dataesr/scientific-paragraphs-categorization huggingface dataset:
This approach resulted in an open reference database of 8920 putative viral genomic contigs ranging in size from 1000 to 207,752 bp. Six hundred ninety-five contigs (1001-151,574 bp long) were circular, suggesting that they may represent complete viral genomes, or other circular extrachromosomal DNA sequences (plasmids, conjugative transposons) inadvertently amplified by the action of polymerase φ29 (Fig. 1b ). Additionally, 1057 contigs were recognised as having a viral origin by Virsorter software. One hundred sixty-one contigs (including 75 positively identified by Virsorter) could be aligned to known viral genomes in the viral section of NCBI RefSeq database. Of the latter, 157 were identified as bacteriophages, two were human papillomavirus 16 and 53 (HPV16 and HPV53), one contig was tomato yellow leaf curl virus (TYLCV)  [47]  and the final one was a sewage-associated gemycircularvirus-4 with unknown host range  [48].
Plotting contig coverage versus length revealed the presence of distinct size classes of circular contigs (Fig. 1b ). For instance, groups of similarly sized contigs likely representing small ssDNA bacteriophages of the subfamily Gokushovirinae (~5 kb) and other subfamilies within the family Microviridae (~6. 3 kb) were readily identifiable  [49]. In addition, a group of loosely inter-related ~97 kb contigs were identified with distant homologies and overall genome structure similarities to uncultured but widespread bacteriophage crAssphage (Fig. 1c ). Collectively, 49. 66% of quality-filtered and trimmed Illumina reads obtained from the faecal VLP fraction across all samples in this study could be aligned to this study's curated database of viral genomic contigs, and we omitted the remaining ~50% of reads from the current analysis.
3 to + 6. 8 relative to base mean according to DESeq2 test, Additional file 7: Figure  S4e , f; Fig. 4e, f ; Additional file 8: Table  S4 ). This probably reflecting the noticeable amounts of operatordependent β-diversity in the phageome dataset. Spiking of faecal homogenates with various amounts (10 5 , 10 6 and 10 7 pfu ml -1 ) of lactococcal phage Q33  [50]  resulted in a quantitative recovery of reads aligning to its genome (Fig. 5a , experiment 1). The percentage of reads aligned to the Q33 genome was a linear function (R 2 of linear regression 0. 73-0. 99) of the amount of spiked phage. The Q33 alignment rates ranged from 0. 009-0. 048% when spiking was performed at a concentration of 10 5 pfu ml -1 , to 0. 95-14. 9% when 10 7 pfu ml -1 of phage was added. Reads aligning to phage Q33 genome could be assembled into a single contig which contained 99.
Levels of bacterial DNA contamination assessed roughly through percentage of reads aligned to bacterial rRNA databases also varied greatly, with a median value of 0. 04%. Assuming a typical gut bacterial symbiont genome size of ~4 Mb and a ~5 kb rRNA operon present in 4 copies per genome, this level of rRNA gene contamination corresponds to only ~8% of DNA being of bacterial chromosomal origin. However, in some of the samples, levels of bacterial DNA contamination were, for unknown reasons, much higher with rRNA gene counts, reaching 0. 47% of the reads in one sample (corresponding to close to 100% of bacterial DNA). When levels of bacterial DNA contamination was assessed through alignment of reads to cpn60 gene database, the median fraction of reads corresponding to the most conserved 549-567 bp segment within cpn60 was 0.
00077%. With assumptions similar to what we used in the above calculations for the rRNA gene, this corresponds to a median level of ~5. 5% of bacterial genomic DNA in a sample. Interestingly, despite the overall moderate correlation (Pearson r = 0. 469) between rRNA and cpn60 alignment rates, the samples that demonstrated unusually high levels of rRNA reads (0. 33 and 0. 47%) had average cpn60 levels (0. 0036 and 0. 00093%, respectively). Additionally, relative abundances of rRNA and cpn60 reads from our faecal virome samples were compared to those from a selection (n = 18) of total faecal metagenomic samples sequenced as part of Human Microbiome Project. These are mostly composed of bacterial genomic DNA. As expected, median cpn60 values from individual experiments within this study were significantly lower than median cpn60 value for the HMP dataset (Additional file 3: Figure  S1 ), with none of the virome samples surpassing the lowest cpn60 value from the HMP samples.
