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106 results for “electrical conductivity”
Soil moisture, temperature, and electrical conductivity data from the black sand extended growing season length experiment, 2018 - 2024, hourly.
As a result of climate change, the Rocky Mountain Front Range is experiencing warmer summers and earlier snowmelt. Due to the importance of snow for regulating soil temperature, growing season length, and available moisture in alpine ecosystems, even small shifts in the snow-free period could have large impacts. The focus of the Growing Season Length Experiment is to examine how terrain-related differences in climate exposure influence the way alpine habitats respond to climate change via earlier snowmelt. To simulate how changes in growing season length may affect biotic and abiotic components, NWT LTER researchers established 5 experimental sites each containing a pair 10 x 40m rectangular plots. These blocks include north and south facing aspects, subalpine and alpine tundra meadows in a range of hydrological conditions (e.g. dry meadows, moist meadows, wet meadows). We accelerated snowmelt in one plot of each block by adding chemically inert black sand, while keeping the second plot as an unmanipulated control (black sand was added to these plots after snow had naturally melted). This dataset includes measurements of soil temperature, moisture, and electrical conductivity.
Dataset of "A Monte Carlo Approach for Simulating Electrical Conductivity in Highly Porous Ceramic Composites: Impact of Internal Structure"
<p>3D structure of lanthanum strontium manganite and yttria-stabilized zirconia composites is predicted based on conductivity measurements using Monte Carlo 3D equivalent circuit network approach. Validation experimental impedance spectra; scanning electron micrographs; cross sections of model simulation or prediction (MSP).</p>
Hydrochemical Data from a Tropical Andean Glacierized Catchment: δ18O, electrical conductivity, maximum fluorescence intensity, and dissolved organic carbon concentrations from short-term sampling campaigns, Ecuador (2022 and 2024)
Fluorescent dissolved organic matter (FDOM) quality, dissolved organic carbon (DOC) concentration, electrical conductivity (EC), and stable water isotopes (δ¹⁸O and δ2H) were determined in water, snow, and ice samples from a tropical glacierized catchment in the Ecuadorian Andes. The sampling locations were selected to capture the major hydrologic inputs to the main stream channel (glacial melt, tributaries, wetlands, and groundwater springs) and constrain the in-stream spatiotemporal variation in DOM quality and other hydrochemical characteristics. Two sets of high-resolution time series were collected on Oct 13, 2022 and Jun 14, 2024. Time series samples were collected at various upper catchment locations and the outlet simultaneously. DOM quality was characterized via fluorescence spectroscopy and processed using parallel factor analysis (PARAFAC). The DOM quality data are expressed as %FMax values obtained through a 4-component PARAFAC model, where %Fmax 1– 4 are interpreted as terrestrial humic-like, tyrosine-like, tryptophan-like, and microbial humic-like fluorescent components, respectively. DOC concentrations were quantified using high-temperature catalytic combustion, stable water isotopes were analyzed using laser-based spectroscopy, and EC was measured in situ with handheld multiparameter water quality probes.
