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1,179 results for “Probe”
Dataset of 'Complete flow characterization from snapshot PIV, fast probes and physics-informed neural networks'
<p>Dataset of the article 'Complete flow characterization from snapshot PIV, fast probes and physics-informed neural networks' (https://doi.org/10.1016/j.cma.2023.116652). The codes processing data here are on https://github.com/AlvaroMS90/Complete-flow-characterization-from-snapshot-PIV-fast-probes-and-physics-informed-neural-networks.</p> <p>This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement No 949085) and by MCIN/AEI /10.13039/501100011033 and the European Union ‘NextGenerationEU/PRTR’ as part of the grant FJC2020-044342-I.</p>
Van Allen Probes toroidal standing Alfvén wave frequencies
<p>This dataset consists of files containing toroidal standing Alfvén wave frequencies at the Van Allen Probes (RBSP) spacecraft. The frequencies were determined for the fundamental (mode 1) thorough third (mode 3) harmonics using the method described by Takahashi et al. (2021). The files cover the RBSP mission period for which both the fluxgate magnetometer data and the spinfit electric field data are available. Main programs used to generate the data files are also included.</p>
Parker Solar Probe Reconnection Exhaust Data Set for Close Encounters 4-11
<p>The data set contained in the ascii text files consist of the current sheet (CS) time intervals and the associated parameters of identified magnetic reconnection exhausts across CSs recorded by the Parker Solar Probe within R<0.26 AU from the Sun for close encounters 4-11. A detailed description is available in a manuscript submitted to The Astrophysical Journal by Eriksson et al. [2024] entitled "Parker Solar Probe Observations of Magnetic Reconnection Exhausts in Quiescent Plasmas Near the Sun".</p><p>The ascii data file zenodo_psp_ce_lmn_cs_output_sorted_tctr.txt contains a center time of the optimized Walen relation (tc, column 1) and columns 2-3 show the CS start (CS1) and end (CS2) times. This is followed by the time duration dtcs in seconds (column 4) and whether a SPAN-I instrument survey (s=sf00) or burst (a=af00) data product exist (column 5). The RTN components of the adjacent time averaged magnetic field before (B1_R, B1_T, B1_N) and after (B2_R, B2_T, B2_N) the CS then follows in columns 6-11 and a corresponding magnetic field rotation angle (Bshear1) at the CS (column 12). The CP dL, dM, dN columns 13-15 list the distances covered along the hybrid-LMN coordinate system based in a Cross-Product normal to the CS in terms of the average ion inertial length diavg (column 16), which is based on a non-corrected proton density (Np1+Np2)/2=Npavg (columns 17-19). The subsequent columns 20-28 are the adjacent VL,VM,VN on the two sides of the CS from the MVAB LMN system as well as their averages (VL1+VL2)=VLavg, (VM1+VM2)/2=VMavg and (VN1+VN2)/2=VNavg. This is followed (columns 29-37) by the corresponding VL,VM,VN and averages in the hybrid-LMN system using the cross-product (CP) normal. The adjacent average total magnetic field Btot1 and Btot2 (columns 38-39) and the LMN components of the magnetic field in the MVAB system [BL1, BL2], [BM1, BM2], [BN1, BN2] in columns 40-45 are followed by the corresponding magnetic fields in the CP hybrid-LMN system (columns 46-51). The ratio of the intermediate to minimum eigenvalue ratio is included (column 52) followed by the unit vectors of the MVAB system L=[L_R, L_T, L_N], M=[M_R, M_T, M_N], and N=[N_R, N_T, N_N] in columns 53-61. The times adjacent to the CS used to obtain this set of MVAB eigenvectors is found in columns 62-63 (MVAB UT1 and MVAB UT2). The magnetic fields (B1 and B2) used to obtain the local hybrid-LMN system relative these MVAB times relatively farther from the CS are associated with a rotation angle (Bshear2) in column 64. The local hybrid-LMN system from the CP normal is then listed in columns 65-73 LCP=[L_R, L_T, L_N], MCP=[M_R, M_T, M_N], NCP=[N_R, N_T, N_N]. The angle (degrees) between the MVAB N and NCP is listed in column 74 (Nmv*Ncp) followed by the last two columns 75-76 that contain the time interval (s) relative CS1 and CS2 to obtain average plasma data (dext) and magentic field data (bext).</p><p>A letter "m" right before column 1 flags the five events in this list of 236 events when the magnetic field did not change sign across the assumed CS. A letter "d" marks the 10 events associated with two opposite (double) exhausts.</p><p>The ascii data file zenodo_psp_ce_lmn_cs_output_sorted_qtn.txt contains the same current sheet (CS) start (CS1) and end (CS2) times (columns 1-2). This file also lists the adjacent average proton (non-corrected) density in columns 3-4 as in the first (tctr) file which is followed by the average proton temperatures (Tp1 and Tp2 in columns 5-6), solar wind speed (Vtot1 and Vtot2 in columns 7-8) and the hybrid system L-components of the velocity (VL1 and VL2 in columns 9-10). Columns 11-16 contain the minimum and maximum values within each CS of the non-correct proton density, proton temperature and VL component. Column 17 lists the daily median density ratio from the electron density (Ne) and the non-corrected proton density (Np) where Ne is obtained through quasi-thermal noise spectroscopy by Kruparova et al. (2023). Column 18 lists the average of this daily ratio. Column 19 lists a value fc. A value larger than 1.0 indicates that a CS event time period is corrected to a local Ne value using Npfc=(Ne/Np)*Np*fc, where Np is a non-corrected proton density, Ne/Np is the median of the daily Ne/Np ratio.