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157 results for “carbon dynamics”
Carbon Dynamics Along a Permafrost Gradient at Caribou-Poker Creeks Research Watershed (CPCRW) in Interior Alaska: Specific Leaf Area (SLA) for alder (Alnus crispa) and black spruce (Picea mariana) in a 75x75m spatial domain along a permafrost and vegetation gradient.
This dataset includes depth-resolved soils data from September 2014 coring: soil pH, gravimetric soil moisture, roots/rocks, bulk density, humification indices as determined by FTIR, total elemental composition (carbon, nitrogen, sulfur), depth to mineral horizon, thickness of the moss layer, percent groundcover at the sampling location of several common species, and soil temperature at the time of coring. Project summary: Specific leaf area (SLA, leaf area per unit dry mass) is a key canopy structural characteristic, a measure of photosynthetic capacity, and an important input into many terrestrial process models. Although many studies have examined SLA variation, relatively few data exist from high latitude, climate-sensitive permafrost regions. We measured SLA and soil and topographic properties across a boreal forest permafrost transition, in which forest composition changed as permafrost deepened from 54 to >150 cm over 75 m hillslope transects in Caribou-Poker Creeks Research Watershed, Alaska. This is an exploratory study to begin understanding SLA variation and controls thereof in a non-contiguous permafrost system.
Carbon Dynamics Along a Permafrost Gradient at Caribou-Poker Creeks Research Watershed (CPCRW) in Interior Alaska: Forest stand structure in a 75x75m spatial domain along a permafrost and vegetation gradient.
This dataset includes forest stand structure. Project summary: Specific leaf area (SLA, leaf area per unit dry mass) is a key canopy structural characteristic, a measure of photosynthetic capacity, and an important input into many terrestrial process models. Although many studies have examined SLA variation, relatively few data exist from high latitude, climate-sensitive permafrost regions. We measured SLA and soil and topographic properties across a boreal forest permafrost transition, in which forest composition changed as permafrost deepened from 54 to >150 cm over 75 m hillslope transects in Caribou-Poker Creeks Research Watershed, Alaska. This is an exploratory study to begin understanding SLA variation and controls thereof in a non-contiguous permafrost system.
Carbon Dynamics Along a Permafrost Gradient at Caribou-Poker Creeks Research Watershed (CPCRW) in Interior Alaska: GPS coordinates for a 75x75m spatial domain along a permafrost and vegetation gradient.
This dataset includes GPS coordinates and elevation data across a 75x75m spatial domain in the Caribou-Poker Creeks Research Watershed. Project summary: Specific leaf area (SLA, leaf area per unit dry mass) is a key canopy structural characteristic, a measure of photosynthetic capacity, and an important input into many terrestrial process models. Although many studies have examined SLA variation, relatively few data exist from high latitude, climate-sensitive permafrost regions. We measured SLA and soil and topographic properties across a boreal forest permafrost transition, in which forest composition changed as permafrost deepened from 54 to >150 cm over 75 m hillslope transects in Caribou-Poker Creeks Research Watershed, Alaska. This is an exploratory study to begin understanding SLA variation and controls thereof in a non-contiguous permafrost system.
