dataesr/scientific-paragraphs-categorization huggingface dataset:
9 and 10) as well as the turnover time of nutrients in specific ecosystem compartments. We find that ORCHIDEE-CNP simulates the openness of nutrient cycles, including differences among biomes that are close to estimates from the model-data fusion framework GOLUM-CNP (Fig. 6 ; Sect. 4. 4). There are differences in the openness of N (O N ) in tropical natural biomes and the openness of P (O P ) in central Africa, which are related to lower but more realistic tropical BNF in ORCHIDEE-CNP (Sect. S4) and a difference in the prescribed P deposition compared to GOLUM-CNP. Simulated nutrient losses due to aquatic transport are generally in good agreement with independent estimates (Sect. S5). Residence times of N and P (τ N and τ P ) in ORCHIDEE-CNP compare generally well to estimates from GOLUM-CNP: ORCHIDEE-CNP simulates shorter τ N and τ P in tropical and temperate biomes compared to boreal ones, in line with GOLUM-CNP (Fig.
Inclusion of nutrient cycling improves the interannual variability of GPP To what extent nutrient effects on vegetation affect the sensitivity of ecosystem CO 2 fluxes to climatic variation is unclear  (Goll et al. , 2018). For instance, drought can reduce nutrient use by decreasing GPP, but it also slows down decomposition, which supplies nutrients for plant uptake. Further, N : P stoichiometry is also strongly modified by drought and warming towards increased N : P in whole plant biomass  (Yuan and Chen, 2015 ). Here we found that the inclusion of N and P cycles in ORCHIDEE affects the interannual variability of GPP for all vegetation types. In ORCHIDEE-CNP, the interannual variation (IAV) of GPP is better correlated with that of observation-based datasets than in ORCHIDEE globally and for the NH, but less correlated for other regions (Fig.
Compared to data-driven estimates for C storage (Sect. S1G and S1H), ORCHIDEE-CNP simulates a higher global aboveground forest biomass (387 Pg C; 283 Pg C for GlobBiomass,  Santoro, 2018; 221 Pg C for GEOCARBON, Operational Global Carbon Observing System, Avitabile et al. , 2016 ; Fig. S2 ) but lower global soil organic carbon (801 Pg C; 4387 Pg C for Soilgrids,  Hengl et al. , 2017; 1680  Pg C for GSDE -Global Soil Dataset for use in Earth system models,  Shangguan et al. , 2014 ; Fig. S3 ).
Code and data availability. The source code is freely available online via the following address: http://forge. ipsl. jussieu. fr/orchidee/wiki/GroupActivities/CodeAvalaibilityPublication/ ORCHIDEE-CN-P_v1. 2_r5986 (last access: 8 May 2020,  Goll, 2020). Please contact the corresponding author if you plan an application of the model and envisage longer-term scientific collaboration. Primary data and scripts used in the analysis and other supplementary information that may be useful in reproducing the authors' work can be obtained at https://doi. org/10. 17632/f54v9zcgbf. 1  (Sun et al. , 2020). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/gmd-14-1987-2021-supplement. Author contributions. YS and DSG carried out the simulation of ORCHIDEE-CNP.
The mean value across TRENDY ensemble models (v6) and three sets of NBP from inversion datasets were used as the reference database for the comparison with different available periods(TRENDY Ensemble:  1959-2016; CAMS: 1979 CAMS:  -2016;; JENA: 1985 JENA:  -2016;; CTracker: 2001 CTracker:  -2016)).
Acknowledgements. Yan Sun ,  Daniel S. Goll ,  Philippe Ciais ,  Haicheng Zhang  and  Yuanyuan Huang  are funded by the  IMBALANCE-P  project of the  European Research Council  ( ERC-2013-SyG610028 ). Julian Helfenstein  is supported by the  Swiss National Science Foundation  (project number  200021_162422 ). Financial support. This research has been supported by the  IMBALANCE-P  project of the  European Research Council  (grant no. ERC-2013-SyG610028 ). Review statement. This paper was edited by  Chiel van Heerwaarden  and reviewed by two anonymous referees.
