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18 results for “carbonate parameters”
Example Perturbed Parameter Ensemble (Black Carbon)
<p>This dataset contains the parameter design and example ECHAM-HAM output from the AeroCom Black Carbon (BC) multi-model Perturbed Parameter Ensemble (PPE) experiment described here: https://wiki.met.no/aerocom/phase3-experiments#multi-model_ppe_bc_experiment</p>
Viskari et al. (2019) The influence of canopy radiation parameter uncertainty on model projections of terrestrial carbon and energy cycling
<p>Zenodo DOI release for permanent archiving outside of GitHub</p>
Supporting data sets for "Estimating Carbon Fixation of Plant Organs for Afforestation Monitoring using a Process-based Ecosystem Model and Ecophysiological Parameter Optimization". (the survey of tree breast diameter and tree height in 11-year old Eucommia ulmoides plantation, values of simulation results used in figures and tables.)
<p>Supporting data sets for Miyauchi et al., Ecology and Evolution, 2019 (accepted).</p> <p>The files store: </p> <p>(1) The survey of tree breast diameter and tree height in <em>Eucommia ulmoides</em> plantation<em>.</em> The ring and stem analysis and dry weight of seven harvested sample trees in the plantation.</p> <p>(2) Values of optimization result used fig.7.</p> <p>(3) Values of prediction result used fig.8. and table 4.</p> <p>(4) Values of optimized parameters by optimization methods, parameter range and constrain.</p>
Data set on soil physicochemical parameters, biomass accumulation and carbon credit generation in different management systems in Rio Verde, GO, Brazil
<h1>Description</h1> <p>This repository contains a comprehensive dataset focused on soil organic carbon and its role in mitigating climate change through carbon sequestration on agricultural lands in Rio Verde, GO, Brazil. With the global imperative to reduce anthropogenic CO2 emissions, our data highlights the effectiveness of no-till agricultural practices in both improving soil quality and enhancing carbon storage. This collection represents extensive soil and biomass sampling from five distinct areas within the Cerrado region, utilizing three priority management systems:</p> <p>No-till with soybean and maize in sequence under rainfed conditions. No-till with soybean and maize in sequence with central pivot irrigation. First and second cuts of sugarcane. The samples were meticulously collected post-harvest and used to estimate both soil biomass accumulation and carbon stock indices. A thorough analysis of the soil's physicochemical parameters was conducted for the 0-20 cm soil profile in each area. This dataset not only provides a valuable resource for studying the impact of different no-till practices on carbon sequestration but also serves as a critical input for modeling future contributions of conservation management systems to carbon trading markets.</p> <div> <div> </div> <div> <h2>Data Contents</h2> </div> <p>Soil organic carbon measurements for various no-till systems. Biomass accumulation data post-harvest. Carbon stock indices derived from biomass samples. Detailed physicochemical profiles of soil samples.</p> <div> <h2>Significance</h2> </div> <p>This dataset is pivotal for researchers and policymakers focusing on the potentials of agricultural carbon sequestration and its implications for carbon trading schemes. It offers insights into the current contributions of no-till conservation management systems and aids in the development of future strategies to enhance carbon</p> <h1>Metadata Description and Script</h1> </div> <p>This repository contains two key data files that encapsulate diverse aspects of soil physicochemical parameters, biomass accumulation, and carbon credit generation across different management systems in Rio Verde, GO, Brazil. Below are descriptions of each file's contents and structure.</p> <div> <h2>all.txt</h2> </div> <p>This text file presents aggregated data from various sites under different agricultural management systems. Each row in the dataset represents measurements from distinct sample plots, with the following fields:</p> <ul> <li><code>Sites</code> - Identifier for the plot location.</li> <li><code>SB</code> - Soil bulk density (g/cm³).</li> <li><code>SOC</code> - Soil organic carbon (%).</li> <li><code>Stock</code> - Carbon stock (ton/ha).</li> <li><code>Biomass</code> - Biomass accumulation (ton/ha).</li> <li><code>Credits</code> - Estimated carbon credits (ton CO2 equivalent/ha).</li> </ul> <div> <h2>Quimica.xlsx</h2> </div> <p>This Excel file provides detailed physicochemical analyses of soil samples from different management zones in the study area. The data is structured to support in-depth analysis of soil characteristics influencing carbon sequestration capabilities. Each sheet in the workbook corresponds to a specific area, with columns typically representing:</p> <ul> <li><code>pH</code> - Soil pH, indicating the acidity or alkalinity.</li> <li><code>EC</code> - Electrical conductivity (dS/m).</li> <li><code>Cation Exchange Capacity (CEC):</code> - (meq/100g).</li> <li><code>Organipont c Matter:</code> - (%).</li> <li><code>NPK levels</code> - Concentrations of Nitrogen (N), Phosphorus (P), and Potassium (K).</li> </ul>
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>
