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629 results for “clays”
Ecosystem metabolism and associated environmental data for a forested, meadow and reforested reach of White Clay Creek, Chester Co., Pennsylvania; 1971-1975 and 1997-2010
Ecosystem metabolism data for a 3rd-order Piedmont stream were collected during two periods: P1- April 1971 – Dec 1975, and P2- May 1997 – January 2010. Measures were made in a meadow and a forested reach during each period and in a reforested (formerly meadow) reach during the latter years of P2. During P1, measures were made by transferring streambed substrata to chambers in water jackets located on the streambank and measuring dissolved oxygen changes over diel periods. During P2, open system measures of dissolved O2 change were made for several days in warm and cold seasons, with reaeration determined from a propane injection experiment. Metabolism estimates were determined from diel curves of dissolved O2 change. Photosynthetically active radiation (PAR) and chlorophyll were measured concurrent with many measurements in P1 and all measures during P2, and temperature with all measures. Water chemistry parameters (NH4-N, NO3-N, PO4-P, SiO2, Cl, SO4, total alkalinity, pH) associated with each run are included in the data set, as are days since storm of various thresholds. Field procedures, analytical methods and data analyses are detailed in Bott, T.L. & J. D. Newbold, 2023. A multi-year analysis of factors affecting ecosystem metabolism in forested and meadow reaches of a Piedmont Stream. Hydrobiologia
Stream Storm Sample Data from White Clay Creek (WCC) Watershed in 2021
These data were collected to explore Nitrogen update dynamics in the water column of small streams during storm events. Data was collected using a combination of passive sampling and grab sampling of suspended sediments from the stream channel. Analysis of samples include: dissolved and total chemistry, suspended sediment load, biological and microbiological characteristics, and Nitrogen uptake rates.
iSDAsoil: soil clay content (USDA system) for Africa predicted at 30 m resolution at 0-20 and 20-50 cm depths
<p>iSDAsoil dataset soil clay content (USDA system) in % predicted at 30 m resolution for 0–20 and 20–50 cm depth intervals. Data has been projected in WGS84 coordinate system and compiled as <a href="https://gdal.org/drivers/raster/cog.html">COG</a>. Predictions have been generated using multi-scale Ensemble Machine Learning with 250 m (MODIS, PROBA-V, climatic variables and similar) and 30 m (DTM derivatives, Landsat, Sentinel-2 and similar) resolution covariates. For model training we use a pan-African compilations of soil samples and profiles (<a href="https://www.isda-africa.com/national-soil-services/">iSDA points</a>, <a href="https://www.isric.org/projects/africa-soil-profiles-database-afsp">AfSPDB</a>, <a href="https://landpotential.org/data-portal/">LandPKS</a>, and other national and regional soil datasets). Cite as:</p> <p>Hengl, T., Miller, M.A.E., Križan, J. <em>et al.</em> African soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learning. <em>Sci Rep</em> <strong>11, </strong>6130 (2021). <a href="https://doi.org/10.1038/s41598-021-85639-y">https://doi.org/10.1038/s41598-021-85639-y</a></p> <p>To open the maps in QGIS and/or directly compute with them, please use the <a href="https://gitlab.com/openlandmap/africa-soil-and-agronomy-data-cube"><strong>Cloud-Optimized GeoTIFF version</strong></a>.