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661 results for “temperature measurement”
Year 2001, 15 minute interval, water quality measurements of water column temperature, salinity, oxygen, and depth near the mouth of Plum Island Sound, Massachusetts
Year 2001, 15 minute measurements of water column temperature, salinity, oxygen and depth in Plum Island Sound at the Ipswich Bay Yacht Club, Ipswich, MA.
Year 2002, 15 minute interval, water quality measurements of water column temperature, salinity, oxygen, and depth near the mouth of Plum Island Sound, Massachusetts
Year 2002, 15 minute measurements of water column temperature, salinity, oxygen and depth in Plum Island Sound at the Ipswich Bay Yacht Club, Ipswich, MA.
Year 2009, 15 minute interval, water quality measurements of water column temperature, salinity, oxygen, and depth in the upper Parker River Estuary at Middle Road Bridge, Newbury, MA.
Year 2009, water quality sonde data.15 minute readings of water column temperature, salinity, oxygen and depth in the upper Parker River Estuary at Middle Rd Bridge, Newbury, MA
MFS-M-00002 Air temperature at 2m measured in a raised bog-ridge, DS18B20 (APIK)
<p>Air temperature at 2m measured in a raised bog ecosystem by DS18B20 (temperature logger), 2018-2019, 30 min frequency, N60.89494 E68.66999, as part of meteorological monitoring in Mukhrino Field Station (https://mukhrinostation.com/).</p>
Debris temperature and ablation measurements from the Lirung Debris-Covered Glacier (2013-2014), Nepal
<p>These data are from the debris-covered portion of Lirung Glacier, Langtang Valley located in the Langtang National Park of the Nepal Himalaya that were collected during three field expeditions between September 2013 and April 2014. The data includes debris temperature measurements at two different sites of the glacier in three different seasons of the year 2013 (Monsoon and Winter) and 2014 (Pre-Monsoon). Debris temperature measurements from thermistors located at the surface (Black-colored dirty ice) to up to 40 cm from the surface. Dataset also included the ablation measurement in three seasons at different debris-thicknesses. Below is a brief description of the two different datasets:</p> <p>- Ablation_stake_data_Lirung_glacier_CHAND.csv: ablation stake measurement in the Monsoon and Winter season of 2013 and pre-monsoon season of 2014 from Chand and Kayastha (2018) and Chand et al. (2015).</p> <p>- Debris_temperature_profile_Lirung_Glacier_CHAND.csv: debris temperature measurements (Degree Celcius) where the depth is reported in cm from Chand and Kayastha (2018).</p> <p>----- Citing datasets -----</p> <p>Chand, M. B., and Kayastha, R. B. (2018). Study of thermal properties of supraglacial debris and degree-day factors on Lirung Glacier, Nepal. Sciences in Cold and Arid Regions, 10(5): 357-368 doi:10.3724/SP.J.1226.2018.00357</p> <p>Chand, M. B., Kayastha, R. B., Parajuli, A., and Mool, P. K. (2015). Seasonal variation of ice melting on varying layers of the debris of Lirung Glacier, Langtang Valley, Nepal, Proc. IAHS, 368, 21–26, https://doi.org/10.5194/piahs-368-21-2015</p> <p> </p>
Data from: Long-term, high frequency in situ measurements of intertidal mussel bed temperatures using biomimetic sensors
At a proximal level, the physiological impacts of global climate change on ectothermic organisms are manifest as changes in body temperatures. Especially for plants and animals exposed to direct solar radiation, body temperatures can be substantially different from air temperatures. We deployed biomimetic sensors that approximate the thermal characteristics of intertidal mussels at 71 sites worldwide, from 1998-present. Loggers recorded temperatures at 10–30 min intervals nearly continuously at multiple intertidal elevations. Comparisons against direct measurements of mussel tissue temperature indicated errors of ~2.0–2.5 °C, during daily fluctuations that often exceeded 15°–20 °C. Geographic patterns in thermal stress based on biomimetic logger measurements were generally far more complex than anticipated based only on 'habitat-level' measurements of air or sea surface temperature. This unique data set provides an opportunity to link physiological measurements with spatially- and temporally-explicit field observations of body temperature.
