dataesr/scientific-paragraphs-categorization huggingface dataset:
2 sets of 2,050 and 1416 sequence indels, shared by at least 23 transcriptomes, obtained from the first 478 protein dataset. The set of 1416 indels corresponds to a more conserved subset for which the quality of the alignment is the most reliable. The nucleotide sequences were aligned with MACSE v. 2. 05, which is an aligner respecting the codon structure of the coding sequences  (Ranwez et al. , 2011). The amino acid sequences were aligned with Probcons v. 1. 12  (Do et al. , 2005). The indels were deduced from the alignments obtained by Probcons on the protein sequences.
Phylogenetic inference and molecular dating Phylogenetic inference using a supergene and the ML approach A global phylogeny was first evaluated from the supergenes of datasets 3 and 4 with the maximum likelihood approach implemented in RAxML-ng v. 1. 1. 0  (Kozlov et al. , 2019)  with 100 bootstrap BRUN ET AL. replicates, either under the GTR+Γ model with 4 discrete Γ classes for nucleotide mutation rates, or under the JTT+Γ model with 4 discrete Γ classes for amino acid mutation rates. The choice of the JTT matrix is justified by the fact that this model is the most suitable for 65% of individual gene trees according to a model cross-validation procedure implemented in Phylobayes (see below). The best ML trees were then compared with consensus trees obtained from the single gene coalescence approach described below.
Otherwise, C20+JTT is retained as the best model. The amino-acid encoded genes were then tested to see wether the best evolutionary model was able to describe observed sequences' features (namely substitution rates, homoplasies, divergence, the proportion of invariant sites, and especially the assumption of amino-acid frequency homogeneity which is the invariance of the amino-acid frequency distribution over time). For this purpose, the Phylobayes function ppred was used on the initial chain corresponding to the best model, with a burn-in of 100 and the sampling of one genealogy every 5 cycles. Only genes for which the evolutionary model was suitable, i. e. well-describing these features with a p -value > 0. 1, were retained. These genes form the dataset (  2 ) and (  4 ), which explains why there are fewer genes in these datasets compared to their untranslated counterparts from dataset (1) and (3).
Less care was needed when filtering the phylogenetic signal in nucleotide BRUN ET AL. alignments. Only the third nucleotide position of each codon was removed to limit homoplasy and phylogenetic signal saturation. Fitting the most probable alternative tree topologies associated with the deepest nodes to the sequence datasets Fifteen specific trees differing in their topologies at the two deepest nodes surrounding the root of the family Alvinellidae, named T1 to T15, were exhaustively evaluated against datasets 3 and 4 with BaseML (GTR+Γ with 4 discrete classes of Γ) for nucleotide mutation rates, or CodeML (JTT+Γ with 4 discrete classes of Γ) for amino-acid mutation rates  (Yang, 2007). The 15 topologies were also evaluated using Phylobayes under the GTR+Γ model with 4 discrete classes of Γ proposed by this software for amino acid sequences.
In this case, 4 MCMC chains were run until convergence (maximum discrepancy < 0. 3, effective size > 50). The log likelihoods of the trees of each topology were sub-sampled with a burn-in of 200 cycles, one genealogy sampled every 5 cycles. The resulting likelihood distribution was fitted according to a normal distribution. These topologies were also evaluated using the coalescent method for datasets 1 and 2. Gene trees were obtained with RAxML-ng (GTR+Γ for nucleotide sequences and JTT+Γ for amino acid data, 100 bootstrap replicates, from 15 random starting trees). The species trees were then evaluated with MP-EST v. 2. 0  (Liu et al. , 2010). The gene trees obtained with Phylobayes from the amino acid sequences are not necessarily binary, and cannot be processed with MP-EST. For trees produced by Phylobayes, we used ASTRAL v.
5. 7. 8  (Zhang et al. , 2018)  which can use non-binary trees. Branches with low bootstrap support (¡0. 1) could also be contracted beforehand to create a polytomy, according to the recommendations of the coalescent-based species tree software ASTRAL. Estimating the age of the radiation event that led to the diversity in the family Alvinellidae The most probable alvinellid phylogeny was dated with the software Phylobayes. The uncorrelated Gamma model  (Drummond et al. , 2006) , Cox-Ingersoll-Ross (CIR) model  (Lepage et al. , 2007)  and the log normal model  (Thorne et al. , 1998)  were tested on the dataset 4. Considering that the oldest known Terebelliformia fossils date from the Carboniferous/Devonian time  (Sepkoski Jr. , 2002) , the age of the tree's root (divergence between P. gouldii and other terebellid, ampharetid and alvinellid species) is given a Gamma distribution prior with a mean of 330 million years and a standard deviation of 200 million years.
