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128 results for “carbon biomass”
NACP Aboveground Biomass and Carbon Baseline Data, V.2 (NBCD 2000), U.S.A., 2000
The NBCD 2000 (National Biomass and Carbon Dataset for the Year 2000) data set provides a high-resolution (30 m) map of year-2000 baseline estimates of basal area-weighted canopy height, aboveground live dry biomass, and standing carbon stock for the conterminous United States. This data set distributes, for each of 66 map zones, a set of six raster files in GeoTIFF format. There is a detailed README companion file for each map zone. There is also an ArcGIS shapefile (mapping_zone_shapefile.shp) with the boundaries of all the map zones. A mosaic image of biomass at 240 m resolution for the whole conterminous U.S. is also included.Please read this important note regarding the differences of Version 2 from Version 1 of the NBCD 2000 data. With Version 1, in some mapping zones, certain land cover types (in particular Shrubs, NLCD Type 52) were missing from and unaccounted for in modeled estimates because of a lack of reference data. In Version 1, when landcover types were missing in the models, the model for the deciduous tree cover type was applied. While more woody vegetation was mapped, the authors think this had little effect on model performance as in most cases NLCD version 1 cover type was not a strong predictor of modeled estimates (See companion Mapping Zone Readme files). In Version 2, after renewed modeling efforts and user feedback, these previously unaccounted for cover types are now included in modeled estimates.All 66 mapping zones were updated with the previously unmapped land cover types now mapped. The authors recommend use of the new version for all analyses and will only support the updated version.Development of the data set used an empirical modeling approach that combined USDA Forest Service Forest Inventory and Analysis (FIA) data with high-resolution InSAR data acquired from the 2000 Shuttle Radar Topography Mission (SRTM) and optical remote sensing data acquired from the Landsat ETM+ sensor. Three-season Landsat ETM+ data were systematically compiled by the Multi-Resolution Land Characteristics Consortium (MRLC) between 1999 and 2002 for the entire U.S. and were the foundation for development of both the USGS National Land Cover Dataset 2001 (NLCD 2001) and the Landscape Fire and Resource Management Planning Tools Project (LANDFIRE). Products from both the NLCD 2001 (landcover and canopy density) and LANDFIRE (existing vegetation type) projects as well as topographic information from the USGS National Elevation Dataset (NED) were used within the NBCD 2000 project as spatial predictor layers for canopy height and biomass estimation. Forest survey data provided by the USDA Forest Service FIA program were made available to the project under a national Memorandum of Understanding. The response variables (canopy height and biomass) used in model development and validation were derived from the FIA database (FIADB). Production of the NLCD 2001 and LANDFIRE projects was based on a mapping zone approach in which the conterminous U.S. was split into 66 ecoregionally distinct mapping zones. This mapping zone approach was also adopted by the NBCD 2000 project.
Temporal Shift of Circadian-Mediated Gene Expression and Carbon Fixation Contributes to Biomass Heterosis in Maize Hybrids
GEO Series GSE67655. Zea mays. 48 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Transcriptional profiling of biomass degradation-related genes during Herbivorax saccincola A7 growth on different carbon sources
GEO Series GSE165680. Acetivibrio saccincola. 5 samples. Type: Expression profiling by RT-PCR.
