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539 results for “organic carbon”
Fig. 1 in Microbial Respiration of Organic Carbon in Freshwater Microcosms: The Potential for Improved Estimation of Microbial CO Emission from Organically Enriched Freshwater Ecosystems
Fig. 1. Densities (ordinate) of ciliates (N × 104 L–1) black bars, and densities of bacteria (N × 109 ml–1) grey bars; for Experiment One and Experiment Two with carbon-enriched (E) and control (C) preparations (abscissa).
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>
Can metal organic frameworks outperform adsorptive removal of harmful phenolic compound 2-chlorophenol by activated carbon?
<p>Dataset supporting publication. High resolution images, and full data set as produced in manuscript figures.</p> <p><strong>Preprint</strong>: <a href="https://doi.org/10.26434/chemrxiv.10320752.v1">https://doi.org/10.26434/chemrxiv.10320752.v1 </a></p> <p><strong>Published article:</strong> <a href="https://doi.org/10.1016/j.cherd.2020.03.017">https://doi.org/10.1016/j.cherd.2020.03.017 </a></p> <p><strong>Abstract:</strong> Removal of persistent organic compounds from aqueous solutions is generally achieved using adsorbent like activated carbon (AC) but it suffers from limited adsorption capacity due to low surface area. This paper describes a pioneering work on the adsorption of an organic pollutant, 2-chlorophenol (2-CP) by two MOFs with high surface area and water stability; MIL-101 and its amino-derivative, MIL-101-NH<sub>2</sub>. Although MOFs have higher surface area than AC, the latter was proven better having the highest equilibrium 2-CP uptake (345 mg.g<sup>-1</sup>), followed by MIL-101 (121 mg.g<sup>-1</sup>) and MIL-101-NH<sub>2</sub> (84 mg.g<sup>-1</sup>). Used MIL-101 could be easily regenerated multiple times by washing with ethanol and even showed improved adsorption capacity after each washing cycle. These results can open the doors to meticulous adsorbent selection for treating 2-CP-contaminated water. </p>
STXM-NEXAFS Analysis of Mineral-Organic Carbon Associations and OC Composition in North Atlantic Sediments
<p>Scanning transmission X-ray microscopy coupled with near edge X-ray absorption spectroscopy (STXM-NEXAFS) data collected on beamline 11.0.2 at the Advanced Light Source (ALS, Lawrence Berkeley National Laboratory) on sediment samples from the North Atlantic gyre collected during piston coring cruise KN223 on the R/V Knorr in 2014. Data include (a) sample geochemistry and lithology (metadata, "STXMNEXAFS_Sample_Geochem.xlsx"), (b) transmission microscopy images with overlaid element masks (organic and inorganic carbon, Al, Fe, Mn, K, and Ca, "mask images.zip") and marked regions ("region images.zip") that carbon NEXAFS spectra were extracted from, (c) NEXAFS spectra extracted from element masks and marked regions ("STXMNEXAFS_mask_reg_spectra_data.xlsx"), and (d) pixel intensity maps of mineral-forming elements used to make element correlation plots ("pixel correlation maps.zip"). The use of the Advanced Light Source is supported by the US Department of Energy, Office of Science, Office of Basic Energy Sciences under contract<br>no. DE-AC02-05CH11231. </p>
Research data of "Distribution, characteristics, and importance of particulate and mineral-associated organic carbon in China forest"
<p>We used the Web of Science (https://www.webofscience.com), Google Scholar (https://scholar. google.com), and China National Knowledge Infrastructure (CNKI, http://www.cnki.net) to compile a list of all peer-reviewed articles that investigated the soil organic carbon components in forest ecosystem of China.</p> <p><span>Apart from the SOC and SOC components, forest types, forest age, sampling depth, climatic properties (MAT and MAP), vegetation (</span>microbial biomass carbon<span>, litter biomass, living fin root biomass, above-ground biomass carbon) and edaphic properties (</span>soil pH, total organic carbon, total nitrogen, total phosphorus, soil type<span>, dissolved organic carbon, bulk density and </span>Silt+Clay%<span>) in sites for each study were extracted from material and method section, tables or supporting information in each study.</span></p> <p>After multiple screening of articles, we totally collected 540 observational data from 59 independent studies, which covers major forest ecosystems of China.</p>
