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Dataset results
14 results for “signal propagation”
Dataset on the Human Body as a Signal Propagation Medium
<p><strong>Overview:</strong> This is a large-scale dataset with impedance and signal loss data recorded on volunteer test subjects using low-voltage alternate current sine-shaped signals. The signal frequencies are from 50 kHz to 20 MHz.</p> <p><strong>Applications:</strong> The intention of this dataset is to allow to investigate the human body as a signal propagation medium, and capture information related to how the properties of the human body (age, sex, composition etc.), the measurement locations, and the signal frequencies impact the signal loss over the human body.</p> <p><strong>Overview statistics:</strong></p> <ul> <li>Number of subjects: 30</li> <li>Number of transmitter locations: 6</li> <li>Number of receiver locations: 6</li> <li>Number of measurement frequencies: 19</li> <li>Input voltage: 1 V</li> <li>Load resistance: 50 ohm and 1 megaohm</li> </ul> <p><strong>Measurement group statistics:</strong></p> <ul> <li>Height: 174.10 (7.15)</li> <li>Weight: 72.85 (16.26)</li> <li>BMI: 23.94 (4.70)</li> <li>Body fat %: 21.53 (7.55)</li> <li>Age group: 29.00 (11.25)</li> <li>Male/female ratio: 50%</li> </ul> <p><strong>Included files:</strong></p> <ul> <li>experiment_protocol_description.docx - protocol used in the experiments</li> <li>electrode_placement_schematic.png - schematic of placement locations</li> <li>electrode_placement_photo.jpg - visualization on the experiment, on a volunteer subject</li> <li>RawData - the full measurement results and experiment info sheets</li> <li>all_measurements.csv - the most important results extracted to .csv</li> <li>all_measurements_filtered.csv - same, but after z-score filtering</li> <li>all_measurements_by_freq.csv - the most important results extracted to .csv, single frequency per row</li> <li>all_measurements_by_freq_filtered.csv - same, but after z-score filtering</li> <li>summary_of_subjects.csv - key statistics on the subjects from the experiment info sheets</li> <li>process_json_files.py - script that creates .csv from the raw data</li> <li>filter_results.py - outlier removal based on z-score</li> <li>plot_sample_curves.py - visualization of a randomly selected measurement result subset</li> <li>plot_measurement_group.py - visualization of the measurement group</li> </ul> <p><br> CSV file columns:</p> <ul> <li>subject_id - participant's random unique ID</li> <li>experiment_id - measurement session's number for the participant</li> <li>height - participant's height, cm</li> <li>weight - participant's weight, kg</li> <li>BMI - body mass index, computed from the valued above</li> <li>body_fat_% - body fat composition, as measured by bioimpedance scales</li> <li>age_group - age rounded to 10 years, e.g. 20, 30, 40 etc.</li> <li>male - 1 if male, 0 if female</li> <li>tx_point - transmitter point number</li> <li>rx_point - receiver point number</li> <li>distance - distance, in relative units, between the tx and rx points. Not scaled in terms of participant's height and limb lengths!</li> <li>tx_point_fat_level - transmitter point location's average fat content metric. Not scaled for each participant individually.</li> <li>rx_point_fat_level - receiver point location's average fat content metric. Not scaled for each participant individually.</li> <li>total_fat_level - sum of rx and tx fat levels</li> <li>bias - constant term to simplify data analytics, always equal to 1.0</li> </ul> <p>CSV file columns, frequency-specific:</p> <ul> <li>tx_abs_Z_... - transmitter-side impedance, as computed by the `process_json_files.py` script from the voltage drop</li> <li>rx_gain_50_f_... - experimentally measured gain on the receiver, in dB, using 50 ohm load impedance</li> <li>rx_gain_1M_f_... - experimentally measured gain on the receiver, in dB, using 1 megaohm load impedance</li> </ul> <p><br> <strong>Acknowledgments:</strong> The dataset collection was funded by the Latvian Council of Science, project “Body-Coupled Communication for Body Area Networks”, project No. lzp-2020/1-0358.