Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
123
datasets available to search
ShareScore release 0.9.0
Dataset results
123 results for “carbon cycling”
Dataset for "Diurnal, seasonal, and interannual variations in δ(18O) of atmospheric O2 and its application to evaluate natural/anthropogenic changes in oxygen, carbon, and water cycles" by Ishidoya et al.
<p>The dataset used in the paper "Diurnal, seasonal, and interannual variations in δ(18O) of atmospheric O2 and its application to evaluate natural/anthropogenic changes in oxygen, carbon, and water cycles" by Ishidoya et al. The doi of the preprint of the paper is https://doi.org/10.5194/egusphere-2024-654. The dataset contains the diurnal cycles of delta_atm(18O), delta(O2/N2), y(CO2), and delta(Ar/N2) at Tsukuba, Japan averaged for the period 2013-2022 ("data_for_Fig4.csv"), the rolling average values of delta_atm(18O) and delta(O2/N2) anomalies for 300 data at TKB during August and February, 2017 ("data_for_Fig6.csv"), and the monthly mean values of delta_atm(18O) and the delta(O2/N2) at TKB for the period 2013-2022("data_for_Fig7.csv"). The raw data before averaging are also presented ("raw_data_2013.csv" to "raw_data_2022.csv"). See text of Ishidoya et al. in more details. </p> <p> </p>
Data set for the study "Interplay between climate and carbon cycle feedbacks could substantially enhance future warming"
<p>This repository contains the data necessary to reproduce the results of the paper: <br>"Interplay between climate and carbon cycle feedbacks could substantially enhance future warming" <br><a href="https://iopscience.iop.org/article/10.1088/1748-9326/adb6be" target="_blank" rel="noopener">https://iopscience.iop.org/article/10.1088/1748-9326/adb6be</a></p> <h3><strong>Data organization:</strong></h3> <p>The Zenodo repository is organized as follows inside of <code>results.zip</code>:</p> <ul> <li>Figure generation are given by "*.pynb" and "*.m" files<br><br></li> <li>Data files as NetCDF output are organized with the following structure inside of <code>data</code>:<br><br> <ul> <li><strong>Experiment/emission scenario</strong>: <code>hist-aer</code>, <code>ssp126</code>, <code>ssp434</code>, and <code>ssp245</code><br><br> <ul> <li><strong>Equilibrium climate sensitivity</strong>: <code>ecs_2.0K</code>, <code>ecs_2.5K</code>, <code>ecs_3.0K</code>, <code>ecs_3.5K</code>, <code>ecs_4.0K</code>, <code>ecs_4.5K</code>, and <code>ecs_5.0K</code><br><br> <ul> <li><strong>Experiment: </strong><code>comp</code>, <code>comp_fix_ch4</code>, <code>comp_fix_co2_ch4</code>, <code>comp_ssp_co2_ch4</code><br><br> <ul> <li><strong>Component</strong>: atmosphere (<code>atm</code>), ocean (<code>ocn</code>), land (<code>lnd</code>), sea ice (<code>sic</code>), carbon dioxide (<code>co2</code>), methane (<code>ch4</code>)<br><br></li> <li><strong>File type</strong>: for some experiments, files are divided into timeseries (<code>*_ts.nc</code>) or 2D data (<code>*.nc</code>)<br><br></li> <li>Note: <code>comp_ssp_co2_ch4</code> are the CLIMBER-X runs which used prescribed concentrations (rather than emissions) and is only available for ECS 3°C<br><br></li> <li>Note: <code>comp_fix_ch4</code> and <code>comp_fix_co2_ch4</code> is only available for ECS 2°C, 3°C, and 5°C (as shown in Fig. 4 in the manuscript)</li> </ul> </li> </ul> </li> </ul> </li> </ul> </li> </ul>
Data-base for : 'Partitioning carbon sources between wetland and well-drained ecosystems to a tropical first-order stream - Implications to carbon cycling at the watershed scale (Nyong, Cameroon)'
<p>Dataset of carbon (pCO2, TA, DIC, DOC, POC) and ancillary parameters (water temperature, oxygen saturation, pH, specific conducitivity) in ground and surface waters of the Nyong watershed (Cameroon). The dataset covers one entire year (in 2016) and thus allows describing the varability of carbon and ancillary paramaters concentrations induced by seasons.</p>
SUPPLEMENTARY TABLES: Defining the Sphagnum core microbiome across the North American continent reveals a central role for diazotrophic-methanotrophs in the nitrogen and carbon cycles of boreal peatland ecosystems
