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101 results for “scintillator”
Gamma dose rate monitoring using a Silicon Photomultiplier-based plastic scintillation detector
<p>Data set in support of the publication "Gamma dose rate monitoring using a Silicon Photomultiplier-based plastic scintillation detector". It contains measurement campaign data, radionuclide sources data, measurement count rate per radionuclide.</p>
The dataset for publication "Characterization of scintillating materials in use for brachytherapy fiber based dosimeters" by S. Commeti, et al., Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 2022.
<p>This dataset is related to paper journal paper with DOI: <a href="http://dx.doi.org/10.1016/j.nima.2022.167083">10.1016/j.nima.2022.167083</a>.</p> <p>The dataset contains raw txt file and matlab files on the transmittance and the attenuation of Gadox and YVO specimens. </p> <p>Data files were prepared by agnieszka.gierej@vub.be</p>
First In-Situ Measurements of Travelling Ionospheric Disturbances at 420 km Altitude by the Scintillation Observations and Response of The Ionosphere to Electrodynamics (SORTIE) CubeSat
<p>Companion dataset to the paper entitled "First In-Situ Measurements of Travelling Ionospheric Disturbances at 420 km Altitude by the Scintillation Observations and Response of The Ionosphere to Electrodynamics (SORTIE) CubeSat". The dataset includes the SORTIE CubeSat IVM Level 2 ion density and GPS TEC data used in the study along with the WRF simulation results.</p>
Additional results for article "A new approach for the generation of real-time GNSS low-latitude ionospheric scintillation maps"
<p>Complete set of interpolation error and correlation metrics for the approaches GDA, IDW, RBF and GPR for all the 12 pre-processing options using the SSS cross-validation scheme for the 10-hour dataset (40 maps with 16-minute interval for each approach and each pre-processing options) - file “Complete table of interpolation errors and correlation.csv”.</p> <p>Complete set of scintillation maps for the approaches GDA, IDW, RBF and GPR for all the 12 pre-processing options covering the 10-hour dataset (40 maps with 16-minute interval for each approach and each pre-processing options) - file “Scintillation maps for the 10-hour dataset.zip”.</p> <p>Comparison plots of the scintillation maps generated by the approaches GDA, IDW, RBF and GPR with the pre-processing options SAR, SMR and VQI for each of the 40 intervals of time of 16 minutes covering the 10-hour dataset - file “Set of maps for all 4 approaches with the SAR, SMR and VQI sets of options.zip”.</p> <p>Sequence of scintillation maps for the 8-hour dataset generated by the GPR(VQI) approach for the three time resolutions (1, 2, and 16-minute) - file “Scintillation maps for the 8-hour comparison dataset.zip”.</p> <p>Animations corresponding to the sequence of maps generated by the GPR(VQI) approach for the 8-hour dataset, and for the three time resolutions (1, 2, and 16-minute) - file “Animations of GPR(VQI) maps for the 8-hour comparison dataset.zip”.</p>
First time-resolved measurement of infrared scintillation light in gaseous xenon
<p>Repository with supplemental data to:<br> <strong>First time-resolved measurement of infrared scintillation light in gaseous xenon</strong>. Piotter, M., Cichon, D., <em>Hammann, R.</em>, Jörg, F., Hötzsch, L.,<em> Marrodán Undagoitia, T. Eur. Phys. J. C</em> <strong>83</strong>, 482 (2023).<br> A pre-print of the article is available <em>on arXiv: </em><a href="https://arxiv.org/abs/2303.09344">2303.09344</a></p> <p><strong>Note: </strong>When re-using the data, please make sure to cite the article (and not only the dataset)</p> <p> </p> <p>The files contain all data related to the observed IR scintillation in gaseous xenon presented in the paper. This comprises the IR time profiles obtained via single photon counting and the measured pressure dependence of the IR light yield for the three extrapolation methods:</p> <ul> <li><strong>waveform_before.csv, waveform_during.csv, waveform_after.csv</strong>: These files contain the IR time profiles before, during, and after the purification of the gas (presented in figure 8 in the publication). The column <em>dt</em> is given in nanoseconds relative to the UV pulse and <em>counts </em>corresponds to counts per nanosecond per 100 UV events.