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1,772 results for “sensors”
Sensor Position Comparison Dataset
<pre> </pre> <p>A dataset containing IMU recordings with full motion capture reference from 14 participants (approx. 10000 strides). Each participant was equipped with 15 synchronised IMUs (6 at different positions at each shoe, 1 at each ankle, and 1 and the lower back).</p> <p>The main goal of the dataset is to compare the recorded signals of the 6 sensors attached to each foot.</p> <p>For more information about the dataset check the `README.md` file in the dataset.</p> <p>If you are using the dataset, please cite the following paper:</p> <p>Küderle, Arne, Nils Roth, Jovana Zlatanovic, Markus Zrenner, Bjoern Eskofier, and Felix Kluge.<br> “The Placement of Foot-Mounted IMU Sensors Does Affect the Accuracy of Spatial Parameters during Regular Walking.”<br> PLOS ONE 17, no. 6 (June 9, 2022): e0269567. https://doi.org/10.1371/journal.pone.0269567.</p>
Sensor data collected at the Nowe Czarnowo carp farm in Poland
<p>This dataset includes data collected from a land-based Carp farm in Nowe Czarnowo, Poland. Included are temperature, dissolved oxygen, and PH data collected at the farm at two locations. More details on the data can be found in the <a href="https://www.unive.it/pag/fileadmin/user_upload/progetti_ricerca/gain/documenti/GAIN_D11_Report_on_instrumentation_of_GAIN_pilot_sites.pdf">Report on instrumentation of site</a></p>
Sensor data collected at the Preore trout farm in Italy
<p>This dataset includes data collected from a land-based trout farm in Preore, Italy. Included are temperature, dissolved oxygen, nitrates, salinity collected at the farm. Further sample measurements of fish size are included. More details on the data can be found in the <a href="https://www.unive.it/pag/fileadmin/user_upload/progetti_ricerca/gain/documenti/GAIN_D11_Report_on_instrumentation_of_GAIN_pilot_sites.pdf">Report on instrumentation of site</a></p>
Diffuse-optical data set measured with a smartphone-based sensor on Potato Hill, Oregon, USA
<p>This data set contains both raw data and derived data obtained on Potato Hill, Oregon, on December 17th 2021 using a diffuse-optical, smartphone-based sensor. The raw image files have been converted to an uncompressed Adobe-.dng file format, file names indicate whether the file contains data for the blue (405nm) or red (650nm) laser or spatial calibration data using a 9mm x 9mm calibration pattern. Spectral albedo measurements are contained in the subfilder ./Albedo, the raw images in ./Phone. The root directory contains the matlab code (Matlab R2021b) needed for analysis as well as the derived data.</p> <p>For analyzing the raw data set, use "CameraMatchPotatoHillFinal.m". It wraps around the function "CameraAnalysisFinal.m", which performs the image analysis and least-square fit to resorted and rescaled data, employing in turn the model function "theosurfGInf.m". It saves a derived data set (attenuation, absorption and scattering coefficients, albedos, absorption enhancement factor and snow density.</p> <p>The script "Albedo.m" analyzes the derived data set along with measured albedo and simulated albedo deposited in the file "snicar_120ppb.txt". The obtained albedo curves and black carbon mixing ratio are as shown in the below manuscript.</p> <p>If you wish to use this data set please contact Markus Allgaier at markusa@uoregon.edu with a description of the work and any questions so that we may offer guidance in regards to the best usage of our dataset. When using the data set within a publication, please cite:</p> <p>Markus Allgaier & Brian Smith, "A Smartphone-Based Sensor for Measuring the Optical Properties of Snow", in preparation, (2022)</p>
Raw Sensor Data for STRIDE Project J "Improving Work Zone Mobility through Planning, Design and Operations"
<p>This dataset contains the raw traffic data from 9 sensors located on I-59 southbound near Tuscaloosa, Alabama, from October 3 to October 16, 2016. These data were used in the research for STRIDE Project J "Improving Work Zone Mobility through Planning, Design and Operations" and described in the STRIDE Final Project for this project.</p>
Coupling Novel Soil Moisture-Suction Sensors and UAV Photogrammetry Technology to the Performance of Highway Embankments