PsPM-SCRV6: Skin conductance responses to pain by electric stimulation
<p>This dataset includes skin conductance response (SCR) measurements for each of 20 healthy unmedicated participants (10 males and 10 females aged 21.8+/-3.3 years) in response to 10 discomforting electric shocks. Stimuli are 0.5ms wide square current pulse repeated at 500Hz for 100ms. Amplitude is varied (mean +/- SD: 0.78mA +/- 0.43mA). ITI is selected randomly on each trial from 29s, 34s or 39s.</p>
Stream discharge, stage, electrical conductivity & temperature dataset from Otemma glacier forefield, Switzerland (from July 2019 to October 2021)
<p>Stream data collected in the Otemma forefield (Switzerland) from July 2019 to Ocober 2021.<br> Data were collected by the research teams of Bettina Schaefli<sup>2</sup> and Stuart N. Lane<sup>1</sup>.</p> <p><sup>1</sup> Institute of Earth Surface Dynamics (IDYST), University of Lausanne, 1015 Lausanne, Switzerland</p> <p><sup>2</sup> Institute of Geography (GIUB), University of Bern, 3012 Bern, Switzerland</p> <p>For further information, please contact:</p> <ul> <li>tom.muller.1@unil.ch</li> <li>floreana.miesen@unil.ch</li> </ul> <p><strong>Description of data </strong></p> <p>A detailed description of the dataset is provided in the <strong>data_description_analysis.pdf</strong> file. In particular, the methodology and stage-discharge rating curves are provided in this file. Stream data were measured in three locations from glacier snout (Station 1); after the outwash plain (Station 2) and at the end of the glacier forefield (Station 3) (<strong>see overview_GS.png</strong>). A <strong>shapefile </strong>is also provided (coordinate system LV95).</p> <p>2 datasets are available in the data.zip file:</p> <ul> <li> <p><strong>River_2019_2021_10T.csv</strong> : contains the measured River Electrical conductivity (EC) [μS/cm], Stage [meters] and Temperature [°C] data for all stations in a tidy data format (see pdf for detailed description), with a 10 minutes timestep.</p> </li> <li> <p><strong>Discharge2020_10T.csv </strong>&<strong> Discharge2021_10T.csv </strong>: contains the estimated discharge [m<sup>3</sup>/s] at Station 1 and Station 2 from July 2020 to October 2021 and estimated error (2 standard deviations) in a tidy data format (see pdf for detailed description), with a 10 minutes timestep.</p> </li> </ul> <p>Additionally, the point discharge measurements covering peak summer discharge to minimal winter baseflow are provided in the <strong>Point_discharge_measurements_2020_2021.xlsx</strong> file.</p> <p>Plots of river parameters and discharge are also provided in data.zip for vizualisation.</p> <p> </p>
Accompanying data for paper "Electrical and Thermal Conductivity of Complex-Shaped Contact Spots"
<div> </div> <div>This repository contains the numerical data of the conductivity of complex-shaped contact spots on isotropic and linear conducting half-space obtained by Boundary and Finite Element methods. These data were used to construct some figures from the manuscript "Electrical and Thermal Conductivity of Complex-Shaped Contact Spots". The data is organized in folders corresponding to different types of contact spots: annular, flower-, star- and gear-shaped, Koch's snowflake, and self-affine spots. Each folder contains the results of numerical simulations in the form of `.npz` files, which can be loaded using `numpy` library in Python. The data is used to construct figures in the manuscript and can be used to reproduce the results or to perform additional analysis.</div> <div> </div>
Water stable isotope, temperature and electrical conductivity dataset (snow, ice, rain, surface water, groundwater) from a high alpine catchment (2019-2021).
<p>Data collected in the Otemma forefield in Switzerland (45°56’03”N,7°24’42”) from July 2019 to October 2021.<br> Data were collected by the research teams of Bettina Schaefli<sup>2</sup> and Stuart N. Lane<sup>1</sup>.</p> <p><sup>1</sup> Institute of Earth Surface Dynamics (IDYST), University of Lausanne, 1015 Lausanne, Switzerland</p> <p><sup>2</sup> Institute of Geography (GIUB), University of Bern, 3012 Bern, Switzerland</p> <p>For further information, please contact:</p> <ul> <li>tom.muller.1@unil.ch</li> </ul> <p><strong>Description of the dataset</strong></p> <p>This dataset contains water stable isotope (δ<sup>2</sup>H, δ<sup>17</sup>O, δ<sup>18</sup>O), water temperature and water electrical conductivity (EC) measurements collected from the Otemma glacier catchment.