Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
89
datasets available to search
ShareScore release 0.9.0
Dataset results
89 results for “backscatter”
Remote versus local impacts of energy backscatter on the North Atlantic SST biases in a global ocean model
<p>The data and scripts used to generate the figures in the manuscript "Remote versus local impacts of energy backscatter on the North Atlantic SST biases in a global ocean model".</p>
Data for "The effect of pattern overlap on the accuracy of high resolution electron backscatter diffraction measurements"
<p>Data for "The effect of pattern overlap on the accuracy of high resolution electron backscatter diffraction measurements"</p> <p>Vivian Tong1, Jun Jiang1, Angus J Wilkinson2, and T Ben Britton1<br /> 1. Department of Materials, Imperial College London, Prince Consort Road, London, SW7 2AZ, UK<br /> 2. Department of Materials, University of Oxford, Parks Road, Oxford, OX1 3PH, UK</p> <p>For more information please contact: b.britton@imperial.ac.uk (Ben Britton)</p> <p>--<br /> The zip contains three subfolders:<br /> Fig4 Interaction volume measurement<br /> Fig14 Error approaching gb<br /> Fig16 GrainBoundaryProbability</p> <p>--<br /> Further details:</p> <p>Fig4 Interaction volume measurement -</p> <p>Measurement and simulation data of EBSD inteaction volume</p> <p>Includes calculated model & EBSD patterns for measurement<br /> EBSD patterns are from Zircaloy-4 and scanned on a Bruker eFlashHR camera in high resolution mode (1600 x 1200) attached to a Zeiss Auriga-40 SEM. The sample was tilted to 70 degrees and the SEM image shows the tilt corrected scanned region.</p> <p><br /> Fig14 Error approaching gb -<br /> 15 patterns are included that were used to create many simulated grain boundary pairs. These were captured from the same sample as used in Fig4.<br /> The spreadsheet details results shown in Fig 4.</p> <p><br /> Fig 16 GrainBoundary Pobability -<br /> This describes results from the simple Voronoi tessalation model (virtual grain structure) and sampling with a fixed step size, similar to a real EBSD scan. Probabilities were calcualted for different interaction volume sizes and critical distances.</p> <p> </p>
TVC Experiment 2018/19: Radarsat-2 backscatter data
<p><span>This dataset contains the processed, backscatter data from Radarsat-2 (RSAT-2) satellite data, as part of Environment and Climate Change Canada's 2018-2019 Trail Valley Creek Snow Experiment (TVC Experiment 18/19). These RSAT-2 data were collected and processed evaluate against a network of Steven’s HydraProbe soil monitoring sensors, and coincident in situ snowpit measurements, airborne radar, and other satellite radar data to better understand soil-snow-radar interactions in a tundra environment. The RSAT-2 data was ordered to provide wintertime coverage from September 2018 to July 2019, over the Trail Valley Creek research station (https://www.trailvalleycreek.ca/) in Northwest Territories, Canada. Three periods of in situ snow measurement took place in November 2018, January 2019, and March 2019. RSAT-2 data was acquired in Wide Fine Quad mode HH+HV+VH+VV. The RSAT-2 products were processed using the European Space Agency’s (ESA), Sentinel Application Platform (SNAP) software which included image calibration to sigma nought and orthorectification. An average of the calibrated backscatter and incidence angles was then calculated for an area 100 x 100 meters surrounding the geographic coordinates of each snowpit.</span></p>
Observation of strong backscattering in valley-Hall photonic topological interface modes
<p><span>Dataset required to reproduce the figures in the main text and supplementary information of: Rosiek, C.A., Arregui, G., Vladimirova, A. <em><span>et al.</span></em> Observation of strong backscattering in valley-Hall photonic topological interface modes. <em><span>Nature Photonics</span></em> <strong><span>17</span></strong>, 386–392 (2023).</span></p> <p><span>Execution of scripts to generate the figures tested on Windows with Matlab 2020b or newer. COMSOL models generated and solved on version 6.0. Unzip folder to access all files.</span></p> <p><span>Scripts are named according to the associated figures of the main manuscript:</span></p> <p><span> - fig1_transmittances.m generates the graphs of Fig. 1.</span></p> <p><span> - fig2_pti_dispersion.m generates the graphs of Fig. 2.</span></p> <p><span> - fig3etc_main_analysis.m (which calls subroutine fig3etc_main_analysis_sub.m) generates graphs of Fig. 3 and 4 as well as Supplementary Figures S6 and S11.</span></p> <p><span> - </span><span>fig5c_fffits.m and fig5d_ffmap.m generates plots of Fig. 5c and d, respectively. fig5_load_images.m performs preprocessing required to run fig5c_fffits.m and fig5d_ffmap.m from the raw data alone.</span></p> <p><span> - fig_s10_w1_loss_analysis.m generates the graphs of Fig. S10.</span></p> <p><span>Note that the data analysis also makes use of data derived from COMSOL simulations. The COMSOL MPH simulation files are contained within the subfolders of contrib.</span></p> <p><span>Contact chanro@dtu.dk for any inquiry on the contents.</span></p> <p> </p>
