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395 results for “detectors”
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>
Measurement data of the response of a Li-glass/multi-anode photomultiplier detector to focused proton and deuteron beams
<p>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>) .</p> <p>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.</p>
2019-2020 woody plants pollen dataset from automatic particle detector in Šiauliai
<p>Dataset acquired from Rapid-E device by testing it with anemophilous woody plants pollen collected in Lithuania. Data is sorted by pollen type.</p> <p>The sampling methodology can be found in publication "Automatic pollen recognition with the Rapid-E particle counter: the first-level procedure, experience and next steps", Šaulienė Ingrida, et al. Atmospheric Measurement Techniques, 2019, 12.6: 3435-3452. <a href="https://doi.org/10.5194/amt-12-3435-2019">https://doi.org/10.5194/amt-12-3435-2019</a></p> <p>The authors would like to hear from you at realtime@sa.vu.lt if you use this dataset.</p>
2019-2020 herbaceous plants pollen dataset from automatic particle detector in Šiauliai
<p>Dataset acquired from Rapid-E device by testing it with anemophilous herbaceous plant pollen collected in Lithuania. Data is sorted by pollen type.</p> <p>The sampling methodology can be found in publication "Automatic pollen recognition with the Rapid-E particle counter: the first-level procedure, experience and next steps", Šaulienė Ingrida, et al. Atmospheric Measurement Techniques, 2019, 12.6: 3435-3452. <a href="https://doi.org/10.5194/amt-12-3435-2019">https://doi.org/10.5194/amt-12-3435-2019</a></p> <p>The authors would like to hear from you at realtime@sa.vu.lt if you use this dataset.</p>
Dataset: Environmental Gamma Dose Rate Measurements using CZT Detectors
<h1>Scope</h1> <p>This dataset compiles the raw and partely processed data for the manuscript <em>Environmental Gamma Dose Rate Measurements using CZT</em><br><em>Detectors </em>by Sebastian Kreutzer, Loïc Martin, Didier Miallier, and Norbert Mercier. </p> <h1>Dataset structure</h1> <ul> <li>00_Measurement_Data: All original spectra recorded with a Kromek GR1+ and Kromek RayMon10 GR1 detector. All files have the file ending .spe</li> <li>10_GEANT4 modelling results as .xlsx and .ods + simulation code in ZIP file</li> <li>20_System_Calibrations: The final detector calibration results as .rda (external representation of R objects)</li> <li>30_R_Scripts: A compact version of R scripts used to derive the results in the manuscript as HTML and Quatro file</li> <li>40_RadionuclideComposition_WH2024: Raw data from the radionuclide measurements of sample WH2024 (.ud, .pdf)</li> <li>60_RadionuclideComposition_Flossi: Radionuclide concentrations of the granute block Flossi extracted from an unpublished Diplom thesis by Uwe Rieser (1991)</li> <li>70_Heidelberg_Dataset4gamma: Dataset for the R package 'gamma' with nuclide concentration results for Weiße-Hohl, Flossi and calculated dose rates </li> <li>80_Strain_Relief_3D_printing: Construction files for printing the strain relief adapter for the GR1. Please note that all files in this subfolder are subject to CC BY-NC licence conditions, excluding commercial use. </li> </ul> <p> </p>
An analyst-created, high-resolution seismic arrival time dataset for evaluating machine learning phase detectors
<p>This data set contains phase arrival times and phase labels for one hour of continuous seismic data recorded at the three-component broadband station WY.YNR on 2014 30 March from 13:00:00 to 14:00:00 UTC. This hour of data follows a M<sub>w</sub> 4.8 occurring at 12:34 UTC in the Yellowstone region and contains many small events close together in space and time. All 687 picks (404 P and 283 S) were made by a seismic analyst trained at the University of Utah Seismograph Stations. The goal was to pick as many reasonable arrivals as possible for evaluating the performance of machine-learning-based phase detectors on continuous data during high seismicity rates. Phase picks were made using the Seismic Analysis Code (SAC; Goldstein and Snoke, 2005) and Pyrocko (Heimann <em>et al</em>., 2017). In general, a 1 to 17 Hz bandpass filter was used.</p> <p>The csv file contains the pick id, phase arrival time in UTC and Unix epoch formats, and the phase labels (P or S).</p>
Data from Simulations of a Magnetic Shielding System to Deflect Background-Inducing Secondary Electrons away from Space-Based X-ray Detectors