Datasets for the article "The temperature and density of a solar flare kernel measured from extreme ultraviolet lines of O IV"
<p>This entry contains the following files:</p><p>20120309_030933_kernel_fe8_shift.save<br>20120309_030933_kernel_fe8_shift_fits.txt<br>20110814_055342_qs_offlimb_si10.save<br>20110814_055342_qs_offlimb_si10_fits.txt</p><p>The .save files are IDL save files that can be restored into IDL using the restore command.</p><p>The 20120309 save file contains:</p><p>swspec - An IDL structure containing a 1D spectrum of the flare kernel for the EIS short wavelength (SW) channel. The format is that returned by eis-mask-spectrum.pro.<br>lwspec - As above, but for the long-wavelength (LW) channel.<br>map185 - An IDL map structure containing the Fe VIII 185.21 image that was used to select the flare kernel.<br>mask185 - An IDL structure containing the pixel mask that is used as input to eis-mask-spectrum.pro.</p><p>The Gaussian fits to the spectra (as performed with the routine spec-gauss-eis.pro) are stored in 20120309_030933_kernel_fe8<i>s</i>hift_fits.txt. This file can be read with read_line_fits.pro in Solarsoft.</p><p>The 20110814 dataset is used to obtain an off-limb coronal spectrum for calibration purposes. The save file contains:</p><p>swspec - An IDL structure containing a 1D spectrum of the off-limb region for the EIS SW channel. The format is that returned by eis-mask-spectrum.pro.<br>lwspec - As above, but for the LW channel.<br>map - An IDL map structure containing the Si X 272 image that was used to select off-limb region.<br>mask - An IDL structure containing the pixel mask that is used as input to eis-mask-spectrum.pro.</p><p>The Gaussian fits to the spectra (as performed with the routine spec-gauss-eis.pro) are stored in 20110814_055342_qs_offlimb_si10_fits.txt. This file can be read with read_line_fits.pro in Solarsoft. </p><p> </p><p> </p><p> </p><p> </p><p> </p><p> </p><p> </p>
CoUDlabs_WP8_T812_EAWAG_001. Sediment depth measurements for surrogate modeling of sediment build-up in gully pots using temperature data
<p>This dataset contains the results of the experimental campaign and how data were collected on the the <a href="https://co-udlabs.eu/">Co-UDlabs</a> <strong>Work Package 8 (Joint Research Activity 3)</strong>: <i>Improving resilience and sustainability in urban drainage solutions</i>; <strong>Task 8.1</strong>: <i>Development of consensus on measurement of hydraulic and water quality performance of urban drainage technologie</i>s; <strong>Subtask 8.1.2</strong>: <i>Development of scalable measurement protocols to assess the pollutant retention and release potential of urban drainage structures</i>. </p><p>Co-UDlabs is a project funded by the European Union's Horizon 2020 research and innovation programme under grant agreement No 101008626.</p><p>This database was developed as part of the Master Thesis in Environmental Engineering at ETH Zurich (Switzerland). Fuchs, L. (2023). Automated surrogate model to estimate sediment accumulation from temperatures in urban drainage systems. MSc Thesis, ETH Zurich. https://polybox.ethz.ch/index.php/s/IyiM38rRy1vlHWD. Accessed on 10th of October of 2023.</p>
TEAMx-PC22 (TEAMx pre-campaing 2022) - ACINN Distributed temperature sensing, fluxes from eddy covariance measurements, and auxiliary measurements from and at the i-Box station (VF-0) Kolsass
