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1,772 results for “Sensor”
New Hampshire Soil Sensor Network: Soil CO2 Fluxes
The goal of the New Hampshire Soil Sensor Network is to examine spatial and temporal changes in soil properties and processes as the climate changes. Data collected can also calibrate and validate models that examine how ecosystems may respond to changing climate and land use. To determine how soil processes are affected by climate change and land management, this soil sensor network measures snow depth, air temperature, soil temperature, soil volumetric water content, and soil electrical conductivity, as well as soil CO2 fluxes. This data package includes air temperature, soil temperature at 5 cm, and soil volumetric water content at 5 cm, and soil CO2 flux at the time of sampling, as well as gap-filled soil CO2 fluxes using non-linear least squares regression. Data were collected at the following sites: BRT = Bartlett Experimental Forest, Bartlett, NH; BDF = Burley-Demmerit Farm, Lee, NH; DCF = Dowst Cate Forest, Deerfield, NH; HUB = Hubbard Brook Experimental Forest, Woodstock, NH; SBM = Saddleback Mountain, Deerfield, NH; THF = Thompson Farm, Durham, NH; and Trout Pond Brook, Strafford, NH.
Hågaån Catchment Continuous Sensor Data, Uppsala, Sweden, 2018-2023
This data package includes continuous sensor data used to model stream metabolism and carbon dioxide emissions from the Hågaån stream catchment in Uppsala, Sweden. Data is in 30 minute increments from 2018 to 2023 and includes discharge, water temperature, turbidity, specific conductivity, temperature, dissolved carbon dioxide, and dissolved oxygen. The data package is complete.
Summer water chemistry, high frequency sensors, zooplankton and benthic macroinvertebrate community composition, periphyton, fish, and macrophyte biomass, along with lake metabolism and greenhouse gas dynamics in six experimental ponds in central Iowa, USA (2020)
This data product contains physical, chemical, and biological data ranging from the minute to daily to weekly scale in six artificial ponds (400 square meter surface area, 2m depth) in central Iowa (USA) 2020. Ponds were paired into three sets of treatment and reference with treatment ponds receiving two nutrient pulses designed to increase ambient phosphorus concentrations ~ 3 - 5%. Nitrogen and phosphorus were added as NH4NO3 and H3PO4, respectively, at a 24:1 molar ratio. The first nutrient pulse occurred on Julian day of year (DOY) 176 corresponding to a 3% increase and the second nutrient pulse occurred on DOY 211 to a 5% increase. Each treatment-reference set had a different food web structure established ranging between low, intermediate, and high complexity based on trophic connectivity and food chain length. Added to this data package is a document titled "2020 Iowa State University Horticultural Farm Experimental Ponds Nutrient Addition Experiment". For experimental set up, context, and a summary table of the data tables archived herein with available variables please review this document. It is added to aid in successful interpretation and to increase ease-of-use. Please email Tyler Butts (tyler.james.butts@gmail.com) for any and all questions regarding context or use of this dataset!
Long-term Atmospheric, Soil and Water Sensor Data from the GCE-LTER Eddy Covariance Flux Tower on Sapelo Island, Georgia
Long-term measurements of various atmospheric, soil and water properties were made using electronic sensors attached to the GCE-LTER eddy covariance flux tower deployed in a Spartina alterniflora salt marsh on Sapelo Island, Georgia. Variables measured include air and water temperature, relative humidity, precipitation, wind speed and direction, soil temperature, water pressure and solar radiation components (i.e. incident and reflected photosynthetically available, total, long-wave and shortwave radiation). Measurements were logged at 5 minute intervals using multiple Campbell Scientific Instruments CR3000 data loggers, and then combined into a single monotonic time series data set. Quality control analyses were performed to remove values deemed invalid due to sensor failure or miscalibration and to assign Q/C qualifiers to values outside expected ranges or failing various sanity and quality checks of the data. Note that some measurements were spatially replicated with multiple sensors deployed in different micro-habitats (e.g. at the tower and in a nearby marsh platform or creek). Sensors were also added to the tower at various times after the initial installation, therefore some variables do not span the entire period of record. Measurements at this site are ongoing, and the data set will be updated annually to include additional observations.
