Find research datasets worth reusing

opencc-by-4.0Nov 2023View details →

Multiple years of Seaglider observations of hydrography, dissolved oxygen, chlorophyll a, and optical backscatter at Station ALOHA

File descriptions: Seaglider missions are identified as GLIDER_MISSION (i.e. sg148_12 is glider 148, mission 12) and each have three files associated. For example: <ol> <li>sg148_12_qc_pass.xlsx contains only quality controlled (QC flags = 1) core data for an entire mission. Core data may include temperature, conductivity, salinity, potential density anomaly, calibrated dissolved oxygen concentrations, calibrated chlorophyll a concentrations, and the backscattering coefficient due to particles (bbp) at up to three wavelengths (470, either 650 or 660, and 700 nm) and spike flags. Bbp data is corrected with an in situ dark subtraction from near 200 m deep. Associated metadata (datetime, latitude, longitude, depth, dive number, and vertical profile direction) is also included.</li> <li>sg148_12_alldata.nc contains all data (i.e. all QC flag levels) and associated quality control flags. In addition to core and metadata, factory-only calibrated observations (e.g. dissolved oxygen concentrations, chlorophyll a concentrations, and bbp) are listed. </li> <li>sg148_12_qctests.nc contains all quality control test values (pass: QC = 1, input flag: QC = 2, questionable data QC = 3, bad data: QC = 4). The maximum test QC flag value (e.g. out of range, density inversions, bioflouling, etc.) was passed to the variable QC flag (e.g chla_qcflag or salin_qcflag). . </li> </ol>   Dataset description: The SCOPE-ALOHA Seaglider dataset was designed to monitor the spatial and temporal variability of physical and biogeochemical properties around the long term sampling site Station ALOHA (22°45′N, 158°W). Seagliders are autonomous underwater vehicles that take high frequency (up to 0.2 Hz in our dataset), depth-resolved observations over several months and can be used to map large spatial features. The gliders depicted in this study were equipped with sensors to measure temperature, salinity, pressure, dissolved oxygen concentration (O2), chlorophyll a concentration (Chl a) from fluorescence (excitation/emission lambda = 470/695 nm), and the particulate backscattering coefficient (bbp) at three wavelengths (lambda = 470 nm, 700 nm, and either 650 or 660 nm depending upon mission). Vertical profiles down to at least 200 m were collected for all sensors over periods of several months per mission. This dataset comprises 18 missions between 2008 and 2023 centered on Station ALOHA, totaling over 20,000 depth profiles. Chlorophyll a and oxygen concentrations are calibrated with discrete observations. Particulate backscattering coefficients are corrected with an additional dark subtraction. This dataset is an improvement on the raw data files as they are quality controlled, calibrated, and corrected. Raw data files can be found at https://hahana.soest.hawaii.edu/seagliders/index.php. version notes: v1.0 original v1.1 Metaadata tab on xlsx files edited, no change to data v1.2 fixed error: variable qc flags added to alldata.nc files v1.3 Added error estimates and CF_standard_name to alldata.nc files Methods: Code for all processing steps is on GitHub (https://github.com/cathygarcia/SeagliderDataprocessing). The steps listed here are a brief summary.  Temperature, Conductivity, Salinity, and Potential Density Anomaly <ul> <li>Both temperature and conductivity profiles were lag corrected.</li> <li>Practical salinity was calculated using the Gibbs Seawater Toolbox (gsw_SP_from_C.m), and then converted to absolute salinity (gsw_SA_from_SP.m). </li> <li>Potential density anomaly was calculated with respect to a reference water pressure of 0 db using the Gibbs Seawater Toolbox (gsw_sigma0.m).</li> </ul> Dissolved oxygen concentrations <ul> <li>Raw optode phase values proceeded through a series of corrections to account for the effects of temperature, salinity, pressure, and time response in addition to sensor drift (Bittig et al., 2018, Barone et al., 2019).</li> <li> Optode phase values were converted to dissolved oxygen concentrations, and re-calibrated using discrete Winkler measurements. </li> </ul> Chlorophyll a <ul> <li>Factory-calibrated chlorophyll a observations were re-calibrated using discrete measurements of either HPLC chlorophyll a (16 missions) or fluorometric chlorophyll a (2 missions).</li> <li>Daytime chlorophyll a values are not quench corrected, and may be lower than actual values. It is recommended to use nighttime profiles near the surface. </li> <li>Additionally, a spike flag is included based on published protocol (Briggs et al., 2011).</li> </ul> Backscattering coefficient due to particles (bbp) <ul> <li>Factory-calibrated bbp values could have a large offset, that was not expected based on natural variability.</li> <li>A mission-specific deep dark correction (1st percentile of bbp at 190-200 m) was subtracted for each bbp dataset. Both the uncorrected and corrected data are available.</li> <li>Additionally, a spike flag is included based on published protocol (Briggs et al., 2011).</li> </ul>

