Find research datasets worth reusing

openOpenFeb 2024View details →

Eight Mile Lake Research Watershed, Carbon in Permafrost Experimental Heating and Drying Research (DryPEHR): Half-hourly soil moisture and temperature data, 2010-2022

This drying and warming experiment addresses the following questions: 1) Does ecosystem drying, warming and permafrost thaw cause a net release or uptake of C from the ecosystem to the atmosphere?, 2) Does the decomposition of old C that comprises the bulk of the soil C pool influence ecosystem C loss? 3) How do drying and warmign affect plant communities and ecosystem properties? We are answering these questions using a combined warming and drying experiment (DryPEHR), which is situated with the Carbon in Permafrost Experimental Heating Research (CiPEHR) project and located in an upland tundra field site near Healy, Alaska in the foothills of the Alaska Range. Warming treatment here refers to growing season air temperature warming (~1C) using open top chambers (OTC) combined with soil 'warming' using snow fences during the snow covered months. Drying is achieve using an automated pumping system that lowers the water table in the dry plots. Soil warming began in 2008; OTCs and drying in 2011.This data set includes half-hourly values of surface moisture content (gravimetric, 0-5cm), depth-integrated soil moisture (volumetric, 0-20 cm), and soil temperature in winter warming and summer warming, drying, and control treatment plots at DryPEHR.

Eight Mile Lake Research Watershed, Carbon in Permafrost Experimental Heating Research (CiPEHR): CiPEHR snow depth manual data 2009-2025

The Carbon in Permafrost Experimental Heating Research (CiPEHR) project addresses the following questions: 1) Does ecosystem warming cause a net release of C from the ecosystem to the atmosphere?, 2) Does the decomposition of old C, that comprises the bulk of the soil C pool, influence ecosystem C loss?, and 3) How do winter and summer warming alone, and in combination, affect ecosystem C exchange? We are answering these questions using a combination of field and laboratory experiments to measure ecosystem carbon balance and radiocarbon isotope ratios at a warming experiment located in an upland tundra field site near Healy, Alaska in the foothills of the Alaska Range. This data set includes manual measurements of snow depth collected in early spring on winter warming and control treatment plots.

Fire Self-Limitation (FiSL) Experiment: Quantifying Wildfire Carbon Combustion Losses in boreal Deciduous and Mixed Forests in Interior Alaska and the Boreal Cordillera I: Site Attribute Data 2022

This dataset contains site characteristics collected in the field for plots in 8 fire scars in Interior Alaska and the Yukon. Data was collected in the summer of 2022. Fire scars sampled included Shovel Creek (2019), Aggie Creek (2015), Hess Creek (2019), Baker (2015), Munson Creek (2021), Isom Creek (2020), 2019MA014 (2019), and 2019BC005 (2019). Data includes detailed site characteristics collected at the site level. Each site included three 10 m * 2 m plots (A, B, and C) laid in a single 30 m transect (or, where constrained, in parallel).

Fire Self-Limitation (FiSL) Experiment: Quantifying Wildfire Carbon Combustion Losses in boreal Deciduous and Mixed Forests in Interior Alaska and the Boreal Cordillera II: Tree Inventory Data 2022

This dataset contains tree combustion measurements collected in the field for plots in 8 fire scars in Interior Alaska and the Yukon. Data was collected in the summer of 2022. Fire scars sampled included Shovel Creek (2019), Aggie Creek (2015), Hess Creek (2019), Baker (2015), Munson Creek (2021), Isom Creek (2020), 2019MA014 (2019), and 2019BC005 (2019). Tree species, diameters (DBH where possible, otherwise BD), condition (living/dead, standing/fallen, etc), and component combustion are recorded for every tree in each 10 m * 2 m plot.

Fire Self-Limitation (FiSL) Experiment: Quantifying Wildfire Carbon Combustion Losses in boreal Deciduous and Mixed Forests in Interior Alaska and the Boreal Cordillera III: Shrub Inventory Data

This dataset contains shrub combustion measurements collected in the field for plots in 8 fire scars in Interior Alaska and the Yukon. Data was collected in the summer of 2022. Fire scars sampled included Shovel Creek (2019), Aggie Creek (2015), Hess Creek (2019), Baker (2015), Munson Creek (2021), Isom Creek (2020), 2019MA014 (2019), and 2019BC005 (2019). Shrub species, stem diameters (BD), and component combustion were recorded for every shrub in each 10 m * 2 m plot.