In addition, the model is optimised on two datasets corresponding to different levels of confidence in the alignment of gaps, considering that alignment errors can distort the information in phylogenies established from indels  (Donath and Stadler, 2018 ): • a "soft filter" dataset in which only indels of 10 amino acids or less explained by a maximum of 4 insertion or deletion events are retained. In addition, the event/length ratio is limited to 2. This set contains 2050 indels; • a "hard filter" dataset in which only indels of 5 amino acids or less explained by a maximum of 1 insertion or deletion event are retained. This set contains 1416 indels. The details of the model equations and parameters are presented in the supplementary data.
This also prevents likelihoods from converging to outlier trees with aberrant grouping of species at the gene level. We therefore assume that outside the root of Alvinellidae, incomplete lineage sorting (ILS) or gene flow events are negligible in the evolutionary history of species divergence. Using either the coding (dataset 1) or the protein (dataset 2) sequence alignments, the evaluation is performed 100 times from a set of randomly resampled genes (100 bootstraps). The tree topology uncertainty at the gene level was taken into account with the previously-described procedure: for each gene, 100 trees are drawn from our set of 15 alternative trees with respect to their likelihoods. For example, a tree topology whose likelihood is 8 times higher than another one will be drawn in average 8 times more than this second topology.
In the analysis, each gene is therefore weighted at a value of 100, and the distribution of topologies corresponds to the relative likelihood score of the topologies. The species trees are evaluated by ASTRAL, which gives a normalized quartet score reflecting the agreement between the gene trees and the species tree. To simultaneously compare the scores obtained with dataset 1 and 2, we calculate the Euclidean distance of each topology from the point (1,1), which would correspond to an ideal topology maximizing both the quartet scores for the amino-acid data and for the nucleotide data. The performance of the best ranked topology (TX) averaged on all bootstraps is then tested against each other topology. For this purpose, for each bootstrap replicate, the difference between the distance of the tested topology with TX is calculated.
Results Phylogenetic inference A first phylogeny of the family Alvinellidae has been established based on the whole transcriptome dataset from alignments of either nucleotide or amino-acid sequences, according to the two methods of phylogenetic inference: either concatenation of the coding sequences (CDS) and maximum likelihood, or coalescence from single-gene trees. Both methods consistently group our terebellomorph species into 3 main clades (Fig. 1 ), which are the Alvinellidae family, the Ampharetidae family with Anobothrus sp. , H. invalida, A. carldarei and A. gunneri , and a third group composed of the Terebellidae species (Terebellidae gen. sp. and N. edwardsi ) as well the Melinnidae species (M. palmata). This suggests that the Melinnidae family is closer to the Terebellidae than to the Ampharetidae.
and 1b. For this reason, we have chosen to address more specifically the resolution of these deep nodes with a topology fixed for the other species, as it is well resolved for the rest of the tree. Evaluating bifurcations at the root of Alvinellidae In order to solve the Alvinellid topology, we exhaustively compared the phylogenetic results obtained for all possible root topologies, given the outgroup subtree (Terebellidae+Ampharetidae+P. gouldii ) which remains quite stable (except the position of Anobothrus sp. ) regardless of the used method. Figure  2  lists all these topologies, numbered from 1 to 15, in which the subtree containing the Paralvinella species (without P. unidentata and P. p. irlandei ) are considered resolved and constrained. The results of the different inference methods on the different datasets (CDS or translated proteins) are presented in Table  1.
This dataset of a greater number of indels (2500) ranks the topologies in the following order: T13>T6=T1=14>T15>T9. A larger dataset therefore ranks T1 and T6 better than T9. However, this dataset is arguably much noisier. In the lack of a method to properly discriminate the quality of gap formation in the alignment, we rather should favor a more conservative approach that increases the quality of the indel alignment, at the expense of the phylogenetic signal. In conclusion, if we restrict ourselves to the comparison of T1, T6 and T9 topologies, T6 is the topology with the best likelihood values with methods using a set of concatenated genes for both nucleotide and amino acid sequences, while T1 and T9 perform better with single-gene coalescent inference method. In the single-gene approach, T9 performs better with MP-EST, while T1 works better under ASTRAL.