<p><strong>Introduction</strong></p> <p>During the TEAMx-precampaign (TEAMx-PC22) in summer 2022, the Innsbruck Box (i-Box) station at the valley floor in Kolsass (CS-VF0) was extended by a vertical array with fiber-optic distributed temperature sensing (DTS). The i-Box is a testbed for studying boundary layer processes in highly complex terrain (<a href="http://journals.ametsoc.org/doi/abs/10.1175/BAMS-D-15-00246.1">Rotach et al. (2017)</a> and <a href="https://fileshare.uibk.ac.at/f/9f1101851849439483de/">i-Box WIKI</a> for further information). The mountain boundary layer is investigated using a 17 m high tower with multi-level observations of turbulence, wind speed, and temperature. DTS measurements with a spatio-temporal resolution of 0.127 m and 1 s were added to these profile measurements. The combination of DTS measurements and point observations has the capability of resolving sub-meso scale motions (<a href="https://doi.org/10.1007/s10546-021-00618-0">Pfister et al. 2021</a>) and can reveal processes within the boundary layer during the morning and evening transition (<a href="https://doi.org/10.1029/2020GL092238">Fritz et al. 2021</a>).</p> <p>The aim of TEAMx-PC22 was to test new instruments, new instrument configurations and new measurement sites to support the planning of the main TEAMx observational campaign (TOC) in 2024/2025. More details about TEAMx can be found at <a href="http://www.teamx-programme.org">http://www.teamx-programme.org</a> as well as in <a href="https://doi.org/10.15203/99106-003-1">Serafin et al. (2020)</a> and in <a href="https://doi.org/10.1175/bams-d-21-0232.1">Rotach et al. (2022)</a>.</p> <p><strong>DATA SET DESCRIPTION</strong></p> <p><strong>1. Location</strong></p> <p>The i-Box valley-floor site is located on the almost flat floor near the town of Kolsass within the Inn Valley roughly 20 km east-north-east of Innsbruck. The site is characterized by different types of agricultural land. The 17-m high tower is a full energy-balance station and is instrumented with three vertical levels of turbulence measurements. The exact location is 47.305341°N, 11.62219°E (UTM: 698215.03 E, 5242420.95 N) at 545 m above mean sea level.</p> <p><strong>2. Temporal coverage</strong></p> <p>The TEAMx-PC22 lasted from mid-May 2022 to early October 2022. The i-Box station is running continuously, however, the provided data only covers the period when DTS data is available. The DTS array was running during the following periods:</p> <ul> <li>08.06.-14.06.2022</li> <li>28.06.-18.07.2022</li> </ul> <p><strong>3. Instrument details</strong></p> <p><em><strong>Distributed temperature sensing</strong></em></p> <p>For spatially continuous measurements of vertical temperature profiles at this tower, a DTS array was installed. Temperatures were measured with two channels at 1~Hz with a spatial resolution of 0.127~m. The used DTS instrument was an Ultima-HS (Silixa Ltd., Hertfordshire, UK) which was combined with a fibre-optic cable (900 µm outer diameter; AFL Telecommunications, Spartanburg, SC, USA) consisting of a bend-optimised optical fiber (125 µm with 50 µm core), buffered with Kevlar in a white plastic jacket. The fiber-optic cable was installed vertically towards the west of the tower such that two temperature profiles could be measured simultaneously. For the full array the approximately 450 m long fiber-optic cable was running from one DTS channel through a warm and cold reference bath towards the tower, then up and down the 17-m tower, and back through the baths towards the second channel. Accordingly, the array could be measured in both directions. For mounting at the top and bottom of the tower PVC pipes (diameter 15 cm) were used. The setup with two channels allows for sampling the array in both directions. Before entering the reference baths roughly 200 m were left on the spool slightly affecting signal-to-noise ratio. The vertical array was mapped by cooling packs. Both reference baths observed at the beginning and end of each fiber-optic cable yielded to four reference sections at two temperatures. The array was a double-ended configuration observed as two single-ended configurations which is different from the manufacturer's provided double-ended mode (<a href="https://doi.org/10.3390/s20082235">des Tombe et al. 2020</a>, <a href="https://doi.org/10.5194/essd-14-885-2022">Lapo