<p>The movement of water plays a critical role in the mechanical performance and service life of transportation infrastructure, especially for pavement subgrades and highway embankments consisting of high-plasticity, expansive soils that saturate and ultimately lead to infrastructure distress. Shallow slides along highway embankments are ubiquitous across Region 6 because long-term wetting and drying cycles considerably weaken these compacted soils. In the aftermath of heavy rains, pore-water pressures increase to a critical threshold such that a failure occurs. The implications of embankment failures range from repeated maintenance repairs to long-term road closures. A comprehensive approach to model highway embankments comprising of laboratory testing, setup, and field data collection using unmanned aerial vehicles has been proposed in this study.</p>
Comprehensive Kinetic and EMG Dataset of Daily Locomotion with 6 types of Sensors
<p> </p> <p><strong><a href="https://www.cambridge.org/core/journals/wearable-technologies/article/wearable-realtime-kinetic-measurement-sensor-setup-for-human-locomotion/488C21B7706FFDFA7FFAB387FD0A1A64?utm_campaign=shareaholic&utm_medium=copy_link&utm_source=bookmark">The paper</a> is now published in the recent Wearable Technology Journal, more detailed information on this dataset can be found there!</strong></p> <p>A human movement experiment with 12 young adults performing 13 daily movement trials (6 walking trials (speed: 0.9, 1.8, 2.7, 3.6, 4.5, 5.4 km/ h; 3 running trials (speed: 6.3, 8.8, 9.9 km/h); and four non-locomotion trials (vertical jump, squat, lunge, and single leg landing) was conducted with the ethic approved by the University of Twente (ET/A.21.19298, reference number 2021.57). </p> <p>Six types of measurement devices were used to capture different information of participants’ movements. They can be divided into two systems: the wearable system and the conventional non-wearable system. In the wearable measurement system, eight IMUs (Xsens Link, Enschede, The Netherlands) were used to measure the kinematic movements of lower limbs and trunk. A pair of pressure insoles (Moticon, Munich, Germany) was used to measure the vertical GRF and CoPs. In the conventional system, an optical motion capture system (OMC) containing 8 infrared light cameras (6+ series, Qualisys, Gothenburg, Sweden) was used to measure body kinematics data using reflective markers. A split-belt instrumented treadmill (Motek-Forcelink B.V, Culemborg, The Netherlands) was used to measure the GRFs under each foot. Two video cameras were also included inside the conventional system to capture the RGB images of participants’ body postures at the sagittal and frontal planes (<a href="https://doi.org/10.5281/zenodo.6644593">https://doi.org/10.5281/zenodo.6644593</a>). In addition, nine electromyography sensors (EMGs) (Delsys Trigno, Delsys, USA) were included to record the activations of nine major muscles in the dominant leg ("soleus", "medial gastrocnemius", "lateral gastrocnemius", "tibialis anterior", " semimembranosus", " biceps femoris long head", "vastus lateral", "rectus femoris", "vastus medial").</p> <p>In this shared data repository, both raw data (to be uploaded) and processed data (Processed_data.zip) are provided. The data processing pipeline can be found in this public GitHub repository: <a href="https://github.com/HuaweiWang/BioMechPro-WearableSystemVaildation">https://github.com/ET-BE/BioMechPro/tree/study/WearableSystemValidation</a>. Guidelines for creating the same wearable system in the corresponding comparison study are shared in this GitHub repo: <a href="https://github.com/HuaweiWang/WearableMeasurementSystem">https://github.com/HuaweiWang/WearableMeasurementSystem</a>.</p> <p><strong>[Note!]