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3,377 results for “scan”
Terrestrial LiDAR Scans in the CTFS-ForestGEO Plot at Harvard Forest 2021
In heavily forested and jungle environments where GPS reception is unavailable due to dense canopy cover, it is difficult to determine one's location. Currently, either visual landmarks are used, or open clearings are found where GPS reception can be reestablished. Alternatively, dead-reckoning systems that rely on Inertial Measurement Unit sensor suites can help over moderate distances, but these devices cannot retain positional accuracy over extended ranges. Creare proposes to address this problem by developing the Tree Positioning System. This technological solution will combine a metrology system for determining local tree maps, and geolocalization algorithms that perform spatial pattern matching of local tree maps against a georegistered reference tree map of the area. This system was tested by scanning trees in the ForestGEO plot at Harvard Forest in June 2021.
North Temperate Lakes LTER Regional Survey Water Color Scans 2015 - current
The Northern Highlands Lake District (NHLD) is one of the few regions in the world with periodic comprehensive water chemistry data from hundreds of lakes spanning almost a century. Birge and Juday directed the first comprehensive assessment of water chemistry in the NHLD, sampling more than 600 lakes in the 1920s and 30s. These surveys have been repeated by various agencies and we now have data from the 1920s (UW), 1960s (WDNR), 1970s (EPA), 1980s (EPA), 1990s (EPA), and 2000s (NTL). The 28 lakes sampled as part of the Regional Lake Survey have been sampled by at least four of these regional surveys including the 1920s Birge and Juday sampling efforts. These 28 lakes were selected to represent a gradient of landscape position and shoreline development, both of which are important factors influencing social and ecological dynamics of lakes in the NHLD. This long-term regional dataset will lead to a greater understanding of whether and how large-scale drivers such as climate change and variability, lakeshore residential development, introductions of invasive species, or forest management have altered regional water chemistry. Color is measured in water samples that are filtered in the field through 0.45 um nucleopore membrane filters. A spectrophotometer is used to quantify color in the lab as absorbance (unitless) at 1 nm intervals between the wavelengths of 200 and 800 nm. Absorbance data are considered suspect for values greater than 2.
Data supplement for "Alignment of scanning lidars in offshore wind farms" - Wind Energy Science Journal
<p>These data are supplements for the calculations of the methods from the article "Alignment of scanning lidars in offshore wind farms".<br> The data was used to produce the results from the publication and is intended to be used here as sample data for illustrative purposes.</p>
Low-voltage Secondary Electron Emission Spectromicroscopy using a Scanning Auger Microscope
<p>Secondary electron emission is considered a well-established nano-scale probe for mapping the surface morphology of materials. It has also been demonstrated that secondary electrons (SE) emitted from materials can provide additional information on the local work function, bulk density of state (DOS), surface potential, charging/discharging characteristics, and elemental/chemical properties of bulk materials. The nano-scale lateral resolution and surface sensitivity of low-voltage scanning microscopes give them a unique advantage for the investigation of surfaces. However, the surface contamination caused by exposure to electron beams has always been a limiting factor for this purpose. Since the yield of SE emission is higher than that of Auger emission, the secondary electron emission spectromicroscopy (SEES) performed in an ultra-high vacuum chamber using a scanning Auger microscope (SAM) can be a very powerful tool for surface characterization, especially in the case of ultra-thin materials.</p> <p>We adapt our scanning auger microscope (SAM), equipped with a cylindrical mirror analyzer (CMA) and operated in an ultra-high vacuum, to SEES by tilting the sample holder and applying a negative bias to the sample. We also presented SEES signals of Chromium thin film at low voltages of 500 and 1000 V.</p>
