dataesr/scientific-paragraphs-categorization huggingface dataset:
| Physiognomic information To achieve a classification into forests versus non-forests that is applicable to all plots irrespective of the structural and habitat data provided by the source database, we defined as forest all plot records that had >25% absolute cover of the tree layer, making use of the attribute data of sPlot. This threshold is similar to the classification of  Ellenberg and Müller-Dombois (1967) , who defined woodland formations with trees covering more than 30%. There were 16,244 tree species in the sPlot database. As tree layer cover was available for only 25% of all plots, we additionally used the information whether the taxa present in a plot were trees (usually defined as being taller than 5 m), using the plant growth form information from TRY (see below). Thus, plots lacking tree cover information were defined as forests if the sum of relative cover of all tree taxa was | 169 >25%.
GIVD ID refers to the ID in the Global Index of Vegetation-Plot Databases (http://www. givd. info), which manages the metadata for sPlot and provides updated online descriptions of these databases; * after the GIVD ID indicates that the respective database description is currently not visible on the GIVD website. Datasets contributed in harmonized format from a continental data aggregator ("collective database" according to the sPlot Rules) are listed under its name. Further references, attributions and disclaimers for particular datasets are found Appendix S1.
| Associated environmental plot information To complement the plot data, we harmonized geographical coordinates (in decimal degrees), elevation (m above sea level), aspect (degrees) and slope (degrees) as provided by the contributing databases. All other variables were too sparsely and too inconsistently sampled across databases to be combined in the global set, but were retained in the original data sources and can be retrieved for particular purposes. We used the geographic coordinates to create a geodatabase in ArcGIS 14. 1 (ESRI, Redlands, CA) to link sPlot 2. 1 to these climate and soil data. We retrieved data for all the 19 bioclimatic variables provided by CHELSA v1. 1  (Karger et al. , 2017)  by averaging climatic data from the period 1979-2013 at 30 arc seconds (about 1 km in grid cells near to the equator).
| Trait information To broaden the potential applications of the global vegetation database in functional contexts, we linked sPlot to TRY. We accessed plant trait data from TRY version 3. 0 on August 10, 2016, and included 18 traits that describe the leaf, wood and seed economics spectra  (Westoby, 1998; Reich, 2014; Table S6. 4  in Appendix S6), and are known to affect different key ecosystem processes and to respond to macroclimatic drivers. These traits were represented across all species in the TRY database by at least 1,000 trait records. We excluded trait records from manipulative experiments and outliers  (Kattge et al. , 2011) , which resulted in a matrix with 632,938 individual plant records on 52,032 taxa in TRY, having data records for an average of 3. 08 of the 18 selected traits.
On average, each trait has been measured at least once in 17. 1% of all taxa. In order to attain data for these 18 traits for all species with at least one trait value in TRY, we employed hierarchical Bayesian modeling, using the R package 'BHPMF'  (Fazayeli, Banerjee, Kattge, Schrodt, & Reich, 2017; Schrodt et al. , 2015) , to fill a gap in the matrix of individual plant records in TRY. Gap filling allows obtaining trait values for a species on which this trait has not been measured, but for which other traits are available. To assess gap-filling quality, we used the probability density distributions provided by BHPMF for each imputation and removed highly uncertain imputations with a coefficient of variation >1. We then log e -transformed all gap-filled trait values and averaged each trait by taxon.
| Phylogenetic information The phylogenetic tree for sPlot was produced from 53,489 vascular plant names contained in the database, comprising 5518 genera (Appendix S7). Moderately to highly frequent species in sPlot 2. 1 are equally distributed across the phylogeny (corresponding to yellowish to reddish colors for low and high peaks, respectively, in Figure  S7. 6 in Appendix S7). Coverage of species included in the phylogeny ranges from 89% of species that occur only once in all plots to 100% of species with a frequency >10,000 plots (Figure  S7. 7 in Appendix S7).
| DATA USAG E The sPlot database (the vegetation-plot data, including the environmental information for each plot and the species phylogeny) is released in fixed versions to allow reproducibility of results, but also due to the enormous effort needed for data integration and harmonization and for updating the phylogeny. By delivering few fixed versions while keeping older versions available, the sPlot consortium ensures that the same data can be used in parallel projects and that the data underlying a specific study remain accessible in the future, thus allowing re-analysis. Each new version will be matched to the current TRY database. Data access to sPlot is regulated by the Governance and Data Property Rules (www. idiv. de/sPlot) to ensure a fair balance between the interests of data contributors and data analysts.
