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626 results for “Methanation”
Figure 5 in A methane seep from the deep-marine, late Eocene Keasey Formation, Rock Creek, Columbia County, Oregon
Figure 5. Stratigraphic sections of the Main Seep Site in relation to Second and Third sites and stratigraphic position of UCMP localities. The Main Seep Site (Fig. 4) consists of a thick limestone lithosome including immediately subjacent black mudstone. The Second Seep Site about 70m west of the main seep, consists of a large conduit about 0.5 m wide by 3 meters high and a complex association of concretions. This site occurs at the east end of the exposure shown in Fig. 7. The Third Seep Site includes a localized concentration of small blebby concretions.
Figure 11 in A methane seep from the deep-marine, late Eocene Keasey Formation, Rock Creek, Columbia County, Oregon
Figure 11. Depiction of solemyid burrow (Fig. 11A, from Hickman 1984) and lucinid burrow (Fig. 11B, from Hickman 1994, 2003b). Such burrows span oxic-anoxic boundaries to provide their symbionts with sulfide from the sediment and oxygen from the water column and as well as meeting their own oxygen needs. The presence of an articulated fossil solemyid in life position (i.e. in situ) indicates the presence of an ancient oxic-anoxic interface, regardless of whether the burrow is clearly preserved. Extensive bioturbation characteristic of the massive Keasey siltstone and mudstone units is likely to obliterate uncemented burrow ichnofabrics of solemyids. See Droser & Bottjer (1989) for history of bioturbation. On the other hand, burrows of decapods are more likely to be preserved since decapod crustaceans secrete a calcareous cement that prevents burrows from caving — e.g. collophanite (an amorphous calcium phosphate). See Weimer and Hoyt (1964).
Figure 1 in A methane seep from the deep-marine, late Eocene Keasey Formation, Rock Creek, Columbia County, Oregon
Figure 1.. Field area maps. A. Study site location in the vicinity of Vernonia, northwest Oregon State, U.S.A. B. Detail of UCMP IP localities along Rock Creek 8 kilometers west of Vernonia. UCMP IP locality 16636 at the west end is from the base of the measured section (Fig. 2) while UCMP IP locality 16621 is from the top of the measured section. The Main Seep Site is located between collections UCMP IP localities 16630 and 16621. C. Geologic map along Rock Creek from Vernonia to the former town of Keasey. Geology generalized from mapping by Alan Niem in Wells et al. (2020). Abbreviated geologic units in the map include the following: Qaf = Holocene and Pleistocene alluvial fan deposits; Qls = Holocene and Pleistocene landslide deposits; Qt = Holocene and Pleistocene talus; Qtd = Holocene and Pleistocene terrace deposits. M22= macrofauna locality from Warren et al. (1945) and benthic foraminiferal localities 249-251 and 239-247 from McDougall (1979).
Figure 2 in A methane seep from the deep-marine, late Eocene Keasey Formation, Rock Creek, Columbia County, Oregon
Figure 2. Stratigraphic section of the Keasey Formation in the vicinity of the Rock Creek seep site, showing position of UCMP localities. Yellow highlighted area above the upper turbidite bed is the equivalent stratigraphic positions of the Main and Second Seep Sites, whereas the minor yellow highlighted area just below the upper turbidite gives the stratigraphic position of small blebby concretions defining the Third Seep Site
Figure 9 in A methane seep from the deep-marine, late Eocene Keasey Formation, Rock Creek, Columbia County, Oregon
Figure 9. Map occurrences of major macrofaunal elements and associations at the cold seep horizon in limestone, muddy siltstone, and siltstone facies and exemplar outcrops of non-chemosymbiotic. A. byssally attached bivalves from the Mud Pecten Association; C, D, E, M. Suspension-feeding heterodont bivalve association; B, F, L, deposit-feeding Protobranch Association; G, H I, J, chemosymbiotic Thyasirid-Solemyid Association; K. Turrid–Naticid–Epitoniid Association.
