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1,772 results for “sensors”
Proximity-sensors on GPS collars reveal fine-scale predator-prey behavior during a predation event: A case study from Scandinavia
<p>Although the advent of high-resolution GPS tracking technology has helped increase our understanding of individual and multi-species behavior in wildlife systems, detecting and recording direct interactions between free-ranging animals remains difficult. In 2023, we deployed GPS collars equipped with proximity sensors (GPS proximity collars) on brown bears (<em>Ursus</em> <em>arctos</em>) and moose (<em>Alces</em> <em>alces</em>) as part of a multi-species interaction study in central Sweden. On 6 June, 2023, a collar on an adult female moose and a collar on an adult male bear triggered on each other's UHF signal and started collecting fine-scale GPS positioning data. The moose collar collected positions every 2 minutes for 89 minutes and the bear collar collected positions every 1 minute for 41 minutes. On 8 June, field personnel visited the site and found a female neonate moose carcass with clear indications of bear bite marks on the head and neck. During the predation event, the bear remained at the carcass while the moose moved back and forth, moving towards the carcass site about 5 times. The moose was observed via drone with 2 calves on 24 May and with only one remaining calf on 9 June. This case study describes, to the best of our knowledge, the first instance of a predation event between two free-ranging, wild species recorded by GPS proximity collars. Both collars successfully triggered and switched to finer-scaled GPS fix rates when the individuals were in close proximity producing detailed movement data for both predator and prey during and after a predation event. We suggest that, combined with standard field methodology, GPS proximity collars placed on free-ranging animals offer the ability for researchers to observe direct interactions between multiple individuals and species in the wild without the need for direct visual observation.</p>
FloodNet flood sensor data - October 2020 to October 2023
<p>Before accessing our data, please review our <a href="https://docs.google.com/document/d/1jd5Q2UYj_0PwMRplFISmhT6LswpS08D9/edit">Data Access License Agreement</a>, which outlines the terms and conditions associated with the use of FloodNet data.</p><p>We also encourage you to thoroughly review this description, particularly the section regarding noise in sensor data. Floods are not the only events that show up in this dataset, and we want to reduce misinterpretation of data as much as possible. If you have questions about the dataset, please contact <a href="mailto:info@floodnet.nyc">info@floodnet.nyc</a>.</p><h2><strong>Data Summary</strong></h2><p>The contents of the CSVs are as follows:</p><p><strong>deployment_id - </strong>This is the unique identifier assigned to the sensor deployment</p><p><strong>time - </strong>The ISO-8601 date-time format in <strong>UTC</strong>. e.g. <strong>2023-02-22T14:20:30.000Z</strong></p><p><strong>depth_proc_mm - </strong>Time-series filtered depth in mm, sampled every ~1min using a series of custom noise filters designed to remove common manifestations of sensor noise. <strong>This is the recommended depth field to use.</strong></p><p><strong>depth_filt_mm - </strong>Time-series raw depth in mm, sampled every ~1min, except that values below 10mm are set to zero. This data has more noise, but you can use this to verify that depth_proc_mm did not accidentally filter a flood.</p><p><strong>depth_raw_mm - </strong>Time-series raw depth in mm, sampled every ~1min.</p><h3><strong>How do the sensors capture data?</strong></h3><p>The FloodNet sensors <strong>measure distance</strong> at a regular interval, currently <strong>once every 60 seconds</strong>. Certain older sensors were programmed to upload once every 5 minutes, so don't be alarmed if you see sensors with different upload rates.</p><p>The sensors work using ultrasound. They send out sound waves at a frequency outside the range of human hearing and then capture the echo when it bounces back off the closest reflective surface. That means that what the sensor is ultimately measuring is a difference in time. The sensor then uses the travel time to calculate the distance to that reflective surface (hopefully the ground!).