dataesr/scientific-paragraphs-categorization huggingface dataset:
For Eukaryotes, the following size fractions were included (and consolidated as described below) in the analyses: '0. 8-5 µm and 0. 8-2000 µm', '3-20 µm and 5-20 µm, '20-180 µm' and '180-2000 µm. Because of these sampling constraints and the non-systematic sequencing of all available samples, the Tara Oceans dataset is heterogeneous. Specifically, at polar stations, fractions 0. 8-5 µm and 5-20 µm are less represented. Conversely, in non-polar stations, sequencing data for the fraction 3-20 µm is nearly absent. To overcome this issue and increase sampling coverage, we considered that fractions 3-20 µm and 5-20 µm, and fractions 0. 8-5 µm and 0. 8-2000 µm were equivalent, as samples from these size fractions capture very similar diversity and community composition (data not shown). When both size fractions were available for the same sampling site, the 0.
Data processing and taxonomic annotations For the prokaryotes-enriched size fraction (0. 2-1. 6µm and 0. 2-3µm), taxonomic profiling was performed using 16S ribosomal gene fragments directly identified in Illumina-sequenced metagenomes (4). Extracted 16S reads, named miTags, were mapped to cluster centroids of taxonomically annotated 16S rRNA gene reference sequences from the SILVA database (59) (release 128: SSU Ref NR 99), that had been clustered at 97% sequence identity beforehand, using USEARCH v9. 2. 64. Additional methodological details are available in (  4 ), and we used the OTU-level abundance matrix as provided by the authors. For the Eukaryotic taxonomic profiling, we used the same methodology as in  (50)  to define OTUs using 18S rRNA gene V9 amplicons with the Swarm2 algorithm  (91) , with an updated version of the PR2 reference database (3).
For protists, an extended version of the functional database of (50) was used. It encompasses a wide variety of protist taxa that are assigned to major functional groups: photosynthetic/mixotrophic protists, endophotosymbionts, hosts with endophotosymbionts (photohosts), parasitic protists, and free-living heterotrophs or phagotrophs (heterotrophic protists). For the mesozooplankton, the categories used corresponded to the most abundant taxonomic groups (such as copepods and chaetognaths) or feeding strategies. Shannon diversity indices were calculated for each sample and provided by  (3).
Network inference and stability procedure The network inference was performed using FlashWeave (FW) v0. 13. 1 with default parameters  (38). FW relies on the Local-to-Global learning framework and infers direct associations by searching for conditional dependencies between OTUs. Several heuristics are then applied to connect these "local" dependencies and infer a network. FW is significantly faster than other methods while achieving better or similar results, and gives the possibility to include meta-variables (such as the temperature). Although the latter feature seemed appealing, very few OTUenvironmental factor associations were detected which advocates for developing a complementary approach to study the environmental influence (see Network attack below). While FW includes a heterogeneous mode (FlashWeaveHE) and the Tara dataset is heterogenous itself, the low number of samples prevented its use.
Estimation of false discovery rate Three null models were generated using two R packages (picante v1. 7 and HMP v1. 6). The HMP library provides the Dirichlet. multinomial function which allows data matrix generation of OTUs following a Dirichlet distribution. picante comes with a randomizeMatrix function and several methods to randomize the matrix. We used the frequency (that maintains OTUs occurrence frequency), and trialswap (maintaining OTUs occurrence frequency and sample OTUs richness) approaches. Then, networks were inferred from these matrices using FW and the same procedure as for the observed matrices. We then estimated a False Discovery Rate by comparing common edges between the observed and simulated networks. The highest FDR we obtained was 3. 6% (with a number of iterations set to 10 e 8) using the trialswap method.
Literature-based validation of predicted interactions To compare the performance and sensitivity of FW to similar co-occurrence network inference methods such as SPIEC-EASI (93), we estimated the graph accuracy by comparing edges with known (marine) biotic interactions. We limited our comparisons to Polar networks and compared edges with known interactions from the PIDA (78) (https://github. com/ramalok/PIDA) and GLOBI (  95 ) databases (https://www. globalbioticinteractions. org/). We used the NCBI taxonomy for prokaryotes and PR2 taxonomy for eukaryotes to identify Superkingdom, Family, Genus, and Species levels. Then, we searched for known interactions from these databases in the networks by detecting all combinations of OTUs at the four taxonomic levels considered. Results are presented in Fig.
