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473 results for “Soil temperatures”
Soil temperature data for Saddle snowfence, 1992 - 1995.
A snowfence was built in 1993 on the Niwot Ridge Saddle grid to determine the effects of changes in snowpack on a number of variables, one of which was soil temperature. The study area was 60m x 125m. Soil temperatures were measured at two depths (0 and 15 cm) at each of 13 locations within the snowfence experiment area. Soil temperatures were measured at the same depths at each of 6 control locations outside of but near the snowfence experiment area. These measurements were made weekly to biweekly using fixed thermistors and data loggers. Sampling locations were each given a unique point identification number in order that these data could be incorporated into the Saddle GIS. The snowfence was oriented in a north/south direction and was 60m long. Each of the point identification numbers had a coordinate within the experiment area. The first number of the coordinate was the distance in m from the snowfence in an east/west direction, negative numbers being west of the snowfence and positive numbers being east of the fence. The second number in the coordinate was the distance in m from the southern terminus of the snowfence in a northerly direction. The point identification numbers and coordinates were: 203 (-40,30), 217 (-30,30), 231 (-20,30), 245 (-10,30), 252 (-5,30), 259 (5,30), 266 (10,30), 281 (20,30), 295 (30,30), 309 (40,30), 323 (50,30), 337 (60,30). Controls were located outside of the snowfence experiment area: 113 (approximately 10 m south of the -30,0 coordinate) in the Acomastylidetum rossii vegetation association, 114 (approximately 11 m south of the -25,0 coordinate) in the Acomastylidetum rossii vegetation association, 115 (approximately 8 m south of the 55,0 coordinate) in the Kobresietum myosuroidis vegetation association, 116 (approximately 5 m north of the 50,60 coordinate) in the Kobresietum myosuroidis vegetation association, 117 (approximately 3 m north of the -23,60 coordinate) in the Rhodiolo integrifoliae vegetation association, 118 (approxi
Warming-El Nino-Nitrogen Deposition Experiment (WENNDEx): Soil Temperature Data from the Sevilleta National Wildlife Refuge, New Mexico (1/2006 - 7/2009)
This data set provides soil temperature data in each plot of the warming experiment (see SEV176). Data are collected with automated soil temperature probes at 15-minute intervals at two soil depths under grass and bare patches in each of the 40 plots.
Warming-El Nino-Nitrogen Deposition Experiment (WENNDEx): Soil Temperature, Moisture, and Carbon Dioxide Data from the Sevilleta National Wildlife Refuge, New Mexico
Humans are creating significant global environmental change, including shifts in climate, increased nitrogen (N) deposition, and the facilitation of species invasions. A multi-factorial field experiment is being performed in an arid grassland within the Sevilleta National Wildlife Refuge (NWR) to simulate increased nighttime temperature, higher N deposition, and heightened El Nino frequency (which increases winter precipitation by an average of 50%). The purpose of the experiment is to better understand the potential effects of environmental drivers on grassland community composition, aboveground net primary production and soil respiration. The focus is on the response of two dominant grasses (Bouteloua gracilis and B eriopoda), in an ecotone near their range margins and thus these species may be particularly susceptible to global environmental change. It is hypothesized that warmer summer temperatures and increased evaporation will favor growth of black grama (Bouteloua eriopoda), a desert grass, but that increased winter precipitation and/or available nitrogen will favor the growth of blue grama (Bouteloua gracilis), a shortgrass prairie species. Treatment effects on limiting resources (soil moisture, nitrogen availability, species abundance, and net primary production (NPP) are all being measured to determine the interactive effects of key global change drivers on arid grassland plant community dynamics and ecosystem processes. This dataset shows values of soil moisture, soil temperature, and the CO2 flux of the amount of CO2 that has moved from soil to air. On 4 August 2009 lightning ignited a ~3300 ha wildfire that burned through the experiment and its surroundings. Because desert grassland fires are patchy, not all of the replicate plots burned in the wildfire. Therefore, seven days after the wildfire was extinguished, the Sevilleta NWR Fire Crew thoroughly burned the remaining plots allowing us to assess experimentally the effects of interactions among multip
Soil study results at Vallon de Nant : Soil moisture and soil temperature time series and granulometry results