</p><p>Sometimes the sensor will send out a ping, but will never receive an echo back. In these cases, the measurement is invalid and we record a null value. <strong>If you see a null measurement, this is likely the cause</strong>.</p><p>Distance is calculated using d=v∆t2 where d is the distance to the ground, v is the speed of sound, and ∆t/2 is the time that it takes for the sound to bounce back and return to the sensor (divided by two because it's traveling twice the distance we're measuring).</p><p>If you know a bit of physics, you might be saying "Wait a second, but the speed of sound isn't constant! It varies depending on the properties of the medium that it's traveling through!" Good catch! The speed of sound depends on a few factors including the molecular composition and density of the medium, in this case, "air". Assuming dry air (low humidity), the speed of sound, in units of m/s, is represented as v=20.05TC where TC is the ambient temperature in Celsius. The FloodNet sensors have an internal temperature sensor that it uses to do these calculations.</p><p>In order to calculate flood depth, the measured distance between the sensor and the ground below must be known under non-flooded conditions. This value is calculated through a dynamic calibration procedure that occurs at 5 AM daily, in which the distance measurements z collected over the previous three nights (between 10 PM and 5 AM are analyzed to determine the median value (z_nighttime~median). Day-time measurements are excluded from the calibration because of their temperature-related variance caused by direct sunlight producing erroneously high measurements on the sensor's internal temperature sensors. If the standard deviation of the night-time distance measurements exceeds 5 mm signifying either a flood or erroneous high variance), the previous day's z_nighttime~median calculation is used. To calculate flood depth D_t at time point t, the sensor's distance measurement at that time z_t) is subtracted from z_nighttime~median: Dt = z_nighttime~median - z_t</p><p>This dynamic calibration approach allows data collection to adapt to changes in sensor height, caused for example by a shift in signpost position if there is a vehicle impact, or seasonal variation in baseline z_nighttime~median readings.</p><h3><strong>Why is there noise in the data?</strong></h3><p>It may have occurred to you "How do you know that you're measuring the ground, and not a pigeon?" Well, the short answer is, we don't! That's why you may see some values that are not zero but are not actually floods.</p><p>The issue of noise is a challenge that our team is focused on and is actively researching. We have developed some methods designed to remove common manifestations of noise. The noise categories we have characterized are:</p><p><strong>Blips:</strong> a momentary jump in the data, where it returns to the previous value a sample or two later. This is often caused by someone or something passing under the sensor while it is taking its measurement.</p><p><strong>Boxes:</strong> a sudden and persistent jump in depth. This is commonly because something is placed beneath the sensor, such as garbage bags, bicycles, or loose trash.</p><p><strong>Pulse Chain:</strong> a chaotic chain of pulse/box-like noise that can occur for an extended period of time. This can be caused by aberrant reflections on uneven, sloped, or complex surfaces such as the spokes on a bike wheel. </p><p>The FloodNet data analysis pipeline uses a series of custom filters to address and filter some of this noise, and includes blip filters, detecting momentary jumps in the data, and box filters, detecting sustained jumps to a higher, near-constant value. These filters were optimized to minimize the risk of distorting the data and removing floods. We also employ a gradient filter that looks at the rate of change in depth to determine if the change observed is physically plausible. The gradient threshold used is 10 inches per minute, which is 7 times the maximum rate observed in Hurricane Ida in late summer of 2021.</p><h3><strong>Why isn't the sensor showing a flood?