dataesr/scientific-paragraphs-categorization huggingface dataset:
Input data and processing Eight peach (P. persica) RNA-sequencing datasets were downloaded from the European Nucleotide Archive (https:// www. ebi. ac. uk/ena) and were used as raw reads for this project. This comprehensive dataset includes data of various peach cultivars under different stress conditions and from various tissues: root  ((Ksouri et al. , 2016)  and PRJNA323761), leaf  (Ksouri et al. , 2016; Bakir et al. , 2016) ), stigma  (Jiao et al. , 2017) , and fruit (  (Li et al. , 2015; Sanhueza et al. , 2015) , PRJNA328435 and PRJNA397885). A detailed list of the project IDs and metadata are provided, respectively, in Table  1  and Supplemental Table  S6. The obtained reads were quality-processed and trimmed using FASTQC v. 0. 11. 5 and Trimmomatic v. 0. 36  (Bolger et al. , 2014) , to discard adaptors and low-quality sequences with mean Phred score (Q 5 30) and window size of 4:15.
The first nucleotides were then head-cropped to ensure a per-position A, C, G, T frequency near to 0. 25. Following the trimming, only sequences longer than 36 bp were retained for further analysis. An overview of our complete workflow is shown in Figure  1  (see Step 1). The high-quality reads from each RNA-seq project were quantified separately using the pseudo-aligner kallisto v. 0. 43. 1 for fast and accurate transcripts count and abundance  (Bray et al. , 2016). Kallisto was run in two steps: (1) a transcriptome index was built from all cDNA transcripts of P. persica v2, from Ensembl Plants release 39  (Verde et al. , 2017; Howe et al. , 2020)  and (2) each sample was pseudoaligned against the index. Transcript-level abundance was estimated and normalized to transcripts per million (TPM) using 100 bootstraps (-b 100) to ascertain the technical variation.
Network-based detection of co-expressed modules Based on the assumption that co-expressed genes may share the same biological signature, WGCNA v. 1. 61 was performed to extract clusters of densely interconnected genes named modules  (Langfelder and Horvath, 2008). Transcripts were clustered to remove sample outliers and transcripts with missing entries. A similarity matrix was constructed by performing pairwise Pearson correlation across all targets. Then an adjacency matrix was built raising the similarity matrix to a soft power (b). Here b was set to 7 reaching thus 83% of the scale free topology fitting index (R 2 ). To minimize the effect of noise, matrix adjacency was transformed to topological overlap measure (TOM). Modules were defined as gene sets with high topological overlap with a minimum module size of 20 targets.
Compared with standard HC, this approach solves the issue of setting the final number of clusters and arranges the genes based on their topological overlap to eliminate spurious associations resulting from the correlation matrix. Three diagnostic module functions were evaluated: The ME, intra-connectivity (Kwithin), and Module membership (MM). ME is considered as a representation of gene expression profiles and defined as the first principal component of a given module. Kwithin measures how connected a given gene is with respect to others of the same module, and MM calculates the correlation between gene expression values and ME  (Langfelder and Horvath, 2008). MM values close to 1 or -1 indicate genes highly connected to the module. De novo cis-regulatory sequences discovery using RSAT::Plants Gene modules resulting from network analysis were subjected to de novo motif discovery pipeline using the RSAT::Plants standalone (Figure  1,  Step 2).
TF prediction and GO analysis Hereafter, the analysis was restricted to modules with significant detected signals. First, genes encoding TFs were predicted using the iTAK database (http://itak. feilab. net/cgi-bin/ itak/index. cgi, last accessed September 2020). Their protein sequences were subsequently submitted to footprintDB to predict their putative target DNA-binding site (https:// github. com/eead-csic-compbio/footprintDBclient). GO functional enrichment analysis was conducted using PlantRegMap/PlantTFDB portal v5. 0 (http://planttfdb. cbi. pku. edu. cn, last accessed September 2020) and the adjusted P-value  (FDR 50. 05; Tian et al. , 2019).
