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626 results for “Methanation”
Raised bogs are the key source of methane in West Siberia terrestrial seeps
<p>The expansive plains of western Siberia contain globally significant carbon stocks, with the largest peatland complex in the world overlying the planet's largest known hydrocarbon basin. Numerous terrestrial methane seeps have been recently discovered on this landscape, located along the Ob and Irtysh River floodplains in hotspots covering more than 2,500 km<sup>2</sup>. The origin of methane from these seeps is a matter of both practical and academic interest. The release of even negligible portions of western Siberia's vast carbon pool will have global climate implications. We articulated three hypotheses to explain the origin and migration pathways of methane within these seeps: (H1) uplift of Cretaceous-aged methane from deep petroleum reservoirs along faults and fractures, (H2) release of Oligocene-aged methane capped or trapped by degrading permafrost, and (H3) horizontal migration of Holocene-aged methane from surrounding peatlands. We tested these hypotheses using a range of geochemical tools on gas and water samples extracted from seeps, peatlands, and aquifers across the 120,000 km<sup>2</sup> study area. Seep-gas composition, radiocarbon age, and stable isotope fingerprints favor the peatland hypothesis of seep-methane origin. We identified the dominant metabolic pathways of mid Holocene-aged <sup>14</sup>CH<sub>4</sub>, absent of oxidation, all the way from raised bogs to seeps along the floodplains of the Ob and Irtysh Rivers. Observed <sup>13</sup>C-depletion of methane, along with concentration decreases between source and seeps, could be associated with mixing between two sources with different conditions for methane production: raised bogs with CO<sub>2</sub> reduction methanogenesis and groundwater with acetate fermentation methanogenesis. Our findings highlight the importance of lateral migration between typical boreal landscapes via groundwater, implying intimate connections between them. Lateral migration may result in high methane emissions from groundwater-fed rivers of the region. These methane hotspots could be potentially overlooked in the West Siberia Lowlands and other bog-dominated regions.</p>
Carbon Dioxide and Methane Flux Meta Analysis, Schaerer et al: Permafrost microbes unleashed: thaw reactors provide timely insights into greenhouse gas feedbacks for climate stewardship
<p>Meta-analysis results and workflow: <strong>Meta-Analysis-Report-V1.pdf</strong> </p> <p>raw data tables for input into meta-analysis:</p> <p><strong>co2_flux_by_layer_temp.csv</strong></p> <p><strong>co2_flux_by_layer_time.csv</strong></p> <p><strong>ch4_flux_by_layer_temp.csv</strong></p> <p><strong>ch4_flux_by_layer_time.csv</strong></p> <p><strong>co2_flux_by_headspace_temp.csv</strong></p> <p>(Data included in these tables was digitized using the R package metaDigitize)</p> <p>****</p> <p>We also attempted to summarize the raw data from 12 studies which is summarized in the <strong><em>Flux_Summary_Report </em></strong>document. we converted all units into mg C / g Soil * d (calculations are included in the <strong><em>co2_meta_analysis</em></strong> spreadsheet). For studies not reporting raw data or data tables (7/12 studies), we estimated the values from the figures manually. This typically resulted in an estimate of the mean flux of several replicates (all studies had 3-10 replicates). We filled in metadata as well as we could based on the information available in the papers, although there were many gaps. This information is summarized in the <strong><em>flux_data_compilation</em> </strong>spreadsheet.</p> <p>Studies in the raw data comparison include: Mackelprang 2011, Waldrop 2010 & 2021, Barbato 2022, Dang 2022, Muller 2018, Monteaux 2020, Dutta 2006, Lee 2012, O'Donnell 2009, Roy Chowdhury 2014, Trubl 2021.</p>
Ebullition drives high methane emissions from a eutrophic coastal basin
<p>Dataset used in the article: "Ebullition drives high methane emissions from a eutrophic coastal basin".</p>
Supplementary material for publication "Pore-scale salinity effects on methane hydrate dissociation" by Almenningen et al