Soil carbon: Successional Dynamics on a Resampled Chronosequence
The purpose of this observational study is to describe the dynamics of ecosystem succession. The change in the number, type, and amount of plant and grazing animal species is monitored in more than 20 fields. These fields were previously cultivated, but then abandoned from agriculture at various times in the past. The fields were left undisturbed for plants to develop from seeds within the soil or brought into the fields by wind or animals. Permanent transects have been established in these abandoned fields for purposes of sampling in a consistent location from year to year. Permanent plots along these transects have been used to sample soil nutrients, (in particular, nitrogen) abundance of vegetation, species composition and herbivore populations. The sampling occurs approximately every 6 years. In the initial survey, 100 quadrats of size 1 by 0.5 m were sampled per field in 23 different fields. Abandoned fields included in E014 are 4, 5, 10, 21, 24, 26, 27, 28, 32, 35, 39, 40, 41, 44, 45, 47, 53, 70, 72, 76, 77. Fields 22(B), 29(A), and 69(C) were originally included in E014 but used for other purposes shortly after the start of the study. This experiment was established in 1983 and 1989 by principal investigators Johannes Knops and David Tilman. Past work at CDR and elsewhere has demonstrated an overriding influence of fire frequency in maintaining prairie openings and oak savanna at the prairie-forest border. Fire regimes harm some types of species while favoring others and drive light and nutrient dynamics, which in turn drive community functional attributes and diversity levels. Ultimately, fire frequency interacts with climate, N deposition, land use, and biotic invasion to determine the outcomes of tree-grass interactions and the dynamics of vegetation at ecotones such as the prairie-forest border in Minnesota. In 2006 each field was divided in half, and one half randomly chosen for periodic prescribed burning (a fire every other year). We anticipate that th
Simulations from the SEIB-DGVM dynamic global vegetation model for the Vegetation Carbon Turnover Intercomparison
<p>Outputs from the SEIB-DGVM dynamic global vegetation model. One forced by CRU-NCEP v5 climate data for the period 1901-2014 and one forced by bias-corrected IPSL-CM5A-LR RCP 8.5 climate data for the period 1901-2099. Only potential natural vegetation was simulated, i.e. no human land-use. Both simulations are at 0.5 x 0.5 degree spatial resolution. Carbon turnover fluxes for live vegetation for each individual turnover-causing process in the model were outputted individually. For a full description of the simulations, please refer to the accompanying paper.</p>
Data for "Sub-7-femtosecond conical-intersection dynamics probed at the carbon K-edge"
<p>Data sets underlying Figs. 1-4, and S1-S6, S8-S20 of the paper entitled "Sub-7-femtosecond conical-intersection dynamics probed at the carbon K-edge".</p>
Fire promotes functional plant diversity and modifies soil carbon dynamics in tropical savanna
<p>The dataset associated with the manuscript "Fire promotes functional plant diversity and modifies soil carbon dynamics in tropical savanna" (Teixeira et al.) includes 6 different datasets, for which we provided one metadata.<br> </p> <p><strong>Version 2</strong> includes an update of the biomass data set, including the correct transformation to g/m2 on fine roots biomass data.<br><br><strong>Version 3 </strong>includes an update of the belowground traits data set based on correcting formatting errors in the belowground traits data. <br><br><strong>Version 4 </strong>Sorry for the inconvenience. This version includes the correct updated belowground traits data file based on the correct formatting errors in the belowground trait data. <br><br>fluxes: it includes data related to net ecosystem C and water exchange. NEE and ET from each plot were measured using the LiCOR 7500 infrared gas analyzer (Li-Cor Inc.). See the method section in the manuscript for full details.</p> <p>soil_carbon: it includes carbon soil data.<br><br>biomass_v2: it includes data related to aboveground and belowground biomass. Aboveground data were collected in 0.5m2 subplot and belowground at 0.25m2 at 20cm depth both within 1m2 sampling plot. See the method section in the manuscript for full details.</p> <p>aboveground_traits: all aboveground functional traits from plant species. See the method section in the manuscript for full details.</p> <p>belowground_traitsv3: all roots functional traits from plant species. See the method section in the manuscript for full details.</p> <p>species_composition: plant community composition. See the method section in the manuscript for full details.</p> <p><br><strong>Abstract</strong><br>Fire is an evolutionary environmental filter in tropical savanna ecosystems altering functional diversity and associated C pools in the biosphere and fluxes between the atmosphere and biosphere. Therefore, alterations in fire regimes (e.g. fire exclusion) will strongly influence ecosystem processes and associated dynamics. In those ecosystems, C dynamics and functions are underestimated by the fire-induced offset between C output and input. To determine how fire shapes ecosystem C pools and fluxes in an open savanna across recently burned and fire excluded areas, we measured the following metrics: (I) plant diversity including taxonomic (i.e. richness, evenness) and plant functional diversity (i.e. functional diversity, functional richness, functional dispersion and community weighted means); (II) structure (i.e. above- and below-ground biomass, litter accumulation); and (III) functions related to C balance (i.e. net ecosystem carbon dioxide (CO<sub>2</sub>)<sub> </sub>exchange (NEE), ecosystem transpiration (ET), soil respiration (soil CO<sub>2</sub> efflux), ecosystem water use efficiency (eWUE) and total soil organic C (SOC). We found that fire promoted aboveground live and belowground biomass, including belowground organs, and coarse and fine root biomass, and contributed to higher biomass allocation belowground. Fire also increased both functional diversity and dispersion. NEE and total SOC were higher in burned plots compared to fire-excluded plots whereas soil respiration recorded lower values in burned areas. Both ET and eWUE were not affected by fire. Fire strongly favored functional diversity, fine root, and belowground organ biomass in piecewise SEM models but the role of both functional diversity and ecosystem structure to mediate the effect of fire on ecosystem functions remain unclear. Fire regime will impact C balance, and fire exclusion may lead to lower C input in open savanna ecosystems.</p>
Skogaryd data used for the paper: Evaluation of long-term carbon dynamics in a drained forested peatland using the ForSAFE-Peat Model.
