dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. We thank the anonymous referee for helping to improve this paper. E. S. G. de Almeida  thanks  OCA  and the  "Ville de Nice" (Nice, France)  for the financial support to this work through the  "Bourse Doctorale Olivier Chesneau " during the period of 2016-2019. E. S. G. de Almeida  acknowledges  A. Rubio  for relevant information about her work on α Columbae. R. Ligi  has received funding from the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Skłodowska-Curie  grant agreement n. 664931. D. M. Faes acknowledges  FAPESP  (grant  2016/16844-1 ). A. C. Carciofi acknowledges support from  CNPq  (grant  307594/2015-7 ). This work was supported by the  "Programme National de Physique Stellaire" (PNPS)  of  CNRS/INSU  cofunded by  CEA  and  CNES ".
This work made use of the computing facilities of the  Laboratory of Astroinformatics (IAG/USP, NAT/Unicsul ), whose purchase was made possible by the  Brazilian agency FAPESP  (grant  2009/54006-4 ) and the  INCT-A. This work is based upon observations obtained with the  Georgia State University Center for High Angular Resolution Astronomy Array at Mount Wilson Observatory. The  CHARA Array  is supported by the  National Science Foundation  under Grants No. AST-1211929  and  AST-1411654. This work used  BeSOS Catalogue , operated by the  Instituto de Física y Astronomía, Universidad de Valparaíso, Chile  : http://besos. ifa. uv. cl and funded by  Fondecyt iniciación N 11130702. Based on observations collected at the  European Southern Observatory  under  ESO  programmes  087-D. 0311and 094.
D-0140. This work has made use of the BeSS database, operated at  LESIA, Observatoire de Meudon, France : http://basebe. obspm. fr. Some of the data presented in this paper were obtained from the  Mikulski Archive for Space Telescopes (MAST). STScI is operated by the  Association of Universities for Research in Astronomy, Inc. , under  NASA  contract  NAS5-26555. Support for MAST for non-HST data is provided by the  NASA Office of Space Science via  grant  NNX13AC07G  and by other grants and contracts. This research has made use of the  Jean-Marie Mariotti Center (JMMC) services LITpro, SearchCal , and AMHRA, co-developped by CRAL, IPAG and Lagrange. This work has made use of the SIMBAD and VizieR databases, operated at  CDS, Strasbourg, France.
OpenProt (www. openprot. org) is the first proteogenomic resource supporting a polycistronic annotation model for eukaryotic genomes. It provides a deeper annotation of open reading frames (ORFs) while mining experimental data for supporting evidence using cutting-edge algorithms. This update presents the major improvements since the initial release of OpenProt. All species support recent NCBI RefSeq and Ensembl annotations, with changes in annotations being reported in OpenProt. Using the 131 ribosome profiling datasets re-analysed by OpenProt to date, non-AUG initiation starts are reported alongside a confidence score of the initiating codon. From the 177 mass spectrometry datasets reanalysed by OpenProt to date, the unicity of the detected peptides is controlled at each implementation. Furthermore, to guide the users, detectability statistics and protein relationships (isoforms) are now reported for each protein.
Finally, to foster access to deeper ORF annotation independently of one's bioinformatics skills or computational resources, Open-Prot now offers a data analysis platform. Users can submit their dataset for analysis and receive the results from the analysis by OpenProt. All data on OpenProt are freely available and downloadable for each species, the release-based format ensuring a continuous access to the data. Thus, OpenProt enables a more comprehensive annotation of eukaryotic genomes and fosters functional proteomic discoveries.
Building on ribosome profiling data, several small ORF repositories have been published to foster the functional characterization of these previously overlooked ORFs  (16) (17) (18). These repositories have greatly participated in the incorporation of small ORFs in genome annotations  (1, 16 ). Yet, because they rely sometimes exclusively on ribosome profiling data analyses, these repositories suffer from the technical biases inherent to the experiment. This may hin-der the detection of overlapping ORFs or ORFs in lowabundance transcripts or in repetitive regions  (1, 10, 19, 20). Furthermore, the accuracy of the detection of isoforms may vary depending on the algorithm used to mine the data  (21) (22) (23). Using a different approach, OpenProt  (24)  first predicts all possible ORFs within the transcriptome retrieved from two annotations [NCBI RefSeq  (25)  and Ensembl  (26) ] and then gathers supporting evidence.
