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167 results for “greenhouse gas”
Data from: Reported U.S. Wild Game Consumption and Greenhouse Gas Emissions Savings
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Global greenhouse gas emissions from agriculture: pathways to sustainable reductions
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Data from: Using greenhouse gas fluxes to define soil functional types
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Energy consumption and greenhouse gas emissions data of activated carbon production using different biomass
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Comparative aquatic greenhouse gas emission rates across multiple land-use types and along impounded river systems
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Data from MECO(n) model simulations on "Urban greenhouse gas emissions from the Berlin area: A case study using airborne CO2 and CH4 in situ observations in summer 2018"
<p>This tar-files contain the results of the MECO(n) model, which are published in</p> <p>T. Klausner, M. Mertens, H. Huntrieser, M. Galkowski, G. Kuhlmann, R. Baumann, A. Fiehn, P. Jöckel, M. Pühl, and A. Roiger: Urban greenhouse gas emissions from the Berlin area: A case study using airborne CO<sub>2</sub> and CH<sub>4</sub> in situ observations in summer 2018, Elementa: Science of the Anthropocene (Ref.: Ms. No. ELEMENTA-D-19-00074R1), 2019.</p>
Linking potential greenhouse gas and nitric oxide fluxes to soil microbial communities in incubation experiments with soil from the SAFE landscape
<b>Description: </b><p>Controlled lab experiment to measure potential GHG emissions and associated parameters from SAFE soil. Soil taken Nov 2016, lab experiment carried out Apr-May 2017. Day 0 is before fertilisation, day 1 application of NH4NO3 solution to simulate N deposition of approx. 5 kg N ha-1 y-1 . Day 15 for (OP2,OP7 and RR) application of NH4NO3 solution to simulate N deposition of approx 50 kg N ha-1 y-1.</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/126"><b>Characterising soil microbial communities and measuring associated biogeochemical fluxes</b></a></p><p><b>Funding: </b>These data were collected as part of research funded by: </p><ul><li>NERC HMTF (Research Programme, (NE/K016091/1), <a href=" http://lombok.nerc-hmtf.info/"> http://lombok.nerc-hmtf.info/</a>)</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><b>Permits: </b>These data were collected under permit from the following authorities:</p><ul><li>Sabah Biodiversity Centre (Research licence JKM/MBS.1000-2/3 JLD.2 (115))</li></ul><p></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=3897394">here</a></p><p><b>Files: </b>This consists of 1 file: Lab_experiment_Melissa_corrected.xlsx</p><p><b>Lab_experiment_Melissa_corrected.xlsx</b></p><p>This file contains dataset metadata and 2 data tables:</p><ol><li><p><b>parameters_repeated_measures</b> (described in worksheet parameters_repeated_measures)</p><p>Description: soil characteristics</p><p>Number of fields: 16</p><p>Number of data rows: 207</p><p>Fields: </p><ul><li><b>core_id</b>: Location measurement was taken (Field type: id)</li><li><b>site</b>: Location measurement was taken (Field type: location)</li><li><b>landuse</b>: Land use of location (Field type: categorical)</li><li><b>day_of_exp</b>: day number (Field type: numeric)</li><li><b>flux_CH4</b>: Soil CH4 flux (Field type: numeric)</li><li><b>flux_CO2</b>: Soil CO2 flux (Field type: numeric)</li><li><b>flux_N2O-N</b>: Soil N2O flux (Field type: numeric)</li><li><b>flux_NO</b>: Soil NO flux (Field type: numeric)</li><li><b>NH4-N</b>: Soil NH4 concentration (Field type: numeric)</li><li><b>NO3-N</b>: Soil NO3 concentration (Field type: numeric)</li><li><b>soil_moisture</b>: Soil moisture around the flux chamber (Field type: numeric)</li><li><b>archaeal amoA</b>: Gene transcript abundance (Field type: numeric)</li><li><b>Proteobacteria_nirS</b>: Gene transcript abundance (Field type: numeric)</li><li><b>AniA_nirK</b>: Gene transcript abundance (Field type: numeric)</li><li><b>nosZ-I</b>: Gene transcript abundance (Field type: numeric)</li><li><b>nosZ-II</b>: Gene transcript abundance (Field type: numeric)</li></ul></li><li><p><b>parameters_one_off</b> (described in worksheet parameters_one_off)</p><p>Description: soil pH and density</p><p>Number of fields: 5</p><p>Number of data rows: 18</p><p>Fields: </p><ul><li><b>core id</b>: Location measurement was taken (Field type: id)</li><li><b>site</b>: Location measurement was taken (Field type: location)</li><li><b>landuse</b>: Land use of location (Field type: categorical)</li><li><b>pH</b>: Soil pH (Field type: numeric)</li><li><b>bulk_density</b>: dry weight of soil (Field type: numeric)</li></ul></li></ol><p><b>Date range: </b>2016-11-01 to 2017-05-30</p><p><b>Latitudinal extent: </b>4.5000 to 5.0700</p><p><b>Longitudinal extent: </b>116.7500 to 117.8200</p>
Data from: Proximate controls on semiarid soil greenhouse gas fluxes across 3 million years of soil development
