dataesr/scientific-paragraphs-categorization huggingface dataset:
Carbon stored in vegetation Estimates of aboveground carbon biomass can be derived from satellite estimates of vegetation optical depth (VOD; related to the aboveground vegetation's water content, density, and biomass). We used aboveground carbon biomass to assess the carbon stored in vegetation in the TRENDY models and note that the datasets are not directly comparable since the satellite product does not account for belowground carbon. We used global estimates of the annual average aboveground biomass carbon for 1993-2012 with a 0. 25 • spatial resolution derived from a series of satellite passive microwave instruments (Version 1. 0;  Liu et al. , 2015). Liu et al. (2015)  used an empirical approach to convert a harmonized time series of VOD to aboveground biomass carbon.
Fire-related CO 2 fluxes We used two different satellite estimates of fire CO 2 emissions: the Global Fire Emissions Database version 4 (GFED4s) described in van der  Werf et al. (2017)  and the Copernicus Atmosphere Monitoring Service Global Fire Assimilation System (CMAS-GFAS;  Kaiser et al. , 2012). GFED4s has a spatial resolution of 0. 25 • with a monthly time step and provides data from 1997-2020. We note that starting from 2017, the GFED4s data are flagged as a beta version; i. e. starting 2017, fire emissions are derived from active fire detections instead of burned area. CAMS-GFAS covers the years 2003-2020 on a 0. 1 • horizontal grid. The source for both estimates is the MODIS satellite, but different variables and methods are used to derive the fire CO 2 emissions (for L. Teckentrup et al.
7c ). CLASS-CTEM, JSBACH, and ISBA-CTRIP captured some features of the variability in the satellite-derived observations. CLASS-CTEM is moderately correlated with both datasets (r > 0. 5), ISBA-CTRIP shows a significant moderate correlation with the GFED4s dataset only, and JS-BACH is highly correlated with both datasets (r > 0. 7; see Table  B3 ). The remaining models are not significantly linked the satellite-derived observations.
, 2020)  may help this process. Australia has a high fraction of endemic vegetation (> 90 %;  Chapman, 2006) , structurally distinct vegetation including opencanopy forests and woodlands (predominately Eucalyptus) and hummock grasslands, and a dominance of sclerophyll leaves  (Box, 1996). Thus, there remains a significant opportunity in future work to directly test the current hypothesis implicitly embedded in DGVMs, i. e. that Australia's vegetation is not distinct from other continents. The forthcoming AusTraits (https://ardc. edu. au/project/austraits-acurated-plant-trait-database-for-the-australian-flora/, last access: 8 March 2021) database presents one possibility to examine how the use of Australia-specific plant traits may change DGVM simulations. One core area in which model development should be focused relates to the processes that govern the vegetation cover.
A3 Land use The land-use datasets are based on updated data from HYDE for the years 1960-2019 as well as the latest wood harvest data provided by the Food and Agriculture Organization (FAO). The land-use states and transitions are identical to the LUH2 v2h dataset  (Hurtt et al. , 2020)  for the years from 1700 to 1949 (i. e. consistent with the input for CMIP6). Starting in 1950, the land-use forcing is based on new inputs from HYDE and new FAO data for the national wood harvest demands. This leads to differences in comparison to LUH2 v2h, primarily in Brazil. In order to convert natural vegetation to managed pasture, the LUH2 guidelines suggested all natural vegetation is cleared. The conversion of natural vegetation to rangeland only requires the clearance of forests. However, each modelling group developed model-specific land-use forcings leading to inter-model differences.
A4 Nitrogen deposition and fertilization For the years from 1850 to 2014, the TRENDY models were forced with the historical nitrogen deposition database and then transitioned to the future RCP8. 5 nitrogen deposition databases for the years from 2015 to 2018. Details are available at https://esgf-node. llnl. gov/search/input4mips/. Nitrogen fertilizer input datasets are available via the N 2 O Model Intercomparison Project ("NMIP";  Tian et al. , 2018). NMIP assumes that the nitrogen input data remain unchanged in the years  2015, 2016, 2017, and 2018. Manure is not included for the TRENDY simulations.