Data from: Soil carbon accumulation differences: allocation of visible plant biomass, carbon and nitrogen in two turfgrasses
<p><span>Carbon accumulation in turfgrass soils by plant material may be beneficial to CO<sub>2</sub> sequestration and soil health, but at high rates can easily lead to declined turfgrass quality (i.e. thatch and mat layer formation). In a field study it was investigated how the fraction of visible plant biomass of turfgrass (sub)species monocultures, with two or three varieties as monoculture per (sub)species, and its C and N concentration and CN ratio in this visible plant biomass in thatch, mat and soil layers contributes to C accumulation. In total three <em>Festuca rubra</em> subspecies; <em>Festcua rubra commutata</em> (Frc), <em>Festuca rubra trichophylla </em>(Frt), <em>Festuca rubra rubra</em> (Frr), and three Agrostis species; <em>Agrostis canina</em> (Acn), <em>Agrostis capillaris</em> (Acp), Agrostis stolonifera (As), were studied. The study was conducted on 3 years old turfgrass demonstration fields of two turfgrass seed companies: <em>Festuca rubra</em> subspecies samples were collected at a site of Barenbrug in Wolfheze (52°00´N, 5°46´E), and <em>Agrostis </em>species were sampled at a site of DLF in Moerstraten (51°32´N, 4°20´E), both in the Netherlands. Both sites were built on a sandy soil. </span></p> <p><span>For every variety, cores of the top 20 cm of the soil including aboveground biomass were taken with a core sampler (diameter 28 mm). The core was immediately divided into 4 distinctive layers, thatch + aboveground biomass, mat, remaining upper 10 cm soil and 10-20 cm soil. Distinction of these layers followed the protocol of Evers et al. (2024). </span><span>Aboveground biomass was separated from the thatch with scissors. Sediment from thatch, mat, remaining upper 10 cm of soil and 10-20 cm soil was carefully washed out with tap water, after which the remaining below-ground (dead and living) visible plant biomass and aboveground biomass was dried at 65 °C until stable weight and weighed. Total C and N analyses were carried out with a Vario Micro Cube Element Analyzer (Elementar, Langenselbold, Germany), from which C and N concentrations (in % of dry matter of plant biomass) and C to N ratios (CN ratio) were calculated.</span></p>
IB-AGC: Annual 25 km global live biomass carbon product from SMOS L-band passive microwave vegetation optical depth
<p>The IB AGC dataset provides global aboveground biomass carbon (AGC) estimates for 2010-2020, derived from SMOS-IC L-band vegetation optical depth (L-VOD) using improved calibration methods and corrections for vegetation water effects. The accuracy assessments revealed that the IB L-VOD-derived AGC shows a very good spatial and temporal consistency with national forest inventory data and forest disturbance events, when compared to other mainstream satellite products. The dataset is stored in netCDF4 format and projected on the global cylindrical Equal-Area Scalable Earth Grid version 2.0 (EASE-Grid 2.0), with dimensions of 584 by 1388 and a grid resolution of 25 km. It includes eight layers, featuring the AGC density map and its associated uncertainty layer, representing the standard error of calibration. Additionally, a sub-product of total biomass carbon (BC) density is provided, calculated using a ratio method. The associated uncertainty, derived via error propagation, accounts for calibration errors in AGC and the AGC-to-BGC ratio.</p>
Example simulation showing spatial and temporal variations in surface carbon biomass of plankton functional groups during a Spring bloom as shown by a 3D hydrodynamic-biogeochemical model (FVCOM-ERSEM), with and without integration of the mixoplankton paradigm.
