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106 results for “organic nitrogen”
Soil nitrogen and carbon from organic and mineral soil of 32 mature black spruce sites across interior Alaska (Sampled 2001)
Soil nitrogen and carbon was collected at 33 sites as part of a bigger study looking at the structure and function of black spruce stands in interior Alaska. These variables can be compared to any of the environmental site descriptions, GPS coordinates, soil characteristics, physical site characteristics, stand and structural characteristics, active layer, collected in the summers of 2000, 2001 for these sites
McMurdo Dry Valleys Lake Bonney Autonomous Lake Profiler and Samplers (ALPS): Particulate Organic Carbon and Nitrogen Concentrations
Knowledge of the McMurdo Dry Valley (MDV) lakes is limited by winter access, a period which is most relevant in understanding the habitability of other icy worlds and critical to understanding the overall function of these lakes. Owing to the lack of winter access, data that normally require human presence are incomplete. Our goal was to conduct the first year-round investigation of the biogeophysics of these unique lakes. An important part of the McMurdo Long Term Ecological Research (LTER) is evaluating carbon and nitrogen budgets in perennial ice-covered lakes. This data set addresses this core area of research and quantifies the particulate carbon and nitrogen found at specific depths in McMurdo Dry Valley lakes.
Data from: Leaching losses of dissolved organic carbon and nitrogen from agricultural soils in the upper US Midwest
<p>Leaching losses of dissolved organic carbon (DOC) and nitrogen (DON) from agricultural systems are important to water quality and carbon and nutrient balances but are rarely reported; the few available studies suggest linkages to litter production (DOC) and nitrogen fertilization (DON). In this study we examine the leaching of DOC, DON, NO<sub>3</sub><sup>-</sup>, and NH<sub>4</sub><sup>+</sup> from no-till corn (maize) and perennial bioenergy crops (switchgrass, miscanthus, native grasses, restored prairie, and poplar) grown between 2009 and 2016 in a replicated field experiment in the upper Midwest U.S. Leaching was estimated from concentrations in soil water and modeled drainage (percolation) rates. DOC leaching rates (kg ha<sup>-1 </sup>yr<sup>-1</sup>) and volume-weighted mean concentrations (mg L<sup>-1</sup>) among cropping systems averaged 15.4 and 4.6, respectively; N fertilization had no effect and poplar lost the most DOC (21.8 and 6.9, respectively). DON leaching rates (kg ha<sup>-1 </sup>yr<sup>-1</sup>) and volume-weighted mean concentrations (mg L<sup>-1</sup>) under corn (the most heavily N-fertilized crop) averaged 4.5 and 1.0, respectively, which was higher than perennial grasses (mean: 1.5 and 0.5, respectively) and poplar (1.6 and 0.5, respectively). NO<sub>3</sub><sup>-</sup> comprised the majority of total N leaching in all systems (59-92%). Average NO<sub>3</sub><sup>-</sup> leaching (kg N ha<sup>-1</sup> yr<sup>-1</sup>) under corn (35.3) was higher than perennial grasses (5.9) and poplar (7.2). NH<sub>4</sub><sup>+</sup> concentrations in soil water from all cropping systems were relatively low (<0.07 mg N L<sup>-1</sup>). Perennial crops leached more NO<sub>3</sub><sup>-</sup> in the first few years after planting, and markedly less after. Among the fertilized crops, the leached N represented 14-38% of the added N over the study period; poplar lost the greatest proportion (38%) and corn was intermediate (23%). Requiring only one third or less of the N fertilization compared to corn, perennial bioenergy crops can substantially reduce N leaching and consequent movement into aquifers and surface waters.</p>
iSDAsoil: soil total organic Nitrogen for Africa predicted at 30 m resolution at 0-20 and 20-50 cm depths
