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.
32
datasets available to search
ShareScore release 0.9.0
Dataset results
32 results for “Carbon plot”
Eight Mile Lake Research Watershed, Carbon in Permafrost Experimental Heating Research (CiPEHR): Off Plot Soil Incubation By Depth II - Soil CO2 Fluxes 2013-2014
The Carbon in Permafrost Experimental Heating Research (CiPEHR) project addresses the following questions: 1) Does ecosystem warming cause a net release of C from the ecosystem to the atmosphere?, 2) Does the decomposition of old C, that comprises the bulk of the soil C pool, influence ecosystem C loss?, and 3) How do winter and summer warming alone, and in combination, affect ecosystem C exchange? We are answering these questions using a combination of field and laboratory experiments to measure ecosystem carbon balance and radiocarbon isotope ratios at a warming experiment located in an upland tundra field site near Healy, Alaska in the foothills of the Alaska Range. We investigated C and nitrogen (N) mineralization within the soil profile by incubating soil cores collected adjacent to (but not within) the CiPEHR site. These soil cores spanned the entire active layer and approximately 30 cm of permafrost. Soil cores were separated into 10 cm depth intervals and incubated for 241 days at 15 degC and field moisture was maintained with water additions. This dataset contains CO2 flux rates measured periodically throughout the incubation.
Eight Mile Lake Research Watershed, Carbon in Permafrost Experimental Heating Research (CiPEHR): Off Plot Soil Incubation By Depth III - Soil Extracts 2013-2014
The Carbon in Permafrost Experimental Heating Research (CiPEHR) project addresses the following questions: 1) Does ecosystem warming cause a net release of C from the ecosystem to the atmosphere?, 2) Does the decomposition of old C, that comprises the bulk of the soil C pool, influence ecosystem C loss?, and 3) How do winter and summer warming alone, and in combination, affect ecosystem C exchange? We are answering these questions using a combination of field and laboratory experiments to measure ecosystem carbon balance and radiocarbon isotope ratios at a warming experiment located in an upland tundra field site near Healy, Alaska in the foothills of the Alaska Range. We investigated C and nitrogen (N) mineralization within the soil profile by incubating soil cores collected adjacent to (but not within) the CiPEHR site. These soil cores spanned the entire active layer and approximately 30 cm of permafrost. Soil cores were separated into 10 cm depth intervals and incubated for 241 days at 15 degC and field moisture was maintained with water additions. This dataset contains extractable pools of dissolved inorganic N (DIN, NH4 + NO3) and non-particulate organic carbon (NPOC). Measurements were made periodically throughout the incubation by destructively harvesting subsamples of soil and performing 2M KCl vacuum extractions.
Eight Mile Lake Research Watershed, Carbon in Permafrost Experimental Heating Research (CiPEHR): GPS Plot Locations
The Carbon in Permafrost Experimental Heating Research (CiPEHR) project addresses the following questions: 1) Does ecosystem warming cause a net release of C from the ecosystem to the atmosphere?, 2) Does the decomposition of old C, that comprises the bulk of the soil C pool, influence ecosystem C loss?, and 3) How do winter and summer warming alone, and in combination, affect ecosystem C exchange? We are answering these questions using a combination of field and laboratory experiments to measure ecosystem carbon balance and radiocarbon isotope ratios at a warming experiment located in an upland tundra field site near Healy, Alaska in the foothills of the Alaska Range. This dataset contains plot locations made using a differential GPS with real time kinematic correction in early August of 2017.
