dataesr/scientific-paragraphs-categorization huggingface dataset:
2 ± 0. 6 kg C m -2 yr -1. Finally, all three models overestimated soil carbon content compared to the information from the Soil-Grid database with LPJ-GUESS, ED2, and ORCHIDEE v2. 2, respectively, simulating mean values of 14. 8 ± 6. 3, 10. 6 ± 8. 2, and 7. 6 ± 2. 9 kg C m -2 for a reference value of 4. 3 ± 1. 7 kg C m -2. Also the simulated spatial distributions of the soil carbon content were drastically different from the reference one (Figs. S3  and S4 ), regardless of the soil scenario. The spatial correlations of soil carbon between Soil-Grids250m and the models were systematically low: 0. 10 for ORCHIDEE, 0. 25 for ED2, and 0. 26 for LPJ-GUESS. The relative better performance of the ORCHIDEE model in capturing the spatial variability in vegetation and average soil carbon stocks can be partly explained by the use of a land-cover map to constrain vegetation type distribution compared to ED2 and LPJ-GUESS for which the PFT distribution is an emergent property of the models.
Appropriately representing soil spatial heterogeneity (through better estimates of the impact of soil-texture uncertainties) and soil-plant coupling, such as the non-linearity of soil-root resistance, is a major challenge that needs to be urgently addressed in TBMs. This will lead to a better representation of the effect of drought stress on vegetation and to a reduction of the carbon budget uncertainties, which is particularly needed in complex and heterogeneous ecosystems such as tropical forests. Code and data availability. SoilGrids250m data are available at: https://soilgrids. org/ (SoilGrids web portal, 2022). CRUNCEPv7 data are available at: https://rda. ucar. edu/datasets/ds314. 3/  (Viovy, 2018). The code used to generate the results, soil scenario, and to reproduce the figures of this manuscript is available on Github (https://github.
com/femeunier/SoilSensitivity (last access: 11 October 2022) with an archived version on Zenodo (https://doi. org/10. 5281/zenodo. 6226622, Meunier, 2022)  corresponding to tag v1. The code source of the ORCHIDEE v2. 2 model is available at: https://forge. ipsl. jussieu. fr/orchidee/ browser/branches/ORCHIDEE_2_2?order=name (ORCHIDEE in tags/ORCHIDEE_2_0 -ORCHIDEE, 2022), please contact the ORCHIDEE team at https://orchidee. ipsl. fr/contact/ (last access: 11 October 2022) before any intended usage of the model. The ED2. 2 model is available at https://doi. org/10. 5281/zenodo. 3365659 (Longo et al. , 2019b). LPJ-GUESS is a worldwide developed and refined terrestrial biosphere model. The model code is managed and maintained by the Department of Physical Geography and Ecosystem Science, Lund University, Sweden.
The source code can be made available with a collaboration agreement under the acceptance of certain conditions. For this reason, a DOI for the model code is not available. Additional details and information can be found at the LPJ-GUESS website (http://web. nateko. lu. se/lpj-guess, last access: 10 October 2022). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/gmd-15-7573-2022-supplement. Author contributions. All co-authors designed the study. FM ran the ED2 simulations, MP ran the ORCHIDEE v2. 2 simulations, and WV ran the LPJ-GUESS simulations. FM analysed the model outputs, and wrote the first version of the manuscript. All co-authors critically revised it.
These classifications are generally based on a combination of remote-sensed proxies of phytoplankton biomass and global climatologies of biogeochemical or physical parameters. However, these approaches are limited in their capacity to account for subsurface variability in these parameters. The deployment of autonomous profiling floats in the Biogeochemical Argo network over the last decade has greatly increased global coverage of subsurface measurements of bio-optical proxies for phytoplankton biomass and physiology. In this study, we used empirical orthogonal function analysis to identify the main components of variability in a global data set of 422 annual time series of Chlorophyll a fluorescence and optical backscatter profiles. Applying cluster analysis to these results, we identified six biomes within the global ocean: two high-latitude biomes capturing summer bloom dynamics in the North Atlantic and Southern Ocean and four mid-and low-latitude biomes characterized by variability in the depth and frequency of deep chlorophyll maximum formation.