Hubbard Brook Experimental Forest: Hourly Temperature Under Canopy by HOBO sensors at Valleywide Plots, 2013-2020
Air temperature at 1.5 m agl is measured at hourly intervals under the canopy at every fifth valleywide plot plus one more between watersheds 1 and 4. The sensors are maintained by bird crews and volunteers. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the US Forest Service, Northern Research Station.
Snow depth sensor measurement data for Upper Sub Alpine site, 2010 - 2015.
Effects of infrared heaters on snow accumulation, snowmelt, and snow–atmosphere energy exchange were examined at Niwot Ridge, Colorado (CO). These .zip data files contains hourly snow depth measurements collected using Judd snow depth sensors for water year 2010-2015 (1 October 2009 – 30 September 2015) at the Upper Sub Alpine site, located just southeast of the Tundra Lab, below treeline in the Niwot Ridge Long-Term Ecological Research (NWTLTER) project area. The file contains both level 0 and level 1 (see details in “Process Description” below) hourly snow depth data measured in centimeters, and an accompanying metadata file.
GPS point locations of plots, subplots, itex subplots, transects and soil sensor in the black sand extended growing season experiment, 2018 - 2023.
As a result of climate change, the Rocky Mountain Front Range is experiencing warmer summers and earlier snowmelt. Due to the importance of snow for regulating soil temperature, growing season length, and available moisture in alpine ecosystems, even small shifts in the snow-free period could have large impacts. The focus of the Black Sand Extended Growing Season Length Experiment is to examine how terrain-related differences in climate exposure influence the way alpine habitats respond to climate change via earlier snowmelt. To simulate how climate exposure may affect plant communities, NWT LTER researchers established 5 experimental sites each containing a pair 10 x 40m rectangular plots. These sites include north and south facing aspects, subalpine and alpine tundra meadows in a range of hydrological conditions (e.g. dry meadows, moist meadows, wet meadows). We accelerated snowmelt in one plot of each block by adding chemically inert black sand, while keeping the second plot as an unmanipulated control; black sand was added to these plots after snow had naturally melted. This dataset includes geolocations of individual subplots and sensors within the experiment, measured in summer 2023.
PIE LTER, Wind sensor data, 15 minute intervals, from the Ipswich Bay Yacht Club pier located in Ipswich, MA, year 2022.
Wind sensor measurements (wind speed and wind direction) for 2022 at the Ipswich Bay Yacht Club, Ipswich, MA, 15 minute average measurements.
Dataset for paper entitled "Developing Reliable Foam Sensors with Novel Electrodes"
<p>This dataset includes all the experimental results presented in the IEEE Sensors 2019 paper "Developing Reliable Foam Sensors with Novel Electrodes" (DOI: 10.1109/SENSORS43011.2019.8956750).<br> URL of IEEE Xplore:<br> https://ieeexplore.ieee.org/document/8956750</p> <p>List of data in this dataset:<br> Fig-1-Stress-Strain curves of PU foam and Coated foam.xlsx<br> Fig-3-Conductive foam without electrodes.xlsx<br> Fig-4-Foam sensor with Ag electrodes.xlsx<br> Fig-5-Foam sensor-stability-100cycles.xlsx</p> <p>All the data included in this dataset were collected by Dr. Hongbo Wang.</p> <p>Contact person:<br> Dr. Hongbo Wang, ustcwhb@gmail.com</p>
Continuous multi-sensor wearable data and daily subject-reported fatigue of heathy adults
<p>Fatigue is a broad, multifactorial concept encompassing feelings of reduced physical and mental energy levels. Fatigue strongly impacts health-related quality of life across a huge range of conditions, yet, to date, tools available to understand fatigue are limited. We collected a total of 28 healthy adult subjects and 973 recording days. Recorded data included continuous multimodal wearable sensor time series on physical activity, vital signs, and other physiological parameters at 1-minute temporal resolution, and daily questionnaires (patient-reported outcome scores, PROs) on fatigue. When matching both sensor data and PROs, the datasets contains data from 27 subjects and 405 recording days.</p> <p>Analysis of these multimodal digital data to inform, quantify, and augment subjectively captured non-pathological fatigue measures were published at <em>Luo H., et. al. (2020), Assessment of Fatigue Using Wearable Sensors: A Pilot Study. Digit Biomark</em>.</p> <p>Demographics, sensor parameters and other information on this dataset can be found in the aforementioned manuscript and related supplementary material.</p> <p>Files included are</p> <ul> <li><em>fatiguePROs.csv</em>: daily PROs for all subjects</li> <li>subjectID_*.csv: sensor time series for each subject</li> </ul>