Long-term biochar and soil organic carbon stability– Evidence from field experiments in Germany-ROW DATA
<p> Row data for researcher paper Long-term biochar and soil organic carbon stability– Evidence from field experiments in Germany</p>
(DATASET) (10,0) carbon nanotubes functionalized with carboxyl and hydroxyl organic groups
<p>Starting from a (10,0) carbon nanotube, 10 000 structures where randomly generated for both functionalizations (carboxyl, -COOH, and hydroxyl, -OH) and for 5 concentrations of the surface being funcionalized (5%, 10%, 15%, 20% and 25%). Then, the entropy of all system was calculated. The structures with highest entropy on each group/percentage where selected as representative of each functionalization.</p> <p>Here are the structures of functionalized (10,0) carbon nanotubes in MOL2 and XYZ formats.</p> <p>These systems were used in the following publications:</p> <ul> <li>M.S. Ribeiro, A.L. Pascoini, W.G. Knupp, I. Camps. <em>Effects of surface functionalization on the electronic and structural properties of carbon nanotubes: A computational approach</em>. Applied Surface Science 426 (2017) 781–787. DOI: <a href="http://dx.doi.org/10.1016/j.apsusc.2017.07.162">10.1016/j.apsusc.2017.07.162</a></li> <li>W.G. Knupp, M.S. Ribeiro, M. Mir, I. Camps. <em>Dynamics of hydroxyapatite and carbon nanotubes interaction</em>. Applied Surface Science 495 (2019) 143493. DOI: <a href="https://doi.org/10.1016/j.apsusc.2019.07.235">10.1016/j.apsusc.2019.07.235</a></li> </ul>
Carbon and mineral data of organic matter fractions in Siberian Yedoma permafrost
<p>This file contains carbon and mineral data of organic matter fractions obtained from two permafrost drill cores L14-02 (73.33616° N; 141.32776° E) and L14-05 (73.34994° N; 141.24156° E) from Bol’shoy Lyakhovsky Island in NE Siberia in 2014. The datasets contain mass fractions of different size and density fractions, OC concentrations, OC/N ratios, data on organic matter composition based on <sup>13</sup>C-NMR, radiocarbon (<sup>14</sup>C) data, as well as data on iron (Fe) mineral phases and CO<sub>2</sub> production rates of mineral-associated organic matter. Further, carbon and organic biomarker data (<em>n</em>-alkanes) of the bulk sediment are included. The data were created to study mass partitioning of Pleistocene permafrost OC among different organic matter fractions to assess the bioavailability and stability of the organic matter. Please refer to the publication listed below for more information.</p>
Soil organic carbon models need independent time-series validation for reliable prediction
<p>Supplementary Data 1 to the paper: Soil organic carbon models need independent time-series validation for reliable prediction</p> <p>By: Le Noë, J., Manzoni, S., Abramoff, R.Z., Bölscher, T., Bruni, E., Cardinael, R., Ciais, P., Chenu, C., Clivot, H., Derrien, D., Ferchaud, F., Garnier, P., Goll, D., Lashermes, G., Martin, M.P., Rasse, D., Rees, F., Sainte-Marie, J., Salmon, E., Schiedung, M., Schimel, J., Wieder, W.R., Abiven, S., Barré, P., Cécillon, L., Guenet, B.</p>
Upscaling soil organic carbon measurements at the continental scale using multivariate clustering analysis and machine learning
