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412 results for “sensor data”
Data set for Wireless SAWR sensors: FFT, EMD or wavelets for the frequency estimation in one shot?
<p>This data set is the basis for the publication "Wireless SAWR sensors: FFT, EMD or wavelets for the frequency estimation in one shot?", submitted to Journal of Sensors and Sensor Systems.<br> It contains the following data:</p> <p>- "Scipioni_JSSS23_Fig5_WaveletChoice.txt" contains results to obtain the best wavelet for this study. For this, a SAWR (Fig. 3a) signal is noised by an additive Gaussian white noise with different SNR values. The signal is then denoised by wavelets for each SNR value. Results are the new SNR values after denoising.</p> <p>- "Scipioni_JSSS23_Fig9_to_15_F_Ref.txt" contains all the frequencies around F=10700 MHz chosen to test the three methods: Fourier, wavelets, EMD.</p> <p>- 10 files "Scipioni_JSSS23_Fig10_to_14_SAW_EMDvsWavelet_FRef_XX_Occ_100.txt" contain results of the frequency and uncertainty measurement for each noisy SAWR signal versus frequencies and SNR values.</p> <p>- 3 files "Scipioni_JSSS23_Fig17_Tab2_3_Experimental SAWR signal_NoX" contain the values of 3 different experimental SAWR signals.</p>
Virtual sensors for wind energy applications benchmark study data - preliminary version
<p>Test version of the time series data for the wind energy virtual sensing benchmark study data.</p>
Gyroscope sensor data of shank motion during normal and barefoot walking
<p>The measurements were performed in closed and disturbance free space, where an unobstructed 10 m walkway was arranged. All tests were performed on a hard floor surface first with shoes that were adapt for walking (indoor sports shoes, sneakers etc.) and afterwards walking barefooted the same protocol. The subjects had clothing which did not restrict lower limb movement. In each test, the 10 m walking was repeated three times. Prior to testing, the procedure was demonstrated, and the sensors were carefully positioned to correct locations. The subjects were instructed to walk with their own natural walking velocity and to begin each 10 m walk from a completely stationary position.</p>
NAIADES Soil moisture sensors raw data
<p>Raw data from initial soil moisture sensors (LSE-01) tests performed during the initial phases of the NAIADES project. Sensors were installed in flower boxes and flowerbeds across the city of Carouge, Switzerland, data was transmitted each 20 minutes via LoRaWAN.</p> <p>This data was exported from influxdb, unrelevant fields were omitted, device addresses were partially obfuscated.</p> <p>Units: (field:unit):</p> <p>water_SOIL: V/V%</p> <p>temp_SOIL:°C</p> <p>conduct_SOIL:uS/cm</p> <p> </p>
Evaluation of ultrasound sensors for transcranial photoacoustic sensing and imaging - Data
<p>Raw data and simulation code for the paper "Evaluation of ultrasound sensors for transcranial photoacoustic sensing and imaging"</p>
Sensor Data - Mrak's Farm
<p>The data set includes data from two sensors located at the Mrak's Farm near Bled, Slovenia. Sensor 1 is located on the 1st floor, and sensor 2 is located on the ground floor. The data set includes dates from 24.6.2020 until 31.12.2020. Both sensors are hanging in the air attached to a string.</p>
Evaluating a Kinematic Data Glove with Pressure Sensors to Automatically Differentiate Free Motion from Product Manipulation (Experimental Data)
<p>Experimental data from <em>"Evaluating a kinematic data glove with pressure sensors to automatically differentiate free motion from product manipulation", </em>available at Applied Sciences.</p> <p>"DATA.zip" contains raw data collected using VMG30 and CyberGlove data gloves, in txt format. </p> <p>For further information please see the details in the manuscript or contact the corresponding author Alba Roda-Sales (rodaa@uji.es).</p>
Supplementary data of article Integrating Data-Driven and Hydraulic Modelling with Acoustic Sensor Information for Improved Leak Location in Water Distribution Networks
<p>This dataset was generated within the research thesis of Axel Hutomo, under the supervision of Leonardo Alfonso and Ioana Popescu at IHE Delft, and it is published as supplementary data for the article <em>Integrating Data-Driven and Hydraulic Modelling with Acoustic Sensor Information for Improved Leak Location in Water Distribution Networks, </em>currently under review. </p> <p>The Excel sheet provides information about the datasets produced to integrate acoustic sensor data and hydraulic model output data, to be used by the Machine Learning model. The acoustic sensor data were obtained by extracting several features in time and frequency domains from each audio file coming from acoustic sensors, whereas hydraulic model data was obtained by modelling these leaks using a pressure-independent analysis.</p> <p>The Python code shows the building of the ANN for leakage modelling prediction, integrating the two datasets above, for different leak rates.</p>
VNMPF-LIS: Validation Network Multiplatform Precipitation Feature (VNMPF) Dataset with International Space Station Lightning Imaging Sensor (ISS LIS) Data
<p>The Multiplatform Precipitation Feature (MPF) database combines ground- and space-based precipitation observations and retrievals from the Global Precipitation Measurement (GPM) mission Validation Network (VN) with space-based lightning measurements from the Lightning Imaging Sensor on board the International Space Station (ISS LIS). The data are synthesized in a thunderstorm-like, feature-based framework that encapsulates the microphysical, kinematic, and electrical properties of the observed storm.<br> <br> A VNMPF includes:</p> <p>- Radar information, GPM orbit, and ISS orbit <br> - Time/date information<br> - Geographical information<br> - Radar reflectivity characteristics<br> - Lightning energetic and identification information (where there is lightning)<br> - 3-dimensional wind information (where radars in dual-Doppler configuration are available)<br> <br> Version 1: 2017-2020</p> <p>Version 2: 2017-2022, updated VN winds </p>
Code and data for: Decoupling channel count from field-of-view and spatial resolution in single-sensor imaging systems for fluorescence image-guided surgery
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Data from: Long-term, high frequency in situ measurements of intertidal mussel bed temperatures using biomimetic sensors
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Data supplement to: Quality control of image sensors using gaseous tritium light sources
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Data for OFDVDnet: A sensor fusion approach for video denoising in fluorescence guided surgery
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Snow depth, air temperature, humidity, soil moisture and temperature, and solar radiation data from the basin-scale wireless-sensor network in American River Hydrologic Observatory (ARHO)
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High frequency soil sensor data for SOM input - Complex drivers of riparian soil oxygen variability revealed using self-organizing maps
The provided datasets contain the original (non-normalized) high-frequency soil and meteorological observations that were fed to the Self-Organizing Map (SOM) in order to identify ranges of values associated with low and high soil O2 conditions. For the Champlain Valley (CV) site we used the natural breaks algorithm to subset the data into high and low O2 datasets. O2 values were consistently low at the Green Mountains (GM) site, so we ran a single SOM for all O2 values at this site. The original values were then range-normalized before they were fed to the SOM.
