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519 results for “organic soil”
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>
Soil resistance and soil moisture data of organic, permaculture and conventional horticultural farms of Central Hungary
<p>This dataset has been produced from the PhD research of Alfréd Szilágyi supervised by Csaba Centeri and Eszter Kovács Tormáné. The study compared permaculture, organic and conventional farming systems regarding their ecosystem-service provision potential and sustainability. Multiple ecological indicators were measured in the field during the field study in 2020, and the basic datasets (soil test results; photo gallery of the studied farms with soil core sample; soil resistance and moisture; decomposition; earthworms; nematodes; soil surface fauna; pollinators; agrobiodiversity and habitat types) are uploaded in Zenodo separately to provide scientific data on permaculture systems. In this way, we hope to contribute to international efforts to evaluate the performance of agroecological agriculture alternatives. These publications also serve as supplements to the PhD thesis. For the sake of further usability of the datasets short description of the used methods is described. For further information please contact the authors.</p>
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>
Dataset to Manuscript: Schiedung et al. (2023; SBB) Enhanced loss but limited mobility of pyrogenic and organic matter in continuous permafrost-affected forest soils.
<p>Dataset to Schiedung et al. (2023; SBB) Enhanced loss but limited mobility of pyrogenic and organic matter in continuous permafrost-affected forest soils.</p> <p>All published data is provided in the files "<strong>dd_</strong>". This includes:</p> <ul> <li>dd_cores: All data of soil cores and with depth</li> <li>dd_fractions: All data obtained from fractionation of the 0-3cm core layers</li> <li>dd_teabag: All data and mass losses of incubated teabags</li> <li>dd_temperature: All data and recorded soil temperatures</li> </ul> <p>All parameters and names are described in the corresponding file starting with "<strong>Var_names_</strong>". Details on methods and calculations are given in the manuscript and supporting information.</p> <p>NanoSIMS data is provided in the folder "<strong>dd_NanoSIMS.zip</strong>". This contains a file with descriptions of the provided tif-files "<strong>dd_NanoSIMS</strong>". Descriptions of the variables and parameters as well as further instructions are given in the file "<strong>Var_names_description_dd_NanoSIMS</strong>". Images and additional data can be requested by the corresponding author (marcusschiedung@gmail.com).</p> <p> </p> <p> </p>
Data and code from "Water availability is a stronger driver of soil microbial processing of organic nitrogen than tree species composition"
<p>#### Data description<br> Data from large scale, long-term tree diversity experiment in southwestern France (<a href="https://sites.google.com/view/orpheeexperiment/home">ORPHEE</a>), additionally manipulating water contraint. Variables presented are soil nitrogen cycling rates measured using isotope pool dilutions.</p> <p>Companion paper is found here:</p> <p>Maxwell TL, Augusto L, Tian Y, Wanek W & Fanin N (2023). Water availability is a stronger driver of soil microbial processing of organic nitrogen than tree species composition. <em>European Journal of Soil Science</em>. <a href="https://doi.org/10.1111/ejss.13350">https://doi.org/10.1111/ejss.13350</a></p> <p>#### Metadata<br> Soil sampling: July 2020<br> Maxwell_ShortComm_Data.csv data description</p> <p>ID: unique identifier per sample<br> Block: numbered 1-6. Blocks 1,3,6 are control (unirrigated), Blocks, 2,4,5 are irrigated<br> Plot: numbered plot according to the ORPHEE design. Plot 1 = BP, Plot 5 = PP, Plot 9 = BP_PP<br> Espece: species ID. BP = pure birch (<em>Betula pendula</em>), PP = pure pine (<em>Pinus pinaster</em>), BP_PP (50% mixed birch-pine)<br> Rep: sample replicate, 3 replicates per plot<br> Sample name: long unique identifier per sample. Concatenation of Block, Plot, and Espece<br> PD: gross protein depolymerization rates (micrograms nitrogen per grams dry soil per day = µg N g-1 d-1)<br> AAU: gross free amino acid uptake rates (µg N g-1 d-1)<br> Cmicrobial_ug_g: microbial biomass carbon (µg C g-1)<br> Nmicrobial_ug_g: microbial biomass nitrogen (µg N g-1)<br> MRT_FAA_hrs: mean residence time of free amino acids (hours)<br> FAA_ugN_g: free amino acids (µg N g-1)<br> Moisture_percent: soil moisture percent (%)<br> N_nonfumige_ug_g: nitrogen from non fumigated soils, i.e. extractable N (µg N g-1)<br> C_nonfumige_ug_g: nitrogen from non fumigated soils, i.e. extractableC (µg C g-1)</p>
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>
Flume Erosion Testing of Unamended and Organic Matter Amended Soil Samples Using an Acoustic Doppler Profiler, 2021