Point cloud data from terrestrial laser scanning for stem volume modelling of Scots pine trees
<p>Stem volume is a key forest inventory attribute characterizing growth and yield of individual trees and forest stands. Three-dimensional information from terrestrial laser scanning (TLS) can be used to reconstruct tree stems and provide information on stem volume as well as stem shape. We collected diameter at breast height and height information with traditional field measurements as well as preprocessed TLS point cloud data on 230 Scots pine trees (<em>Pinus sylvestris L.</em>) from southern Finland. The data set here includes three-dimensional information on Scots pine tree stems derived from TLS point clouds. The usage of this data set can include, but is not limited to, development of point cloud processing algorithms for single tree stem reconstruction and investigations of of stem volume modelling for Scot pine. </p> <p>This data set includes two files: Scots_pines.txt includes DBH and height information based on field measurements from the 230 Scots pine trees. File includes the following columns: treeID, DBH, and h, where DBH is presented in cm and h (i.e. tree height) in m. Stem_points.zip, on the other hand, includes 230 laz-files where figure in the name of the laz-file refers to the tree ID in Scots_pines.txt-file. Laz-files include three columns that describe x, y, and z, coordinates (in meters) of stem points in a local coordinate system extracted from the normalized TLS point clouds (i.e. z coordinate describes height above ground).</p>
Binaural room scanning files for sound field synthesis localization experiment
<p>Binaural room scanning files that were used together with the SoundScape Renderer to perform the localization experiments described in Wierstorf [1].</p> <p>The results of the corresponding listening experiments are summarized in Fig. 5.4, see https://github.com/hagenw/phd-thesis/tree/master/05_psychoacoustics/fig5_04</p> <p>[1] H. Wierstorf, Perceptual Assessment of Sound Field Synthesis, PhD dissertation, TU Berlin, 2014.</p>
Overcoming Limitation of AlphaFold2 by Deep-mutational Scanning and Stability-Selection of Protein Sequences
<p>This repository contains the processed datasets and corresponding code used in our study. While AlphaFold2 revolutionizes protein structure prediction, its accuracy critically depends on evolutionary information from natural homologs—limiting applications for proteins with sparse sequence families. Here, we bypass this bottleneck by employing deep mutational scanning and stability-guided selection to generate artificial homologs. Fed into AlphaFold2, these synthetic sequences match the accuracy achieved on well-predicted proteins with rich natural homology, while providing highly accurate predictions for difficult targets—including orphan proteins previously deemed "unpredictable." Our approach achieves high accuracy (<3 Å RMSD for 5/8 and <2 Å RMSD for 7/8 targets after excluding intrinsically flexible regions). Thus, integrating simple, scalable molecular biology (mutagenesis/selection) with high-throughput sequencing can deliver the accuracy similar to but at a fraction of the cost and time of traditional experimental structure-determination methods. This hybrid framework could democratize high-resolution structural biology, opening avenues to determine structures of protein complexes, modified proteins, and condition-dependent conformations. </p>
Extracted trails from airborne laser scanning in the Oostvaardersplassen nature reserve
<p>Ungulates and other mammalian herbivores can create trails in dense vegetation by trampling and browsing. This can affect vegetation structure and results in the fragmentation of closed, high vegetation, with subsequent impacts on biodiversity. Manually mapping trails in the field or from aerial photographs can be challenging and time consuming, especially in inaccessible or difficult to access habitats such as wetlands and if trails occur beneath the canopy. Airborne laser scanning provides an alternative method because it penetrates vegetation canopies and efficiently obtains highly accurate data in the form of dense 3D point clouds. This repository consists of the extracted trails in wetland area of the Oostvaardersplassen nature reserve in the Netherlands using 3D airborne point cloud data (AHN4) and the manually created 50 plots of ground truth in two regions, i.e. grazed only by red deer and grazed by both red deer and geese. </p>
