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539 results for “organic carbon”
Supplementary materials for "Effects of mesozooplankton growth and reproduction on plankton and organic carbon dynamics in a marine biogeochemical model"
<h2>Overview</h2> <p>This folder contains supplementary materials corresponding to the analysis conducted for "Effects of mesozooplankton growth and reproduction on plankton and organic carbon dynamics in a marine biogeochemical model". The folder is structured into two .zip files. <a href="../api/records/10720907/draft/files/ZENODO_PISCES_MLC.zip/content" target="_blank" rel="noopener noreferrer">ZENODO_PISCES_MLC.zip</a> contains the analysis presented in the paper. BDM-MAREDAT-ZENODO.zip contains the outputs from the Biomass Distribution Models pipeline developped by Nielja Knecht (<a href="../doi/10.5281/zenodo.7888451">10.5281/zenodo.7888451</a>) applied to the MAREDAT mesozooplankton product. </p> <h2>ZENODO_PISCES_MLC Folder Structure</h2> <h3>BDM</h3> <ul> <li><strong>MAREDAT_TUNED_SDM.csv</strong>: This file contains the BDM mesozooplankton biomass monthly climatology from MAREDAT data.</li> </ul> <h3>CODE</h3> <p>This directory contains Jupyter Notebook files (<code>.ipynb</code>) and related Python scripts used for data analysis and visualization. Below is a list of the files:</p> <ul> <li><strong>Code_Fig3_FigA8_FigA17.ipynb</strong>: Jupyter Notebook for generating figures 3, A8, and A17.</li> <li><strong>Code_Fig4.ipynb</strong>: Jupyter Notebook for generating figure 4.</li> <li><strong>Code_Fig5_FigA12_FigA13.ipynb</strong>: Jupyter Notebook for generating figures 5, A12, and A13.</li> <li><strong>Code_Fig6.ipynb</strong>: Jupyter Notebook for generating figure 6.</li> <li><strong>Code_Fig7.ipynb</strong>: Jupyter Notebook for generating figure 7.</li> <li><strong>Code_FigA10.ipynb</strong>: Jupyter Notebook for generating figure A10.</li> <li><strong>Code_FigA11.ipynb</strong>: Jupyter Notebook for generating figure A11.</li> <li><strong>Code_FigA14.ipynb</strong>: Jupyter Notebook for generating figure A14.</li> <li><strong>Code_FigA15.ipynb</strong>: Jupyter Notebook for generating figure A15.</li> <li><strong>Code_FigA16.ipynb</strong>: Jupyter Notebook for generating figure A16.</li> <li><strong>Code_FigA1.ipynb</strong>: Jupyter Notebook for generating figure A1.</li> <li><strong>Code_FigA2.ipynb</strong>: Jupyter Notebook for generating figure A2.</li> <li><strong>Code_FigA6_FigA7.ipynb</strong>: Jupyter Notebook for generating figures A6 and A7.</li> <li><strong>Code_FigA9.ipynb</strong>: Jupyter Notebook for generating figure A9.</li> <li><strong>Code_POC_metrics_not_in_the_paper.ipynb</strong>: Jupyter Notebook containing metrics related to particulate organic carbon (POC) not included in the paper.</li> <li><strong>Code_Table3.ipynb</strong>: Jupyter Notebook for generating table 3.</li> <li><strong>Code_Table4.ipynb</strong>: Jupyter Notebook for generating table 4.</li> <li><strong>Code_Table5.ipynb</strong>: Jupyter Notebook for generating table 5.</li> <li><strong>GlobalEstimatesAbstract.ipynb</strong>: Jupyter Notebook containing global estimates abstract.