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57 results for “Carbon sink”
Increased interannual precipitation variability enhances the carbon sink in a semiarid grassland
<p>1. The amplifying interannual precipitation variability has been observed globally and is projected to intensify under climate change scenarios. However, its impacts on terrestrial vegetation and carbon (C) sink have not been well investigated.</p> <p>2. As part of a field manipulative experiment with three precipitation variabilities (20%, 40%, and 60%) in a semiarid grassland, this study was conducted to examine the responses of ecosystem C cycling to increased precipitation variability.</p> <p>3. Across the 3 experimental years from 2010 to 2012, amplified precipitation variability enhances gross primary productivity (GPP) and ecosystem respiration (ER) as both GPP and ER were more sensitive to above- than below-average precipitation. In addition, the larger responses of GPP than ER to precipitation variability resulted in an enhancement of net ecosystem productivity (NEP), and the magnitudes of NEP increased with the increasing precipitation variability. More species with higher sensitivity to increased precipitation under wet condition and the insensitive response of root growth to decreased precipitation could largely be responsible for the above observations, which suggest that the semiarid grassland was more sensitive to wet treatment yet strongly resistant to drought.</p> <p>4. Our results provide empirical evidence that intensified precipitation variability could stimulate grassland C sink. The findings revealed in this study could facilitate the mechanistic understanding and imply the potential positive feedback of climate variability-terrestrial C sink.</p>
The datasets for "Substantial Nitrogen Abatement Accompanying Decarbonization Suppresses Terrestrial Carbon Sinks in China".
<p><span>The modeled nitrogen deposition data</span><span>,</span><span> ecosystem carbon flux data and processed data generated in this study.</span></p>
Urban Vegetation Carbon Sink Response to Urbanization Depends on Urban Expansion Rate
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A robust estimate of continental-scale terrestrial carbon sinks using GOSAT XCO2 retrievals
<p>The dataset contains monthly terrestrial ecosystem carbon fluxes (NEE) for 51 terrestrial regions from 2011-2014. We used simulations from 12 terrestrial biosphere models (TBMs) as the prior carbon fluxes, therefore the posterior carbon fluxes correspond to the 12 TBMs.</p>
TRENDYv10 DGVM output for: Process-oriented analysis of dominant sources of uncertainty in the land carbon sink
<p><strong>New datasets saved as NCDF files.</strong></p> <p><strong>Data</strong></p> <p>Post-processed TRENDYv10 DGVM data covering the period 1901-2020. Each file contains monthly/annual mean values for 19 models: CABLE-POP, CLASSIC, CLASSIC-N, CLM5.0, DLEM, IBIS, ISAM, ISBA-CTRIP, JSBACH, JULES-ES-1.1, LPJ-GUESS, LPJ, LPX-Bern, OCN, ORCHIDEE, ORCHIDEEv3, SDGVM, VISIT, YIBs. See https://blogs.exeter.ac.uk/trendy/</p> <p>List of variables can be found at https://blogs.exeter.ac.uk/trendy/documents/ . File name: 'trendy_listofvariables_GCP2021'.</p> <p>Data for three experiments (S1 - S3) is provided. For experiment details, see 'GlobalCarbonBudget-Protocol-2021-web' at https://blogs.exeter.ac.uk/trendy/documents/</p> <p><strong>Code</strong></p> <p>All data processing code is also provided. Files starting "raw#..." are run in order 1-4. Then the figure/manuscript files are executed, starting with "proc1.." - "proc3..". The remaining Figure scripts (Figures 1-5 and S1-S8) and "temp_changes.R" can be run in any order.</p> <p>Any enquiries, contact Mike O'Sullivan at m.osullivan@exeter.ac.uk</p>
Data of global wetland carbon sinks
<p>A database of the annual net ecosystem production (NEP) across global wetlands. In total, the database comprised 2,385 observations of annual NEP data across global wetlands.</p>
Increased interannual precipitation variability enhances the carbon sink in a semiarid grassland
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Decreased precipitation in the late growing season weakens an ecosystem carbon sink in a semiarid grassland
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Data from: Montane meadows: A soil carbon sink or source?
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Dataset for the article entitled "Macroalgal metabolism and lateral carbon flows can create significant carbon sinks"
<p>The dataset for the article entitled “Macroalgal metabolism and lateral carbon flows can create significant carbon sinks” by Watanabe et al., 2020</p>
Supplementary information_Limited organic carbon burial by the rusty carbon sink in Swedish fjord sediments.
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Carbon:234Thorium ratios of sinking particles collected by sediment trap and large particles collected by in situ pump on CCE-LTER Process Cruises. 2006 - 2017 (ongoing).