S11. Conserved associations across taxonomy ranks were estimated as follows. First, taxonomic ranks were extracted from NCBI Taxonomy database for prokaryotes and from PR2 database for eukaryotes. Next, for each pair of ranks, we counted the number of edges between nodes of each rank. Next, we repeated the procedure but now applied to the subnetwork induced by considering only nodes from a particular biome. Finally, we calculated the proportion of edges for each rank pair in each biome with respect to the total network.
Marine biome assignations to OTUs In the Tara Oceans data set, each sample is associated with one specific marine biome (Coastal, Trades, Westerlies or Polar). Using this information, we assigned each OTU to a biome or a combination of biomes according to its abundance profile. We did this by identifying biome(s) in which a given OTU is overrepresented, based on relative abundance, compared to other biomes using a 14 Kruskal-Wallis (KW) test implemented in the Python package SciPy (version 1. 2. 1). Adjustments for multiple testing were performed using the Benjamini-Hochberg (BH) procedure implemented in statsmodels (version 0. 9. 0). For significant tests (FDR < 0. 05), a post hoc Dunn's test implemented in scikitposthocs (version 0. 6. 1) was performed to determine in which biome(s) a given OTU was significantly over-represented (FDR < 0.
Network community detection and biome assignation to communities We detected five communities in the GPI using an eigenvector-based network community detection algorithm  (96)  implemented in the networkx 2. 3 python package. To assign biomes to these communities, OTUs abundance tables were CLR transformed and aggregated by community for each size fraction. CLR values for each community were grouped by biome, and a KW test was run to verify mean differences of communities among biomes (KW test column in Table  S4 ). As all pvalues were significant while controlling the FDR using the BH procedure, post hoc Dunn tests were performed to detect community pairwise differences between biomes (Dunn test p-value columns in Table  S4 ). Biomes that were found significantly lower via the Dunn test were discarded from the biome assignation (Dunn test z-score column in Table  S4 ).
General keystone index The generalized keystone index (51) combines several centrality metrics in a single measure, which can then be used to rank nodes, revealing their topological importance in the network. Degree, betweenness, closeness and subgraph centralities have been calculated using the Python library networkx (version 2. 3), capturing the relevance of each node at different topological scales. Factor analysis was performed with the Python library sklearn (version 0. 20. 3) on those centralities to get the generalized keystone index associated with each node.
Acknowledgments General:  Tara Oceans  (which includes both the Tara Oceans and  Tara Oceans Polar Circle  expeditions) would not exist without the leadership of the  Tara Ocean Foundation  and the continuous support of 23 institutes (http://oceans. taraexpeditions. org). Funding: We further thank the commitment of the following sponsors:  CNRS  (in particular Groupement de Recherche GDR3280 and the  Research Federation for the study of Global Ocean Systems Ecology and Evolution ,  FR2022/Tara Oceans-GOSEE ),  European Molecular Biology Laboratory  (EMBL),  Genoscope/CEA , The  French Ministry of Research , and the  French Government 'Investissements d'Avenir '.
programmes  OCEANOMICS  ( ANR-11 - BTBRGENOMIQUE  ( ANR-10-INBS-09-08 ),  MEMO LIFE  ( ANR-10-LABX-54 ),  PSL* Research University  ( ANR-11-IDEX-0001-02 ),  ETH  and the  Helmut Horten Foundation ,  MEXT/JSPS/KAKENHI  (projects  16H06429 ,  16K21723 ,  16H06437 ,  18H02279 ), the  Spanish Ministry of Economy and Competitiveness  (project  MAGGY -CTM2017-87736-R ),  ERC   Advanced Award Diatomic  (grant agreement  835067  to CB), the  CNRS MITI  through the  interdisciplinary program Modélisation du Vivant  ( GOBITMAP grant  to SC), and the  H2020  project  AtlantECO  (award number  862923 ). We also thank the support and commitment of Agnès b. and  Etienne Bourgois , the  Prince Albert II de Monaco Foundation , the  Veolia Foundation ,  Region Bretagne, Lorient Agglomeration ,  Serge Ferrari ,  Worldcourier , and  KAUST.
