Find research datasets worth reusing

opencc-by-4.0Aug 2020View details →

Measurement data of the response of a Li-glass/multi-anode photomultiplier detector to focused proton and deuteron beams

Data taken at the LIBAF accelerator in Lund 2019 using a prototype SoNDe detector based on a Lithium-6 scintillating glass and Hamamatsu multi-anode photomultiplier tube. See further details in the paper based on this dataset (<a href="https://doi.org/10.1016/j.nima.2020.164604">doi:10.1016/j.nima.2020.164604</a>) . The .csv data is ordered so every 64th line is a new event. The line number within an event represents the pixel number according to the translation in lines_to_pixel_numbers.txt . The column 'sample' contains the readout ADC channel for that pixel and event.

opencc-by-4.0Nov 2024View details →

Proton fluxes from the REleASE system from 1995 to 2016

This dataset has been generated utilizing the REleASE method described in Posner (2007, doi:10.1029/2006SW000268). It utilizes data from the Electron Proton Helium INstrument aboard SOHO (Müller-Mellin et al., 1995, doi:10.1007/BF00733437).  Yearly files of 22 proton energy bins created from level 1 EPHIN .PHA and .SCI data from 1995 to 2016. Each file contains data with a 1 minute time resolution.    Contents of each file: Columns  #1:     year #2:     day of year (DOY) #3:     milliseconds of the day #4:     flag for data status: ->      0 = not nominal data (do not use) ->      1 = ok #5:     criterium for high fluxes: ->      1 if count rate in anti coincidence > 25e5 * 59.95312 ->      1 if a00+a01+a02+a03+a04+a05 count rate > 47500 * 59.95312 ->      else 0 #6:     <2: ring on, >=2: ring off proton fluxes in (cm^2 s sr MeV)^-1 in the folowing energy bins #7:     3.98 - 4.47 MeV #8:     4.47 - 5.01 MeV #9:     5.01 - 5.62 MeV #10:    5.62 - 6.31 MeV #11:    6.31 - 7.08 MeV #12:    7.08 - 7.94 MeV #13:    7.94 - 8.91 MeV #14:    8.91 - 10.00 MeV #15:    10.00 - 11.22 MeV #16:    11.22 - 12.59 Mev #17:    12.59 - 14.13 MeV #18:    14.13 - 15.85 MeV #19:    15.85 - 17.78 MeV #20:    17.78 - 19.95 MeV #21:    19.95 - 22.39 MeV #22:    22.39 - 25.12 MeV #23:    25.12 - 28.18 MeV #24:    28.18 - 31.62 MeV #25:    31.62 - 35.48 MeV #26:    35.48 - 39.81 MeV #27:    39.81 - 44.67 MeV #28:    44.67 - 50.12 MeV

opencc-by-sa-4.0Nov 2017View details →

Metabomatching: Using Genetic Association to Identify Metabolites in Proton NMR Spectroscopy. CoLaus Pseudospectra.

Summary statistics between urine NMR metabolome features and genotypes in the CoLaus cohort. Used as test pseudospectra for metabomatching, a method for metabolite identification using genetic spiking.

opencc-by-sa-4.0Dec 2016View details →

Metabomatching: Using Genetic Association to Identify Metabolites in Proton NMR Spectroscopy. SHIP Pseudospectra.

Summary statistics between urine NMR metabolome features and genotypes in the SHIP cohort. Used as test pseudospectra for metabomatching, a method for metabolite identification using genetic spiking.

opencc-by-4.0Apr 2023View details →

Dataset of paper "GNN for Deep Full Event Interpretation and hierarchical reconstruction of heavy-hadron decays in proton-proton collisions"

