Find research datasets worth reusing

openCC (other)Oct 2021View details →

Spatiotemporal variation in internal phosphorus loading, sediment characteristics, water column chemistry, and thermal mixing in a hypereutrophic reservoir in southwest Iowa, USA (2019-2020)

The primary aim of the data product is to quantify seasonal and spatial variation in sediment phosphorus fluxes in a temperate reservoir and evaluate mechanisms responsible for instances of elevated sediment phosphorus release. We studied Green Valley Lake, a hypereutrophic reservoir in southwest Iowa, USA, from 2019 to 2020. We measured sediment phosphorus flux rates and potential explanatory variables at three sites along the longitudinal gradient of the reservoir over six sampling events during winter and summer stratification as well as mixing events in the spring, summer, and fall. Ex situ sediment core incubations were used to measure sediment P release rates under ambient temperature and dissolved oxygen conditions. Explanatory variables measured included sediment phosphorus chemistry, sediment physical characteristics, epilimnetic and hypolimnetic nutrient concentrations, and thermal stratification patterns. These data will be used to identify mechanisms driving hot spots and hot moments of sediment phosphorus release, which will contribute to our understanding of how areas of lakebed and times of the year can disproportionately influence whole-lake water chemistry.

openCC (other)Apr 2024View details →

Data from “A Mixed Method Approach to Understanding the Public Health Impact of a School-Based Citizen Science Program to Reduce Arsenic in Private Well Water”

Objectives We have approached the problem of low well water testing rates in Maine and New Hampshire communities by developing the All About Arsenic (AAA) project, which engages secondary school teachers and students as citizen scientists in collecting well water samples for analysis of arsenic and other toxic metals and supports their outreach efforts to their communities. Methods We assessed this project’s public health impact by analyzing student data relative to existing well water quality datasets in both states. In addition, we surveyed private well owners who contributed well water samples to the project to determine the actions taken to mitigate arsenic in well water. Data The data presented here are used in the analyses performed for the publication: "A Mixed Method Approach to Understanding the Public Health Impact of a School-Based Citizen Science Program to Reduce Arsenic in Private Well Water.” Additional data may be available at: The Anecdata Project Page: https://anecdata.org/projects/view/299 The project website: https://www.allaboutarsenic.org/

openCC (other)Sep 2024View details →

Factors Influencing Aboveground Carbon Storage in Mixed Oak-Pine Forests: USDA FIA Data from Southeastern U.S. (2009-2019)

This study explores factors affecting aboveground carbon (AGC) storage in mixed oak-pine forests across the Southeastern United States. Utilizing USDA Forest Inventory and Analysis (FIA) data from 2009 to 2019, the research spans nine states: Alabama, Mississippi, Florida, Georgia, North Carolina, South Carolina, Texas, Louisiana, and Virginia. Data processing in R included converting units to the metric system and calculating structural diversity using Shannon diversity indices. Climate data from the PRISM Climate Group were integrated with FIA data using longitude and latitude. The research aims to uncover how various factors influence AGC storage and contribute to informed forest management practices.

openCC (other)Jan 2025View details →

Groundwater-derived nutrient fluxes and offshore mixing rates along the New Jersey coast 21-23

Radium isotopes are natural tracers useful for studying the magnitude of groundwater discharge and the transport and fate of nutrients in the coastal ocean. We collected radium and nutrient samples from groundwater and surface waters along the southern New Jersey coast to calculate the flux of groundwater-derived nutrients and coastal mixing rates. These data serve as baselines for assessing future changes in the magnitude and quality of groundwater discharge driven by human activity and climate change.

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 IV: Organic Soil Carbon and Nitrogen Content from Organic Soil Samples 2022

This dataset contains lab-quantified (and some field-measured) characteristics for post-fire residual organic 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 summer and fall of 2022 at UAF and NAU.

Fire Self-Limitation (FiSL) Experiment: Quantifying Wildfire Carbon Combustion Losses in boreal Deciduous and Mixed Forests in Interior Alaska and the Boreal Cordillera V: Organic Soil Depth 2022

This dataset contains field-measured characteristics for post-fire residual organic soil samples and for additional organic soil depths 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).

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 VII: Coarse and Fine Woody Debris Inventory 2022

This dataset contains characteristics of coarse woody debris and snags 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).

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

This dataset contains characteristics of post-fire seedlings and resprouts 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).