</strong> If you have unstable network that not able to download the huge data files, please check this version of dataset with small file sizes(2GB each)</p> <ul> <li>Raw data: <a href="https://doi.org/10.5281/zenodo.7422043">https://doi.org/10.5281/zenodo.7422043</a></li> <li>Processed data: <a href="https://doi.org/10.5281/zenodo.7422031">https://doi.org/10.5281/zenodo.7422031</a> </li> </ul> <p> </p> <p>Dataset structure descriptions:</p> <p><a href="https://zenodo.org/api/files/871df791-82f9-495a-ac56-eafd195c56ad/Raw_data.rar?versionId=41a0df36-b9b2-4e48-a50e-a6e81b23609b">Raw_data.rar</a>: Raw dataset</p> <ul> <li><em>Subjxx</em>: subject folder <ul> <li><em>Qualisys</em>: Qualisys project folder of the recordings</li> <li><em>Xsens</em>: Xsens project folder of the recordings</li> <li><em>Insoles</em>: Pressure insole project folder of the recordings</li> </ul> </li> </ul> <p><a href="https://zenodo.org/api/files/871df791-82f9-495a-ac56-eafd195c56ad/Processed_data.rar?versionId=d035b581-a334-48bb-88e1-3c655943b5cc">Processed_data.rar</a>: Processed dataset.</p> <ul> <li><em>allAverage.mat</em>: the overall summarization data of all subject at all movement trials. </li> <li><em>dataValidation.m</em>: the Matlab code to plot the summarization data by loading the allAverage.mat.</li> <li><em>subjs_info.txt</em>: general information of all participants. </li> <li><em>ComparisonPlots</em>: folder that contains the comparison plots between the laboratory-based system and the wearable measurement systems.</li> <li><strong><em>Subjxx</em></strong>: processed data for participants xx <ul> <li><em>dynMVCvalue.mat</em>: the dynamic Maximal Voluntary Contraction of measured muscles (highest value among all recorded movements)</li> <li><em>MVCvalue.mat</em>: the Maximal Voluntary Contraction of measured muscles (highest value in MVC recording trial only)</li> <li><em>Subjxx_xxxx_xx.mat</em>: the processed data (generated by the above mentioned processing pipeline) of current subject at specific movement trial.</li> <li><em>Qualisys</em>: The exported mat files from the Qualisys recordings, including markers, EMGs, GRFs.</li> <li><em>Xsens</em>: The exported .mvnx files from the Xsens MVN software reprocessing. This file can be directly loaded by Matlab without requiring the Xsens license.</li> <li><em>Insole</em>: Insole recorded data, parsed from the Moticon endpoint SDK output.</li> <li><em>OS</em>: The folder that contains the scaled OpenSim model and corresponding .xml and data files for IK and ID processing. Majority content in this folder is automatically generated by the processing pipeline</li> <li><em>Figures</em>: plots of the processed data, including joint angles, GRFs, joint torques, and EMGs. They are all generated in the last module of the processing pipeline.</li> </ul> </li> </ul> <p>Structure of the <strong>Subjxx_xxxx_xx.mat</strong>:</p> <p><em><strong>Subjxx_xxxx_xx</strong>.</em><em><strong>mat:</strong></em></p> <ul> <li><em>Info</em>: the general information of the participant and corresponding processing steps.</li> <li><em>Marker</em>: Marker data from Qualisys</li> <li><em>Force</em>: GRFs data from Qualisys</li> <li><em>EMG</em>: EMG data from Qualisys</li> <li><em>IMU</em>: Motion data from Xsens IMU system (from .mvnx)</li> <li><em>Insole</em>: The recorded pressure insole data</li> <li><strong>Resample</strong>: This data structure that contains the resampled data of the above mentioned sensor data <ul> <li><em>FrameRate</em>: the sampling rate for all resampled sensor data</li> <li><em>Marker</em>: Resampled marker data</li> <li><em>Force</em>: resampled force data</li> <li><em>EMG</em>: resampled EMG data</li> <li><em>IMU</em>: resampled IMU data</li> <li><em>Insole</em>: resampled Insole data</li> <li><em>CoM</em>: resampled center of mass data from Xsens IMU system</li> <li><strong>Sych</strong>: this data structure contains the IK & ID data of two measurement systems. They are also synchronized by calculate the highest correlation coefficient. <ul> <li><em>DeltaT</em>: the time differences between the laboratory-based system and the wearable measurement system.</li> <li><em>IKAngData</em>: the joint angle data from marker data inverse kinematics</li> <li><em>ForcePlateGFRData</em>: the ground reaction force data from instrumented treadmill</li> <li><em>IDTrqData</em>: the joint torque data from laboratory measurement system (optical + treadmill)</li> <li><em>IMUAngData</em>: the joint angle data from Xsens MVN software</li> <li><em>InsoleGRFData</em>: the ground reaction force data from pressure insoles</li> <li><em>IDTrqData_portable</em>: the joint torque data from the wearable system inverse dynamics</li> <li><em>EMG</em>: synchronized EMG data</li> <li><em>CoM</em>: synchronized CoM data</li> <li><em>xxxxxLabel</em>: the labels of each data column of corresponding data matrix</li> <li><strong>Average</strong>: this data structure contains the averaged gait/moment cycles <ul> <li><em>hsMatrix_right</em>: The heel strike data points of the right leg</li> <li><em>hsMatrix_left</em>: the heel strike data points of the left leg</li> <li><em>EMGAvedynNorFlag</em>: whether dynamic MVC normalization is applied on EMG.