</p><p>The ascii data file zenodo_psp_ce_lmn_cs_output_sorted_positions.txt contains the Parker Solar Probe median of its radial position within each CS1-CS2 time period in both solar radius and astronomical unit.</p><p>The PDF figures psp_ceXX_apj_plots_qtn_final.pdf for XX=04,05,06,07,08,09,10 and 11 contain all CS events with a reconnection exhaust for each close encounter 04-11 on the basis of the agreement with a Walen prediction. Each plot shows the pitch-angle distribution (0-180 degrees) of the supra-thermal energy flux, proton temperature (MK), proton density (black) corrected to a daily median ratio Ne/Np as Npcorr=(Ne/Np)*Np, where Ne (red dots) is obtained from a QTN analysis by Kruparova et al. (2023), L-components of the magnetic field and proton velocity with a red trace showing a Walen prediction to this VL across the CS, the M and N components of the magnetic field with BM shifted by its time-period average, and the M and N components of the proton velocity with VM shifted by its time-period average. The LMN unit vectors of the employed hybrid LMN system are shown below each plot for reference.</p><p>Finally, the eight ~11-day overview plots for each close encounter 4-11 marks the center times (tc) of each confirmed exhaust interval in this study of 231 events (red vertical dotted line) with the five marginal events marked as a black vertical dotted line. The panels from the top show the pitch-angle distribution (0-180 degrees) of the supra-thermal energy flux, proton temperature (MK), magnetic field strength, R-components of B and V, N-components of B and V, and the radial postion of Parker Solar Probe in terms of the solar radius. Here, R and N are two of the three RTN system components of B and V.</p>
Data from: The effect of probe density coverage on the detection of oenological tannins in quartz crystal microbalance with dissipation monitoring (QCM-D) experiments
<p>Polyphenols, crucial compounds in grapes, musts, and wines, influence grape ripening, must fermentation, and final wine quality. Current detection methods for polyphenols are expensive, time-consuming, and reliant on specialized laboratories and personnel. This study proposes the use of a functionalized acoustic sensor to address these limitations and efficiently detect oenological polyphenols.</p> <p>The method employs a quartz crystal microbalance with dissipation monitoring (QCM-D) combined with a gelatin-based probe layer to detect the target analyte. The sensor is functionalized by optimizing probe coverage density, accomplished through the use of 12-mercaptododecanoic acid (12-MCA) for probe immobilization onto the gold sensor surface, along with dithiothreitol (DTT) as a reducing and competitive binding agent. Varying concentrations of 12-MCA and DTT allow for control over probe density, with QCM-D measurements demonstrating effective adjustment, ranging from 0.2 × 10^13 to 2 × 10^13 molecules cm^−2. The study also explores the interaction between the probe and tannins, confirming the ability of the sensor to detect them. Notably, lower probe coverage yields higher detection signals when normalized to probe immobilization signals. Additionally, significant alterations in the mechanical properties of the functionalization layer occur after interaction with samples.</p> <p>Combining QCM-D with gelatin functionalization presents promising applications in the wine industry. This approach enables real-time monitoring, requires minimal sample preparation, and offers high sensitivity for quality control purposes.</p>
Sensor Response Files for the Relativistic Proton Spectrometer aboard NASA's Van Allen Probes
<p>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.</p>
Multi-Needle Langmuir probe (mNLP) data on the Investigation of Cusp Irregularities (ICI) 4 sounding rocket
<p>Documentation for file: ICI4_mNLP_01112021.mat</p> <p> </p> <ol> <li> <p><strong>The mNLP system on ICI-4</strong></p> </li> </ol> <p>The mNLP system on ICI-4 consisted of four cylindrical Langmuir probes with a diameter of 0.51 mm and a length of 25 mm [1,2]. The instruments allowed for current measurements at a sampling rate of 8680.5 Hz. The mNLP data included in the file are 1) the raw currents (‘I_mnlp’) in which spikes have been removed using a median filter over ten data points, and 2) the “filtered currents” (‘I_mnlp_filt’). For the latter, the spin of the payload and the three first harmonics were removed using a band-pass filter [1,2]. Additionally, components with frequencies larger than 1kHz were also removed.</p> <p> </p> <p> </p> <ol> <li> <p><strong>Variables:</strong></p> </li> </ol> <table> <tbody> <tr> <td> <p>Variable name</p> </td> <td> <p>Units</p> </td> <td> <p>Description/Comment</p> </td> </tr> <tr> <td> <p>time_noNans</p> </td> <td> <p>seconds</p> </td> <td> <p>Time of flight since launch.</p> </td> </tr> <tr> <td> <p>Alt</p> </td> <td> <p>Km</p> </td> <td> <p>Altitude of the payload.</p> </td> </tr> <tr> <td> <p>I_mnlp</p> </td> <td> <p>Ampere</p> </td> <td> <p>Currents obtained by the four cylindrical Langmuir probes. The 1<sup>st</sup>-4<sup>th</sup> columns contain the currents obtained by the probes with bias voltages of 3V, 4.5V, 6V, and 7.5 V, respectively. The data was filtered using a median filter over ten data points.</p> </td> </tr> <tr> <td> <p>I_mnlp_filt</p> </td> <td> <p>Ampere</p> </td> <td> <p>Same as I_mnlp with additional filtering of currents using band-pass filters [1,2].</p> </td> </tr> </tbody> </table> <p> </p> <p><strong>Acknowledgement</strong></p> <p>The mNLP experiment and the ICI-4 campaign were funded through the Research Council of Norway. Thanks to Lasse Clausen, Espen Trondsen, Jøran I. Moen, David Michael Bang-Hauge, Bjørn Lybekk, and the Mechanical Workshop at the University of Oslo, Norway. </p> <p><br> </p> <p>1. Bekkeng, T. A., K. S. Jacobsen, J. K. Bekkeng, A. Pedersen, T. Lindem, J.‐P. Lebreton, and J. I. Moen (2010), Design of a multi‐needle Langmuir probe system, Meas. Sci. Technol., 21, 085,903, doi:10.1088/0957‐0233/21/8/085903</p> <p>2. Jacobsen, K. S., Pedersen, A., Moen, J. I., & Bekkeng, T. A. (2010), A new Langmuir probe concept for rapid sampling of space plasma electron density. Measurement Science and Technology, 21(8), <a href="https://doi.org/10.1088/0957%E2%80%900233/21/8/085902">https://doi.org/10.1088/0957‐0233/21/8/085902</a></p>