<p>Peat mosses of the genus <em>Sphagnum</em> are ecosystem engineers that frequently predominate over photosynthetic production in boreal peatlands. <em>Sphagnum</em> spp. host diverse microbial communities capable of nitrogen-fixation (diazotrophy) and methane oxidation (methanotrophy), thereby potentially supporting plant growth under severely nutrient-limited conditions. Moreover, diazotrophic-methanotrophs represent a possible "missing link" between the carbon and nitrogen cycles, but the functional contributions of the <em>Sphagnum</em>-associated microbiome remain in question<em>.</em> A combination of metagenomics, metatranscriptomics, and dual-isotope incorporation assays was applied to investigate <em>Sphagnum</em> microbiome community composition across the North American continent and provide empirical evidence for diazotrophic-methanotrophy in <em>Sphagnum</em>-dominated ecosystems. Remarkably consistent prokaryotic communities were detected in over 250 <em>Sphagnum</em> SSU rRNA libraries from peatlands across the US (5 states, 17 bog/fen sites, 18 <em>Sphagnum</em> species), with twelve genera of the core microbiome comprising 60% of the relative microbial abundance. Additionally, nitrogenase (<em>nifH</em>) and SSU rRNA gene amplicon analysis revealed that nitrogen-fixing populations made up nearly 15% of the prokaryotic communities, predominated by <em>Nostocales</em> cyanobacteria and <em>Rhizobiales</em> methanotrophs. While cyanobacteria comprised the vast majority (>95%) of diazotrophs detected in amplicon and metagenome analyses, obligate methanotrophs of the genus <em>Methyloferula</em> (order <em>Rhizobiales</em>) accounted for one-quarter of transcribed <em>nifH</em> genes. Furthermore, in dual-isotope tracer experiments, members of the <em>Rhizobiales</em> showed substantial incorporation of <sup>13</sup>C-CH<sub>4</sub> and <sup>15</sup>N-N<sub>2</sub> isotopes into their rRNA. Our study characterizes the core <em>Sphagnum</em> microbiome across large spatial scales and indicates that diazotrophic methanotrophs, here defined as obligate methanotrophs of the rare biosphere (<em>Methyloferula</em> spp. of the <em>Rhizobiales</em>) that also carry out diazotrophy, play a keystone role in coupling of the carbon and nitrogen cycles in nutrient-poor peatlands.</p>
Freshwater viral metagenome assembled genomes (vMAGs) used for vContact2 analysis in publication Genome-resolved metaproteomics decodes the microbial and viral contributions to coupled carbon and nitrogen cycling in river sediments
<p>This dataset contains all freshwater viruses that were mined from publicly available data in an effort to provide biogeographical context to viral communities identified from the Columbia River. These two files include data from:</p> <p>1) East River, CO (PRJNA579838)</p> <p>2) A previous study from the Columbia River, WA (PRJNA375338)</p> <p>3) Prairie Potholes, ND (PRJNA365086)</p> <p>4) Amazon River (PRJNA237344)</p> <p> </p> <p>Manuscript title Genome-resolved metaproteomics decodes the microbial and viral contributions to coupled carbon and nitrogen cycling in river sediments</p>
Changes in Mean Seasonal Carbon Cycle Due to Climate Change
<p>This repository contains the output data of the 3D-CMCC-FEM model (https://github.com/Forest-Modelling-Lab/3D-CMCC-FEM/tree/v.5.6) related to the manuscript entitled "Changes in Mean Seasonal Carbon Cycle Due to Climate Change" by Morichetti M. and Collalti A. 2024</p>
Southern Ocean's circulation impact on the marine carbon cycle