</li> <li><em>EMGAveNorFlag</em>: whether MVC normalization is applied on EMG.</li> <li><em>xxxx</em>: The averaged data of corresponding variables</li> <li><em>ForcePlateGRFDataInCalCn</em>: transferred treadmill GRF data from the treadmill global coordinate to the local Calcaneus coordinate of the scaled OpenSim model. </li> </ul> </li> </ul> </li> </ul> </li> </ul>
Data corresponding to "The Impact of Multi-sensor Land Data Assimilation on River Discharge Estimation"
<p>This dataset is corresponding to the input and output files that were used in this study:</p> <p>Wu, W.-Y., Z.-L. Yang, L. Zhao, P. Lin (2022), Joint Multi-sensor Data Assimilation for Constraining Water Storages and its Impact on Global Discharge Estimation (<em>in revision, RSE</em>)</p>
Dataset of paper "Critical assessment of optical sensor parameters for the measurement of ultraviolet LED lamps"
<p>Dataset of paper "Critical assessment of optical sensor parameters for the measurement of ultraviolet LED lamps"</p> <ul> <li>Sensor specifications</li> <li>Peak wavelength of light sources studied. </li> <li>Relative spectral intensity of each light source.</li> <li>Angle of Acceptance of sensors.</li> <li>Summary of angular response of detectors.</li> <li>Change of acceptance angle of detectors with wavelength.</li> <li>Raw reference counts as measured by a saturated and unsaturated sensor.</li> <li>Change in measured intensity with integration time.</li> <li>Effect of temperature on intensity measured by the sensor. </li> <li>Comparison between sensor measurements.</li> </ul>
REIP: a Reconfigurable Environmental Intelligence Platform and Software Framework for Fast Sensor Network Prototyping - use case dataset
<p>Sensor networks have dynamically expanded our ability to monitor and study the world. Their presence and need keep increasing, and new hardware configurations expand the range of physical stimuli that can be accurately recorded. Sensors are also no longer simply recording the data, they process it and transform into something useful before uploading to the cloud. However, building sensor networks is costly and very time consuming. It is difficult to build upon other people’s work and there are only a few open-source solutions for integrating different devices and sensing modalities. We introduce REIP, a Reconfigurable Environmental Intelligence Platform for fast sensor network prototyping. REIP’s first and most central tool, implemented in this work, is an open-source software framework, an SDK, with a flexible modular API for data collection and analysis using multiple sensing modalities. REIP is developed with the aim of being user-friendly, device-agnostic, and easily extensible, allowing for fast prototyping of heterogeneous sensor networks. Furthermore, our software framework is implemented in Python to reduce the entrance barrier for future contributions. We show the potential and versatility of REIP in real world applications, along with performance studies and benchmark REIP SDK against similar systems.</p> <p>This dataset was created for the case study in Section 5 of the paper.</p>
Two light sensors decode moonlight versus sunlight to adjust a plastic circadian/circalunidian clock to moon phase
