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zenodo44/100

Data: Boreal forest soil carbon fluxes one year after a wildfire: Effects of burn severity and management

2018 Boreal forest fires in Sweden: Measurements of soil CO2 and CH4 fluxes, soil microclimate and nutrient content during the first growing season after a wildfire, from forest sites impacted by different fire severity (tree mortality) and post-fire management.   Data used in: Boreal forest soil carbon fluxes one year after a wildfire: Effects of burn severity and management; Julia Kelly, Theresa S. Ibáñez, Cristina Santín, Stefan H. Doerr, Marie-Charlotte Nilsson, Thomas Holst, Anders Lindroth, Natascha Kljun; Global Change Biology, 27, 4181-4195, https://doi.org/10.1111/gcb.15721

opencc-by-4.0Mar 2022View details →

zenodo44/100

High resolution soil moisture and soil temperature data during Hurricane Florence, 2018, over the Carolina region (U.S.)

We set the study domain over the U.S. east coast to cover the Carolinas and the regions that were affected by Hurricane Florence. Therefore, the selected domain covered the area between -86º to -75º longitude and 30º to 40º latitude. We should note that Hurricane Florence made landfall in the Carolinas on September 14, 2018, as a Category 1 storm.  Hurricane reports indicate that “Hurricane Florence made landfall near Wrightsville Beach, North Carolina at 7:15 AM EDT (1115 UTC) on September 14 with estimated maximum winds of 90 mph (150 km/h), and a minimum central pressure estimate of 958 millibars. Winds gusts topping 105 mph (169 km/h) were reported in the Outer Banks of North Carolina.” Rainfall from Florence, as per the initial reports, suggest possible new records for North Carolina (breaking the record set by Hurricane Floyd in 1999). we used the latest development of the high-resolution land surface assimilation system (HRLDAS) that was retrieved from the Github repository (<a href="https://github.com/NCAR/hrldas-release">https://github.com/NCAR/hrldas-release</a>). The model was coupled to the Noah land surface modeling system and used the multi-layer soil model, complex canopy resistance with the Penman method for calculating evapotranspiration, and frozen ground physics for the simulations.  The atmospheric forcing including 2-meter air temperature, shortwave, and long-wave radiation, 2-meter wind speed, 2-meter specific humidity, and surface pressure data was obtained from the NCEP-DOE Reanalysis 2 (available from <a href="https://psl.noaa.gov/data/gridded/data.ncep.reanalysis2.html">psl.noaa.gov/data/gridded/data.ncep.reanalysis2.html</a>). For the precipitation, we used the high-resolution GCIP/EOP surface precipitation NCEP/EMC gridded data (Stage IV) with 4 km of grid spacing. All the forcing data have been retrieved at an hourly frequency from 2016 to 2019. The model was configured with the initial soil moisture and soil temperature conditions at 4 depths (0-10, 10-40, 40-100, 100-200 cm), retrieved from the NCEP data. Other initialization fields including skin temperature and water equivalent snow depth were retrieved from NCEP. The NCEP reanalysis data, however, does not provide “plant canopy surface water” data which is required as an initialization field. This data was retrieved from the NLDAS dataset. The first two years of the model run (2016 and 2017) were considered as the spin-up, and the outcome during 2018 was used for further analysis and public release.

opencc-by-4.0Jan 2022View details →

zenodo44/100

Globally-gridded data for manuscript: Global stocks and capacity of mineral-associated soil organic carbon

Supporting globally-gridded data products for manuscript: Georgiou K., Jackson R. B., Vindu&scaron;ková O., Abramoff R. Z., Ahlström A., Feng W., Harden J. W., Pellegrini A. F. A., Polley H. W., Soong J. L., Riley W. J., Torn M. S. Global stocks and capacity of mineral-associated soil organic carbon. Nature Communications, 2022. We leveraged data from a global synthesis of soil fractionation measurements (DOI: 10.5281/zenodo.5987415) along with ancillary data on climate, vegetation, and soil characteristics to produce spatially-explicit global estimates of mineral-associated soil organic carbon stocks (MOC) and mineralogical carbon capacity (MOCmax) in non-permafrost, non-desert mineral soils. Globally-gridded datasets are given in kgC/m2 for topsoil (0-30cm) and subsoil (30-100cm) at 0.5 degree by 0.5 degree spatial resolution.

opencc-by-4.0Dec 2021View details →

zenodo44/100

Synthesis data for manuscript: Global stocks and capacity of mineral-associated soil organic carbon

Supporting synthesis data for manuscript: Georgiou K., Jackson R. B., Vindu&scaron;ková O., Abramoff R. Z., Ahlström A., Feng W., Harden J. W., Pellegrini A. F. A., Polley H. W., Soong J. L., Riley W. J., Torn M. S. Global stocks and capacity of mineral-associated soil organic carbon. Nature Communications, 2022. We performed an observational synthesis of soil fractionation data constituting 1,144 globally-distributed soil profiles from 78 studies that reported fractionation and bulk measurements of organic carbon across depths. This dataset includes measurements of mineral-associated, particulate, and bulk soil organic carbon, as well as ancillary data on edaphic, climate, and vegetation characteristics. We also performed a separate observational synthesis of soil carbon accrual from manipulation and chronosequence studies, which included changes in carbon stocks or concentrations, bulk density, experimental duration, and edaphic properties. This latter synthesis included 103 observations from 34 studies that spanned crop, pasture, grassland, and forest ecosystems across climates and soil types. Further details for both syntheses can be found in the methods and supplementary materials of the associated manuscript.