A5. 3 CO 2 After the spin-up, the CO 2 run used a transient atmospheric CO 2 forcing. The simulation continued to recycle the 1901-1920 climate forcing, and the land-use was prescribed to the distribution of the year 1700 (as in the "CTRL" run). The TRENDY protocol required all participating models to be in equilibrium for the CTRL simulation after the spin-up. Further, all models had to simulate the net annual land flux as a carbon sink over the 1990s and/or 2000s for the CO 2 + CLIM + LUC run as constrained by global atmospheric and oceanic observations  (Keeling and Manning, 2014). Lastly, the global net annual land-use flux (ELUC) had to be a carbon source over the 1990s (based on the CO 2 + CLIM and CO 2 + CLIM + LUC simulation). Code and data availability. All eddy covariance data are available from http://www.
ozflux. org. au/ (OzFlux, 2017). The Global Aboveground Biomass Carbon (version 1. 0) dataset  (Liu et al. , 2015)  is freely available from http://wald. anu. edu. au/global-biomass/, and the GOSIF-GPP product  (Li and Xiao, 2019)   Competing interests. Some authors are members of the editorial board of Biogeosciences. The peer-review process was guided by an independent editor, and the authors have also no other competing interests to declare. Disclaimer. Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
DiMiceli et al. , 2017)ttps://globalecology. unh. edu/data/GOSIF-GPP. html. Fire CO 2 emissions were provided by the Copernicus Atmosphere Monitoring Service Global Fire Assimilation System (CAMS GFAS; https://apps. ecmwf. int/datasets/data/cams-gfas/,ECMWF,2021),and the Global Fire Emissions Database version 4 (GFED4s) described in van der Werf et al. (2017) is available from https: //globalfiredata. org/pages/data/. The MODIS/Terra Vegetation Continuous Fields dataset was provided by NASA's Land Processes Distributed Active Archive Center (https://lpdaac. usgs. gov/products/mod44bv006/,DiMicelietal. , 2017). The TRENDY v8 model output is available upon request (https://sites. exeter. ac. uk/trendy, last access: 27 September 2021). All analysis scripts are accessible on https://doi. org/10.
5281/zenodo. 5570974(Teckentrup,2021). Author contributions. LT, MGDK, and AJP designed the experimental analysis. LT wrote the code and analysed the TRENDY models. DSG, VH, AKJ, EJ, EK, SL, DL, PCM, JRM, JEMSN, and SZ conducted the model runs to produce the TRENDY data. LT wrote the first draft with contributions from MGDK and AJP. All authors contributed to the final manuscript. Correspondence and requests for materials should be addressed to LT (l. teckentrup@unsw. edu. au).
Acknowledgements. This work utilized data from the  OzFlux network , which is supported by the  Australian Terrestrial Ecosystem Research Network  (TERN; http://www. tern. org. au, last access:  8 March 2021 ). Lina Teckentrup,  Martin G. De Kauwe , and  Andrew J. Pitman  acknowledge support from the  Australian Research Council (ARC) Centre of Excellence for Climate Extremes  ( CE170100023 ). Martin G. De Kauwe  and  Andrew J. Pitman  acknowledge support from the  ARC   Discovery Grant  ( DP190101823 ). Martin G. De Kauwe  was also supported from the  NSW Research Attraction and Acceleration Program. Sebastian Lienert  acknowledges funding from the  Swiss National Science Foundation  (grant no. 172476 ) and from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement no.
821003  (project  4C ,  Climate-Carbon Interactions in the Current Century ). ORNL is managed by  UT-Battelle, LLC , for the DOE under contract  DE-AC05-1008 00OR22725. Emilie Joetzjer  would like to thank the  European Union 's  Horizon 2020 research and innovation programme  with the  CRESCENDO  project under the grant agreement no. 641816  and the  H2020 CONSTRAIN  under the grant agreement no. 820829. JSBACH simulations used resources of the  Deutsches Klimarechenzentrum (DKRZ)  under project  ID bm0891. The  CESM  project is supported primarily by the  National Science Foundation (NSF). This material is based upon work supported by the  National Center for Atmospheric Research , which is a major facility sponsored by the  NSF  under cooperative agreement 1852977. Computing and data storage resources, including the Cheyenne supercomputer (https://doi.
