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.
167
datasets available to search
ShareScore release 0.9.0
Dataset results
167 results for “greenhouse gas”
Dataset for greenhouse gas modelling in diesel dependent communities transitioning to bioenergy
<p>The data presented here are from the research article entitled "Greenhouse gas mitigation potential of replacing diesel fuel with wood-based bioenergy in an arctic Indigenous community: A pilot study in Fort McPherson, Canada". Based on a pilot study realized in Northern Canada and life cycle assessment, we provide a set of key parameters and operational data gathered along the biomass supply chain to build a GHG mitigation scenario and compute the quantity and timing of GHG savings in the off-grid community of Fort McPherson, NWT. Given that GHG mitigation scenarios are often assessed against a relative fossil-fuel reference scenario, we are providing two categories of data; 1) data for the reference fossil fuel scenario and; 2) data along the upstream operations of biomass supply chains. Both categories contain data related to the operational processes as well as forest growth or decomposition of unused feedstock. Although the data presented are mostly derived from the boreal forest, they could help guide other communities beyond the boreal to develop a renewable bioenergy system and assess their GHG mitigation options.</p>
Number of chamber measurement locations for accurate quantification of landscape-scale greenhouse gas fluxes: Importance of land use, seasonality, and greenhouse gas type
<p>Contains all raw data measured in the Schwingbach Earth Observatory (SEO) from Spring, Summer and Autumn 2020. Data was measured with an on-site LGR laser from the GHG emissions, and with 100cm³ soil cores for the soil characteristics. Details can be found in the corresponding manuscript "Number of chamber measurement locations for accurate quantification of landscape-scale greenhouse gas fluxes: Importance of land use, seasonality, and greenhouse gas type"</p>
Data and code for 'Worldwide greenhouse gas emissions of green hydrogen production and transport'
<p>This data and code accompanies a Nature Energy article with the title 'Worldwide greenhouse gas emissions of green hydrogen production and transport'. In the article ‘Worldwide greenhouse gas emissions of green hydrogen’, we quantify project-specific greenhouse gas emissions for 1,025 green hydrogen projects in 2030, as well as green hydrogen transport emissions for three transport modes: pipeline, liquid hydrogen shipping and ammonia shipping. This repository entry contains the data and code used to produce the outputs presented in the article.</p>
Dataset for "The Role of Microbial Communities in Biogeochemical Cycles and Greenhouse Gas Emissions within Tropical Soda Lakes"
<p>Here, we make available 27 raw metagenomic files in fastq.gz associated to the article: "The Role of Microbial Communities in Biogeochemical Cycles and Greenhouse Gas Emissions within Tropical Soda Lakes". This files is not paired, with forward as _1.fastq.gz and reverse as _2.fastq.gz. The abstract of manuscript is described below:<br><br></p> <p>Abstract</p> <p>Although anthropogenic activities are the primary drivers of increased greenhouse gas (GHG) emissions, it is crucial to acknowledge that wetlands are a significant source of these gases. Brazil's Pantanal, the largest tropical inland wetland, includes numerous lacustrine systems with freshwater and soda lakes. This study focuses on soda lakes to explore potential biogeochemical cycling and the contribution of biogenic GHG emissions from the water column, particularly methane. Both seasonal variations and the eutrophic status of each examined lake significantly influenced GHG emissions. Eutrophic turbid lakes (ET) showed remarkable methane emissions, likely due to cyanobacterial blooms. The decomposition of cyanobacterial cells, along with the influx of