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709 results for “soil carbon”
Soil Organic Carbon Content estimations over the Lithuanian pilot area (2021)
<p>In the context of the EU-funded project DIONE (No. 870378), Soil Organic Carbon Content (SOC) estimations have been released as outputs of novel machine learning algorithms which combined the point measurements (i.e. soil properties detected by the Soil Scanning Systems) with temporal EO multispectral imagery and other ancillary variables, enabling end-users, and for the DIONE case, the national paying agency of Lithuania (National Paying Agency - NPA) to mine meaningful information about overall soil health and the effects applied agricultural practices at a parcel level.</p> <p>The dataset is delivered in a shapefile format (DIONE_SOC_estimations_LT_2021.shp - EPSG:3346) containing the SOC content (SOC %) labeled as SOC.</p>
Knowledge gaps on trade-offs of soil carbon sequestration related to soil management strategies
<p>The database contains 87 unique literature items (29 reviews, 42 meta-analyses, 16 original papers) describing the effect of a soil management strategy (tillage management, cropping systems, water management, cover crops, crop residues, livestock manure, slurry, compost, biochar, liming) on the trade-offs between soil carbon sequestration or SOC change and N2O emission, CH4 emission and nitrogen leaching. Since some literature items describe effects of several SMS categories, the database_summary tab comprises a total of 112 unique inputs. For each input it is indicated in the Database_summary tab if it was used as input for the "Soil management effect assessment" in Maenhout et al. (2024) [Maenhout, P., Di Bene, C., Cayuela, M. L., Diaz-Pines, E., Govednik, A., Keuper, F., Mavsar, S., Mihelic, R., O'Toole, A., Schwarzmann, A., Suhadolc, M., Syp, A., & Valkama, E. (2024). Trade-offs and synergies of soil carbon sequestration: Addressing knowledge gaps related to soil management strategies. European Journal of Soil Science, 75(3), e13515. https://doi.org/10.1111/ejss.13515] and/or to define knowledge gaps ("Knowledge gap in tab"-column). Knowledge gaps and research recommendations are gouped per soil management strategy in different tabs in this database. Per soil management strategy, knowledge gaps are clustered per theme in groups. These themes include: the specific soil management strategy, pedoclimatic conditions, establishment of experiments, other soil management strategies, meta-analysis, modelling and other</p>
Soil organic carbon content in x 5 g / kg at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution
<p>Soil organic carbon content in × 5 g / kg (to convert to % divide by 2) at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution. The maps are provided using Byte type to significantly reduce file size. Predicted from a global compilation of soil points. Also available for download: soil organic stock maps in in kg / m<sup>2</sup> (<a href="https://doi.org/10.5281/zenodo.1475453">https://doi.org/10.5281/zenodo.1475453</a>) and bulk density maps in kg / m<sup>3</sup> (<a href="https://doi.org/10.5281/zenodo.1475970">https://doi.org/10.5281/zenodo.1475970</a>). Processing steps are described in detail <strong><a href="https://gitlab.com/openlandmap/global-layers/tree/master/soil">here</a></strong>. Antarctica is not included.</p> <p>To access and visualize maps use: <a href="http://www.openlandmap.org/">OpenLandMap.org</a></p> <p>If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels:</p> <ul> <li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/issues">https://gitlab.com/openlandmap/global-layers/issues</a> </li> <li>General questions and comments: <a href="https://disqus.com/home/forums/landgis/">https://disqus.com/home/forums/landgis/</a></li> </ul> <p>All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:</p> <ul> <li>sol = theme: soil,</li> <li>organic.carbon = variable: soil organic carbon content in x 5 g / kg,</li> <li>usda.6a1c = determination method: laboratory method code,</li> <li>m = mean value,</li> <li>250m = spatial resolution / block support: 250 m,</li> <li>b10..10cm = vertical reference: 10 cm depth below surface,</li> <li>1950..2017 = time reference: period 1950–2017,</li> <li>v0.2 = version number: 0.2,</li> </ul>
