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661 results for “temperature measurement”
Dataset of publication "Speed of Sound Measurements in Helium at Pressures from 15 to 100 MPa and Temperatures from 273 to 373 K"
<p>This is a dataset of the speed of sound in helium, which was measured along five isotherms in a temperature range from 273 to 373 K at pressures from 15 to 100 MPa with a relative expanded uncertainty (k = 2) from 0.02 to 0.04%. A dual-path pulse-echo device was utilized to conduct these measurements.</p>
Temperature data on forest plots recorded with hourly measurements in LandKlif Project
<p><span>Temperature data on forest plots in LandKlif project recorded with hourly measurements (EasyLOG USB, measured accurate to 0.5°C). Thermologgers were attached to the wildlife cameras within the forest. Due to battery leakage, corrosion, and programming errors, only 39 out of 56 loggers collected temperature data.</span></p> <p><span>LandKlif is funded by the Bavarian State Ministry of Science and the Arts within the Bavarian Climate Research Network (bayklif). Within the five year funding period of bayklif, five interdisciplinary senior research associations and five junior research groups are be financed with a total sum of 18 million Euro. LandKliF, as one of the five interdisciplinary senior research associations, addresses the effects of climate change on biodiversity and ecosystem services in semi-natural, agricultural and urban landscapes.</span></p>
Compilation of parallel measurements comparing the temperatures recorded in Stevenson screens with those recorded in pre-Stevenson screen thermometer exposures
<p>Compilation of parallel measurements comparing the temperatures recorded in Stevenson screens with those recorded in pre-Stevenson screen thermometer exposures. This dataset accompanies Wallis et al. (2024); further details of the dataset and its creation can be found in the attached readme file and Wallis et al. (2024).</p> <p>---</p> <p><strong>References</strong></p> <p>Wallis, E.J., Osborn, T.J., Taylor, M., Jones, P.D., Joshi, M. & Hawkins, E. (2024) Quantifying exposure biases in early instrumental land surface air temperature observations. <em>International Journal of Climatology, </em>https://doi.org/10.1002/joc.8401</p>
Surface brightness temperatures measured by the HATPRO microwave radiometer onboard the RV Polarstern during the ATWAICE expedition PS144 to the Arctic in summer 2022
<p>The data set contains daily files of raw microwave radiation measurements by the HATPRO microwave radiometer (see Rose et al., 2015) onboard about 22 m height at the top deck (starboard) of RV Polarstern during cruise PS131 (ATWAICE expedition, see Kanzow, 2023). Via a mirror construction the radiometers were observing the surface at a viewing angle of about 53° off-nadir for 15 min each hour. The actual viewing angle could vary by a few degree because of ship motion. The data covers the range July 11, 2022 to August 11, 2022. The radiation measurements are given as brightness temperatures in seven K band channels (22.24 - 31.4 GHz), vertical polarization, and seven V band (51.26 - 58 GHz) channels, horizontal polarization. </p> <p>Version 2 of the uploaded data is quality-controlled (see the flag variable).</p>
Surface brightness temperatures measured by the MiRAC-P microwave radiometer onboard the RV Polarstern during the ATWAICE expedition PS144 to the Arctic in summer 2022
<p>The data set contains daily files of raw microwave radiation measurements by the MiRAC-P (or LHUMPRO-243-340) microwave radiometer (see Mech et al., 2019) onboard about 22 m height at the top deck (starboard) of RV Polarstern during cruise PS131 (ATWAICE expedition). Via a mirror construction the radiometers were observing the surface at a viewing angle of about 53° off-nadir for 15 min each hour. The actual viewing angle could vary by a few degree because of ship motion. The data covers the range July 11, 2022 to August 11, 2022. The radiation measurements are given as brightness temperatures in six double side band averaged G band (183.31 +/- 0.6 to 183.31 +/- 7.5 GHz), vertical polarization, and one higher frequency (243 GHz) channel, horizontal polarization. The 340 GHz channel was malfunctioning. </p> <p>Version 2 of the uploaded data is quality-controlled (see the flag variable).</p>