<p>Many species synchronize their physiology and behavior to specific hours. It is commonly assumed that sunlight acts as the main entrainment signal for ~24h clocks. However, the moon provides similarly regular time information. Consistently, a growing number of studies have reported correlations between diel behavior and lunidian cycles. Yet, mechanistic insight into the possible influences of the moon on ~24hr timers remains scarce.</p> <div> <div> <div class="msocomtxt"> <p class="MsoNormal"><span>We have explored the marine bristleworm </span><em><span>Platynereis dumerilii</span></em><span> to investigate the role of moonlight in the timing of daily behavior. We uncover that moonlight, besides its role in monthly timing, also schedules the exact hour of nocturnal swarming onset to the nights' darkest times. Our work reveals that extended moonlight impacts on a plastic clock that exhibits <24h (moonlit) or >24h (no moon) periodicity. Abundance, light sensitivity, and genetic requirement indicate that the <em>Platynereis </em>light receptor molecule r-Opsin1 serves as a receptor that senses moonrise, whereas the cryptochrome protein L-Cry<em> </em>is required to discriminate the proper valence of nocturnal light as either moon- or sunlight. Comparative experiments in <em>Drosophila </em>suggest that cryptochrome's principal requirement for light valence interpretation is conserved. Its exact biochemical properties differ, however, between species with dissimilar timing ecology.</span></p> <p class="MsoNormal"><span>Our work advances the molecular understanding of lunar impact on fundamental rhythmic processes, including those of marine mass spawners endangered by anthropogenic change.</span></p> </div> </div> </div>
Data accompanying: Performance characterization of low-cost air sensors for off-grid deployment in rural Malawi
<p>Low-cost gas and particulate sensor packages offer a compact, lightweight, and easily transportable solution to address global gaps in air quality (AQ) observations. However, regions that would benefit most from widespread deployment of low-cost AQ monitors often lack the reference grade equipment required to reliably calibrate and validate them. In this study, we explore approaches to calibrating and validating three integrated sensor packages before a one year deployment to rural Malawi using collocation data collected at a regulatory site in North Carolina, USA. We compare the performance of five computational modelling approaches to calibrate the electrochemical gas sensors: k-Nearest Neighbor (kNN) hybrid, random forest (RF) hybrid, high-dimensional model representation (HDMR), multilinear regression (MLR), and quadratic regression (QR). For the CO, O<sub>x</sub>, NO, and NO2 sensors, we found that kNN hybrid models returned the highest coefficients of determination and lowest error metrics when validated. Hybrid models also were the most transferable approach when applied to deployment data collected in Malawi. We compared kNN-hybrid calibrated CO observations from two regions in Malawi to remote sensing data and found qualitative agreement in spatial and annual trends. However, ARISense monthly mean surface observations were 2 to 4 times higher than the remote sensing data, due to proximity to residential biomass combustion activity not resolved by satellite imaging. We also compared the performance of the integrated Alphasense OPC-N2 optical particle counter to a filter-corrected nephelometer using collocation data collected at one of our deployment sites in Malawi. We found the performance of the OPC-N2 varied widely with environmental conditions, with the worst performance associated with high relative humidity (RH > 70%) conditions and influence from emissions from nearby residential biomass combustion. We did not find obvious evidence of systematic sensor performance decay after the one year deployment to Malawi. Data recovery (30-80%) varied by sensor and season and was limited by insufficient power and access to resources at the remote deployment sites. Future low-cost sensor deployments to rural Sub-Saharan Africa would benefit from adaptable power systems, standardized sensor calibration methodologies, and increased regional regulatory grade monitoring infrastructure. </p>
Localizing Hydrological Drought Early Warning using In-Situ Groundwater Sensors - Veness et al. (2022) - Dataset
