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167 results for “greenhouse gas”
Lake Mendota Carbon and Greenhouse Gas Measurements at North Temperate Lakes LTER 2016
This original dataset contains carbon and greenhouse gas (GHG) data collected in Lake Mendota during the summer of 2016. Data were collected between 15 April 2016 and 14 November 2016 on both Lake Mendota and its surrounding streams—four major inflows and the primary outflow of Lake Mendota. The dataset is comprised of four linked tables, corresponding to carbon and GHG measurements on Lake Mendota (lake_weekly_carbon_ghg), weekly physico-chemical sonde casts on Lake Mendota (lake_weekly_ysi), ebullition rate estimates on Lake Mendota (lake_weekly_ebullition), and carbon and physico-chemical data from the four major inflows and primary outflow of Lake Mendota (stream_weekly_carbon_ysi). These data were used to explore the relationship between organic carbon dynamics and greenhouse gas production on a eutrophic lake. From these data, it is possible to estimate daily oxygen, methane, and carbon dioxide flux on Lake Mendota during the study time period. Additional methods and applications of this data can be found in J.A. Harts Masters Thesis, University of Wisconsin-Madison Center for Limnology, May 2017.
Time series of methane and carbon dioxide diffusive fluxes using an Ultraportable Greenhouse Gas Analyzer (UGGA) for Falling Creek Reservoir and Beaverdam Reservoir in southwestern Virginia, USA during 2018-2025
Diffusive fluxes of methane and carbon dioxide were measured using an Ultraportable Greenhouse Gas Analyzer (UGGA) at the surface of Falling Creek Reservoir (FCR) and Beaverdam Reservoir (BVR; Vinton, Virginia, USA). FCR and BVR are owned and operated by the Western Virginia Water Authority as drinking water sources for Roanoke, Virginia. The dataset consists of calculated diffusive fluxes of methane and carbon dioxide measured at the deepest site of the reservoir adjacent to the dam (2018–2025) and additional reservoir upstream sites in FCR (2018, 2023) and BVR (2022). In 2025, two littoral sites were measured at the northernmost wetland inflow to FCR. Measurements were collected approximately fortnightly in FCR throughout the summer stratified periods of 2018–2021 and 2023-2025, while measurements from BVR were only taken in 2018 and 2022-2024.
Greenhouse gas partial pressure (CO2, CH4, N2O) and environmental variables (physical, chemical, and biological) measured in urban ponds of Barcelona during summer and winter (2023-2024)
This dataset provides information on the partial pressure of greenhouse gases (CO₂, CH₄, and N₂O) measured in 41 artificial urban ponds—28 naturalized and 13 non-naturalized—using the headspace technique. Additionally, GPS coordinates, as well as physical, chemical, and biological variables for each pond, are included. Data were collected during the summer and winter seasons, during daytime. Furthermore, a subset of 16 ponds (8 naturalized and 8 non-naturalized) was also sampled at night in both seasons. All samples were taken from the water surface.
Soil dissolved organic matter and greenhouse gas fluxes from intact Delmarva Bay wetland soil cores during laboratory simulation of groundwater level rise
Wetlands in low-relief landscapes have dynamic terrestrial-aquatic interfaces as surface water and groundwater levels fluctuate seasonally and these variable water levels influence wetland carbon cycling. Seasonal changes in groundwater levels determine which soils are hydrologically connected to the wetland surface water-groundwater continuum and therefore which soils act as carbon sources. To quantify groundwater-mediated soil dissolved organic matter (DOM) and greenhouse gas (CO2 and CH4) fluxes, we performed a laboratory simulation of groundwater rise on intact soil cores. Soil cores were collected from four Delmarva Bay wetlands located in the low-relief landscape of the Delmarva Peninsula in the Mid-Atlantic United States. At each wetland, two cores (length = 60 cm, diameter = 10.2 cm) were collected: one from within the wetland basin and the second from the transitional zone near the edge of the wetland basin (total number of cores collected = 8). To characterize seasonal hydrologic conditions at each wetland site where intact soil cores were collected (e.g., mean water level, number of saturation events, duration of saturation), we used high frequency water level data collected in wetland center and upland groundwater monitoring wells. In the lab, cores were re-saturated with groundwater over 15 days and after cores were fully re-saturated, core water levels were maintained for an additional 25 days. Water levels in each head tank and soil core were manually recorded on sub-daily timesteps. Rhizon soil porewater samplers were installed at 8, 20, and 40 cm below the soil surface reflecting the expected depths of the O, A, and B soil horizons across the wetland sites. Source groundwater, soil porewater, and exfiltrated surface water samples were collected daily from the soil cores and analyzed for pH, ORP, and DOM concentration (dissolved organic carbon) and DOM composition (absorbance and fluorescence metrics). Discrete measurements of CO2 and CH4 fluxes were
