dataesr/scientific-paragraphs-categorization huggingface dataset:
Georeferencing Locations of samples were digitalized if available, and an associate ArcGIS data layer in shapefile format (see MORE-POC_v1. 1. rar) is provided with all points projected in a geographic coordinate system using the World Geodetic System 1984 (WGS1984). For references only providing a sampling map without any numerical information on the sampling location, sampling coordinates were manually extracted using ArcGIS 10. 3 after georeferenced adjustment. In the end, 3339 SPM samples have coordinate information among the 3546 compiled SPM entries (Fig. 1 ). Furthermore, it can be noted that most studies chose sampling locations where SPM can be taken as representative of biogeochemical processes at catchment scale, i. e. , the river mouth, to better understand the compositions, transport behavior, and fluxes of POC go- ing either to a confluence or an estuary (e.
Definitions of POC composition variables and units In MOREPOC v1. 1, all data are either taken directly from the references or are calculated from the reference data. POC content (POC %) and total nitrogen content (TN %) are reported as dry weight percentage (%). In addition, POC concentration in river water (mg L -1 ) can be calculated using the SPM concentration reported as dry weight per liter (mg L -1 ) and percentage content of POC (%). Most importantly, the fundamental component of MORE-POC v1. 1 consists of an extensive dataset for stable carbon isotope values (δ 13 C, in ‰ relative to VPDB) and radiocarbon compositions (provided as both 14 C in ‰ or as F 14 C; see below). Fraction modern, F 14 C, is the deviation of a sample's 14 C atoms from that of the modern standard. Conventional radiocarbon ages (RCAs) are given in MORE-POC v1.
, 2008b). POC contents are usually positively correlated with proportions of fine-grained fractions  (Mayer, 1994; Galy et al. , 2008b; Bouchez et al. , 2014). https://doi. org/10. 5194/essd-14-4743-2022 Earth Syst. Sci. Data, 14, 4743-4755, 2022 2. 8 Extent of MOREPOC v1. 1 Although MOREPOC v1. 1 features data from river systems worldwide, it does not offer the same degree of representativeness for all the continents, with, for instance, an overrepresentation of Asian rivers and an underrepresentation of rivers draining Europe and Oceania (Table  1 ). It can be noticed that there are relatively abundant POC studies on North American fluvial systems. However, the MOREPOC database also indicates the lack of studies on POC in fluvial systems in the cryosphere regions such as Antarctica and Greenland as well as in arid regions, including Australia and vast areas spanning from northern Africa to Middle Eastern Asia (Fig.
The ages of riverine POC span from "modern" (that is, recording bombderived carbon) to "ancient" (strongly influenced by fossil petrogenic sources), with 14 C values (n = 2283) ranging from -990. 1 ‰ to 147. 7 ‰ with a statistical average of -386 ‰. A large fraction of the 14 C values (n = 775, 33. 9 % of total entries) falls within the range -300 ‰ to 0 ‰; this range dominates the database in  Marwick et al. (2015)  as well (n = 278, 52. 3 % of Marwick's total entries). The MOREPOC v1. 1 dataset is on average more 14 C-depleted than that of  Marwick et al. (2015). Around the globe, the most ancient POC ( 14 C = -990 ‰) is found in small mountainous rivers in Taiwan  (Hilton et al. , 2010) , in which the entirety of POC is derived from the erosion of sedimentary rocks. In the riverine POC dataset of MOREPOC v1.
Relationships between riverine SPM and POC MOREPOC v1. 1 features data from rivers with SPM concentrations ranging from 0. 35 to 199 000 mg L -1 , with POC content from 0. 01 % to 91. 67 %. SPM and POC concentrations (both mg L -1 ; n = 2115) are positively correlated (Fig. 4 ). However, the global trend shows that an increase in SPM concentration is accompanied by a decrease in POC content (%), which is largely due to a dilution effect by inorganic materials  (Fig. 4, Ittekkot, 1988; Ludwig et al. , 1996; Meybeck, 1993). In MOREPOC v1. 1, large SPM concentrations (over 10 000 mg L -1 ) are generally observed in mountainous rivers, such as the Choshui and Liwu rivers in Taiwan  (Hilton et al. , 2008; Kao et al. , 2014) , the Santa Clara River (USA)  (Masiello and Druffel, 2001) , or the Minjiang (a major tributary of the upper Changjiang, China)  (Wang et al.