opencc-by-4.0Sep 2021View details →

Finite amplitude sound propagation effects in volume backscattering measurements for fish abundance estimation

The upload contains measurement and simulation data for finite-amplitude sound propagation effects in volume backscattering measurements. The experimental data are from a trawl survey conducted in the North Sea with R/V "G. O. Sars", 6-7 November 2004, passing several times over a group of Atlantic mackerel schools. The measurements are of the relative area backscattering coefficient, relative to 38 kHz, 2000 W power setting, at (1) 120 kHz with 250 W transmit power setting, 200 kHz with 120 W transmit power setting (2) 120 kHz with 1000 W power setting, 200 kHz with 1000 W power setting. A Simrad EK60 echosounder system was used, alternating between the low (1) and high (2) power settings through the measurement series. The corresponding simulation data are calculated using the Bergen Code numerical solver of the KZK Equation. The medium parameters input to the simulations are based on CTD data from the field survey . The transducer and amplitude data were found by laboratory measurements on echo sounders of the same type as used in the survey. .m files are included for both .mat data files, with details on how to read the data. An article describing the data has been submitted by the authors to Acta Acustica, 2022.

opencc-by-4.0Sep 2022View details →

Refinements for Bragg coherent X-ray diffraction imaging: Electron backscatter diffraction alignment and strain field computation

Here we present the final crystal reconstructions and analysis scripts for the paper titled "Refinement for Bragg coherent X-ray diffraction imaging: Electron backscatter diffraction alignment and strain field computation" published in Journal of Applied Crystallography, 55, 2022. Please see the README file for more information.

opencc-by-4.0Jul 2023View details →

Paired field measurements of suspended-sediment concentration, turbidity, acoustic backscatter, and particle size compiled from various estuaries in the United States and Australia

Field measurements of suspended-sediment concentration, turbidity, acoustic backscatter, and particle size are compiled from various estuaries in the United States and Australia to investigate the utility of combining optical and acoustic backscatter measurements for the estimation of suspended-sediment concentration under changes in floc particle size and density.  Theory, analysis, and interpretation of the data is available in Livsey et al (2023). Data collected from the Chesapeake Bay, US were compiled from Fall et al (2022). Data collected on the Brisbane River were collected by Livsey et al (2022).  Data collected for all other locations were compiled from Livsey et al (2022).   Data collected by Fall et al (2022) utilized a LISST 100x. Data collected by Livsey et al (2022, 2023) utilized a LISST 200x. Data files for each instrument are provided.  Funding for this research was provided by an Advance Queensland Industry Research Fellowship, Queensland University of Technology, and Queensland Department of Environment and Science. References Fall, Kelsey A., Massey, Grace M., and Friedrichs, Carl T., (2020). The importance of organic content to fractal floc properties in estuarine surface waters, insights from video, LISST, and pump sampling: Supporting data. Data. William & Mary. https://doi.org/10.25773/7gbc-794 6739 Livsey, D., Turner, R., Grace, P., and Crosswell, & Andy Steven. (2022). Field and laboratory measurements of suspended-sediment particle size and concentration from nine rivers draining to the Great Barrier Reef (1.0). Data. Zenodo. https://doi.org/10.5281/zenodo.6788303 Livsey, D., Turner, R., and Grace, P. (2023). Combining optical and acoustic backscatter measurements for monitoring of fine suspended-sediment concentration under changes in particle size and density. Water Resources Research. <a href="https://doi.org/10.1029/2022WR033982">https://doi.org/10.1029/2022WR033982</a>