organic carbon through photosynthesis, accelerated the degradation of high organic matter content in the water column by the heterotrophic community. This process released byproducts that were subsequently metabolized in the sediment leading to methane production, more pronounced during periods of increased drought. In contrast, oligotrophic turbid lakes (OT) avoided methane emissions due to high sulfate levels in the water, though they did emit CO2 and N2O. Clear vegetated oligotrophic turbid lakes (CVO) also emitted methane, possibly from organic matter input during plant detritus decomposition, albeit at lower levels than ET. Over the years, a concerning trend has emerged in the Nhecolândia subregion of Brazil's Pantanal, where the prevalence of lakes with cyanobacterial blooms is increasing. This indicates the potential for these areas to become significant GHG emitters in the future. The study highlights the critical role of microbial communities in regulating GHG emissions in soda lakes, emphasizing their broader implications for global GHG inventories. Thus, it advocates for sustained research efforts and conservation initiatives in this environmentally critical habitat.</p> <p><strong> </strong></p>
Additional greenhouse gas emissions under different scenarios of permafrost melt'
<p>This dataset contains the underlying data for the following publication Significant implications of permafrost thawing for climate change control, Climatic Change, DOI: 10.1007/s10584-016-1666-5. </p> <p>This data set contains the permafrost emissions used as inputs for the DICE model. These are estimates of the emissions release from permafrost under the RCP 2.6 scenario (GtCO 2 -eq y −1. Three inputs were used: the median, 16th percentile and 84th percentile pathway.</p>
Climate Watch Historical Country Greenhouse Gas Emissions Data (1990-2018)
<p>Climate Watch Historical Emission data contains sector-level greenhouse gas (GHG) emissions data for 194 countries and the European Union (EU) for the period 1990-2018, including emissions of the six major GHGs from most major sources and sinks. Non-CO2 emissions are expressed in CO2 equivalents using 100-year global warming potential values from IPCC Fourth Assessment Report. See <a href="http://cait.wri.org/docs/CAIT2.0_CountryGHG_Methods.pdf">http://cait.wri.org/docs/CAIT2.0_CountryGHG_Methods.pdf</a> for details regarding data source and methodology.</p> <p> </p> <p>Climate Watch Historical GHG Emissions. 2021. Washington, DC: World Resources Institute. Available online at: <a href="https://www.climatewatchdata.org/ghg-emissions">https://www.climatewatchdata.org/ghg-emissions</a></p>
Canadian fossil fuel production, greenhouse gas emissions, emissions targets and carbon budgets
<p>This spreadsheet shows the amounts of coal, oil and natural gas produced in Canada from 2010 to 2020 using governmental sources. It includes calculations of the corresponding emissions according to a life-cycle analysis. The total greenhouse gas emissions from fossil fuels extracted annually in Canada (including those burned abroad) are computed. McGlade and Ekins (2015) proposed budgets for the production of each type of fossil fuel in order to provide a 67% chance to limit warming to 2.0 °C by 2100. The proportion of each budget that is already spent is calculated. Emissions targets from 21 scenarios originating from five effort-sharing studies are compared with Canadian 2020 emissions to evaluate the difference. Carbon budgets from 18 scenarios originating from seven studies are compared with Canadian cumulative emissions to evaluate the percentage of the budgets within the period 2010-2050 already emitted.</p>
Rates of greenhouse gas (carbon dioxide, methane and nitrous oxide) fluxes, denitrification-derived N2O and N2 fluxes and nitrification-derived N2O fluxes from salt marsh soils in Quebec, Canada and Louisiana, U.S. under ambient and elevated temperature and nutrient loading.
