fourth for test. </p> <p>Thus the users might play around with merging the train and validation dataset as they prefer. These two sets can be used during model training, hyperparameter tuning, and model selection. However, the test set should be kept as an independent set to be used for benchmarking against the values reported in the two studies indicated below. </p> <h1>Citation</h1> <p>To cite this datasets and for a more detailed description use:</p> <p>Wielgosz, M., Puliti, S., Xiang, B., Schindler, K. and Astrup, R., 2024. SegmentAnyTree: A sensor and platform agnostic deep learning model for tree segmentation using laser scanning data. <em>Remote Sensing of Environment; </em></p> <h2>Other studies using these data</h2> <p>Wielgosz, M., Puliti, S., Wilkes, P. and Astrup, R., 2023. Point2Tree (P2T)—Framework for parameter tuning of semantic and instance segmentation used with mobile laser scanning data in coniferous forest. <em>Remote Sensing</em>, <em>15</em>(15), p.3737; available <a href="https://www.mdpi.com/2072-4292/15/15/3737" target="_blank" rel="noopener">here</a></p> <h1>Funding</h1> <p>This work is part of the Center for Research-based Innovation SmartForest: Bringing Industry 4.0 to<br>the Norwegian forest sector (NFR SFI project no. 309671, smartforest.no).</p> <h1>⚖️ Licensing</h1> <p>📄 Please refer to the specific licenses below for details on how the data can be used.</p> <h4>🔑 Key Licensing Principles:</h4> <ul> <li>✅ You may access, use, and share the dataset and models freely.</li> <li>🔄 Any derivative works (e.g., trained models, code for training, or prediction tools) must also be made publicly available under the same licensing terms.</li> <li>🌍 These licenses promote <strong>collaboration</strong> and <strong>transparency</strong>, ensuring that research using this dataset benefits the broader scientific and open-source community 🙌</li> </ul>
Field-Acquired DBH Measurements of Trees in Durango, Mexico, using Terrestrial Laser Scanning (TLS) and Tree Caliper
<p><strong>Introduction<br></strong>The data generated in this study provide a valuable resource for developing and testing algorithms focused on circumference fitting and diameter estimation of tree stems. By combining field-measured diameters and high-resolution terrestrial laser scanning (TLS) data, the dataset includes precise point clouds representing individual trees, with diameter at breast height (DBH) measured at 1.30 meters above ground level. This standardized height ensures consistency in diameter measurements, a crucial metric in forestry and ecological studies. The dataset supports advancements in remote sensing applications, offering researchers an opportunity to refine methods for accurately determining DBH and enhancing the reliability of biomass and carbon estimation models.<strong><br><br>Study Area<br></strong>The research was conducted in Mexico within a permanent forest research plot, established based on the methodology outlined by Corral-Rivas et al. The plot covers a quadrangular area of 625 m² and is situated in the Sierra Madre Occidental region, specifically within the "La Victoria" management unit in the municipality of Pueblo Nuevo, Durango, Mexico. This area experiences a temperate climate, with average annual temperatures between 20 and 22 °C and rainfall ranging from 800 to 1200 mm per year. The dominant vegetation type is a coniferous forest, primarily consisting of <em>Pinus cooperi</em>, with an estimated tree density of 960 trees per hectare.<strong><br><br>Field Data</strong><br>In this study, we focused on 50 trees with diameters exceeding 10 cm, a commonly used threshold in forest inventory protocols for estimating biomass and carbon, chosen to enhance accuracy and consistency. According to Hoover and Smith, excluding smaller trees generally has a negligible effect on biomass estimates across most forest types, which supports the practicality of this 10 cm cutoff. This threshold also helps eliminate saplings, which can add variability due to their inconsistent growth patterns, and aligns with standard inventory practices. We conducted a conventional inventory to measure DBH for each tree, using a Häglof caliper and taking two measurements at perpendicular angles to achieve a reliable average (<strong>data_field.xlsx</strong>).<br><br><strong>Data Capture and Acquisition via Terrestrial Laser Scanning</strong><br>The terrestrial laser scanning data were collected using a FARO Focus M70 scanner, capable of measuring distances up to 70 meters with an accuracy of ±3 mm. The scanner was set to an "exterior" profile with a resolution of 1/4 and quality level of 4x, achieving an average point density of 234,679 points per square meter and a resolution of 10,310 x 4,268 points. Before starting the scans, ten targets were strategically positioned on-site to assist with alignment during post-processing. Four scans were then performed, with a 180-degree vertical angle and a 360-degree horizontal angle, capturing a complete view of the surrounding environment. The scans were merged using FARO Scene software to ensure accurate alignment. All details regarding the scanner and tree positions can be found in Appendix A.