dataesr/scientific-paragraphs-categorization huggingface dataset:
Libraries for transcriptome analysis were established by using RNA collected from the YG and NG groups. Analysis of RNA-Seq data. The fastq format raw data were treated with Trimmomatic (v. 0. 36) to obtain the clean reads  64  , with mapping to NCBI Rfam databases for removing the rRNA sequences by Bowtie2 (v2. 33). The residual mRNA sequences were compared with the reference genome using Hisat2 (2. 1. 0)  65. The read count and function of each gene were obtained using HTSeq-count (v0. 9. 1). The reads per kilobase per million for each gene were used to compare the expression level of genes among the experimental and control groups. The read count of each gene (acquired from HTSeq-count) was mainly used for analyzing the differential expression. The DEGs between two groups were identified using the edgeR (v3.
Statistical analyses. Data were analyzed using one-way analysis of variance (ANOVA) and SPSS software (ver. 18. 0, SPSS Inc. , Chicago, IL, United States). The resulting p-value was adjusted for judging the false discovery rate. Genes with the false discovery rate correction, p ≤ 0. 05, and |log 2 (fold change)|≥ 1 were taken as the candidates of DEGs  43, 70. The GO terms and KEGG pathways with p ≤ 0. 05 were identified as significantly enriched by DEGs.
Acknowledgements This research was partially supported by the  National Natural Science Foundation of China  ( 32070004 ,  31670006 , and  41720104007 ), the  Major National R & D Projects for the Chinese Ministry of Science and Technology  ( 2019YFC1803500 ), and the 1000-Talents plan (WQ2017110423). This study was also supported by the  Fundamental Research Funds for Central Non-profit Scientific Institution  ( 1610132020009 ),  Central Public-interest Scientific Institution Basal Research Fund  ( Y2021GH18 ), and  Key Laboratory of Microbial Resources Exploitation and Application of Gansu Province  ( 2019cx-04 ). We also acknowledge the support of the  Centre National de la Recherche Scientifique  ( CNRS PRC1416 , France) and  Xinjiang Key Laboratory of Special Conservation and Regulatory Biology, School of Life Sciences, Xinjiang Normal University  ( 2019D04015 , Microbial flora structure and strain resource excavation for remediation of pesticide-contaminated soil in Xinjiang).
In this study, we tested if the PARTY SOC machine-learning model, built on a totally independent dataset from northwestern Europe, could be used to initialize the distribution of SOC pools of the simple AMG model  (Clivot et al. , 2019)  and improve the accuracy of its simulations. The default version of AMG is currently the most accurate model for reproducing the observed SOC stock dynamics in diverse French agricultural LTEs at the pluri-decadal scale  (Martin et al. , 2019). The efficient use of this model at sites covering an important pedological and climatic variability (including oceanic, continental, and tropical climate) provides further support to its robustness  (Levavasseur et al. , 2020; Farina et al. , 2021; Saffih-Hdadi and Mary, 2008). In this model, SOC is simply divided into two pools, the "stable SOC (C S )" that is considered inert at the timescale of the simulation and the "active SOC (C A )" that has a mean turnover time of a few decades.
Experimental sites This work was conducted on nine French agricultural LTEs (Supplement Fig. S1 ); s 7 LTEs including 29 treatments were selected from the dataset presented in  Clivot et al. (2019) , from sites with available initial topsoil samples. Two additional LTEs (Colmar and Feucherolles) including a total of three treatments were obtained from the dataset published in  Levavasseur et al. (2020) , selecting control treatments without organic amendments and with available initial topsoil samples. Basic site and topsoil characteristics are reported in Table  1  and Supplement Table  S1. Information necessary to run AMG simulations on a total of 32 treatments (initial soil physicochemical properties, detailed information on management practices, and observed climatic data during all ex-periments) were obtained from  Clivot et al.
For the calculation of all of the above parameters, only the part of each thermogram corresponding to organic carbon was taken into account. For this purpose, upper temperature integration limits for Rock-Eval ® temperature parameters were set to 560 • C for the CO and CO 2 pyrolysis thermograms and to 611 • C for the CO 2 oxidation thermograms  (Cécillon et al. , 2018 ; Supplement Fig. S2 ). R scripts used for computing Rock-Eval ® parameters are available on the Zenodo platform  (Cécillon, 2021b).