opencc-by-4.0Dec 2021View details →

zenodo44/100

Soil carbon stock, litter decomposition, and weather data from Ethiopian forests

Introduction 100 sampling units (SU) were selected from the total of 631 SUs of the Forest Reference Level submission 2017 (FRL 2017). The sampling was designed unbiased for total growing stock per SU, altitude,and mean litter depth per SU. The actual field sampling succeeded on 98 of the pre-selected SUs due to accessibility restrictions. Soil profile sampling Soil sampling was performed from November 2017 till mid-January 2018. Samples were taken from undisturbed soil from depths of 0-10 cm, 10-20 cm, and 20-30 cm below the organic layer. Volumetric samples of 107.5 cm3 were taken vertically, using a 10 cm long conically shaped corer with a cutting lower edge diameter of 37 mm and upper diameter of 40 mm.  Composite samples were formed by combining the volumetric samples taken from different depths of two parallel soil profiles. The samples were transported to EEFRI Soil Laboratory in Addis Ababa after 1-4 weeks of sampling at distant locations.    Soil physical characteristics The soil samples were air-dried, homogenized, and subjected to oven-drying at 105°C until constant mass. Total bulk density was determined using the total dry mass and volume of the composite samples.  Organic carbon content (C % by wet oxidation method), and soil physical characteristics: moisture content, bulk density of the total sample, and bulk density of fine fraction (particles passing the 2 mm sieve). The mass of the coarse fraction was weighed. The soil fine fraction was also subjected to laser diffraction for more accurate particle size analysis for proportions of clay, silt, and sand.    In addition to this 28 samples were also analyzed for C content in the laboratory of Natural Resources Institute Finland to determine C content by LECO CHN analyzer. This was done to calibrate the bulk of wet digestion-based estimates (Fig. 1). Before analysis, the soils were tested for the presence of inorganic C.   For Figure 1. See Soil_C_Ethiopia.pdf Figure 1. Comparison of results from wet oxidation (Walkley-Black) and dry oxidation (CHN analyzer). The dotted line shows the theoretical 1:1 match between the axis, the solid line shows linear regression (intercept = 0) between the methods. The estimated slope value of 1.165 was used in adjusting the wet digestion results to match those obtained by dry oxidation: OCadj = 1.165 * OCwet. Based on a linear regression between the wet and dry oxidation analysis results, a correction factor of 1.165 was applied to adjust the organic C% obtained by wet digestion. The adjusted data are shown in the file “SOC_Ethiopia_2017-2018.csv”.   SOC stocks were calculated by multiplying the proportion of organic C with BD of fine earth, after which the result was corrected for stoniness, a visually estimated proportion of large stones (S, value from 0 to 1) in the soil profile that could not be included in the volumetric soil samples (FAO VS-FAST). \(SOCstock = C_{org} * BD_{fe} * (1-S)\) Soil organic carbon stock data Files: “SOC_Ethiopia_2017-2018.csv” and “SOC_Ethiopia_2017-2018.xlsx” The file includes soil characteristics from layers of 0-10 cm, 10-20 cm, and 20-30 cm below the loose organic layer on top of the soil. The data are used for SOC stock estimation in the respective layers as described above. In the .csv file individual columns are for  LAT is the latitude of the sampling site corresponding to FieldCode and SU_nr LON is the longitude of the sampling site corresponding to FieldCode and SU_nr <ul> <li>The coordinates are expressed as decimal degrees of the WGS84 system</li> </ul> FieldCode refers to the Region and Sampling Unit number of the Ethiopian NFI (see below)  SU_nr is the Sampling Unit number of the Ethiopian NFI Region is the name of the administrative region where the sample was taken Biome is the name of the forest biome type where the sample was collected BiomeSimplified is the name of a biome with some close types combined DepthRange is the upper and lower limit of the soil sample in the field, cm StoninessVFAST is a percentage of stones (VS-FAST by FAO) in the ca. 40 cm deep soil profile exposed during the sampling FreshMassInField is the mass of the total composite soil sample of the given layer, g, primarily indicative of checking the correct number of subsamples in composite NrComposites is the number of subsamples included in the composite for each soil layer CorerVolume is a constant of 107.5 cm3 because only one type of corer was used for undisturbed, volumetric sampling CompositeVolume is the volume of the composite sample for each soil depth layer CoarseFractionMass is the dry mass, g of soil particles > 2mm that did not pass the sieve, but were included in the sample volume FE_DryMass is oven-dry mass, g of the fine fraction that passed the 2 mm sieve. BDtot is total bulk density, g m-3, calculated for the composite sample BDfe is the bulk density of the fine earth fraction, g m-3 OC_adj is organic carbon (OC) content (%) in the composite sample, adjusted according to the comparison between dry and wet oxidation methods (Fig. 1) SOCfe is SOC stock calculated for soil fine earth fraction, t ha-1 in the 10 cm deep soil layer SOCfe_stoniness is SOC stock of the fine earth fraction, t ha-1 in the 10 cm deep soil layer, adjusted for stoniness. The correction assumes that the volume occupied by larger stones would be void of OC.    Litter stock data File: “Litter_Ethiopia_2017-2018.csv” The file includes measurements of litter layer on Ethiopian NFI Sampling Unit (SU) sites where sampling for SOC stock determination was done. The depth of the litter layer was measured in the SU’s of the NFI, and this data contains in addition to depth also a volumetric sample of the litter layer. The dry bulk density was used to calculate the carbon stocks in the litter pool.   