<p>Supplementary materials for publication "Pore-scale salinity effects on methane hydrate dissociation"</p> <p>Abstract: Sedimentary methane gas hydrates may become a significant source of methane gas in the global energy mix for the next decades. The widespread distribution of methane hydrates, primarily in subsea sediments on continental margins, makes the crystalline compound attractive for countries with shorelines that seek self-sustainable energy. Fundamental understanding of pore-level methane hydrate distribution and dissociation pattern is important to anticipate the gas production from hydrate reservoirs. Especially the effect of local salinity gradients on dissociation characteristics must be understood as the aqueous phase in most reservoirs is saline. We evaluate the pore-level salinity effect on hydrate dissociation experimentally using silicon-wafer micro-models capable of withstanding high internal pressures. Methane hydrates were formed with brines for a range of salinities (0.0, 2.0, 3.5 and 5.0 wt% NaCl), and we study hydrate dissociation during both depressurization and thermal stimulation, which currently are the most cost-effective production methods. The laboratory results show how initial pore-scale hydrate distribution prior to dissociation affect the melting and mobilization of gas. The local pore-water salinities influenced the stability of the hydrate structure, and led to distinct dissociation patterns due to water freshening.</p>
Supplementary Data for "Structure−Activity Relationships that Identify Metal−Organic Framework Catalysts for Methane Activation"
<p>DFT-optimized structures, energies, and computed physicochemical properties of metal-organic frameworks that correspond with work in "Structure−Activity Relationships that Identify Metal−Organic Framework Catalysts for Methane Activation" (DOI: <a href="https://pubs.acs.org/doi/10.1021/acscatal.8b05178">10.1021/acscatal.8b05178</a>).</p>
Technical note: Interferences of volatile organic compounds (VOC) on methane concentration measurements - Raw Data
<p>Technical note: Interferences of volatile organic compounds (VOC) on methane concentration measurements - Raw Data</p>
Dataset for "Monthly Gridded Data Product of Northern Wetland Methane Emissions Based on Upscaling Eddy Covariance Observations"
<p>This dataset provides wetland methane (CH<sub>4</sub>) emissions, their uncertainties and underlying CH<sub>4</sub> flux densities north from 45 N using three different wetland maps. The data products are derived using data from several eddy covariance CH<sub>4</sub> flux sites, random forest machine learning algorithms and three prescribed wetland maps. The data are at 0.5 by 0.5 deg or 1 by 1 deg resolution, depending on the wetland map used. The dataset covers years 2013 and 2014. CH<sub>4</sub> flux densities are provided only for grid cells with > 5 % wetland coverage.</p> <p>The three data products are provided in netCDF format files (.nc). Please see more details in the attributes saved in the netCDF files.</p> <p>RF-DYPTOP.nc<br> Upscaling based on DYPTOP dynamic wetland map. At 1 by 1 deg resolution.</p> <p>RF-GLWD.nc<br> Upscaling using GLWD static wetland map. At 0.5 by 0.5 deg resolution.</p> <p>RF-PEATMAP.nc<br> Upscaling using PEATMAP static wetland map. At 0.5 by 0.5 deg resolution.</p> <p> </p> <p>This dataset is related to Peltola et al. (2019) manuscript submitted to Earth System Science Data. Please cite this publication if you use this dataset in your work.</p> <p>Peltola, O., Vesala, T., Gao, Y., Räty, O., Alekseychik, P., Aurela, M., Chojnicki, B., Desai, A. R., Dolman, A. J., Euskirchen, E. S., Friborg, T., Göckede, M., Helbig, M., Humphreys, E., Jackson, R. B., Jocher, G., Joos, F., Klatt, J., Knox, S. H., Kowalska, N., Kutzbach, L., Lienert, S., Lohila, A., Mammarella, I., Nadeau, D. F., Nilsson, M. B., Oechel, W. C., Peichl, M., Pypker, T., Quinton, W., Rinne, J., Sachs, T., Samson, M., Schmid, H. P., Sonnentag, O., Wille, C., Zona, D., and Aalto, T.: Monthly Gridded Data Product of Northern Wetland Methane Emissions Based on Upscaling Eddy Covariance Observations, Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2019-28, in review, 2019.</p>
Study on the effects of heterogeneous distribution of methane hydrate on permeability of porous media using low-field NMR technique
<p>Data sets for the Publication 'Study on the effects of heterogeneous distribution of methane hydrate on permeability of porous media using low-field NMR technique' in Water Resources Research.</p>
Fig. 6 in Catshark egg capsules from a Late Eocene deep-water methane-seep deposit in western Washington State, USA
Fig. 6. Pyrite framboids in the egg capsule wall. SEM image of an etched fracture surface.
Fig. 4 in Catshark egg capsules from a Late Eocene deep-water methane-seep deposit in western Washington State, USA
Fig. 4. Raman spectra of globules and microsparitic matrix of the capsule wall.
