dataesr/scientific-paragraphs-categorization huggingface dataset:
Participants and procedure The study consisted of a survey that was accessible online and circulated via FB and other social networks and research networks, as well as via email by using snowballing techniques among the researchers' contacts. The survey was disseminated by using an online platform (Qualtrics, Provo, UT). All items used in the online survey can be obtained from https://osf. io/ xt4zv/. The survey items were administered in fixed order (FB actual-use items, FB problematic use items, a self-esteem item, and impulsivity items; see below). The survey was available between October 1 and November 30, 2015. Anonymity of the participants was guaranteed (no personal data or Internet Protocol [IP] address was collected). Inclusion criteria were being French-speaking, aged 18 years or older, and being a FB user.
2 ). Specifically, 20 morphotypes belonged to the phylum Annelida, 37 were Gastrotricha, 22 Mollusca, 37 Nemertea, 23 Platyhelminthes, 38 Nematoda, and 10 morphotypes belonged to the phylum Xenacoelomorpha (Table  1 ; see additional tables for a detailed list of morphotypes  15  ). DNA taxonomy obtained with the Generalized Mixed Yule Coalescent (GMYC) model revealed 116 evolutionary independent entities, among which 23 Annelida, 27 Gastrotricha, 6 Mollusca, 2 Nematoda, 9 Nemertea, 42 Platyhelminthes, and 7 Xenacoelomorpha. Results obtained by using the GMYC species delineation model were not significant (Annelida, Mollusca, Nematoda, p = 0. 773; Nemertea, p = 0. 205; Xenacoelomorpha, p = 0. 199), except for species of Gastrotricha (p = 0. 0015) and Platyhelminthes (p = 0. 016) whose 18S datasets seemed to better fit the GYMC model (see additional tables for a detailed list of evolutionary independent entities  15  ).
The same genetic dataset as described above was considered to determine the number of Operational Taxonomic Units (OTUs) for each phylum. A total of 211 OTUs were obtained, among which were 27 Annelida, 38 Gastrotricha, 17 Mollusca, 37 Nematoda, 34 Nemertea, 48 Platyhelminthes, and 10 Xenacoelomorpha (see additional tables for a detailed list of OTUs assessed on single specimens  15  ). Meanwhile, biodiversity was assessed with metabarcoding for each of the 19 investigated sites. Taxonomic units obtained with the metabarcoding approach were categorized by groups in order to remove all organisms that are not meiofaunal invertebrates (e. g. , fungi, vertebrates, plants, algae, known big invertebrates, and protists). The final dataset consisted of 120133 genetic reads and 1345 environmental OTUs (eOTUs) distributed in 20 phyla.
The richest phylum was Arthropoda with 699 eOTUs, most of which (659) were Copepoda. By pruning the dataset to the seven focal phyla, we obtained a total of 32206 genetic reads and 547 eOTUs. In order of richness, 178 eOTUs were Annelida, 155 Nematoda, 120 Platyhelminthes, 36 Mollusca, 26 Xenacoelomorpha, 20 Gastrotricha, and 12 Nemertea (see additional tables for a detailed list of eOTUs assessed with metabarcoding  15  ). The same initial genetic dataset was analyzed and filtered to obtained sequence variants. Compared to eOTUs, we obtained a similar number of genetic reads (127,002 for the total meiofaunal community dataset) but these clustered in a lower number of unique units (496 sequence variants). The seven focused phyla were present with a total number of 29279 genetic reads and 293 sequence variants.
Richness of Annelida significantly differed between eOTUs and the three methods based on single individuals, namely morphotypes (Z = 5. 258, p = 0. 0001), OTUs (Z = -5. 198, p = 0. 0001), and evolutionary entities (Z = -7. 803, p < 0. 0001). For Nematoda, significant differences in predicted richness were observed between evolutionary entities and both eOTUs (Z = -6. 191, p < 0. 0001) and sequence variants (Z = -4951, p = 0. 0004). The total number of species belonging to the seven phyla estimated in the sampled area spanned between 365 and 609 depending on the species estimator function applied (chao2 and jack1) to the two taxonomic datasets considered (morphological units and sequence variants; (see ref. 15  for values of richness estimation obtained using Chao2 and Jackknife algorithms).
