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2,078 results for “soil data”
Soil nitrous oxide and carbon dioxide flux data for Niwot Ridge and Loch Vale watershed, 1994.
Fluxes of carbon dioxide and nitrous oxide from snow-covered alpine soils were measured at Niwot Ridge. Six sites characterized by relatively shallow snowpacks were sampled in 1993. A total of 27 sites were sampled in 1994. Nine of the 1994 sites were located in the naturally shallow snowfield sampled in 1993, 9 sites were located in a formerly shallow snowpack site where snow depth was augmented by the construction of a 2.8-m high, 60-m long snowfence, and the 9 remaining sites were located in a naturally deep snowpack. Concentrations of N2O and CO2 at the soil surface were measured monthly from January until March, biweekly through April, and weekly until snowmelt was complete. Elevated levels of CO2 under the snowpack, suggesting microbial activity, were first observed under the shallow snowpacks in early March of 1993. N2O production under snow was first observed in April 1993, when soil temperatures had warmed above -3 degrees C. In 1994 shallow snowpack sites exhibited diminished and sporadic production of both CO2 and N2O, apparently due to the inconsistent snow cover compared to 1993. The snowfence sites exhibited increased CO2 and N2O fluxes beginning in February 1994. Both CO2 and N2O fluxes from the snowfence site were similar to those measured under the naturally deep snowpack. These data suggest that the timing and depth of snow cover during the alpine winter control microbial activity by insulating soils from extreme air temperatures. To obtain a regional perspective on subnivean trace gas fluxes, both CO2 and N2O samples were determined at sites below treeline on Niwot Ridge and at Loch Vale in Rocky Mountain National Park.
Data for "Soil CO2 efflux errors are lognormally distributed - Implications and guidance."
<p>Soil CO2 flux data at site ES-LMa of four automatic chambers in the control-openLand-subplot for the period from 2015-11-10 to 2016-11-10.</p> <p>These data were used for the publication:</p> <p>Wutzler, et al. (2020) "Soil CO2 efflux errors are lognormally distributed - Implications and guidance." Geoscientific Instrumentation, Methods, and Data Systems</p> <p>Variables, units and description are found in the ReadmeDataDescription.csv file</p> <p> </p>
Data on ground ice, organic carbon and soluble cations in tundra permafrost and active-layer soils near Lac de Gras in the Slave Geological Province, N.W.T., Canada
<p>Data and computer code for producing figures for the manuscript:</p> <p>Subedi, R., Kokelj, S. V., and Gruber, S.: Ground ice, organic carbon and soluble cations <br> in tundra permafrost soils and sediments near a Laurentide ice divide in the Slave <br> Geological Province, N.W.T., Canada. The Cryosphere, accepted for publication in October 2020. </p> <p>Discussion paper and final version: https://doi.org/10.5194/tc-2020-33</p> <p> </p> <p>==========================================================================================<br> CONTENT OF DIRECTORIES<br> ==========================================================================================<br> -– data [input data to produce plots]<br> |–– BoreholesMeta.csv<br> |–– brackets_photos_ice.csv<br> |–– brackets_photos_thawed.csv<br> |–– Lac_de_Gras_permafrost_20200612.csv<br> |–– NordicanaD<br> <br> |–– ds_000582159 [authoritative copy at doi: 10.5885/45558XD-EBDE74B80CE146C6]<br> |–– Cored_Drill_TCR.csv<br> |–– Cored_Drill_TCR.csv_ReadMe.txt<br> <br> |–– ds_000582163 [authoritative copy at doi: 10.5885/45558XD-EBDE74B80CE146C6]<br> |–– Cored_Drill_Logs.csv_ReadMe.txt<br> |–– Cored_Drill_Logs.csv</p> <p>–– plot [R scripts write plots into this subdirectory]</p> <p>–– src [R scripts to generate plots]<br> |–– Combined_Plots.R [produces Figures 3–6]<br> |–– Eskers.R [helper function called by Combined_Plots.R]<br> |–– Organics.R [helper function called by Combined_Plots.R]<br> |–– plot_boreholes_DD_single.R [produces Figures S3]<br> |–– plot_boreholes_DD.R [produces raw Figure S2 for further graphic processing]<br> |–– Till.R [helper function called by Combined_Plots.R]<br> |–– Valley.R [helper function called by Combined_Plots.R]</p> <p><br> ==========================================================================================<br> RUNNING SCRIPTS<br> ==========================================================================================</p> <p>Adjust the variable 