Data for: Probing electron and hole co-localization by resonant four-wave mixing spectroscopy in the extreme-ultraviolet
<p>Data for: Probing electron and hole co-localization by resonant four-wave mixing spectroscopy in the extreme-ultraviolet</p>
Nanomechanical probing and strain tuning of the Curie temperature in suspended Cr2Ge2Te6-based heterostructures
<p>Data files for Figs. 1-5 of the article "Nanomechanical probing and strain tuning of the Curie temperature in suspended Cr<sub>2</sub>Ge<sub>2</sub>Te<sub>6</sub>-based heterostructures" published in <em>npj 2D Materials and Applications</em>, DOI: , URL: </p>
Spatiotemporal multiplexed immunofluorescence imaging of living cells and tissues with bioorthogonal cycling of fluorescent probes
<p>Raw multichannel and/or Z-stack source data from time series images in TIF format to accompany publication of:</p> <p><strong>Spatiotemporal multiplexed immunofluorescence imaging of living cells and tissues with bioorthogonal cycling of fluorescent probes</strong></p> <p>Jina Ko<sup>1</sup>, Martin Wilkovitsch<sup>2</sup>, Juhyun Oh<sup>1</sup>, Rainer Kohler<sup>1</sup>, Evangelia Bolli<sup>1,3</sup>, Mikael J. Pittet<sup>1,3,4,5</sup>, Claudio Vinegoni<sup>1</sup>, David B. Sykes<sup>6,7</sup>, Hannes Mikula<sup>2</sup>, Ralph Weissleder<sup>1,8</sup>*, Jonathan C. T. Carlson<sup>1,7</sup>*</p> <p><sup>1 </sup>Center for Systems Biology, Massachusetts General Hospital, 185 Cambridge St, CPZN 5206, Boston, MA 02114 </p> <p><sup>2</sup> Institute of Applied Synthetic Chemistry, TU Wien, 1060 Vienna, Austria </p> <p><sup>3</sup> Department of Pathology and Immunology, University of Geneva, Geneva, Switzerland</p> <p><sup>4</sup> Ludwig Institute for Cancer Research, Lausanne Branch, Switzerland</p> <p><sup>5</sup> AGORA Cancer Center, Lausanne, Switzerland</p> <p><sup>6</sup> Center for Regenerative Medicine, Massachusetts General Hospital, Boston, MA, USA</p> <p><sup>7 </sup>Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA</p> <p><sup>8 </sup>Department of Systems Biology, Harvard Medical School, 200 Longwood Ave, Boston, MA 02115</p>
Icing Wind Tunnel Measurements of Supercooled Large Droplets Using the 12 mm Total Water Content Cone of the Nevzorov Probe: Measurement Data
<p>This repository contains the measurement data that was used for the publication "Icing Wind Tunnel Measurements of Supercooled Large Droplets Using the 12 mm Total Water Content Cone of the Nevzorov Probe".</p>
Climatology of deep O+ dropouts in the night-time F-region in solar minimum measured by a Langmuir Probe onboard the International Space Station
<p>Dataset contains data pertaining to an accepted JGR Space Physics article of the same name as the dataset. The link to the article is the following: <a href="https://doi.org/10.1029/2022JA030446">https://doi.org/10.1029/2022JA030446</a>. The dataset contains the high level data that were used to generate Figs 2-5 in the aforementioned paper. </p> <p>The observations recorded by ISS FPMU will be uploaded to NASA SPDF as well. A previous dataset already exists in CDAweb under ISS/FPMU. The O+ information will be added with the new upload.</p> <p>For any questions about the data or the tools used to derive the figures from the data, please take a look at the paper <a href="https://doi.org/10.1029/2022JA030446">https://doi.org/10.1029/2022JA030446</a>, or contact Shantanab Debchoudhury at debchous@erau.edu. </p> <p> </p>
The chemical enrichment in the early Universe as probed by JWST via direct metallicity measurements at z~8
<p>Reduced and flux calibrated JWST/NIRSpec 1D spectra for the three sources (ID_4590 at z=8.4953, ID_6355 at z=7.6643 and ID_10612 at z=7.6592) analysed in Curti et al., 2022, "The chemical enrichment in the early Universe as probed by JWST via direct metallicity measurements at 𝑧~8" (published on MNRAS, Volume 518, Issue 1, pp.425-438)</p> <p>For more details on the data processing we refer to the Section 2.1 of the paper.</p> <p> </p> <p> </p>
The data for SKYSURF-5: Probing the Integrated Galaxy Light with a SDSS-SKYSURF Cross-Matched Catalog
<p>The SKYSURF Project (Windhorst et al. 2022) analyzes the extragalactic background light (both directly using sky background measurements and indirectly using galaxy counts) using the HST Archive. While HST images probe faint galaxies unseen by ground-based imaging, its small field of view prevents it from probing the large-scale structure around its observations.</p> <p>To supplement SKYSURF analysis, we cross-match SKYSURF pointings with SDSS observations able to probe the surrounding large-scale environment (Bhatia et al. 2024). The tables in this database include galaxies brighter than r=22.5 AB mag photometrically identified in SDSS, within +/-5 arcmin around a SKYSURF pointing.</p> <p>The tables in the Object_AB directory include information on all SDSS objects, organized by the HST camera and filter of the central pointing.</p> <p>The tables in the IGL directory include the total galaxy counts and integrated galaxy light (down to AB mag=22.5) for all SDSS objects surrounding a given SKYSURF image.</p>
