dataesr/scientific-paragraphs-categorization huggingface dataset:
[3] , while for sc116, the storm surge component is estimated based on the simulations performed by Le Roy et al. [2]. The CFMDG dataset can be used to estimate the effect that such past forcing conditions would have on the territory. We should keep in mind that the territory, topography, and coastal defences may have significantly changed such that the same forcing conditions (X) may lead to different flooding (Y). This is the case for the territory of Gâvres  [3] , where, for the same forcing conditions, the flood (obtained with our modelling approach for a digital elevation model and coastal defence configuration representative of 2018) should be smaller than the past observed floods (see  [3] ). Indeed, Figure  6a  (sc95 to sc116, Group S1) illustrates that these past forcing conditions would lead to smaller flood intensity, with events leading to no flooding or light flooding (from sc = 95 to 116, the maximal value of Smax is 16,839 m 2 for sc = 116).
This is even better illustrated when focussing on the most recent major flood event, which occurred in 2008 (Johanna storm, forcing conditions corresponding to sc116): the present topography and coastal defences (DEM2018, Figure  9a ) appear much more protective against floods than those of 2008 (DEM2008, Figure  3a ). The CFMDG dataset also includes scenarios of sea level rise (the present mean sea level-NM-is 0. 532 m in the IGN datum, while the NM values of the dataset range from 0. 3 to ~2 m) and thus may provide knowledge on future floods and hazards. Among the scenarios of variations around past real forcing conditions, scenario N • 118 corresponds to scenario N • 116 (Johanna, best estimate), but with a sea level rise of +1 m (i. e. , NM (sc = 118) = NM (sc = 116) + 1). Figure  9  illustrates the significant effect that such a sea level rise of 1 m would have if the Johanna forcing conditions occurred again in the future, assuming an unchanged DEM (DEM2018).
Examples of Use of the CFMDG Dataset for Methods Development and Forecast The dataset introduced here is directly of use to develop, test and compare metamodelling techniques, especially regarding problems of functional input (i. e. , time-varying forcing conditions) and scalar (i. e. , a single value) or functional (i. e. , time and/or space varying) outputs (in general). One part of this dataset has, for instance, already been used to develop and train metamodels aiming at predicting either the flooded area (Smax) or maps of water depths inland (Hmax). These developments rely on Gaussian processes (GP) and allow prediction in a few seconds and a few minutes, respectively, instead of the 3 days of computations on 48 cores. First, a GP-based metamodelling approach was developed by Betancourt  [14]  to predict Smax.
Implementations for functional input GP-based metamodelling and structural optimisation supported on ACO-Gp are available through the funGp R package  [15] , freely accessible from CRAN at https://cran. rproject. org/package=funGp accessed on 13 June 2023 and GitHub at https://github. com/djbetancourt-gh/funGp accessed on 13 June 2023. Betancourt et al. [15].
Implementations for functional input GP-based metamodelling and structural optimisation supported on ACO-Gp are available through the funGp R package  [15] , freely accessible from CRAN at https://cran. r-project. org/package=funGp, accessed on 13 June 2023 and GitHub at https://github. com/djbetancourt-gh/funGp, accessed on 13 June 2023. Betancourt et al. [15]  and Idier et al. [10]  used the above approach and a subset of 174 scenarios (sc1 to sc117, sc119 to sc132, sc139 to sc178, sc180 to sc182; Table  2 ) to train and cross-validate a metamodel predicting Smax. The obtained metamodel exhibits high predictive skills with a coefficient of determination value, denoted Q 2 (in a leave-one-out LOO cross-validation procedure) of 0. 958, but with some weaknesses to properly detect the zero cases (no flood).
Regarding the water depth inland, López-Lopera et al. [16]  developed a metamodelling technique that provided the ability to account not only for the functional inputs (time series) but also for functional (spatial) outputs. Implementations in both Python and R are freely available at https://github. com/anfelopera/spatfGPs, accessed on 13 June 2023. They applied this method to predict Hmax in each inland point at Gâvres using a subset of 131 scenarios (sc1 to sc118 and sc120 to sc132) and different subsets of the inland points (103 and 1003 points) to train the metamodel. Considering for instance the predictions for the 131 LOO tests and the 103 points subset, they resulted in Q 2 and CA (coverage accuracy of the predictive intervals defined at ±2 standard deviation) median values of approximately 95.
