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”
Data for 'The effect of δ-hydride on the micromechanical deformation of a Zr alloy studied by in situ high angular resolution electron backscatter diffraction'
<p>This is the data bundle for <br> "The effect of delta-hydride on the micromechanical deformation of a Zr alloy studied by in situ high angular resolution electron backscatter diffraction" <br> published in Scripta Materialia in 2019</p> <p>Siyang Wang 1, Szilvia Kalácska 2, Xavier Maeder 2, Johann Michler 2, Finn Giuliani 1, T. Ben Britton 1</p> <p>1 Imperial College London, London, UK SW7 2AZ<br> 2 EMPA, Swiss Federal Laboratories for Materials Science and Technology, Laboratory for Mechanics of Materials and Nanostructures, Feuerwerkerstrasse 39, 3602, Thun, Switzerland</p> <p>Please refer to the newest version of this data bundle, if there are multiple versions.</p> <p>For more information email siyang.wang15@imperial.ac.uk (Mr. Siyang Wang).</p>
Data for 'Indexing electron backscatter diffraction patterns with a refined template matching approach'
<p>Data for 'Indexing electron backscatter diffraction patterns with a refined template matching approach'</p> <p>Alexander Foden, T Ben Britton<br> Department of Materials, Imperial College London, Prince Consort Road, London, SW7 2AZ, UK</p> <p>For more information please contact: b.britton@imperial.ac.uk (Ben Britton) or a.foden16@imperial.ac.uk (Alex Foden)</p> <p>---</p> <p>File contains:</p> <p>High resolution image for Figures 1 - 11</p> <p>CSV data files for Figures 2, 3, 4, 5, 7 and 8. Figures 1 and 6 are illustrative and contain no data.</p> <p>EBSD data for figures 9, 10 and 11 can be found here <a href="https://zenodo.org/record/3459415#.XYym9C5KhaQ">https://zenodo.org/record/3459415#.XYym9C5KhaQ</a></p>
Code and model outputs for article "Assessing the potential of backscattering as a proxy for phytoplankton carbon biomass"
<p>Scripts from the scientific article "Assessing the potential of backscattering as a proxy for phytoplankton carbon biomass"<br> Submitted to Global Biogeochemical Cycles<br> Camila Serra-Pompei, Anna Hickman, Gregory L. Britten, Stephanie Dutkiewicz</p> <p>This repository contains all the files needed to run the MITgcm Biogeochemical and Optical model (MITgcmBgc, a.k.a. The Darwin model) and generate the figures in the paper.<br> Model outputs are also provided, so there is no need to run the entire model to generate the figures.</p> <p>The main folder "codes_phyto_bbp_paper.zip" contains all the files to process model output and reproduce the figures from the paper. The main files are:<br> - "Script_main.m" loads and processes model outputs and generates figures 2 to 9.<br> - "Script_new_optical_params" generates the optical parameters that are used as inputs in the MITgcmBgc model. It also generates figure 1.<br> - "Script_sensitivity_analysis" Performs offline sensitivity analysis and figure 10.</p> <p>The "mitgcm_darwin_3.zip" folder contain the code of the MITgcm with the Biogeochemical and Optical modules (Darwin package), with the configurations used in this study. Explanations of the model can be found in https://darwin3.readthedocs.io/en/latest/phys_pkgs/darwin.html </p> <p>These folders also contain the BGC-Argo floats data and climatological bbp and Chl NASA MODIS-Aqua GIOP data used in this study.</p> <p>The BGC-Argo data used in this article (download date: 27/10/2021) is available in "Other_extra_files/Argo_matrix.mat". The BGC-Argo data were collected and made freely available by the International Argo Program and the national programs that contribute to it (https://argo.ucsd.edu, https://www.ocean-ops.org, https://doi.org/10.17882/42182). The Argo Program is part of the Global Ocean Observing System. BGC-Argo float data was extracted using the BGC-Argo-Mat Matlab toolbox (Frenzel et al. 2021).</p> <p>The MODIS-Aqua satellite remote sensing data used in this article (download date: 23/12/2022) is available here in the folder "nasa_data". Satellite remote sensing data was extracted from NASA Goddard Space Flight Center, Ocean Ecology Laboratory, Ocean Biology Processing Group; (2014): MODIS-Aqua Ocean Color Data; NASA Goddard Space Flight Center, Ocean Ecology Laboratory, Ocean Biology Processing Group. http://dx.doi.org/10.5067/AQUA/MODIS_OC.2014.0</p> <p> </p>
Using generalized regression neural network to retrieve bare surface soil moisture from Radarsat-2 backscatter observations, regard less of roughness effect
<p>Description of soil moisture, correlation length, and RMS height from ground measurements of 147 sampling sites, full-polarized backscattering coefficients extracted from Radarsat-2 scene corresponding to those ground measurement sites.</p>
Graphs for Attenuated Backscatter in Dublin and Palaiseau (covid-19 months of 2020 and recent 2023)
Open the record for dataset details and reuse information.