<p>Data from simulations examining the effect of cosmic rays and secondary particles generated by them on background induced in an X-ray astronomy space telescope, and the effectiveness of a surrounding magnetic field at reducing this background.</p> <p>These simulations were performed for the paper “Effectiveness of a dual solenoid magnetic shield at reducing X-ray-like background in silicon-based X-ray detectors” (2023) published in the Journal of Astronomical Telescopes Instruments and Systems. The paper can also be found at https://openresearch.surrey.ac.uk/esploro/outputs/journalArticle/The-Effectiveness-of-a-Dual-Solenoid/99777566602346/filesAndLinks?forceView=true&mode=quickaccess&index=0 .</p> <p>This data was also used for the simulations and analysis described in the thesis "The Simulation, Composition and Shielding of Radiation-Induced X-ray-like Background in Space-Based X-ray Astronomy Missions" (2021), which can be found at <a href="https://doi.org/10.21954/ou.ro.00012e1f">https://doi.org/10.21954/ou.ro.00012e1f</a> .</p>
Data release for "Rapid pre-merger localization of binary neutron stars in third generation gravitational wave detectors"
<p>We publish skymap files in fits format of the simulation in our work "Rapid pre-merger localization of binary neutron stars in third generation gravitational wave detectors". There are 68000 BNS events, and results of different negative latencies are zipped in different tar files. An example jupyter notebook for using the data is provided.</p> <p> </p> <p> </p>
Datasets acquired using Dectris ARINA detector in paper "Using a fast hybrid pixel detector..." doi:10.1088/2515-7639/acf524
<p>Here are the raw data presented in the paper "Using a fast hybrid pixel detector for dose-efficient diffraction imaging beam-sensitive organic molecular thin films"<br> doi:10.1088/2515-7639/acf524</p> <p>It contains multiple datasets:</p> <p>SmB6: The sample is a monocrystalline domain of SmB6 oriented along the <110> zone axis, prepared with FIB by Elisabeth Mueller at PSI. Data was collected with a probe-corrected 200kV TEM microscope, by Daniel Stroppa (Dectris) supported by Mingjian Wu (FAU). Further experimental parameters are mentioned in the paper.</p> <p>Gd2O3 Ptycho: The sample is a quasi-2D poly-crystalline Gd2O3 supported on a carbon TEM grid provided by Baixu Zhu and Xingchen Ye (Indiana University Bloomington). Data was collected with a probe-corrected 200kV TEM microscope, by Philipp Pelz supported by Mingjian Wu. Raw data was a scan of 512x512; a crop of 256x256 was used for reconstruction as presented in the paper. Further experimental parameters are mentioned in the paper</p> <p>Gold thin film: poly-crystalline gold thin film (nominal thickness ~20 nm) deposited on SiN membrane, provided by Peter Denninger (FAU). Data was analyzed using ACOM in py4dstem (ver. 13.17). The analysis notebook is included in the zip. Further experimental parameters are mentioned in the paper.</p> <p>DRCNT_PCBM: bulk hetero-junction organic solar cell thin film provided by Christina Harreiss (FAU). Further experimental parameters (4D-SCED and NBD 4D-STEM) are mentioned in the paper. The same, but pre-processed version (in Gatan dm4 format) of the datasets are in the previous version of this publication.</p> <p>Data visualization and processing can be done with NOVENA software, freely available at DECTRIS website. Alternatively, the files can be opened using a HDF5 file reader.</p>
Interstitial null-distance time-domain diffuse optical spectroscopy using a superconducting nanowire detector
<p>We demonstrate a novel realization of Interstitial fiber, broadband, Time Domain Diffuse Optical Spectroscopy (TD-DOS) in Null Source-Detector separation (NSDS) approach without temporal gating, by using a Superconducting Nanowire single photon detector (SNSPD) for acquisition. As per the MEDPHOT protocol, we test experimentally, the absorption linearity of the system on tissue-equivalent liquid phantoms, and demonstrate the scattering-independent retrieval of the absorption spectrum of water using Intralipid phantoms in the wavelength range of 600-1100 nm.</p> <p>This work has been published in the Journal of Biomedical Optics - https://doi.org/10.1117/1.JBO.28.12.121202. Here, we present the dataset containing the acquired data pertaining to the aforementioned publication, including a brief overview, the tools to read it and the analysis corresponding to the figures in the article.</p>
68 image lysozyme dataset recorded on the Jungfrau 16M detector at SwissFEL and formatted as a NeXus file