<p>iSDAsoil dataset soil total organic Nitrogen (N) log-transformed 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>, 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_log.n_tot_mehlich3_m_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil total N mean value,</li> <li>sol_log.n_tot_mehlich3_md_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil total N model (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: log.n_tot_ncs R-square: 0.732 Fitted values sd: 0.326 RMSE: 0.197 Random forest model: Call: stats::lm(formula = f, data = d) Residuals: Min 1Q Median 3Q Max -1.87298 -0.09584 -0.00985 0.07613 3.14728 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.267429 0.493235 0.542 0.588 regr.ranger 1.128208 0.005766 195.669 < 2e-16 *** regr.xgboost -0.048780 0.006108 -7.987 1.4e-15 *** regr.cubist 0.143954 0.004424 32.539 < 2e-16 *** regr.nnet -0.482261 0.797938 -0.604 0.546 regr.cvglmnet -0.170889 0.004955 -34.489 < 2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.1972 on 99249 degrees of freedom Multiple R-squared: 0.7319, Adjusted R-squared: 0.7319 F-statistic: 5.419e+04 on 5 and 99249 DF, p-value: < 2.2e-16</code></pre> <p>To back-transform values (y) to g/kg use the following formula:</p> <pre><code>g/kg = expm1( y / 100 )</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>
Accompanying Data - Nitrogenous Compound Utilization and Production of Volatile Organic Compounds among Commercial Wine Yeasts Highlight Strain-Specific Metabolic Diversity
<p>This repository contains all the data and some of the performed statistical analysis from all the measurements in the following paper: Scott Jr WT, Van Mastrigt O, Block DE, Notebaart RA, Smid EJ. Nitrogenous compound utilization and production of volatile organic compounds among commercial wine yeasts highlight strain-specific metabolic diversity. Microbiology spectrum. 2021 Aug 31;9(1):10-128.</p><p>Please cite: Scott Jr WT, Van Mastrigt O, Block DE, Notebaart RA, Smid EJ. Nitrogenous compound utilization and production of volatile organic compounds among commercial wine yeasts highlight strain-specific metabolic diversity. Microbiology spectrum. 2021 Aug 31;9(1):10-128.</p><p>Contact: Dr. William T. Scott (william.scott@wur.nl) or Dr. Eddy J. Smid (eddy.smid@wur.nl) for more information</p>
Engineering Machine Learning features to predict adsorption of carbon dioxide and nitrogen in metal-organic frameworks
<p>This repository contains CIF files for metal-organic frameworks and Grand canonical Monte Carlo (GCMC) simulation results for the article <em>Engineering Machine Learning features to predict adsorption of carbon dioxide and nitrogen in metal-organic frameworks</em> by Zijun Deng and Lev Sarkisov.</p>
Decay by ectomycorrhizal fungi couples soil organic matter to nitrogen availability
<p>Interactions between soil nitrogen (N) availability, fungal community composition, and soil organic matter (SOM) regulate soil carbon (C) dynamics in many forest ecosystems, but context dependency in these relationships has precluded general predictive theory. We found that ectomycorrhizal (ECM) fungi with peroxidases decreased with increasing inorganic N availability across a natural inorganic N gradient in northern temperate forests, whereas ligninolytic fungal saprotrophs exhibited no response. Lignin-derived SOM and soil C were negatively correlated with ECM fungi with peroxidases and were positively correlated with inorganic N availability, suggesting decay of lignin-derived SOM by these ECM fungi reduced soil C storage. The correlations we observed link SOM decay in temperate forests to tradeoffs in tree N nutrition and ECM composition, and we propose SOM varies along a single continuum across temperate and boreal ecosystems depending upon how tree allocation to functionally distinct ECM taxa and environmental stress covary with soil N availability.</p>
Dataset: Substantial organic and particulate nitrogen and phosphorus export from geomorphologically stable African tropical forest landscapes
<p>Raw chemical and stream data from the publication 'Substantial organic and particulate nitrogen and phosphorus export from geomophologically stable African tropical forest landscapes'. </p> <p>Data shows dissolved and particulate nitrogen and phosphorus concentrations of stream waters of two forested first order streams withing the Congo Basin. </p>
More soil organic carbon is sequestered through the mycelium-pathway than through the root-pathway under nitrogen enrichment in an alpine forest