<p>Dataset of abiotic and carbon exchange variables for Skogaryd drained afforested peatland. The dataset include measurements of soil temperature, ground water level, and carbon exhange as well as modelled carbon fluxes performed with the model ForSAFE-Peat </p>
Data from: Drought and recovery effects on belowground respiration dynamics and the partitioning of recent carbon in managed and abandoned grassland
<p>The supply of soil respiration with recent photoassimilates is an important and fast pathway for respiratory loss of carbon (C). To date it is unknown how drought and land-use change interactively influence the dynamics of recent C in soil respired CO<sub>2</sub>. In an <em>in situ </em>common-garden experiment, we exposed soil-vegetation monoliths from a managed and a nearby abandoned mountain grassland to an experimental drought. Based on two <sup>13</sup>CO<sub>2</sub> pulse-labelling campaigns, we traced recently assimilated C in soil respiration during drought, rewetting and early recovery. Independent of grassland management, drought reduced the absolute allocation of recent C to soil respiration. Rewetting triggered a respiration pulse, which was strongly fueled by C assimilated during drought. In comparison to the managed grassland, the abandoned grassland partitioned more recent C to belowground respiration than to root C storage under ample water supply. Interestingly, this pattern was reversed under drought. We suggest that these different response patterns reflect strategies of the managed and the abandoned grassland to enhance their respective resilience to drought, by fostering their resistance and recovery, respectively. We conclude that while severe drought can override the effects of abandonment of grassland management on the respiratory dynamics of recent C, abandonment alters strategies of belowground assimilate investment, with consequences for soil-CO<sub>2</sub> fluxes during drought and drought-recovery.</p>
Data used to reconstruct carbon dynamics of Siikaneva, Kalevansuo and Lompolojänkkä.
<p>Peat core data from three Finnish peatlands used to reconstruct site-scale carbon exchange between the peatlands and the atmosphere. Basal peat radiocarbon ages; reconstructed peat area over time based on these basal ages; carbon accumulation data along multiple peat cores; additionally reconstructed peat type and methane emission rates.</p>
Dataset: Blue carbon dynamics across a salt marsh-seagrass ecotone in a cool-temperate South African estuary
<p>This is a dataset of organic carbon, nitrogen, and phosphorus content from the Olifants estuary. The study was designed to investigate drivers of variability at different spatial scales and across the salt marsh-seagrass ecotone. Samples were collected in March 2023 from three selected sites (upper, middle, and lower) in the estuary. Each site featured three transects, extending from the salt marsh vegetation of mixed species through the <em>Zostera capensis</em> seagrass meadows towards the water. Sediment cores were taken to a depth of 50 cm, but only the top 0-5 cm section was analyzed. Carbon and nitrogen content were measured using an Elementar Vario EL Cube Elemental CHNS Analyzer, while phosphorus was determined by ICP at Central Analytical Facilities (Stellenbosch University).</p> <p> </p> <table> <tbody> <tr> <td> <p><strong>Sampling site</strong></p> </td> <td> <p><strong>GPS coordinates</strong></p> </td> </tr> <tr> <td> <p>Upper</p> </td> <td> <p>31°39'45.27"S, 18°11'42.40"E</p> </td> </tr> <tr> <td> <p>Middle</p> </td> <td> <p>31°40'56.46"S, 18°12'3.72"E</p> </td> </tr> <tr> <td> <p>Lower</p> </td> <td> <p>31°41'39.85"S, 18°11'15.95"E</p> </td> </tr> </tbody> </table> <p> </p> <p><strong>File description</strong></p> <p><em>CHNS_Dataset_SM&INT.xlsx</em>: Data for intermediate sample measurements including percent organic carbon content and and percent nitrogen content for all sites</p> <p><em>ICP_Data_INT.xlsx</em>: Data for Phosphorus content and other related measurements for the upper site. </p>