However, because such provinces are typically defined using data from satellites or from climatologies averaging many decades of ship-based measurements, they are limited in their ability to account for environmental variability beneath the sea surface. Over the last 10 years, the development of the global Biogeochemical Argo (BGC-Argo) networks of autonomous profiling floats has significantly relieved this constraint, providing measurements of physical, bio-optical, and biogeochemical parameters within the upper kilometer of the ocean. In this study, we explore the potential for using BGC-Argo time series data to develop a novel biogeographical classification based on the direct measurement of subsurface bio-optical parameters. We identify six phytoplankton biomes in the global BGC-Argo data set: two high-latitude biomes characterized by large phytoplankton blooms during summer months and four mid-to-low-latitude biomes characterized by complex subsurface dynamics reflecting basin-scale trends in environmental forcings.
, 2020; Roemmich et al. , 2019). These data have made it possible to characterize subsurface phytoplankton distributions and their drivers at both the basin  (Barbieux et al. , 2019; Lacour et al. , 2017; Mignot et al. , 2014)  and global scale  (Cornec et al. , 2021). Because BGC-Argo floats typically complete transects to maximal depths of 1,000 m, they additionally provide an insight into linkages between ecological processes in the euphotic zone and carbon export in the mesopelagic zone  (Briggs et al. , 2011; Dall'Olmo & Mork, 2014). In this study, we use empirical orthogonal function (EOF) analysis to characterize variability in annual time series of bio-optical parameters measured by BGC-Argo floats: in vivo chlorophyll fluorescence (FChla) and particle backscattering coefficients (b bp ).
Construction of Bio-Optical Time Series This study relies on data acquisition from 205 BGC-Argo floats sampling physical and biogeochemical parameters across diverse regions of the global ocean  (Argo, 2000; Claustre et al. , 2020; Roemmich et al. , 2019). In addition to FChla and b bp , these measurements include temperature, salinity, and up to four additional core BGC variables (PAR, dissolved oxygen, nitrate concentration, and pH). BGC-Argo float data are accessible through the Coriolis database (ftp://ftp. ifremer. fr/ifremer/argo). Monthly satellite [Chla] and b bp measurements (hereafter  [Chla]  sat and b bp_sat , respectively) corresponding to each profile were obtained at a 4 km resolution from the Ocean Color-Climate-Change Initiative v4. 0 database, which merges observations from MERIS, MODIS, VIIRS, and SeaWiFs (ftp://oc-cci-data:ELaiWai8ae@oceancolou.
10. 1029/2021GB007233 3 of 24 from the OCCCI database, 665 nm retrievals were downloaded and converted to 700 nm following the power law equation described in  Sauzède et al. (2016). Hydrological data were collected by the SBE 41 Seabird CTD sensors, which were processed and quality-controlled following Argo protocols as described by  Wong et al. (2020). Floats were equipped with Seabird-Wetlabs sensors of three types (FLBB, ECO-Triplet, or MCOMS) that include a chlorophyll fluorometer (excitation at 470 nm; emission at 695 nm), and a backscattering sensor at 700 nm. Values for [Chla] and b bp were retrieved from these measurements and quality-controlled (only data with good and probably good QC flags were used) following BGC-Argo protocols  (Schmechtig et al. , 2015 (Schmechtig et al. , , 2018a (Schmechtig et al.
The ratio of b bp_pz :b bp_mz was interpreted as a proxy for transfer efficiency (T effp ), following  Galí et al. (2021) , albeit in our case based on comparative depth-integrated b bp values rather than POC estimates. Average b bp values were also calculated for each of the four equally sized depth ranges within the mesopelagic zone (b bp_m1 -b bp_m4 ) by integrating b bp profiles within each depth range and then normalizing to the width of the depth range (Figure  S3  in Supporting Information S1). The width of each depth range was defined as (Z m -Z p )/4. Because Z p was calculated as the greater value of the MLD or Z eu for each profile in the data set,  10. 1029/2021GB007233 7 of 24 mesopelagic depth ranges were calculated for each profile. For ease of reporting, all b bp values are reported in units of 10 -3 m -1.
Conclusions To date, characterizing phytoplankton distributions at the global scale has been limited by the near invisibility of subsurface phytoplankton distributions to remote sensing platforms. As the issue was described by Platt and Sathyendranath, "to characterize the ocean water column using satellite-based observations of its surface layer is certainly not ideal. But is the only option available at these scales, now and into the foreseeable future"  (Platt & Sathyendranath, 1988). However, with modern advances, our results demonstrate that the amount and extent of data collected by BGC-Argo profiling floats are now sufficient to resolve this constraint. Beyond demonstrating the utility of data from BGC-Argo floats for partitioning biogeographical provinces within the global ocean, this analysis provides a new insight into the drivers of subsurface variability in phytoplankton distributions at the global scale, while also highlighting the role of subsurface processes in driving biomass The classifications in this study additionally extend beyond existing methods by fully accounting for vertical variability in phytoplankton distributions and particle concentrations within the upper kilometer of the ocean.