Temperature measurements at Saint Thomas and Saint Philip Neri church
<p>This dataset contains temperature measurements in Celsius degrees collected at the church of Saint Thomas and Saint Philip Neri in Valencia (Spain) by multiple wireless sensors.</p> <p>The data were collected from August 2017 to March 2019 and aligned at the same time point using linear interpolation.</p> <p> </p>
Correlation Between Insulation Resistance and Temperature Measurement Error in Type K and Type N Mineral Insulated, Metal Sheathed Thermocouples
<p>Mineral insulated, metal sheathed (MI) Type K and Type N thermocouples are<br> widely used in industry for process monitoring and control. One factor that limits<br> their accuracy is the dramatic decrease in the insulation resistance at temperatures<br> above about 600 °C which results in temperature measurement errors due to electrical<br> shunting. In this work the insulation resistance of a cohort of representative MI<br> thermocouples was characterised at temperatures up to 1160 °C, with simultaneous<br> measurements of the error in indicated temperature by in situ comparison with a reference<br> Type R thermocouple. Intriguingly, there appears to be a systematic relationship<br> between the insulation resistance and the error in the indicated temperature. At<br> a given temperature, as the insulation resistance decreases, there is a corresponding<br> increasingly negative error in the temperature measurement. Although the measurements<br> have a relatively large uncertainty (up to about 1 °C in temperature error and<br> up to about 10 % in insulation resistance measurement), the trend is apparent at all<br> temperatures above 600 °C, which suggests that it is real. Furthermore, the correlation<br> disappears at temperatures below about 600 °C, which is consistent with the<br> well-established diminution of insulation resistance breakdown effects below that<br> temperature. This raises the intriguing possibility of using the as-new MI thermocouple<br> calibration as an indicator of insulation resistance breakdown: large deviations<br> of the electromotive force (emf) in the negative direction could indicate a correspondingly<br> low insulation resistance.</p>
Data for "Measurement of temperature induced X-ray tube transmission target displacements for dimensional computed tomography"
<p>Raw data used to create figures for the paper "Measurement of temperature induced X-ray tube transmission target displacements for dimensional computed tomography" <a href="https://doi.org/10.1016/j.precisioneng.2021.06.002">https://doi.org/10.1016/j.precisioneng.2021.06.002</a></p> <p>Data is available in tab delimited format (.txt) and in Excel (.xls).</p> <p> </p>
Temperature measurements from the SMS Gazelle, Valdivia, and SMS Planet in the Indian Ocean
<p>This dataset contains digitized temperature records from the SMS Gazelle (1874–1876), Valdivia (1898–1899), and SMS Planet (1906–1907) observations in the Indian Ocean. The data is described in:</p> <p>Wenegrat, J.O., E. Bonanno, U. Rack, and G. Gebbie, 2022: A century of observed temperature change in the Indian Ocean. <em>Geophys. Res. Letters.</em> doi:10.1029/2022GL098217.</p> <p>Data was digitized from the original cruise reports using independent double-entry, and checked for consistency. A number of observations were discarded due to data problems, as described in Wenegrat et al. 2022 (see also associated code repository doi:10.5281/zenodo.6646645).</p>
Raw Data for Evaluation of Measurement Uncertainty in Structural Health Monitoring Systems Under Temperature Influence