In brief, the sPlot Rules state that: with particular responsibilities may also be appointed as personal members to the sPlot consortium; (d) sPlot data can be requested for projects that involve at least one member of the sPlot consortium; (e) whenever a project has been proposed, all sPlot consortium members will be informed and can declare their interest in becoming co-authors of manuscripts resulting from this project and then becoming actively involved in data evaluation and writing; and (f) if also the matched gap-filled or original trait data from TRY are requested for a project, likewise members from the TRY consortium can opt-in as co-authors. The sPlot database is, therefore, available according to a 'give-and-receive' system. Moreover, the data are available to any researcher by establishing a collaboration that includes and is supported by at least one sPlot consortium member.
| E XPEC TED IMPAC T AND LIMITATI ON S The main aim of the sPlot database is to catalyze a collaborative network for understanding global diversity patterns of plant communities in space and time. sPlot provides a unique, integrated global repository of data that would otherwise be fragmented in unconnected and structurally inconsistent databases at regional, national or continental levels. Together with the provision of harmonized phylogenetic, functional and environmental information, sPlot allows, for the first time, global analyses of plant community data. Compared to approaches using data aggregated from species occurrences in grid cells, sPlot will significantly advance ecological analyses and future interdisciplinary research in at least four different ways. 1. Using sPlot, one can predict the species that can co-exist in a community and also the frequencies of their co-occurrence.
| 179 Journal of Vegetation Science BRUELHEIDE Et aL. (Breitschwerdt, Jandt, & Bruelheide, 2015)  or niche overlap  (Broennimann et al. , 2012). In addition, emerging tools such as Markov networks can be used to infer strengths of interspecific interactions  (Harris, 2016). When investigating community assembly rules, the same information can be used to derive species pools for specific vegetation types  (de Bello et al. , 2016; Karger et al. , 2016; Lewis, Szava-Kovats, & Pärtel, 2016). Moreover, the co-occurrence data from sPlot can be used to address fundamental patterns and drivers of plant invasions better than information on large geographic entities (e. g. , van  Kleunen et al. , 2015)  alone could. 2. sPlot data can be aggregated across all types of plots, by grid cells, ecoregions, environment, or even vegetation type or formation.
Furthermore, replicated plots within grid cells, ecoregions, or any other subdivision of environmental conditions or vegetation types allow users to derive measures of compositional differences between plant communities within grid cells (= beta diversity; Table  1 ). Thus, the community data are an important complement to regional-scale species occurrence data (e. g. ,  Enquist et al. , 2016; Kreft & Jetz, 2007). 3. sPlot data provide information on the proportion of species in a community (in terms of cover, basal area, frequency). When combined with functional trait information, relative abundance of species allows calculation of community abundance-weighted mean trait values  (Bruelheide et al. , 2018). Information on the relative contribution of species to a community-aggregated trait value is particularly necessary when traits are used as proxies for vegetation functions and processes, allowing to test, among other things, the mass ratio hypothesis  (Garnier et al.
ACK N OWLED G EM ENTS We are grateful to thousands of vegetation scientists who sampled vegetation plots in the field or digitized them into regional, national or international databases. We also appreciate the support of the German Research Foundation for funding sPlot as one of the iDiv (DFG FZT 118) research platforms, and the organization of three workshops through the sDiv calls. We acknowledge this support with naming the database "sPlot", where the "s" refers to the sDiv synthesis workshops. The study was supported by the TRY initiative on plant traits (http://www. try-db. org). For all further acknowledgements see Appendix S10. We thank Meelis Pärtel.
