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4,775 results for “carbon”
IODP Expedition 379 Carbonates composite report
This composite report includes data from two analyses (total carbon from Elemental analysis [CHNS], and inorganic carbon from [Coulometer]). Each row combines the CHNS and Coulometer data from measurements made on the same sample at the same time for a particular section and section offset (depth). If data do not exist for a particular expedition, the column does not appear. To identify individual samples and tests, see each separate data type (Elemental analysis and Coulometer). If the same sample was measured multiple times by any of the methods, results in the report will be combined on one line where possible. Each additional replicate result will be shown in subsequent rows and will be combined where possible. Report includes results for carbon forms: total, inorganic, calcium carbonate, and organic by difference, along with total hydrogen, nitrogen, and sulfur.
IODP Expedition 379 Inorganic carbon (coulometer)
Inorganic carbon (carbonate) is determined by coulometry, which uses a photodetection cell to measure carbon dioxide evolved during sample acidification. Report includes percent inorganic carbon and calcium carbonate.
Data from "Into the unknown: The role of post-fire soil erosion in the carbon cycle"
<p>Wildfires directly emit 2.1 Pg carbon (C) to the atmosphere annually. The net effect of wildfires on the C cycle, however, involves many interacting source and sink processes beyond these emissions from combustion. Among those, the role of post-fire enhanced soil organic carbon (SOC) erosion as a C sink mechanism remains essentially unquantified. Wildfires can greatly enhance soil erosion due to the loss of protective vegetation cover and changes to soil structure and wettability. Post-fire SOC erosion acts as a C sink when off-site burial and stabilization of C eroded after a fire, together with the on-site recovery of SOC content, exceed the C losses during its post-fire transport. Here we synthesize published data on post-fire SOC erosion and evaluate its overall potential to act as longer-term C sink. To explore its quantitative importance, we also model its magnitude at continental scale using the 2017 wildfire season in Europe. Our estimations show that the C sink ability of SOC water erosion during the first post-fire year could account for around 13% of the C emissions produced by wildland fires. This indicates that post-fire SOC erosion is a quantitatively important process in the overall C balance of fires, and highlights the need for more field data to further validate this initial assessment.</p> <p>Here we provide the post-fire SOC erosion dataset ("Post-fire SOC erosion rates" file) used for calculating the SOC ratio of eroded sediments implemented in the RUSLE modelling; as well as the list of data sources ("List of data sources" file).</p>
Supporting data for "Forest carbon uptake as influenced by snowpack and length of photosynthesis season in seasonally snow-covered forests of North America"
<p>This is a supporting dataset for the paper :</p> <div> <div>Yang, J. C., Bowling, D. R., Smith, K. R., Kunik, L., Raczka, B., Anderegg, W. R. L., Bahn, M., Blanken, P. D., Richardson, A. D., Burns, S. P., Bohrer, G., Desai, A. R., Arain, M. A., Staebler, R. M., Ouimette, A. P., Munger, J. W., and Litvak, M. E.: Forest carbon uptake as influenced by snowpack and length of photosynthesis season in seasonally snow-covered forests of North America, Agricultural and Forest Meteorology, 353, 110054, <a href="https://doi.org/10.1016/j.agrformet.2024.110054">https://doi.org/10.1016/j.agrformet.2024.110054</a>, 2024.</div> </div> <p>Descriptions and units for each column can be found in a dedicated page within the data file. Methods are decribed in the paper.</p>
Physical inconsistencies in the representation of the ocean heat-carbon nexus in simple climate models (Ocean and Climate variables from Simple Climate Models)
<p>This dataset provide global ocean and climate variables from 8 Simple Climate Models used in the study "Physical inconsistencies in the representation of the ocean heat-carbon nexus in simple climate models"</p>
IODP Expedition 371 Inorganic carbon (coulometer)
Inorganic carbon (carbonate) is determined by coulometry, which uses a photodetection cell to measure carbon dioxide evolved during sample acidification. Report includes percent inorganic carbon and calcium carbonate.