</li> <li><strong>light_yield_ir.csv: </strong>This file contains the IR light yield as a function of pressure obtained with the three extrapolation models together with the respective statistical and systematic uncertainties. The data is presented in figure 9 in the publication and all values are given in units of photons per MeV.</li> <li><strong>waveform_495.csv, waveform_742.csv, waveform_1047.csv:</strong> These files contain the IR time profiles for xenon gas pressures of 495.0 mbar, 742.5 mbar, and 1047.0 mbar, respectively (presented in figure 10 in the publication). The column <em>dt</em> is given in nanoseconds relative to the UV pulse and <em>counts </em>corresponds to counts per nanosecond per 100 UV events.</li> </ul> <p> </p> <p><strong>Code examples for plotting the data:</strong></p> <p>The following Python code reproduces figure 9 in the publication:</p> <pre><code class="language-python">import pandas as pd import matplotlib.pyplot as plt if __name__ == '__main__': df = pd.read_csv("light_yield_ir.csv") color_pairs = [("#365898", "#B7D0FF"), ("#AB123B", "#F0B5C5"), ("#E1992E", "#F1DAB9")] fig, ax = plt.subplots(1, figsize=(4, 3)) for fit_func_str, cs in zip(["Recombination model fit", "Exponential fit", "Linear fit"], color_pairs): # Plot systematic error ax.errorbar(df["Pressure"], df[f"IR Light Yield ({fit_func_str} fit)"], yerr=df[f"Syst. uncertainty ({fit_func_str} fit)"], ls="", elinewidth=3, capsize=0, ecolor=cs[1] ) # Plot estimator with statistical error ax.errorbar(df["Pressure"], df[f"IR Light Yield ({fit_func_str} fit)"], yerr=df[f"Stat. uncertainty ({fit_func_str} fit)"], ls="", c=cs[0], ecolor=cs[0], elinewidth=1, capsize=1, marker=".", label=fit_func_str) # Cosmetics ax.set_xlabel("Pressure [mbar]") ax.set_ylabel("IR light yield [ph / MeV]") ax.set_ylim(1200, 12_500) ax.legend(frameon=False, loc="upper left") plt.show()</code></pre> <p> </p> <p>The IR time response of figure 8 can be redrawn as follows:</p> <pre><code class="language-python">import pandas as pd import matplotlib.pyplot as plt if __name__ == '__main__': fig, ax = plt.subplots(1, figsize=(4, 3)) for label in ["before", "during", "after"]: df = pd.read_csv(f"waveform_{label}.csv") ax.step(df["dt"], df["counts"], label=label) # Cosmetics ax.set_xlabel("$\Delta t$ between IR and UV signal [ns]") ax.set_ylabel("Counts per 1 ns per 100 UV events") ax.legend(frameon=False, loc="upper right") plt.show()</code></pre> <p> </p>
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.
<p>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>).</p> <p> </p> <p>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.</p> <p> </p> <p>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.</p> <p> </p>
ScintPi: A low-cost, easy-to-build GPS ionospheric scintillation monitor for DASI studies of space weather, education, and citizen science initiatives
<p>These data sets contain ionospheric scintillation (GPS L1) observations (S4 indices) collected by a ScintPi prototype during 2018 at a low magnetic latitude station (Presidente Prudente). ScintPi is a low-cost, easy-to-build GPS ionospheric scintillation monitor for DASI studies of space weather, education, and citizen science initiatives.</p> <p>The file named "ScintPi_PPR_S4_2018.mat" contains S4 values (s4mat) for 2018 as a function of universal time (utmat), day-of-year (doymat), GPS satellite identifier number (prnmat), GPS satellite elevation (elmat) and azimuth (azmat) angles.</p> <p>The file named "ScintPi_PPR_20180211.mat" contains example raw (10 Hz) measurements made by ScintPi on February 11, 2018. The file contains values of receiver's altitude, latitude and longitude (variables alt,lat, and lon), GPS satellite azimuth and elevation (variables el and az), GPS identifier number (prn), signal-to-noise ratio (snr), and day-of-year (doy).</p>
Signal frequency dependence of ionospheric scintillations: An indicator of irregularity spectrum characteristics
