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412 results for “sensor data”
Calibration data for Climate Change Tower temperature sensors - may 2017
<p>Calibration data for the temperature sensors hosted at Climate Change Tower in Ny-Ålesund, Svalbard, during May 2017. Each namefile contains the temperature point (e.g. m25 is -25 °C, while p5 is +5 °C) and the sensor type ("ref" for reference, "DUT" for Device under test).</p> <p>Reference files contain data for the 2 reference sensors employed, while DUT files contain data for the 4 CCT sensors.</p>
Data products from "GNSS reflectometry from low-cost sensors for continuous in-situ contemporaneous glacier mass balance and flux divergence"
<p>GNSS, GNSS-IR, and mass balance data from "GNSS reflectometry from low-cost sensors for continuous in-situ contemporaneous glacier mass balance and flux divergence". Contains the following folders and files</p> <ul> <li>GNSS <ul> <li><em>Precise point positioning solution (.pos, etc) using the CSRS-PPP tool for each GNSS system</em></li> </ul> </li> <li>GNSS_basefix <ul> <li><em>Precise point positioning solution (.pos) with base station observations using the Emlid Studio desktop application for GNSS systems AB floating and AB fixed</em></li> </ul> </li> <li>GNSSIR<br> <ul> <li><em>Reflector height solutions for site AB floating, AB fixed, and D floating (see Fig. 7). The filename is the day of year 2023.</em></li> </ul> </li> <li>Monitored Ablation Stake<br> <ul> <li><em>Processed daily and seasonal climatic mass balance height changes (see Fig. 5)</em></li> </ul> </li> </ul>
Data for "Quantifying nocturnal thrush migration using sensor data fusion between acoustics and vertical-looking radar"
<p>This repository contains the data used for the analyses conducted and described in the article "Quantifying nocturnal thrush migration using sensor data fusion between acoustics and vertical-looking radar", as well as the Random Forest classifier built from such data. </p> <p>Files:</p> <p><em>2021_AcousticDataset.xlsx:</em> excel dataset containing the daily number of calls for each of the three thrush species considered recorded in Helsinki (Finland) during the study period (year 2021). The dates refer to UTC time.</p> <p><em>2022_ AcousticDataset.xlsx: </em>excel dataset containing the daily number of calls for each of the three thrush species considered recorded in Helsinki (Finland) during the study period (year 2022) . The dates refer to UTC time.</p> <p><em>RandomForestClassifier.Rdata: </em>R object containing the Random Forest classifier built using the eight most important echo features. The purpose of the classifier is to categorize echoes into 'thrush' and 'non-thrush' classes. </p> <p><em>2021_RadarDataset.rds: </em>R object containing the radar data collected with AVLR BirdScan MR1 in Helsinki (Finland) during the study period (year 2021).</p> <p><em>2022_RadarDataset.rds:</em> R object containing the radar data collected with AVLR BirdScan MR1 in Helsinki (Finland) during the study period (year 2022).</p> <p><em>license.txt: </em>the license applying to the data.</p>
A Big Data Analysis Algorithm for Massive Sensor Medical Images
<p><span>The smart sensor based big data analysis recommendation system has significant privacy and security concerns when it comes to using sensor medical images for suggestions and monitoring. The danger of security breaches and unauthorized access which might lead to identity theft and privacy violations increases when sending and storing sensitive medical data on the cloud. Insufficient or erroneous patient data can lead to poor treatment decisions, misdiagnoses, and unreliable recommendations. By creating an anomaly detection system based on machine learning specifically for medical image and providing timely treatments and notifications, our effort will improve patient care and well-being. We infer the feature extraction, feature selection, attack detection, and data collection data processing procedures in order to anticipate the anomaly in patient data. We transfer the data, take care of any missing values, and sanitize it using the data pre-processing mechanism. We employed the RFE and DPCA algorithms for feature selection and extraction, respectively. In addition, we applied the AGRNN approach to identify abnormalities. Data arrival rate, resource consumption, propagation delay, transaction epoch, true positive rate, false alarm rate, and RMSE are some of the metrics used to evaluate the proposed task.</span></p>
Environmental Data, PurpleAir Sensors, Athens, August 2021
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2021 DATA SET OF TRAFFIC COUNTES BASED ON CITIZEN SENSORS TO MONITOR DAILY TRAFFIC SOUTHERN INNER BYPASS RING IN LJUBLJANA
<p>Traffic counters for Southern inner bypass ring in Ljubljana, Slovenia. Corresponding roads: Aškerčeva road, Zoisova road, Karlovška road and Roška road.</p>
DOE1 and DOE2 - Sensor data set radial forging at AFRC testbed
<p><strong>Sensor data set, radial forging at AFRC testbed</strong></p> <p><strong>General information on the data set</strong></p> <p>Radial forging is widely used in industry to manufacture components for a broad range of sectors including automotive, medical, aerospace, rail and industrial. The Advanced Forming Research Centre (AFRC) at the University of Strathclyde, Glasgow, houses a GFM SKK10/R radial forge that has been used as a testbed for this project. Using two pairs of hammers operating at 1200 strokes/min, and providing a maximum forging force per hammer of 150 tons, the radial forge is capable of processing a range of metals, including steel, titanium and inconel. Both hollow and solid material can be formed with the added benefit of creating internal features on hollow parts using a mandrel. Parts can be formed at a range of temperatures from ambient temperature to 1200 °C.</p> <p>For the provided data set, a total of 95 parts were forged over two days of operation. Each part was forged with machine parameters outlined in the file <code>Met4FoF STRATH test plan v4.xlsx</code>. Deviations from the test plan occurred during the second batch and are outline in the sheet <code>Batch 2</code>. Each forged part was then measured using a CMM to provide dimensional output relative to a target specification and tolerances. T<strong>he CMM records 16 dimensional measurements</strong>.