<p>Dataset used in <a href="https://link.springer.com/article/10.1007/s10533-023-01104-0?utm_source=rct_congratemailt&utm_medium=email&utm_campaign=oa_20231214&utm_content=10.1007/s10533-023-01104-0#citeas">Elevated temperature and nutrients lead to increased N<sub>2</sub>O emissions from salt marsh soils from cold and warm climates</a>.</p> <p>The dataset contains fluxes calculated from headspace gas samples taken over a 24 hour period from intact soil cores, as well as corresponding environmental data. Intact soil cores (0-15 cm depth, 2.5 cm diameter) were taken at five sampling locations along a 20 m transect using a soil auger or piston corer. Samples were collected along a transect in four marsh sites in Quebec, Canada (La Pocatière: 47°22'24.7"N 70°03'26.3"W) and Louisiana, U.S. (Barataria Basin: 29°33'47.3"N 90°04'22.8"W and 29°29'52.2"N 89°55'00.2"W) from two vegetation types (<em>Sporobolus alterniflorus</em> formerly known as <em>Spartina alterniflora </em>and<em> Sporobolus pumilus</em> formerly known as<em> Spartina patens</em>). In Quebec, the two vegetation zones were in the same marsh whereas in Louisiana two separate marshes, dominated by the relevant vegetation, were chosen. Soil samples were collected on the 20-21<sup>st</sup> July 2021 from Louisiana and the 9-10<sup>th</sup> August 2021 from Quebec. Environmental data was collected including <em>in-situ</em> soil temperature and salinity, and gravimetric soil moisture, extractable soil dissolved organic carbon (DOC), extractable soil total dissolved nitrogen (TDN), extractable soil nitrate, extractable soil ammonium, extractable soil soluble reactive phosphate, soil total carbon, soil total nitrogen, soil carbon to nitrogen ratio, soil d<sup>13</sup>C and soil d<sup>15</sup>N determined from additional 0-15 cm core samples. This project has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under Grant Agreement no. 838296, a NSERC Discovery Grant and a Natural Environment Research Council grant number (NE/T012323/1).</p> <p>Stable <sup>15</sup>N tracers were added to the intact soil cores so that at each location, at each treatment level (ambient and elevated, described below), there was one core receiving no tracer for greenhouse gas fluxes, one core receiving <sup>15</sup>N-NO<sub>3</sub><sup>‑ </sup>for denitrification rates and one core receiving <sup>15</sup>N-NH<sub>4</sub><sup>+</sup> for nitrification rates. The cores were incubated at ambient temperature (16 ℃ and 28.1 ℃ for Quebec and Louisiana, respectively) and nutrient concentrations (3.2 NO<sub>3</sub><sup>-</sup>, 2.0 NH<sub>4</sub><sup>+</sup>; 2.9 NO<sub>3</sub><sup>-</sup>, 2.5 NH<sub>4</sub><sup>+</sup>; 0.5 NO<sub>3</sub><sup>-</sup>, 7.3 NH<sub>4</sub><sup>+ </sup>and 5.7 NO<sub>3</sub><sup>-</sup>, 2.8 NH<sub>4</sub><sup>+</sup> mg g wet soil<sup>-1</sup> for Quebec <em>S. alterniflorus</em>, Quebec <em>S. pumilus</em>, Louisiana <em>S. alterniflorus</em> and Louisiana <em>S. pumilus</em>, respectively), and elevated temperature (ambient temperature +5 ℃) and nutrient concentration (double ambient concentration). Gas samples were collected from the headspace of 0-15 cm intact cores in a 20 cm high PVC pipe, capped at the top and bottom to create a 5 cm headspace. Gas samples were analysed for greenhouse gases (GHGs: N<sub>2</sub>O, CH<sub>4</sub>, CO<sub>2</sub>) and <sup>15</sup>N in denitrification-derived N<sub>2</sub>O, denitrification-derived N<sub>2</sub> and nitrification-derived N­<sub>2</sub>O.</p> <p>Soil temperature (YSI 30, Baton Rouge, USA or DeltaTrak 11050, Pleasanton, USA) and porewater salinity (YSI 30, Baton Rouge, USA or portable ATC refractometer) were measured in-situ or in the laboratory using the portable refactometer. Additional soil samples were used for multiple analyses; one subsample was extracted with ultrapure water (18.2 MΩ) for DOC and TDN analysis, one subsample was extracted with 2M KCl for NO<sub>3</sub><sup>-</sup> and NH<sub>4</sub><sup>+</sup>, one subsample was extracted with Olsen-P solution (0.5 M NaHCO<sub>3</sub>, pH 8.5), for soluble reactive phosphate analysis and one subsample was weighed and dried for soil moisture and then finely ground and analysed for total carbon, total nitrogen, d<sup>13</sup>C and d<sup>15</sup>N.