Soil organic carbon stock (0–30 cm) in kg/m2 time-series 2001–2015 based on the land cover changes
<p>Estimated SOC loss based on the European Space Agency (ESA) Climate Change Initiative (ESACCI-LC) land cover maps 2001–2015. This only shows estimated SOC loss (in kg/m2) as a result of change in land use / land cover (assuming standard change factors based on the literature and IPCC reports). Methodology produced for the purpose of the Land Degradation Neutrality (UNCCD) project. Processing steps are described in detail <strong><a href="https://gitlab.com/openlandmap/global-layers/tree/master/soil/LDN">here</a></strong>. Antartica is not included.</p> <p>To access and visualize maps use: <a href="http://www.openlandmap.org/">OpenLandMap.org</a></p> <p>If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels:</p> <ul> <li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/issues">https://gitlab.com/openlandmap/global-layers/issues</a> </li> <li>General questions and comments: <a href="https://disqus.com/home/forums/landgis/">https://disqus.com/home/forums/landgis/</a></li> </ul> <p>All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:</p> <ul> <li>sol = theme: soil,</li> <li>organic.carbon.stock = variable: soil organic carbon stock in kg/m2,</li> <li>msa.kgm2 = determination method: derived from carbon content, bulk density and coarse fragments,</li> <li>m = mean value,</li> <li>250m = spatial resolution / block support: 250 m,</li> <li>b0..30cm = vertical reference: standard layer 0-30 cm below surface,</li> <li>2014 = time reference: year 2014,</li> <li>v0.2 = version number: 0.2,</li> </ul>
Soil organic carbon stock in kg/m2 for 5 standard depth intervals (0–10, 10–30, 30–60, 60–100 and 100–200 cm) at 250 m resolution
<p>Soil organic carbon stock in kg/m<sup>2</sup> for 5 standard depth intervals (0–10, 10–30, 30–60, 60–100 and 100–200 cm) at 250 m resolution. To convert to t/ha multiply by 10. Derived using soil organic carbon content (<a href="https://doi.org/10.5281/zenodo.1475457">https://doi.org/10.5281/zenodo.1475457</a>), bulk density (<a href="https://doi.org/10.5281/zenodo.1475970">https://doi.org/10.5281/zenodo.1475970</a>) and coarse fragments (<a href="https://doi.org/10.5281/zenodo.2525681">https://doi.org/10.5281/zenodo.2525681</a>), predicted from point data at 6 standard depths. Depth to bed rock has been ignored, hence total stocks might be about 10–15% lower then reported. Processing steps are described in detail <strong><a href="https://gitlab.com/openlandmap/global-layers/tree/master/soil">here</a></strong>. Antarctica is not included.</p> <p>To access and visualize maps use: <a href="https://openlandmap.org"><strong>https://openlandmap.org</strong></a></p> <p>If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels:</p> <ul> <li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/issues">https://gitlab.com/openlandmap/global-layers/issues</a> </li> <li>General questions and comments: <a href="https://disqus.com/home/forums/landgis/">https://disqus.com/home/forums/landgis/</a></li> </ul> <p>All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:</p> <ul> <li>sol = theme: soil,</li> <li>organic.carbon.stock = variable: soil organic carbon stock in kg/m2,</li> <li>msa.kgm2 = determination method: derived from organic carbon content, bulk density and coarse fragments,</li> <li>m = mean value,</li> <li>250m = spatial resolution / block support: 250 m,</li> <li>b0..10cm = vertical reference: 0-10 cm layer below surface,</li> <li>1950..2017 = time reference: period 1950-2017,</li> <li>v0.2 = version number: 0.2,</li> </ul>
