Find research datasets worth reusing

openCC (other)Dec 2025View details →

Chamber level gas fluxes and soil biogeochemical properties from a tidal salt marsh and an impounded brackish wetland in South Carolina, USA

Archived data from a research project assessing the role of plants in driving methane fluxes from coastal wetland systems. Data collection occurred at the inland edge of a salt marsh and a diked, brackish impounded wetland in Georgetown County, South Carolina during 2022 and 2023. The first archived data table includes soil biogeochemical properties for all soil samples (0-10 cm and 10-50 cm mineral soil depth) collected approximately monthly from our two study sites. Specific biogeochemical properties include copy numbers of the mCRA gene, soil C and N concentrations, soil C:N ratios, soil moisture, pH, conductivity, organic matter content and alive and dead root biomass. The second archived data table includes methane and carbon dioxide fluxes from chambers over plants (whole-plant gas fluxes), adjacent to plants (plant-adjacent chambers) and in non-vegetated areas (non-vegetated fluxes) measured approximately monthly at our two study sites. Metadata associated with flux measurements are also included: leaf area, dead stems, oxidation reduction potential at four depths, atmospheric pressure, incoming solar radiation, relative humidity, chamber temperature, windspeed, water salinity, water temperature and water column depth.

openCC (other)Mar 2025View details →

Soil nitrogen availability vs. acidification: effects on soil respiration, heterotrophic respiration, and soil physicochemical properties in mixed temperate forests in central New York, USA (2019-2022)

In 2011, an experimental nitrogen x pH manipulation study was initiated in mixed temperate forests in central New York, USA to disentangle the often-confounded roles of nitrogen (N) and soil pH in driving various ecosystem processes. This data package contains soil physicochemical properties (soil pH, resin available nitrogen), soil temperature, in situ soil respiration, and heterotrophic respiration measured from laboratory incubations of soils collected from experimental plots. Soil pH was measured both pre-treatment (2009-2010) and after 8 and 11 years of experimental treatment. All other properties were measured between 9 and 12 years after treatment initiation.

openCC0Jun 2025View details →

Long-term soil properties after different biochar feedstock treatments in a Southwest Virginia Pasture, 2024

Biochar is an agricultural amendment that can improve soil health and promote carbon (C) sequestration. Effects of biochar can vary and depend on the biochar feedstock, method of production, soil conditions, and amendment method and frequency. These data include soil physicochemical properties from plots amended with hay, softwood, and hardwood biochar types produced under similar conditions (479°C – 522°C for 3.5-10.2 hours) with and without a nitrogen addition (porcine blood meal) in a randomized complete block design after 4.5 years. Plots were first established at the Virginia Tech Catawba Sustainability Center in Catawba, VA in June of 2019 and sampled in March 2024. Soil measurements include total nitrogen, total carbon, carbon:nitrogen ratios, gravimetric moisture, pH, electrical conductivity, dissolved inorganic nitrogen (NO3 and NH4), bulk density, and moisture from bulk density measurements. These data contribute to a long-term understanding of different biochar feedstock effects on Southwest Virginia pasture soils.

openCC0Dec 2023View details →

Soil Properties in CRUI Land Use Project at Harvard Forest 1995-1998

Soil properties and processes were evaluated on three types of colonial agricultural land-use - plowing, pasturing, and selective tree removal in a woodlot that ceased in the mid to late 1800s. Plowing, the most intensive type of agricultural disturbance, mixes soil to a depth of approximately 15cm, homogenizing the soil resources and likely reducing diversity in microenvironments. Removing trees and replacing them with grasses for pasture decreases the organic matter amount and types of inputs to the system, decreasing resource diversity. Woodlots, altered by selective and chronic tree removal, would have more limited decreases in resources and microenvironments. This study defines forest soil legacies using data from plots located at Harvard Forest in both amounts of soil resources and spatial heterogeneity of those soil resources. We found that for several soil parameters measured on previously cultivated and preciously pastured lands at the Harvard Forest, a legacy exists in the mineral soil, but the forest floor appears to have largely recovered from the agricultural disturbance. Parameters examined included soil mass, bulk density, organic matter content, pH, C, N, nitrogen mineralization and nitrification, Ca, Mg, K, and P.