<p>Data provided by Meitian Wang at PSI and master file revised May 2020 for full NXmx compliance.</p> <p>To create a new NeXus master file, assuming DIALS is installed in the folder $DIALS, use this command:</p> <p>libtbx.python $DIALS/modules/cctbx_project/xfel/swissfel/jf16m_cxigeom2nexus.py unassembled_file=lyso009a_0087.JF07T32V01.h5 geom_file=16M_bernina_backview_optimized_adu_quads.geom wavelength=1.368479 detector_distance=97.830 mask_file=lyso009a_0087.JF07T32V01.mask.h5 nexus_details.start_time=2018-01-00T00:00:00.000 nexus_details.end_time=2018-01-00T00:00:02.720Z nexus_details.end_time_estimated=2018-01-00T00:00:02.720Z nexus_details.sample_name=Lysozyme nexus_details.total_flux=1000000000000</p> <p>Some notes about the parameters:<br> - Geometry file is in CrystFEL format but has been realigned to group the modules hierarchically into quadrants.<br> - Wavelength is a single wavelength for the whole dataset, but options exist to do 1 wavelength per image, or a whole spectrum per image.<br> - Start and end times are example timestamps for illustration. End times are estimated for 68 frames using a 25 Hz recording rate.<br> - Total flux of 1e12 photons is an estimate.</p> <p>View the data using DIALS: dials.image_viewer lyso009a_0087.JF07T32V01_master.h5</p> <p>Process the data using DIALS, treating the images as stills, assuming 64 cores available on the system:<br> dials.stills_process mp.nproc=64 lyso009a_0087.JF07T32V01_master.h5 dispersion.gain=10 known_symmetry.space_group=P43212 known_symmetry.unit_cell=77,77,37,90,90,90 refinement_protocol.d_min_start=2.5</p> <p>Download DIALS at dials.github.io.</p>
Discrimination of aluminum from silicon by electron crystallography with the JUNGFRAU detector
<p>Electron diffraction data of two different aluminosilicates, zeolite A and albite. They were used to implement the JUNGFRAU detector (PSI Switzerland) at Vienna University, and develop the software for data conversion and data collection. The archives also contain the XDS files for processing. The metadata of the CBF-files mostly not filled in properly (pixel size and wavelength only are correct). Please refer to the XDS.INP files for experimental parameters (oscillation width, detector distance, etc.). The structural CIF files have been submitted to the Cambridge Structural Database CSD.</p>
A simple, static and stage mounted direct electron detector based electron backscatter diffraction system
<h3><strong>Data set for </strong><i><strong>A simple, static and stage mounted direct electron detector based electron backscatter diffraction system</strong></i></h3><p>T.Zhang, T. B. Britton</p><p> </p><h3><strong>Contents</strong></h3><p><strong>- New in v2.0.0: CAD drawings of the stage</strong></p><p> </p><p>- Single Si(100) diffraction patterns at 4 camera lengths, and at 4 corners of the sample</p><p>- Horizontal and vertical line scan on Si(100) with 20 grid points</p><p>- 20x20 mapping scan on a polycrystalline Cu sample</p><p>Scan parameters for the line scans and map are included in logfiles within each subfolder.</p><p> </p><p>All pattern files are provided in .h5 format and .tif format. Analyses of the patterns were performed with AstroEBSD and MTEX.</p>
Simulated datasets for detector and particle flow reconstruction: CLIC detector, machine learning format
<p><strong>Synopsis</strong></p> <p>Machine-learning friendly format of tracks, clusters and target particles in electron-positron events, simulated with the CLIC detector. Ready to be used with <a href="https://zenodo.org/records/14930299">jpata/particleflow:v2.3.0</a>. Derived from the EDM4HEP ROOT files in <a href="https://zenodo.org/record/8260741">https://zenodo.org/record/8260741</a>.</p> <ul> <li>clic_edm_ttbar_pf.zip: e+e- -> ttbar, center of mass energy at 380 GeV</li> <li>clic_edm_qq_pf.zip: e+e- -> Z* -> qqbar, center of mass energy at 380 GeV</li> <li>clic_edm_ww_fullhad_pf.zip: e+e- -> WW -> W decaying hadronically, center of mass energy at 380 GeV</li> <li>clic-tfds.ipynb: an example notebook on how to load the files</li> </ul> <p><strong>Contents</strong></p> <p>Each .zip file contains the dataset in the <a href="https://github.com/tensorflow/datasets">tensorflow-datasets</a>, <a href="https://github.com/google/array_record">array_record</a> format. We have split the full datasets into 10 subsets, due to space considerations on zenodo, two subsets from each dataset are uploaded. Each dataset contains a train and test split of events.