<p>Dataset:</p><ul><li><a href="https://zenodo.org/api/records/10136586/draft/files/Particule_size_distribution.csv/content">Particule_size_distribution.csv : </a><br>Particle size distribution obtained for 34 samples in Vallon de Nant. Please refer to the pdf report for technical information.<br>Columns description : <br>point,depth= identification of the point. Please refer to the pdf notice<br>size (in micrometers) : Particle size ranging from 0.003mum to 2 mm<br>value (in %) : Fraction of the material volume corresponding to the size<br>USDAclass : Soil class according to the USDA classification for each sample<br> </li><li><a href="https://zenodo.org/api/records/10136586/draft/files/measure_T_HU_5TM_3stations.csv/content">measure_T_HU_5TM_3stations.csv :</a><br>Hourly soil moisture and soil temperature measurements at 3 points and different depths in the catchment. Please refer to the pdf report for technical information.<br>Columns description : <br>Time,Hour : recording time stamp<br>portX_HU : Soil moisture recorded at the X slot. Please refer to the notice for sensor depth.<br>portX_T : Soil temperature recorded at the X slot.<br>Station : Name of the measurement point (Auberge, Chalet or LaChaux). Auberge and LaChaux are at the exact same location than the corresponding weather stations. Chalet point is on the left bank on the river, near little bridge. Please refer to the pdf notice.</li></ul>
NOAA PSL Soil Moisture and Surface Temperature Probe Data for SPLASH
<p>This dataset contains measurements from a hand-held FieldScout TDR Soil Moisture Meter within the 0-10 cm soil depth of: Time (UTC), GPS locations, Electrical Conductivity (EC), compensated percent volumetric water content (VWC), soil surface temperature (T), and rod length (inches) obtained during the Study of Precipitation, the Lower Atmosphere, and Surface for Hydrology (SPLASH) campaign sponsored by the National Oceanic and Atmospheric Administration (NOAA). These data were collected around the SPLASH campaign areas near Avery Picnic (38.972425 degrees N,106.996855 degrees W) and Kettle Ponds (38.942005 degrees N,106.973006 degrees W) in the East River Watershed in Colorado from between June 1st, 2022 and September 18th, 2023, under support from the NOAA Physical Sciences Laboratory and NOAA Weather Program Office under award NA21OAR4590363.</p><p>Two file formats are provided: one version is text csv format and the second version is in NetCDF.</p><p><strong>Volumetric water content calculations: </strong></p><p>Data were calibrated and adjusted, with a soil-specific sample set, to improve accuracy and compensate for the meter's default "standard" soil type used in the sampling. VWC data was correlated by measuring the weight of a known volume of soil from a range of saturation values. Samples were measured and weighed, dried at 105 degrees C for 48 hours, then weighed again. Calculations of VWC (VWC<strong> </strong>= 100*(Mwet - Mdry)/(w*Vtot) )were plotted against TDR readings. Where: </p><p>Mwet, Mdry = mass (g) of wet and dry soil respectively </p><p>Vtot = total soil volume (ml) </p><p>w = density of water (1g/ml) </p><p>A regression analysis to correlate TDR readings to the samples is below and was applied to the dataset.</p><p>vwc_calculated = vwc_probe * slope + intercept</p><p>slope = 1.20665, intercept = 0.0837017 m3/m3, slope_std_error = 0.09229, intercept_std_error = 0.0217403 m3/m3</p><p><strong>Definitions:</strong></p><p>TDR (Time Domain Reflectometry): A technique for measuring soil moisture content that uses the fact that water has a much higher dielectric permittivity than air, soil minerals, and organic matter. </p><p>VWC (Volumetric Water Content): The ratio of the volume of water in a given volume of soil to the total soil volume expressed as a decimal or a percentage. The percent of the soil volume that is filled with water. At saturation, the VWC will equal the soil porosity (Saturation is typically around 50%).</p><p>EC (Electrical Conductivity): A measure of how well the soil solution conducts electricity. The EC is influenced by the amount of salt and water in the soil. </p><p>The VWC measured by TDR is an average over the length of the waveguide. </p><p><strong>Soil Characteristics:</strong></p><p>Soil at both Kettle Ponds (KEP1 and KPA) locations and Avery Picnic (AYP) were lab tested for composition as follows:</p><p><strong>Sample ID Depth(in.) Sand(%) Silt(%) Clay(%) Soil Texture</strong></p><p>------------------------------------------------------------------------------------------------------ </p><p>KEP1 2 43 35 22 Loam</p><p>AYP 2 40 35 25 Loam</p><p>KPA 2 35 42 22 Loam</p><p>------------------------------------------------------------------------------------------------------</p>
Data from: Soil incubation methods lead to large differences in inferred methane production temperature sensitivity