Methane drone flights over a cattle farm in Tjøtta, Norway
<p>This dataset includes observations of atmospheric methane concentrations at a cattle farm in Norway.</p> <p>The data was collected by using a DJI Matrice 300 RTK drone equipped with an Aeries MIRA Strato LDS methane sensor.</p> <p>The files contain the raw data from the methane sensor with an additional column, 'OSD.height [m]', for the estimated height of the drone relative to its home location next to the barn. The height data comes from the drone and is resampled (linear interpolation) to the datetimes of the methane sensor. The datetimes are in UTC.</p> <p>The data was collected on 11.08.2023 and 12.08.2023 in Tjøtta, Norway. The cattle farm is located at 65.902044 N, 12.541739 E.</p> <p>The following flights were performed:</p> <table> <tbody> <tr> <td>20230811_101700</td> <td>Flight along the approximate mean wind direction at ~16m and ~28m height.</td> </tr> <tr> <td>20230811_140340</td> <td>Flight in horizontal plane at ~14m, hovering for ~1min at specific locations.</td> </tr> <tr> <td>20230811_144650</td> <td>Flight in vertical plane at ~16m and ~32m, hovering for ~1min at specific locations.</td> </tr> <tr> <td>20230811_153040</td> <td>Lawnmower flight at ~25m height.</td> </tr> <tr> <td>20230811_161430</td> <td>Lawnmower flight at ~25m height.</td> </tr> <tr> <td>20230812_073100</td> <td>Lawnmower flight at ~25m height.</td> </tr> <tr> <td>20230812_075830</td> <td>Lawnmower flight at ~25m height.</td> </tr> <tr> <td>20230812_082530</td> <td>Continuous vertical profile to ~100m height above the chimney of the barn.</td> </tr> <tr> <td>20230812_102930</td> <td>Lawnmower flight at ~25m height.</td> </tr> </tbody> </table>
Supporting data for 'Rapid quantification of methane in water with parts-per-billion sensitivity using a metal-organic framework-functionalized quartz crystal resonator'
<p>Supporting data for the preprint 'Rapid quantification of methane in water with parts-per-billion sensitivity using a metal-organic framework-functionalized quartz crystal resonator' published at ChemRxiv (doi://10.26434/chemrxiv-2024-x62zz)</p> <p> </p>
Mapping Russian Wetlands and Estimating Methane Fluxes
<h3>Mapping Russian Wetlands and Estimating Methane Fluxes</h3> <p><strong>Introduction</strong></p> <p>Wetlands are crucial in regulating the Earth’s climate, acting as both carbon sinks and significant methane sources. Russian wetlands represent one of the largest and most diverse wetland complexes globally, extending across biomes from Arctic tundra to boreal forests. Despite their importance, these wetlands remain underexplored, particularly in terms of their spatial distribution and greenhouse gas contributions. This dataset provides a detailed typological map of Russian wetlands and accompanying methane flux estimates, representing the most comprehensive methane emissions dataset for Russian wetlands to date. The maps and calculations were developed in Google Earth Engine (GEE) through a combination of multi-seasonal Landsat composites, PALSAR radar imagery, and extensive field-based validation data from peatland sites across Western Siberia.</p> <h3>Data Overview</h3> <p><strong>Input Layers</strong></p> <p>The wetland mapping relied on seasonal Landsat composites (spring, summer, fall) and PALSAR radar data to capture the distinct structural and hydrological characteristics of each wetland type. Additional layers, such as GMTED topographic slope and Hansen’s TreeCover, were included to exclude non-wetland areas and to enhance the classification by distinguishing forested from non-forested wetlands.</p> <p><strong>Training Points</strong></p> <p>A comprehensive training site database was created, integrating field knowledge, high-resolution imagery, and georeferenced photos. Approximately 2,450 representative points were selected to capture 12 primary wetland types across Russia, with each point validated against high-resolution imagery to ensure accuracy. Points were collected to represent the wide-ranging wetland ecosystems in Russia, from open water and patterned bogs to swampy and forested fens, providing robust ground-truth data for training the classification model.</p> <p><strong>Random Forest Classifier</strong></p> <p>The random forest classifier was chosen for its capacity to handle large datasets and complex relationships among input layers. Optimized for Landsat and PALSAR inputs, the classifier used over 100 trees, each making independent predictions based on subsets of data, which were averaged to produce the final classification. This ensemble approach minimized overfitting, a crucial factor for the varied ecological regions across Russia.