Assessment of whole-site methane emissions from anaerobic digestion plants: towards establishing emission factors for various plant configurations
<p>This dataset supplements the publication "Assessment of whole-site methane emissions from anaerobic digestion plants: towards establishing emission factors for various plant configurations" by Wechselberger et al. (2025).</p> <p>The dataset contains primary and secondary data underlying the statistical analysis and reported methane emission factors. Emission factors were calculated as described in section 2.3 of the paper. </p> <p>Available files (UTF-8 encoded):</p> <ul> <li>Data.csv (dataset)</li> <li>Glossary.csv (column/variable descriptions of dataset)</li> </ul> <p>The dataset includes plant characteristics and whole-site methane losses of 135 anaerobic digestion plants, covering normal and various other-than-normal operating conditions (155 rows). For statistical analysis, only periods during normal operation and plants with information on the analyzed emission factors and plant characteristics were considered (cf. supplementary information C of the paper). Consequently, the final dataset contained 109 anaerobic digestion plants for statistical analysis on the methane emission factor (% of methane produced) and 28 plants when analyzing the wastewater-specific emission factor (kg methane per population equivalent and year). All but one facility continuously processed feedstock without any post-rotting stages. Plant DE-MH_WP5_1 of the secondary data implemented garage digesters.</p> <p>Data from three plants were collected only after completion of statistical analyses. These data were used to compare methane losses during normal and other-than-normal operating conditions. The respective rows are marked accordingly in the dataset (column “data_collected_after_statistical_analyses”).</p> <p>Version v2 contains the final reference to the publication Wechselberger et al. (2025). The data are the same as in version v1.</p>
Methane and Carbon Dioxide Production and Emission Pathways in the Belowground and Draining Water Bodies of a Tropical Peatland Plantation Forest
<p>This is the data repository for the second version (revised) of the manuscript "Methane and Carbon Dioxide Production and Emission Pathways in the Belowground and Draining Water Bodies of a Tropical Peatland Plantation Forest<strong>"</strong> submitted to Geophysical Research Letters on 10 January 2025.</p>
Dataset of the AVIRIS-NG methane campaign in Romania in 2021
<p>The dataset contains CH4 emission data from the AVIRIS-NG campaign conducted in Romania in July 2021.</p>
The key role of production efficiency changes in livestock methane emission mitigation
<p>This dataset contains the R code, the input data, the parameters used, and the updated livestock methane emission for the period 1961-2023 using methods from Chang, J., Peng, S., Yin, Y., Ciais, P., Havlik, P., Herrero, M. (2021). The key role of production efficiency changes in livestock methane emission mitigation. AGU Advances, 2, e2021AV000391. DOI: https://doi. org/10.1029/2021AV000391 </p> <p>1. R code: Chang_et_al_Global_Livestock_CH4_Assessment_1961_2023.R<br>2. Input data and parameters: Data.zip (statistics on historical livestock numbers and production need to be downloaded from FAOSTAT (http://www.fao.org/faostat/en/)<br>3. Results on global livestock methane emissions during 1961-2023 were presented in the Global_Results.xlsx<br>4. Results on livestock methane emissions from enteric fermentation and manure management during the period 1961-2023 in each country/area were shown in the folder named Country_Results: Files are organized as "Country_[XX]CH4_[YY]_[ZZ].csv" where XX indicate emission from enteric fermentation (EF) or manure management (MM); YY indicates method used for the estimates; and ZZ indicates livestock categories.<br>5. Results on gridded livestock methane emissions at a resolution of 5 arc-min using the IPCC Mixed Tier 1 and Tier 2 (2019MT) and Tier 1 (2019T1) methods following the 2019 refinement to the 2006 IPCC guidelines for National Greenhouse Gas Inventories (Vol. 4) (IPCC, 2019): Livestock_CH4_map_5arcmin_1961_2023_2019MT_2019T1.nc4</p> <p>Please contact: Dr. Jinfeng Chang (changjf@zju.edu.cn) for any question on the dataset.</p>
