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539 results for “organic carbon”
NOAA-20 VIIRS Global Mapped Particulate Organic Carbon (POC) - Near Real Time (NRT) Data, version R2022.0
The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.
Remote-sensing-derived particulate organic carbon (POC) validation
Measurements taken for the purpose of validating remote-sensing-derived particulate organic carbon.
Aqua MODIS Global Mapped Particulate Organic Carbon (POC) - Near Real Time (NRT) Data, version R2022.0
The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.
OrbView-2 SeaWiFS Global Mapped Particulate Organic Carbon (POC) Data, version R2022.0
The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.
NOAA-21 VIIRS Global Mapped Particulate Organic Carbon (POC) - Near Real Time (NRT) Data, version R2022.0
The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.
Chip-SIP analysis of Monterey Bay surface waters incubated with organic carbon substrates
GEO Series GSE71228. marine metagenome. 17 samples. Type: Other.
Metagenomic analysis revealed the microbial-mediated soil organic carbon loss under the degeneration succession in alpine meadow
GEO Series GSE93158. uncultured soil microorganism. 20 samples. Type: Other.
Carbon input of Flanders' organic fertilizers
<p>Deze dataset bevat de C input waarden voor verschillende types organische meststoffen die worden gebruikt in het BeSOCC model en de Koolstoftool in het bodempaspoort.</p> <p> </p> <p>For access to dataset contact Greet Ruysschaert at greet.ruysschaert@ilvo.vlaanderen.be </p>
A global distribution of dissolved organic carbon in soil and in leaching - (database)
<p>Current global carbon (C) models are not representing the fraction of C which is displaced along the terrestrial aquatic continuum thus overestimating the land sink capacity. In order to obtain more reliable C budgets, we need to integrate the lateral transfers of C from terrestrial ecosystems through the inland water network down to the oceans, including biogeochemical transformation during transport and C exchange with the atmosphere.Representing the production and cycling of dissolved organic C (DOC) in the soil column and the leaching of DOC into the inland water network is a first major step in this development.</p> <p>In this study we used newly developed model JULES-DOCM to obtain the first global estimate of global soil DOC stock and DOC concentration, DOC concentration in runoff and DOC leaching flux.</p> <p>In this dataset model produced files are stored as netcdf files including soil DOC stocks (at Top (0-35 cm) and Total soil (0-300cm), soil DOC concentration (at Top (0-35 cm) and Bottom ( 35-300cm)), DOC leaching flux (averaged over 1980-2010) and DOC concentration in runoff (averaged over 1980-2010).</p> <p>The measured DOC collected database is enclosed as the Excel file.</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><span>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</span><span>⁻</span><span>¹ with substantial spatial variability, ranging from 15.06 Mg C ha</span><span>⁻</span><span>¹ to 138.03 Mg C ha</span><span>⁻</span><span>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</span><span>⁻</span><span>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</span></p>
Differential effects of nitrogen addition on soil organic carbon decomposition correlate with changes in microbial C-degradation functional potentials in a Pinus tabulaeformis forest
GEO Series GSE147041. uncultured soil microorganism; Bacteria; Eukaryota; Viruses; Archaea. 16 samples. Type: Other.
Dataset for McClelland et al. 2022. Infrequent compost applications increased plant productivity and soil organic carbon in irrigated pasture but not degraded rangeland. Agriculture, Ecosystems, and Environment.
<p>Raw data files accompanying the published article "Infrequent compost applications increased plant productivity and soil organic carbon in irrigated pasture but not degraded rangeland" in <em>Agriculture, Ecosystems, and Environment</em>. <a href="https://authors.elsevier.com/a/1etJPcA-Ik6yb">https://authors.elsevier.com/a/1etJPcA-Ik6yb</a></p> <p>Units for response variables in .csv files are as follows. Please reach out to scm229@cornell.edu with any questions about using the files or the data within.</p> <p>--</p> <p>Aboveground biomass: total (Mg ha-1), carbon (Mg C ha-1), nitrogen (kg N ha-1)</p> <p>Bulk density: g cm-3</p> <p>Respiration (Rs): micro mol m-2 s-1</p> <p>Roots: Mg C ha-1</p> <p>Soil C and N: organic and inorganic carbon (Mg C ha-1), nitrogen (Mg N ha-1)</p> <p> </p>
Particulate organic carbon controlled the upper limit of soil organic carbon in natural alpine ecosystems of northeast Qinghai-Tibet Plateau
Open the record for dataset details and reuse information.