Meanwhile, it is worth noting, for a given SPM concentration, that 14 C abundance can be contrasted. For example, rivers draining low-latitude, tropical regions (especially 10 • N-10 • S; e. g. , African rivers) or high-latitude regions (60-75 • N; e. g. , Siberian Arctic rivers) are usually characterized by relatively low SPM concentration and abundant POC composition. Nevertheless, riverine POC from the low-latitude African rivers is much younger compared to that from the Arctic Siberian regions. This difference most likely stems from the contrasting radiocarbon activities and turnover times of the soil organic carbon between these two regions, which are primarily driven by climate  (Eglinton et al. , 2021; Marwick et al. , 2015; Wild et al. , 2019; Vonk et al. , 2015). The MOREPOC v1. 1 dataset also reveals that, under a given climate, river systems can be heterogeneous in terms of SPM concentration and the associated POC composition.
Conclusions In this paper, we introduce MOREPOC, the largest and most comprehensive database for riverine suspended particulate matter (SPM) concentration and particulate organic carbon (POC) composition, including POC and total nitrogen (TN) content, stable carbon isotope ( 13 C), cosmogenicradioactive carbon isotope ( 14 C), as well as aluminumto-silicon (Al / Si) mass ratios. MOREPOC will benefit the scientific community carrying out research on riverine POC sources, transport, and fate. Furthermore, it will help feed and validate Earth system models in order to improve the ability of models to constrain all the components of the global carbon cycle. Combined with ocean sediment databases, such as CASCADE (Circum-Arctic Sediment Carbon DatabasE;  Martens et al. , 2021)  or MOSAIC (Modern Ocean Sediment Archive and Inventory of Carbon;  Van der Voort et al.
, 2021) , MOREPOC will enable a better understanding of the fate of POC from the terrestrial source to sink at the ocean bottom. Existing environmental raster global datasets for climate, geomorphology, lithology, tectonics, hydrology, and land use also offer promising prospects for the use of MOREPOC for identifying the controls on POC fluxes and composition, in particular using advanced statistical analysis or machine-learning techniques. Future updates of MOREPOC should include new bulk POC parameters as well as data on molecular fractions, thermal labile fractions, or specific components such as black carbon or fossil carbon, which should, in turn, provide additional insight into the alteration of riverine POC from source to sink, an essential feature of the global carbon cycle.
Acknowledgements. The development of the MOREPOC database was funded by the  Agence Nationale de la Recherche (ANR) SEDIMAN  (grant no. ANR-15-CE01-0012 ). Yutian Ke  was supported by the  China Scholarship Council  with a PhD scholarship (grant no. 201706180008 ). Financial support. This research has been supported by the  Agence Nationale de la Recherche  (grant no. ANR-15-CE01-0012 ) and the  China Scholarship Council  (grant no. 201706180008 ). Review statement. This paper was edited by  Attila Demény  and reviewed by  Jin Wang  and three anonymous referees.
Results The CLSYs are unique in their degree of tissue-specific expression. As a first step in determining whether the CLSYs play a role in shaping tissue-specific patterns of DNA methylation, their expression patterns were assessed during Arabidopsis development. Based on previously published data available in the ePlant database  38  , (Supplementary Fig. 1  and Fig. 1a ), four tissues [flower buds (Fl; stage 12 and younger), ovules (Ov; mature, but unfertilized), 1 st and 2 nd true leaves (Lv; 25 day old plants), and rosettes (Rs; 15 days old, no roots)] were selected for further characterization using GUS reporters driven by the CLSY promoters (Fig. 1b ) and mRNA sequencing (mRNA-seq) (Supplementary Data 1 and Fig. 1c ). For the GUS reporters, the CLSY promoter regions were vetted in constructs driving the expression of the CLSY genes, which complemented the 24nt-siRNA defects at the clsy-dependent loci identified in Zhou et al.