</strong></h3><p>As ultrasonic sensors need to be perpendicular and directly above the location that they are measuring, they are highly dependent on available mounting locations. To deploy these sensors, we are using existing street infrastructure such as signposts. This largely limits the locations on a street that we can use to install sensors, meaning that the sensors are not always located at the lowest point on the street, where floods would first develop. </p><p>Streets have variable topography and therefore the depth measurements our sensors capture will not always reflect the depth at every part of the street. For example, if our sensor is 4 inches higher than the lowest point in the area, our sensor would be reading zero for any flooding below 4 inches, and a reading of 10 inches actually corresponds to 14 inches of depth at the lowest point. That offset calculation requires detailed elevation maps and is information that our data scientists are working to compile for any of our sensor deployments, but is not yet available.</p>
Screen-Printed Piezoelectric Sensors on Tattoo Paper Combined with All-Printed High Performance Organic Electrochemical Transistors for Electrophysiological Signal Monitoring
<p>Dataset of the journal article "Screen-Printed Piezoelectric Sensors on Tattoo Paper Combined with All-Printed High Performance Organic Electrochemical Transistors for Electrophysiological Signal Monitoring" published in <i>ACS Applied Materials & Interfaces</i>, 28 November 2023, <a href="https://eur05.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.1021%2Facsami.3c10299&data=05%7C01%7Cpeter.andersson.ersman%40ri.se%7Cbabb1e939a0d4fa7a40908dbeb5e3902%7C5a9809cf0bcb413a838a09ecc40cc9e8%7C0%7C0%7C638362562144125762%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=pwQmN%2BZMzDOvXQcJ1B4gpHf1S5TEKXtjslPldL%2F9kYQ%3D&reserved=0">https://doi.org/10.1021/acsami.3c10299</a></p>
Influences of Satellite Sensor and Scale on Derivation of Ecosystem Functional Types and Diversity
<p>These are the datasets where were generated for paper "Influences of Satellite Sensor and Scale on Derivation of Ecosystem Functional Types and Diversity"</p>
Data for: Melt electrowriting enabled 3D liquid crystal elastomer structures for cross-scale actuators and temperature field sensors
<p>Liquid crystal elastomers have garnered significant attention due to their remarkable capability to undergo reversible strains and shape transformations under various stimuli. Early studies on LCE primarily focused on limited shape changes of macrostructures or quasi-3D microstructures. However, fabricating complex cross-scale LCE-based 3D structures still remains challenging. Here, we report a compatible method, the Melt-Electrohydrodynamic (Melt-EHD) 3D printing, to create LCE-based microfiber actuators and various 3D actuators across micrometer to centimeter scales, showcasing their actuation to thermal airflow stimulus. By controlling printing parameters, microfiber actuators with different diameters (5 μm~70 μm), and tunable properties including actuation strain (10%~55%), actuation stress (0~0.6 MPa), and large work density (~160J/kg) have been demonstrated. Under dynamic thermal airflow stimulus at 15 Hz, the microfiber actuators lift weights over 3500 times heavier than themselves. These 3D structures were obtained by depositing LCE microfibers along pre-programmed paths, including various gradient-responsive elementary structural units, 1 mm-sized microgripper, and various large area 3D lattice structures. In addition, by integrating a Deep Learning model, we have demonstrated, for the first time, large area (≥ centimeter scale), real-time (24 Hz sampling frequency), high-precision (~95%) LCE grid based spatial temperature field sensors with a spatial resolution of only 4 mm.</p>
NOAA PSL thermodynamic profiles retrieved from a combination of active and passive remote sensors and numerical weather prediction models with the optimal estimation physical retrieval TROPoe at Platteville, CO, USA