opencc-by-4.0Nov 2023View details →

Data archive for journal paper "Assimilation of Sentinel-1 Backscatter into a Land Surface Model with River Routing and Its Impact on Streamflow Simulations in Two Belgian Catchments"

The datasets archived here include data assimilation results presented in the journal paper, "Assimilation of Sentinel-1 Backscatter into a Land Surface Model with River Routing and Its Impact on Streamflow Simulations in Two Belgian Catchments" (https://doi.org/10.1175/JHM-D-22-0198.1). The output was produced by combining land surface modeling (Noah-MP with HYMAP river routing) and Sentinel-1 backscatter data, applying a 1D Ensemble Kalman Filter using the NASA Land Information System. We provide Netcdf daily output files for 6 different experiments- OLfd and OLgw: model-only (open-loop, OL) for two different model settings (fd: free drainage and gw: SIMTOP groundwater option)  - DASMfd and DASMgw: data assimilation (DA) with soil moisture (SM) updating for two different model settings (fd: free drainage and gw: SIMTOP groundwater option)  - DASMLAIfd and DASMLAIgw: data assimilation (DA) with soil moisture (SM) and leaf area index (LAI) updating for two different model settings (fd: free drainage and gw: SIMTOP groundwater option) Each experiment directory contains five subdirectories (DAOBS, EnKF, ROUTING, RTM, SURFACEMODEL) with corresponding outputs as described in https://nasa-lis.github.io/LISF/LIS_users_guide/LIS_users_guide.html

opencc-by-4.0Nov 2023View details →

A simple, static and stage mounted direct electron detector based electron backscatter diffraction system

<h3>Data set for A simple, static and stage mounted direct electron detector based electron backscatter diffraction system</h3>T.Zhang, T. B. Britton <h3>Contents</h3>- New in v2.0.0: CAD drawings of the stage - Single Si(100) diffraction patterns at 4 camera lengths, and at 4 corners of the sample- Horizontal and vertical line scan on Si(100) with 20 grid points- 20x20 mapping scan on a polycrystalline Cu sampleScan parameters for the line scans and map are included in logfiles within each subfolder. All pattern files are provided in .h5 format and .tif format. Analyses of the patterns were performed with AstroEBSD and MTEX.

opencanada-crownMar 2024View details →

TVC Experiment 2018/19: TerraSAR-X backscatter data

This dataset contains the processed, backscatter data from TerraSAR-X (TSX) satellite data, as part of Environment and Climate Change Canada's 2018-2019 Trail Valley Creek Snow Experiment (TVC Experiment 18/19). These TSX data were collected and processed to 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 TSX data was ordered to provide a <a>wintertime series of repeat overpasses</a> from September 2018 to June 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. TSX data was acquired from two stripmap orbits, one in HH/HV and the other in VV/VH. The TSX 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.

opencc-by-4.0Mar 2022View details →

Satellite monthly surface chlorophyll-a concentration, particulate backscattering, Secchi Disk depth, Mixed Layer Depth, Sea Surface Temperature at 25 km resolution optimally interpolated for the North Atlantic Ocean (1998-2018)