</p> <p><strong>References:</strong> For a more detailed information, see this article: J. Ormanis, V. Medvedevs, A. Sevcenko, V. Aristovs, V. Abolins, and A. Elsts. Dataset on the Human Body as a Signal Propagation Medium for Body Coupled Communication. Submitted to Elsevier Data in Brief, 2023.</p> <p><strong>Contact information:</strong> info@edi.lv</p>
Data for "Direct evidence reveals transmitter signal propagation in the magnetosphere"
<p>The data and codes for figures in "Direct evidence reveals transmitter signal propagation in the magnetosphere"</p>
Geophysical Signals from Magma Propagation: Experimental and Processed Data
<p>This repository contains the experimental data analyzed and interpreted in the manuscript titled "<em>Geophysical signals induced by magma propagation: Insights from analog experiments</em>" by S. Furst, J. Vandemeulebrouck, and V. Pinel. It includes video recordings, timelapse photos, accelerometer data, and deformation data. Additionally, there are three MATLAB scripts for post-processing the timelapse photos following the approach described in the manuscript. The results of the MFP analysis on the accelerometer data, as well as the outcomes from the COMSOL Multiphysics simulations, are also included in the repository.</p>
Simulation data for the manuscript "Characterizing Optimal Signal Propagation in the Human Brain Network."
<p>See <a href="https://github.com/kuffmode/OI-and-CMs">https://github.com/kuffmode/OI-and-CMs</a></p>
Signal propagation within the MCL-1/BIM protein complex - DATA
<p>Data supporting: "Signal propagation within the MCL-1/BIM protein complex"; Journal of Molecular Biology (JMB), February 2022 (DOI: 10.1016/j.jmb.2022.167499). </p>
A DNA-Micropatterned Surface for Propagating Biomolecular Signals by Positional on-off Assembly of Catalytic Nanocompartments
<p>Data underlying the figures in the publication: Maffeis, V. <em>et al.</em> “A DNA-Micropatterned Surface for Propagating Biomolecular Signals by Positional on-off Assembly of Catalytic Nanocompartments” <em>Small</em> <strong>2022</strong>, 2202818, <a href="https://doi.org/10.1002/smll.202202818">https://doi.org/10.1002/smll.202202818</a></p> <p>Concept figures, Unicode origin graph (opj), TEM pictures, AFM pictures, LSM pictures</p> <p>TOC</p> <ol> <li><strong>Figure 1</strong> Concept figure representing the 3D view of micropatterned CNC immobilization promoting a cascade reaction between two distinct CNCs that ultimately results in a bioluminescent surface.</li> <li><strong>Figure 2</strong> Schematic reactions involved in the biomolecular signal propagation.</li> <li><strong>Figure 3</strong> Design and characterization of CNC-1 and CNC-2 (SLS and TEM).</li> <li><strong>Figure 4</strong> Schematic rapresentation of DNA synthesis inside Klenow-CNCs by SYBR green I.</li> <li><strong>Figure 5</strong> <em>a)</em> FCS autocorrelation curves of free Atto-488-template DNA (red) and Atto-488-template DNA CNC (blue); <em>b)</em> Activity of free Klenow polymerase, Klenow polymerase CNC with melittin, Klenow polymerase CNC without melittin at 25 °C; <em>c)</em> Enzyme activity of melittin-permeabilized Klenow CNCs and free Klenow fragment treated for 1 h at 55 °C; <em>d)</em> Enzyme activity of melittin-permeabilized Klenow CNCs and free Klenow fragment treated for 1 h at 75 °C.