Surprisingly, with rRNA data, we observed that not only did a number of virome samples have alignment rates overlapping with those of the HMP dataset, but that some virome samples had cpn60 levels even surpassing the highest values from the HMP dataset (Additional file 3: Figure  S1 ). One possible explanation for this discrepancy and the striking overrepresentation of rRNA genes in some virome samples is that in those samples, large amounts of bacterial rRNA molecules could be introduced into nucleic acid samples by co-purification of ribosomes with viruses, with subsequent reverse transcription of rRNA into cDNA. Therefore, we believe that the conserved segment of cpn60 gene can represent a viable alternative to rRNA as a marker of bacterial DNA contamination in faecal virome samples. Meta-analysis conducted by Roux et al.
[31]  demonstrated presence of significant amounts of contaminating bacterial DNA in a quarter of viral metagenomes included in their study. It was recommended by the authors that levels of contamination corresponding to > 0. 02% of rRNA reads should be considered as non-negligible and results from such samples should be treated with caution. At the same time, it seems that in practice, a certain level of bacterial DNA contamination in phageomes is inevitable even in studies where viral fractions are purified through CsCl density gradient centrifugation. Several studies have to date discussed that contamination with bacterial DNA can sometimes lead to considerable misinterpretations of sequencing data  [56, 57]. Therefore, in the present study, we applied a number of in silico steps for removal of possible bacterial genomic sequences from our dataset at the expense of leaving behind ~50% of reads.
However, our attempt to artificially spike faecal samples with an RNA bacteriophage at three different concentrations (phage Qβ of the family Leviviridae) did not result in recovery of any reads aligning to its genome. This suggests that although some larger rod-shaped plant RNA viruses of family Virgaviridae were detected using our protocol, the protocol failed to quantitatively recover the smaller icosahedral, RNAcontaining bacteriophage Qβ. Similar to previous reports  [24] , the vast majority of contigs assembled from the data generated in this study (8759 out of 8920) could not be aligned to any known viral genomes in NCBI RefSeq database. Interestingly, a distinct separation of complete circular viral contigs into several size classes can be seen in Fig. 1b. A large group of contigs with sizes ranging from 4.
Consistent with previous literature data  [39, 40] , a single or repeated (up to 5) freeze-thaw cycles did not cause any significant alterations of faecal bacterial community structure as assessed by 16S rRNA profiling. A very similar result was observed for the phageome data. Interestingly, analysis of α-diversity showed a certain trend for increase in richness (Shannon entropy index) of the bacteriome with increasing number of freeze-thaw cycles (as demonstrated in Additional file 4: Figure  S2a ). Such an effect could possibly be explained by an assistive action of repeated freeze-thaw cycles on efficiency of DNA extraction from difficult-to-lyse bacteria (Actinobacteria, Firmicutes) which leads to an increase in visible richness and evenness of bacteriome composition. No such trend could be seen in the corresponding faecal phageome dataset.
In comparison, total DNA extraction from faeces was performed entirely using a commercially available kit-based protocol with little space for operator error. We believe that although the magnitude of the operator effect was not considerable, it still highlights the necessity of strict standardisation in faecal phageome profiling, especially in large scale comparative and longitudinal studies. As previously mentioned, published data on the absolute concentrations of VLPs in human faecal and mucosal samples are often conflicting and highly dependent on the extraction procedure and method of quantification  [17, 18, 20]. To the best of our knowledge, none of the published metagenomic studies have attempted to assess viral loads quantitatively or semi-quantitatively by spiking faecal samples with an exogenous virus as an internal standard.
Analysis of 16S rRNA amplicon sequencing data The quality of the raw reads was visualised with FastQC v0. 11. 3. The reads were then imported into R v3. 3. 0 for analysis with the DADA2 package (v1. 03) [70]. Errors introduced during the sequencing process were corrected to generate ribosomal sequence variants (RSVs). These were exported and further chimera filtered using both the de novo and reference-based chimera filtering implemented in USEARCH v8. 1. 1861 with the ChimeraSlayer gold database v20110519  [71]. The remaining RSVs were classified with mothur v1. 38  [72]  against the RDP database version 11. 4, as well as classified with SPINGO to species level  [73]. Only RSVs with a domain classification of bacteria or archaea were kept for further analysis. A phylogenetic tree of the RSV sequences rooted on the midpoint was generated with FastTree  [74].