<p>This dataset contains retrieved profiles of thermodynamic variables obtained using the Tropospheric Remotely Observed Profiling via Optimal Estimation (TROPoe) physical retrieval from various combinations of input data collected by passive and active remote sensing instruments, in-situ surface platforms, and numerical weather prediction models deployed at the Platteville, CO, USA, site in fall 20221-winter 2022. Among the employed instruments are Microwave Radiometers (MWRs), Infrared Spectrometers (IRS), Radio Acoustic Sounding Systems (RASS), ceilometers, surface sensors, and information from the operational Rapid Refresh numerical weather prediction model.</p> <p>The dataset also includes 15 radiosounding launched for assessing the retrievals.</p> <p>For further information, please see:</p> <p>Bianco, L., Adler, B., Bariteau, L., Djalalova, I. V., Myers, T., Pezoa, S., Turner, D. D., and Wilczak, J. M.: Sensitivity of thermodynamic profiles retrieved from ground-based microwave and infrared observations to additional input data from active remote sensing instruments and numerical weather prediction models, Atmos. Meas. Tech. Discuss. [preprint], https://doi.org/10.5194/amt-2023-263, in review, 2024.</p>
LI-COR (LI-850) sensor data obtained by the Antarctic Modeling Observation System (ATMOS) project during the 40th Brazilian Antarctic Operation (OPERANTAR XL) and were used to calculate the partial pressure of CO2 (pCO2)
<p>LI-COR (LI 850) sensor data obtained by the "Antarctic Modeling Observation System" (ATMOS) project. These data were collected in the southern summer of 2021/2022 during the 40th Brazilian Antarctic Operation (OPERANTAR XL) and were used to calculate the partial pressure of seawater CO2 (pCO2sea)</p> <p>The LI-850 carbon dioxide analyzer was installed in the laboratory aft of H41 together with a balancer to measure the CO2 concentration of the water. The collection system occurs as follows: the ship's saltwater piping system collects seawater, when this water enters the balancer it generates turbulence. The turbulence generated causes the CO2 present in the water to come into balance with the air. The air that comes out of the balancer is pumped into the LI-850, by its internal pump, and thus, the equipment measures the concentration of CO2 present in the water. To ensure that the air inside the balancer is actually balanced with the seawater, the air leaving the LI-850 is pumped back into the balancer, closing the circuit. From these data it is possible to calculate pCO2sea.</p>
Phosphate Sensor Search Engine (P-SENSEE) Snapshot
<p>We demosntrate a snapshot of the Phosphate Sensor Search Engine (P-SENSEE) for EuroSensors 2024, which represents a small select fraction of the database collected by our team.</p>
AUV-Based Multi-Sensor Dataset: Forward-Looking Camera (FLC) and Forward-Looking Sonar (FLS) Observations in the Red Sea
<p><strong>Context</strong></p> <p>This dataset is the first part of a dataset collection comprised of forward-looking sonar (FLS) and forward-looking camera (FLC) underwater images. The entire data was collected during the years 2021-2023 using 2 underwater vehicles in both the Red Sea and the Mediterranean along the Israeli shoreline, depicting both man-made and natural underwater environments. The data is part of a research project aimed at developing fusion models for improved obstacle detection and navigation in autonomous underwater vehicles.</p> <p><strong>Content</strong></p> <p>This dataset consists of FLC and FLS images and their metadata, collected by the ALICE-AUV. Both sensors were installed in the front payload section in a configuration having aligned fields of view to achieve matching pairs of data. The data was collected to train and evaluate a complete perception and obstacle avoidance framework.</p> <p>A series of diving sessions were performed in the Red Sea, off the coast of Eilat, Israel. The experiments focused on two main sites: A "Sunboat" shipwreck and the Eilat-Ashkelon Pipeline Company (EAPC) pier pillars. The "Sunboat" shipwreck is a 40-meter long vessel resting at a depth of approximately 12 meters, with the surrounding seabed at a depth of 18-24 meters. This dataset contains approximately 8,000 FLC-FLS sample pairs from the first session conducted at the "Sun boat" shipwreck site on September 3, 2023. The data was recorded at depths ranging from 10 to 15 meters.