Dataset From: Measuring Inner Layer Capacitance with the Colloidal Probe Technique
<p>The dataset for the publication "Measuring Inner Layer Capacitance with the Colloidal Probe Technique". doi:10.3390/colloids2040065</p> <p>Files containing data have .dat extension and are in text format.</p>
Replication Data for: Probing magnetism in 2D materials at the nanoscale with single spin microscopy
<p>Data repository for: <strong>Probing magnetism in 2D materials at the nanoscale with single spin microscopy</strong></p> <p><em>Data description.pdf </em>describes the uploaded data.<br> <em>Data.xlsx</em> is the data represented in the paper.<br> <em>MzFromBNV.m</em>, <em>kvalues.m</em>, <em>NVZeemanShiftFromMagnetizedSampleEdge.m</em> are Matlab code files used to transform and fit the data.</p> <p> </p>
Triazole-Extended Anthracenes as Optical Force Probes
<p><sup>1</sup>H- and <sup>13</sup>C-NMR FID of compounds <strong>2</strong> (Figure S1-S2), <strong>3 </strong>(Figure S4-S5), and <strong>6 </strong>(Figure S7-S8) of the Supporting Information.</p>
Development of a diffuse reflectance probe for in situ measurement of inherent optical properties in sea ice
<p>Included are the data presented in the publication entitled: <em>Development of a diffuse reflectance probe for in situ measurement of inherent optical properties in sea ice</em> accepted for publication in The Cryosphere Journal (2021). The data set includes Data and codes:</p> <p>1. Data (duplicated in .xlsx and .mat):</p> <p> </p> <p>1.1 Sites coordinates- (figure 5) -Geolocalisation of both sea ice sampling sites visited for this study (1 and 4)</p> <p> </p> <p>1.2 cumu_sg- (figure 6)- cumulative signal vs depth vs source-detector distance vs scattering coefficient obtained with Monte Carlo simulations</p> <p> —cumu_sg- cumulative signal (%)</p> <p> — depth (mm)</p> <p> —standard deviation on depth where signal is cumulated</p> <p> —ddet (mm)- radial distance between source and detection point </p> <p> — b (m^-1)-scattering coefficient</p> <p> </p> <p>1.3 validation-(figure 7)- Error on IOPs vs IOP value estimated measuring on microspheres solutions </p> <p> </p> <p>—vf (-)- microspheres volume fraction (in water)</p> <p> —a_theo (m^-1) - theoretical value of the absorption coefficient</p> <p> — mean_error_a(%) - error between theoretical value and measured value</p> <p> —std_error_a_x (%) - standard deviation on theoretical value (based on the standard deviation on microspheres diameter)</p> <p> —std_error_a_y (%) -standard deviation on error_a </p> <p> —rb_theo (m^-1) - theoretical value of the reduced scattering coefficient</p> <p> —mean_error_rb(%) - error between theoretical value and measured value</p> <p> —std_error_rb_x (%) - standard deviation on theoretical value (based on the standard deviation on microspheres diameter)</p> <p> —std_error_rb_y (%)) -standard deviation on error_rb </p> <p> —gamma_theo (-) - theoretical value of gamma</p> <p> —mean_error_gamma (%) - standard deviation on theoretical value (based on the standard deviation on microspheres diameter)</p> <p> —std_error_gamma (%) - standard deviation on error_gamma</p> <p> </p> <p>-1.4 T-S-(figure 8)- Vertical profiles of temperature and bulk salinity of sampled sea ice available at both snow covered site 1 and bare ice site 4</p> <p> </p> <p> —T (celsius) - ice temperature</p> <p> —S_si (ppt) - ice bulk salinity</p> <p> —depth (cm)</p> <p> </p> <p>1.5 Rmes-(figure 9)-Vertical profiles of spatially resolved diffuse Reflectance in sea ice using different covers to shade available at both snow covered site 1 and bare ice site 4</p> <p> </p> <p> —Rmes (-) - spatially resolved diffuse Reflectance</p> <p> —Rmes_nbg (-) - spatially resolved diffuse Reflectance with no background sunlight subtraction in calculation of Rmes</p> <p> —dmes (mm) - distance between source and detecting fibre (named rho in the paper)</p> <p> —depth (cm)</p> <p> — cover - cover used to shade from the sun: te=tent,nc= no cover, ta=tarp</p> <p> </p> <p>1.6 IOPprofiles-(figure 9)-Vertical profiles of reduced scattering coefficient in sea ice using different covers to shade available at both snow covered site 1 (ice+snow) and bare ice site 4</p> <p> </p> <p> —infferedrb (m^-1) - reduced scattering coefficient</p> <p> —infferedrb_nbg (m^-1) - reduced scattering coefficient with no background sunlight subtraction in calculation of Rmes</p> <p> —cr1 (binary)— criteria determining if the measurement is kept or not</p> <p> —depth (cm)- depth from the surface . **watch out** at site 1 , the measurments start from the surface of the snow. Substract 24 cm to get measurement from surface of the ice.</p> <p> — cover - cover used to shade from the sun: te=tent,nc= no cover, ta=tarp</p> <p> </p> <p>2. Code (written in .m with MATLAB_R2018b ®) :</p> <p> </p> <p>2.1 inversion algorithm—(figure 9 ) — used to find rb from Rmes (dmes) vertical profiles in sea ice</p> <p> </p> <p>— Main_vprofiles_Rtorb-qik2019_article.m - Main script of the inversion alorithm to get rb from Rmes (dmes)</p> <p>—importfiledata.m-subfunction to import data from .csv </p> <p>—importfiledatamay8.m-subfunction to import data from .csv (specific to may 8th because file was corrupted)</p> <p>—interp1lookup_HR_enlarged_bin10.mat - lookup table of Reflectance vs dmes vs a vs b’ vs gamma used in the inversion</p> <p>—calibjune6_ha_interp1_indcalib2.mat - calibration factor with microspheres as a reference</p> <p>—site1_c20-picture of the ice core taken at site 1</p> <p>—site4_c20-picture of the ice core taken at site 4</p> <p>—may8th+othertests_fixed.csv-raw data from may 8 (site1)</p> <p>—may9day3.csv-raw data from may 9 (site4)</p> <p> </p> <p> </p>