et al. 2022</a>). Reference temperature probes were PT100 from the Ultmia-HS itself. DTS data was calibrated using the weighted-least squares approach described in <a href="https://doi.org/10.3390/s20082235">des Tombe et al. (2020)</a> and implemented in the <em>dtscalibration</em> software package (<a href="https://doi.org/10.5281/zenodo.7111585">des Tombe et al. 2022</a>) and all processing was completed using the <em>pyfocs</em> software package (<a href="https://doi.org/10.5281/zenodo.7111585">Lapo and Freundorfer 2020</a>) . As the fiber-optic cable runs through both calibration baths before and after the array creating four locations within a temperature controlled environment. Of those locations three are used for calibration (every time step) and the fourth is used for validation. A schematic of the setup is given within the files.</p> <p>After calibration a mean bias of -0.05 K and root mean squared difference of 0.22 K was determined with the validation water bath.</p> <p>Unfortunately the mounting towards the west created an artifact as the tower was partially shading the fiber-optic cable creating unphysical temperature gradients. Accordingly, data from 04.00 - 09.00 UTC should not be used for data analysis. The given data is only a single fiber of the paired vertical sections on the 17-m tower. Artifacts from the plastic ring holders are removed from the fiber.</p> <p>The DTS experiment was named the Innsbruck DTS Experiment (InnDEX22), hence, data names were chosen accordingly. But keep in mind that InnDEX22 was part of TEAMx-PC22.</p> <p><em><strong>i-Box tower</strong></em></p> <p>Full site description and all data exceeding the DTS observations can be found on <a href="https://acinn-data.uibk.ac.at/pages/i-box-kolsass.html">https://acinn-data.uibk.ac.at/pages/i-box-kolsass.html</a>. Utilized and uploaded data are mainly within three categories: eddy covariance (EC) fluxes at level 1 (4 m agl) and at level 2 (8.7 m agl) and low-frequency data:</p> <ul> <li>EC flux level 1:<br>Combination of ultrasonic anemometer CSAT3 (orientation from North: 30°) and infrared gas analyzer EC150 from Campbell Scientific</li> <li>EC flux level 2:<br>Ultrasonic anemometer CSAT3 (orientation from North: 30°) from Campbell Scientific</li> <li>low-frequency data: <ul> <li>pressure: Setra 278 (Setra Systems, Inc., Boxborough, Maine, USA) at 1.4 m agl</li> <li>radiation: ventilated CGR4 pyrgeometers and CMP21 pyranometers (Kipp & Zonen, Delft, Netherlands) at 2 m agl</li> <li>temperature profile: Rotronic HC2-S3 actively ventilated at 2, 4, 8.7, and 16.9m</li> <li>wind profile: 2D ultrasonic anemometer Gill Windsonic4 at 2, 4, 6, and 12m</li> </ul> </li> </ul> <p>For the EC processing further quality criteria can be applied to assure good data quality. More information on processing of data and quality criteria is given here:</p> <ul> <li>EC fluxes processing:<br>Averaging interval of 30 min performed by the software <a href="https://www.geos.ed.ac.uk/homes/jbm/micromet/EdiRe/">EdiRe</a><br>Processing includes despiking; double-rotation of the wind components; detrending with a recursive filter and a time constant of 200 s; and applying frequency-response corrections, heat-flux corrections for humidity effects, oxygen corrections for KH20, and WPL corrections. The datafile contains several quality flags and added as description within the netcdf files; zero-plane displacement height of 0 m</li> <li>EC flux Quality Criteria (QC) flags: <ul> <li>-1: all data</li> <li>0: excluding instrument malfunction</li> <li>1: additionally skewness within range (-2 to 2) and kurtosis <8 following Vickers and Mahrt 1997</li> <li>2: additionally exclude non-stationary data</li> </ul> </li> <li>EC flux Flags: <ul> <li>0: data ok</li> <li>1: data not ok (see description of individual flag for further details)</li> </ul> </li> </ul> <p><strong>4. Data