<p>The files contain the following data for 11 days in March 2015:</p> <p>1) L-band S4 for the L1 signal from geostationary satellite GSAT-10 recorded at two stations: MUM and TRV.</p> <p> The data columns represent: Indian Standard Time (IST), signal path ELEVATION and AZIMUTH, S4 , S4_corrected , IPP_LATITUDE and IPP_LONGITUDE. The file name contains the day information.</p> <p>2) VHF S4 for the 251 MHz signal from geostationary satellite UFO10 recorded at two stations: MUM and TIR.</p> <p>The data columns represent: Year Month Day IST S4.</p>
Data sets for distributed ionospheric L-band scintillation and TEC observations made in the American sector during the March 23-24, 2023 geomagnetic storm
<p>These data sets contain the scintillation measurements presented in the manuscript titled, "On the extraordinary L-band scintillation event observed in the American sector during the March 23-24, 2023 geomagnetic storm".</p> <p><br>The HDF5 files are organized by constellations and satellites. Each satellite includes the following parameters: Azimuth (AZIM), Elevation (ELEV), Number of Samples (NOS), Amplitude Scintillation Index (S4), 1-minute average SNR (SNR), relative Total Electron Content (PTEC), and Time of Week in seconds (S_TW)</p>
ScintPi 2.0 and 3.0: low-cost GNSS-based monitors of ionospheric scintillation and total electron content
<p> </p> <p>This data set provides measurements made by PolaR5x and ScintPi3.0 receivers in Presidente Prudente, Brazil for two consecutive days.</p>
Experimental data for the publication: "Evaluating scintillator performance in time-resolved, hard X-ray studies at synchrotron light sources"
<p>In accordance with the expectations outlined in <em><strong>Clarifications of EPSRC expectations on research data management</strong></em> (09/10/14) this data has been made publicly available to complement the open access publication "Evaluating scintillator performance in time-resolved, hard X-ray studies at synchrotron light sources". </p> <p>There are six data sets, corresponding to the six experimental data sets presented in the article. In each data set, which may be identified by their file names and reference to the article, the 1st column is the RF trigger - to - ICCD exposure delay in [ns], and the second column in the intensity recorded on the ICCD in [counts]. This intensity accounts for any online and offline processing outlined in the article, such as on-CCD exposures, dark frame correction etc. </p>
Data release for "Scintillator ageing of the T2K near detectors from 2010 to 2021"
<p>The data release is associated with the paper "Scintillator ageing of the T2K near detectors from 2010 to 2021”, published in the <a href="https://iopscience.iop.org/article/10.1088/1748-0221/17/10/P10028">Journal of Instrumentation</a> and <a href="https://arxiv.org/abs/2207.12982">arXiv:2207.12982 [physics]</a>.</p> <p>The data release contains the data points and associated fits from the paper within two root files:</p> <ul> <li>The file T2KNearDetector_ScintAgeing_Standard_DataRelease.root contains the data points and fits shown in the standard ageing results described in Sections 4 and 5 of the paper.</li> <li>The file T2KNearDetector_ScintAgeing_ECalSupplemental_DataRelease.root contains the data points and fits shown in the supplemental ageing results described in Section 6 of the paper.</li> </ul> <p>All data points are provided as ROOT TGraphError objects and fit as ROOT TF1 objects. A full description of the contents of each root file is provided in the README.txt.</p>
dataset for "A Scintillation Arc Survey of 22 Pulsars with Low to Moderate Dispersion Measures"
<p>These are the 54 Dynamic Spectrum files (.FITS format) behind the paper "A Scintillation Arc Survey of 22 Pulsars with Low to Moderate Dispersion Measures," submitted to the AAS Journals on 2022 April 06. We also include the Tables, Figures, and Text from the work, as well as some explanatory README files.</p>
ScintiPi3 data sets for "First observations of severe scintillation over low-to-mid latitudes driven by quiet-time extreme equatorial plasma bubbles: conjugate measurements enabled by citizen science initiatives"
<p>ScintPi 3.0 data sets for "First observations of severe scintillation over low-to-mid latitudes driven by quiet-time extreme equatorial plasma bubbles: conjugate measurements enabled by citizen science initiatives" by Sousasantos et al. (2024).</p>
