dataesr/scientific-paragraphs-categorization huggingface dataset:
Sequences were then grouped into operational taxonomic units (OTUs) using a 97% cut-off parameter, and the relative abundance of all OTUs was computed using number of sequences. Relative abundances of OTUs recovered from blank samples were then subtracted to their respective relative abundance in all other samples. Rarefaction curves obtained from all samples are provided in Additional file 1: S11. Non-parametric Chao's coverage estimator was calculated using entropart R-package, and averaged 0. 93 ± 0. 05 across all samples. The dominant sequence for each OTU was selected as reference and added into the SILVA reference phylogenetic tree (release 128) using the ARB parsimony insertion tool  [71]. The full phylogenetic tree was then pruned using the ape R-package to remove all but the added sequences, while keeping the topology of the tree.
Computing phylogenetic diversity We measured phylogenetic entropy accounting for the relative abundance of OTUs, using Allen's index  [25] , which is a phylogenetic extension of Shannon's taxonomic entropy index. Allen's index was computed using our own R-function (https://github. com/marlenec/chao, q = 1) based on the entropart R-package  [76]. Allen's index increases when the most abundant OTUs are phylogenetically distant. Phylogenetic dissimilarities between pairs of microbial assemblages were assessed using the abundance weighted Unifrac index (W-Unifrac) computed using the GUniFrac R-package  [77]. Phylogenetic dissimilarity indices accounting for structure ranges from 0 when assemblages share the same dominant phylogenetic lineages to 1 when assemblages are or dominated by phylogenetically distant OTUs.
Fish phylogeny and ecological traits Phylogenetic relationships between studied fish species were extracted from a published time-calibrated phylogeny containing 7822 fish species, covering all Actinopterygian orders  [78]. Out of the 44 fish species, 13 were not present in the phylogenetic tree and were manually grafted next to their closest species accordingly to literature. One species (Cephalopholis argus) was incorrectly branched next to Scaridae in the initial tree and was therefore also grafted next to its closest relative (see Additional file 1: S13). The ecology of the 44 species was described using a set of 6 categorical traits describing body size at fish maturity, mobility, period of activity, schooling behavior, position in water column, and diet. Values were taken from a global database of functional traits for 6316 tropical reef fishes  [79].
To calculate Moran's I, we used the inverse of divergence times between fish species as a measure of phylogenetic proximity  [80]. Then, Moran's I observed value was compared to the ones obtained when shuffling diversity values 999 times on the phylogenetic tree using adephylo R-package. Observed Pagel's Lambda was calculated using the function 'fitContinuous' from geiger R-package and compared to the ones obtained when shuffling diversity values 500 times (due to extensive calculation time) on the phylogenetic tree. These tests were performed using the two methodologies described above (see the "A and B methodologies used to test interspecific drivers of skin microbiomes" section).
Determinants of dissimilarity between skin microbiomes The comparison of the structure of fish-associated microbial communities and the planktonic ones was performed on the full dataset using a permutational multivariate ANOVA (PERMANOVA) performed on dissimilarity values (W-Unifrac) using vegan R-package. To assess how each microbial clade contributed to the dissimilarity between planktonic and skin-associated microbial communities, we performed a LefSe analysis  [81]. LefSe provides linear discriminant analysis (LDA) scores for the bacterial clades contributing the most to the differences between communities (Additional file 1: S3). The assessment of the effect of fish species on skin microbial community structure was done using a PERMA-NOVA on the dissimilarities between individuals (n = 128) of the 34 species that contained at least 2 individuals.
[27]  on OTU abundances found in skin fish microbiome and planktonic communities using the R-script from Burns and coworkers  [82]. This analysis was performed using method B only. The correlation between interspecific dissimilarities and hosts' phylogeny (phylosymbiosis) was measured using Mantel tests based on Pearson's coefficient, using vegan R-package and 999 permutations. This analysis was performed using the two methods described above (A and B). In order to assess the effects of host phylogeny at higher phylogenetic levels than OTUs, we used the beta diversity through time (BTTD) approach developed by Groussin et al. [11] , which computes various beta-diversity indices at different time periods (slices) along the bacterial phylogenetic tree. We went back in time this way until 900 Mya, which corresponds approximately to divergence between bacterial orders, and computed Bray-Curtis index at each slice of 100 Mya.