Although operative in the RISCOPE application case, this approach could be replaced by alternative techniques of wider theoretical ground. One way to improve the metamodel prediction relying on the GP-based metamodels close to 0 is to increase the number of learning scenarios providing no flood (Y = 0). This strategy was tested by adding 40 non-flood event scenarios (sc183 to sc222) to the existing 174 scenarios used in the learning dataset, but no significant improvement was found. A more promising perspective of improvement is to use refinements of GP models that are tailored for modelling positive functions and may provide more accurate metamodel predictions of output values close to 0. Two examples of these refinements are truncated GP models  [30]  and constrained GP models  [31, 32].
Use of the Dataset for Method Development and Forecasting For the flood forecast, the added value of the present dataset is twofold: (1) local forecast to the Gâvres site (i. e. , site-specific, as in  [10] ); (2) development of forecasting methods. As highlighted in  [10] , the need of the users is to know if there will be a flood event or not, and if so, of which intensity. The need of spatial maps only appears in the second phase. Such maps allow better predicting where there will be a flood and thus support risk prevention or crisis preparation and management actions. Thus, an alternative to prototypes that are fully based on metamodels (as in  [10] ), is to estimate the primary information of interest (flooded surface based on a metamodel predicting Smax) and then to use the associated rp maps (see, e.
g. , Figure  8  for the flood intensity classes defined in Section 4. 1) to estimate the envelope of potential flooded zones (map) corresponding to any Smax classes defined by the user of the dataset depending on its specific need. In addition, the FEWS prototype developed by Idier et al. [10]  is determinist (i. e. , metamodel simulations are performed for single meteo-oceanic forcing condition X). The next step would be to use such a metamodelling approach (fast running) to move from deterministic to probabilistic forecast using as inputs the outputs of Ensemble meteo-oceanic prediction systems. A second approach for forecasting floods is to rely on a lookup table method. Such a method consists, for each desired input condition (e. g. , forecasted 6 h time series for the next tide), of identifying in a pre-existing computed database (here, the CFMDG dataset) which existing scenario (scenarios) is (are) the closest to the desired conditions, i.
e. , based on a distance (or similarity) measure in the input domain of variation. This approach has, for instance, been used in many applications when the inputs and outputs are treated as scalar continuous variables; for instance, for transforming wave characteristics from deep to shallow water conditions (see, e. g. ,  [33]  along the Long Beach peninsula in the US) or for fast prediction of a hurricane-induced surge in coastal Mississippi  [34]. When the inputs are functions (e. g. , time series such as in the Gâvres case), more advanced distance functions must be defined (see  [15]  and references therein). Thus, the present dataset may be of use in developing and testing methods to compute the distances in the X space (when X is made of time series) and the uncertainty associated with the selected scenario of the dataset.
The limits of a Steady Topo-Bathymetry In addition to hydro-meteorological forcing, floods are also related to topo-bathymetry and especially to the coastal defences in place. The present dataset has been built for a constant topo-bathymetry (and coastal defences) representative of the 2018 winter. In case of significant change in this topo-bathymetry and coastal defences in the future, the present dataset for local applied studies aiming at investigating coastal hazards should be used with caution. For instance, based on the analyses of the DEM differences, the user may estimate if the new configuration should lead to more (or less) flooding for the same hydrometeorological forcing conditions and thus should be able to stress if the use of the present dataset for such a new site configuration should under-or over-predict the flooding.