<p>In the context of past and present climate change, the Southern Ocean (SO) has been identified as a crucial region modulating the concentration of atmospheric CO<sub>2</sub>. The sustained upwelling of carbon-rich deep waters and inefficient nutrient utilisation at the surface of SO leads to an outgassing of natural CO<sub>2</sub>, while anthropogenic CO<sub>2</sub> is entrained to depth during the formation of Antarctic Bottom water (AABW), Antarctic intermediate water (AAIW) and sub-Antarctic mode water (SAMW). Changes to the SO circulation resulting from both dynamic and buoyancy forcing can alter the rate of upwelling as well as formation and subsequent transport of AABW, AAIW and SAMW, thus impacting the air-sea CO<sub>2</sub> exchange in SO. Models of all complexity robustly show that stronger southern hemispheric (SH) westerlies enhance the SO upwelling, thus leading to stronger natural CO<sub>2</sub> outgassing, with a sensitivity of ∼0.13 GtC/yr for a 10% increase in SH westerly windstress. While the impact of changes in the position of the SH westerly winds was previously unclear, recent simulations with high-resolution ocean/sea-ice/carbon cycle models show that a poleward shift of the SH westerlies also enhances natural CO<sub>2</sub> outgassing with a sensitivity of ∼0.08GtC/yr for a 5• poleward shift. While enhanced AABW transport reduces deep ocean natural DIC concentration and increases surface natural DIC concentration, it acts on a centennial timescale. Future work should better constrain both the natural and anthropogenic carbon cycle response to changes in AABW and the compound impacts of dynamic and buoyancy changes on the SO marine carbon cycle.</p>
Dataset used in publication titled " Dependence of climate and carbon cycle response in net zero emission pathways on the magnitude and duration of positive and negative emission pulses"
<p>Essential model data use to produce figures and tables for the publication titled " Dependence of climate and carbon cycle response in net zero emission pathways on the magnitude and duration of positive and negative emission pulses"</p>
Processed Data for Short Gianotti et al., "Two Sub-Annual Time-Scales and Coupling Modes for Terrestrial Water and Carbon Cycles" (2024), Global Change Biology.
<p>These files include all data used to create Figures in Short Gianotti et al., "Two Sub-Annual Time-Scales and Coupling Modes for Terrestrial Water and Carbon Cycles" (2024), Global Change Biology. Raw data provenances and methodological processing are cited in the published manuscript.</p> <p>See README file for metadata information.</p>
Daily cycles in soil carbon flux
<p><strong>Description: </strong></p> <p>Measurements of 24 hour cycles in soil CO2 flux taken from soil collars in the Belian Carbon plot at Maliau. Measurements were taken from 12 subplots over four days at 5-hourly intervals, ensuring good coverage of the complete 24 hour cycle. Air and soil temperatures, soil moisture content and CO2 flux were taken from each plot at each visit. 9 subplots only have a single total soil respiration collar, but 3 subplots also have soil flux partitioning treatments to separate contributions to total respiration from soil organic matter, mycorrhizae and roots.<br> <br> This data was collected by the 2019 cohort of the Tropical Forest Ecology MRes at Imperial College London.</p> <p><strong>Project: </strong>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/152"><strong>MRes Tropical Forest Ecology Field Course</strong></a></p> <p><strong>XML metadata: </strong>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=3247592">here</a></p> <p><strong>Files: </strong>This dataset consists of 2 files: Carbon_corrected_slopes.xlsx, raw_egm.zip</p> <p><strong>Carbon_corrected_slopes.xlsx</strong></p> <p>This file contains dataset metadata and 2 data tables:</p> <ol> <li> <p><strong>24 hour observations of CO2 flux patterns</strong> (described in worksheet Carbon_flux_data)</p> <p>Description: Estimated CO2 flux values, soil moisture and air and soil temperatures from Carbon subplots</p> <p>Number of fields: 14</p> <p>Number of data rows: 296</p> <p>Fields:</p> <ul> <li><strong>record_no</strong>: EGM 'Plot' value - record number on EGM machine for this collar (Field type: ID)</li> <li><strong>plot</strong>: Carbon subplot number (Field type: Location)</li> <li><strong>date</strong>: Calendar date that measurements taken (Field type: Date)</li> <li><strong>time</strong>: Time that measurements taken (Field type: Time)</li> <li><strong>soil_wmc</strong>: Soil water moisture content (Field type: Numeric)</li> <li><strong>soil_temp</strong>: Soil temperature (Field type: Numeric)</li> <li><strong>air_temp</strong>: Air temperature (Field type: Numeric)</li> <li><strong>treatment</strong>: Exclusion treatments for partitioning soil respiration components (Field type: Categorical)</li> <li><strong>field_flux</strong>: CO2 flux reported in the field by EGM (Field type: Numeric)</li> <li><strong>Source</strong>: EGM dat file of source data used for corrected fluxes where available (Field type: File)</li> <li><strong>corrected_flux</strong>: Corrected flux measurements using by eye exclusion of raw flux data (Field type: Numeric)</li> <li><strong>n_points</strong>: Number of points in EGM record (Field type: Numeric)</li> <li><strong>n_used</strong>: Number of points used for corrected slope estimation (Field type: Numeric)</li> <li><strong>flux</strong>: Final flux values, using corrected values where available (Field type: Numeric)</li> </ul> </li> <li> <p><strong>EGM raw data</strong> (described in worksheet EGM_raw_data)</p> <p>Description: Duplicates key information from raw EGM files and indicates points excluded in calculation of corrected flux values</p> <p>Number of fields: 7</p> <p>Number of data rows: 7114</p> <p>Fields:</p> <ul> <li><strong>Plot</strong>: EGM recorder 'plot' code, actually just the record sequence number. (Field type: ID)</li> <li><strong>RecNo</strong>: EGM record number - time points of gas measurement at a single plot (Field type: ID)</li> <li><strong>Datetime</strong>: Time of gas concentration measurement (Field type: Datetime)</li> <li><strong>CO2.Ref</strong>: Measured CO2 (Field type: Numeric)</li> <li><strong>Input.E</strong>: EGM internal variable used in slope estimation (Field type: Numeric)</li> <li><strong>Source</strong>: Original EGM dat file containing the flux data (Field type: File)</li> <li><strong>ignore</strong>: Indicates where points from raw data excluded from corrected slope calculations (Field type: Categorical)</li> </ul> </li> </ol> <p><strong>raw_egm.zip</strong></p> <p>Description: Zipfile of raw EGM dat files</p> <p><strong>Date range: </strong>2019-02-18 to 2019-02-21</p> <p><strong>Latitudinal extent: </strong>4.7467 to 4.7480</p> <p><strong>Longitudinal extent: </strong>116.9693 to 116.9704</p>
Does land use age influence carbon cycling in the Tibetan Plateau?
<p>Although substantial information had been generated on the effects of land use change on soil organic carbon (SOC) and total nitrogen (TN) storage, studies are absent on multifactorial effects of land use types, land use age, and elevation on SOC and TN storage. SOC and TN were therefore investigated in 30 field sites comprising natural forests, planted forests, shrub, and grasslands. SOC and TN stocks differed and correlated significantly with land use age; the C stocks correlates significantly with land use change compared the TN stocks. However, there was no relation between the C and N stocks with elevation, implying that SOC and TN are solely dependent on land use age. SOC sequestration potentials of the sampled ecosystems were 345.86, 293.19, 266.45, and 251.23 t ha<sup>−1</sup> for the natural forests, planted forests, shrub, and grasslands with total mean value of 289.18 t·ha<sup>−1</sup> (1,060.42 t·ha<sup>−1</sup> CO<sub>2−</sub>eq). A significant SOC stock loss (17.96%, 29.80%, and 37.66%) occurred in converting natural forests to planted forests, shrub, and grasslands, whereas gains (27.36%, 14.31%, and 5.71%) would occur in reconverting grassland to natural forests, planted forests, and shrublands. Therefore, the C that was lost during deforestation and conversion of natural forests into other land use types could not match the carbon gains thereafter. Our results suggest that land use change and land use age have influenced soil C and N stocks. Moreover, natural forests are better in ecological conservation and restoration of degraded lands. This study provides baseline information for C and N management in ecologically restored and degraded lands.</p>