<p>This upload contains full input data and modelling scripts to support AGU WRR's 'Localizing Hydrological Drought Early Warning using In-situ Groundwater Sensors' (Veness et al., 2022).</p> <p>'WRR_Data_Extraction' contains input data and processing of these for model input.</p> <p>'WRR_Modelling_Methods' contains two folders. 'Groundwater Model & Calibration' provides the code for the modified AquiMod, formatted for input in to an automated calibration procedure (run time approx. 5 minutes with 10,000 runs). The 'Plotting Files' folder contains the code converting the groundwater levels from the automated calibration procedure to plots seen in the paper.</p> <p>These scripts require use of the data and functions within the 'data_and_functions' folder, which may require careful directory management and minor edits to the code to ensure the scripts can communicate.</p>
Contaminations on Lidar Sensor Covers: Performance Degradation including Fault Detection and Modeling as Potential Applications
<p><strong>Data description of contamination measurements with lidar sensors RIEGL LD05-A20 and Ouster OS1-64</strong></p> <p><em><strong>Photos of the measurement setup</strong></em></p> <p>We provide photos of the measurement setup and the contaminations applied in the folder "/photos".</p> <p> </p> <p><em><strong>Riegl LD05-A20 data</strong></em></p> <p>The data of the Riegl LD05-A20 can be found in the folder "riegl_LD05-A20_full_waveforms" - one file per experiment. An example notebook for reading the data is provided in "/notebooks/example_riegl_LD05-A20.ipynb". Note that the files contain only the prominent peaks of the full waveform calculated by the V08Wave software provided by RIEGL. If you are interested in the entire full waveforms, please contact the authors.</p> <p> </p> <p><em><strong>Ouster OS1-64</strong></em></p> <p>The data of the Ouster OS1-64 can be found in the folder "ouster_OS1-64_point_clouds" - one folder per experiment. An example notebook for reading the data is provided in "/notebooks/example_ouster_OS1-64.ipynb". The python package <strong><em>pointcloudset</em></strong> (https://github.com/virtual-vehicle/pointcloudset) and its documentation is suggested for further data analytics of the point cloud data.</p> <p> </p>
Spatio-thermal depth correction of RGB-D sensors based on Gaussian Processes in real-time
<p>This RGB-D dataset is part is part of our publication</p> <p>Heindl, Christoph, et al. "Spatio-thermal depth correction of RGB-D sensors based on Gaussian processes in real-time." <em>Tenth International Conference on Machine Vision (ICMV 2017)</em>. Vol. 10696. SPIE, 2018.</p> <p>Our capture setup consists of a RGB-D sensor looking towards a known planar object. The sensor is coupled with an electronic linear axis to adjust distance. We captured data at distances [40cm, 90cm, 10cm steps] in the temperate range of [25°C, 35°C, 1°C steps]. At each temperature/distance tuple we grabbed 50 images from both RGB and IR (aligned with RGB) sensors. We then created an artificial depth map for all RGB images utilizing the known calibration target in sight.</p> <p>For more information visit https://github.com/cheind/rgbd-correction</p>
ImPure Injection Molding Sensor Data - Trial 12th May
<p>ImPure project, open access data from PASCOE IM line. </p>
ImPure Injection Molding Sensor Data - Trial 17th May
<p>ImPure project, open access data from PASCOE IM line. </p>
ImPure Injection Molding Sensor Data - Trial 16th May
<p>ImPure project, open access data from PASCOE IM line. </p>
Sensor data for "Influence of X-Ray Radiation on Historical Paper"
<p>This is the raw and processed data used in the paper "Influence of X-Ray Radiation on Historical Paper". It consists of the following:</p> <ol> <li>all images in the CR3 format, taken before, during, and after irradiation of the paper,</li> <li>the images converted to JPEG, as well as crops of the relevant parts stored as nupy arrays</li> </ol>
mmWave Radar and RGB-D Camera Sensor Data for Human Activity Recognition
<p>This is a human activity recognition dataset with measurements from both mmWave radar and camera sensor. Meanwhile, we set multiple people scenario to mimic more realistic scenes. The other dataset collected in non-LOS(line-of-sight) environment, you can visit https://zenodo.org/record/7096889#.YynBvuhBwQ8 to get it. The mmWave radar sensors used in our experiments are composed of TI IWR6843ISK-ODS, eradar ESRR(corner radar), eradar EMRR(front radar). We appreciate the support of the eradar company, that provides corner radars and front radars for us, you can visit http://en.eradartech.com/ to get more information. </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.