Soils are important sources and sinks of three greenhouse gases (GHGs): carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O). However, it is unknown whether semiarid landscapes are important contributors to global fluxes of these gases, partly because our mechanistic understanding of soil GHG fluxes is largely derived from more humid ecosystems. We designed this study with the objective of identifying the important soil physical and biogeochemical controls on soil GHG fluxes in semiarid soils by observing seasonal changes in soil GHG fluxes across a three million year substrate age gradient in northern Arizona. We also manipulated soil nitrogen (N) and phosphorus availability with 7 years of fertilization and used regression tree analysis to identify drivers of unfertilized and fertilized soil GHG fluxes. Similar to humid ecosystems, soil N2O flux was correlated with changes in N and water availability and soil CO2 efflux was correlated with changes in water availability and temperature. Soil CH4 uptake was greatest in relatively colder and wetter soils. While fertilization had few direct effects on soil CH4 flux, soil nitrate was an important predictor of soil CH4 uptake in unfertilized soils and soil ammonium was an important predictor of soil CH4 uptake in fertilized soil. Like in humid ecosystems, N gas loss via nitrification or denitrification appears to increase with increases in N and water availability during ecosystem development. Our results suggest that, with some exceptions, the drivers of soil GHG fluxes in semiarid ecosystems are often similar to those observed in more humid ecosystems.
Climate Changes in the Upper Atmosphere: Contributions by the Changing Greenhouse Gas Concentrations and Earth's Magnetic Field
<p>These are data that were used to write the paper: "Climate Changes in the Upper Atmosphere: Contributions by the Changing Greenhouse Gas Concentrations and Earth's Magnetic Field " by Liying Qian, Joseph M. McInerney, Stan S. Solomon, Hanli Liu, Alan G. Burns.</p>
Data from greenhouse gas study in Lake Ormstrup 2022
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Optimization of global crop distribution could reduce greenhouse gas emissions by one third but requires expanding international trade
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Dataset for "Projected thermally driven elderly mortality for Beijing under greenhouse gas and stratospheric aerosol geoengineering scenarios"
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Data from: Driving factors on greenhouse gas emissions in permafrost region of Daxing'an Mountains, Northeast China
<p>Permafrost regions are an important source of greenhouse gases. However, the effects of different permafrost wetland types on greenhouse gas emissions and the driving factors are still unclear in the permafrost region. Here, we selected three typical permafrost wetlands from the Daxing'an Mountains to investigate the effects of permafrost wetland types on greenhouse gas emissions. <span class="fontstyle71"><span>The cumulative </span></span>N<sub>2</sub>O, CO<sub>2</sub>, and CH<sub>4</sub> emissions were 84–122, 657,942–1,446,121, and 173–16,924 kg km<sup>−2</sup>, respectively. The linear mixed effects model indicated that N<sub>2</sub>O emissions were significantly affected by the NO<sub>3</sub><sup>−</sup>-N content, whereas CO<sub>2</sub> emissions were mainly driven by soil temperature, water table level, and NO<sub>3</sub><sup>−</sup>-N content. CH<sub>4</sub> emissions were affected by soil temperatue and water table level. Permafrost wetland types significantly affected the average and cumulative N<sub>2</sub>O, CO<sub>2</sub>, and CH<sub>4</sub> emissions. The cumulative N<sub>2</sub>O emissions were highest in the <i>Larix gmelinii - Carex</i> <i>appendiculata </i>(<i>LC</i>) wetland and lowest in the <em>Betula fruticosa Pall. </em>(<em>B</em>) wetland<span class="fontstyle71"><span>, driven by </span></span>NO<sub>3</sub><sup>−</sup>-N content. The cumulative CO<sub>2</sub> emissions were highest in the (<em>B</em>) wetland and lowest in the <em>L. gmelinii</em> - Ledum palustre var. dilatatum (<em>LL</em>) wetland. The cumulative CH<sub>4</sub> emissions from <span class="fontstyle71"><span><i>B</i></span></span><span class="fontstyle71"><span> wetland were significantly higher than those from </span></span><i>LL</i> and <i>LC</i> wetlands. The differences in cumulative CO<sub>2</sub> and CH<sub>4 </sub>emissions were driven by the water table level. Our findings indicate that NO<sub>3</sub><sup>−</sup>-N content affect the spatial-temporal variation of N<sub>2</sub>O emissions, whereas water table level influence the spatial-temporal variation of CO<sub>2</sub> and CH<sub>4</sub> emissions in the permafrost region of the Daxing'an Mountains.</p>
Supplementary data for Life-cycle assessment shows that retrofitting coal-fired power plants with fuel cells will substantially reduce greenhouse gas emissions