DFEI dataset The full description can also be found in README.md. The dataset was used in the paper “GNN for Deep Full Event Interpretation and hierarchical reconstruction of heavy-hadron decays in proton-proton collisions”. The project describes a full event interpretation at the LHCb experiment, situated at the Large Hadron Collider in CERN, Geneva. An “event” consists of detector responses that were converted to tracks - each track represents a particle. The aim of the algorithm is to make sense of the tracks and bundle together tracks coming from the same origin, as well as interpreting their decay hierarchy. Generated events The events in this dataset are based on simulation generated with <a href="https://www.pythia.org/">PYTHIA8</a> and <a href="https://evtgen.hepforge.org/">EvtGen</a>, in which the particle-collision conditions expected for the LHC Run 3 are replicated as shown in the table. <table> <thead> <tr> <th>LHCb period</th> <th>Num. vis. pp collisions</th> <th>Num. tracks</th> <th>Num. b hadrons</th> <th>Num. c hadrons</th> </tr> </thead> <tbody> <tr> <td>Runs 3-4 (Upgrade I)</td> <td> &sim; 5</td> <td> &sim; 150</td> <td> ≪ 1</td> <td> &sim; 1</td> </tr> </tbody> </table> Additionally, an approximate emulation of the LHCb detection and reconstruction effects is applied, as described in the paper in the appendix “Simulation”. In the generated dataset, each event is required to contain at least one b-hadron, which is subsequently allowed to decay freely through any of the standard decay modes present in PYTHIA8. On average, 40% of those events contain more than one b-hadron decay, with a maximum b-hadron decay multiplicity of five. Only charged stable particles that have been produced inside the LHCb geometrical acceptance and in the Vertex Locator region (as defined in the paper) are included in the datasets. Datasets The datasets are divided in three categories Training and testing The file <code>Dataset_InclusiveHb_Training.root</code> contains the training dataset (40,000 events) test dataset (10,000 events) of inclusive decays. Evaluation The inclusive dataset <code>Dataset_InclusiveHb_Evaluation.root</code> contains the evaluation events (50,000). Exclusive decays In addition to this inclusive dataset, several other smaller samples (of few thousand events each) have also been generated, requiring that all the events in each sample contained a specific (exclusive) type of b-hadron decay. The specific modes have been chosen to be representative of the most common classes of decay topologies of physics interest for LHCb. These samples contain only events in which all the particles originating from each of the considered exclusive decays have been produced inside the LHCb geometrical acceptance and in the Vertex Locator region. The datasets contained are: <ul> <li><code>Dataset_Bd_DD.root</code></li> <li><code>Dataset_Bd_Kpi.root</code></li> <li><code>Dataset_Bd_Kstmumu.root</code></li> <li><code>Dataset_Bs_Dspi.root</code></li> <li><code>Dataset_Bs_Jpsiphi.root</code></li> <li><code>Dataset_Bu_KKpi.root</code></li> <li><code>Dataset_Lb_Lcpi.root</code></li> </ul> More information on them can be found in the paper. Loading the data The dataset is saved in the binary ROOT format with a key-array mapping. It can be loaded using the <a href="https://github.com/scikit-hep/uproot5#readme">uproot</a> Python library to convert it to a pandas DataFrame or similar. An example snippet is given here: <pre><code>import uproot # treename = "Particles" treename = "Relations" with uproot.open('/path/to/file.root') as file: df = file[treename].arrays( # we can specify only a set of branches # ['EventNumber', "FromSamePV_true"], library='pd') # 'pd' for pandas </code></pre> The returned <code>file</code> behaves like a mapping that contains two different data holders. They are accessible with <code>Relations</code> or <code>Particles</code> that contain either the relations between the particles or the particles themselves. Regarding the <code>Relations</code>, only edges connecting two different particles are contained in the dataset. The edges are treated as not directional, so a single edge is considered for each pair of particles. Variables The relevant features used in the GNN are described in the following. A cartesian right-handed coordinate system is used, with the z axis pointing along the beamline, the x axis beinng parallel to the horizontal and the y axis being vertically oriented. When specified in the name of the variables, the suffix “_true” refers to ground-truth information, and the suffix “_reco” refers to the output of the emulated LHCb reconstruction. <ul> <li> General: <ul> <li>EventNumber: unique number to identify the event that the entry belongs to.</li> </ul> </li> <li> Node variables: <ul> <li> ParticleKey: unique number to identify each particle in a given event. </li> <li> Identity (ID): numerical code identifying the type of particle, following the <a href="https://pdg.lbl.gov/2019/reviews/rpp2019-rev-monte-carlo-numbering.pdf">Monte Carlo Particle Numbering Scheme</a>. </li> <li> FromPrimaryBeautyHadron: boolean variable indicating whether the particles has been produced in a beauty hadron decay or not. </li> <li> Transverse momentum (pT): component of the three-momentum transverse to the beamline, i.e. the x and y component combined. </li> <li> Impact parameter with respect to the associated primary vertex (IP): distance of closest approach between the particle trajectory and its associated primary vertex (proton-proton collision point), defined as the one with the smallest IP for the given particle amongst all the primary vertices in the event. </li> <li> Pseudorapidity (η): spatial coordinate describing the angle of a particle relative to the beam axis, computed as η = arctanh(pz/∥p⃗∥). </li> <li> Charge (q): for the stable particles under consideration, the charge can take the value 1 or -1. </li> <li> Ox, Oy, Oz: cartesian coordinates of the origin point of the particle. </li> <li> px, py, pz: cartesian coordinates of the three-momentum. </li> <li> PVx, PVy, PVz: cartesian coordinates of the position of the associated primary vertex. </li> </ul> </li> <li> Edge variables: <ul> <li> FirstParticleKey: ParticleKey of one of the two particles connected by the edge. </li> <li> SecondParticleKey: ParticleKey of the other particle, verifying FirstParticleKey > SecondParticleKey. </li> <li> FromSamePrimaryBeautyHadron: boolean variable indicating whether the two particles originate from the same beauty hadron decay. </li> <li> Opening angle (θ): angle between the three-momentum directions of the two particles. </li> <li> Momentum-transverse distance (d &perp; P⃗): distance between the origin point of the two particles defined on a plane which is transverse to the combined three momentum of the two particles. </li> <li> Distance along the beam axis (Δz): difference between the z-coordinate of the origin points of the two particles. </li> <li> FromSamePV: boolean variable indicating whether the two particles share the same associated primary vertex. </li> <li> Order of the “topological” Lowest Common Ancestor (TopoLCAOrder): variable that can take the values 0, 1, 2 or 3, as explained in the paper. </li> <li> Identity of the “topological” Lowest Common Ancestor (TopoLCAID): numerical code identifying the particle type of the ancestor, following the <a href="https://pdg.lbl.gov/2019/reviews/rpp2019-rev-monte-carlo-numbering.pdf">Monte Carlo Particle Numbering Scheme</a>. </li> </ul> </li> </ul>