The global sampling effort was enabled by countless scientists and crew who sampled aboard the Tara from 2009-2013, and we thank  MERCATOR-CORIOLIS  and  ACRI-ST  for providing daily satellite data during the expedition. ED is supported by the  RFI   ATLANSTIC2020 grant  ( PROBIOSTIC grant  to DE). M. B. received financial support from the  French Facility for Global Environment (FFEM)  as part of the " Ocean Plankton, Climate and Development"  project. PCJ was supported by  Fundação de Amparo à Pesquisa do Estado de São Paulo -FAPESP  (PhD grant  2017/26786-1 ). HS is supported by a  Brazilian Research Council (CNPq)  productivity grant (process  309514/2017-7 ). We are also grateful to the countries who graciously granted sampling permissions. Computational support was provided by the bioinformatics core facility of  Nantes (BiRD -Biogenouest ),  Nantes Université, France.
INTRODUCTION 2] [3]  The American College of Rheumatology (ACR) recommendations suggest initiating treatment with non-steroidal anti-inflammatory drugs (NSAIDs) or methotrexate or, in more severe cases, biologics. 4 [6] [7] [8] [9] [10] [11]  Adalimumab has a favourable benefit-risk profile in children with JIA. 1 6 12-14  A long-term, 4-period, phase III study of up to 360 weeks of adalimumab treatment with or without methotrexate (ClinicalTrials. gov Identifier: NCT00048542) was conducted in children with JIA who had active disease and had failed NSAIDs. 6 he results from the double-blind, placebo-controlled period demonstrated that in patients who initially responded to open-label adalimumab treatment, significantly fewer disease flares occurred in patients continuing to receive adalimumab compared with those who switched to placebo; treatment response was sustained during an additional 104 weeks of open-label adalimumab treatment.
Statistical analysis Efficacy analyses were performed in the intention-totreat population (any patient who received ≥1 dose of study drug). Data were assessed as observed without imputation (in patients with non-missing responses at the assessed visit; patients who discontinued from the study were considered as non-responders from that point on) and using non-responder imputation (NRI; for patients who did not enrol into double-blind trial, discontinued from the study or had missing data for any reason) as well as last observation carried forward (LOCF). A subgroup analysis was performed stratifying patients into those using concomitant methotrexate and those not using methotrexate. The safety population consisted of all patients exposed to adalimumab at any point during the study. Rates of AEs are reported as events per 100 patient-years (PY) of exposure.
DISCUSSION This long-term open-label extension study demonstrated that adalimumab was well tolerated and effective among children with polyarticular JIA through 6 years of exposure with or without background methotrexate therapy but was associated with a relatively low treatment retention rate. No new safety signals were identified in this study. Serious AEs and serious infections were within the range of those reported for other pediatric populations treated with adalimumab. 21 Overall, the adalimumab safety profile in this study was consistent with that of adult populations. 22 No increased safety risk was observed in patients who were AAA positive versus those who were AAA negative in the presence or absence of methotrexate. These findings were also consistent with data from the real-world STRIVE registry in patients with active polyarticular JIA.
CS has nothing to disclose. JFB has nothing to disclose. DE has received research grants, consulting fees and/or speakers fees from AbbVie. CG has nothing to disclose. GH has nothing to declare. IK-P has received consulting fees from AbbVie, Pfizer, Roche, CHUGAI, Novartis, SOBI and Novimmune. OYJ has nothing to disclose. VV has received speaker fees from AbbVie and Pfizer. EC has received speaker fees from AbbVie. CW has received research grants to her institution from Roche, Pfizer and GSK. IL and YS are full-time employees of AbbVie and may hold stock or stock options. Funding This study was funded by AbbVie.
, 2015). Similarly, sequencing depth was also claimed to be an important parameter to increase the detection probability of rare taxa  (Smith & Peay, 2014). Aquatic eDNA metabarcoding studies hence exhibit considerable variations in the protocols. Considering that those variations can impact species detection probability, it can be expected that different protocols will produce distinct biodiversity measures for the same sampled fauna. Here, we investigate the robustness of an aquatic eDNA metabarcoding method through the comparison of two biodiversity datasets generated by two different protocols. Those two protocols differ mainly in the eDNA collection step, each protocol using a distinct filter, with different membrane composition, surface, and pore size, leading also to different volumes of filtered water and in the extraction step, with two different extraction kits and different sample preparation prior to the extraction (Figure  1a ).
