dataesr/scientific-paragraphs-categorization huggingface dataset:
Negative control samples (extraction and PCR blanks) contained 0. 001-0. 6% of total processed reads, compared to 1. 3-1. 5% in a true samples. DNA extracts obtained from the joint DNA/RNA protocol based on 2 g of sediment produced less eukaryotic reads than DNA extracts from the 10 g kit, while similar yields were obtained for prokaryotes. RNA extracts produced more reads than DNA extracts with the ribosomal loci, while they produced less reads with the mitochondrial COI locus (Table  S 5 ). After data refining and abundance renormalisation  (Wangensteen & Turon, 2016) , the final datasets comprised between 8. 6 and 16. 2 million target reads for eukaryotes and 21. 7 million prokaryote reads for 16S-V4V5. Target reads delivered 4,333 and 6,031 metazoan OTUs for COI and 18S-V1 respectively, 40,868 micro-eukaryote 18S-V4 ASVs, and 138,478 prokaryote 16S ASVs (Table  S5 ).
16), yet, 288 average cluster numbers per sample were higher in RNA than in DNA-10g extracts in 289 numerous groups (Fig. 2 ). 290 291 Table  1. ANODEVs and PERMANOVAs for the four studied genes. The ANODEVs were performed on mixed models with negative binomial distributions based on rarefied datasets. The PERMANOVAs were calculated on normalised datasets by permuting 10,000 times with Site as a blocking factor, using Jaccard distances for 18S-V1V2 and COI, and Bray-Curtis distances for 18S-V4 and 16S-V4V5. Significant p values are in bold. For pairwise comparisons, DNA 10g comprises all DNA 10g processing methods, and significance codes are p<0. 001: '***'; p<0. 01: '**'; p<0. 05: '*'.
Extraction kit vs nature of nucleic acid PERMANOVA of the dataset containing DNA-10g, DNA-2 g, and RNA-2g extracts confirmed that site was the predominant effect, explaining ~20% of variation for metazoans, 33% of variation for micro-eukaryotes, and 54% of variation for prokaryotes. The analysis also indicated that the differences observed between processing methods were predominantly due to the type of nucleic acid rather than the kit used for extraction. Nucleic acid nature Figure  3. PCoA ordinations showing community differences between RNA and DNA molecular processing methods, using either DNA/RNA extracted jointly from 2 g of sediment or DNA extracted from 10g of sediment in five deep-sea sites using four barcode markers targeting metazoans (18S-V1, COI), micro-eukaryotes (18S-V4), and prokaryotes (16S-V4V5).
Discussion The aim of this study was to evaluate different molecular methods in order to select the most appropriate eDNA metabarcoding protocol to inventory contemporary deep-sea communities, with the lowest possible bias due to aDNA. Using RNA rather than DNA to inventory contemporaneous communities has been suggested as a means of avoiding the bias due to long-term persistence of DNA in marine sediments. Indeed, RNA is only produced by living organisms and is thought to quickly degrade when released in the environment, due to spontaneous hydrolysis and the abundance of RNases  (Torti et al. , 2015). Expectedly, in our COI dataset, RNA resulted in less OTUs (Fig. 1 ) and detected less phyla (Fig. 2 ) than co-extracted DNA. Contrastingly, for ribosomal loci, RNA detected higher cluster numbers than co-extracted DNA (Fig.
1 ), resulting in more clusters per sample for most of the taxonomic groups detected (Fig. 2 ). In these joint datasets, 45-63% of clusters were unique to RNA (Fig. S  2). These unique clusters were not singleton clusters as only up to 2. 2% of them had less than three reads, even if 5-28% had less than ten reads (data not shown). Although proportions vary strongly among investigations, other studies using ribosomal loci have also reported increased recovery of OTUs in RNA datasets, and considerable amounts of unshared OTUs between joint RNA and DNA data  (Guardiola et al. , 2016; Laroche et al. , 2017 and references therein). This difference observed here between COI and ribosomal loci is likely related to the nature of the targeted RNA molecule. The rapid hydrolysis of RNA mostly applies to random coils (like mRNA), while helical conformations (including most types of RNA, such as ribosomal RNA, transfer RNA, viral genomic RNA, or ribozymes) are less prone to hydrolysis by water molecules  (Torti et al.