</p> <p>Layer description:</p> <ul> <li>sol_clay_tot_psa_m_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil clay content mean value,</li> <li>sol_clay_tot_psa_md_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil clay content (prediction) errors,</li> </ul> <p>Model errors were derived using bootstrapping: md is derived as standard deviation of individual learners from 5-fold cross-validation (using spatial blocking). The model 5-fold cross-validation (<a href="https://mlr.mlr-org.com/reference/makeStackedLearner.html">mlr::makeStackedLearner</a>) for this variable indicates:</p> <pre><code>Variable: clay_tot_psa R-square: 0.746 Fitted values sd: 16.5 RMSE: 9.63 Random forest model: Call: stats::lm(formula = f, data = d) Residuals: Min 1Q Median 3Q Max -75.803 -4.512 -0.178 3.748 82.146 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 4.494652 8.914671 0.504 0.61413 regr.ranger 1.076957 0.003611 298.210 < 2e-16 *** regr.xgboost -0.012617 0.004678 -2.697 0.00699 ** regr.cubist 0.030730 0.003930 7.820 5.32e-15 *** regr.nnet -0.238376 0.365390 -0.652 0.51415 regr.cvglmnet -0.044547 0.004379 -10.174 < 2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 9.629 on 122269 degrees of freedom Multiple R-squared: 0.7458, Adjusted R-squared: 0.7458 F-statistic: 7.175e+04 on 5 and 122269 DF, p-value: < 2.2e-16</code></pre> <p>To submit an issue or request support please visit <a href="https://isda-africa.com/isdasoil"><strong>https://isda-africa.com/isdasoil</strong></a></p>
Soil clay content [%] for continental Europe at 30 m spatial resolution for period 2000-2020: Open Soil Data Cube for Europe
<p>Predictions are based on the 3D Ensemble Machine Learning framework, as implemented in the R environment for statistical computing (Hengl & MacMillan, 2019; Hengl, et al., 2021). For each pixel we provide prediction errors as 1 standard deviation in either log or the original variable scale.</p> <p>The short description of currently available soil properties: clay.tot = clay content [percent];</p> <p>Soil properties were predicted at fixed depths:</p> <p> Surface soil = s0..0cm,<br> Subsoil 1 = s30..30cm,<br> Subsoil 2 = s60..60cm,<br> Subsoil 3 = s100..100cm.</p> <p>To produce estimates for depth intervals e.g. 0–30 cm, 0–100 cm best use the trapezoidal rule formula.</p> <p>Periods: 2000 (2000–2003), 2004 (2004–2007), 2008 (2008–2011), 2012 (2012–2015), 2016 (2016–2019), 2020;</p>
Clay content in % (kg / kg) at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution
<p>Clay content in % (kg / kg) at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution. Based on machine learning predictions from global compilation of soil profiles and samples. Processing steps are described in detail <strong><a href="https://gitlab.com/openlandmap/global-layers/tree/master/soil">here</a></strong>. Antarctica is not included.</p> <p>To access and visualize maps use: <a href="http://www.openlandmap.org/">OpenLandMap.org</a></p> <p>If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels:</p> <ul> <li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/issues">https://gitlab.com/openlandmap/global-layers/issues</a> </li> <li>General questions and comments: <a href="https://disqus.com/home/forums/landgis/">https://disqus.com/home/forums/landgis/</a></li> </ul> <p>All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:</p> <ul> <li>sol = theme: soil,</li> <li>clay.wfraction = variable: sand weight fraction,</li> <li>usda.3a1a1a = determination method: laboratory method code,</li> <li>m = mean value,</li> <li>250m = spatial resolution / block support: 250 m,</li> <li>b10..10cm = vertical reference: 10 cm depth below surface,</li> <li>1950..2017 = time reference: period 1950-2017,</li> <li>v0.2 = version number: 0.2,</li> </ul>
Environmental data from Neversink River, White Clay Creek, and Rio Tempisquito watersheds
This dataset presents dissolved stream water chemistry and other environmental variables collected from Neversink River in NY, USA, White Clay Creek in PA, USA, and Rio Tempisquito in Costa Rica.
Bagan, Manadalay, Myanmar. Clay tablet inscription, National Museum of Myanmar, Yangon.