Data from: Shift of bacterial and fungal communities upon soil amelioration is driven by carbon degradability of organic amendments
<p>Microbial communities of bacteria and fungi have been analyzed in soil. Agricultural soil was amended with different organic amendments including straw, compost, biogas residues, and biochar, and incubated in the lab. After 6 months, DNA extracted from soil samples was analyzed via Illumia MiSeq DNA sequencing (16S V3V4 for bacteria, ITS1 for fungi) to evaluate changes to the microbial community structure.</p> <p>For details, please see the respective publication (DOI: 10.1007/s44378-024-00012-5).</p>
Map of soil organic carbon loss of mineral soils in Estonia
<p>The internal EJP SOIL project SERENA contributed to the evaluation of soil multifunctionality aiming at providing assessment tools for land planning and soil policies at different scales. By co-working with relevant stakeholders, the project provided co-developed indicators and associated cookbooks to assess and map them, to report both on soil degradation, soil-based ecosystem services and their bundles, under actual conditions and for climate and land-use changes, at the regional, national, and European scales.</p> <p>The map was generated to evaluate soil organic carbon (SOC) loss in Estonian agricultural soils. It is directly related to SERENA project WP3, T3.2, D3.3 with the aim of applying cookbooks to assess soil threats or ecosystem services. This map is the outcome of applying a cookbook developed by ISRIC (Genova, G., Poggio, L., Kempen, B., & Colman, B. DSM Workflow Seedling. ISRIC - World Soil Information. https://doi.org/10.17027/ISRIC-FSX2-2691).</p> <p>The generated map of SOC loss expressed as absolute sequestration rate (t C ha-1 a-1) between 2015 and 2021 is in GEOTIFF format at the resolution of 100m. The input data for the cookbook was from the PANDA database, which contains regular soil monitoring and voluntary soil sampling data by farmers in Estonia. To achieve the aim for accounting SOC loss in agricultural soils temporal pairs were selected resulting in 1037 paired points where the interval between second sampling was more than 5 years. SOC stocks were calculated for the depth of 20 cm using the equation by Adams (1973) to calculate soil bulk density. The calculated SOC stock for time0 and time2 (> 5 years resampled locations) were used as input points for digital soil mapping, that is the ISRIC cookbook. </p>
EJPSOIL_SERENA: Maps of Soil Organic Carbon Loss Scenarios in Elva Parish, Estonia
<p>The internal EJP SOIL project SERENA contributed to the evaluation of soil multifunctionality aiming at providing assessment tools for land planning and soil policies at different scales. By co-working with relevant stakeholders, the project provided co-developed indicators and associated cookbooks to assess and map them, to report both on soil degradation, soil-based ecosystem services and their bundles, under actual conditions and for climate and land-use changes, at the regional, national, and European scales. </p> <p>The study examined the effects of winter cropping systems on long-term soil fertility and their potential to mitigate SOC (Soil Organic Carbon) loss compared to bare soil during the winter months. It analyzed changes in SOC stocks (0–30 cm) at the field level in Elva Parish over the period 2020–2040, under different land-use scenarios. The modeling was based on a SOC stock map layer for Estonian mineral arable soils, developed by the Centre of Estonian Rural Research and Knowledge, which represented the baseline conditions in 2020. SOC stock projections were made using the RothC model, which simulates soil carbon turnover. </p> <p>In the first scenario (Scenario 1), the average SOC stock in Elva Parish by 2040 was estimated assuming the land would remain bare, without vegetation, during the winter months from October to April. In the second scenario (Scenario 2), the SOC stock projection accounted for the presence of winter vegetation, which means the soil is covered with vegetation year-round. The dataset includes four files: a projected SOC stock map for Elva Parish in 2040 and the stock changes from 2020–2040 under Scenario 1, along with a projected SOC stock map for 2040 and the stock changes from 2020–2040 under Scenario 2. </p>