<p>Quantifying the temperature sensitivity of methane (CH4) production is crucial for predicting how wetland ecosystems will respond to climate warming. Typically, the temperature sensitivity (often quantified as a Q10 value) is derived from laboratory incubation studies and then used in biogeochemical models. However, studies report wide variation in incubation-inferred Q10 values, with a large portion of this variation remaining unexplained. Here we applied observations in Stordalen Mire, a thawing permafrost peatland, and a well-tested process-rich model, ecosys, to interpret incubation observations and investigate controls on inferred CH4 production temperature sensitivity. We developed a Field-Storage-Incubation (FSI) modeling approach to mimic the full incubation sequence, including field sampling at a particular time in the growing season,refrigerated storage, and the laboratory incubation process, followed by model evaluation. We found that CH4 production rates during incubation are regulated by seasonally-dependent substrate availability and active microbial biomass of key microbial functional groups. Applying a model sensitivity analysis, we found that storage duration, storage temperature, and field sampling time significantly affect CH4 production during incubation. Shorter storage duration and lower storage temperature led to larger CH4 production during incubation. Our findings revealed a wide range of inferred Q10 values (1.2 to 3.5), which we attribute to incubation temperatures, incubation duration, storage duration, and sampling time. Q10 of CH4 production is controlled by many interacting biological, biochemical, and physical processes, which cause the aggregated Q10 values to differ from those of the component processes. Terrestrial ecosystem models that use a constant Q10 value to represent temperature responses may therefore predict biased soil carbon cycling under future climate scenarios.</p> <p>This dataset includes all the data used to plot figures in the manuscript, including Fig.2-6 and Fig.S2-S11. Each sheet in the aggregated spreadsheet corresponds to one figure in the manuscript. The simulation experiment setup and analyses are thoroughly described in the manuscript. Here we provide a brief summary. The data includes field greenhouse gas observations and laboratory incubation measurements of CH4 production in Stordalen Mire. These datasets were already published and references were provided in the manuscript and spreadsheet. The data also includes simulation data, including modeled cumulative CH4 production, CH4 production rates, substrate concentrations, and active microbial biomass under different incubation temperature, sampling time and storage conditions. This data also includes inferred temperature sensitivity of CH4 production as Q10 values under different scenarios. Please refer to the manuscript for more detailed information.</p> <p>Please see "Related works" at the bottom of this page and the "References" tab in the spreadsheet for a full list of source datasets and associated publications.</p> <p> </p> <p>FUNDING:</p> <p>This research is a contribution of the EMERGE Biology Integration Institute, funded by the National Science Foundation, Biology Integration Institutes Program, Award # 2022070.</p> <p>We thank the Swedish Polar Research Secretariat and SITES for the support of the work done at the Abisko Scientific Research Station. SITES is supported by the Swedish Research Council’s grant 4.3-2021-00164. This research used resources of the National Energy Research Scientific Computing Center (NERSC) which is a U.S. Department of Energy Office of Science user facility. This research used the Lawrencium computational cluster resource provided by the IT Division at the Lawrence Berkeley National Laboratory (Supported by the Director, Office of Science, Office of Basic Energy Sciences, of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231). Incubation and field observation data were collected under the IsoGenie Project, which was funded by the Genomic Science Program of the United States Department of Energy Office of Biological and Environmental Research, grant #s DE-SC0004632, DE-SC0010580, and DE-SC0016440.</p>
Data from: High temperatures and low soil moisture synergistically reduce switchgrass yields from marginal field sites and inhibit fermentation
