dataesr/scientific-paragraphs-categorization huggingface dataset:
Houghton and Nassikas (2017)  also provide emissions from forest degradation (i. e. , biomass-reducing activities that do not result in the land parcel being reclassified as a non-forest) and subsequent recovery as part of F LUC. The various methods available to quantify F LUC (Table  2 ) rely on different input datasets and models with different abilities to represent land use practices. They further use different terminology and assumptions of which component fluxes to include, leading to inconsistencies between one another. For RECCAP2, the best data available in each region should be used. However, it is crucial to clearly define the methods and assumptions made and which F LUC fluxes are included in the corresponding results. If possible, regional F LUC fluxes estimated by the "best method" should be compared with those estimated by the global datasets from the most up-to-date Global Carbon Budget coordinated by the Global Carbon Project (GCP) Global Carbon Budget in order to ensure consistency and comparability between regions.
Examples of already available remote-sensing-based datasets than can be used for land cover and land cover change mapping are the ESA-CCI land cover product, based on five different satellite missions at 300 m spatial resolution and annual time steps between 1992 and 2018  (ESA, 2017) , and the Landsat 30 m spatial-resolution forest cover change product, covering 2000 to 2018  (Hansen et al. , 2013)  and extended to land cover change for forest, short vegetation, and bare soil  (Song et al. , 2018). For vegetation C densities, the ESA GlobBiomass dataset provides aboveground biomass data for a period centered on the year 2010 at 100 m spatial resolution  (Santoro, 2018). Because of its fine spatial resolution, this dataset could, in principle, be used to evaluate undisturbed C densities  (Erb et al.
, 2018; Luyssaert et al. , 2012). Other datasets currently under development include the ESA-CCI high-resolution land-cover product, expected to provide a long-term record since the 1990s of regional high-resolution land cover maps at 30 m spatial resolution every 5 years in regions of interest, and the ESA-CCI biomass dataset, which will provide aboveground biomass data for four epochs, i. e. , the  mid-1990s, 2007-2010, 2017/2018, and 2018/2019 , at 100 m spatial resolution with a relative error of less than 20 %. The NASA Carbon Monitoring System program is also supporting the development of regional-to global-scale biomass products based on optical reflectance data from MODIS (as well as active lidar-based approaches using ICESAT-1 and now ICESAT-2 GLAS-retrievals) and the Global Ecosystem Dynamics Instrument or GEDI aboard the International Space Station.
Land use change transitions, definitions, and assumptions The land use change transitions and land management fields used in the latest version of the GCP Global Carbon Budget  (Friedlingstein et al. , 2019)  to calculate the net land use change flux, called F LUC latest , are from the harmonized land use change data (LUH2v2. 1h) dataset  (Hurtt et al. , 2011) , which is based on HYDE3. 1  (Klein Goldewijk et al. , 2011). These data have the advantage of being globally consistent and covering a long period (850 CE-present) but have relatively coarse spatial resolution (0. 25 × 0. 25 • ), and due to a globally consistent methodology they may not account for regional specificities  (Bastos et al. , 2018; Li et al. , 2018). For each region, the best available information (in terms of spatiotemporal resolution or detail of processes covered) on land use change should be used.
The Global Carbon Project now conducts greenhouse gas budget accounting for the three major greenhouse gases, i. e. , carbon dioxide, methane, and nitrous oxide, where each budget provides detailed sectoral information for sources and sinks using a system that is more closely aligned with Tier 1 approaches. Beginning with Tier 1 data can help initiate regional budgets and identify areas of uncertainty or opportunities for regionally and locally calibrated approaches to be used to reduce uncertainty. -2, 2022). RECCAP2 studies will be hosted as a special collection at the American Geophysical Union (AGU). Following AGU data policy, the datasets used in each paper will be made available upon publication https://www. agu. org/Publish-with-AGU/Publish/ Author-Resources/Policies/Data-policy (last access: 8 February 2022).
Code and data availability. There is no code associated with this paper. Many datasets we work with are publicly available (e. g. , CMIP6 [https://esgf-node. llnl. gov/projects/cmip6/, WCRP, 2019], ocean models [https://reccap2-ocean. github. io/, last access: 8 February 2022, Hauck and Gruber, 2022], ICOS FLUXNET [https://www. icos-cp. eu/data-products, ICOS, 2022], FLUX-COM [https://www. fluxcom. org/, FLUX-COM, 2022], LUH2 [https:// luh. umd. edu/, Chini et al. , 2022],. ). Most of the global datasets that are not public yet are already available to the REC-CAP2 teams through the MPI-data portal: https://www. bgc-jena. mpg. de/geodb/projects/Data. php (RECCAP.