Fire Self-Limitation (FiSL) Experiment: Quantifying Wildfire Carbon Combustion Losses in boreal Deciduous and Mixed Forests in Interior Alaska and the Boreal Cordillera VI: Mineral Soil Sample and pH Data 2022

This dataset contains field- and lab-measured characteristics for post-fire mineral soil samples collected in the field for plots in 8 fire scars in Interior Alaska and the Yukon. Data was collected in the summer of 2022. Fire scars sampled included Shovel Creek (2019), Aggie Creek (2015), Hess Creek (2019), Baker (2015), Munson Creek (2021), Isom Creek (2020), 2019MA014 (2019), and 2019BC005 (2019). Lab analyses were conducted in fall of 2022 at NAU.

Fire Self-Limitation (FiSL) Experiment: Quantifying Wildfire Carbon Combustion Losses in boreal Deciduous and Mixed Forests in Interior Alaska and the Boreal Cordillera IX: metrics derived from All Raw Data Collected Plus Data from Previous Studies on the 2004 Alaska Wildfires Included in Analysis 2022

This data set includes metrics derived from field and lab data collected for deciduous and mixed deciduous-confier plots collected in the summer of 2022 (Shovel Creek (2019), Aggie Creek (2015), Hess Creek (2019), Baker (2015), Munson Creek (2021), Isom Creek (2020), 2019MA014 (2019), and 2019BC005 (2019)), as well as additional data for conifer plots from previous studies of the Taylor Highway Complex (2004), Dall Creek/Yukon Crossing (2004), and Boundary (2004) fires. Those additional data were acquired from: https://www.lter.uaf.edu/d1/d1-detail/id/773 and https://daac.ornl.gov/ABOVE/guides/ABoVE_Plot_Data_Burned_Sites.html. From this complete data set of 333 plots, 311 plots were used in analyses in Black at al. (NCC) paper: "Increased deciduous tree dominance reduces wildfire carbon losses in boreal forests". Plots excluded (from 2022 FiSL data) were poplar-dominated, mixed poplar/conifer dominated, missing soil C data, or conifer-dominated (adventituous root heights were not recorded consistently at sites in 2022 making it impossible to estimate pre-fire conifer stand organic soil C pools for 2022-collected conifer plots). Only 2005-collected conifer plots were used in NCC paper analyses. For all plots, in addition to field/lab derived site characteristics and combustion metrics, post hoc remotely sensed metrics were derived: pre-fire NDVI/EVI-2 trends, 1980-2010 climate normals, and DOB weather metrics.

openCC0Feb 2020View details →

Organic and inorganic data for soil cores from Brazil and Florida Bay seagrasses to support Howard et al 2018, CO2 released by carbonate sediment production in some coastal areas may offset the benefits of seagrass “Blue Carbon” storage, Limnology and Oceanography, DOI: 10.1002/lno.10621

Using piston corers, soils from Florida Bay and Brazilian seagrass meadows were collected to complete organic and inorganic carbon inventories for the top 1 m of soil. Instrumental analyses and loss on ignition at 500C were used to measure C content of downcore slices.

openCC (other)Jun 2023View details →

Monsoon Rainfall Manipulation Experiment (MRME) Soil Temperature, Moisture and Carbon Dioxide Data from the Sevilleta National Wildlife Refuge, New Mexico

The Monsoon Rainfall Manipulation Experiment (MRME) is designed to understand changes in ecosystem structure and function of a semiarid grassland caused by increased precipitation variability, by altering rainfall pulses, and thus soil moisture, that drive primary productivity, community composition, and ecosystem functioning. The overarching hypothesis being tested is that changes in event size and frequency will alter grassland productivity, ecosystem processes, and plant community dynamics. Treatments include (1) a monthly addition of 20 mm of rain in addition to ambient, and a weekly addition of 5 mm of rain in addition to ambient during the months of July, August and September. It is predicted that changes in event size and variability will alter grassland productivity, ecosystem processes, and plant community dynamics. In particular, we predict that many small events will increase soil CO2 effluxes by stimulating microbial processes but not plant growth, whereas a small number of large events will increase aboveground NPP and soil respiration by providing sufficient deep soil moisture to sustain plant growth for longer periods of time during the summer monsoon.