Unfortunately, GGMs or variants thereof can not properly account for the extremely sparse patterns occurring in realworld metagenomic taxonomic profiles. In particular, structural zeros (as opposed to sampling zeros) corresponding to true absences of biological signals fail to be properly handled by most statistical methods. We present here a zero-inflated log-normal graphical model (available at https://github. com/vincentprost/Zi-LN) specifically aimed at handling such "biological" zeros, and demonstrate significant performance gains over state-of-the-art statistical methods for the inference of microbial association networks, with most notable gains obtained when analyzing taxonomic profiles displaying sparsity levels on par with real-world metagenomic datasets.
Data transformation. Because of variations in sequencing depths across samples, the total number of reads in a sample only reflects the relative proportion of microbes it contains  [30]. This compositional nature of sequence data prevents naive correlation-based analyses without appropriate correction  [29] , of which log-ratio based methods are the most commonly used. In the latter, the handling of zeros by adding a fixed pseudocount (i. e. , irrespective of the sequencing depth of the various samples) has been shown to introduce important biases when the datasets include rare taxa and/or are inhomogeneous in coverage  [10]. Downsampling or ignoring samples to level off sequence depth, or aggressive thresholding to remove rare taxa, can somewhat alleviate this bias, although this has been criticised as statistically unsound  [31]  and is performed at the cost of discarding large amounts of possibly biologically relevant data.
One way to address this difficulty, as originally shown by Aitchison  [29]  and implemented in popular toolkits (e. g. [23] ), is to apply a centered-log-ratio (clr) transformation to the counts: y clr ij ¼ logðy ij Þ À 1 p X p k¼1 logðy ik Þ ð3Þ An important property of this transformation is scale-invariance, which guarantees that two samples with similar read proportions will have similar transformed count data. Indeed, if counts represent absolute abundances with a scaling factor a i � λy i , then we would have a clr i � y clr i. As clr is not defined in zero, a typical workaround consists in adding a unit pseudocount to the original data in order to avoid numerical problems. However, doing so breaks the scale invariance property, as shown in S2 Text. We therefore propose a slightly different version of clr, preserving scale invariance in the presence of zeros and defined as following: ỹij ¼ ( logðy ij Þ À P k; y ik 6 ¼0 logðy ik Þ jk; y ik 6 ¼0j if y ij 6 ¼ 0 0 if y ij ¼ 0 ð4Þ where |k, y ik 6 ¼ 0| denotes the number of non zero entries of y i.
Synthetic datasets In order to compare the performance of various methods without knowing the true underlying associations among taxa in real datasets, we designed synthetic datasets with a controlled ground truth. For our simulations, we tested two generative models. The first is the model described in section 3. 1 with Eqs (1) and (2) (ZiLN). The second is the model implemented in  [23]  using Gaussian copulas and zero-inflated negative binomial marginals (combined in the NorTA protocol). The covariance matrix S associated to a given graph topology is generated using the Spiec-Easi framework  [23] , with condition number κ = 100 and the number of edges e equal to the number of variables p (the sparsity assumption of the association graph is translated into the scaling of the number of taxon-taxon associations with the number of taxa).
Accounting for sparsity and overdispersion In order to assess the ability of our model to deal with the high levels of sparsity and overdispersion that are inherent to natural microbial abundance datasets, we compared the extent to which synthetic count data generated under our model and two other model-based approaches are able to reproduce abundances of taxonomic profiles generated from human gut microbiomes of the population-based LifeLines-Deep cohort  [33]. This cohort includes 1,135 individuals (474 men and 661 women) from the general Dutch population, whose gut microbiomes were shotgun sequenced using the Illumina short-read technology, generating an average of 32 million reads per sample (EBI dataset EGAD00001001991). We phylogenetically profiled the sequences from the LifeLines-Deep cohort using the metapalette software  [34] , and several samples by taxa matrices corresponding to different taxonomic ranks were constructed from these results, e.
e. the graphical lasso) for network structure inference. SparCC is a relatively older but widely used method dealing with compositional data and measuring the linear relationship between log transformed abundances  [36]. Flashweave  [37]  is a recently developed tool based on the HITON  [38]  constraint-based conditional independence solver and leverages the so-called local-to-global learning framework to infer directly associated neighborhoods of variables in large systems  [37]. We performed a comparison of these three model-based methods in terms of their ability to accurately recover the structure of controlled networks of different dimensions, topologies and sampling regimes (described in the subsection 3. 3. 1 of the Material and Methods). The performance of the methods was quantified by the area under precision-recall curves (AUPR, which should be preferred over ROC-AUC on imbalanced datasets with a lot of true negatives like microbiome taxonomic data), and is displayed in In practice, this comparison process does not rely on the inference of a single graph but of a series of graphical models of varying sparsity (including the empty and complete network as extremes), which together constitute the solution path.