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.

openCC (other)Dec 2021View details →

Aggregate mesquite litter chemistry following soil-mixing and decomposition in a semi-arid grassland at the Jornada Basin LTER, 2010-2012

This dataset contains litter carbon content, nitrogen content, and associated chemistry data from a litter decomposition experiment at the Jornada Basin LTER in 2010 to 2012. To assess the role of soil-litter mixing (SLM) in aridland litter decomposition, litterbags were deployed in the Chihuahuan Desert and interrelationships between vegetation structure, SLM, and rates of decomposition were quantified. To assess the role of vegetation structure, litterbags were deployed in contrasting vegetation microsites, including grass, shrub, and bare ground microsites. This dataset contains litter chemistry data from the experiment including percent carbon, percent nitrogen, ash corrections, and the carbon to nitrogen ratio of litter in recovered bags. This study is complete.

openCC (other)Dec 2021View details →

Aggregate mesquite litter mass-loss following soil-mixing and decomposition in a semi-arid grassland at the Jornada Basin LTER, 2010-2012

This package contains litter mass loss data from a litter decomposition experiment at the Jornada Basin LTER. To assess the role of soil-litter mixing (SLM) in aridland litter decomposition, litterbags were deployed in the Chihuahuan Desert and interrelationships between vegetation structure, SLM, and rates of decomposition were quantified. To assess the role of vegetation structure, litterbags were deployed in contrasting vegetation microsites, including grass, shrub, and bare ground microsites. This dataset contains the mass-loss data (including ash-corrections) from the experiment. This study is complete.

opencc-by-4.0Apr 2020View details →

CO2 fertilization, transpiration deficit and vegetation period drive the response of mixed broadleaved forests to a changing climate in Wallonia: Dataset

This repository is linked to the paper "CO2 fertilization, transpiration deficit and vegetation period drive the response of mixed broadleaved forests to a changing climate in Wallonia" submitted to Annals of Forest Science and written by Louis DE WERGIFOSSE (corresponding author), Frédéric ANDRE, Hugues GOOSSE, Steven CALUWAERTS, Lesley DE CRUZ, Rozemien DE TROCH, Bert VAN SCHAEYBROECK and Mathieu JONARD. The files stored in the repository are the input files that should be used in the model HETEROFOR to retrieve the results displayed in the study and the corresponding results themselves. The source code of the model HETEROFOR can be freely accessed and downloaded (https://doi.org/10.5281/zenodo.3591348). Additional information on the model can be found in the following description papers: Jonard et al., 2020 (https://doi.org/10.5194/gmd-13-905-2020) and de Wergifosse et al., 2020 (https://doi.org/10.5194/gmd-13-1459-2020). The repository contains three directories. The first (HETEROFOR_input_files) comprises the additional files to those in the model repository presented in the previous paragraph needed to run the model for the purpose of this study. The second directory (Simulation_outputs_raw) contains the data directly provided by the model without any processing. The  third directory (Simulation_outputs_raw) includes the model outputs after processing. The directory "HETEROFOR_input_files" is constituted of two directories called "Climate_files" and "Stand_files". "Climate_files" is subdivided in three sub-directories. Sub-directory "Original_downscaled_CORDEX_timeseries" contains the climate projections of the four sites and scenarios described in the study. These downscaled timeseries have been  produced by the Royal Meteorological Institute of Belgium under the program CORDEX.be, which is part of EURO-CORDEX. A bias correction has been further applied to these climate timeseries that are stored in the "Bias_corrected_timeseries" sub-directory. The files of these two sub-directories should be used in HETEROFOR as "Meteorological data" input files. The "CO2_concentrations" sub-directory includes the yearly averaged projected concentrations for the three RCP scenarios described in the paper. In HETEROFOR, they should be put as input in the "Atmospheric CO2 concentration" part after selecting the option "Variable over time". The second directory called "Stand files" contain the six inventory files described in the study for which a thinning has been applied. They should be used in HETEROFOR as "Inventory data" input files. The directory "Simulation_outputs_raw" is divided similarly to the study into two simulation experiments. The "First simulation experiment" directory is further subdivided into constant and time-dependent CO2 concentrations like in the study and contains one file for the regular modality and one for the thinning modality. All the files are constructed the same way with, for each tree and site (or stand, soil and climate), annual values of Net Primary Production (NPP) in kg of carbon, transpiration and potential transpiration in L under the different climate scenarios. In addition, the "Phenology" directory contains, for each day and under all climate scenarios, the green proportion (proportion of green leaves comprised between 0 and 1) for the two tree species considered in the study (Common oak and European beech). Finally, the directory "Simulation_outputs_processed" is constructed similarly to "Simulation_outputs_raw" but all the data are integrated in one file at the yearly time step. However, the units change with the NPP expressed in gC/m2 and transpiration and potential transpiration in mm (or L/m2) while the vegetation period is averaged according to the percentage of species occurrence in the different stands. For more information concerning this repository or the study, please do not hesitate to contact Louis DE WERGIFOSSE (louis.dewergifosse@uclouvain.be) or Mathieu JONARD (mathieu.jonard@uclouvain.be).

opencc-by-4.0Nov 2020View details →

Argo-based ocean surface mixed layer depths using the buoyancy gradient definition of Whitt Nicholson and Carranza (2019)