Supporting Information for the article "Enhanced formation of interstellar complex organic molecules on carbon monoxide ice"
<p>Chemical models ("Models.pkl") and structures (*.xyz files, figures 2 and 3) supporting the calculations presented in the article : "Enhanced formation of interstellar complex organic molecules on carbon monoxide ice"</p> <p>The chemical models are stored as a dictionary of dictionaries with the following name per model:<br> <br> e_XXXX_T_YYYY_CR_ZZZZ</p> <p>where XXXX are the values of the epsilon parameter (see original article) sampled for \epsilon=0.5 and \epsilon=1.0 (additionally we release models with \epsilon=0.40, 0.45, 0.55, 0.60, 0.65, not discussed in the original article), YYYY is the dust temperature sampled within np.linspace(8,18, 26) and ZZZZ is the cosmic ray ionization rate, sampled within np.logspace(-18, -15, 22).<br> <br> To open the chemical models, prepare a python script of the type:</p> <p>++++<br> import pickle<br> <br> def ice(dictionary, name, species): #Similar functions for gas molecules or surface molecules can be defined<br> gas = dictionary[name][f"{species}"]<br> surface = dictionary[name]["J"+f"{species}"+"(1)"]<br> mantle = dictionary[name]["K"+f"{species}"+"(1,1)"]<br> return surface + mantle</p> <p><br> Data = read_pkl_file('Models.pkl')<br> name = "e_1.00_T_10.0_CR_1.00e-17" #as an example<br> time = ice(Data, name, "Time")<br> CO= ice(Data, name, "CO") </p> <p>print(CO)<br> ++++</p> <p><br> Legend for large COMs: Y=CH2OH, X=CH3O, Q=NH2, M=CH2. Please check the reaction network in https://iopscience.iop.org/article/10.1088/0004-637X/765/1/60 for further details.</p> <p><br> Version 1.0: Revision release, including chemical models and cartesian coordinates supporting Figures 2 and 3 of the manuscript.<br> <br> If you request further data, please do not hesitate to contact us.</p>
Phosphorus addition decreases microbial residual contribution to soil organic carbon pool in a tropical coastal forest
<p>This is the data supporting the study of 'Phosphorus addition decreases microbial residual contribution to soil organic carbon pool in a tropical coastal forest'. Data in the excel sheet were used for the figures and tables in the article. </p>
Contribution of wheat and maize to soil organic carbon in a wheat-maize cropping system: a field and laboratory study
<ol> <li>Retention of crop biomass is widely recommended to improve soil organic carbon (SOC). However, the magnitude of contribution of aboveground residues and belowground roots from C3 and C4 crops to SOC is unclear.</li> <li>Data from a 10-year field experiment and a 60-day laboratory incubation were synthesized to identify the respective contribution of C3 (e.g., wheat) and C4 (e.g., maize) residues and roots to SOC, as well as its underlying mechanisms under no-till (NT) using <sup>13</sup>C labelling trace in wheat-maize rotations.</li> <li>The field experiment showed that residue retention significantly increased SOC accumulation, and SOC derived from wheat was 126.0% higher than that from maize. Conversion to NT promoted SOC derived from wheat and thus accumulated 17.6% higher SOC stock compared with plow tillage (PT) under residue returning at 0-20 cm soil depth (<em>P</em><0.05). The data from laboratory incubation revealed the mechanisms that lower priming effects at 0-10 cm depth decreased total mineralization by 91.8% after inputs of wheat residues and roots compared with that of maize residues and roots, especially under NT compared with PT. Priming effects were negatively correlated with enzyme activities associated with the C recycle, SOC, and total nitrogen (TN) contents (<em>P</em><0.01). NT increased enzyme activities, SOC, and TN contents and thus reduced priming effects and improved residual carbon.</li> <li><em>Synthesis and applications.</em> These results suggested that wheat may contribute more to SOC accumulation than maize, and carbon increment efficiency in farmland could be enhanced by considering the crucial roles of C3 crops in SOC accumulation. NT practice sustains the benefits of C3 crops to SOC sequestration in the upper soil depths.</li> </ol>