Global coverage of sPlot 2. 1. (a) Contributing databases identified by different colours with indication of the two data aggregators (EVA, TAVA) and a few particularly large individual databases; (b) available plot numbers per WWF Ecoregion; and (c) available plot density in grid cells of 100 km × 100 km F I G U R E 4 Examples of global community-level patterns that can be derived from (a) sPlot alone and (b-d) sPlot combined with TRY, here shown as raw data averaged by 1-degree grid cells. There are only a very few cells (142 out of 2633) comprising only a single plot. For the maps, only plots with full vascular species composition and spatial accuracy <5 km were used. They are based on 148,474 and 218,051 plots for forests and non-forests, respectively. Note that these maps are not corrected for biases caused by the facts that not all community types were recorded in all grid cells and that plot sizes as well as the fraction of species with available trait data varied spatially.
In addition, analyses of functional diversity with sPlot data are limited by the absence of trait data for a (small) portion of the species and by the lack of plotspecific trait measures. Furthermore, the non-random and geographically and ecologically very unequal distribution of the plots contained in sPlot call for stratified resampling to balance records of different environments (e. g. , stratified by climate, Figure2) or physiognomic formations (Figure4). Users of sPlot need to be aware of these and other limitations and to correct potential biases for their specific research question. 6 | CON CLUS IONsPlot is a unique global database of plant community records sampled with relatively similar methods widely used in vegetation ecology. The integration of co-occurrence data into a unified database that can be directly linked to environmental, functional and phylogenetic information, makes sPlot an unprecedented and essential tool for analyzing global plant diversity, the structure of plant communities and the co-occurrence of plant species.
The compatibility of this consolidated database with other global databases, e. g. , via a joint taxonomic backbone with TRY and the Global Naturalized Alien Flora(GloNAF; van Kleunen et al. , 2015)  (via taxon names), or via standardized geo-reference with databases of environmental information such as CHELSA, WorldClim or SoilGrids (Bruelheide et al. , 2018) , facilitates data integration and creates new research opportunities. The adaptive management of the database employed by the sPlot consortium allows regular incorporation of new data, resulting in a dynamic platform for storing and analyzing the most comprehensive compilation of plant community data worldwide.
for his very fast and constructive feedback on an earlier version of this manuscript. AUTH O R CO NTR I B UTI O N S H. Bru. had the original idea and led the consortium from the start, while O. Pu. and J. D. coordinated the sPlot workshops. J. D. , S. M. H. and U. J. compiled the databases to be included in sPlot. J. D. and later B. J. -A. and F. M. S. coordinated the network and the database. O. P. prepared the taxonomic and phylogenetic data. S. M. H programmed the Turboveg software. B. Sa. , F. J. , H. Bru. , J. D. , J. K. , M. Ch. , and V. D. P.
DATA ACC E S S I B I L I T Y The data contained in sPlot (the vegetation-plot data complemented by species phylogeny and environmental information) are available on request, through contacting any of the consortium members for submitting a paper proposal. The proposals should follow the Governance and Data Property Rules of the sPlot Working Group, which are available on the sPlot website (www. idiv. de/sPlot). After acceptance, the.
Introduction Real-Time quantitative PCR (RT-qPCR) has been used in the last two decades by biologists in several fields of biology. The main application is to quantify relative gene expression  [3]. The wide success of this technique can be attributed to low cost, speed, sensitivity and specificity of the measurements compared to techniques such as northern blot. It has also benefited from the increase of gene sequences in data banks. RT-qPCR can quantify low gene expression but also simultaneous multiple gene expressions in a single tube  [11]  with the use of specific primers. The accuracy of RT-qPCR techniques has contributed to the development of biomarkers in various fields from medical diagnostic to bioagressors detection for agricultural crops as well as gene expression knowledge in academic research area.