Conventional clustering of genes from expression data To demonstrate that the proposed protocol is not strictly dependent on co-expression network analyses, we also generated gene clusters with conventional clustering methods in the clValid R package  (Brock et al. , 2008). Indeed, clValid allows the simultaneous comparison of multiple algorithms in a single function call. Furthermore, it can determine the best clustering method and the optimal number of clusters (k). K_min and K_max were, respectively, set up to 3 and 50. Hereafter, we use the term "module" to refer to a group of interconnected genes derived from network analysis and the term "cluster" to group of genes resulting from classical clustering. We paired clusters to network modules having similar expression profile and sharing at least 15% of the genes in the module.
Positive control: gene clusters based on ChIPseq peaks Peak sequences of 10 ChIPseq datasets from A. thaliana were downloaded from JASPAR database  (Fornes et al. , 2020). They were locally aligned with BLASTN against the Arabidopsis TAIR10. 42 genome from Ensembl Plants to obtain the closest neighbor genes. The Blast parameters were as follows: E-value 4 1e -5 , max_target_seqs = 1, max_hsps = 1 query-coverage of 80% and percentage of identity 98%. Similarity between references (JASPAR) and de novo discovered motifs was computed with the normalized Ncor score (see above). Additionally, motif predictions based on the protein sequences of the ChIPped TFs were done using footprintDB.
Acknowledgments We acknowledge support of the publication fee by the  CSIC Open Access Publication Support Initiative  through its  Unit of Information Resources for Research (URICI). We thank  Eric OLO NDELA  for his support and help in creating the HTML report and providing useful feedback. We also thank  Claudio Antonio Meneses Araya ,  Dayan Sanhueza , and  Tomas Carrasco  for giving us access to their RNA-seq data.
The development and validation are performed at the study site of Gâvres (France). This site is subject to wave overtopping, so the numerical phase-resolving SWASH model is used to build the learning dataset required for the metamodel setup. Gaussian process-and random forest classifier-based metamodels are used and post-processed to estimate 14 indicators of interest for FEWS users. These metamodelling and post-processing schemes are implemented in an FEWS prototype, which is employed by local users and exhibits good warning skills during the validation period. Based on this experience, we provide recommendations for the improvement and/or application of this methodology and individual steps to other sites.
In contrast, FEWSs relying on solutions S1, S2, or S3 provide model-based flooding information. For instance, the Coastal and Inland Flooding Observation and Warning (CI-FLOW) demonstration project for coastal North Carolina relies on models using the shallow-water equations  [9] ; nevertheless, while this approach is well-suited to overflow events, it is not adequate for overtopping. In the UK, employing some simplifications and expecting that HPC will allow their integration into operational systems, developments have allowed flood predictions to account for both overflow and overtopping  [10]. In the Netherlands, the issuance of warnings by the operational FEWS is based on forecasted nearshore conditions (as in other FEWSs); in the case of a warning, the flood is estimated using a database of pre-calculated flooding scenarios for a pre-defined set of dike breach locations and water levels with a model resolution of approximately 10 m (personal communication).
For instance, data-driven machine learning techniques are widely used for continental flood prediction  [13] [14] [15] [16]. These approaches rely on observational datasets (typically from water-level sensors installed in rivers and others channels) that are relatively large (for instance, comprising hourly measurements at several locations over several months or years). However, the characteristics of coastal domains are quite different: inland sensors are typically lacking, with the exception of smart cities such as Macao  [17] , and while tide gauges are occasionally available, these types of measurements are not sufficient to deduce coastal flood information (e. g. , the flood extent or inland water depth), especially on sites subject to wave overtopping. The alternative is to rely on hydrodynamic models to build learning datasets.
However, the computation time of such models significantly limits the number of runs/scenarios; as a result, the statistical method being used must be adapted to the scarcity of the dataset. In recent years, metamodelling techniques have been explored for coastal hydrodynamics and coastal water level predictions and have shown promising results (e. g. ,  [18, 19] ). Metamodels (also called surrogate or response models; for an introduction, see  [20] ) are functions that aim to reproduce the behaviour of a "true" model (e. g. , a numerical hydrodynamic model) for given input variables (for instance, offshore conditions). In addition, as highlighted by  [21] , FEWSs should be user-centred and not designed following the linear paradigm of model-based FEWSs (i. e. , a linear set of connections from observations to the generation of warnings and their transmission to users).