This could explain the higher cluster numbers detected for ribosomal loci with eRNA compared to eDNA for several taxa (Fig. 2 ). Moreover, many of the unique RNA OTUs may be artefacts from the reverse transcription of RNA to cDNA, a process known to generate errors that are difficult to measure and detect in bioinformatic analyses  (Laroche et al. , 2017) , but highlighted by the greater amounts of chimeras detected in RNA extracts with ribosomal loci. This overestimation of RNA-based data will affect non-clustered data more than clustered datasets, in line with the results observed here for microbial ASVs and metazoan OTUs. In terms of beta diversity patterns, although RNA and DNA detected significantly different communities (Table  1 , Fig. 3 ), DNA and RNA samples resolved similar spatial configurations, with samples clustering by site (Fig.
3 ). This is consistent with  Guardiola et al. (2016) , who also reported similar patterns of ecological differentiation between DNA and RNA in deep-sea sites although both datasets resolved different communities. However, spatial variation was more pronounced with DNA samples for eukaryotes, which is congruent with  Laroche et al. (2017) , who suggested that eDNA may be more reliable for assessing differences in community composition. Thus, due to its suspected persistence in the environment, and the unknown but potentially additional sources of bias suspected here, using eRNA for metabarcoding of deepsea sediments does not seem to effectively address the problem of aDNA, and even less so for ribosomal loci. Other studies suggested that a more efficient way to deal with aDNA may be to use joint RNA and DNA datasets, and trim for shared OTUs  (Laroche et al.
ACKNOWLEDGEMENTS This work is part of the " Pourquoi Pas les Abysses ?" project funded by  Ifremer , and the project eDNAbyss ( AP2016 -228 ) funded by  France Génomique  ( ANR-10-INBS-09 ) and Genoscope-CEA. This work also received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant the agreement No 678760 (ATLAS). This output reflects only the author's view and the  European Union  cannot be held responsible for any use that may be made of the information contained therein. We wish to thank  Laure Quintric ,  Patrick Durand , and  Caroline Belser  for bioinformatic support, as well as  Jan Pawlowski  and  Eva Ramirez Llodra  for useful advice on this work. We also wish to express our gratitude to the crew, participants, and mission chiefs of the MarMine cruise ( Eva Ramirez Llodra , project  247626/O30  ( NRC-BI ) and the MEDWAVES cruise supported by the  ATLAS  project and the  Spanish Ministry of Economy, Industry and Competitivity  ( Covadonga Orejas  and all the crew from the  Sarmiento de Gamboa) , as well as to all the people who helped collecting samples ( Perregrino Cambeiro ,  Juancho Movilla ,  Maria Rakka ,  Joana Boavida ,  Anna Addamo ).
DATA ACCESSIBILITY The data for this work can be accessed in the European Nucleotide Archive (ENA) database (Study accession number will be given upon manuscript acceptance). The dataset, including sequences, databases, as well as raw and refined ASV/OTU tables, have been deposited on ftp://ftp. ifremer. fr/ifremer/dataref/bioinfo/merlin/abyss/abyss-molecularcomparisons/. Bioinformatic scripts and config files are available on Gitlab (https://gitlab. ifremer. fr/abyss-project/).
Moreover, the alveolinids, which belong to miliolids, exhibit a characteristic planispiral-fusiform with an extremely complex internal architecture  (Reichel 1931 (Reichel , 1933 (Reichel , 1937 (Reichel , 1941 (Reichel , 1953;; Hottinger 1960 and 1974 , Hottinger, 1983; Hottinger et al. , 1993). This now appears to be a better possibly due to the advances in µCT, which have become available to foraminiferologists since 2008  (Sasaki et al. , 2008). For these reasons and to explore this new technology, we have chosen a single specimen of the extant Borelis schlumbergeri  Reichel (1937)  for a pilot experiment. Following the classification scheme of the World Foraminifera Database (http:/ /marinespecies. org/foraminifera/index. php) the genus Borelis belongs to the family Alveolinidae Ehrenberg, 1839.