Satellite monthly records of surface chlorophyll-a concentration (CHL), particulate backscattering at 443nm (bbp), Secchi Disk depth (zsd), Mixed Layer Depth (MLD), Sea Surface Temperature (SST) at 25 km resolution optimally interpolated via Multivariate Singular Spectrum Analysis (MSSA) for the North  Atlantic Ocean for the period 1998-2018. This dataset has been used for the article "Ultra-oligotrophic waters expansion in the North Atlantic Subtropical Gyre revealed by 21 years of satellite observations" Leonelli et al. 2022, where details of interpolation method are fully explained.

opencc-by-4.0May 2022View details →

Dataset for Surface waves prediction based on acoustic backscattering

Underwater acoustic measurements dataset is represented by three types of files “.raw", “.mat", ".dat" as follows: * “.raw" format also represented by three types of data. - “...search.raw" files contain complex envelop from all hydrophones calculated at four emitted frequencies - “...chan.raw" files is a signal in a wide band from one of the hydrophones - for control and noise analysis. -  the largest files are the raw wideband signal from all hydrophones. One such file was saved per eight-hour sound emission cycle. * Spectrogram files are saved in MATLAB format “.mat” v7 . Phasing of the antenna array (all-round view) and calculation of window spectra near each emitted pulse  was carried out. * ".dat" files contain features of the average spectrum of the backscattered signal. * Direct measurements of surface wave characteristics, made by a Datawell DWR-G4 wave-rider buoy, accompanied the acoustic measurements. This data is included too. In this archive, we upload all available files of the "dat" and "mat" type and a limited number of "raw" files. You may unpack all ".tar.gz" files into one folder, preserving the directory tree, existing in the archives. Users should refer to the included “.pdf” file for the data format description and to a published preprint for a description of the experimental conditions and instrumentation characteristics. See [arXiv:arXiv:2204.10153] via <a href="https://arxiv.org/abs/2204.10153">https://arxiv.org/abs/2204.10153</a> (Also check when the link is updated to the journal paper)  The authors are grateful to their colleges, who helped during the expedition. Data acquisition would be impossible without their contribution. This research was supported by the Russian Science Foundation, grant number 20-77-10081 (the expedition and motivation for study) and the State Contract with the Ministry of Education and Science of the Russian Federation, grant number 0030-2021-0017 (the instruments for underwater acoustic measurements).

opencc-by-4.0Sep 2022View details →

Spectral Effects of Absorbing Aerosols on Backscattered UV Radiation

Satellite measurements of backscattered UV radiation are sensitive to the presence of UV-absorbing aerosols in the atmosphere. These measurements are commonly used for determining the concentration of atmospheric trace gases such as O3, SO2, and H2CO. The theoretical results in this dataset describe the effects of UV-absorbing mineral dust and carbonaceous smoke aerosols on these backscatter satellite measurements between 300-400 nm. The information provided is independent of any specific trace gas retrieval algorithm and does not require detailed a priori knowledge of aerosol and surface properties. The results are derived from the analysis of the radiative transfer model simulations performed with the optical property data used in the Ozone Monitoring Instrument (OMI) UV aerosol retrieval algorithm, OMAERUV. Results from this algorithm have been validated with Aerosol Robotic Network (AERONET) observations (Jethva & Torres, 2011; Torres et al., 2018). This dataset is associated with the following publication: Jethva, H., Haffner, D., Bhartia, P. K., & Torres, O. (2022). Estimating Spectral Effects of Absorbing Aerosols on Backscattered UV Radiation, Earth Space Sci., Accepted.