opencc-by-4.0Dec 2019View details →

Figure data for "Detection of tar brown carbon with the single particle soot photometer (SP2)"

Data contained in Figures 2 and 4 of Corbin and Gysel-Beer 2019. https://doi.org/10.5194/acp-2019-568

opencc-by-4.0Nov 2020View details →

Data from: Dwarf shrubs impact tundra soils: drier, colder, and less organic carbon

In the tundra, woody plants are dispersing towards higher latitudes and altitudes due to increasingly favourable climatic conditions. The coverage and height of woody plants are increasing, which may influence the soils of the tundra ecosystem. Here, we use structural equation modelling to analyse 171 study plots and to examine if the coverage and height of woody plants affect the growing-season topsoil moisture and temperature (< 10 cm) as well as soil organic carbon stocks (< 80 cm). In our study setting, we consider the hierarchy of the ecosystem by controlling for other factors, such as topography, wintertime snow depth and the overall plant coverage that potentially influence woody plants and soil properties in this dwarf-shrub dominated landscape in northern Fennoscandia. We found strong links from topography to both vegetation and soil. Further, we found that woody plants influence multiple soil properties: the dominance of woody plants inversely correlated with soil moisture, soil temperature, and soil organic carbon stocks (standardised regression coefficients = -0.39; -0.22; -0.34, respectively), even when controlling for other landscape features. Our results indicate that the dominance of dwarf shrubs may lead to soils that are drier, colder, and contain less organic carbon. Thus, there are multiple mechanisms through which woody plants may influence tundra soils. Kemppinen, Niittynen, Virkkala, Happonen, Riihimäki, Aalto & Luoto (2021). Dwarf shrubs impact tundra soils: drier, colder, and less organic carbon. Ecosystems. These are the data from Kemppinen et al. (2021).

opencc-by-4.0Nov 2020View details →

Case study result data set for Energy Economics article "Demystifying market clearing and price setting effects in low-carbon energy systems"

The data set contains country-specific power generation and consumption time series data for the European energy system, including both traditional and new market participants due to cross-sectoral integration. Country codes: ALPHA-3 Unit: Megawatt (electric) (interval average values, i.e. MWh/h) Generation technology types <ul> <li>batteryStorage (Li-Ion)</li> <li>conventionalHydro (aggregated for different equivalent hydropower systems)</li> <li>natural_gas_CC_COND (Combined Cycle Gas Turbine)</li> <li>natural_gas_CC_EXCOND (Combined Cycle Gas Turbine as extraction condensing CHP plant for district heating)</li> <li>natural_gas_GT_COND (Open-Cycle Gas Turbine)</li> <li>natural_gas_GT_EXCOND (Open-Cycle Gas Turbine as extraction condensing CHP plant for industry)</li> <li>offshoreWind (aggregated for different LCOE and IEC wind turbine classes)</li> <li>offshoreWindExplicit (offshore wind generation considered for offshore grid investments in the North Seas area, aggregated for different LCOE classes)</li> <li>onshoreWind (solar PV, aggregated for different LCOE classes)</li> <li>other (geothermal, waste)</li> <li>pumpedHydro (aggregated for different equivalent hydropower systems)</li> <li>solar (solar PV, aggregated for different LCOE classes)</li> <li>uran_ST_COND (steam turbine condensing power plant)</li> </ul> Consumption technology types <ul> <li>BEV (Battery Electric Vehicles, aggregated for different market segments)</li> <li>PHEV (Battery Electric Vehicles, aggregated for different market segments)</li> <li>airConditioning</li> <li>batteryStorage (Li-Ion)</li> <li>conventionalLoad</li> <li>heatPump (aggregated for different combinations of building, e.g. residential and non-residential, and technology, e.g. air-source, ground-source, types)</li> <li>hybridHeatPump (aggregated for different combinations of building, e.g. residential and non-residential, and technology, e.g. air-source, ground-source, types)</li> <li>hybridTruck (Hybrid Overhead-Line truck)</li> <li>largeScaleDirectResistiveHeating (Centralised CHP systems)</li> <li>natural_gas_CC_EXCOND_electrodeHeater</li> <li>natural_gas_CC_EXCOND_heatpumpHeater</li> <li>natural_gas_GT_EXCOND_electrodeHeater</li> <li>natural_gas_GT_EXCOND_heatpumpHeater</li> <li>powerToGas</li> <li>pumpedHydro (aggregated for different equivalent hydropower systems)</li> </ul>

opencc-by-4.0Nov 2020View details →

Mechanical data of rotary shear experiments and temperature measurements for the manuscript: "Fast and localized temperature measurements during simulated earthquakes in carbonate rocks"

