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661 results for “temperature measurement”
Data for 'Using 40 years of spot measurements to assess stream temperature response and recovery for different harvesting systems in northern hardwood forests' by Jason Leach, Danielle Hudson and R. Dan Moore. Submitted to Hydrological Processes.
<p>This dataset contains spot stream temperature measurements taken at 5 headwater streams draining forested hillslopes (C31, C32, C33, C34, C35) in the Turkey Lakes Watershed, approximately 65 km northwest of Sault Ste. Marie, Ontario, Canada.</p>
Subdaily meteorological measurements of temperature, direction of the movement of the clouds, and cloud cover in the Late Maunder Minimum by Louis Morin in Paris
<p>Supplementary dataset of the publication: Pliemon, T., Foelsche, U., Rohr, C., and Pfister, C.: Subdaily meteorological measurements of temperature, direction of the movement of the clouds, and cloud cover in the Late Maunder Minimum by Louis Morin in Paris, Clim. Past, 2022</p> <p>For more details see the file.</p> <p> </p>
Temperature measurements of an internal fumarolized area of Vesuvius
<pre>Periodic temperature measurements of an internal fumarolized area of Vesuvius (Italy) carried out starting from 2003 with a Gemini Tinytag plus 2 datalogger TGP4020 range -40+125°C with resolution 0.02°C. </pre> <pre>Measurements are made at a depth of 0.1 m. </pre> <pre>The measurements are taken every 30 minutes for the years 2003-2006 and every hour for the following years.</pre> <pre>The coordinates of the measuring point are 33T 451537.2m E, 4519098.5 m N.</pre> <pre>Due to great difficulties in accessing the measurement site, unfortunately, long periods of non-measurement are present in the record. </pre> <pre>Since the measurement area was affected by a landslide, the data before and after 2007 appear to be slightly different.</pre> <p> </p>
GLORIA 3-D temperature and ALIMA temperature measurements from flight 12 of the SouthTRAC measurement campaign
<p>This dataset consists of temperature measurements acquired from the German HALO research aircraft during a research flight over Southern Andes on 20-21 September, 2019, as part of the SouthTRAC measurement campaign. </p> <p>ALIMA lidar instrument is developed and operated by the German Aerospace center (Deutsches Zentrum für Luft- und Raumfahrt, DLR). The data included here covers the whole research flight.</p> <p>GLORIA infrared limb imaging spectrometer is jointly developed and operated by the Jülich Research Center (Forschungszentrum Jülich). The data included here is the 3-D temperature retrieval from the hexagonal flight pattern, which was flown from 02:50 UTC till 06:10 UTC on 21 Septermber, 2019.</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>
Ground surface temperature measurements at grazed and ungrazed plots in Central Mongolia
<p>Ground surface temperature measurements from two sites with different topographic aspect in Central Mongolia. The dataset includes both grazed and ungrazed plots, and covers ca. 14 months from May 2022 to August 2023.</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>
Evaluation of the influence of rain on air surface temperature measurements
<h2>Description</h2> <p>The dataset is constituted by three .csv files, which contain the measurements performed in an experiment aiming to evaluate the influence of rain on temperature readings. Two devices under tests (DUTs), one naturally ventilated and one artificially ventilated, are compared with a reference system. A .csv file is produced for DUT1, DUT2 and the reference system. Here below the content of each file is briefly described:</p> <ul> <li>Dataset_reference: accurate air temperature measurements obtained using the reference system, which is not affected by rain. The system is constituted by four aspirated thermometers (called Meteo1, Meteo2, Meteo 3, Meteo 4) manufactured at the Danish Technology Institute. The column "PT500" contains instead the rain temperature measurements. The readings are produced using a Fluke Super-DAQ (1586A). </li> <li>Dataset_DUT1: measurements of the naturally ventilated thermometer under an artificially generated rainfall. The readings are produced using the manufacturer datalogger.</li> <li>Dataset_DUT2: measurements of the artificially ventilated thermometer under an artificially generated rainfall. The readings are produced using the manufacturer datalogger.</li> </ul>