Excess soil moisture and fresh carbon input are prerequisites for methane production in podzolic soil
<p>This package contains the data used in the research article: "Excess soil moisture and fresh carbon input are prerequisites for methane production in podzolic soil" published in Biogeosciences.</p> <p>flux_data.csv contains the measured CH4 fluxes and corresponding ambient air temperature, 5 cm soil moisture and 5 cm soil temperature during the flux measurement.</p> <p>CH4_potentials.xlsx contains the measured CH4 oxidation and production potential data</p> <p>data_soil_moisture.csv contains the time series of measured 5 cm soil moisture at the flux points.</p> <p>data_soil_temperature.csv contains the time series of measured 5 cm soil temperature at the flux points.</p> <p>microcosm_data.csv contains the data of the microcosm experiment. Columns are: datetime, sample name, sample temperature (15 or 25 c), sample moisture (control, M1 (moderate moisture), M2 (high moisture)), glucose addition (no glucose or with glucose), week (measurement week), ch4 flux.</p>
Global mangrove soil carbon data set at 30 m resolution for year 2020 (0-100 cm)
<p>Global soil organic carbon stocks in mangrove forests at 30 m resolution, and predicted for 2020 using spatiotemporal ensemble machine learning. Soil organic carbon stock (t/ha) was derived using predictions of soil organic carbon content and bulk density (BD) to 1 m soil depth, which were then aggregated to calculate soil organic carbon stocks.</p> <p>The "mangroves_tiles_SOC_predictions_2020.zip" file contains predictions of SOC content, Bulk Density (BD) and aggregated SOC stocks (t/ha) for 0—100 cm depth interval. Example of a tile:</p> <ul> <li>089E_21N (89E to 90E, 21N to 22N): <ul> <li>sol_db.od_mangroves.typology_m_30m_s0..100cm_2020_global_v0.1.tif = predicted BD aggregated to 0—100 cm;</li> <li>sol_soc.wpct_mangroves.typology_m_30m_s0..0cm_2020_global_v1.1.tif = predicted SOC content (%) at 0 cm depth (surface soil);</li> <li>sol_soc.wpct_mangroves.typology_m_30m_s0..100cm_2020_global_v1.1.tif = predicted SOC content (%) for 0—100 cm;</li> <li>sol_soc.tha_mangroves.typology_m_30m_s0..100cm_2020_global_v0.1.tif = predicted SOC stocks in t/ha (mean value);</li> <li>sol_soc.tha_mangroves.typology_l.std_30m_s0..100cm_2020_global_v0.1.tif = predicted SOC stocks in t/ha lower 95% probability prediction interval;</li> <li>sol_soc.tha_mangroves.typology_u.std_30m_s0..100cm_2020_global_v0.1.tif = predicted SOC stocks in t/ha upper 95% probability prediction interval;</li> </ul> </li> </ul> <p>Example of a tile:</p> <ul> <li>class : RasterLayer</li> <li>dimensions : 4004, 4004, 16032016 (nrow, ncol, ncell)</li> <li>resolution : 0.00025, 0.00025 (x, y)</li> <li>extent : 88.9995, 90.0005, 20.9995, 22.0005 (xmin, xmax, ymin, ymax)</li> <li>crs : +proj=longlat +datum=WGS84 +no_defs</li> <li>source : sol_db.od_mangroves.typology_m_30m_s0..0cm_2002_global_v0.1.tif</li> </ul> <p>To load global mosaics <strong><strong>Soil Carbon t/ha Maps (0—100cm)</strong></strong> as COGs directly into QGIS or similar, best use:</p> <ul> <li> <p><a href="https://s3.eu-central-1.wasabisys.com/openlandmap/mangroves/sol/soc.tha_tnc.mangroves.typology_m_30m_b0..100cm_2019_2020_go_epsg.4326_v1.2.tif">https://s3.eu-central-1.wasabisys.com/openlandmap/mangroves/sol/soc.tha_tnc.mangroves.typology_m_30m_b0..100cm_2019_2020_go_epsg.4326_v1.2.tif</a></p> </li> <li> <p><a href="https://s3.eu-central-1.wasabisys.com/openlandmap/mangroves/sol/soc.tha_tnc.mangroves.typology_l.std_30m_b0..100cm_2019_2020_go_epsg.4326_v1.2.tif">https://s3.eu-central-1.wasabisys.com/openlandmap/mangroves/sol/soc.tha_tnc.mangroves.typology_l.std_30m_b0..100cm_2019_2020_go_epsg.4326_v1.2.tif</a></p> </li> <li> <p><a href="https://s3.eu-central-1.wasabisys.com/openlandmap/mangroves/sol/soc.tha_tnc.mangroves.typology_u.std_30m_b0..100cm_2019_2020_go_epsg.4326_v1.2.tif">https://s3.eu-central-1.wasabisys.com/openlandmap/mangroves/sol/soc.tha_tnc.mangroves.typology_u.std_30m_b0..100cm_2019_2020_go_epsg.4326_v1.2.tif</a></p> </li> </ul>
