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15 results for “magnetic sensor”
Displacement measurements of the open-hardware sandbox using the AS5311 high-resolution magnetic sensor
<p>This dataset includes the experimental data from the AS5311 sensor for measuring the displacement of the Open-Hardware Geological Sandbox.</p> <p>These experiments are explained in the journal article: <a href="https://doi.org/10.1109/ACCESS.2023.3262617">Designing low-cost open-hardware electromechanical scientific equipment: A geological analogue modeling sandbox</a></p> <p>To understand this dataset, go to the Tectonic Open Hardware (TectOH) Sandbox project: <a href="https://github.com/URJCMakerGroup/TectOH">https://github.com/URJCMakerGroup/TectOH</a>. Then go to the <a href="https://github.com/URJCMakerGroup/TectOH/tree/main/optional">optional</a> folder and to the <a href="https://github.com/URJCMakerGroup/TectOH/tree/main/optional/as5311_magn_sens">magnetic sensor</a> folder.</p> <p>This data set contains two kind of files:</p> <ul> <li><strong>bin</strong>: raw binary files received from the AS5311 high resolution sensor. Although this sensor sends 12 bit data, we have truncated the most significant bits and receive only 8 bits (one byte). Therefore, each byte of these binary files is a measurement of the distance. Each distance increment corresponds to ~0.488nm (2mm/2048)</li> <li><strong>csv</strong>: csv files that can be opened with any spreadsheet app, such as Libreoffice Calc or Microsoft Excel, or even with a text editor. This file contains the processed data from the binary files. These files have been generated with the proc_magn_sensor.py Python script located in the <a href="https://github.com/URJCMakerGroup/TectOH">project repository</a>. There are some columns, which are: <ul> <li>index: measurement number</li> <li>time in milliseconds: each measurement is taken every 250 us</li> <li>median2: in micrometers, since the sensor may jitter, we have applied the median filter twice. All the data has been subtracted to the lowest value, making the lowest value equal to zero.</li> <li>median1: in micrometers, median filter only applied once. All the data has been subtracted to the lowest value, making the lowest value equal to zero.</li> <li>mean: in micrometers, mean filter. All the data has been subtracted to the lowest value, making the lowest value equal to zero.</li> <li>mean int: in micrometers, mean filter rounded to an integer value. All the data has been subtracted to the lowest value, making the lowest value equal to zero.</li> <li>orig_base: this is not in micrometers, but in the units of the sensor (~0.488nm). The only processing done is that when there is an overflow of 255 to 0, or from 0 to 255, it adds the overflow to continue the trend. All the data has been subtracted to the lowest value, making the lowest value equal to zero.</li> <li>original: this is the data received from the sensor with no processing, each value is ~0.488nm</li> <li>mean2: in micrometers, mean filter applied twice. All the data has been subtracted to the lowest value, making the lowest value equal to zero.</li> </ul> </li> </ul> <p>There are two set of experiments:</p> <ul> <li><strong>Experiments with no load</strong>. These files start with <em>noload_</em><br> In these experiments the gantry is moved 1 mm alternatively to the front and then reversing direction. Moving in this alternate way a few times. There are five experiments each of them with a different speed: v= 10 mm/h; 25 mm/h; 50 mm/h; 82 mm/h and 100 mm/h. The name of the file indicates the speed: <ol> <li>noload_100mmh_1mm: FBFBF: 1mm forth, 1mm back, 1mm forth, 1mm back, 1 mm forth</li> <li>noload_25mmh_1mm: FBFFBBFB</li> <li>noload_50mmh_1mm: FBFBFB</li> <li>noload_82mmh_1mm: FBFBFB</li> <li>noload_100mmh_1mm: FBFBFB</li> </ol> </li> <li><strong>Experiments pushing a 5kg sand load</strong>. These files start with <em>load5kg_</em> <ol> <li>load5kg_25mmh_5mm: moving 5kg at 25mm/h a distance of 5mm</li> <li>load5kg_25mmh_10mm: moving 5kg at 25mm/h a distance of 10mm</li> <li>load5kg_25mmh_20mm: moving 5kg at 25mm/h a distance of 20mm</li> <li>load5kg_75mmh_20mm: moving 5kg at 75mm/h a distance of 20mm</li> <li>load5kg_75mmh_50mm: moving 5kg at 75mm/h a distance of 50mm</li> <li>load5kg_100mmh_25mm: moving 5kg at 100mm/h a distance of 20mm</li> <li>load5kg_100mmh_50mm: moving 5kg at 100mm/h a distance of 50mm</li> </ol> </li> </ul> <p> </p> <p> </p> <p> </p>
MEMS-Based Cantilever Sensor for Simultaneous Measurement of Mass and Magnetic Moment of Magnetic Particles (Data)
<p>Origin project and figures used for the article "MEMS-Based Cantilever Sensor for Simultaneous Measurement of Mass and Magnetic Moment of Magnetic Particles", published in <em>Chemosensors</em> on 04 Aug 2021.</p>
Broadband microwave detection using electron spins in a hybrid diamond-magnet sensor chip
<p>Dataset accompanying "Broadband microwave detection using electron spins in a hybrid diamond-magnet sensor chip". </p>
Localizing On-scalp MEG Sensors using an Array of Magnetic Dipole Coils
<p>Matlab scripts and data necessary to reproduce the results from the PLOS ONE paper. For more information see README.</p>
Data for A miniaturized magnetic field sensor based on nitrogen-vacancy centers
<p>Here, data sets as plotted in the preprint "A miniaturized magnetic field sensor based on nitrogen-vacancy centers" are uploaded. <br><br>The zip file Data_Zenodo_v3.zip contains a folder for each figure and one additional table supporting the findings in the preprint/publication. Folders of figures containing only images include the images in the preprint/publication as .png and .svg or .pdf data. Folders of figures containing plots include the plot as .png and .pdf aswell as the data points and fit parameters collected in a .xlsx, .csv or .h5 file.</p>
Dataset for P. Ripka, M. Mirzaei, J. Maier: Flat Magnetic X-Y Alignment sensor, IEEE Sensors Letters Vol. 8, Iss. 7, 2024, pp. 1-4 10.1109/LSENS.2024.3414375
Open the record for dataset details and reuse information.