This data accompanies a publication titled "Soil Amended with Organic Matter Increases Fluvial Erosion Resistance of Cohesive Streambank Soil". Briefly, fluvial erosion testing was conducted on soil samples using an indoor flume channel. Soil samples were previously collected from the riparian zone of a river near Virginia Tech's campus in Blacksburg, VA, USA. The soil was subsequently air-dried and stored until use. Prior to erosion testing, soil samples were amended with varying amounts of organic matter (0%, 1%, and 4% OM by mass), compacted to a bulk density of 0.95 KilogramsPerCubicCentiMeters in growth containers, and allowed to mature in a greenhouse setting for 50 days prior to flume erosion testing. An Acoustic Doppler Profiler (ADP) was used to measure soil erosion and collect three-dimensional velocity data during erosion tests; raw velocity and soil depth data for each sample tested were stored in MATLAB files. Follow testing, the soil remaining from each sample was collected, stored, and analyzed for aggregate stability, soil organic matter (SOM), and extracellular polymeric substances (EPS). Additionally, soil temperature, water temperature, and volumetric water content were also measured prior to or during erosion testing. Data collected from this study, and the accompanying ADP MATLAB files, are presented here.
Water soluble organic matter from Delmarva Bay soils
Little is known about how hydrologic processes along the terrestrial-aquatic interface in wetland dominated landscapes influence carbon dynamics, particularly regarding soil-derived dissolved organic matter (DOM) transport and transformation. To understand the role of different soil horizons as potential sources of DOM to wetland systems, we measured water soluble organic matter (WSOM) in soil horizons collected from upland to wetland transects at four Delmarva Bay wetlands. The Delmarva Bays used in this study are located on property managed by The Nature Conservancy on the Delmarva Peninsula in the eastern United States. Transects ranged from 25 – 45 m in length beginning from a monitoring well in the wetland center to an upland monitoring well. Each transect had four points (Upland, Transition, Edge, and Wetland). Soils were sampled in the late winter (January 17 and March 10) and autumn (September 21 and November 1) of 2020. Soils were sampled by horizon to a depth of approximately 50 cm at each transect point. WSOM extracted in the laboratory was analyzed for WSOM concentration, reported as Water Soluble Organic Carbon (mg WSOC / g soil). WSOM absorbance and fluorescence data were used to calculate composition metrics, providing insight to organic matter sources and chemical characteristics. WSOM fluorescence excitation-emission matrices were evaluated using the 13 component Cory and McKnight (2005) PARAFAC model. Extracted leaf litter, surface water, and groundwater samples were collected in addition to soil samples for the purpose of comparing WSOM to DOM end-members along the Delmarva Bay terrestrial-aquatic continuum. Continuous water level data, averaged to a daily time-step, was collected over the 2020 water year (October 1, 2019 to September 30, 2020) in previously established wetland and upland monitoring wells. The hydrologic conditions (e.g. mean water level, number of saturation events, duration of saturation) at each transect point were characterize
Water-soluble organic matter and nutrients from stormwater control measure and urban wetland soils
Water-soluble organic matter (WSOM) represents organic matter that has the potential to be readily released from soils. WSOM has been understudied in urban, engineered soils relative to natural soils. To understand the potential for organic matter and nutrient release, we extracted WSOM from the soils of stormwater control measures (SCM) and urban wetlands. In February 2022, we sampled soils from 20 SCMs and natural wetlands in the Rappahannock River watershed of the mid-Atlantic United States. The SCMs reflected a variety of design configurations including bioretention, rain gardens, wet ponds, and swales. We also sampled naturally occurring floodplain wetlands that are located in this urban watershed. Soils were sampled to a depth of approximately 40 cm. If there was standing water present in the SCMs and wetlands at the time of sampling, we also collected surface water samples. If present, grab samples of leaf litter or biomass were collected. Soil characteristics, such as pH, bulk density, soil moisture, soil organic matter, and cation exchange capacity were also determined for each site. WSOM was extracted from soils and biomass in the laboratory and analyzed for organic matter concentration (dissolved organic carbon) and composition (absorbance and fluorescence metrics), along with dissolved nutrient concentrations (total dissolved nitrogen, total dissolved phosphorus, nitrate, ammonium, and orthophosphate). In addition to the 20 sites in the Rappahannock watershed, soils from 2 additional bioretention SCMs on the Virginia Tech campus were sampled on a monthly basis from February 2022 to February 2023 to explore temporal variability in WSOM. To characterize changes in soil hydrologic conditions during monthly SCM sampling, we applied a Thornthwaite-type monthly water balance model. Finally, we performed a simple scaling exercise to WSOM results based on SCM area, sample depth, and soil bulk density to estimate potential SCM contributions of organic matter.