Ecological conclusions across different richness estimates. The correlations between richness and environmental parameters differed across taxonomic methods. Specifically, for the datasets based on single individuals, our final model inferred that sample size (number of individuals or genetic sequences; Wald χ 2 = 51. 01, p < 0. 0001), depth (Wald χ 2 = 6. 38, p = 0. 011), grain size sorting (Wald χ 2 = 4. 91, p = 0. 027), and the dichotomous (dummy) variable distinguishing morphology and one of the two DNA taxonomy methods (OTU) from the other (evolutionary independent entity; Wald χ 2 = 9. 71, p = 0. 002) were statistically significant predictors of richness. Interestingly, none of the interactions with the dichotomous variable were found to be statistically significant (interaction between depth and dummy: Wald χ 2 = 1.
Specifically, for morphological taxonomy there were significant correlations between the community structure and depth (p = 0. 003), mean grain size (p = 0. 039), and the sedimentological parameter kurtosis (p = 0. 027); and a nearly significant correlation with (iv) salinity (p = 0. 082). Community compositions obtained via OTU showed a nearly significant correlation with depth (p = 0. 064). For the metabarcoding datasets on focal phyla, a nearly significant correlation was found between both eOTU and sequence variant communities and the mean grain size (p-values = 0. 099 and 0. 087, respectively). When the whole meiofaunal community was analyzed with metabarcoding (15 phyla; Fig. 4 ), significant correlations were present between eOTUs and both mean grain size (p = 0. 039) and the sedimentological parameter skewness (p = 0.
039), and nearly significant correlations between eOTU communities and depth (p = 0. 098). Sequence variant whole community structures were significantly correlated to depth (p = 0. 05), and nearly significant correlated to the mean grain size (p = 0. 069). Phylogenetic diversity. Our fitted model inferred that phylum (F = 43. 15, df = 6, p < 0. 0001), taxonomic method (F = 103. 63, df = 2, p < 0. 0001), and the interaction between phylum and method (F = 9. 48, df = 12, p < 0. 0001) were all statistically significant predictors of phylogenetic diversity (Fig. 5 ; see additional tables for values of phylogenetic diversity measured for each phylum and sampled site  15  ). For each phylum, we identified significant differences only between the phylogenetic diversity obtained from single individual datasets and the one obtained from one or both metabarcoding datasets (all associated p-values < 0.
05 as specified below), and no significant differences between the two metabarcoding approaches (all associated p-values = 1; Fig. 5 ). In detail, the values of phylogenetic diversity for species of Annelida, Nematoda, Platyhelminthes, and Xenacoelomorpha significantly differed between single individual datasets and both metabarcoding approaches (all associated p-values < 0. 0001). The phylogenetic diversity of Mollusca significantly differed between single individual dataset and eOTU dataset (p = 0. 0214). The phylogenetic diversity of Gastrotricha and Nemertea did not differ significantly across methods (all associated p-values = 1). Similar results were obtained for both Mean Phylogenetic Diversity (MPD) and Mean Nearest Taxon Distance (MNTD). The ANOVA model fit to MPD data exhibited a lack of Normality in the residuals, therefore, a robust ANOVA model was pursued.
By contrast, sequence variant methods are developed with specific pipelines to reduce such errors, they have demonstrated sensitivity and specificity as good as or better than eOTU methods  22, 23. The lack of difference in the phylogenetic diversity between eOTU and sequence variant datasets supports that eOTU clustering does not increase the actual species diversity, but only overestimates the number of biological units in the samples. For nematodes, richness was significantly different between metabarcoding and individual DNA taxonomy predictions obtained with the GMYC model (p-values equal or lower than 0. 0004). However, the number of species estimated by that model was not statistically supported (p = 0. 773). Moreover, the application of this species delineation model to the 18S rRNA locus has been shown to underestimate diversity in many meiofaunal taxa  24, 25.
Preliminary surveys of diversity across sites using environmental DNA methods would be extremely helpful to drive, in advance, the focus of taxonomists toward specific sites with a higher and/or particularly interesting diversity. More evidence of bias by the taxonomic method applied is the strong correlation between biodiversity and the grain size of the sediment that was found only in the morphological dataset. Grain size is considered among the most important factors that affect meiofaunal biodiversity  8, 26  , and such biases need to be removed in order to reach an inclusive understanding of the ecological effect in marine sediments. Lastly, differences in the ecological correlations are particularly relevant when the same metabarcoding method (eOTU or sequence variant) is applied to either the focal or complete meiofaunal dataset.