To determine the extent to which the observed tissue-specific differences in 24nt-siRNA levels contribute to altered DNA methylation patterns, differentially methylated regions (DMRs) between tissues were first determined in 100 bp bins across the Fig. 1  Tissue-specific expression patterns of the CLSY genes. a Relative expression levels of the four CLSY genes in select tissues from two ePlant expression viewers  [71] [72] [73]  (see also Supplementary Fig. 1a, b ). Here, all tissues and organs are labeled as in the ePlant viewers  38   whole genome and then mapped onto the 24nt-siRNA landscape. Notably, this method required the identification of DMRs in all pairwise combinations of wild-type data sets (e. g. 3 wild-type ovule datasets versus 3 wild-type flower datasets = 9 pairwise combinations), and required 10%, 20%, or 40% changes for CHH, CHG and CG methylation, respectively (Supplementary Data 7).
4c  and Supplementary Fig. 11e, f ), suggesting that the 24nt-siRNAs remaining in this mutant are sufficient to maintain near wild-type levels of methylation. Although it cannot be excluded that some additional floral tissues produce 24nt-siRNAs at siren loci, these findings suggest that most, if not all, 24nt-siRNAs at these sites arise from ovules, that they are required to maintain DNA methylation in all sequence contexts, and that their production is most strongly dependent on CLSY3. To determine whether CLSY3 acts directly at siren loci and whether it is required for Pol-IV recruitment to these sites, chromatin immunoprecipitation (ChIP)-seq experiments (Supplementary Data 13) were conducted using flower tissue from wild-type (Col) plants or clsy3 mutant plants expressing a 3xFLAG-tagged version of CLSY3.
14a ). As observed for the pol-iv mutant, the majority of the upregulated loci, as well as a subset of the downregulated loci, are associated with local (± 2 kb) reductions in 24nt-siRNAs and/or non-CG DNA methylation across all tissues in the clsy mutants (Supplementary Fig. 14b ), consistent with these expression changes being associated with defects in the RdDM pathway. Although it remains to be determined why loci controlled by CLSY3 and CLSY4 vary more in their distribution and their effects on 24nt-siRNA levels, DNA methylation patterns, and TE silencing, these data demonstrate that the relatively smaller set of sites controlled by these factors play a disproportionately large role in shaping the epigenomes across the four tissues analyzed. To investigate the relative contributions of the CLSYs in shaping the differences in the 24nt-siRNA and DNA methylation landscapes between tissues, the effect of each clsy mutant combination on the profiles at 24nt-siRNA clusters and WTvsWT CHH DMRs were determined on a genome-wide scale for each tissue.
Methods Plant materials, growth conditions, and tissue collection Plant materials. All plant materials used in this study were in the Columbia-0 (Col) ecotype. Previously characterized mutant lines include: clsy1-7 (SALK_018319)  49, 50  , clsy2-2 (SAIL_484_F03)  37, 51  , clsy3-1 (SALK_040366)  37, 50  , clsy4-1 (SALK_003876)  37, 50  , nrpd1-4 (SALK_083051)  50, 52  and shh1-1 (SALK_074540C)  35, 50. Growth conditions and tissue collection. Growth conditions, tissue collection, and data for flower buds (Fl; stage 12 and younger) are described in Zhou et al. ,  37  (GEO accession: GSE99694). Growth conditions and tissue collection for 15 day old rosettes (Rs) and 25 day old leaves (Lv) for all genotypes were as follows: seeds were sterilized with bleach and plated on half strength Linsmaier and Skoog medium (1/ 2 LS; Caisson Labs, Cat# LSP03) supplemented with 1% agar.