CT scans of COVID-19 patients
<p>Datasets contain CT scans of COVID-19 patients from Faculty hospital of Královké Vinohrady in DICOM (and TIFF) used in paper <em>Estimation of Covid-19 lungs damage based on computer tomography images analysis</em> presenting the tool is available on F1000reserach DOI: <a href="http://dx.doi.org/10.12688/f1000research.109020.1">10.12688/f1000research.109020.1</a>. The tool sued for the analysis of the dataset is published in Zenodo (<a href="https://doi.org/10.5281/zenodo.5805990">10.5281/zenodo.5805990</a>). Data were anonymized before exporting. Each patient has a folder with a unique ID, subfolder contains TIFF image for reach CT slice, and whenever possible DICOM files are added. All files contain ID and data format in the name. The CT data overview is in CSV for the whole dataset.</p> <p>Contributions:<br> Martin SCHÄTZ: Dataset preparation and couration<br> Olga RUBEŠOVÁ: Data selection and cleaning<br> David GIRSA: Data measuring and selection<br> Katarína NAĎOVA: Data measuring and selection</p> <p>The work was funded by the Ministry of Education, Youth and Sports by grant ‘Development of Advanced Computational Algorithms for evaluating post-surgery rehabilitation’ number LTAIN19007. The work was also supported from the grant of Specific university research – grant No FCHI 2022-001.</p>
Dataset supporting the paper "Superconducting Scanning Tunneling Microscope Tip to Reveal Sub-millielectronvolt Magnetic Energy Variations on Surfaces. J. Phys. Chem Lett. 12, 2983 (2021)"
<p>Dataset corresponding to theoretical calculations in the supporting information of the paper "Superconducting Scanning Tunneling Microscope Tip to Reveal Sub-millielectronvolt Magnetic Energy Variations on Surfaces" J. Phys. Chem Lett. 12, 2983 (2021), <a href="https://doi.org/10.1021/acs.jpclett.1c00328">https://doi.org/10.1021/acs.jpclett.1c00328</a></p> <p>List of files:</p> <p>Several folders corresponding to the figures of the supporting information. They contain:</p> <ul> <li>.siesta files: STM images in WsXM format (http://www.wsxm.eu/) simulated using STMpw (<a href="https://doi.org/10.5281/zenodo.3581159">https://doi.org/10.5281/zenodo.3581159</a>).</li> <li>CONTCAR files: relaxed structures in VASP format. They can be visualized with VESTA (<a href="https://jp-minerals.org/vesta/en/">https://jp-minerals.org/vesta/en/</a>).</li> <li>.agr files: grace files (<a href="https://plasma-gate.weizmann.ac.il/Grace/">https://plasma-gate.weizmann.ac.il/Grace/</a>).</li> </ul>
Mapping mineralogical heterogeneities at the nm-scale by scanning electron microscopy in modern Sardinian stromatolites: Deciphering the origin of their laminations
<p>These are the raw or processed data used for a paper published in Chemical Geology by Debrie et al. (2022), entitled "Mapping mineralogical heterogeneities at the nm-scale by scanning electron microscopy in modern Sardinian stromatolites: Deciphering the origin of their laminations", <a href="https://doi.org/10.1016/j.chemgeo.2022.121059">https://doi.org/10.1016/j.chemgeo.2022.121059</a></p> <p>The data content is summarized in the List_description_of_data.xlsx file</p>
MicroCT scans of a hybrid poplar leaf dehydrating, with annotated slices for model training