</li> <li><strong>mlctools</strong>: Python package containing utility functions for the analysis.</li> </ul> <h3>OBS</h3> <p>This directory contains observed data used in the analysis:</p> <ul> <li><strong>BATS_zooplankton.csv</strong>: Zooplankton data from the Bermuda Atlantic Time-series Study (BATS).</li> <li><strong>CHL2.nc</strong>: Chlorophyll data in NetCDF format.</li> <li><strong>climatology_n_0_5.nc</strong>: Climatological data in NetCDF format.</li> <li><strong>HOTS_zooplankton.csv</strong>: Zooplankton data from the Hawaii Ocean Time-series (HOTS).</li> </ul> <h3>OUTPUT</h3> <p>This directory contains output files from PISCES simulations (yearly, monthly and 5-day-average outputs). </p> <ul> <li><strong>0class</strong>: Output files for the '0class' classification corresponding to PISCES-v2.</li> <li><strong>0classregrid</strong>: Regridded output files for the '0class' classification corresponding to PISCES-v2.</li> <li><strong>10classes</strong>: Output files for the '10classes' classification corresponding to PISCES-MOG.</li> <li><strong>10classesregrid</strong>: Regridded output files from PISCES-MOG.</li> <li><strong>2classes</strong>: Output files from PISCES-MOG-2LS.</li> <li><strong>2classesregrid</strong>: Regridded output files from PISCES-MOG-2LS.</li> <li><strong>NOALLOregrid</strong>: Regridded output files from PISCES-MOG-NA.</li> </ul> <h3>PLOT</h3> <p>This directory contains plots generated during the analysis:</p> <h3>TEMP</h3> <p>This directory contains temporary files used during the analysis, including data files and matrices.</p> <h2>BDM-MAREDAT-ZENODO Folder </h2> <p>BDM-MAREDAT-ZENODO.zip contains the outputs from the Biomass Distribution Models pipeline developped by Nielja Knecht (<a href="../doi/10.5281/zenodo.7888451">10.5281/zenodo.7888451</a>) applied to the MAREDAT mesozooplankton product. </p> <p>For any inquiries or data access requests, please contact corentin.clerc -at- usys.ethz.ch</p>
New parameter estimates for exogenous organic materials including biochar for the Rothamsted carbon model
<p><span>This parameter set has been established within the EJP Soil project Carboseq and is fully explained in the corresponding report (Leifeld, J., Hardy, B., Budai, A., Elsgaard, L., Keel, S.G., Levavasseur, F., Liang, Z., Mondini, C., Plaza, C., Rodrigues, L. 2024. Soil organic carbon sequestration potential of agricultural soils in Europe. Final report EJP Soil CarboSeq work package 3 – Biochar and other organic amendments).</span></p>
Fig. 5 in Seasonal Carbon Emissions And Sequestration In Agroecosystems Of Organic Crops In Central Lithuania
Fig. 5. Net ecosystem production (NEP) in organic agroecosystems (mean±SE).
Fig. 4 in Seasonal Carbon Emissions And Sequestration In Agroecosystems Of Organic Crops In Central Lithuania
Fig. 4. Atmospheric CO2 sequestration in crops GPP (mean±SE).
Fig. 3 in Seasonal Carbon Emissions And Sequestration In Agroecosystems Of Organic Crops In Central Lithuania
Fig. 3. LAI and SLA in crops agroecosystems (mean±SE).
Fig. 2 in Seasonal Carbon Emissions And Sequestration In Agroecosystems Of Organic Crops In Central Lithuania
Fig. 2. Plant respiration (Ra) in organic agroecosystems, 2014-2016 (mean±SE).