<p><strong>Data Description</strong>:</p> <p>To improve SOC estimation in the United States, we upscaled site-based SOC measurements to the continental scale using multivariate geographic clustering (MGC) approach coupled with machine learning models. First, we used the MGC approach to segment the United States at 30 arc second resolution based on principal component information from environmental covariates (gNATSGO soil properties, WorldClim bioclimatic variables, MODIS biological variables, and physiographic variables) to 20 SOC regions. We then trained separate random forest model ensembles for each of the SOC regions identified using environmental covariates and soil profile measurements from the International Soil Carbon Network (ISCN) and an Alaska soil profile data. We estimated United States SOC for 0-30 cm and 0-100 cm depths were 52.6 + 3.2 and 108.3 + 8.2 Pg C, respectively.</p> <p>Files in collection (32):</p> <p>Collection contains 22 soil properties geospatial rasters, 4 soil SOC geospatial rasters, 2 ISCN site SOC observations csv files, and 4 R scripts</p> <p>gNATSGO TIF files:</p> <p>├── available_water_storage_30arc_30cm_us.tif [30 cm depth soil available water storage]<br> ├── available_water_storage_30arc_100cm_us.tif [100 cm depth soil available water storage]<br> ├── caco3_30arc_30cm_us.tif [30 cm depth soil CaCO3 content]<br> ├── caco3_30arc_100cm_us.tif [100 cm depth soil CaCO3 content]<br> ├── cec_30arc_30cm_us.tif [30 cm depth soil cation exchange capacity]<br> ├── cec_30arc_100cm_us.tif [100 cm depth soil cation exchange capacity]<br> ├── clay_30arc_30cm_us.tif [30 cm depth soil clay content]<br> ├── clay_30arc_100cm_us.tif [100 cm depth soil clay content]<br> ├── depthWT_30arc_us.tif [depth to water table]<br> ├── kfactor_30arc_30cm_us.tif [30 cm depth soil erosion factor]<br> ├── kfactor_30arc_100cm_us.tif [100 cm depth soil erosion factor]<br> ├── ph_30arc_100cm_us.tif [100 cm depth soil pH]<br> ├── ph_30arc_100cm_us.tif [30 cm depth soil pH]<br> ├── pondingFre_30arc_us.tif [ponding frequency]<br> ├── sand_30arc_30cm_us.tif [30 cm depth soil sand content]<br> ├── sand_30arc_100cm_us.tif [100 cm depth soil sand content]<br> ├── silt_30arc_30cm_us.tif [30 cm depth soil silt content]<br> ├── silt_30arc_100cm_us.tif [100 cm depth soil silt content]<br> ├── water_content_30arc_30cm_us.tif [30 cm depth soil water content]<br> └── water_content_30arc_100cm_us.tif [100 cm depth soil water content]</p> <p>SOC TIF files:</p> <p>├──30cm SOC mean.tif [30 cm depth soil SOC]<br> ├──100cm SOC mean.tif [100 cm depth soil SOC]<br> ├──30cm SOC CV.tif [30 cm depth soil SOC coefficient of variation]<br> └──100cm SOC CV.tif [100 cm depth soil SOC coefficient of variation]</p> <p>site observations csv files:</p> <p>ISCN_rmNRCS_addNCSS_30cm.csv 30cm ISCN sites SOC replaced NRCS sites with NCSS centroid removed data</p> <p>ISCN_rmNRCS_addNCSS_100cm.csv 100cm ISCN sites SOC replaced NRCS sites with NCSS centroid removed data</p> <p><br> <strong>Data format</strong>:</p> <p>Geospatial files are provided in Geotiff format in Lat/Lon WGS84 EPSG: 4326 projection at 30 arc second resolution.</p> <p><strong>Geospatial projection</strong>: </p> <pre><code>GEOGCS["GCS_WGS_1984", DATUM["D_WGS_1984", SPHEROID["WGS_1984",6378137,298.257223563]], PRIMEM["Greenwich",0], UNIT["Degree",0.017453292519943295]] (base) [jbk@theseus ltar_regionalization]$ g.proj -w GEOGCS["wgs84", DATUM["WGS_1984", SPHEROID["WGS_1984",6378137,298.257223563]], PRIMEM["Greenwich",0], UNIT["degree",0.0174532925199433]] </code></pre> <p> </p>
Dataset for the paper submitted for peer-review with the title "Quantifying heterotrophic bacteria parameters and dissolved organic carbon biodegradability through oxygen data assimilation in a river water quality model"
<p>The proposed dataset is related to the following article submitted for peer review:</p> <p>Hasanyar, M., Flipo, N., Romary, T., Wang, S. (2023), Quantifying heterotrophic bacteria parameters and dissolved organic carbon biodegradability through oxygen data assimilation in a river water quality model, UNDER PEER-REVIEW</p> <p>It consists of command files for the prose-pa0.74 software available here: https://gitlab.com/prose-pa/prose-pa </p> <p>To run the model :</p> <p>1. Compile prose-pa0.74</p> <p>2. Copy the executable in the current directory</p> <p>3. In a terminal launch</p> <p>> ./prose-pa0.74 simulation.COMM test.log</p> <p>The “simulation.COMM” holds the settings for the ProSe-PA simulation related to the paper mentioned in the front head of the current file. </p> <p>The information on different parameters of “simulation.COMM” are included in “bathymetrie”, “Cmds”, “Inflows”, “layers”, “meteo”, “o2_obs”, “param_bio”, “Reaches” and “Singularities” folders.