IODP Expedition 371 Carbonates composite report
This composite report includes data from two analyses (total carbon from Elemental analysis [CHNS], and inorganic carbon from [Coulometer]). Each row combines the CHNS and Coulometer data from measurements made on the same sample at the same time for a particular section and section offset (depth). If data do not exist for a particular expedition, the column does not appear. To identify individual samples and tests, see each separate data type (Elemental analysis and Coulometer). If the same sample was measured multiple times by any of the methods, results in the report will be combined on one line where possible. Each additional replicate result will be shown in subsequent rows and will be combined where possible. Report includes results for carbon forms: total, inorganic, calcium carbonate, and organic by difference, along with total hydrogen, nitrogen, and sulfur.
Data and code from: Climate-based prediction of carbon fluxes from deadwood in Australia
This repository contains the code for the publication 'Climate-based prediction of carbon fluxes from deadwood in Australia'.
IODP Expedition 360 Carbonates composite report
This composite report includes data from two analyses (total carbon from Elemental analysis [CHNS], and inorganic carbon from [Coulometer]). Each row combines the CHNS and Coulometer data from measurements made on the same sample at the same time for a particular section and section offset (depth). If data do not exist for a particular expedition, the column does not appear. To identify individual samples and tests, see each separate data type (Elemental analysis and Coulometer). If the same sample was measured multiple times by any of the methods, results in the report will be combined on one line where possible. Each additional replicate result will be shown in subsequent rows and will be combined where possible. Report includes results for carbon forms: total, inorganic, calcium carbonate, and organic by difference, along with total hydrogen, nitrogen, and sulfur.
IODP Expedition 360 Inorganic carbon (coulometer)
Inorganic carbon (carbonate) is determined by coulometry, which uses a photodetection cell to measure carbon dioxide evolved during sample acidification. Report includes percent inorganic carbon and calcium carbonate.
IODP Expedition 397 Inorganic carbon (coulometer)
Inorganic carbon (carbonate) is determined by coulometry, which uses a photodetection cell to measure carbon dioxide evolved during sample acidification. Report includes percent inorganic carbon and calcium carbonate.
IODP Expedition 397 Carbonates composite report
This composite report includes data from two analyses (total carbon from Elemental analysis [CHNS], and inorganic carbon from [Coulometer]). Each row combines the CHNS and Coulometer data from measurements made on the same sample at the same time for a particular section and section offset (depth). If data do not exist for a particular expedition, the column does not appear. To identify individual samples and tests, see each separate data type (Elemental analysis and Coulometer). If the same sample was measured multiple times by any of the methods, results in the report will be combined on one line where possible. Each additional replicate result will be shown in subsequent rows and will be combined where possible. Report includes results for carbon forms: total, inorganic, calcium carbonate, and organic by difference, along with total hydrogen, nitrogen, and sulfur.
Data repository for "Spatio-temporal trends of Holocene peat carbon accumulation in China: climatic and human drivers"
<p>Dating results collected from peatlands in China are used to calculate the spatiotemporal trends of the Holocene peat accumulation rate (PAR) and net carbon balance (NCB), including all original dating, calculated intermediate results, and final composite results. This file includes a total of 14 tables (Supplementary Tables S1-S14).</p>
Seasonal Carbonate Chemistry Variability in Marine Surface Waters of the Pacific Northwest. Data Archive.