Lamprey River New Hampshire Greenhouse Gas Data, 2014-2023
The Lamprey River watershed, located in southeastern New Hampshire (USA), drains 554 km² of low elevation land before discharging into the Great Bay Estuary (Wymore and others 2021). The watershed is classified as suburban with mixed land-use that includes forests (73%), wetlands (10%), development (7%), and agriculture (NOAA Coastal Change Analysis Program 2016). We selected four distinct sites within the watershed to capture the dynamics of both tributaries and the mainstem, while accounting for variations in land use, land cover, and nutrient availability. Wednesday Hill Brook (WHB) is a 1st-order stream that drains a residential landscape and has the highest NO3 concentrations among the sites due to a high density of septic systems (Flint and McDowell 2015). Dowst Cate Forest (DCF) is a 2nd-order stream draining a headwater wetland and forested landscape and is characterized by the highest concentrations of dissolved organic carbon (DOC). The two main stem Lamprey River sites, LMP72 and LMP73, exhibit moderate concentrations of both DOC and NO3 and are located approximately 1 km apart. LMP72 is located at a low-head run-of-river dam while LMP73 is free-flowing water downstream of the reservoir. Our dataset includes weekly water chemistry and dissolved gas data collected from April 2014 through May 2023, except for DCF where data collection ended in 2021, resulting in a total of 1,179 observations across the four different sites.
Greenhouse gas emissions from streams at North Temperate Lakes LTER 2012
Aquatic ecosystems can be important components of landscape carbon budgets. In lake-rich landscapes, streams may be important sources of greenhouse gases (CO2 and CH4) to the atmosphere in addition to lakes, but their source strength is poorly documented. The processes which control gas concentrations and emissions in these interconnected landscapes of lakes, streams and groundwater have not been adequately addressed. In this paper we use multiple datasets that vary in their spatial and temporal extent to investigate the carbon gas source strength of streams in a lake-rich landscape and to determine the roles of lakes and groundwater. We show that streams emit roughly the same mass of CO2 as regional lakes, and that stream CH4 emissions are an important component of the regional greenhouse gas balance.
Snow Manipulation Greenhouse Gas Measurements at South Sparkling and Trout Bog 2020-2021
To investigate the effect of a winter with decreased snow cover on greenhouse gas emissions, we experimentally removed snowfall from a small dystrophic lake in northern Wisconsin. As a comparative study, we were able to explore the role of light in under-ice gas dynamics and spring emissions in dimictic lakes. This dataset contains greenhouse gas and temperature/dissolved oxygen profile data collected on South Sparkling and Trout Bog during the winter of 2020 through the winter of 2021. Data were collected between 09 January 2020 and 13 April 2021 in the deep hole of both bogs. Dissolved greenhouse gas concentrations of carbon dioxide and methane were measured using the headspace equilibrium method.
UNFCCC country-submitted greenhouse gas emissions data until 2024-07-05
<p>Dataset containing all greenhouse gas emissions data submitted by countries under climate change convention (including CRF data) as published by the UNFCCC secretariat at 2024-07-05.</p>
GHG-depths: Greenhouse gas depth-profile data in 522 lakes worldwide
Lakes, ponds, and reservoirs (hereafter: “lakes”) are significant sources of the greenhouse gases carbon dioxide (CO2) and methane (CH4). Emissions of CO2 and CH4 from lakes are regulated in part by in-lake processes, including the production and storage of gases in the lower parts of the water column (bottom waters). However, while substantial efforts have been made to improve estimates of greenhouse gas emissions from lakes, limited data on gas concentrations along depth profiles have prevented the incorporation of bottom-water processes in global emission estimates. Here, we present GHG-depths: the largest existing dataset of depth-profile CO2 and CH4 measurements worldwide, including 522 lakes across 38 countries and all seven continents. These data include contributions from 45 research teams and 56 published studies, totaling 2558 discrete sampling events. As global change continues to alter biogeochemical cycling in lakes, these data can help improve mechanistic models to better predict greenhouse gas production and emission from lakes worldwide.