The depth of the litter layer was measured in the field. Litter from the respective spot was sampled quantitatively from a frame of 0.01m2 of area for litter dry mass estimate. The organic C stock in a litter (L) was calculated as,   \(L = {M\over z} * {C_{om}\over A}, \)   where M = Dry mass of the litter sample, g z = Depth of the litter layer in the field, m Com = Conversion factor from dry organic matter to carbon (C), 0.5 A = area of quantitative collection of litter (0.01 m2)   In the .csv file individual columns are for LAT, LON is the GPS coordinates (decimal degrees of WGS84) for the Sampling Units (SU_ID) SU_ID is the Sampling Unit identification number of the Ethiopian NFI FieldCode refers to the Region and Sampling Unit number of the Ethiopian NFI (see below) Region is the name of the administrative region where the sample was taken Litter_dry is the dry mass, g of the litter sample Area_m2 is the area, m2 of litter sampling MeanLitterDepth is the mean depth of the litter layer at the sampling area CDensityLitter is the dry bulk density of the litter, g m-2 multiplied by the assumed organic C proportion of the oven-dry litter materials (0.50) LitterCStock_tha is the litter stock, t ha-1 calculated from the C density of the litter layer   Litter bag data (decomposition and quality) The leaves and twigs were sampled from 2 species (Juniperus and Podocarpus) and 3 locations of the elevation gradient in the Chilimo forest (Table 1). The forest was considered an old-growth with Juniperus procera and Podocarpus falcatusbeing the main species forming the tree canopy. The sites form an elevation gradient (Table 1).   Table 1. Geographical locations of the study sites in the Chilimo forest.   <table> <tbody> <tr> <td> id </td> <td> Latitude (deg.) </td> <td> Longitude (deg.) </td> <td> Elevation (m a.s.l) </td> </tr> <tr> <td> 1 </td> <td> 9.0672 </td> <td> 38.1443 </td> <td> 2500 </td> </tr> <tr> <td> 2 </td> <td> 9.0712 </td> <td> 38.1556 </td> <td> 2670 </td> </tr> <tr> <td> 3 </td> <td> 9.0869 </td> <td> 38.1684 </td> <td> 2800 </td> </tr> </tbody> </table>   The dying and dead leaves were sampled directly from the trees later referred to as “fresh” and from the branches found on the ground, referred to as “old”. The old leaves were assumed to be dead for around 3 months. The diameter of the branches/twigs was less than 1 cm in diameter. The samples were first sorted and air-dried in an elevated temperature of the greenhouse and thereafter oven-dried in the oven overnight at 45 °C.  The samples were analyzed for acid, water, ethanol dissolved,and undissolved fractions (AWEN) (Table 2) and for the decomposition rates of the litter installed into the litter bags corresponding to each of the Chilimo sites.    Table 2. Acid, water, ethanol (A, W, E, respectively) dissolved and undissolved fractions (N) from the litter components of the dominant tree species in the Chilimo forest. <table> <tbody> <tr> <td> Litter type </td> <td> Species </td> <td> A </td> <td> W </td> <td> E </td> <td> N </td> </tr> <tr> <td> leaves fresh </td> <td> Juniperus  </td> <td> 0.45 </td> <td> 0.13 </td> <td> 0.1 </td> <td> 0.33 </td> </tr> <tr> <td> leaves fresh </td> <td> Podocarpus  </td> <td> 0.42 </td> <td> 0.28 </td> <td> 0.05 </td> <td> 0.25 </td> </tr> <tr> <td> leaves old </td> <td> Juniperus  </td> <td> 0.44 </td> <td> 0.07 </td> <td> 0.08 </td> <td> 0.41 </td> </tr> <tr> <td> leaves old </td> <td> Podocarpus  </td> <td> 0.44 </td> <td> 0.09 </td> <td> 0.05 </td> <td> 0.42 </td> </tr> <tr> <td> twigs </td> <td> Juniperus  </td> <td> 0.61 </td> <td> 0.04 </td> <td> 0.02 </td> <td> 0.32 </td> </tr> <tr> <td> twigs </td> <td> Podocarpus  </td> <td> 0.56 </td> <td> 0.15 </td> <td> 0.02 </td> <td> 0.27 </td> </tr> </tbody> </table>   A sufficient amount of litter was placed into the litter bags (polyurethane mesh 1 mm) and the mesh bags were installed on top of the soil surface under the forest canopy (later referred to as “canopy”) and in the forest gap caused by harvesting (later referred as “open”). The installation of the litter bags (for each species 3 replicates of each litter type for each site and canopy type for the 3 periods, in total 12 litter bags for leaves and 6 bags for twigs) was done on 22.9.2017. The mesh bags were left on the ground, protected from grazing by the fence, and retrieved subsequently on 12.10.2017, 31.10.2017, and 12.12.2017. Despite the efforts took few samples were lost. The retrieved samples were oven-dried and initial mass and mass loss data for each period and litter type with a detailed description of the variables can be found in the file “litter.chilimo_07.02.22.xlsx”.   Soil temperature data During the period from 22.9.2017 to 12.12.2017, we monitored the soil temperature at 5 cm depth under the canopy and in the open canopy on all Chilimo sites continuously every 4 hours intervals with the Maxim iButton temperature loggers. However, some sensors were lost. Daily means and their standard deviation of the continuous temperatures can be found in the file “soil.temp.chilimo_07.02.22.xlsx”.   Processed weather data The air temperature and precipitation data for 98 sampling units corresponding to soil carbon data originated from 73 weather stations located across Ethiopia and were obtained from Ethiopian Meteorological Agency (http://www.ethiomet.gov.et/). Sampling units were joined with weather data by the closest proximity to their corresponding weather stations. Precipitation was unaltered. The air temperature required correction by elevation is described in more detail in Lehtonen et al. (2020). The monthly values of air temperature and precipitation with an accompanied readme description of the variables can be found for 98 sampling units in the file “sampling.units98_meteo_07.02.22.xlsx” and the Chilimo study sites in the file “monthly.weather.chilimo_07.02.22.xlsx”. The monthly values in the file "sampling.units98_meteo_07.02.22.xlsx" correspond to long-term average over the period from 1986 to 2017.     References:   Lehtonen, A., Ťupek, B., Nieminen, T.M., Balázs, A., Anjulo, A., Teshome, M., Tiruneh, Y. and Alm, J., 2020. Soil carbon stocks in Ethiopian forests and estimations of their future development under different forest use scenarios. Land Degradation & Development, 31(18), pp.2763-2774.   FRL 2017. https://redd.unfccc.int/files/ethiopia_frel_3.2_final_modified_submission.pdf