Mechanical data of rotary shear experiments and temperature measurements Each experiment is presented in a file with the experiment name (mechanical data of rotary shear experiment) and a file with the experiment name and _Temp (temperature measurement with the optical fiber). Mechanical data are presented in a tab-delimited file with calibrated measurements of: <ul> <li>Time (milliseconds)</li> <li>Normal stress: Normal (MPa) </li> <li>Fault displacement: Slip (mm)</li> <li>Fault velocity: Velocity (mm/s)</li> <li>Shear stress: Shearstress (MPa)</li> <li>Axial shortening: Shortening (mm).</li> </ul>  In a separate file, temperature data are presented as tab-delimited file with calibrated measurements of: <ul> <li>Time (milliseconds)</li> <li>Temperature from optical fiber in the channel at 1.5 µm : Temperature_1,5 (°C) </li> </ul>

opencc-by-4.0Jan 2020View details →

Carbon dioxide, methane, and chemical data from Batang Ai reservoir

The dataset contains biogeochemical in situ field measurements taken in Batang Ai reservoir (located on the Borneo Island, Malaysia). Samples were taken over four sampling campaigns from 2016 to 2018. Data was used to analyse carbon dioxide and methane flux patterns and to calculate the carbon footprint of the reservoir in the paper: “The carbon footprint of a Malaysian tropical reservoir: measured versus modeled estimates highlight the underestimated key role of downstream processes” (<a href="https://doi.org/10.5194/bg-17-1-2020">https://doi.org/10.5194/bg-17-1-2020</a>). Data were also used to calculate budgets of CO2 and CH4 in the epilimnion of Batang Ai reservoir in the paper: “Changing sources and processes sustaining surface CO2 and CH4 fluxes along a tropical river to reservoir system” (<a href="https://doi.org/10.5194/bg-2020-258">https://doi.org/10.5194/bg-2020-258</a>).

opencc-by-4.0Dec 2023View details →

Global Carbon Budget 2023, surface ocean fugactiy of CO2 (fCO2) and air-sea CO2 flux of individual global ocean biogechemical models and surface ocean fCO2-based data-products

Surface ocean fugacity of CO2 (fCO2) and air-sea CO2 flux data from individual Global Ocean Biogeochemistry Models (GOBMs) and surface ocean fCO2-based data-products (fCO2-products). There are three types of files: (1) one file per fCO2-product with gridded fields and regionally-integrated CO2 flux time-series, (2) one file per GOBM with gridded fields, and (3) one file with the regionally-integrated time-series for the GOBMs.  Note: These provided gridded outputs from fCO2-based data-products and GOBMs are regridded datasets, without adjustments. The best estimates of the annual global ocean carbon sink, based on the native grids of fCO2-products and GOBMs and with the adjustments described in the Global Carbon Budget 2023 (https://doi.org/10.5194/essd-15-5301-2023), are available in the Global Carbon Budget 2023 spreadsheet.The regionally-integrated time-series are as provided by the contributing groups, i.e. integrated from their native grids. In order to reproduce Figure 13 of the Global Carbon Budget 2023 paper (https://doi.org/10.5194/essd-15-5301-2023), the river flux adjustment needs to be added to the CO2 flux estimated from the data-products (North: 0.14 GtC yr-1, Tropics: 0.42 GtC yr-1, South: 0.09 GtC yr-1, see GCB 2023 paper, section 2.5.1). The sum of the regional fluxes may differ from the global estimates as reported in the GCB spreadsheet, because some adjustments were applied only for global fluxes.What is in the files?(1) The files for the fCO2-based data-products contain the following variables (temporal resolution: monthly): fgco2_reg: Regionally integrated air-sea CO2 flux (positive downward), monthly, for regions: north, tropics, south fgco2: Flux density of the total air-sea CO2 flux (positive downward), dimensions: time, latitude, longitude sfco2: Surface ocean fCO2, dimensions: time, latitude, longitude area: Area per pixel, dimensions: latitude, longitude area_reg: Total surface ocean area covered by native grid, for global, north, tropics, south(2) The files for the GOBMs contain the following fields, for simulation A ('contemporary simulation', including effects of rising CO2, climate change and variability) and simulation B ('control simulation', constant CO2, no climate change and variability). Temporal resolution: monthlyfgco2: Flux density of the total air-sea CO2 flux (positive downward), dimensions: time, latitude, longitude sfco2: Surface ocean fCO2, dimensions: time, latitude, longitude area: Area per pixel, dimensions: latitude, longitude (3) One file 'GCB-2023_OceanModel_RegionalBreakdown_1959-2022.nc' with the regionally-integrated CO2 flux time-series for all individual GOBMs, and for simulations A and B. Temporal resolution: annual.Fair data use statement: The data and model output provided on this site are freely available and were furnished by individual scientists who encourage their use. Citation: Please cite the Global Carbon Budget 2023 (Friedlingstein et al., 2023, ESSD, https://doi.org/10.5194/essd-15-5301-2023) for all data. In addition, please also cite the corresponding original reference for each dataset that has been used - see Table 4 in Global Carbon Budget 2023 for references of all the individual Global Ocean Biogeochemical Models and fCO2-based data-products. Further, for an overview of the Global Ocean Biogeochemical Model output, you may find it useful to cite Hauck et al. (2020, Frontiers, doi:10.3389/fmars.2020.571720). Acknowledgement: Please add the following text in the acknowledgement of your paper: "We acknowledge the Global Carbon Project, which is responsible for the Global Carbon Budget and we thank the ocean modeling and fCO2-mapping groups for producing and making available their model and fCO2-product output." Co-authorship: An invitation of co-authorship to the contributing groups is encouraged if these data are the central data set of the publication.Besides the surface fCO2 and air-sea CO2 flux data that is made available open access, we make additional 3D output from the Global Ocean Biogeochemical models (GCB-ocean) available upon request and with its own data policy. Please refer to the Global Carbon Budget website for these additional data: https://globalcarbonbudgetdata.org/closed-access-requests.html