</p> <p>The “bathymetrie” folder holds the geometric information of several cross-sections along the river. </p> <p>The Cmds folder holds the “simulation.COMM” file. </p> <p>The “Inflows” folder the information about the boundary condition inflows to the river such as discharge, concentration of organic carbon, etc.</p> <p>The layer folder holds data of the initial conditions of the model (Table 2 in the article).</p> <p>The “meteo” folder holds the meteorological information.</p> <p>The “o2_obs” folder holds the observed oxygen data needed to do data assimilation. </p> <p>The “param_bio” folder holds information on the physiology of bacteria, phytoplankton, and other model species.</p> <p>The “Reaches” folder holds information about river reaches and their manning coefficient. </p> <p>The “param_range” file holds the variation range of model parameters considered in data assimilation together with their perturbation percentage.</p> <p>The output files are written in $HOME/Outputs folder. It is possible to change it directly in simulation.COMM, last entry “Output_folder”.</p>
Potassium fertilization effects on cereal yield and soil organic carbon in agricultural ecosystems at the global scale
<p>This dataset includes the raw data of a global meta-analysis study on the responses of cereal yield and soil organic carbon to potassium fertilization in agricultural ecosystems.</p>
Binned dissolved organic carbon (DOC), dissolved organic nitrogen (DON), and dissolved organic phosphorus (DOP) concentration observations in the ocean
<p>Here we provided binned dissolved organic carbon (DOC), dissolved organic nitrogen (DON), and dissolved organic phosphorus (DOP) concentration observations in the ocean used for manuscript "Global patterns of surface ocean dissolved organic matter stoichiometry " submitted to Global Biogeochemical Cycles.</p> <p>DOC and DON concentrations observations are from a compilation of DOM data obtained from global ocean observations from 1994 to 2021 (Hansell et al., 2021, https://doi.org/10.25921/s4f4-ye35)</p> <p>DOP concentration observations are from the DOPv2021 database (Liang et al., 2022, https://doi.org/10.1038/s41597-022-01873-7)</p> <p>We binned the data into the OCIM2 grid with a resolution of 2˚x2˚ with 24 vertical layers. More info about OCIM2 grid can be found on <a href="https://tdevries.eri.ucsb.edu/models-and-data-products/">https://tdevries.eri.ucsb.edu/models-and-data-products/</a></p>
Export of organic carbon by ocean biological pump from GYRE ocean model
<p>Model data of the biological pump of organic carbon computed from the idealized ocean model GYRE used in: Resplandy, Lévy and McGillicuddy (2019). Effects of eddy-driven subduction on ocean biological carbon pump. Global Biogeochemical Cycles. Readme file describes the data.</p>
Data from: Soil organic carbon stability in forests: distinct effects of tree species identity and traits
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Impacts of an omnivorous ungulate on plant communities and soil organic carbon
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Total data for global pattern of organic carbon pools in forest soil
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Soil organic carbon loss decreases biodiversity but stimulates multitrophic interactions that promote belowground metabolism
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A global dataset of soil particulate organic carbon
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Data from: Responses of subsoil organic carbon to climate warming and cooling is determined by microbial community rather than its molecular composition
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Allen Brain Atlas
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.