Cloning and visualization of GUS reporter lines Cloning. All primers are listed in Supplementary Data 15. For the GUS reporters, approximately 2. 0-3. 0 kb of the promoter regions from CLSY1, CLSY2, CLSY3 and CLSY4 were amplified from wild-type genomic DNA and cloned into the pDONR-P4P1R vector using the Gateway BP Clonase II Enzyme kit (Invitrogen, Cat# 11789020). These donor plasmids were recombined with a pDONR221 vector containing the GUS gene, a pDONR-P2RP3 vector with a mock fragment, and the pH7m34GW  54  destination vector, using the Gateway LR Clonase II kit (Invitrogen, CA, Cat# 11791020). For the four CLSY genes, their coding regions, either ending just upstream of the stop codon or including both the stop codon and 3′UTR region, were amplified from genomic DNA and cloned into the pDONR221 or pDONR-P2RP3 vectors, respectively, using the Gateway BP Clonase II Enzyme kit (Invitrogen, Cat# 11789020).
These samples were then incubated in the dark at 37 °C for 16-18 h. The GUS enzymatic reaction was stopped and cleared by submerging tissues in a sequential series of ethanol washes (20, 35, 50, and 70% ethanol) for 30 min each. The cleared tissues were treated in chloral hydrate solution (1. 5 mM in 30% glycerol), observed under a stereo microscope (Fisher, Cat# 03000015), and photographed with a SeBaCam14C digital camera (Fisher, Cat # SEBACAM14C CMOS 5 V 14 mp CMOS, Laxco) using SeBaView digital imaging software (Laxco). mRNA-seq library preparation, mapping, and data analysis RNA isolation, mRNA-seq library construction, and sequencing. Total RNA from rosette and leaf tissue was isolated using the Quick-RNA MiniPrep kit (Zymo Research, Cat# R1055), while total RNA from ovules was isolated using the Directzol RNA Microprep Kits (Zymo Research, Cat# R2061).
1. 0 μg total RNA (0. 1-0. 5 μg for ovules) was used to generate mRNA-seq libraries using the NEB-Next Ultra II RNA Library Prep Kit (New England Biolabs, Cat# E7775). Sera-Mag Magnetic SpeedBeads (Thermo Scientific, Cat# 65152105050250) were used for all size selection and clean-up steps and the resulting libraries were pooled and sequenced (single end 50 bp, SE50) on a HiSeq 2500 machine (Illumina). mRNA-seq mapping. mRNA-seq reads were mapped to the TAIR10 reference genome using STAR (v2. 5. 0c)  56  with the following options: "--outFilterMismatchNmax 2", to allow 2 mismatches, and "--outFilterMultimapNmax 1", to include only uniquely mapped reads (Supplementary Data 1). The sorted bam files were then used to generate Tag Directories using the (Hypergeometric Optimization of Motif EnRichment) HOMER  57  makeTagDirectory script with the "-mis 2 and -unique" options and UCSC format tracks were generated using the makeUCSCfile script with the "tair10 -fragLength given and -style rnaseq" options.
Quantification of expression levels. To identify differentially expressed loci in each tissue and compare the expression of specific genes across tissues, raw read counts or normalized values for each sample were obtained using the HOMER 57 analy-zeRepeats. pl script with the "-noadj" or "-fpkm" options, respectively, and a custom. gtf file (Supplementary Data 16). The. gtf file was generated based on the Araport11 annotation  58  and includes genes, transposons/repeats, as well as several novel transcripts that were characterized in Zhou et al. 37. The resulting expression files were then filtered to report just the isoform with highest expression across all genotypes (condense_genes_by_highest_sum_exp_header. py). The expression patterns [log 2 (fpkm + 1)] of genes required for DNA methylation were visualized for representative wild-type samples from each tissue using MORPHEUS (https:// software.