Pre-processing, Segmentation, Rendering and 3D-Reconstruction The commercially available software AVIZO 7. 0 from FEI Visualization Sciences Group was applied for the segmentation and rendering of the FIGURE 1. The μCT-system nanotom® m. 2, Vertical placement of the glass pipette in front of the X-ray source. White arrow points to the specimen. 3, Typical radiograph of the specimen taken from the screen. The glue is less Xray absorbing than the object of interest, see lower part of the image. internal microstructures. To reduce the size of the dataset to 526 slices, only one out of four slices has been employed. Using AVIZO, the specimen was first segmented chamber-wise from one pole to the other one, i. e. , parallel to the coiling axis of the specimen. Segmentation was started from the latest chamber, i.
e. , #48, until the proloculus, i. e. , #0. In a second step, each chamber was labelled using a color code. The manual segmentation process required about one week. A video that illustrates the sequence of chamber fillings from the proloculus through to the terminal chamber was generated by means of the extended functions in AVIZO 8. In addition, axial, equatorial and tangential virtual sections were obtained by the software Voxler2 (http://www. goldensoftware. com/), to compare them with the historical drawings from alveolinid tests given in  Reichel (1937)  and  Hottinger (1960). Alternatively to the usage of AVIZO and Voxler software products, 3D reconstruction and visualization can be performed using open source Java ImageJ and/or Fiji plugins that were inspired by NIH Image initiative and which is in the public domain (https://imagej.
Deposition of Specimen and Data The investigated specimen is deposited in the reference collection to this article at the NMB in Basel. The full and reduced image stacks as well as the AVIZO and Voxler2 generated networks can be downloaded as supplementary data at https:// doi. org/10. 1594/PANGAEA. 884663. A mirror of the same set of data is stored on the internal media server at the NMB.
3D Representation and Axial, Equatorial and Tangential Sections To describe the microstructural features, the structural terms of  Reichel (1937)  and  Hottinger (1960 Hottinger ( , 1974 Hottinger ( , and 2006) )  are applied. The latter reference is also applied to terms of ontogenetic growth stages. Figure  2  displays the µCT-data in four representations. These images show the structural elements of the elongated Borelis test including the apertural face with a single line of main apertures and without inter-calar apertures (in contrast to Alveolinella, which for example has multiple rows of supplementary apertures in the apertural face). The proloculus is surrounded by three whirls of irregularly (streptospirally) arranged chambers, cf. Figure  2. 1 and 2. 2. In the axial section, the more adult chambers are increasingly subdivided in chamberlets.
A columnar structure is evident toward the two pole regions. In the equatorial section of Figure  2. 3, the bending of the septum is easily visible. The preseptal passages occur before each septum as deep holes. There are no postseptal passages. The nepionic (juvenile) stage of the test having a more compressed and irregular arrangement of chambers can be easily distinguished from the ephebic (adult) stage until about the third whorl after the proloculus. The ephebic stage is characterized by regular planispiral coiling around the coiling axis and chambers get increasingly elongated. Until the gerontic or terminal stage our specimen has grown to a test length of 2,230 µm and a maximum diameter of 470 µm, and reached 49 chambers (the proloculus being counted as chamber #0). Figure  3  shows an alternative volume rendering in axial section of the test from the same CT-data but using the Voxler2 software.
Comparison of Borelis µCT-Data with Historical Drawings and Models from Thin Sections The internal Bauplan of an alveolinid described above has been extensively documented by serial sections in a series of pioneering studies  (Reichel, 1931 (Reichel, through 1937 (Reichel, , 1941 (Reichel, , 1953;; Hottinger 1960 Hottinger , 1974)). A µCT-study of the internal microstructures of alveolinids, however, is not reported yet. A potential reason for this lack lies in the fact that the majority of alveolinids has been found in Cretaceous to Paleogene shallow water carbonate platform deposits and Alveolina-limestones, where the cavities became filled by carbonate cement during diagenesis, which prevents µCT imaging with common photon energies. In order to draw attention to the stunning development of methodologies in micropaleontology, it is tempting to search and compare early studies about the inner structure and nepiont reconstructions of Borelis with the results pre-  sented herein.