</p> <p>N<sub>2</sub>O, CH<sub>4</sub> and CO<sub>2</sub> concentrations were measured in the gas samples using a gas chromatograph interfaced with a PAL3 autosampler (Agilent 7890A, Agilent Technologies Ltd, USA) fitted with a flame ionisation detector (FID) for CH<sub>4</sub> analysis and a micro electron capture detector (mECD) for N<sub>2</sub>O analysis. CO<sub>2</sub> was methanised to CH<sub>4</sub> before analysis on the FID. The instrument precision as the relative standard deviation was < 5 % for all of the gases, while the minimum detectable concentration difference (MDCD) was 9 ppb N<sub>2</sub>O, 72 ppb CH<sub>4 </sub>and 31 ppm CO<sub>2</sub>. Potential GHG fluxes were calculated from the linear portion or where the highest production was observed in the concentration-time series ( https://doi.org/10.2134/jeq2003.2436). If fluxes were below the MDCD they were set to zero see (https://doi.org/10.1002/2017JG003783). The <sup>15</sup>N content of the N<sub>2</sub> and N<sub>2</sub>O was determined using a continuous flow isotope ratio mass spectrometer (Elementar Isoprime PrecisION; Elementar Analysensysteme GmbH, Hanau, Germany) coupled with a trace-gas pre-concentrator inlet with autosampler (isoFLOW GHG; Elementar Analysensysteme GmbH, Hanau, Germany), with a standard deviation of d<sup>15</sup>N < 0.05 %. Extractable dissolved organic carbon and total dissolved nitrogen were analysed in soil extractant (ultrapure water 18.2 MΩ, 7:1 of extractant to soil) on a TOC/TDN analyser (TOC VCSn + TMN-1, Shimadzu, Kyoto, Japan), with 50 mg C l<sup>-1</sup> and 10 mg l<sup>-1</sup> standards resulting in accuracy and precision of 0.3 and ±0.3 mg C l<sup>-1</sup>, and 0.5 and ±0.3 mg N l<sup>-1</sup>, respectively. Extractable nitrate+nitrite (assumed to be nitrate) and ammonium were analysed in soil extractant (2M KCl, 5:1 of extractant to soil) using a microplate reader and methods in Sims et al., 1995 (<a href="https://doi.org/10.1080/00103629509369298">https://doi.org/10.1080/00103629509369298</a>) with a limit of detection of 0.1 ppm and accuracy of ±5 %. Extractable phosphate was analysed in soil extractant (Olsen-P solution 0.5M NaHCO­<sub>3</sub>, pH 8.5, 10:1 of extractant to dry soil) using a microplate reader and methods in Jeannotte et al., 2004 (https://doi.org/10.1007/s00374-004-0760-4) with a limit of detection of 1 mg P l<sup>-1</sup> and accuracy of ±6 %. Soil total carbon, total nitrogen, d<sup>13</sup>C and d<sup>15</sup>N analysis was performed using a continuous flow isotope ratio mass spectrometer (Elementar Isoprime PrecisION; Elementar Analysensysteme GmbH, Hanau, Germany) coupled with an elemental analyser (EA) inlet (vario PYRO cube; Elementar Analysensysteme GmbH, Hanau, Germany). The precision was < 5 % for both C and N and the precision as a standard deviation was < 0.06 % for both d<sup>13</sup>C and d<sup>15</sup>N. Results from the experiments were entered into an Excel spreadsheet for ingestion into the Zenodo data repository.</p>
Dataset: A unified modelling framework for projecting sectoral greenhouse gas emissions
<p>Data provided includes results from the unified framework described in "A unified modelling framework for projecting sectoral greenhouse gas emissions". Contains posterior draws of emission intensities and resulting emissions for 173 countries, five main sectors up to the year 2050. Historical GHG emissions data based on <a href="https://doi.org/10.5194/essd-13-5213-2021">Minx et al (2021)</a>.</p> <p>Code for the processing of results can be found on <a href="https://github.com/oDNAudio/GHG_sector_projections">Github</a>.</p>