opencc-by-4.0Feb 2024View details →

Data for: First experimental time-of-flight-based proton radiography using low gain avalanche diodes

Data for: First experimental time-of-flight-based proton radiography using low gain avalanche diodes The associated publication can be found on https://iopscience.iop.org/article/10.1088/1361-6560/ad3326. All graphs inside the publication can be recreated with this dataset. Similar to the publication, the data for the timewalk and offset correction are only given for one sensor and one channel as they only serve a representative purpose. The raw data for all other channels can be shared upon request. Furthermore, as in the publication, the data for the water-equivalent-thickness (WET) calibration and proton radiography (pRAD) creation are given by the median and the interquartile range of the measured quantities of the individual graphs. Those data are also calibrated. If required, the raw, unprocessed data of each measurement can be shared upon request. In the following, a description of the individual files and corresponding figures in the publication is given. If not specified otherwise, the physical units are given in brackets next to the name of the corresponding physical quantity (usually first line in file): <ul> <li>Figure 6: <ul> <li> RawToTspectrumrescaledLGAD3.txt: <ul> <li>Describes the re-scaled time-over-threshold (ToT) spectrum measured inside the third LGAD of the time-of-flight-based ion computed tomography (TOF-iCT) demonstrator using 800 MeV protons (Figure 6a). The first column gives the channel number on the LGAD (channelnr[#]), the second column, the ToT value measured in this channel (ToT[ps]) and the third channel, the corresponding occurrence  (counts[#]).</li> </ul> </li> <li>ToTspectrumrescaledLocMaxLGAD3.txt <ul> <li>Describes the re-scaled ToT spectrum measured inside the third LGAD of the TOF-iCT demonstrator using only the local ToT maxima inside each 4D-cluster. The spectrum was obtained using 800 MeV protons (Figure 6b). The first column gives the channel number on the LGAD (channelnr[#]), the second column, the ToT value measured in this channel (ToT[ps]) and the third channel, the corresponding occurrence  (counts[#]).</li> </ul> </li> </ul> </li> <li>Figure 7: <ul> <li>offsetpraecalib.txt: <ul> <li>Describes the raw, uncalibrated time difference spectrum in LGAD3 measured between all channels on LGAD3 and a central reference channel on LGAD4 (figure 7a). The first column represents the detector channel nr in LGAD3, the second column the raw, uncalibrated time difference between LGAD3 and LGAD4 (TDiff[ns]) and the third column the number of corresponding counts (counts[#]).</li> </ul> </li> <li>offsetpraecalib.txt: <ul> <li>Describes the time walk and offset-calibrated time difference spectrum in LGAD3 measured between all channels on LGAD3 and a central reference channel on LGAD4 (figure 7b). The first column represents the detector channel nr in LGAD3, the second column the calibrated time difference between LGAD3 and LGAD4 (TDiff[ns]) and the third column the number of corresponding counts (counts[#]).</li> </ul> </li> <li> praetwdata.txt: <ul> <li>Describes the ToT dependence of the measured time difference between LGAD1 and LGAD2 using the raw ToT of channel 31 in LGAD1 (figure 7c). The first column represents the raw, unscaled and uncalibrated ToT in LGAD 1 (ToT[ns]), the second column the measured time difference (TDiff[ns]) and the last column, the number of corresponding counts (counts[#]). A ToT cut on the reference channel on LGAD2 has been applied in advance to guarantee a correlation between only true particle hits in the second sensor.</li> </ul> </li> <li>posttwdata.txt <ul> <li>Describes the time walk-calibrated ToT vs TDiff spectrum using the measured time difference between LGAD1 and LGAD2 and the  ToT of channel 31 in LGAD1 (figure 7d). The first column represents the  ToT in LGAD 1 (ToT[ns]), the second column the measured time difference (TDiff[ns]) and the last column the number of corresponding counts (counts[#]). A ToT cut on the reference channel on LGAD2 has been applied in advance to guarantee a correlation between only true particle hits in the second sensor.