</p> <p><strong>Dataset semantics (to be updated)</strong></p> <p>Each dataset consists of events that can be iterated over using the tensorflow-datasets library and used in either tensorflow or pytorch. Each event has the following information available:</p> <ul> <li>X: the reconstruction input features, i.e. tracks and clusters</li> <li>ytarget: the ground truth particles with the features ["PDG", "charge", "pt", "eta", "sin_phi", "cos_phi", "energy", "jet_idx"], with "jet_idx" corresponding to the gen-jet assignment of this particle</li> <li>ycand: the baseline Pandora PF particles with the features ["PDG", "charge", "pt", "eta", "sin_phi", "cos_phi", "energy", "jet_idx"], with "jet_idx" corresponding to the gen-jet assignment of this particle</li> </ul> <p>The full semantics, including the list of features for X, are available at https://github.com/jpata/particleflow/blob/v2.3.0/mlpf/heptfds/clic_pf_edm4hep/utils_edm.py and https://github.com/jpata/particleflow/blob/v2.3.0/mlpf/data/key4hep/postprocessing.py.</p>
4 image lysozyme dataset recorded on the Jungfrau 16M detector at SwissFEL and formatted as a NeXus file
<p>This is a 4 image lysozyme datasets derived from https://doi.org/10.5281/zenodo.3352357. The specific 4 images are able to be processed by the software package DIALS using commands in the linked dataset above. The images were rounded to integer and compressed to save file space using this script:</p> <pre><code class="language-python">import shutil, h5py import numpy as np shutil.copyfile('../lyso009a_0087.JF07T32V01_master.h5', 'lyso009a_0087.JF07T32V01_master_4img.h5') h5 = h5py.File('lyso009a_0087.JF07T32V01_master_4img.h5', 'r+') data = h5["entry/data/data"][()] del h5["entry/data/data"] h = h5["entry/data"] subset = data[5:9].astype(np.int32) h.create_dataset("data", subset.shape, subset.dtype, subset, compression="gzip", compression_opts=9) h5.close()</code></pre> <p>The .expt file was created by dials.import and is useful for regression testing in DIALS.</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>
Dataset for the comparison of performance of two leak detectors using hydrogen reference leaks (supplement to paper "Advancing Hydrogen Leak Detection: Design and Calibration of Reference Leaks")
<p>Excel file containing some measurements made in December 2023, using three hydrogen reference leaks, to assess the performance of two distinct leak detectors, one portable and made specifically for hydrogen and one MSLD in hydrogen-mode.</p>
STOP-IT Human Presence Detector (HPD)
<p>Detecting human movement using WiFi commercial off-the-shelf devices can be effected by exploiting Channel State Information (CSI). CSI is part of the WiFi protocol and models the propagation of a WiFi signal from the transmitter to the receiver, supporting many subcarriers due to the Orthogonal Frequency Division Multiplexing (OFDM) principle. Taking profit of the information provided by the CSI it is possible to detect an intruder in a room or even detect the intruder through the wall. The Human Presence Detector (HPD) uses this technology to detect the presence of a human in movement.</p>
STOP-IT REAL-TIME ANOMALY DETECTOR (RTAD)
<p>ICS were designed with automation reliability in mind and most communication technologies were proprietary with no compatibility with TCP/IP Stack. Nowadays most devices have connectivity features inheriting attacks that do not require physical access to plant or systems and organizations are dedicating resources to protect their assets converging physical, logical and IT resources. Task 5.5 objective is to detect known and unknown threats to ICS systems affecting integrated sensors or actuators and SCADA systems by monitoring and learning from its normal behaviour and giving the ICS operators the ability to detect advanced attacks and anomalies before they can cause damage or spread inside the network. Machine Learning techniques and custom attacks were developed for those purposes in order to identify attack patterns and unwanted behaviour before they can interact with sensors/actuators or spoof SCADA reporting messages for removing visibility of the threat to the operator.</p>
Nanobeam electron diffraction dataset from ion irradiated DIN 1.4970 austenitic stainless steel with G-phase precipitates collected on pixelated TVIPS detector