broadinstitute. org/morpheus) (Fig. 1c ). Differentially expressed loci for each tissue (log 2 fold change | (FC) ≥ 1 or 1. 5| and FDR ≤ 0. 05) were determined based on the raw read counts in R using Rstudio with the DESeq2 59 package and default parameters including batch corrections for the ovule and rosette datasets that included samples from two different experiments. Analysis of pol-iv-dependent up and downregulated loci across tissues. From the poliv up and downregulated loci in the four tissues, a list of 326 and 213 nonoverlapping pol-iv regulated loci, respectively, were generated (e. g. if a gene and a TE annotation overlap, the most highly upregulated locus was selected) (Supplementary Data 10). Of these loci, those with significant upregulation (log 2 FC ≥ 1 and FDR ≤ 0. 05) or downregulation (log 2 FC ≤ -1.
smRNA-seq and MethylC-seq data sets were converted from wiggle (. wig) to bigwig format using the bedGraphToBigWig script and then compared to wild-type controls with the bigwigCompare tool using the "--ratio = log2 and -bs 10" options or with the bigwigCompare tool using the "--ratio=subtract" options, respectively. The resulting bigwig files were used to calculate a matrix with the computeMatrix tool using the "scale-regions -a 2000 --regionBodyLength 2000 -b 2000 and -bs = 100" options and the data was plotted using the plotHeatmap tool and sorted based on the 24nt-siRNA values. The associations between pol-iv-dependent differentially expressed loci and epigenetic features for all mutants and tissues were determined by padding the genes and TEs 2 kb up and downstream and identifying the tissue and genotype matched reduced 24nt-siRNA clusters and non-CG DMRs (hyper and hypo) in each region using the bedtools intersect with the -loj option.
For each locus, the average log 2 fold change in expression across all overlapping reduced 24-nt siRNA clusters was calculated [Average (log 2 FC mut/WT)] along with the average difference in methylation levels across all overlapping DMRs [Average (% methylation mut -WT)]. These data were visualized as bubble plots using ggplot2 in RStudio (Supplementary Fig. 14b ). ChIP-seq library preparation, mapping, and data analysis Chromatin immunoprecipitation, library construction, and sequencing. A FLAGtagged CLSY3 line, pCLSY3::CLSY3-3xFLAG in the clsy3-1 mutant background, and non-transgenic wild-type plants were used for ChIP experiments. The ChIP was performed as previously described in Zhou et al. 37. Briefly, for each genotype, ~2. 0 g of un-opened flower buds (stage 12 and younger) was collected, ground in liquid nitrogen, and in vitro crosslinked with 1% formaldehyde (20 min at RT; Sigma, Cat# F8775).
The chromatin was then fragmented to ~500 bp by sonication, incubated with anti-FLAG M2 Magnetic beads (4 °C for 2 h; Sigma, Cat# M8823), washed 5 times and then eluted with 3xFLAG peptide ([0. 1 mg mL -1 ]; Sigma, Cat# F4799). The crosslinking was reversed overnight at 65 °C and purified DNA (Thermo Scientific, Cat# 17908) was used to generate libraries with the NEBNext Ultra II DNA Library Prep Kit (New England Biolabs, Cat# 7645). The resulting libraries were sequenced (single end 50 bp, SE50) on a HiSeq 2500 machine (Illumina). ChIP-seq data mapping, peak calling, and data analysis. The ChIP-seq data was aligned to TAIR10 reference genome using bowtie (v1. 1. 0)  61 and the "-m 1 -v 2 --all --best and --strata" options to allow 2 mismatches and including only uniquely mapping reads (Supplementary Data 13).
pl script from HOMER  57  with the "tair10 -m -mbed -matrix -nmotifs -multi" options (Supplementary Data 14). The total number of motif 1 occurrences genome-wide were determined using the scanMotifGenomeWide. pl script from HOMER  57  with the "tair10 -bed -keepAll" options. Metaplots showing enrichment of reads from ChIP-seq datasets and the distribution of motif 1 over the CLSY3 peaks (Fig. 4d, e ) were generated using the annotatePeaks. pl script from HOMER  57  with the "tair10 -m -mbed -size 4000 -hist 100 -fpkm" options and plotted in Excel. Heatmaps showing enrichment of reads from ChIP-seq datasets (Fig. 4d, e ) were generated using the annotatePeaks. pl script from HOMER  57  with the "none -size 4000 -hist 600 -ghist -fpkm" options and plotted in Morpheus. The Pol-IV ChIP-seq data sets in different clsy mutants were downloaded from GSE99693  37  , and were mapped, analyzed, and visualized as described for the CLSY ChIP.