file structure</strong></p> <p><em><strong>Zip folders</strong></em></p> <p>Different data sets were generated, as measurements had different temporal resolutions or different sets of parameter. Accordingly the following data is given:</p> <ul> <li>DTS data (1 s): InnDEX22_distributed_temperature_sensing.zip</li> <li>EC flux level 1 (30 min): InnDEX22_EC_flux_lvl1.zip</li> <li>EC flux level 2 (30 min): InnDEX22_EC_flux_lvl1.zip</li> <li>Low-frequency data (1 min): InnDEX22_low_frequency_data.zip</li> </ul> <p><em><strong>File format</strong></em></p> <p>Each above mentioned folder is filled with netcdf files. One for each day. Global information contains location, instrument type etc. Parameter description is given as attributes for each parameter.</p> <p><strong>6. Contact</strong></p> <p>Contact lena.pfister(at)uibk.ac.at for any questions regarding the data set.</p> <p><em><strong>Acknowledgements</strong></em></p> <p>Special thanks to the "Institut für Meteorologie und Klimaforschung Atmosphärische Umweltforschung" (IMK-IFU), KIT-Campus Alpin, Garmisch-Partenkirchen, for lending us the DTS measurement device for TEAMx-PC22.</p> <p><strong>7. References</strong></p> <p>Fritz, A. M., Lapo, K., Freundorfer, A., Linhardt, T., & Thomas, C. K. (2021): Revealing the morning transition in the mountain boundary layer using fiber-optic distributed temperature sensing. <em>Geophysical Research Letters</em>, 48, e2020GL092238. <a href="https://doi.org/10.1029/2020GL092238">https://doi.org/10.1029/2020GL092238</a></p> <p>des Tombe, B., Schilperoort, B., Bakker, M. (2020): Estimation of Temperature and Associated Uncertainty from Fiber-Optic Raman-Spectrum Distributed Temperature Sensing. <em>Sensors</em>, 20, 2235. <a href="https://doi.org/10.3390/s20082235">https://doi.org/10.3390/s20082235</a></p> <p>des Tombe, Bas François, & Schilperoort, Bart. (2022): Dtscalibration Python package for calibrating distributed temperature sensing measurements (v1.1.2). <em>Zenodo</em>. <a href="https://doi.org/10.5281/zenodo.7111585">https://doi.org/10.5281/zenodo.7111585</a></p> <p>Pfister, L., Lapo, K., Mahrt, L., Thomas, C.K. (2021): Thermal Submesoscale Motions in the Nocturnal Stable Boundary Layer. Part 1: Detection and Mean Statistics. <em>Boundary-Layer Meteorol</em> 180, 187–202. <a href="https://doi.org/10.1007/s10546-021-00618-0">https://doi.org/10.1007/s10546-021-00618-0</a></p> <p>Lapo, K., Freundorfer, A., (2020): klapo/pyfocs v0.5: Fully-functional python package intended for atmospheric deployments of distributed temperature sensing. <em>Zenodo</em>, <a href="https://doi.org/10.5281/zenodo.7111585">https://doi.org/10.5281/zenodo.7111585</a></p> <p>Lapo, K., Freundorfer, A., Fritz, A., Schneider, J., Olesch, J., Babel, W., and Thomas, C. K. (2022): The Large eddy Observatory, Voitsumra Experiment 2019 (LOVE19) with high-resolution, spatially distributed observations of air temperature, wind speed, and wind direction from fiber-optic distributed sensing, towers, and ground-based remote sensing, <em>Earth Syst. Sci. Data</em>, 14, 885–906 <a href="https://doi.org/10.5194/essd-14-885-2022">https://doi.org/10.5194/essd-14-885-2022</a></p> <p>Serafin, S., M. W. Rotach, M. Arpagaus, I. Colfescu, J. Cuxart, S. F. J. De Wekker, M. Evans, V. Grubišić, N. Kalthoff, T. Karl, D. J. Kirshbaum, M. Lehner, S. Mobbs, A. Paci, E. Palazzi, A. Raudzens Bailey, J. Schmidli, G. Wohlfahrt, B. Zardi, (2020): Multi-scale transport and exchange processes in the atmosphere over mountains: Programme and experiment. <em>Innsbruck University Press</em>. <a href="https://doi.org/10.15203/99106-003-1">https://doi.org/10.15203/99106-003-1</a></p> <p>Rotach, M. W., S. Serafin, H. C. Ward, M. Arpagaus, I. Colfescu, J. Cuxart, S. F. J. D. Wekker, V. Grubišic, N. Kalthoff, T. Karl, D. J. Kirshbaum, M. Lehner, S. Mobbs, A. Paci, E. Palazzi, A. Bailey, J. Schmidli, C. Wittmann, G. Wohlfahrt, D. Zardi, (2022): A collaborative effort to better understand, measure, and model atmospheric exchange processes over mountains. <em>Bulletin of the American Meteorological Society</em>, 103, E1282–E1295. <a href="https://doi.org/10.1175/bams-d-21-0232.1">https://doi.org/10.1175/bams-d-21-0232.1</a></p>