PARTY SOC -based initialization of C S / C 0 The PARTY SOC -based initialization of C S / C 0 was derived from data obtained with Rock-Eval ® analysis of initial topsoil samples from each LTE. Here, C S / C 0 was estimated using the following simple four-step procedure: first, topsoil samples from the LTE's onset were analysed with Rock-Eval ® , and the 18 thermal parameters described in Sect. 2. 3 were calculated for each sample. Second, the thermal parameters were used as input for the PARTY SOC machine-learning model described in Sect. 2. 4 which was run for this sample set resulting in a sample-specific prediction of the centennially stable-SOC proportion. Third, the obtained values were averaged per LTE. Fourth, the site mean of the centennially stable-SOC proportion was used (as C S / C 0 ) to initialize simulations of SOC stocks for the various treatments of every site (the site standard deviation is reported in Fig.
Prediction bias and model error were expressed as the mean difference (BIAS) and relative mean square error (RMSE). The relative root mean square error (RRMSE) and the normalized root mean square error (NRMSE) were used to compare the error of different datasets (with a different range of predictions)  (Smith et al. , 1996; Wallach, 2006; Otto et al. , 2018). R 2 =       n i=1 O i -O • S i -S n i=1 O i -O 2 • n i=1 S i -S 2       2 , ( 3 ) BIAS = 1 n n i=1 (S i -O i ) , (4) RMSE = 1 n n i=1 (S i -O i ) 2 , ( 5 ) RRMSE = RMSE O , ( 6 ) NRMSE = RMSE O max -O min , (7) where O and S are the observed and simulated n is the number of observations; O and S are the means of observations and simulations, respectively; and O max and O min are the maximum and the minimum value observed. The observed and simulated total SOC stock change dQC was calculated as follows for each treatment: dQC obs = QC obs,t 2 -QC obs,t 1 , ( 8 ) dQC sim = QC sim,t 2 -QC obs,t 1 , (9) where QC obs is the observed SOC stock at time t, QC sim is the SOC stock at time t simulated with AMG, and t 1 indicates the start and t 2 indicates the end of simulation period.
Conclusions Combining Rock-Eval ® thermal analysis with the PARTY SOC machine-learning model should be considered an emerging key approach with demonstrated ability to improve the accuracy of simulations of SOC dynamics, complementary to other SOC cycling proxies  (Bailey et al. , 2018; Wiesmeier et al. , 2019). The progressive large-scale delivery of these complementary data related to SOC dynamics will strengthen model predictions of SOC stock changes at the national to global scale, necessary for implementing efficient climate change mitigation policies (FAO, 2020). Code availability. Code used to produce the results presented in this study, as well as the AMG model, can be made available upon reasonable request. The code of the Rock-Eval ® -based PARTY SOC v2. 0 EU random forest model is available on Zenodo at the permanent link of https://doi.
org/10. 5281/zenodo. 4446138 (Cécillon, 2021b). Data availability. Pedoclimatic and land management information used to run the AMG model are published in  Clivot et al. (2019)  and can be obtained from Hugues Clivot and Fabien Ferchaud upon request concerning seven sites and from Florent Levavasseur and Sabine Houot concerning the remaining two sites  (Levavasseur et al. , 2020). The Rock-Eval ® data obtained and processed during this study are available upon request from the corresponding authors. Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/bg-19-375-2022-supplement. Author contributions. EK performed the research and produced the first version of the manuscript. PB and LC designed the study with contributions from CC and FB, whereas BM, FF, HC, FL, and SH organized the search for archive soil samples, provided data needed to run AMG simulations as well as the latest version of the AMG model, and helped with the interpretation of the results.
Acknowledgements. The authors would like to thank  ADEME (Ministry for an Ecological Transition of the Ministry for Higher Education, Research and Innovation)  and the  ANR (French National Research Agency)  for the financial support. The authors would also like to thank  Florence Savignac  for her help in performing Rock-Eval ® analyses and all the people involved in maintaining and monitoring the field trials used in this study over multiple decades. We would also like to thank everyone involved in the conception and creation of the AMG consortium, the AIAL database, and the  SOERE PRO network  for kindly providing access to their data. Financial support. This work was supported by  ADEME (French Agency for Ecological Transition , under the Ministry for an Ecological Transition and the  Ministry for Higher Education, Research and Innovation ; PhD grant for  Eva Kanari ,  PROTERR  project) and the  ANR (French National Research Agency ; PhD grant for  Eva Kanari ,  StoreSoilC  project  ANR-17-CE32-0005 ).