Rock-temperature, fracture displacement and acoustic/micro-seismic data measured at Matterhorn Hörnligrat, Switzerland
<p>This repository contains data, which were acquired in the context of project X-Sense2 (financed by nano-tera.ch, ref. no. 530659) at the Matterhorn Hörnligrat fieldsite on 3500 m a.s.l. from 2015 until 1 April 2018. These data were used in the following publication:</p> <p>Weber, S., Faillettaz, J., Meyer, M., Beutel, J., and Vieli, A.: Acoustic and micro-seismic characterization in steep bedrock permafrost on Matterhorn (CH), Journal of Geophysical Research: Earth Surface, 123(6), 1363-1385, doi: 10.1029/2018JF004615, 2018.</p> <p><strong>AM-DATA</strong> This repository contains selected accelerometer data with SI unit m/s<sup>2</sup> (hourly .miniseed-files, MH40 refers to AM<sub>scarp</sub>). These data were measured continuously using an accelerometer based on a Wilcoxon 728A/T (10 − 10000 Hz, 24 kHz resonance frequency), netADC data acquisition system and netSP+ seismological processor of Institute of Mine Seismology. Data were synchronized to a global time reference using GPS (<1 μs). The data is stored in .miniseed-format and splitted in hourly files.</p> <p><strong>SM-DATA</strong> This repository contains selected raw seismometer data in counts (hourly .miniseed-files, MHDL refers to SM<sub>scarp</sub> and MHDT refers to SM<sub>ridge</sub>). These data were measured using a Lennartz electronic low-noise seismometer LE-3Dlite MKIII (1−100 Hz) and Nanometrics Centaur digital recorder, a 24-bit high-resolution seismic data acquisition system disciplined by GPS (<100 μs) with a sampling rate of 1000 sps. The data is stored in .miniseed-format and splitted in hourly files.</p> <p><strong>TIMESERIES</strong> This repository contains 8 timeseries:</p> <ul> <li> <p><em>AS_scarp_high.csv</em> describes the threshold triggeres acoustic emission hits acquired with a piezoelectric sensor Mistras Physical Acoustics Corporation R6α, 35−100 kHz, 55 kHz resonance frequency.</p> </li> <li> <p><em>AS_scarp_low.csv</em> describes the threshold triggeres acoustic emission hits acquired with a piezoelectric sensor Mistras Physical Acoustics Corporation R.45, 5−30 kHz, 20 kHz resonance frequency.</p> </li> <li> <p><em>CR_old.csv</em> described the measured fracture displacement in mm.</p> </li> <li> <p><em>SMridge_nofilter.csv</em> describes automatically triggered events using a recursive short-term/long-term average (STA/LTA ) algorithm without filtering. Peak amplitude in µm/s and energy in µm<sup>2</sup>/s<sup>2</sup>.</p> </li> <li> <p><em>SMridge_filtered.csv</em> describes automatically triggered events using a recursive short-term/long-term average (STA/LTA ) algorithm in the frequency band 33-67 Hz. Peak amplitude in µm/s and energy in µm<sup>2</sup>/s<sup>2</sup>.</p> </li> <li> <p><em>SMscarp_filtered.csv</em> describes automatically triggered events using a recursive short-term/long-term average (STA/LTA ) algorithm without filtering. Peak amplitude in µm/s and energy in µm<sup>2</sup>/s<sup>2</sup>.</p> </li> <li> <p><em>SMscarp_nofilter.csv</em> describes automatically triggered events using a recursive short-term/long-term average (STA/LTA ) algorithm in the frequency band 33-67 Hz. Peak amplitude in µm/s and energy in µm<sup>2</sup>/s<sup>2</sup>.</p> </li> <li> <p><em>temperature.csv</em> describes the rock temperature (in °C) at different depths: 5, 10, 20, 30, 50 and 100 cm.</p> </li> </ul> <p>All time stamps are in UTC.</p>
Movies and temperature and pressure measurements associated with the study "Experimental evidence for lava-like mud flows under Martian surface conditions"
<p>Movies and temperature and pressure data associated with the study "<strong>Experimental evidence for lava-like mud flows under Martian surface conditions</strong>".</p>
Figure 1 in Assessment of the accuracy of determining the Caspian Sea surface temperature by Landsat-5, -7 satellites based on the measurements of drifters
Figure 1. Drifter device [Lagrangian drifter laboratory, 2024]