On-farm study reveals positive relationship between gas transport capacity and organic carbon content in arable soil (Data set)
<p>Data used for "On-farm study reveals positive relationship between gas transport capacity and organic carbon content in arable soil" by Colombi T, Walder F, Büchi L, Sommer M, Liu K, Six J, van der Heijden M, Charles R and Keller T. (2019). SOIL. 5, 91-105, https://doi.org/10.5194/soil-5-91-2019.</p> <p>.txt file "MetaInformation_On-farm study reveals positive relationship between gas transport capacity and organic carbon content in arable soil" contains all necessary meta-information </p>
A Review on Emerging Organic-containing Microporous Material Membranes for Carbon Capture and Separation
<p><strong>Published article:</strong> Prasetya, N., Himma, N. F., Doddy Sutrisna, P., Wenten, I. G. & Ladewig, B. P. <em>Chem. Eng. J.</em> 123575 (2019). <a href="https://doi.org/10.1016/j.cej.2019.123575">https://doi.org/10.1016/j.cej.2019.123575</a></p> <p><strong>Abstract</strong></p> <p>Membrane technology has gained great attention as one of the promising strategies for carbon capture and separation. Intended for such application, membrane fabrication from various materials has been attempted. While gas separation membranes based on dense polymeric materials have been long developed, there is a growing interest to use porous materials as the membrane material. This review then focuses on emerging porous materials to be used for the fabrication of membranes that are designed for CO<sub>2</sub>separation. Criteria for selecting microporous material are first discussed, including physical and chemical properties, and parameters in membrane fabrication. Membranes based on emerging porous materials,such as metal-organic frameworks, porous organic frameworks, and microporous polymers,are then reviewed. Finally, special attention is given to recent advances, challenges, and perspectives in the development of such membranes for carbon capture and separation. </p>
Can metal organic frameworks outperform adsorptive removal of harmful phenolic compound 2-chlorophenol by activated carbon?
<p>A more complete version of this dataset, along with publication details, is available from <a href="https://zenodo.org/record/2586955">https://zenodo.org/record/2586955</a></p>
Data and Code for Analysis of Bottom Trawling Impact on Sedimentary Organic Carbon in the North Sea
<p>Data and Code for Analysis of Bottom Trawling Impact on Sedimentary Organic Carbon in the North Sea</p>
Three-dimensional soil organic carbon density by logarithmic function and coefficient scaling in Yangtze River Delta, China
<h3>Three-dimensional soil organic carbon density (SOCD) dataset with 90-m resolution generated by Lin, S., Zhu, Q., Yin, B., Yang, G., Liao, K., Lai, X., Guo, C., 2025. Generating three-dimensional soil organic carbon density dataset by soil depth function and correction methods in Yangtze River Delta, China. Environmental Modelling & Software, <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.envsoft.2025.106582" target="_blank" rel="noreferrer noopener"><span><span>https://doi.org/10.1016/j.envsoft.2025.106582.</span></span></a></h3> <h3>Here, based on the best performance, the three-dimensional SOCD generated by LF corrected with coefficient scaling method were provided. The accurate SOCD maps with the spatial resolution of 90-m at any specific depth interval can be generated by our method. This dataset includes:</h3> <ul> <li>Spatial distribution map of parameter 1 (p1) of LF (LF_p1.tif)</li> <li>Spatial distribution map of parameter 2 (p2) of LF (LF_p2.tif)</li> <li>The calculation code and fitted functions of scaling coefficient a, k of LF (fitted_fx_scalingcoff.m)</li> <li>Readme.docx</li> </ul> <p>Note: the unit of SOCD is kg m-2; the spatial distribution maps provided by this dataset does not mask any water bodies.</p> <p><strong>How to use our dataset? Please refer to our article and Readme.docx for more details.</strong></p> <p> </p> <p> </p>
Wetland sediment soil organic carbon sequestration data to support radiometric technique comparisons
<p>This workbook shows the ID, the geographical location, the year of sampling, and sediment core information in samples collected from undisturbed wetlands situated across four provinces of Canada (Alberta, Saskatchewan, Manitoba, and Ontario) from 2016 to 2019.</p>