Data for continuous and discrete measurements of carbonate parameters in a productive coastal region in the Northwestern Pacific (36°09'13.5''N, 129°24'04.9''E) from January to September in 2019 and from March to December in 2020
<p><span>Photosynthetic organisms shift the dynamics of surface pCO<sub>2</sub> driven by the sea surface temperature change (thermodynamic driver) by assimilating C from seawater. Here we measured net C uptake </span><span>in a macroalgal habitat</span> <span>of coastal Korea for two years (2019–2020) and found that the macroalgal habitat</span> <span>contributed </span><span>5.8 g</span><span> C m</span><sup><span>-</span><span>2</span></sup><span> month</span><sup><span>-</span><span>1</span></sup><span> of </span><span>the net C uptake during the growing period (the cooling period, September</span><span>-</span><span>May). This massive C uptake changed the thermodynamics-driven seasonal dynamics such that the air</span><span>-</span><span>sea equilibrium of </span><span>pCO<sub>2</sub></span><span> was pushed into disequilibrium. T</span><span>he </span><span>surface </span><span>pCO<sub>2</sub></span><span> dynamics during the cooling period were </span><span>mostly influenced by the seasonal decrease in temperature and the proliferation of macroalgae, while the dynamics </span><span>during the warming period </span><span>(the stagnant period, </span><span>June</span><span>-</span><span>August) </span><span>closely followed that predicted based solely on the change in sea surface temperature only </span><span>(thermodynamic driver)</span><span>.</span><span> In contrast to the phytoplankton-dominated offshore waters (where phytoplankton populations are large in spring and summer), the impact of coastal water macroalgae on surface </span><span>pCO<sub>2</sub></span><span> dynamics was most pronounced during the cooling period, when the magnitude of </span><span>pCO<sub>2</sub></span><span> change was as much as twice that resulting from temperature change. Our study shows that</span><span> t</span><span>he distinctive features of the macroalgal habitat—in particular the seasonal temperature extremes (~18°C difference), the </span><span>active macroalgal metabolism,</span><span> and anthropogenic </span><span>nutrient</span><span> inputs—collectively influenced</span><span> the seasonal decoupling of seawater and air </span><span>pCO<sub>2</sub></span><span> dynamics</span><span>.</span></p>
In-situ parameters, nutrients and dissolved carbon distribution in the water column and pore waters of Arctic Fjords (Western Spitsbergen) during a melting season
<p>A nutrient distribution such as phosphate (PO₄³⁻), ammonium (NH₄⁺), nitrate (NO₃⁻), dissolved silica (Si), total dissolved nitrogen (TN), dissolved organic nitrogen (DON) together with dissolved organic carbon (DOC) and inorganic carbon (DIC), was investigated during a high melting season in 2021 in the western Spitsbergen fjords (Hornsund, Isfjorden, Kongsfjorden, and Krossfjorden). Both the water column and the pore water were investigated for nutrients and dissolved carbon distribution and gradients. The water column concentrations of most measured parameters such as PO₄³⁻, NH₄⁺, NO₃⁻, Si, and DIC showed significant changes among fjords and water masses. In addition, pore water gradients of PO₄³⁻, NH₄⁺, NO₃⁻, Si, DIC and DOC revealed significant variability between fjords and are likely substantial sources of the investigated elements for the water column. The obtained dataset reflects differences in hydrography and biogeochemical ecosystem function of the western Spitsbergen fjords and may form the base for further modelling of physical oceanographic and biogeochemical processes within the investigated fjord systems.</p> <p> </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>
Monitoring data for oyster reefs and nearby controls, including high frequency environmental parameters, carbonate chemistry, oyster growth, and other data
<p>Data for Oyster reefs' control of carbonate chemistry - implications for oyster reef restoration in estuaries subject to coastal ocean acidification. </p>
Monitoring data for oyster reefs and nearby controls, including high frequency environmental parameters, carbonate chemistry, oyster growth, and other data
Open the record for dataset details and reuse information.
Data for continuous and discrete measurements of carbonate parameters in a productive coastal region in the Northwestern Pacific (36°09'13.5''N, 129°24'04.9''E) from January to September in 2019 and from March to December in 2020
Open the record for dataset details and reuse information.
Dataset for light absorption parameter and molecular composition of atmospheric brown carbon in Xi'an
<p>This is the raw data for light absorption parameter and molecular composition of atmospheric brown carbon in Xi'an.</p>
Soil Moisture Active Passive (SMAP) L4 Carbon Ancillary Biome Parameter Look Up Table V001
This ancillary SMAP product contains biophysical characteristics (biome parameters) used to estimate carbon fluxes and soil organic carbon in the SMAP L4 Carbon algorithm. Biophysical characteristics were established from previous studies and the parameters defined for the MODIS MOD17 operation GPP algorithm. This data set was refined through regional and global comparisons and calibration of prototype SMAP L4 Carbon calculations.