ERA5 overviews complementing temperature measurements of ground-based Rayleigh lidars for the investigation of gravity waves generated by moving sources
<p>ERA5 overviews to associate stratospheric gravity waves in temperature measurements from vertically staring (zenith-pointing) ground-based Rayleigh lidars with atmospheric processes. Animations are for a virtual lidar location over the Southern Ocean during research flight RF25 of the DEEPWAVE campaign (July 17 to 19, 2014) and for the location of the COmpact Rayleigh Autonomous Lidar (CORAL) in the lee of the southern Andes. Here, the first overview is for the CORAL measurement from June 22 to 23, 2018. The second one is for the nightly measurements between August 7 and 9, 2020.</p> <p>(a) and (b) emulate the measurement of a vertically staring ground-based lidar and show temperature perturbations after subtracting a temporal running mean of 12h (a) and the mean absolute temperature profile (b). Panels (c) and (d) are vertical sections of stratospheric 𝑇′ along sectors of the latitude circle (c) and meridian (d) of the virtual lidar location. (e) and (f) are corresponding vertical sections of thermal stability 𝑁2 (10−4 s−2, color-coded), potential temperature (K, thin grey lines), and potential vorticity (1, 2, 4 PVU: black, 2 PVU: green). Thin black lines in the vertical sections are zonal (d, f) and meridional (c, e) wind components (solid: positive, dashed: negative). Panel (g) is a horizontal section of the height of the 2 PVU surface (km, color-coded), geopotential height (m, solid lines) and wind barbs at the 850 hPa level. The black vertical line in (a) marks the time for (c)-(g) and dashed lines in (c)-(g) highlight the location of the virtual lidar and profiles in (a) and (b).</p> <p>The provided NETCDF files contain the corresponding CORAL temperature measurements for the two periods with CORAL measurements in 2018 and 2020.</p>
Measurements of diurnal variations of meteorological parameters and subsurface water temperature in Lake Kinneret, Israel, during the period (Sept. 6 – 20, 2015)
<p>The datasets include in-situ 10-minute measurements of subsurface water temperature taken at a depth of 20 cm, at a site A (32.82 <sup>o</sup>N; 35.60 <sup>o</sup>E) located near the center of Lake Kinneret, during the period (Sept. 6 – 20, 2015). Lake Kinneret is located in Israel. The Campbell 107-L temperature probe was used (specifications are available online at <a href="https://www.campbellsci.asia/107-l">https://www.campbellsci.asia/107-l</a> ). The datasets also include meteorological measurements taken at the same site, such as air temperature, relative humidity, wind speed, upwelling and downwelling longwave (4.5 - 42 µm) radiation. The above meteorological measurements were taken at a height of 2 - 3 m above the lake surface. Measurements at the site A are associated with the Kinneret Limnological Laboratory, Israel Oceanographic and Limnological Research (<a href="https://www.ocean.org.il/kinneret-limnological-laboratory-center/">https://www.ocean.org.il/kinneret-limnological-laboratory-center/</a> ).</p> <p><em>Data format: xlsx file. The file includes water temperature (WT, <sup>o</sup>C), wind speed (WS, m/s), air temperature (Tair, <sup>o</sup>C), relative humidity (RH, %), upwelling longwave radiation (Upwelling LW, W/m<sup>2</sup>) and downwelling longwave radiation (Downwelling LW, W/m<sup>2</sup>).</em></p> <p>Files (140.40 KB)</p>