Acknowledgements. Philippe Ciais acknowledges funding from the  ANR CLAND Convergence Institute. Ana Bastos ,  Frédéric Chevallier , and  Philippe Ciais  acknowledge support from the  VERIFY H2020  project and the  RECCAP2 ESA Climate Change Initiative (CCI)  project. The authors are very grateful to the many data providers (measurements, models, inventories, atmospheric inversions, hybrid products, etc. ) that are directly or indirectly used in this study. Financial support. This study has been co-funded by the  European Space Agency Climate Change Initiative ESA-CCI   RECCAP2  project  1190  ( ESRIN/grant  no. 4000123002/18/I-NB ) and by the Observation-based system for monitoring and verification of greenhouse gases (VERIFY, grant no. 725546 ). Review statement. This paper was edited by  Carlos Sierra  and reviewed by  Richard Houghton  and one anonymous referee.
While MOC constitutes the majority (69%) of total SOC in the top meter of non-permafrost mineral soils (totaling 899 Pg C), we find that this represents less than half of the mineralogical carbon-storage capacity. The understanding of how climate and vegetation affect MOC stocks, and the pronounced undersaturation in managed lands, in particular, suggests that there is significant potential for restoring or enhancing MOC storage. Our estimate of MOC max can be refined in future studies as scientists collect more data on additional mineral types, but already provides a data framework to inform ESMs, paleo reconstructions of soils, and policies for soil carbon sequestration. Long-term changes in C inputs may have lasting effects on MOC that are particularly pertinent for land management decisions that seek to sequester carbon  87.
g. , cation exchange capacity, iron, and aluminum content, among others), where available. To locate studies, we searched Google Scholar for combinations of the keywords "soil carbon", "mineral-associated", "clay", "silt", "size fractionation", "density fractionation", and "carbon saturation". 0] [91]  ) were consistent and within the limit of MOC max for each mineral type, and that both methods concluded similar ranges of MOC to SOC ratios 10 (Supplementary Fig. 1 ). These findings support the use of either fractionation method for the goals of our particular study. The observational synthesis totalled 1144 mineral soil profiles from 78 studies that reported fractionation and bulk measurements of carbon and nitrogen across depths. Specifically, the dataset included: 359 (<2 μm; clay fraction only) and 1451 (<63 μm; clay and silt fraction) MOC measurements, 1107 POC measurements, and 1432 SOC measurements, as well as corresponding MAT, MAP, CS, soil order, mineral type, and vegetation type (see section "Defining and evaluating ecosystem categories" for classification details).
Within natural lands, there was a greater representation of temperate ecosystems, as opposed to tropical and boreal ecosystems (Supplementary Figs. 2, 3 , and 14)especially when including the European LUCAS database  11, 92  (Supplementary Fig. 2b, d ), but our model and results were robust to its exclusion. Furthermore, measurements in tundra and deserts were limited, highlighting the need for studies in these regions (Fig. 1c , Supplementary Figs. 2  and 3 ). Nevertheless, our observational synthesis spanned diverse climates and soil types: MAT ranged from -2. 9 to 29 °C, MAP from 79 to 3806 mm yr -1 , and CS from 1. 5 to 100%, across different mineral and vegetation types (Fig. 1b , c and Supplementary Fig. 2 ). Indeed, our synthesis spanned the majority of the climate-edaphic covariate space encompassed by the world's soils in WoSIS (World Soil Information Service)  93  (Supplementary Fig.
We used MOC observations from clay and silt (<63 μm) size classes to derive MOC max (Fig. 1 ) and note that our results were robust to the choice of particle size (e. g. , <20 μm or <63 μm; Supplementary Fig. 5 ). The 63 μm silt size threshold chosen is also that used in global datasets and mapped products of soil clay and silt  4, 98. We also note that our estimates of MOC max for HM and LM were robust across topsoils and subsoils. Both topsoils and subsoils approached MOC max (Fig. 2b ), and thus, a separate MOC max for each depth was not theoretically or empirically warranted. While it is more difficult to reach MOC max in subsoils due to lower carbon inputs, MOC max is an intrinsic property of the soil mineralogy and constitutes a theoretical upper limit that is independent of depth; indeed, this depth-independence is important for capturing MOC stocks and ages in process-based soil carbon models  99, 100.