HAQES 3-Hourly Ensemble mean surface PM2.5 Organic Carbon concentration at county level, North America V1 (HAQES_NA_PM25_OC_COUNTY) at GES DISC
This product provides HAQES 3-hourly ensemble mean surface PM2.5 Organic Carbon concentration at the county level over the continental United States (CONUS). The Hazardous Air Quality Ensemble System (HAQES) is a real-time ensemble forecast of hazardous air quality events, such as wildfires, dust storms, and Volcanic eruptions. Both regional and global models from multiple agencies are used to create the ensemble, including the Goddard Earth Observing System (GEOS) from the National Aeronautics and Space Administration (NASA), the Navy Aerosol Analysis and Prediction System (NAAPS) from Naval Research Laboratory, the Global Ensemble Forecast System Aerosols (GEFS), High-Resolution Rapid Refresh (HRRR), and National Oceanic and Atmospheric Administration-U.S. Environmental Protection Agency (NOAA-EPA) Atmosphere-Chemistry Coupler-Community Multiscale Air Quality model (NACC-CMAQ) from NOAA. The prototypes of HAQES products were developed by the George Mason University Air Quality Laboratory as part of the NASA Health Air Quality Applied Science Team (HAQAST).
HAQES 3-Hourly Ensemble mean surface PM2.5 Organic Carbon concentration at census level, North America V1 (HAQES_NA_PM25_OC_CENSUS) at GES DISC
This product provides HAQES 3-hourly ensemble mean surface PM2.5 Organic Carbon concentration at the census level over the continental United States (CONUS). The Hazardous Air Quality Ensemble System (HAQES) is a real-time ensemble forecast of hazardous air quality events, such as wildfires, dust storms, and Volcanic eruptions. Both regional and global models from multiple agencies are used to create the ensemble, including the Goddard Earth Observing System (GEOS) from the National Aeronautics and Space Administration (NASA), the Navy Aerosol Analysis and Prediction System (NAAPS) from Naval Research Laboratory, the Global Ensemble Forecast System Aerosols (GEFS), High-Resolution Rapid Refresh (HRRR), and National Oceanic and Atmospheric Administration-U.S. Environmental Protection Agency (NOAA-EPA) Atmosphere-Chemistry Coupler-Community Multiscale Air Quality model (NACC-CMAQ) from NOAA. The prototypes of HAQES products were developed by the George Mason University Air Quality Laboratory as part of the NASA Health Air Quality Applied Science Team (HAQAST).
HAQES 3-Hourly Ensemble mean surface PM2.5 Organic Carbon concentration, North America V1 (HAQES_NA_PM25_OC) at GES DISC
This product provides HAQES 3-hourly ensemble mean surface PM2.5 Organic Carbon concentration over the continental United States (CONUS) and surrounding regions. The data is mapped on Lambert projection. The Hazardous Air Quality Ensemble System (HAQES) is a real-time ensemble forecast of hazardous air quality events, such as wildfires, dust storms, and Volcanic eruptions. Both regional and global models from multiple agencies are used to create the ensemble, including the Goddard Earth Observing System (GEOS) from the National Aeronautics and Space Administration (NASA), the Navy Aerosol Analysis and Prediction System (NAAPS) from Naval Research Laboratory, the Global Ensemble Forecast System Aerosols (GEFS), High-Resolution Rapid Refresh (HRRR), and National Oceanic and Atmospheric Administration-U.S. Environmental Protection Agency (NOAA-EPA) Atmosphere-Chemistry Coupler-Community Multiscale Air Quality model (NACC-CMAQ) from NOAA. The prototypes of HAQES products were developed by the George Mason University Air Quality Laboratory as part of the NASA Health Air Quality Applied Science Team (HAQAST).
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
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.