It should be noted, however, that repeating the likelihood optimisation with the other parameter sets did change the absolute likelihood, but not the relative likelihoods. Thus, the observed differences in likelihoods between the topologies remain true with the other values for r, l 1 (0) and l 2 (0) summarized in 2a. The results of this second optimization are presented in Table  2b. , for both the 'soft filter' (L ⩽ 40, n ⩽ 10, n/L ⩽ 2) and the 'hard filter' datasets (L ⩽ 5, n ⩽ 1, n/L ⩽ 1). For the three most probable topologies, the optimisation gave satisfactory results where the estimated parameters have very similar values under each filtering condition, which is expected considering that the differences between the topologies are small. For the 'hard filter' condition, the branch lengths obtained are very short, on the order of 10,000 times smaller than the branch lengths in "number of expected amino acid substitution" units.
P. bactericola; BRUN ET AL. three traits must have been gained at least twice in the branches leading to P. p. irlandei and P. unidentata (or two losses in the branches leading to Alvinella and all other Paralvinella). The other most parsimonious topologies regarding the evolution of these criteria are T3, T5 and T13, but none of these topologies are well supported by the molecular dataset (Table  1 ). It is also noteworthy that an analysis of 278 loci  (Jollivet and Hourdez, 2021)  had concluded to a topology where P. p. irlandei +P. p. pandorae and P. unidentata were basal to the genus Paralvinella, although they were not sister species. This topology, close to T4 in our study, is only well supported in the analysis combining coalescent-based approach with constrained gene trees (Fig. 4 ).
However, the size of the dataset (2050 indels) and the quality of the alignment are two important limitations of the method at the moment. Indeed, we obtained substantial differences depending on the filter applied to the indels. Some indels have much higher evolutionary rates than others, which is more likely to be related to misalignments than to a biological reality. Ideally, the two steps of alignment and phylogenetic likelihood calculation should be conducted in parallel according to the same evolutionary model. Nonetheless, this seems to be an interesting model to explore in future works, as the evaluation of phylogenies from well-defined gaps gave fairly consistent results despite this two constraints. Table  2 : ML optimization of the indel model of evolution on different topologies and with different filtering levels.
L is the maximum allowed length of gaps, and n is the maximum number of insertions and deletions allowed to explain the formation of a gap parsimoniously. To be included, a gap must meet the filtering criteria for at least one of the 15 topologies shown in Figure  2. Fig. 4 : Evaluation of species tree topologies by ASTRAL for the 15 topologies, with a 95% confidence interval. Each evaluation is the result of 100 replicates on gene resampling in the gene sets. (a) biplots of quartet scores (in %) for each topology from the protein and coding sequence datasets. A higher proportion indicates a species-tree topology more in agreement with our set of gene trees. (b) Euclidean distance of each topology to the point (1,1), which should represent the 'true' species-tree topology that verifies all quartet trees both for the two sequence datasets.
The recent development of the FYRE climate (French Hydroclimate REanalysis), a high-resolution ensemble daily reanalysis of precipitation and temperature covering the period 1871-2012 and the whole of France, offers the opportunity to derive streamflow series over the country from 1871 onwards. The FYRE Climate dataset has been used as input for hydrological modelling over a large sample of 661 near-natural French catchments using the GR6J lumped conceptual model. This approach led to the creation of the 25-member hydrological reconstructions HyDRE spanning the 1871-2012 period. Two sources of uncertainties have been taken into account : (1) the climate uncertainty by using forcings from all 25 ensemble members provided by FYRE Climate, and (2) the streamflow measurement error by perturbing observations used during the calibration.
, 1998)  over the 661 catchments of the study were computed using mean hourly values of Safran over the 1 January 1958-29 December 2012 period. The GR6J model was calibrated over the 1973-2006 period using the Kling-Gupta Efficiency  (Gupta et al. , 2009)  on the squared root of streamflow as objective function. The hydrological reconstruction obtained through the modeling using Safran and GR6J spans the 1958-2012 period and produces a deterministic simulation of daily streamflow over the 661 stations  (Caillouet et al. , 2017). This dataset -called Safran Hydro -will be used to asses the quality of the hydrological reconstructions produced in this study over the recent past. The Safran Hydro reconstruction is available through the Recherche Data Gouv platform  (Caillouet et al. , 2023a). Attach to : None (  158 ) Loire (  129 ) Garonne (  167 ) Seine (  80 ) Rhône (  128 ) Seine@Poses Loire@Montjean-sur-Loire Garonne@Tonneins Ubaye@Barcelonnette Aveyron@Laguépie Rhône@Beaucaire Gave d'Oloron@Sainte-Marie.
