Find research datasets worth reusing

Synchrotron X-ray Diffraction Dataset - Measuring Bulk Crystallographic Texture from Differently-Orientated Ti-6Al-4V Samples

A dataset of raw synchrotron X-ray diffraction (SXRD) images, recording crystallographic texture from two different pre-processed Ti-6Al-4V (Ti-64) materials, analysing six differently orientated samples from each material. The aim of the work was to provide a large dataset for testing and improving crystallographic texture refinement from SXRD patterns, with the use of different computational fitting methods. Prior to the experiment, the Ti-64 materials had been pre-rolled and then air-cooled to develop the microstructure, rolling to 50% and 87.5% reduction at 915ºC using a rolling mill at The University of Manchester. Rectangular samples (2 mm thick) were then machined from these rolled blocks. The samples were cut along different directions, three samples along different orthogonal rolling directions, and three at different angles to the rolling directions. The samples are referenced according to alignment of the rolling directions (RD – rolling direction, TD – transverse direction, ND – normal direction) with the long horizontal (X) axis and short vertical (Y) axis of the rectangular specimens.  Data was recorded using a high energy 99.8 keV synchrotron X-ray beam and a 5 second exposure at the detector. The slits were adjusted to give a 0.5 x 0.5 mm beam area, chosen to optimally resolve both the α (hexagonal close packed, hcp) and β (body-centred cubic, bcc) phase peaks. The SXRD data was recorded across each of the specimens by stage-scanning the beam in sequential X-Y positions at 0.5 mm increments, forming a rectangular grid of measurement points across each sample. A powder Ti-64 sample was also measured as a random texture standard. As well as the main experiment, 3 samples (sample 1, 2 and 3) were held together in different orders (1, 2, 3 ; 2, 1, 3 ; 2, 3, 1) and analysed through-thickness, to measure how beam attenuation might affect the bulk texture measurement. In addition, different detector exposure times (1 to 0.04 seconds) were also tested to analyse the impact of exposure time on overall intensity, to see how well the α and β peaks could be resolved from background noise at very fast acquisition frequencies. The raw data is in the form of synchrotron diffraction pattern images which has been separated according to experiment type. An accompanying YAML text file contains associated beamline metadata for each measurement. Further details of the experimental setup can be found in a pdf document. The material data folder contains further details about the material and sample orientations, including an electron backscatter diffraction (EBSD) map that can be used to verify the crystallographic texture.

opencc-by-4.0May 2020View details →

PsPM-PCF2: PSR, SCR, ECG, respiration and startle-eyeblink EMG measurements in a delay fear conditioning task with 4 CS and different reinforcement rates

This dataset includes eyetracker, skin conductance response (SCR), electrocardiogram (ECG), respiration, electromyogram (EMG), and auditory startle output (snd, as delivered by sound card) measurements. Also included are CS and US information, keypress responses and keypress response times for 19 healthy unmedicated participants (5 males and 14 females aged 24.68 +/- 3.65 years) participating in a classical (Pavlovian) discriminant delay fear conditioning task. CS were 4 coloured rectangles. US consisted of 0.5 s square electric pulses with 0.2 ms duration and 500 Hz frequency. SOA between the CS onset and US was 3.5 s. CS and US co-terminated. In the last learning block, an auditory startle probe (ST) and no US was delivered 3.5 s after CS onset via headphones (100 dB, 50 ms duration with 2ms on- and offset ramp). The ITI was randomly determined on each trial to be 7, 9, or 11 s.

An Empirical Evaluation of the "Cognitive Complexity" Measure as a Predictor of Code Understandability

The paper provides an evaluation of the “Cognitive Complexity” measure as an indicator of code understandability. The evaluation is performed via an empirical study. ""Cognitive Complexity" is compared with traditional code measure, like LoC and McCabe's complexity. The data used in the empirical study are provided.

opencc-by-4.0Oct 2022View details →

Supplementary Materials for "Influence of Measured Radio Environment Map Interpolation on Indoor Positioning Algorithms"