Eight Mile Lake Research Watershed, Carbon in Permafrost Experimental Heating and Drying Research (DryPEHR): GPS Plot Locations
This drying and warming experiment addresses the following questions: 1) Does ecosystem drying, warming and permafrost thaw cause a net release or uptake of C from the ecosystem to the atmosphere?, 2) Does the decomposition of old C that comprises the bulk of the soil C pool influence ecosystem C loss? 3) How do drying and warming affect plant communities and ecosystem properties? We are answering these questions using a combined warming and drying experiment (DryPEHR), which is situated with the Carbon in Permafrost Experimental Heating Research (CiPEHR) project and located in an upland tundra field site near Healy, Alaska in the foothills of the Alaska Range. Warming treatment here refers to growing season air temperature warming (~1C) using open top chambers (OTC) combined with soil 'warming' using snow fences during the snow covered months. Drying is achieved using an automated pumping system that lowers the water table in the dry plots. Soil warming began in 2008; OTCs and drying in 2011. This dataset contains plot locations made using a differential GPS with real time kinematic correction in early August of 2017.
Hubbard Brook Experimental Forest: Soil-atmosphere fluxes of carbon dioxide, nitrous oxide and methane on snow removal plots
Soil atmosphere fluxes of the trace gases; carbon dioxide (CO2), nitrous oxide (N2O) and methane (CH4) have been measured at several locations at the Hubbard Brook Experimental Forest (HBEF) including 1) the “freeze” study reference plots that provide contrast between stands dominated (80%) by sugar maple versus yellow birch and low and high elevation areas, 2) the Bear Brook Watershed where trace gas sampling is coordinated with long-term monitoring of microbial biomass and activity and 3) watershed 1 where trace gas sampling locations were co-located with long-term microbial biomass and activity monitoring sites that are located near a subset of the lysimeter sites established for the calcium addition study on this watershed. This dataset contains the Freeze study data. Watershed 1 and Bear Brook trace gas data can be found in: https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-hbr&identifier=116. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station. These data have been published in: Groffman, P. M., Hardy, J. P., Driscoll, C. T., & Fahey, T. J. (2006). Snow depth, soil freezing, and fluxes of carbon dioxide, nitrous oxide and methane in a northern hardwood forest. Global Change Biology, 12, 1748–1760.
Components of the complete budget for SAFE intensive carbon plots
<p><strong>Description: </strong></p> <p>Measured components of total carbon budget at SAFE project, values, with standard errors, for each 1-ha carbon plots for 11 plots investigated across a logging gradient from unlogged old-growth to heavily logged.<br> <br> These data are also published in below-ground carbon cycle in Riutta et al 2021 GBC and allocation of net primary productivity from Riutta et al 2019 GCB. This worksheet include two addititional carbon plots from Lambir Hills National Park (see Kho et al. 2013 JGR), which are not part of the SAFE Project. Below-ground carbon cycle data can be found at DOI 10.5281/zenodo.3266770 and leaf respiration at DOI 10.5281/zenodo.3247630.<br> <br> SAFE Intensive Carbon Plots, part of the Global Ecosystem Monitoring (GEM) network, see http://gem.tropicalforests.ox.ac.uk/. All the methods and installation is described in detail in the GEM Intensive Carbon Plots manual, available at http://gem.tropicalforests.ox.ac.uk/files/rainfor-gemmanual.v3.0.pdf.