</li> </ul> </li> </ul> </li> <li>Figure 8: <ul> <li>tofinaridata.txt: <ul> <li>Describes the measured TOF in air through the scanner w.r.t the TOF measured at 800MeV, i.e. the median TOF value at 800MeV was subtracted from all data points (Figure 8a). The first column describes the beam energy (beamenergy[MeV]), the second column the first quartile of the measured TOF per pixel (TOFperpixelQ1[ps]), the second column the median TOF per pixel (TOFperpixelQ2[ps]) and the last column the third quartile of the measured TOF per pixel (TOFperpixelQ3[ps]).</li> </ul> </li> <li>tofinairtheodata.txt: <ul> <li>Describes the theoretical TOF in air through the scanner w.r.t the theoretical TOF at 800MeV, i.e. the theoretical TOF value at 800MeV was subtracted from all data points (Figure 8a).</li> </ul> </li> <li>intrinsictimeresolution.txt: <ul> <li>Describes the energy dependence of the intrinsic time resolution per channel measured inside LGAD1 (figure 8b). The first column represents the primary beam energy (beamenergy[MeV), the second column the corresponding energy loss in MIPs (relativeenergylossi[MIP]), the third column the first quartile of the intrinsic time resolution per LGAD channel (timeresperpixelQ1[ps]), the fourth column the median of the intrinsic time resolution per LGAD channel and the last column the third quartile of the intrinsic time resolution per LGAD channel (timeresperpixelmedian[ps],timeresperpixelQ3[ps]).</li> </ul> </li> </ul> </li> <li>Figure 9: <ul> <li>wetcalib.txt <ul> <li>Describes the measured TOF increase per pixel w.r.t to the TOF in air (i.e. without a phantom) for a given WET and primary beam energy. The first column represents the WET of the irradiated sample (WET[mm]), the second column the used beam energy (beamenergy[MeV]), the third column the first quartile of the measured TOF distribution (TOFperpixelQ1[ps]), the fourth column the median (TOFperpixelQ2[ps]) and the sixth column the third quartile (TOFperpixelQ3[ps]).</li> <li>For each energy, a fifth-order polynomial was used to fit the WET and the TOF increase (Delta TOF(E)~sum_i a_i*(WET_i )^i, with i in [0,5] ). The fit parameters are given in the following for each beam energy: <ul> <li>83 MeV: a_i=[-4.70496227e-02,4.64323118e-01, -2.71391535e-02,4.23655842e-03, -1.13034255e-04,1.23725678e-06]</li> <li>100.4 MeV: a_i=[-3.28976022e-02,-3.68818468e-02,1.96339858e-02,7.31585040e-04, -4.38697681e-05 ,7.52163384e-07]</li> </ul> </li> </ul> </li> </ul> </li> <li>Figure 10: <ul> <li>wetsperpixel83MeV.txt <ul> <li>Describes the proton radiography (pCR) for 83 MeV (Figure 10a). The first column represents the x position of the pixel (x[mm]), the second column the y position of the pixel (y[mm]) and the last column the corresponding WET (WET[mm]).</li> </ul> </li> <li>wetsperpixel83MeV.txt <ul> <li>Describes the proton radiography (pCR) for 100.4 MeV (Figure 10b). The first column represents the x position of the pixel (x[mm]), the second column the y position of the pixel (y[mm]) and the last column the corresponding WET (WET[mm]).</li> </ul> </li> </ul> </li> <li>Figure 11: <ul> <li>wetdistrdata83MeV.txt <ul> <li>Describes the measured TOF per pixel inside the ROI for 83 MeV protons (Figure 11a). The first column represents the lower boundary of each WET bin (WETlowerbinboundary[mm]), the second column the upper boundary of each WET bin (WETupperbinboundary[mm) and the last column the corresponding counts per bin (counts[#]).</li> </ul> </li> <li>wetdistrdata100MeV.txt <ul> <li>Describes the measured TOF per pixel inside the ROI for 100.4 MeV protons (Figure 11b). The first column represents the lower boundary of each WET bin (WETlowerbinboundary[mm]), the second column the upper boundary of each WET bin (WETupperbinboundary[mm) and the last column the corresponding counts per bin (counts[#]).</li> </ul> </li> </ul> </li> </ul>

opencc-by-4.0Oct 2021View details →

Sensor Response Files for the Relativistic Proton Spectrometer aboard NASA's Van Allen Probes

This data set provides the NASA Van Allen Probes Relativistic Proton Spectrometer (RPS) sensor response function files. These files provide the sensor’s response to protons and electrons as a function of energy and angle of incidence.

opencc-by-4.0Feb 2022View details →

Proton collision producing top pair, decaying hadronically via bottom quarks and W bosons