SCOPE Hydro The GR6J model calibrated with the Safran reanalysis (see Section 2. 2) have also been used with long-term climate reconstruction SCOPE Climate  (Caillouet et al. , 2019)  as input  (Caillouet et al. , 2017). The SCOPE method  (Caillouet et al. , 2016 (Caillouet et al. , , 2017) )  is based on the analog downscaling approach, i. e the hypothesis that similar large-scale patterns of atmospheric circulation lead to similar local meteorological conditions  (Lorenz, 1969). The SCOPE Climate dataset consists of a daily 25-member 90 ensemble reconstruction of precipitation  (Caillouet et al. , 2018a) , temperature  (Caillouet et al. , 2018b) , and Penman-Monteith reference evapotranspiration  (Caillouet et al. , 2018c)  on the 8-km Safran grid. Data from SCOPE Climate were extracted between 1 January 1871 and 29 December 2012, i.
e the entire period of availability of SCOPE Climate, in order to compute cathement-average daily mean values over the 661 catchments. The hydrological reconstruction obtained through the modeling using SCOPE Climate and GR6J spans the 1871-2012 period and produces a 25-member ensemble daily streamflow recon-95 struction at the 661 stations  (Caillouet et al. , 2017). This dataset -called SCOPE Hydro -will be compared to the hydrological reconstruction produced in this study over a long period of time (> 100 years). The SCOPE Climate reconstruction is available through the Recherche Data Gouv platform  (Caillouet et al. , 2023b).
GRUN The GRUN dataset  (Ghiggi et al. , 2019a ) is a global gridded reconstruction of monthly runoff at 0. 5 • grid over the 1904-2014 period. It is based on a machine learning algorithm trained during a recent period  (Ghiggi et al. , 2019b)  with in-situ streamflow observations of small catchments and uses precipitation and temperature from the Global Soil Wetness Project Phase 3  (Kim et al. , 2017)  as predictors to reconstruct gridded monthly runoff. In order to account for uncertainty, the random forest algorithm was trained on 50 subsets of data thus producing a 50-member ensemble in the reconstruction. Considering the coarse resolution of the GRUN data, we can not compare it directly to the hydrological reconstructions at the 661 catchments. Hence, GRUN values over the 1904-2012 period were extracted over the catchments of the 4 mains rivers of France in order to compare long-term variability properties.
Note that since FYRE Climate does not provide any estimation of the evapotranspiration, we used the Penman-Monteith reference evapotranspiration from SCOPE Climate  (Caillouet et al. , 2018c)  to complete the forcing datasets. on the 176 catchments were the ratio snow/precipitation -computed using the Safran reanalysis -is higher than 10% the 8 parameters are calibrated freely, on the other catchments, the two parameters of CemaNeige are fixed to the median values from the previous 176 catchments. Thus only the 6 parameters of GR6J are calibrated. The criteria chosen for the calibration is the KGE  (Gupta et al. , 2009) , as it allows understanding the quality of the reconstruction through is decomposition in correlation, bias and variability. The KGE is computed on the square root of streamflow in order to give similar weights to high and low flows.
Metrics Several metrics are used to compare the different reconstructions. The Continuous Ranked Probability Score  (Brown, 1974 ) is a commonly used score for ensemble verification and is defined as follow CRPS[x, y] = 1 M M i=1 ∞ -∞ [F (x) -H(y)] 2 dx, (8) with x the ensemble to be evaluated, y the observation, F the cumulative distribution function, M the number of observations and H the Heaviside function. The decomposition of the CRPS  (Hersbach, 2000)  is also computed to study the reliability part and the potential CRPS. As explained in  Hersbach (2000)  the reliability (Reli) gives an information similar to the rank histogram and the potential CRPS (P ot) is linked to the spread of the ensemble -the uncertainty -and to the number of outliers. In addition, the KGE  (Gupta et al. , 2009)  and his decomposition were used to provide a more insightful description of the datasets.