Investigation of morphotypes. Live material was studied using stereo and compound light microscopes Microscopes used were: optical Leitz Orthoplan 2 microscope with Canon Rebel T2i digital camera (Fig. 2a, c, e, f ); differential interference contrast Leitz dialux 20 microscope with DS-Fi1 Nikon digital camera driven by the Nikon NIS-F v. 4. 0 software (Fig. 2b ); optical Nikon Eclipse microscope with Cool Pix 4300 Nikon digital camera; optical Zeiss Axiostar microscope with Power Shot G6 Canon digital camera (Fig. 2d, g ); differential interference contrast Nikon Eclipse 80i microscope with Canon EOS 5D mk III digital camera (Fig. 2h ). Individuals from each identified morphotype (maximum 20 individuals per morphotype) were preserved in ethanol 90% for further molecular analyses. The taxonomic effort was focused on species belonging to the phyla Annelida, Gastrotricha, Mollusca, Nematoda, Nemertea, Platyhelminthes, and Xenacoelomorpha (Fig.
The summarized trees were calculated with the program TreeAnnotator (part of the BEAST package) after discarding the first 20% of the trees as burn-in. As outgroups for rooting, we used sequences obtained in the present work from the phylogenetically closest clade. Test for evolutionary independent entities and OTUs. In order to quantify evolutionary independent entities, the ultrametric trees obtained with BEAST were used as input for the single-threshold GMYC approach in R 2. 15. 3  45  with the package splits (http://splits. r-forge. r-project. org/)  46. The GMYC method is a likelihood method for delimiting species under coalescent theory by fitting withinspecies and between-species branching models to reconstructed ultrametric gene trees  4. To obtain OTUs, the amplified 18S rRNA sequences were concatenated into a single FASTA file to mimic the input of a processed metabarcoding dataset.
OTUs are based on sequence similarity  47. The sequences were clustered into OTUs using the QIIME open-reference OTU picking pipeline and the SILVA (release 128) 99% identity SSU ribosomal sequence reference database  47. Metabarcoding. DNA extraction, amplification, and sequencing: DNA was extracted for metabarcoding from the sediments samples, for each of the 19 locations, at the Laboratories of Analytical Biology, Smithsonian Institution National Museum of Natural History. Each sample was vortexed for 10 s after which 10 mL were transferred to 2-mL tubes. The ethanol was evaporated in a 60 °C vacuum spinning centrifuge. 0. 5 mL of extraction buffer (0. 1M Tris-HCl, 0. 1M NaCl, 0. 1M EDTA, 1% SDS and 250 μg/mL proteinase K)  48  was added to the dry sample which was then incubated for 2-12 h at 56 °C.
Thermal cycler conditions included an initial denaturing step of 94 °C for 3 min, followed by 35 cycles of 94 °C for 45 s, 57 °C for 60 s, 72 °C for 60 s, and a final extension step of 72 °C for 10 min. The PCR products were purified with Rapid PCR Purification System Kit Marligen Bioscience, Inc. Construction of eOTU and sequence variant tables. To reconstruct eOTU tables, forward and reverse reads were merged using PEAR V0. 9. 6; bases with a phred quality score less than Q3 were trimmed  16. The merged sequencing reads were clustered into operational taxonomic units using the QIIME open-reference OTU picking pipeline and the SILVA (release 128) 99% identity SSU ribosomal sequence reference database  47. Within the pipeline, reverse-strand matches were allowed and a percent identity threshold of 99 was used for UCLUST clustering  49.
The final result of the QIIME pipeline is an OTU table with per sample counts of each eOTU  16. Sequence variant tables were constructed following the DADA2 V1. 4 pipeline  22. Forward and reverse reads were truncated to 130 base-pairs on quality profiles. Reads were then de-replicated and binned into unique sequence variants. Final sequence variants across the samples were then inferred using a parametric error model customized for each sample dataset. Forward and reverse reads were then merged and a 'sequence table' was constructed with per sample total counts for each sequence variant (analogous to the classical OTU table). Chimeric sequence variants were removed from the final table  16. Taxonomic classifications of eOTUs and sequence variants. Each sequence from the dataset was compared to the SILVA 18S reference set using BLAST.