This dataset contains the matrix element calculations for 10,000 events of `p p > t t~ , (t > b W+) , (t~ > b~ W-)`, as produced by MadGraph, without showering or hadronisation, and applying no cuts.

opencc-by-4.0Jun 2022View details →

Combined proton radiography and irradiation for high-precision preclinical studies in small animals

Data used for the publication "Combined proton radiography and irradiation for high-precision preclinical studies in small animals". The repository contains all data that was used to generate the quantitative results and figures in the submitted manuscript. Further documentation for the provided code can be found here: https://github.com/jo-mueller/radiographic_workflow_evaluation

opencc-by-4.0Oct 2024View details →

Saturn's magnetospheric proton and electron intensities from Cassini

Differential intensities of 3keV – 40MeV protons and 10eV – 10MeV electrons in Saturn’s magnetosphere and radiation belts at L-shell distances between 1 and 20 Saturn radii from mission-averages of the MIMI/LEMMS, MIMI/CHEMS, and CAPS/ELS instruments on the Cassini spacecraft that was in orbit between 2004 and 2017.

opencc-by-4.0Apr 2021View details →

Spectrum data for calculation of biological effectiveness of proton beams

Datasets used in Bellinzona, E.V.; Grzanka, L.; Attili, A.; Tommasino, F.; Friedrich, T.; Krämer, M.; Scholz, M.; Battistoni, G.; Embriaco, A.; Chiappara, D.; Cirrone, G.A.P.; Petringa, G.; Durante, M.; Scifoni, E. Biological Impact of Target Fragments on Proton Treatment Plans: An Analysis Based on the Current Cross-Section Data and a Full Mixed Field Approach. Cancers 2021, 13, 4768. https://doi.org/10.3390/cancers13194768

opencc-by-4.0Jun 2022View details →

Synthetic proton radiographs for testing direct inversion algorithms

Proton radiographs generated by particle tracing in specified radial force profiles in cylinders and spheres saved in pradformat (github.com/phyzicist/pradformat) in a zipped folder intended as tests for direct inversion algorithms. For details see: J. R. Davies, and P. V. Heuer, https://arxiv.org/abs/2203.00495 Version 2 includes 3 additional radiographs for a spherical Gaussian potential with a reduced bin width and more bins (0.02R and 200x200 bins) Version 3 corrects an error in the x values given for the original spherical Gaussian potentials with negative mu values sphGauss_mum0p25 and sphGauss_mum0p5. The bin widths were half that of the spherical Gaussian results with positive mu values.  Version 4 corrects an error in the x values given for the mesh run and adds a smoothed version of the source intensity (mu0)

opencc-by-4.0Jun 2023View details →

Machine Learning Features from Proton Therapy Treatment Simulations with the Bergen DTC Prototype for Range Verification

Extracted features from the simulation data found at DOI: <a href="https://doi.org/10.5281/zenodo.8192778">10.5281/zenodo.8192778</a> Each simulation constitutes a single data sample. The following features were extracted. Detector features: <ul> <li>Total number of active pixels</li> <li>Total number of clusters (hits)</li> <li>Number of clusters over threshold (5, 20 pixels)</li> <li>Mean and standard deviation of cluster sizes</li> <li>The number of clusters of any given size (1–72)</li> <li>Mean and standard deviation of x- and y-coordinates over each layer (0–42), and the entire detector</li> <li>Number of active pixels in each layer (0–42)</li> <li>Number of clusters (hits) in each layer (0–42)</li> <li>Total energy deposition of the hits in each layer (0–42)</li> </ul> Higher-level detector features, i.e., function fits (linear, cubic, exponential) with their mean squared residuals over the following quantities: <ul> <li>Active pixels over layer</li> <li>Number of clusters over layer</li> <li>Total deposited energy over layer</li> </ul> 201 RSP features extracted from the beam spot, the phantom rotation, and its 3D RSP image. Two datasets are included in two separate archive files: <ul> <li>features.tar.gz: 715-HN phantom by CIRS Inc. (Norfolk, VA, United States), digitized by Giacometti et al. (2017).</li> <li>features-vhf.tar.gz: The Visible Human Female (VHF) Head phantom (Ackermann et al. 1995), courtesy of the U.S. National Library of Medicine, resampled to 1 mm voxels and scaled down to 80% size in the simulation.</li> </ul> After extracting features, some outliers were removed from the datasets: 14 samples for 715-HN and 3 samples for VHF. The rest of the samples were split into train (70%), validation (10%), and test (20%) sets, for both phantoms separately, which can be found in separate CSV files: features_train.csv, features_val.csv, features_test.csv (715-HN) and features-vhf_train.csv, features-vhf_val.csv, features-vhf_test.csv (VHF). The last file (features_shifted_test.csv (715-HN) and features-vhf_shifted_test.csv (VHF)) contains 40 additional samples for each data point in the respective test set, representing a simulated lateral shift between 1 mm and 10 mm in 1 mm intervals in all directions along the x- and y-axis of the beam.