<p>Dataset of a leaf segment of a hybrid poplar (<em>P. maximowiczii x P. nigra</em> ‘Max3’) leaf scanned using microcomputed tomography (microCT) over time as it dehydrates.</p> <p> </p> <p><strong>Data acquisition methodology</strong></p> <p>Plants were brought to the TOMCAT tomographic beamline of the Swiss Light Source at the Paul Scherrer Institute (Villigen, Switzerland). Before microCT scanning, a young fully expanded leaf was detached from the plant and a short strip (0.4 x 1.5 cm) was cut between second-order veins. The base of the strip was wrapped in polyimide tape and inserted into a styrofoam block fixed on a sample holder. The strip was immediately scanned by imaging 1801 projections of 100 ms under a beam energy of 21 keV and a magnification of 40x, yielding a final voxel size of 0.1625 µm (field of view: ~416x416x312 µm). The leaf was left to dehydrate in the holder and additional scans were taken 10, 20, 25, and 30 minutes after the initial scan. Scanned projections were reconstructed to a transverse view using both absorption (gridrec; Marone <em>et al.</em> 2012) and phase contrast enhancement (Paganin <em>et al.</em> 2002) reconstruction.</p> <p> </p> <p><strong>Dataset description</strong></p> <p>On the reconstructed images a region of interest was identified using a paradermal view (i.e. top to bottom of the leaf) and used to manually align the scans of each time step. Thereafter, all images were cropped to that ROI, ensuring that the same region of the leaf was present in all image stacks.</p> <p>For all stacks, files start with:<br> <em>DEHYDRATION_small_Leaf4_time_N_</em><br> where N is the time point, with values from 1 to 5 equaling 0, 10, 20, 25, and 30 minutes.</p> <p>Following this prefix is either GRID (gridrec reconstruction), PAGANIN (phase contrast enhancement reconstruction), or LABELLED (hand labelled slices or ground truth). For GRID and PAGANIN, 8-bit grayscale stacks are provided. The AOI suffix indicates the region of interest.</p> <p>Stacks have been hand labelled over three orientations (for visual examples of the orientations see <a href="https://zenodo.org/api/files/6f06d15b-3ee9-412d-82ca-a20336c4bffa/Labeled_Sections_order_time1.png?versionId=26fc15aa-702e-4052-b162-702cc567634c">Labeled_Sections_order_time1.png</a> and <a href="https://zenodo.org/api/files/6f06d15b-3ee9-412d-82ca-a20336c4bffa/Labeled_Sections_order_time2.png">Labeled_Sections_order_time2.png</a>):</p> <ol> <li>CROSS (cross sectional, or transverse, view)</li> <li>LONGI (longitudinal view: similar to cross sectional view but starting normal to it, i.e. along the depth of the stack starting from the left of the cross-sectional view)</li> <li>PARADERMAL (top to bottom view: starting at the upper epidermis)</li> </ol> <p>A general idea of the slice range within one LABELLED stack is presented after the orientation, as:<br> <em>STARTtoENDbyRANGE</em><br> The exact position of the labelled slices for each time point can be found in the <a href="https://zenodo.org/api/files/6f06d15b-3ee9-412d-82ca-a20336c4bffa/Labeled_slices_positions.txt?versionId=93d7e22f-9f07-4f98-8c49-d93e9a2e1ce5">Labeled_slices_positions.txt </a>file. <strong>Note that one-based indexing is used (as in ImageJ), not zero-based indexing (as in e.g. Python).</strong></p> <p> </p> <p><strong>References</strong></p> <p>Marone F, Stampanoni M. 2012. Regridding reconstruction algorithm for realtime tomographic imaging. Journal of Synchrotron Radiation 19: 1029–1037.</p> <p>Paganin D, Mayo SC, Gureyev TE, Miller PR, Wilkins SW. 2002. Simultaneous phase and amplitude extraction from a single defocused image of a homogeneous object. Journal of Microscopy 206: 33–40.</p>
Dataset of Scanning Tunneling Microscopy (STM) images of model surfaces for elementary steps in catalytic reactions
<p>STM images presented in the dataset were recorded by the STRAS research group using a Omicron Variable Temperature STM (VT-STM) microscope, in the TASC laboratory of the CNR-IOM in Trieste.</p> <p>This work has been done within the NFFA-DI project funded by the European Union – NextGenerationEU - Missione 4, “Istruzione e Ricerca” – Componente 2, “Dalla ricerca all'impresa” – Linea di investimento 3.1,“Fondo per la realizzazione di un sistema integrato di infrastrutture di ricerca e innovazione” – Azione 3.1.1, “Creazione di nuove IR o potenziamento di quelle esistenti che concorrono agli obiettivi di Eccellenza Scientifica di Horizon Europe e costituzione di reti”.</p>
Three Annotated Anomaly Detection Datasets for Line-Scan Algorithms