This dataset was created as suplementary material for research article: Influence of Measured Radio Environment Map Interpolation on Indoor Positioning Algorithms This package contains packet capture files of 802.11 probe requests captured at Geotec office at University Jaume I, Spain by 5 ESP32 microcontrollers. The packet capture files are in the standardized *.pcap binary format and can be opened with any packet analysis tool such as Wireshark or scapy (Python packet analysis and manipulation package). The data are split between radio map data captured at all accessible reference positions in our office spread in 1m grid and evaluation data gathered alligned to 0.5m grid, as well as in hard to access locations. The location the data were collected are available in the office. The dataset has 4 parts, and all subsets of the dataset can be generated from the captured pcap files: Data This folder contains pcap files from all 5 ESP32 stations representing the whole radio environment map. The folder name stands for each of the 5 ESP32 sniffer stations and the name of the file points to a reference location the data were captured in. Example of the coordinates matching the reference location grid names are in following table: <table> <caption>Data Point Coordinates</caption> <thead> <tr> <th scope="row"> </th> <th scope="col">X</th> <th scope="col">Y</th> <th scope="col"> </th> <th scope="col">X</th> <th scope="col">Y</th> <th scope="col">...</th> </tr> </thead> <tbody> <tr> <th scope="row">A1</th> <td>0.85</td> <td>0.1</td> <td>B1</td> <td>1.85</td> <td>0.1</td> <td>...</td> </tr> <tr> <th scope="row">A2</th> <td>0.85</td> <td>1.1</td> <td>B2</td> <td>1.85</td> <td>1.1</td> <td>...</td> </tr> <tr> <th scope="row">A3</th> <td>0.85</td> <td>2.1</td> <td>B3</td> <td>1.85</td> <td>2.1</td> <td>...</td> </tr> <tr> <th scope="row">...</th> <td>...</td> <td>...</td> <td>...</td> <td>...</td> <td>...</td> <td>...</td> </tr> <tr> <th scope="row">A11</th> <td>0.85</td> <td>10.1</td> <td>B11</td> <td>1.85</td> <td>10.1</td> <td>...</td> </tr> </tbody> </table> Data_Eval This folder contains pcap files from all 5 ESP32 stations with data captured at 31 locations not found in the original reference location grid. The naming corresponds to the X and Y location in which the data were collected. Processed_Data Additionally, there are 3 folders with processed CSV files. One folder that combines all radio map values, second folder contains combined evaluation values and third is with linearly interpolated radio map values. The CSV files are in a format: <blockquote> <code>X, Y, RSSI_1, RSSI_2, RSSI_3, RSSI_4, RSSI_5</code> </blockquote> Data_Scenarios This folder for the ease of use, contains data for exact reproducibility of our results in the paper. There 14 scenarios described in the following table: <table> <caption>Scenario Descriptions</caption> <thead> <tr> <th scope="col"> Data Name </th> <th scope="col"> Scenario Description </th> </tr> </thead> <tbody> <tr> <td>GPR00</td> <td>Only measured data, 50 samples per reference position</td> </tr> <tr> <td>GPR01</td> <td>Measured data with empty spots filled using Linear interpolation, 50 samples per reference position</td> </tr> <tr> <td>GPR02</td> <td>Gaussian Regression trained only on measured data - 1m output grid, 50 samples per reference position</td> </tr> <tr> <td>GPR03</td> <td>Gaussian Regression trained only on measured data - 0.5m output grid, 50 samples per reference position</td> </tr> <tr> <td>GPR04</td> <td>Gaussian Regression trained on linearly interpolated data - 1m output grid, 50 samples per reference position</td> </tr> <tr> <td>GPR05</td> <td>Gaussian Regression trained on linearly interpolated data - 0.5m output grid, 50 samples per reference position</td> </tr> <tr> <td>GPR06</td> <td>Gaussian Regression trained selection of linearly interpolated data - 1m output grid, 50 samples per reference position</td> </tr> <tr> <td>GPR07</td> <td>Gaussian Regression trained selection of linearly interpolated data - 0.5m output grid, 50 samples per reference position</td> </tr> <tr> <td>GPR08</td> <td>Gaussian Regression trained only on measured data - 1m output grid, 1 sample per reference position</td> </tr> <tr> <td>GPR09</td> <td>Gaussian Regression trained only on measured data - 0.5m output grid, 1 sample per reference position</td> </tr> <tr> <td>GPR10</td> <td>Gaussian Regression trained on linearly interpolated data - 1m output grid, 1 sample per reference position</td> </tr> <tr> <td>GPR11</td> <td>Gaussian Regression trained on linearly interpolated data - 0.5m output grid, 1 sample per reference position</td> </tr> <tr> <td>GPR12</td> <td>Gaussian Regression trained selection of linearly interpolated data - 1m output grid, 1 sample per reference position</td> </tr> <tr> <td>GPR13</td> <td>Gaussian Regression trained selection of linearly interpolated data - 0.5m output grid, 1 sample per reference position</td> </tr> </tbody> </table> The folder contains 4 files for each scenario. The Beginning of the filename corresponds to the data name, with suffix describing what data are in the file. The descriptions of used suffixes are in the following table: <table> <caption>File Suffix Descriptions</caption> <tbody> <tr> <td> Suffix </td> <td> Suffix Description </td> </tr> <tr> <td>_trncrd</td> <td>Training Labels</td> </tr> <tr> <td>_trnrss</td> <td>Training RSSI Values</td> </tr> <tr> <td>_tstcrd</td> <td>Evaluation Labels</td> </tr> <tr> <td>_tstrss</td> <td>Evaluation RSSI Values</td> </tr> </tbody> </table> These data are in format compatible with systems that apart from X and Y coordinates also detect, building, floor etc. The RSSI data are in format: <blockquote> RSSI_1, RSSI_2, RSSI_3, RSSI_4, RSSI_5 </blockquote> The Labels are in format: (Since we only use positioning in 1 office, apart X and Y coordinates are set to 0) <blockquote> X, Y, 0, 0, 0 </blockquote>

WUSA surface wave amplification measurements (Schardong et al. 2019)

#Western U.S. surface wave amplification measurements If using this data please cite:  Schardong, L., Ferreira, A.M., Berbellini, A. and Sturgeon, W., 2019. The anatomy of uppermost mantle shear-wave speed anomalies in the western US from surface-wave amplification. Earth and Planetary Science Letters, 528, p.115822. Directory format: USA.RAV... = vertical-component Rayleigh wave amplification measurements USA.RAH... = horizontal-component Rayleigh wave amplification measurements USA.TOR... = Love wave amplification measurements File format: motion type index  -- mode order (n) -- angular order (l) -- period -- relative local amplification w.r.t. PREM -- estimated error on local amplification The number of data points varies depending on the available data in Hendrik van Heijst's database, and the various selection steps.

Dataset supporting publication: "A novel ROM methodology to support the estimation of the energy savings under the Measurement and Verification protocol."

DATASET suporting: "A novel ROM methodology to support the estimation of the energy savings under the Measurement and Verification protocol." Piccinini, Alessandro; Hajdukiewicz, Magdalena; D'Angelo, Letizia; Blanes, Luis Miguel; Keane, Marcus M. This paper presents a novel Reduced Order grey box Model (ROM) methodology, based on a ResistorCapacitor (RC) network, which supports the creation of the baseline energy consumption and the estimation of energy savings due to Energy Conservation Measures (ECMs) under the Measurement and Verification protocol. Within this scope, a description of the RC network, including a calculation of the parameters’ needed to execute the ROM, are presented. This ROM methodology is demonstrated on an educational building located in Sant Cugat, Spain as part of the H2020 GEOFIT project. The results presented in this paper demonstrate that the ROM is sufficiently accurate for the creation of the baseline energy consumption and for estimating the energy savings of different ECMs.

opencc-by-4.0Jan 2022View details →

Pre-Publication Dataset: Is Remote Sensing a Better Measure of Internet Censorship than Expert Analysis? Analyzing Tradeoffs for International Donors and Advocacy Organizations

These are the underlying data and do file to support the analysis in the forthcoming paper "Is Remote Sensing a Better Measure of Internet Censorship than Expert Analysis? Analyzing Tradeoffs for International Donors and Advocacy Organizations" that has been submitted to the Data & Policy Journal.  This is an expanded and updated version of the Data For Policy conference paper "Comparing Measures of Internet Censorship: Analyzing the Tradeoffs between Expert Analysis and Remote Measurement" (10.5281/zenodo.3967398).