ACKNOWLEDGEMENTS We first thank the three anonymous reviewers for their helpful and relevant comments which improved the manuscript. We thank  Thibaut Lurton  and  Adriana Sima  for running experiments with radiative temperature increment outputs, which allowed us to compute the clear-sky radiatively-driven variables essential to the study. We thank the IPSL climate modelling centre for developing the model and running the CMIP6 experiments. We thank  Bernard Legras  for making the ERA5 MODA data available and for useful discussions,  Maxime Leuliet  for his interest and help during the study,  Olivier Boucher  for his useful insights, and  Marianne Pietschnig  and  Jules Kajtar  for editing the text. MSL is funded by  JPI climate/JPI  ocean  ROADMAP  ( ANR-19-JPOC-003 ) via the CNRS. SB and JLD are supported by the  European Unions   Horizon 2020 research and innovation program  ( European Research Council  grant no  694768  and  CONSTRAIN  grant no  820829 ).
This work was supported by  CNES. The IPSL experiments were performed using the HPC resources of TGCC under the allocations  2019-A0060107732 ,  2020-A0080107732 , and  2021-A0100107732  ( gencmip6 ) provided by  GENCI  ( Grand Equipement National de Calcul Intensif ). To process the GOCCP,  ERA5 , and IPSL-CM6A-LR data, this study benefited from the  IPSL mesocenter ESPRI  facility which is supported by  CNRS ,  Sorbonne Université , Labex L-IPSL, CNES, and  École Polytechnique.
DATA AVAILABILITY GOCCP 3D cloud fraction are available at https://climserv. ipsl. polytechnique. fr/cfmipobs/Calipso_goccp. html. ERA5 monthly means of daily means (MODA) on single levels are available on the Climate Data Store (CDS) online catalogue, at https:// cds. climate. copernicus. eu/#!/search?text=ERA5, except for clear-sky mean temperature tendency due to long-wave and short-wave radiation ("mttlwrcs", "mttswrcs") used to compute Q r , which can be downloaded in a raw format from the CDS API client following instruction at https://confluence. ecmwf. int/display/CKB/How+to +download+ERA5#HowtodownloadERA5-4-DownloadERA5familydatathroughtheCD SAPI. CMIP6 data are available through the Earth System Grid Federation (ESGF)  35  , see https://pcmdi. llnl. gov/CMIP6/Guide/dataUsers. html#3-accessing-model-output.
All data points are potentially subject to revision, not just the latest year. For full details see  Andrew and Peters (2021). Other estimates of global fossil CO 2 emissions exist, and these are compared by  Andrew (2020a). The most common reason for differences in estimates of global fossil CO 2 emissions is a difference in which emissions sources are included in the datasets. Datasets such as those published by BP energy company, the US Energy Information Administration, and the International Energy Agency's "CO 2 emissions from fuel combustion" are all generally limited to emissions from combustion of fossil fuels. In contrast, datasets https://doi. org/10. 5194/essd- 14-1917-2022  Earth Syst. Sci. Data, 14, 1917 Data, 14, -2005 Data, 14, , 2022  such as PRIMAP-hist, CEDS, EDGAR, and GCP's dataset aim to include all sources of fossil CO 2 emissions.
Outgassing of natural CO 2 occurs mostly in the tropics, especially in the equatorial upwelling region, and to a lesser extent in the North Pacific and polar Southern Ocean, mirroring a well-established understanding of regional patterns of air-sea CO 2 exchange (e. g. Takahashi et al. , 2009; Gruber et al. , 2009). These patterns are also noticeable in the Surface Ocean CO 2 Atlas (SOCAT) dataset, where an ocean f CO 2 value above the atmospheric level indicates outgassing (Fig. B1 ). This map further illustrates the data sparsity in the Indian Ocean and the Southern Hemisphere in general. Interannual variability of the ocean carbon sink is driven by climate variability with a first-order effect from a stronger ocean sink during large El Niño events (e. g. 1997-1998) (Fig. 9 ;  Rödenbeck et al.
The persistent unexplained variability in the carbon budget imbalance limits our ability to verify reported emissions (Peters et al. , 2017) and suggests we do not yet have a complete understanding of the underlying carbon cycle dynamics on annual to decadal timescales. Resolving most of this unexplained variability should be possible through different and complementary approaches. First, as intended with our annual updates, the imbalance as an error term is reduced by improvements of individual components of the global carbon budget that follow from improving the underlying data https://doi. org/10. 5194/essd-14-1917-2022 Earth Syst. Sci. Data, 14, 1917-2005, 2022 and statistics and by improving the models through the resolution of some of the key uncertainties detailed in Table  9. Second, additional clues to the origin and processes responsible for the variability in the budget imbalance could be obtained through a closer scrutiny of carbon variability in light of other Earth system data (e.