openCustomMar 2025View details →

Barrier Island Plant and Soil Properties on Hog and Metompkin Islands, Virginia, 2021-2022

Dune building has the potential to impact the entire barrier island ecosystem, and these grasses therefore serve as ecosystem engineers. Protection offered by dune ridges directly impacts the adjacent swale habitat, modifying both biotic and abiotic factors. In order to better understand how dune building impacts the island ecosystem as a whole, we quantified sediment accretion, plant percent cover, stem numbers, and soil characteristics (chlorides, bulk density, %OM, %C, %N). These characteristics were assessed on two islands with varied disturbance intensities. Hog island is infrequently disturbed, and resists change driven by storms and overwash. Metompkin island is frequently disturbed and undergoes high rates of overwash and island migration.

zenodo52/100

Soil moisture sensor network, design, location attributes and soil properties, Hainich, Germany, project AquaDiva

<p>This dataset contains information of the small scale highly resolved soil moisture measurement network that is part of the of the AquaDiva Critical Zone exploratory, Hainich National Park, Germany. The dataset contains information on soil measurement locations, as well as attributes to the location, the design type (random locations vs transects), as well as locations attributes like distance to the next tree and soil properties. Measurement design was first introduced by Metzger et al., (2017), and used in Fischer et al., 2023. See there for more information.</p> <p><strong>References</strong></p> <p>Fischer-Bedtke, C., Metzger, J. C., Demir, G., Wutzler, T., and Hildebrandt, A.: Throughfall spatial patterns translate into spatial patterns of soil moisture dynamics – empirical evidence, Hydrology and Earth System Sciences, https://doi.org/10.5194/hess-2022-418, 2023.</p> <p>Metzger, J. C., Wutzler, T., Dalla Valle, N., Filipzik, J., Grauer, C., Lehmann, R., Roggenbuck, M., Schelhorn, D., Weckmüller, J., Küsel, K., Totsche, K. U., Trumbore, S., and Hildebrandt, A.: Vegetation impacts soil water content patterns by shaping canopy water fluxes and soil properties, Hydrological Processes, 31, 3783–3795, https://doi.org/10.1002/hyp.11274, 2017.</p>

opencc-by-4.0Jun 2023View details →

edi52/100

Physical and chemical properties of soils on Watershed 5 of Hubbard Brook Experimental Forest, before and after whole-tree harvest

We sampled soils on watershed 5 at the Hubbard Brook Experimental Forest in 1983, prior to a whole-tree harvest conducted in the winter of 1983-84. We resampled in 1986, 1991, and 1998. All sampling was performed using a quantitative soil pit method. Samples of the combined Oi and Oe horizons; the Oa horizon; 0-10 cm, 10-20 cm, and >20 cm layers of mineral soil; and the C horizon were collected. Grab samples of pedogenic mineral horizons were also taken from the sides of a subset of pits in each year. Here we report soil chemistry, mass of soil, percent rock, bulk density, and organic matter. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.

openCC (other)Feb 2022View details →

edi52/100

Soil physical and chemical properties of gypsum & non-gypsum soils from the Chihuahuan and Mojave Deserts in 2023

This dataset contains data for soil physical and chemical properties of gypsum and non-gypsum soils in the northern Chihuahuan and eastern Mojave Deserts. Data were obtained from 20 study sites total, 10 located on soils derived from gypsum parent material and 10 located on soils derived from non-gypsum parent materials. Sites were grouped into 10 pairs, in which every gypsum site was partnered with a non-gypsum site located in the same region. Apart from soil type, partnered-site characteristics (topography, climate, elevation, slope, aspect, and presence of biocrusts) were held relatively constant. Site info and characteristics data can be accessed at knb-lter-jrn.210616001. Soil physical properties included: percent gravel, percent < 2mm fraction, soil aggregate stability, and soil compaction. Soil chemical properties were: percent gypsum content, pH, EC, and soil soluble concentrations of calcium, magnesium, potassium, sulfur, and phosphorus. The resulting soil data was used to understand physical and chemical differences between gypsum and non-gypsum soils and to examine how biocrust community types and moss species abundance and composition were associated with the measured soil variables. This study and dataset are complete.