(2016)  and protocol 2 (P2) described in  Pont et al. (2018). These protocols were developed by SPYGEN and optimized with large volumes of filtered water, high number of PCR replicates and with the amplification of short fragments. P1 and P2 were used to inventory fish fauna from 15 rivers and stream sites in French Guiana (Figure  1b  and Table  S1 ). P1 was used to collect eDNA in 2014 and 2015 by  Cilleros et al. (2019). 15 sampled sites were selected for which the bioinformatic pipeline was reprocessed to perform taxonomic assignments based on an updated reference database (containing 24 species more than the one used in  Cilleros et al. (2019) ). P2 was used to collect eDNA from 2016 to 2019 on the same 15 sites and the taxonomic assignments were performed based on the same updated reference database.
Sequencing were performed using an Illumina HiSeq2500 (2 × 125 bp) (Illumina, San Diego, CA, USA) and a HiSeq SBS Kit v4 (Illumina, San Diego, CA, USA) following the manufacturer's instructions at Fasteris facilities (Geneva, Switzerland). For both protocols, the sequence reads were analyzed using the functions of the OBITools package (http://metab arcod ing. org/ obitools) following the protocol described in  Valentini et al. (2016). The ecotag function was used for the taxonomic assignment of molecular operational taxonomic units (MOTUs) using a threshold of 98% of identity with the reference database updated from  Cilleros et al. (2019)  which references 255 Guianese fish species. The MOTUs occurring with a frequency bellow 0. 0003 per library sample were considered as tag-jumps and discarded  (Schnell et al.
| RE SULTS A total number of 2,251,622 and 2,430,314 reads were retained after bioinformatic screening for the sites sampled with P1 and P2, respectively. They, respectively, represented 48. 09% and 51. 91% of the 4,681,936 total reads retained. After the bioinformatic filtering step, reads were detected in all our 360 PCR replicates while no reads were found in the PCR and extraction controls. Among all the samples, we detected 160 species out of the 255 species referenced in our database. We obtained 779 occurrences in the 15 sites, and all the species detected were consistent with their known distribution.
, 2018). Still, while it has been proven that the protocols and the material used have an impact on the total quantity of DNA recovered  (Capo et al. , 2019; Thomas et al. , 2018) , it does not necessarily implies that the different datasets generated by distinct protocols are strongly affected by those protocol variations  (Djurhuus et al. , 2017). In fact, protocol variations may not produce heterogeneous datasets if the achieved sampling effort provides a valuable representation of the communities. Despite the evident need to search for a unique standardized approach offering the best biological results, testing the robustness of existing methods remains a way to potentially rescue biological data collected using deprecated protocols. This is of particular importance to consider time series data, or biological inventories gathered in remote locations difficult to access.
ACK N OWLED G M ENTS This work benefited from Investissement d'Avenir grants managed by the Agence Nationale de la Recherche (CEBA: ANR-10-LABX-25-01; TULIP: ANR-10-LABX-0041) and ANR grant (DEBIT: ANR-17-CE02-0007-01). We are indebted to the DGTM Guyane, the "Office de l'eau" Guyane, the Guiana National Park (PAG), the CNRS Guyane (LEEISA lab), and HYDRECO for financial and technical support. We also thank Amaia Iribar, Axel Cerdan, Gael Grenouillet, and Pablo Tedesco who provided assistance during field sampling.
I G U R E 5 β jac dissimilarity (a) and nestedness and turnover contribution to β jac dissimilarity (b) of sample pairs for each site. White and striped bars represent nestedness and turnover, respectively. Sample pairs refer to samples collected with P1 and P2 in the same site Non-metric multi-dimensional scaling (NMDS) ordinations based on β jac (a) and β RC (b) indices. The segments connect each pair of samples from the same site collected with P1 and P2. River sites and stream sites are clustered with a grey area. Communities that are closer together, using modified Raup-Crick dissimilarities, are more deviant from the null expectation, whereas communities that are farther apart are less deviant from the null expectation protocols are continuously improved and optimized with scientific advances, heterogeneous datasets are produced, some of which may become deprecated, for either technical or financial reasons (Deiner.