It should be noted however that all methods show only limited accuracy for this network topology when operating in the under-determined regime, where the number of variables (taxa, p) exceeds the number of   [23] , MAGMA (gray)  [24]  and ours (ZiLN, orange), as well as no transformation at all (green)), was tested with two structure inference algorithms (glasso and neighborhood selection). Two other orthogonal (i. e. based on distinct rationales) network inference methods, sparcc (dark-brown)  [36]  and Flashweave (dark-red)  [37] , were included to broaden the comparisons. https://doi. org/10. 1371/journal. pcbi. 1009089. g002 samples (n). It should also be noted that the MB algorithm appears to perform better than the graphical lasso. A similar figure, presenting network reconstruction accuracy metrics for a distinct dataset generated using the NorTA protocol with zero inflated negative binomials, is shown in the Supplemental Information (see S1 Fig).
Inference of real-world microbial association networks We finally applied our method to infer microbial association networks from real-world taxonomic profiles generated from healthy gut microbiomes of the LifeLines-Deep  [33]  population cohort. Even though confirmed ecological associations within natural microbial communities are only scarcely known, several global network properties have been reproducibly documented, including the salient observation of preferential associations among phylogenetically close organisms  [39, 40]. This property, known as assortativity, is manifest at the level of highly connected components in our analysis, and is displayed for the species taxonomic level in Fig 4. To provide a more quantitative support for the observed assortativity phenomenon, we computed assortativity coefficients (homophyly of the graph) for the different methods at various phylogenetic levels using the igraph R package  [41]  (shown in S1 Table ).
It can be seen that our method has the highest ratio (239/100) of edges predicted by all methods versus idiosyncratic ones, but that overall substantial differences remain among the predictions of the various methods. that the problem lies in the GGM formalism itself or in inherently flawed inference algorithms; instead, it is most likely rooted in difficulties to properly account for the idiosyncrasies of biological data. Beyond compositionality and overdispersion, high levels of sparsity are common attributes of real-world biological datasets, which are challenging to account for in a statistically sound manner. In particular, an important distinction between sampling zeros and structural zeros is often neglected, and nearly all of the actual analyses of multidimensional metagenomic count data perform some form of aggressive thresholding to discard rare but possibly biologically relevant data.
Molecular data We used a fragment of the mitochondrial gene 12S rRNA gene (12S, amplicon ~ 400 bp) as a barcoding marker. DNA extraction, PCR amplification, and sequencing for all newly generated sequences were carried out at Service de Systématique moléculaire of the MNHN, following the protocols as described in  Dong et al. (2018) , including primers and annealing temperatures. Sequences were cleaned and checked for sequencing errors in Sequencher ver. 4. 9 (Gene Codes Co. ) or Geneious ver. R9. 0. 2 (Biomatter Ltd. , New Zealand, www. geneious. com; Kearse et al. 2012) , and BioEdit ver. 7. 0. 5. 3  (Hall 1999 ), then blasted with NCBI blast tools, and submitted to GenBank (Table  1 ). Fig. 3. Forewing venations (with labels of the veins and areas) of Nisitrus vittatus  (Haan, 1844). A.
♂. B. ♀. Scale bar = 5 mm. The sequences were aligned with MAFFT ver. 7 online  (Kuraku et al. 2013; Katoh et al. 2017 ). The complete combined dataset consisted of 403 aligned base pairs (bp) for a total of 28 terminals, including 3 sequences of the species Paranisitra longipes  Chopard, 1925  to represent the sister genus Paranisitra, and 1 sequence of Gryllus bimaculatus De Geer, 1773, used as outgroup in the phylogenetic analysis. Phylogenetic analyses were carried out using the IQ-TREE web server (http://iqtree. cibiv. univie. ac. at/; Trifinopoulos et al. 2016) ; maximum likelihood phylogenetic reconstruction was run using IQ-TREE ver. 1. 6. 2 web portal (Nguyen et al. 2015)  (http://iqtree. cibiv. univie. ac. at/) and ModelFinder  (Kalyaanamoorthy 2017). Clade support was assessed by conducting 1000 ultrafast bootstrap replicates  (Hoang et al.