Predicted soil organic carbon stock at 30 m in t/ha for 0-100 cm depth global / update of the map of mangrove forest soil carbon
<p>This is the 2nd update of maps produced by <a href="https://doi.org/10.1088/1748-9326/aabe1c">Sanderman et al (2018)</a>. The improvements to the <a href="https://opengeohub.github.io/spatial-prediction-eml/spatiotemporal-prediction-of-soil-organic-carbon.html">3D spatial predictions</a> include:</p> <ul> <li> <p>new updated global mangrove coverage map (contact Thomas Worthington),</p> </li> <li> <p>spatiotemporal predictions to account for differences in spectral reflectance at the time of field work,</p> </li> <li> <p>additional SOC points <a href="https://static-content.springer.com/esm/art%3A10.1038%2Fs41558-018-0162-5/MediaObjects/41558_2018_162_MOESM2_ESM.xlsx">published in Rovai et al. (2018)</a> used in model training (see gpkg file).</p> </li> </ul> <p>To open map in QGIS or similar, drag and drop the *.tif files. You can than add also the gpkg file contain the training points.</p> <p>Production steps (ensemble predictions using SuperLearner) are explained in detail at: </p> <ul> <li>R code: <a href="https://github.com/whrc/Mangrove-Soil-Carbon/">https://github.com/whrc/Mangrove-Soil-Carbon/</a> (see "R_code/GMW_mangroves_SOC_30m.R")</li> <li>Tutorial: <a href="https://envirometrix.github.io/PredictiveSoilMapping/soilmapping-using-mla.html#ensemble-predictions-using-superlearner-package">"Predictive Soil Mapping with R"</a></li> </ul> <p>Produced for the purpose of Mangrove Restoration Potential Map funded by The Nature Conservancy and IUCN. Contact TNC: Emily Landis <<a href="mailto:elandis@TNC.ORG">elandis@TNC.ORG</a>>. Contact IUCN / University of Cambridge: Thomas Worthington <<a href="mailto:taw52@cam.ac.uk">taw52@cam.ac.uk</a>>.</p> <ul> <li>The mangrove restoration potential map is available at: <a href="https://www.researchgate.net/deref/http%3A%2F%2Fmaps.oceanwealth.org%2Fmangrove-restoration%2F">http://maps.oceanwealth.org/mangrove-restoration/</a></li> </ul>
Palmyra Atoll soil and/or wood density sampling locations used in the carbon storage analysis
This dataset provided soil and/or wood density sampling locations and values from Palmyra Atoll (2016 and 2019). Soil samples were extracted to measure organic carbon content associated with different vegetation communities in Palmyra. Wood samples were collected to measure basic wood density values for dominant woody vegetation types found in Palmyra to calculate aboveground carbon values.
Lignin, litter, and soil carbon decomposition from soil samples collected from 20 National Ecological Observatory Network (NEON) sites in 2019
These data support the findings of a manuscript by Huang et al. (2023) published in Nature Communications (doi pending). We used incubations of soil and stable isotope measurements to measure lignin, litter, and SOC decomposition over an 18-month lab incubation and assessed their relationships with geochemical, microbial, N-related and climatic factors across 156 mineral soils collected from 20 National Ecological Observatory Network (NEON) sites, which span broad biophysical gradients (climate, soil, and vegetation type) across North America. The soils were collected in 2019. Lignin decomposition and biogeochemical variables were also measured in an approximately 12-month field incubation.