<p>The documentation on these laboraty tests is titled "Documentation.pdf"</p> <p> </p> <p>Raw data from distance measurements using laser triangulation sensors acquired under different temperatures are provided. Six sensors were tested per experiment (CSV file), and in each experiment the boundary conditions are varied as follows:<br><br>00RawData_LTS_1m: The entire measurement system is subject to temperature change, with initial distances chosen as LTS1/LTS2=17 mm, LTS3/LTS4=21 mm nd LTS5/LTS6=25 mm.<br><br>01RawData_LTS_1m_SwitchedDistances: The entire measurement system is subject to temperature change, with the selected initial distances of LTS1/LTS2=25 mm, LTS3/LTS4=17 mm nd LTS5/LTS6=21 mm.<br><br>02RawData_LTS_1m_SwitchedDistances2: The entire measurement system is subject to temperature change, with initial distances selected as LTS1/LTS2=21 mm, LTS3/LTS4=25 mm nd LTS5/LTS6=17 mm.<br><br>03RawData_LTS_1m_OnlySensor: Only the sensors of the measuring system are subject to temperature change, where the selected initial distances are LTS1/LTS2=21 mm, LTS3/LTS4=25 mm nd LTS5/LTS6=17 mm.<br><br>04RawData_LTS_1m_OnlyMeasuringAmplifier: Only the measuring amplifiers of the measuring system are subject to temperature change. The selected initial distances are LTS1/LTS2=21 mm, LTS3/LTS4=25 mm nd LTS5/LTS6=17 mm.<br><br>05RawData_LTS_1m_OnlyCable: Only the cables of the measurement system are subject to the temperature change. The selected initial distances are LTS1/LTS2=21 mm, LTS3/LTS4=25 mm nd LTS5/LTS6=17 mm.<br><br>Tested temperature range: -10°C to 50°C<br>Measuring frequency: 1 Hz<br>Measuring amplifier: Q.bloxx.XL A107 Gantner Instruments<br>Cable: 4-pole, 1.00 m length<br>Sensor: OM20-P0026.HH.YIN laser triangulation sensor from Baumer</p>
Estimation of the variation in specific discharge over large depth using Distributed Temperature Sensing (DTS) measurements of the heat pulse response
<p>The data contains measurements and derived values that are used for the manuscript "Estimation of the variation in specific discharge over large depth using Distributed Temperature Sensing (DTS) measurements of the heat pulse response, [Paper # 2018WR024171]" Currently under review at the Water Resources Research journal.</p> <p>The data is stored in netCDF files with xarray (Python), and should be readable with any other netCDF reader. </p> <ul> <li>TEMP is the measured temperature in degrees Celsius relative to the background temperature</li> <li>tempinfty is one of the calibration parameters. Represents the steady state temperature increase</li> <li>A is one of the calibration parameters. Represents the timescale in days</li> <li>b is one of the calibration parameters. Represents the scaled distance to the heat source</li> <li>err_alpha is one of the calibration parameters. Represents the autoregressive parameter</li> <li>TEMPmodel is the best fit temperature response in degrees Celsius relative to the background temperature</li> <li>Innovation is termed the noise in the article, in degrees Celsius.</li> <li>q is the estimated specific discharge in meters per day</li> <li>q_MC_XX are the confidence intervals of the estimated specific discharge calculated with Monte Carlo as presented in the article</li> <li>q_lmfit_XX are the confidence intervals of the estimated specific discharge calculated with LMFIT. Is a rough estimate for q_MC_XX calculated by lmfit (Python package).</li> </ul> <p>Time is measured in days with respect to when the heating cable is turned on.</p> <p>Additionally, a Jupyter notebook is supplemented to the article. It demonstrates the calibration routine and the calculation of the confidence interval for the temperature response at a single depth.</p>
Plankton Temperature Measurements - Data Management - University of Tennessee - Mock Data