The taxonomy from the top five BLAST hits were then compared and the best consensus taxonomy was obtained. BLAST hits were only considered if the percent identity of the match falls within 0. 5% identity of the top hit and if the alignment of the hit spans >120 bp. Taxonomic identifications were made to the species, family, and phylum level if the percent identity of the best hit was >97, 93, and 90, respectively. These threshold values were arbitrarily chosen according to diverse literature sources and investigations that were mostly based on mock communities of selected taxa  50, 51. To validate the taxonomic classifications of the sequences, a phylogenetic tree was constructed for each dataset and the phylum-level taxonomic identities for each clade were examined. Previously unidentified sequences were dubbed a certain phylum when belonging to a supported clade including at least five other sequences, all them belonging to that phylum.
A constrained phylogenetic tree was reconstructed to show the taxonomic composition of the whole meiofaunal dataset in the sampled area. First, sequence variants were grouped in their respective phyla based on the taxonomic classifications. Separated alignments were performed with Uclast and PyNAST method, a python implementation of the NAST alignment  47. Maximum Likelihood phylogenies were reconstructed using RAxML v8. 2  52. Finally, the separate phyla trees were combined into a single tree. The topology of the final tree was constrained following the animal phylogeny proposed by Dunn et al. 53  V1-V2 and V9 primer region comparison. Primer locations were identified from each of the metazoan 18S rRNA sequences in the SILVA SSU database using blastn. Variable regions (V1-V2 and V9) were then extracted and separated into multifastas for each phylum.
Analyses of data obtained with metabarcoding were performed for the seven focal phyla as well as the total number of eOTUs or sequence variants for all the meiofauna groups present in the sediments. Species composition. Differences in community structure (β-diversity) between samples were measured for units obtained by every single method using the Jaccard dissimilarity index  54, 55  calculated with the function beta. pair included in the package betapart 1. 3 of R statistical software system  56. Biological units obtained by each of the five methods were organized in presence/absence (incidence) datasets. Phylogenetic diversity. A quantitative measure of phylogenetic diversity, defined as the minimum total length of all the phylogenetic branches required to span a given set of taxa on the phylogenetic tree, was measured  57.
Diversity and sorting at the phylogenetic level were measured on ultrametric BEAST trees calculated for the focal groups. Phylogenetic diversity was calculated with the function pd whereas phylogenetic sorting was estimated using the standardized effect size of the MPD, equivalent to 1 -Nearest Relative Index (NRI) and MNTD  58  , calculated with the functions ses. mpd and sesmntd, included in the R package picante 1. 6-2  59. Datasets used are unique genetic sequences obtained from single individuals and environmental samples. Statistical models. Richness estimation: In order to estimate the species richness for our sampled locality, we computed the species estimator functions chao2 and jack1 in the R package fossil  55, 60, 61. These functions apply the algorithms Chao and Jackknife using incidence data.
In fact, the number of individuals in our qualitative samples did not reflect the natural population abundance. Moreover, measuring abundance based on the number of genomic reads may be misleading because body size and cell numbers differ between species and individuals, the number of rRNA gene copies can vary within-species  62  , the occurrence of extensive intragenomic variation  63  , and possible PCR biases. The seven focal phyla and complete meiofaunal community were considered for the whole investigated area and sorted by three depth descriptors (littoral: 0-0. 5 m; sublittoral: 1-4 m; and offshore: 5-16 m). To evaluate richness estimation, we did not consider datasets including evolutionary independent entities, OTUs, and eOTUs for the following reasons: the number of entities was not supported statistically for most phyla; the richness obtained via OTU analysis was comparable to the evaluation of distinct morphotypes, thus, we considered morphotypes in order to include also additional units, from which DNA amplification and/or sequencing failed.
Lastly, in order to be more conservative in species estimation, we chose sequence variant approach which estimates a lower richness than eOTU. Effect of the method on richness. To identify significant predictors of genetic richness (response), a generalized linear mixed-effects model was pursued. More specifically, a Poisson (log-linear) model form was assumed, a model form that is theoretically appropriate for count data such as species richness. Using the Poisson model form, our full model included a single random effect for the 19 station locations, and several fixed effects including a continuous variable for sample size; continuous ecological variables such as depth, salinity, grain size, sorting, skewness, and kurtosis; and a categorical variable for taxonomic method. The Poisson model form has a fairly restrictive assumption that the response follows a Poisson distribution (equal mean and variance), and our particular data did show evidence of overdispersion.