Data from: Desiccation and rehydration of mosses greatly increases resource fluxes that alter soil carbon and nitrogen cycling
1. Mosses often have positive effects on soil carbon and nitrogen cycling, but we know little about how environmentally determined cycles of desiccation and rehydration in mosses influence these processes. 2. In this context, we compared carbon and nitrogen in throughfall after precipitation passed through eight moss species that were either hydrated continuously or desiccated and rehydrated. Also, the throughfall of four moss species was added to soil and used to determine the net effect of carbon and nitrogen added in moss throughfall on soil CO2 and N2O efflux. 3. Depending on the species, desiccated-rehydrated (rehydrated) mosses lost 2-31 times more carbon in throughfall than mosses that were continuously hydrated (hydrated). Hydrated mosses lost little to no detectable nitrogen; whereas most rehydrated mosses lost some nitrogen in throughfall. Throughfall from both hydrated and rehydrated mosses generated higher CO2 and N2O efflux than water treated soils, but rehydrated moss throughfall promoted larger N2O efflux than hydrated moss throughfall. Throughfall from hydrated mosses caused net negative changes in soil carbon and had very little effect on soil nitrogen, whereas throughfall from rehydrated mosses generated positive changes in soil carbon and nitrogen. 4. Synthesis. Our results indicate that resources lost from desiccated mosses during rehydration influence soil carbon and nitrogen transformations and may be important drivers of carbon and nitrogen cycling and storage in ecosystems.
Effects of salt marsh vegetation zonation on carbon and nitrogen cycling in Connecticut
<p>Coastal marshes fringing the Long Island Sound (Connecticut, USA) are dynamic ecosystems positioned at the interface between land and sea, and provide an array of essential ecosystem services to society associated with improved water quality, carbon sequestration, and disturbance regulation. However, these wetlands are increasingly altered by rising seas and invasive species, and have been affected by historical management such as tidal manipulation. We conducted a survey of 20 Connecticut salt marshes (10 tidally restored, 10 unrestricted references) in 2017 to quantify carbon mineralization, denitrification potential, microbial community composition, a<span>bove and belowground biomass and a suite of sediment characteristics. Carbon density was our only paramenter that differed between unrestricted and tidally restored marshes, but we observed strong differences across vegetation zones, with vegetation being a top predictor of microbial respiration and potential denitrification rates. Based on sea-level rise model projections, the replacement of <i>S. patens</i> by short-form <i>S. alterniflora</i> is expected to be widespread across the Connecticut coastline, decreasing statewide potential denitrification from the low-to-high marsh transitional zone. Our results suggest that changes in vegetation zones can serve as landscape-scale predictors of the rapid changes occurring in salt marshes.</span></p>
Precipitation manipulation and terrestrial carbon cycle: the roles of treatment magnitude, experimental duration, and background climate
<p><b>Aim: </b><a name="_Hlk62140683">Precipitation manipulation experiments have shown diverse terrestrial carbon (C) cycling responses when the ecosystem is subjected to different magnitudes of altered precipitation, various experimental durations, or heterogeneity in local climates. However, how these factors combine to affect C cycle responses to changes in precipitation remains unclear.</a></p> <p><b>Location</b>: Global.</p> <p><b>Time period</b>: 1990–2019.</p> <p><b>Major taxa studied</b>: Terrestrial ecosystems.