'path' in these scrips, then run: <br> Combined_Plots.R<br> plot_boreholes_DD_single.R<br> plot_boreholes_DD.R </p> <p>Tested with R version 3.6.3 (2020-02-29) -- "Holding the Windsock"</p> <p> </p> <p>==========================================================================================<br> REFRERENCE<br> ==========================================================================================<br> Please note that the data contained in data/NordicanaD is published as Gruber et al. (2018)<br> and only included here for convenience. The full reference for the authoritative copy is: <br> <br> Gruber, S., Brown, N., Stewart-Jones, E., Karunaratne, K., Riddick, J., Peart, C., <br> Subedi, R., Kokelj, S. 2018. Drill logs, visible ice content and core photos from 2015 <br> surficial drilling in the Canadian Shield tundra near Lac de Gras, Northwest Territories, <br> Canada, v. 1.0 (2015-2015). Nordicana D38, doi: 10.5885/45558XD-EBDE74B80CE146C6. <br> http://www.cen.ulaval.ca/nordicanad/dpage.aspx?doi=45558XD-EBDE74B80CE146C6 </p>
Data from: Carbon accumulation of cool season sports turfgrass species in distinctive soil layers
<p>Carbon sequestered by turfgrasses may contribute to reducing atmospheric CO<sub>2 </sub>levels, to improved soil health and to increased turfgrass quality. Therfore in a field study conducted in the Netherlands, the amount of soil C accumulated by nine cool season turfgrass monocultures and 12 mixtures of turfgrass species during the first three years of establishment was analysed and compared. Thatch, mat and other soil layers and the layers were sampled and thickness of these layers was quantified. From these samples, dry matter, C and N concentrations, and CN ratio were measured.</p> <p>The study was conducted on a 3 years old turfgrass field of the turfgrass seed company DLF. The site was located in the Netherlands (51°32´N, 4°20´E), on a sandy soil (Hortic Anthrasol as described in the FAO/Unesco soil map of the world (2006)). The monocultures consisted of different varieties of the (sub)species <em>Lolium perenne (lp), Poa pratensis (Pp), Festuca arundinacea (Fa), Festuca rubra commutata (Frc), Festuca rubr trichophylla (Frt), Festuca rubra rubra (Frr), Festuca ovina duriuscala (Fod), Festuca ovina vulgaris (Fov), Agrostis stolonifera (As). </em>Varieties were treated as replicates per (sub)species, which resulted in some variation in the number of replicates, as not all species were available in the same number of varieties.<em> </em>Varieties of the<em> (s</em>ub)species and mixtures were on the market as commercial turfgrass seeds. </p> <p>In 2016 a soil profile sampler with a depth of 20 cm, a horizontal length of 10 cm and a width of 2 cm was used to take an undisturbed soil profile in each plot and the thickness of each layer, thatch, matt and remainder soil, was measured using the protocol as described in Evers et al. (2024). Plant biomass in the plots was quantified by taking cores of the top 20 cm of the soil with a core sampler (diameter 28 mm). Cores were divided into thatch, mat, the remainder soil till 10 cm depth, and 10-20 cm depth, respectively, based on the earlier measurement of layer thicknesses in the field. Sediment of each section was then carefully washed out with tap water, after which the remaining below-ground (dead and living) plant biomass was dried at 65°C until stable weight and weighed. Total C and N analyses were carried out at the General Instrumentation Department of Radboud University with a Vario Micro Cube Element Analyzer (Elementar, Langenselbold, Germany), from which C and N concentrations (in % of dry matter or in mg cm<sup>-3</sup> C from total plant biomass in a layer) and CN ratios were calculated.</p> <p>Statistical analyses were carried out using the open source program R version 3.5.2 (2018-12-20). Differences in thickness of thatch and mat as well as differences in the C accumulation and C- and N concentration in thatch, mat and soil layers between (sub)species of turfgrasses in were based on the calculated means per species. Normality of residuals and the equality of variances was checked with diagnostic plots and Levene’s test, respectively. Non-normal and heteroscedastic data were either log transformed in linear models from the car package, or general least square (gls) models using varIdent from the nlme package were used. All data were further analyzed with ANOVA-type3 from the car package, followed by the Tukey post hoc test of the emeans package. Correlations between thatch and mat thickness were analyzed with linear regression models in R of the ggplot package. Similar procedures were performed for correlation between thatch, mat or soil thickness and C accumulation as well as for the correlation between C concentration and N concentration on C accumulation in a particular layer.</p>