<p><strong>Comparison of conochilus unicornis (CONI) and conochilus hippocrepus (CHIP) depth and colony size over time at three different locations. </strong></p> <p>This contains colony size, depth, and density measurements from Name Pond and the data on which these data were gathered wereTHESE DATES.</p> <p><a href="https://zenodo.org/api/files/984109f0-dde8-46b7-bf8c-5556ac8b76b2/SEH_PlanktonNamePond_v7.27.2012.csv">SEH_PlanktonNamePond_v7.27.2012.csv </a></p> <p>This contains time, temperature, colony size, depth, and density measurements from Name Pond and the data on which these data were gathered wereTHESE DATES.</p> <p><a href="https://zenodo.org/api/files/984109f0-dde8-46b7-bf8c-5556ac8b76b2/SEH_PlanktonTempB_v.7.27.2012.csv">SEH_PlanktonTempA_v.7.27.2012.csv </a></p> <p>This contains time, temperature, colony size, depth, and density measurements from site A and the data on which these data were gathered wereTHESE DATES.</p> <p><a href="https://zenodo.org/api/files/984109f0-dde8-46b7-bf8c-5556ac8b76b2/SEH_PlanktonTempB_v.7.27.2012.csv">SEH_PlanktonTempB_v.7.27.2012.csv </a></p> <p>This contains time, temperature, colony size, depth, and density measurements from Site B and the data on which these data were gathered wereTHESE DATES.</p> <p><strong>Metadata</strong></p> <p>Date: Day that samples were collected</p> <p>Time_Day_Night: Gives time that the sample was gathered in the day</p> <p>Temp_C: Temperature of the water containing the plankton.</p> <p>CONI: Conochilus unicornis - species of plankton</p> <p>CHIP: Conochilus hippocrepis - plankton</p> <p>XXXX_ColonySize_mm: Average diameter (mm) of 5 randomly chose plankton colonies in the sample </p> <p>Temp: TemperatureYSI probe taken once at each depth</p> <p>ChlorophyllA_Units: Chlorophyll A values</p>
Water temperature measurements collected during austral summer 2017/2018 on lakes located in the Schirmacher oasis, East Antarctica.
<p>Lakes’ water temperature are measured on lakes of three types (epiglacial, epishelf and land-locked) located in the Schirmacher oasis, East Antarctica. The temporal hydrological network is equipped by 11 temperature sensors, which are measured both surface and bottom water temperature of lakes. The surface temperature is recorded with the temperature loggers iButton DS1922L/DS1922T (https://www.maximintegrated.com/en/datasheet/index.mvp/id/4088) on 8 lakes. The sensors are deployed within a distance of 1–3 m from a lake’s coast, on a depth of 0.02 m. The lake’s surface temperature is also measured on two lakes with the temperature sensors by HOBO Water Level U20L (https://www.onsetcomp.com/products/data-loggers/u20l-01), which are deployed on the depth of 0.2 m. One HOBO sensor is installed to be attached to a lake’s ground on a depth approximately 0.5 m. The measurements cover the period of over 12–36 days depending on a lake. The data set includes the field campaign’s report of 63 RAE in the Schirmacher oasis (a text, in Russian) as a pdf-file, the metadata for the measurements (name, elevation, lon/lat of the temperature sensors deployed; name of the lakes; period with measurements; comments) as a shp-file, and the tables with water temperature measured for each lakes (as files of CSV format). Also, the deployment of the temperature sensor on the Lake Pomornik is presented in the jpg-file.</p> <p> </p>
Validation of Emission Spectroscopy Gas Temperature Measurements Using a Standard Flame Traceable to the International Temperature Scale of 1990 (ITS-90)
<p>Data underpinning the associated publication (https://doi.org/10.1007/s10765-019-2557-6) on accurate traceable measurement of post-flame temperatures.</p>
Nocturnal leaf respiratory CO2 release in different species measured at constant temperature
<p>RAW data for GCB publication by Dan Bruhn, Martijn Slot, and Lina M Mercado, '<span>Simple and accurate representation of cumulative night-time leaf respiratory CO<sub>2</sub>-efflux'</span></p> <p><span>Nocturnal leaf respiratory CO2 release in 14 different species measured at constant temperature measured in the field.</span></p>
Fast Method for Calibrated Self-Discharge Measurement of Lithium-Ion Batteries including Temperature Effects and Comparison to Modelling
<p>Self-discharge data related to the manuscript entitled: 'Fast Method for Calibrated Self-Discharge Measurement of Lithium-Ion Batteries including Temperature Effects and Comparison to Modelling', submitted to Energy Reports on 26 April 2023.</p>
HotBin composter temperature measurements