Data and R-scripts for estimating carbon dioxide emissions from drained peatland forest soils for the greenhouse gas inventory of Finland
<p><strong> Introduction</strong></p> <p>A new method for estimating carbon dioxide emissions from rained peatland forest soils was developed for the Greenhouse Gas Inventory of Finland (GHG inventory). The method is based on a set of models (Ojanen et al. 2014, Tuomi et al., 2009) that dynamically compile all relevant carbon inputs and outputs into a time series of soil CO<sub>2</sub> emission. A complete description of the method is described in Alm et al. (2023). Here we present the input data and R-scripts (R Core Team, 2020) for computing the time series from year 1990 to 2022 of CO<sub>2</sub> emission from soil in forest land on drained organic soil, like it was reported by the Finnish GHG inventory (Statistics Finland, 2023).</p> <p><strong>Time series data </strong></p> <p>The source of forest and area data is the Finnish National Forest Inventory (NFI) as a part of Luke Statutory Services. The NFI standing forest data in the data files includes annual country-wide estimates of mean basal area and standing biomass of Scots pine (<em>Pinus sylvestris</em> L.), Norway spruce (Picea abies (L.) H. Karst) and all the broadleaved forest trees combined. The data concerns forest land on drained organic soil only (class FRA 1 according to the FAO forest land definition).</p> <p>The NFI data for each year has been averaged by different drained peatland forest site types (FTYPE) and by inventory regions of southern and northern Finland. The areas and proportions of FTYPEs of all drained peatland “forests remaining forests” (i.e., forests that have not undergone another change in land use in the past 20 years) in southern and northern Finland (Alm et al., 2023), derived from NFI12 (2014–2018).</p> <p>Annual litter input from harvest residues was estimated using statistics of harvested stem volumes by species, collected and published by Luke (Luke statistics). The stem volumes were converted to whole trees and further to litter fractions and further to The share of residues remaining in forest is estimated by subtracting the amount of the logging residues collected for energy use, the data obtained from Luke statistics/energy. The biomass of live trees, annual litterfall from live trees aboveground and root litter belowground are derived from the National Forest Inventory of Finland (inventory rounds NFI8 to NFI13). The R-code also includes calculation of annual litter production from the harvesting residues.</p> <p>The regression-based transfer models, implemented in the R-code, also need meteorological time series inputs: The soil organic matter decomposition model (Ojanen et al. 2014) uses May-October mean temperature. Decomposition model yasso07 (Tuomi et al., 2009), applied for estimating the CO<sub>2</sub> release by decomposition of harvesting residues and above ground litter from natural mortality, is constrained by annual temperature, annual temperature amplitude and annual precipitation. Starting from the original country-wide grid produced by the Finnish Meteorological Institute (FMI) the weather time series were spatially averaged so that the FMI weather grid values were collected from those locations where peatlands representing each FTYPE in southern and northern Finland were observed by the NFI, respectively.</p> <p>The pre-prepared input data are given in files, see Table 1 for descriptions.</p> <p> </p> <p> </p> <p>Table 1. Description of input data files.</p> <table> <tbody> <tr> <td> <p><strong>File</strong></p> </td> <td> <p><strong>Description of data</strong></p> </td> </tr> <tr> <td> <p>basal.areas.csv</p> </td> <td> <p>Time series of years 1990-2022 for annual average basal area (m<sup>2</sup> ha<sup>-1</sup>) by year, by peatland forest site type (peat_type) and by tree species or group (tree_type).