From the beginning of RT-qPCR development, scientists have proposed rules to design and analyze these biological data through Minimum Information for Publication of Quantitative Real-Time PCR Experiments (MIQE) guidelines  [3, 4]. However, a single gene is often use to normalize expression of gene of interest (GOI) and primers efficiency are not given or used for this normalization. Student t-test is often misused in RT-qPCR analysis. Tools to implement MIQE rules exists such as geNorm or RefFinder (review in  [7] ) to evaluate reference genes and as HTqPCR for normalization, quantification and statistics (review in  [8] ). These tools are either performing only one step of MIQE rules or are implemented in programming languages such as R that necessitate coding knowledge. Our web-application aims to facilitate the analytical work of biologists since we provide a ready to use tool to statistically analyze RT-qPCR data according to the MIQE guidelines.
SATQPCR is a website that supports all quantitative PCR machine data as it uses Cq "parameters" usually provided by the machine. Cq value results from fluorescence measurement of a sample obtained and collected after each PCR cycle. It represents the accumulation of an amplicon in a sample. Our application needs a text file associating the basic Cq data and the efficiency calculated for each gene to quantify. The normalization of the data is based on the search of genes with the lowest gene expression variability using geNorm method  [10]. The statistical analysis of our previous R script was improved  [6]  by including one-way and two-ways ANOVA. All graphs and tables resulting from the statistical analysis are visible on the website and can be downloaded using a single link.
Conclusion Even if new statistical analysis methods have been proposed for RT-qPCR such as the Unified Censored Normal Regression Model  [9] , the procedure using references genes as performed in our web-application SATQPCR is adequate for small datasets which are the most often used. Our web application is free compare to commercial packages such as qBASE+ Biogazelle NV  [5]  and GenEx (MultiD Analyses AB) and the biologist does not need to learn R language as in the open source tool DAG Expression  [1]. Our web-application is following MIQE rules with normalization steps including gene efficiency and the use of at least two" reference" genes. The application is very fast and provides graphs as well as statistical tables summarizing the results of the chosen methods (t-test or ANOVA with Tukey test).
The final mutational signature, here named SBS-MM1, did not correspond to any of the COSMIC reference mutational signatures but was compatible with a recently reported MM mutational signature of unknown etiology (Fig. 1a  and Supplementary Fig. 1A, B )  9, [17] [18] [19]. The existence of SBS-MM1 as a distinct mutational process was further validated by extending the mutational signature analysis to include the two flanking bases 5' and 3' to the mutated base (1536 classes or 5-nucleotide context; Supplementary Fig. 1C, D ). Despite their statistical robustness, in heterogeneous cancer types such as MM, de novo extraction algorithms can be biased by the bleeding of mutational signatures between samples, where mutational signatures present in only part of the dataset are erroneously assigned to every sample  17, 18.
Clock-like mutational process in MM. Similar to other cancers 2,6,9 , we found SBS1 and SBS5 to be active in all phases of MM in all patients. In many cancers and normal tissues, these mutational signatures are correlated with patient age  4, [6] [7] [8] 38, 39. Previ us results from a small cohort of MM exomes (n = 68) showed a modest correlation for SBS1 but not for SBS5  6. Here we employed several approaches to determine whether MM does indeed have a clock-like mutational process, integrating our WGS cohort with a large exome series. First, we assessed the relationship between patient age and the mutational burden attributed to each mutational signature at diagnosis of MM in the CoMMpass exome dataset (n = 764) using linear regression models. Significant relationships were identified for SBS5 [slope 1.
55 (95% confidence interval (CI) 0. 97-2. 14); p < 0. 0001] and SBS1 [slope 0. 29 (95% CI 0. 13-0. 45); p = 0. 0003], but none of the other mutational signatures (Fig. 6a  and Supplementary Data 8). Despite the strong correlation, there was considerable variation in SBS1 and SBS5 mutational burden between individual patients that could not be explained by age alone. Next, we applied linear mixed-effects (LME) models to 72 patients in the CoMMpass dataset with paired diagnosis and relapse exomes, allowing for each patient to accumulate mutations at a different rate  5. The LME model confirmed a strong linear relationship between SBS5 and age (p < 0. 0001) and a weaker association for SBS1 (Fig. 6b  and Supplementary Data 8). Finally, to address the SBS5 mutation rate over time in more detail, we applied similar LME models to our WGS cohort (the entire computational workflow is reported in Supplementary Data 9).
