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58 results for “temperature sensors”
Temperature-modulated gas sensor signal
<p><strong>Temperature-modulated</strong><strong> gas sensor signal</strong></p> <p><strong>Abstract</strong>: Data is the conductance of a temperature-modulated gas sensor exposed to several concentrations of several gases. Both classification (gas type) and (selective) quantification are of interest.</p> <p><strong>Source</strong>:<br> Creator: Lab for Measurement Technology, Saarland University, 66123 Saarbrücken, Germany<br> Contact: m.bastuck@lmt.uni-saarland.de, info@lmt.uni-saarland.de</p> <p><strong>Dataset</strong>:<br> The data set was experimentally obtained from a semiconductor gas sensor (UST GGS 1330) with temperature cycled operation (TCO). The sensor temperature was linearly increased from 200 °C to 400 °C within 20 s, and back to 200 °C within another 20 s. This cycle is repeated during the whole measurement (~18 h). During the measurement, the sensor was exposed to four different gases (carbon monoxide, CO, ammonia, NH3, nitrogen dioxide, NO2, and methane, CH4) in three different concentrations each.<br> The aim is either to classify the type of gas that is currently seen by the sensor independent of its concentration, or the concentration of one specific gas type.</p> <p><strong>Attribute Information</strong>:</p> <ul> <li><strong>sensordata.csv</strong><br> The data set consists of the measured conductance in nS (nanosiemens) of the sensor. Each row represents one cycle (200-400-200 °C in 40 s) with 4001 data points. </li> <li><strong>targetvectors.csv</strong><br> Different target vectors have been prepared manually. All of them include an ‘ignore’ label that indicates cycles where the exact gas concentrations are unknown, e.g. due to a change in concentration which can take several cycles. Cycles during an “init peak” in the beginning to test the setup are labeled as ‘ignore’ as well. These cycles should always be discarded. The objectives of the ten target vectors are as follows: <ol> <li>categorical. A short segment at the beginning is labeled as ‘background’, i.e. no relevant test gas is present. In the following, the presence of a test gas is indicated with its name, i.e. CO, NH3, NO2, and CH4</li> <li>continuous. A short segment at the beginning is labeled as ‘0’, CO exposures are labeled with their respective concentrations, everything else is ignored.</li> <li>continuous. A short segment at the beginning is labeled as ‘0’, NH3 exposures are labeled with their respective concentrations, everything else is ignored.</li> <li>continuous. A short segment at the beginning is labeled as ‘0’, NO2 exposures are labeled with their respective concentrations, everything else is ignored.</li> <li>continuous. A short segment at the beginning is labeled as ‘0’, CH4 exposures are labeled with their respective concentrations, everything else is ignored.</li> <li>categorical. Like (1), but the previously ignored background cycles at the beginning and between gas exposures are now labeled as ‘background’.</li> <li>continuous. Like (2), but the previously ignored background cycles at the beginning and between gas exposures are now labeled as ‘0’.</li> <li>continuous. Like (3), but the previously ignored background cycles at the beginning and between gas exposures are now labeled as ‘0’.</li> <li>continuous. Like (4), but the previously ignored background cycles at the beginning and between gas exposures are now labeled as ‘0’.</li> <li>continuous. Like (5), but the previously ignored background cycles at the beginning and between gas exposures are now labeled as ‘0’.</li> </ol> </li> </ul> <p>Target vectors (6)-(10) contain some sensor drift in the background. Additionally, the background class is much larger compared to the gas exposure classes. Target vectors (7)-(10) also label all but one gas as ‘0’, so that a selective quantification of the target gas must be performed.</p> <ul> <li><strong>gasexposures.png</strong><br> A graphical summary of the dataset.</li> <li><strong>temperaturecycle.png</strong><br> A graphical representation of the temperature cycle used when operating the gas sensor.</li> </ul>
Soil temperature profiles, measured using a coil-shaped fiber-optic distributed temperature sensor
<p>Measurements of soil temperature temperature profile, by reference sensors and a coil-shaped fiber optic distributed temperature sensor.</p> <p>Retrieved at the Speulderbos measurement site, 52.251048 N, 5.690061 E.</p> <p> </p> <p>A full description can be found in:</p> <p>Schilperoort, B. (2022). <em>Heat Exchange in a Conifer Canopy: A Deep Look using Fiber Optic Sensors</em> [Delft University of Technology]. https://doi.org/10.4233/uuid:6d18abba-a418-4870-ab19-c195364b654b</p>
Data from: Long-term, high frequency in situ measurements of intertidal mussel bed temperatures using biomimetic sensors
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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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Water-level and subsurface water temperature at sensor from the Toolik River Thermokarst, 2010-2013
Data were collected to investigate if formation of gully thermokarst (TK) results in lowering of the water table and more rapid evacuation of water from above the frost table. Data were collected from 24 shallow screened wells. 2 replicate rows of 4 wells were located at: (a) a hillslope (HS) ~120m away from the gully TK, (b) perpendicular to the gully TK (TK) and (c) perpendicular to an unimpacted water track (WT) upstream of the gully TK. Note that water levels are the distance below the ground surface and may have organic/peat layers of different thicknesses. Also note that sensors were progressively lowered deeper into the well during the season, resulting in steps in the temperature and raw depth data. Data were collected with support from the NSF ARCSS-TK project, (OPP – 0806399) .