OpenProt thus offers a deep annotation of the genome of 10 species, identifying novel isoforms and novel proteins in an unbiased yet data-driven manner. The initial release of OpenProt provided a much needed resource for functional proteomic discoveries  (7, (28) (29) (30) (31) (32)  and was the first proteogenomic resource supporting a polycistronic model of annotation for eukaryotic genomes  (24). Here, we present an update of the OpenProt database incorporating the re-analysis of an additional 63 MS datasets and 44 ribosome profiling datasets, for a total of 177 and 131 datasets, respectively. This increase in the number of analysed datasets demonstrates the constant growth of the OpenProt resource. However, it does not constitute the core of this update. In this release, OpenProt implemented the transition to the latest annotations from NCBI RefSeq and Ensembl, the annotation of non-AUG initiation sites from ribosome profiling datasets, the incorporation of a timeindependent quality control of the unicity of detected peptides and the annotation of protein identities within a gene (isoform prediction).
Additionally, OpenProt website was upgraded to a more user-friendly interface and it now provides a platform for users to submit their datasets for reanalysis with the OpenProt pipeline. This platform aims to grant access to a deeper ORF annotation for data analysis to the wider community, independently of bioinformatics skills or computational resources. With a user-friendly and interactive interface, OpenProt (www. openprot. org) aims to foster discoveries of functional yet currently non-annotated proteins (Figure  1 ; Supplementary Material S1).
Thus, in humans 736 novel isoforms and 69 AltProts annotated in the OpenProt initial release are now RefProts since they have been included in annotations (NCBI RefSeq, Ensembl and/or UniProt). For example, the protein previously annotated IP 211724 in the initial release of Open-Prot (https://openprot. org/p/savedSearch/jCa) was detected and described (renamed altDDIT3) in a publication building on OpenProt data  (7). Consequently, the protein was included in the Ensembl annotation (ENSP00000494177) and in UniProt (A0A2R8YD15) in 2018, and became a Ref-Prot in the latest OpenProt release (https://openprot. org/p/ savedSearch/kCa). The numbers of AltProts and novel isoforms that have been included in NCBI RefSeq, Ensembl and/or UniProt since the initial release of OpenProt, across all supported species, are listed in Table  2.
ORF calling from ribosome profiling datasets In its initial release, OpenProt only reported evidence of translation for ORFs starting with an AUG. Yet pervasive translation with alternative start codons is increasingly reported by the community (14-17), with up to 83% of the sORF repository consisting of non-AUG small ORFs  (28). The latest release of OpenProt now integrates data-driven annotation of non-AUG initiating codons. The pipeline of analysis for ribosome profiling datasets (RIBO-seq) uses the PRICE algorithm  (21) , which can accurately predict non-AUG start ORFs (Supplementary Material S1). Briefly, PRICE uses a logistic regression model to accurately predict initiating codons in RIBO-seq data, whether the dataset is enriched or not for initiating ribosomes. PRICE thus reconstitutes a set of codons most likely to yield the observed reads.
We observed that when including all codons as possible initiation sites, the P-value of the reported ORF would increase as the overlap with the ORF predicted by OpenProt decreases (Supplementary Material S2). In PRICE, the Pvalue is the result of a generalized binomial test; thus, it in-dicates the confidence of that ORF not being attributable to noise. As the length of the ORF decreases, it is expected to be more difficult to distinguish true translation event from noise. In order to only annotate the most confident alternative initiation starts, we set the following filters based on our observation (Supplementary Material S2): any codon as initiating codon, and an overlap of the ORF candidate with the ORF predicted by OpenProt above 70%. Using these new filters, almost half a million (488 231) translation events could be retrieved in human datasets, corresponding to 33 836 ORFs across OpenProt.