</p> <p> </p> <p>Values of peat_type correspond to FTYPE:</p> <p>1 Herb-rich type</p> <p>2 <em>Vaccinium myrtillus</em> type</p> <p>4 <em>Vaccinium vitis-idaea</em> type</p> <p>6 Dwarf shrub type</p> <p>7 <em>Cladina</em> type</p> <p> </p> <p>Values of tree species or group correspond to:</p> <p>1 Scots pine</p> <p>2 Norway spruce</p> <p>3 Broadleaved species</p> </td> </tr> <tr> <td> <p>biomass.csv</p> </td> <td> <p>Time series of years 1990-2022 for annual biomass (biomass, t ha<sup>-1</sup> of dry mass) by year, by biomass component, by tree species and by peatland forest site type (tkg).</p> <p> </p> <p>Values of peat_type correspond to FTYPE:</p> <p>1 Herb-rich type</p> <p>2 <em>Vaccinium myrtillus</em> type</p> <p>4 <em>Vaccinium vitis-idaea</em> type</p> <p>6 Dwarf shrub type</p> <p>7 <em>Cladina</em> type</p> <p> </p> </td> </tr> <tr> <td> <p>dead_litter.csv</p> </td> <td> <p>Time series of years 1990-2022 of annual aboveground litter from dead wood: Harvesting residues and natural mortality combined (C, t ha<sup>-1</sup> of dry mass; lognat_litter).</p> <p> </p> <p>Values of region correspond to GHG inventory region:</p> <p>south South Finland</p> <p>north North Finland</p> </td> </tr> <tr> <td> <p>ghgi_litter.csv</p> </td> <td> <p>Time series of years 1990-2022 for litter AWEN-fractions (A=acid soluble, W=water soluble, E=ethanol soluble, N=non-soluble; C, t ha<sup>-1</sup>) by different litter types: Above-ground coarse woody litter (coarse_woody_litter), fine woody litter (fine_woody_litter), non-woody litter (non_woody_litter) by litter source and deposition type by region. “org” denotes organic soil.</p> <p> </p> <p>Values of region correspond to GHG inventory region:</p> <p>south South Finland</p> <p>north North Finland</p> <p> </p> <p>Values of ground correspond to litter deposition environment:</p> <p>above Above-ground litter</p> <p>below Below-ground litter</p> </td> </tr> <tr> <td> <p>lognat_decomp.csv</p> </td> <td> <p>Time series of years 1990-2022 for C, t ha<sup>-1</sup> of dry mass, decomposed from logging residues and natural mortality by region.</p> <p> </p> <p>Values of variable “region” correspond to GHG inventory region:</p> <p>south South Finland</p> <p>north North Finland</p> </td> </tr> <tr> <td> <p>logyasso_weather_data.csv</p> </td> <td> <p>Time series of years 1990-2022 for regional (region) precipitation sum (mm, sum_P), average annual temperature (°C, mean_T) and amplitude of the annual temperature (°C , ampli_T).</p> <p> </p> <p>Values of region correspond to GHG inventory region:</p> <p>south South Finland</p> <p>north North Finland</p> <p> </p> </td> </tr> <tr> <td> <p>total_area.csv</p> </td> <td> <p>Areas (ha) of drained peatland forests remaining forest land by region and peat_type.</p> <p> </p> <p>Values of variable “region” correspond to GHG inventory region:</p> <p>south South Finland</p> <p>north North Finland</p> <p> </p> <p>Values of peat_type correspond to FTYPE:</p> <p>1 Herb-rich type</p> <p>2 <em>Vaccinium myrtillus</em> type</p> <p>4 <em>Vaccinium vitis-idaea</em> type</p> <p>6 Dwarf shrub type</p> <p>7 <em>Cladina</em> type</p> <p> </p> </td> </tr> <tr> <td> <p>weather_data.csv</p> </td> <td> <p>Time series of years 1990-2022 for 30-year rolling mean temperature for the May-October period (roll_T) used by the soil decomposition models. The values are calculated for each FTYPE (peat_type) using their spatial distributions (see details in Alm et al., 2023).