MCR LTER: Coral Reef: Seawater pH, Temperature and Depth Time Series from Bottom-mounted Sensors on the Fringing Reef, January-March 2012
Bottom-mounted instrumentation (SeaFET, Seabird thermisters, Hobo water level data loggers) sampled for 8 weeks on the fringing reef of Moorea Island, French Polynesia at site LTER Fringe 1. Sampling began in January 2012. The instruments were secured to a cement piling at 3.3 meters depth and 0.7 meters above the sandy bottom. The SeaFET recorded voltages from a thermistor and pH electrodes at a 10-minute sampling interval. Discrete seawater samples were collected using a Niskin bottle during the deployment; pH, salinity, and total alkalinity of this sample were measured to calculate seawater pH (total scale) from raw SeaFET data as well as other carbonate chemistry parameters. Adjacent to the SeaFET were two thermistors and two HOBO® water level data loggers, synchronized with the SeaFET to simultaneously record temperature and depth.
Taiwan Coral Reef: Seawater pH, Temperature and Depth Time Series from Bottom-mounted Sensors on the Fringing Reef in Nanwan Bay, May-July 2012
Bottom-mounted instrumentation (SeaFET, Seabird conductivity/temperature sensor, Hobo water level data loggers) sampled for 7 weeks on the Hobihu fringing reef in Nanwan Bay, Taiwan. Sampling began in May 2012. The instruments were secured to anchored fencing stakes at 4 meters depth and 0.6 meters above the sandy bottom. The SeaFET recorded voltages from a thermistor and pH electrodes at a 10-minute sampling interval. Discrete seawater samples were collected using a Niskin bottle during the deployment; pH, salinity, and total alkalinity of this sample were measured to calculate seawater pH (total scale) from raw SeaFET data as well as other carbonate chemistry parameters. Adjacent to the SeaFET were a Seabird sensor and two HOBO® water level data loggers, synchronized with the SeaFET to simultaneously record conductivity, temperature and depth.
Dataset/Development of a novel calorimetry setup based on metallic paramagnetic temperature sensors
<p>Dataset related to the publication "Development of a novel calorimetry setup based on metallic paramagnetic temperature sensors" submitted to Review of Scientific Instruments.</p>
Test data for Sonic Kayaks particulate matter sensor, temperature and GPS
<p>These data sets are the first trials for adding a particulate matter sensor to the Sonic Kayak project https://fo.am/activities/kayaks/</p> <p>There are three data files - gps.csv is the GPS co-ordinates, pm.csv is the particulate matter data (using a PMS7003), and temp.csv is the temperature data (two separate but identical digital thermometer sensors). Time is included in all datasets and can be used to align them. Together the data can be used to make a fine scale heat line map of particulate matter and temperature.</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>
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>
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>
Data from DHT22 a sensor humidity and temperature
<p>data from DHT22 a sensor humidity and temperature</p>
Derived environmental temperatures at Jezero crater from Air Temperature Sensors' measurements on the Perseverance rover.
<p><strong>Material from Version 2</strong> extends derived Air Temperature Sensor data to the first 700 sols of the Mars 2020 mission used in the analysis of <em>Munguira et al. (2024). "One Martian Year of Near-Surface Temperatures at Jezero from MEDA measurements on Mars2020/Perseverance". Journal of Geophysical Research: Planets. [in revision]. </em>We also include the tables needed to generate and reproduce the figures in the paper. Most importantly, the tables include the results from different analyses of temperatures through Fourier series and Reynolds averaging. </p>
Raw temperature measurements from SmartSantander sensors reported between January 1st 2021 and July 31st 2022
<p>This is a smart city domain dataset, and more specifically a environmental one generated within the framework of the SmartSantander research testbed.</p> <p>It contains raw temperature measurements reported by SmartSantander sensors deployed in the spanish city of Santander, covering a period of 17 months between January 1st 2021 and July 31st 2022, and comprising more than 24 million data points. The dataset includes not only the temperature dimension but also spatial and temporal information, as well as the specific device identifier and some labels to differentiate between static/mobile and indoor/outdoor devices.</p> <p>As is common with large-scale sensor deployments, there are occasional sensor malfunctions, which have deliberately not been filtered out of this raw dataset.</p>
Dataset supplementing H. Kohler, B. Ojha, N. Illyaskutty, I. Hartmann, C. Thiel, K. Eisinger, M. Dambacher: In situ high-temperature gas sensors: continuous monitoring of the combustion quality of different wood combustion systems and optimization of combustion process, Journal of Sensors and Sensor Systems (JSSS), 2018
<p>Dataset supplementing H. Kohler, B. Ojha, N. Illyaskutty, I. Hartmann, C. Thiel, K. Eisinger, M. Dambacher: In situ high-temperature gas sensors: continuous monitoring of the combustion quality of different wood combustion systems and optimization of combustion process, Journal of Sensors and Sensor Systems (JSSS), 2018</p>
Data for: Melt electrowriting enabled 3D liquid crystal elastomer structures for cross-scale actuators and temperature field sensors
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Temperature sensor data for locations in Smart-Santander testbed of Fed4FIRE+
<p>This is a database dump with temperature sensor-values (called "phenomenons" in testbed API), collected during the Fed4FIRE+ project SECTOR (Algorithm to determine a cost-effective, optimal SpatiaL-dEployment for smart-City environmenTal sensOr netwoRks).</p> <p> </p>
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>
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>
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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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.