2. 97 and an 8-Pre MOTU sound card at a sampling frequency of 96 ksamples / s  (16 bit). Song features were measured using Avisoft Lite ver. 2. 0. 0. Power spectra using Fast Fourier Transformation (FFT) were generated using the 'meanspecʼ function at 256 000 sampling frequency, using a Hanning window of window length 512 from the R package ʻseewaveʼ  (Sueur et al. 2008). All recording files are deposited in the Sound Library of the MNHN under accession numbers MNHN-SO-2020-2851 to MNHN-SO-2020-2860. To examine how the call properties of N. malaya sp. nov. , N. musicus and N. vittatus differ, the call properties (which were not mutually independent and likely to be highly intercorrelated) were summarised into major gradients of variation by performing a Principal Coordinates Analysis (PCoA) on the scaled call properties using the 'cmdscaleʼ function in R ver.
Acknowledgements We thank  Andrej Gorochov  ( ZIN ) for providing samples from the collections of Saint Petersburg; Jérôme Constant and  Carole Paleco  ( RBINS ) for their help during the study of the collections of the  Royal Belgian Institute of Natural sciences, Brussels , funded by the  SYNTHESYS European program  ( BE-TAF-6640 );  Rob de Vries ,  Caroline Pepermans  and  Luc Willemse  ( NBC Naturalis ) for their help with the Leiden collections;  Sigfrid Ingrisch (ZFMK)  for providing specimens from  Sumatra and Borneo; Ellen Sandström Biological Museum, Lund University ;  Martin Husemann  for the loan of material from Hamburg museum;  Russell Cox  and  Dan Bickel  for the loan of material from the  Australian Museum ;  Judith Marshall  and  George Beccaloni  ( BMNH ) for their help during the study of the  Natural History Museum of London collections  (funded by the  SYNTHESYS European program   GB-TAF-531 );  Peter Schwendinger  and  John Hollier  ( MHNG ) for their help during the study of Genève collections; and  Oscar Effendi  and  Erni Ernawati  ( MZB, Indonesia ) for their help during the study of eneopterine crickets in Cibinong, Indonesia (MZB), and all the other persons helping us in the collection of crickets and in some other aspects.
Inter-laboratory mass spectrometry dataset based on passive sampling of drinking water for non-target analysis Bastian Schulze, Denice van Herwerden, Ian Allan, Lubertus Bijlsma, Nestor Etxebarria, Martin Hansen, Sylvain Merel, Branislav Vrana, Reza Aalizadeh, Bernard Bajema, et al. Non-target analysis (Nta) employing high-resolution mass spectrometry is a commonly applied approach for the detection of novel chemicals of emerging concern in complex environmental samples. Nta typically results in large and information-rich datasets that require computer aided (ideally automated) strategies for their processing and interpretation. Such strategies do however raise the challenge of reproducibility between and within different processing workflows. An effective strategy to mitigate such problems is the implementation of inter-laboratory studies (ILS) with the aim to evaluate different workflows and agree on harmonized/standardized quality control procedures.
Here we present the data generated during such an ILS. this study was organized through the Norman Network and included 21 participants from 11 countries. A set of samples based on the passive sampling of drinking water pre and post treatment was shipped to all the participating laboratories for analysis, using one pre-defined method and one locally (i. e. in-house) developed method. The data generated represents a valuable resource (i. e. benchmark) for future developments of algorithms and workflows for NTA experiments.