Summer water chemistry, high frequency sensors, zooplankton and benthic macroinvertebrate community composition, periphyton, fish, and macrophyte biomass, along with lake metabolism and greenhouse gas dynamics in six experimental ponds in central Iowa, USA (2020)
This data product contains physical, chemical, and biological data ranging from the minute to daily to weekly scale in six artificial ponds (400 square meter surface area, 2m depth) in central Iowa (USA) 2020. Ponds were paired into three sets of treatment and reference with treatment ponds receiving two nutrient pulses designed to increase ambient phosphorus concentrations ~ 3 - 5%. Nitrogen and phosphorus were added as NH4NO3 and H3PO4, respectively, at a 24:1 molar ratio. The first nutrient pulse occurred on Julian day of year (DOY) 176 corresponding to a 3% increase and the second nutrient pulse occurred on DOY 211 to a 5% increase. Each treatment-reference set had a different food web structure established ranging between low, intermediate, and high complexity based on trophic connectivity and food chain length. Added to this data package is a document titled "2020 Iowa State University Horticultural Farm Experimental Ponds Nutrient Addition Experiment". For experimental set up, context, and a summary table of the data tables archived herein with available variables please review this document. It is added to aid in successful interpretation and to increase ease-of-use. Please email Tyler Butts (tyler.james.butts@gmail.com) for any and all questions regarding context or use of this dataset!
Dissolved greenhouse gas concentrations derived from the NEON dissolved gases in surface water data product (DP1.20097.001)
This dataset contains partial pressure and molar concentration of dissolved carbon dioxide, methane, and nitrous oxide in 34 streams, rivers, and lakes calculated from headspace equilibration samples collected by the National Ecological Observatory Network (NEON). All input data were collected by NEON and is available on the NEON data portal at https://data.neonscience.org. Specifically, in situ dissolved gas concentrations were calculated from the air and headspace mixing ratios provided by the NEON Dissolved gases in surface water data product (DP1.20097.001), adjusted for sample and water temperature (DP1.20097.001, DP1.20264.001, DP1.20053.001), barometric pressure (DP1.20097.001, DP1.00004.001), and alkalinity (DP1.20093.001). The final set of inputs is found in the file, input_file, and the processing scripts are available at https://github.com/kellyaho/NEON-GHG-processing. The file, output_file, contains the raw outputs from running the input_file through the processing scripts. There are three outputs for each gas for each sample, one for each of three different pre-equilibration headspace mixing ratios (paired atmospheric samples, loess smoothing of atmospheric samples, and site-specific median). The file, GHG_final, contains the final dataset. This GHG_final uses the outputs from output_file calculated with paired atmospheric samples, and substitutes 0.01 μatm, 0.001 μM, 0.001 μatm, and 0.001 μM for any negative instances of pCH4, [CH4], pN2O, and [N2O], respectively. See methods for more detail. Please cite the NEON data inputs (listed below), in addition to this dataset, when using the data. NEON is sponsored by the National Science Foundation (NSF) and operated under cooperative agreement by Battelle. This material is based in part upon work supported by NSF through the NEON Program.
APEX beta greenhouse gas flux data from both the permafrost plateau area and the active thaw margin, from 2017 on
This dataset contains greenhouse gas flux data for the bog and permafrost plateau sites at the Alaskan Peatland Experiment (APEX) from 2017 on. Includes some environmentals, raw gas concentrations, and derived gas flux rates.