<p><strong>Summary</strong></p> <p>This is a 4D scanning transmission electron microscopy (4D STEM) dataset collected in near-parallel beam mode (NBED) from a sample of ion irradiated austenitic (FCC) stainless steel of the DIN 1.4970 specification, collected on a high quality pixelated detector inside a transmission electron microscope (TEM). The dataset is represented by a 4D array, comprising a 2D grid of scan points, with each scan point mapping to an electron diffraction spot pattern. From this kind of dataset it is possible to derive local crystal orientations and strains. The dataset is in the .hspy format, the native hdf5 format of the <a href="https://zenodo.org/record/5082777">HyperSpy</a> library.</p> <p>The main features in this dataset are:</p> <ul> <li>a single crystal of the matrix is sampled, close to a 110 zone axis</li> <li>inside the matrix, irradiation induced G-phase precipitates of 10-20 nm in size can be found which contribute weakly to some of the diffraction patterns. From these patterns it is possible to derive the orientation relationship of the precipitates with respect to the matrix.</li> <li>irradiation also resulted in the formation of faulted frank loops, which also show up in some diffraction patterns.</li> </ul> <p><strong>Material and sample preparation</strong></p> <p>The sample was prepared from DIN 1.4970 steel (composition by weight: 15% Ni, 15% Cr, 1.8% Mn, 1.2% Mo, 0.5% Ti, 0.5% Si, 0.1% C, Fe Bal.) with the intended application of nuclear fuel cladding material. The material was originally in the shape of thin walled tubes and cold worked to 24% (measured by cross sectional area reduction). The material was aged for 2 hours at 800 °C. It was then irradiated to 40 dpa surface damage as calculated using the SRIM program and the Kinchin and Pease model with displacement energy of 40 eV, using 4.5 MeV Fe<sup>2+</sup> ions with a flux of arround 9x10<sup>11</sup> ions.s<sup>-1</sup>.cm<sup>-2</sup>. The irradiation was performed at 600 °C. Full details on the material, irradiation conditions, and context can be found in:</p> <p>Cautaerts, N., Delville, R., Stergar, E., Pakarinen, J., Verwerft, M., Yang, Y., Hofer, C., Schnitzer, R., Lamm, S., Felfer, P., & Schryvers, D. (2020). The role of Ti and TiC nanoprecipitates in radiation resistant austenitic steel : A nanoscale study. <em>Acta Materialia</em>, <em>197</em>, 184–197. https://doi.org/10.1016/j.actamat.2020.07.022</p> <p>A TEM sample was prepared by regular focused ion beam (FIB) lift-out techniques in a Ga-ion FIB. Additional details on the dataset can be found in the paper and supplementary materials of</p> <p>Cautaerts, N., Rauch, E. F., Jeong, J., Dehm, G., & Liebscher, C. H. (2021). Investigation of the orientation relationship between nano-sized G-phase precipitates and austenite with scanning nano-beam electron diffraction using a pixelated detector. <em>Scripta Materialia</em>, <em>201</em>, 113930. https://doi.org/10.1016/j.scriptamat.2021.113930</p> <p><strong>Microscopy parameters and data collection</strong></p> <p>NBED was performed in a JEM-2200FS TEM (JEOL) operating at 200 kV. The microscope was operated in nanobeam diffraction mode with the smallest spot size (Spot 5). The probe diameter was ~ 1 nm with a semi-convergence angle of ~0.5 mrad. Data was collected on a TemCam-XF416 pixelated CMOS detector (TVIPS). The camera length as indicated in the operating software was 80 cm, and collected images were 1024 by 1024 in size (hardware binning of 4). The dataset comprises 260x200 scan points and pixel depth is 2 bytes (unsigned 16 bit integers).</p> <p><strong>Data processing</strong></p> <p>The raw data was collected in the .tvips format. The original dataset was about 50 GB in size and can be shared upon request to the author. This dataset was converted to the .hspy format using the <a href="https://zenodo.org/record/4288857">TVIPSconverter</a> tool. In the conversion, the images were binned by an additional factor of 4 to a final size of 256x256. A median filter was also applied to the data to remove pixel noise.</p> <p><strong>Data characteristics</strong></p> <p>Scan shape: 260 x 200 pixels</p> <p>Image shape: 256 x 256 pixels</p> <p>Pixel dtype: uint16</p> <p>Scan pixel size: about 1 nm, scan dimensions were never calibrated</p> <p>Image pixel size: 0.01261 Angstrom<sup>-1</sup> / pixel</p> <p>Note that scale factors are not stored in the dataset! The dataset can be read with HyperSpy using the load function (please see the HyperSpy documentation) and the pixel scale can be set through the axes manager. It is highly recommended to have a working installation of <a href="https://zenodo.org/record/5075520">Pyxem</a> as well to process the data.</p> <p><strong>Additional notes</strong></p> <p>Data was collected with the TVIPS scan generator which can be quite buggy. The scan lines show "jitters" due to the unstable snake-scan pattern, hysteresis and instability.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.