<p>This is a CSV data table with temperature measurements of a HotBin composter, as displayed by the thermometer on its top. Environment temperature, rain is also recorded. A home-made heater unit is installed near the lid of the HotBin, this is a simple kettle that boils the water when a button is pressed, then heats up the water to boiling temperature and does no more heating until the button is pressed again. The composter is fed mainly with kitchen waste, occasionally with garden waste.</p>
Satellite-based measurements of brightness temperatures (AMSR2 sensor) colocated to MOSAiC ground measurements
<p>The file contains measurements of brightness temperatures of satellite overpasses of the research vessel Polarstern during the MOSAiC expedition from October 26, 2019 - May 26, 2020 as well as co-located measurements of different parameters. For every overpass of Polarstern, the satellite measurement closest to the hourly position of Polarstern is taken.</p> <p>The satellite sensor is AMSR2 (six frequencies between 6.9 and 89 GHz and both polarizations) and we use the Level 1R (<em>Madea et al., 2016)</em> product available at JAXA <a href="https://gportal.jaxa.jp/gpr/">https://gportal.jaxa.jp/gpr/</a></p> <p>The co-located parameters are liquid water path, total water vapor, sea ice concentration, multi-year ice fraction, snow depth, snow-air interface temperature, snow-ice interface temperature, wind speed and sea surface temperature. In addition to the co-located parameters as ground truth, the dataset also contains their “uncertainties” given as temporal and/or spatial variability.</p> <p>Note: The dataset contains <strong>only</strong> satellite overpasses where co-located data is available.</p> <p>More information on the parameters are found below and they are described in more detail in <em>Rückert et al., 2023</em><em> </em>and the references given therein.</p> <ul> <li> <p><strong>scantime</strong>: time of satellite observation as included in the satellite data from JAXA</p> </li> <li> <p><strong>lon</strong>: longitude in decimal degrees (DD) of satellite observations as included in the satellite data from JAXA</p> </li> <li> <p><strong>lat</strong>: latitude in decimal degrees (DD) of satellite observations as included in the satellite data from JAXA</p> </li> <li> <p><strong>distance</strong>: distance to the hourly Polarstern position</p> </li> <li> <p><strong>TB6.9V, TB6.9H, TB10.7V, TB10.7H, TB18.7V, TB18.7H, TB23.8V, </strong><strong>T</strong><strong>B23.8H, TB36.5V, TB36.5H, TB89V, TB89H</strong>: Brightness temperatures (TB) measured by AMSRE2, the name includes the frequency in GHz and the polarization (either H for horizontal or V for vertical polarization), e.g, TB6.9V is the brightness temperature at 6.9 GHz and vertical polarization</p> </li> <li> <p><strong>LWP</strong>: liquid water path in kg/m² measured by a radiometer onboard the ship (<em>Walbröl et al., 2022</em>), averaged within +/- 10 minutes of the satellite observations</p> </li> <li> <p><strong>sigma_LWP</strong>: temporal variability of liquid water path (see previous point) given as standard deviation within +/- 10 minutes of the satellite observation time</p> </li> <li> <p><strong>TWV</strong>: total water vapor (integrated water vapor) in kg/m² measured by a radiometer onboard the ship (<em>Walbröl et al. (2022)</em>), averaged within +/- 10 minutes satellite observation time</p> </li> <li> <p><strong>sigma_TWV</strong>: temporal variability of total water vapor (see previous point) given as standard deviation within +/- 10 minutes of the satellite observation time</p> </li> <li> <p><strong>WSP</strong>: wind speed in m/s from the vessel’s meteorological observatory (<em>Schmithüsen et al., 2021</em>), averaged within +/- 10 minutes of the satellite observation time</p> </li> <li> <p><strong>sigma_WSP</strong>: temporal variability of wind speed (see previous point) given as standard deviation within +/- 10 minutes of the satellite observation time</p> </li> <li> <p><strong>SST</strong>: sea water temperature in K from the vessel’s meteorological observatory (<em>Schmithüsen et al., 2021</em>), averaged within +/- 10 minutes of the satellite observation time</p> </li> <li> <p><strong>sigma_SST</strong>: temporal variability of sea water temperature (see previous point) given as standard deviation within +/- 10 minutes of the satellite observation time</p> </li> <li> <p><strong>SND</strong>: snow depth in m obtained from the median of daily snow depth from available Snow and Ice Mass Balance Apparatus (SIMBA) buoys (<em>Lei et al., 2021</em><em>a</em>) in the proximity of Polarstern.