Database Manuscript Temperature and moisture are minor drivers of regional-scale soil organic carbon dynamics - Gonzalez Dominguez et al
<p>The database contained the data used in the manuscript <strong>Temperature and moisture are minor drivers of regional-scale soil organic carbon dynamics, by Gonzalez Dominguez et al. </strong></p>
Data for the Carbon Erosion Dynamics Model (CE-DYNAM)
<p>Data on soil erosion by rainfall and runoff and data on the turnover rates between carbon pools on land of the Rhine catchment for the period 1850-2005. This dataset belongs to the model code that will be published in the near future along with a paper in submission to the GMD journal.</p>
Data and Processing from "Carbon-centric dynamics of Earth's marine phytoplankton"
<div><strong>Brief Summary:</strong></div> <div>This documentation is for associated data and code for: </div> <div>A. Stoer, K. Fennel, Carbon-centric dynamics of Earth's marine phytoplankton. Proceedings of the National Academy of Sciences (2024).</div> <div> </div> <div>To cite this software and data, please use:</div> <div> <div>A. Stoer, K. Fennel, Data and processing from "Carbon-centric dynamics of Earth's marine phytoplankton". Zenodo. <a href="https://doi.org/10.5281/zenodo.10949682" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.10949682</a>. Deposited 1 October 2024.</div> </div> <div> </div> <div><strong>List of folders and subfolders and what they contain:</strong></div> <div> <ol> <li>raw data: Contains raw data used in the analysis. This folder does not contain the satellite imagery, which will need to be downloaded from the NASA Ocean Color website (https://oceancolor.gsfc.nasa.gov/). <ol> <li>bgc-argo float data (subfolder): Includes Argo data from its original source or put into a similar Argo format</li> <li>global region data (subfolder): Includes data used to subset the Argo profiles into each 10deg lat region and basin.</li> <li>graff et al 2015 data (subfolder): Include the data digitized from Graff et al.'s Fig. 2.</li> </ol> </li> <li>processed data: data processing by this study (Stoer and Fennel, 2024) <ol> <li>processed bgc-argo data (subfolder): A binned processed file is present for each Argo float used in the analysis. Note these files include those describe in Table S1 (these are later processed in "3_stock_bloom_calc.py")</li> <li>processed satellite data (subfolder): includes a 10-deg latitude averaged for each satellite image processed (called "chl_sat_df_merged.csv"). This is later used to calculate a satellite chlorophyll-a climatology in "3_stock_bloom_calc.py".</li> <li>processed chla-irrad data (subfolder): includes the quality-controlled light diffuse attenuation data coupled with the chlorophyll-a fluorescence data to calculate slope factor corrections (the file is called "processed chla-irrad data.csv").</li> <li>processed topography data (subfolder): includes smoothed topography data (file named "ETOPO_2022_v1_60s_N90W180_surface_mod.tiff").</li> </ol> </li> <li>software: <ol> <li>0_ftp_argo_data_download.py: This program downloads the Argo data from the Global Data Assembly Center's FTP. Running this program will provide new Argo float profiles. However, there will be new floats and profiles present if downloaded. This will not match the historical record of Argo floats used in this analysis but could be useful for replicating this analysis when more data becomes available. The historical record of BGC-Argo floats are present in "/raw data/bgc-argo float data/" path. If you wish to downloaded other float data, see Gordon et al. (2020), Hamilton and Leidos (2017) and the data from the misclab website (https://misclab.umeoce.maine.edu/floats/).</li> <li>1_argo_data_processing.py: This program quality-controls and bins the biogeochemical data into a consistent format. This includes corrections and checks, like the spike/noise test or the non-photochemical quenching correction.</li> <li>2_sat_data_processing.py: this program processes the satellite data downloaded from the NASA Ocean Color website.