Fast measurement of the gradient system transfer function at 7 T

Measurement data complementing our publication "Fast measurement of the gradient system transfer function at 7 T" (DOI: https://doi.org/10.1002/mrm.29523). The corresponding MATLAB code is available at https://github.com/expRad/Fast_GIRF .

Measuring Bulk Crystallographic Texture from Ti-6Al-4V Hot-Rolled Sample Matrices using Synchrotron X-ray Diffraction (Analysis Dataset)

A dataset of synchrotron X-ray diffraction (SXRD) analysis files, recording the refinement of crystallographic texture from a number of Ti-6Al-4V (Ti-64) sample matrices, containing a total of 93 hot-rolled samples, from three different orthogonal sample directions. The aim of the work was to accurately quantify bulk macro-texture for both the α (hexagonal close packed, hcp) and β (body-centred cubic, bcc) phases across a range of different processing conditions. Material Prior to the experiment, the Ti-64 materials had been hot-rolled at a range of different temperatures, and to different reductions, followed by air-cooling, using a rolling mill at The University of Manchester. Rectangular specimens (6 mm x 5 mm x 2 mm) were then machined from the centre of these rolled blocks, and from the starting material. The samples were cut along different orthogonal rolling directions and are referenced according to alignment of the rolling directions (RD – rolling direction, TD – transverse direction, ND – normal direction) with the long horizontal (X) axis and short vertical (Y) axis of the rectangular specimens. Samples of the same orientation were glued together to form matrices for the synchrotron analysis. The material, rolling conditions, sample orientations and experiment reference numbers used for the synchrotron diffraction analysis are included in the data as an excel spreadsheet. SXRD Data Collection Data was recorded using a high energy 90 keV synchrotron X-ray beam and a 5 second exposure at the detector for each measurement point. The slits were adjusted to give a 0.5 x 0.5 mm beam area, chosen to optimally resolve both the α and β phase peaks. The SXRD data was recorded by stage-scanning the beam in sequential X-Y positions at 0.5 mm increments across the rectangular sample matrices, containing a number of samples glued together, to analyse a total of 93 samples from the different processing conditions and orientations. Post-processing of the data was then used to sort the data into a rectangular grid of measurement points from each individual sample. Diffraction Pattern Averaging The stage-scan diffraction pattern images from each matrix were sorted into individual samples, and the images averaged together for each specimen, using a Python notebook <a href="https://github.com/LightForm-group/sxrd-tiff-summer">sxrd-tiff-summer</a>. The averaged .tiff images each capture average diffraction peak intensities from an area of about 30 mm2 (equivalent to a total volume of ~ 60 mm3), with three different sample orientations then used to calculate the bulk crystallographic texture from each rolling condition. SXRD Data Analysis A new Fourier-based peak fitting method from the <a href="https://pypi.org/project/continuous-peak-fit/">Continuous-Peak-Fit</a> Python package was used to fit full diffraction pattern ring intensities, using a range of different lattice plane peaks for determining crystallographic texture in both the α and β phases. Bulk texture was calculated by combining the ring intensities from three different sample orientations. A .poni calibration file was created using <a href="http://www.clemensprescher.com/programs/dioptas">Dioptas</a>, through a refinement matching peak intensities from a LaB6 or CeO2 standard diffraction pattern image. Two calibrations were needed as some of the data was collected in July 2022 and some of the data was collected in August 2022. Dioptas was then used to determine peak bounds in 2θ for characterising a total of 22 α and 4 β lattice plane rings from the averaged Ti-64 diffraction pattern images, which were recorded in a .py input script. Using these two inputs, Continuous-Peak-Fit automatically converts full diffraction pattern rings into profiles of intensity versus azimuthal angle, for each 2θ section, which can also include multiple overlapping α and β peaks. The Continuous-Peak-Fit refinement can be launched in a notebook or from the terminal, to automatically calculate a full mathematical description, in the form of Fourier expansion terms, to match the intensity variation of each individual lattice plane ring. The results for peak position, intensity and half-width for all 22 α and 4 β lattice plane peaks were recorded at an azimuthal resolution of 1º and stored in a .fit output file. Details for setting up and running this analysis can be found in the <a href="https://github.com/LightForm-group/continuous-peak-fit-analysis">continuous-peak-fit-analysis</a> package. This package also includes a Python script for extracting lattice plane ring intensity distributions from the .fit files, matching the intensity values with spherical polar coordinates to parametrise the intensity distributions from each of the three different sample orientations, in the form of pole figures. The script can also be used to combine intensity distributions from different sample orientations. The final intensity variations are recorded for each of the lattice plane peaks as text files, which can be loaded into MTEX to plot and analyse both the α and β phase crystallographic texture. Metadata An accompanying YAML text file contains associated SXRD beamline metadata for each measurement. The raw data is in the form of synchrotron diffraction pattern .tiff images which were too large to upload to Zenodo and are instead stored on The University of Manchester's Research Database Storage (RDS) repository. The raw data can therefore be obtained by emailing the authors. The material data folder documents the machining of the samples and the sample orientations. The associated processing metadata for the Continuous-Peak-Fit analyses records information about the different packages used to process the data, along with details about the different files contained within this analysis dataset.

Dataset Single-subject EEG measurement of interhemispheric transfer-time for the in-vivo estimation of axonal morphology