Soil Moisture Active Passive (SMAP) L4 Carbon Ancillary MODIS Preprocessor Run Time Input Parameters V001
This ancillary SMAP product contains MODIS Fractional Photosynthetically Active Radiation (FPAR) model configurations, including model inputs.
Soil Moisture Active Passive (SMAP) L4 Carbon Ancillary Parameters V001
This ancillary SMAP product contains assorted static ancillary parameters, such as reference grids and land cover classifications, also referred to as Plant Function Type (PFT) maps.
Soil Moisture Active Passive (SMAP) L4 Carbon Ancillary Meteorology Preprocessor Run Time Input Parameters V001
This ancillary SMAP product contains meteorological model configurations, including model inputs. The meteorological model is derived from the Modern-Era Retrospective Analysis for Research and Applications (MERRA) data set and used as an input in the SMAP L4 Carbon algorithm.
Soil Moisture Active Passive (SMAP) L4 Carbon Ancillary Model Run Time Input Parameters V001
This ancillary SMAP product contains SMAP L4 Carbon model configurations, including model inputs.
Aerosol optical parameters and Equivalent Black Carbon (EBC)
<p>The produced dataset (in csv format) contains physical and optical characteristics of aerosol population as measured for the period 2016-2019 at the Italian Arctic station Dirigibile Italia (DI) in Ny Alesund (Svalbard Archipelago).</p> <p>Long-term in situ aerosol measurements at DI Svalbard are performed at Gruvebadet observatory (78.918' °N, 11.895' °E; 61m above sea level), which is located 800m south-west of the Ny-Ãlesund research village.</p> <p>At Gruvebadet aerosol scattering is measured at one wavelenght (530 nm, Nephelometer Radiance Research M903), while aerosol absorption is evaluated at three wavelengths with a Radiance Research PSAP at 1-minute time resolution (Bond et al. 1999), since 2010.</p> <p>Data are then averaged over 1-hour period. Absorption coefficients are measured at 467 nm, 530 nm, and 660 nm, with a precision ranging between 20 and 25%. Measurements are performed generally from April to September, with a limited number of data during the winter season. Absorption measurements are corrected according to Bond et al. (1999) and Virkkula (2010) for filter transmission, flow, and sampling filter area. Data are not corrected for aerosol scattering, while shadowing effect is considered negligible due to the low aerosol loading.</p> <p>Absorption coefficients are normalized at standard pressure and temperature conditions (1 atm and 0 °C).</p> <p>The BC content in evaluated considering the central value according to Zanatta et al. (2018), with a MAC (mass absorption cross section) of 7.72 m<sup>2</sup>/g</p> <p> </p> <p>Scattering and Absortion coefficient are in Mm<sup>-1</sup>,</p> <p>Equivalent Black Carbon (EBC) content in ng/m<sup>3</sup></p> <p> </p> <p>Data are stored in annual files that contains columns of collected and evaluated data as followin the following order:</p> <p> </p> <p><strong>column 1</strong> Date time (DD/MM/AAAA HH:MM) in UTC</p> <p><strong>column 2</strong> Scattering coefficient at 530 nm (Mm<sup>-1</sup>)</p> <p><strong>column 3</strong> Absorption coefficient at 467 nm (Mm<sup>-1</sup>)</p> <p><strong>column 4</strong> Absorption coefficient at 530 nm (Mm<sup>-1</sup>)</p> <p><strong>column 5</strong> Absorption coefficient at 660 nm (Mm<sup>-1</sup>)</p> <p><strong>column 6</strong> Equivalent Black Carbon (EBC) (ng/m<sup>3</sup>)</p> <p>HEADER<br> date time, Scattering coefficient , Absorption1 coefficient, Absorption1 coefficient, Absorption1 coefficient, equivalentBC content</p> <p>where the Scattering and Absortion coefficient are in Mm^-1,<br> equivalent BC content in ng/m^3</p> <p><br> References</p> <p>Bond, T. C., Anderson, T. L., and Campbell, D.: Calibration and Intercomparison of Filter-Based Measurements of Visible Light Absorption by Aerosols, Aerosol Sci. Tech.,<br> 30, 582–600, https://doi.org/10.1080/027868299304435, 1999. </p> <p>Virkkula, A.: Correction of the Calibration of the 3-wavelength Particle Soot Absorption Photometer (3λ PSAP),<br> Aerosol Sci. Tech., 44, 706–712, https://doi.org/10.1080/02786826.2010.482110, 2010. </p> <p>Zanatta, M., Laj, P., Gysel, M., Baltensperger, U., Vratolis, S., Eleftheriadis, K., Kondo, Y., Dubuisson, P., Winiarek, V., Kazadzis, S., Tunved, P., and Jacobi, H.-W.: Effects of mixing state on optical and radiative properties of black carbon in the European Arctic, Atmos. Chem. Phys., 18, 14037-14057, https://doi.org/10.5194/acp-18-14037-2018, 2018</p>
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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.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
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.