At each slice, correlation between pairwise beta-diversity values and host phylogeny was tested using a Mantel test based on Pearson's coefficient and 999 permutations. This analysis was performed using both methods described above (see the "A and B methodologies used to test interspecific drivers of skin microbiomes" section). For this analysis, due to extensive computation time, method A was performed using only 500 subsamples instead of 999. The effect of fish ecological traits was assessed using PERMANOVAs, using both methods described above (see the "A and B methodologies used to test interspecific drivers of skin microbiomes" section). The ecological traits were ordered in the model according to their independent contribution (greatest to least) to the total variability. For all analyses involving dataset subsampling (method A), results were reported as the percentage of significant P values (P < 0.
Slides were finally dried under argon and scanned. Data Analyses. Scans were analyzed with Mapix. To analyze the entire range of spots including inhomogeneous ones, a fixed diameter was imposed (200 μm), equivalent to the minimal diameter observed on the slide. Data were treated with Excel and GraphPad Prism 6. To evaluate the interaction and approximatively determine K d values, a hyperbole model was used to fit the data, with Y being the mean fluorescence and X being the concentration in lectin. To obtain an accurate value of K d , a linear regression Y = AX + B was plotted (see equation below) and K d was determined. To evaluate IC 50 values, a sigmoidal model (4PL model), which is a variable slope model, was used to fit the data, with Y being the mean fluorescence and X being the log of the concentration in competitor introduced.
■ ACKNOWLEDGMENTS This work was supported by CNRS, UniversitéGrenoble Alpes, the "Communautéd'agglomeŕation Grenoble-Alpes Metropole" (Nanobio program), the French ANR project Glyco@Alps (ANR- (3) Bernardi, A. ; Jimeńez-Barbero, J. ; Casnati, A. ; Castro, C. D. ; Darbre, T. ; Fieschi, F. ; Finne, J. ; Funken, H. ; Jaeger, K. -E. ; Lahmann,.
15-IDEX-02) and Labex ARCANE (ANR-11-LABX-003). O. R. acknowledges the European Research Council Consolidator Grant "LEGO" (647938) for D. G. and E. L. Glycomimetics versus Multivalent Glycoconjugates for the Design of High Affinity Lectin Ligands. Chem. Rev. 2015, 115, 525-561. (2) Galan, M. C. ; Dumy, P. ; Renaudet, O. Multivalent Glyco-(Cyclo)Peptides. Chem. Soc. Rev. 2013, 42, 4599-4612.
15-IDEX-02) and Labex ARCANE (ANR-11-LABX-003). O. R. acknowledges the European Research Council Consolidator Grant "LEGO" (647938) for D. G. and E. L. Glycomimetics versus Multivalent Glycoconjugates for the Design of High Affinity Lectin Ligands. Chem. Rev. 2015, 115, 525-561. (2) Galan, M. C. ; Dumy, P. ; Renaudet, O. Multivalent Glyco-(Cyclo)Peptides. Chem. Soc. Rev. 2013, 42, 4599-4612. ■ REFERENCES (1) Cecioni, S. ; Imberty, A. ; Vidal, S.
During the course of the experiments (typical duration: 60-120 minutes), the knickpoints retreated up the channel to the water inlet (Fig. 2A ). High-resolution topography of the channel was surveyed using a terrestrial laser scanner (green laser that penetrates water) at ), with the location of knickpoints identified with the red dots. The red shaded area highlights the area of the landscape that has adjusted to the new base level conditions while the relict low relief landscape can be seen upstream of the knickpoints (Inset made with Natural Earth, Free vector and raster map data @ naturalearthdata. com). (B) Conceptual diagram illustrating how waterfalls or reaches of steepened channel slope, collectively termed 'knickpoints' , represent dynamic locations within the river network, separating the part of the landscape adjusted to the new conditions (downstream -red) and those that are yet to adjust (green) (adapted from Hurst et al.