opencc-by-4.0Jan 2018View details →

Backscattering from randomly placed Dirichlet particles

Summary: A simulated data set of backscattered waves, governed by the 2D wave equation, from randomly placed Dirichlet particles. The particles are circular with 0.2 < radius  < 2.0 and 1% < volume fraction < 20%. The incident wave is of the form ei k (x - xR -  t)  where the particles are placed in the halfspace x>0 and the backscattering is emitted and received at (xR, 0) with xR = -10.0. The data named bunny and bunnytest is for incident waves with 0 < k < 1 (where the term e- i k t factors out). The data named train and test is for incident waves of the form e0.1(x - xR - t)^2, with  9.5 < t < 98.        Format: An example of how to use the data is given  in [4]. To load the data requires the packages [1] and [2] in the language Julia v0.5> [3]. When using load(filename) the data will automatically be loaded as an array of type FrequencySimulation or of type TimeSimulation.  [1] https://github.com/jondea/MultipleScattering.jl [2] https://github.com/JuliaIO/JLD.jl  [3] https://julialang.org/ [4] https://github.com/gowerrobert/MultipleScatteringLearnMoments/tree/master/examples

opencc-by-4.0Sep 2019View details →

Electron backscatter patterns from Nickel acquired with varying camera gain

Ten electron backscatter diffraction (EBSD) datasets from a recrystallized, polycrystalline sample of nickel. The datasets, each comprising 29 800 patterns, were collected from the same region of interest (ROI) with EBSD camera gains varying from 0 dB to 24 dB (camera maximum). A backscattered electron image of the ROI is also included. The data was acquired in order to study denoising of EBSD patterns, more specifically the increased signal-to-noise ratio obtained after principal component analysis followed by dimensionality reduction, as presented in H W Ånes, J Hjelen, B E Sørensen, A T J van Helvoort, K Marthinsen "Processing and indexing of electron backscatter patterns using open-source software," IOP Conf. Ser.:Mater. Sci. Eng. (2019), doi:<a href="https://doi.org/10.1088/1757-899X/891/1/012002">10.1088/1757-899X/891/1/012002</a>. This conference paper is part of the proceedings of EMAS 2019 - 16th European Workshop on Modern Developments and Applications in Microbeam Analysis held in Trondheim, Norway. However, the data is released with the hope that it can be used to compare the performance of denoising methods in general. The datasets, Pattern.dat, are stored in the NORDIF (binary) format, 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. They can be opened in for example the open-source Python package kikuchipy (https://github.com/pyxem/kikuchipy). Assuming Python 3.7 or above and the package is installed, the patterns in scan 1 can be read and plotted with the following commands: <pre><code class="language-python">import kikuchipy as kp s = kp.load('/path/to/nickel_scan_gain/scan1_gain0db/Pattern.dat') s.plot()</code></pre>

opencc-by-4.0Sep 2020View details →

Precipitating Solar Wind Hydrogen at Mars: Improved Calculations of the Backscatter and Albedo with MAVEN Observations

These files contain the derived data products used in the paper, including the penetrating and backscatter energy spectra and directional fluxes. See Readme.txt for a description of the data that is stored in each file.

opencc-by-4.0Dec 2020View details →

Side scan sonar backscatter mosaic offshore Thailand, Andaman Sea

The attached mosaics of side scan sonar data were recorded during 3 field campaigns in 2007, 2008 and 2010.  High backscatter values are represented by darker colours. The mosaic is georeferenced in EPSG:32647 - WGS 84 / UTM zone 47N. Please refer to Feldens, P.; Schwarzer, K.; Sakuna, D.; Szczuciński, W.; Sompongchaiyakul, P. Sediment distribution on the inner continental shelf off Khao Lak (Thailand) after the 2004 Indian Ocean tsunami. Earth, Planets and Space, 2012, 64, 875-887; DOI:10.5047/eps.2011.09.001 and Sakuna-Schwartz, D.; Feldens, P.; Schwarzer, K.; Khokiattiwong, S.; Stattegger, K. Internal structure of event layers preserved on the Andaman Sea continental shelf, Thailand: tsunami vs. storm and flash-flood deposits. Natural Hazards and Earth System Sciences, 2015, 15, 1181-1199; DOI:10.5194/nhess-15-1181-201 and Feldens, P., Schwarzer K., Sakuna-Schwartz, D., Khokiattiwong, S. (in prep) Offshore geomorphological evolution in Phang Nga province (Thailand) during the Holocene: An example for a sediment starving shelf and  references therein for further information on the dataset.  The research was funded by Deutsche Forschungsgemeinschaft (DFG) grant No. SCHW 572/11-1 and National Research Council of Thailand (NRCT)

opencc-by-4.0Sep 2022View details →

Supplemental materials for "Benchmarking magnetized three-wave coupling for laser backscattering: Analytic solutions and kinetic simulations"