Argo-based mixed layer depth profiles derived from the CORA product as described in Whitt Nicholson Carranza. A binned 2-degree climatology was published previously: https://github.com/danielwhitt/globalimpacts_2019_whittetal/blob/master/MonthlyClimatology_ARGO_MLDbmax_TEOS10_Copernicus_PF_2000-2017_all_jun252019_nc.nc with: Whitt, D. B., Nicholson, S. A., & Carranza, M. M. (2019). Global Impacts of Subseasonal (< 60 Day) Wind Variability on Ocean Surface Stress, Buoyancy Flux, and Mixed Layer Depth. Journal of Geophysical Research: Oceans, 124(12), 8798-8831 Contact the authors with questions.  The chosen mixed layer depth definition is the same as "HMXL", a standard output of the Community Earth System Model (CESM) ocean component.

opencc-by-4.0Oct 2018View details →

Mix-and-Match Dataset

Benchmark results for "Mix-and-Match: A Model-driven Runtime Optimisation Strategy for BFS on GPUs" paper.   Performance data for Breadth-First Search on NVidia TitanX. Including trained Binary Decision Tree model for predicting the best implementation on an input graph.

opencc-by-4.0Jul 2020View details →

Soil profile, climatic, physiographic, overstory and understory data in mixed and monospecific plots of Pinus sylvestris and Pinus pinaster in Spain