opencc-by-4.0May 2022View details →

zenodo44/100

GPM_API - Global Hourly Soil Moisture from GPM IMERG Data - 2019

# GPM_API 2019 GPM_API data root: <a href="https://zenodo.org/record/6489998">https://zenodo.org/record/6489998</a> # Related article: Ramsauer, T., & Marzahn, P. (2023). Global Soil Moisture Estimation based on GPM IMERG Data using a Site Specific Adjusted Antecedent Precipitation Index. International Journal of Remote Sensing, 44(2), 542-566. Article: <a href="https://doi.org/10.1080/01431161.2022.2162351">https://doi.org/10.1080/01431161.2022.2162351</a> Free PDF: <a href="https://www.tandfonline.com/eprint/6VHQ7JCCJXSPAMG3PVIG/full?target=10.1080/01431161.2022.2162351">www.tandfonline.com/eprint/6VHQ7JCCJXSPAMG3PVIG/full?target=10.1080/01431161.2022.2162351</a>

opencc-by-4.0Apr 2022View details →

zenodo44/100

GPM_API - Global Hourly Soil Moisture from GPM IMERG Data - 2016

# GPM_API 2016 GPM_API data root: <a href="https://zenodo.org/record/6489998">https://zenodo.org/record/6489998</a> # Related article: Ramsauer, T., & Marzahn, P. (2023). Global Soil Moisture Estimation based on GPM IMERG Data using a Site Specific Adjusted Antecedent Precipitation Index. International Journal of Remote Sensing, 44(2), 542-566. Article: <a href="https://doi.org/10.1080/01431161.2022.2162351">https://doi.org/10.1080/01431161.2022.2162351</a> Free PDF: <a href="https://www.tandfonline.com/eprint/6VHQ7JCCJXSPAMG3PVIG/full?target=10.1080/01431161.2022.2162351">www.tandfonline.com/eprint/6VHQ7JCCJXSPAMG3PVIG/full?target=10.1080/01431161.2022.2162351</a>

opencc-by-4.0Apr 2022View details →

zenodo44/100

GPM_API - Global Hourly Soil Moisture from GPM IMERG Data - 2018

# GPM_API 2018 GPM_API data root: <a href="https://zenodo.org/record/6489998">https://zenodo.org/record/6489998</a> # Related article: Ramsauer, T., & Marzahn, P. (2023). Global Soil Moisture Estimation based on GPM IMERG Data using a Site Specific Adjusted Antecedent Precipitation Index. International Journal of Remote Sensing, 44(2), 542-566. Article: <a href="https://doi.org/10.1080/01431161.2022.2162351">https://doi.org/10.1080/01431161.2022.2162351</a> Free PDF: <a href="https://www.tandfonline.com/eprint/6VHQ7JCCJXSPAMG3PVIG/full?target=10.1080/01431161.2022.2162351">www.tandfonline.com/eprint/6VHQ7JCCJXSPAMG3PVIG/full?target=10.1080/01431161.2022.2162351</a>

opencc-by-4.0Apr 2022View details →

zenodo44/100

GPM_API - Global Hourly Soil Moisture from GPM IMERG Data - 2017

# GPM_API 2017 GPM_API data root: <a href="https://zenodo.org/record/6489998">https://zenodo.org/record/6489998</a> # Related article: Ramsauer, T., & Marzahn, P. (2023). Global Soil Moisture Estimation based on GPM IMERG Data using a Site Specific Adjusted Antecedent Precipitation Index. International Journal of Remote Sensing, 44(2), 542-566. Article: <a href="https://doi.org/10.1080/01431161.2022.2162351">https://doi.org/10.1080/01431161.2022.2162351</a> Free PDF: <a href="https://www.tandfonline.com/eprint/6VHQ7JCCJXSPAMG3PVIG/full?target=10.1080/01431161.2022.2162351">www.tandfonline.com/eprint/6VHQ7JCCJXSPAMG3PVIG/full?target=10.1080/01431161.2022.2162351</a>