Toward this end, we found a random forest classifier trained on latitude, longitude, and the standard deviation of [Chla] sat for each time series to correctly predict biome membership for individual profiles in more than 96% of the cases (data not shown). And while our current data set is too sparse to apply this classifier to historical MODIS data with informative results, such a goal will surely become feasible in the near-future with the increasing deployment of additional BGC-Argo floats.
Acknowledgments This study was funded by a  European Research Council   Advanced Grant  to  Hervé Claustre  for the  REFINE  project (Grant Agreement 834177). Participation by  N. Bock  and  S. Duhamel  was additionally supported by  National Science Foundation (NSF )  OCE-1458070  awarded to  S. Duhamel. The authors wish to thank the Argo Data Management team (ADMT) and the  BGC-Argo Data Management task team (BGC ADMT ).
Data Availability Statement All bio-optical and environmental data used in this study were collected and made freely available by the International Argo Program and the national programs that contribute to it (https://argo. ucsd. edu; https://www. oceanops. org). The Argo Program is part of the Global Ocean Observing System. All analyses were performed in R 4. 0. 2 using RStudio 1. 3. 107. RStudio is an open-source software freely available through the publisher website (https://www. rstudio. com/).
Enhanced crosslinking and immunoprecipitation (eCLIP) sequencing is a powerful method for transcriptome-wide detection of binding sites of RNA-binding proteins (RBPs). However, identified crosslink sites can profoundly deviate from experimentally established functional elements of even well-studied RBPs. Current peak-calling strategies result in low replication and high false-positive rates. Here, we present the R/Bioconductor package DEWSeq that makes full use of replicate information and size-matched input controls. We benchmarked DEWSeq on 107 RBPs for which both eCLIP data and RNA sequence motifs are available and were able to more than double the number of motif-containing binding regions relative to standard eCLIP processing (2. 3-fold median). The improvement not only relates to the number of binding sites (e.
Enhanced CLIP (eCLIP) introduced changes to the sequence library generation as well as a size-matched input (SMI) control to address background noise and false positives in CLIP data  10. The ENCODE Consortium has used eCLIP to generate the largest coherent public set of CLIP data, covering 150 RNA-binding proteins in two cell types (HepG2 and K562), processed with the computational peak-calling analysis pipeline CLIPper  10. Detected binding sites were compared against SMI controls individually for each replicate and extended by 50 nucleotides from their 5' end for functional analyses, with the reasoning that the 5' end of a peak represents the crosslink site  14. An analysis of these data shows the relatively low reproducibility of reported binding sites between replicates (Fig. 1a ). While RBPs such as the SBDS Ribosome Maturation Factor (SBDS), NOP2/Sun RNA Methyltransferase 2 (NSUN2), and Small RNA Binding Exonuclease Protection Factor La (SSB) show almost perfect reproduction of the respective binding sites, Transforming Growth Factor Beta Regulator 4 (TBRG4), Splicing Factor 3b Subunit 1 (SF3B1), and WD Repeat Domain 3 (WDR3) binding sites display high replicate to replicate variation.
More recently, a 'CLIPper reproducible' (CLIPper Rep. ) dataset was introduced, featuring only binding sites that were identical at the base level in both replicates  14. This approach greatly reduced the number of reported binding sites. However, it raises the question whether better data analysis approaches exist. A particular challenge in the analysis of eCLIP data is that the measured crosslink peaks can be at an offset from the RBP's actual binding regions, which can result from an RBP's particular structure and physicochemical crosslinking behaviour. CLIP methods are often tested against classical RBPs such as the RNA Binding Fox-1 Homolog 2 (RBFOX2) or splicing factor Heterogeneous Nuclear Ribonucleoprotein C (hnRNPC) with well-known binding sites and RNA sequence motifs  10, 13, 15.
Given the varying crosslinking behaviour of each protein and the relatively low reproducibility of binding site detections in eCLIP experiments, we developed a method, DEWSeq, that allows accurate and robust identification of RBP interactions in eCLIP data by detecting regions enriched in crosslink sites compared to the control. DEWSeq is a slidingwindow-based approach that uses single-nucleotide precision information across multiple replicates and control experiments for significance testing. To test DEWSeq and to facilitate a comprehensive analysis of RBP-RNA interactions, we benchmarked it on the eCLIP data for RBPs provided by ENCODE. Notably, 107 out of 150 RBPs in the dataset have known experimentally determined RNA sequence motifs, and one, SLBP, is known to recognise a specific secondary structure (histone mRNA stem-loops).