</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/113"><strong>Changing carbon dioxide and water budgets from deforestation and habitat modification</strong></a></p> <p><strong>Funding: </strong>These data were collected as part of research funded by:</p> <ul> <li>Sime Darby Foundation (Grant, SAFE Core data)</li> <li>European Research Council Advanced Investigator Grant, GEM-TRAIT (Grant, Grant number 321131)</li> <li>NERC Human-Modified Tropical Forests Programme: Biodiversity And Land-use Impacts on tropical ecosystem function (BALI) Project (Grant, NE/K016369/1)</li> <li>NERC standard grant: The multi-year impacts of the 2015/2016 El Niño on the carbon cycle of tropical forests worldwide (Grant, NE/P001092/1)</li> <li>HSBC Malaysia (Grant)</li> <li>The University of Zurich (Grant)</li> </ul> <p>This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs.</p> <p> </p> <p><strong>Permits: </strong>These data were collected under permit from the following authorities:</p> <ul> <li>Sabah Biodiversity Council (Research licence JKM/MBs.1000-2/2 JLD.6 (76))</li> </ul> <p> </p> <p><strong>XML metadata: </strong>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=7307449">here</a></p> <p><strong>Files: </strong>This consists of 1 file: SAFE_CarbonBalanceComponents.xlsx</p> <p><strong>SAFE_CarbonBalanceComponents.xlsx</strong></p> <p>This file contains dataset metadata and 1 data tables:</p> <ol> <li> <p><strong>Carbon balance components data</strong> (described in worksheet Data)</p> <p>Description: Carbon balance components and carbon budget of intensive carbon plots at SAFE project</p> <p>Number of fields: 64</p> <p>Number of data rows: 11</p> <p>Fields:</p> <ul> <li><strong>ForestType</strong>: Old-growth or Logged (Field type: categorical)</li> <li><strong>SAFEPlotName</strong>: SAFE plot name, as in the SAFE Gazetteer (Field type: location)</li> <li><strong>PlotName</strong>: Plot name (used in field work) (Field type: id)</li> <li><strong>ForestPlotsCode</strong>: Plot code, as in the ForestPlots database (this should be used in publications, instead of plot name) (Field type: id)</li> <li><strong>WoodyNPP_Stem</strong>: Woody stem productivity (subcomponent of woody net primary productivity) (Field type: numeric)</li> <li><strong>WoodyNPP_CoarseRoot</strong>: Coarse root productivity (subcomponent of woody net primary productivity) (Field type: numeric)</li> <li><strong>WoodyNPP_BranchTurnover</strong>: Branch turnover productivity (subcomponent of woody net primary productivity) (Field type: numeric)</li> <li><strong>WoodyNPP_Total</strong>: Total woody net primary producivity (Field type: numeric)</li> <li><strong>CanopyNPP_Leaf</strong>: Leaf productivity (subcomponent of canopy net primary productivity) (Field type: numeric)</li> <li><strong>CanopyNPP_Twig</strong>: Twig productivity (subcomponent of canopy net primary productivity) (Field type: numeric)</li> <li><strong>CanopyNPP_Reproductive</strong>: Reproductive productivity, i.e. fruit, seed and flowers (subcomponent of canopy net primary productivity) (Field type: numeric)</li> <li><strong>CanopyNPP_Miscellaneous</strong>: Unidentified canopy debris (subcomponent of canopy net primary productivity) (Field type: numeric)</li> <li><strong>CanopyNPP_Herbivory</strong>: Leaf productivity lost to herbivory (subcomponent of canopy net primary productivity) (Field type: numeric)</li> <li><strong>CanopyNPP_Total</strong>: Total canopy net primary producivty (Field type: numeric)</li> <li><strong>FineRootNPP</strong>: Fine root productivity (Field type: numeric)</li> <li><strong>TotalNPP_WithoutMycorrhiza</strong>: Total net primary productivity without mycorrhiza (Field type: numeric)</li> <li><strong>TotalNPP_WithMycorrhiza</strong>: Total net primary productivity including mycorrhiza (Field type: numeric)</li> <li><strong>GPP_WithoutMycorrhiza</strong>: Gross primary productivity without mycorrhiza (Field type: numeric)</li> <li><strong>GPP_WithMycorrhiza</strong>: Gross primary productivity including mycorrhiza (Field type: numeric)</li> <li><strong>R_Stem</strong>: Respiration from woody stems (Field type: numeric)</li> <li><strong>R_Leaf</strong>: Leaf Respiration (Field type: numeric)</li> <li><strong>R_FineRoots</strong>: Respiration from fine roots (Field type: numeric)</li> <li><strong>R_CoarseRoots</strong>: Respiration from coarse roots (Field type: numeric)</li> <li><strong>R_SOM</strong>: Respiration from soil organic matter (Field type: numeric)</li> <li><strong>R_Mycorrhiza</strong>: Respiration from mycorrhiza (Field type: numeric)</li> <li><strong>R_Litter</strong>: Respiration from litter layer (Field type: numeric)</li> <li><strong>R_Deadwood</strong>: Deadwood respiration (Field type: numeric)</li> <li><strong>R_auto</strong>: Total autotrophic respiration (Field type: numeric)</li> <li><strong>R_het</strong>: Total heterotrophic respiration (Field type: numeric)</li> <li><strong>R_eco</strong>: Total ecosystem respiration (Field type: numeric)</li> <li><strong>NEP_WithoutMycorrhiza</strong>: Total net ecosystem productivity (also known as net ecosystem exchange) without including mycorrhiza, whereby positive values indicate a net source of carbon to the atmosphere (Field type: numeric)</li> <li><strong>NEP_WithMycorrhiza</strong>: Total net ecosystem productivity (also known as net ecosystem exchange) including mycorrhiza, whereby positive values indicate a net source of carbon to the atmosphere (Field type: numeric)</li> <li><strong>AbovegroundBiomassCarbonStock</strong>: Plot above-ground biomass carbon stock (Field type: numeric)</li> <li><strong>CoarseRootBiomassCarbonStock</strong>: Biomass carbon stock of coarse roots (Field type: numeric)</li> <li><strong>SE_WoodyNPP_Stem</strong>: Standard error of woody stem productivity (Field type: numeric)</li> <li><strong>SE_WoodyNPP_CoarseRoot</strong>: Standard error of coarse root productivity (Field type: numeric)</li> <li><strong>SE_WoodyNPP_BranchTurnover</strong>: Standard error of branch turnover productivity (Field type: numeric)</li> <li><strong>SE_WoodyNPP_Total</strong>: Standard error of total woody net primary producivity (Field type: numeric)</li> <li><strong>SE_CanopyNPP_Leaf</strong>: Standard error of leaf productivity (Field type: numeric)</li> <li><strong>SE_CanopyNPP_Twig</strong>: Standard error of twig productivity (Field type: numeric)</li> <li><strong>SE_CanopyNPP_Reproductive</strong>: Standard error of reproductive productivity, i.e. fruit, seed and flowers (Field type: numeric)</li> <li><strong>SE_CanopyNPP_Miscellaneous</strong>: Standard error of unidentified canopy debris (Field type: numeric)</li> <li><strong>SE_CanopyNPP_Herbivory</strong>: Standard error of leaf productivity lost to herbivory (Field type: numeric)</li> <li><strong>SE_CanopyNPP_Total</strong>: Standard error of total canopy net primary producivty (Field type: numeric)</li> <li><strong>SE_FineRootNPP</strong>: Standard error of fine root productivity (Field type: numeric)</li> <li><strong>SE_TotalNPP_WithoutMycorrhiza</strong>: Standard error of total net primary productivity without mycorrhiza (Field type: numeric)</li> <li><strong>SE_TotalNPP_WithMycorrhiza</strong>: Standard error of total net primary productivity including mycorrhiza (Field type: numeric)</li> <li><strong>SE_GPP_WithoutMycorrhiza</strong>: Standard error of gross primary productivity without mycorrhiza (Field type: numeric)</li> <li><strong>SE_GPP_WithMycorrhiza</strong>: Standard error of gross primary productivity including mycorrhiza (Field type: numeric)</li> <li><strong>SE_R_Stem</strong>: Standard error of respiration from woody stems (Field type: numeric)</li> <li><strong>SE_R_Leaf</strong>: Standard error of leaf Respiration (Field type: numeric)</li> <li><strong>SE_R_FineRoots</strong>: Standard error of respiration from fine roots (Field type: numeric)</li> <li><strong>SE_R_CoarseRoots</strong>: Standard error of respiration from coarse roots (Field type: numeric)</li> <li><strong>SE_R_SOM</strong>: Standard error of respiration from soil organic matter (Field type: numeric)</li> <li><strong>SE_R_Mycorrhiza</strong>: Standard error of respiration from mycorrhiza (Field type: numeric)</li> <li><strong>SE_R_Litter</strong>: Standard error of litter layer respiration (Field type: numeric)</li> <li><strong>SE_R_Deadwood</strong>: Standard error of deadwood respiration (Field type: numeric)</li> <li><strong>SE_R_auto</strong>: Standard error of total autotrophic respiration (Field type: numeric)</li> <li><strong>SE_R_het</strong>: Standard error of total heterotrophic respiration (Field type: numeric)</li> <li><strong>SE_R_eco</strong>: Standard error of total ecosystem respiration (Field type: numeric)</li> <li><strong>SE_NEP_WithoutMycorrhiza</strong>: Standard error of total net ecosystem productivity (Field type: numeric)</li> <li><strong>SE_NEP_WithMycorrhiza</strong>: Standard error of total net ecosystem productivity (Field type: numeric)</li> <li><strong>SE_AbovegroundBiomassCarbonStock</strong>: Standard error of plot above-ground biomass carbon stock (Field type: numeric)</li> <li><strong>SE_CoarseRootBiomassCarbonStock</strong>: Standard error of biomass carbon stock of coarse roots (Field type: numeric)</li> </ul> </li> </ol> <p><strong>Date range: </strong>2011-08-25 to 2018-07-17</p> <p><strong>Latitudinal extent: </strong>4.1830 to 5.0700</p> <p><strong>Longitudinal extent: </strong>114.0190 to 117.8200</p>
Raw data for "Plot-scale variability of organic carbon in temperate agricultural soils - Implications for soil monitoring"
<p>This dataset is the raw data that belongs to a peer-reviewed study on the small-distance variability of soil organic carbon in agricultural soils in Germany. It consists of three different files. The first file gives the coordinates of the 16 soil cores that were taken at each of the 16 sites (eight cropland and eight grassland sites). The second file gives the soil properties measured at each individual core (n=16 per site) and the third file the soil properties measured at each indivdual soil profile (n=6 per site).</p>
The global distribution and drivers of wood density across angiosperms and gymnosperms and their impact on forest carbon stocks (Data and Plots)
<p>Abstract:</p> <div>The density of wood is a key indicator of trees’ carbon investment strategies, impacting productivity and carbon storage. Despite its importance, the global variation in wood density and its environmental controls remain poorly understood, preventing accurate predictions of global forest carbon stocks. Here, we analyze information from 1.1 million forest inventory plots alongside wood density data from 10,703 tree species to create a spatially-explicit understanding of the global wood density distribution and its drivers. Our findings reveal a pronounced latitudinal gradient, with wood in tropical forests being up to ~30% denser than that in boreal forests. In both angiosperms and gymnosperms, hydrothermal conditions represented by annual mean temperature and soil moisture emerged as the primary factors influencing the variation in wood density globally. This indicates similar environmental filters and evolutionary adaptations among distinct plant groups, underscoring the essential role of abiotic factors in determining wood density in forest ecosystems. Additionally, our study highlights the prominent role of disturbance, such as human modification and fire risk, in influencing wood density at more local scales. Factoring in the spatial variation of wood density notably changes the estimates of forest carbon stocks, leading to differences of up to 21% within biomes. Therefore, our research contributes to a deeper understanding of terrestrial biomass distribution and how environmental changes and disturbances impact forest ecosystems.</div> <div> </div> <p>This repository only provides the folders of 'Data' and 'Plots' that are needed for for the repository on GitHub: https://github.com/LidongMo/GlobalWoodDensityProject</p>
Data from: Spatial variability in soil organic carbon in a tropical montane landscape: associations between soil organic carbon and land use, soil properties, vegetation, and topography vary across plot to landscape scales
Open the record for dataset details and reuse information.