Analysis of Growth The µCT data allow in an unprecedented manner for the quantification of the cell growth by measuring the volumes of the individual chambers. With the exceptions of a few recent studies on foraminifera  (Speijer et al. , 2008; Schmidt et al. , 2013) , this kind of quantitative analysis was previously impossible. Usually, ontogenetic growth was investigated from the 1930s onward in larger benthonic foraminifera in thin sections. The growth is frequently quantified by 2D plots of equatorial chamber areas, diameters or radii from the proloculus to the center of a chamber as function of successive instars (chambers), but these measurements allow only estimates for the volume increase of chambers. The same 2D technique was occasionally applied in planktonic foraminifera  (Brummer and Kroon, 1988; Brown, 2007; Mary, 2013) , but has the same limitations if one is out to measure true FIGURE 5.
ACKNOWLEDGEMENTS The project was possible through financial support of the  Stiftung zur Förderung des Naturhistorischen Museums Basel, Switzerland. The financial support of the  Swiss National Science Foundation  in the frame of the R' Equip initiative  (project number  316030_133802 ) is gratefully acknowledged. We thank  S. Schumacher  ( Alfred-Wegener-Institut, Bremerhaven, Germany ) for her generous help to archive the data at PANGAEA. Thanks are given to  C. Haug ,  P. Getty ,  J. Rumford  and the team of  Palaeontologia Electronica  for their assistance and two anonymous reviewers for their critical comments on the manuscript.
Comparison with and re-analysis of previously investigated bird datasets Several previous studies have addressed the relationship between d N /d S and life-history traits in birds  [25, [30] [31] [32]. Table  2  provides an overview of the relationship between d N /d S and body mass among studies (including our study), which illustrates the consistent lack of a significant positive correlation if gBGC is not accounted for. Although all these studies, like us, used a substitution mapping approach to estimate branch-specific d N /d S , our study differs from previous work in three key aspects. First, the underlying substitution model that we used explicitly allows for non-stationary base composition, in contrast to the assumption of stationary base composition used in previous studies. Second, the subset of genes analyzed and the alignment procedures varied between studies (Table  2 ).
Finally, only in our study, d N /d S was separately estimated for S-to-W, W-to-S, and GCconservative substitution categories. Comparison of the relationships between d N /d S and life-history traits among studies is therefore limited to estimates of d N /d S based on all substitutions together (Table  2 ). In order to improve comparability between results and to be able to distinguish between the effects of the underlying methodological differences (related to the stationarity assumption) and gene dataset, we re-analyzed two datasets with our revised approach, the cleaned alignments of Figuet et al. [30]  (referred to as Figuet+HMMclean) and the alignments of Botero-Castro et al. [32]. Note that these two datasets are subsets of the avian dataset published by Jarvis et al. [57]. We addressed two different aspects when re-analyzing these two data sets.
First, we applied our modified (non-stationary) model to analyze all substitution categories together. So, instead of a method that assumes stationarity in base composition as implemented in the original publications, we used a method that allows for non-stationarity. Second, we re-analyzed previous datasets also using the modified (non-stationary) model but this Fig. 2  Relationship between ΔGC and body mass. ΔGC nonsynonymous (light green circles) and ΔGC synonymous (dark green triangles). Regression lines for each substitution category are shown using the same color scheme time limited to only GC-conservative changes in order to investigate the impact of gBGC. Similar to the original publications, correlations between life-history traits and d N /d S based on all substitution categories together (which are affected by gBGC) were only marginally or non-significant (Table  3 ).
This suggests that the relaxation of the stationarity assumption has only a minor impact on the results. On the other hand, correlations between life-history traits and d N /d S based on GC-conservative changes only (which are not affected by gBGC), were strong and significant for all datasets, which suggests that gBGC has a major impact on the results regardless of the data set analyzed. In addition, as observed in the original studies, Paleognathae (ratites and tinamous) and Galloanserae (gamefowl and waterfowl) appeared to be outliers in the Figuet+HMMclean and the Botero-Castro et al. datasets (Additional file 2: Figures  S3-S5 ); estimates of d N /d S were lower than expected by their respective body mass. We therefore followed Botero-Castro et al. [32]  and repeated the correlation analysis within the group of Neoaves only.