Recommendations for Generating Similar Datasets in Other Locations As guidelines to support the development of similar training datasets for flood investigations in another location, our recommendations are as follows. A preliminary step is to identify if flood events occurred in the past, and, in this case, if they result mainly from overflow or wave overtopping processes. This will support the choice of the numerical modelling approach. Then, the availability and quality of local topo-bathymetric data (and coastal defences) should be analysed. In case of too-low vertical precision (e. g. , errors larger than 10 cm), data should be acquired to obtain a precise enough DEM. Then, following the method described in Figure  2 , the locations in which forcing conditions are needed should be identified.
g. ,  [9] , which considered a regular discretisation of the driver space but applied filtering based on the analysis of the distribution of these drivers in the past). Depending on the specific use of the dataset, attention should also be paid to including cases in which no flood is expected (such scenarios are needed both for metamodel development and for lookup table approaches). Then, the hydrodynamic simulations can be performed for scenarios including specific past conditions and fictive conditions (and, when needed, scenarios in which no floods are expected). Finally, as in the present dataset, the model outputs may be post-processed to ease the dataset diffusion (light files) and the analysis of the final dataset. One recommendation we may provide when the total number of simulations is imposed would be to build the dataset iteratively, especially when the aim is to develop metamodels for fast prediction.
Conclusions The CFMDG dataset compiles a set of 250 scenarios of forcing conditions and induced flooded surfaces (Smax) and water depths inland (Hmax), obtained via hydrodynamic modelling and post-processing. Each scenario is a combination of 6 h long time series of the different forcing variables, centred on high tide. The dataset covers a wide range of coastal flood intensities. Several examples of past and future potential use of this dataset have been provided, from direct analysis for site-specific knowledge (processes, hazard, prevention) to method development (metamodels, forecast). We hope that making this dataset publicly accessible will trigger further methodological developments in machinelearning/metamodel-based techniques to support flood prediction. The procedure we used was adapted to the characteristics of the site (e.
g. , macro-tidal, complex seafronts) and the modelling choices (here, a quasi-3D modelling accounting for the nearshore and inland dynamics, including wave overtopping processes). In the case of micro-tidal sites-and depending on the selected modelling approach-while the main steps remain valid, the design of the scenarios may be adapted (as illustrated in this paper, it may be indeed simpler). In terms of practice, especially when the number of simulations is limited, we recommend building such datasets iteratively to ensure that the final dataset is as optimal as possible regarding the initial need. For specific studies related to the site of Gâvres, the dataset presents the limit to have been built for topo-bathymetry and coastal defences representative of the 2018 situation, restricting the validity domain.
Results: Synthetic View of the DatasetThis subsection provides a synthetic view of the built CFMDG dataset. The dataset is made of input data (X, with X = (NM, T, S, Hs, Tp, Dp, U, DU) (t)) and output data (Y, with Y = (Smax; Hmax (longitude, latitude) for the Np points)). Figures5 and 6show a synthetic view of the X time series and Y outputs for the 250 scenarios.
3. 1. 31 Results: Synthetic View of the DatasetThis subsection provides a synthetic view of the built CFMDG dataset. The dataset is made of input data (X, with X = (NM, T, S, Hs, Tp, Dp, U, DU) (t)) and output data (Y, with Y = (Smax; Hmax (longitude, latitude) for the Np points)). Figures5 and 6show a synthetic view of the X time series and Y outputs for the 250 scenarios.
In recent years, a huge amount of data on ncRNA interactions has been described in scientific papers and databases. Although considerable effort has been made to annotate the available knowledge in public repositories, there are still significant discrepancies in how different resources capture and interpret data on ncRNAs functional and physical associations. In the present paper, we have focused on microRNAs which regulate genes associated with rare diseases, as a case study to investigate data availability. The list of protein-coding genes with a known role in specific rare diseases was retrieved from the Genome England PanelApp, and associated microRNA-mRNA interactions were annotated in the IntAct database, and compared with other datasets. The annotation follows recognised standard criteria approved by the IMEX consortium.
RNAcentral identifiers were used for unambiguous, stable identification of ncRNAs. The information about the interaction was enhanced by a detailed description of the cell types and experimental conditions, providing a computerinterpretable summary of the published data, integrated with the huge amount of protein interactions already gathered in the database. Furthermore, for each interaction, the binding sites of the microRNA are precisely mapped on a well-defined mRNA transcript of the target gene. This information is crucial to conceive and design optimal microRNA mimics or inhibitors, to interfere in vivo with a deregulated process. As these approaches become more feasible, high-quality, reliable networks of microRNA interactions are needed to help, for instance, in the selection of the best target to be inhibited and to predict potential secondary offtarget effects.