Atom probe tomography nomad-FAIR demonstrator dataset R76-20231-v01.epos.apth5
<p>This is the dataset of an atom probe tomography experiment which is provided open source for testing the possibility of implementing an open source encyclopedia for experimental materials science datasets, including techniques to begin with such as Scanning Transmission Electron Microscopy (STEM), Multidimensional Photo Emission Spectroscopy (MPES), and Atom Probe Tomography (APT) / Field Ion Microscopy (FIM).</p> <p><strong>This repository serves three aims:</strong></p> <p>1. The dataset is of scientific interest. Specifically, it captures the result of a cutting-edge APT experiment detailed exemplarily in DOI 10.1017/S1431927616012654 Fig. 1d by Zirong Peng and coworkers.</p> <p>2. The dataset contributes to tests of an extension to "The NOMAD Laboratory" (https://nomad-coe.eu/): nomad-FAIR. Specifically, to test various aspects of an automatized metadata parsing and processing pipeline to enable the extraction of domain-specific JSON metadata files into a NOMAD-conformant JSON file, ultimately aiming for searchable and repurposable dataset documentation. This serves two purposes: on the one hand to contextualize each dataset within NOMAD. On the other hand to serve as a starting point to parse potential interesting content from the heavy data HDF5 file to reduce unnecessary file access.</p> <p>The implementation of nomad-FAIR is coordinated by Markus Scheidgen.<br> The APT domain-specific parser is developed by Markus Kühbach.</p> <p>3. The dataset constitutes further a test of an open format specification for storing atom probe tomography data using the Hierarchical Data Format (HDF5). This is a recent initiative of the International Field Emission Society's (IFES) atom probe tomography technical committee. In this repository it is detailed an exemplar proposal of how to store acquisition-side relevant results and context of an APT experiment into a HDF5 file and complementary metadata files such as JSON. Implementation of this HDF5-based storage solution for APT data is lead by Markus Kühbach.</p> <p><br> <strong>The organization of this repository with respect to above aims is as follows:</strong></p> <p>-The original EPOS file of the measured is contained in the compressed *.epos.tar.gz archive.</p> <p>-The *.apth5 file is a transcoded version of the EPOS file. Therein, x,y,z data columns are stripped.</p> <p>-The correspondingly named *.json file is the file which nomad-FAIR parses metadata from.</p> <p>-Other files constitute logs of the transcoding process.</p> <p><strong>Funding:</strong><br> The work was partially supported by BiGmax, the Max Planck Society's Research Network on Big-Data-Driven Materials-Science.</p>
Labeled dataset of IEEE 802.11 probe requests
<p><strong>Introduction</strong> <br> <br> The 802.11 standard includes several management features and corresponding frame types. One of them are probe requests (PR). They are sent by mobile devices in the unassociated state to search the nearby area for existing wireless networks. The frame part of PRs consists of variable length fields called information elements (IE). IE fields represent the capabilities of a mobile device, such as data rates. <br> The dataset includes PRs collected in a controlled rural environment and in a semi-controlled indoor environment under different measurement scenarios. <br> It can be used for various use cases, e.g., analysing MAC randomization, determining the number of people in a given location at a given time or in different time periods, analysing trends in population movement (streets, shopping malls, etc.) in different time periods, etc.</p> <p> <br> <strong>Measurement setup</strong> <br> <br> The system for collecting PRs consists of a Raspberry Pi 4 (RPi) with an additional WiFi dongle to capture Wi-Fi signal traffic in monitoring mode. Passive PR monitoring is performed by listening to 802.11 traffic and filtering out PR packets on a single WiFi channel.<br> The following information about each PR received is collected: MAC address, Supported data rates, extended supported rates, HT capabilities, extended capabilities, data under extended tag and vendor specific tag, interworking, VHT capabilities, RSSI, SSID and timestamp when PR was received.<br> The collected data was forwarded to a remote database via a secure VPN connection. A Python script was written using the Pyshark package for data collection, preprocessing and transmission.</p> <p><br> <strong>Data preprocessing</strong></p> <p>The gateway collects PRs for each consecutive predefined scan interval (10 seconds). During this time interval, the data are preprocessed before being transmitted to the database.<br> For each detected PR in the scan interval, IEs fields are saved in the following JSON structure:<br> PR_IE_data =<br> {<br> 'DATA_RTS': {'SUPP': DATA_supp , 'EXT': DATA_ext},<br> 'HT_CAP': DATA_htcap,<br> 'EXT_CAP': {'length': DATA_len, 'data': DATA_extcap},<br> 'VHT_CAP': DATA_vhtcap,<br> 'INTERWORKING': DATA_inter,<br> 'EXT_TAG': {'ID_1': DATA_1_ext, 'ID_2': DATA_2_ext ...},<br> 'VENDOR_SPEC': {VENDOR_1:{<br> 'ID_1': DATA_1_vendor1,<br> 'ID_2': DATA_2_vendor1<br> ...},<br> VENDOR_2:{<br> 'ID_1': DATA_1_vendor2,<br> 'ID_2': DATA_2_vendor2<br> ...}<br> ...