openCC (other)Oct 2024View details →

opencc-by-4.0Mar 2020View details →

Dataset of Soil hydraulic properties of Valle Telesina (Italy)

<p>The dataset contain a .xls file with the hydraulic properties georeferenced of 47 soil profiles of the "Valle Telesina (Italy) site, according to the parametrization of the van Genuthen-Mualem model (van Genuchten, 1980). Moreover a zipped folder with the shape files for the same area is provided.</p> <p>Following there is the description of the methods applied for the soil hydraulic characterization:</p> <p>Undisturbed soil samples were collected from the horizons using cylindrical steel samplers (8.5 cm diameter and 12.0 cm high). In the laboratory, the samples were saturated by slowly wetting from the bottom in order to remove all the air entrapped in the soil. The maximum water content,θ<sub>0</sub>, was gravimetrically determined and the saturated hydraulic conductivity, ks, was measured by a falling-head permeameter. Then, the Wind method was applied to simultaneously determine the water retention and hydraulic conductivity functions by subjecting the soil samples to an evaporation process. After sealing the bottom surface to prevent drainage, during the evaporation process - at appropriate pre-set time intervals - the weight of the whole sample and the pressure head at three different depths were measured. An iterative procedure was applied for estimating the water retention curve from these measurements. Then, the instantaneous profile method was applied to determine the unsaturated hydraulic conductivity. θr, θs, α and n parameters were derived by fitting the soil water retention data; under the restriction m=l−l/n, τ and k<sub>0</sub> parameters were derived by fitting the hydraulic conductivity data. Details of the tests and overall calculation procedures are described in Basile et al. (2012). The parameters obtained in the laboratory were then scaled to better reproduce the field behaviour by following the procedure suggested by Basile et al. (2003; 2006). Finally, for the few soils having considerable stone content, a correction of θs and k<sub>0</sub>, to take into account the stoniness, was applied (Coppola et al., 2013).</p> <p>References:</p> <p>Van Genuchten, M. T. (1980). A closed-form equation for predicting the hydraulic conductivity of unsaturated soils. Soil Science Society of America Journal, 44(5), 892–898.</p> <p>Basile, A., Buttafuoco, G., Mele, G., & Tedeschi, A. (2012). Complementary techniques to assess physical properties of a fine soil irrigated with saline water. Environmental Earth Sciences,66(7), 1797–1807.</p> <p>Basile, A., Ciollaro, G., & Coppola, A.(2003). Hysteresis in soil water characteristics as a key to interpreting comparisons of laboratory and field measuredhydraulic properties.Water Resources Research, 39(12).</p> <p>Basile, A., Coppola, A., De Mascellis, R., & Randazzo, L. (2006). Scaling approach to deduce field unsaturated hydraulic properties and behavior from laboratory measurements on small cores. Vadose Zone Journal,5(3), 1005–1016.</p> <p>Coppola, A., Dragonetti, G., Comegna, A., Lamaddalena, N., Caushi, B., Haikal, M., & Basile, A. (2013). Measuring and modeling water content in stony soils. Soil and Tillage Research,128, 9–22.</p>

Soil properties as point estimations over the Lithuanian pilot area (2022)