A high-resolution gridded inventory of coal mine methane emissions for India and Australia
<p>The dataset contains the high-resolution gridded coal mine methane emissions file (.csv) for India and Australia. The emissions are estimated for the year 2018 at a resolution of 0.1° × 0.1°. The emission unit is ton/grid/year.</p>
Dataset - Controlled release experiment to investigate uncertainties in UAV-based emission quantification for methane point sources
<p>This dataset was created by Randulph Morales (randulph.morales@empa.ch) and was used for Morales et al. (2021) AMT publication (amt-2021-314). A short description of the files is written in <strong>readme.txt</strong></p> <p>The dataset contains:</p> <ul> <li>QCLAS methane measurement</li> <li>Active AirCore methane measurement</li> <li>Meteorology files</li> </ul>
Data supporting "Solar radiation drives methane emissions from the shoots of Scots pine"
<p>Data supporting our New Phytologist publication "Solar radiation drives methane emissions from the shoots of Scots pine". </p>
Methane fluxes from four elevation zones in a St. Lawrence Estuary salt marsh
<p>Dataset used in <a href="https://iopscience.iop.org/article/10.1088/2752-664X/ac706a/meta">Spartina alterniflora has the highest methane emissions in a St. Lawrence estuary salt marsh - IOPscience</a>.</p> <p>The dataset contains methane fluxes calculated from gas measurements taken over a 40 or 60 minute period using a dark static chamber method. Methane fluxes were measured at six locations in four elevation zones of a northern salt marsh on the St. Lawrence River estuary at La Pocatière, Quebec (47°22'24.7"N 70°03'26.3"W). Additional environmental data was collected including carbon dioxide fluxes, extractable soil nitrate, extractable soil ammonium, extractable soil dissolved organic carbon, extractable soil total dissolved nitrogen, salinity, temperature, water table depth, soil total organic carbon, soil total nitrogen, soil organic carbon to nitrogen ratio and bulk density. Soil cores were collected from 0-15 cm and used for extractable nutrient analysis, bulk density and soil organic carbon and nitrogen analysis. The work was carried out with funding from the European Union’s Horizon 2020 Research and Innovation Programme under the Marie Sklodowska-Curie Grant Agreement 838296, a NSERC Discovery Grant and a Natural Environment Research Council grant number (NE/T012323/1). This dataset is used in a publication entitled <em>Spartina alterniflora</em> has the highest methane emissions in a St. Lawrence Estuary salt marsh in Environmental Research: Ecology (https://doi.org/10.1088/2752- 664X/ac706a), which also contains more details on fieldsite and methodology.</p> <p>Gas samples were collected from dark, static chambers (18L, 26 cm diameter), which were placed onto pre-inserted collars in the vegetated zones (inserted to 2.5 cm, 3 days prior to sampling) or placed directly onto the mudflat. The chambers were insulated and fitted with fans and venting tubes. Gas samples were collected on the 23rd August 2020 from all sites, soil cores were collected between the 24-25th August 2020 and the 19-20th September 2020. Soil samples were collected at 0-15 cm using a 2.5 cm diameter dutch gouge corer.