</p> <p>All water isotope samples were collected directly from the source and stored in 12 mL amber glass vials with an air-tight caps. River samples were first collected with an automatic ISCO 6712 portable water sampler with 1L open plastic bottles and transferred in 12 mL vials every one to two weeks. All isotope analysis were performed using a Wavelength-Scanned Cavity Ring Down Spectrometer (Picarro 2140-I, Santa Clara, California, USA) and expressed relative to the international Vienna Standard Mean Ocean Water (VSMOW) standards.</p> <p>All EC and water temperature measurements were performed with a WTW Multi 3510 IDS logger with a IDS TetraCon® 925 probe.</p> <p>The dataset contains measurements performed at various locations within the catchment. A total of approximately 1500 measurements are provided. In the dataset each point correspond to a measurement station (column "<strong>Station</strong>") which we classified in specific class of water (column "<strong>Type</strong>") as follows :</p> <ul> <li><strong>Stream </strong>: samples collected at three locations, from the glacier snout, after a small outwash plain and 2km downstream.</li> <li><strong>Tributary </strong>: 5 hillslopes tributaries originating from small seasonal overland flow or small springs at the base of the morainic hillslope. Those tributaries were monitored weekly. In addition, a few other seasonal lateral streams were sampled in various locations (Type: Other tributaries).</li> <li><strong>Bedrock </strong>: A few exfiltrations directly leaking out of the bedrock outcrop were sampled.</li> <li><strong>Ice </strong>: Ice was sampled either as surface ice (small cores 5 cm deep), as deeper cores (5 to 8m deep) or as meltwater from supraglacial gullies. All solid ice samples were melted at ambiant air temperature in air-tight plastic bags before being transferred into 12 mL vials.</li> <li><strong>Snow </strong>: The snowpack was sampled either at the surface (0 to 5cm) or at about 20 cm depth. Where possible, meltwater leaking from the snowpack was sampled. At 3 locations in 2021, we dug snowpits from which we sampled snow at different layers with depth. All solid snow samples were melted at ambiant air temperature in air-tight plastic bags before being transferred into 12 mL vials</li> <li><strong>Rain </strong>: Rainwater was mostly sampled at our camp site at 2450 m. asl. Rainwater samples represent single rain events which are identified by dry periods of at least one day long.</li> <li><strong>Groundwater </strong>: shallow (2 to 3 meters) fully-screened groundwater wells were installed in the outwash plain and water sampled monthly in the snow-free season.</li> </ul> <p>- GPS coordinates are provided with each point (Swiss coordinate system CH1903+ / LV95<strong> (EPSG: 2056)).</strong></p> <p>- Dates are provided in local timezone (GMT+1 with daylight saving time) and in UTC date format.</p> <p>- Analyitcal error from the Picarro spectrometer is reported as 1 standard deviation.</p> <p>More information can be accessed in the corresponding publication by Müller et al. (to be published in 2023).</p> <p><strong>Data files</strong></p> <ul> <li><em>Otemma_isotope_EC_T_2019_2021.csv</em> : file containing all data with GPS coordinates</li> <li> <p><em>isotope_locations_Otemma.jpg</em> : an overview of the locations of each measurement point</p> </li> <li> <p><em>Otemma_Isotopes_2019-2020.html </em>: interactive plots of all datasets (δ<sup>2</sup>H, EC, temperature), classified by Type.</p> </li> </ul>
2017 seasonal high frequency monitoring of Upper Clark Fork River (Montana, USA) dissolved carbon dioxide, pH, and electrical conductivity
These data were collected by the University of Montana and Montana State University to support the Upper Clark Fork River restoration monitoring project supported by the US NSF Long Term Research in Environmental Biology (LTREB) program. The original analytical intent for these data was to assess the response of river metabolic regime to the floodplain restoration via inference of whole-ecosystem metabolism from the daily variation in carbon dioxide concentrations. Data are primarily measurements of dissolved carbon dioxide, pH, electrical conductivity, and temperature in well-mixed river thalweg water. Additional miscellaneous data were collected for quality control. Data are from late in the 2017 field season. Data were collected on the Upper Clark Fork River (USGS HUC 17010201) at the Galen and Racetrack sites northeast of Anaconda, Montana, USA. High frequency carbon dioxide and pH data were collected using field deployments SAMI sensors from Sunburst Sensors (Missoula, Montana, USA). Electrical conductivity data was collected with a HOBO sensor from Onset Computer Corporation (Bourne, Massachusetts, USA).