<p>This dataset contains supplementary data for "Life-cycle assessment shows that retrofitting coal-fired power plants with fuel cells will substantially reduce greenhouse gas emissions" DOI: <strong>10.1016/j.oneear.2022.03.009</strong>.</p> <p>S1-S10 contains life-cycle inventories of solid oxide fuel cells, molten carbonate fuel cells, phosphoric acid fuel cells, and proton exchange membrane fuel cells with either natural gas or wind-electrolysis hydrogen as a feedstock.</p> <p>S11-S13 contains technological information of coal-fired power plants in China</p>
Increasing global precipitation whiplash due to anthropogenic greenhouse gas emissions
<p>Supporting processed data for 'Increasing global precipitation whiplash due to anthropogenic greenhouse gas emissions' <br> </p>
Data from: Greenhouse gas emissions from reservoir water surfaces: a new global synthesis
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Data from: Driving factors on greenhouse gas emissions in permafrost region of Daxing’an Mountains, Northeast China
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Data from: Proximate controls on semiarid soil greenhouse gas fluxes across 3 million years of soil development
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AVIRIS-3 L2B Greenhouse Gas Enhancements, Facility Instrument Collection
This dataset contains Level 2B (L2b) enhancements of greenhouse gasses (GHG) derived from imagery collected by the Airborne Visible / Infrared Imaging Spectrometer-3 (AVIRIS-3) instrument. Products include methane and carbon dioxide enhancements, each with per-pixel uncertainties and sensitivities to the background. Concentration enhancements are estimated from radiance measurements using a column-wise adaptive matched filter approach, which searches each pixel's radiance spectrum for deviations that are characteristic of a GHG's absorption spectrum. This is the NASA Earth Observing System Data and Information System (EOSDIS) facility instrument archive of these data. The NASA AVIRIS-3 is a spectral mapping system that measures reflected radiance at 7.4-nm intervals in the Visible to Shortwave Infrared (VSWIR) spectral range from 390-2500 nm.
NACP Regional: National Greenhouse Gas Inventories and Aggregated Gridded Model Data
This data set provides two products that were derived from the recently published North American Carbon Program (NACP) Regional Synthesis 1-degree terrestrial biosphere model (TBM) and inverse model (IM) outputs (Gridded 1-deg Observation Data and Biosphere and Inverse Model Outputs, Wei et al., 2013). The first product is the aggregation of the standardized gridded 1-degree TBM and IM outputs to the Greenhouse Gas (GHG) inventory zones as defined for North America (United States, Canada, and Mexico). Depending on the data availability, the monthly/yearly Net Ecosystem Exchange (NEE), Net Primary Production (NPP), Total Vegetation Carbon (VegC), Heterotrophic Respiration (Rh), and Fire Emissions (FE) outputs from the 22 TBM and 7 IM models were aggregated from the 1-degree resolution gridded format to the inventory zones and then, further divided into Forest Lands, Crop Lands, and Other Lands sectors within each inventory zone based on the 1-km resolution GLC2000 land cover map (GLC2000, 2003).The second product is the North American national GHG inventories on the scale of inventory zones which contain estimated land-atmosphere exchange of CO2 (NEE) in forest lands, crop lands, and other lands sectors. NEE estimates were synthesized from inventory-based data on productivity, ecosystem carbon stock change, and harvested product stock change, and additional information from national-level GHG inventories of the United States, Canada, and Mexico including EPA (2011) and Environment Canada (2011).An additional summary file of annual mean NEE (2000-2006)is provided for both land sectors and reporting zones in North America and was created by combining the aggregated model output and the national GHG database and is provided. The aggregated monthly and yearly model output data and the national GHG inventories data are available in comma separated value (*.csv) format files. Also provided are detailed inventory zone spatial data as an ESRI Shapefile. Included are zone names, boundaries, and zone and land cover type area attributes. For mapping convenience, the inventory zones shapefile was merged with 1-km forest, crop, and other lands masks to create a 1-km resolution reference data file that was converted to GeoTIFF format. The GeoTIFF defines to which inventory zone and land cover type each 1-km grid cell belongs.This document provides detailed information about the content, format, and processing procedures of these two data products. Detailed descriptions of the TBMs and IMs can be found in a separate companion document: NACP Regional Synthesis - Description of Observations and Models.
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.