dataesr/scientific-paragraphs-categorization huggingface dataset:
The fact a significant portion of the iPPI-DB compounds are missing from major public generic databases (13% are missing from PubChem and up to 51% are missing from ChemBL) is also a good illustration of the importance of combining different data sources and that community curation of specialized database like iPPI-DB is a strong strategy. A growing number of life science data resources adopt community contribution to support data collection and curation, and to ensure their long term sustainability  (Jassal et al. , 2020; Lee et al. , 2018; Lock et al. , 2020). Yet, fully automated processes and community curation both required substantial developments in order to become efficient. Although it is obvious for automated curation procedures especially when they rely on artificial intelligence, community curation also mandates the development of adapted technologies that enable the automation of every process that does not require human supervision and the management of numerous entry sources from different groups of experts.
A web application for maintenance relying on community curation In order for iPPI-DB to rely on community curation, major developments were necessary to design a web application for expert curators. It was built to assist them in their extraction of DARCLP relationships from bibliographic sources (see Fig. 3 ). It follows the pillars of DARCLP: bibliographic source, biological assays, bioactivities, compound and compound location and PPI target. This constitutes a significant improvement over the previous version of the database, which was a complex and largely manual process relying on numerous scripts and external data sheets. This interface has been designed with the aim of facilitating the contributions as much as possible, even from experts who are not familiar with the technical aspects of this database.
Each contribution is based on a description of the content of a publication or a patent. A wizard-based web interface assists the expert curators in retrieving the DARCLP relationships disseminated in the document. Indeed, contributors indicate (step-by-step), the architecture of the PPI complex(es), the chemical compounds tested for modulation and the various assays in which these compounds were tested. An important step consists for the contributor in indicating a PDB ID for the entry containing the investigated PPIs and select the associated class of PPI architecture from a list of provided schematics as described in  (Zarzycka et al. , 2016). This input from the expert curator is a key element to accurately define the PPI target and the mode of action of the active compounds. The combination of the expert's insight and the automated processes allows to efficiently translate the DARCLP relationships into tractable database entries.
The contribution interface only requires minimal information from contributors. Whenever they indicate references to some entities such as bibliographic references (DOI, PubMed ID or Patent), PDB codes, proteins or compounds, the server automatically retrieves additional details from other reference databases using web services. This contributes to reducing the risk of errors and facilitates contributions. When the process is over, the contribution is deposited into the administration portal of the core curators for an ultimate validation that will eventually launch final calculations such as compound properties and push the contributions into production. This validation process makes the contribution data publicly visible, and updates all plots within the query interface including chemical space, efficiency plots, etc.
Versatile and interactive query capabilities While the new web application for contribution was designed to assist expert curators in feeding iPPI-DB with pharmacological data and DARCLP, the querying interface was entirely redesigned to access these data in the most tractable manner. Thus, the design of new querying interface was also articulated around the key elements of DARCLP relationships: bibliographic source, biological assays, bioactivities, compound and compound location and PPI target. The compounds available in iPPI-DB can be queried through an interface (see Fig. 4 ) that allows for the exploration of the data through the combination of multiple chemical and pharmacological filters. These filters include the properties of the compounds themselves (e. g. the chemical similarity to a query compound), or the compliance with chemistry rules such as the Lipinski's RO5  (Lipinski et al.
, 2001) , as well as the properties of their targets, and the data available to assess their mode of action (e. g. X-ray crystallographic structure, cellular assay, pharmacokinetic data). Depending on the nature of the data, the filters will be displayed with different widgets used to pick values from a list, to specify ranges for numeric values or to draw chemical compound structures, using MarvinJS (www. chem axon. com, version 20. 5. 0). Users can easily combine multiple filters to constitute complex queries, and the modification of the filter criteria is immediately reflected in the results page. This interactive approach enables users to iteratively refine their queries by adding new filters. Query results can be sorted on multiple criteria, and displayed as thumbnails, as a list of cards or as a table.