Precise positioning of gamma ray interactions in multiplexed pixelated scintillators using artificial neural networks
<p>Data used to train multiclass and binary neural networks to analyse SiPM (Silicon Photomultiplier) signals in a multiplexed array of 16 detectors and detect the signal detector origin. Data acquired using an oscilloscope. Results compared with previous anger logic methods. </p> <p>Dataset used in the publication</p> <p>"Precise positioning of gamma ray interactions in multiplexed pixelated scintillators using artificial neural networks"</p> <p>https://doi.org/10.1088/2057-1976/ad4f73</p>
Dataset from the ground-based TEC and scintillation receiver in Troll station for events on March 18th and 25th, 2018
<p>Here is data from the GNSS Ionospheric Scintillation and TEC Monitor (GISTM) receiver NovAtel GPStation-6 that located at the Norwegian Research Station Troll in Queen Maud Land, Antarctica. The receiver records signals from the GPS, GLONASS, and Galileo satellites. Every minute, it provides extended summary messages, including satellite azimuth/elevation angles, C/NO, lock time, code-minus-carrier, calculations of amplitude (S4) and phase (σϕ) scintillation indices.</p> <p>The data is presented as tables. Each .txt file contains data and a header.</p> <p> </p> <p><span>This work was supported by European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (ERC Consolidator Grant agreement No. 866357, POLAR-4DSpace).</span></p>
GNSS amplitude scintillation index (S4) and ionospheric pierce point (IPP) from Zhuhai on 11 May 2024
<p>The ionospheric pierce point (IPP) location is named as "IPP_lla_CXX", sampled by 1-sec (86400 a day), where the columns are latitude, longitude, and altitude, respectively.</p> <p>The amplitude scintillation index (S4) is named as "S4_CXX_SYSU_132", sampled by 1-minute cadence (1440 a day).</p> <p>The data are provided in MATLAB format.</p>
Radiation Performance Data of the Fast-, Light-cured Plastic Scintillator Formulations
<p>This repository contains the waveforms from the CAEN DT5730 digitizer of the fast-, light-cured plastic scintillators for the different formulations used in the two level-factorial parameter study (AFIT101-130) and ethanol treatment study (AFIT220-230). Each scintillator was wrapped with two layers of MIL-T-27730A PTFE tape and coupled to a 2" Hamamatsu R7724 PMT biased at -1050 V. The signals were recorded using CAEN CoMPASS software on a CAEN DT5730 digitizer while the scintillators were exposed to Na-22, Cs-137, and AmBe radiation sources. Some of the scintillator formulations were measured more than once, but due to the limitations of the data that can be saved in this repository, not all the experimental data is included; however, this repository consists of the data closest to the average performance of each scintillator. Additionally, the EJ-276 dataset is included.<br> <br> The table below lists the weight percentage of the compounds in each scintillator formulations. The compounds for each formulation were weighed and placed into a 20 mL reaction vial. The compounds were thoroughly mixed together using an alternating combination of an ultrasonic bath and hot plate to gently warm the mixture. To reduce oxygen-induced quenching, the reaction vial was transferred to a glove box pressurized with a nitrogen atmosphere and dissolved oxygen was removed by sparging with nitrogen for 10 min. The reaction vial was sealed before removal from the glove box. Next, the resin solution was cured using a commercially-available 405 nm Formlabs Form Cure for 5 mins and Dymax BlueWave AX-550 VisiCure for 4 mins.<br> Finally, all solid scintillators were sanded and polished using 180 grit, 400 grit, 1000 grit, and 2000 grit sandpaper and a 300~nm alumina slurry.