This dataset provides valuable environmental information about a triplets’ essay of Scots pine and Maritime pine in Spain. The data characterizes the soil profile (physicochemical parameters of organic and mineral horizons), climate, physiography, understory and overstory. The essay, located in North-Central Spain, consists of eighteen forest plots divided in six triplets. Each triplet includes three circular plots of 15 m-radius located less than 1 km from each other: two monospecific plots dominated by P. sylvestris or P. pinaster, and one mixed plot of both species. In each plot, one pit up to 50 cm depth, one 15 m-radius overstory features inventory and ten understory 1x1 m inventories were carried out. Additionally, physiographic and climatic variables were collected per plot. The file contains information about the 218 environmental variables studied in the eighteen forest plots. Triplet: Triplet to which the plot belongs(1: Triplet 1; 2: Triplet 2; 3: Triplet 3; 4: Triplet 4; 5: Triplet 5; 6: Triplet 6). Stand_type: Type of stand (PS: monospecific stand of Pinus sylvestris L.; PP: monospecific stand of Pinus pinaster Ait.; MM: mixed stand of Pinus sylvestris L.and Pinus pinaster Ait.). Plot: Plot identification (PS01: monospecific stand of Pinus sylvestris L. of triplet 1; PS02: monospecific stand of Pinus sylvestris L. of triplet 2; PS03: monospecific stand of Pinus sylvestris L. of triplet 3; PS04: monospecific stand of Pinus sylvestris L. of triplet 4; PS05: monospecific stand of Pinus sylvestris L. of triplet 5; PS06: monospecific stand of Pinus sylvestris L. of triplet 6; MM01: mixed stand of Pinus sylvestris L.and Pinus pinaster Ait. of triplet 1; MM02: mixed stand of Pinus sylvestris L.and Pinus pinaster Ait. of triplet 2; MM03: mixed stand of Pinus sylvestris L.and Pinus pinaster Ait. of triplet 3; MM04: mixed stand of Pinus sylvestris L.and Pinus pinaster Ait. of triplet 4; MM05: mixed stand of Pinus sylvestris L.and Pinus pinaster Ait. of triplet 5; MM06: mixed stand of Pinus sylvestris L.and Pinus pinaster Ait. of triplet 6; PP01: monospecific stand of Pinus pinaster Ait. of triplet 1; PP02: monospecific stand of Pinus pinaster Ait. of triplet 2; PP03: monospecific stand of Pinus pinaster Ait. of triplet 3; PP04: monospecific stand of Pinus pinaster Ait. of triplet 4; PP05: monospecific stand of Pinus pinaster Ait. of triplet 5; PP06: monospecific stand of Pinus pinaster Ait. of triplet 6). Lat: Plot latitude in degrees. Long: Plot longitude in degrees. Province: Province to which the plot belongs (B: Province of Burgos; Sp: Province of Soria). Municipality: Municipality to which the plot belongs (M: Town of Mamolar; HP: Town of Hontoria del Pinar; N: Town of Navaleno; St: Town of Soria; CP: Town of Cabrejas del Pinar). Forest: Name of the forest where is located the plot (MB: Mata Blanca; MR: Mata Robledo; FP: Fuente del Pardo; PM: Pajar de la molinera; MP: Mojon Pardo; CM: Cueva de Matarubias). Alti: Plot elevation above sea level in m a.s.l. Slope: Slope (gradient) of the plot in percentage. Ori: Plot orientation in degrees. Clim: Climate classification according to Köppen classification (1936) (Cfb: Temperate without a dry season and temperate summer climate; Csb: Temperate with dry summer climate). XR: Accumulated rainfall in one year according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in mm. JR: January rainfall according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ mm FR: February rainfall according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in mm. MR: March rainfall according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in mm. AR: April rainfall according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in mm. MyR: May rainfall according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in mm. JnR: June rainfall according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in mm. JlR: July rainfall according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in mm. AgR: August rainfall according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in mm. SR: September rainfall according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in mm. OR: October rainfall according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in mm. NR: November rainfall according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in mm. DR: December rainfall according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in mm. XT: Anual mean temperature according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in ºC. JT: January temperature according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in ºC. FT: February temperature according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in ºC. MT: March temperature according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in ºC. AT: April temperature according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in ºC. MyT: May temperature according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in ºC. JnT: June temperature according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in ºC. JlT: July temperature according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in ºC. AgT: August temperature according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in ºC. ST: September temperature according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in ºC. OT: October temperature according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in