<p>In the context of the EU-funded project DIONE (No. 870378), VNIR topsoil reflectance was captured with DIONE’s Soil Scanning System and transformed through Machine Learning modelling to a set of soil properties that are meaningful for the assessment of soil health. The captured reflectance measurements correspond to locations distributed within the pilot areas of Lithuania and are indicated after the analysis of EO multispectral imagery, aiming to create a collection of point locations that well represent the soil characteristics of the area, and provide valuable information about soil condition through the estimations of the following soil properties:</p> <ul> <li>Sand %</li> <li>Clay %</li> <li>Silt %</li> <li>Electrical Conductivity (mS/m)</li> <li>pH</li> <li>Calcium carbonate %</li> <li>Soil Organic Carbon %</li> </ul> <p>The dataset is delivered in a shapefile format (DIONE_LT_point_estimations_2022_WP4.shp - EPSG:4326 - WGS 84) containing the following fields:</p> <table> <caption><strong>Description of the information contained in the corresponding "LT points estimations" dataset</strong></caption> <thead> <tr> <th scope="col">Field </th> <th scope="col">Type </th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td>Sample ID</td> <td>String </td> <td>Unique ID</td> </tr> <tr> <td>Lat</td> <td>Real </td> <td>Latitude </td> </tr> <tr> <td>Lon</td> <td>Real </td> <td>Longitude</td> </tr> <tr> <td>Sand </td> <td>Real </td> <td>Sand fraction</td> </tr> <tr> <td>Clay</td> <td>Real </td> <td>Clay fraction</td> </tr> <tr> <td>Silt</td> <td>Real </td> <td>Silt fraction</td> </tr> <tr> <td>EC </td> <td>Real </td> <td>Electrical Conductivity</td> </tr> <tr> <td>ph_H<sub>2</sub>0</td> <td>Real </td> <td>ph</td> </tr> <tr> <td>CaCO<sub>3</sub></td> <td>Real </td> <td>Calcium Carbonate</td> </tr> <tr> <td>SOC </td> <td>Real </td> <td>Soil Organic Carbon</td> </tr> </tbody> </table>

Soil properties as point estimations over the Cypriot pilot area (2022)

<p>In the context of the EU-funded project DIONE (No. 870378), VNIR topsoil reflectance was captured with DIONE’s Soil Scanning System and transformed through Machine Learning modelling to a set of soil properties that are meaningful for the assessment of soil health. The captured reflectance measurements correspond to locations distributed within the pilot areas of Cyprus and are indicated after the analysis of EO multispectral imagery, aiming to create a collection of point locations that well represent the soil characteristics of the area, and provide valuable information about soil condition through the estimations of the following soil properties:</p> <ul> <li>Sand %</li> <li>Clay %</li> <li>Silt %</li> <li>Electrical Conductivity (mS/m)</li> <li>pH</li> <li>Calcium carbonate %</li> <li>Soil Organic Carbon %</li> </ul> <p>The dataset is delivered in a shapefile format (DIONE_CY_point_estimations_2022_WP4.shp - EPSG:4326 - WGS 84) containing the following fields:</p> <table> <caption><strong>Description of the information contained in the corresponding "CY points estimations" dataset</strong></caption> <thead> <tr> <th scope="col">Field </th> <th scope="col">Type </th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td>Sample_ID</td> <td>String</td> <td>Unique ID</td> </tr> <tr> <td>Lat</td> <td>Real</td> <td>Latitude </td> </tr> <tr> <td>Lon</td> <td>Real</td> <td>Longitude</td> </tr> <tr> <td>Sand</td> <td>Real</td> <td>Sand fraction</td> </tr> <tr> <td>Clay</td> <td>Real</td> <td>Clay fraction</td> </tr> <tr> <td>Silt</td> <td>Real</td> <td>Silt fraction</td> </tr> <tr> <td>EC</td> <td>Real</td> <td>Electrical Conductivity</td> </tr> <tr> <td>ph_H<sub>2</sub>0</td> <td>Real</td> <td>pH</td> </tr> <tr> <td>CaCO<sub>3</sub></td> <td>Real</td> <td>Calcium Carbonate</td> </tr> <tr> <td>SOC</td> <td>Real</td> <td>Soil Organic Carbon</td> </tr> </tbody> </table> <p> </p>

Soil properties as point estimations over the Lithuanian pilot area (2021)