Place the unzipped Data and Programs folders in the same directory. The contents of these folders are as follows: <ul> <li>Data Post processed data underlying each figure in the paper. The data files are .txt files with self-contained explanations. The files are organized in subfolders according to their purposes. </li> <li>Programs <ul> <li>./PlotFigures Contains python scripts for reading and plotting Data</li> <li>input.deck Example input for EPOCH PIC code that generates raw data </li> <li>setup_batch.csh Linux/Unix shell script for setting up batch simulations</li> <li>submit_batch Slurm script for submitting jobs on computing clusters</li> </ul> </li> </ul>

opencc-by-4.0Jun 2024View details →

Electron Backscatter Diffraction Patterns from Titanium-added Interstitial-free Steel Containing Subgrains

<h3>Associated Publications</h3> <ol> <li>Bennett IV, T.J. and Taleff, E.M. Dynamic Grain Growth Driven by Subgrain Boundaries in an Interstitial-Free Steel During Deformation at 850 °C. Metall Mater Trans A 55, 429–446 (2024). <a href="https://doi.org/10.1007/s11661-023-07256-w">https://doi.org/10.1007/s11661-023-07256-w</a>.</li> <li>Bennett IV, T.J. and Taleff, E.M. Imaging and Segmenting Grains and Subgrains using Backscattered Electron Techniques. Under review (2024).</li> </ol> <h3>Data Description</h3> These data were collected by Thomas J. Bennett IV on July 28, 2022. The electron backscatter diffraction (EBSD) data and associated electron backscatter diffraction patterns (EBSPs) contained herein were acquired from a titanium-added interstitial-free (Ti-IF) steel sheet material containing numerous subgrains.  The Ti-IF steel specimen that provided these data was ramped to 850 degrees Celsius over 30 minutes, held at this temperature for one hour, and then deformed at a constant true-strain rate of 10^-4 s^-1. Upon reaching a final true strain of 0.225, the specimen was air quenched while maintaining a constant stress to preserve subgrains formed during high-temperature deformation. The tensile specimen was cut from a Ti-IF steel sheet received in a hard as-rolled condition with the tensile axis parallel to the sheet rolling direction. EBSPs were acquired from a section cut from the center of the deformed gage region using a JEOL JSM-IT300HR SEM equipped with an EDAX Velocity EBSD camera at the Center for Integrated Nanotechnologies. The following conditions were used for EBSD data acquisition: <table> <tbody> <tr> <td>Accelerating Voltage:</td> <td>20 kV</td> </tr> <tr> <td>Beam Current:</td> <td>80%</td> </tr> <tr> <td>Working Distance:</td> <td>20.0 mm</td> </tr> <tr> <td>Magnification:</td> <td>200×</td> </tr> <tr> <td>Dynamic Focus:</td> <td>44 (out of 255, arbitrary units)</td> </tr> <tr> <td>Specimen Tilt:</td> <td>70 degrees</td> </tr> <tr> <td>Scanning Grid Type:</td> <td>Square</td> </tr> <tr> <td>Step Size (x and y):</td> <td>0.5 μm</td> </tr> <tr> <td>Scan Size:</td> <td>520 (across) × 340 (down) pixels</td> </tr> <tr> <td>EBSD Camera Resolution:</td> <td>446 × 446 pixels</td> </tr> <tr> <td>EBSD Camera Binning:</td> <td>1 × 1</td> </tr> <tr> <td>EBSD Camera Exposure Time:</td> <td>10 ms</td> </tr> <tr> <td>Frame Averaging:</td> <td>None</td> </tr> <tr> <td>Specimen Tensile Direction:</td> <td>Horizontal</td> </tr> <tr> <td>Specimen Rolling Direction:</td> <td>Horizontal</td> </tr> <tr> <td>Specimen