Analysis of phageome shotgun sequencing data The quality of the raw reads was visualised with FastQC v0. 11. 3. Nextera adapters were removed with cutadapt v1. 9. 1  [75]  followed by read trimming and filtering with Trimmomatic v0. 36  [76]  to ensure a minimum length of 60, maximum length of 250 and a sliding window that cuts a read once the average quality in a window size of 4 follows below a Phred score of 30. Reads were then assembled on a per sample basis with the metaSPAdes assembler  [77]. Redundancy between samples was removed by aligning all contigs against each other with BLAST+ v2. 2. 28. Only the longer contig was kept in the reference set when contigs aligned to each other with at least 90% identity over 90% of their length. In order for a contig to be included in the final analysis, it must have been detected as viral by Virsorter  [44]  in the phageome decontamation mode, or had a significant BLAST hit (50% identity over 90% of the length) to a genome in RefSeq Virus  [45, 46] , or had no significant BLAST hits (alignments longer than 100 nucleotides with an e value greater than 1e-5) against the 'nt' database.
This allowed us to include known viruses, putative viruses predicted by Virsorter and completely novel viral sequences not yet included in any database. The quality-filtered reads were then aligned to this contig set using bowtie2 v2. 1. 0 [78]  using the end-to-end alignment mode. A count table was generated with samtools v0. 1. 19 which was then imported into R v3. 3. 1 for statistical analysis. Levels of bacterial DNA contamination were estimated by classifying reads with SortMeRNA v2. 0  [79]  against the SILVA database  [80]  to assess 16S rRNA abundance and by aligning reads using bowtie2 (-very-sensitive preset mode) against the cpnDB  [43]  to assess abundance of cpn60 gene. A selection of total faecal DNA metagenomic samples (sample IDs SRS012902, SRS013158, SRS013215, SRS014613, SRS014923, SRS0 15854, SRS016095, SRS016753, SRS017521, SRS018575, SRS018656, SRS019601, SRS020328, SRS022137, SRS0 24549, SRS045645, SRS053398, SRS054956) deriving from Human Microbiome Project was used for comparisons.
Statistical analysis Alpha diversity and beta diversity were generated using PhyloSeq v1. 16. 2, which also was used for a principle coordinate analysis as implemented in Ape v3. 5. As gut microbiome datasets typically do not follow a normal distribution, the non-parametric Mann-Whitney U test was implemented for assessing differences between two groups, and the non-parametric Kruskal-Wallis test where there are more than two groups. Differential abundance analysis was carried out with DESeq2 v1. 12. 4  [81] , an algorithm originally designed for RNA-Seq analyses but has found to be efficient in microbiome datasets  [82]. All visualisation in R was performed with ggplot2 v2. 2. 1. The Adonis function in the Vegan library v2. 4. 3 was used to look for associations with metadata factors. This implements a permutational multivariate analysis of variance (PERMANOVA), which allows for identifying associations between distance matrices and meta data variables assigning significance through a permutation-based test  [83].
Availability of data and materials All sequence data used in the analyses were deposited in the Sequence read Archive (SRA) (http://www. ncbi. nlm. nih. gov/sra) under BioProject PRJNA407341. Sample IDs, meta data and corresponding accession numbers are summarised in Additional file 2: Table  S2. All raw count tables, 16S taxonomic assignments, BLAST top hits for viral contigs and R code used for the analysis are available at (https://figshare. com/s/71163558b4f78e3e7ed6).
Estimation of the ICCs For observations below the LOD, values were imputed using a quantile regression approach for the imputation of left-censored missing data implemented in the imputeLOD function available in the rexposome package in the R software  (R Core, 2016). Supplemental material, Fig. S2  illustrates how the distribution of imputed data mimics observed raw data distribution. Concentrations of all compounds were log-transformed to improve normality of their distribution. In order to estimate the within-person variation, we used linear mixed models (with random effect on woman or child) adjusted for cohort and creatinine concentrations (continuous, non-transformed) to decompose the total variability of concentrations and estimate: i) the between-trimester (or between-week) variability of the two weekly pools in pregnant women; ii) the between-day (or within-week) variability of the four pools of two urines of consecutive days in children; and iii) the between-season (or between-week) variability of the two weekly pools in children.