Peptide unicity in MS datasets Unique and recurrent peptide detection in datasets of MSbased proteomics is considered a gold standard evidence of novel protein expression  (1, 34). However, the characterization of a unique peptide is often dependent on the database used at the time of the analysis. In classical MS analyses, a peptide is considered unique if it matches to one and only one protein. However, if a protein is not included in the database, it will not be taken into account in the evaluation of the unicity of the peptide. In the initial release of OpenProt, we ensured unicity by using an exhaustive proteome derived from both NCBI RefSeq and Ensembl annotations, as well as UniProt  (24). Yet, as mentioned earlier, annotations evolve and with that the proteomic landscape. In the latest OpenProt release, we ensured a control of peptide unicity from MS datasets through time and releases.
At each OpenProt release, the set of peptides mapped from all MS datasets to proteins is rescanned for protein assignation enforcing the OpenProt assignation rules. (i) When a peptide matches to multiple protein sequences from different genes, it is discarded. (ii) When a peptide matches to multiple protein sequences from the same gene, with at least one of the proteins being a RefProt, the peptide is given to the RefProt(s) only. (iii) When a peptide matches multiple Alt-Prots or novel isoforms but not RefProts, the peptide is assigned to all AltProts and novel isoforms. Using such rules, OpenProt reports MS-based evidence of expression of novel proteins (AltProts and novel isoforms) only if the detected peptide does not match any known protein. Furthermore, during this control of peptide unicity, the isobaric residues leucine and isoleucine are treated as undistinguishable.
MS statistics to guide data interpretation Based on the OpenProt pipeline and stringent peptide assignation criteria regarding novel proteins, one must realize that not all predicted AltProts or novel isoforms are detectable by MS  (35). To help the OpenProt users with the interpretation of an MS score of 0, we added MS coverage statistics to the MS tab of the details page for each protein. The coverage statistics tab contains the number of independent datasets in which the protein was detected, the number of unique peptides detected across all datasets and the number of peptide spectrum matches across all datasets. Alongside these metrics, the theoretical and current coverages by MS for each given protein are indicated (named possible sequence coverage and detected sequence coverage, respectively).
The theoretical coverage is calculated from all possible tryptic peptides that would fit the OpenProt criteria to be assigned: a minimal length of seven amino acids, a maximal mass of 4600 Da and peptide unicity given the protein type (RefProt, novel isoform or AltProt). Thus, in H. sapiens  44   A summary of experimental detections across all species supported by OpenProt is provided in Table  3  and general statistics are now provided in the About page of the Open-Prot website (https://openprot. org/p/about; Supplementary Material S3) alongside a link to a stand-alone, freely accessible version of the script for calculating MS statistics.
Identification of similarities of coding sequences within genes In this OpenProt update, the relationships between proteins from a same gene are now reported. In order to predict proteins as isoforms, OpenProt evaluates the protein sequence identity between proteins from a same gene using an all-versus-all BLAST (Basic Local Alignment Search Tool)  (36). OpenProt reports proteins as isoforms if the result of their BLAST search yields a bit score over 40 for an overlap over 50% of the queried sequence, as previously published  (37, 38). The bit score was chosen as it is dependent on the size of the alignment, but not on the size of the database used. Thus, the higher the bit score, the better the sequence similarity. This isoform feature allows the user to grasp the complexity brought by alternative splicing and by the polycistronic nature of a transcript.
Summary of the features presented in the initial release At the time of the initial release, the OpenProt website provided three interfaces: a genome browser, a search page and a platform for downloads. The genome browser enables rapid lookup of all transcripts, proteins and detected peptides for a specific genomic region, while the search interface is designed for exploration of specific genes, transcripts and/or proteins. The platform for downloads allows custom retrieval of any data on the OpenProt resource as tsv, fasta or bed files  (24, 35). Each predicted ORF and encoded protein annotated in OpenProt has its own page that contains a genome browser centred on the dedicated ORF alongside all supporting information. All data from the initial release are still present on the website (under the release number 1.