COMPAIR carbon footprint calculations and greenhouse gas emissions reduction scenarios
<p>Citizens' carbon footprint calculation results and citizen-created scenarios on how Greenhouse Gas emissions can be reduced by 55% by 2030 are available that were gathered as part of the <a href="https://cordis.europa.eu/project/id/101036563">EU Horizon2020 COMPAIR project</a> in Europe. The pilot cities/regions are Berlin, Athens, Sofia, Plovdiv, and Flanders.</p>
Greenhouse gas profiles from the 2021 HEMERA-TWIN balloon launch
<p>The dataset contains mixing ratio observations of o long-lived greenhouse gase carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and (SF6) from the HEMERA TWIN gonodola launch from Kiruna (Sweden) on 12. August 2021. Profile data for CO2 and CH4 from AirCore sampling and CH4 data from the PICO IR laser spectrometer cover altitudes from the PBL to 32km. 14 cryogenic air samples collected between 14 and 31 km altitude have been analysed for all four gases.</p>
IPCC Climate Zones (from the 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories)
<p><strong>Description</strong></p> <p>These data (re)create spatial data for the 2019 IPCC Climate Zones, shown in <em>Figure 3A.5.1</em> of <a href="https://www.ipcc-nggip.iges.or.jp/public/2019rf/pdf/4_Volume4/19R_V4_Ch03_Land%20Representation.pdf">Chapter 3: Consistent Representation of Lands</a> in <a href="https://www.ipcc-nggip.iges.or.jp/public/2019rf/vol4.html">Volume 4: Agriculture, Forestry and Other Land Use</a> of the <a href="https://www.ipcc-nggip.iges.or.jp/public/2019rf/index.html">2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories</a>. I recreated these data because I could not readily identify the data in a spatial format online, a problem which has previously been noted by ESDAC, who produced a <a href="https://esdac.jrc.ec.europa.eu/content/support-renewable-energy-directive#tabs-0-description=1">spatial version of <em>Figure 3A.5.1</em> from the original 2006 guidelines</a>.</p> <p>Resolution: 0.5 arc degree</p> <p>CRS: lon/lat WGS 84</p> <p><strong>If you use these data please ensure you also cite the IPCC</strong> - Calvo Buendia, E et al. (2019). 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories. IPCC, Switzerland.</p> <p> </p> <p><strong>Methods</strong></p> <p>The data were derived using the classification scheme shown in <em>Figure 3A.5.2</em> based on the gridded Climate Research Unit (CRU) Time Series (TS) monthly climate data (<a href="https://rmets.onlinelibrary.wiley.com/doi/10.1002/joc.3711">Harris et al., 2014</a>) for the period from 1985 to 2015 following the methods described in <em>Annex 3A.5 Default climate and soil classifications </em>of the above Chapter. All data were processed in <em>R</em> version 4.2.1, with the packages <a href="https://cran.r-project.org/web/packages/elevatr/index.html"><em>elevatr</em></a> (v0.4.2), <a href="https://cran.r-project.org/web/packages/lubridate/index.html"><em>lubridate</em></a> (v1.8.0), <a href="https://cran.r-project.org/web/packages/magrittr/index.html"><em>magrittr</em></a> (v2.0.3), and <a href="https://cran.r-project.org/web/packages/terra/index.html"><em>terra</em></a> (v1.6-7)<em> </em>attached. The full session info is included as a <em>.txt</em> file. As these methods are not exhaustively described in the Annex, the following assumptions were made:</p> <ul> <li><a href="http://http://dx.doi.org/10.5285/c311c7948e8a47b299f8f9c7ae6cb9af">CRU TS3.25</a> was used as the most recently published data (published on 2017-09-22) that could have been incorporated into the Refinement. Other possibilities include CRU TS3.24 (which are the first data to include 2015), or CRU TS4.00 or CRU TS4.01 (both of which were published in parallel to 3.24 and 3.25). These data were all investigated, and CRU TS3.25 produced results that were the most visually similar to the published <em>Figure 3A.5.1</em> (though non-identical).</li> <li>As the methods did not mention a preferred elevation data source, the <a href="https://cran.r-project.org/web/packages/elevatr/index.html"><em>elevatr</em></a> R package was used to obtain data at zoom level 2 (approx resolution of 0.15 arc degree), that was then resampled to match the 0.5-degree resolution of the CRU data. These data originally come from the <a href="https://www.ngdc.noaa.gov/mgg/global/global.html">ETOPO1 global relief model</a>.</li> </ul> <p> </p> <p><strong>Known discrepancies</strong></p> <ul> <li>The distribution of Tropical Wet and Tropical Moist in South America does not exactly match the original data.</li> <li>There are small discrepancies in Tropical Montane classifications (likely arising from the use of a different elevation layer). These are most noticeable in, but not restricted to, Africa.</li> <li>The classification of Boreal Dry, Polar Dry, and Polar Moist in northern Russia and (to a lesser extent) in northern Canada does not exactly match the original data.</li> <li>There are a small number of Cool Temperate Dry pixels in the UK, and Warm Temperate Dry pixels around Brittany which do not occur in the original data.