opencc-by-4.0Apr 2022View details →

zenodo44/100

GPM_API - Global Hourly Soil Moisture from GPM IMERG Data - 2020

# GPM_API 2020 GPM_API data root: <a href="https://zenodo.org/record/6489998">https://zenodo.org/record/6489998</a> # Related article: Ramsauer, T., & Marzahn, P. (2023). Global Soil Moisture Estimation based on GPM IMERG Data using a Site Specific Adjusted Antecedent Precipitation Index. International Journal of Remote Sensing, 44(2), 542-566. Article: <a href="https://doi.org/10.1080/01431161.2022.2162351">https://doi.org/10.1080/01431161.2022.2162351</a> Free PDF: <a href="https://www.tandfonline.com/eprint/6VHQ7JCCJXSPAMG3PVIG/full?target=10.1080/01431161.2022.2162351">www.tandfonline.com/eprint/6VHQ7JCCJXSPAMG3PVIG/full?target=10.1080/01431161.2022.2162351</a>

opencc-by-4.0Apr 2022View details →

zenodo44/100

Soil carbon and nitrogen stock data for dominant geomorphological terrain units in Qarlikturvik Valley, Bylot Island, Arctic Canada

Dataset for the manuscript 'The distribution of soil carbon and nitrogen stocks among dominant geomorphological terrain units in Qarlikturvik Valley, Bylot Island, Arctic Canada.' to appear in the 'Journal of Geophysical Research: Biogeosciences'.

opencc-by-4.0Jun 2022View details →

zenodo44/100

Soil greenhouse gas emissions (CO2 and N2O) data and metadata derived from H2020 Diverfarming project

Soil greenhouse gas emissions (CO2 and N2O) data and metadata of an almond crop diversified with Thymus hyemalis (diversification 1) and with Capparis spinosa (diversification 2). This data comes from WP5 "Environmental impact and delivery of ecosystem services by crop diversification", derived from H2020 Diverfarming project. This workpackage has been designed to provide sound and robust scientific understanding of the benefits and drawbacks of the tailored diversified cropping systems for improvement of the environmental quality and delivery of ecosystem services in each pedoclimatic region. http://www.diverfarming.eu

opencc-by-4.0Jul 2022View details →

zenodo44/100

Data to support the publication "Impact of agricultural management on soil aggregates and associated organic carbon fractions: Analysis of long-term experiments in Europe"

Raw data: Experimental plot ids and information, mass distribution of all aggregate fractions after wet sieving, Sand content of each fraction to conduct the sand correction, mass distribution of all fractions after isolating the micro-aggregates held within the macroaggregates, yields per treatment, carbon content per fraction (raw data) All data per plot: SOC content, MAOM and POM content of each fraction presented in the fractionation scheme included in the manuscript, together with the mass of the relative fractions.

opencc-by-4.0Sep 2022View details →

zenodo44/100

Data from "Into the unknown: The role of post-fire soil erosion in the carbon cycle"

Wildfires directly emit 2.1 Pg carbon (C) to the atmosphere annually. The net effect of wildfires on the C cycle, however, involves many interacting source and sink processes beyond these emissions from combustion. Among those, the role of post-fire enhanced soil organic carbon (SOC) erosion as a C sink mechanism remains essentially unquantified. Wildfires can greatly enhance soil erosion due to the loss of protective vegetation cover and changes to soil structure and wettability. Post-fire SOC erosion acts as a C sink when off-site burial and stabilization of C eroded after a fire, together with the on-site recovery of SOC content, exceed the C losses during its post-fire transport. Here we synthesize published data on post-fire SOC erosion and evaluate its overall potential to act as longer-term C sink. To explore its quantitative importance, we also model its magnitude at continental scale using the 2017 wildfire season in Europe. Our estimations show that the C sink ability of SOC water erosion during the first post-fire year could account for around 13% of the C emissions produced by wildland fires. This indicates that post-fire SOC erosion is a quantitatively important process in the overall C balance of fires, and highlights the need for more field data to further validate this initial assessment. Here we provide the post-fire SOC erosion dataset ("Post-fire SOC erosion rates" file) used for calculating the SOC ratio of eroded sediments implemented in the RUSLE modelling; as well as the list of data sources ("List of data sources" file).

opencc-by-4.0May 2024View details →

zenodo44/100

Soil, climatic, physiographic and stand data in Pinus sylvestris and Pinus halepensis plantations in Spain