}<br> }</p> <p> <br> Supported data rates and extended supported rates are represented as arrays of values that encode information about the rates supported by a mobile device. The rest of the IEs data is represented in hexadecimal format. Vendor Specific Tag is structured differently than the other IEs. This field can contain multiple vendor IDs with multiple data IDs with corresponding data. Similarly, the extended tag can contain multiple data IDs with corresponding data. <br> Missing IE fields in the captured PR are not included in <em>PR_IE_DATA</em>.</p> <p>When a new MAC address is detected in the current scan time interval, the data from PR is stored in the following structure:</p> <p>{'MAC': MAC_address, 'SSIDs': [ SSID ], 'PROBE_REQs': [PR_data] },</p> <p>where <em>PR_data</em> is structured as follows:<br> {<br> 'TIME': [ DATA_time ],<br> 'RSSI': [ DATA_rssi ],<br> 'DATA': PR_IE_data<br> }.</p> <p>This data structure allows storing only <em>TOA</em> and <em>RSSI</em> for all PRs originating from the same MAC address and containing the same <em>PR_IE_data</em>. All SSIDs from the same MAC address are also stored. <br> The data of the newly detected PR is compared with the already stored data of the same MAC in the current scan time interval. <br> If identical PR's IE data from the same MAC address is already stored, then only data for the keys <em>TIME</em> and <em>RSSI</em> are appended.<br> If no identical PR's IE data has yet been received from the same MAC address, then PR_data structure of the new PR for that MAC address is appended to <em>PROBE_REQs</em> key. <br> The preprocessing procedure is shown in Figure ./Figures/Preprocessing_procedure.png <br> At the end of each scan time interval, all processed data is sent to the database along with additional metadata about the collected data e.g. wireless gateway serial number and scan start and end timestamps. For an example of a single PR captured, see the ./Single_PR_capture_example.json file.</p> <p><br> <strong>Environments description</strong> <br> <br> We performed measurements in a controlled rural outdoor environment and in a semi-controlled indoor environment of the Jozef Stefan Institute.<br> See the Excel spreadsheet Measurement_informations.xlsx for a list of mobile devices tested. <br> <br> <strong>Indoor environment </strong><br> <br> We used 3 RPi's for the acquisition of PRs in the Jozef Stefan Institute. They were placed indoors in the hallways as shown in the ./Figures/RPi_locations_JSI.png. Measurements were performed on weekend to minimize additional uncontrolled traffic from users' mobile devices. While there is some overlap in WiFi coverage between the devices at the location 2 and 3, the device at location 1 has no overlap with the other two devices.</p> <p> <strong>Rural environment outdoors</strong> <br> <br> The three RPi's used to collect PRs were placed at three different locations with non-overlapping WiFi coverage, as shown in ./Figures/RPi_locations_rural_env.png. Before starting the measurement campaign, all measured devices were turned off and the environment was checked for active WiFi devices. We did not detect any unknown active devices sending WiFi packets in the RPi's coverage area, so the deployment can be considered fully controlled.<br> All known WiFi enabled devices that were used to collect and send data to the database used a global MAC address, so they can be easily excluded in the preprocessing phase. MAC addresses of these devices can be found in the ./Measurement_informations.xlsx spreadsheet.<br> Note: The Huawei P20 device with ID 4.3 was not included in the test in this environment.</p> <p><br> <strong>Scenarios description</strong> <br> <br> We performed three different scenarios of measurements. <br> <br> <strong>Individual device measurements</strong><br> <br> For each device, we collected PRs for one minute with the screen on, followed by PRs collected for one minute with the screen off. In the indoor environment the WiFi interfaces of the other devices not being tested were disabled. In rural environment other devices were turned off. Start and end timestamps of the recorded data for each device can be found in the ./Measurement_informations.xlsx spreadsheet under the <em>Indoor environment of Jozef Stefan Institute</em> sheet and the <em>Rural environment</em> sheet.</p> <p> <strong>Three groups test</strong></p> <p>In this measurement scenario, the devices were divided into three groups. The first group contained devices from different manufacturers. The second group contained devices from only one manufacturer (Samsung). Half of the third group consisted of devices from the same manufacturer (Huawei), and the other half of devices from different manufacturers. The distribution of devices among the groups can be found in the ./Measurement_informations.xlsx spreadsheet. <br> <br> The same data collection procedure was used for all three groups. Data for each group were collected in both environments at three different RPis locations, as shown in ./Figures/RPi_locations_JSI.png and ./Figures/RPi_locations_rural_env.png. <br> At each location, PRs were collected from each group for 10 minutes with the screen on. Then all three groups switched locations and the process was repeated. Thus, the dataset contains measurements from all three RPi locations of all three groups of devices in both measurement environments. The group movements and the timestamps for the start and end of the collection of PRs at each loacation can be found in spreadsheet ./Measurement_informations.xlsx.