The ratio of carbon to 234Thorium on particles can be used to convert water column 234Th measurements to sinking carbon flux estimates and to investigate particle scavenging in the ocean. Carbon:234Th ratios of two types of particles are measured: true sinking particles collected by sediment trap and large (>50-µm) particles collected using a McLane in situ Pump. Sinking particles are collected in VERTEX-style particle interceptor traps (PIT) with an 8:1 aspect ratio, 70-mm diameter, and a baffle on top comprised of 13 smaller beveled tubes with a similar 8:1 aspect ratio. Tubes are deployed with a formalin-brine for a duration of 2-5 days. After recovery, samples were gravity filtered through a 200-µm filter. The 200-µm filtered was then immediately examined under a stereomicroscope and mesozooplankton swimmers were removed from the sample. On P0704, P0810, and P1106 cruises the remaining (non-swimmer) portion of the material on the >200-µm filter was then re-combined with the <200-µm portion of the sample. Samples were then filtered through a pre-combusted quartz (QMA) filter. On P1208, P1604, and P1706 cruises the >200-µm and <200-µm fractions were filtered separately onto QMA filters to determine C:234Th ratios of different size classes of sinking particles. Typically, triplicate whole PIT tubes were filtered for C:234Th ratios. However, on some cycles with very high flux, samples were first split on a Folsom splitter. On some cycles, the mesozooplankton ‘swimmers’ were saved and filtered onto a separate QMA filter to quantify the C:234Th ratio of mesozooplankton. QMA filters were all dried and mounted in RISO sample holders. >50-µm particles were also collected using McLane in situ pumps (typically deployed for a period of ~1 hour at the same depth of the sediment traps). In situ pump samples were rinsed off of the 147-mm nitex mesh filters used with the pump onto a pre-combusted QMA filter, which was similarly dried and mounted in a RISO sample holder. Samples
N deposition and carbon sink
<p>Datasets for modeled nitrogen deposition and ecosystem carbon fluxes in China.</p>
Data and code for: Cost-effective priorities for land carbon sink conservation in China
<p>This is the data and code for: Cost-effective priorities for land carbon sink conservation in China</p> <p>The code in this study is organized into two parts:</p> <ol> <li><strong>machine_learning:</strong> This folder contains the code for training machine learning models and making predictions. The code, written in Python 3.8, runs on Ubuntu 20.04 with CUDA 11.8.</li> <li><strong>data_analysis:</strong> This folder includes the code for analyzing predictions and generating figures. The code is also written in Python 3.8 and can be executed on both Ubuntu and Windows environments.</li> </ol> <p>All the code is provided as Jupyter Notebooks, allowing for immediate, step-by-step results.</p> <p>The data generated by the code can be found in "00_data/output" directory within each folder.</p>
Modulation of Polar Auxin Transport Identifies the Molecular Determinants of Source-Sink Carbon Relationships and Sink Strength in Poplar
GEO Series GSE232245. Populus tremula x Populus alba. 64 samples. Type: Expression profiling by high throughput sequencing.
Data from paper: Large carbon sink potential of Secondary Forests in Brazilian Amazon to mitigate climate change
<p><strong>Title</strong>: Large carbon sink potential of Secondary Forests in the Brazilian Amazon to mitigate climate change</p> <p><strong>Contact:</strong> Viola Heinrich (viola.heinrich@bristol.ac.uk)</p> <p><strong>This repository contains</strong>:</p> <ol> <li>Zipped folder:<strong> Fig1_data_input.zip</strong> - all the files needed to produce Figure 1a-e of the main paper. Set the working directory to folder containing the file and use the script "Fig1_analysis_all_variables_asAGC.R" to run (see below). The folder contains the input files of the 6 driving variables used to build regrowth models seen in Figure 1 - these files are in the format "<strong><driver>_assessment_v2.csv</strong>". The columns in the files are: A: age of secondary forest; B: 50th percentile (median) of the modal Aboveground Biomass (AGB) value for the given age (note, units are in biomass not carbon: Mg/ha/yr); C: The bias-corrected AGB value, calculated by subtracting the lowest AGB value in column B such that the AGB data starts at or near 0Mg/ha/yr at age 1. D: the number of secondary forest pixels observed to have the given age, E: "Threshold" : the threshold limits of the given driver e.g. 0 Fires in fire_assessmentv2.csv implies the corresponding secondary forest pixels experienced 0 fires throughout the analysis period. </li> <li>Zipped folder:<strong> Fig1_confidence_intervals.zip</strong> - all the files need to produce the confidence intervals seen in Figure 1a-e of the main paper: units are in MgC/ha/yr as they appear in the Figure. column A: lower limit; B: upper limit</li> <li>Zipped folder:<strong> Fig2_regions_outline.zip</strong> - contains the boundaries of the 4 regions identified in Figure 2a of the main paper in a shapefile (.shp) format and the corresponding file formats needed to produce and load a shapefile. </li> <li>Zipped folder: <strong>Fig1g_2b_e_variable_importance.zip</strong> - contains the output files of the random forest