</p> <p>Soil temperature was measured at 10 cm depth using a soil thermometer, (°C, DeltaTrak 11050, Pleasanton, USA), salinity was measured in the laboratory using a portable ATC refractometer. Water table depth was measured using a PVC piezometer, a plastic pipe with tubing was inserted into the piezometer and blown into to determine water table depth through bubbling sound (cm). Soil cores were dried at 60 °C to constant weight and the dry weight over core volume used to calculate bulk density (g cm-3), soil was finely ground and analysed for total organic carbon and total nitrogen (%) using an Elemental Analyser (ThermoFinnigan Flash EA 1112 CN analyser, Carlo Erba, Milan, Italy) with an accuracy of ±5 % for N and ±1 % for C, and a limit of 171 detection of 0.05 % for both N and C. Extractable nitrate+nitrite (assumed to be nitrate) 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 dissolved organic carbon and total dissolved nitrogen were analysed in soil extractant (ultrapure water 18.2 MΩ, 5:1 of extractant to soil) on a TOC/TDN analyser (TOC VCSn + TMN-1, Shimadzu, Kyoto, Japan), with a 50 mg C l -1 standard resulting in an accuracy and precision of 3.0 and ±4.4 mg l-1, respectively. CH4 and CO2 concentrations were measured in the gas samples using a gas chromatograph (GC-14, Shimadzu, Kyoto, Japan) fitted with a flame ionisation detector, CO2 was methanised to CH4 before analysis. Standards of CH4 (5.1 ppm) and CO2 (5000 ppm) resulted in an accuracy and precision of 6.6±1.5 and 0.4 ppm, and 5324±324 and 78 ppm, respectively, for CH4 and CO2. Changes in gas concentration over time were converted to fluxes using a linear regression of the linear portion fo the flux and if fluxes were below the minimum detectable concentration difference (see <a href="https://doi.org/10.1002/2017JG003783">https://doi.org/10.1002/2017JG003783</a>), they were set to zero. Results from the experiments were entered into an Excel spreadsheet for ingestion into the Zenodo data repository.</p>
Data for Microkinetic modeling of the transient CO2 methanation with DFT-based uncertainties in a Berty reactor
<p>Dataset and scripts for the manuscript "Microkinetic modeling of the transient CO2 methanation with DFT-based uncertainties in a Berty reactor", which has been submitted for review. The file contains all the raw data and the evaluation of the experiments. Additionally, all scripts for the microkinetic model are provided to perform transient simulations with all 5000 methanation mechanisms investigated in the manuscript.</p>
Global input datasets for use in constraints on global seafloor biogenic methane production from deterministic and machine learning modeling
<p>This dataset includes 9 grids used as model input for manuscript "Constraints on global seafloor biogenic methane production from deterministic and machine learning modeling". Additionally, there are four grids (heat flow, total organic carbon, porosity, and crust age) for which variable uncertainty was given.</p> <p>Grids here are available in xyz (longitude in decimal degrees, latitude in decimal degrees, and variable) ascii file format. Each reference is below is the grids native reference. For more information on the creation of these grids please visit the main manuscript.</p> <p>Below are respective file names and variable name/units:</p> <p>Dataset 1: Elevation in Meters (+ indicates above sea level, - below sea level)</p> <p>Tozer, B., Sandwell, D. T., Smith, W. H. F., Olson, C., Beale, J. R., & Wessel, P. (2019). Global bathymetry and topography at 15 arc sec: SRTM15+. <em>Earth and Space Science</em>, 6. https://doi.org/10.1029/ 2019EA000658</p> <p>Dataset 2: Seawater Density in Kilograms per Cubic Meter</p> <p>Boyer, T. P., Antonov, J. I., Baranova, O. K., Garcia, H. E., Johnson, D. R., Mishonov, A. V., … Grodsky, A. (2013). World Ocean Database 2013. In S. Levitus, A. Mishonov (Ed.), Technical Ed.; <em>NOAA Atlas NESDIS</em> 72 (pp. 209).</p> <p>Dataset 3: Seawater Temperature in Degrees Celcius </p> <p>Boyer, T. P., Antonov, J. I., Baranova, O. K., Garcia, H. E., Johnson, D. R., Mishonov, A. V., … Grodsky, A. (2013). World Ocean Database 2013. In S. Levitus, A. Mishonov (Ed.), Technical Ed.; <em>NOAA Atlas NESDIS</em> 72 (pp. 209).