org/10. 5065/D6RX99HX),were provided by the  Computational and Information Systems Laboratory  (CISL) at  NCAR. Danica Lombardozzi  was supported by the  US Department of Agriculture NIFA  award  2015-67003-23485. Sönke Zaehle  was supported by the  European Union 's  Horizon 2020 research and innovation programme  under the grant agreement no. 821003  ( 4C  project). Atul K. Jain  acknowledges his funding support by the  US Department of Energy  (grant no. DE-SC0016323 ). Daniel S. Goll  benefited from support from the  Agence Nationale de la Recherche (ANR)  grant  ANR-16-CONV-0003  (CLAND). We thank  Vladislav Bastrikov  and  Andrew J. Wiltshire  for providing model output as part of the TRENDY v8 ensemble. Review statement. This paper was edited by  Ben Bond-Lamberty  and reviewed by  Ben Bond-Lamberty  and one anonymous referee.
Modern assays have enabled high-throughput studies of epigenetic regulation of gene expression using DNA sequencing. In particular, the assay for transposase-accessible chromatin using sequencing (ATAC-seq) allows the study of chromatin configuration for an entire genome. Despite the gain in popularity of the assay, there have been limited studies investigating the analytical challenges related to ATAC-seq data, and most studies leverage tools developed for bulk transcriptome sequencing (RNA-seq). Here, we show that GC-content e↵ects are omnipresent in ATAC-seq datasets. Since the GC-content e↵ects are sample-specific, they can bias downstream analyses such as clustering and di↵erential accessibility analysis. We introduce a normalization method based on smoothquantile normalization within GC-content bins, and evaluate it together with eleven di↵erent normalization procedures on eight public ATAC-seq datasets.
However, the impact of GC-content e↵ects in ATAC-seq data on downstream analyses has not been studied in depth. Notwithstanding the recent surge in popularity for the assay, surprisingly few studies investigate the analytical challenges of ATAC-seq data, e. g. , normalization and di↵erential accessibility (DA) analysis. Indeed, most data analysis workflows rely on statistical methods originally developed for ChIP-seq or bulk RNA-seq data to analyze bulk ATAC-seq datasets  [16] [17] [18]. Recently, Reske et al. 18  compared pipelines for DA analysis and showed that normalization has a large influence on the results. While the authors advised comparing multiple normalization methods for a particular dataset at hand, they did not elaborate on GC-content e↵ects nor normalization methods that take this into account.
We further study the impact of GC-content e↵ects on the accuracy and interpretation of di↵erential accessibility analysis results. While no normalization method uniformly performs best across all datasets, smooth GC-FQ performs best on average, and GC-aware normalization methods typically perform better than GC-unaware methods, emphasizing the need to correct for GC-content e↵ects. We recommend that researchers use exploratory data analysis methods to guide the choice of normalization method.
GC-content e↵ects are sample-specific and confound downstream analyses In ATAC-seq, accessibility counts are often positively associated with GC-content. We explore this using data from Calderon et al. 23  , who generated an ATAC-seq atlas of human immune cell types. Here, we focus on six replicate samples of memory natural killer (MNK) cells in a control condition. Figure  1a  shows that the accessibility count of a particular genomic region is associated with its GC-content. However, the slope and shape of the curves may di↵er between samples, which indicates that GC-content e↵ects are sample-specific and can therefore bias between-sample comparisons. This can be similarly observed for other cell types in this dataset (Supplementary Figure  1 ). Note that, while the width of each peak is also associated with its accessibility, this e↵ect tends to be more uniform across di↵erent samples and therefore have lower impact on between-sample comparisons (Supplementary Figure  2 ).
GC-aware normalization GC-content e↵ects have been observed and accounted for in other work on ATAC-seq data  14, 26  , typically using conditional-quantile normalization (cqn)  21  (see Methods). While this method removes GC-content e↵ects in many datasets, Risso et al. 10  observed that, in the context of RNA-seq, cqn's regression approach may be "too weak" for some datasets and "more aggressive normalization procedures" may be required. They proposed a method, implemented in the Bioconductor R package EDASeq, based on two rounds of full-quantile normalization: First, FQ normalization within a sample across bins of features (e. g. , peaks or genes) with similar GC-content and, subsequently, FQ normalization between samples. We will denote this method as FQ-FQ normalization. However, read count distributions may in some datasets be more comparable across samples within a GC-content bin than across GC-content bins within a sample (see Figure  2a ).