</p> </li> <li> <p><strong>sigma_SND</strong>: spatial variability of snow depth (see previous point) given as standard deviation of all buoys available on that day.</p> </li> <li> <p><strong>Tsi:</strong> Snow-ice interface temperature in K obtained from the median of daily measurements from available Snow and Ice Mass Balance Apparatus (SIMBA) buoys (e.g. <em>Lei et al., 2021b</em>, for references of all buoys the reader is referred to the references given in <em>Rückert et al., 2023</em>) in the proximity of Polarstern.</p> </li> <li> <p><strong>sigma_Tsi:</strong> spatial variability of snow-ice interface temperature (see previous point) given as standard deviation of all buoys available on that day.</p> </li> <li> <p><strong>MYIF:</strong> multi-year ice fraction (from 0 to 1) based on classified TerraSAR-X scenes (<em>Guo et al., 2023</em>) in the proximity of Polarstern, interpolated to daily values.</p> </li> <li> <p><strong>sigma_MYIF:</strong> estimated (constant) uncertainty of multi-year ice fraction (see previous point).</p> </li> <li> <p><strong>SIC</strong>: sea ice concentration (from 0 to 1) based on classified TerraSAR-X scenes (<em>Guo et al., 2023</em>) in the proximity of Polarstern, interpolated to daily values.</p> </li> <li> <p><strong>sigma_SIC:</strong> estimated (constant) uncertainty of sea ice concentration (see previous point).</p> </li> <li> <p><strong>Tsa</strong>: Snow-air interface temperature in K based on infrared thermometer data (<em>Cox et al., 2023 a)-d)</em>) installed at four positions in the proximity of Polarstern, averaged within +/- 20 minutes of the satellite observation time.</p> </li> <li> <p><strong>sigma_Tsa:</strong> spatial variability of snow-ice interface temperature (see previous point), given as spatial (4 sites) and temporal (within +/- 20 minutes of the satellite observation time) standard deviation.</p> </li> </ul>
Data from air, englacial and permafrost temperature measurements on Mt. Ortles (Eastern European Alps)
<p>The *.csv files report the temperature data recorded between 2010 and 2016 and presented in the paper “Modern air, englacial and permafrost temperatures at high altitude on Mt. Ortles, (3905 m a.s.l.) in the Eastern European Alps” (Carturan et al., 2023, submitted). The data were used to display the time series reported in the paper, which details variable names, data quality flags, maintenance logs of field operations, and characteristics of measurement sites.</p> <p>The data files contain measurements of air temperature, englacial temperature, soil surface temperature and rockwall temperature.</p> <p>The file named ‘Ortles_Temperature_Metadata.pdf’ contains information regarding variable names, structure of data files, quality codes, geolocation, and topographic and geomorphological characteristics of sites instrumented for temperature measurements.</p> <p>Reference:</p> <p>Carturan, L., De Blasi, F., Dinale, R., Dragà, G., Gabrielli, P., Mair, V., Seppi, R., Tonidandel, D., Zanoner, T., Zendrini, T. L., and Dalla Fontana, G.: Modern air, englacial and permafrost temperatures at high altitude on Mt. Ortles, (3905 m a.s.l.) in the Eastern European Alps, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2023-164, in review, 2023.</p>
Temperature Measurements of Southern California Deserts 2022.
The following data was recorded at various desert field sites within Southern California. Data was collected between May 2022 and June 2022. 3 different deserts; Carrizo, Cuyama, and Mojave, were tested. Temperature pendants were deployed for 30 days and recorded local temperature at 1 hour intervals.
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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OpenNeuro
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