</p> <p>Dataset 4: Seawater Salinity in Percent Salinity Units</p> <p>Boyer, T. P., Antonov, J. I., Baranova, O. K., Garcia, H. E., Johnson, D. R., Mishonov, A. V., … Grodsky, A. (2013). World Ocean Database 2013. In S. Levitus, A. Mishonov (Ed.), Technical Ed.; <em>NOAA Atlas NESDIS</em> 72 (pp. 209).</p> <p>Dataset 5: Heat Flow in Milliwatts per Square Meter</p> <p>Global Heat Flow Compilation Group (2013). Component parts of the World Heat Flow Data Collection. <em>PANGAEA</em>, https://doi.org/10.1594/PANGAEA.810104</p> <p>Hornbach, M. J., Harris, R. N. & Phrampus, B. J. (2020). Heat flow on the U.S. Beaufort Margin, Arctic Ocean: Implications for ocean warming, methane hydrate stability, and regional tectonics. <em>Geochemistry, Geophysics, Geosystems</em>, 21(5). e2020GC008933. https://doi.org/10.1029/2020GC008933</p> <p>Dataset 6: Sediment Thickness in Meters</p> <p>Straume, E. O., Gaina, C., Medvedev, S., Hochmuth, K., Gohl, K., Whittaker, J. M., … Hopper, J. R. (2019). GlobSed: updated total sediment thickness in the world’s oceans. <em>Geochemistry, Geophysics, Geosystems</em>, 20(4), 1756–1772.</p> <p>Dataset 7: Seafloor Porosity in Fraction</p> <p>Martin, K. M., Wood, W. T., & Becker, J. J. (2015). A global prediction of seafloor sediment porosity using machine learning. <em>Geophysical Research Letters</em>, 42(24), 2015GL065279. https://doi.org/10.1002/2015GL065279</p> <p>Dataset 8: Seafloor Total Organic Carbon in Percent Dry Weight</p> <p>Lee, T.R., Wood, W.T., & Phrampus, B.J. (2019). A machine learning (kNN) approach to predicting global seafloor total organic carbon. <em>Global Biogeochemical Cycles</em>. 33, 37–46, doi:10.1029/2018GB005992.</p> <p>Dataset 9: Crust Age in Million Years</p> <p>Müller, R. D., Sdrolias, M., Gaina, C., & Roest, W. R. (2008). Age, spreading rates, and spreading asymmetry of the world’s ocean crust. <em>Geochemistry, Geophysics, Geosystems</em>, 9, Q04006. https://doi.org/10.1029/2007GC001743</p> <p>Dataset 10: Seafloor Porosity Uncertainty in Fraction</p> <p>Dataset 11: Seafloor Total Organic Carbon Uncertainty in Percent Dry Weight</p> <p>Lee, T.R., Wood, W.T., & Phrampus, B.J. (2019). A machine learning (kNN) approach to predicting global seafloor total organic carbon. <em>Global Biogeochemical Cycles</em>. 33, 37–46, doi:10.1029/2018GB005992.</p> <p>Dataset 12: Heat Flow Uncertainty in Milliwatts per Square Meter</p> <p>Dataset 13: Crust Age Uncertainty in Million Years</p> <p>Müller, R. D., Sdrolias, M., Gaina, C., & Roest, W. R. (2008). Age, spreading rates, and spreading asymmetry of the world’s ocean crust. <em>Geochemistry, Geophysics, Geosystems</em>, 9, Q04006. https://doi.org/10.1029/2007GC001743</p>
Estimating drivers and pathways for hydroelectric reservoir methane emissions using a new mechanistic model (estimated methane emissions for hydropower reservoir surfaces and potential dam emissions)
<p>Methane emissions data from hydropower reservoir surfaces and dams, as estimated with the ResME model. Emissions estimates available for hydropower reservoirs in the GRanD database (Lehner et al., 2011). </p> <p> </p> <p>References:</p> <p>Lehner, B., Liermann, C. Reidy, Revenga, C., Vörösmarty, C., Fekete, B., Crouzet, P., Döll, P., Endejan, M., Frenken, K., Magome, J., Nilsson, C., Robertson, J.C., Rodel, R., Sindorf, N., and Wisser, D. (2011). High-resolution mapping of the world’s reservoirs and dams for sustainable river-flow management. Frontiers in Ecology and the Environment, 9 (9): 494-502. https://doi.org/10.1890/100125.</p>
Tables and data for "Downward Trend in Methane Detected in a Northern Colorado Oil and Gas Production Region Using AIRS Satellite Data"