Data to support the publication "Soil Water Retention as Affected by Management Induced Changes of Soil Organic Carbon: Analysis of Long-Term Experiments in Europe", https://doi.org/10.3390/land10121362
<p>Soil organic carbon content and water content at the different pressure points, as measured by Ioanna Panagea for the publication "Soil Water Retention as Affected by Management Induced Changes of Soil Organic Carbon: Analysis of Long-Term Experiments in Europe", https://doi.org/10.3390/land10121362 from the the long term experiments belonging in some of the SoilCare project partners. </p>
Data for "Modelling soil carbon stocks following reduced tillage intensity: a framework to estimate decomposition rate constant modifiers for RothC-26.3, demonstrated in north-west Europe"
<p>Dataset of paired observations of conventional tillage (CT) with no tillage (NT) and reduced tillage (RT) from studies in temperate oceanic regions of Western Europe, extracted from a recent systematic review (Jordon et al. preprint, see DOI below).</p> <p>R code of modelling framework to estimate tillage rate modifiers (TRM) for simulating adoption of RT and NT using RothC-26.3, and meta-estimates of TRM across studies.</p>
Soil bulk density [10x kg/m3] for continental Europe at 30 m spatial resolution for period 2000-2020: Open Soil Data Cube for Europe
<p>Predictions are based on the 3D Ensemble Machine Learning framework, as implemented in the R environment for statistical computing (Hengl & MacMillan, 2019; Hengl, et al., 2021). For each pixel we provide prediction errors as 1 standard deviation in either log or the original variable scale.</p> <p>The short description of currently available soil properties: db_od = bulk density over dry [kg/m3 ⨉ 10];</p> <p>Soil properties were predicted at fixed depths:</p> <p> Surface soil = s0..0cm,<br> Subsoil 1 = s30..30cm,<br> Subsoil 2 = s60..60cm,<br> Subsoil 3 = s100..100cm.</p> <p>To produce estimates for depth intervals e.g. 0–30 cm, 0–100 cm best use the trapezoidal rule formula.</p> <p>Periods: 2000 (2000–2003), 2004 (2004–2007), 2008 (2008–2011), 2012 (2012–2015), 2016 (2016–2019), 2020;</p> <p>The bulk density maps are also provided in 10 kg / m-cubic to reduce total data size; to convert values to kg / m-cubic multiply by 10 e.g. 120 = 1200 kg / m-cubic = 1.2 t / m-cubic.</p>
Soil pH in H2O [-] for continental Europe at 30 m spatial resolution for period 2000-2020: Open Soil Data Cube for Europe
<p>Predictions are based on the 3D Ensemble Machine Learning framework, as implemented in the R environment for statistical computing (Hengl & MacMillan, 2019; Hengl, et al., 2021). For each pixel we provide prediction errors as 1 standard deviation in either log or the original variable scale.</p> <p>The short description of currently available soil properties: soil pH in H2O;</p> <p>Soil properties were predicted at fixed depths:</p> <p> Surface soil = s0..0cm,<br> Subsoil 1 = s30..30cm,<br> Subsoil 2 = s60..60cm,<br> Subsoil 3 = s100..100cm.</p> <p>To produce estimates for depth intervals e.g. 0–30 cm, 0–100 cm best use the trapezoidal rule formula.</p> <p>Periods: 2000 (2000–2003), 2004 (2004–2007), 2008 (2008–2011), 2012 (2012–2015), 2016 (2016–2019), 2020;</p>
Soil organic carbon content [g/kg] for continental Europe at 30 m spatial resolution for period 2000-2020: Open Soil Data Cube for Europe
<p>Predictions are based on the 3D Ensemble Machine Learning framework, as implemented in the R environment for statistical computing (Hengl & MacMillan, 2019; Hengl, et al., 2021). For each pixel we provide prediction errors as 1 standard deviation in either log or the original variable scale.</p> <p>The short description of currently available soil properties: log organic carbon [g/kg] to back-transform use exp(x/10)-1;</p> <p>Soil properties were predicted at fixed depths:</p> <p> Surface soil = s0..0cm,<br> Subsoil 1 = s30..30cm,<br> Subsoil 2 = s60..60cm,<br> Subsoil 3 = s100..100cm.</p> <p>To produce estimates for depth intervals e.g. 0–30 cm, 0–100 cm best use the trapezoidal rule formula.</p> <p>Periods: 2000 (2000–2003), 2004 (2004–2007), 2008 (2008–2011), 2012 (2012–2015), 2016 (2016–2019), 2020;</p> <p>To back-transform the log.oc maps use formula: exp(x/10)-1. These are examples of back-transformed values:</p> <p> log.oc = 15 → 0.3% SOC;<br> log.oc = 20 → 0.6% SOC;<br> log.oc = 25 → 1.1% SOC;<br> log.oc = 30 → 1.9% SOC;<br> log.oc = 35 → 3.2% SOC;<br> log.oc = 40 → 5.3% SOC;<br> log.oc = 50 → 14.8% SOC;</p>