Electrical conductivity of the world ocean and marine sediments
<p>Copy of dataset (as was on 2022-01-12) of electrical conductivity and conductance grids for the ocean and marine sediments at 0.1 degree lateral resolution, from https://github.com/agrayver/seasigma. These models are presented in the work</p> <p>Grayver, A. V. (2021). Global 3-D electrical conductivity model of the world ocean and marine sediments. Geochemistry, Geophysics, Geosystems, 22, e2021GC009950. <a href="https://doi.org/10.1029/2021GC009950">doi: 10.1029/2021GC009950</a></p> <p>Please cite this publication if you use the provided models in your work.</p>
Dataset for "A capillary bundle model for the electrical conductivity of saturated frozen porous media"
<p>This dataset supports the research study 'A capillary bundle model for the electrical conductivity of saturated frozen porous media' by H. L. Luo, D. Jougnot, A. Jost, J. D. Teng and L. D Thanh.</p> <p>We provide the experimental data from this study and the published data from Coperey et al. (2019a,b) and Duvillard et al. (2018, 2021) for verifing the proposed model with different PSDs (lognormal and fractal distribution).</p> <p>Matlab code Description:</p> <p>Untitled 1- the code for determining the electrical conductivity as a function of temperature and the sensitive analysis;</p> <p>Untitled 2- the code for comparison between the experimental data and the proposed model;</p> <p>Untitled 3- the code for comparison of the contribution between the bulk conduction and surface conduction to the total electrical conductivity;</p> <p>Untiled 4- the code for evolution of the effective formation factor as a function of the temperature.</p>
2707 water samples from Vallon de Nant analyzed for stable water isotopes (dD, d17O, d18O) and electric conductivity
<p>Database gathering 2707 water samples from the Vallon de Nant (Westerm Swiss Alps) analyzed for stable isotopes of water (dD, d17O, d18O, d-excess, LC-excess, 17O-excess) and electric conductivity.</p> <p>The "VDN_database.csv" file includes the following columns:<br> - time [dd/mm/yyyy HH:MM, UTC]<br> - latitude [decimal degrees, WGS84]<br> - longitude [decimal degrees, WGS84]<br> - elevation [masl]<br> - point: point name<br> - dD [‰]<br> - d17O [‰]<br> - d18O [‰]<br> - d-excess [‰]<br> - LC-excess [‰]<br> - 17O-excess [per meg]<br> - conductivity [µS/cm]</p> <p>Water is sampled from:<br> - 2 points of main stream (Avançon de Nant) at HyS1 and HyS2<br> - 5 different springs at GRAS, AUBG, ROCK, BRDG and ICEC<br> - 3 piezometers at PZ1, PZ2 and PZ3<br> - 2 weather stations for rain at Auberge and Chalet<br> - various locations (GPS points) for the Snowpack<br> - various locations (GPS points) for the Glacier</p> <p>The points with regular sampling are shown on the map "VDN_sampling_points.png".</p>
Direct synthesis of a stable radical doped electrically conductive coordination polymer
