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155 results for “conductivity measurements”
Which physicochemical variables should zooplankton ecologists measure when they conduct field studies?
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Electric field and conductivity measurements in the stratosphere
<p>This dataset is a compressed archive that includes 2 data files in binary format, 4 files in csv format, and 2 metadata files (pdf documents) that provide information on how to interpret the data. This data was collected from instruments deployed on two stratospheric balloons, launched a day apart in late June 2021 from central Oregon. They flew on upper level winds to the west, out over the northeastern Pacific Ocean. The measurement objective was a multi-day set of vertical electric field and polar conductivity measurements at roughly a 10 minute cadence, and from widely separated locations in the stratosphere. The binary format data is comprehensive, including everything that was measured. The csv files have been processed from the raw data files into calibrated, timed, and time-ordered ASCII files containing the primary science measurements and some essential auxiliary measurements such as measurement location and time. This data was collected in an effort to (1) compare the fair-weather return current density at different geographic locations and (2) to compare the fair-weather current density with global thunderstorm activity.</p>
Electric field and conductivity measurements in the stratosphere
Open the record for dataset details and reuse information.
Datasets for "Two improvements to Gardner's method of measuring the hydraulic conductivity of non-saturated media: accounting for impedance effects and non-constant imposed suction increment"
<p>In Tables S1 to S3 are presented transient outflow measurements for suction steps 1, 4 and 11, carried out on a coarse volcanic substrate using the apparatus described in the paper "Two improvements to Gardner’s method of measuring the hydraulic conductivity of non-saturated media: accounting for impedance effects and non-constant imposed suction increment". Data are presented in the form of the change in water level [mm] in the outer (suction step 1 – Table S1) and inner tube (suction steps 4 and 11 – Tables S2 and S3), captured by means of the differential pressure transducer of high precision (0.1 mm).</p> <p>In Tables S4 and S5 are presented transient outflow measurements for the remolded poorly graded sand and undisturbed silty clay, respectively, published by Wayllace and Lu (2011). In case of both soils, only the first suction step is analyzed. Data are presented in the form of the change in mass of the water drained, which is monitored by means of the weight scale.</p>
Pre TAVI Membranous Septum Measurement Predicts High Grade Iatrioventricular Conduction Disorders in Patients With Specific Pacemaker Implanted After TAVI. The STIM TAVI-MS Study
ClinicalTrials.gov study NCT04906837. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Skin Conduction Device for Measuring Hot Flashes in Postmenopausal Women With Hot Flashes
ClinicalTrials.gov study NCT00427622. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Effect of Partner-Conducted Fetal Heart Sound Measurement on Maternal and Paternal Attachment
ClinicalTrials.gov study NCT06965647. IPD Sharing: NO. Countries: 1. Publications: 0.
Assessment of Pacing Stimulus Conduction and Latency Measurements in CRT-D Patients
ClinicalTrials.gov study NCT03314675. IPD Sharing: NO. Countries: 3. Publications: 0.
Uroflow Measurement With Electromyography (EMG) to Identify Lower Urinary Tract Symptoms (LUTS): Conducted on Healthy Children
ClinicalTrials.gov study NCT03339609. IPD Sharing: NO. Countries: 1. Publications: 0.
Skin Conduction Device and Patient Diary for Measuring Hot Flashes in Postmenopausal Women With Hot Flashes
ClinicalTrials.gov study NCT00427531. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Prediction of Pain During Old People Care Measured by Skin Conductance
ClinicalTrials.gov study NCT02975778. IPD Sharing: NO. Countries: 1. Publications: 0.
S-MODE L2 Shipboard Conductivity, Temperature, and Depth Measurements Version 1
This dataset contains shipboard conductivity, temperature, and depth (CTD) measurements taken during the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) field campaign. The experiment was conducted approximately 300 km offshore of San Francisco, during a pilot campaign that spanned two weeks in October 2021, and two intensive operating periods in Fall 2022 and Spring 2023. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. The shipboard CTD rosette is cast from the R/V Oceanus where it records ocean temperature, conductivity, and pressure as it descends to depth and then returns to the surface. IOP1 and IOP2 measurements also contain biological data. Data are available in netCDF format.