</p> <table> <tbody> <tr> <td>Scintillator</td> <td>DIN</td> <td>PPO</td> <td>Exalite</td> <td>IBOA</td> <td>HDDMA</td> <td>BPADMA</td> <td>TPO</td> </tr> <tr> <td>AFIT101</td> <td>-</td> <td>30</td> <td>0.2</td> <td>49</td> <td>21</td> <td>-</td> <td>0.1</td> </tr> <tr> <td>AFIT102</td> <td>10</td> <td>20</td> <td>0.2</td> <td>49</td> <td>21</td> <td>-</td> <td>0.1</td> </tr> <tr> <td>AFIT103</td> <td>20</td> <td>10</td> <td>0.2</td> <td>49</td> <td>21</td> <td>-</td> <td>0.1</td> </tr> <tr> <td>AFIT104</td> <td>30</td> <td>-</td> <td>0.2</td> <td>49</td> <td>21</td> <td>-</td> <td>0.1</td> </tr> <tr> <td>AFIT105</td> <td>-</td> <td>30</td> <td>0.2</td> <td>49</td> <td>14</td> <td>7</td> <td>0.1</td> </tr> <tr> <td>AFIT106</td> <td>10</td> <td>20</td> <td>0.2</td> <td>49</td> <td>14</td> <td>7</td> <td>0.1</td> </tr> <tr> <td>AFIT107</td> <td>20</td> <td>10</td> <td>0.2</td> <td>49</td> <td>14</td> <td>7</td> <td>0.1</td> </tr> <tr> <td>AFIT109</td> <td>-</td> <td>30</td> <td>0.2</td> <td>49</td> <td>7</td> <td>14</td> <td>0.1</td> </tr> <tr> <td>AFIT110</td> <td>10</td> <td>20</td> <td>0.2</td> <td>49</td> <td>7</td> <td>14</td> <td>0.1</td> </tr> <tr> <td>AFIT111</td> <td>20</td> <td>10</td> <td>0.2</td> <td>49</td> <td>7</td> <td>14</td> <td>0.1</td> </tr> <tr> <td>AFIT113</td> <td>-</td> <td>30</td> <td>0.2</td> <td>49</td> <td>-</td> <td>21</td> <td>0.1</td> </tr> <tr> <td>AFIT114</td> <td>10</td> <td>20</td> <td>0.2</td> <td>49</td> <td>-</td> <td>21</td> <td>0.1</td> </tr> <tr> <td>AFIT130</td> <td>15</td> <td>15</td> <td>0.2</td> <td>49</td> <td>10.5</td> <td>10.5</td> <td>0.1</td> </tr> <tr> <td>AFIT230</td> <td>-</td> <td>30</td> <td>0.2</td> <td>49</td> <td>21</td> <td>-</td> <td>0.1</td> </tr> <tr> <td>AFIT225</td> <td>5</td> <td>25</td> <td>0.2</td> <td>49</td> <td>21</td> <td>-</td> <td>0.1</td> </tr> <tr> <td>AFIT220</td> <td>10</td> <td>20</td> <td>0.2</td> <td>49</td> <td>21</td> <td>-</td> <td>0.1</td> </tr> <tr> <td>AFIT221</td> <td>10</td> <td>20</td> <td>0.2</td> <td>49</td> <td>14</td> <td>7</td> <td>0.1</td> </tr> </tbody> </table> <p>DIN: diisopropyl naphthalene<br> PPO: 2,5-diphenyloxazole<br> Exalite: Exalite 416<br> IBOA: isobornyl acrylate<br> HDDMA: difunctionized 1,6-hexanediol dimethacrylate<br> BPADMA: bisphenol-A dimethacrylate<br> TPO: diphenyl (2,4,6-trimethylbenzoyl) phosphine oxide</p>
Data sets for "On the detection of a solar radio burst event that occurred on 28 August 2022 and its effect on GNSS signals as observed by ionospheric scintillation monitors distributed over the American sector" by Wright et al.
<p>Copy of data described in 'On the detection of a solar radio burst event that occurred on 28 August 2022 and its effect on GNSS signals as observed by ionospheric scintillation monitors distributed over the American sector' by Wright et. al and to appear in the Journal of Space Weather and Space Climate (JSWSC), 2023.</p><p> </p>
Performance of 6 Different GNSS Receivers at Low Latitude under Moderate and Strong Scintillation
<p>These data sets contain ionospheric scintillation (GPS L1) observations (S<sub>4</sub> and <span class="math-tex">\(\mathbf{\sigma_\phi}\)</span> indexes) from 5 different GNSS receivers.</p> <p>The data are from two nights, 20/21 February and 27/28 November of 2013, and stored in different files. The files are in the HDF5 format using zlib compression and were generated by a Python script using the Pandas library.</p> <p>Each file contain the following folders:</p> <ul> <li> <p>/csm_maker_calc</p> </li> <li> <p>/csm_own_calc</p> </li> <li> <p>/csm_raw</p> </li> <li> <p>/gpstation6_maker_calc</p> </li> <li> <p>/gpstation6_own_calc</p> </li> <li> <p>/gpstation6_raw</p> </li> <li> <p>/gsv_4004b_lisn_calc</p> </li> <li> <p>/gsv_4004b_maker_calc</p> </li> <li> <p>/septentrio_maker_calc</p> </li> <li> <p>/septentrio_own_calc</p> </li> <li> <p>/septentrio_raw</p> </li> <li> <p>/su_iono_sdr_own_calc</p> </li> <li> <p>/su_iono_sdr_raw</p> </li> </ul> <p>The suffix <strong>maker_calc</strong> means that the indices were calculated in real time or post-processed using receiver manufacturer's tools. The suffix <strong>own_calc</strong> means that the indices were calculated using our own methodology. The suffix <strong>raw</strong> corresponds to the raw data (power and phase cycles). And finally the suffix <strong>lisn_calc</strong> corresponds to the S<sub>4</sub> index obtained from the LISN network website.</p>
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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.