<p>'Marginal lands' are low productivity sites abandoned from agriculture for reasons such as low or high soil water content, challenging topography, or nutrient deficiency. To avoid competition with crop production, cellulosic bioenergy crops have been proposed for cultivation on marginal lands, however on these sites they may be more strongly affected by environmental stresses such as low soil water content. In this study we used rainout shelters to induce low soil moisture on marginal lands and determine the effect of soil water stress on switchgrass growth and the subsequent production of bioethanol. Five marginal land sites that span a latitudinal gradient in Michigan and Wisconsin were planted to switchgrass in 2013 and during the 2018-2021 growing seasons were exposed to reduced precipitation under rainout shelters in comparison to ambient precipitation. The effect of reduced precipitation was related to the environmental conditions at each site and biofuel production metrics (switchgrass biomass yields and composition and ethanol production). During the first year (2018), the rainout shelters were designed with 60% rain exclusion, which did not affect biomass yields compared to ambient conditions at any of the field sites, but decreased switchgrass fermentability at the Wisconsin Central - Hancock site. In subsequent years, the shelters were redesigned to fully exclude rainfall, which led to reduced biomass yields and inhibited fermentation for three sites. When switchgrass was grown in soils with large reductions in moisture and increases in temperature, the potential for biofuel production was significantly reduced, exposing some of the challenges associated with producing biofuels from lignocellulosic biomass grown under drought conditions.</p>
Derived daily timeseries of weather, soil moisture and temperature, flow and nitrogen species (nitrate and nitrite, ammonium) concentrations data for the North Wyke Farm Platform National Biosciences Research Infrastructure, England
<p>For a selection of catchments from the North Wyke Farm Platform in southwest England, where land use conversions have been introduced, daily time series data covering weather conditions (minimum temperature, maximum temperature, total rainfall, wind speed and solar radiation), near-surface soil status (moisture content and temperature), flow and concentrations of key nitrogen species (nitrate and nitrite, ammonium) have been filtered based on attached data quality tags . The datasets run between 2013 and March 2024. For the main climate variables, data gaps were infilled with preceding- and following-on daily data, observations from a nearby weather station or existing national datasets to generate a continuous data series for modelling. For the other data series, annual and seasonal summary statistics on data coverage are provided. Information on significant field events, such as ploughing, drilling and harvest, fertiliser applications and manure spreading were also tabulated.</p>
Global Soil Moisture-Air Temperature Interactions from Linear and Nonlinear Granger Causalities
<p>These datasets were generated to assess linear and nonlinear Granger causalities in the submitted manuscript, Global Soil Moisture-Air Temperature Interactions from Linear and Nonlinear Granger Causalities by Bhatti et al. submitted to AGU-GRL. Nonlinear GC here is achieved with the Kernel Granger causality by Marinazzo et al. (2008). The data was used to develop theoretical experiments that help validate the strengths and limitations of both the linear Granger causality and the Kernel Granger causality before applying to real world datasets</p>
Soil Moisture and Sea Surface Temperature Data for Wikle et al. (2022)
<p>Raw data (.nc) in NetCDF4 format, and formatted and rearranged data (.csv) in CSV format. With R Markdown document detailing the steps taken. All data obtained originally from NOAA's NCEP and NCDC data store systems. </p> <p>Data used in developing and demonstrating explainable AI models for <em>An Overview of Model Agnostic Explainability Methods for Machine Learning Applied to Environmental Data</em>, Wikle et al. (2022), for the <em>Special Issue on Environmental Data Science</em> for <em>Environmetrics. </em>See https://zenodo.org/record/6353636 for the corresponding model codebase. </p>
Unexpected microbial metabolic responses to elevated temperatures and nitrogen addition in subarctic soils under different land-use
<p>This repository contains all necessary raw data as well as the R code used to conduct statistical analysis and create figures of the publication<br> <br><strong>Unexpected microbial metabolic responses to elevated temperatures and nitrogen addition in subarctic soils under different land-use</strong></p><p>Julia Schroeder1, Tino Peplau1, Edward Gregorich2, Christoph C. Tebbe3, Christopher Poeplau1</p><p>1 Thünen Institute of Climate-Smart Agriculture, Bundesallee 68, 38116 Braunschweig, Germany<br>2 Research and Development Centre, Central Experimental Farm, Agriculture and Agri-Food Canada, Ottawa, Canada<br>3 Thünen Institute of Biodiversity, Bundesallee 65, 38116 Braunschweig, Germany</p><p>DOI: https://doi.org/10.1007/s10533-022-00943-7 </p><p>This study investigated how subarctic soils under different land use will respond to warming and increasing N