<p>These are tables and data files for the paper "Downward Trend in Methane Detected in a Northern Colorado Oil and Gas Production Region Using AIRS Satellite Data" submitted to the Journal of Atmospheric Research: Atmospheres</p>
Data published in manuscript "Highest methane concentrations in an Arctic River linked to local terrestrial inputs"
<p>This data is published in the manuscript:</p> <p>Castro-Morales, K., Canning, A., Arzberger, S., Overholt, W.A., Küsel, K., Kolle, O., Göckede, M., Zimov, N. and Körtzinger, A. (2022). Highest methane concentrations in an Arctic River linked to local terrestrial inputs. <em>Biogeosciences.</em> XX, XXX-XXX. https://doi.org/10.5194/bg-XX-XXX-2022.</p> <p>The data contains the water properties, the dissolved gas concentrations and flux densities at 1-min resolution corresponding to two transects in the Kolyma River main channel and two tributaries (Ambolikha and Leonid). The data was collected between 15 and 17 June, 2019.<strong> </strong></p> <p>This folder contains four data files and the file "README_Data_access_Castro-Morales_etal_CH4_Kolyma_River.txt" provides more details on the data.</p> <p> </p> <p> </p>
Paddy rice methane emissions across Monsoon Asia
<p>Although rice cultivation is one of the most important agricultural sources of methane and contributes ~8 % of total global anthropogenic emissions, large discrepancies remain among estimates of global methane emissions from rice cultivation due to a lack of observational constraints. The spatial distribution of paddy-rice emissions has been assessed at regional-to-global scales by bottom-up inventories and land surface models over coarse spatial resolution (e.g., > 0.5 degrees) or spatial units (e.g., agro-ecological zones). However, high-resolution CH4 flux estimates capable of capturing the effects of local climate and management practices on emissions, as well as replicating in situ data, remain challenging to produce because of the scarcity of high-resolution maps of paddy-rice and insufficient understanding of CH4 predictors. Here, we combined paddy-rice methane-flux data from 23 global eddy covariance sites and MODIS remote sensing data with machine learning, and produced gridded up-scaling estimates of rice methane emissions at 5000-m resolution at 8-day intervals across Monsoon Asia, where ~87% of global rice area is cultivated and ~90% of global rice production occurs.<br> </p>
Unveiling the impact of soil methane sink on atmospheric methane concentrations in 2020
<p>In 2020, anthropogenic methane (CH<sub>4</sub>) emissions decreased due to COVID-19 containment policies, but there was a substantial increase in the concentration of atmospheric CH<sub>4</sub>. Previous research suggested that this abnormal increase was linked to higher wetland CH<sub>4</sub> emissions and a decrease in the atmospheric CH<sub>4</sub> sink. However, the impact of changes in the soil CH<sub>4</sub> sink remained unknown. To address this, we utilized a process-based model to quantify the alterations in the soil CH<sub>4</sub> sink of terrestrial ecosystems between 2019 and 2020. By implementing the model with various datasets, we consistently observed an increase in the global soil CH<sub>4</sub> sink, reaching up to 0.35 ± 0.06 Tg in 2020 compared to 2019. This increase was primarily attributed to warmer soil temperatures in northern high latitudes. These findings emphasize the importance of considering the CH<sub>4</sub> sink in terrestrial ecosystems, as neglecting it can lead to an underestimation of both emission increases and reductions in atmospheric CH<sub>4</sub> sink capacity. Furthermore, they highlight the potential role of increased soil warmth in terrestrial ecosystems in slowing the growth of CH<sub>4</sub> concentrations in the atmosphere.</p>
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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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.