Statistical modeling and predictive relationships. Due to potential nonlinear relationships between MOC and environmental variables, we used an ensemble machine learning method-namely, Random Forest (RF)-to identify key predictors and their effects on MOC. This method has been shown to reduce overfitting to a training dataset compared to other machine learning methods, and does not suffer from multi-collinearity (i. e. , the linear dependence among predictor variables), as do multiple regression analyses  103. We used the RandomForest package in R for RF analyses  103, 104. We used an ensemble of 300 independently trained RF models (using a 75-25% train-test split; 400 decision trees each) with bootstrapped sampling to robustly assess model performance  105  -mean absolute error (MAE) and mean-squared error (MSE) to quantify the model error and R 2 to estimate the proportion of variance in MOC explained by the model (Supplementary Fig.
The Scikit-learn Library in the Python environment was used for global predictions and Matplotlib Basemap for global mapping. We again used an ensemble of 300 independently trained RF models (using a 75-25% train-test split; 400 decision trees each) with bootstrapped sampling to rigorously assess model performance  105. For these RF models that included SOC, a comparison of predicted and observed MOC on the independent test datasets across the ensemble yielded an R 2 = 0. 79 ± 0. 05 (mean ± s. d. ; Supplementary Fig. 13 ). We note that, while the predictability of SOC is often lower across large global datasets  4, 107  , the predictability of MOC is generally high given readily measured SOC, climate, and edaphic variables (e. g. , see refs. 11,92 on a regional scale). Indeed, the R 2 was consistently high across the ensemble cross-validation (Supplementary Fig.
13b ). We also performed a spatially buffered leave-one-out cross-validation  108  to ensure that spatial auto-correlation did not significantly compromise the RF model. Namely, for each leave-one-out test data point, all data within a given buffer radius (0-150 km) were excluded from the training set; i. e. , only data points outside the buffer zone were used to train each RF model. R 2 values did not vary with buffer radius (regression slope not statistically different from zero; p = 0. 81) and were in agreement with the ensemble cross-validation approach (Supplementary Fig. 13c ). Furthermore, we also used a hold-out (or block) cross-validation  109  on the basis of soil order to assess extrapolation performance and confirm the robustness of our RF estimates across soil types-i. e. , all soil profiles from a given soil order were completely withheld from the training dataset and then used to independently test the resulting RF model.
Model performance was consistently high across the outof-sample test datasets (R 2 = 0. 75 ± 0. 11; mean ± s. d. across soil orders); only Andisols (which span ~1% of the ice-free land surface) were more difficult to predict when completely withheld from the training dataset (R 2 = 0. 40), and we encourage more measurements in these soils (see section "Defining and evaluating ecosystem categories"). Hold-out cross-validation on the basis of individual studies also confirmed the robustness of our RF estimates to additional data (e. g. , our results were insensitive to adding the independent LUCAS dataset 92 ; Supplementary Fig. 2 ). We rigorously demonstrate the RF model performance and provide uncertainty ranges for the 90% prediction intervals, derived from the 5th and 95th quantiles (Supplementary Fig.
13a ). The climatic and edaphic driver variables used for the global predictions were all re-gridded to 0. 5°× 0. 5°resolution (Supplementary Fig. 14 ). Mean annual temperature (MAT) was estimated from the CRU data set (version 3. 10)  110  , and mean annual precipitation (MAP) from the GPCC dataset  111  , both as 30-year annual averages. Clay and silt (CS) content was obtained from the Harmonized World Soil Database (HWSD)  98. Soil organic carbon (SOC) was obtained from both the HWSD and SoilGrids  4, 98  , and also as the average of the two gridded data products  112  (Supplementary Fig. 19  and Supplementary Table  2 ). Land cover (vegetation type) was obtained from the MODIS MCD12C1 product  113  , and the 16 categories were combined into 10 for consistency with those reported in the observations (Fig.
15 ; Supplementary Tables  1  and 2  summarize results for all SOC data products). We include MOC uncertainty ranges for the 90% prediction intervals (5th and 95th quantiles) globally (Supplementary Fig. 15 ; Supplementary Table  2 ). Interestingly, we note that the distributions of MOC and SOC (and %C saturation) from our globally distributed observational dataset most closely agreed with SOC and predicted MOC (and %C saturation) from the averaged data product (Supplementary Figs. 18  and 19 ; Supplementary Table  2 ), whereas the distribution of SOC (and %C saturation) from SoilGrids was higher and HWSD was substantially lower (Supplementary Fig. 19 ; Supplementary Table  2 ). We focus here on reporting results using the averaged SOC data product, as a conservative estimate of MOC stocks given the uncertainty and range between soil carbon data products, and note that more accurate estimates and consensus on current soil carbon stocks are critical for improved predictions  116.