This dataset contains information about soil physical, chemical and biochemical, climatic, physiographic and stand parameters of 32 plots belonging to the Spanish National Forest Inventory (SNFI) located in Pinus halepensis Mill. plantations and 35 plots belonging to the Sustainable Forest Management Research Institute (iuFOR; University of Valladolid and INIA) located in Pinus sylvestris L. plantations in Spain. Parameters included in the dataset:  Plot: plot identification in the SNFI and iuFOR networks. Species: species present in each plot (1: Pinus sylvestris; 2: Pinus halepensis) Slope: gradient in the plot in percentage. Altitude: elevation of the plot in meters above the sea level Latitude and Longitude: geographical coordinates of the plots in degrees Density: number of trees per hectare in the plot Dg: quadratic mean diameter in centimetersç Hm: mean height in meters of the trees in the plot H0; dominant height in meters of the trees in the plot BA: basal area of the plot in square meters per hectare SI: site index; dominant height of the trees in the plot at the reference age (80 years for Pinus halepensis and 50 years for Pinus sylvestris stands)  SQ: the site quality class Age: average age in years of the trees in the plot AW: soil available water in percentage CO: soil coarse particles in percentage Porosity: soil porosity in percentage CLAY: clay content in soil in percentage SILTUS: silt content in soil following the USDA criteria in percentage SILTIS: silt content in soil following the International criteria, in percentage SANDUS: sand content in soil following the USDA criteria, in percentage SANDIS: sand content in soil following the International criteria, in percentage OHT: organic horizon thickness in the plot in centimeters ([C/N]L):  the total carbon to total nitrogen ratio in the litter fraction of the organic horizon ([C/N]FH):  the total carbon to total nitrogen ratio in the fragmented plus humified fractions of the organic horizon  L: amount of litter fraction in the organic horizon in tons per hectare FH: amount of fragmented plus humified fraction in the organic horizon in tons per hectare.  pH: soil pH value  CEC: cation exchange capacity in soil in centimoles of charge per kilogram of soil (Bascomb, 1964) EOC: amount of easily oxidizable C in soil in percentage (Walkley and Black, 1934) AP: amount of available phosphorus in soil in miligrams per kilogram of soil extracted with anion exchange membranes and determined with colorimetry (Murphy and Riley, 1962) TN: total N in soil in percentage TOC/TN: total organic C to total N ratio in soil Ca, Mg, Na, K: exchangeable calcium, magnesium, sodium and potassium in soil in centimoles of charge per kilogram of soil (Schollenberger and Simon, 1945) WSP: water soluble phenols in soil in micrograms of TAE per gram of soil (Box, 1983) Carbonates: amount of carbonates in soil in percentage (Bundy and Bremner, 1972) React_carb: amount of reactive carbonates in soil in percentage (Bashour and Sayegh, 2007) Gypsum: amount of gypsum in soil in centimoles of charge per kilogram of soil (Richards, 1954) Cu, Fe, Mn, Zn: amount of copper, iron, manganese and zinc in miligrams per kilogram of soil (Lindsay and Norvell, 1978) EA: soil exchangeable acidity in centimoles of charge per kilogram of soil (Bascomb, 1964)  Sat: base saturation of soil in percentage  AlA, FeA, MnA: amorphous aluminum, iron and manganese (AlA, FeA, MnA) in soil in centimoles of charge per kilogram of soil (Bascomb, 1968) AlM, FeM, MnM: organically bound aluminum, iron and manganese in soil in centimoles of charge per kilogram of soil (Blakemore et al. 1987)  AlE: exchangeable aluminum in soil in centimoles of charge per kilogram of soil (Bertsch & Bloom, 1996) AlI: inorganic aluminum in soil in centimoles of charge per kilogram of soil (Mc-Keague et al., 1971) Cmic, Nmic, Pmic: amount of microbial biomass carbon, nitrogen and phosphorus in soil in milligrams per kilogram of soil (Vance et al. 1987) Cmin: amount of mineralizable carbon in soil in milligrams per kilogram of soil (Isermeyer, 1952) Cmin/TOC: mineralizable carbon to total organic carbon ratio  Cmic/TOC: microbial biomass carbon to total organic carbon ratio qCO2: microbial metabolic quotient (Cmin/Cmic) in soil in grams per week and gram of soil FDA: fluorescein diacetate hydrolysis reaction (Alef and Nannipieri, 1995) in milliunits per gram of dry soil (nanomoles of fluorescein diacetate produced per gram of soil and minute) DHA: dehydrogenase activity (Casida et al., 1964) in milliunits per gram of dry soil (nanomoles of triphenyl formazan produced per gram of soil and minute) AcPhos, AlkPhos: acid and alkaline phosphatase activity (Tabatabai and Bremner, 1969) in milliunits per gram of dry soil (nanomoles of p-nitrophenol produced per gram of soil and minute) Urease: urease activity in soil (Hofmann, 1963) in milliunits per gram of dry soil (nanomoles of N per gram of soil and minute) Catalase: catalase activity (Tabatabai and Beck, 1971) in milliunits per gram of dry soil (nanomoles of O2 produced per gram of soil and minute) MAT: mean annual temperature in degrees centigrade  (Ninyerola et al., 2005) MMWM: mean maximum temperature of the warmest month in degrees centigrade (Ninyerola et al., 2005) MMCM: mean maximum temperature of the coldest month in degrees centigrade  (Ninyerola et al., 2005) MTWM: mean temperature of the warmest month in degrees centigrade (Ninyerola et al., 2005) MTCM: mean temperature of the coldest month in degrees centigrade  (Ninyerola et al., 2005) TP: total precipitation in millimeters  (Ninyerola et al., 2005) PW, PSP, PSU, PA: winter, spring, summer and autumn precipitation in millimeters (Ninyerola et al., 2005) PET, RET: potential and real evapotranspiration in millimetres (Thornthwaite, 1949 and Thorntwaite and Mather, 1955)  Deficit: mean annual hydric deficit in millimeters (Thornthwaite, 1949 and Thorntwaite and Mather, 1955)  Surplus: mean annual hydric surplus in millimetres (Thornthwaite, 1949 and Thorntwaite and Mather, 1955)  AHI: Annual Hydric Index (Thornthwaite, 1949) Martonne: Martonne index (De-Martonne, 1926) Lang: Lang index  (Lang, 1919) Code -999.99 indicates missing values.

opencc-by-4.0Jun 2018View details →

zenodo44/100

Fuzzy modelling and mapping soil moisture in Germany, link to research data and scientific software