Data from: Electron backscatter diffraction (EBSD) analysis of maniraptoran eggshells with important implications for microstructural and taphonomic interpretations
Open the record for dataset details and reuse information.
SMEX03 QuikSCAT/SeaWinds Backscatter Data, Brazil, Version 1
This data set includes data collected over the Soil Moisture Experiment 2003 (SMEX03) areas of Alabama, Georgia, Oklahoma, USA , and Brazil.
OPERA Radiometric Terrain Corrected SAR Backscatter from Sentinel-1 Static Layers validated product (Version 1)
The Observational Products for End-Users from Remote Sensing Analysis (OPERA) Radiometric Terrain Corrected (RTC) SAR Backscatter from Sentinel-1 (S1) Static Layers (RTC-S1-STATIC) validated product contains static radar geometry layers associated with the OPERA Radiometric Terrain Corrected (RTC) SAR Backscatter from Sentinel-1 (S1) (RTC-S1) validated product. Due to the S1 mission’s narrow orbital tube, radar-geometry layers such as incidence angle, local incidence angle, number of looks, and RTC Area Normalization Factor (ANF) vary slightly over time for each position on the ground, and therefore are considered static. These static layers are provided separately from the OPERA RTC-S1 product, as they are produced only once or a limited number of times, to account for changes in the DEM, in the S1 orbit, or in the static-layers generation algorithm. Static layers are provided as single-band cloud-optimized GeoTIFF (COG) files, with map grid matching RTC-S1 products with the same burst ID. The standard OPERA RTC-S1 product is derived from the original Copernicus Sentinel-1 (S1) interferometric wide (IW) single-look complex (SLC) data, provided by the European Space Agency, with a temporal sampling coincident with the availability of Sentinel-1A and Sentinel-1B SLC data. The OPERA RTC-S1-STATIC and RTC-S1 products are provided at a near global scope (land masses excluding Antarctica). The RTC-S1 products are available in the associated OPERA Radiometric Terrain Corrected SAR Backscatter from Sentinel-1 validated product (Version 1) dataset.
SMEX02 QuikSCAT/SeaWinds Backscatter Data, Iowa, Version 1
This data set includes data collected over the Soil Moisture Experiment 2002 (SMEX02) area of Iowa, USA, during June and August, 2002.
SMEX03 QuikSCAT/SeaWinds Backscatter Data, Alabama, Version 1
This data set includes data collected over the Soil Moisture Experiment 2003 (SMEX03) areas of Alabama, Georgia, and Oklahoma, USA, and Brazil. The SeaWinds scatterometer on NASA's Quick Scatterometer (QuikSCAT) satellite collected backscatter data.
SMEX03 QuikSCAT/SeaWinds Backscatter Data, Oklahoma, Version 1
This data set includes data collected over the Soil Moisture Experiment 2003 (SMEX03) areas of Alabama, Georgia, and Oklahoma, USA.