Mapping Soil Organic Carbon in the World's Largest Arid Mangrove Forest (Indus Delta, Pakistan): A Multi-Sensor Remote Sensing and Machine Learning Approach
<p>Mangrove forests play a crucial role in carbon sequestration, especially in arid regions where their ability to store carbon in soil is vital for mitigating climate change. The Indus Delta in Pakistan, the world’s largest arid mangrove forest system, lacks spatially explicit data on Soil Organic Carbon (SOC) despite its importance for conservation and carbon budgeting. This study aims to establish a baseline SOC map 2020 at 10 m spatial resolution using Sentinel-1 (Synthetic Aperture Radar) and Sentinel-2 (MultiSpectral Instrument) satellite imagery, integrated with in-situ soil sampling. SOC predictions were made using a Classification and Regression Tree (CART) machine learning model within the Google Earth Engine platform, leveraging 40 predictor variables, including spectral bands and derived indices. A total of 53 topsoil (0-10 cm) samples were collected in February 2020 across the Indus Delta, and SOC was analyzed using the Walkley-Black method. The results showed an average SOC value of 65.88 Mg C ha⁻¹ with substantial spatial variability, ranging from 15.06 Mg C ha⁻¹ to 138.03 Mg C ha⁻¹ with a total of 0.91 Pg C. The CART model demonstrated high accuracy, with an R² of 0.95 and an RMSE of 9.18 Mg C ha⁻¹. However, the region faces challenges such as seawater intrusion and salinity, which threaten its ability to sequester carbon. With the first high-resolution SOC map for the Indus Delta, this study provides valuable insights for ecosystem management, conservation planning, and carbon budgeting. These findings of this study have the potential to significantly influence initiatives like REDD+ and Blue Carbon projects, which aim to enhance carbon sequestration while addressing the ecological challenges facing Pakistan’s mangroves</p>
Climate warming and soil drying lead to a reduction of riverine dissolved organic carbon in China
<p>The raw datasets for spatio-temporal analysis of riverine dissolved organic carbon in China</p>
Global warming may turn ice-free areas of Maritime and Peninsular Antarctica into potential soil organic carbon sinks
<h2>Dear researchers and interested parties,</h2> <p>We are excited to announce the publication of our recent research on Zenodo, presenting <strong>high-resolution</strong> (8 m) spatial models of <strong>soil organic carbon (SOC) stocks in ice-free areas of Maritime and Peninsular Antarctica</strong>. This research evaluates the potential impacts of climate change on SOC stocks under three Shared Socioeconomic Pathways (SSPs), providing a comprehensive understanding of the role these regions may play as carbon sinks in the face of intensified global warming.</p> <h2>Available resources:</h2> <h3>SOC stock predictions:</h3> <p>We provide detailed maps of SOC estimates and uncertainties for different soil depths across various IPCC Shared Socioeconomic Pathways, including mean values (Mg ha⁻¹) and coefficients of variation (%). All maps are available in "tif" format, using the South Pole Stereographic projection system (<a href="https://epsg.io/102021" target="_blank" rel="noopener">ESRI:102021</a>).</p> <p>Open-Source Code and Data: The entire analytical workflow, developed in R, <strong>is accessible through our <a href="https://github.com/moquedace/soc_stock_antarctica" target="_blank" rel="noopener">GitHub repository</a></strong>, ensuring reproducibility and transparency. Additional methodological details are provided in our publication:</p> <p>Mello, D., Francelino, M. R., Moquedace, C. M., Baldi, C. G. O., Silva, L., Siqueira, R. G., Veloso, G. V., Fernandes-Filho, E. I., Thomazini, A., Demattê, J., Ferreira, T., Gomes, L. C., Senra, E., Schaefer, C. E. G. R. Global warming may turn ice-free areas of Maritime and Peninsular Antarctica into potential soil organic carbon sinks. <em>Commun Earth Environ</em>, v. 6, n. 1, p. 143, 2025. DOI: <a href="https://doi.org/10.1038/s43247-024-01937-z" target="_blank" rel="noopener">10.1038/s43247-024-01937-z</a></p> <h2>Availability objectives:</h2> <h3>Advancing scientific collaboration:</h3> <p>We invite scientists, researchers, and organizations to explore our findings to support additional studies on soil carbon dynamics and climate change.</p> <h3>Supporting environmental understanding:</h3> <p>By providing open access to these models, we aim to contribute to global knowledge on Antarctic soil carbon dynamics and assist in formulating sustainable climate mitigation strategies.</p> <h3>Fostering innovation:</h3> <p>Sharing this data aims to stimulate advances in spatial modeling and SOC prediction methodologies, especially in high-latitude environments.</p> <h2>We appreciate your interest and collaboration. We look forward to advancing knowledge and promoting sustainable solutions to essential environmental challenges together.</h2>