<br><br></p> <p><strong>Extraction of 2D Planes from Individual Tree Stem Point Clouds</strong><br>The point cloud was initially loaded into CloudCompare, where selected trees were manually segmented, and ground points were removed to minimize slope effects. Each tree was represented as a series of n points in three-dimensional space: <br><br>(x_1, y_1, z_1), (x_2, y_2, z_2), ..., (x_i, y_i, z_i), ..., (x_n, y_n, z_n)</p> <p>For each tree stem, a specific cylindrical section was isolated by selecting points with z-coordinates between 1.25 m and 1.35 m, filtering out points outside this range. This method enabled the extraction of a segment corresponding to the diameter at breast height (DBH).</p> <p>Following this step, the z-coordinates within the selected cylindrical sections were excluded, and duplicate points were removed, resulting in a two-dimensional dataset. This reduction in dimensionality produced a new collection of n points:</p> <p>(x_1,y_1),(x_2,y_2),…,(x_i,y_i),…,(x_n,y_n).<br><br>The information about the planes and their representation can be found in <strong>tls_tree_discs.zip</strong> and <strong>tls_tree_imgs.zip</strong>.</p>
Data from: Spatiotemporal rank filtering improves image quality compared to frame averaging in 2-photon laser scanning microscopy
Live imaging of biological specimens using optical microscopy is limited by tradeoffs between spatial and temporal resolution, depth into intact samples, and phototoxicity. Two-photon laser scanning microscopy (2P-LSM), the gold standard for imaging turbid samples in vivo, has conventionally constructed images with sufficient signal-to-noise ratio (SNR) generated by sequential raster scans of the focal plane and temporal integration of the collected signals. Here, we describe spatiotemporal rank filtering, a nonlinear alternative to temporal integration, which makes more efficient use of collected photons by selectively reducing noise in 2P-LSM images during acquisition. This results in much higher SNR while preserving image edges and fine details. Practically, this allows for at least a four fold decrease in collection times, a substantial improvement for time-course imaging in biological systems.
Country-wide data of ecosystem structure from the fourth Dutch airborne laser scanning survey (AHN4)
<p>This data repository contains country-wide data products for the ecosystem structure metrics generated from Airborne Laser Scanning (ALS) data across the Netherlands (AHN4). Twenty-five ecosystem structure metrics (at 10-meter resolution, GeoTIFF format) were derived from the AHN4 dataset (<a href="https://www.ahn.nl/ahn-viewer">https://www.ahn.nl/ahn-viewer</a>) using <a href="https://laserfarm.readthedocs.io/en/latest/">Laserfarm</a> workflow (<a href="https://zenodo.org/record/5636773">https://zenodo.org/record/5636773</a>). Laserfarm is a free and open-source workflow that enables efficient, scalable, and distributed processing of multi-terabyte LiDAR point clouds from national and regional ALS surveys into LiDAR metrics of ecosystem structure. All code of Laserfarm is hosted and freely available on GitHub (<a href="https://github.com/eEcoLiDAR/Laserfarm">https://github.com/eEcoLiDAR/Laserfarm</a>).</p> <p>Note that we also published the data products generated from AHN3 (2014-2019), which you can find in the repository here: <a href="https://doi.org/10.5281/zenodo.6421381">https://doi.org/10.5281/zenodo.6421381</a>. Laserfarm workflow was also employed to generate the data products from AHN4 (2020-2022). </p> <p>The twenty-five LiDAR metrics are related to three key dimensions of ecosystem structure (ecosystem height, ecosystem cover, and ecosystem structural complexity). Each GeoTIFF layer represents one LiDAR metric at 10 m resolution covering the whole Netherlands (file name as "ahn4_10m_featrue_name.tiff").</p> <p>A detailed description of all the metrics can be found in the README file (README.pdf). </p> <p>Relevant publications:</p> <p>Kissling, W.D., Shi, Y., Koma, Z., Meijer, C., Ku, O., Nattino, F., Seijmonsbergen, A.C., & Grootes, M.W. (2022). Laserfarm – A high-throughput workflow for generating geospatial data products of ecosystem structure from airborne laser scanning point clouds. <em>Ecological Informatics, 72</em>, 101836 (<a href="https://doi.org/10.1016/j.ecoinf.2022.101836">https://doi.org/10.1016/j.ecoinf.2022.101836</a>)</p> <p>Kissling, W.D., Shi, Y., Koma, Z., Meijer, C., Ku, O., Nattino, F., Seijmonsbergen, A.C., & Grootes, M.W. (2023). Country-wide data of ecosystem structure from the third Dutch airborne laser scanning survey. <em>Data in Brief, 46</em>, 108798 (<a href="https://doi.org/10.1016/j.dib.2022.108798">https://doi.org/10.1016/j.dib.2022.108798</a>)</p> <p> </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.