ºC. NT: November temperature according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in ºC. DT: December temperature according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in ºC. Par_mat: Soil parental material according to Spanish Geological Map on a 1M scale. (IGME , 2015) (SM: Sandstones and Marls). Geo_age: Geological age of plot according to Spanish Geological Map on a 1M scale. (IGME, 2015) (Mz: Mesozoic age). Soil: Soil type according to Soil-Survey-Staff (2014) (TpDx: Typic Dystroxerept; TpHx:: Typic Humixerept; AqHx:: Aquic humixerept) Litter_B: Total Leaf Litter Biomass in Mg/ha. FF_Th: Forest floor Thickness in cm. Fs: Percentage of Fresh to Total Leaf Litter in %. Fr: Percentage of Fragmented to Total Leaf Litter in %. Hm: Percentage of Humified to Total Leaf Litter in %. GH1: Fist genetic soil horizon according to Soil Survey-Staff (2014) (Ah: Mineral horizon with accumulation of organic matter. This horizon is formed at the soil surface or below an O horizon). GH2: Second genetic soil horizon according to Soil Survey-Staff (2014) (AB: Transition horizon between A and B. A is a mineral horizon formed at the surface or below an O horizon, B is a subsurface horizon in which the structure of the rock is obliterated; AC: Transition horizon between A and C. A is a mineral horizon formed at the surface or below an O horizon; C is a mineral horizon, excluding hard bedrock, that is little affected by pedogenetic processes; Bw: Mineral B horizon where the development of color or structure are its more important diagnostic characteristics). GH3: Third genetic soil horizon according to Soil Survey-Staff (2014) (Bw: Mineral B horizon where the development of color or structure are its more important diagnostic characteristics; C: Mineral horizon, excluding hard bedrock, that is little affected by pedogenetic processes; Cg: Mineral horizon in which a distinct pattern of mottling occurs that reflects alternating conditions of oxidation and reduction of sesquioxides, caused by seasonal surface waterlogging). Th_H1: Thickness of the first soil horizon in cm. Th_H2: Thickness of the second soil horizon in cm. Th_H3: Thickness of the third soil horizon in cm. moistCol_H1: Wet matrix color (Hue Value/Chroma) of the first soil horizon according to Munsell soil color chards (10YR2/1: black; 10YR2/2: very dark brown; 10YR3/1: very dark grey; 10YR3/2: very dark greyish brown; 10YR4/1: dark grey; 10YR6/3: pale brown). moistCol_H2: Wet matrix colour (Hue Value/Chroma) of the second soil horizon according to Munsell soil color chards (5YR5/8: yellowish red; 7.5YR4/6: strong brown; 10YR3/2: very dark greyish brown; 10YR4/1: dark grey; 10YR4/2: dark greyish brown; 10YR4/4: dark yellowish brown with chroma 4; 10YR4/6: dark yellowish brown with chroma 6; 10YR5/3: brown; 10YR5/4: yellowish brown with chroma 4; 10YR5/6: yellowish brown with chroma 6; 10YR5/8: yellowish brown with chroma 8; 10YR6/4: light yellowish brown; 10YR6/6: brownish yellow). moistCol_H3: Wet matrix colour (Hue Value/Chroma) of the third soil horizon according to Munsell soil color chards (5YR4/6: yellowish red; 10YR4/4: dark yellowish brown with chroma 4; 10YR4/6: dark yellowish brown with chroma 6; 10YR5/8: yellowish brown; 10YR6/1: grey). dryCol_H1:Dry matrix color (Hue Value/Chroma) of the first soil horizon according to Munsell soil color chards (10YR4/1: dark grey; 10YR4/2: dark greyish brown; 10YR5/1: grey with value 5; 10YR5/2: greyish brown; 10YR5/3: brown; 10YR6/1: grey with value 6; 10YR6/2: light yellowish brown; 10YR7/2: light grey). dryCol_H2: Dry matrix color (Hue Value/Chroma) of the second soil horizon according to Munsell soil color chards (7.5YR6/6: redish brown; 10YR4/1: dark grey; 10YR6/1: grey with value 6; 10YR6/2: light yellowish brown with chroma 2; 10YR6/3: pale brown; 10YR6/4: light yellowish brown with chroma 4; 10YR6/6: brownish yellow; 10YR7/3: very pale brown with value 7 and choma 3; 10YR7/4: very pale brown withvalue 7 and choma 4; 10YR8/4: very pale brown with value 8 and choma 4). dryCol_H3: Dry matrix color (Hue Value/Chroma) of the third soil horizon according to Munsell soil color chards (5YR5/6: yellowish red; 7.5YR5/6: strong brown; 10YR6/4: light yellowish brown; 10YR6/6: brownish yellow; 10YR7/4: very pale brown; 10YR8/1: white). Sand_H1: Percentage of sand of the first soil horizon determined by the pipette method (Van-Reeuwijk 2002) according to Soil Survey Staff (2014) in % weight/weight. Sand_H2: Percentage of sand of the second soil horizon determined by the pipette method (Van-Reeuwijk 2002) according to Soil Survey Staff (2014) in % weight/weight. Sand_H3: Percentage of sand of the third soil horizon determined by the pipette method (Van-Reeuwijk 2002) according to Soil Survey Staff (2014) in % weight/weight. Silt_H1: Percentage of silt of the first soil horizon determined by the pipette method (Van-Reeuwijk 2002) according to Soil Survey Staff (2014) in % weight/weight. Silt_H2: Percentage of silt of the second soil horizon determined by the pipette method (Van-Reeuwijk 2002) according to Soil Survey Staff (2014) in % weight/weight. Silt_H3: Percentage of silt of the third soil horizon determined by the pipette method (Van-Reeuwijk 2002) according to Soil Survey Staff (2014) in % weight/weight. Clay_H1: Percentage of clay of the first soil horizon determined by the pipette method (Van-Reeuwijk 2002) according