opencc-by-4.0May 2023View details →

Proton Therapy Treatment Simulations with the Bergen DTC Prototype for Range Verification

This dataset contains proton therapy Monte Carlo simulations using GATE (Jan et al. 2004) version 9.2 and Geant4 (Agostinelli et al. 2003; Allison et al. 2006; Allison et al. 2016) version 11.0.0. The treatment targets are the pediatric head phantom 715-HN by CIRS Inc. (Norfolk, VA, United States), digitized by Giacometti et al. (2017), and The Visible Human Female (VHF) head, courtesy of the U.S. National Library of Medicine, resampled to 1 mm voxels and scaled down to 80%. Distal to the phantom, is a simplified geometry of the digital tracking calorimeter designed by the <a href="https://www.uib.no/en/ift/142356/medical-physics-bergen-pct-project">Bergen pCT collaboration</a> for proton computed tomography (Alme et al. 2020). <ul> <li>treatment_simulations.tar.gz: 36258 simulations with the 715-HN phantom</li> <li>treatment_simulations_vhf.tar.gz: 35673 simulations with the VHF phantom</li> </ul> Each simulation is a single pencil beam with \(10^7\) primary protons. To determine valid spots to use, probing simulations with \(10^5\) primaries were conducted, where the beam position, energy, and phantom rotation were varied: 10 mm interval in the xy-plane for the beam position, 3 mm water range interval for the beam energy, and 30° rotations of the phantom. If no primary was found in the detecter after the probing simulation, it is a valid spot for the dataset. Resulting hit files, containing simulated detector readout, can be found in the archive files for the respective phantom. Each simulation is accompanied by a metafile in JSON format, specifying all parameters used to produce the output. E.g., beam position and energy can be found in "parameters" -> "beam_spot_x", "beam_spot_y", "beam_energy" and the phantom rotation can be found in "parameters" -> "phantom_rotation_angle". Simulation data has been pre-processed with two steps. First, hits for the same track in the same layer are binned together into a single entry by averaging the positions (posX, posY, posZ) and summing the energy depositions (edep). Second, the energy deposition values are discretized into a cluster size and back into MeV, to represent the energy resolution of the ALPIDE chip (Alme et al. 2020). Clusters of size 0 are removed. Additionally, some of the unused GATE output columns are removed and a column specifying the detector layer is created through the formula layer = 2*volumeID[2] + volumeID[3]. Details about the meaning of the other output columns can be found in the <a href="https://opengate.readthedocs.io/en/latest/data_output_management.html">GATE documentation</a>.

opencc-by-4.0Sep 2023View details →

MCU data in a Cypress 65 nm SRAM from heavy ions and protons collected at ground facilities

The dataset contains the raw MCU data collected at ground facilities under heavy ion and proton irradiation in the scope or RADSAGA and RADNEXT project. The device under consideration is the CY62167GE30-45ZXI, a 65 nm commercial SRAM available from Infineon (formerly Cypress). Note that the internal ECC has been disabled for this data collection. More information on data collection are available through this paper (<a href="https://doi.org/10.1109/REDW51883.2020.9325822">10.1109/REDW51883.2020.9325822</a>). The MCU were determined through the procedure explained in these two papers (<a href="https://doi.org/10.1109/TNS.2014.2313742">10.1109/TNS.2014.2313742</a> and <a href="https://doi.org/10.1109/TNS.2015.2496874">10.1109/TNS.2015.2496874</a>).

opencc-by-4.0Jan 2020View details →

Supplementary Materials for "Simultaneous single-shot radiographic imaging using a laser-driven x-ray and proton micro-source"

Simulation Data Repository, please read the contained README file in the contained simulation/ directory. This directory contains a copy of the used PIConGPU source code, version 0.5.0-dev-60ad9eb85 and analysis scripts. The PIConGPU source code is archived including its complete git history (git version 2.17.1) in source/picongpu.tar.gz with the input parameter template inside in share/picongpu/examples/Wneedle . Generally, PIConGPU source code is available via <a href="https://doi.org/10.5281/zenodo.591746">https://doi.org/10.5281/zenodo.591746</a> with its public git repository being maintained on <a href="https://github.com/ComputationalRadiationPhysics/picongpu">https://github.com/ComputationalRadiationPhysics/picongpu</a> . The two simulations’ exact input is modified accordingly in the directory input/ inside: 2D_a0-45_Z-10_ppc-20_002_light.tar.gz  (p-polarized; along X) 2D_a0-45_Z-10_ppc-20_003_light.tar.gz  (s-polarized; along Z). “Heavy” simulation data (checkpoints in simOutput/checkpoints/, full-resolution field and particle output in simOutput/bp/ ) has been stripped from this archive and are archived on NERSC’s HPSS tape archive. Analysis scripts are provided as Jupyter notebooks (DensityPlot_polX.ipynb and DensityPlot_polZ.ipynb) and depend on the following software: - adios 1.13.1 python bindings with enabled c-blosc transformations - numpy 1.17.1 - matplotlib 3.1.1 - PIConGPU post-processing helper modules located in each simulation root directory under “input/lib/python/” The detailed conda environment can be found in the README.