Research data and scientific software related to spatio-temporal estimations of ecological soil moisture with available data covering the whole territory of Germany and the Kellerwald National Park (Hesse). Temporal trends of modelled soil moisture for the time period 1961–2070 were statistically analyzed. Soil moisture changes (drying-out) at both national and regional levels were mapped.

opencc-by-4.0Nov 2017View details →

zenodo44/100

ArcGIS Map Packages and GIS Data for: A Geospatial Method for Estimating Soil Moisture Variability in Prehistoric Agricultural Landscapes, Gillreath-Brown et al. (2019)

ArcGIS Map Packages and GIS Data for Gillreath-Brown, Nagaoka, and Wolverton (2019) **When using the GIS data included in these map packages, please cite all of the following: <blockquote> Gillreath-Brown, Andrew, Lisa Nagaoka, and Steve Wolverton. A Geospatial Method for Estimating Soil Moisture Variability in Prehistoric Agricultural Landscapes, 2019. PLoSONE 14(8):e0220457. <a href="http://doi.org/10.1371/journal.pone.0220457">http://doi.org/10.1371/journal.pone.0220457</a> Gillreath-Brown, Andrew, Lisa Nagaoka, and Steve Wolverton. ArcGIS Map Packages for: A Geospatial Method for Estimating Soil Moisture Variability in Prehistoric Agricultural Landscapes, Gillreath-Brown et al., 2019. Version 1. Zenodo. <a href="https://doi.org/10.5281/zenodo.2572018">https://doi.org/10.5281/zenodo.2572018</a> </blockquote> OVERVIEW OF CONTENTS This repository contains map packages for Gillreath-Brown, Nagaoka, and Wolverton (2019), as well as the raw digital elevation model (DEM) and soils data, of which the analyses was based on. The map packages contain all GIS data associated with the analyses described and presented in the publication. The map packages were created in ArcGIS 10.2.2; however, the packages will work in recent versions of ArcGIS. (Note: I was able to open the packages in ArcGIS 10.6.1, when tested on February 17, 2019). The primary files contained in this repository are: <ul> <li>Raw DEM and Soils data <ul> <li>Digital Elevation Model Data (Map services and data available from U.S. Geological Survey, National Geospatial Program, and can be downloaded from the <a href="https://viewer.nationalmap.gov/basic/">National Elevation Dataset</a>) <ul> <li>DEM_Individual_Tiles: Individual DEM tiles prior to being merged (1/3 arc second) from USGS National Elevation Dataset.</li> <li>DEMs_Merged: DEMs were combined into one layer. Individual watersheds (i.e., Goodman, Coffey, and Crow Canyon) were clipped from this combined DEM. </li> </ul> </li> <li> Soils Data (Map services and data available from <a href="https://data.nal.usda.gov/dataset/natural-resources-conservation-service-web-soil-survey">Natural Resources Conservation Service Web Soil Survey</a>, U.S. Department of Agriculture) <ul> <li>Animas-Dolores_Area_Soils: Small portion of the soil mapunits cover the northeastern corner of the Coffey Watershed (CW).</li> <li>Cortez_Area_Soils: Soils for Montezuma County, encompasses all of Goodman (GW) and Crow Canyon (CCW) watersheds, and a large portion of the Coffey watershed (CW).</li> </ul> </li> </ul> </li> <li>ArcGIS Map Packages <ul> <li>Goodman_Watershed_Full_SMPM_Analysis: Map Package contains the necessary files to rerun the SMPM analysis on the full Goodman Watershed (GW).</li> <li>Goodman_Watershed_Mesa-Only_SMPM_Analysis: Map Package contains the necessary files to rerun the SMPM analysis on the mesa-only Goodman Watershed.</li> <li>Crow_Canyon_Watershed_SMPM_Analysis: Map Package contains the necessary files to rerun the SMPM analysis on the Crow Canyon Watershed (CCW).</li> <li>Coffey_Watershed_SMPM_Analysis: Map Package contains the necessary files to rerun the SMPM analysis on the Coffey Watershed (CW).</li> </ul> </li> </ul> For additional information on contents of the map packages, please see see "Map Packages Descriptions" or open a map package in ArcGIS and go to "properties" or "map document properties." LICENSES Code: <a href="http://opensource.org/licenses/MIT">MIT</a> year: 2019  Copyright holders: Andrew Gillreath-Brown, Lisa Nagaoka, and Steve Wolverton CONTACT Andrew Gillreath-Brown, PhD Candidate, RPA <a href="https://anthro.wsu.edu/">Department of Anthropology</a>, Washington State University <a href="mailto:andrew.brown1234@gmail.com">andrew.brown1234@gmail.com</a> – Email <a href="https://andrewgillreathbrown.wordpress.com/">andrewgillreathbrown.wordpress.com</a> – Web

openmit-licenseJul 2019View details →

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Data archive associated with "Landscape age as a major control on the geography of soil weathering" (https://doi.org/10.1029/2019GB006266)

(1) Table including parameter values and weathering model outputs associated with NASGLP sampling locations (SLP_data.csv).  (2) List of rivers used for calibrating erosion estimates (river_list.csv). (3) R workspace with same data as (1), plus a data frame of global parameter values ("gm") and spatial polygons giving continent boundaries ("con"). (4) Scripts with functions for running the single-compartment weathering model at individual point locations or running a global sample of locations and computing summary statistics by continent (run_soilgenesis.R; soilgenesis.R).