to Soil Survey Staff (2014) in % weight/weight. Clay_H2: Percentage of clay of the second soil horizon determined by the pipette method (Van-Reeuwijk 2002) according to Soil Survey Staff (2014) in % weight/weight. Clay_H3: Percentage of clay of the third soil horizon determined by the pipette method (Van-Reeuwijk 2002) according to Soil Survey Staff (2014) in % weight/weight. Tex_H1: Textural class of the first soil horizon according to Soil Survey Staff (2014) (SL: Sandy Loam; L: Loam). Tex_H2: Textural class of the second soil horizon according to Soil Survey Staff (2014) (SL: Sandy Loam; L: Loam). Tex_H3: Textural class of the third soil horizon according to Soil Survey Staff (2014) (SL: Sandy Loam; L: Loam; CL: Clay loam; C: Clay). Stones_H1: Coarse soil material (> 2 mm) of the first soil horizon in % weight/weight. Stones_H2: Coarse soil material (> 2 mm) of the second soil horizon in % weight/weight. Stones_H3: Coarse soil material (> 2 mm) of the third soil horizon in % weight/weight. bD_H1: Bulk density of the first soil horizon according to (Van-Reeuwijk 2002) in g/cm3. bD_H2: Bulk density of the second soil horizon according to (Van-Reeuwijk 2002) in g/cm3. bD_H3: Bulk density of the third soil horizon according to (Van-Reeuwijk 2002) in g/cm3. pD_H1: Particle density of the first soil horizon according to (Van-Reeuwijk 2002) in g/cm3. pD_H2: Particle density of the second soil horizon according to (Van-Reeuwijk 2002) in g/cm3. pD_H3: Particle density of the third soil horizon according to (Van-Reeuwijk 2002) in g/cm3. Poro_H1: Porosity of the first soil horizon according to (Van-Reeuwijk 2002) in % vol/vol. Poro_H2: Porosity of the second soil horizon according to (Van-Reeuwijk 2002) in % vol/vol. Poro_H3: Porosity of the third soil horizon according to (Van-Reeuwijk 2002) in % vol/vol. pH_H1: pH (1:2.5 H2O) of the first soil horizon according to (Van-Reeuwijk 2002) pH_H2: pH (1:2.5 H2O) of the second soil horizon according to (Van-Reeuwijk 2002) pH_H3: pH (1:2.5 H2O) of the third soil horizon according to (Van-Reeuwijk 2002) EC_H1: Electrical conductivity of the first soil horizon according to (Van-Reeuwijk 2002) in dS/m. EC_H2: Electrical conductivity of the second soil horizon according to (Van-Reeuwijk 2002) in dS/m. EC_H3: Electrical conductivity of the third soil horizon according to (Van-Reeuwijk 2002) in dS/m. avP_H1: Available phosphorus of the first soil horizon according to Olsen and Sommers (1982) in mg/kg. avP_H2: Available phosphorus of the second soil horizon according to Olsen and Sommers (1982) in mg/kg. avP_H3: Available phosphorus of the third soil horizon according to Olsen and Sommers (1982) in mg/kg. avPstock_H1: Available phosphorus stock of the first soil horizon according to López-Marcos et al. (2019) in Mg/ha. avPstock_H2: Available phosphorus stock of the second soil horizon according to López-Marcos et al. (2019) in Mg/ha. avPstock_H3: Available phosphorus stock of the third soil horizon up to 50 cm depth according to López-Marcos et al. (2019) in Mg/ha. avPstock_50: Available phosphorus stock of whole soil profile up to 50 cm depth according to López-Marcos et al. (2019) in Mg/ha. TN_Fs: Total nitrogen of the fresh forest floor analyzed with a LECO-CHN 2000 elemental analyzer in g/kg TN_Fg: Total nitrogen of the fragmented forest floor analyzed with a LECO-CHN 2000 elemental analyzer in g/kg TN_Hm: Total nitrogen of the humified forest floor analyzed with a LECO-CHN 2000 elemental analyzer in g/kg TN_H1: Total nitrogen of the first soil horizon analyzed with a LECO-CHN 2000 elemental analyzer in g/kg. TN_H2: Total nitrogen of the second soil horizon analyzed with a LECO-CHN 2000 elemental analyzer in g/kg. TN_H3: Total nitrogen of the third soil horizon analyzed with a LECO-CHN 2000 elemental analyzer in g/kg. TNstock_H1: Total nitrogen stock of the first soil horizon according to López-Marcos et al. (2019) in Mg/ha. TNstock_H2: Total nitrogen stock of the second soil horizon according to López-Marcos et al. (2019) in Mg/ha. TNstock_H3: Total nitrogen stock of the third soil horizon up to 50 cm depth according to López-Marcos et al. (2019) in Mg/ha. TNstock_50: Total nitrogen stock of whole soil profile up to 50 cm depth according to López-Marcos et al. (2019) in Mg/ha. TOC_Fs: Total organic carbon of the fresh forest floor analyzed with a LECO-CHN 2000 elemental analyzer in g/kg TOC_Fg: Total organic carbon of the fragmented forest floor analyzed with a LECO-CHN 2000 elemental analyzer in g/kg TOC_Hm: Total organic carbon of the humified forest floor analyzed with a LECO-CHN 2000 elemental analyzer in g/kg TOC_H1: Total organic carbon of the first soil horizon analyzed with a LECO-CHN 2000 elemental analyzer in g/kg. TOC_H2: Total organic carbon of the second soil horizon analyzed with a LECO-CHN 2000 elemental analyzer in g/kg. TOC_H3: Total organic carbon of the third soil horizon analyzed with a LECO-CHN 2000 elemental analyzer in g/kg. TOCstock_H1:Total organic carbon stock of the first soil horizon according to López-Marcos et al. (2019) in Mg/ha. TOCstock_H2: Total organic carbon stock of the second soil horizon according to López-Marcos et al. (2019) in Mg/ha. TOCstock_H3: Total organic carbon stock of the third soil horizon up to 50 cm depth according to López-Marcos et al. (2019) in Mg/ha. TOCstock_50: Total organic carbon stock of whole soil profile up to 50 cm depth according to