<ul> <li><strong>Data type</strong>: Experimental spectroscopic measurements.</li> <li>Files are with filename extensions: <strong>DSC</strong>, <strong>DAT</strong>,<strong>txt</strong></li> <li>Information on <strong>origin of the data</strong>:</li> </ul> <ul> <li>EPR spectroscopic measurements with filename extensions <strong>DSC</strong>, <strong>DTA.</strong></li> <li>EPR spectra are exported as <strong>txt</strong> files in ASCII format.</li> </ul> <ul> <li>X-band CW-EPR spectroscopic measurements were generated by EMX spectrometer equipped with SHQ cavity produced by Bruker.</li> <li><strong>If the dataset includes multiple files that relate to each other:</strong> <ul> <li>Files in <strong>PARACAT_WP4_20220202_ULEI_01_ONDICathedral_RT </strong>folder includes X-band CW-EPR spectroscopic measurements; original data are in DTA/DSC and txt formats.</li> </ul> </li> <li><strong>Information on</strong>: <ul> <li>specialized abbreviations: <strong>ONDICatechol– </strong>ONDI with Catechol, <strong>ONDIDMF– </strong>ONDI with DMF, <strong>ONDI_piperidine– </strong>ONDI with piperidine,<strong> ONDICdCl– </strong>ONDI with CdCl, <strong>ONDIDMSO– </strong>ONDI with DMSO.</li> <li>_RT– measured at 300 K</li> <li>definitions of variables: <strong>Magnetic field, Temperature.</strong></li> <li>units of measurement: <strong>Gauss (G), K, degree (°), milliTesla (mT)</strong>.</li> </ul> </li> </ul>
Dataset for "Predicting the electrical conductivity of partially saturated frozen porous media, a fractal model for wide ranges of temperatures and salinities"
<p>This dataset supports the research study "Predicting the electrical conductivity of partially saturated frozen porous media, a fractal model for wide ranges of temperatures and salinities" by H. L. Luo, D. Jougnot, A. Jost, J. D. Teng, A. Mendieta, G. Lin, and L. D. Thanh.<br> We provide the experimental data of electrical condutivity and unfrozen water saturation with different initial water saturations and salinities. Meanwhile, we also offer the matlab code for calculating the predicted values of electrical conductivity and apparent formation factor.</p> <p>Each file has its header, describing each column.</p> <p> </p>
New Hampshire Soil Sensor Network: Air Temperature, Soil Temperature, Soil Water Content, and Soil Electrical Conductivity, 2012 - ongoing
The goal of the New Hampshire Soil Sensor Network is to examine spatial and temporal changes in soil properties and processes as the climate changes. Data collected can also calibrate and validate models that examine how ecosystems may respond to changing climate and land use. To determine how soil processes are affected by climate change and land management, this soil sensor network measures snow depth, air temperature, soil temperature, soil volumetric water content, and soil electrical conductivity, as well as soil CO2 fluxes. This data package includes data from the air temperature, soil temperature, soil volumetric water content, and electrical conductivity sensors. Data were collected at the following sites: BRT = Bartlett Experimental Forest, Bartlett, NH; BDF = Burley-Demmerit Farm, Lee, NH; DCF = Dowst Cate Forest, Deerfield, NH; HUB = Hubbard Brook Experimental Forest, Woodstock, NH; SBM = Saddleback Mountain, Deerfield, NH; THF = Thompson Farm, Durham, NH; and Trout Pond Brook, Strafford, NH.