Other high-throughput sequencing approaches of immunoprecipitated RNAs, after cross-linking to proteins of the RISC complex (CLIP-seq, PAR-CLIP etc. ), provide datasets of potential RNA-RNA interactions  [10] , largely improving the performance of the binding predictors, although specific couples of miRNA-mRNA should be further verified  [11]. While the number of publications describing microRNA interactions is constantly increasing, several databases have begun to collect them, often including high throughput indirect or weak evidence. It is worth noting that miRTarBase  [12]  and RAID  [13, 14]  allow filtering of collected ncRNAs associations, to reduce false positive rates when selecting "strong evidence" interactions. The inclusion of potentially erroneous targets in network analysis may result in misleading data interpretation, as extensively discussed in  Huntley et al.
, 2018. The UCL Functional Gene Annotation group has focused on the gene ontology annotation of human microRNAs collecting a set of highly reliable microRNA-mRNA pairs  [15, 16]. Since 2002 the HUPO Proteomic Standard Initiative (HUPO-PSI) has provided a standardised annotation system for molecular interactions, and has defined the minimal information requirements and the syntax of terms used to describe an interaction experiment (MIMIx)  [17] , approved by members of the International Molecular Exchange (IMEx) Consortium  [18, 19]. Common guidelines help to elude false positives, and increase the level of detail and contextual information captured when describing molecules, such as protein binding  [20]. The IntAct database, which is a member of the Consortium, has expanded its activities into the annotation of ncRNA interactions, initially focusing on S.
cerevisiae ncRNAs  [21] , and subsequently on mammalian ncRNAs. In this paper, we present the collection of microRNA-mRNA interactions annotated in IntAct and we integrate and compare it with other resources. In particular, we have focused on the microRNAs that regulate genes associated with rare diseases, in order to provide information potentially useful for the development of new drugs. A rare disease is a health condition that affects a minority of people compared with other diseases that are prevalent in the population  [22]. Although the increased focus on these diseases in the last few years, very little is known on microRNAs involved in rare diseases and their collection may help to gain insights into pathological mechanisms. The interactions can be downloaded from the IntAct database (https://www.
Data collection and deposition MicroRNA-mRNA interactions were manually annotated in the IntAct database, according to the curation standards established by the IMEx Consortium. We retrieved relevant papers by searching the literature with text-mining seeking for the term "microRNA" (or synonymous) AND a specific gene name in the abstract, and luciferase assay or RIP, or CLASH in the methods. The collected papers were manually filtered to identify those relevant for the curation process. More than 260 papers were selected and annotated. Genes associated with rare diseases were prioritised for annotation. The list of these genes was generated according to green panels from GenomeEngland PanelApp for the following diseases: Aniridia, Anemia Fanconi, Autism, Cakut, Cerebrak Folate Deficiency, Epidermolysis Bullosa, Familial Hirschsprung Disease, Growth Failure in Early Childhood, Early Onset Disease and Mitochondrial Disorders.
These last three presented a higher number of genes regulated by microRNAs and were used in the subsequent analysis. The microRNAs are identified with RNAcentral IDs and a short label, which specify the strand of the mature microRNA  [23]. The database can be queried with Ensembl transcript IDs or gene common names, (then selecting mRNA) or with mRNA common names (mrna_name), as well as with the microRNA name or ID. To retrieve information on mutagenesis analysis, in order to map the binding nucleotides, from the Intact web page, click on the lens in the "Select" column of the displayed interactions, then select "Features" in the following web page. Data are also linked to RNA central  [23]  in the microRNA entry.
Network analysis and databases comparison The molecular interaction network of microRNA-mRNA interactions was downloaded from IntAct using the IntAct app  [24]. To build diseasespecific networks, the list of Ensembl transcript IDs was used. Networks were built and analysed with Cytoscape  [25]. For comparison with other databases, miRTarBase and Raid were chosen to enable the selection of strong evidences of direct binding, comparable to those annotated in IntAct. The data were downloaded, IDs were uniformed, and the tables were uploaded in Cytoscape to build and intersect the networks.