ABoVE/ASCENDS: Atmospheric Backscattering Coefficient Profiles from CO2 Sounder, 2017
This dataset provides atmospheric backscattering coefficient profiles collected during Active Sensing of CO2 Emissions over Nights, Days, and Seasons (ASCENDS) deployments from 2017-07-20 to 2017-08-08 over Alaska, U.S., and the Yukon and Northwest Territories of Canada. These profiles were measured by the CO2 Sounder Lidar instrument carried on a DC-8 aircraft. The airborne CO2 Sounder is a pulsed, multi-wavelength Integrated Path Differential Absorption lidar that estimates column-averaged dry-air CO2 mixing ratio (XCO2) in the nadir path from the aircraft to the scattering surface. In addition to XCO2, the lidar receiver recorded the time-resolved atmospheric backscatter signal strength as the laser pulses propagated through the atmosphere. Raw lidar data were converted to the atmospheric backscatter cross-section product and the two-way atmosphere transmission, also known as attenuated backscatter profiles. These ASCENDS flights were coordinated with the 2017 Arctic-Boreal Vulnerability Experiment (ABoVE) campaign and are provided in ICARTT format.
SPURS-2 shipboard X-band radar backscatter images for the 2016 E. Tropical Pacific field campaign
The SPURS-2 X-band marine navigation radar image dataset was collected from the ship during both the 2016 and 2017 cruises. The dataset consists of screenshots of rain echoes captured directly from the science-use X-band marine navigation radar. Raw data could not be saved. The screenshots show qualitative (uncalibrated) echoes of backscatter from rain. For full details on the screenshots, how they should be used, and what they show about rainfall, please refer to our publication: Thompson, E.J., W.E. Asher, A.T. Jessup, and K. Drushka. 2019. High-Resolution Rain Maps from an X-band Marine Radar and Their Use in Understanding Ocean Freshening. Oceanography 32(2):58–65, https://doi.org/10.5670/oceanog.2019.213 . The SPURS (Salinity Processes in the Upper Ocean Regional Study) project is a NASA-funded oceanographic process study and associated field program that aims to elucidate key mechanisms responsible for near-surface salinity variations in the oceans.
SMEX04 QuikSCAT/SeaWinds Backscatter Data: Sonora, Version 1
This data set includes data collected by the SeaWinds scatterometer on NASA's Quick Scatterometer (QuikSCAT) satellite collected backscatter data.
S-MODE DopplerScatt Level 1 Surface Doppler and Radar Backscatter Version 1
This dataset contains concurrent airborne DopplerScatt radar retrievals of surface vector winds and ocean currents from the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) during a pilot campaign conducted approximately 300 km offshore of San Francisco over two weeks in October 2021. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. DopplerScatt is a Ka-band (35.75 GHz) scatterometer with a swath width of 24 km that records Doppler measurements of the relative velocity between the platform and the surface. It is mounted on a B200 aircraft which flies daily surveys of the field domain during deployments, and data is used to give larger scale context, and also to compare with in-situ measurements of velocities and divergence. Level 1 data includes geolocated physical measurements for a measurement footprint, which are the basis for the DopplerScatt L2 surface winds and currents estimates. Data are available in netCDF format and are ordered by measurement acquisition time and radar range, and are not on a geospatial grid.
OPERA Radiometric Terrain Corrected SAR Backscatter from Sentinel-1 validated product (Version 1)
The Observational Products for End-Users from Remote Sensing Analysis (OPERA) Radiometric Terrain Corrected (RTC) SAR Backscatter from Sentinel-1 (S1) validated product consists of radar backscatter normalized with respect to the topography. The product maps signals related to the physical properties of ground scattering objects, such as surface roughness and soil moisture and/or vegetation. The OPERA RTC-S1 product is derived from Copernicus Sentinel-1 Interferometric Wide (IW) Single Look Complex (SLC) data with a near global scope and temporal sampling coincident with the availability of S1 SLC data. Each OPERA RTC-S1 product corresponds to a single S1 burst projected onto a pre-defined UTM/Polar stereographic map projection system map grid with a 30-meter spacing. The Copernicus global 30 m (GLO-30) Digital Elevation Model (DEM) is the reference DEM used to correct for the impacts of topography and to geocode the product. The OPERA RTC-S1 product is normalized to the backscatter coefficient gamma-naught, ɣ0, obtained from the original radar brightness beta-naught, β0, through radiometric terrain correction. The RTC-S1 product is distributed as cloud optimized GeoTIFFs with one GeoTIFF file per processed polarization. The RTC-S1 product metadata is provided in the Hierarchical Data Format version 5 (HDF5) format. The OPERA RTC-S1 product contains modified Copernicus Sentinel data (2022-2025).Due to the S1 mission’s narrow orbital tube, radar-geometry layers such as incidence angle, local incidence angle, number of looks, and RTC Area Normalization Factor (ANF) vary slightly over time for each position on the ground, and therefore are considered static. These static layers are provided separately from the OPERA RTC-S1 product, as they are produced only once or a limited number of times, to account for changes in the DEM, in the S1 orbit, or in the static-layers generation algorithm. The static layers are available in the associated OPERA Radiometric Terrain Corrected SAR Backscatter from Sentinel-1 Static Layers validated product (Version 1) dataset.