Conclusions The estimation of global CO 2 emissions and sinks is a major effort by the carbon cycle research community that requires a careful compilation and synthesis of measurements, statistical estimates, and model results. The delivery of an annual carbon budget serves two purposes. First, there is a large demand for up-to-date information on the state of the anthropogenic perturbation of the climate system and its underpinning causes. A broad stakeholder community relies on the datasets associated with the annual carbon budget including scientists, policy makers, businesses, journalists, and non-governmental organizations engaged in adapting to and mitigating human-driven climate change. Second, over the last decades we have seen unprecedented changes in Earth Syst. Sci. Data, 14, 1917 Data, 14, -2005 Data, 14, , 2022  https://doi.
org/10. 5194/essd-14-1917-2022 the human and biophysical environments (e. g. changes in the growth of fossil fuel emissions, impact of COVID-19 pandemic, Earth's warming, and strength of the carbon sinks), which call for frequent assessments of the state of the planet, a better quantification of the causes of changes in the contemporary global carbon cycle, and an improved capacity to anticipate its evolution in the future. Building this scientific understanding to meet the extraordinary climate mitigation challenge requires frequent, robust, transparent, and traceable datasets and methods that can be scrutinized and replicated. This paper via "living data" helps to keep track of new budget updates.
Data availability The data presented here are made available in the belief that their wide dissemination will lead to greater understanding and new scientific insights into how the carbon cycle works, how humans are altering it, and how we can mitigate the resulting human-driven climate change. Full contact details and information on how to cite the data shown here are given at the top of each page in the accompanying database and summarized in Table  2. The accompanying database includes two Excel files organized in the following spreadsheets: The file Global_Carbon_Budget_2021v1. 0. xlsx includes the following:     Atmospheric CO 2 for simulation A provided by the GCP; converted to pCO 2 temper-ature formulation  (Sarmiento et al. , 1992)   Rödenbeck et al. , 2013. Specifically, the sea-air CO 2 fluxes and the pCO 2 field are numerically linked to each other and to the spatio-temporal field of ocean-internal carbon sources/sinks through process pa-rameterizations, and the ocean-internal source/sink field is then fit to the SOCATv2021 pCO 2 data  (Bakker et al.
, 2021)   Land-schützer et al. , 2013 Land-schützer et al. , , 2016)). An ensemble of neural network models trained on 100 subsampled datasets from the Sur-face Ocean CO 2 Atlas v2021 (SOCATv2021,  Bakker et al. , 2021). Like the original data, subsamples are distributed after in-terpolation on 1 × 1 grid cells along ship tracks. Sea surface salinity, temperature, sea surface height, mixed layer depth, atmospheric CO 2 mole fraction, chlorophyll a, pCO 2 climatology, latitude, and longitude are used as predictors. The models are used to reconstruct sea surface pCO 2 and convert to air-sea CO 2 fluxes (see the proposed ensemble-based approach and analysis in  Chau et al. , 2020 Chau et al. , , 2022 ). An ensemble average of six machine learning estimates of surface ocean pCO 2 using the approach described in  Gregor et al.
(2019)  with the updated prod-uct using SOCAT v2021  (Bakker et al. , 2016). All ensemble members use a cluster-regression approach. Two different cluster configurations are used: (1) based on K-means clustering; (2)  Fay and McKinley (2014) 's CO 2 biomes. Three regression algorithms are used: (1) gradient-boosted decision trees; (2) feed-forward neural network; (3) support vector regression. The product of the cluster configurations and the regression algorithms results in an ensemble with six members; hence the CSIR-ML6. Derived from the SO-CAT(v2021) pCO 2 database, but cor-rected to the subskin temperature of the ocean as measured by satellite, using the methodology described by  Goddijn-Murphy et al. (2015). A correction to the flux calculation is also applied for the cool and salty surface skin.
See  Fay and Gregor et al. (2021). https://doi. org/10. 5194/essd-14-1917-2022 Earth Syst. Sci. Data, 14, 1917 Data, 14, -2005 Data, 14, , 2022  Table  A4. Comparison of the inversion set-up and input fields for the atmospheric inversions. Atmospheric inversions include the full CO 2 fluxes, including the anthropogenic and pre-industrial fluxes. Hence they need to be adjusted for the pre-industrial flux of CO 2 from the land to the ocean that is part of the natural carbon cycle before they can be compared with S OCEAN and S LAND from process models. See  Table  A5. Attribution of f CO 2 measurements for the year 2020 included in SOCATv2021  (Bakker et al. , 2016 (Bakker et al. , , 2021) )   B2. Evaluation of the GOBMs and data products using the root mean squared error (RMSE) for the period 1990 to 2020, between the individual surface ocean f CO 2 mapping schemes and the SOCAT v2021 database.