This dataset is a subset of the data presented in the article Single‐subject electroencephalography measurement of interhemispheric transfer time for the in‐vivo estimation of axonal morphology Rita Oliveira, Marzia De Lucia, Antoine Lutti <a href="https://onlinelibrary.wiley.com/doi/full/10.1002/hbm.26420">https://onlinelibrary.wiley.com/doi/full/10.1002/hbm.26420</a> For a complete description of our approach for axonal morphology estimation in-vivo, see: Oliveira, R., Pelentritou, A., Di Domenicantonio, G., De Lucia, M., and Lutti, A. (2022). In vivo Estimation of Axonal Morphology From Magnetic Resonance Imaging and Electroencephalography Data. Front. Neurosci. 16, 1–18. doi: 10.3389/fnins.2022.874023. This dataset contains the following Matlab files: <ul> <li>CD_CondNameVisualField_LeftBrainOccipital.mat - Current source densities (pA.m) of each brain vertice, EEG trial, and time point for the left brain occipital cortex [#trials x #vertices x #timepoints]</li> <li>CD_CondNameVisualField_RightBrainOccipital.mat - Current source densities (pA.m) of each brain vertice, EEG trial, and time point for the right brain occipital cortex [#trials x #vertices x #timepoints]</li> <li>Stats_Source_CondNameVF_Occipital_LeftBrainOccipital.mat - Result of the cluster permutation for the left brain cortex for the CondNameVF, CondName being Left or Right visual stimulation (Fieldtrip stat structure)</li> <li>Stats_Source_CondNameVF_Occipital_RightBrainOccipital.mat - Result of the cluster permutation for the right brain cortex for the CondNameVF, CondName being Left or - Right visual stimulation (Fieldtrip stat structure)</li> <li>time_vec.mat - Time vector associated with the timecourses [1 x #timepoints]</li> <li>Occipital_vertices.mat - Structure containing the vertices of the brain mesh of the region of interest. Occipital_vertices.Vertices [1 x #vertices]</li> <li>G_ratio_samples.mat - MRI g-ratio sampled along the occipital transcallosal tract [#samples x 1]</li> <li>Tract_length.mat - Length of the occipital transcallosal tract (double)</li> </ul> The analysis scripts that allow the users to replicate the results of the original publication can be found here: <a href="https://github.com/DNC-EEG-platform/SingleSubjectIHTTEstimation">https://github.com/DNC-EEG-platform/SingleSubjectIHTTEstimation</a> Funding: Swiss National Science Foundation (grant no 320030 184784 and 32003B 212981), ROGER DE SPOELBERCH Foundation and Bertarelli Catalyst Foundation.   Author: Rita Oliveira PIs: Marzia De Lucia, Antoine Lutti Laboratory for Neuroimaging Research Lausanne University Hospital & University of Lausanne, Lausanne, Switzerland Copyright (C) 2022 Laboratory for Neuroimaging Research

Measuring Bulk Crystallographic Texture from Ti-6Al-4V Hot-Rolled Sample Matrices using Synchrotron X-ray Diffraction (Results Dataset)

A dataset of crystallographic texture results for both α (hexagonal close packed, hcp) and β (body-centred cubic, bcc) phases, measured from 31 different hot-rolled Ti-6Al-4V (Ti-64) materials and 3 differently orientated samples using synchrotron X-ray diffraction (SXRD). The aim of the work was to accurately quantify bulk macro-texture for both the α and β phases across a range of different processing conditions, and to compare results with electron backscatter diffraction (EBSD) measurements. The synchrotron intensities were extracted using a new Fourier-based peak fitting method from the <a href="https://pypi.org/project/continuous-peak-fit/">Continuous-Peak-Fit</a> Python package, and then directly used to calculate the pole figures, orientation distribution functions (ODFs) and numerical values for the texture indices in <a href="https://mtex-toolbox.github.io">MTEX</a> Material The Ti-64 materials had been hot-rolled at a range of different temperatures, and to different reductions, followed by air-cooling. Three samples of different orientation were cut from the centre of these rolled blocks, and from the starting material. The material and hot-rolling conditions are recorded in this <a href="https://doi.org/10.5281/zenodo.7438090">analysis dataset</a> as an excel spreadsheet and summarised in the table below. <table align="center"> <caption>A table recording the sample number and associated hot-rolling condition.</caption> <tbody> <tr> <td> Sample Number </td> <td> Rolling Condition </td> </tr> <tr> <td>1</td> <td>825ºC, 87.5% Reduction</td> </tr> <tr> <td>2</td> <td>865ºC, 87.5% Reduction</td> </tr> <tr> <td>3</td> <td>895ºC, 87.5% Reduction</td> </tr> <tr> <td>4</td> <td>915ºC, 87.5% Reduction</td> </tr> <tr> <td>5</td> <td>935ºC, 87.5% Reduction</td> </tr> <tr> <td>6</td> <td>950ºC, 87.5% Reduction</td> </tr> <tr> <td>7</td> <td>960ºC, 87.5% Reduction</td> </tr> <tr> <td>8</td> <td>975ºC, 87.5% Reduction</td> </tr> <tr> <td>9</td> <td>1020ºC, 87.5% Reduction</td> </tr> <tr> <td>10</td> <td>β-annealed, 825ºC, 87.5% Reduction</td> </tr> <tr> <td>11</td> <td>β-annealed, 915ºC, 87.5% Reduction</td> </tr> <tr> <td>12</td> <td>β-annealed, 975ºC, 87.5% Reduction</td> </tr> <tr> <td>13</td> <td>Reduced heating from 915ºC, 87.5% Reduction</td> </tr> <tr> <td>14</td> <td>Reduced heating from 975ºC, 87.5% Reduction</td> </tr> <tr> <td>15</td> <td>825ºC, 75% Reduction</td> </tr> <tr> <td>16</td> <td>865ºC, 75% Reduction</td> </tr> <tr> <td>17</td> <td>895ºC, 75% Reduction</td> </tr> <tr> <td>18</td> <td>915ºC, 75% Reduction</td> </tr> <tr> <td>19</td> <td>935ºC, 75% Reduction</td> </tr> <tr> <td>20</td> <td>950ºC, 75% Reduction</td> </tr> <tr> <td>21</td> <td>960ºC, 75% Reduction</td> </tr> <tr> <td>22</td> <td>975ºC, 75% Reduction</td> </tr> <tr> <td>23</td> <td>1020ºC, 75% Reduction</td> </tr> <tr> <td>24</td> <td>β-annealed, 825ºC, 75% Reduction</td> </tr> <tr> <td>25</td> <td>β-annealed, 915ºC, 75% Reduction</td> </tr> <tr> <td>26</td> <td>β-annealed, 975ºC, 75% Reduction</td> </tr> <tr> <td>27</td> <td>Reduced heating from 915ºC, 75% Reduction</td> </tr> <tr> <td>28</td> <td>Reduced heating from 975ºC, 75% Reduction</td> </tr> <tr> <td>29</td> <td>As-received</td> </tr> <tr> <td>30</td> <td>As-received, β-annealed</td> </tr> <tr> <td>31</td> <td>975ºC, 50% Reduction</td> </tr> </tbody> </table> MTEX Data Analysis The lattice plane intensities for 22 α and 4 β phase peaks were extracted from the Continuous-Peak-Fit analysis, also included in this <a href="https://doi.org/10.5281/zenodo.7438090">analysis dataset</a>, and saved as text files in the form of pole figures. The lattice intensity text files were analysed in MTEX using scripts from the <a href="https://github.com/LightForm-group/continuous-peak-fit-analysis">continuous-peak-fit-analysis</a> package, to plot pole figures and ODF slices, and to calculate pole figure maxima, ODF maxima, texture indices and texture component phase fractions. A kernel half-width of 10° was found to produce optimal data fitting, for highly accurate texture strength intensity values. Metadata An accompanying YAML text file contains associated processing metadata for the SXRD analysis, recording information about the packages used to process the data, along with details about the different files contained within this results dataset.