Acknowledgements ISGlobal is a member of the  CERCA Programme, Generalitat de Catalunya. We are grateful to all the participants in the six countries who took part in this study. We thank  Francesc Figueras  and  Maribel Grande  ( Maternal-Fetal Medicine Service -Hospital Clínic ),  Joane Quentin  ( CHU Grenoble-Alpes and Inserm ), and  Carin Helena Meinhard-Kjellstad, Pernille Mathiesen (NIPH) ,  Martine Angell ,  Kari Selsjord Bjørseth ,  Ingeborg Frøysland ,  Karina Heiberg , and  Lilian Johansen  ( Oslo and Akershus University College of Applied Sciences ), for their assistance in carrying out the  Pregnancy Panel Studies. We also thank  Julia Blackburn ,  Samina Ashraf ,  Samina Janjua  and  Amy Findley  from  Born in Bradford ;  Sandy Bertin ,  Manuela Boué ,  Pierre-Jean Saulnier ,  Michèle Grosdenier , and  Sonia Brishoual  ( CHU Poitiers ),  Anne Forhan , and  Barbara Heude  from the EDEN cohort;  Silvia Fochs ,  Nuria Pey , and  Marina Biel  from the  INMA  Project;  Asta Danileviciute  ( Vytauto Didziojo Universitetas ),  Asta Bernatiene  and  Jolanta Kudzyte  ( Department of Paediatrics, Lithuanian University of Health Sciences, Kaunas ) from the Kaunas cohort; and  Eirini Michalaki  from the RHEA cohort for their assistance in conducting the  Child Panel Studies.
Funding The research leading to these results has received funding from the  European Community 's  Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement no 308333 -the  HELIX  project. Dr. Maribel Casas  received funding from  Instituto de Salud Carlos III (Ministry of Economy and Competitiveness)  ( MS16/00128 ). Dr. Regina Grazuleviciene  received the grant of the  Lithuanian Agency for Science Innovation and Technology  (No. 31V-66 ). Dr. Juan R Gonzalez  is supported by  Ministerio de Economía y Competitividad y Fondo Europeo de Desarrollo  ( MTM2015-68140-R ). The pregnant women recruited in Grenoble are part of SEPAGES mother-child cohort, which has been partly funded by a grant from the  European Research Council  ( ERC , consolidator grant # 311765 -EDOHaD, PI,  R. Slama ).
, 1999b). Thirdly, Du Pontavice et al. (2018)  only tested the three-subunits hypothesis whereas alternative hypotheses deserve to be tested to investigate the stock structure. Here, we investigate the spatiotemporal consistency of a stock sub-structure in the EEC by analyzing spatiotemporal patterns of key life history traits over 26 years. We expanded from Du Pontavice et al. (  2018 ) by analyzing, over a longer series , density-atage in addition to length-at-age data, both estimated from a scientific survey dataset. We tested for different configurations of spatial structure, including three isolated subunits  (Rochette et al. , 2012; Archambault et al. , 2016; ICES, 2017a) ; Fig. 1 ); two isolated subunits (combination of the 3) or a single stock (no spatial structure, i. e. the present management unit).
Length-at-age and density-at-age datasets The UK-Beam Trawl Survey (BTS) is an annual scientific survey conducted by the Center for Environment Fisheries and Aquaculture Science (CEFAS) since 1989. It covers the EEC and the southern part of the North Sea. Sampling takes place in July and August, following the same survey design each year (Fig. 1 ), using a commercial 4 m beam trawl  (ICES, 2009). Each sole caught is measured (total length), sexed and aged by reading otolith increments. Data from this survey provide consistent time series of lengths and abundances-at-age by sex throughout the EEC over the period 1989-2015. However, to ensure sufficient numbers of common sole per age, sex and subunit categories, we removed three cohorts  (1992, 2006 and 2007)  from the dataset, due to the low occurrence and abundance of sole.