</li> <li>3_stock_bloom_calc.py: this is the main program used to described the results of the study. The program takes the processed Argo data and groups it into regions and calculates slope factors, phytoplankton carbon & chlorophyll-a, global stocks, and bloom metrics.</li> <li>4_stock_calc_longhurst_province.py: This program repeats the global stocks calculations performed in "3_stock_bloom_calc.py" but bases the grouping on Longhurst Biogeochemical Provinces.</li> </ol> </li> </ol> </div> <div><strong>How to Replicate this Analysis:</strong></div> <div>Each program should be run in the order listed above. Path names where the data files have been downloaded will need to be updated in the code.</div> <div> </div> <div>To use the exact same Sprof files as used in the paper, skip running "0_ftp_argo_data_download.py" and start with "1_argo_data_processing.py" instead. Use the float data from the folder "bgc-argo float data". The program "0_ftp_argo_data_download.py" downloads the latest data from Argo database, so it is useful for updating the analysis. The program "1_argo_data_processing.py" may also be skipped to save time and the processed BGC-Argo float data may be used instead (see folder named "processed bgc-argo data"). </div> <div> </div> <div>Similarly, the program "2_sat_data_processing.py" may also be skipped, which otherwise can take multiple hours to process. The raw data is available from the NASA Ocean Color website (<a href="https://oceancolor.gsfc.nasa.gov/">https://oceancolor.gsfc.nasa.gov/</a>). The processed data from "2_sat_data_processing.py" is available so this step may be skipped to save time as well.</div> <div> </div> <div>The program "3_stock_bloom_calc.py" will require running "ocean_toolbox.py" (see below) in another tab. The portion of the program that involves QC for the irradiance profiles has been commented out to save processing time, and the pre-processed data used in the study has been linked instead (see folder "processed light data"). Similarly, pre-processed topography data is present in this repository. The original Earth Topography data can be accessed at <a href="https://www.ncei.noaa.gov/products/etopo-global-relief-model">https://www.ncei.noaa.gov/products/etopo-global-relief-model.</a></div> <p> </p> <p>A version of "3_stock_bloom_calc.py" using Longhurst provinces is available for exploring alternative groupings and their effects on stock calculations. See the program named "4_stock_calc_longhurst_province.py". You will need to download the Longhurst biogeochemical provinces from <a href="https://www.marineregions.org/">https://www.marineregions.org/</a>.</p> <p>To explore the effects of different slope factors, averaging methods, bbp spectral slopes, etc, the user will likely want to make changes to "3_stock_bloom_calc.py". Please do not hesitate to contact the correponding author (Adam Stoer) for guidance or questions.</p> <p><strong>ocean_toolbox.py:</strong></p> <p>import statsmodels.formula.api as smf<br>import os<br>import matplotlib.pyplot as plt<br>import numpy as np<br>from uncertainties import unumpy as unp<br>from scipy import stats</p> <p>def file_grab(root,find,start): #grabs files by file extensions and location<br> filelst = []<br> for subdir, dirs, files in os.walk(root):<br> for file in files:<br> filepath = subdir + os.sep + file<br> if filepath.endswith(find):<br> if filepath.startswith(start):<br> filelst.append(filepath)<br> return filelst</p> <p>def sep_bbp(data, name_z, name_chla, name_bbp):<br> <br> '''<br> data: Pandas Dataframe containing the profile data<br> name_z: name of the depth variable in data<br> name_chla: name of the chlorophyll-a variable in data<br> name_bbp: name of the particle backscattering variable in data <br> <br> returns: the data variable with particle backscattering partitioned into <br> phytoplankton (bbpphy) and non-algal particle components (bbpnap).