</p> <p> <strong>One group test</strong> <br> <br> In the last measurement scenario, all devices were grouped together. In rural evironement we first collected PRs for 10 minutes while the screen was on, and then for another 10 minutes while the screen was off. In indoor environment data were collected at first location with screens on for 10 minutes. Then all devices were moved to the location of the next RPi and PRs were collected for 5 minutes with the screen on and then for another 5 minutes with the screen off.</p> <p><strong>Folder structure</strong> <br> <br> The root directory contains two files in JSON format for each of the environments where the measurements took place (Data_indoor_environment.json and Data_rural_environment.json). Both files contain collected PRs for the entire day that the measurements were taken (12:00 AM to 12:00 PM) to get a sense of the behaviour of the unknown devices in each environment. The spreadsheet ./Measurement_informations.xlsx. contains three sheets. <em>Devices description</em> contains general information about the tested devices, RPis, and the assigned group for each device. The sheets <em>Indoor environment of Jozef Stefan Institute</em> and <em>Rural environment</em> contain the corresponding timestamps for the start and end of each measurement scenario. For the scenario where the devices were divided into groups, additional information about the movements between locations is included. The location names are based on the RPi gateway ID and may differ from those on the figures showing the locations of the RPIs for each environment.<br> The ./Figures folder contains the figures already mentioned above.</p>
Dataset of IEEE 802.11 probe requests from an uncontrolled urban environment
<p><strong>Introduction</strong></p> <p>The 802.11 standard includes several management features and corresponding frame types. One of them are Probe Requests (PR), which are sent by mobile devices in an unassociated state to scan the nearby area for existing wireless networks. The frame part of PRs consists of variable-length fields, called Information Elements (IE), which represent the capabilities of a mobile device, such as supported data rates.</p> <p>This dataset contains PRs collected over a seven-day period by four gateway devices in an uncontrolled urban environment in the city of Catania.</p> <p>It can be used for various use cases, e.g., analyzing MAC randomization, determining the number of people in a given location at a given time or in different time periods, analyzing trends in population movement (streets, shopping malls, etc.) in different time periods, etc.</p> <p><strong> Related dataset</strong></p> <p>Same authors also produced the <a href="https://zenodo.org/record/7503594">Labeled dataset of IEEE 802.11 probe requests</a> with same data layout and recording equipment.</p> <p><br> <strong>Measurement setup</strong> </p> <p>The system for collecting PRs consists of a Raspberry Pi 4 (RPi) with an additional WiFi dongle to capture WiFi signal traffic in monitoring mode (gateway device).<br> Passive PR monitoring is performed by listening to 802.11 traffic and filtering out PR packets on a single WiFi channel.</p> <p>The following information about each received PR is collected:<br> - MAC address<br> - Supported data rates<br> - extended supported rates<br> - HT capabilities<br> - extended capabilities<br> - data under extended tag and vendor specific tag<br> - interworking<br> - VHT capabilities<br> - RSSI<br> - SSID<br> - timestamp when PR was received.</p> <p>The collected data was forwarded to a remote database via a secure VPN connection.<br> A Python script was written using the Pyshark package to collect, preprocess, and transmit the data.</p> <p><br> <strong>Data preprocessing</strong></p> <p><br> The gateway collects PRs for each successive predefined scan interval (10 seconds). During this interval, the data is preprocessed before being transmitted to the database.<br> For each detected PR in the scan interval, the IEs fields are saved in the following JSON structure:</p> <pre><code class="language-json">PR_IE_data = { 'DATA_RTS': {'SUPP': DATA_supp , 'EXT': DATA_ext}, 'HT_CAP': DATA_htcap, 'EXT_CAP': {'length': DATA_len, 'data': DATA_extcap}, 'VHT_CAP': DATA_vhtcap, 'INTERWORKING': DATA_inter, 'EXT_TAG': {'ID_1': DATA_1_ext, 'ID_2': DATA_2_ext ...}, 'VENDOR_SPEC': {VENDOR_1:{ 'ID_1': DATA_1_vendor1, 'ID_2': DATA_2_vendor1 ...}, VENDOR_2:{ 'ID_1': DATA_1_vendor2, 'ID_2': DATA_2_vendor2 ...} ...} }</code></pre> <p><br> Supported data rates and extended supported rates are represented as arrays of values that encode information about the rates supported by a mobile device. The rest of the IEs data is represented in hexadecimal format. Vendor Specific Tag is structured differently than the other IEs. This field can contain multiple vendor IDs with multiple data IDs with corresponding data. Similarly, the extended tag can contain multiple data IDs with corresponding data. <br> Missing IE fields in the captured PR are not included in <em>PR_IE_DATA</em>.</p> <p>When a new MAC address is detected in the current scan time interval, the data from PR is stored in the following structure:</p> <pre><code class="language-json">{'MAC': MAC_address, 'SSIDs': [ SSID ], 'PROBE_REQs': [PR_data] },</code></pre> <p>where <em>PR_data</em> is structured as follows:</p> <pre><code class="language-json">{ 'TIME': [ DATA_time ], 'RSSI': [ DATA_rssi ], 'DATA': PR_IE_data }.</code></pre> <p> </p> <p>This data structure allows to store only 'TOA' and 'RSSI' for all PRs originating from the same MAC address and containing the same 'PR_IE_data'. All SSIDs from the same MAC address are also stored.