Dataset from two meteorological stations with water and soil temperature measurements in the Alqueva reservoir (Portugal)
<p>In the multidisciplinary <strong>AL</strong>entejo <strong>O</strong>bservation and <strong>P</strong>rediction systems<strong> </strong>project (ALT20-03-0145-FEDER-000004), which aims to strengthen research and innovation in the Alentejo region (southern Portugal), one of the main objectives was to study and model the meteorological conditions in the Alqueva reservoir, in particular their spatial variations within a few hundred meters.</p> <p>The shared hourly dataset, using Coordinated Universal Time (UTC), covers the period from 2018 to 2023 and includes measurements from two meteorological stations located in the Alqueva reservoir, the largest artificial lake in Europe. One station, Montante, is located on a floating platform with a water depth of approximately 70 meters (38.2235 N, 7.4595 W), to the west of the second station, CidAlmeida (38.21539 N, 7.45454 W), which is about 1 km away on land, very close to the water.</p> <p>According to the World Meteorological Organisation (WMO) standards, the data were sampled every second, and hourly data were calculated in post-processing. The dataset includes hourly accumulated precipitation (<em>mm</em>) and hourly average measurements of surface water temperature (at a depth of 0.25 m), soil temperature (at a depth of 0.15 m) and various meteorological parameters: wind speed (<em>m/s</em>) and direction (<em>degrees</em>), relative humidity (%), upward/downward solar radiation (<em>W/m</em><sup><em>2 </em></sup>) and air temperature (<em>°C </em>). All parameters are measured at both stations, except the hourly average water temperature at a depth of 0.25 m, which is only available at the Montante station, and the hourly accumulated precipitation, hourly average soil temperature and wind direction, which are only available at the CidAlmeida station.</p> <p>Hourly data were not subjected to rejection criteria based on the percentage of errors; instead, a column with this percentage is provided, allowing potential data users to apply their own rejection criteria. Daily extremes (daily maximums and minimums for air temperature, relative humidity, and daily maximum gust) are only provided for days where the percentage of errors does not exceed 25% of the 1440 minutes of each day. The daily error percentage for each of the measured parameters at the two stations for the period 2018-2023 is available.</p> <p>Finally, the repository also includes two codes (one for each weather station) written in Visual Basic, which enable data transmission and real-time statistical processing.</p> <p><strong>Fundings:</strong></p> <p>Gonçalo Rodrigues was supported by the Portuguese Foundation for Science and Technology, I.P (Grant 2020.05752.BD). The work is co-funded by national funds through FCT – Fundação para a Ciência e Tecnologia, I.P., in the framework of the ICT project (references UIDB/04683/2020 and UIDP/04683/2020) and by the ALOP project (ALT20-03-0145-FEDER-000004). </p>
Co2 concentration, temperature and humidity in primary classrooms during the Covid-19 Safety Measures in Spain
<p>Co2 concentration, temperature and humidity in primary classrooms during the Covid-19 Safety Measures in Spain. The data presented were collected between 1 May 2020 and 23 June 2021 using a low-cost CO<sub>2</sub> sensor called SCD30 (https://bit.ly/3dDWXu1). This sensor can messure CO<sub>2</sub>, temperature and air humidity. Six nodes were built and deployed in six classrooms in two different schools in two different periods. In the first school, located in Vilafamés (Castellón, Spain), a total of 38,891 observations were carried out. Altogether 34,570 measurements were captured in the second school located in Vall d’Alba (Castellón, Spain).</p>
Time series of the longitudinal gradient of Venus's brightness temperature measured by Akatsuki LIR