</p> <p><b>Methods</b>: Using observations from 230 published studies in which precipitation was manipulated and terrestrial C cycling variables were measured, we conducted a global meta-analysis to investigate responses of diverse C cycle processes to altered precipitation, including gross ecosystem productivity, ecosystem respiration, net ecosystem productivity, ecosystem carbon use efficiency, net primary productivity, aboveground and belowground net primary productivity, aboveground and belowground biomass, shoot:root ratio, soil respiration, and soil microbial biomass C.</p> <p><b>Results</b>: <a name="_Hlk50913646"></a><a name="_Hlk62140761">We found that C cycling responses correlated linearly and positively with the magnitude of precipitation treatments, in that C cycling variables increased under increased precipitation, and decreased under decreased precipitation. </a>We also detected that the responses of net primary productivity (NPP) and its aboveground component (ANPP) to altered precipitation weakened with experimental duration. Furthermore, gross ecosystem productivity, ecosystem respiration, and net ecosystem productivity had larger responses to precipitation treatments of greater magnitude over shorter time periods. The response of soil respiration, a key component of the C budget in most terrestrial ecosystems, particularly depended on the background climate. Local temperature and precipitation not only influenced the magnitude of the response of soil respiration to altered precipitation but also affected its sensitivity to the magnitude of the precipitation treatments, with higher sensitivities in the response of soil respiration to treatment magnitude at drier and colder sites.</p> <p><b>Main conclusions</b>: <a name="_Hlk62140806">Our findings highlight the importance of the interactions between the magnitude of precipitation treatments, their duration, and local climate in the response of ecosystem C cycling to precipitation, which is critical to better understanding and projecting ecosystem C processes and functioning under changing precipitation regimes.</a></p>
Dataset for The effect of charcoal production on carbon cycling in African biomes
<p>LPJ-GUESS simulated carbon dynamics data for African biomes under different charcoal management regimes</p>
A comparison of the climate and carbon cycle effects of carbon removal by Afforestation and Reduction of Fossil fuel emissions
<p>This dataset is for the journal article titled "A comparison of the climate and carbon cycle effects of carbon removal by Afforestation and Reduction of Fossil fuel emissions".</p>
Simulations for impacts of large-scale Saharan solar farms on the global terrestrial carbon cycle
<p>Four experiments were conducted to investigate the impact of a large scale Saharan solar farm on the global carbon cycle. Here a fully coupled Earth System model (ESM) EC-Earth was used to investigate the impact of a Saharan solar farm on the terrestrial carbon cycle, simulated with prescribed reduced surface albedo approximating the albedo effect of Photovoltaic (PV) solar panels over the Sahara desert. These experiments considered solar panel coverage at 5, 20 or 50% of a region of the Sahara Desert. The dataset contains selected output data from the simulations. Simulations were run for 200 years. The last 60 years were used for analysis after the model reached an equilibrium. </p> <p>The dataset contains Earth system model results from EC-Earth3 presented in the study by Power et al. (2023).</p>
Supplementary material for "Filter-feeding gelatinous macrozooplankton response to climate change and implications for benthic food supply and global carbon cycle"