</p> <p><strong>Russian Wetlands Map</strong></p> <p>The final <strong>Russian Wetlands Map</strong> encompasses 12 wetland types, detailing their distribution and extent across the country:</p> <ul> <li> <p><strong>Total Wetland Area</strong>: 173.96 million hectares of mapped wetlands, capturing diverse ecosystems, including bogs, fens, and swampy areas.</p> </li> <li> <p><strong>Open Water Area</strong>: Lakes, rivers, and smaller water bodies within wetland zones were separately mapped, totaling 42.6 million hectares.</p> </li> </ul> <h3>Emission Modeling and Ecosite Analysis</h3> <p><strong>Ecosite Proportions for Methane Emission Modeling</strong></p> <p>Each wetland type was further divided into <strong>ecosite units</strong> representing distinct, smaller areas with uniform hydrological and geochemical properties. This level of detail enabled precise methane emission estimates by capturing the variability within complex wetland ecosystems. For instance, ridges and hollows within patterned bogs exhibit unique methane emission dynamics due to differences in vegetation and water levels. Ecosite proportions for methane emission were calculated from 20-30 representative field sites per wetland type, capturing the typical area breakdown of each wetland type across Russia.</p> <p><strong>Methane Emission Period Calculation</strong></p> <p>To estimate seasonal methane emission periods across Russia’s climatic zones, the average summer temperature (Bio10) parameter from WorldClim data was used. Bio10 values reflect seasonal variation in emission potential, correlating with longer and warmer summers in southern regions versus shorter, cooler summers in the north. Using these data, an emission period was calculated for<strong> each 50 km x 50 km grid</strong> cell based on a regression model derived from Western Siberia data:<br>Emission Period (hours) = 303 * Bio10 – 675</p> <p>This equation, which explained 98% of the variation in emission duration, provided a dynamic method for estimating emission periods across Russia’s diverse landscape.</p> <h3>Methane Emission Estimates</h3> <p><strong>Calculation Approach</strong></p> <p>Methane emission estimates were derived from a multi-step approach that incorporated ecosystem-specific emission factors, ecosystem area, and the estimated emission period:</p> <ol> <li> <p><strong>Ecosystem Area Calculation</strong>: Area estimates for each ecosite type were derived from field-based proportions applied to the classified wetland map.</p> </li> <li> <p><strong>Emission Period</strong>: Calculated for each grid cell based on Bio10 data, varying continuously across climatic zones.</p> </li> <li> <p><strong>Methane Flux Values</strong>: Based on quantiles from field measurements within three main zones (Tundra, Northern Taiga, and Southern Taiga) to account for natural variability in methane emissions.</p> </li> </ol> <p>Using this approach, methane emissions were calculated for each 50 km per 50 km grid cell, factoring in the unique emission characteristics of each wetland type and zone. This produced a spatially detailed estimate of methane fluxes, reflective of the temperature and vegetation gradients across Russia.</p> <p> </p> <p><strong>Resulting National Estimate</strong></p> <ul> <li> <p><strong>Total Annual Methane Emissions</strong>: 11.39 MtCH₄ per year from all mapped wetland areas.</p> </li> <li> <p><strong>Open Water Contributions</strong>: 2.54 MtCH₄ per year from open water bodies, including intra-wetland lakes and rivers.</p> </li> </ul> <h3>Data Highlights</h3> <ul> <li> <p><strong>High-resolution wetland classification</strong> covering 173.96 million hectares across diverse wetland ecosystems.</p> </li> <li> <p><strong>Detailed methane emission data</strong> derived from multi-year field measurements and validated against climatic data, providing spatially continuous methane flux estimates across Russia.</p> </li> <li> <p><strong>50x50 km² grid cell calculations</strong>, accounting for methane emission rates, emission periods, and ecosystem proportions for each cell.</p> </li> </ul> <p>This dataset serves as an essential tool for environmental scientists, climate modelers, and conservationists, supporting further research into wetland carbon dynamics, climate mitigation strategies, and regional land-use planning. The high resolution data availbale at url: https://code.earthengine.google.com/d6a9d4045255fd84298777e56a38ae03</p>
ML scripts and data for Buzacott et al. "Drivers and annual totals of methane emissions from Dutch peatlands (2024)"
<div> <div>This repository includes the machine learning (ML) scripts, data, and output for the article "Drivers and annual totals of methane emissions from Dutch peatlands" submitted to Global Change Biology. The scripts utilise the ML FCH4 gapfilling framework described in Irvin et al. (2021) (https://doi.org/10.1016/j.agrformet.2021.108528) which is available at https://github.com/stanfordmlgroup/methane-gapfill-ml and needed to run the scripts.</div> </div>