Characterization of Interventricular Conduction Measurements
ClinicalTrials.gov study NCT01362465. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Electrical and hydraulic properties estimation from complex conductivity measurements of limestones from Beauce aquifer (O-ZNS, France)
<p><span>This the dataset (.zip) contains the data used in paper and <span>supplementary materials</span> of the same name intended to be submitted at Water Resource Research. </span><span>I</span><span>n this folder you will find multiple datasets of different format including SIP measurements of carbonate samples, hydraulic parameters (porosity, permeability) measurements and image analysis (CT-scans and SEM) data. There is a MATLAB code allowing to read the raw data (SIP data) regrouped according the water conductivity. The same code also allows to obtain the different plots find in the paper and supplementary materials based on the processed data in excel files. Then, a Python code locating in the folder ‘’Fitting Cole-Cole model’’ allows to fit and compute Cole-Cole parameters. Finally, in the ‘’Image analysis’’ folder, there excel files showing the results of image analysis find in the paper and supplementary materials. <span> </span></span></p>
Soil salinity measured as electrical conductivity (ec, saturated paste method) soil maps of the Upper Colorado River Basin
<p>The data here were originally posted to facilitate timely and transparent peer review. The final public data release with formal metadata is now available from at the following location:</p> <p>Nauman, T.W., and Duniway, M.C., 2020, Predictive soil property maps with prediction uncertainty at 30 meter resolution for the Colorado River Basin above Lake Mead: U.S. Geological Survey data release,<a href="http://https//doi.org/10.5066/P9SK0DO2"> https://doi.org/10.5066/P9SK0DO2</a>.</p> <p>Associated publication:</p> <p>Nauman, T. W., and Duniway, M. C., 2020, A hybrid approach for predictive soil property mapping using conventional soil survey data: Soil Science Society of America Journal, v. 84, no. 4, p. 1170-1194. <a href="https://doi.org/10.1002/saj2.20080">https://doi.org/10.1002/saj2.20080</a>.</p> <p>Repository includes maps of soil saturated paste electrical conductivity (ec) as defined by United States soil survey program. </p> <p>These data are preliminary or provisional and are subject to revision. They are being provided to meet the need for timely best science. The data have not received final approval by the U.S. Geological Survey (USGS) and are provided on the condition that neither the USGS nor the U.S. Government shall be held liable for any damages resulting from the authorized or unauthorized use of the data.</p> <p>This data should be used in combination with a soil depth or depth to restriction layer map (both layers that will be released soon as part of this project) to eliminate areas mapped at deeper depths than the soil actually goes. This is a limitation of this data which will hopefully be updated in future updates. </p> <p>The creation and interpretation of this data is documented in the following article. Please note this article has not been reviewed yet and this citation will be updated as the peer review process proceeds.</p> <p>Nauman, T. W., Duniway, M. C., In Preparation. Predictive reconstruction of soil survey property maps for field scale adaptive land management. Soil Science Society of America Journal.</p> <p>File Name Details:</p> <p>ACCURACY!! Please see manuscript and Github repository (https://github.com/naumi421/SoilReconProps) for full details on accuracy. We do provide cross validation (CV) accuracy plots in this repository for both the overall sample (NRCS field pedons plus NRCS laboratory pedons; file ending _CV_plots.tif) and for just the CV results at laboratory pedons (file ending _CV_SCD_plots.tif). These plots compare CV predictions with observed values relative to a 1:1 line. Values plotted near the 1:1 line are more accurate. Note that values are plotted in hex-bin density scatter plots because of the large number of observations (most are >3000).</p> <p>Elements are separated by underscore (_) in the following sequence:</p> <p>property_r_depth_cm_geometry_model_additional_elements.extension</p> <p>Example: ec_r_0_cm_2D_QRF_bt.tif</p> <p>Indicates soil electircal conductivity (ec) at 0 cm depth using a 2D model (separate model for each depth) employing a quantile regression forest. This file is the raster prediction map for this model. There may be additional GIS files associated with this file (e.g. pyramids) that have the same file name, but different extensions. The _bt indicates that the map has been back transformed from ln or sqrt transformation used in modeling.</p> <p>The following elements may also exist on the end of filenames indicating other spatial files that characterize a given model's uncertainty (see below).</p> <p>_95PI_h: Indicates the layer is the upper 95% prediction interval value.</p> <p>_95PI_l: Indicates the layer is the lower 95% prediction interval value.</p> <p>_95PI_relwidth: Indicates the layer is the 95% relative prediction interval (RPI). The RPI is a standardization of the prediction interval that indicates that model is constraining uncertainty relative to the original sample. RPI values less than one represent uncertainty is being improved by the model relative to the original sample, and values less than 0.5 indicate low uncertainty in predictions. See paper listed above and also Nauman and Duniway (In revision) for more details on RPI.</p> <p>References</p> <p> Nauman, T. W., and Duniway, M. C., In Revision, Relative prediction intervals reveal larger uncertainty in 3D approaches to predictive digital soil mapping of soil properties with legacy data: Geoderma</p>
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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.