<p>This archive includes two .nc files (NetCDF format) containing observational data (discrete and mooring) from marine surface waters of the Pacific Northwest that have not yet been submitted to a long-term data repository. These data contributed to the development of seasonal cycle data products described in the manuscript by Fassbender et al. A metadata file is provided for the discrete data subset (upper 10 m of discrete observational data); however, the complete cruise datasets and metadata will be submitted for archival in the National Centers for Environmental Information’s (NCEI) Ocean Carbon and Acidification Data repository (<a href="https://www.nodc.noaa.gov/oceanacidification/">https://www.nodc.noaa.gov/oceanacidification/</a>). Data subsets are provided here for accelerated public access. Data users are encouraged to download the complete datasets from NCEI once they are available (<a href="https://www.nodc.noaa.gov/oceanacidification/stewardship/data_portal.html">https://www.nodc.noaa.gov/oceanacidification/stewardship/data_portal.html</a>). Metadata for the University of Washington Oceanic Remote Chemical/Optical Analyzer (ORCA) mooring observations used by Fassbender et al., including the temperature and salinity data from the Dabob Bay and Twanoh moorings, are not provided here. Quality control protocols applied to the ORCA mooring data are outlined in the Quality Assurance Project Plan (<a href="http://nwem.ocean.washington.edu/ORCA_QAPP.pdf">http://nwem.ocean.washington.edu/ORCA_QAPP.pdf</a>; Newton and Devol, 2012).</p>
Carbon sequestration in riparian forests: a global meta-analysis data set
<p>Data collected for a global meta-analysis of riparian forest biomass and soil carbon stocks. Includes studies estimating the carbon stored in the soil or standing live and dead woody vegetation, or the total biomass of woody vegetation in plots described as "riparian" or "floodplain". Also includes soil carbon metrics for plots considered to be "baseline" plots paired with a riparian plot. Excludes studies focused solely on depressional or tidal wetlands, plots lacking woody vegetation, greenhouse experiments, or those that measured only the biomass or carbon content of individual plants.</p> <p>The data file includes DOIs for all studies included (where available), study area coordinates, descriptions of study plots, vegetation age and soil texture (if known), reported values for woody biomass, biomass carbon stock, soil bulk density, soil carbon concentration, soil carbon stock, and/or soil sampling depth. All field descriptions are provided in the accompanying metadata file.</p>
Simulated climate change reduced the capacity of lichen-dominated biocrusts to act as carbon sinks in two semi-arid Mediterranean ecosystems
<p> Biocrust gas exchange measurements used as input data for this study. The methods are described in detail in the related identifier paper.</p>
Global derived datasets for use in k-NN machine learning prediction of global seafloor total organic carbon
<p>This dataset includes 663 predictor grids used for k-NN global prediction of seafloor total organic carbon.</p> <p>663 predictor grids available in netCDF4 HDF5 file format. Grids are cell-centered sized 4320 x 2160. File names adhere to the naming conventions discussed below. The naming structure is partioned by underscores and periods in the following order: interface to which the gridded values refer to, quantity of values contained within the grid, units and reference values/units (e.g. meters below sea level), data source, statistic calculated (if applicable), grid pitch, and file extension.</p> <p>Possible interfaces from the top – down:</p> <p>SS – Sea surface – atmosphere interface (may also be average of the entire water column)</p> <p>SF – Seafloor – water interface (may also be denoted by GL)</p> <p>GL – Ground level (e.g. bottom of pure liquid, top of dirt)</p> <p>SC – Sediment – crust interface (e.g. sediment above, igneous/metamorphic below)</p> <p>CM – Crust – mantle interface (e.g. Mohorovicic discontinuity)</p> <p>Appropriate reference naming marker (bold), original data source, and date of last access:</p> <p><strong>Becker</strong></p> <p>Becker, J. J., Wood, W. T., & Martin, K. M. (2014). <em>Global crustal heat flow using random decision forest prediction</em>, Abstract NG31A-3788 presented at 2014 Fall Meeting, AGU, San Francisco, California, U.S.A. Last access: 06/23/2015.