The novelty of the present work is to tackle the entire process, from the identification of user needs to the deployment of the fast-running FEWS and its validation. A side objective is to investigate whether the needed elements (data, models, etc. ) are adequately mature to allow the prediction of local floods with good confidence. The FEWS is developed in Gâvres, France. We first describe the materials and methods (site, indicators, numerical model, metamodel, and FEWS elements). Then, we present and discuss the results (learning dataset, metamodel skills, and FEWS skills). Finally, we discuss future challenges and provide some recommendations. All the abbreviations and acronyms used in the present manuscript are listed in Acronyms and Abbreviations.
For the present study, as the topo-bathymetry has significantly changed since 2008 due to the heightening of some coastal defences. We use the above-mentioned model chain, but we include a DEM that is as representative of the present-day configuration as possible. From the available bathymetric surveys (SHOM, DHI), lidar data (the public RGE ALTI IGN product and limited access data of the Lorient Agglomeration), and Global Positioning System (GPS) survey of the coastal defences, we have constructed a DEM (denoted DEM2015-2018) that is representative of the 2015 bathymetry (winter) and 2018 coastal defences. In addition, Ref. [6] highlights the significant effect of the stochastic character of waves on floods at the study site, i. e. , that the same offshore wave spectrum leads to different instantaneous waves and to different volumes of water entering inland.
Design of Experiments We start by selecting a limited number n = 100 of offshore meteo-oceanic scalar conditions X = (SWL, Hs, Tp, Dp, U, Du), as was performed by  [22]. This selection (S1) is designed by applying the clustering procedure described by  [42]  to a large dataset of extreme conditions (here, ≈150,000). This database is constructed through a combination of Monte Carlo random sampling and multivariate extreme value analysis performed on the database of hindcast conditions constructed by  [7]  over the period 1900-2016. This approach ensures that the selected X input conditions are sufficiently extreme to lead to flooding but are also realistic. Then, using this first selection S1 (n = 100) and the 1900-2016 hydro-meteorological dataset  [7] , a probabilistic classifier is used to locate the time instants of these maximum values (based on quadratic discriminant analysis;  [43] ), and a multivariate Gaussian Monte Carlo-based sampling procedure is applied to generate 94 time series (S2), i.
e. , functional inputs X = (NMR(t), T(t), S(t), Hs(t), Tp(t), Dp(t), U(t), Du(t)) over 6 h, with 10-minute time steps. This functional input dataset is complete with 21 historical events (9 flood events and 12 non-flood events; see  [7] , for more details on the historical floods) and 16 scenarios simulated from small variations of the 9 historical flood events. Thus, the final grid experiment of time-varying forcing conditions contains 174 scenarios. Regarding the scenarios of the offshore meteo-oceanic scalar conditions, the initial S1 selection is complete with 44 high tide conditions extracted from the S2 selection such that the design of experiments for the steady forcing conditions (i. e. , the scalar inputs for simulation type Sim1) now contains n = 144 offshore meteo-oceanic scalar conditions X = (SWL, Hs, Tp, Dp, U, Du).
In many studies, the structural characteristics of the metamodels are chosen beforehand (e. g. , PCA decomposition in  [51] , B-spline decomposition in  [52] , the Matérn 5/2 covariance function in  [53] ) without a specific sensitivity analysis or benchmarking. In the present work, we use the ant colony-based optimisation algorithm ACO-Gp proposed in  [54]  to fix several structural characteristics of each metamodel, such as the set of inputs to use for prediction and the type of kernel function of the model. This algorithm helps to efficiently explore the space of potential metamodel configurations while optimising the prediction quality. Implementations for functional input GP-based metamodelling and structural optimisation supported on ACO-Gp are available through the R  [55]  funGp package  [56] , which is freely accessible from CRAN at https://cran.
r-project. org/package=funGp and GitHub at https://github. com/djbetancourt-gh/funGp, accessed on 25 July 2021. Alternatives to ant colony-based optimisation could be other methods for combinatorial optimisation, such as simulated annealing or genetic algorithms. Here, ant colony-based optimisation is adapted to the tree structure of the decision space (see  [54] ). For instance, whether a functional input is active affects the dimension of the rest of the decision parameter to be selected. This tree structure is naturally exploitable with the pheromone paths of ant colony-based optimisation, whereas it is much less clear how to exploit this tree structure with simulated annealing or genetic algorithms. Finally, to account for spatial outputs (as for metamodel Y 18 ), we rely on the work of  [24] , the method of which is summarised as follows.