Novel OpenProt web interface The OpenProt resource is hosted at www. openprot. org since its initial release. The graphics of the website have, however, considerably changed for a more user-friendly and graphical interface. The home page (https://openprot. org/) now hosts a presentation of the project with tutorials and visual explanations. The query interface has been updated with an additional advanced search filter. The users can now filter results for specific MS or ribosome profiling datasets (Figure  1 ). Furthermore, a novel interface has been added for the submission of MS or ribosome profiling datasets to be analysed by OpenProt. This novel submission platform allows users to query their datasets for unannotated proteins, independently of their bioinformatics skills or computational resources.
To ensure the quality of the data inserted into the OpenProt database, users first have to deposit their data into public repositories, the PRIDE archive  (39)  or ProteomeXchange  (40)  for MS datasets and Gene Omnibus (41) for ribosome profiling ones. After submission, the parameters are validated by the members of the OpenProt team prior to the analysis with the OpenProt pipeline (24) (Supplementary Material S1). Upon completion of the analysis, the results are sent to the users and the data are stored to be inserted in the next OpenProt release. For computational reasons, releases will be launched when 10 datasets are ready for implementation, or if it is 60 days since a dataset has been analysed. The Help page and contents have been updated to support this new interface (https://openprot.
Website development The OpenProt web platform was built using the Flask framework (version 1. 1. 2) and developed using HTML, SQL and JavaScript. The OpenProt website uses an HTTPS protocol to ensure protection of personal information. This table contains the accession of the RefProts responsible for the isoform annotation of the looked-up protein (here, II 794710), and the associated reason. Please note that because the filter for a novel protein to be annotated as novel isoform is more inclusive than that of the prediction of related proteins from the same gene, the RefProts listed in the table may not appear in the predicted isoform tree. D386 Nucleic Acids Research, 2021, Vol. 49, Database issue OpenProt is accessible via all major web browsers supporting JavaScript, such as Safari, Firefox, Chrome or Internet Explorer.
DISCUSSION AND COMPARISON TO EXISTING RE-SOURCES The inaccuracies of current genome annotations have been increasingly reported in the last decades  (1, 4, 14, 28, 29, 42, 43) , and the OpenProt resource has offered a systematic approach to mend the gap between annotations and experimental data  (24, 35). To the best of our knowledge, OpenProt was the first database to fully endorse a polycistronic model of eukaryotic genome annotation. OpenProt differs from other small ORF databases  (16, 17)  in that it does not uphold a maximum length threshold (below 100 codons for smORFs); it allows for multiple ORFs per transcript, supports two transcriptome annotations and allows for the identification and detection of novel isoforms of annotated proteins. Thus, OpenProt reaches a deeper ORF annotation throughout the genome.
Furthermore, OpenProt now supports data submission to be analysed using the pipeline of OpenProt and with the results returned to the users. To the best of our knowledge, other small ORF databases do not offer this service although they may accept data suggestions  (16). In addition, OpenProt differentiates itself from UniProt  (27)  or NextProt  (44)  as it provides a genome browser and implements a polycistronic annotation model. Notwithstanding, as UniProt and NextProt are well-established proteomic resources, when a novel protein regroups sufficient evidence to meet UniProt and NextProt annotation requirements, it becomes a RefProt in the OpenProt database with the UniProt accession for ease of translation between databases. This symbiotic behaviour is exemplified by hundreds of novel proteins that have been annotated in NCBI RefSeq, Ensembl and/or UniProt since the first release of OpenProt (Table  2 ).
OpenProt is a release-based platform, developed in accordance to the FAIR guiding principles for scientific data management and stewardship  (58). This ensures an up-to-date, continuous availability of all OpenProt data through time. Furthermore, the shareable links created from the 'Share' feature at the top of the results table on the query interface are also release-based and persistent in time. This allows the inclusion of such links in publications for an exact snapshot of the OpenProt annotation at the time. All of the scripts behind OpenProt are available upon reasonable requests to the authors. Standalone scripts related to the mining of OpenProt data are available in the About page, under the Related Scripts section (https://openprot. org/p/about). In future releases, OpenProt aims to tackle remaining challenges.