opencc-by-4.0Oct 2023View details →

Land, carbon and biodiversity data for supply chain impact calculations

Monitoring, halting and reversing land conversion is fundamental to meeting international biodiversity and climate targets, and agriculture is the major driver of land conversion. We present an open access set of global data for calculating land use change impacts of agricultural supply chains. These data, originally prepared for the LandGriffon service, include indicators of deforestation, conversion of natural ecosystems, greenhouse gas emissions, and loss of intact or high integrity ecosystems following international standards and guidelines for reporting and target setting in the agriculture, forestry, and land use sector. In order to assign impacts to agricultural production, we prepare data using a spatial adaptation of the statistical Land Use Change (sLUC) accounting approach distributing impact to human activities across the local area using a 50km radius. The results are high resolution global maps of impact per hectare of land occupation. These can then be combined with land footprint data, cropland extent, or productivity maps to calculate land use change related impacts for specific crop volumes sourced from specific regions. Carbon and deforestation results are validated against FAO statistics at the national level.

opencc-by-4.0Jan 2022View details →

Carbon fluxes data over Indian spring wheat agro-ecosystem

The data consists of the following: <ol> <li>Site-scale carbon flux data for an IARI experimental wheat site for the growing season 2013–2014 in New Delhi (28°40' N, 77°12' E).</li> <li>The simulation data in NetCDF format comprises carbon fluxes such as GPP, NPP, Ra, Rh, and NEE.</li> <li>Harvested wheat area of spring wheat across the Indian wheat-growing regions.</li> <li>Site-scale NEP (gC/m2/mon) measured at Meerut (29°05′33″N, 77°41′53″E; growing season 2009-2010) and Saharanpur (29° 52′ 19.139″ N and 077° 34′ 01.621″ E; growing season 2014-15) extracted from published work (Patel et al., 2011; Patel et al., 2021, respectively)</li> </ol>

opencc-by-4.0Apr 2024View details →

JR100 Expedition 379T Site J1002 beryllium isotope, XRF element count and carbon isotope data sets