Data for: Optimizing broad ion beam polishing of zircaloy-4 for electron backscatter diffraction analysis
<p>This is the data to support a manuscript that explores how to optimize sample preparation of zircaloy-4 using broad ion beam polishing.</p> <p>If you wish to follow-up on this data, please contact Dr Ben Britton (ben.britton@ubc.ca).</p> <p>The data was collected and curated by Ning Fang and Ruth Birch.</p>
Electron backscatter diffraction data and backscatter electron images from a cold-rolled and recovered Al-Mn alloy
<p>Three electron backscatter diffraction (EBSD) data sets and three sets of backscatter electron (BSE) images from the same region of interest in a cold-rolled and recovered Al-Mn alloy.</p> <p>The data forms part of the supplementary material to the paper H W Ånes, A T J van Helvoort, K Marthinsen "Correlated subgrain and particle analysis of a recovered Al-Mn alloy by directly combining EBSD and backscatter electron imaging" (2022), published in Materials Characterization.</p> <p>The data was acquired in order to study the effect of particles on recovery and recrystallization in the Al-Mn alloy. The particles detected in the BSE images were inserted in the EBSD map after the EBSD map had been corrected for distortions by image registration using the BSE images.</p> <p>See the GitHub repository https://github.com/hakonanes/correlated-grains-particles-workflow for Jupyter notebooks and (MATLAB) MTEX scripts used to analyze the data.</p>
October 2019 700 kHz multibeam echo sounder data used for Seasonal Change of Multifrequency Backscatter in three Baltic Sea Habitats
<p>The raw data used for the study</p> <p>Seasonal Change of Multifrequency Backscatter in three Baltic Sea Habitats</p> <p>by Schulze et al.; currently under review at Frontiers in Remote Sensing. </p> <p> </p> <p>Files are stored in the s7k-Format, and sorted by date of acquisition and frequency. 200 and 400 kHz data were manufacturer-calibrated. Correct absorption values have been applied duirng the export. Refer to the paper for further dataset information.</p> <p> </p> <p>This upload stores the 700 kHz data recorded in October 2019.</p>
Stochastic Ocean Energy Backscatter via Pressure and Momentum Perturbation
<p>Our research aims to enhance the representation of mesoscale eddies in ocean models, particularly for eddy-permitting resolutions, by incorporating a dynamic backscatter parameterization and additional stochastic perturbations. This study addresses several key modeling issues, including enhancing the representation of missing variability through stochastic forcing, the need for incorporating stochastic terms alongside dynamic backscatter, the propagation of energy across scales and regimes, and distinguishing between different stochastic approaches.</p> <div> <div> <div> <div> <div> </div> </div> </div> </div> </div> <div> <div> <div> <div> <div> <div> <p>The output data from the FESOM2 model (<a href="https://fesom.de/" target="_new" rel="noreferrer">https://fesom.de/</a>) is available here. The file names include information about the corresponding plot in the paper, the simulation name, and the relevant diagnostic variable. Additionally, the uploaded data includes the high-resolution array used to produce stochastic perturbation.</p> </div> </div> </div> </div> </div> </div>
Data from: Investigating the Association of Seasonal Dynamics in GEDI Canopy Cover Profiles and Sentinel-1 Backscatter in Temperate Forests
<p>This dataset supports the analysis about <em>Investigating the Association of Seasonal Dynamics in GEDI Canopy Cover Profiles and Sentinel-1 Backscatter in Temperate Forests</em></p>
Backscatter tuned laser absorption spectroscopy in additive manufacturing
<p>Raw data accompanying our paper </p> <p><strong>Backscatter absorption spectroscopy for process monitoring in powder bed fusion</strong></p> <p> </p>
MiniMPL data for 'Supercooled liquid water cloud classification using lidar backscatter peak properties'