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
Surface and SubSurface Soil Organic Matter Processing following Hurricane Harvey, Texas, USA
Coastal wetland plant identity and cover is changing, as many subtropical salt marshes dominated by low-stature herbaceous species transition to woody mangroves. How changes in dominant plant species affect carbon processing in coastal wetlands during storms is uncertain. We experimentally manipulated patch-scale (3 × 3 m) cover of black mangroves (Avicennia germinans) and saltmarsh plants (e.g., Spartina alterniflora, Batis maritima) in fringe and interior locations of ten plots (24 × 42 m) to create a gradient in mangrove cover in coastal Texas, USA. Hurricane Harvey made direct landfall over our site on 25 August 2017. To test how mangrove cover affected carbon retention after the storm, we measured litter breakdown rates (k) of A. germinans and S. alterniflora in surface soils and fast- and slow-decomposing standard litter substrates (green and red tea, respectively) in subsurface soils (15 cm depth). Soil temperatures were lower in mangrove than marsh patches, and prior microclimate measurements showed non-linear increases in air and soil temperatures with increasing mangrove cover (highest temperatures at intermediate % cover). Litter breakdown rates (k) were 2 higher in surface than in subsurface soils. Avicennia germinans litter k increased linearly in surface soils with plot-level mangrove cover, whereas slow-decomposing red tea had similar k in subsurface soils of all plots. Litter k of S. alterniflora in surface soils and fast-decomposing green tea in subsurface soils increased non-linearly with mangrove cover (highest k at intermediate % cover), explained largely by temperature. Microbial respiration rates (R) were highest in interior marsh patches for S. alterniflora litter and increased with plot-level mangrove cover, whereas R associated with A. germinans litter was similar among fringe and interior patches and highest at higher mangrove cover. Despite widespread declines in soil nutrient concentrations throughout marsh and mangrove patches in all pl
Multiple biogeochemical variables were measured for organic and mineral soils on Arctic LTER experimental plots in moist acidic and non-acidic tundra, Arctic LTER Toolik Field Station, Alaska 2013.
Measures of soil nutrient content (available N and P, Extractable N and P, Total C, N and P), and microbial biomass and activity (exoenzyme activity) were measured for organic and mineral soils on Arctic LTER experimental plots at Toolik field station in moist acidic and non-acidic tundra (organic soils only).
Soil hydraulic and thermal properties determined in surface organic and mineral soils in the region near Toolik Lake on the North Slope of Alaska, 2016-2019
Soil cores of 5 cm diameter down to frozen soil were taken from a subset of sample sites for laboratory analysis. Determinations of hydraulic conductivity, thermal conductivity, porosity, and bulk density were made for each core. For a further subset of sites we developed soil moisture retention curves.
Daily water-column rates of sunlight absorption by chromophoric dissolved organic matter (CDOM) 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. Daily rates of sunlight absorption by chromophoric dissolved organic matter (CDOM) from the permafrost soil leachates over the water column depth of an arctic headwater stream were quantified.
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.
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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)
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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.