Soil organic carbon and associated uncertainty at 90 m resolution for peninsular Spain
Soil organic carbon (SOC) must be quantified and monitored to assess soil management practices, adapt policies, and evaluate environmental impacts. However, due to SOC spatial variability, soil surveys become a very challenging task because of the high costs of acquiring data, operational complexity, and updating. Digital soil mapping based on machine learning approaches in combination with remote sensing techniques have enabled soil carbon spatial distribution to be significantly improved, even with limited soil samples. A legacy soil database of 8,361 georeferenced profiles and a selection of environmental data-driven covariates intimately related to soil-forming factors (e.g., biota, climate, parent material) were used to generate SOC maps. Modeling of data was based on three supervised learning approaches: quantile regression forest, ensemble machine learning and auto-machine learning. For the final SOC spatial distribution maps, each pixel was assigned the prediction from the most accurate model, i.e., lowest uncertainty. We applied this modeling technique to generate cost-effective, high-resolution maps (90 m pixel resolution) of SOC distribution, and its associated spatially explicit uncertainty, in peninsular Spain. These maps showed 15.7 g.kg-1 mean SOC concentration at 0-30 cm and 3.6 g.kg-1 at 30-100 cm depth. The total SOC stock at its effective depth was 3.8 Pg C, storing the 74% in the upper 30 cm (2.82 Pg C). The correlation between SOC observed and predictions final values showed R2=0.68 for SOCc and R2=0.54 for SOCs at the upper 30cm. The methodology proposed in this study aims to improve benchmark SOC estimates in support of the National GHG Emissions Inventory Report
Land use and management effects on soil carbon in the Lake States, Northern United States - Nave, et al 2022
There is growing need to quantify and communicate how land use and management activities influence soil organic carbon (SOC) at scales relevant to, and in the tangible control of landowners and forest managers. The continued proliferation of publications and growth of datasets, data synthesis and meta-analysis approaches allows the application of powerful tools to such questions at ever finer scales. In this analysis, we combined a literature review and effect-size meta-analysis with two large, independent, observational databases to assess how land use and management impact SOC stocks, primarily with regards to forest land uses. We performed this work for the (Great Lakes) Lake States, which comprise 6% of the land area, but 7% of the forest and 9% of the forest SOC in the U.S., as the second in a series of ecoregional SOC assessments. Most importantly, our analysis indicates that natural factors, such as soil texture and parent material, exert more control over SOC stocks than land use or management. With that for context, our analysis also indicates which natural factors most influence management impacts on SOC storage. We report an overall trend of significantly diminished topsoil SOC stocks with harvesting, consistent across all three datasets, while also demonstrating how certain sites and soils diverge from this pattern, including some that show opposite trends. Impacts of fire grossly mirror those of harvesting, with declines near the top of the profile, but potential gains at depth and no net change when considering the whole profile. Land use changes showing significant SOC impacts are limited to reforestation on barren mining substrates (large and variable gains) and conversion of native forest to cultivation (losses). We describe patterns within the observational data that reveal the physical basis for preferential land use, e.g., cultivation of soils with the most favorable physical properties, and forest plantation establishment on the most marginal so
Accession numbers for radiocarbon and stable carbon isotopes of dissolved organic carbon (DOC) and dissolved inorganic carbon (DIC) in soil leachates from permafrost soils collected from the North Slope of Alaska in the summers of 2018 and 2022
Leachates of dissolved organic carbon (DOC) from permafrost soils were prepared from soils collected from the North Slope of Alaska in 2018 and 2022. Soil leachates were then either kept in the dark or exposed to light from LEDs at 305 nm (UV) and 405 nm (visible), and then inoculated with native microbial communities and incubated. At the start of the biological incubations, single replicates of the DOC after dark or light treatment and inoculation were assigned accession numbers and analyzed for 14C and 13C at the National Ocean Sciences Accelerator Mass Spectrometry (NOSAMS) facility. At the end of the biological incubations, duplicates of the dissolved inorganic carbon (DIC) in those waters were assigned accession numbers and analyzed for 14C and 13C at the NOSAMS facility.