availability to allow for better predictions of C cycling under global change. The short-term temperature sensitivity as well as N-input effects on microbial CUE, respiration, growth and turnover were assessed in a one-day incubation experiment according to the 18O-CUE approach. The warming and N response of SOM decomposition were assessed in a 50-days incubation experiment via measurement of cumulative respiration. Both experiments were conducted with the following three treatments: incubation at 10 °C, incubation at 20 °C, and incubation at 20 °C plus N-fertiliser addition at an amendment rate of 100 kg N ha-1. The response to warming or N addition were expressed as response ratios RRT = 20°C/10°C and RRN = 20°C+N/20°C for warming and N response, respectively.</p><p>The R code was developed under R v3.6.3 and adapted to work under version R v.4.1.2.</p><p>The repository includes the following files:</p><ul><li>general_soil_parameters_per_sample.csv - general soil data for each field sample (n=27)</li><li>general_soil_parameters_per_plot.csv - general soil data assessed on pooled replicated field samples (n=9)</li><li>respiration_over_50d_incubation.csv - respiration rate and cumulative respiration for each time-point and laboratory sample over the 50-days incubation</li><li>sample_data.csv - data measured for each laboratory sample (n=81)</li></ul><p> </p><ul><li>Warming_and_nitrogen_response_of_CUE_in_subarctic_soils.Rproj - Rproject (load project to work on provided scripts and data)</li><li>load_data_script.R - loads required data</li><li>absolute_values_script.R - summary of absolute ranges of parameters per land-use type and site</li><li>absolute_linear_mixed_effects_model_script.R - run statistical analysis</li><li>correlograms_absolute_soil_params_script.R - correlation analysis to identify what drives absolute values</li><li>plot_correlations_absolute_soil_params_script.R - plot drivers of CUE and cumulative respiration</li><li>RRT_RRN_calculation_script.R - calculates response ratios</li><li>plot_RRT_RRN_script.R - plot response ratios</li><li>RRT_RRN_linear_mixed_effects_models_script.R - run statistical analysis</li><li>correlograms_RRT_RRN_soil_param_script.R - correlation analysis to identify drivers of response ratios</li><li>plot_correlations_RRT_RRN_soil_params_script.R - plot drivers of response ratios</li><li>RRT_RRN_resprate_cumulresp_over_time_50d_incubation_script.R - plot response ratios over time course</li></ul>
Universal temperature sensitivity of denitrification nitrogen losses in forest soils
<p><span>Soil nitrous oxide (N<sub>2</sub>O) and dinitrogen (N<sub>2</sub>) emissions from denitrification are crucial to the global nitrogen (N) cycle. </span><span>However, the temperature sensitivities of gaseous N losses in forest soils are poorly understood, limiting our ability to predict N cycling responses to global warming. We quantified temperature sensitivities (Q10) of denitrification-derived potential N<sub>2</sub>O and N2 production ex-situ for 18 forest soils across China.</span><span> N<sub>2</sub>O</span><span> and N<sub>2</sub> production rates increased exponentially with temperature, showing large variation among soils. By contrast, the Q10 values for N<sub>2</sub>O (</span><span>2.1±0.5</span><span>) and N<sub>2 </sub>(</span><span>2.6±0.6</span><span>) were surprisingly similar across soils. N<sub>2</sub> was more sensitive to temperature than N<sub>2</sub>O, suggesting warming could promote complete denitrification. The Q10 values for denitrification (</span><span>2.3±0.5)</span><span> were similar to those reported for aquatic sediments. Collectively, our results indicate a universal temperature sensitivity of gaseous N losses from denitrification, which will facilitate modelling N losses in response to warming on the global scale<a>.</a></span><span> </span></p>
Reconstruction of a soil microbial network induced by stress temperature
<p>By applying a nonlinear time-series analysis to the metagenomic data of the soil microbiota cultured under suitable (30℃) or stressful (37℃) conditions, we show how the microbial interaction network responds to temperature stress. While the genera that persisted only under the suitable condition gave fewer positive effects, the genera that appeared only under the stressful condition received more positive effects in agreement with SGH. However, temperature changes also induce reconstruction of a community network, leading to an increased proportion of negative interactions at the whole community level. The anti-SGH pattern can be explained by the stronger competition caused by increased metabolic rate and population densities. </p>
Dataset of measurements of the soil CO2 flux and soil brightness temperature at Le Biancane (geothermal field of Larderello-Travale, Tuscany, Italy) in the May-June 2021 period.