As shown by  [24] , the resulting GP model can also be written as a multi-output model where the outputs are driven by a given set of functions. Implementations in both Python and R are freely available at https://github. com/anfelopera/spatfGPs, accessed on 25 July 2021. In the present work, we use 1003 spatially designed points. Those points are selected based on a dedicated k-means-based methodology accounting for both the spatial locations (i. e. , longitude, latitude) and the flood ratios (see Section 3. 1 and  [24] , for a further discussion). For a comparison with the more classic multi-output GP method based on linear models of coregionalisation, see also  [24].
e. , class 1) when Z 1 = 0, with Z 1 being the maximum of Y 1 over the tide. In addition, Z 1 is used to discriminate between events at the flood limit (i. e. , few wave overtopping events may occur; Z 1 < V1) and events corresponding to minor flooding (Z 1 ≥ V1) such that if Z 1 < V1, then the intensity (I 1 ) falls in the second class (Figure  3 ). At this stage, the threshold V1 is not defined; this is accomplished by optimising the numbers of true and false alarms (Section 3. 3). For this optimisation step, we employ a damage database covering the period 1900-2010 together with the oceanographic forcing database  [7]. These corrections can be seen as hierarchical constraints (Figure  5 ). In addition, some physically logical constraints are considered. For instance, the locations of indicators I 9 (town hall), I 10 (gymnasium), and I 12 (road2) are such that they cannot be flooded if the intensity of the flood is too small (typically for Z 7 < S1 with S1 = 29,200 m 2 , S1 corresponding to the upper limit of class no.
In a second step, the automatic detection of high tides is performed on the dataset, and then, the X data are extracted over the 6 h windows centred on the high tides for the 6 next tides. Then, the metamodels (Y i,i=1:18 ) are run, providing the 18 outputs for each tide. These outputs are post-processed (providing Z i,i=1:15 ) and aggregated into the specific indicators (I i,1:14 ). Finally, the I i indicators are published through the FEWS. Another related objective was to provide simple figures and outputs through the FEWS. This ambition is fulfilled not only by showing the classes in which each indicator falls rather than the indicator values but also by designing an interface as simple as possible (based on user feedback). From a technical point of view, the FEWS relies on open-source technologies, while the predictions and complementary information are provided through a website (Figure  6 ).
The background of the FEWS is based on big data technologies that allow the storage of a large amount of data and increase the speed at which data can be visualised. More precisely, the FEWS relies on the Elasticsearch engine  [58] , which is a highly scalable opensource full-text search and analytics engine that allows the rapid and near-real-time storage, search, and analysis of large volumes of data. The computational aspect of the FEWS relies on Python and R (the metamodels are built using R), and R Shiny  [59]  is utilised to visualise the predictions. Finally, the Lyxea © software suite supports the FEWS web platform. To guarantee the scalability and shareability of the system, dedicated application programming interfaces (APIs) have been developed for the data processing chain. Finally, the accessibility of the FEWS is restricted to the members of the consortium and the final beneficiaries (the local user group) via secured access.
Grid Experiment and Numerical Modelling Results: Preliminary Analysis Figure  7  illustrates the scenarios (learning dataset) that were run with the numerical model to build the scalar input metamodels (panel a) and the functional input metamodels (panel b) in comparison with the distribution of the continuous time series of X over 1900-2016 (panel c). This comparison illustrates how the grid experiment covers the X input space, which covers the distribution of the 1900-2016 X hindcast and includes past extreme events. Figure  7  further shows that the learning dataset effectively incorporates more extreme events than those observed. From the numerical modelling, the "Y" values are extracted (denoted Y Num for the Y values extracted directly from the numerical results, i. e. , to avoid misunderstanding with the Y metamodels); these are the (X,Y Num ) datasets used to build the metamodels.