</p> <p><strong>CRUST1</strong> </p> <p>Pasyanos, M.E., Masters, G., Laske, G. & Ma, Z. (2012). <em>LITHO1.0 - An Updated Crust and Lithospheric Model of the Earth Developed Using Multiple Data Constraints</em>, Abstract T11D-09 presented at 2012 Fall Meeting, AGU, San Francisco, California, U.S.A. Last access: 07/01/2014.</p> <p><strong>CRUST1_NOAA</strong></p> <p> As the NOAA sediment thickness database is globally not complete, data gaps in the NOAA grid with this have been supplemented by the CRUST1 sediment thickness (see above citation).</p> <p>Whittaker, J., Goncharov, A., Williams, S., Müller, R. D., & Leitchenkov, G. (2013) Global sediment thickness dataset updated for the Australian-Antarctic Southern Ocean, <em>Geochemistry, Geophysics, Geosystems. </em>https://doi.org/10.1002/ggge.2018.<em> </em>Last access: 09/02/2018.</p> <p><strong>GVP</strong></p> <p>Global Volcanism Program (2013) Volcanoes of the World. In E. Venzke (ed.). (Vol. 4.7.3). Smithsonian Institution. https://doi.org/10.5479/si.GVP.VOTW4-2013. Last access: 09/22/2014.</p> <p><strong>ETOPO2v2</strong></p> <p>National Geophysical Data Center (2006). 2-minute Gridded Global Relief Data (ETOPO2) v2. National Geophysical Data Center, NOAA. DOI: 10.7289/V5J1012Q. Last access: 02/06/2013.</p> <p><strong>PLATES</strong></p> <p>Coffin, M.F., Gahagan, L.M., & Lawver, L.A. (1998). Present-day Plate Boundary Digital Data Compilation. University of Texas Institute for Geophysics Technical Report (No. 174, pp. 5). Last access: 09/15/2014.</p> <p><strong>ONRL</strong></p> <p>Ludwig,W., Amiotte-Suchet, P., & Probst, J. L. (2011). ISLSCP II Global River Fluxes of Carbon and Sediments to the Oceans. In F. G. Hall, G. Collatz, B. Meeson, S. Los, E. Brown de Colstoun, and D. Landis (Eds.), <em>ISLSCP Initiative II Collection</em>. Oak Ridge National Laboratory Distributed Active Archive Center, Oak Ridge, Tennessee, U.S.A. http://dx.doi.org/10.3334/ORNLDAAC/1028. Last Access: 02/15/2015.</p> <p><strong>Muller</strong></p> <p>Müller, R. D., Sdrolias, M., Gaina, C., & Roest, W. R. (2008). Age, spreading rates, and spreading asymmetry of the world’s ocean crust, <em>Geochemistry, Geophysics, Geosystems</em>, 9(4), Q04006. https://doi.org/10.1029/2007GC001743. Last accessed: 07/19/2011.</p> <p><strong>Woa13x</strong></p> <p>Boyer, T.P., Antonov, J. I., Baranova, O. K., Coleman, C., Garcia, H. E., Grodsky, A., et al. (2013) World Ocean Database 2013. In S. Levitus, A. Mishonov (Ed.), <em>NOAA Atlas NESDIS 72, Technical Ed</em>. Silver Spring, MD. http://doi.org/10.7289/V5NZ85MT. Last Access: 09/18/2014.</p> <p><strong>KIM</strong></p> <p>Kim, S.S. & Wessel, P. (2011). New global seamount census from the altimetry-derived gravity data, <em>Geophysical Journal International</em>, 186, 615-631. https://doi.org/10.1111/j.1365-246X.2011.05076.x. Last access: 09/22/2014.</p> <p><strong>HYCOM</strong></p> <p>The 1/12 deg global HYCOM+NCODA Ocean Reanalysis was funded by the U.S. Navy and the Modeling and Simulation Coordination Office. Computer time was made available by the DoD High Performance Computing Modernization Program. The output is publicly available at https://hycom.org/publications/acknowledgements/ocean-reanalysis-data.Last access: 03/19/2014.</p> <p><strong>NCEDC</strong></p> <p>NCEDC (2016). Northern California Earthquake Data Center. UC Berkeley Seismological Laboratory. Dataset. doi:10.7932/NCEDC. Last access: 09/21/2014.</p> <p><strong>Wei2010</strong></p> <p>Wei, C.-L., Rowe, G. T., Escobar-Briones, E., Boetius, A., Soltwedel, T., Caley, M. J., et al.(2010). Global patterns and predictions of seafloor biomass using random forests. <em>PLoS ONE</em>,5(12), e15323. https://doi.org/10.1371/journal.pone.0015323 Last access: 06/20/2016.</p> <p><strong>NGA_egm2008</strong></p> <p>Pavlis, N.K., Holmes, S. A., Kenyon, S. C., & Factor, J. K. (2008). <em>The</em> <em>EGM2008 Global Gravitational Model</em>, Abstract 2008AGUFM.G22A..01P presented at the 2008 General Assembly of the European Geosciences Union, Vienna, Austria. Last access: 07/10/2014.</p> <p><strong>WAVEWATCH3</strong></p> <p>The 1/12 deg global HYCOM+NCODA Ocean Reanalysis was funded by the U.S. Navy and the Modeling and Simulation Coordination Office. Computer time was made available by the DoD High Performance Computing Modernization Program. The output is publicly available at https://hycom.org/publications/acknowledgements/ocean-reanalysis-data. Last access: 03/19/2014.