<p>Dataset of measurements of the soil CO<sub>2</sub> flux and soil brightness temperature at Le Biancane (geothermal field of Larderello-Travale, Tuscany, Italy) in the period May-June 2021. The dataset is structured as follows:</p> <p>Column A is the progressive number of the point (#);</p> <p>Column B is the Longitude of the point, datum WGS 1984;</p> <p>Column C is the Latitude of the point, datum WGS 1984;</p> <p>Column D is the Universal Transverse Mercator (UTM) Longitude coordinate, datum WGS 1984, zone 32N;</p> <p>Column E is the Universal Transverse Mercator (UTM) Latitude coordinate, datum WGS 1984, zone 32N;</p> <p>Column F is the soil brightness temperature, in °C;</p> <p>Column G is the soil CO<sub>2</sub> flux in grams of CO<sub>2</sub> per square meter, per day (g m<sup>-2</sup> day<sup>-1</sup>)</p>
Fine-root biomass production, sedge root, sedge leaf, and moss shoot decomposition, soil water-table level, and temperature data from two sedge fens in Finland
<p>Dataset including fine-root biomass production, mass loss of sedge (<em>Carex rostrata</em>) roots and leaves, and moss (<em>Sphagnum</em> <em>fallax</em>) shoots, along with environmental data (soil water-table level, air temperature, soil temperature at 5 cm, and soil temperature at 15 cm) from two sedge fens located in southern Finland (Lakkasuo, Orivesi, 61°48' N 24°19'E) and northern Finland (Lompolojänkkä, Kittilä, 68°N 24°12'E). Data are from a climate change experiment, where warming was induced with open top chambers (OTCs) and drying with shallow ditching. Data are from years 2011-2013.</p>
Fig. 1 in Infection of Anastrepha ludens (Diptera: Tephritidae) adults during emergence from soil treated with Beauveria bassiana under various texture, humidity, and temperature conditions
Fig. 1. Adult mortality of Anastrepha ludens infected with different concentrations of Beauveria bassiana conidia, afer emerging from treated soil. Different letters indicate significant differences among treatments based on 1-way ANOVA followed by the Tukey Honest Significant Difference test, P <0.05).
Database Manuscript Temperature and moisture are minor drivers of regional-scale soil organic carbon dynamics - Gonzalez Dominguez et al
<p>The database contained the data used in the manuscript <strong>Temperature and moisture are minor drivers of regional-scale soil organic carbon dynamics, by Gonzalez Dominguez et al. </strong></p>
Soil temperature, moisture, and ground heat flux measurements at LPTEG-TREES-1 site, 2019/07/01-2019/09/09
<p>This dataset includes the original measurements of soil temperature, moisture, and surface ground heat flux reconstructed from heat flux plate measurements at the LPTEG-TREES-1 site (N66°53’55’’, E66°45’27’’). Soil temperature (T_soil, °C) was measured at 2 cm below the peat layer surface. Soil liquid water content (theta_liq, m<sup>3</sup>/m<sup>3</sup>) was measured 2 cm below the mineral soil layer surface. Observation for ground heat flux at the soil surface (G_obs, W/m<sup>2</sup>) was reconstructed from the heat flux plate (buried 6 cm below the mineral soil surface) measurement plus the energy storage above the heat flux plate calculated based on soil temperature and soil heat capacity.</p>
Rates of greenhouse gas (carbon dioxide, methane and nitrous oxide) fluxes, denitrification-derived N2O and N2 fluxes and nitrification-derived N2O fluxes from salt marsh soils in Quebec, Canada and Louisiana, U.S. under ambient and elevated temperature and nutrient loading.