The y axis shows the amplitude of the interannual variability (A-IAV, taken as the standard deviation of a detrended time series calculated as a 12-month running mean over the monthly flux time series,  Rödenbeck et al. , 2015). Results are presented for the globe, north (> 30 • N), tropics (30 • S-30 • N), and south (< 30 • S) for the GOBMs (see legend circles) and for the f CO 2 -based data products (star symbols). The f CO 2 -based data products use the SOCAT database and therefore are not independent from the data (see Sect. 2. 4. 1). (Saatchi et al. , 2011; and GlobalCarbon unpublished data; Avitabile et al. , 2016) , GPP  (Jung et al. , 2010; Lasslop et al. , 2010) , leaf area index  (De Kauwe et al. , 2011; Myneni et al. , 1997) , net ecosystem exchange  (Jung et al. , 2010; Lasslop et al.
, 2010) , ecosystem respiration  (Jung et al. , 2010; Lasslop et al. , 2010) , soil carbon  (Hugelius et al. , 2013; Todd-Brown et al. , 2013 ), evapotranspiration (De Kauwe et al. , 2011) , and runoff  (Dai and Trenberth, 2002). For each model-observation comparison a series of error metrics are calculated, scores are then calculated as an exponential function of each error metric, and finally for each variable the multiple scores from different metrics and observational datasets are combined to give the overall variable scores shown in (a). Overall variable scores increase from 0 to 1 with improvements in model performance. The set of error metrics vary with dataset and can include metrics based on the period mean, bias, root mean squared error, spatial distribution, interannual variability, and seasonal cycle.
, 2011a). We estimate the sector-level CO 2 emissions using the GTAP data and methodology, include the flaring and cement emissions from CDIAC, and then scale the national totals (excluding bunker fuels) to match the emission estimates from the carbon budget. We do not provide a separate uncertainty estimate for the consumption-based emissions, but based on model comparisons and sensitivity analysis, they are unlikely to be significantly different than for the territorial emission estimates  (Peters et al. , 2012a).
India We use monthly emissions estimates for India updated from Andrew (2020b) through August 2021. These estimates are derived from many official monthly energy and other activity data sources to produce direct estimates of national CO 2 emissions, without the use of proxies. Emissions from coal are then extended to September using a regression relationship based on power generated from coal, coal dispatches by Coal India Ltd. , the composite PMI, time, and days per month. For the last 3-4 months of the year, each series is extrapolated assuming typical trends.
, 2017). In contrast, the BLUE and OSCAR models include sub-grid-scale transitions between all vegetation types. Furthermore, the updated H&N2017 assume conversion of natural grasslands to pasture, while BLUE and OShttps://doi. org/10. 5194/essd-14-1917-2022 Earth Syst. Sci. Data, 14, 1917 Data, 14, -2005 Data, 14, , 2022  CAR allocate pasture proportionally on all natural vegetation that exists in a grid cell. This is one reason for generally higher emissions in BLUE and OSCAR. Bookkeeping models do not directly capture carbon emissions from peat fires, which can create large emissions and interannual variability due to synergies of land-use and climate variability in Southeast Asia, particularly during El-Niño events, nor emissions from the organic layers of drained peat soils. To correct for this, the updated H&N2017 includes carbon emissions from burning and draining of peatlands in Indonesia, Malaysia, and Papua New Guinea (based on the Global Fire Emission Database, GFED4s; van der  Werf et al.
, 2020). This is implemented in BLUE. OSCAR was run with both LUH2-GCB2021 and FAO/FRA (as used by  Houghton and Nassikas, 2017) , where emissions from the latter were extended beyond 2015 with constant 2011-2015 average values. The best-guess OSCAR estimate used in our study is a combination of results for LUH2-GCB2021 and FAO/FRA land-use data and a large number of perturbed parameter simulations weighted against an observational constraint. All three bookkeeping estimates were extended from 2020 to provide a projection for 2021 by adding the annual change in emissions from tropical deforestation and degradation and peat burning and drainage to the respective model's estimate for 2020  (van der Werf et al. , 2017; Conchedda and Tubiello, 2020). For E LUC from 1850 onwards we average the estimates from BLUE, the updated H&N2017 and OSCAR.