This motivates a variant of FQ-FQ normalization, which we call GC-FQ, that applies full-quantile normalization across samples for each GC-content bin separately. Note that this attempts to equalize the GC-content e↵ect across samples and therefore performs between-sample normalization simultaneously. For the ATAC-seq dataset of Calderon et al. 23  , all three GC-aware methods (cqn, GC-FQ, and FQ-FQ) indeed e↵ectively remove GC-content e↵ects (Figure  2b-d ) on the fold-changes. All three of these GC-aware normalization methods rely on full-quantile between-sample normalization, which is an aggressive normalization method that does not come without assumptions. Since FQ normalization forces distributions to be equal across all samples, the underlying assumption is that global di↵erences between distributions are the result of technical e↵ects.
In other words, if there were no technical nor sampling variability in the data, the distributions of all samples should be identical, hence comparable, and this is what FQ normalization is trying to achieve. This assumption is restrictive and is not guaranteed to hold for all datasets. Hicks et al. 27  recently developed a generalization of FQ normalization, smooth-quantile normalization, or qsmooth, that can account for global di↵erences between distributions due to biological e↵ects of interest. The method is based on the assumption that the read count distribution of each sample should be equal within biological groups or conditions, but could vary between groups. Essentially, the method is a weighted combination of FQ normalization between samples for each biological group separately and FQ normalization across all samples of all biological groups.
Benchmarking ATAC-seq normalization We evaluate normalization methods using eight public bulk ATAC-seq datasets  14, 17, 23, [28] [29] [30] [31] [32]  , including ATAC-seq atlases of mouse tissues  30  , human blood cells  23  , and human brain cells  29  , thus spanning a multitude of biological systems. For each dataset, we use the publicly-available raw (i. e. , unnormalized) accessibility count matrix; the di↵erent datasets hence also span a realistic range of preprocessing and peak-calling pipelines. We compare GCaware normalization methods (smooth) GC-FQ, cqn, and FQ-FQ, with GC-unaware normalization methods qsmooth, TMM, DESeq2, full-quantile, total-count, upper-quartile, and no normalization; see Methods for a description of each normalization procedure. Our benchmarking framework evaluates di↵erent aspects of each normalization method, which can broadly be categorized as follows: (i) between-sample comparison of normalized expression measures, (ii) performance in di↵erential accessibility analysis, (iii) removal of GC-content e↵ects, each of which are described in more detail below.
(b-d) There is no visible GC-content e↵ect on fold-changes estimated using edgeR following normalization with GC-aware methods cqn, EDASeq, and GC-FQ, in the mock comparisons for the dataset from Calderon et al. 23. The blue curve represents a GAM fit. normalization methods over others. We therefore use simulated mock datasets as well as real datasets to select relevant measures for our context, as described in Supplementary Results. Based on this evaluation, we benchmark normalization methods using five summary measures. The first three are the average silhouette width for (i) a clustering of samples according to biological covariate(s) of interest (Bio Sil ); (ii) a clustering of samples according to (unwanted) batch e↵ects (Batch Sil ); (iii) an empirical clustering of samples using partitioning around medoids (PAM Sil ).
We also evaluate normalized data by the correlation of their log-count principal components with (iv) principal components of QC variables (see Methods section for which QC variables are used in each dataset), and (v) principal components of factors of unwanted variation, derived from negative control features. Here, we use peaks that overlap with housekeeping genes as negative control features. Performance in di↵erential accessibility analysis. The di↵erential accessibility analysis performance evaluation relies on two scenarios, based on synthetic null and synthetic signal datasets. First, a mock null analysis is performed for each real dataset where, for each stratum of the biological covariate of interest, samples are split randomly into two groups to create a mock variable. Since the two mock groups therefore contain a similar number of samples from each stratum, we expect no systematic di↵erences between the groups.