Stress, Strain, and Temperature measurement data for AISI 301 and AISI 316 stainless steels from tension tests with a sudden increase of strain rate
<p>This publication comprise data obtained during a tension test where the strain rate was suddenly changed from 10<sup>-4</sup> s<sup>-1 </sup>to 1.3x10<sup>3 </sup>s<sup>-1</sup>. The sudden increase in strain rate was carried out at different amounts of plastic strain. The tests were carried out with a modified tension Split-Hopkinson Pressure bar device. The force was measured from the stress bars using strain gages, whereas the strain was measured using high speed photography and digital image correlation. The temperature was measured using high speed infrared imaging. The data is part of the original publication in Journal of Dynamic Behavior of Materials. The original manuscript is available at https://doi.org/10.1007/s40870-022-00333-y</p> <p> </p>
Dataset of measurements of the soil CO2 flux and soil brightness temperature at Le Biancane (geothermal field of Larderello-Travale, Tuscany, Italy) in the May-June 2021 period.
<p>Dataset of measurements of the soil CO<sub>2</sub> flux and soil brightness temperature at Le Biancane (geothermal field of Larderello-Travale, Tuscany, Italy) in the period May-June 2021. The dataset is structured as follows:</p> <p>Column A is the progressive number of the point (#);</p> <p>Column B is the Longitude of the point, datum WGS 1984;</p> <p>Column C is the Latitude of the point, datum WGS 1984;</p> <p>Column D is the Universal Transverse Mercator (UTM) Longitude coordinate, datum WGS 1984, zone 32N;</p> <p>Column E is the Universal Transverse Mercator (UTM) Latitude coordinate, datum WGS 1984, zone 32N;</p> <p>Column F is the soil brightness temperature, in °C;</p> <p>Column G is the soil CO<sub>2</sub> flux in grams of CO<sub>2</sub> per square meter, per day (g m<sup>-2</sup> day<sup>-1</sup>)</p>
CoUDlabs_WP8_T812_Deltares_001. Measuring sediment deposits in gully pots from temperature signals
<p>This dataset contains the results of the experimental campaign and how data were collected on the the <a href="https://co-udlabs.eu/">Co-UDlabs</a> <strong>Work Package 8 (Joint Research Activity 3)</strong>: <i>Improving resilience and sustainability in urban drainage solutions</i>; <strong>Task 8.1</strong>: <i>Development of consensus on measurement of hydraulic and water quality performance of urban drainage technologie</i>s; <strong>Subtask 8.1.2</strong>: <i>Development of scalable measurement protocols to assess the pollutant retention and release potential of urban drainage structures</i>. </p><p>The experimental campaign was funded under the European Union's Horizon 2020 research and innovation programme under grant agreement No 101008626.</p><p>The experiments were designed to further develop an innovative methodology for measuring sediment bed deposits in UDS based on temperature data analysis (<a href="https://doi.org/10.5281/zenodo.7258998">Anta et al., 2022</a>; <a href="https://doi.org/10.1039/D2EW00820C">Regueiro-Picallo et al., 2023</a>). Particularly, the aim of these campaigns was to test the application of this methodology in gully pots for measuring sediment build-up. For this purpose, we focused on understanding the heat transfer processes in gully pots in relation to the volume of bed deposits. Thus, the aim of this research is to estimate or at least obtain proof for the presence/absence of sediments by analyzing the differences between the temperature time series measured in the water phase and at the bottom of bed deposits. Results from the experimental campaigns will help to develop new technologies to estimate accumulation in urban drainage infrastructures.</p><p>The data are described so that others can use and reproduce.</p>
Miniaturization and expansion of the contactless temperature measurement system. Facial temperatures in relation to age, pulse and gender.