<p>The files contain the time series of the longitudinal gradient of Venusian cloud's brightness temperature measured by LIR onboard JAXA's Venus orbiter Akatsuki. The data were derived and analyzed in the paper "Planetary-Scale Waves Seen in Thermal Infrared Images of Venusian Cloud Top" by Kajiwara et al. The filename represents the latitude for each time series (For example, "10N" means 10 degrees north, and "EQ" means the equator). In all files, the first column gives the approximate elapsed time in days from 18 May 2017: the exact dates are given in the paper. The second column gives the longitudinal gradient of the brightness temperature in unit of K/degree.</p>
Measurement of the fumarole temperature of the north eastern crater rim of Vesuvius -----Long-term variations of fumarole temperature of the north eastern crater rim of Vesuvius (Italy)
<pre>Measurements of the fumarole temperature of the north eastern edge of Vesuvius have been carried out since 1995. The temperature is measured at a depth of 10 cm using a K-type thermocouple. The coordinate of the fumarole is 33T 451833.1m E - 4519224.8m N.</pre> <pre>The monitored fumarole is the one at the highest altitude present on Vesuvius </pre> <p>The fumarole is characterized by low temperatures (59.5 - 75.6 ˚C) and discharge a mixture of air (46% -72%), steam (25% -45%) and CO2 (0.2% -2%). The air is the main component of fumarole, is most likely included in the upper fluids part of the fumarolic ducts found in the highly permeable products of the Vesuvian cone. </p>
Dataset related to publication "Improved acoustic thermometry for long-distance temperature measurements"
<p>The zip folder contains dataset used in paper "Improved acoustic thermometry for long-distance temperature measurements" to be published in <em>Sensors </em><strong>2023</strong><em>, 23. </em>Two txt files are provided to explain the content of the csv files.</p> <p>The data were produced within the EMPIR project 17IND03 LaVA, Large Volume Metrology Application.</p>
Movies, temperature and pressure measurements associated with the study "Volumetric changes of mud on Mars: evidence from laboratory simulations"
<p>Movies, temperature and pressure measurements logs associated with the study "Volumetric changes of mud on Mars: evidence from laboratory simulations" that are showing behavior of muds with different viscosities.</p>
Temperature measurements of full-scale wall element using Type K thermocouples to observe internal convection in loose-fill wood fiber insulation
<p>Internal convection of insulation materials is a phenomenon that occurs when a construction element is subjected to a temperature difference on either side of the element, as the temperature difference inside the insulation will facilitate an onset of air movement due to thermal buoyancy. This dataset represents the results of 11 unique experiments conducted at Aalborg University at the Department of the Built Environment, where a full-scale wall element insulated with loose-fill wood fiber insulation is investigated for internal convection. A large guarded hotbox is used to control the boundary conditions of either side of the wall element, to imitate a construction element subjected to external and internal boundary conditions, similar to a wall in a house. This dataset can be used to benchmark other insulation materials investigated at similar boundary conditions.</p> <p>The dataset is structured into steady-state experiments and dynamic experiments, where a total of 7 unique cases are conducted in steady-state conditions, and 4 unique cases are conducted in dynamic conditions. The dataset for the steady-state experiments is structured by the temperature difference that the full-scale wall element is exposed to, from the cold and hot side, while the dynamic experiments are structured by the amplitude of the temperature variation, along with if an artificial sun is used or not.</p> <p>The results for the internal convection of the loose-fill wood fiber insulation show similar results as other studies that have conducted experiments on other insulation materials.</p> <p>For more information, see doi: 10.54337/aau488363266</p>
Real time temperature measurements during aplication of PFA to rat ventricular tissue
<p>Dataset containing the temperature recordings performed during PFA in ventricular tissue as described in publication : doi: 10.1161/CIRCEP.122.010992.</p> <p> </p> <p> </p>
Light and temperature measurements and untargeted proteomic measurements
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