opencc-by-4.0Nov 2024View details →

Supporting Information for "Electron and proton peak intensities as observed by a five-spacecraft fleet in solar cycle 25"

Table of parameters employed in the study "Electron and proton peak intensities as observed by a five-spacecraft fleet in solar cycle 25". The table contains the original peak intensities directly taken from the SERPENTINE SEP event catalog, without any scaling or inter-calibration factors that are applied in the study. All information provided in the table is based on the SERPENTINE SEP event catalog and SERPENTINE CME and coronal shocks catalog, only limiting the variables to those used in this study. The dataset is in CSV format. For more information, and if you use this table, please refer to the corresponding publication: <blockquote> <div> Electron and proton peak intensities as observed by a five-spacecraft fleet in solar cycle 25 G. U. Farwa, N. Dresing, J. Gieseler, L. Vuorinen, I. G. Richardson, C. Palmroos, S. Valkila, B. Heber, S. Jensen, P. Kühl, L. Rodríguez-García and R. Vainio A&A, 693 (2025) A198 DOI: <a href="https://doi.org/10.1051/0004-6361/202450945">10.1051/0004-6361/202450945</a> </div> </blockquote> <div> <div> </div> </div> Field descriptions <ul> <li>id: ID</li> <li>date: Event date [UTC]</li> <li>flare_time: Flare time [UTC]</li> <li>flare_lat: Flare Carrington latitude [deg]</li> <li>flare_lon: Flare Carrington longitude [deg]</li> <li>flare_class: Flare class (GOES)</li> <li>flare_comments: Flare Comments</li> <li>radio_type2: Radio type II bursts</li> <li>decametric_type2_start: Decametric type II burst start time [UT]</li> <li>decametric_type2_stop: Decametric type II burst end time [UT]</li> <li>radio_type2_start: Metric radio type II burst start time [UT]</li> <li>radio_type2_stop: Metric radio type II burst end time [UT]</li> <li>solar_mach_link: Solar-Mach link</li> <li>S/C codes <ul> <li>BepiC: BepiColombo</li> <li>L1: L1 (SOHO/Wind)</li> <li>PSP: Parker Solar Probe</li> <li>STA: STEREO A</li> <li>SolO: Solar Orbiter</li> </ul> </li> <li>S/C related field descriptions <ul> <li>{sc}_sc_lat: S/C Carrington latitude [deg]</li> <li>{sc}_sc_lon: S/C Carrington longitude [deg]</li> <li>{sc}_dist: S/C radial distance [au]</li> <li>{sc}_p25MeV_onset_date: S/C protons 25 MeV onset date [UTC]</li> <li>{sc}_p25MeV_onset_time: S/C protons 25 MeV onset time [UTC]</li> <li>{sc}_p25MeV_onset_time_formatted: S/C protons 25 MeV onset time [UTC] (Formatted)</li> <li>{sc}_p25MeV_onset_averaging: S/C protons 25 MeV averaging used for onset [min]</li> <li>{sc}_p25MeV_onset_sector: S/C protons 25 MeV sector used for onset</li> <li>{sc}_p25MeV_peak_date: S/C protons 25 MeV peak date [UTC]</li> <li>{sc}_p25MeV_peak_time: S/C protons 25 MeV peak time [UTC]</li> <li>{sc}_p25MeV_peak_time_formatted: S/C protons 25 MeV peak time [UTC] (Formatted)</li> <li>{sc}_p25MeV_peak_flux: S/C protons 25 MeV original peak flux [cm^-2 s^-1 sr^-1 MeV^-1]</li> <li>{sc}_p25MeV_peak_flux_formatted: S/C protons 25 MeV original peak flux [cm^-2 s^-1 sr^-1 MeV^-1] (Formatted)</li> <li>{sc}_p25MeV_peak_averaging: S/C protons 25 MeV averaging used for peak [min]</li> <li>{sc}_p25MeV_peak_sector: S/C protons 25 MeV sector used for peak</li> <li>{sc}_p25MeV_injection_date: S/C protons 25 MeV inferred injection date [UTC]</li> <li>{sc}_p25MeV_injection_time: S/C protons 25 MeV inferred injection time [UTC]</li> <li>{sc}_p25MeV_sw_speed: S/C protons 25 MeV onset solar wind speed [km/s]</li> <li>{sc}_p25MeV_comments: S/C protons 25 MeV comments</li> <li>{sc}_e100keV_onset_date: S/C electrons 100 keV onset