Anomalies of density-at-age time series We used data on number of individuals in Table  2  to build abundance indices at age, per cohort, per sex and for each spatial unit considered in Table  1. Based on the density-at-age dataset (i. e. , age 1-5, from cohort 1989 to 2010), we first calculated the density per sex, age, cohort and subunit, as the abundance relative to the trawled surface (Eq. 4): = ∑ + Density Abundance (Surface trawled ) Sex Subunit Age Cohort Sex Subunit Age Cohort Sex Subunit Cohort Age , , , , , , , , (4) The decline in abundance with age resulting from both natural and fishing mortality is strong. This decline could blur the statistical signal of covariations in abundance among age classes throughout a cohort. To avoid such limitation, we first standardized density indices from BTS surveys into anomalies using average density at age (Eq.
Acknowledgments The data used were retrieved from DATRAS and constitute public data from the UK-BTS survey operated by CEFAS. We are grateful to  Colette Wabnitz  ( Institute for the Oceans and Fisheries, University of Bristish Columbia, Vancouver, Canada ) for English corrections,  Amélie Régimbart  ( Agrocampus Ouest, Rennes, France ) who helped in mapping, and  Marie Savina  ( Ifremer, Lorient, France ),  Youen Vermard  and  Sigrid Lehuta  ( Ifremer, Nantes, France ) for preliminary discussion on the approach.
Nobel prize winner Elinor Ostrom advocated for embracing social-ecological complexity. Ostrom recognized that there is no universal solution to problems of destruction of overuse of natural resources  (5)  and further developed a Social-Ecological Systems' (SES) framework hoping it would help accumulate knowledge and better understanding of what works and what does not in different SES contexts  (6). The SES framework is a nested multi tier set of variables that has been suggested as features that characterise distinctive aspects of SES. The framework has been typically applied to local case studies that cover relatively small areas and short periods of time (documented in two publicly available datasets: the SES Library and the SES Meta Analysis Database). Over a hundred case studies have been coded in these databases.
Despite the differences in purpose, scale, resolution and datasets used, what the aforementioned studies have in common is that they attempt to map SES by combining multivariate methods of ordination and clustering algorithms to identify i) systems' typologies and ii) potential underlying variables of change -what explains the variability of the typologies. However, the studies do not provide guidance on how to make choices regarding optimal number of clusters or algorithmic selection, limiting their replicability when applied to different places or data. These choices remain idiosyncratic in the SES literature and there has been no way of assessing best practices. Here we apply clustering techniques to SES data while explicitly addressing these limitations. To guide us in choice of variables, we use Ostrom's SES framework.
In Ostrom's parlance a SES has 6 key subsystems: i) resource units (RU), resource system (RS), governance system (GS), users (U), interactions (I) and outcomes (O); all framed by social, economic and political settings (S) as well as by related ecosystems (ECO). Each of these subsystems has a nested second tier of variables (53 in total in the original proposal) aimed to capture key features of the first tier  (6). We obtained publicly available datasets covering the second administrative level for Ghana and Burkina Faso (districts and provinces respectively). From these, we matched any data that in a meaningful way could be used as proxies of the Ostrom variables.
Data and choice of variables Since our analysis focuses on food security we use crop production as the defining key interaction (I) of our SES characterisation. It is a proxy that reflects both the capacity of the ecosystem to provide food services, and also the human input (e. g. fertilisers, labor) and preferences (e. g. crop choices) necessary to co-produce the service. Crop data, both production and cultivated area, were obtained from national statistical bureaux. While data does exist for 32 crops from 1993-2012, there are large data gaps in the time series of these data (~36%) (SM  Fig 1). We thus chose 7 crops with minimum missing values (3. 85%), and used averages based on the last 7 years to correct for outliers (See SM for crop selection). Users (U) and their social, economic and political settings (S) were here characterised by national census statistics and their change between the years 1996 and 2006 for Burkina Faso and between 2000 and 2010 for Ghana.
Although indicators of governance and fertilizer use do exist at the national scale (e. g. governance indicators, World Bank database, only available for Ghana; fertilizers FAO database), they cannot provide insight at the scale of this study, limiting our conclusions. This suggests that including governance indicators such as how often people share food, what is the structure of the social networks, or efficiency of local institutions at managing existing water infrastructure in national monitoring programs such as census or national surveys would markedly improve the ability to characterize SES. If and when these type of data become available for the Figure  4 : Influence of water reservoirs in kilocalorie produced per crops and its variability. The kilocalorie and its standard deviation in log scale is regressed against several factors that can influence crop production.