LoRaWAN Dense Indoor Sensor Network (DISN) Transmission Meta Data
<p>We present a large data of indoor Long Range Wide Area Network (LoRaWAN) network metadata to study Dense Indoor Sensor Networks (DISN). We collected 14 million transmissions from 390 sensors between date February 2020 and date September 2020. The transmissions have been received by 3 gateways across 8 floors and distances up to 64 m. The prototype will run in the background throughout the project and the data set will be regularly updated.</p> <p> </p>
D4.1 Review on Accuracy of Proximal Soil Sensors Dataset
<p><span lang="EN-US">The dataset includes the raw data about PSS accuracy based on the root-mean-square error (RMSE) and wet chemistry analytical data on which we based our analysis, organized in a comma-separated value (CSV) file. A table with the metadata of the accuracy dataset is included in this document. A previous version of the dataset present in this supplementary has already been published in the Zenodo repository with the following associated DOI: <a href="https://doi.org/10.5281/zenodo.14035520">https://doi.org/10.5281/zenodo.14035520</a>. The current version updates the previous one due to incongruencies found after the upload. The PDF file contains a table that give information about the fields in the CSV.</span></p>
AirMLP - SPS30 low-cost sensors and Tecora reference station PM 2.5 data
<p>The information below describes a dataset related to a study conducted in Turin, Italy, involving low-cost laser-scattering SPS30 sensors placed by Wiseair SRL and a Tecora reference station placed by Arpa Piemonte (Italian Air Quality Agency). This dataset spans two different time periods in 2022, specifically from March 1, 2022, to April 29, 2022, and from October 26, 2022, to December 30, 2022. The data in this dataset pertains to the mass concentration of PM2.5 (particulate matter with a diameter of 2.5 micrometres or less).</p><p> </p><p>The reference station's data is divided into two periods and is provided in files named "rf_x.csv." These files contain hourly data and timestamps in GMT+1. Each file has three columns:</p><ul><li>"valid_at" (in Rome local hour, GMT+1)</li><li>"valore_originale" (PM 2.5 raw mass concentration values recorded by the reference station)</li><li>"pm2p5" (PM 2.5 mass concentration validated values by the air quality agency)</li></ul><p>The low-cost sensors, referred to as "ari_xxxx.csv," provide data at approximately 15-minute frequency. These files contain the following columns:</p><ul><li>"valid_at" (in GMT)</li><li>"pm2p5" (PM 2.5 raw mass concentration measured by the SPS30 sensor)</li><li>"relative_humidity" (expressed as a percentage)</li><li>"temperature" (in degrees Celsius)</li><li>"pressure" (in hPA)</li><li>"wind_speed" (in meters per second)</li><li>"cloud_coverage" (expressed as a percentage)</li></ul><p>Notably, the "relative_humidity" and "temperature" values are gathered from sensors placed within a device containing the SPS30 low-cost sensor.</p><p> </p><p>Here's a summary of the specific data files in this dataset:</p><ul><li>"<strong>rf_1.csv</strong>": Hourly data provided by the Air Quality Agency for the first period.</li><li>"<strong>rf_2.csv</strong>": Hourly data provided by the Air Quality Agency for the second period.</li><li>"<strong>arpa_1727.csv</strong>," "<strong>arpa_1952.csv</strong>," and "<strong>arpa_1953.csv</strong>": Three low-cost sensors placed by Wiseair, which refer to the first period.</li><li>"<strong>arpa_1885.csv</strong>" and "<strong>arpa_2049.csv</strong>": Two low-cost sensors placed by Wiseair, that refer to the second period.</li></ul>