</p> <p>Updated global seafloor porosity grid using our k-nearest neighbors algorithm using 5 nearest neighbors. Observed data used for prediction from Martin et al. (2015). </p> <p>Martin, K. M., Wood, W. T., & Becker, J. J. (2015). A global prediction of seafloor sediment porosity using machine learning. <em>Geophysical Research Letters</em>, 42(24), 10640. https://doi.org/10.1002/2015GL065279</p> <p>Other grids which have been generated by empirical means are latitude (and derivatives), longitude (and derivatives), Coriolis, coast_is_1.0, and the random noise grids. </p> <p>Units referenced are as follows:</p> <p>KGM3 - kilogram per cubic meter<br> MS - meters per second<br> KM - kilometer<br> M_ASL - meters above sea level (i.e. meters referenced to sea level)<br> MWM2 - milliwatt per square meter<br> TGCYR - terragram of carbon per year<br> TGYR - terragram per year<br> MA - megaannum<br> M - meters<br> MGCM2 - milligram of carbon per square meter<br> DEG - degree<br> S - seconds</p> <p>Statistics grids are calculated within a given radius (e.g. 10km, 50km, 125km, 250km, 500km, 1000km) of the respective cell-centered value. The statistics grids include mean (.men), average absolute deviation from the mean (.aad), and the common logarithm (.log) of the absolute value of the mean (.mlg). Additionally, some grids are a weighted count for given radii (e.g. seamounts) where weight is a cosine taper from the center of the grid cell. </p> <p>The grid pitch for this dataset is uniformly at 5-arc minute denoted by “.5m”. Additionally, the extension used (netCDF4) is denoted by “.nc”.</p>
Towards a more complete quantification of the global carbon cycle
<p>These are the data and IDL code required to create Table3 from the paper.</p> <p><strong>Abstract.</strong></p> <p>The main components of global carbon budget calculations are the emissions from burning fossil fuels, cement production, and net land-use change, partly balanced by ocean CO<sub>2</sub> uptake and CO<sub>2</sub> increase in the atmosphere. The difference between these terms is referred to as the residual sink, assumed to correspond to increasing carbon storage in the terrestrial biosphere through physiological plant responses to changing conditions (Δ<em>B</em><sub>phys</sub>). It is often used to constrain carbon exchange in global earth-system models. More broadly, it guides expectations of autonomous changes in global carbon stocks in response to climatic changes, including increasing CO<sub>2</sub>, that may add to, or subtract from, anthropogenic CO<sub>2</sub> emissions.</p> <p>However, a budget with only these terms omits some important additional fluxes that are important to correctly infer Δ<em>B</em><sub>phys</sub>. They are cement carbonation and fluxes into increasing pools of plastic, bitumen, harvested-wood products, and landfill deposition after disposal of these products, and carbon fluxes to the oceans via wind erosion and non-CO<sub>2</sub> fluxes of the intermediate break-down products of methane and other volatile organic compounds. While the global budget includes river transport of dissolved inorganic carbon, it omits river transport of dissolved and particulate organic carbon, and the deposition of carbon in inland water bodies.</p> <p>Each one of these terms is relatively small, but together they can constitute important additional fluxes that would significantly reduce the size of the inferred Δ<em>B</em><sub>phys</sub>. We estimate here that inclusion of these fluxes would reduce Δ<em>B</em><sub>phys</sub> from the currently reported 3.6 GtC yr<sup>–1 </sup>down to about 2.1 GtC yr<sup>–1</sup> (excluding losses from land-use change). The implicit reduction in the size of ΔB<sub>phys</sub> has important implications for the inferred magnitude of current-day biospheric net carbon uptake and the consequent potential of future biospheric feedbacks to amplify or negate net anthropogenic CO<sub>2</sub> emissions.</p>
Carbon x-ray Raman scattering mapping and spectroscopy of a fragment of Lepidodendron trunk from the Upper Carboniferous
<p>Carbon x-ray Raman scattering mapping and spectroscopy of a fragment of Lepidodendron trunk from the Upper Carboniferous (ca. 305 Mya) of Noyelles-lez-Lens, France</p>
NMR data - Mechanism and regioselectivity of the anionic oxidative rearrangement of 1,3-diketones towards all-carbon quaternary carboxylates
<p>NMR characterisation raw data for the publication:</p> <p>Mechanism and regioselectivity of the anionic oxidative rearrangement of 1,3-diketones towards all-carbon quaternary carboxylates</p>
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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.