<br> '''<br> #name_chla = 'chla'<br> #name_z = 'depth'<br> #name_bbp = 'bbp470'<br> dcm = data[data.loc[:,name_chla]==data.loc[:,name_chla].max()][name_z].values[0] # Find depth of deep chla maximum<br> part_prof = data[(data.loc[:,name_bbp]<np.median(data.loc[:,name_bbp]))] # find median bbp of profile<br> <br> mod = smf.quantreg('bbp470 ~ ' + str(name_z), <br> part_prof).fit(q=0.01) # Find model to 1 percentile<br> y_pred = mod.predict(part_prof.loc[:,name_z]) # Create predicted bbp_nap<br> <br> part_prof.loc[:,'bbp_back'] = y_pred.values # Predicted bbp NAP from linear trend<br> z_lim = part_prof.loc[(part_prof.loc[:,'bbp_back'].div(part_prof.loc[:,name_bbp])>=1), name_z].min() <br> <br> # Find depth where bbp NAP and bbp intersect<br> data.loc[data[name_z]>=z_lim, 'bbp_back'] = data.loc[data[name_z]>=z_lim, name_bbp].tolist()<br> data.loc[data[name_z]<z_lim,'bbp_back'] = data.loc[data[name_z]==z_lim, name_bbp].values[0] #data.loc[data[name_z]<z_lim, name_z].mul(lr.slope).add(lr.intercept)<br> <br> <br> data.loc[:,'bbpphy'] = data.loc[:, name_bbp].sub(data.loc[:,'bbp_back']) # Subtract bbp NAP from bbp for bbp from phytoplankton<br> data.loc[(data['bbpphy']<0)|(data['depth']>z_lim),'bbpphy'] = 0 # Subtract bbp NAP from bbp for bbp from phytoplankton</p> <p> return data['bbpphy'], z_lim</p> <p>def bbp_to_cphy(bbp_data, sf):<br> <br> '''<br> data: Pandas Dataframe containing the profile data<br> name_bbp: name of the particulate backscattering variable in data<br> name_bbp_err: name of particulate backscattering error variable in data<br> <br> returns: the data variable with particle backscattering [/m] converted into<br> phytoplankton carbon [mg/m^3].<br> '''<br> <br> cphy_data = bbp_data.mul(sf) </p> <p> return cphy_data</p>
(VIDEOS) Dynamics of hydroxyapatite and carbon nanotubes interacting
<p>These files correspond to the dynamics results for all the structures studied in the paper:</p> <ul> <li>W.G. Knupp, M.S. Ribeiro, M. Mir, I. Camps. <em>Dynamics of hydroxyapatite and carbon nanotubes interaction</em>. Applied Surface Science 495 (2019) 143493. DOI: <a href="https://doi.org/10.1016/j.apsusc.2019.07.235">10.1016/j.apsusc.2019.07.235</a></li> </ul> <p>The nomenclature to identify the systems is:</p> <ul> <li>HAP, for hydroxyapatite.</li> <li>CNT, for pristine carbon nanotube.</li> <li>HAP+CNT, for the complex hydroxyapatite interacting with pristine carbon nanotube.</li> <li>HAP+CNTOHx, for hydroxyapatite interacting with -OH functionalized carbon nanotube.</li> <li>HAP+CNTCOOHx, for hydroxyapatite interacting with -COOH functionalized carbon nanotube.</li> <li>x = 5%, 10%, 15%, 20%, 25% represents the concentration of -OH or -COOH, respectively.</li> </ul>
Data on: Dynamics of short-term ecosystem carbon fluxes induced by precipitation events in a semiarid grassland
<p>Data correspond to mean daytime net ecosystem carbon exchange (NEE) obtained through the eddy covariance method along six years from 2011 to 2016 (For more details of data see <a href="https://doi.org/10.1029/2018JG004799">https://doi.org/10.1029/2018JG004799</a>).</p> <p>Database contain changes of daytime NEE after a precipitation event (difference between previous day and the day after a precipitation event). Moreover, environmental and soil variables are included: 1) daily mean, previous and the change of soil water content at 2.5 and 15 cm depth, 2) previous NEE rate, 3) change of photosynthetic photon flux density, and 4) air temperature.</p> <p>Data was used to test the effect of environmental and soil variables on the daytime net ecosystem exchange. We was interested in short-term effects, i.e. the priming effect or the Birch effect.</p> <p>Manuscript where this database was used is under review.</p> <p> </p>
High trophic level feedbacks on global ocean carbon uptake and marine ecosystem dynamics under climate change (Dupont et al., GBC)
