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Dataset results
13 results for “DTS”
Supplementary data for analysing distributed temperature sensing (DTS) measurements from Helsinki, Finland
<p>Supplementary data used in the analysis of distributed temperature sensing (DTS) measurements from Helsinki, Finland, as described in a journal article manuscript "Quantifying coastal urban surface layer structure using distributed temperature sensing in Helsinki, Finland".</p> <p>Eddy covariance, radiation and precipitation data is provided from the SMEAR III station by the Institute for Atmospheric and Earth System Research at the University of Helsinki under Creative Commons Attribution 4.0 International license (https://creativecommons.org/licenses/by/4.0/). The data can also be accessed programmatically via https://smear.avaa.csc.fi/. All SMEAR III data is time referenced to UTC+2.</p> <p>The 2-metre temperature data is provided by the Finnish Meteorological Institute under Creative Commons Attribution 4.0 International license (https://creativecommons.org/licenses/by/4.0/). All Finnish Meteorological Institute data is referenced to UTC.</p>
UTCI-to-DTS
<p>Mapping values of the Universal Thermal Climate Index (UTCI) to Dynamic Thermal Sensation (DTS), cf. Fig. 10 in https://doi.org/10.1007/s00484-011-0454-1<br> Tab-delimited ASCII file with the two columns UTCI (in °C) and DTS representing values from the 7-point thermal sensation scale (-3:cold; -2:cool:; -1:slightly cool; 0:neutral; +1:slightly warm; +2:warm; +3:hot)</p> <p>Headers in line 3, first data row in line 4</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>
Dataset containing DTS-data used in Karttunen et al. "Quantifying coastal urban surface layer structure using distributed temperature sensing in Helsinki, Finland"
<p>This record contains DTS-data used in the following study:</p> <p>Karttunen et al. (2021): Quantifying coastal urban surface layer structure using distributed temperature sensing in Helsinki, Finland, submitted to AMTD</p> <p> </p> <p>DTS_highfreq_SMEARIII_Karttunen_et_al.zip contains continuous high frequency potential temperature profiles measured along the SMEAR III 31-metre tall mast. See more information on the data in the netCDF-file attributes and on the measurement setup in the related manuscript.</p> <p>DTS_statistics_SMEARIII_Karttunen_et_al.nc contains profiles for the turbulence temperature statistics calculated from the continuous DTS potential temperature profiles.See more information in the netCDF-file attributes and the related manuscript.</p> <p> </p>
Animated DTS temperature profiles Speulderbos forest
<p><strong><em><a href="https://www.youtube.com/playlist?list=PL0Ojbg2cPwM8u4ZaGC9WWMtCGCQ5GUTiJ">These videos are also on YouTube for easier viewing</a>.</em></strong></p> <p>Vertical temperature profiles of the air temperature (red) and the wet-bulb temperature (blue) are animated through time. The measurement tower and an example tree is shown on the right. The time shown is in UTC+1</p> <p>Different radiation shielding above canopy was available (or absent), which is shown on the right hand side of the video. From 2017/08/03 onwards, a coiled fiber measures the temperature profile in the bottom 1.0 meters.</p> <p>The measurement method is described in <a href="http://doi.org/10.5194/hess-22-819-2018">doi.org/10.5194/hess-22-819-2018</a></p> <p>Part of the raw data (2016) is available on <a href="https://doi.org/10.4121/uuid:5c81f10a-1249-4b85-8dec-2b029dd88b99">doi.org/10.4121/uuid:5c81f10a-1249-4b85-8dec-2b029dd88b99</a></p>
Raw results of the simulation experiments performed to evaluate different Aloha-based schemes for LoRa-based DtS uplink transmissions
<p>This zip file contains the gnuplot and data files needed to generate the figures showing the experimental results presented in [1].</p> <p>[1] S. Herrería-Alonso, M. Rodríguez-Pérez, R. F. Rodríguez-Rubio and F. Pérez-Fontán, "Improving Uplink Scalability of LoRa-Based Direct-to-Satellite IoT Networks," in <em>IEEE Internet of Things Journal</em>, vol. 11, no. 7, pp. 12526-12535, April 2024, doi: 10.1109/JIOT.2023.3333934.</p>
Lantern Refine DTS D8
Chinese Bamboo Lantern for decoration. Source: Objaverse 1.0 / Sketchfab
DTS Blood Glucose Monitor System Surveillance Program SubStudy 1
ClinicalTrials.gov study NCT02789319. IPD Sharing: NO. Countries: 1. Publications: 1.
Data for DTS-adapSTNet
Open the record for dataset details and reuse information.
Efficacy and Safety of the DTS-G2 System in Patients With Axillary Hyperhidrosis
ClinicalTrials.gov study NCT00931359. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Dts-seq: a simple method of library preparation for a highly reproducible characterization of the tRNA epitranscriptome by deep sequencing
GEO Series GSE135937. Escherichia coli str. K-12 substr. MG1655. 15 samples. Type: Non-coding RNA profiling by high throughput sequencing.
APTIMA HPV 16 18/45 Genotype Assay on the TIGRIS DTS System in Women With ASC-US or Negative Pap Test Results
ClinicalTrials.gov study NCT01384370. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Drosophila temperature sensitive col4a1 mutant (DTS-3) and control (OreR) comparison at permissive and at restrictive temperature
GEO Series GSE52579. Drosophila melanogaster. 4 samples. Type: Expression profiling by array.
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