All data are inserted in the OpenProt resource and freely available to the community (yellow arrow). OpenProt also allows data submission by users for analysis using the OpenProt pipeline (purple dashed arrows). The results are then returned to the users and inserted in OpenProt at the next release (purple dashed arrows). With a symbiotic behaviour between the scientific community, linking experimental data, bioinformatics resources and deep ORF annotation, OpenProt participates in the implementation of novel, experimentally supported proteins in genome annotations and protein databases (red arrows).
2) and Python (version 3. 6. 9) scripts and stored in a Post-greSQL database (version 9. 6). All re-analysed MS and ri-D384 Nucleic Acids Research, 2021, Vol. 49, Database issue Table 3. OpenProt (v1. 6) evidence collection output Species Conservation evidence Translation evidence (RIBO-seq) of species evaluated for orthology relationships (not counting the queried species); St = number of studies re-analysed by OpenProt; Ref = currently annotated CDS (RefORF); II = novel isoforms of known CDS; IP = novel CDS from alternative ORF (AltORF); N/A = when no dataset has been re-analysed for this species yet (OpenProt release 1. 2). Conservation evidence = all proteins with at least one ortholog in at least one species. Translation evidence = all ORFs detect in at least one detection by PRICE analysis of RIBO-seq data.
OpenProt annotates thousands of novel predicted proteins supported by experimental evidence and functional predictions. As our pipeline evolves and more RIBO-seq and MS datasets are constantly added to the database, evidence for functional yet unannotated proteins is growing. At the time of this manuscript, the OpenProt resource lists 1477 novel proteins (AltProts or novel isoforms) in humans that were detected by both ribosome profiling and MS. Out of these, 935 are located on at least one mRNA that contains another annotated coding sequence. These numbers highlight not only the need for resources like the OpenProt database, but also the polycistronic nature of eukaryotic transcripts.
ACKNOWLEDGEMENTS We thank our collaborators for providing additional MS datasets. We thank  Florian Erhard  for his help updating the PRICE pipeline on RIBO-seq data. We also thank  Mylène Brunelle ,  Alan A. Cohen  and  Darel Hunting  for helpful discussions and contributions. X. R. is a member of the  Fonds de Recherche du Québec Santé (FRQS)-supported Centre de Recherche du Centre Hospitalier Universitaire de Sherbrooke. We thank the team at  Calcul Québec and Compute Canada  for their support with the use of the supercomputer mp2 from  Université de Sherbrooke. S. L. was supported by a  CIHR  scholarship for graduate studies. Operation of the mp2 supercomputer was funded by the  Canada Foundation of Innovation (CFI), le ministère de l' Économie, de la science et de l'innovation du Québec (MESI)  and les  Fonds de Recherche du Québec--Nature  et technologies (FRQ-NT).
OpenProt annotates thousands of novel predicted proteins supported by experimental evidence and functional predictions. As our pipeline evolves and more RIBO-seq and MS datasets are constantly added to the database, evidence for functional yet unannotated proteins is growing. At the time of this manuscript, the OpenProt resource lists 1477 novel proteins (AltProts or novel isoforms) in humans that were detected by both ribosome profiling and MS. Out of these, 935 are located on at least one mRNA that contains another annotated coding sequence. These numbers highlight not only the need for resources like the OpenProt database, but also the polycistronic nature of eukaryotic transcripts. Indeed, in each species for which OpenProt re-analysed at least one MS dataset and one ribosome profiling dataset, novel proteins detected by both methods and encoded in at least one mRNA containing another annotated sequence were reported.
A simplified nonlinear dispersive BOUSSINESQ system of the BENJAMIN-BONA-MAHONY (BBM)-type, initially derived in  [2] , is employed here in order to model the generation and propagation of surface water waves over variable bottom. The simplification consists in prolongating the so-called BOUSSINESQ approximation to bathymetry terms as well. Using the finite element method and the FreeFem++ software [3], we solve numerically this system for three different complexities for the bathymetry function: a flat bottom case, a variable bottom in space, and a variable bottom both in space and in time. The last case is illustrated with the JAVA 2006 tsunami event. This article is designed to be a pedagogical paper presenting to tsunami wave community a new technology and a novel adaptivity technique along with all source codes necessary to implement it.