Soil clay content [%] for continental Europe at 30 m spatial resolution for period 2000-2020: Open Soil Data Cube for Europe
<p>Predictions are based on the 3D Ensemble Machine Learning framework, as implemented in the R environment for statistical computing (Hengl & MacMillan, 2019; Hengl, et al., 2021). For each pixel we provide prediction errors as 1 standard deviation in either log or the original variable scale.</p> <p>The short description of currently available soil properties: clay.tot = clay content [percent];</p> <p>Soil properties were predicted at fixed depths:</p> <p> Surface soil = s0..0cm,<br> Subsoil 1 = s30..30cm,<br> Subsoil 2 = s60..60cm,<br> Subsoil 3 = s100..100cm.</p> <p>To produce estimates for depth intervals e.g. 0–30 cm, 0–100 cm best use the trapezoidal rule formula.</p> <p>Periods: 2000 (2000–2003), 2004 (2004–2007), 2008 (2008–2011), 2012 (2012–2015), 2016 (2016–2019), 2020;</p>
Soil sand content [%] for continental Europe at 30 m spatial resolution for period 2000-2020: Open Soil Data Cube for Europe
<p>Predictions are based on the 3D Ensemble Machine Learning framework, as implemented in the R environment for statistical computing (Hengl & MacMillan, 2019; Hengl, et al., 2021). For each pixel we provide prediction errors as 1 standard deviation in either log or the original variable scale.</p> <p>The short description of currently available soil properties: sand.tot = sand content [percent];</p> <p>Soil properties were predicted at fixed depths:</p> <p> Surface soil = s0..0cm,<br> Subsoil 1 = s30..30cm,<br> Subsoil 2 = s60..60cm,<br> Subsoil 3 = s100..100cm.</p> <p>To produce estimates for depth intervals e.g. 0–30 cm, 0–100 cm best use the trapezoidal rule formula.</p> <p>Periods: 2000 (2000–2003), 2004 (2004–2007), 2008 (2008–2011), 2012 (2012–2015), 2016 (2016–2019), 2020;</p>
Data for "Temperate Regenerative Agriculture practices increase soil carbon but not crop yield – a meta-analysis"
<p>Supplementary Files for systematic review and meta-analysis: Temperate Regenerative Agriculture practices increase soil carbon but not crop yield – a meta-analysis</p> <p> </p>
Data from: Effects of plastic fragments on plant performance are mediated by soil properties and drought
<p>In recent years, the effects of plastic contamination on soil and plants have received growing attention. Plastic can affect soil water content and thus may interact with the effects of drought on soil and plants. However, the effects of plastic on soil are highly context-dependent, and interactions with drought have been hardly tested. We conducted two greenhouse experiments to test the combined effects of plastic fragments (of varying size and concentration), water availability and soil texture, on soil water content and performance of the plant <em>Arabidopsis thaliana</em>. Plastic fragments had stronger negative effects on soil water content in low water availability, and the shape of this response (linear <em>vs.</em> unimodal) was mediated by soil texture. Conversely, increasing concentration of plastic had positive effects on plant growth. We suggest that plastic fragments introduce fracture points within soil aggregates. This increases number and size of soil pores favoring water loss but also facilitating root growth. Our results suggest complex interactive effects of plastic and drought, that may lead to a decoupling of plant and soil response. These processes should be taken into account in ecological studies and agricultural practices.</p>
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>
Data in: Reduced predation and energy flux in soil food webs by introduced tree species