BGC-Argo matchups with Ocean Color Satellite Sensors and MERRA-2 updated for 2023
<p>Updated matchup dataset as described in "Begouen Demeaux et al., Algorithms to Retrieve the Spectral Diffuse Attenuation Coefficient of Light in the Ocean from Remote Sensing, Optics Express, 2023".</p> <p>Composed of Satellites matchup from the MODIS, VIIRS and OLCI sensors with BGC-Argo floats, including Kds derived from float measurements (Kd_WV_Xing), Rrs at all wavelengths from each sensor, solar zenith angle and information on the atmospheric composition from Merra-2 matchups. </p> <p>New recomputed Kds using the Lee et al., 2005 algorithm with individual sensor coefficients are also listed (new_kd_WV_Lee_indiv), as well as recomputed Kds for a new global m2 coefficient (new_kd_WV_Lee_global). Recomputed Kds for the new coefficients of the NASA/ESA algorithm are also available (new_Kd_Aus). Lastly, Kds for the new GF algorithm depending on the MERRA inputs and IOPs is listed : (kd_WV_f). </p> <p>For any questions, do not hesitate to be in touch. </p>
Two Wearable Sensor Datasets recording the Countermovement Jump
<p>These datasets come from two independent studies using wearable inertial sensors to estimate countermovement jump performance. The participants were healthy sports science students, free of injury, all of whom had given their prior written consent. Ethical approval was given by the governing institutions’ ethics committees, which included further analysis of the data.</p> <ul> <li><strong>Smartphone Dataset:</strong> <ul> <li>119 valid jumps</li> <li>Peak power 40.7 +/- 8.9 W/kg</li> <li>22 males, 10 females (26.5 +/- 4.1 yrs; standing height 1.74 +/- 0.08 m; body mass 70.0 +/- 10.9 kg)</li> <li>Redmi 9T phone (Xiaomi Technology, Beijing, China)</li> <li>128 Hz sampling frequency</li> <li>Accelerometer & gyroscope</li> <li>Handheld at sternum level</li> <li>Mascia, G.; De Lazzari, B.; Camomilla, V. Machine learning aided jump height estimate democratization through smartphone measures. Frontiers in Sports and Active Living 2023, 5, 1112739. <a href="https://doi.org/10.3389/fspor.2023.1112739">https://doi.org/10.3389/fspor.2023.1112739</a>.</li> </ul> </li> <li><strong>Accelerometer Dataset:</strong> <ul> <li>347 valid jumps</li> <li>Peak power 45.1 +/- 7.6 W/kg</li> <li>48 males, 25 females (21.6 +/- 3.3 yrs; standing height 1.75 +/- 0.10 m; body mass 71.2 +/- 15.1 kg)</li> <li>Trigno sensor (Delsys Inc, MA, USA)</li> <li>250 Hz sampling frequency</li> <li>Accelerometer</li> <li>Taped to lower back (L4)</li> <li>White, M.G.E.; Bezodis, N.E.; Neville, J.; Summers, H.; Rees, P. Determining jumping performance from a single body-worn accelerometer using machine learning. PLOS ONE 2022, 17, e0263846. <a href="https://doi.org/10.1371/journal.pone.0263846">https://doi.org/10.1371/journal.pone.0263846</a></li> </ul> </li> </ul> <p>MATLAB .mat files</p> <p>This repository was used by the paper currently under review for the open journal Mathematics:</p> <p>White, M.; De Lazzari, B.; Bezodis, N., Camomilla, V. Title. Mathematics 2024, 1, 0. Wearable Sensors for Athletic Performance: A Comparison of Discrete and Continuous Feature Extraction Methods for Prediction Models</p>
Development of piezocapacitive pressure sensor by DLP
<p>This data set corresponds to the analyses carried out in the following article: Arias‐Ferreiro, G., Ares‐Pernas, A., Lasagabáster‐Latorre, A., Dopico‐García, M. S., Ligero, P., Pereira, N., ... & Abad, M. J. (2022). <br>Photocurable printed piezocapacitive pressure sensor based on an acrylic resin modified with polyaniline and lignin. Advanced Materials Technologies, 7(8), 2101503.<br>DOI: 10.1002/admt.202101503</p>
Spectral and Chemical Dataset for Ripeness Monitoring in cv. Tempranillo Grapes Using a Multispectral Sensor