Again in agreement with previous observations  [32] , correlations between d N /d S and life-history traits were stronger within Neoaves than within Neornithes (Table  3  and Additional file 2: Figures  S3-S5, ). However, after accounting for the phylogenetic relationships of species, the difference in the strength of correlations within Neoaves and within Neornithes reduced to only a minor effect (Additional file 2: Table  S5 ). This suggests that correction for phylogenetic relationships of species is important and indicates that contrary to previous suggestions  [32] , life-history traits do not seem to lose power to properly describe variation in N e over large evolutionary distances. Nevertheless, our results provide evidence that gBGC weakens the correlation between d N /d S and life-history traits in birds and that this observation is robust to the underlying gene dataset.
Local GC content and chromosome size affect estimates of d N /d S but not its correlations with life-history traits Previous studies have observed differences in estimates of d N /d S between GC-rich and GC-poor genes  [32]. In order to test if our results are robust to local variation in GC content, we split the dataset into two groups according to their GC content, i. e. , high GC content and low GC content. We then estimated d N /d S separately for the two datasets and investigated the correlation between body mass and d N /d S for different substitution categories. The correlation between body mass and d N /d S based on GC-conservative changes was significantly positive both for GC-rich and GC-poor genes, but not significant when analyzing all substitutions together. Interestingly, correlations were similar between datasets, but estimates of d N /d S were consistently lower for GC-rich genes than for GC-poor genes, irrespective of the substitution category (Fig.
Yet other factors that could impact estimates of d N /d S and consequently the correlation between life-history traits and d N /d S in birds are alignment quality and/or missing genes  [32]. After accounting for alignment errors (and inclusion of previously undetected genes), Botero-Castro et al. [32]  found a significant positive correlation between d N /d S and longevity. They also found that d N /d S was significantly correlated with body mass in the Neoaves clade, i. e. , all birds except Paleognathae (ratites and tinamous) and Galloanserae (gamefowl and waterfowl). The authors suggested that the latter group of birds might be outliers, since for those birds current life-history traits might not properly reflect long-term N e (see also ref. [25] ). However, after correction for phylogenetic relationships of species, we no longer observe striking differences between correlations for their dataset.
Moreover, in the larger dataset analyzed in this study, we do not observe that Paleognathae and Galloanserae deviate from the overall pattern. Taken together, the hypothesis that gBGC conceals the prediction of the nearly neutral theory appears robust to the set of genes analyzed as well as to alignment quality. d N /d S based on GC-conservative changes showed strong positive correlations with all life-history traits analyzed in this study in all datasets, while correlations were weak when analyzing all substitutions together. This demonstrates that gene set and alignment quality alone do not explain the lack of a positive correlation between life-history traits and d N /d S in birds. The correlations between life-history traits and d N /d S based on GC-conservative changes were robust to variation in local GC content and chromosome location.
Multiple sequence alignments We downloaded publicly available coding sequence alignments of 8253 orthologous genes from 48 avian genomes and their inferred phylogenetic tree from Jarvis et al. [57]. We excluded the white-tailed eagle (Haliaeetus albicilla), since the branch length between this species and another eagle species included in the data set (bald eagle, Haliaeetus leucocephalus), was too short to reliably estimate d N /d S ratios  [62]. We further excluded Z-linked genes according to the chicken annotation from Ensembl v90 (Gallus_gallus-5. 0) [63, 64] , since the efficacy of selection differs between sex-linked and autosomal genes  [65, 66]. This resulted in a dataset of 7986 orthologous genes of 47 bird species. In addition, following previous approaches  [30] , only codons represented in at least 41 species were retained.
Two additional sets of multiple sequence alignments were retrieved from Botero-Castro et al. [32]. The first of these, which we refer to as the Figuet+HMMclean dataset, is a set of 1077 avian genes analyzed by Figuet et al. [30]. This dataset is a subset of the Jarvis et al. [57]  data, which are used in the present study. The dataset consisted of genes shared by sauropsids and mammals based on orthology prediction between chicken, green anole, and human. Misaligned sites in these alignments were subsequently filtered using HMMclean  [32]. The second set of alignments, which we refer to as Botero-Castro dataset, included all genes in the Figuet+HMMclean dataset plus a set of 1245 previously undetected avian GC-rich genes  [32]. For further description on the gene sets and cleaning methods, we refer to the original publications.