</li> </ul> <p> </p> <p><strong>Disclaimer</strong></p> <p><strong>I am not affiliated with the IPCC in any way</strong>, I just needed spatial data of the Climate Zones, and could not readily identify any online. This is a problem which has previously been noted by ESDAC, who produced a <a href="https://esdac.jrc.ec.europa.eu/content/support-renewable-energy-directive#tabs-0-description=1">spatial version of <em>Figure 3A.5.1</em> from the original 2006 guidelines</a>.</p> <p> </p> <p><strong>File description</strong></p> <ul> <li><em>README.html</em> - ~this description file.</li> <li><em>IPCC_Climate_Zones_ts_3.25.tif</em> - the output Climate Zones map at 0.5-arc degree resolution based on the CRU TS3.25 data.</li> <li><em>IPCC_Climate_Zones_colour_map.clr </em>- a colour map file to render the output map with the same colours as in the IPCC 2019 Refinement figure.</li> <li><em>IPCC_Climate_Zones_ts_3.25.png</em> - an image file of the output Climate Zones map.</li> <li><em>ipcc_climate_zones_2019.R</em> - the script used to produce these data.</li> <li><em>session_info.txt</em> - the R session info.</li> </ul>
Greenhouse gas fluxes and concentrations and associated habitat data in western Dane County, Wisconsin, USA, streams during the 2018 growing season
Streams are often sources of carbon dioxide (CO2) and methane (CH4), particularly in agricultural regions where sediment and organic matter inputs can be substantial. Floods are occurring more often and more intensely in southern Wisconsin, one such agricultural region, due to climate change and few studies have investigated how floods impact stream CO2 and CH4 fluxes and concentrations. I compared concentrations and fluxes of CO2 and CH4 with greater than 30 variables representing in-stream and watershed attributes at 10 sites in mixed agricultural and suburban locations in southern Wisconsin. Sampling was conducted 10 times at each site during the growing season (May-November) in 2018
2015 Drought soil biogeochemistry and greenhouse gas emissions study at El Verde
We report the effects of the severe 2015 Caribbean drought on soil moisture, oxygen (O2), temperature, phosphorus (P), iron (Fe), pH, and GHG emissions (CO2 and CH4) across a catena sensor array field outside of El Verde Research Station, Luquillo LTER, Puerto Rico. Seven sensors of each type were installed at 12 cm depth along a ridge to valley catena; the entire catena transect was replicated five times for a total of 105 sensors. Within the sensor field we also installed nine automated gas flux chambers randomly located in each topographic zone (ridge, slope and valley). Soil carbon and nitrogen, extractable phosphorus (P) pools, iron (Fe) species, and pH were sampled before and during the drought as indicators of biogeochemical conditions. 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 the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
Greenhouse gas and water chemistry data from urban ponds in Madison, Wisconsin during the summer and under-ice period of 2021-2022
Stormwater ponds are common features in urbanized landscapes and can suffer from rapid oxygen depletion when thermally stratified or ice-covered. To investigate under-ice oxygen dynamics and drivers of bottom water oxygen saturation, we sampled 20 stormwater ponds in Madison, Wisconsin, USA during the summer of 2021 and winter 2022. The urban ponds ranged in age, shape, size, and depth. We repeatedly took YSI profiles of water temperature, oxygen, and specific conductance 7 times in the summer and 3 times in the winter. Water chemistry variables were collected in the surface waters, habitat surveys were conducted in the summer, and ice/snow thickness was recorded in the winter. We also measured the concentration of greenhouse gases in the surface waters as a consequence to oxygen depletion using the headspace equilibrium method.
Electron shuttling capacity and greenhouse gas production of soils for three high-elevation wetlands at Niwot Ridge, 2024.
High-elevation wetlands are important indicators of how mountain ecosystems may respond to global climate change. These wetlands also act as locations of disproportionate biogeochemical processing on the landscape, but they remain relatively understudied compared to lowland wetlands. This study aimed to characterize redox-active organic matter (RAOM) reduction, a known key control on carbon cycling in high-latitude peatland ecosystems, to better understand biogeochemical cycling in high elevation wetlands and carbon greenhouse gas production at Niwot Ridge LTER. Soils were collected from three different types of wetlands, a subalpine wetland, a periglacial solifluction lobe, and an alpine wet meadow. Samples were incubated at a common temperature in the laboratory to measure RAOM reduction, carbon dioxide production, and methane production over 63-d. This dataset reports the electron shuttling values, a measure of RAOM reduction, and the greenhouse gas production over the incubation period.