Multidecadal variations over large catchments In order to further describe HydRE and HydREM, multidecadal variations in the reconstruction are compared to the ones in SCOPE Hydro, in the observations over the 4 main rivers of France (see Section 2. 1), and in the GRUN dataset (see Section 2. 4). As those products have different spatial and temporal resolutions, transformations are first applied: - of the main rivers (see Figure  1 ). Finally, for each member and main river, the mean of the all catchment anomalies is computed. -For GRUN, yearly anomalies over 1871-2012 in respect to the 1970-2000 period are computed for each grid cell over France for each of the 50 members. Each cell is then assigned to a main river based on its location (see Section 2. 4 for the number of cells by catchments).
As previously, for each member and main river, the average of grid cell anomalies is computed. -For the observations, yearly anomalies over 1871-2012 in respect to the 1970-2000 period are simply computed for the four main rivers. Finally, a 30-yr centered rolling mean is applied to each time series of anomalies to highlight multidecadal variations. Results are presented in Fig. 8. First, applying the error model does not affect multidecadal variations as HydRE and Hy-dREM display similar ones, even if the dispersion is different. Overall, the GRUN dataset is closer to the HydRE and HydREM reconstructions than to SCOPE Hydro, especially before 1940, except for the Loire@Montjean. Indeed, during the 1900-1940 period, SCOPE Hydro shows strong positive anomalies which are not shown by HydRE, HydREM, or GRUN.
Concerning the Garonne@Tonneis, the observations are closer to SCOPE Hydro than HydRE, HydREM or GRUN before 1950. For the Rhône@Beaucaire, multidecadal variations in the observations shows less variability than in the different reconstructions, GRUN included. This could reflect the strong anthropogenic modifications of the Rhône catchment. For the Seine@Poses, HydRE and HydREM are closer to the observations than SCOPE Hydro or GRUN over 1950-2012, but the time series is rather short. Lastly, for the Loire@Montjean, the GRUN dataset seems to better represent observed variations. Indeed, over the 1920-1940 period, a overestimation is seen in SCOPE Hydro whereas HydRE/HydREM underestimate anomalies. However, for HydRE/HydREM this tendency is almost null over 1890-1920 whereas it still present in SCOPE Hydro.
e. with FYRE Climate. Still, some events seem more or less important when looking at SCOPE Hydro or HydRE/HydREM, e. g. high-flows in 1910 and low-flows in 1971 and 1990. The study of the high-flows and low-flows records in HydRE and HydREM reconstructions shows the interest of such datasets to better apprehend extremes of the past, and a good coherence with other indirect sources of data.
Two examples are the Generalized Split-Sample Test  (Coron et al. , 2012)  using of calibration-validation periods using a 10 year sliding window, Secondly, the stability of the reconstructions also depends on the stability of the input data. For HydRE and HydREM, FYRE Climate is used to forced the GR6J model. This reanalysis is a combination of SCOPE Climate and in-situ measurements of precipitation and temperature. Then some sources of instability can be potentially present : (1) the SCOPE Climate dataset is driven by the Twentieth Century Reanalysis which may include some trend inconsistencies  (Krueger et al. , 2013) , (2) the SCOPE climate used a bais-correction of precipitation based on the 1958-2008 period as reference  (Caillouet et al. , 2019)  that could not be stable over time and, (3) the assimilation of station measurements in FYRE Climate could also lead to temporal inconsistencies due to the evolution of the observation network and of its quality.
, 2017)   The various results of these study have put forward the interest of both HydRE and HydREM when comparing to other existing datasets. Those two 25-member reconstructions make available daily streamflow over 661 near-natural catchments of France between 1 January 1871 and 29 December 2012. For both, the 25-member ensemble spread reflects the uncertainty of the reconstructed streamflow. A preference should be given to HydREM as results have put forward its higher reliability. However, as the two products show good results in reproducing observations, long-term variations and flood events, we chose to provide both through two joined datasets : HydRE  (Devers et al. , 2023a)  and HydREM  (Devers et al. , 2023b).
Data availability. HydRE (Devers et al. , 2023a)  and HydREM (Devers et al. , 2023b)  are made available as netcdf files on the Recherche Data Gouv platform. Each dataset comprises 25 netcdf files, one for each ensemble member. Please note that ensemble member #1 of HydRE is associated to member #1 for HydREM, and so on.