<h1>Summary</h1> <p>This dataset contains two hyperspectral and one multispectral anomaly detection images, and their corresponding binary pixel masks. They were initially used for real-time anomaly detection in line-scanning, but they can be used for any anomaly detection task.</p> <p>They are in .npy file format (will add tiff or geotiff variants in the future), with the image datasets being in the order of (height, width, channels). The SNP dataset was collected using sentinelhub, and the Synthetic dataset was collected from AVIRIS. The Python code used to analyse these datasets can be found at: https://github.com/WiseGamgee/HyperAD</p> <h1>How to Get Started</h1> <p>All that is needed to load these datasets is Python (preferably 3.8+) and the NumPy package. Example code for loading the Beach Dataset if you put it in a folder called "data" with the python script is:</p> <pre><code>import numpy as np # Load image file hsi_array = np.load("data/beach_hsi.npy") n_pixels, n_lines, n_bands = hsi_array.shape print(f"This dataset has {n_pixels} pixels, {n_lines} lines, and {n_bands}.") # Load image mask mask_array = np.load("data/beach_mask.npy") m_pixels, m_lines = mask_array.shape print(f"The corresponding anomaly mask is {m_pixels} pixels by {m_lines} lines.")</code></pre> <h1>Citing the Datasets</h1> <p>If you use any of these datasets, please cite the following paper:</p> <pre><code>@article{garske2024erx,</code><br><code> title={ERX - a Fast Real-Time Anomaly Detection Algorithm for Hyperspectral Line-Scanning},</code><br><code> author={Garske, Samuel and Evans, Bradley and Artlett, Christopher and Wong, KC},</code><br><code> journal={arXiv preprint arXiv:2408.14947},</code><br><code> year={2024},</code><br><code>}</code></pre> <div> <pre>If you use the beach dataset please cite the following paper as well (original source):</pre> </div> <pre><code>@article{mao2022openhsi, title={OpenHSI: A complete open-source hyperspectral imaging solution for everyone}, author={Mao, Yiwei and Betters, Christopher H and Evans, Bradley and Artlett, Christopher P and Leon-Saval, Sergio G and Garske, Samuel and Cairns, Iver H and Cocks, Terry and Winter, Robert and Dell, Timothy}, journal={Remote Sensing}, volume={14}, number={9}, pages={2244}, year={2022}, publisher={MDPI} }</code></pre>
A high-resolution 4D geospatial laser scan dataset of the beach at Mariakerke Bad, Belgium
<p>This dataset contains a high resolution (in both time and space) laser scan data set of a 1-year measurement campaign in 2017 and 2018 in the seaside resort of Mariakerke Bad in Belgium. The measurements consist of 8417 hourly laserscans of a 400 meter stretch of beach. The measurement campained was performed to study variations in shoreward sand transport at urbanized beaches. </p> <p>Laserscan data is stored in local coordinates. Time dependent corrections per laserscan epoch are provided next to a global transformation matrix to transform the local coordinates to the Belgium Lambert 2008 coordinate system.</p> <p>This data is provided as is and is licensed under the Creative Commons Attribution 4.0 International (CC-BY-4.0). See the provided PDF on more information about the CC-BY-4.0.</p> <p>Version 1 contained an error in the global transformation matrix. Version 2 corrects this.</p>
Dataset of Scanning Tunneling Microscopy (STM) images of graphene on nickel
<p>STM images presented in the dataset were recorded by the STRAS research group using a Omicron Variable Temperature STM (VT-STM) microscope, in the TASC laboratory of the CNR-IOM in Trieste.</p> <p> </p>
Scanning electron microscope images of spruce needle homogenate and scanning electron microscope images of isolated small cellular particles from spruce needle homogenate
<p>Scanning electron microscope images of spruce needle homogenate and of isolated small cellular particles from spruce needle homogenate are presented. Each image is supplemented by description of the preparation of the sample and the data on the imaging technique and equipment. The data are curated by Veronika Kralj-Iglic and University of Ljubljana, Faculty of Health Sciences, Laboratory of Clinical Biophysics, and Anna Romolo, presently at University of Ljubljana, Faculty of Electrical Engineering, Laboratory of Physics, Ljubljana, Slovenia. Present address of Marko Jeran is: Department of Inorganic Chemistry and Technology, “Jožef Stefan” Institute, Ljubljana, Slovenia.</p>