<h2>JR100 Expedition 379T Site J1002 beryllium isotope, XRF element count and carbon isotope data sets (Finalised 8th of April 2024)</h2> <h3>How to cite these data:</h3> The full data were published in Sproson et al., 2024. Sproson AD, Yokoyama Y, Miyairi Y, Aze T, Clementi VJ, Riechelson H, Bova SC, Rosenthal Y, Childress LB & Expedition 379T Scientists. Near-synchronous Northern Hemisphere and Patagonian ice sheet variation over the last glacial cycle. Nature Geoscience <a href="https://doi.org/10.1038/s41561-024-01436-y">https://doi.org/10.1038/s41561-024-01436-y</a> (2024). <h3>Files:</h3> Supplementary Table 1: Multiple linear regression results between 10Be/9Be ratios and sedimentation rate, K/Ca, Fe/Ca, Al/Ti (this study), Green/Blue (Li et al., 2022), and Global Mean Sea Level (Lambeck et al., 2014). The multiple linear regression was calculated using the MATLAB(R) function “regress”. Supplementary Table 2:  Age-depth model and beryllium isotope measurements for Site J1002. The age-depth model was calculated from radiocarbon dates and oxygen isotope stratigraphy (Li et al., 2022) using the BIGMACS modelling routine (Lee et al., 2022). Beryllium-9 and beryllium-10 were measured by Adam D. Sproson by HR-ICP-MS and AMS at the Atmosphere and Ocean Research Institute (Sproson et al., 2021) and University of Tokyo (Matsuzaki et al., 2007), respectively. 10Be/9Be* ratios were corrected for 10Be paleo-production following von Blanckenburg et al. (2015).  Supplementary Table 3: X-ray Fluorescence Ti, K, Fe, Ca, and Al element counts per second for Site J1002 measured at the Lamont-Doherty Earth Observatory by Vincent J. Clementi. Supplementary Table 4: Carbon isotope measurements for the benthic foraminifera, U. peregrina, measured at Rutgers University by Vincent J. Clementi. <h3>Format:</h3> Depth (m CCSF-A) = core composite depth below seafloor. Calendar age (kyr BP) = age in thousand years before present. [10Be]reac, [9Be]reac = the concentration of 10Be and 9Be in the reactive phase of marine sediments.  Sample ID = expedition sample designation specifying hole (e.g., A), core number (e.g., 1), type (i.e., H), section number (e.g., 1), and then section half (i.e., W). σ = standard deviation. <h3>References:</h3> Lambeck K, Rouby H, Purcell A, Sun Y, Sambridge M. Sea level and global ice volumes from the Last Glacial Maximum to the Holocene. Proceedings of the National Academy of Sciences. 2014;111(43):15296-15303.  Lee T, Rand D, Lisiecki LE, Gebbie G, Lawrence CE. Bayesian age models and stacks: Combining age inferences from radiocarbon and benthic δ18O stratigraphic alignment. EGUsphere. 2022;2022:1-29. Li C, Clementi VJ, Bova SC, et al. The sediment green‐blue color ratio as a proxy for biogenic silica productivity along the Chilean Margin. Geochemistry, Geophysics, Geosystems. 2022:e2022GC010350.  Matsuzaki H, Nakano C, Tsuchiya Y, et al. Multi-nuclide AMS performances at MALT. Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms. 2007;259(1):36-40.  Sproson AD, Aze T, Behrens B, Yokoyama Y. Initial measurement of beryllium‐9 using high‐resolution inductively coupled plasma mass spectrometry allows for more precise applications of the beryllium isotope system within the Earth Sciences. Rapid Communications in Mass Spectrometry. 2021;35(8):e9059.  Von Blanckenburg F, Bouchez J, Ibarra DE, Maher K. Stable runoff and weathering fluxes into the oceans over Quaternary climate cycles. Nature Geoscience. 2015;8(7):538-542.

opencc-by-4.0Nov 2024View details →

Soil grid data for 3 agricultural fields in Italy (Soil Moisture, soil organic carbon)

Soil data collected in an agricultural area with annual crops in Italy (west-central Lombardia Po Valley, province of Pavia). The data refers to soil properties of 320 soil samples for Soil Moisture and  120 for SOC, collected in the topsoil (around 5-10 cm), considering a regular sampling grid, within three agricultural field with different crops (spring-summer cycle) and soils type, Rice-Loamy, Sorghum-Sandy and, Maize-Clay, in a period (before the seeding of the crops),  when the soil was bare, in the framework of the EJP Steropes project. The aim of the collected dataset was to be able to analyse the influence of soil moisture in SOC (WP2 of the STEROPES project) prediction models from remote sensing. Data in the form of shape file (one shapefile for each agricultural field, for oth Soil Moisture and SOC), and pictures of the soil surface in .jpg format.