</p> <p> </p> <p>Values of variable “region” correspond to GHG inventory region:</p> <p>south South Finland</p> <p>north North Finland</p> <p> </p> <p>Values of peat_type correspond to FTYPE:</p> <p>1 Herb-rich type</p> <p>2 <em>Vaccinium myrtillus</em> type</p> <p>4 <em>Vaccinium vitis-idaea</em> type</p> <p>6 Dwarf shrub type</p> <p>7 <em>Cladina</em> type</p> <p> </p> </td> </tr> </tbody> </table> <p> </p> <p><strong>The R-scripts</strong></p> <p>The scripts are an excerpt from the Finnish greenhouse gas inventory code set, applying the necessary pre-processed input data and producing the soil CO<sub>2</sub> emissions for each FTYPE separately. The necessary R-packages (R Core Team, 2020) are managed in the script LIBRARIES.R.</p> <p>Guidance for running the R-scripts is given in the README.txt.</p> <p><strong>References</strong></p> <p>Alm, J., Wall, A., Myllykangas, J-P., Ojanen, P., Heikkinen, J., Henttonen, H. M., Laiho, R., Minkkinen, K., Tuomainen, T. and Mikola, J. A new method for estimating carbon dioxide emissions from drained peatland forest soils for the greenhouse gas inventory of Finland. Biogeosciences https://doi.org/10.5194/bg-20-1-2023, 2023.</p> <p>LUKE Statistics</p> <ul> <li>https://www.luke.fi/en/statistics/total-roundwood-removals-and-drain, last access 8.12.2022.</li> </ul> <ul> <li>https://www.luke.fi/en/statistics/commercial-fellings/commercial-fellings-72023. last access 8.12.2022.</li> </ul> <p>Statistics Finland 2023. URL: https://unfccc.int/documents/627718 (last access 13.9.2023).</p> <p>Ojanen, P., Lehtonen, A., Heikkinen, J., Penttilä, T., and Minkkinen, K.: Soil CO2 balance and its uncertainty in forestry drained peatlands in Finland, Forest Ecol. Manage., 325, 60–73, 2014.</p> <p>R Core Team: R: A language and environment for statistical computing. R Foundation forStatistical Computing, Vienna, Austria, URL https://www.R-project.org, 2020.</p> <p>Tuomi, M., Thum, T., Järvinen, H., Fronzek, S., Berg, B., Harmon, M., Trofymow, J.A., Sevanto, S. and Liski, J.: Leaf litter decomposition - Estimates of global variability based on Yasso07 model, Ecol. Modell. 220 (23):3362-3371, 2009.</p>
Spatially and taxonomically explicit characterisation factors for greenhouse gas emission impacts on biodiversity
<p>Gridded global potentially affected fraction of species (PAF) in 2050 and 2100 per kg GHG for 3 RCPs (2.6, 4.5,8.5) averaged over all species groups. </p> <p>Full method description is available in the article: <a href="https://www.sciencedirect.com/science/article/pii/S092134492300294X">Spatially and taxonomically explicit characterisation factors for greenhouse gas emission impacts on biodiversity - ScienceDirect</a></p>
Dataset for greenhouse gas modelling in diesel dependent communities transitioning to bioenergy
Open the record for dataset details and reuse information.
Data from: Earthworms do not increase greenhouse gas emissions (CO2 and N2O) in an ecotron experiment simulating a realistic three-crop rotation system
Open the record for dataset details and reuse information.
Data from: Promoting success in thin layer sediment placement: effects of sediment grain size and amendments on salt marsh plant growth and greenhouse gas exchange
Open the record for dataset details and reuse information.
Gas exchange, dieback, leaf water potential and chlorophyll content during a greenhouse drought experiment: An evolutionary perspective on functional diversity in co-occuring willow(salix) species
Thirteen willow (Salix) species occur in southeastern Minnesota and often co-occur within the same wetlands. This high local diversity is challenging to explain since closely related species are often functionally similar and density-dependent interactions such as competition and susceptibility to pests and pathogens should limit their co-occurrence. However, if willow species are partitioning resources, or if they are phylogenetically structured so that closely related species rarely co-occur, then the impact of these density-dependent processes could be reduced. In this study, I examined the role of niche partitioning in maintaining local willow diversity by comparing species physiology in a greenhouse.