</li> <li><strong>Figure 6</strong> <em>a)</em> Chemical functionalization of the micro-printed glass surface with the amino-functionalized ssDNA; <em>b)</em> AFM height image of the DNA-functionalized glass slide recorded in 10 mm Tris-HCl buffer at pH 7.2 at the resolution of 128 lines; <em>c)</em> CLSM image of glass surface microprinted with Cy5-labeled NH2-modified 31-mer.</li> <li><strong>Figure 7</strong> <em>a)</em> left, AFM height image, middle, phase type image, and right, height profile (corresponding to dashed white line) of tandem CNCs attached via DNA hybridization on the microprinted glass surface recorded in 10 mm Tris-HCl buffer at pH 7.2; <em>b)</em> left, AFM height image, middle, phase type image and right, corresponding height profile of a single CNC; <em>c)</em> CLSM micrographs of polymersomes labeled with either cholesterol functionalized Atto-488 (green) or Dylight-633 (red)-DNA and immobilized by hybridization on a microprinted glass surface. Left panel, 488-channel, middle panel, 633-channel, right panel, merged image. Scale bars: 5 µm; <em>d)</em> Bioluminescence generation by permeable Klenow-CNCs and permeable ATP sulfurylase-CNCs (blue), by nonpermeable Klenow-CNCs and nonpermeable ATP sulfurylase-CNCs (pink), by the substrate mix alone (black), and by D-Luciferin and luciferase (red). Error bands represent ±SD, n = 3 replicates; <em>e)</em> QCM-D measurement of immobilized CNCs following repeated loading-removal cycles. Frequency (blue) and dissipation (brown) were recorded at three overtones (n = 3, 5, 7) as a function of time. (i, iv, vii) Addition of adaptor DNA, (ii, v, viii) immobilization of 22-mer polymersomes, and (iii, vi, ix) separation of DNA strands with 1 m NaOH.</li> </ol>
Signal Propagation and Its Relationship to Cognitive Performance in the Aging Human Brain (Focus or Spread)
ClinicalTrials.gov study NCT04361760. IPD Sharing: YES. Countries: 1. Publications: 6.
Data from: Modeling GPS signal propagation through volcanic plumes
Open the record for dataset details and reuse information.
The propagation paths of GPS radio signals over the tropical ocean from 16–18 January 2022
<p>This dataset is the propagation paths of RO radio signals over the tropical ocean from 16–18 January 2022, simulated using the 2D non-local observation operator. The ray integration uses a variable step size of 2 km or smaller. The vertical interval is 100 m. The data format is HDF5. Variables include the 2D simulation bending angle, the COSMIC-2 retrieval bending angle, geometric height, impact heights, the vertical gradient of refractivity, latitude, longitude, and observation time of the RO profile.</p>
Data from: Assessing Electrogenetic Activation via a Network Model of Biological Signal Propagation
<p>Simulation data from Assessing Electrogenetic Activation via a Network Model of Biological Signal Propagation, doi: 10.3389/fsysb.2024.1291293</p> <p>There are 10 csv files per network type, each dataset contains the Timestep, Node, Strain, Inducer Concentration/Duration, and Node weights for the timecourse of the simulation. </p>
Islet-Autonomous Inflammatory Signaling Propagates Autoimmunity and Promotes Diabetes in Nonobese Diabetic Mice
GEO Series GSE166572. Mus musculus. 4 samples. Type: Expression profiling by high throughput sequencing.
Propagation of Adipogenic Signals through an Epigenomic Transition State
GEO Series GSE21898. Mus musculus. 10 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
A self-propagating c-Met-SOX2 axis drives cancer-derived IgG signaling that promotes lung cancer cell stemness [RNA-seq]
GEO Series GSE222023. Homo sapiens. 6 samples. Type: Expression profiling by high throughput sequencing.
Neonatal monocytes in tracheal aspirates from infants at risk for Bronchopulmonary Dysplasia propagate IL-1 signaling in the first weeks of lung injury
GEO Series GSE127455. Homo sapiens. 72 samples. Type: Expression profiling by high throughput sequencing.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.