smRNA-seq mapping and 24nt-siRNA cluster calling. The smRNA-seq data were mapped (Supplementary Data 2) and 24nt-siRNA clusters identified (Supplementary Data 4) as described in Zhou et al. 37. Briefly, the adapters were removed from the de-multiplexed reads using cutadapt (v1. 9. 1)  63  and trimmed reads longer that 15nt were mapped to the TAIR10 genome using ShortStack (v3. 8. 1)  64  with the following options: --mismatches 1 and either --mmap f, to include multimapping reads, or --mmap n, to include only unique mapping reads. As described in Zhou et al. 37  only perfectly matched reads or reads with a single 3′ mismatch were used to call smRNA clusters using ShortStack  64  with the --mincov 20, --pad 100, --dicermin 21 and --dicermax 24 options, to generate Tag Directories using the makeTagDirectory script from the HOMER  57  with the -format sam -mis 1 and -keepAll options, and to make genome browser tracks specifically for the 24nt sized smRNAs using the makeUCSCfile script from HOMER  57  with the -fragLength 24 and -norm 10000000 options.
Core, Master, and clsy1,2-versus clsy3,4-dependent Fl,Rs,Lv,Ov 24nt-siRNA cluster lists (Supplementary Data 5). An inclusive list of 24nt-siRNA clusters present amongst the four tissues was generated by first identifying the core set of 24nt-siRNA clusters present in all three replicates for each tissue using the mergePeaks script from HOMER  57  , as was done for flower tissue in Zhou et al. 37. These regions were then combined using the BEDOPS  62  --merge function to identify a non-redundant set of clusters based on all four tissues, hereafter referred to as the "master 24nt-siRNA clusters" list. Lists of clusters reduced in the clsy1,2 or clsy3,4 mutants across all tissues were identified first using the BEDOPS  62  -u function with the clusters from each of the four tissues as input files, and then the sort -u function to remove duplicates.
Differentially expressed (DE) 24nt-siRNA clusters analysis. For differential expression analyses, 24nt-siRNA levels at each of the master 24nt-siRNA clusters were quantified for each tissue and genotype with the HOMER 57 annotatePeaks. pl script using the "-noadj, -size given and -len 1" options and differentially expressed 24nt-siRNA clusters compared to the three wild-type controls were identified using DESeq2  59  (log 2 FC ≤ 1 and FDR ≤ 0. 01) (Supplementary Data 11). To account for the extreme decreases in 24nt-siRNA counts in some mutants (i. e. pol-iv), the size factor estimates calculated by DESeq2  59  for the wild-type samples from each tissue were used to adjust the size factor estimates for the other samples to normalize the data to mapped reads of all size classes rather than to just 24nt-siRNAs.
pl script using the "tair10 -size given -fpkm and -len 1" options (Supplementary Fig. 6a ). MethylC-seq library preparation, mapping, and DMR calling DNA isolation, methyl-seq library construction, and sequencing. ~0. 1 g of tissue from 15 day old rosettes, 25 day old leaves, or >5000 unfertilized ovules were collected for genomic DNA isolation using the DNeasy Plant Mini Kit (Qiagen, Cat# 69104). MethylC-seq libraries were generated using 2. 0 μg of DNA from rosettes and leaves, and 0. 5-1. 0 μg of DNA from ovules as described in Li et al. 65. The libraries were then pooled and sequenced (single end 50 bp, SE50) on a HiSeq 2500 machine (Illumina). MethylC-seq data processing. MethylC-seq reads were mapped and processed using BS-Seeker2 66 as described in Zhou et al. 37. Briefly, reads were mapped with the bs_seeker2-align.