Discussion In this study, we drew on large WGS and whole-exome sequencing datasets to reconstruct the timeline of mutational processes shaping the MM genome, providing new insights into its origin and evolutionary trajectories. We identified a mutational signature related to melphalan exposure in relapsed MM, indicating a mutagenic effect of this agent in clonal plasma cells. This is important given that highdose melphalan is considered to be part of the standard first-line treatment in eligible patients  41. Interestingly, not all patients exposed to melphalan showed evidence of SBS-MM1. We propose two potential explanations for this observation. First, according to the recently proposed single-cell expansion model, cells acquiring melphalan-induced mutations must undergo clonal expansion for those mutations to be detectable by bulk WGS  42.
We performed 108 base/100 base (genomic) paired-end sequencing on HiSeq X10 genome analyzers. The average sequence coverage was 38. 7-fold. Short insert paired-end reads were aligned to the reference human genome (GRCh37) using Burrows-Wheeler Aligner, BWA (v0. 5. 9). Sequencing data have been deposited in the European Genome phenome Archive (EGA) under accession number EGAD00001001898. To this cohort, we added 22 MM patients with available WGS data (dbGap: phs000348. v2. p1). Raw data was downloaded and processed similar to the in-house samples. Whole-genome analysis pipeline. All 89 MM samples from the 2 cohorts were uniformly analyzed at the Wellcome Sanger Institute  15. Briefly, deduplicated aligned BAM files were analyzed using the following published tools: (  1  Fig. 8  Single-cell expansion model for melphalan and GC-related mutational signatures.
Phylogenetic tree reconstruction was performed applying the pigeonhole principle to mutational clusters defined by the Dirichlet process  15. The MRCA was defined as the cluster of mutations detected as clonal in all samplers (i. e. , trunk of the phylogenetic tree). Kataegis, or foci of localized hypermutation, was defined as ≥6 consecutive mutations with an average inter-mutation distance of ≤1 Kb. Whole-exome sequencing and analysis. Two cohorts of exome data were included in this study. One comprised of 933 MM patients enrolled within the CoMMpass trial (NCT01454297; phs000748. v1. p1). The CoMMpass data were generated as part of the Multiple Myeloma Research Foundation Personalized Medicine Initiative (https://research. themmrf. org). PhyloWGS  52  was used to reconstruct the phylogenetic tree structure from whole-exome sequencing of 788 patients with available copy number data from low coverage long-insert WGS (median 4-8×).
DNA was enriched for the coding fraction of the genome using the SureSelect All Exon V6 Kit (Agilent Technologies) and the NextSeq500 machine (Illumina) was used to sequence an average of 15. 63 Gb per patient on a 75-bp paired-end protocol. Samples were sequenced to an average depth of 97×. Paired-end reads were aligned to the reference human genome (GRCh37) using Burrows-Wheeler Aligner, BWAmem (v0. 7. 12) and postprocessed following GATK best practices 3. 7. Similarly to the CoMMpass work flow, somatic SNVs were called with three different variant callers: MuTect2 53 , CaVEMan  54  , and MuSE  55. Small insertions-deletions was called with MuTect2. To create the confident variant list, we chose the variants called by at least two algorithms. Single-cell RNA sequencing. A recently published cohort of single-cell RNA sequencing from 29 plasma cell dyscrasia patients and 11 controls was interrogated for AID expression in single cells (GSE117156)  36.
VDJ and HCDR3 reconstruction from WGS data. VDJ gene segment usage and HCDR3 sequences were reconstructed from short reads data using custom scripts (Supplementary Methods). Molecular time. The relative timing of each multi-gain event was estimated using the recently published R package mol_time (https://github. com/nicosangelopoulos/mol_time)  15. Briefly, we selected all chromosomal duplications with >50 clonal mutations estimated by the Dirichlet Process (DP). These mutations were divided using a mixed model (mclust R function) in duplicated (i. e. , present on more than one allele) and non-duplicated (i. e. , present on one single allele). Next, we used the corrected ratio between duplicated and non-duplicated to estimate the relative time of each large chromosomal gain acquisition (i.