<p>The introduction of non-native tree species has become a global concern and may disruptnative communities and related ecosystem functions. Soil food webs regulate organic matter decomposition and nutrient cycling in forests with their feeding activities, butevaluating consequences of tree species introduction on soil invertebrates is challengingdue to the complex trophic structure and wide range in body size of soil invertebrates. Here, we employed an energetic food web approach, and estimated the energy flux in soil food webs using a four-node model including soil meso- and macrofauna decomposers and predators. We examined pure and mixed stands of native European beech (<em>Fagus sylvatica</em>), introduced Douglas fir (<em>Pseudotsuga menziesii</em>) and native range-expanding Norway spruce (<em>Picea abies</em>) across site conditions. Compared to native forests, introduced tree species reduced total mass of macrofauna predators by 92% at sandy sites but not that of decomposers, suggesting trophic downgrading in soil food webs by Douglas fir. The energy flux in mixed forests was intermediate between respective monocultures, suggesting that tree mixtures mitigate potential negative impacts of introduced tree species on food web functioning. Across size classes, soil macrofauna responded more sensitively to changes in environmental conditions than soil mesofauna. Despite the lower total mass, the energy flux through mesofauna outweighed that through macrofauna when consideringenergy loss to predators, highlighting the importance of mesofauna for decomposition processes in forest soil food webs. Additionally, total energy flux positively correlated with species richness, pointing to the significance of soil biodiversity for trophic functionality. Overall, the study emphasizes the critical role of tree species composition, site conditionsand soil biodiversity in driving energy flux through soil food webs and maintaining forest ecosystem functions.</p>
QST - open data of the article Vanwindekens & Hardy (2022) - table 1 soil properties
<p>Soil properties of the long term fields trials linked to the paper "The QuantiSlakeTest, dynamic weighting of soil under water to measure soil structural stability" submitted to the SOIL journal by Vanwindekens & Hardy (2022).</p>
Open Soil Spectral Library (training data and calibration models)
<p><strong>Open Soil Spectral Library</strong> contains training MIR (91,631) and VisNIR (65,063) spectral scans + soil calibration data (>60,000 unique locations) and calibration models. Key data set:</p> <ul> <li>ossl_all_L1_v1.2.qs: soil laboratory, site and spectra information;</li> </ul> <p>Important note: The data set spatially over-represents USA and European Union, with little training data in Asia, South America and Australia, hence calibration models reflect primarily soils of USA and Europe.</p> <p>To use the models and data please install <a href="https://hub.docker.com/r/opengeohub/r-geo">R and required packages</a>. Read more about the <strong><a href="https://github.com/traversc/qs">QS data format</a></strong> and how to convert it to CSV or similar. Modeling steps are explained in detail in: <a href="https://github.com/soilspectroscopy/ossl-models">https://github.com/soilspectroscopy/ossl-models</a>. To visualize database please use: <a href="https://explorer.soilspectroscopy.org/">https://explorer.soilspectroscopy.org/</a></p> <p>Complete OSSL documentation can be found at: <a href="https://soilspectroscopy.github.io/ossl-manual/">https://soilspectroscopy.github.io/ossl-manual/</a></p> <p><a href="https://soilspectroscopy.org/"><strong>Soil Spectroscopy for the Global Good</strong></a> is a Coordinated Innovation Network funded by USDA NIFA Food and Agriculture Cyberinformatics Tools Program (<a href="https://nifa.usda.gov/press-release/nifa-invests-over-7-million-big-data-artificial-intelligence-and-other">Award #2020-67021-32467</a>).</p> <p>Input datasets are property of the <a href="https://www.nrcs.usda.gov/wps/portal/nrcs/main/soils/research">USDA NRCS National Soil Survey Center – Kellogg Soil Survey Laboratory</a>, <a href="https://www.worldagroforestry.org/">ICRAF-World Agroforestry</a>, <a href="https://www.isric.org/">ISRIC-World Soil Information</a>, the <a href="http://africasoils.net/services/data/soil-databases/">Africa Soil Information Service</a> funded by the Bill and Melinda Gates Foundation, the <a href="https://esdac.jrc.ec.europa.eu/">European Soil Data Centre</a>, the <a href="https://www.neonscience.org/">National Ecological Observatory Network</a>, and <a href="https://sae.ethz.ch/">ETH Zurich</a>. </p> <p>For more advanced uses of the soil spectral libraries <strong>we advise to contact the original data producers</strong> especially to get help with using, extending and improving the original SSL data.</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>
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>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.