Because, based on our fitted model, we identified a statistically significant interaction effect (Wald χ 2 = 153. 01, p < 0. 0001), we pursued a post-hoc analysis to compare levels of combinations of our categorical variables (phylum and methodology), or factors. To do this, we used the package lsmeans  65  to calculate predicted values of each combination of factors. So, for our two factors, phylum and methodology, with 7 and 5 levels, respectively, we have 35 different combinations of factors. Then, for these 35 combinations, using a Tukey procedure for multiple comparisons to ensure a fixed family-wise error rate (α = 0. 05), we performed all pairwise comparisons of predicted values  66. Effect of the sample size and ecological parameters on richness. Because the values related to the sample size from single individuals (number of specimens or genetic sequences) and environmental samples (number of genetic reads) were not comparable, we evaluated the effect of the sample size and ecological variables on richness by performing separate analyses.
Acknowledgements The meiofauna workshop in Panama was funded to F. L. primarily by the  Global Genome Initiative (GGI)  Award (Grant No. GGI-Rolling-2015-020 ). The research was also supported by a  Smithsonian Institute Buck GGI Fellowship  (to F. L. ) and  Gulf of Mexico Research Initiative (GOMRI)  Grant  16-052  (to W. K. T. ). In consideration of partial funding by  GOMRI , data are publicly available through the  Gulf of Mexico Research Initiative Information & Data Cooperative (GRIIDC)  at https://data. gulfresearchinitiative. org(https://doi. org/10. 7266/n7-szj8-1531). We are grateful to  Juan Mate  and  Lil Camacho  of  Smithsonian Tropical Research Institution  for their scientific support; the director of  Achotines Laboratory Facility ,  Vernon P. Scholey , and all his staff for hosting the workshop;  Marcelo Lamour  of  Laboratório de Oceanografia Geológica (UFPR)  for supporting the sedimentological analysis;  Ester Eckert  for sharing the DNA extraction protocol for metabarcoding.
Data availability. All data supporting the findings of this study are available within the article and its supplementary information files, which can be found at https:// doi. org/10. 6084/m9. figshare. 6790640  15. Sequence data that support the findings of this study have been deposited in GenBank with the accession codes MH302536-MH303208 for sequences obtained from single individuals, sequences obtained via metabarcoding are in the SRA database with accession code SRP142495. Additional outcomes that support results obtained by comparing the 18S rRNA V1-V2 region with 18S rRNA V9 region are available in GitHub with the identifier https://doi. org/10. 5281/zenodo. 1308829  16. In consideration of partial funding by GOMRI, data are publicly available through the Gulf of Mexico Research Initiative Information & Data Cooperative (GRIIDC) at https://data.
gulfresearchinitiative. org (https://doi. org/10. 7266/n7-szj8-1531). Code availability. Computer codes used for bioinformatics analyses that support the findings of this study are available in GitHub at https://doi. org/10. 5281/zenodo. 1308829  16. Computer codes used for statistical analyses that support the findings of this study are available from the figshare record associated with this publication at https://doi. org/10. 6084/m9. figshare. 6295061  66.
The PCR product was cloned into pGEM-T vector (Promega, UK) following the manufacturer's instructions and transformed into E. coli JM109 competent cells. Following isolation of the transformants, plasmid DNA was purified and sequenced. 16S rDNA sequence of each strain was submitted to a BLAST search of the NCBI (National Center for Biotechnology Information) GenBank database (www. blast. ddbj. nig. ac. jp/) to identify the organism.
The selected constructs were sequenced using universal primers T7 promoter and terminator. ORF searches and sequence analysis were performed using the DNASTAR program and the obtained sequences were subjected to homology search using the National Centre for Biotechnological Information (NCBI) on-line program BLAST. Sequence comparison was performed using pairwise and multiple sequence alignment program CLUSTAL W  [26, 27]. Primers were designed based on the ORF of the two new CPG2, and unique restriction enzymes (NdeI/HindIII) were introduced to facilitate the cloning of the gene(s). CPG2-Fwd 5' ACC GGA TCC CAT ATG CAG AAG CGC GAC AAC GTG CTG TTC C 3' (the underlined sequence is NdeI site). CPG2-Rev 5' CCT AAG CTT TCA CTT GCC GGC GCC CAG ATC CAT G 3' (the underlined sequence is HindIII site).