Dataset S1 - Noelaerhabdaceae organic carbon isotope culture data compilation
<p class="BodyA">The carbon isotope fractionation in algal organic matter (E<sub>p</sub>), including the long-chain alkenones produced by the coccolithophorid family Noelaerhabdaceae, is used to reconstruct past atmospheric CO<sub>2</sub> levels. The conventional proxy linearly relates E<sub>p</sub> to changes in cellular carbon demand relative to diffusive CO<sub>2</sub> supply, with larger E<sub>p</sub> values occurring at lower carbon demand relative to supply (<i>i</i>.<i>e</i>. abundant CO<sub>2</sub>). However, the response of <i>Gephyrocapsa oceanica</i>, one of the dominant alkenone producers of the last few million years, has not been studied closely. Here we subject <i>G. oceanica</i> to various CO<sub>2</sub> levels by increasing pCO<sub>2</sub> in the culture headspace, as opposed to increasing dissolved inorganic carbon (DIC) and alkalinity concentrations at constant pH. We note no substantial change in physiology, but observe an increase in E<sub>p</sub> as carbon demand relative to supply decreases, consistent with DIC manipulations. We compile existing Noelaerhabdaceae E<sub>p</sub> data and show that the diffusive model poorly describes the data. A meta-analysis of individual treatments (unique combinations of lab, strain, and light conditions) shows that the slope of the E<sub>p</sub> response depends on the light conditions and range of carbon demand relative to CO<sub>2</sub> supply in the treatment, which is incompatible with the diffusive model. We model E<sub>p</sub> as a multilinear function of key physiological and environmental variables and find that both photoperiod duration and light intensity are critical parameters, in addition to CO<sub>2</sub> and cell size. While alkenone carbon isotope ratios indeed record CO<sub>2</sub> information, irradiance and other factors are also necessary to properly describe alkenone E<sub>p</sub>.</p>
Soil dissolved organic carbon in terrestrial ecosystems: global budget, spatial distribution and controls
<p><strong>Aims: </strong>Soil dissolved organic carbon (DOC) is a primary form of labile carbon in terrestrial ecosystems and therefore plays a vital role in soil carbon cycling. This study aims to quantify the budgets of soil DOC at biome- and global levels and to examine the variations in soil DOC and their environmental controls. Location: Global Time period: 1981 - 2019 Method: We compiled a global dataset and analyzed the concentration and distribution of DOC across 10 biomes.</p> <p><strong>Results: </strong>Large variations in DOC are found among biomes across space and the soil DOC concentration declines exponentially along soil depths. Tundra has the highest soil DOC concentration in 0 - 30 cm soils (453.75 (95% confidence interval: 324.95 – 633.5) mg·kg-1); whereas tropical and temperate forests have relatively lower DOC concentrations, ranging from 30.20 (24.78 - 36.80) mg·kg-1 to 54.54 (49.77 – 59.77) mg·kg-1. DOC generally accounts for < 1% of total organic carbon in soils, and DOC in 0 - 30 cm contributes more than half of total DOC in 0 - 100 cm soil profile. Furthermore, variations in DOC are primarily controlled by soil texture, moisture, and total organic carbon.</p> <p><strong>Main conclusion: </strong>A global synthesis is combined with an empirical model to extrapolate the DOC concentration along soil profiles across the globe, and global budgets of DOC are estimated as 7.20 Pg C in top 0 - 30 cm and 12.97 Pg C in 0 - 100 cm, respectively, with a considerable variation among biomes. The strong soil texture control but weak TOC control on DOC variations suggest that the investigation of physical protection of soil organic carbon might need to expand to consider the labile C in soils. The global maps of DOC concentration serve as a benchmark for validating land surface models in estimating carbon storage in soils.</p>