<p><span>Plant roots and associated mycorrhizae exert a large influence on soil carbon (C) cycling. Yet, little was known whether and how roots and </span><span>ectomycorrhizal</span><span> extraradical mycelia differentially contribute to soil organic C (SOC) accumulation in alpine forests under increasing nitrogen (N) deposition. Using ingrowth cores, the relative contributions of the root-pathway (RP) (i.e., roots and rhizosphere processes) and mycelium-pathway (MP) (i.e., extraradical mycelia and hyphosphere processes) to SOC accumulation were distinguished and quantified in an ectomycorrhizal-dominated forest receiving chronic N addition (25 kg N ha<sup>-1</sup> yr<sup>-1</sup>). Under the non-N addition, the RP facilitated SOC accumulation, while the MP reduced SOC accumulation. Nitrogen addition enhanced the positive effect of RP on SOC accumulation from +18.02 mg C g<sup>-1</sup> to +20.55 mg C g<sup>-1</sup> but counteracted the negative effect of MP on SOC accumulation from -5.62 mg C g<sup>-1</sup> to -0.57 mg C g<sup>-1</sup>, as compared to the non-N addition. Compared to the non-N addition, the N-induced SOC accumulation was 1.62~2.21 mg C g<sup>-1</sup> and 3.23~4.74 mg C g<sup>-1</sup>, in the RP and the MP, respectively. The greater contribution of MP to SOC accumulation was mainly attributed to the higher microbial C pump (MCP) efficacy (the proportion of</span><span> increased microbial residual C to the increased SOC under N addition) in the MP (72.5%) relative to the RP (57%). The higher MCP efficacy in the MP was mainly associated with the higher fungal metabolic activity (i.e., the greater fungal biomass and N-acetyl glucosidase activity) and greater binding efficiency of fungal residual C to mineral surfaces than those of RP. Collectively, our findings highlight the indispensable role of mycelia and hyphosphere processes in the formation and accumulation of stable SOC in the context of increasing N deposition.</span></p>
Global Ocean particulate organic phosphorus, carbon, oxygen for respiration, and nitrogen (GO-POPCORN) data from Bio-GO-SHIP cruises
<p>Here, we present the Global Ocean Particulate Organic Phosphorus, Carbon, Oxygen for Respiration, and Nitrogen (GO-POPCORN) dataset with data from the recent Bio-GO-SHIP cruises between 2011 and 2020 supplemented with data from Arctic IERP cruises. The dataset contains 2581 paired measurements of particulate organic carbon, nitrogen, and phosphorus from 70°S to 73°N across all major ocean basins. The dataset also includes 965 measurements of <span>particulate chemical oxygen demand</span>. This new dataset is valuable for improving our understanding of how biological elemental stoichiometry plays a role in regulating both the marine nutrient cycles and the global carbon cycle.</p>
Laboratory Assessment of the Impact of Chemical Oxidation, Mineral Dissolution, and Heating on the Nitrogen Isotopic Composition of Fossil-bound Organic Matter
<p>Results of laboratory experiments reported in the manuscript "<em>Laboratory Assessment of the Impact of Chemical Oxidation, Mineral Dissolution, and Heating on the Nitrogen Isotopic Composition of Fossil-bound Organic Matter</em>", published in the journal <em>Geochemistry, Geophysics, Geosystems</em></p>
Seasonal dynamics of sinking organic matter in the Pacific Arctic Ocean revealed by nitrogen isotope ratios of amino acids
<p><span>The Pacific Arctic Ocean has experienced a rapidly changing climate, sea-ice retreat, and enhanced primary production over the past few decades. The export production generated by photoautotrophs and heterotrophs has been characterized in the Arctic Ocean, but their seasonal variations in relative proportion are largely unknown due to the limited access in the ice-covered season. We measured the concentration and nitrogen isotope ratio of individual amino acids from sinking particles in the northern east Siberian Sea (KAMS1), northern Chukchi Sea (KAMS2), and Northwind Ridge (KAMS4) from August 2017 to July 2019. </span><span>The average trophic position, based on differences in the nitrogen isotope ratios of glutamic acid and phenylalanine, can indicate the relative proportions of biogenic organic