Leveraging our global MOC predictions, we calculated the proportion of SOC that is mineral-associated globally (Supplementary Fig. 16 ). Our global estimates of MOC/SOC ratios agree with those observed in our observational synthesis and confirm the dominant contribution of MOC to soil carbon at the global scale (Supplementary Figs. 1  and 16 ). Furthermore, our predictions suggest that highlatitudes have a lower fraction of MOC/SOC globally, and thus a higher fraction of particulate organic carbon (POC), but data limitations of mineral-associated carbon measurements at high-latitudes warrant further exploration of these patterns. Estimating the global soil mineralogical carbon capacity. To estimate the spatially explicit mineralogical carbon capacity (MOC max ) globally, we used global data products of clay and silt content (CS), soil order and mineral composition, and bulk density (BD), together with our derived relationship for MOC max (Eq.
Where necessary, initial and final SOC stocks were standardized to equal depths using the maximum depth of the shallower profile as a cutoff ranging between 30 and 39 cm (n = 5 profiles). We calculated the mineralogical %C saturation as the initial MOC stock divided by the initial MOC max stock. Because MOC was not measured in all accrual studies, we used the measured SOC and CS values to calculate the corresponding MOC using our random forest model trained on the MOC synthesis data with analogous SOC and CS predictors (R 2 = 0. 70 ± 0. 06; using Scikit-Learn in Python, see section "Global mineral-associated carbon predictions and uncertainty"). Carbon accrual (t C ha -1 y -1 ) was calculated as the difference between final and initial C stock divided by the time period between the repeated samplings (n = 32), the duration of the experiment in the case of a paired plot experimental design (n = 30), the age difference between two sites of a chronosequence (n = 24), the time span at which a regression across multiple samplings was used to obtain an average rate for a chronosequence (n = 10), or the time of soil formation after emergence of a SOC-free parent material (n = 7).
e. , low MOC and MOC max ) displayed seemingly high %C saturation, yet still within expected ranges (Fig. 4d ). Furthermore, this soil was categorized as an Andisol, which could be treated as a third category in future studies (see section "Defining and evaluating ecosystem categories"), due to its propensity to form stable Al-and Fe-complexes and accumulate carbon  14. Nevertheless, removing this individual measurement, or even all Andisols (n = 6) in the C accrual synthesis, did not change the fitted model. Furthermore, we note that the reclaimed mine soils in our dataset constituted two major categories of abandonment and regeneration-those with and without topsoil application (Supplementary Fig. 23b ). Mine reclamation that employs the application of topsoil (usually a 30 cm layer, originally excavated before mining and stored in stockpiles; n = 31) is, in terms of management procedures, comparable to the conversion of cropland (disturbed by tillage and/or erosion) to grassland or forest.
23c ). Statistical analyses were conducted using the 'nls' package in R to fit a nonlinear asymptotic relationship between carbon accrual rates and the mineralogical percent saturation of the respective soil profiles. Specifically, we used a three-parameter self-starting asymptotic regression model (SSAsymp) on the 103 rate measurements (R 2 = 0. 18; Fig. 4d ). The same approach was also used to fit the 10th and 90th quantiles (Fig. 4d ). There was substantial variability in observed accrual rates for a given %C saturation, due to differences in climate and land use across sites (Supplementary Fig. 23 ). However, most of this variability appeared in soils furthest from C saturation, where environmental and management factors also play a role in observed accrual rates. In soils closest to saturation, this variability was far lower as carbon accrual rates tended towards zero, illustrating the importance of the mineralogical limit.
Acknowledgements K. G. was supported as a Lawrence Fellow at  Lawrence Livermore National Laboratory (LLNL)  and by a  U. S. Department of Agriculture NIFA Postdoctoral Fellowship  (Grant No. 2018-67012-27982 ). Work at LLNL was conducted under the auspices of  DOE  Contract  DE-AC52-07NA27344  and was supported by the  LLNL-LDRD Program  under Project No. 21-ERD-045. O. V. and R. Z. A. were supported by the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie  grant agreement No. 793485  (O. V. ) and No. 834169 (R. Z. A. ). R. Z. A. was also supported by the  US DOE, Office of Science, Office of Biological and Environmental Research  at  Oak Ridge National Laboratory , which is managed by  UT-Battelle, LLC , for the  US DOE  under contract  DE-AC05-00OR22725.