Functional enrichment analysis For the "Biological Processes" enrichment analysis of microRNA targets, each transcript ID was converted to the common gene name. The tables were imported in Cytoscape to build the network, and BINGO tool  [26]  was used to perform the enrichment with 0. 003 as significance level for GFEC and EOD related genes, while 0. 05 was set for MD because there was no enrichment at lower values.
Statistical analysis Correlation analysis: A table was generated containing the number of interacting microRNAs for each gene in each of the three databases. Pearson correlation coefficient was calculated with the ggpairs function in the GGally R package  [27, 28]  Venn Diagram: Intersections among datasets were outlined by ggVennDiagram library  [29]  in R  [28]  and Jaccard index was calculated as : J(A, B) = ! ∩ $ ! ∪ $ [30].
Building microRNA network in IntAct The main molecular function of microRNAs is to down-regulate the target gene by binding to the mRNA through few complementary nucleotides located at the 5' of the microRNA and commonly (although not always) at the 3' UTR of the messenger RNA. As discussed in the introduction, several experimental approaches have been developed to investigate microRNA function, but only a subset provides clear evidence of microRNA binding to mRNA, while discrimination between direct binding and causal interaction is crucial for the design of interfering drugs. The partners of the IMEx consortium of interaction databases  [18, 19]  have agreed to consider luciferase assay as a proof of RNA-RNA interaction, when validated with mutagenesis analysis (Figure  1 ). Papers describing microRNA-mRNA interactions validated with the above-mentioned methods, were selected from the literature and annotated in the IntAct database.
As the interaction with mRNA results in the down-regulation of the expression, through the mRNA degradation, or in the inhibition of the translation, the microRNA is featured as "regulator" and a causality statement was used to annotate its effect. Most of the repositories which collect microRNA interactions, identify the targets with the gene name or ID. However, microRNAs bind to messenger RNAs and, in order to map the binding sites on the target, it is necessary to refer to a specific transcript. To this aim, it is worth noting that each gene produces several messengers by alternative splicing of the precursor transcript. The number of the collected transcripts is destined to increase as new sequences will be produced from different cell-types and stages. To avoid ambiguities, we have therefore created one full-detailed entry for each target gene, cross-referred it with its Ensembl transcript ID  [31] , and, whenever possible, we have mapped all the annotated interactions on this entry.
A small number of genes associated with mitochondrial dysfunctions (MD) are regulated by microRNAs, and the enrichment analysis did not show significant enrichment for any term. Interestingly, however, there is a strong enrichment of specific metabolic and biosynthetic processes, such as: ATP synthesis coupled electron transport, carboxylic acid metabolic process, generation of precursor metabolites and tRNA metabolic processes, in genes associated with MD and not regulated by microRNAs. We compared our results with two other databases: miRTarBase (https://mirtarbase. cuhk. edu. cn/~miRTarBase/miRTarBase_2022/php/in dex. php interactions confirmed by "Luciferase assay") and RAID (https://www. rna-society. org/raid2/ interactions confirmed by "strong evidence"). The low percentage of microRNA regulated genes was confirmed in the two datasets.
Supplementary Table  1  compares genes found in each of the 3 databases to interact with microRNAs. Interestingly there is a very high correlation among the number of interacting microRNAs annotated in the 3 databases for each gene, and frequently those with no interactions in one database are not annotated in the others either (Figure  5A ). This suggests that, although the coverage of annotated microRNA interactions may still be low, the networks reflect what is currently known in the literature, with more than half genes of each list not affected by microRNA regulation, many involved in one or few interactions, and a small number targeted by a huge number of microRNAs. Despite the high correlation, the three datasets do not contain the same interactions: the Venn diagram shows that only 8% of them (calculated from the union of the three datasets) are present in all databases, while each dataset contains a percentage of 21%, 15% or 13% of interactions not annotated in the others (Figure  5B ).