opencc-by-4.0Aug 2022View details →

Radar and ground-level measurements collected during the POPE 2020 campaign at Princess Elisabeth Antarctica

This repository contain the datasets of radar and ground-level measurements collected in the vicinity of the Belgian research base Princess Elisabeth Antarctica (PEA). The measurement campaign has been conducted by the Environmental Remote Sensing Laboratory (LTE) of the Scole Polytechnique Fédérale de Lausanne (EPFL), with the logistical support of the International Polar Foundation (IPF). The datasets and their processing are described in the article “Radar and ground-level measurements of clouds and precipitation collected during the POPE 2020 campaign at Princess Elisabeth Antarctica”, by Alfonso Ferrone and Alexis Berne. The article was submitted to Earth System Science Data in August 2022, and is available at the following URL: <a href="https://doi.org/10.5194/essd-2022-295">https://doi.org/10.5194/essd-2022-295</a> . Content of the archives The datasets have been divided into compressed archives. Each of them contains a series of data files, all saved in NetCDF4 format. The content of each archives is listed below. - WProf.zip This archive contains the radar variables collected by the W-band Doppler profiling cloud radar (WProf) deployed at PEA. The liquid water path and integrated water vapor (retrieved thanks to the 89 GHz radiometer included in the instrument) has also been included in the files. - MXPol_PPI.zip This archive contains the polarimetric radar variables collected by the X-band Doppler dual-polarization scanning weather radar (MXPol) during the nearly-vertical PPI scans. - MXPol_sector_scans_2019.zip This archive contains the polarimetric radar variables collected by MXPol during the sector scans (PPI scans limited to a sector of the full azimuth circle) scans performed in December 2019. Sector scans collected in the following months have been stored separately, due to a limitation on the maximum number of files in input to the creation of the zip archive. - MXPol_sector_scans_2020.zip This archive contains the polarimetric radar variables collected by MXPol during the sector scans scans performed in January and February 2020. - MXPol_hydrometeor_types.zip This archive contains the polarimetric radar variables collected by MXPol during the RHI scans. The files also contain information on the proportion of hydrometeor classes and the dominant hydrometeor type computed from the measurements of MXPol. - MRR_PRO_06.zip, MRR_PRO_22.zip, and MRR_PRO_23.zip The three archives contain the radar variables collected by the K-band Doppler profiling radars (MRR-PRO) deployed in a transect across the mountain range south of PEA. - aws_radiometers.zip This archive contains the measurements collected by the Automated Weather Station (AWS) installed alongside each of the three MRR-PRO. The data from the two radiometers deployed at the MRR-PRO 06 site have also been included in the archive.   Content of the NetCDF4 files A “short name” is associated to each variables in the NetCDF4 files. This section provides a list of all the relevant variables collected by each instruments, alongside their short name.   The following polarimetric variables are available for all the scans performed by MXPol, stored in the archives MXPol_PPI.zip, MXPol_hydrometeor_types.zip, MXPol_sector_scans_2019.zip, and MXPol_sector_scans_2020.zip: – the horizontal reflectivity factors (ZH), identified in the files by the short name “Zh”; – the vertical reflectivity factors (ZV), identified by the short name “Zv”; – the differential reflectivity (ZDR), identified by the short name “Zdr”; – the signal-to-noise ratio on the horizontal polarization channel (SNRH), identified by the short name “SNRh”; – the signal-to-noise ratio on the vertical polarization channel (SNRV), identified by the short name “SNRv”; – the mean Doppler radial velocity (V), identified by the short name “RVel”; – the spectral width (SW), identified by the short name “Sw”; – the total differential phase shift (ΨDP), identified by the short name “Psidp”; – the differential phase shift (ΦDP), identified by the short name “Phidp”; – the specific differential phase on propagation (KDP), identified by the short name “Kdp”; – the co-polar correlation coefficient (ρhv), identified by the short name “Rhohv”.   The hydrometeor classification (Besic et al., 2016) and the retrieval of the proportion of each hydrometeor category in the radar volume (Besic et al., 2018) has been applied to all RHI scans of MXPol (archive MXPol_hydrometeor_types.zip), producing the following variables: – the dominant hydrometeor type, identified by the short name “hydro”, – the entropy computed by the classification algorithm, which provides an estimate of the confidence on the label assigned to the volume, identified by the short name “hydroclass_entropy”; – the proportion of each hydrometeor type in the volume, stored in the variables “proportion_AG” (aggregates), “proportion_CR” (ice crystals), “proportion_LR” (light rain), “proportion_RP” (rimed particles), “proportion_RN” (rain), “proportion_VI” (vertically-aligned ice), “proportion_WS” (wet snow), “proportion_MH” (melting hail); – the entropy computed by the demixing algorithm, identified by the short name “entropy”.   The following radar variables are available for all the profiles collected by WProf, stored in the archive WProf.zip: – the equivalent reflectivity factor (Ze), identified in the files by the short name “Ze” – the signal-to-noise ratio (SNR), identified in the files by the short name “SnR”; – the mean Doppler radial velocity, identified in the files by the short name “Mean-velocity”; – the spectral width, identified in the files by the short name “Spectral-width”; – the skewness, identified in the files by the short name “Spectral-skewness”; – the kurtosis, identified in the files by the short name “Spectral-kurtosis”; – the noise level at each range gate gate, identified in the files by the short name “Noise_level”; – the noise floor at each range gate gate, identified in the files by the short name “Noise_floor”.   The following retrievals (Billault-Roux et al., 2021) have been included in the WProf data files, stored in the archive WProf.zip: – the Integrated Water Vapor (IWV), identified in the files by the short name “Integrated-water-vapor”; – the Liquid Water Path (LWP), identified in the files by the short name “Liquid-water-path”.   The following atmospheric variables, recorded by the automated weather station integrated in the radar, have been included in the WProf data files, stored in the archive WProf.zip: – the atmospheric pressure, identified in the files by the short name “Barometric-pressure”; – the air temperature, identified in the files by the short name “Environment-temp”; – the relative humidity with respect to liquid water, identified in the files by the short name “Rel-humidity”; – the horizontal wind direction, identified in the files by the short name “Wind-direction”; – the horizontal wind speed, identified in the files by the short name “Wind-speed”.   The following variables are available for all the profiles collected by the three MRR-PRO, stored in the archives MRR_PRO_06.zip, MRR_PRO_22.zip, and MRR_PRO_23.zip: – the attenuated equivalent reflectivity factor (Zea), identified in the files by the short name “Zea”; – the mean Doppler radial velocity, identified in the files by the short name “VEL”; – the spectral width, identified in the files by the short name “SW”; – the signal-to-noise ratio, identified in the files by the short name “SNR”; – the noise level computed by ERUO (Ferrone et al., 2022) at each range gate gate, identified in the files by the short name “noise_level”; – the noise floor computed by ERUO at each range gate gate, identified in the files by the short name “noise_floor”.   The following variables are available for all the measurements collected by the three weather stations, stored in the archive aws_radiometers.zip: – the atmospheric pressure, identified in the files by the short name “pressure”; – the air temperature, identified in the files by the short name “temperature”; – the relative humidity with respect to liquid water, identified in the files by the short name “relative_humidity”; – the horizontal wind direction, identified in the files by the short name “wind_speed”; – the horizontal wind speed, identified in the files by the short name “wind_direction”.   The following variables are available for all the measurements collected by the pyrgeometer and pyranometer, stored in the archive aws_radiometers.zip:: – the total downwelling irradiance in the shortwave, identified in the files by the short name “shortwave_irradiance”; – the total downwelling irradiance in the longwave, identified in the files by the short name “longwave_irradiance”.     References Besic, N., Figueras i Venturra, J., Grazioli, J., Gabella, M., Germann, U., and Berne, A.: Hydrometeor classification through statistical clustering of polarimetric radar measurements: a semi-supervised approach, Atmospheric Measurement Techniques, 9, 4425–4445, https://doi.org/10.5194/amt-9-4425-2016, 2016. Besic, N., Gehring, J., Praz, C., Figueras i Ventura, J., Grazioli, J., Gabella, M., Germann, U., and Berne, A.: Unraveling hydrometeor mixtures in polarimetric radar measurements, Atmospheric Measurement Techniques, 11, 4847–4866, https://doi.org/10.5194/amt-11-4847-2018, 2018 Billault-Roux, A.-C. and Berne, A.: Integrated water vapor and liquid water path retrieval using a single-channel radiometer, Atmospheric Measurement Techniques, 14, 2749–2769, https://doi.org/10.5194/amt-14-2749-2021, 2021 Ferrone, A., Billault-Roux, A.-C., and Berne, A.: ERUO: a spectral processing routine for the Micro Rain Radar PRO (MRR-PRO), Atmospheric Measurement Techniques, 15, 3569–3592, https://doi.org/10.5194/amt-15-3569-2022, 2022