<p>In the context of the EU-funded project DIONE (No. 870378), VNIR topsoil reflectance was captured with DIONE’s Soil Scanning System and transformed through Machine Learning modelling to a set of soil properties that are meaningful for the assessment of soil health. The captured reflectance measurements correspond to locations distributed within the pilot areas of Lithuania and are indicated after the analysis of EO multispectral imagery, aiming to create a collection of point locations that well represent the soil characteristics of the area, and provide valuable information about soil condition through the estimations of the following soil properties:</p> <ul> <li>Sand %</li> <li>Clay %</li> <li>Silt %</li> <li>Electrical Conductivity (mS/m)</li> <li>pH</li> <li>Calcium carbonate %</li> <li>Soil Organic Carbon %</li> </ul> <p>The dataset is delivered in a shapefile format (DIONE_LT_point_estimations_2021.shp - EPSG:4326 - WGS 84) containing the following fields:</p> <table> <caption><br> <strong>Description of the information contained in the corresponding "LTH points estimations" dataset</strong></caption> <thead> <tr> <th scope="col">Field</th> <th scope="col">Type</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td>Sample_ID</td> <td>String</td> <td>Unique ID</td> </tr> <tr> <td>lat</td> <td>Real</td> <td>Latitude </td> </tr> <tr> <td>lon</td> <td>Real</td> <td>Longitude</td> </tr> <tr> <td>Sand</td> <td>Real</td> <td>Sand fraction</td> </tr> <tr> <td>Clay</td> <td>Real</td> <td>Clay fraction</td> </tr> <tr> <td>Silt</td> <td>Real</td> <td>Silt fraction</td> </tr> <tr> <td>EC</td> <td>Real</td> <td>Electrical Conductivity</td> </tr> <tr> <td>pH_H20</td> <td>Real</td> <td>pH</td> </tr> <tr> <td>CaCO3</td> <td>Real</td> <td>Calcium Carbonate </td> </tr> <tr> <td>SOC</td> <td>Real</td> <td>Soil Organic Carbon</td> </tr> </tbody> </table> <p><br>  </p>

Soil properties as point estimations over the Cypriot pilot area (2021)

<p>In the context of the EU-funded project DIONE (No. 870378), VNIR topsoil reflectance was captured with DIONE’s Soil Scanning System and transformed through Machine Learning modelling to a set of soil properties that are meaningful for the assessment of soil health. The captured reflectance measurements correspond to locations distributed within the pilot areas of Cyprus and are indicated after the analysis of EO multispectral imagery, aiming to create a collection of point locations that well represent the soil characteristics of the area, and provide valuable information about soil condition through the estimations of the following soil properties:</p> <ul> <li>Sand %</li> <li>Clay %</li> <li>Silt %</li> <li>Electrical Conductivity (mS/m)</li> <li>pH</li> <li>Calcium carbonate %</li> <li>Soil Organic Carbon %</li> </ul> <p>The dataset is delivered in a shapefile format (DIONE_CY_point_estimations_2021.shp - EPSG:4326 - WGS 84) containing the following fields:</p> <table> <caption><strong>Description of the information contained in the corresponding "CY points estimations" dataset</strong></caption> <thead> <tr> <th scope="col">Field</th> <th scope="col">Type</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td>Sample_ID</td> <td>String</td> <td>Unique ID</td> </tr> <tr> <td>lat</td> <td>Real</td> <td>Latitude </td> </tr> <tr> <td>lon</td> <td>Real </td> <td>Longitude</td> </tr> <tr> <td>Sand</td> <td>Real</td> <td>Sand fraction</td> </tr> <tr> <td>Clay</td> <td>Real</td> <td>Clay fraction</td> </tr> <tr> <td>Silt</td> <td>Real</td> <td>Silt fraction</td> </tr> <tr> <td>EC</td> <td>Real</td> <td>Electrical Conductivity</td> </tr> <tr> <td>pH_H20</td> <td>Real</td> <td>pH</td> </tr> <tr> <td>CaCO3</td> <td>Real</td> <td>Calcium Carbonate </td> </tr> <tr> <td>SOC</td> <td>Real</td> <td>Soil Organic Carbon</td> </tr> </tbody> </table> <p> </p>

Data from: Effects of plastic fragments on plant performance are mediated by soil properties and drought

<p>In recent years, the effects of plastic contamination on soil and plants have received growing attention. Plastic can affect soil water content and thus may interact with the effects of drought on soil and plants. However, the effects of plastic on soil are highly context-dependent, and interactions with drought have been hardly tested. We conducted two greenhouse experiments to test the combined effects of plastic fragments (of varying size and concentration), water availability and soil texture, on soil water content and performance of the plant <em>Arabidopsis thaliana</em>. Plastic fragments had stronger negative effects on soil water content in low water availability, and the shape of this response (linear <em>vs.</em> unimodal) was mediated by soil texture. Conversely, increasing concentration of plastic had positive effects on plant growth. We suggest that plastic fragments introduce fracture points within soil aggregates. This increases number and size of soil pores favoring water loss but also facilitating root growth.  Our results suggest complex interactive effects of plastic and drought, that may lead to a decoupling of plant and soil response. These processes should be taken into account in ecological studies and agricultural practices.</p>