Magnetic Hair Tactile Sensor for Directional Pressure Detection
<p><span>Tactile sensing in the human body is achieved via the skin. This has inspired the fabrication of synthetic skins with pressure sensors for potential applications in robotics, bio-medicine, and human-machine interfaces. Tactile sensors based on magnetic elements are promising as they provide high sensitivity and a wide dynamic range. However, current magnetic tactile sensors mostly detect pressures of solid objects and operate at relatively high forces about 100 mN. Here, we address these limitations by manufacturing soft, stretchable, and hair-like structures that are permanently magnetized to achieve high-resolution, cost-effective, and high-resolution pressure sensing. Combining these hair-like structures with advances in 3D magnetic-field measurements allows us to monitor directional tactile pressures without solid contact. To prove the concept of this technology, we built a bio-inspired soft device with a hairy structure that senses and reports environmental mechanical stresses, similar to that of human skin. Simple self-assembly of the soft magnetic hair structure makes our approach easy to scale for large-area applications.</span></p>
Magnetic Distance Estimation Data from Gait Experiments with Magnetoelectric Sensors
<h2>Overview</h2> <p><br>This is the "Magnetic Distance Estimation Data from Gait Experiemtns with Magnetoelectric Sensors" dataset. <br>It represents a pilot study on magnetic motion tracking with novel magnetoelectric sensors during treadmill walking.<br>Therefore, it contains both technical (calibration) data and clinical (gait) data of five healthy participants.</p> <p>Example scripts for loading and processing data are available in the linked respository.</p> <p>The dataset is formatted according to the Brain Imaging Data Structure. See the `dataset_description.json` file for the specific version used.</p> <p>The work was supported by the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG) through the Collaborative Research Center CRC 1261 Magnetoelectric Sensors: From Composite Materials to Biomagnetic Diagnostics. The data was recorded in the project B9 on "Magnetoelectric Sensors for Movement Detection and Analysis".</p> <p>All measurements were approved by the ethics committee of Kiel University (File number: A122/20) and conducted in accordance with the Declaration of Helsinki.</p> <h2><br>Details about the experiment</h2> <p><br>Magnetic motion tracking enables a relative tracking, in which the distance between each sensor and actuator node can be estimated. <br>The full setup contains two actuator nodes (a0, a1) and four sensor nodes (s0, s1, s2, s3).<br>Each actuator-sensor pair produces nine magnetic signals (x,y,z by x,y,z) as well as three magnetic dipole moment signals that represent the currents through the coils (actuators).<br>Additionally, each node was tracked with an optical motion capture (OMC) system. The resulting position and orientation data of the attached rigid body act as a reference (ground truth) to evaluate the magnetic estimation. <br>Subfolders sub-01 to sub-08 each contain up to three calibration tasks which each contain between 60 and 120s of arbitrary movement of one coil (wand-mounted) around one stationary sensor (base).<br>Subfolders sub-09 to sub-13 each contain two 120s walking tasks (0.5 and 1 m/s) of five subjects in total with the full sensor and actuator setup. Two actuators were mounted to the shanks, two sensors to the thighs and two sensors were placed stationary next to the threadmill. <br>The dataset also contains a folder with derived data, which contains calibration parameters for each actuator-sensor pair. See the provided matlab script for details on how to load, visualize, and compare the results.</p>
Molecular diagnostics based on DNA amplification and magnetic sensor arrays
<p>On May 12, 2020, the third Scientific Online Lecture of the IPANEMA project was organized. The lecture has been given by INESC MN’s Researchers: Verónica Romão, Sofia Martins , Sara Viveiros, and Débora Albuquerque. Through this lecture, our researchers were able to get acquainted with the Molecular diagnostics based on DNA amplification and magnetic sensor arrays.</p>
Validation of a Magnetic Sensor in Arterial Flow Recording, Compared With the Reference Method, at Various Peripheral Arterial Sites
ClinicalTrials.gov study NCT06274697. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Magnetic Sensor Validation of Hemodynamic Non-invasive Measurements Pressure During Cardiac Catheterization
ClinicalTrials.gov study NCT05943275. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Database Creation and Evaluation of Motor Function of Finger Tapping Measured by Magnetic Sensor System in Healthy Subjects
ClinicalTrials.gov study NCT01015573. IPD Sharing: Not stated. Countries: 1. Publications: 0.
DMSP F17 Special Sensor Magnetometer, SESS/SSM-Boom, Magnetic Field Measurements taken at 850 km, 1 s Data
Defense Meteorology Satellite Program, DMSP, F17 Vector Magnetometer Measurements, 850 km Altitude
DMSP F18 Special Sensor Magnetometer, SESS/SSM-Boom, Magnetic Field Measurements taken at 850 km, 1 s Data
Defense Meteorology Satellite Program, DMSP, F18 Vector Magnetometer Measurements, 850 km Altitude
DMSP F16 Special Sensor Magnetometer, SESS/SSM-Boom, Magnetic Field Measurements taken at 850 km, 1 s Data
Defense Meteorology Satellite Program, DMSP, F16 Vector Magnetometer Measurements, 850 km Altitude
ScienceDex guides
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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.