<p>Bagan, Mandalay. Clay tablet inscription in Pāli, referring to an image made by Aniruddha. Now in the National Museum of Myanmar, Yangon. Provisional translation: This [image of] the Lord was made by Mahārāja Śrī Aniruddhadeva for the purpose of liberation by his own hand.</p>
Clay sealing with dhāraṇī
<p>Eastern India (find-spot not recorded). Clay sealing with the Vimaloṣṇīṣa dhāraṇī, eastern India, circa 11th century, currently in Budapest, acquired through Imre Schwaiger (1868-1940).</p>
Telhara, Bihar. Clay sealing of the Maukhari dynasty, 6th century.
<p>Telhara, Bihar. Clay sealing of the Maukhari dynasty, 6th century. Found at Telhara, Nalanada district, Bihar, located at 25.226334°N 85.181587°E.</p>
Interpolated Clay Fraction and Resistivity model of the Aare Valley, Switzerland
<p>The dataset is an underground model of the Upper Aare Valley in Switzerland. It has been made in the framework of the Phenix project at the University of Neuchâtel. It has been produced by applying the CF prediction method (doi : 10.5194/hess-18-4349-2014) to an EM dataset (doi : 10.5194/essd-13-2743-2021).</p> <p>The Model was then interpolated using Multiple Point statistics, with robust uncertainty quantification (doi : In review).</p> <p>The two files contain the same data, as pointset or gridVTK files. The data contained are :</p> <ul> <li>Log10(Resistivity)</li> <li>Log10(Resistivity) Uncertainty (STD)</li> <li>ClayFraction</li> <li>ClayFraction Uncertainty (STD)</li> </ul> <p>The X,Y,Z positions are provided in UTM32N (epsg : 32632).</p>
Spectral induced polarization of non-consolidated heterogeneous clay mixtures
<p>We present a spectral induced polarization dataset on heterogeneous mixtures of illite and red montmorillonite, with two longitudinal, and one transversal arrangement. Additionally, there is a 50-50% in volume content homogeneous mixture of illite and red montmorillonite.</p> <p>Each file has its header, describing each column. The ReadMe file also explains the content and format of each dataset.</p>
Experimental characterization of transversal-heterogeneous clay mixtures by the spectral induced polarization method
<p>In this folder you will find multiple datasets (*.txt) from SIP measurements of transversal-heterogeneous clay mixtures using spectral induced polarization acquired between April and May 2022. Additionally, we include two python codes to read and process the data.</p> <p>SIP_Plot_ReWrite.py is a python program aimed to process a .res file from a SIP Fuchs III.<br> It gives a .txt file with the frequency, the resistivity, the phase and the associated errors.<br> In order for the program to give the resistivity, you will need to enter the geometric factor of the studied sample.</p> <p><br> The six text files in the folder (excluding README.txt) were created using SIP_Plot_ReWrite.py.</p> <p>IL_1by1.txt and IL_1by1_V2.txt are from two different homogeneous mixtures of illite and water with a concentration of initially 0.01 mol/L of NaCl.<br> MtR_1by1.txt is from a homogeneous mixture of red montmorillonite and water with a concentration of initially 0.01 mol/L of NaCl.</p> <p>IL_MtR_1by2.txt, IL_MtR_1by4.txt and IL_MtR_1by8.txt are from three transversal-heterogeneous mixtures of illite and red montmorillonite with water containing a concentration of initially 0.01 mol/L of NaCl.</p> <p><br> For IL_MtR_1by2.txt, there was one portion of each clay types, occupying a half of the cylindrical container each.</p> <p><br> For IL_MtR_1by4.txt, there was two portions of each clay types, occupying a quarter of the cylindrical container each.</p> <p><br> These two samples were made using the same mixtures as for IL_1by1.txt and MtR_1by1.txt.</p> <p><br> For IL_MtR_1by8.txt, there was four portions of each clay types, occupying an eighth of the cylindrical container each.<br> This sample was made using the same mixtures as for IL_1by1_V2.txt and MtR_1by1.txt.</p> <p><br> TestDoubleColeColeFit.py is a python program which optimizes a double Cole-Cole model by multiplication on SIP data.<br> This program needs a file with the same structure as the .txt file made by SIP_Plot_ReWrite.py.</p>
"Hydration of a clay-rich unit on Mars, comparison of orbital data to rover data" supplementary data
<p>MCMC results from individual DAN measurements from sols 1814 to 3069 used in this work with material classifications from Figure 11.</p>
GEOLAB - Transnational Access project QC-CEM - Mapping quick clay with geophysical methods