<p>Simulation Outputs Description<br> ---------------------------</p> <p>This text file outlines the contents of the simulation outputs utilized in the analysis presented in Clerc et al. (2023). Pre-treated outputs are not provided for storage reasons, but the complete output can be made available upon request. Spatial files are regridded to a 1-degree resolution regular grid using the treatment: cdo remapdis,r360x180 in.nc out.nc.</p> <p>Folder Names:<br> --------------</p> <p>Here is a brief overview of the content within each folder.</p> <p>- TS_PICONTROL: Globally averaged time series for the PI-control runs spanning 1850-2100. (Units: PgC, or PgC/yr for "fluxes" filenames.)</p> <p>- TS_RCP85: Globally averaged time series for the RCP8.5 runs spanning 1850-2100. (Units: PgC, or PgC/yr for "fluxes" filenames.)</p> <p>- TS_RCP26: Globally averaged time series for the RCP2.6 runs spanning 1850-2100. (Units: PgC, or PgC/yr for "fluxes" filenames.)</p> <p>- EXPORT_LAYERS_PICONTROL: Time-averaged Pi-control flux outputs at specified depth horizons over the indicated period. (Regular 360x180 grid; Units: gC/m2/yr.)</p> <p>- EXPORT_LAYERS_RCP85: Time-averaged RCP8.5 flux outputs at specified depth horizons over the indicated period. (Regular 360x180 grid; Units: gC/m2/yr.)</p> <p>- EXPORT_LAYERS_RCP26: Time-averaged RCP2.6 flux outputs at specified depth horizons over the indicated period. (Regular 360x180 grid; Units: gC/m2/yr.)</p> <p>- SURFACE_LAYERS_RCP85: Vertically integrated (0-300m) time-averaged RCP8.5 outputs over the specified period. (Regular 360x180 grid; Units: mmolC/m3.)</p> <p>- SURFACE_LAYERS_RCP26: Vertically integrated (0-300m) time-averaged RCP2.6 outputs over the specified period. (Regular 360x180 grid; Units: mmolC/m3.)</p> <p>- SURFACE_LAYERS_PICONTROL: Vertically integrated (0-300m) time-averaged PiControl outputs over the specified period. (Regular 360x180 grid; Units: mmolC/m3.)</p> <p>- CHLOROPHYLL: Surface chlorophyll on a 360x180 degree grid, time-averaged over the indicated period. (Units: mg Chl/m3.)</p> <p>- TS_BIOME: Biome-specific averaged time series spanning 1850-2100. (Units: PgC, or PgC/yr for "fluxes" filenames.)</p> <p>- TS_CFLX: Globally averaged carbon uptake time series spanning 1850-2100. (Units: PgC/yr.)</p> <p>- BIOMES: Biome masks on a regular 360x180 grid.</p> <p>Filename Conventions:<br> -----------------------</p> <p>Please refer to the following conventions for filenames.</p> <p>MODEL:<br> - PISCES-FFGM: SLP<br> - PISCES-v2: STD<br> - PISCES-GM: MAC</p> <p>FORCING:<br> - Pi-control: PI<br> - Climate-change scenario: AD</p> <p>PERIOD:<br> - 1881-1900: PRI<br> - 1995-2014: HIS<br> - 2081-2100: FUT</p> <p>DEPTH:<br> - 0-300 m: dmin0_dmax300<br> - 100 m: ilevel9<br> - 1000 m: ilevel21<br> - Seafloor: seafloor</p> <p>BIOMES:<br> - High Chlorophyll: HC<br> - Intermediate Chlorophyll: IC<br> - Low Chlorophyll: LC<br> - Southern Ocean: SO<br> - Arctic Ocean: LCA</p> <p>NetCDF Variables:<br> ------------------</p> <p>- SALP: Filter-feeding gelatinous Macrozooplankton (FFGM)<br> - ZOO3: Generic Macrozooplankton (GM)<br> - ZOO2: Mesozooplankton<br> - ZOO: Microzooplankton<br> - PHY: Nanophytoplankton<br> - PHY2: Diatoms<br> - POC: Small particles (quantity or flux)<br> - GOC: Large particles (quantity or flux)<br> - SPCC: FFGM carcasses carbon (content or flux)<br> - SPFC: FFGM fecal pellets carbon (content or flux)<br> - MPCC: GM carcasses carbon (content or flux)<br> - MPFC: GM fecal pellets carbon (content or flux)<br> - CHL: Chlorophyll<br> - Cflx: Carbon uptake<br> - NO3: Nitrate<br> - PO4: Phosphate<br> - O2: Oxygen</p> <p>For any inquiries or data access requests, please contact corentin.clerc -at- usys.ethz.ch.</p>
Results dataset for research paper "Ultra-long-duration energy storage anywhere: methanol with carbon cycling"
<p>Results dataset for research paper <a href="https://doi.org/10.1016/j.joule.2023.10.001">Ultra-long-duration energy storage anywhere: methanol with carbon cycling</a>. The code is also available on <a href="https://github.com/PyPSA/methanol-uldes">GitHub</a>. Results include both the raw PyPSA NetCDF networks as well as summary files for each run. The main runs from the paper are in the directory "final-main". Runs for the other countries (Ireland, France, Sweden) are in "final-IEFRSE". Runs without wind power are in "final-nowind".</p>
Precipitation regime controls bryosphere carbon cycling similarly across contrasting ecosystems
Open the record for dataset details and reuse information.
ScienceDex guides
Understand access before you commit
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.