DATA AVA I L A B I L I T Y S TAT E M E N T The site locations (Table  S1 ) and the species by sites matrices (Table  S2  and Table  S3 ) underlying the main results of the study are provided in the online version of the article. All Illumina raw sequence data are available on Dryad https://doi. org/10. 5061/dryad. dc25730 for the samples collected by Cilleros et al. (2019) in 2014 with P1. The reference database sequences and all Illumina raw sequence data are available on figshare https://doi. org/10. 6084/m9. figsh are. 13129703 for the samples collected from 2016 to 2019 with P2.
F I G U R E 5 β jac dissimilarity (a) and nestedness and turnover contribution to β jac dissimilarity (b) of sample pairs for each site. White and striped bars represent nestedness and turnover, respectively. Sample pairs refer to samples collected with P1 and P2 in the same site Non-metric multi-dimensional scaling (NMDS) ordinations based on β jac (a) and β RC (b) indices. The segments connect each pair of samples from the same site collected with P1 and P2. River sites and stream sites are clustered with a grey area. Communities that are closer together, using modified Raup-Crick dissimilarities, are more deviant from the null expectation, whereas communities that are farther apart are less deviant from the null expectation protocols are continuously improved and optimized with scientific advances, heterogeneous datasets are produced, some of which may become deprecated, for either technical or financial reasons (Deiner.
| Participants and procedure Sixty-six distinct samples were collected by researchers from 30 countries. We aimed for 150 participants per sample, so that we had at least 150 participants per country to give us approximately 80% power to detect a small to medium effect (i. e. , r =. 22) within each sample. Data collection was not continued after analysis. To ensure that respondents were part of the social and political system, we only included participants who indicated they were either born in the country or had lived in the country for six or more years and so not every sample resulted in 150 participants. 4 All exclusions are reported. Countries, samples, type of sample, proportion of men, mean age, and sample size are presented in Table  3. Samples included a mix of student samples, community samples, and samples with both students and members of the community (the latter two were both considered non-student samples for the sake of analyses).
| Societal level measures of moderators We included three measures to assess societal level conditions. To capture the amount of civil liberties, we used the 2015 Civil Liberties subscale of the Democracy index created by the Economist Intelligence Unit (2016). This index uses a combination of survey data and expert ratings to estimate how democratically free individual countries are. It has been used in prior work testing similar questions  (Brandt, 2013). To assess the extent to which the culture holds meritocratic values, we combined four items from international surveys. The first two are the importance of ambition and hard work for getting ahead in bring success-it's more a matter of luck and connections". All four items were standardized, the third and fourth items were reverse scored, and all four items were combined to form a scale (α =.
82). To measure the objective inequality, we used the Gini index from 2013 and obtained from the World Bank (World Bank, 2016). For some countries, the 2013 data were not available, and in these cases the most recent data were included instead. The Gini index assesses the amount of income inequality within a region, with higher scores indicating greater inequality. It is a common measure of inequality within a society (e. g. ,  Oishi, Kesebir, & Diener, 2011).
01,. 02,. 05, and. 10. These analyses showed that we had adequate power to detect effects of at least. 02 (i. e. a 2% difference in the outcome variable between the minimum and maximum of the predictor variable; power ≈ 49%, 97%, 100%, 100% respectively). 10 The variance for the intercept at each level of the multilevel models that included covariates was also calculated (Participants σ =. 049, Labs σ =. 006, Countries σ =. 098, Residual σ =. 18). Descriptively, there is more variation between countries than there is between labs. 11 We used the simr package in R  (Green & MacLeod, 2016)  to see how much power our model and sample size had to detect individual-level interaction effects when b =. 02,. 05,. 10, and. 20. We used the model with the identification moderator as our base model for these analyses.
These analyses showed that we had adequate power to detect moderation effects of at least. 10 (i. e. , a. 10 difference in the unstandardized slope of status/power at the minimum and maximum of the moderator variable; power ≈12%, 46%, 98%, 100% respectively). 12 We used the simr package in R  (Green & MacLeod, 2016)  to see how much power our model and sample size had to detect country-level interaction effects for both subjective status and sense of power when b =. 01,. 02,. 05,. 10, and. 20 We used the model with the meritocracy moderator as our base model for these analyses. These analyses showed that we had adequate power to detect country-level moderation effects of at least. 20 (power ≈6%, 10%, 45%, 87% respectively). 13 To check the robustness of our results to outliers at the country-level (cf.