A di↵erential accessibility analysis is then performed using each of the normalization procedures (see Methods). The following two evaluation measures are computed: The fraction of peaks returned at a nominal marginal significance level of 5% (FPR) and the Hellinger distance of the p-value distribution with a uniform distribution on the interval [0, 1] (P-val unif ). Both measures aim to assess control of false positives in a DA analysis. Second, we use each real dataset to construct synthetic signal datasets of 12 samples each, based on the simulation framework described in Methods. We use the simulated datasets to assess DA analysis performance based on the area under the receiver operating characteristic (AUROC) curve (auroc). GC-content e↵ect removal. Finally, we use the evaluations in both components above in combination with three measures that assess the removal of GC-content e↵ects.
In the scone normalization performance evaluation, we use a measure based on relative log-expression (RLE) values  34  to investigate whether the normalization works across the range of GC-content values (RLE GC ), see Supplementary Methods for details. In the mock comparison, we assess deviation of p-value uniformity as a function of GC-content, by calculating the variability in Hellinger distance between the p-value distributions in each of 20 equally-sized GC-content bins and a uniform distribution. Good normalization methods should have a similar p-value distribution across GC-content bins (p-val GC ). Finally, we use the DA analysis on the simulated datasets to calculate the distance in empirical cumulative distribution functions between the observed GC-content distribution of called DA peaks and the GC-content distribution of truly DA peaks (GC-dist DA).
The benchmark results for each dataset are shown in Supplementary Figure  5 , and summarized across datasets in Figure  3. While no method uniformly outcompetes all others, smooth GC-FQ performs best in 7 out of 8 datasets. Other GC-aware normalization methods, such as FQ-FQ and GC-FQ, also often perform well, while the performance of cqn is variable across datasets. GC-unaware normalization methods typically perform worse than GC-aware methods. Out of the former, qsmooth and FQ consistently perform reasonably well. The good performance of both smooth GC-FQ and qsmooth suggests that, in bulk ATAC-seq data, often large numbers of di↵erentially abundant features between biological conditions may exist, possibly more so than what is typically observed in bulk RNA-seq data. Indeed, this is also what we observe in the two case studies discussed below, where, for both datasets, many methods flag over 35% of all features as di↵erentially accessible between biological conditions.
To check the robustness of these results, we also rank normalization methods for each of the three benchmarking components separately (Supplementary Figure  6 ). In terms of both scone normalization performance as well as GC-content e↵ect removal, all GC-aware methods perform better than all GC-unaware methods. In terms of di↵erential analysis performance, smooth GC-FQ is still the top performing method, followed by qsmooth and TMM normalization. GC-FQ and FQ-FQ aslo show fairly consistent good performances. Interestingly, while cqn performs well in terms of normalization performance as well as GC-content bias removal, it performs badly in di↵erential analysis performance, being second-to-last. These results confirm that, even for benchmarking methods not explicitly using GC-content bias removal for evaluation, accounting for GC-content bias is beneficial for the normalization of ATAC-seq datasets.
GC-content bias removal Figure  3 : Benchmark of twelve normalization methods across eight public ATAC-seq datasets. The benchmark assesses normalization, di↵erential accessibility performance, and GC-content bias removal, the results of which are each represented as a heatmap. The pseudo-color images, where darker color represents better performances, display matrices of average ranks (see Methods), with rows corresponding to normalization procedures and columns to datasets. Methods are ordered according to their average rank across all evaluation criteria and datasets, and their names colored based on whether they explicitly account for GC-content (blue) or not (black).
Brain Open Chromatin Atlas Fullard et al. 29  published the Brain Open Chromatin Atlas (BOCA), where chromatin accessibility is measured in five human postmortem brain samples. The dataset consists of a total of 14 brain regions and two cell types (neuronal and glial/non-neuronal). These 14 brain regions can be classified into six broader regions, namely, the neocortex (NCX), primary visual cortex (PVC), amygdala (AMY), hippocampus (HIP), mediodorsal thalamus (MDT), and striatum (STR). After normalizing the counts using each normalization method (see Methods), we first assess how well each normalization method is able to recover the cell types and the major brain regions within each of these cell types by clustering the datasets using partitioning around medoids (PAM) based on the first 2 to 10 principal components.