<p><strong><span>The dataset consists of 1010 samples of Tempranillo grape berries, offering a comprehensive record of spectral and chemical measurements that serve as a valuable resource for evaluating berry ripeness and sugar content (ºBrix). Each row in the dataset corresponds to a single berry, and the columns include a unique identifier (ID), the date of sampling (spanning 21 different days during the ripening period in 2024), expressed as Day of the Year (DOY) from DOY 210 to DOY 284. The dataset also includes measurements for nine spectral bands which represent the reflectance values recorded by the sensor (F1–F8 and NIR), a dedicated channel to detect ambient light flicker (CLEAR), and the sugar content (</span><span>°Bx</span><span>), ranging from 4.8 to 45 </span><span>°Bx</span><span>, encompassing all maturity stages from early ripeness to over-ripeness. The dataset is structured so that rows correspond to individual berries, and columns represent the measured variables, enabling statistical and machine learning analyses</span></strong></p> <p><strong><span>Center wavelength (λp) (F1: 415 nm, F2: 445 nm, F3: 480nm, F4: 515nm, F5: 555nm, F6: 590nm, F7: 630nm, F8: 680nm)</span></strong></p>
Chemical sensors for fire detection and nuisance rejection under EN-5420 standard conditions and reduced-scale chamber
<p>The dataset was acquired using a gas sensor array placed in the celling of a validated standard fire room (240 m3) located in Minimax Company. The dataset includes measurements of three different campaigns that were performed over 15 months. The dataset includes standard EN-54 smoldering fires and non-standard smoldering fires (such as plastic fires; PVC, cables Fire). In order to generate scenarios that may result in false-positive alarms when gas sensors are used, different nuisance experiments were also performed (such as cleaners, and air fresheners). Additionally, an additional measurement campaign was performed in a small chamber. The small-scale experiments dataset includes scale-down replicates of the fire and nuisances experiments performed in the standard fire room (EN-54 smoldering fire experiments, non-standard fires, and nuisance experiments).</p> <p>Citation request: Ana Solórzano et al, Early fire detection based on gas sensor arrays: Multivariate calibration and validation, Sensors and Actuators B: Chemical, 2021, <a href="https://doi.org/10.1016/j.snb.2021.130961">https://doi.org/10.1016/j.snb.2021.130961</a>.</p>
Single aerosol measurements from a wideband integrated bioaerosol sensor, collected during the Antarctic Circumnavigation Expedition (ACE).
<p><strong>Dataset abstract</strong></p> <p>This data set contains the time series of single particle data, measured by the wideband integrated bioaerosol sensor (WIBS-4, University of Hertfordshire, Hatfield, UK), during the Antarctic Circumnavigation Expedition (ACE), which was conducted between 20th of December 2016 and 19th of March 2017. WIBS provides aerosol optical diameter (5 μm - 14 μm), asymmetry factor and fluorescent signals on three different channels. WIBS measures single aerosol particles at a sampling rate of 125 Hz. More technical details about WIBS could be found in Kaye et al. (2005).</p> <p><strong>Dataset contents</strong></p> <ul> <li>part_1_Cape_Town_Kerguelen.csv, data file, comma-separated values</li> <li>part_2_Kerguelen_Hobart.csv, data file, comma-separated values</li> <li>part_3_Hobart_Mertz.csv, data file, comma-separated values</li> <li>part_4_Mertz_Punta Arenas.csv, data file, comma-separated values</li> <li>part_5_Punta_Arenas_Cape_Town.csv, data file, comma-separated value</li> <li>part_6_Cape_Town_Bremerhaven.csv, data file, comma-separated values</li> <li>data_file_header.txt, metadata, text</li> <li>README.txt, metadata, text</li> </ul> <p><strong>Dataset license</strong></p> <p>This aerosol measurement dataset collected using a WIBS during ACE is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p>
Sensor deployment to support the integrated energy management system in residential buildings in ReCO2ST LoRa Dataset
<p>LoRa Radio Testing Datasets for preliminary performance tests. These datasets were taken in order to ensure that the LoRa radios were capable of transmitting through concrete and testing various preamble settings of the radio. As per the paper,</p> <p>"Although these testing methodologies were indicative but not exact or perfect, to test in a manner that was qualitative would have been both costly and beyond the scope of the project." </p> <p>These tests were to help us verify feasibility of the chosen LoRa Radio</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.