López-Marcos et al. (2019) in Mg/ha. C/N_Fs: Ratio of total organic carbon to total nitrogen of the fresh forest floor  C/N_Fg: Ratio of total organic carbon to total nitrogen of the fragmented forest floor  C/N_Hm: Ratio of total organic carbon to total nitrogen of the humified forest floor  C/N_H1: Ratio of total organic carbon to total nitrogen of the first soil horizon C/N_H2: Ratio of total organic carbon to total nitrogen of the second soil horizon C/N_H3: Ratio of total organic carbon to total nitrogen of the third soil horizon OxC_H1: Easily oxidizable carbon of the first soil horizon according to Walkley (1947) in mg/kg. OxC_H2: Easily oxidizable carbon of the second soil horizon according to Walkley (1947) in mg/kg. OxC_H3: Easily oxidizable carbon of the third soil horizon according to Walkley (1947) in mg/kg. OxCstock_H1: Easily oxidizable carbon stock of the first soil horizon according to López-Marcos et al. (2019) in Mg/ha. OxCstock_H2: Easily oxidizable carbon stock of the second soil horizon according to López-Marcos et al. (2019) in Mg/ha. OxCstock_H3: Easily oxidizable carbon stock of the third soil horizon up to 50 cm depth according to López-Marcos et al. (2019) in Mg/ha. OxCstock_50: Easily oxidizable carbon stock of whole soil profile up to 50 cm depth according to López-Marcos et al. (2019) in Mg/ha. CEC_H1: Cation exchange capacity of the first soil horizon according to Mehlich (1953) in cmol+/kg. CEC_H2: Cation exchange capacity of the second soil horizon according to Mehlich (1953) in cmol+/kg. CEC_H3: Cation exchange capacity of the third soil horizon according to Mehlich (1953) in cmol+/kg. Na+_H1: Exchangeable sodium of the first soil horizon by means of extracting with 1N ammonium acetate (pH=7) (Schollenberger and Simon 1945) in cmol+/kg. Na+_H2: Exchangeable sodium of the second soil horizon by means of extracting with 1N ammonium acetate (pH=7) (Schollenberger and Simon 1945) in cmol+/kg. Na+_H3: Exchangeable sodium of the third soil horizon by means of extracting with 1N ammonium acetate (pH=7) (Schollenberger and Simon 1945) in cmol+/kg. Na+stock_H1: Exchangeable sodium stock of the first soil horizon according to López-Marcos et al. (2019) in Mg/ha. Na+stock_H2: Exchangeable sodium stock of the second soil horizon according to López-Marcos et al. (2019) in Mg/ha. Na+stock_H3: Exchangeable sodium stock of the third soil horizon up to 50 cm depth according to López-Marcos et al. (2019) in Mg/ha. Na+stock_50: Exchangeable sodium stock of whole soil profile up to 50 cm depth according to López-Marcos et al. (2019) in Mg/ha. K+_H1: Exchangeable potassium of the first soil horizon by means of extracting with 1N ammonium acetate (pH=7) (Schollenberger and Simon 1945) in cmol+/kg. K+_H2: Exchangeable potassium of the second soil horizon by means of extracting with 1N ammonium acetate (pH=7) (Schollenberger and Simon 1945) in cmol+/kg. K+_H3: Exchangeable potassium of the third soil horizon by means of extracting with 1N ammonium acetate (pH=7) (Schollenberger and Simon 1945) in cmol+/kg. K+stock_H1: Exchangeable potassium stock of the first soil horizon according to López-Marcos et al. (2019) in Mg/ha. K+stock_H2: Exchangeable potassium stock of the second soil horizon according to López-Marcos et al. (2019) in Mg/ha. K+stock_H3: Exchangeable potassium stock of the third soil horizon up to 50 cm depth according to López-Marcos et al. (2019) in Mg/ha. K+stock_50: Exchangeable potassium stock of whole soil profile up to 50 cm depth according to López-Marcos et al. (2019) in Mg/ha. Ca+2_H1: Exchangeable calcium of the first soil horizon by means of extracting with 1N ammonium acetate (pH=7) (Schollenberger and Simon 1945) in cmol+/kg. Ca+2_H2: Exchangeable calcium of the second soil horizon by means of extracting with 1N ammonium acetate (pH=7) (Schollenberger and Simon 1945) in cmol+/kg. Ca+2_H3: Exchangeable calcium of the third soil horizon by means of extracting with 1N ammonium acetate (pH=7) (Schollenberger and Simon 1945) in cmol+/kg. Ca+2stock_H1: Exchangeable calcium stock of the first soil horizon according to López-Marcos et al. (2019) in Mg/ha. Ca+2stock_H2: Exchangeable calcium stock of the second soil horizon according to López-Marcos et al. (2019) in Mg/ha. Ca+2stock_H3: Exchangeable calcium stock of the third soil horizon up to 50 cm depth according to López-Marcos et al. (2019) in Mg/ha. Ca+2stock_50: Exchangeable calcium stock of whole soil profile up to 50 cm depth according to López-Marcos et al. (2019) in Mg/ha. Mg+2_H1: Exchangeable magnesium of the first soil horizon by means of extracting with 1N ammonium acetate (pH=7) (Schollenberger and Simon 1945) in cmol+/kg. Mg+2_H2: Exchangeable magnesium of the second soil horizon by means of extracting with 1N ammonium acetate (pH=7) (Schollenberger and Simon 1945) in cmol+/kg. Mg+2_H3: Exchangeable magnesium of the third soil horizon by means of extracting with 1N ammonium acetate (pH=7) (Schollenberger and Simon 1945) in cmol+/kg. Mg+2stock_H1: Exchangeable magnesium stock of the first soil horizon according to López-Marcos et al. (2019) in Mg/ha. Mg+2stock_H2: Exchangeable magnesium stock of the second soil horizon according to López-Marcos et al. (2019) in Mg/ha. Mg+2stock_H3: Exchangeable magnesium stock of the third soil horizon up to 50 cm depth according to López-Marcos et al. (2019) in Mg/ha. Mg+2stock_50: Exchangeable magnesium stock of whole soil