High-frequency sensor data collected by Stroud Water Research Center in a recovery reach of White Clay Creek site ID WCC017 from April through December 2018
High-frequency sensor data at 5-min intervals using a s::can®oxy::lizer IITM and Solinst level logger, an Apogee SQ-212 sensor, and an s::can® field spectrophotometer in a recovery reach at White Clay Creek from April through December 2018. These data were collected as part of a study focused on baseflow dynamics of DOC and nitrate in White Clay Creek, Stroud Water Research Center. The parameters in this data package are water temperature, dissolved oxygen, depth, Photosynthetically Active Radiation (PAR), dissolved oxygen concentration, dissolved organic carbon concentration (DOC) and nitrate concentration. Data are presented in two tables. All the parameters and table are further explained in the metadata.
Environmental and behavioral sensor data to identify heat stress in dairy cows
Heat stress impairs the health and performance of dairy cows, yet only a few studies have investigated the diversity of cattle behavioral responses to heat waves. This research was conducted on an Italian Holstein dairy farm equipped with precision livestock farming sensors to assess potential different behavioral patterns of the animals. Three heat waves, defined as at least five consecutive days with mean daily temperature-humidity index higher than 72, were recorded in the farm area during the summer of 2021. Individual daily milk yield data of 102 cows were used to identify ‘heat-sensitive’ animals, meaning the cows that, under a given heat wave, experienced a milk yield drop that was not linked with other health events (e.g., mastitis). Milk yield drops were detected as perturbations of the lactation curve estimated by iteratively using Wood’s equation. Individual daily minutes of lying, chewing, and activity were retrieved from ear-tag-based accelerometer sensors. Semi-parametric generalized estimating equations models were used to assess behavioral deviations of heat-sensitive cows from the herd means under heat stress conditions. Heat waves were associated with an overall increase in the herd’s chewing and activity times, along with an overall decrease of lying time. Heat-sensitive cows spent approximately 15 min/d more chewing and performing activities (p < 0.05). The findings of this research suggest that the information provided by high-frequency sensor data could assist farmers in identifying cows for which personalized interventions to alleviate heat stress are needed.
Hypereutrophic lake sensor data during summer algae blooms in Iowa, USA, 2014 - 2018
High frequency measurements of temperature, dissolved oxygen saturation, pH, chlorophyll a and phycocyanin concentration were measured using a multiparameter sonde in the surface waters of four lakes during the ice-free season to monitor for harmful algal blooms. The lakes are located in the state of Iowa, USA. Green Valley Lake as monitored in 2014 and 2015, Blackhawk Lake was monitored in 2015, and Swan Lake and South Twin Lake were monitored in 2018. The high frequency data were aggregated to a daily average value for each parameter in each lake. These data were used to test for statistical early warning indicators of harmful algal blooms.
Hypereutrophic lake spatial sensor data during summer bloom, Swan Lake, Iowa, USA 2018
Weekly spatial measurements of chlorophyll a, phycocyanin, dissolved oxygen, pH, conductivity, total dissolved solids from a multiparameter handheld sonde in 65 meter grid across the entire lake during an ice-free algal bloom. While sonde measurements were being recorded macrophyte presence was also documented. Spatial measurements where made in Swan Lake in Iowa, USA during 2018. Spatial data are unaltered from data collection from sonde. These data were used to test for spatial heterogeneity during, before, and after a harmful algal bloom.
High-frequency sensor turbidity data collected by Stroud Water Research Center in a meadow reach of White Clay Creek site ID WCC017 from January 2014 through May 2021
High-frequency sensor data from a Campbell Scientific OSB3 (every 5-minutes) in a recovery reach at White Clay Creek from January 2014 through May 2021. Funded by NSF and DEB as part of the LTREB grant to study the recovery of stream ecosystem structure and function during reforestation, Stroud Water Research Center. The parameter in this data package is turbidity. Data are presented in two tables which likely have significant overlap. The raw data table presents the data exactly as it was downloaded from the Aquarius Database. It is formatted as a "wide" human-readable table. Turbidity at White Clay Creek Site WCC017 since 2014 OSB3 presents only the data from the respective sensors. These tables are gap filled for intervals greater than 10 minutes and less than 24 hours, formatted as a "wide" human-readable table. All of the parameters and table are further explained in the metadata.
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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.