Results In these experiments, the main control on the knickpoint retreat rate is the relative cohesion of the substrate material (Fig. 3A ), with the fastest retreat rates occurring in the silica mix with the highest proportion of spherical silica beads. The experimental datasets show that knickpoint retreat rate is insensitive to river discharge, suggesting m = 0 (Fig. 3A ). These results do not agree with the predictions of the stream power incision model. The knickpoint retreat occurred through a combination of plunge pool undercutting, cantilever failure of the overhanging knickpoint face and hydraulic detachment on steep segments. The constant knickpoint retreat rate with increasing discharge cannot be explained by a systematic change in the knickpoint geometry. Steepened reaches, vertical faces and knickpoints with undercutting plunge pools occurred across the range of discharges, and with similar knickpoint retreat rates (Fig.
Additionally, a time lapse camera was mounted directly above the flume and collected imagery every 60 seconds. In some experiments, due to the cohesivity of the silica, the channel began to undercut the adjoining banks. This overhanging material was removed manually so the laser scanner could maintain line-of-sight with all parts of the channel bed (visible in Fig. 2A  downstream of the knickpoint). In practice, this meant that no major bank collapse occurred in the channel, and the majority of the transported material comes from channel erosion at the knickpoint location. Bank collapse would only occur downstream of the knickpoints due to elevation change, so the manual removal of bank material will not have impacted the results for knickpoint retreat presented here. Data processing, extraction and analysis of the point clouds were carried out using the open source software CloudCompare 2.
7  53. The analysis of knickpoint location and retreat rate was carried out on a subset of the point cloud data in order to accelerate the timescale of data processing. A 5 mm wide swath was extracted from the length of the thalweg in the channel for each point cloud. The location of the knickpoint lip and base was identified from each of these swaths visually, based on the inflection point of the channel slope at the upstream and downstream edges of the steepened reach. The locations of the knickpoints were compared between subsequent point clouds and the retreat rate calculated by averaging the distance between the lip and base of subsequent knickpoints and dividing it by the time between scans (extracted from the time-stamp of the point cloud collection by the laser scanner). Estimated measurement error is of the order of 1 mm, but the large number of measurements mitigate this.
The consistency in retreat rates over short and long distances during each experiment suggests that the measurement error does not have a significant impact on the overall retreat rate calculations. The data analysis was performed directly on the point clouds rather than more traditional 'DEM-of-Difference' algorithms because some of the knickpoints developed undercutting plunge pools, a feature that would have been lost by converting the point clouds into raster layers. The knickpoint width measurements were performed at the lip of the knickpoint and the channel slope measurements were performed by calculating the change in elevation over a measured distance of ~2 channel widths upstream of the knickpoint lip. Hydraulic information (shear stress, water depth) was calculated using the Floodos hydrodynamic model  39  over digital elevation models of the topography (SI Section 2).
Acknowledgements We would like to thank  Jean-Jacques Kermarrec  for his technical assistance during the experiments, and also for his work in developing the experimental apparatus. Jens Turowski  and  Alzeena Bata  are also thanked for their past work in developing the experimental techniques employed here. The research was funded by  Natural Environment Research Council Ph. D. Studentship No. NE/H525270/1  (to E. R. C. B. ) and  Marie Sklodowska-Curie Individual  Fellowship No. 703230  (to E. R. C. B. ).
We present a new surface-atmospheric dataset for driving ocean-sea-ice models based on Japanese 55-year atmospheric reanalysis (JRA-55), referred to here as JRA55-do. The JRA55-do dataset aims to replace the CORE interannual forcing version 2 (hereafter called the CORE dataset), which is currently used in the framework of the Coordinated Ocean-ice Reference Experiments (COREs) and the Ocean Model Intercomparison Project (OMIP). A major improvement in JRA55-do is the refined horizontal grid spacing ( ∼ 55 km) and temporal interval (3 hr). The data production method for JRA55-do essentially follows that of the CORE dataset, whereby the surface fields from an atmospheric reanalysis are adjusted relative to reference datasets. To improve the adjustment method, we use high-quality products derived from satellites and from several other atmospheric reanalysis projects, as well as feedback on the CORE dataset from the ocean modelling community.