<p>Quick clay is characterised by complete collapse and liquid-like mobility when overloaded. Quick clay is found primarily in Norway and Sweden, but also exists in Finland, Russia, Canada and Alaska. Quick clay landslides, with their retrogression characteristics and extreme mobility, pose significant risk to human lives, infrastructure, property and surrounding ecosystems. Hence, the proper characterization of quick clay sites is essential for ensuring the safety and resilience of infrastructure in Norway and elsewhere in Europe.<br> The current practice for mapping quick clay in Norway relies heavily on borehole data with either rotary sounding or total sounding and core samples tested in the laboratory. The only method for identifying quick clay with certainty is physical testing in the laboratory, but it is time-consuming, expensive and gives limited information, i.e., only at the depths and locations where the samples are taken. In Norway, rotary sounding and total soundings are frequently used in mapping of quick clay. There is increasing interest in using geophysical methods such as Electrical Resistivity Tomography (ERT) to supplement the results from soundings, particularly in early stage of ground investigation for mapping of quick clay. ERT is a near surface geophysical method that uses direct current to measure the earth's electrical resistivity. The current is injected into the subsurface through steel electrodes installed 10-20 cm into the ground, and the apparent resistivity distribution along a profile or area is measured. Using data processing and inverse modelling a 2D or 3D resistivity model of the subsurface can be derived.<br> Geophysical methods such as ERT show capability to identify not quick clay such as sand, silt, dry crust, moraine and bed rock reasonably accurate, but the identification of quick clay is still generally limited. The detection of leached clay (thus potentially quick clay) is however possible.<br> Transnational Access project QC-CEM is funded through the 1st call for proposal for the GEOLAB project. This project aims at testing various geophysical methods for their capability for soil characterisation, particularly for detecting quick clay.</p> <p>The objectives of the QC-CEM project are:<br> (i) to test different configurations of Electrical Resistivity Tomography survey for detection of quick clay<br> (ii) to test innovative and efficient electromagnetic based methods for mapping of quick clay. Results from this investigation is not available to share at this stage.<br> (iii) to investigation the effectiveness of cross-interpretation using different geophysical methods for soil characterisation. The results from this activity will be published in open publication after they are processed.</p>
Dissolved organic carbon concentrations in White Clay Creek, Pennsylvania, 1977-2017
Dissolved organic carbon concentrations were measured over a period of 4 decades in a 3rd-order reach of White Clay Creek, a stream with a forested riparian zone in the Southeastern Pennsylvania Piedmont. Stream water was filtered through pre-combusted glass fiber filters and analyzed in a variety of dissolved organic carbon analyzers. The data have been used to investigate organic matter biogeochemistry and the role of dissolved organic matter in supporting heterotrophic metabolism in the stream.
High-frequency sensor data collected by Stroud Water Research Center in a meadow reach of White Clay Creek from February 2016 through December 2016
High-frequency sensor data from a YSI 600 OMS Optical Monitoring System (every 15 minutes) and Sontek IQ (every 10 minutes) in a meadow reach at White Clay Creek from February through December 2016. Funded by NSF and DEB as part of the LTREB grant to study the recovery of stream ecosystem structure and function during reforestation, Stroud Water Research Center. The parameters in this data package are water temperature, depth, turbidity, conductivity, specific conductance, water pressure, discharge, rivers ection area, and velocity. Data are presented in four tables which likely have significant overlap. The raw data table presents the data exactly as it was downloaded from the Aquarius Database. It is formatted as a "wide" human-readable table. IQ_stream and YSI_stream present only the data from the respective sensors. These tables are gapfilled so that there are no time gaps. Formatted as a "wide" human-readable table. The full_stream table is all of the data, raw and cleaned, from both sensors. It is organized as a long, tidy table and is optimal for machine readability. All of the parameters and table are further explained in the metadata.