<p><span>The dataset contains temperature measurements on the surface of the face taken on 109 people. Each patient (identified by Patient ID in dataset) acclimatized in a room with a temperature of 22-24 degrees Celsius. Then the person completed a survey, during which they provided their:</span></p> <ul> <li><span>age (column Survey - age [years]),</span></li> <li><span>gender (column Survey - Gender),</span></li> <li><span>temperature measurement using a pyrometer thermometer (column Survey - temperature [°C]),</span></li> <li><span>and pulse measurement using a pulse oximeter (column Survey - measured pulse [BPM]).</span></li> </ul> <p><span>After that, the examined person stood in front of the contactless temperature measurement system (using a thermal camera), which was continuously calibrated to the black body at a distance of 1.5-3 meters (column Distance between camera and patient [m]). Then, several hundred temperature measurements were taken on each person in the following ways:</span></p> <ul> <li><span>Median temperature on face [°C]</span></li> <li><span>Median temperature on face, 1% of pixels with max temperature [°C]</span></li> <li><span>Median temperature on face, 5% of pixels with max temperature [°C]</span></li> <li><span>Median temperature on face, 10% of pixels with max temperature [°C]</span></li> <li><span>Median temperature in the center of the eyes (3x3 pixels) [°C]</span></li> <li><span>Median temperature measured at the corners of the eyes (3x3 pixels) [°C]</span></li> </ul> <p><span>Additionally, the system automatically estimated:</span></p> <ul> <li><span>the age of the examined person (column Estimated Age [years]),</span></li> <li><span>the pulse of the examined person (column Estimated Pulse [BPM]),</span></li> <li><span>and gender (Estimated Gender).</span></li> </ul> <p><span>According to [1], the measured temperature on the surface of the face is influenced by the age of the measured person. As part of the project, a Binary Regression Tree was developed, which considers (estimated) age when calculating the temperature on the surface of the face (column Temperature calculated by Binary Tree Regression algorithm [°C]).</span></p> <p><span>[1] Cheung, Ming & Chan, Lung & Lauder, I & Kumana, Cyrus. (2012). Detection of body temperature with infrared thermography: accuracy in detection of fever. Hong Kong medical journal = Xianggang yi xue za zhi / Hong Kong Academy of Medicine. 18 Suppl 3. 31-4.</span></p>
Figure 8 in Assessment of the accuracy of determining the Caspian Sea surface temperature by Landsat-5, -7 satellites based on the measurements of drifters
Figure 8. The relationship between the temperature of the sea surface layer obtained from drifters and SST according to Landsat-5, -7 Level-2 data: (a) measurements that have a time difference of no more than two hours with the flight of the satellite; (b) all measurements on the day of the satellite flyby.
Figure 4 in Assessment of the accuracy of determining the Caspian Sea surface temperature by Landsat-5, -7 satellites based on the measurements of drifters
Figure 4. An example of the absence in the archives of images over the central part of the Caspian Sea (flight track N 166 of the Landsat-7 satellite on 22 July 2008.
Figure 2 in Assessment of the accuracy of determining the Caspian Sea surface temperature by Landsat-5, -7 satellites based on the measurements of drifters
Figure 2. Drifter tracks in the Caspian Sea: (a) from 4 October 2006 to 20 February 2007, and (b) from 19 July 2008 to 10 October 2008.
Figure 10 in Assessment of the accuracy of determining the Caspian Sea surface temperature by Landsat-5, -7 satellites based on the measurements of drifters
Figure 10. Dependence between SST from the drifter and according to data from Landsat-5, -7 sensors having different levels of processing.
Figure 7 in Assessment of the accuracy of determining the Caspian Sea surface temperature by Landsat-5, -7 satellites based on the measurements of drifters
Figure 7. Histogram of temperature determination error values according to Landsat Level-1 data: (a) measurements that have a time difference of no more than two hours with the flight of the satellite; (b) all measurements on the day of the satellite flyby.
ScienceDex guides
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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.