Data DeScrIptor opeN In this study, we present the data collected during an international collaborative ILS organized through the Norman Network. This study aimed at assessing the uncertainty in the identified compounds caused by different NTA workflows and spectral databases. We used a passive sampling (PS) strategy to sample river water at a drinking water intake, and post treatment for the production of drinking water. This will enable us to sample substances (i) present in river water at the drinking water intake, (ii) present in drinking water, as well identify those (iii) removed during treatment and (iv) generated during drinking water treatment. PS enables to pre-concentrate chemicals from a complex matrix while leaving behind a significant proportion of unwanted matrix affecting the performance of the analysis, therefore allowing to sample chemicals often found at trace-levels in water and in the environment  30, 31.
The participants were allowed the freedom to use any data processing strategy for the identification of CECs in those samples (details are provided in section Experimental Design). This dataset includes the raw data from 21 different laboratories (Fig. 1 ), using different reversed-phase LC (RPLC) columns and instrumentations (Tables  3  and 4 , respectively), chromatographic gradients, and MS acquisitions. This dataset provides the means to assess the impact of method transfer, chromatographic conditions, and data processing workflows on the results of NTA approaches. Additionally, this dataset may, potentially, be used as a benchmark for future development of algorithms for NTA workflows.
As for the injection volumes, 16 participants followed the instructions in the pre-defined method (i. e. 5 µL) while three participants opted for 10 µL injection volume. One of the participants performed a large volume injection of 200 µL. All participants used C18 columns for both the pre-defined and in-house methods. 14 out of 21 participants used the 0. 4 mL/min flow rate suggested by the pre-defined method while six participants used different flow rates between 0. 21 and 0. 3 mL/min and one of 1 mL/min. Additionally, it should be noted that not all the participants employed the same column for both pre-defined and in-house methods (Table  3 ). In terms of mass spectrometers, 8 Thermo Fisher Scientific, 6 Waters, 3 Bruker, 3 Sciex and 1 Agilent instruments were used for the data acquisition.
All eight Thermo Fisher Scientific instruments were Orbitraps, while all other instruments relied on the time-of-flight (TOF) principle (Table  4 ). All the participants were asked to perform the analysis via data independent acquision (DIA). This acquisition mode of accurate-mass full-scan spectra under different collision induced dissociation conditions within a single injection is known under different names depending on the manufacturer: MSE in the case of Waters, broadband collision-induced dissociation (bbCID) in the case of Bruker, or all-ions MS/MS in the case of Agilent. Likewise for Q-Orbitrap instruments from Thermo, this type of acquisition is also possible, and known as All Ion Fragmentation (AIF) or variable Data-Independent Analysis (vDIA). An alternative DIA was Sequential Window Acquisition of All Theoretical Mass Spectra (SWATH-MS), which maily performed via AB Sciex instruments.
Data records This ILS resulted in three different data records -i. e. mzXML files related to the pre-defined method, mzXML files related to the in-house method, and the experimental conditions in XLSX format. Record 1. includes the mzXML files for both the samples related to the river water and drinking water with exposure times of 2 and 4 days analyzed via the pre-defined method. This record includes 157 mzXML files  41, 42. Record 2. includes the mzXML files for both the samples related to the river water and drinking water with exposure times of 2 and 4 days analyzed via the own method. This record includes 134 mzXML files  42, 43. Record 3. consists of an excel file that includes the details of participants and the experimental conditions associated with each mzXML file, including the software packages used for the data acquisition and pre-processing  44.
technical Validation We used the detection of IS, their mass accuracy, retention factor (i. e. run-time normalized retention time), and their intensity to assess the quality of the generated data by the participants. The extracted ion chromatograms (XIC) of the IS were generated using a mass window of ±0. 01 Da. The peak of each IS was identified as the maximum signal in the selected XIC. Cases where we were not able to identify the IS signal as the most prominent signal, were considered as not detected. The measured mass (i. e. the median mass of three scans around apex), retention time, and intensity of apex were recorded for quality assurance of the data. For 98% of datasets, the participants were able to detect all six IS, independently from the method used (i. e. pre-defined versus in-house).
We did not observe a statistically significant correlation between the retention time, m/z value and the measured mass error. Pre-defined method In-house method Fig. 4  The detection frequency of each IS in pre-defined and in-house method. As for the intensities of the IS a median intensity of 10 6 was measured across the datasets and the methods. The variance in the intensities of around five orders of magnitude appeared to be independent from the method used for the analysis.