opencc-by-4.0Oct 2024View details →

The impact of beech deadwood on soil properties and microbial diversity

<p><span><span>Our research is an attempt to determine the role of decaying wood in shaping the properties of forest soils in mountain ecosystems.</span></span><span><span> </span></span></p>

opencc-by-4.0Dec 2022View details →

QST - open data of the article Vanwindekens & Hardy (2022) - table 1 soil properties

<p>Soil properties of the long term fields trials linked to the paper "The QuantiSlakeTest, dynamic weighting of soil under water to measure soil structural stability" submitted to the SOIL journal by Vanwindekens & Hardy (2022).</p>

openCC (other)Apr 2021View details →

Soil moisture and soil properties in Watershed 1 of the HJ Andrews Experimental Forest, 2016–2020

Soil water content was measured at 0–30 cm and 0–60 cm depth at 54 sites within a 10-ha north-facing forested slope of Watershed 1 on 14 dates from August 2016–October 2017. Soil properties were measured at 13 soil moisture sites during the summer of 2017. Soil properties include bulk density, percent sand, percent clay, percent silt, gravimetric rock content, volumetric water content at field capacity, volumetric water content at turgor loss point, and saturated hydraulic conductivity at 15 and 45 cm depth. Total soil depth and resistance to penetration were also measured at 38 sites using a dynamic cone penetrometer. Volumetric water content (VWC) and soil water potential were recorded every 30 min at 9 locations in Watershed 1. VWC sensors were installed at 5, 50, and 100 cm and soil water potential were installed at 50 cm at each location.

openCC (other)Jan 2020View details →

Soil hydraulic and thermal properties determined in surface organic and mineral soils in the region near Toolik Lake on the North Slope of Alaska, 2016-2019

Soil cores of 5 cm diameter down to frozen soil were taken from a subset of sample sites for laboratory analysis. Determinations of hydraulic conductivity, thermal conductivity, porosity, and bulk density were made for each core. For a further subset of sites we developed soil moisture retention curves.

openCC (other)Dec 2023View details →

Baltimore Ecosystem Study: Physical, chemical and biological properties of forest and home lawn soils

Abstract: One-meter soil cores were taken to evaluate soil texture, bulk density, carbon and nitrogen pools, microbial biomass carbon and nitrogen content, microbial respiration, potential net nitrogen mineralization, potential net nitrification and inorganic nitrogen pools in 32 residential home lawns that differed by previous land use and age, but had similar soil types. These were compared to soils from 8 forested reference sites. Purpose: Soil cores were obtained from residential and forest sites in the Baltimore, MD USA metropolitan area. The residential sites were mostly within the Gwynns Falls Watershed (-76.012008W, -77.314183E, 39.724847N, 38.708367S and approximately 17 km2) Lawns on residential sites were dominated by a variety of cool season turfgrasses. Forest soil cores were taken from permanent forest plots of the Baltimore Ecosystem Study (BES) LTER (Groffman et al. 2006). These remnant forests are over 100 years old with soils that were comparable in type and texture to those underlying the residential study sites. Soils from all sites were from the Manor series (coarse-loamy, micaceous, mesic Typic Dystrudepts), which are well-drained upland soils with loamy textures and bedrock at 5 to 10 feet below the soil surface. To aid the site selection process we used neighborhoods in the Baltimore City metropolitan area that have been mapped using HERCULES, a high resolution land cover classification system designed to assist in the study of human-ecological systems (Cadenasso et al. 2007). Using HERCULES and additional data sources, we identified residential sites that were similar except for single factors that we hypothesized to be important predictors of ecosystem dynamics. These factors included land use history (agriculture and forest, n = 10 and n = 22), housing density (low and medium/high, n = 9 and n = 23), and housing age (4 to 58 yrs old, n = 32). Housing age was acquired from the Maryland Property View database. Prior land use was determined b