opencc-by-4.0Jan 2023View details →

Reproduction package for the paper "Measuring the variability of directly imaged exoplanets using vector Apodizing Phase Plates combined with ground-based differential spectrophotometry"

This is a basic reproduction package for the paper <a href="https://doi.org/10.1093/mnras/stad249">"Measuring the variability of directly imaged exoplanets using vector Apodizing Phase Plates combined with ground-based differential spectrophotometry" by Sutlieff et al. (2023)</a>. It aims to provide the most important data products to check and reproduce the main results of the paper.

opencc-by-4.0Jan 2023View details →

Moored echo and turbidity measurements in the Southern Adriatic Sea at mooring site BB and FF, March 2012-June 2020

This data set includes four files (CSV format) containing observational data from two oceanographic moorings, BB and FF, located in the Southern Adriatic Sea from the period between March 2012 and June 2020. The stand-alone moorings are equipped with a 300 kHz ADCP-RDI system, which measures currents along the last 100 meters of the water column and a CTD recorder equipped with SeaPoint turbidity meter sensor located approximatively 10 m above the bottom. The turbidity sensor measures in a range of 0-25 FTU. Moorings were configured and maintained for continuous long-term monitoring following the approach of the CIESM Hydrochanges Program (www.ciesm.org/marine/programs/hydrochanges.html). The moorings are currently operational as from 2021 they have joined the southern Adriatic Sea submarine observatory system of the EMSO-ERIC European Consortium. The data were subjected to quality control (QC) and the coding numbers used, shown in a dedicated column, follow the SeaDataNet L20 measurement qualifiers flags. QC applied on echo data consists of detecting signal anomalies due to interactions with the seafloor and identifying if the signal falls below a minimum threshold for which the value is no longer considered reliable. For turbidity data, QC is addressed to the detections of possible spikes, anomalies, and sensor saturation in the recordings.

Electron microscopy of particles collected by different techniques from field measurements in the Moroccan Sahara during FRAGMENT 2019

An intensive field campaign between 4-30 September 2019 was conducted at a major source region on the edge of the Saharan desert in Morocco (29.83 °N 5.87 °W) in the context of the FRontiers in dust minerAloGical coMposition and its Effects upoN climaTe (FRAGMENT) project. Samples were collected with three different sampling techniques, namely: flat-plate sampler (FPS), free-wing impactor (FWI), and a micro-orifice uniform deposit impactor (MOUDI). Substrates in the MOUDI and FWI were collected two times a day with a typical sampling duration of a few minutes to avoid overloading the substrate for individual particle analysis. For the flat-plate sampler, the average exposure time was half a day. Here we present dataset of the elemental composition and morphology of more than 300,000 freshly emitted individual particles by performing offline analysis in the laboratory using Scanning Electron Microscopy (SEM) coupled with Energy-Dispersive X-ray Spectrometry (EDX).