<p>This depository contains MiniMPL data collected in Christchurch, New Zealand from May 2021 to December 2022 for 'Supercooled liquid water cloud classification using lidar backscatter peak properties' by Whitehead et al. (2024). The dataset contains:</p> <ul> <li> MiniMPL data processed with the Automatic Lidar and Ceilometer Framework (ALCF; Kuma et al., 2021)</li> <li>Reference cloud phase mask</li> <li>G22-Christchurch model-generated cloud phase mask</li> <li>Figures comparing the G22-Davis and G22-Christchurch masks to the reference mask</li> </ul>
Electron backscatter diffraction data and backscatter electron images from four conditions from a cold-rolled and annealed Al-Mn alloy
<p>Raw electron backscatter diffraction (EBSD) datasets and backscatter electron (BSE) images acquired from four conditions from a cold-rolled and non-isothermally annealed Al-Mn alloy: as deformed, 175 C, 300 C and 325 C. The heating rate is 50 C/h. The material is recovered after 300 C and partly recrystallized after 325 C.</p> <p>The data forms part of the supplementary material to the paper "Orientation dependent pinning of (sub)grains by dispersoids during recovery and recrystallization in an Al-Mn alloy" (2023) published in Acta Materialia (https://doi.org/10.1016/j.actamat.2023.118761).</p> <p>The data was acquired in order to study the effect of particles on recovery and recrystallization in the Al-Mn alloy. The particles detected in the BSE images were inserted in the EBSD map after the EBSD map had been corrected for distortions by image registration using the BSE images.</p> <p>See the <em>GitHub</em> repository https://github.com/hakonanes/p-texture-al-mn-alloys for <em>Jupyter</em> notebooks and <em>MTEX</em> (<em>MATLAB</em>) and <em>ImageJ</em> scripts used to process and analyze the data.</p> <p>See the <em>README.txt </em>file for a description of the file contents.</p>
Electron backscatter diffraction patterns from a single crystal silicon wafer
<p>An electron backscatter diffraction (EBSD) dataset of (50, 50) patterns of (480, 480) pixel resolution from a single crystal silicon wafer. The patterns were acquired on a NORDIF UF-1100 detector in a Zeiss Supra 55 VP FEG SEM operated at 20 kV. The working distance was 16.1 mm and the nominal sample tilt was 70<span class="math-tex">\(^{\circ}\)</span>. The nominal step size is 40 μm, so the scan covers a nominal area of (2 x 2) μm<sup>2</sup>.</p> <p>The patterns are stored in NORDIF's binary file format (Pattern.dat) with the top-left pixel in the top-left pattern as the first byte, and the bottom-right pixel in the bottom-right pattern as the last byte. The patterns can be opened in for example the open-source Python package kikuchipy (https://github.com/pyxem/kikuchipy) with the following commands:</p> <pre><code class="language-python">import kikuchipy as kp s = kp.load("Pattern.dat")</code></pre>
A comprehensive open-access database of electron backscattering coefficients for energies ranging from 0.1 KeV to 15 MeV
<p>The database provides measured values of electron backscattering coefficient for 50 elements and 19 compounds at electron energies from 0.1keV to 15MeV.</p>
Dataset for: Characterization of local deformation around hydrides in Zircaloy-4 using conventional and high angular resolution electron backscatter diffraction
<p>Datasets for:</p> <p>Characterization of local deformation around hydrides in Zircaloy-4 using conventional and high angular resolution electron backscatter diffraction</p> <p>Ruth M. Birch<sup>1,2*</sup>, James O. Douglas<sup>1</sup>, T. Ben Britton<sup>1,2</sup></p> <ol> <li>Department of Materials, Imperial College London, Exhibition Road, London, UK, SW7 2AZ</li> <li>Department of Materials Engineering, University of British Columbia, Frank Forward Building, 309-6350 Stores Road, Vancouver, BC, Canada V6T 1Z4</li> </ol> <p>---</p> <p>h5 files for all 4 examples used in the paper:</p> <ul> <li>Example 1: JustGBZrH_20kx_WD16-4_DD17_T10-4_Px100nm</li> <li>Example 2: ZrH_WD16_DD18_T10-4_px0</li> <li>Example 3: 20kx_WD16-5_DD17_T10-2_Px0-1um.</li> <li>Example 4: ZrHSpikes_18kx_WD16-5_DD17_T10-2_Px100nm<br> </li> </ul> <p>High quality figures for all figures in the paper (600 dpi)</p> <p> </p>
Data for "TrueEBSD: correcting spatial distortions in electron backscatter diffraction maps"