Data availability The globally gridded maps of mineral-associated carbon and mineralogical carbon capacity derived in this study are freely available and archived at Zenodo (https://doi. org/ 10. 5281/zenodo. 6539765). The observational syntheses (mineral-associated carbon and accrual measurements) in support of these findings are detailed in Supplementary Tables  3  and 4 , and archived at Zenodo (https://doi. org/10. 5281/zenodo. 5987415).
a b s t r a c t The data presented in this article are related to the published research of "Effect of nitrogen content on structural and mechanical properties of AlTiZrTaHf(-N) high entropy films deposited by reactive magnetron sputtering". This database contains X-ray photoelectron spectroscopy (XPS) measurements, performed in order to determine the extents of nitrides formed in AlTiTaZrHf high entropy films. The latter were prepared by DC magnetron sputtering technique in reactive mode by adding the nitrogen to argon gas. The nitrogen flow rate is calculated by R N2 = N 2 /(N 2 + Ar). XPS measurements were done one month later. Oxides were detected on the top surface of the samples. 2p, 3d and 4f core level peaks were fitted in order to determine accurately the chemical composition of the nitride films.
All these data are uploaded with excel files including then the fitting peaks of N1s, Al2p, Ti2p, Zr3d, Ta4h and Hf4f as a function of the nitrogen flow rate (R N2 varies from 5 to 50%). A table of all parameters (peak position, FWHM, intensity, etc. ) of each fitting for each peak is provided. The data are accessible with the following link: https://figshare. com/s/a4d038f514016b19bd6b.
Ta-N is fitted with two peaks 4f 7/2 at 24. 9 eV and 4f 5/2 at 26. 8 eV  [10]. As the nitrogen increases, a total transformation of Ta metal to nitride occurred. The peak areas of Ta-N increase when R N2 grows from 20 to 50%. In the case of Hf4f, the spectrum at R N2 = 5% was fitted into Hf metal and nitrides (oxides are presents, green color). The peaks of Hf metal are fund around 14. 6 eV and 16. 3 eV for orbital 4f 7/2 and 4f 5/2 respectively. They disappear when R N2 increases from 20 to 50% and Hf-N nitrides are formed. The peaks of these nitrides are located at 15. 9 eV for orbital 4f 7/2 and 17. 6 eV for orbital 4f 5/2. Fig. 5  presents the evolution of nitridation effect of each element as a function of R N2. The atomic percentage was estimated from the fitting of all XPS data according to R N2.
6. 3  [40]. The relationship between soil ecosystem, depth, and physicochemical properties was also verified using a multivariate approach consisting of principal component analysis (PCA) and permutational ANOVA (PERMANOVA) with the package vegan v2. 5-6 in R v3. 6. 3 (functions rda and adonis, respectively)  [40, 41]. C, N, and C:N ratio were not included in the multivariate dataset due to a high amount of missing data, and moisture and SOM were log-transformed prior to analysis. Due to the limited number of samples from barren sites, these were not included in the ANOVA and PER-MANOVA procedures.
Metagenome sequencing and processing of raw data Total DNA and RNA were co-extracted as previously described  [42]. Briefly, extraction was performed on 0. 5 g of soil using a hexadecyltrimethyl ammonium bromide (CTAB), phenol-chloroform, and bead-beating protocol. DNA was purified using the AllPrep DNA Mini Kit (QIAGEN, Hilden, Germany) and quantified using the Qubit dsDNA BR Assay Kit (ThermoFisher Scientific, Waltham, MA, USA). Library preparation for Illumina metagenome sequencing was performed using the Nextera XT DNA Library Preparation Kit (Illumina, San Diego, CA, USA). Metagenomes were obtained for the 69 samples across two paired-end NextSeq (132-170 bp) and one NovaSeq (2 × 151 bp) runs. Two samples were additionally sequenced with Nanopore MinION. For this, libraries were prepared using the SQK-LSK109 Ligation Sequencing Kit with the long fragment buffer (Oxford Nanopore Technologies, Oxford, UK) and the NEBNext Companion Module for Oxford Nanopore Technologies Ligation Sequencing Kit (New England Biolabs).