High-frequency sensor data collected by Stroud Water Research Center in a meadow reach of White Clay Creek from Janurary 2017 through December 2017
High-frequency sensor data from a YSI 600 OMS Optical Monitoring System (every 15 minutes) and Sontek IQ (every 10 minutes) in a meadow reach at White Clay Creek from January 2017 through December 2017. Funded by NSF and DEB as part of the LTREB grant to study the recovery of stream ecosystem structure and function during reforestation, Stroud Water Research Center. The parameters in this data package are water temperature, depth, turbidity, conductivity, specific conductance, water pressure, discharge, rivers ection area, and velocity. Data are presented in four tables which likely have significant overlap. The raw data table presents the data exactly as it was downloaded from the Aquarius Database. It is formatted as a "wide" human-readable table. IQ_stream and YSI_stream present only the data from the respective sensors. These tables are gapfilled so that there are no time gaps. Formatted as a "wide" human-readable table. The full_stream table is all of the data, raw and cleaned, from both sensors. It is organized as a long, tidy table and is optimal for machine readability. All of the parameters and table are further explained in the metadata.
High-frequency sensor data collected by Stroud Water Research Center in a meadow reach of White Clay Creek from Janurary 2018 through December 2018
High-frequency sensor data from a YSI 600 OMS Optical Monitoring System (every 15 minutes) and Sontek IQ (every 10 minutes) in a meadow reach at White Clay Creek from January 2018 through December 2018. Funded by NSF and DEB as part of the LTREB grant to study the recovery of stream ecosystem structure and function during reforestation, Stroud Water Research Center. The parameters in this data package are water temperature, depth, turbidity, conductivity, specific conductance, water pressure, discharge, rivers ection area, and velocity. Data are presented in four tables which likely have significant overlap. The raw data table presents the data exactly as it was downloaded from the Aquarius Database. It is formatted as a "wide" human-readable table. IQ_stream and YSI_stream present only the data from the respective sensors. These tables are gapfilled so that there are no time gaps. Formatted as a "wide" human-readable table. The full_stream table is all of the data, raw and cleaned, from both sensors. It is organized as a long, tidy table and is optimal for machine readability. All of the parameters and table are further explained in the metadata.
Hydraulic Properties of Expansive Yazoo Clay subjected to wet dry cycles
<p>The data file includes the investigation of the behavior of yazoo clay under different wet-dry cycles. The investigation was conducted using laboratory testing and numerical analysis.</p>
MarTREC Project Datasets for Effect of Permeability Variation of Expansive Yazoo Clay at the Maritime and Multimodal Transportation Infrastructure in Mississippi
<p>The existence of Yazoo clay soil in Mississippi frequently causes distress to the pavement and cause deformation at the slopes in highways and levees, which are a critical component in Maritime and multimodal transportation infrastructure. Each year, fixing the pavement requires a significant maintenance budget of MDOT. Also, the infiltration of the rainwater in the highway and levee slopes leads to landslides, which require millions of maintenance dollars each year. Due to the shrinkage and swelling behavior of the Yazoo clay, the hydraulic conductivity varies over the different seasons and has higher vertical permeability during the dry season. With high vertical permeability, the rainwater can easily percolate in the pavement subgrade and slopes, which accelerates the failure. The current study investigates the change in unsaturated vertical and horizontal permeability and its effect on the maritime and multimodal infrastructures, especially on the pavement and slopes of highway embankment and levees. The attached datasets include the laboratory test and finite element modeling findings.</p>
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