Measurements of savanna landscap fire emission factors for CO2, CO, CH4 and N2O using a UAV-based sampling methodology

This dataset contains direct measurements of biomass burning emission factors for CO2, CO, CH4 and N2O. It includes over 4500 EF bag measurements sampled using an unmanned aerial system (UAS), and measured fuel parameters and fire severity proxies during 129 individual fires. The measurements cover a variety of savanna ecosystems in Brazil, Australia, Botswana, Zambia, South-Africa and Mozambique under different seasonal conditions, sampled over the course of six fire seasons between 2017 and 2022. The table in the included word file explains the individual columns in the excell file.

opencc-by-4.0Mar 2023View details →

Energy-Saving Strategies for Mobile Web Apps and their Measurement: Results from a Decade of Research - Dataset

In 2022, over half of the web traffic was accessed through mobile devices. By reducing the energy consumption of mobile web apps, we can not only extend the battery life of our devices, but also make a significant contribution to energy conservation efforts. For example, if we could save only 5% of the energy used by web apps, we estimate that it would be enough to shut down one of the nuclear reactors in Fukushima. This paper presents a comprehensive overview of energy-saving experiments and related approaches for mobile web apps, relevant for researchers and practitioners. To achieve this objective, we conducted a systematic literature review and identified 44 primary studies for inclusion. Through the mapping and analysis of scientific papers, this work contributes: (1) an overview of the energy-draining aspects of mobile web apps, (2) a comprehensive description of the methodology used for the energy-saving experiments, and (3) a categorization and synthesis of various energy-saving approaches.

Smart Meter Water Consumption Measurements

Data Between September 2021 and September 2022, we collected the cumulative water consumption data of 17 households in Germany using the commercially available smart meters Hydrus 1.3, DN 20,00 from Diehl Metering. Further information on data collection and description of the dataset can be found in section 3 of the original article, which is available at the following DOI: <a href="https://doi.org/10.3390/jsan12030046">https://doi.org/10.3390/jsan12030046</a> Structure The root folder of the Zenodo archive contains the documents readme.md, info.txt, and info.json, as well as the 17 folders for the individual households. The readme.md gives some basic information on the data set and its use. The documents main.txt and main.json contains the metadata for the measurements of all households in both human and machine-readable format. For each test household, there is a separate folder with the documents info.txt and info.json. These contain household-specific metadata. The total water consumption measurements are stored in smartmeter.csv.

opencc-by-4.0Mar 2023View details →

[Dataset] One year of high-precision operational data including measurement uncertainties from a large-scale solar thermal collector array with flat plate collectors, located in Graz, Austria

Highlights: <ul> <li>High-precision measurement data acquired within a scientific research project, using high-quality measurement equipment and implementing extensive data quality assurance measures.</li> <li>The dataset includes data from one full operational year in a 1-minute sampling rate, covering all seasons.</li> <li>Measured data channels include global, beam and diffuse irradiances in horizontal and collector plane. Heat transfer fluid properties were determined in a dedicated laboratory test.</li> <li>In addition to the measured data channels, calculated data channels, such as thermal power output, mass flow, fluid properties, solar incidence angle and shadowing masks are provided to facilitate further analysis.</li> <li>Uncertainties of data channels are provided based on data sheet specifications and GUM error propagation.</li> <li>The dataset refers to a real-scale application which is representative of typical large-scale solar thermal plant designs (flat plate collectors, common hydraulic layout).</li> <li>Additional information is provided in a "Data in Brief" journal article: <a href="https://doi.org/10.1016/j.dib.2023.109224">https://doi.org/10.1016/j.dib.2023.109224</a></li> </ul>   Collector array description: The data is from a flat plate collector array with a total gross collector area of 516 m2 (361 kW nominal thermal power). The array consists of four parallel collector rows with a common inlet and outlet manifold. Large-area flat-plate collectors from Arcon-Sunmark A/S are used in the plant. Collectors are all oriented towards the south (180°), have a tilt angle of 30° and a row spacing of 3.1 m. The collector array is part of a large-scale solar thermal plant located at Fernheizwerk Graz, Austria (latitude: 47.047294 N, longitude: 15.436366 E). The plant feeds into the local district heating network and is one of the largest Solar District Heating installations in Central Europe.   Data files: <ul> <li>FHW_ArcS__main__2017.csv – This is the main dataset. It is advised to use this file for further analysis. The file contains the full time series of all measured and all calculated data channels and their (propagated) measurement uncertainty (53 data channels in total). Calculated data channels are derived from measured channels (see script make_data.py below) and have the suffix __calc in their channel names. Uncertainty information is given in terms of standard deviation of a normal distribution (suffix __std); some data channels are assumed to have no uncertainty (e.g., sun azimuth or shadowing).</li> <li>FHW_ArcS__main__2017.parquet – Same as FHW_ArcS__main__2017.csv, but in parquet file format for smaller file size and improved performance when loading the dataset in software.</li> <li>FHW_ArcS__parameters.json – Contains various metadata about the dataset, in both human and machine-readable format. Includes plant parameters, data channel descriptions, physical units, etc.</li> <li>FHW_ArcS__raw__2017.csv – Dataset with time series of all measured data channels and their measurement uncertainty. The main dataset FHW_ArcS__main__2017.csv, which includes all calculated data channels, is a superset of this file.</li> </ul>   Scripts: <ul> <li>make_data.py – This Python script exposes the calculation process of the calculated data channels (suffix __calc), including error propagation. The main calculations are defined as functions in the module utils_data.py.</li> <li>make_plots.py – This Python script, together with utils_plots.py, generates several figures based on the main dataset.</li> </ul>   Data collection and preparation: AEE — Institute for Sustainable Technologies (AEE INTEC), Feldgasse 19, 8200 Gleisdorf, Austria; and SOLID Solar Energy Systems GmbH (SOLID), Am Pfangberg 117, 8045 Graz, Austria   Data owner: solar.nahwaerme.at Energiecontracting GmbH, Puchstrasse 85, 8020 Graz, Austria   Additional information is provided in a journal article in "Data in Brief", titled <a href="https://doi.org/10.1016/j.dib.2023.109224">"One year of high-precision operational data including measurement uncertainties from a large-scale solar thermal collector array with flat plate collectors in Graz, Austria"</a>.   Note: A Gitlab repository is associated with this dataset, intended as a companion to facilitate maintenance of the Python code that is provided along with the data. If you want to use or contribute to the code, please do so using the Gitlab project: <a href="https://gitlab.com/sunpeek/zenodo-fhw-arconsouth-dataset-2017">https://gitlab.com/sunpeek/zenodo-fhw-arconsouth-dataset-2017</a>