Data for McGill et.al. 2018. The greenhouse gas cost of agricultural intensification with groundwater irrigation in a Midwest US row cropping system. at the Kellogg Biological Station, Hickory Corners, MI (2013 to 2017)
Dataset Abstract Data for Data for McGill et.al. 2018. The greenhouse gas cost of agricultural intensification with groundwater irrigation in a Midwest US row cropping system. original data source http://lter.kbs.msu.edu/datasets/176
Greenhouse gas fluxes before and after Hurricane Maria
We used several methods to estimate forest damage in the neighborhood of each focal tree in order to test whether there may be a relationship between GHG fluxes and local damage severity. First, we assigned each damage category a numeric value of damage based on % canopy damage (Light = 0%-25%, Medium = 25%-75%%, and Heavy > 75%). We then calculated three mean neighborhood damage estimates for each gas sampling location by taking 1) the mean damage for all trees > 10 cm DBH in the same 20 x 20 m quadrat as our gas measurements; 2) the mean damage for all trees within a 10 m radius of our gas measurements; and 3) the mean damage for all trees within a 20 m radius of our gas measurements. We then fit simple linear models in R to test for significant relationships between CO2, CH4, and N2O and our neighborhood damage estimates. Approximately every five yearssince 1990, stems are re-measured and their statusis assessed, and new stems are added. The lastcensus of the LFDP prior to Hurricane Marı´a wascompleted in 2016, representing pre-hurricaneconditions in this study.Beginning in January 2018, all trees at least10 cm dbh in the LFDP were surveyed to assessdamage and immediate mortality from H. Marı´a(Uriarte and others 2019). The survey recordedseveral qualitative and quantitative observations oftree damage resulting from the hurricane, such asuprooting or stem break, and type of damage tostems, tree crowns and branches. Using this information,we classified each stem at least 10 cm indbh into three damage classes: (1) no or lightdamage ( £ 25% of crown volume removed by thestorm), (2) medium damage (25–75% of crownvolume lost through a combination of branchdamage and crown break), or (3) heavy or complete(> 75% of the crown lost, stem snapped, rootbreak or tip-up) Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to
A holistic analysis of passenger travel energy and greenhouse gas intensities
<p>Dataset supporting the analysis of the journal article: Schäfer, A. & Yeh, S. A holistic analysis on passenger travel energy and GHG-intensities. <em>Nature Sustainability</em>, <strong>2020. </strong></p>
Disentangling the effects of methanogen community and environment on peatland greenhouse gas production by a reciprocal transplant experiment
<p>1. Northern peatlands consist of a mosaic of peatland types that vary spatially and temporally and differ in their methane (CH<sub>4</sub>) production. Microbial community composition and environment both potentially control the processes that release carbon from anoxic peat either as CH<sub>4</sub> or carbon dioxide (CO<sub>2</sub>), a less potent greenhouse gas than CH<sub>4</sub>. However, the respective roles of these controls remain unclear, which prevents incorporating microbes in the predictions of peatland CH<sub>4</sub> emissions.</p> <p>2. Here, a reciprocal transplant experiment was carried out to separate the influences of microbial community and environment in CH<sub>4</sub> and anaerobic CO<sub>2</sub> production. Peat from an acidic <i>Sphagnum</i> bog and a sedge fen with higher pH was enclosed in membrane bags with a pore size of 0.2 µm, preventing microbial colonization from the outside, and transplanted in the field for two months.</p> <p>3. Potential CH<sub>4</sub> production was primarily controlled by the environment. The conditions in the bog suppressed the initially higher activity of fen methanogens and reduced CH<sub>4</sub> production by 79%. Against expectations, the inhibition was not specific to acetate-using Methanotrichaceae. Reciprocal transplantation favoured Methanosarcinaceae and potentially methylotrophic methanogenesis in general. Bog methanogens, mostly hydrogenotrophic Methanoregulaceae, retained their community structure and activity in the fen with a slight increase (+37%) in CH<sub>4</sub> production.</p> <p>4. Anaerobic CO<sub>2</sub> production was controlled by both the microbial community and the environment. Transplantation led to increased CO<sub>2</sub> production in both bog (+50%) and fen peat (+57%) with distinct bacterial community, showing that the new environment directed more carbon to other anaerobic processes than methanogenesis. 5. Taken together, these results relate differences in CH<sub>4</sub> production of bogs and fens to ecophysiology of specific methanogen groups. The sensitiveness of fen methanogens to the acidic conditions in <i>Sphagnum</i> bogs can help explain the decrease of CH<sub>4</sub> emission in the typical boreal peatland succession from young fens to older bogs. Increase in anaerobic CO<sub>2</sub> vs. CH<sub>4</sub> production with transplantation shows that disturbances of boreal peatlands can activate poorly defined pathways of anaerobic decomposition.</p>
Dataset: Greenhouse Gas and Noxious Emissions from Dual Fuel Diesel and Natural Gas Heavy Goods Vehicles
<p>This dataset contains the data underlying all figures of the paper entitled 'Greenhouse Gas and Noxious Emissions from Dual Fuel Diesel and Natural Gas Heavy Goods Vehicles'.</p>
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.