Long Transverse Direction:</td> <td>Vertical</td> </tr> <tr> <td>Specimen Short Transverse Direction:</td> <td>Normal to plane</td> </tr> <tr> <td>Pattern Center (EMSphInx Convention):</td> <td>(x_pc, y_pc, L) = (-0.2 pixels, 112.76 pixels, 21736.4 μm)</td> </tr> <tr> <td>EBSD Camera Elevation Angle:</td> <td>3 degrees</td> </tr> <tr> <td>EBSD Camera Screen Width:</td> <td>32 mm</td> </tr> <tr> <td>Pixel size on EBSD Camera Screen:</td> <td>71.749 μm/pixel ( = 32000 μm / 446 pixels)</td> </tr> </tbody> </table>   Note: Conversions between different pattern center conventions may be found in the journal article below or at the following link: <a href="https://github.com/EMsoft-org/EMsoft/wiki/DItutorial">https://github.com/EMsoft-org/EMsoft/wiki/DItutorial</a>. <ul> <li>Jackson, M.A., Pascal, E., and De Graef, M. Dictionary Indexing of Electron Back-Scatter Diffraction Patterns: a Hands-On Tutorial. Integr Mater Manuf Innov 8, 226–246 (2019). <a href="https://doi.org/10.1007/s40192-019-00137-4">https://doi.org/10.1007/s40192-019-00137-4</a>.</li> </ul> <h3>File Descriptions</h3> <ul> <li>Specimen_orientation.pdf - A schematic showing specimen reference directions and the orientation used for EBSD data acquisition.</li> <li>Patterns.zip - A compressed archive containing Patterns.up2. This file contains 16-bit EBSPs and is 70,336,697,616 bytes (70.3 GB) uncompressed.</li> <li>SHT_Indexed.ang - A file containing orientation data produced by indexing Patterns.up2 using EMSphInx. Orientations are represented by Euler angles (Bunge convention) and are to be interpreted using the EDAX Setting 2 convention (see MTEX documentation at <a href="https://mtex-toolbox.github.io/EBSDReferenceFrame.html">https://mtex-toolbox.github.io/EBSDReferenceFrame.html</a>).</li> <li>SHT_Indexed.h5 - A file in HDF5 format containing orientation data and other relevant information produced by indexing Patterns.up2 using EMSphInx.</li> <li>SHT_Indexed_IPFmap.png - An image of an inverse pole figure map colored with respect to the short transverse direction showing the data from SHT_Indexed.ang.</li> </ul> Note: The basic format of "up2" files is the following. The first 4 bytes provide the version number. The second 4 bytes are the width of the patterns. The third 4 bytes are the height of the patterns. The fourth 4 bytes are the starting position of the pattern image data. <h3>Acknowledgments</h3> The authors gratefully acknowledge support from the National Science Foundation under Grant DMR-2003312 and instrumentation under Grant DMR-9974476.  The authors also gratefully acknowledge support from the U.S. Department of Energy, Office of High Energy Physics under Grant DE-SC0009960.  This work was performed, in part, at the Center for Integrated Nanotechnologies, an Office of Science User Facility operated for the U.S. Department of Energy (DOE) Office of Science by Los Alamos National Laboratory (Contract 89233218CNA000001) and Sandia National Laboratories (Contract DE-NA-0003525).  The authors thank Mr. Thomas Cayia (Arcelor Mittal) for providing the interstitial-free steel material used for this study.

opencc-by-4.0Jul 2024View details →

Data used in "The Backscatter Gating method for time, energy, and position resolution characterization of long form factor organic scintillators" by Hunter N. Ratliff et al.