date [UTC]</li> <li>{sc}_e100keV_onset_time: S/C electrons 100 keV onset time [UTC]</li> <li>{sc}_e100keV_onset_time_formatted: S/C electrons 100 keV onset time [UTC] (Formatted)</li> <li>{sc}_e100keV_onset_averaging: S/C electrons 100 keV averaging used for onset [min]</li> <li>{sc}_e100keV_onset_sector: S/C electrons 100 keV sector used for onset</li> <li>{sc}_e100keV_peak_date: S/C electrons 100 keV peak date [UTC]</li> <li>{sc}_e100keV_peak_time: S/C electrons 100 keV peak time [UTC]</li> <li>{sc}_e100keV_peak_time_formatted: S/C electrons 100 keV peak time [UTC] (Formatted)</li> <li>{sc}_e100keV_peak_flux: S/C electrons 100 keV original peak flux [cm^-2 s^-1 sr^-1 MeV^-1]</li> <li>{sc}_e100keV_peak_flux_formatted: S/C electrons 100 keV original peak flux [cm^-2 s^-1 sr^-1 MeV^-1] (Formatted)</li> <li>{sc}_e100keV_peak_averaging: S/C electrons 100 keV averaging used for peak [min]</li> <li>{sc}_e100keV_peak_sector: S/C electrons 100 keV sector used for peak</li> <li>{sc}_e100keV_injection_date: S/C electrons 100 keV inferred injection date [UTC]</li> <li>{sc}_e100keV_injection_time: S/C electrons 100 keV inferred injection time [UTC]</li> <li>{sc}_e100keV_sw_speed: S/C electrons 100 keV onset solar wind speed [km/s]</li> <li>{sc}_e100keV_comments: S/C electrons 100 keV comments</li> <li>{sc}_e1MeV_onset_date: S/C electrons 1 MeV onset date [UTC]</li> <li>{sc}_e1MeV_onset_time: S/C electrons 1 MeV onset time [UTC]</li> <li>{sc}_e1MeV_onset_time_formatted: S/C electrons 1 MeV onset time [UTC] (Formatted)</li> <li>{sc}_e1MeV_onset_averaging: S/C electrons 1 MeV averaging used for onset [min]</li> <li>{sc}_e1MeV_onset_sector: S/C electrons 1 MeV sector used for onset</li> <li>{sc}_e1MeV_peak_date: S/C electrons 1 MeV peak date [UTC]</li> <li>{sc}_e1MeV_peak_time: S/C electrons 1 MeV peak time [UTC]</li> <li>{sc}_e1MeV_peak_time_formatted: S/C electrons 1 MeV peak time [UTC] (Formatted)</li> <li>{sc}_e1MeV_peak_flux: S/C electrons 1 MeV original peak flux [cm^-2 s^-1 sr^-1 MeV^-1]</li> <li>{sc}_e1MeV_peak_flux_formatted: S/C electrons 1 MeV original peak flux [cm^-2 s^-1 sr^-1 MeV^-1] (Formatted)</li> <li>{sc}_e1MeV_peak_averaging: S/C electrons 1 MeV averaging used for peak [min]</li> <li>{sc}_e1MeV_peak_sector: S/C electrons 1 MeV sector used for peak</li> <li>{sc}_e1MeV_injection_date: S/C electrons 1 MeV inferred injection date [UTC]</li> <li>{sc}_e1MeV_injection_time: S/C electrons 1 MeV inferred injection time [UTC]</li> <li>{sc}_e1MeV_sw_speed: S/C electrons 1 MeV onset solar wind speed [km/s]</li> <li>{sc}_e1MeV_comments: S/C electrons 1 MeV comments</li> <li>{sc}_ep_ratio: Ratio of Electrons (~1MeV) / Protons (25-40 MeV)</li> </ul> </li> <li>CME related descriptions <ul> <li>cme_id: CME ID</li> <li>L1_date: Date of CME identification at L1</li> <li>L1_time: Time of CME identification at L1</li> <li>L1_pos_speed: Plane of sky speed of CME measured at L1</li> </ul> </li> </ul> <div>CHANGELOG:</div> <div> <ul> <li>2025-06-12 <ul> <li>Updated peak fluxes and peak times of PSP 1 MeV electrons, as well as PSP's e/p ratios (the previous flux values are erroneous!)</li> </ul> </li> </ul> </div>

opencc-by-4.0Nov 2023View details →

Data Set for the Journal Article "Automated Preparation of Nanoscopic Structures: Graph-Based Sequence Analysis, Mismatch Detection, and pH-Consistent Protonation with Uncertainty Estimates"

This repository containes the data generated by ASAP and discussed in the journal article [Csizi, K.-S. and Reiher, M., 2023, arXiv:2307.16344], including Cartesian coordinates of training and test set molecules, and MD trajectories.