Notably, the surface air temperature and specific humidity are adjusted using multi-reanalysis ensemble means. In JRA55-do, the downwelling radiative fluxes and precipitation, which are affected by an ambiguous cloud parameterisation employed in the atmospheric model used for the reanalysis, are based on the reanalysis products. This approach represents a notable change from the CORE dataset, which imported independent observational products. Consequently, the JRA55-do dataset is more self-contained than the CORE dataset, and thus can be continually updated in near real-time.
Introduction The framework of the Coordinated Ocean-ice Reference Experiments (COREs;  Griffies et al. 2009 , Danabasoglu et al. 2014 ) and the subsequent Ocean Model Intercomparison Project (OMIP;  Griffies et al. , 2016)  provides ocean and climate modellers with a common facility for performing coupled global ocean-sea-ice simulations. This framework defines protocols for running coupled ocean-seaice models with boundary forcing derived from common atmospheric datasets. Its most essential element is the forcing dataset developed by  Large and Yeager (2004; 2009) , hereafter referred to as the CORE dataset, which is largely based on the surface-atmospheric fields derived from NCEP/NCAR atmospheric reanalysis  (Kalnay et al. 1996; Kistler et al. 2001). To reduce biases and uncertainties in the reanalysis fields, surface-atmospheric fields such as winds, air temperature, and specific humidity are adjusted with respect to available observations.
The reanalysis products also contain downward surface fluxes such as the downwelling shortwave and longwave radiative fluxes and precipitation (rain and snow), but these data are replaced by estimates that are more directly based on observational data, such as from satellites. Although the CORE framework has become increasingly visible and successfully distributed, no significant developments or maintenance of the dataset or protocol have occurred since 2009. For the CORE dataset, the discontinuity in the source data for radiation fields is the main reason for prohibiting updates. Given that the scientific success of COREs has only recently been demonstrated to the broader community, this situation might be expected. However, the foundation of CORE/ OMIP cannot remain frozen indefinitely. Furthermore, although the present CORE dataset and protocol have become widely used, there are various shortcomings revealed through many recent studies based on the CORE protocol  (Griffies et al.
2009; Danabasoglu et al. 2014; Griffies et al. 2014; Downes et al. 2015; Farneti et al. 2015; Wang et al. 2016a; 2016b; Ilicak et al. 2016; Danabasoglu et al. 2016; Tseng et al. 2016). Hence, there is an urgent need to advance the scientific and engineering foundation of the CORE/OMIP framework. This advance must proceed in a timely manner to benefit ocean modelling communities around the world. The ocean modelling community requires the following features of a forcing dataset: • All forcing datasets must be up-to-date (currently, the CORE dataset is available only through 2009). • The datasets must not be tuned and adjusted to improve simulation results of a particular ocean-sea-ice model or to conform to a particular choice of a set of numerical schemes and parameters for ocean interior processes in coupled ocean-sea-ice models.
• The heat and water budgets of forcing datasets must be balanced with respect to the applied surface flux bulk formulae. • The spatial and temporal resolutions of the datasets must be as sufficient as possible for forcing high-resolution (e. g. , eddying, coastal) ocean and sea-ice models. The horizontal resolution of the CORE dataset is approximately 200 km (T62 grids). • All available datasets, such as other reanalysis products, and ra- diation datasets, should be considered. • The datasets should be extended to include pre-1948 data. The CORE dataset satisfied only the second (no model-based tuning) and third (global balance) items in the above list. The following scientific and engineering backgrounds of forcing datasets also warrant revisiting: • The assumptions and corrections made in Large and Yeager (2009), who created the CORE dataset; • Incorporation of new corrections based on new/different observa- tional data; • Forcing over regions covered by sea-ice; • Surface ocean wave fields; • River runoff field, including runoff from ice-sheet melting; • Diurnal cycling of wind and solar radiation.
By referring to new datasets that resolve some of the above issues, ocean modellers hope to better simulate recent extreme climate events, such as sea-ice reduction in the Arctic and the on-going El Niño/La Niña cycle, and to understand these events in the context of long-term variability. The present developmental study is an international collaborative effort to produce a new atmospheric dataset for driving coupled ocean-sea-ice models. Based on the Japanese 55-year Reanalysis (JRA-55;  Kobayashi et al. , 2015)  project conducted by the Japan Meteorological Agency (JMA), it aims to complement and eventually replace the existing forcing dataset from  Large and Yeager (2009). JRA-55 is among the more recent long-term reanalysis projects. It adopts a relatively high-resolution ( ∼ 55 km) atmospheric model and uses state-of-the-art assimilation techniques.