Gridded EPA U.S. Anthropogenic Methane Greenhouse Gas Inventory (gridded GHGI)
<h2><strong>About</strong></h2><p>The gridded EPA U.S. anthropogenic methane greenhouse gas inventory (gridded methane GHGI) includes spatially and temporally resolved (gridded) maps of annual anthropogenic methane emissions (0.1°×0.1°) for the contiguous United States (CONUS). Total gridded methane emissions for each emission source sector are consistent with national annual U.S. anthropogenic methane emissions reported in the U.S. EPA <a href="https://www.epa.gov/ghgemissions/inventory-us-greenhouse-gas-emissions-and-sinks"><i>Inventory of U.S. Greenhouse Gas Emissions and Sinks</i></a> (U.S. GHGI). More information is available on the <a href="https://www.epa.gov/ghgemissions/gridded-methane-emissions">U.S. EPA website</a>. </p><p>This repository accompanies the peer-reviewed manuscript <a href="https://pubs.acs.org/doi/10.1021/acs.est.3c05138"><i>Maasakkers, et al., 2023</i></a>. Data in this repository are an update to the gridded GHGI version 1, previously described in <a href="https://pubs.acs.org/doi/10.1021/acs.est.6b02878"><i>Maasakkers, et al., 2016</i></a> and available on the <a href="https://www.epa.gov/ghgemissions/gridded-2012-methane-emissions">U.S. EPA website</a>. </p><h4><strong>This repository contains two data products:</strong></h4><ol><li><strong>Gridded GHGI v2 (main product; 2 file types). </strong>Gridded annual U.S. anthropogenic methane emissions for 2012-2018 for 26 source categories (gridded GHGI). This dataset is developed to be consistent with the national U.S. GHGI published in 2020 (<i>U.S. EPA, Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990 - 2020. U.S. Environmental Protection Agency, 2020, EPA 430-R-22-003, </i><a href="https://www.epa.gov/ghgemissions/inventory-us-greenhouse-gas-emissions-and-sinks-1990-2018"><i>https://www.epa.gov/ghgemissions/inventory-us-greenhouse-gas-emissions-and-sinks-1990-2018</i></a>).<br><br>This dataset includes 2 file types: <br>a. Annual methane emission fluxes for 26 inventory source categories. Files contain one year of emissions per source category and include a time dimension variable to make the data suitable (COARDS-compliant) for atmospheric models.<br> (Dimensions: latitude x longitude x time; units: molecules CH4 cm-2 s-1):<br><i> - Gridded_GHGI_Methane_v2_YYYY.nc</i><br><br>b. Monthly emission scaling factors for inventory source categories with strong interannual variability (see 'Data Details' below). To use these factors to calculate absolute monthly methane emission fluxes, multiply the scaling factors for each relevant source category by the corresponding emission fluxes in the annual flux files.<br> (Dimensions: latitude x longitude x month; units: dimensionless): <br> - <i>Gridded_GHGI_Methane_v2_Monthly_Scale_Factors_YYYY.nc</i><br> </li><li><strong>Gridded GHGI v2 Express Extension (1 file type).</strong> The v2 Express Extension includes gridded annual U.S. anthropogenic methane emissions for 2012-2020 for 27 source categories (one additional source category compared to the main v2 dataset above). This dataset is developed to be consistent with total methane emissions from the U.S. GHGI published in 2022 (<i>EPA (2022) Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020. U.S. Environmental Protection Agency, EPA 430-R-22-003. </i><a href="https://www.epa.gov/ghgemissions/draft-inventory-us-greenhouse-gas-emissionsand-sinks-1990-2020"><i>https://www.epa.gov/ghgemissions/draft-inventory-us-greenhouse-gas-emissionsand-sinks-1990-2020</i></a><i>)</i>. <br><br><i>**Note**:</i><strong> </strong>This dataset is <strong>not</strong> a full update to the main gridded GHGI v2 product. To quickly incorporate more recent national methane emission estimates into gridded products, national methane emissions from a more recent U.S. GHGI were spatially allocated (i.e., gridded) using the annual source-specific spatial emission patterns developed for the 2012-2018 main v2 product. Emissions for years 2019 and 2020 were allocated using 2018 spatial patterns.<br><br>This dataset includes 1 file type:<br>a. Annual emission files<br> (Dimensions: latitude x longitude x time; units: molecules CH4 cm-2 s-1):<br> - <i>Express_Extension_Gridded_GHGI_Methane_v2_YYYY.nc</i></li></ol><p><i>--------------------------------------------------</i></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.