matters derived from photoautotrophs and heterotrophs in sinking particles. Decreasing values (close to 1.0) in summer at KAMS2 in 2018 suggest that primary producers are responsible for most of the downward flux of sinking particles. However, the average trophic position at KAMS1 in 2017 increased to > 1.5 in autumn and was maintained at approximately 1.7 during ice-covered winter periods, likely due to greater contributions from heterotrophic organisms. Exceptionally high average trophic positions (close to 2.0) of sinking particles in summer at KAMS1 in 2017 and KAMS4 in 2018 were likely due to small export of photoautotrophs due to the surface seawater stratification and limited pelagic production. </span><span>T</span><span>he average trophic position in sinking particles should reflect the spatiotemporal variation in export particle composition in the </span><span>Pacific Arctic Ocean</span><span>.</span></p>
Supplementary Data to: "Organic Phases in Bivalve (Arctica islandica) Shells: Their Bulk and Amino Acid Nitrogen Stable Isotope Compositions" in Geochemistry, Geophysics, Geosystems
<p>This dataset contains the data generated for publication "Organic Phases in Bivalve (<em>Arctica islandica</em>) Shells: Their Bulk and Amino Acid Nitrogen Stable Isotope Compositions", including the following files:</p> <p>Specimen_ID_shell_morphology_measurement: Description of locality of collection, shell ID, shell working ID and the shell morphology measurement which can be referred to Section 2.1 of the manuscript.</p> <p>EA_IRMS_raw_error_propagate: This file contains data showing the m/z 28 signal intensities of all measurements and the error propagation that mentioned in Section 2.3 of the manuscript. It also includes the calculation of analytical accuracy and precision. </p> <p>Statistics: The file contains the summary of statistical analyses performed in this manuscript. Refer to Section 2.7 and 3.3.</p> <p>Recovery: It contains the recovery of the total nitrogen contents or amino acid concentrations after different treatments and this was mentioned in Section 4.1 of the manucript.</p> <p>Mass_balance: The computation procedure that showed in Section 3.4 and discussed in Section 4.3 can be traced in this file.</p> <p>GC-C-IRMS_long_term_sd: The file shows the long-term precision of GC-C-IRMS computed from the mixed standard of amino acids </p> <p>BSIA_d15N: The file contains all the bulk d15N data that used in this manuscript.</p> <p>CSIA_d15N: The file contains all the amino acid d15N data that used in this manuscript.</p> <p>AA_Composition: The file shows AA percent data that used in this manuscript.</p> <p><br> For details see main text of the manuscript.</p> <p><br> </p>
Global Ocean particulate organic phosphorus, carbon, oxygen for respiration, and nitrogen (GO-POPCORN) data from Bio-GO-SHIP cruises
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Data from: Leaching losses of dissolved organic carbon and nitrogen from agricultural soils in the upper US Midwest
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Impact of soil inoculation on crop residue breakdown and carbon and nitrogen cycling in organically and conventionally managed agricultural soils
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Seasonal dynamics of sinking organic matter in the Pacific Arctic Ocean revealed by nitrogen isotope ratios of amino acids
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More soil organic carbon is sequestered through the mycelium-pathway than through the root-pathway under nitrogen enrichment in an alpine forest
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Telemetry validated nitrogen stable isotope clocks identify ocean-to-estuarine habitat shifts in mobile organisms
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Decay by ectomycorrhizal fungi couples soil organic matter to nitrogen availability
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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.
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International Brain Laboratory public data
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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.