<br> The data of the newly detected PR is compared with the already stored data of the same MAC in the current scan time interval.<br> If identical PR's IE data from the same MAC address is already stored, only data for the keys 'TIME' and 'RSSI' are appended.<br> If identical PR's IE data from the same MAC address has not yet been received, then the PR_data structure of the new PR for that MAC address is appended to the 'PROBE_REQs' key.<br> The preprocessing procedure is shown in Figure ./Figures/Preprocessing_procedure.png</p> <p>At the end of each scan time interval, all processed data is sent to the database along with additional metadata about the collected data, such as the serial number of the wireless gateway and the timestamps for the start and end of the scan. For an example of a single PR capture, see the <em>Single_PR_capture_example.json</em> file.</p> <p><br> <strong>Folder structure</strong></p> <p>For ease of processing of the data, the dataset is divided into 7 folders, each containing a 24-hour period.<br> Each folder contains four files, each containing samples from that device.</p> <p>The folders are named after the start and end time (in UTC).<br> For example, the folder [2022-09-22T22-00-00_2022-09-23T22-00-00](2022-09-22T22-00-00_2022-09-23T22-00-00) contains samples collected between <em>23th of September 2022 00:00 local time</em>, until <em>24th of September 2022 00:00</em> local time.</p> <p>Files representing their location via mapping:<br> - 1.json -> location 1<br> - 2.json -> location 2<br> - 3.json -> location 3<br> - 4.json -> location 4</p> <p><strong>Environments description</strong> </p> <p>The measurements were carried out in the city of Catania, in Piazza Università and Piazza del Duomo<br> The gateway devices (rPIs with WiFi dongle) were set up and gathering data before the start time of this dataset.<br> As of September 23, 2022, the devices were placed in their final configuration and personally checked for correctness of installation and data status of the entire data collection system.<br> Devices were connected either to a nearby Ethernet outlet or via WiFi to the access point provided.</p> <p>Four Raspbery Pi-s were used:<br> - location 1 -> Piazza del Duomo - Chierici building (balcony near Fontana dell’Amenano)<br> - location 2 -> southernmost window in the building of Via Etnea near Piazza del Duomo<br> - location 3 -> nothernmost window in the building of Via Etnea near Piazza Università<br> - location 4 -> first window top the right of the entrance of the University of Catania</p> <p>Locations were suggested by the authors and adjusted during deployment based on physical constraints (locations of electrical outlets or internet access)<br> Under ideal circumstances, the locations of the devices and their coverage area would cover both squares and the part of Via Etna between them, with a partial overlap of signal detection. The locations of the gateways are shown in Figure ./Figures/catania.png.</p> <p> <strong>Known dataset shortcomings</strong></p> <p>Due to technical and physical limitations, the dataset contains some identified deficiencies.</p> <p>PRs are collected and transmitted in 10-second chunks.<br> Due to the limited capabilites of the recording devices, some time (in the range of seconds) may not be accounted for between chunks if the transmission of the previous packet took too long or an unexpected error occurred.</p> <p>Every 20 minutes the service is restarted on the recording device.<br> This is a workaround for undefined behavior of the USB WiFi dongle, which can no longer respond.<br> For this reason, up to 20 seconds of data will not be recorded in each 20-minute period.</p> <p>The devices had a scheduled reboot at 4:00 each day which is shown as missing data of up to a few minutes.</p> <p> <strong> Location 1 - Piazza del Duomo - Chierici</strong></p> <p> The gateway device (rPi) is located on the second floor balcony and is hardwired to the Ethernet port. This device appears to function stably throughout the data collection period.<br> Its location is constant and is not disturbed, dataset seems to have complete coverage.</p> <p> <strong>Location 2 - Via Etnea - Piazza del Duomo</strong></p> <p> The device is located inside the building.<br> During working hours (approximately 9:00-17:00), the device was placed on the windowsill. However, the movement of the device cannot be confirmed.<br> As the device was moved back and forth, power outages and internet connection issues occurred.<br> The last three days in the record contain no PRs from this location.</p> <p> <strong> Location 3 - Via Etnea - Piazza Università</strong></p> <p> Similar to Location 2, the device is placed on the windowsill and moved around by people working in the building.<br> Similar behavior is also observed, e.g., it is placed on the windowsill and moved inside a thick wall when no people are present.<br> This device appears to have been collecting data throughout the whole dataset period.<br> <br> <strong> Location 4 - Piazza Università</strong></p> <p> This location is wirelessly connected to the access point.<br> The device was placed statically on a windowsill overlooking the square.<br> Due to physical limitations, the device had lost power several times during the deployment.<br> The internet connection was also interrupted sporadically.</p> <p><strong>Recognitions</strong></p> <p>The data was collected within the scope of <a href="https://www.resilocproject.eu/">Resiloc project</a> with the help of City of Catania and project partners.</p>
ScienceDex guides
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.