</p> <p>The dataset is organized into separate sessions, each representing a specific dive or experiment. Within each session, the data is further categorized into modalities: camera (FLC images), sonar (FLS images), and navigation (dead reckoning data). The navigation data is derived from a combination of GPS, DVL, and IMU sensors, providing estimated positions when GPS is unavailable. Inside each modality directory, you will find the corresponding data files in PNG format for images and CSV format for navigation data. The file names follow a sequential numbering scheme (e.g., 00001.png, 00002.png, etc.). Each modality directory also contains a CSV file (e.g., camera.csv) that maps each data file to its respective timestamp. Additionally, the samples.json file documents the relationship between uni-modal and multi-modal samples, allowing for easy association of data from different modalities.</p> <p>By providing synchronized and aligned camera and sonar imagery, along with corresponding navigation data, this dataset enables researchers to explore novel algorithms and techniques for multi-modal sensor fusion in the context of autonomous underwater vehicles.</p> <p><strong>Technical Details</strong></p> <ul> <li>Sonar: Blueprint Oculus M1200d <ul> <li>Operating frequency: 1.2 MHz (low frequency mode)</li> <li>Maximum range: 40 m (set to 20 m for this dataset)</li> <li>Horizontal aperture: 130°</li> <li>Vertical aperture: 20°</li> <li>Number of beams: 512</li> <li>Angular resolution: 0.6°</li> <li>Beam separation: 0.25°</li> <li>Image resolution: 902x497 pixels</li> <li>Coordinate system: Polar</li> </ul> </li> <li>Camera: Allied-Vision Manta G-917 <ul> <li>Image dimensions: 3384x2710 pixels (downscaled to 1692x1355 for this dataset)</li> <li>Sensor type: CCD Progressive</li> <li>Sensor bit depth: 12-bit</li> <li>Captured bit depth: 8-bit</li> <li>Camera model: Pinhole with Plumb Bob (Brown–Conrady) distortion coefficients</li> <li>Focal length (fx, fy): (1638.36157, 1641.95202)</li> <li>Principal point (cx, cy): (1705.03529, 1380.27954)</li> <li>Radial distortion coefficients (k1, k2, k3): (-0.124823, 0.048851, 0.000000)</li> <li>Tangential distortion coefficients (p1, p2): (0.000259, -0.002945)</li> </ul> </li> <li>Navigation: <ul> <li>Data format: CSV</li> <li>Contains fused dead reckoning data based on GPS, DVL, and IMU sensors</li> <li>Columns: <ul> <li>timestamp: Unix timestamp (seconds)</li> <li>latitude: Latitude (degrees)</li> <li>longitude: Longitude (degrees)</li> <li>altitude: Altitude (meters)</li> <li>yaw: Yaw angle (degrees)</li> <li>pitch: Pitch angle (degrees)</li> <li>roll: Roll angle (degrees)</li> <li>velocity_x: Velocity along the x-axis (meters per second)</li> <li>velocity_y: Velocity along the y-axis (meters per second)</li> <li>velocity_z: Velocity along the z-axis (meters per second)</li> <li>depth: Depth (meters)</li> </ul> </li> </ul> </li> <li>Frame rate: 2 Hz for both sonar and camera</li> </ul> <p>More datasets from this collection will be uploaded in the future, and a link to access them will be provided on this page.</p> <p><strong>Acknowledgements</strong></p> <p>The data in this repository is part of the DeeperSense project that received funding from the European Commission, Program H2020-ICT-2020-2 ICT-47-2020, Project Number: 101016958.</p>
Transition Edge Sensor Signals (Boulder)
<h1>Content</h1> <p>Transition Edge Sensor (TES) signals from an attenuated coherent source. The mean photon number of the source goes from 2.26 to 7 082 112 (29 dB to 7 dB of attenuation).</p> <p> </p>
Data for "Measuring mean radiant temperature for indoor comfort assessment using low-resolution optical sensors"
<p>Data for "Measuring mean radiant temperature for indoor comfort assessment using low-resolution optical sensors".</p>
Real-Time Dataset of Fire Sensor Measurements Collected During the Resisto Project
<p>This dataset contains real-time environmental measurements from fire detection sensors across multiple locations. These sensors has been deployed on diferent locations, principally on the Doñana National Park. </p>
Optimal IoT Sensor Deployment in the WUI: A Comparative Analysis of Strategies