<p>Data for "TrueEBSD: correcting spatial distortions in electron backscatter diffraction maps" published in Ultramicroscopy.</p> <p>Journal DOI: <a href="https://doi.org/10.1016/j.ultramic.2020.113130">https://doi.org/10.1016/j.ultramic.2020.113130</a>;<br> Preprint DOI: <a href="https://arxiv.org/abs/1909.00347">https://arxiv.org/abs/1909.00347</a>.</p> <p>The zipped folder contains:</p> <ol> <li>Readme (text file)</li> <li>'Ti-64' data subfolder: data for one of the maps in the Ti-64 map stitching example</li> <li>'ZrH' data subfolder: data for the hydride-containing Zircaloy-4 example</li> <li>'CP-Zr' data subfolder: data for the in-situ deformed Zr example</li> <li> 'MATLAB scripts' subfolder: TrueEBSD source code.</li> </ol> <p> </p> <p>Each data subfolder contains:</p> <ul> <li>Input image files</li> <li>EBSD orientation files in Bruker CTF format</li> <li>'Outputs' subfolder containing output figures as image files.</li> </ul> <p>The 'MATLAB scripts' subfolder contains TrueEBSD source code:</p> <ul> <li>The primary user interface is 'input_deck.m'. Most user settings can be changed here. <ul> <li>The input deck entries here have been pre-filled for the Ti-64 dataset.</li> </ul> </li> <li>To use TrueEBSD, run 'input_deck.m' in MATLAB.</li> <li>The method is outlined in 'main.m', which calls functions in 'MATLAB scripts\code\'.</li> </ul>
Dataset for "An innovative pure rotational Raman lidar for accurately profiling atmospheric temperature and aerosol/cloud backscatter coefficients"
<p>This is the dataset used in the paper "<span>An innovative pure rotational Raman lidar for accurately profiling atmospheric temperature and aerosol/cloud backscatter coefficients"</span></p>
Dataset for: Improving parent-austenite twinned grain reconstruction using electron backscatter diffraction in low carbon austenite
<p><strong>Improving parent-austenite twinned grain reconstruction using electron backscatter diffraction in low carbon austenite</strong></p> <p><strong> </strong>Ruth M. Birch<sup>1</sup>*, T. Ben Britton<sup>1</sup>, W. J. Poole<sup>1</sup></p> <p>1. Department of Materials Engineering, University of British Columbia, Frank Forward Building, 309-6350 Stores Road, Vancouver, BC, Canada V6T 1Z4</p> <p>*corresponding author: ruth.birch@ubc.ca</p> <p>---</p> <p><strong>Abstract: <br></strong></p> <p>Thermomechanical controlled processing (TMCP) is widely used to optimize the final properties of high strength low alloy (HSLA) steels, via microstructure engineering. The room temperature microstructures are influenced by the high temperature austenite phase, and the austenite microstructure <span>is commonly</span><span>can be</span> accessed by reconstruction using electron backscatter diffraction (EBSD) data of the final microstructure. A challenge for reconstruction of the <span>PAG </span><span>parent austenite grain (PAG) </span>microstructure and subsequent austenite grain size measurement is the presence of austenite-phase annealing twins, and we address<span> this</span> challenge with a new <span>‘</span>re-sort<span>’</span> algorithm. Our algorithm has been validated using the retained austenite regions (which were recovered via advanced pattern matching of EBSD patterns). We demonstrate that the re-sort algorithm improves the PAG reconstruction significantly, especially for the grain boundary network and correlation with other methods of grain size assessment and development of TMCP steels.</p> <p>---</p> <p><strong>Dataset includes:</strong></p> <ul> <li>Higher quality figures</li> <li>EBSD dataset with/without pattern matching:<br> <ul> <li>1mm map Specimen 1 Site 1 Map Data 1-Subset 1.h5oina</li> <li>1mm map Specimen 1 Site 1 Map Data 1-Subset 1-PatternMatching.h5oina</li> </ul> </li> <li>Code bundle</li> </ul>
Dataset for "Long-wavelength pulse generation via light-sail backscattering"
<p>Dataset for "Long-wavelength pulse generation via light-sail backscattering", paper submitted to PPCF</p>
Data Bundle for "Rapid electron backscatter diffraction mapping: Painting by numbers"
<p>This data is a release of EBSD data for "Rapid electron backscatter diffraction mapping: Painting by numbers"<br> Figure 5 and Figure 6 contain the EBSD data.<br> FFArgus.png = far field ARGUS image <br> NFArgus.png = near field ARGUS image<br> IPF = image data for the EBSD data<br> *.ctf = export of Bruker CTF data for full EBSD map to plot EBSD maps (e.g. in MTEX)<br> *.txt = reconstructed EBSD data in columns: euler1 euler 2 euler 3 euler 3 xpos ypos phaseID<br> *.prg = Bruker project file (use this to link the EBSD patterns to the NF Argus image)<br> EBSP folder = EBSPs as captured.</p> <p>The data bundle was prepared by Ben Britton (b.britton@imperial.ac.uk).</p> <p>The figures are presented in the powerpoint (which can be extracted as a zip if needed).</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.