analysis assessing the variable importance for the whole Amazon ("whole_Amazon" subfolder) and for the different regions identified in Figure2a. Files are given as .RDS files that can be loaded in R and the corresponding figures produced using the script "Fig1g_2b_e_variable_importance.R". Files start with the region of interest e.g. "whole_Amazon" or "NE_sector". Middle part of the filename - importance_conditionalTrue/False - this determines whether the importance was calculated using the conditional permutation (True) or not (False). The end of the file name - seed<NUM> - denotes the number of the random seed that was set to extract the sample data. e.g. whole_Amazon_2500_cforest_important_conditionalTrue_seed200.RDS - shows the conditional permutation importance assessment using a sample size of 2500 when the setseed parameter was set to 200 to extract a random sample representing the whole Amazon. The remaining files include the sample data used to build the random forest model at each iteration - as a .csv file and the random forest output - as .RDS file. Please note the code to produce the random forest model and the importance assessment has not been included here - this code takes multiple days to run, so only the input and outputs have been included here. Please contact the corresponding author (see end) for more information on this. </li> <li>Zipped folder: <strong>Fig3_data_input.zip</strong> - all the files needed to produce Figure 3a-d of the main paper. Set the working directory to folder containing the file and use the script "Fig3_analysis_byAllRegions_asAGC.R" to run (see below). The folder contains the input files of the 6 driving variables used to build regrowth models seen in Figure 3 - these files are in the format "<strong><REGION>-Group.csv</strong>". See bullet point 1 for explanations for the columns in the file. Again column E -"threshold" denotes the code used to identify the the 4 subclasses of regrowth seen in the Figure. Where 11 = No disturbance; 12 = Only burning; 21 = Only (multiple) deforestations; 22 = Both burning and multiple deforestations as disturbance. The folder also contains another set of files "<strong><REGION>_whole_class.csv" </strong>these files do not distinguish disturbance and can be used to model the regrowth for the whole region (this is not shown in any of the Figures). The code takes data in AGB and converts to AGC.</li> <li>Zipped folder:<strong> Fig3_confidence_intervals.zip</strong> - all the files needed to produce the confidence intervals seen in Figure 3a-d of the main paper. These filenames are in the format <region>_number of the region_<number referring to the disturbance combination>_confidence_interval_asAGC.csv. Where the number of the region: 1 - SW; 2 - SE; 3 - NW; 4 - NE. Where the disturbance combination: 1 - No disturbance; 2 - Only fire disturbance; 3 - Only deforestation disturbance; 4 - Both disturbances. so the file NE_4_1_confidence_interval_as_AGC.csv, contains the confidence intervals of the regrowth model in the NE sector of the Amazon under No disturbance. " Units are in MgC/ha/yr as they appear in the Figure. column A: lower limit; B: upper limit. </li> <li> Zipped folder: <strong>Fig4_5_carbon_sink_2017.zip </strong>- Contains two subfolders: a) <strong>Map_aggre_0.1deg</strong> -this folder contains .tiff files (and associated files) of the losses, gains and net change in AGC between 2016 - 2017 in secondary forests in Amazonia - this has been aggregated to 0.1 degree grid cells so each cell contains the total sum of the losses/gains experienced by secondary forests in that 0.1degree grid cell. b) <strong>secondary_forest_by_region_and_disturbance </strong>- this folder contains .tiff files (and associated files) of the secondary forest data at the original resolution (30m) for 2016 and 2017 split up according to the regions identified in Figure 2, and the type of disturbance (if any). The associated files include a .dbf file which includes additional data [read "README.txt" file in folder] - upon loading the data in a GIS software - the age of the secondary forest pixel will be displayed - open the attribute table to see more data associated with that given pixel e.g. modelled associated AGB for a given pixel. Files in this folder can be used to make Figure 4d and Figure 5 - see script "Fig4_Fig5_analysis.R" in the code repository (see below). </li> </ol> <p><strong>Code: </strong>The corresponding code mentioned here can be access here: <a href="https://github.com/heinrichTrees/secondary-forest-amazonia-regrowth">heinrichTrees/secondary-forest-amazonia-regrowth: This repository contains the code used to produce data shown in Heinrich et al. (github.com)</a> </p> <p><strong>Data usage: </strong>When using any code or data in this repository or another related to this study please cite Heinrich et al.2021 and the original paper. </p> <p>If you need anything else, please contact the corresponding author: Viola Heinrich (viola.heinrich@bristol.ac.uk)</p>
Fire-prone monodominant vegetation acts as carbon sinks after fire - Data
<p>These data are part of the analysis and results found in the article in process of publication entitled -</p> <p><strong>Fire-prone monodominant vegetation acts as carbon sinks after fire</strong></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.