BOREAS RSS-17 1994 ERS-1 Level-3 Freeze/Thaw Backscatter Change Images
The BOREAS RSS-17 team acquired and analyzed imaging radar data from the ESA's ERS-1 over a complete annual cycle at the BOREAS sites in Canada in 1994 to detect shifts in radar backscatter related to varying environmental conditions. Two independent transitions corresponding to soil thaw and possible canopy thaw were revealed by the data. The results demonstrated that radar provides an ability to observe thaw transitions at the beginning of the growing season, which in turn helps constrain the length of the growing season. The data set presented here includes change maps derived from radar backscatter images that were mosaicked together to cover the southern BOREAS sites. The image values used for calculating the changes are given relative to the reference mosaic image. Due to copyright issues, the 01-March-1994 reference image is not included on the CD-ROM and is not publically available. See the accompanying guide document for information about how to possibly acquire the data. The data are stored in binary image format files.
SPURS-2 shipboard X-band radar backscatter data for the E. Tropical Pacific field campaign
The SPURS-2 X-band marine navigation radar image dataset was collected from the ship during both the 2016 and 2017 cruises. The dataset consists of screenshots of rain echoes captured directly from the science-use X-band marine navigation radar. Raw data could not be saved. The screenshots show qualitative (uncalibrated) echoes of backscatter from rain. For full details on the screenshots, how they should be used, and what they show about rainfall, please refer to our publication: Thompson, E.J., W.E. Asher, A.T. Jessup, and K. Drushka. 2019. High-Resolution Rain Maps from an X-band Marine Radar and Their Use in Understanding Ocean Freshening. Oceanography 32(2):58–65, https://doi.org/10.5670/oceanog.2019.213 . The SPURS (Salinity Processes in the Upper Ocean Regional Study) project is a NASA-funded oceanographic process study and associated field program that aims to elucidate key mechanisms responsible for near-surface salinity variations in the oceans.
LBA-ECO CD-03 Cloud Base-Backscatter Data, km 67 Tower Site, Tapajos National Forest
A Vaisala CT-25K ceilometer was installed at an old-growth forest site located at the km 67 Eddy Flux Tower site in the Tapajos National Forest, Para, Brazil, off Kilometer 67 of BR-163 south of Santarem in April 2001 and remained operational through December 2003, with reliable data being collected between May 2001 and June 2003.Annual, 2001 to 2003, 30-minute average cloud base and backscatter profile data and measurement statistics (sample count, variance, skewness, and kurtosis) are presented in 15 ASCII comma-delineated files. In addition, the cloud base values (m) and measurement statistics for the three reported cloud base levels have been consolidated in 3 annual comma-separated files.The ceilometer provides 15-second measurements of cloud base (three levels up to 7500 m), echo intensity, and a 30-m resolution backscatter profile. The ceilometer reports vertical visibility during periods when the sky is obscured but a cloud base is not detectable. The ceilometer was operational for a sufficient amount of time to examine wet-to-dry season variations in cloud cover fraction and cloud base height.
The Characteristics of Backscattering With Depth in the Progression of Keratoconus
ClinicalTrials.gov study NCT06050629. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
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.