<p>Files used to make the analysis in the paper "High trophic level feedbacks on global ocean carbon uptake and marine ecosystem dynamics under climate change" (Dupont et al., accepted in GBC)</p> <p>- HTL_LTL_figures.ipynb is the python notebook in which are computed the different terms to make the figures of the paper </p> <p>- histrcp85.1-PISAPE-N-OW** and piCtrl2-PISAPE-N-OW** files contain the raw outputs of the one way (OW) simulation</p> <p>- histrcp85.1-PISAPE-N-TW* and piCtrl2-PISAPE-N-TW* files contain the raw outputs of the two way (TW) simulation</p> <p>- all files ending with *rmp_f.nc/ *regrid.nc/ *f20.nc are regridded files to make maps used in the paper. More details can be found in the python noteboook (briefly, dAT_* = change in active export, dDIC_*= change in dissolved inorganic carbon, dEPC200_* = change in carbon export at 200m depth, OW/TW_<a href="https://zenodo.org/api/files/cc2ae8cc-a150-4bc6-9125-04d9e3465859/TW_dBMapermp_f.nc">dBMape</a>* = OW/TW change in small high trophic levels biomass, OW/<a href="https://zenodo.org/api/files/cc2ae8cc-a150-4bc6-9125-04d9e3465859/TW_dBMapermp_f.nc">TW_dBM</a>meszo* = OW/TW change in mesozooplankton biomass)</p> <p>- <a href="https://zenodo.org/api/files/cc2ae8cc-a150-4bc6-9125-04d9e3465859/egestt2_2.nc">egestt2_2.nc</a>, excrett2_2.nc and graztt2_2.nc are the outputs of egestion, excretion and grazing terms used to compute the active export (AT). </p>
Local controls modify the effects of timber harvesting on surface soil carbon and nitrogen dynamics
Open the record for dataset details and reuse information.
Long-term Carbon and Nitrogen, and Phosphorus Dynamics of Leaf and Fine Root Litter project (LIDET-Long-term Intersite Decomposition Experiment Team) data for the ARC, Arctic LTER. 1990 to 2000.
This file is from the Long-term Carbon and Nitrogen, and Phosphorus Dynamics of Leaf and Fine Root Litter project (LIDET-Long-term Intersite Decomposition Experiment Team). This file contains only the Arctic LTER data. In particular the mass looses over the ten year study. Three types of fine roots (graminoid, hardwood, and conifer), six types of leaf litter (which ranged in lignin/nitrogen ratio from 5 to 75), and wooden dowels were used for litter incubations over a ten year period.
Temporal Dynamics of Soil Carbon and Nitrogen Resources Within a Grassland-Creosote Ecotone at the Sevilleta National Wildlife Refuge, New Mexico (1992-1994)
Plant communities across large portions of the southwestern United States have shifted from grassland to desert shrubland. Studies have demonstrated that soil nutrient resources become spatially more heterogeneous and are redistributed into islands of fertility with this shift in vegetation. This research addressed the additional question of whether soil resources become more temporally heterogeneous along a grassland-shrubland ecotome. Within adjacent grassland and creosotebush sites, soil profiles were described at 3 pits and samples collected for description of nutrient resources within the profile. Relative cover of plant species and bare soil were determined within each site by line transects. The top 20-cm of bare soil or soil beneath the canopy of grasses/creosotebush were collected 17 times during 1992-1994. Soil samples were analyzed for soil moisture, extractable ammonium and nitrate, nitrogen mineralization potential, microbial biomass carbon, total organic carbon, microbial respiration, dehydrogenase activity, ratio of microbial C to total C (C[mic]-to-C[org]), and microbial respiration to biomass carbon (metabolic quotient). The major differences in the structure of soils between sites were the apparent loss of a 3 to 5-cm depth of sandy surface soil at the creosotebush site and an associated increase in calcium carbonate content at a more shallow depth. Soils under plants at both sites had greater total and available nutrient resources with higher concentrations under creosotebush than under grasses. Greatest temporal variation in available soil resources was shown in soils under creosotebush. When expressed on an area basis, greater temporal variation in the total amount of available soil resources was shown in the grassland site, primarily due to greater plant cover (45% in grassland vs. 8% in creosote).
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.