Unlocking higher methane yields and digestate nitrogen availability in soil through thermal treatment of feedstocks in a two-step anaerobic digestion- Dataset
<p>This is a data set for the article "<span>Unlocking higher methane yields and digestate nitrogen availability in soil through thermal treatment of feedstocks in a two-step anaerobic digestion", published in Chemical and Biological Technologies in Agriculture journal.</span></p>
Measurement-Based Spatially Explicit Methane Emission Inventory (EI-ME)
<p>Accurate and comprehensive assessment of methane emissions, a powerful climate warming pollutant, is a key first step in reducing these emissions, while supporting the ability to track progress toward such reductions over time. While national bottom-up source-level inventories are useful for understanding the sources of methane emissions, they are often unrepresentative across spatial scales, adn their reliance on generic emission factors produces underestimations when compared with measurement-based inventories.</p> <p>In this work, we compile and analyze previous peer-reviewed measurement-based data on facility-level methane emissions in the US oil and gas sector and use these data to develop statistically robust emissions models from which we estimate total methane emissions for the population of major US oil and gas facilities.</p> <p>This dataset (EI_ME_v1.0.gpkg) aggregates the results of this measurement-based methane emission inventory (EI-ME), which is focused on oil and gas methane emissions in the US onshore production regions. The emissions estimates are spatially resolved at 0.1 x 0.1 degree spatial scales.</p> <p>The data layers in the GeoPackage are:</p> <ul> <li><em>EI-ME_gridded_ch4_emissions:</em> estimated methane emissions, spatially resolved at 0.1x0.1 degree spatial grids</li> <li><em>EI-ME_US_oil_gas_basins:</em> major US oil and gas basin boundaries, based on <a href="https://www.eia.gov/maps/maps.php">EIA</a> basin boundary definitions.</li> <li><em>EI-ME_facility_ch4_measurements_data: </em>A compilation of previous peer-reviewed facility-level measurement-based data for oil and gas methane emissions in the US.</li> </ul> <p>We also provide a netcdf version ("EI_ME_2021_inventory_CONUS_point1_degrees_v1.nc") which includes estimated mean oil and gas methane emissions over the contiguous US (excludes Alaska) aggregated over a slightly offset spatial grid compared to the full domain in the above .gpkg.</p> <p>Complete details of the emissions model development and dataset creation can be found in the following manuscript:</p> <ul> <li><strong>How to cite: </strong>Omara, M., Himmelberger, A., MacKay, K., Williams, J. P., Benmergui, J., Sargent, M., Wofsy, S. C., and Gautam, R.: Constructing a measurement-based spatially explicit inventory of US oil and gas methane emissions (2021), Earth Syst. Sci. Data, 16, 3973–3991, https://doi.org/10.5194/essd-16-3973-2024, 2024.</li> </ul> <p>-----------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p>UPDATE (10/10/2025):</p> <p>The spatially explicit measurement-based oil and gas methane emissions inventory (EI-ME) is developed by MethaneSAT, a wholly owned subsidiary of Environmental Defense Fund, to support comprehensive oil and gas methane assessment, methane source attribution, and mitigation. The inventory combines ground-based measurement-based data with statistically robust methane emissions modeling to provide representative estimates of total methane emissions for key facility categories in the contiguous US oil and gas supply chain, including well sites, natural-gas compressor stations, processing plants, crude-oil refineries, and pipelines. It is spatially resolved at 0.1x.0.1 degree spatial scales.</p> <p> Version 1 of the EI-ME inventory for the contiguous United States was published in 2024 and provided an estimate of the 2021 oil and gas methane emissions and uncertainties that are spatially resolved at 0.1x0.1 degree spatial scales.</p> <p> Here, we provide an update to the EI-ME inventory for the years 2023 and 2024. In this update, we follow the same methodology and use the same input emissions datasets as described in detail in Omara et al. (2024), https://doi.org/10.5194/essd-16-3973-2024. We incorporate the latest available oil and gas activity data for the years 2023 and 2024 based on data from Enverus Prism (<a href="http://www.eneverus.com/">www.eneverus.com</a>), supplemented with additional information from the Oil and Gas Infrastructure Mapping database (OGIM v2.7, <a href="https://doi.org/10.5281/zenodo.15103476">https://doi.org/10.5281/zenodo.15103476</a>) and global annual gas flaring data from VIIRS (Visible Infraed Imagin Radiometer Suite), available from the Earth Observation Group (<a href="https://eogdata.mines.edu/products/vnf/global_gas_flare.html">https://eogdata.mines.edu/products/vnf/global_gas_flare.html</a>).</p> <p>---</p> <p>Contact at Environmental Defense Fund: Mark Omara (momara@edf.org), Anthony Himmelberger (ahimmelberger@methanesat.org)</p> <p>---</p>