<p>The outputs are from simulations from using the FVCOM hydrodynamic model coupled to two different versions of ERSEM – (i) ERSEM and (ii) ERSEM-PB (the latter includes the implementation of the mixoplankton paradigm through integration of the 'Perfect Beast' PB model; Flynn and Mitra 2009 <em>Journal of Plankton Research</em>).</p> <p>The FVCOM domain was configured to represent Lyme Bay: a protected bay on the South Coast of England. This region is an important area for shellfish aquaculture. The domain was configured at 350 m – 5 km high-resolution, resolving sub-km scale dynamics in the area. A nested modelling approach of increasing model resolution was set up using two model domains. For the coupled hydrodynamic-biogeochemical model, a parent domain of 1.5 km – 10 km resolution was used to drive Lyme Bay model domain. The atmospheric forcing was provided by a 3-step downscaling of GFS global datasets to reach the 3 km of the final model domain using the Weather Research Forecast (WRF) model. Hydrodynamic boundary conditions are extracted from the European Copernicus Marine System North West European Shelf Forecast system. River flows were extracted from a National scale hydrology model run by the Center for Hydrology and Ecology in the UK. Simulations were initialised at Jan 1<sup>st</sup> 2005, and spun up for 3 months prior to the output of the data visualised in these videos. </p> <p>The 6 videos portray spatial and temporal variation of daily averaged surface carbon biomass (μgC L<sup>-1</sup>) during the month of April 2005 for the different plankton functional types (FTs) as follows:</p> <ul> <li>Video 1: all phytoplankton FTs in standard ERSEM grouped together. These thus include diatoms, nano-, pico- and micro- plankton; i.e., these simulations do not discriminate between phytoplankton and constitutive mixoplankton (CM).</li> <li>Video 2: phytoplankton FT in ERSEM-PB now considering only diatoms and picoplankton (i.e., cyanobacteria) only; CM are now included in Video 3 outputs.</li> <li>Video 3: all mixoplankton FTs grouped together in ERSEM-PB. These outputs thus include biomasses of micro-CM, nano-CM and NCM.</li> <li>Video 4: all zooplankton FTs grouped together in standard ERSEM. Thus, these include nanoflagellates, meso- and micro- zooplankton and thus includes the primary producing non-constitutive mixoplankton</li> <li>Video 5: zooplankton FT representing only the heterotrophic nano- and micro- zooplankton in ERSEM-PB.</li> <li>Video 6: spatio-temporal variability between the constitutive and non-constitutive mixoplankton functional groupings within FVCOM-ERSEM-PB. </li> </ul> <p>For further information about the mixoplankton paradigm, please see the following open access publications and references there in:</p> <p>Mitra A, Caron DA, Faure E, Flynn KJ, Leles SG, Hansen PJ, McManus GB, Not F, Gomes HR, Santoferrara L, Stoecker DK, Tillmann U (2023) <strong>The Mixoplankton Database – diversity of photo-phago-trophic plankton in form, function and distribution across the global ocean</strong>. <em>Journal of Eukaryotic Microbiology</em>, e12972. <a href="https://doi.org/10.1111/jeu.12972">https://doi.org/10.1111/jeu.12972</a></p> <p>Glibert PM, Mitra A (2022) <strong>From webs, loops, shunts, and pumps to microbial multitasking: evolving concepts of marine microbial ecology, the mixoplankton paradigm, and implications for a future ocean</strong>. <em>Limnology and Oceanography</em> 67: 585-597 <a href="https://doi.org.10.1002/lno.12018">https://doi.org.10.1002/lno.12018</a> </p> <p>Mitra A, Irigoien X (2022) <strong>Mixoplankton – Marine Organisms that break the rules</strong>. EU Researcher. <a href="https://issuu.com/euresearcher/docs/mixitin_eur28_h_res">https://issuu.com/euresearcher/docs/mixitin_eur28_h_res</a> </p> <p>Flynn KJ, Mitra A, Anestis K, Anschütz AA, Calbet A, et al. (2019) <strong>Mixotrophic protists and a new paradigm for marine ecology: where does plankton research go now?</strong> <em>Journal of Plankton Research</em> 41: 375-391 <a href="https://doi.org/10.1093/plankt/fbz026">https://doi.org/10.1093/plankt/fbz026</a></p>
New insight for comprehensive utilization of Jujube biomass by pyrolysis and activation to prepare biochar and by-products: carbonization process and adsorption nitrogen
<p>Table S1. The all components of by-products from jujube biomass analyzed by GC-MS.</p> <p>Figure S1. The mass spectral peaks of by-products during jujube biomass pyrolysis process</p>
Potential aboveground biomass carbon density
<p>This dataset is the potential above-ground biomass carbon density in the eight provinces of southern China from 2002 to 2017 at the resolution of 500m x 500m, with the urban and water areas, cropland, and the southeast margin of the Tibet Plateau masked. The difference between observed carbon density and the carbon carrying capacity is the potential carbon density. </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)
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.