This repository contains the raw experimental and PHITS-simulated data used in the JINST article “The Backscatter Gating method for time, energy, and position resolution characterization of long form factor organic scintillators” by Hunter N. Ratliff et al., available at <a href="https://doi.org/10.1088/1748-0221/19/07/P07002">https://doi.org/10.1088/1748-0221/19/07/P07002</a> (the accepted manuscript can also be found at <a href="https://hdl.handle.net/11250/3145287">https://hdl.handle.net/11250/3145287</a> and <a href="https://hratliff.com/publications/">https://hratliff.com/publications/</a>).   The data consists of three top-level directories, each with various subdirectories.  Within the “PHITS-simulated-data” directory are the PHITS simulations (including input and output files) used in producing Figures 4 and 5 in the manuscript, showing energy spectra in the bar for BSG events from a Cs-137 emission for placement of the source on the bar and on the BSG detector and with spacings between the bar and BSG detector of 20, 80, and 150 mm, along with the energy-dependent spatial distribution of these recoil electrons in the bar.   The other two top-level directories contain raw data produced by the CAEN CoMPASS software when acquiring data experimentally.  In the analysis for each measurement, the contents of the “RAW” folders were used.  The “Energy-calibrations-of-CeBr3” directory contains the energy spectra of the CeBr3 detector (used as the BSG detector) when exposed to a variety of radioactive sources, used together to energy-calibrate the CeBr3 detector.  The “Backscatter-gating-measurements” directory contains all of the list-mode data acquired with CoMPASS used for all of the other experimental measurements presented in the manuscript.  It contains subdirectories for varied sources, source positions along the bar, distances between the bar and BSG detector, measurements with the source affixed to the BSG detector instead, and a few miscellaneous measurements.  The “images” directories within each measurement’s directory (at the same level as the “RAW” folders) contain images produced by the analysis script written for this work used for diagnostics and presenting analyzed data.

opencc-by-4.0Jul 2019View details →

Suspended sediment concentration inversion from acoustic backscatter in rivers

This repository contains scripts and data related to the article entitled Using a down-looking multi-frequency Acoustic Backscatter System (ABS) for measuring suspended sediments in rivers published in Water Resources Research. The scripts are provided through Python Jupyter Notebooks. Instructions for running the scripts are given in readme.txt file.

opencc-by-4.0Dec 2022View details →

NOAA PSL CL31 Ceilometer Backscatter and Cloud Base Height Data for SPLASH

This dataset contains daily files from a CL31 ceilometer manufactured by Vaisala that was deployed at Roaring Judy in the East River Watershed in Colorado (38.7169321 N,  106.853031 W, 2494 m above mean sea level) from 21 October 2021 to 28 January 2022 as part of the National Oceanic and Atmospheric Administration (NOAA) Study of Precipitation, the Lower Atmosphere, and Surface for Hydrometeorology (SPLASH) campaign.  The files contain backscatter profiles, cloud base heights, and visibility. The ceilometer measures vertical profiles of backscatter using laser technology. From the backscatter profiles, cloud base height and vertical visibility are determined using with the Vaisala software CL-view. For details on the instrument specifics and the methods, see the manufacturer manual (cl31usersguide.pdf).   The file format is the original Vaisala format (.DAT). For a description of the format see the manufacturer manual. To convert the .DAT file format to netcdf format, the open source command line Python program ‘cl2nc’ (https://github.com/peterkuma/cl2nc) can, for example, be used.  The file naming conventions for the .DAT files are as follows: NOAA_PSL_CL31_Roaring Judy_yyyymmdd_HH.DAT with yyyy: Year mm: Month dd: Day HH: Hour when the first sample was written to the file The time stamp of all data is in UTC.