Cryoturbation leads to iron-organic carbon associations along a permafrost soil chronosequence in northern Alaska
<p>In permafrost soils, substantial amounts of organic carbon (OC) are potentially protected from microbial degradation and transformation into greenhouse gases by association with reactive iron (Fe) minerals. As permafrost environments respond to climate change, increased drainage of thaw lakes in permafrost regions is predicted. Soils will subsequently develop on these drained thaw lakes, but the role of Fe-OC associations in future OC stabilization during this predicted soil development is unknown. To fill this knowledge gap, we have examined Fe-OC associations in organic, cryoturbated and mineral horizons along a 5500-year chronosequence of drained thaw lake basins in Utqiaġvik, Alaska. By applying chemical extractions, we found that ~17 % of the total OC content in cryoturbated horizons is associated with reactive Fe minerals, compared to ~10 % in organic or mineral horizons. As soil development advances, the total stocks of Fe-associated OC more than double within the first 50 years after thaw lake drainage, because of increased storage of Fe-associated OC in cryoturbated horizons (from 8 to 75 % of the total Fe-associated OC stock). Spatially-resolved nanoscale secondary ion mass spectrometry showed that OC is primarily associated with Fe(III) (oxyhydr)oxides which were identified by <sup>57</sup>Fe Mössbauer spectroscopy as ferrihydrite. High OC:Fe mass ratios (>0.22) indicate that Fe-OC associations are formed via co-precipitation, chelation and aggregation. These results demonstrate that, given the proposed enhanced drainage of thaw lakes under climate change, OC is increasingly incorporated and stabilized by the association with reactive Fe minerals as a result of soil formation and increased cryoturbation.</p>
Data and Code for Atmospheric oxygen abundance, marine nutrient availability, and organic carbon fluxes to the seafloor
<p>Code and Data for manuscript "<strong>Atmospheric oxygen abundance, marine nutrient availability, and organic carbon fluxes to the seafloor"</strong></p>
Large-scale drivers of relationships between soil microbial properties and organic carbon across Europe
<p>The aim of this study was to quantify direct and indirect relationships between soil microbial community properties (potential basal respiration, microbial biomass) and abiotic factors (soil, climate) in three major land-cover types.</p> <p>Location: Europe</p> <p>Time period: 2018</p> <p>Major taxa studied: Microbial community (fungi and bacteria)</p> <p>We collected 881 soil samples from across Europe in the framework of the Land Use/Land Cover Area Frame Survey (LUCAS). We measured potential soil basal respiration at 20ºC and microbial biomass (substrate-induced respiration) using an O2-microcompensation apparatus. Climate and soil data were obtained from previous LUCAS surveys and online databases. Structural equation modeling (SEM) was used to quantify relationships between variables, and equations extracted from SEMs were used to create predictive maps. Fatty acid methyl esters were measured in a subset of samples to distinguish fungal from bacterial biomass. Soil microbial properties in croplands were more heavily affected by climate variables than those in forests. Potential soil basal respiration and microbial biomass were correlated in forests but decoupled in grasslands and croplands, where microbial biomass depended on soil carbon. Forests had a higher ratio of fungi to bacteria than grasslands or croplands. Soil microbial communities in grasslands and croplands are likely carbon-limited in comparison with those in forests, and forests have a higher dominance of fungi indicating differences in microbial community composition. Notably, the often already-degraded soils of croplands could be more vulnerable to climate change than more natural soils. The provided maps show potentially vulnerable areas that should be explicitly accounted for in coming management plans to protect soil carbon and slow the increasing vulnerability of European soils to climate change.</p>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.