Results We developed DEWSeq as a new R/Bioconductor statistical analysis package for the robust detection of RBP binding regions from i/eCLIP data sets. The DEWSeq workflow starts from the output of an accompanying Python package for post-processing i/eCLIP alignment files, htseq-clip  25  , which extracts crosslink site counts at single-nucleotide positions adjacent to the end of reads, flattens annotation of multiple transcripts and uses sliding windows to count and aggregate crosslink sites. DEWSeq performs one-tailed significance testing using DESeq2  26  , result summarisation and binding site visualisation (Supplementary Fig. 2 ). Similar to the csaw package  27  for ChIP-seq data, DEWSeq incorporates biological variation with significance testing, which reduces the false discovery rate  27  , with the difference that DEWSeq is tailored to single-nucleotide position data.
Sequence motif-based evaluation strategy We compared DEWSeq's results to the CLIPper method used by the ENCODE Project, which uses peak-calling on two individual replicates compared against a single SMI control. We extended each peak 50 nt in the 5' direction, as first introduced by the authors specifically for motif-based analyses  14  , which is referred to as CLIPper original (CLIPper Orig. ) in our study. This dataset was improved on by the authors to produce CLIPper Rep. , which is the subset of CLIPper Orig. peaks that are reproducible at the nucleotide level across both replicates  10, 14. An overview of the RNA sequence motif-based benchmarking strategy we adopted in our study is shown in Fig. 3a. As a proxy for the likely biological relevance of the identified binding sites, we obtained known experimentally determined RNA sequence motifs of 6 nucleotides or longer from catRAPID omics v2.
0  29  (Supplementary File 1). This curated motif dataset covered 107 of the 150 RBPs for which ENCODE eCLIP data were available. To identify positions of known motifs within binding regions identified by CLIPper and DEWSeq from ENCODE datasets, we used FIMO from the MEME suite of motif analysis software  30. To compare these results to orthogonal datasets beyond eCLIP, we obtained iCLIP, HITS-CLIP, PAR-CLIP binding sites determined using different peak callers in the POSTAR2 database  31  as well as a separate dataset of author-processed PAR-CLIP binding sites  32  , and scanned for motifs using FIMO. For each method, we then estimated the accuracy of binding site detection by calculating the proportion of reported binding sites that contained at least one expected sequence motif for the RBP of interest.
FASTKD2 FASTKD2 (FAST kinase domain-containing protein 2) is a mitochondrial RBP that has been shown to interact with a defined set of mitochondrial transcripts  34, 35. In this analysis, we compared the number of FASTKD2-bound regions of DEWSeq against CLIPper Rep. results. In HepG2 cells, CLIPper Rep. reports 7 out of 451 bound genes as mitochondrial, compared to DEWSeq with 16 out of 268 bound genes being mitochondrial (Fig. 4b ). A similar pattern emerges in the K562 cell line, where the numbers for CLIPper and DEWSeq were 19 out of 364 and 29 out of 426, respectively. Fisher's Exact test confirmed a significant enrichment in the number of FASTKD2-bound mitochondrial genes reported by DEWSeq compared to CLIPper Rep results in both cell lines (Fig. 4b , Supplementary Table  3 , Sheet 2).
SLBP (Stem-Loop Binding Protein ) is an RBP that binds to a conserved stem-loop structure motif at the 3' end of mRNAs that encode replication-dependent histones  2, 36, 37. To the best of our knowledge, it represents the only RBP included in the ENCODE project that recognises a secondary structure motif. We scanned SLBP binding regions and surroundings (binding site were extended with 50, 100, 150, 200, and 300 nt in both 5' and 3' direction) from both CLIPper Rep. and DEWSeq results for the SLBP stem-loop structure binding site using Infernal suite and Histone 3' UTR stem-loop covariance model (Rfam: RF00032). A higher proportion of DEWSeq binding sites (39. 2%) contain predicted SLBP binding structures, as compared to CLIPper Rep. binding regions (17. 9%) (Fig. 4c ). This trend becomes more pronounced with the extension of binding regions in both 5' and 3' directions (Fig.