Preparation of dissolved organic carbon (DOC) leachates from permafrost soils collected from the North Slope of Alaska in the summers of 2018 and 2022
Dissolved organic carbon (DOC) was leached from permafrost soils collected from the frozen permafrost layer at four sites underlying tussock tundra or wet sedge tundra vegetation and from both undisturbed soil and a thermokarst failure on the North Slope of Alaska during the summers of 2018 and 2022.
Radiocarbon and stable carbon isotopes of carbon dioxide produced by respiration of dissolved organic carbon (DOC) leached from permafrost soils collected from the North Slope of Alaska in the summers of 2018 and 2022
Dissolved organic carbon (DOC) was leached from permafrost soils near the Toolik Field Station in the Alaskan Arctic, either kept in the dark or exposed to light treatments, and then incubated with native permafrost microbial communities. The radiocarbon (14C) and stable carbon (13C) isotopic compositions of the initial DOC present in the dark or light-exposed permafrost soil leachates and the carbon dioxide (CO2) produced by microbial respiration of dark or light-exposed permafrost DOC were quantified.
Methane concentrations in dissolved organic carbon (DOC) leachates from permafrost soils collected from the North Slope of Alaska in the summers of 2018 and 2019
Methane (CH4) concentrations were measured in dissolved organic carbon (DOC) leachates of permafrost soils collected from the frozen permafrost layer at five sites underlying tussock tundra or wet sedge vegetation on the North Slope of Alaska during the summers of 2018 and 2019.
Baltimore Ecosystem Study: Soil atmosphere fluxes of carbon dioxide, nitrous oxide and methane, 1998 - ongoing
The Baltimore Ecosystem Study (BES) established a network of long-term permanent biogeochemical study plots in 1998. These plots provide long-term data on vegetation, soil and hydrologic processes in the key ecosystem types within the urban ecosystem. The network of study plots includes forest plots (upland and riparian), chosen to represent the range of forest conditions in the area and grass plots (to represent home lawns). Plots are instrumented with lysimeters (drainage and tension) to sample soil solution chemistry, time domain reflectometry probes to measure soil moisture, dataloggers to measure and record soil temperature, and trace gas flux chambers to measure the flux of carbon dioxide, nitrous oxide and methane from soil to the atmosphere. Measurements of in situ nitrogen mineralization, nitrification and denitrification were made at approximately monthly intervals from Fall 1998 - Fall 2000. Detailed vegetation characterization (all layers) was done in summer 1998 and 2015. Data from these plots has been published in Groffman et al. (2006, 2009), Groffman and Pouyat (2009), Savva et al. (2010), Costa and Groffman (2013), Duncan et al. (2013), Waters et al. (2014), Ni and Groffman (2018), Templeton et al. (2019). Literature Cited Costa, K.H. and P.M. Groffman. 2013. Factors regulating net methane flux in urban forests and grasslands. Soil Science Society of America Journal 77:850 - 855. Duncan, J. M., L. E. Band, and P. M. Groffman. 2013. Towards closing the watershed nitrogen budget: Spatial and temporal scaling of denitrification. Journal of Geophysical Research Biogeosciences 118:1-5; DOI: 10.1002/jgrg.20090 Groffman PM, Pouyat RV, Cadenasso ML, Zipperer WC, Szlavecz K, Yesilonis IC,. Band LE and Brush GS. 2006. Land use context and natural soil controls on plant community composition and soil nitrogen and carbon dynamics in urban and rural forests. Forest Ecology and Management 236:177-192. Groffman, P.M., C.O. Williams, R.V. Pouyat, L.E. Band and I.C.
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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.