profile up to 50 cm depth according to López-Marcos et al. (2019) in Mg/ha. SB_H1: Sum of bases of the first soil horizon by means of extracting with 1N ammonium acetate (pH=7) (Schollenberger and Simon 1945) in cmol+/kg. SB_H2: Sum of bases of the second soil horizon by means of extracting with 1N ammonium acetate (pH=7) (Schollenberger and Simon 1945) in cmol+/kg. SB_H3: Sum of bases of the third soil horizon by means of extracting with 1N ammonium acetate (pH=7) (Schollenberger and Simon 1945) in cmol+/kg. SBstock_H1: Sum of bases stock of the first soil horizon according to López-Marcos et al. (2019) in Mg/ha. SBstock_H2: Sum of bases stock of the second soil horizon according to López-Marcos et al. (2019) in Mg/ha. SBstock_H3: Sum of bases stock of the third soil horizon up to 50 cm depth according to López-Marcos et al. (2019) in Mg/ha. SBstock_50: Sum of bases stock of whole soil profile up to 50 cm depth according to López-Marcos et al. (2019) in Mg/ha. FC_H1: Field capacity of the first soil horizon according to Van-Reeuwijk (2002) in %. FC_H2: Field capacity of the second soil horizon according to Van-Reeuwijk (2002) in %. FC_H3: Field capacity of the third soil horizon according to Van-Reeuwijk (2002)) in %. PWP_H1: Permanent wilting point of the first soil horizon according to Van-Reeuwijk (2002) in %. PWP_H2: Permanent wilting point of the second soil horizon according to Van-Reeuwijk (2002) in %.  PWP_H3: Permanent wilting point of the third soil horizon according to Van-Reeuwijk (2002) in %. AW_H1: Available water of the first soil horizon according to Van-Reeuwijk (2002) in %. AW_H2: Available water of the second soil horizon according to MAPA (1994) in %. AW_H3: Available water of the third soil horizon according to Van-Reeuwijk (2002) in %. WHC_H1: Water holding capacity of the first soil horizon according to López-Marcos et al. (2019) in g/cm2. WHC_H2: Water holding capacity of the second soil horizon according to López-Marcos et al. (2019) in g/cm2. WHC_H3: Water holding capacity of the third soil horizon up to 50 cm depth according to López-Marcos et al. (2019) in g/cm2. WHC_50: Water holding capacity of whole soil profile up to 50 cm depth according to López-Marcos et al. (2019) in g/cm2. Pot_veg: Potential vegetation according to Rivas-Martínez (1987) (LfQp: Luzulo forsteri-Querceto pyrenaicae S.; FhQp: Festuco heterophyllae-Querceto pyrenaicae S.; Jht: Junipereto hemisphaerico-thuriferae S.). Cur_veg: Current vegetation according to WMS service of MAPAMA(<a href="http://wms.mapama.es/sig/Biodiversidad">http://wms.mapama.es/sig/Biodiversidad</a>) (ps: Pinus sylvestris L.; pp: Pinus pinaster Ait.; pi: Pinus sylvestris L. and Pinus pinaster Ait.). NT: Stems per hectare of both Pinus species (Pinus sylvestris L. and Pinus pinaster Ait.) in trees/ha. NPs: Stems per hectare of Pinus sylvestris L. in trees/ha. NPp: Stems per hectare of Pinus pinaster Ait. in trees/ha. GT: Basal area per hectare of both Pinus species (Pinus sylvestris L. and Pinus pinaster Ait.) in m2/ha. GPs: Basal area per hectare of Pinus sylvestris L. in m2/ha. GPp: Basal area per hectare of Pinus pinaster Ait. in m2/ha. %PS: Percentage of basal area of Pinus sylvestris L. from total basal area %PP: Percentage of basal area of Pinus pinaster Ait. from total basal area dgT: Quadratic mean diameter of both Pinus species (Pinus sylvestris L. and Pinus pinaster Ait.) in cm. dgPs: Quadratic mean diameter of Pinus sylvestris L. in cm. dgPp: Quadratic mean diameter of Pinus pinaster Ait. in cm. HoT: Dominant height of both Pinus species (Pinus sylvestris L. and Pinus pinaster Ait.) in cm. HoPs: Dominant height of Pinus sylvestris L. in m. HoPp: Dominant height of Pinus pinaster Ait. in m. AgePs: Normal age of Pinus sylvestris L. in years. AgePp: Normal age of Pinus pinaster Ait. In years. SIPs: Site index of Pinus sylvestris L. related at age 100 for total plot according to Rojo and Montero (1999) SIPp: Site index of Pinus pinaster Ait. related at age 100 for total plot according to Bravo-Oviedo et al. (2007) Litter_cov: Cover of leaf litter in %. Vasc: Cover of understory vascular plants in %. Bryo: Cover of understory bryophytes in %. Under_sp: More abundant specie of understory vegetation (Aica: Aira caryophyllea L.; Aruv: Arctostaphylos uva-ursi (L.) Spreng.; Cavu: Calluna vulgaris (L.) Hull; Erar: Erica arborea L.; Erau: Erica australis L.; Pipi: Pinus pinaster Aiton. (seedlings/saplings); Pisy: Pinus sylvestris L. (seedlings/saplings; Ptaq: Pteridium aquilinum (L.) Kuhn) Aqu: Understory cover of family Aquifoliaceae in %. Aste: Understory cover of family Asteraceae in %. Cari: Understory cover of family Cariophyllaceae in %. Cist: Understory cover of family Cistaceae in %. Cupr: Understory cover of family Cupresaceae in %. Eric: Understory cover of family Ericaceae in %. Faba: Understory cover of family Fabaceae in %. Faga: Understory cover of family Fagaceae in %. Junc: Understory cover of family Juncaceae in %. Lili: Understory cover of family Liliaceae in %. Pina: Understory cover of family Pinaceae in %. Poac: Understory cover of family Poaceae in %. Poli: Understory cover of family Poligalaceae in %. Rosa: Understory cover of family Rosaceae in %. Rubi: Understory cover of family Rubiaceae in %. Scro: Understory cover of family Scrofulariaceae in %. Viol: Understory cover of family Violaceae in %. Xant: Understory cover of family Xanthorrhoeaceae in %.