4c  and Supplementary Fig. 4 ), as DEWSeq discovers increasingly more stem-loops: 83. 5% of detected binding sites are in proximity to histone stem-loops, whereas only 30. 4% of CLIPper Rep. binding sites are in the vicinity of known targets, suggesting a significant decrease of false positives for DEWSeq. Further, we calculated the true positive rate (sensitivity) of these predicted stem-loop structures using Histone 3' UTR stem-loop annotations from the Rfam database as a reference set (Fig. 4d ). DEWSeq without extension shows a marginal increase in true positive rate compared to CLIPper Rep. (from 0. 201 to 0. 208), with slight improvement in extensions (Supplementary Table  4 ). In addition to the increased presence of expected stem-loop structures, we also noted that CLIPper Rep. identified binding of SLBP to mRNAs deriving from a total of 44 histone genes (66.
Evaluation of eCLIP compared to iCLIP, HITS-CLIP and PAR-CLIP To validate newly discovered binding sites, we used the motif exclusivity benchmark to compare eCLIP binding sites assigned by DEWSeq and CLIPper Rep. , respectively, to sites from iCLIP, HITS-CLIP and PAR-CLIP protocols retrieved from the POSTAR2  31  and DoRiNA 32 databases. To address differences in detection methods for these different CLIP protocols, we employed multiple established analysis methods: Piranha  38  and CIMS  39  for iCLIP and HITS-CLIP data, and Piranha 38 , PARalyzer  40  as well as Mukherjee  32  for PAR-CLIP data. 5a-c , Supplementary Fig. 6a-c  and Supplementary Table  2 , Sheet 2). Interestingly, the overlaps of binding sites found in either eCLIP and iCLIP, HITS-CLIP, or PAR-CLIP are modest compared to binding sites detected in one of the protocols alone.
1c ). However, the majority of CLIP protocols were primarily benchmarked on selected RBPs like RBFOX2 or hnRNPC which do show peak-like behaviour on top of the known target sites, justifying the choice of peak-callers for data analysis. To increase robustness to the observed crosslinking patterns and therefore to improve the reliability of binding site identification, we developed a computational method that detects enriched regions of crosslink sites in single-nucleotide resolution CLIP called DEWSeq. Similar to csaw for ChIP-seq  27  , DEWSeq takes into account biological variation between replicates for significance testing of the IP samples against size-matched input (SMI) controls. We reanalysed 223 ENCODE eCLIP data sets covering 150 RBPs in one or both of two cell lines (K562 and HepG2), of which 107 RBPs had known experimentally determined RNA sequence motifs and one, SLBP, is known to target a specific RNA stem-loop secondary structure  17.
DEWSeq does consistently improve the overlap with binding sites from other CLIP protocols compared to CLIPper Rep. , although the overlap across protocols is very low overall (Fig. 5a-c ). Our study includes a meta-analysis of binding sites generated using different protocols, methods and analysis tools. The general trend indicates that compared to iCLIP, HITS-CLIP and PAR-CLIP datasets, ENCODE eCLIP dataset analysed with DEWSeq contains a higher number of motif-containing binding sites. Also, binding sites discovered exclusively by DEWSeq can be found in other CLIP protocols, providing independent validation. However, a further large-scale investigation is needed to study the differences between CLIP-type protocols. Where, as in ChIP-seq, bell-shape patterns are displayed in eCLIP or iCLIP crosslinking data, peak-callers should perform well.
eCLIP data In order to compare the results from our newly developed DEWSeq package, we chose the eCLIP data published by the ENCODE Project  10. This dataset provides consistently produced 223 experimental studies, covering 150 RBPs in either one or both of the two human cell lines: HepG2 and K562. Each study in this dataset consists of two biological replicate IP samples and one size-matched input (SMI) control sample  42. For data reanalysis using DEWSeq, we downloaded alignment files (. bam) with reads mapped to the GRCh38 genome annotations from ENCODE project data portal  43. Bam file accessions and additional details are given in Supplementary Table  5.
CLIPper binding sites In this manuscript, the results from the reanalysis of this ENCODE dataset were compared against two sets of binding site results: the original set of called on individual IP samples with respect to SMI controls 10 (referred to as 'CLIPper original' (CLIPper Orig. ) in this manuscript) and the refined set of binding sites based on stringent thresholds, IDR analysis and blacklist region removal  14  (referred to as 'CLIPper reproducible' (CLIPper Rep. ) in this manuscript). Files providing these CLIPper results were also downloaded from the ENCODE Project data portal in narrowPeak BED format.