Multiple Element Limitation in Northern Hardwood Ecosystems (MELNHE): Foliage Scans and Photographs
In the MELNHE project, we are conducting nutrient manipulations in three study sites in the White Mountain National Forest in New Hampshire: Bartlett Experimental Forest, Hubbard Brook Experimental Forest, and Jeffers Brook. We monitored foliar chemistry in 11 of our stands pre-treatment (2008-2010) and post-treatment (2014-2016 and 2021-22). In 2021-22 , we also measured specific leaf area, leaf dry matter content, carbon isotope composition, and stomatal density. This dataset includes scans of the foliage sampled in 2021-22, used to measure leaf area, and photos of all foliage samples used for trait measurements and chemical analysis. For the corresponding trait and chemistry data, please see the following dataset: https://portal.edirepository.org/nis/mapbrowse?packageid=knb-lter-hbr.313.1 Additional detail on the MELNHE project, including a datatable of site descriptions and a pdf file with the project description and diagram of plot configuration can be found in this data package: https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-hbr&identifier=344 These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station. Some of these data have been published in: Jenna M Zukswert, Matthew A Vadeboncoeur, Ruth D Yanai, Responses of stomatal density and carbon isotope composition of sugar maple and yellow birch foliage to N, P and CaSiO3 fertilization, Tree Physiology, Volume 44, Issue 1, January 2024, tpad142, https://doi.org/10.1093/treephys/tpad142
Simulated calibration dataset for 4D scanning transmission electron microscopy
<p>4D-STEM data frequently requires a number of calibrations in order to make accurate measurement: for instance, in various cases, it can be essential to measure and correct for diffraction shifts, account for ellipticity in the diffraction patterns, or determine the rotational offset between the real and diffraction planes.</p> <p>We've prepared a simulated 4D-STEM dataset which includes diffraction shifting, elliptical distortion, and an r-space/k-space rotational offset. Two HDF5 files each include the simulated data for two different electron probes: a standard probe, using a circular probe-forming aperture, and a 'bullseye' probe, using a patterned aperture. Each HDF5 file contains the following data objects:</p> <p>(a) the 'experimental' 4D-STEM scan of a strained single-crystal gold nanoparticle (size: (100,84,250,250) )</p> <p>(b) a 4D-STEM scan of a calibration sample of polycrystalline gold (size: (100,84,250,250) )</p> <p>(c) a stack of diffraction images of the electron probe over vacuum (size: (250,250,20) )</p> <p>(d) a single image of the electron probe over the sample and far from focus, such that the CBED forms a shadow image (size: (512,512) )</p>
Data bundle for "Advancing characterisation with statistics from correlative electron diffraction and X-ray spectroscopy, in the scanning electron microscope"
<p>Prepared by Tom McAuliffe (t.mcauliffe17@imperial.ac.uk)</p> <p>This repository is a release of the raw data and analysis results for: 'Advancing characterisation with statistics from correlative <br> electron diffraction and X-ray spectroscopy, in the scanning electron microscope' <br> https://doi.org/10.1016/j.ultramic.2020.112944</p> <p>The raw data is given as 'RawData.h5' - this contains patterns, spectra, and metadata in the Bruker-exported format.</p> <p>Outputs of our analysis code (which will be made available via AstroEBSD) are contained in 'PCA_Outputs' subfolders. Exported plots and <br> .mat results files are contained within. These are organised by Figure number in the paper.</p> <p>The provided results are divided into two major sections:<br> (1) Variation in the variance tolerance limit (and corresponding numbers of retained components), and the weighting of the PCA in favour of EBSD or EDS information.<br> RCCs are validated by cross-correlation with the corresponding raw data point pattern and/or spectrum. <br> (2) Full outputs of PCA analysis having varied the weighting parameter. This contains IPF maps, quantified chemical maps, PC scores, and label maps. <br> </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.