<p>Included here are individual burn maps used for evaluating algorithm results in the paper: Optimal IoT Sensor Deployment in the WUI: A Comparative Analysis of Strategies. This paper was accepted for presentation at IEEE HONET 2024, the 21st IEEE International Conference on Smart Communities. (For financial/travel reasons the paper has since been withdrawn)</p> <p>Also included are maps of fuel load and elevation (geotifs) and the daily weather (in .csv format) for the region of interest used in the burn probability simulator Burn-P3+ to generate the individual burn maps.</p> <p>This paper investigates optimized IoT sensor deployment strategies within the Wildland-Urban Interface (WUI), a key component of smart communities. It presents a comparative analysis of a novel dynamic grid approach against traditional random and greedy algorithms. By employing the Burn-P3+ simulator, detailed burn probability maps are generated for two Canadian geographically distinct areas: the Halifax Regional Municipality and Kelowna. Our analysis reveals that the dynamic grid method significantly enhances fire detection capabilities by strategically distributing IoT sensors in alignment with calculated burn probability. This approach shows a marked improvement of as much as 35% in burn detection over traditional deployment methods. The paper highlights the advantages of structured IoT sensor placement in supporting smart communities, offering more efficient and effective wildfire management strategies in the WUI through real-time data available to both fire mitigation teams and AI.</p> <p>Partial code for the sensor deployment algorithms discussed in the above mentioned paper is <a href="https://github.com/richardjpurcell/sensor-deployment-algorithms">available on GitHub</a>.</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.
Plasmon-enhanced fluorescence (bio)sensors and other bioanalytical technologies
<pre>DATASET DESCRIPTION This Dataset contains the raw data from the following publication: ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Plasmon-enhanced fluorescence (bio)sensors and other bioanalytical technologies Dario Cattozzo Mor, Gizem Aktug, Katharina Schmidt, Prasanth Asokan, Naoto Asai, Chun Jen Huang, Jakub Dostalek* *corresponding author: dostalek@fzu.cz (J. Dostalek) Trends in Analytical Chemistry </pre>
Polyaniline/CNT composites for piezoresistive presure sensors
<p>This data set corresponds to the analyses carried out in the following article: Arias‐Ferreiro, G., Lasagabáster‐Latorre, A., Ares‐Pernas, A., Dopico‐García, M. S., Pereira, N., Costa, P., ... & Abad, M. J. (2022). <br>Flexible 3D printed acrylic composites based on polyaniline/multiwalled carbon nanotubes for piezoresistive pressure sensors. Advanced Electronic Materials, 8(12), 2200590.<br>DOI: 10.1002/aelm.202200590</p>
Supporting datasets used in the paper entitled "Aircraft-based observation of mineral dust particles over the western North Pacific in summer using a complex amplitude sensor"
<p>This archive contains datasets used in the paper entitled "Aircraft-based observation of mineral dust particles over the western North Pacific in summer using a complex amplitude sensor."</p>
Data from: Handheld lidar sensors can accurately measure herbaceous biomass
<p>Data and code used in <em>Handheld lidar sensors can accurately measure herbaceous biomass</em>. Lidar data is provided for MLS and iPad sensors in <em>las_files.zip</em>. Las files are named by site, plot and subplot (e.g., FP-1-5). Data for response and predictors are avilable in <em>data.zip</em>. R code is provided for predictor creation, modeling, and figure creation in <em>Rcode.zip</em>. </p>
On Synchronization of Wireless Acoustic Sensor Networks in the Presence of Time-varying Sampling Rate Offsets and Speaker Changes
<p>We present an open-source database for evaluation of time synchronization algorithms for wireless acoustic sensor networks . More Information and examples on how to use the database can be found on our GitHub page: <a href="https://github.com/fgnt/paderwasn">https://github.com/fgnt/paderwasn</a></p>
Raw data for the application of temperature and light intensity as intermittency sensors in a temporary pond in Jamaica
<p>Data provided represent the raw data collected for the paper on the application of temperature and light intensity as intermittency sensors in a temporary pond in Jamaica.</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.