As in the case of the scalar input metamodels, the results show a satisfactory prediction quality, which is further supported by a fairly high Q 2 value. In addition, the class is correctly predicted for the vast majority of the points (white dots). However, at this stage, we note that Y 7num values equal to 0 are overestimated. Appendix B presents the validation plots for the metamodels for the prediction of Y 8 to Y 17. To assess the predictability of the spatial GP-based model, we again perform LOO cross-validation where each flood scenario from the dataset is predicted using data from the other scenarios. First, we compute the root mean square error (RMSE) for the 1003 designed points, i. e. , the RMSE over the 174 LOO predictions. For the purposes of our FEWS, we remove the points for which the RMSE is larger than 0.
Indicator Predictions: Raw Results, Optimisation, Hindcast, and Reinforced Hindcast To estimate the quality of the metamodel predictions, we consider the period 1900-2010. From the analysis of historical information, five major flooding events occurred during this period, while another four events led to a sparse number of overtopping phenomena  [7]. We do not have knowledge of the historical values of each indicator, but we do know whether each scenario was a non-flood, minor flooding (few instances of overtopping, equivalent of the class-2 intensity indicator), or major flooding event. We also attribute a confidence index to each event within the damage event database  [7]. Indeed, for some of the damage events, it is not possible to be fully certain that either there was no flood or there were few instances of wave overtopping.
In addition, it is important to note that the territory of Gâvres is now more protected from floods than it was during 1900-2010. This implies that the predicted indicators following our method can be considered satisfactory if they fulfil the following conditions: (1) if the historical database indicates no submersion, then the prediction should be a non-flood event; (2) if the historical database indicates minor flooding, then the prediction should provide either a minor flooding or a non-flood event; and (3) if the historical database indicates major flooding, then the prediction should provide at least a minor flooding event. Our main calibration parameter is V1 (threshold of the water volume entering inland in 15 min; see Section 2. 6). Thus, we optimise the V1 values to minimise (maximise) the number of false (true) alarms over the 48 events within the damage events database.
The 1904 event is classified with moderate confidence as a major flood from an analysis of the historical information  [7]  such that this event may have been a minor flood instead of a major flood, but we are sure that there was a flood. Thus, the number of false alarms (i. e. , two) should be considered as the upper limit of the real number of false alarms. Finally, two events are not clearly identified in the damage event database: those on 18 December 1945 and 19 December 1945. However, after  [60] , strong erosive events may have occurred in December 1945. Focusing on the four remaining flood events of the damage database, (1) consistent with our validation assumption, Z 1 = 0 for two past minor flooding events and 0 < Z 1 < V1 for one past major flooding event (considering the less protective topo-bathymetry during the studied period), and (2) Z 1 = 0 for the past major flooding event on 10 January 2001.
The result for this last event is not inconsistent. Indeed, in comparison to the other major flooding events, the water level, wave, and wind conditions are not exceptional (with a joint exceedance return period of the water level and significant wave height of less than 1 year); in fact, this event was caused by a coastal defence failure, which led to significant flooding  [7]. The failure of coastal defences is not accounted for in our method/FEWS (see Section 4. 1 for a more detailed discussion). ). In (a), the bolded dates correspond to highly confident information in the damage event database, while italicised dates correspond to moderately confident information in the damage event database, i. e. , meaning that there is damage but that we are not fully certain of our classification. In (b), a true (false) alarm corresponds to case where Z 1 ≥ V1 (Z 1 < V1) and a (no) historical flood has been reported.
The methods used to construct the scalar input metamodels have previously been used with a smaller learning dataset and with numerical modelling relying on a topo-bathymetry representative of 2008 (i. e. , DEM2008, less protective). These metamodels exhibit good prediction skills compared to the historical flood events over 1900-2016 and yield much larger predicted quantities than those obtained with the (more protective) 2015-2018 topo-bathymetry.