</p> <p>The aim of the measurement setup is to predict the quality (in terms of dimensional properties) of the forged part from the sensor measurements during the forging process.</p> <p><strong>Structure of the data</strong></p> <ul> <li>The sensor readings for the forging of the parts are provided in csv files in the folders <code>ScopeTraces_DOE1</code> and <code>ScopeTraces_DOE2</code> for DOE1 and DOE2 respectively. For DOE1 the scope traces are named <code>Scope0001.csv</code> to <code>Scope0050.csv</code>. For DOE2 there were deviations and the Scope Traces to billet mapping is outlined in <code>Met4FoF STRATH test plan v4.xlsx</code>. Each file contains the readings (columns) against time (rows). The first column displays the clock times (in milliseconds).</li> <li>A commentary on the sensors is provided in the file <code>ForgedPartDataStructureSummaryv3.xlsx </code>(NOTE: Some columns do not have sensor descriptions as this information is not available).</li> <li>The CMM data is provided in the file <code>CMM_DOE1.xlsx</code> and <code>CMM_DOE2.xlsx</code> for DOE1 and DOE2 respectively.</li> </ul>
Sensor data from industrial splitting saw machine
<p>Sensor data from industrial splitting saw machine used for predictive maintenance and artificial neural networks model.</p>
Mapping test data with various range sensors
<p>This is a test data for range-IMU SLAM systems recorded with various range sensors:</p> <p>- Ouster OS0-32 & OS0-64</p> <p>- Livox Avia</p> <p>- Intel Realsense L515 & D455</p> <p>- Microsoft Azure Kinect</p> <p>- Stereolabs ZED2i</p> <p> </p> <p>The groundtruth trajectories are estimated by aligning point cloud scans with a 3D environment map created with a survey-grade LiDAR (FARO Focus).</p> <p> </p> <p> </p>
mmWave Radar and RGB-D Camera Sensor Data for Human Activity Recognition(2)
<p>This is a supplementary dataset, which is linked to https://zenodo.org/record/7088054#.YyVF3ehBwQ8. The dataset is composed of corner radar point cloud data and front radar point cloud data collected from environments existing obstacles between volunteers and sensors. </p>
Sensor data with Reference
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Demo data and model weights for "Direct Observation of Atmospheric Turbulence with a Video-rate Wide-field Wavefront Sensor"
<p>Demo data and model weights for "Direct Observation of Atmospheric Turbulence with a Video-rate Wide-field Wavefront Sensor". For usage, please refer to https://github.com/freemercury/Widefield_wavefront_sensor.</p>
Data of "Stabilizing classical accelerometers and gyroscopes with a quantum inertial sensor"
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Spatial data for 11 sensors in an apartment in Leuven, Belgium between March to May, 2021.
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Campus Daily Life Behaviors Data based on Smartphone Sensors
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Data related to manuscript: Comparison of size distribution and electrical particle sensor measurement methods for particle lung deposited surface area (LDSAal) in ambient measurements with varying conditions (accepted for publication in Aerosol Research)
<p>These files include data which was utilised to calculate the results in the manuscript (https://doi.org/10.5194/ar-2024-13). </p>
Data from: Performance of social network sensors during Hurricane Sandy
Information flow during catastrophic events is a critical aspect of disaster management. Modern communication platforms, in particular online social networks, provide an opportunity to study such flow and derive early-warning sensors, thus improving emergency preparedness and response. Performance of the social networks sensor method, based on topological and behavioral properties derived from the "friendship paradox", is studied here for over 50 million Twitter messages posted before, during, and after Hurricane Sandy. We find that differences in users' network centrality effectively translate into moderate awareness advantage (up to 26 hours); and that geo-location of users within or outside of the hurricane-affected area plays a significant role in determining the scale of such an advantage. Emotional response appears to be universal regardless of the position in the network topology, and displays characteristic, easily detectable patterns, opening a possibility to implement a simple "sentiment sensing" technique that can detect and locate disasters.
A neural circuit linking two sugar sensors regulates satiety-dependent fructose drive in Drosophila (raw data)
<p>This is the raw numerical data used in Musso et al., 2021, A neural circuit linking two sugar sensors regulates satiety-dependent fructose drive in Drosophila.</p>
PLEIAData:consumption, HVAC (Heating, Ventilation & Air Conditioning), temperature, weather and motion sensor data for smart buildings applications
<p>This dataset presents detailed building operation data from the three blocks (A, B and C) of the Pleiades building of the University of Murcia, which is a pilot building of the European project PHOENIX. The aim of PHOENIX is to improve buildings efficiency, and therefore we included information of:<br> (i) consumption data, aggregated by block in kWh; (ii) HVAC (Heating, Ventilation and Air Conditioning) data with several features, such as state (ON=1, OFF=0), operation mode (None=0, Heating=1, Cooling=2), setpoint and device type; (iii) indoor temperature per room; (iv) weather data, including temperature, humidity, radiation, dew point, wind direction and precipitation; (v) carbon dioxide and presence data for few rooms; (vi) relationships between HVAC, temperature, carbon dioxide and presence sensors identifiers with their respective rooms and blocks. Weather data was acquired from the IMIDA (Instituto Murciano de Investigación y Desarrollo Agrario y Alimentario).</p>
Data and Software for "Metal Pad-Enhanced Resistive Pulse Sensor Reveals Complex-Valued Braess Paradox"
<p>Data and software for the paper entitled "Metal Pad-Enhanced Resistive Pulse Sensor Reveals Complex-Valued Braess Paradox", by Alan Dong, Lydia Sohn, and Michael Lustig, for Physical Review E.</p>
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.