<p>Dataset used in <a href="https://link.springer.com/article/10.1007/s10533-023-01104-0?utm_source=rct_congratemailt&utm_medium=email&utm_campaign=oa_20231214&utm_content=10.1007/s10533-023-01104-0#citeas">Elevated temperature and nutrients lead to increased N<sub>2</sub>O emissions from salt marsh soils from cold and warm climates</a>.</p> <p>The dataset contains fluxes calculated from headspace gas samples taken over a 24 hour period from intact soil cores, as well as corresponding environmental data. Intact soil cores (0-15 cm depth, 2.5 cm diameter) were taken at five sampling locations along a 20 m transect using a soil auger or piston corer. Samples were collected along a transect in four marsh sites in Quebec, Canada (La Pocatière: 47°22'24.7"N 70°03'26.3"W) and Louisiana, U.S. (Barataria Basin: 29°33'47.3"N 90°04'22.8"W and 29°29'52.2"N 89°55'00.2"W) from two vegetation types (<em>Sporobolus alterniflorus</em> formerly known as <em>Spartina alterniflora </em>and<em> Sporobolus pumilus</em> formerly known as<em> Spartina patens</em>). In Quebec, the two vegetation zones were in the same marsh whereas in Louisiana two separate marshes, dominated by the relevant vegetation, were chosen. Soil samples were collected on the 20-21<sup>st</sup> July 2021 from Louisiana and the 9-10<sup>th</sup> August 2021 from Quebec. Environmental data was collected including <em>in-situ</em> soil temperature and salinity, and gravimetric soil moisture, extractable soil dissolved organic carbon (DOC), extractable soil total dissolved nitrogen (TDN), extractable soil nitrate, extractable soil ammonium, extractable soil soluble reactive phosphate, soil total carbon, soil total nitrogen, soil carbon to nitrogen ratio, soil d<sup>13</sup>C and soil d<sup>15</sup>N determined from additional 0-15 cm core samples. This project has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under Grant Agreement no. 838296, a NSERC Discovery Grant and a Natural Environment Research Council grant number (NE/T012323/1).</p> <p>Stable <sup>15</sup>N tracers were added to the intact soil cores so that at each location, at each treatment level (ambient and elevated, described below), there was one core receiving no tracer for greenhouse gas fluxes, one core receiving <sup>15</sup>N-NO<sub>3</sub><sup>‑ </sup>for denitrification rates and one core receiving <sup>15</sup>N-NH<sub>4</sub><sup>+</sup> for nitrification rates. The cores were incubated at ambient temperature (16 ℃ and 28.1 ℃ for Quebec and Louisiana, respectively) and nutrient concentrations (3.2 NO<sub>3</sub><sup>-</sup>, 2.0 NH<sub>4</sub><sup>+</sup>; 2.9 NO<sub>3</sub><sup>-</sup>, 2.5 NH<sub>4</sub><sup>+</sup>; 0.5 NO<sub>3</sub><sup>-</sup>, 7.3 NH<sub>4</sub><sup>+ </sup>and 5.7 NO<sub>3</sub><sup>-</sup>, 2.8 NH<sub>4</sub><sup>+</sup> mg g wet soil<sup>-1</sup> for Quebec <em>S. alterniflorus</em>, Quebec <em>S. pumilus</em>, Louisiana <em>S. alterniflorus</em> and Louisiana <em>S. pumilus</em>, respectively), and elevated temperature (ambient temperature +5 ℃) and nutrient concentration (double ambient concentration). Gas samples were collected from the headspace of 0-15 cm intact cores in a 20 cm high PVC pipe, capped at the top and bottom to create a 5 cm headspace. Gas samples were analysed for greenhouse gases (GHGs: N<sub>2</sub>O, CH<sub>4</sub>, CO<sub>2</sub>) and <sup>15</sup>N in denitrification-derived N<sub>2</sub>O, denitrification-derived N<sub>2</sub> and nitrification-derived N­<sub>2</sub>O.