Litter biomass carbon nitrogen from water treatment plots:BioCON: Biodiversity, CO2, and Nitrogen
BioCON (Biodiversity, CO2, and Nitrogen) is an ecological experiment started in 1997 at the University of Minnesota's Cedar Creek Ecosystem Science Reserve. BioCON's goal is to explore the ways in which plant communities will respond to three environmental changes that are known to be occurring on a global scale: increasing nitrogen deposition, increasing atmospheric CO2, and decreasing biodiversity. Why Biodiversity, CO2, and Nitrogen? While there are many uncertainties in global change biology, there are also some well documented facts. Some of these are: 1. The amount of carbon dioxide (CO2) in the atmosphere is rising. Since the industrial revolution, the CO2 concentration in the atmosphere has increased from approximately 275 parts per million (ppm) to about 378 ppm today. This has been largely the result of fossil fuel burning. It is expected that CO2 levels will continue to rise, and that by the year 2050 these levels will be approximately 550 ppm. CO2 is the raw material for photosynthesis and is known to affect plant growth and development. 2. The amount of nitrogen moving through terrestrial ecosystems has increased in the recent past. While natural "background" levels of nitrogen fixation have remained constant, human additions to the system through fertilizer production and fossil fuel use have increased dramatically. Nitrogen is a key nutrient for plant growth and plays a critical role in plant community structure and composition in many environments. 3. Biodiversity levels are falling. While the research and data are not as complete as they are for CO2 and nitrogen, data indicate that the number of species globally, is being reduced. Perhaps more important for ecosystem function, diversity levels on local to regional scales have fallen due to land use change, biotic invasion and many other drivers. While much is known about how each of these factors affects ecosystem functioning, many questions remain. There is also little data on how these issues affe
ABoVE: Wildfire Carbon Emissions and Burned Plot Characteristics, NWT, CA, 2014-2016
This dataset provides estimates of wildfire carbon emissions and uncertainties at 30-m resolution, and measurements collected at burned and unburned field plots from the 2014 wildfire sites near Yellowknife, Northwest Territories (NWT), Canada. Field data were collected at 211 burned plots in 2015 and include site characteristics, tree cover and species, basal area, delta normalized burn ratio (dNBR), plot characteristics, soil carbon, and carbon combusted. Data were collected at 36 unburned plots with characteristics similar to the burned plots in 2016. The emission estimates were derived from a statistical modeling approach based on measurements of carbon consumption at the 211 burned field plots located in seven independent burn scars. Estimates include uncertainty of field observations of aboveground and belowground combustion, as well as prediction uncertainty from a multiplicative regression model. To apply the model across all 2014 NWT fire perimeters, the final model covariates were re-gridded to a common 30-m grid defined by the Arctic Boreal and Vulnerability Experiment (ABoVE) Project. The regression model was then applied to burned pixels defined by a threshold of Landsat-derived differenced Normalized Burn Ratio (dNBR) within fire perimeters. Derived carbon emissions and uncertainty in g/m2 are provided for each 30-m grid cell. The modeled NWT domain encompasses 29 tiles within the ABoVE 30-m reference grid system.
ABoVE: Characterization of Carbon Dynamics in Burned Forest Plots, NWT, Canada, 2014
This dataset provides field data from boreal forests in the Northwest Territories (NWT), Canada, that were burned by wildfires in 2014. During fieldwork in 2015, 211 burned plots were established. From these plots, thirty-two forest plots were selected that were dominated by black spruce and were representative of the full moisture gradient across the landscape, ranging from xeric to sub-hygric. Plot observations included slope, aspect, and moisture. At each plot, one intact organic soil profile associated with a specific burn depth was selected and analyzed for carbon content and radiocarbon (14C) values at specific profile depth increments to assess legacy carbon presence and combustion. Vegetation observations included tree density. Stand age at the time of the fire was determined from tree-ring counts. Estimates of pre-fire below and aboveground carbon pools were derived. The percent of total NWT wildfire burned area comprising of "young" stands (less than 60 years old at time of fire) was estimated.
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.