Measurement-based MIMO channel model at 140GHz

1. Introduction The file “gen_dd_channel.zip” is a package of a wideband multiple-input multiple-output (MIMO) stored radio channel model at 140 GHz in indoor hall, outdoor suburban, residential and urban scenarios. The package consists of 1) measured wideband double-directional multipath data sets estimated from radio channel sounding and processed through measurement-based ray-launching and 2) MATLAB code sets that allows users to generate wideband MIMO radio channels with various antenna array types, e.g., uniform planar and circular arrays at link ends. 2. What does this package do? Outputs of the channel model The MATLAB file “ChannelGeneratorDD_hexax.m” gives the following variables, among others. The .m file also gives optional figures illustrating antennas and radio channel responses. <table> <tbody> <tr> <td> Variables </td> <td> Descriptions </td> </tr> <tr> <td> CIR </td> <td> MIMO channel impulse responses </td> </tr> <tr> <td> CFR </td> <td> MIMO channel frequency responses </td> </tr> </tbody> </table> Inputs to the channel model In order for the MATLAB file “ChannelGeneratorDD_hexax.m” to run properly, the following inputs are required. <table> <tbody> <tr> <td> Directory </td> <td> Descriptions </td> </tr> <tr> <td> data_030123_double_directional_paths </td> <td> Double-directional multipath data, measured and complemented by ray-launching tool, for various cellular sites. </td> </tr> </tbody> </table> User’s parameters When using “ChannelGeneratorDD_hexax.m”, the following choices are available. <table> <tbody> <tr> <td> Features </td> <td> Choices </td> </tr> <tr> <td> Channel model types for transfer function generation </td> <td> <ul> <li> 'snapshot': single time sample per link = static, random phase for each path, amplitude from measurements </li> <li>'virtualMotion': Doppler shifts & temporal fading, static propagation parameters, random phase for each path, amplitude from measurements, Doppler frequency per path from AoA and velocity vector</li> </ul> </td> </tr> <tr> <td> Antenna / beam shapes </td> <td> <ul> <li> 'single3GPP': single antenna element with power pattern shape defined in 3GPP, adjustable HPBW etc. </li> <li> 'URA': uniform rectangular array, omni-directional elements </li> <li>'UCA': uniform circular array, omni-directional elements</li> </ul> </td> </tr> </tbody> </table> List of files in the dataset MATLAB codes that implement the channel model The MATLAB files consist of the following files. <table> <tbody> <tr> <td> File and directory names </td> <td> Descriptions </td> </tr> <tr> <td> readme_100223.txt </td> <td> Readme file; please read it before using the files </td> </tr> <tr> <td> ChannelGeneratorDD_hexax.m </td> <td> Main code to run; a code to integrate antenna arrays and double-directional path data to derive MIMO radio channels. No need to see/edit other files. </td> </tr> <tr> <td> gen_pathDD.m, randl.m, randLoc.m </td> <td> Sub-routines used in ChannelGeneratorDD_hexax.m; no need of modifications. </td> </tr> <tr> <td> Hexa-X channel generator DD_presentation.pdf </td> <td> User manual of ChannelGeneratorDD_hexax.m. </td> </tr> </tbody> </table>   Measured multipath data The directory "data_030123_double_directional_paths" in the package contains the following files. <table> <tbody> <tr> <td> Filenames </td> <td> Descriptions </td> </tr> <tr> <td> readme_100223.txt </td> <td> Readme file; please read it before using the files </td> </tr> <tr> <td> RTdata_[scenario]_[date].mat </td> <td> Containing double-directional multipath parameters at 140 GHz in the specified scenario, estimated from radio channel sounding and ray-tracing. </td> </tr> <tr> <td> description_of_data_dd_[scenario].pdf </td> <td> Explaining data formats, the measurement site and sample results. </td> </tr> </tbody> </table> References Details of the data set are available in the following two documents: The stored channel models A. Nimr (ed.), "Hexa-X Deliverable D2.3 Radio models and enabling techniques towards ultra-high data rate links and capacity in 6G," April 2023, available: https://hexa-x.eu/deliverables/ @misc{Hexa-XD23,     author    = {{A. Nimr (ed.)}},     title     = {{Hexa-X Deliverable D2.3 Radio models and enabling techniques towards ultra-high data rate links and capacity in 6G}},     year     = {2023},     month    = {Apr.},      howpublished               = {https://hexa-x.eu/deliverables/}, } Derivation of the data, i.e., radio channel sounding and measurement-based ray-launching M. F. De Guzman and K. Haneda, "Analysis of wave-interacting objects in indoor and outdoor environments at 142 GHz," IEEE Transactions on Antennas and Propagation, vol. 71, no. 12, pp. 9838-9848, Dec. 2023, doi: 10.1109/TAP.2023.3318861 @ARTICLE{DeGuzman23_TAP,   author={De Guzman, Mar Francis and Haneda, Katsuyuki},   journal={IEEE Transactions on Antennas and Propagation},    title={Analysis of Wave-Interacting Objects in Indoor and Outdoor Environments at 142 {GHz}},    year={2023},   volume={71},   number={12},   pages={9838-9848}, } Finally, the code “randl.m” are from the following MATLAB Central File Exchange. Hristo Zhivomirov (2023). Generation of Random Numbers with Laplace Distribution (https://www.mathworks.com/matlabcentral/fileexchange/53397-generation-of-random-numbers-with-laplace-distribution), MATLAB Central File Exchange. Retrieved February 15, 2023. Data usage terms Any usage of the data must be upon consent on the following conditions: <ul> <li>The file “ChannelGeneratorDD_hexax.m” is owned by OUL. Contact: Dr. Pekka Kyösti, Pekka.Kyosti@oulu.fi.</li> <li>The other files and those in the directories, except for “randl.m”, are owned by AAU. Contact: Mr. Mar Francis de Guzman, francis.deguzman@aalto.fi.</li> <li>When a scientific paper is published that exploits the data and code, please cite this data set; the citation can be downloaded from the zenodo page of this data set.</li> </ul>