</p> <p>Soil temperature (YSI 30, Baton Rouge, USA or DeltaTrak 11050, Pleasanton, USA) and porewater salinity (YSI 30, Baton Rouge, USA or portable ATC refractometer) were measured in-situ or in the laboratory using the portable refactometer. Additional soil samples were used for multiple analyses; one subsample was extracted with ultrapure water (18.2 MΩ) for DOC and TDN analysis, one subsample was extracted with 2M KCl for NO<sub>3</sub><sup>-</sup> and NH<sub>4</sub><sup>+</sup>, one subsample was extracted with Olsen-P solution (0.5 M NaHCO<sub>3</sub>, pH 8.5), for soluble reactive phosphate analysis and one subsample was weighed and dried for soil moisture and then finely ground and analysed for total carbon, total nitrogen, d<sup>13</sup>C and d<sup>15</sup>N.</p> <p>N<sub>2</sub>O, CH<sub>4</sub> and CO<sub>2</sub> concentrations were measured in the gas samples using a gas chromatograph interfaced with a PAL3 autosampler (Agilent 7890A, Agilent Technologies Ltd, USA) fitted with a flame ionisation detector (FID) for CH<sub>4</sub> analysis and a micro electron capture detector (mECD) for N<sub>2</sub>O analysis. CO<sub>2</sub> was methanised to CH<sub>4</sub> before analysis on the FID. The instrument precision as the relative standard deviation was < 5 % for all of the gases, while the minimum detectable concentration difference (MDCD) was 9 ppb N<sub>2</sub>O, 72 ppb CH<sub>4 </sub>and 31 ppm CO<sub>2</sub>. Potential GHG fluxes were calculated from the linear portion or where the highest production was observed in the concentration-time series ( https://doi.org/10.2134/jeq2003.2436). If fluxes were below the MDCD they were set to zero see (https://doi.org/10.1002/2017JG003783). The <sup>15</sup>N content of the N<sub>2</sub> and N<sub>2</sub>O was determined using a continuous flow isotope ratio mass spectrometer (Elementar Isoprime PrecisION; Elementar Analysensysteme GmbH, Hanau, Germany) coupled with a trace-gas pre-concentrator inlet with autosampler (isoFLOW GHG; Elementar Analysensysteme GmbH, Hanau, Germany), with a standard deviation of d<sup>15</sup>N < 0.05 %. Extractable dissolved organic carbon and total dissolved nitrogen were analysed in soil extractant (ultrapure water 18.2 MΩ, 7:1 of extractant to soil) on a TOC/TDN analyser (TOC VCSn + TMN-1, Shimadzu, Kyoto, Japan), with 50 mg C l<sup>-1</sup> and 10 mg l<sup>-1</sup> standards resulting in accuracy and precision of 0.3 and ±0.3 mg C l<sup>-1</sup>, and 0.5 and ±0.3 mg N l<sup>-1</sup>, respectively. Extractable nitrate+nitrite (assumed to be nitrate) and ammonium were analysed in soil extractant (2M KCl, 5:1 of extractant to soil) using a microplate reader and methods in Sims et al., 1995 (<a href="https://doi.org/10.1080/00103629509369298">https://doi.org/10.1080/00103629509369298</a>) with a limit of detection of 0.1 ppm and accuracy of ±5 %. Extractable phosphate was analysed in soil extractant (Olsen-P solution 0.5M NaHCO­<sub>3</sub>, pH 8.5, 10:1 of extractant to dry soil) using a microplate reader and methods in Jeannotte et al., 2004 (https://doi.org/10.1007/s00374-004-0760-4) with a limit of detection of 1 mg P l<sup>-1</sup> and accuracy of ±6 %. Soil total carbon, total nitrogen, d<sup>13</sup>C and d<sup>15</sup>N analysis was performed using a continuous flow isotope ratio mass spectrometer (Elementar Isoprime PrecisION; Elementar Analysensysteme GmbH, Hanau, Germany) coupled with an elemental analyser (EA) inlet (vario PYRO cube; Elementar Analysensysteme GmbH, Hanau, Germany). The precision was < 5 % for both C and N and the precision as a standard deviation was < 0.06 % for both d<sup>13</sup>C and d<sup>15</sup>N. Results from the experiments were entered into an Excel spreadsheet for ingestion into the Zenodo data repository.</p>
Soil temperature measurements at Gobabeb in December 2017
<p>Dataset containing the measurements at 1 min interval of the soil temperature at different depths (5,10,20,30,50,100 cm) in the Gobabeb site in Namibia (-23.56N 15.04E).</p> <p> </p>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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OpenNeuro
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