PsPM-HRA1: Skin conductance responses in fear conditioning with visual CS and electrical US
<p>This dataset includes skin conductance response (SCR) measurements, CS and US information, keypress responses, keypress response times, key correctness and shock ratings for each of 20 healthy unmedicated participants (10 males and 10 females aged 22.2+/-4.0 years) participating in a classical (Pavlovian) discriminant delay fear conditioning task. CS is a visual stimulus appearing in the middle of the screen with variation in color. US is an electric shock as a 500 Hz current pulses train (individual pulse width: 0.5ms, varying current amplitudes (0.90+/-0.63 mA) train width:500 ms). SOA between the CS and US is 3.5 s. The ITI is randomly determined on each trial to be 7, 8, 9, 10 or 11 s. (This was correctly stated in Staib et al. (2015) but wrongly described in Bach et al. (2010).)</p>
Electrical and Thermal Conductivities of Single CuxO Nanowires - Dataset
<p>This is the dataset of "Electrical and Thermal Conductivities of Single CuxO Nanowires".</p><p>This research is funded by the project 19ENG05 NanoWires. The project has received funding from the EMPIR program co-financed by the Participating States and from the European Union's Horizon 2020 research and innovation program.</p>
Predicted room temperature electrical conductivity of molecular mixtures
<p>In the associated manuscript, we propose the MolSets machine learning model for molecular mixture properties. Using the MolSets architecture, we train a model on a dataset curated by Bradford et al. (2023) to predict the room temperature (298 K) electrical conductivity of mixtures. Here, we report the model-predicted conductivities of all equal-weight binary mixtures among 28 types of small molecules, combined with 30 types of Li<sup>+</sup> salts (1 mol·kg<sup>-1</sup>), totaling 11,340 candidate lithium battery electrolytes. Note that the current model has a limitation of not taking salt solubility into account. This dataset is for demonstration purposes and should be used with caution.</p>
Time series of electrical conductivity, temperature and relative stream stage recorded in surface water and streambed sediments of River Erpe and River Gruendlach, Germany
<p><span><a href="../api/records/13336325/draft/files/temp_EC_timeseries.csv/content" target="_blank" rel="noopener noreferrer">temp_EC_timeseries.csv</a></span>: Time series of electrical conductivity (mS cm<sup>-1</sup>), temperature (degC) and relative stream stage (cm) recorded in the surface water and in streambed sediments (depth in cm) of River Erpe and River Gruendlach, Germany.</p> <p> </p> <p><span><a href="../api/records/13336325/draft/files/porewater_ec_timeseries.csv/content" target="_blank" rel="noopener noreferrer">porewater_ec_timeseries.csv</a></span>: Time series of electrical conductivity (mS cm<sup>-1</sup>), temperature (degC), relative stream stage (cm) and total pressure (hPa) recorded in the surface water and in streambed sediments (depth in cm) of River Erpe and River Ammer, Germany, and the Sturt River, South Australia.</p>
Data for "Site-Specific Management Zones Delineation Based on Apparent Soil Electrical Conductivity in Two Contrasting Fields of Southern Brazil"
<p>This dataset contains soybean yield, soil property values, and apparent electrical conductivity (ECa) from two farms located in the South of Brazil. The data description and methods can be found in the paper:</p> <p><strong>Bottega, E. L., Safanelli, J. L., Zeraatpisheh, M., Amado, T. J. C., Queiroz, D. M. de, & Oliveira, Z. B. de. (2022). Site-Specific Management Zones Delineation Based on Apparent Soil Electrical Conductivity in Two Contrasting Fields of Southern Brazil. In Agronomy (Vol. 12, Issue 6, p. 1390). MDPI AG. https://doi.org/10.3390/agronomy12061390</strong></p> <p>Abstract:</p> <p>Management practices that aim to increase the profitability of agricultural production with minimal environmental impact must consider within-field soil variability, and this site-specific management can be addressed by precision agriculture (PA). Thus, this work aimed to investigate which key soil attributes are distinguishable management zones (MZ) delineated based on the soil apparent electrical conductivity (ECa), using fuzzy k-means, in two fields with contrasting soil textures in southern Brazil. For this, a grid scheme (50 × 50 m) was applied to measure ECa, conduct soil sampling for analysis, and determine soybean yield. The MZ were delineated based on the ECa spatial distribution, and statistical non-parametric tests (p < 0.05) were employed to compare the soil chemical and physical attributes among MZ. The management zones were able to distinguish the average values of Clay, Silt, pH, Ca<sup>2+</sup>, Mg<sup>2+</sup>, SB, Al<sup>3+</sup>, H<sup>+</sup> + Al<sup>3+</sup>, AS%, and BS%. In the field classified as sandy clay loam texture, management zones were able to differentiate the average values of soybean yield, Clay, Ca<sup>2+</sup>, Mg<sup>2+</sup>, SB, and CEC. Thus, this study supports the ECa as an efficient tool for delineating MZ of contrasting cropland soils in southern Brazil to understand the within-field soil variability and adjust the inputs accordingly.</p>
Predicted room temperature electrical conductivity of molecular mixtures
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