Dataset for "Interpreting eddy covariance data from heterogeneous Siberian tundra: land cover-specific methane fluxes and spatial representativeness"
<p>The micrometeorological dataset used in</p> <p>Tuovinen, J.-P., Aurela, M., Hatakka, J., Räsänen, A., Virtanen, T., Mikola, J., Ivakhov, V., Kondratyev, V. and Laurila, T.: Interpreting eddy covariance data from heterogeneous Siberian tundra: land cover-specific methane fluxes and spatial representativeness. <em>Biogeosciences Discussions</em>, https://doi.org/10.5194/bg-2018-155, 2018 (accepted for publication in <em>Biogeosciences</em>).</p> <p> </p>
Data for: Single-blind determination of methane detection limits and quantification accuracy using aircraft-based LiDAR
<p>Methane detection limits, emission rate quantification accuracy, and potential cross-species interference are assessed for Bridger Photonics' Gas Mapping LiDAR (GML) system utilizing data collected during laboratory testing and single-blind controlled release testing. Laboratory testing identified no significant interference in the path-integrated methane measurement from the gas species tested (ethylene, ethane, propane, n-butane, i-butane, and carbon dioxide). The controlled release study, comprised of 650 individual measurement passes, represents the largest dataset collected to date to characterize GML with respect to point-source emissions. Binomial regression is utilized to create detection curves illustrating the likelihood of detecting an emission of a given size under different wind conditions and for different flight altitudes. Wind-normalized methane detection limits (90% detection rate) of 0.25 (kg/h)/(m/s) and 0.41 (kg/h)/(m/s) are observed at a flight altitude of 500 feet and 675 feet above ground level, respectively. Quantification accuracy is also assessed for emissions ranging from 0.15 to 1400 kg/h. When emission rate estimates were generated using wind from High-Resolution Rapid Refresh (HRRR) model (the primary wind source that Bridger uses for their commercial operations), linear regression indicates bias of 8.1% (R2 = 0.89). For 95% of controlled releases above Bridger's stated production-sector detection sensitivity (3 kg/h with 90% probability of detection), accuracy of individual emission rate estimates produced using HRRR wind ranged from -64.1% to 87.0%. Across all controlled releases 38.1% of estimates had error within +/- 20%, and 87.3% of measurements were within a factor of two (-50% to +100% error). At low wind speed (less than 2 m/s) and low emission rates (less than 3 kg/h) emission estimates are biased high; however, when removed do not impact the regression significantly. The aggregate quantification error including all detected emission events was +8.2% using the HRRR wind source. The resulting detection curves and quantification accuracy illustrate important implications which must be considered when using measurements from GML or other remote emission measurement techniques to inform or validate inventory models, or to audit reported emission levels from oil and gas systems.</p>
Maps of predicted carbon dioxide and methane fluxes from waterbodies in the Yukon-Kuskokwim Delta, Alaska
<p>In the Arctic, waterbodies are abundant, and rapid thaw of permafrost is destabilizing the carbon cycle and changing hydrology. It is particularly important to quantify and accurately scale aquatic carbon emissions in arctic ecosystems. Recently available high-resolution remote sensing datasets capture the physical characteristics of arctic landscapes at unprecedented spatial resolution. We demonstrate how machine learning models can capitalize on these spatial datasets to greatly improve accuracy when scaling waterbody CO<sub>2</sub> and CH<sub>4</sub> fluxes across the Yukon-Kuskokwim (YK) Delta of south-west AK. These datasets include carbon dioxide and methane dissolved concentrations and diffusive fluxes from a research watershed in the central YK Delta. </p>
Pathways of methane removal in the sediment and water column of a seasonally anoxic eutrophic marine basin - dataset
<p>Dataset used in the article: "Pathways of methane removal in the sediment and water column of a seasonally anoxic eutrophic marine basin", Zygadlowska et al., 2023.</p>
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.