opencc-by-4.0Oct 2019View details →

zenodo44/100

Geo-referenced Harmonized Financial Data on Soil Defense Public Works in Italy

The dataset collects financial data about public works in Italy, specifically, it focuses on soil defense investments. The data is sourced from three distinct platforms: the OpenCoesione website, the OpenBDAP database, the Ministry of Economy and Finance's open data platform, and the ReNDiS database, provided by ISPRA, that exclusively gathers information about interventions in soil defense. The data obtained is interconnected using unique project codes (CUP) to prevent duplication. Georeferencing involves integrating geographic references into the three datasets. It enhances the accuracy of spatial analyses of spatial defense investments and provides valuable context for understanding the geographical distribution of available financial data. By incorporating geographic references such as regions, provinces, and municipalities analysts can gain insights into the spatial patterns and relationships within the datasets. This step is crucial for effective decision-making and policy formulation in the field of soil defense investments. Geographical references for each project were integrated using codes and names of regions, provinces, and municipalities from the ISPRA database. This database retrieves information directly from ISTAT websites, ensuring constant updates to names and codes, thus enhancing the accuracy of spatial analyses. Furthermore, geographical codes facilitated the association of centroids coordinates and polygon shapes for each financial observation, enhancing spatial visualization and analysis of soil defense investments, empowering decision-makers with a deeper understanding of the geographic distribution and impact of these initiatives. This comprehensive approach allows for a deeper exploration of the geographical factors influencing soil defense investments, including identifying hotspots of activity, assessing spatial trends, and understanding the localized impact of interventions on environmental sustainability and community resilience. The zip folder comprises four subfolders and two files. Among the files, one is a text file containing metadata, while the other is a CSV file consolidating merged data at the national level from three repositories. The subfolders contain data categorized by region and data categorized by region sourced from the three distinct repositories.  Datasets present 28 variables:  <ul> <li>Columns 1-2: descriptive variables;</li> <li>Column 3: total amount financed for each intervention;</li> <li>Columns 4-9: geo-reference variables;</li> <li>Columnn 10:25: key dates of the public works process;</li> <li>Column 26: source of the data;</li> <li>Columns 27-28: geo-referencing (centroids and areal shape).</li> </ul> An additional dataset has been added comprising all Italian municipalities, including thos that lack information on soil defense investments. In such a way, there are geographical information regarding all the peninsula.

opencc-by-4.0Mar 2024View details →

zenodo44/100

Data on respiration, substrate incorporation, and soil compound concentration in response to simulated root exudation

In this study we used reverse microdialysis to release a mixture of 13C-labeled substrates into intact meadow and forest soil cores (6-hour long) to simulate root exudation. We utilized three different artificial root exudates: sugars (glucose, fructose), organic acids (acetate, succinate), and a combination of sugars and organic acids (glucose, fructose, acetate, succinate); alongside a water-only control for comparison. We collected compounds from soil solutions and measured respiration. Due to 13C-labeled substrate we could differentiate between substrate-derived respiration and SOM-derived respiration. Additionally, we extracted lipid fatty acids from soil and measured their 13C incorporation.

opencc-by-4.0Aug 2024View details →

ScienceDex guides

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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

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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.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

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.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

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.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

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.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

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.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record

Find research datasets worth reusing

Dataset results

Data: Boreal forest soil carbon fluxes one year after a wildfire: Effects of burn severity and management

High resolution soil moisture and soil temperature data during Hurricane Florence, 2018, over the Carolina region (U.S.)

Globally-gridded data for manuscript: Global stocks and capacity of mineral-associated soil organic carbon

Synthesis data for manuscript: Global stocks and capacity of mineral-associated soil organic carbon

Soil carbon stock, litter decomposition, and weather data from Ethiopian forests

GPM_API - Global Hourly Soil Moisture from GPM IMERG Data - 2019

GPM_API - Global Hourly Soil Moisture from GPM IMERG Data - 2016

GPM_API - Global Hourly Soil Moisture from GPM IMERG Data - 2018

GPM_API - Global Hourly Soil Moisture from GPM IMERG Data - 2017

GPM_API - Global Hourly Soil Moisture from GPM IMERG Data - 2020

Soil carbon and nitrogen stock data for dominant geomorphological terrain units in Qarlikturvik Valley, Bylot Island, Arctic Canada

Soil greenhouse gas emissions (CO2 and N2O) data and metadata derived from H2020 Diverfarming project

Data to support the publication "Impact of agricultural management on soil aggregates and associated organic carbon fractions: Analysis of long-term experiments in Europe"

Data from "Into the unknown: The role of post-fire soil erosion in the carbon cycle"

Soil, climatic, physiographic and stand data in Pinus sylvestris and Pinus halepensis plantations in Spain

Fuzzy modelling and mapping soil moisture in Germany, link to research data and scientific software

ArcGIS Map Packages and GIS Data for: A Geospatial Method for Estimating Soil Moisture Variability in Prehistoric Agricultural Landscapes, Gillreath-Brown et al. (2019)

Data archive associated with "Landscape age as a major control on the geography of soil weathering" (https://doi.org/10.1029/2019GB006266)

Geo-referenced Harmonized Financial Data on Soil Defense Public Works in Italy

Data on respiration, substrate incorporation, and soil compound concentration in response to simulated root exudation

Understand access before you commit

Allen Brain Atlas

Annotated Behaviour and Observability Dataset (ABODe)

DANDI Archive for NWB datasets

International Brain Laboratory public data

OpenNeuro

Your academic CV and NIH biosketch