The dataset extends from 1958 to the present, and will be updated in forthcoming years as part of the JMA operational climate services. All atmospheric variables necessary for computing surface fluxes are taken from the forecast phase of JRA-55. The temporal interval is 3 h. Data are originally provided on the reduced TL319 ( ∼ 55 km) grid. But as a forcing dataset for ocean modelling, we interpolate the data onto the normal TL319 grid (see Appendix A. 1 for details). The necessary variables are the downward shortwave and longwave radiative fluxes, precipitation (separated into rain and snow), 10-m wind vector, sea level pressure, and the air temperature and specific humidity at 10 m (shifted from their original 2-m height). Our preliminary evaluation indicated that the surface fields of JRA-55 must be adjusted (bias-corrected locally and modified globally to impose flux balances) similarly to the NCEP/NCAR reanalysis in the CORE dataset and the ECMWF reanalysis in the DRAKKAR forcing set  (Brodeau et al.
, 2010)  (see also Fig. 1 ). We adjust all variables except the sea level pressure. We also consider time-dependent adjustments to correct the abrupt shifts in the data quality of JRA-55. These shifts are introduced by changes in the observing systems. However, to provide data in near real-time, the adjustment factors for the most recent decades will be continually used for future updates. The JRA55-do forcing dataset also includes the daily river runoff produced by operationally running a global river hydrodynamic model forced by an adjusted runoff from the land-surface component of JRA-55  (Suzuki et al. , 2017). However, river runoff from Greenland and Antarctica is derived from observational climatology based on independent estimates that account for the discharge and melting of icesheets and ice-shelves.
The JRA55-do dataset does not satisfy all the above-listed items. Specifically, future development is required to extend the data to pre-1948, to incorporate surface ocean wave effects, and to improve atmospheric fields over regions covered by sea-ice. In this paper we introduce the new forcing dataset, JRA55-do (based on JRA-55), designed for driving ocean-sea-ice models. As part of this presentation, we describe general features of JRA55-do, the adjustment methods used in its construction, and overall assessments of its quality. Because (at least in the foreseeable future) JRA55-do is intended mainly for global modelling in the CORE/OMIP framework, our assessments focus on large-scale features and global balances of the surface fluxes. In a companion paper  (Taboada et al. , 2018) , we assess the wind patterns regionally including the eastern-boundary upwelling zones.
In Section 2 of the present paper, we define the variables needed for forcing ocean-sea-ice models. Section 3 explains the method and rationale for adjusting the surface-atmospheric variables of JRA-55. Section 4 details the river runoff fields, and Section 5 discusses the final adjustment procedure for global flux balance. The large-scale features of the dataset are evaluated in Section 6. Finally, in Section 7 we present a summary and concluding remarks. We offer a suite of appendices that present the many technical details relevant for JRA55-do. Appendix A explains the processing of the raw JRA-55 data. Appendix B presents formulas to compute properties of moist air that we advocate as a replacement to those used by the current CORE/OMIP framework. Appendix C gives details of the dataset.
In Eqs. (  2 ) and (3), R denotes the total continental runoff. The river runoff is treated as part of the surface freshwater flux in Eq. (  3 ) and Q R represents the heat transport due to runoff. At present, it is difficult to compute the ice -ocean fluxes from observational data. Moreover, the computations are strongly affected by the imperfect representation of the thermodynamic and dynamic processes in sea-ice models. Therefore, we do not try to adjust local ice -ocean fluxes in this study. However, we use their global ocean averaged values for the purpose of closing the global ocean averaged heat and freshwater flux budget in a multi-decadal time scale (see Section 3. 2. 3). We use approximate global mean ice -ocean fluxes derived from a global ocean-sea-ice model simulation. In a global ocean-sea-ice simulation under the CORE dataset conducted at the Meteorological Research Institute (MRI) (e.