Each of these successive views corresponds to various levels of detail for the properties of the compounds and their targets: • the thumbnail view provides a quick overview of the results, focused on essential properties of the compounds (chemical structure, PPI target family and molecular weight), • the list view offers more details for each entry, including the multiple identifiers, cross links to other databases, bibliography references, compliance with chemistry rules and a summary of the pharmacological profile of the compound. • the table view displays a number of properties which may be further customized in different columns and exported as a CSV file. The filters, sorting and display options corresponding to these queries are directly reflected in the URL of the results page, which can be easily bookmarked for later reuse, or shared between collaborators.
Each compound in iPPI-DB possesses its own ID card through a persistent URL, for example https://ippidb. pasteur. fr/compounds/ 1602 for JQ1, a pan-Bromodomain inhibitor. This page provides all available data on the compound, including its structure, its physicochemical/pharmacological profiles, but also the structures of its most chemically similar marketed or investigated drugs. Each of these pages includes links to the external sources of information that have either been used to collect the data or that might be of interest to the user, such as Uniprot  (Bateman, 2019) , PubChem, ChEMBL, ChemSpider (http://www. chemspider. com) or DrugBank  (Wishart et al. , 2018). Each of these cards, as well as the other main pages of the site, includes BioSchemas-based  (Gray et al. , 2017)  schema.
Evolution of the iPPI-DB database contents and features This new version of iPPI-DB represents a significant improvement over the previous versions, both in terms of data contents and features. The web application has been completely reinvented and designed as a community-centered database. The development of the new interface for community curation, which was released in July2020, has allowed us to rapidly add new data without having yet to rely on external expert curators (Fig. 5 ). Indeed, the number of compounds (  2374 ) has increased of 48% from the first version and of 35% since July2020, while the number of bioactivities (3895) has increased of 84% since the first version and of 58% since July2020. Moreover, the number of PPI families of homologous PPIs (  108 ), the number of PPI targets (  115 ) and the number of publications (  231 ), have increased of 246%, 342% and 114% respectively.
Finally, the new contribution mode based on community curation is a major feature improvement and has clearly impacted the rate of data entries and will allow us to initiate new projects on special pathologies or disease-associated pathways mediated by specific PPIs. The iPPI-DB initiative aims to be centered on the community. Thus, this major update of the database had to place the database users and contributors at the center of the development strategy. We therefore adopted a User-Centered Design (UCD) approach (de  Matos et al. , 2013). We based this process on a constant dialogue with the project stakeholders, and the support of various participatory design techniques. It allowed us first to identify three different user roles: (i) data explorers, who browse the contents of the database; (ii) external contributors, who suggest new entries based on publications and (iii) core curators, who validate new suggestions.
Web application The iPPI-DB web application is based on Django, a framework we chose for its industry-grade robustness and the size of its community. Another factor for the choice of this framework is its use of the Python programming language, therefore easily interoperable with many existing Bioinformatics, Chemoinformatics and data processing libraries, such as Open Babel  (O'Boyle et al. , 2011) and pandas (McKinney, 2011). The data are stored in a PostgreSQL database. Besides the HTML code generated by the Django templates, the interface of the application relies on a number of Javascript components for visualisation. Marvin JS (20. 5. 0, 2020, http://www. chem axon. com) and SmilesDrawer  (Probst and Reymond, 2018)  are used to draw and display the molecular structure of the compounds.
The dynamic charts that display the various statistics on iPPI-DB (see https://ippidb. pasteur. fr/about-pharmacology/ and https://ippidb. pas teur. fr/about-physicochemistry/), or let users explore the compounds in the context of their chemical space (see https://ippidb. pasteur. fr/ about-pca/) or their efficiencies (see https://ippidb. pasteur. fr/aboutle-lle/) use the ChartsJs javascript library (https://www. chartsjs. org).
Web services The data structure stored in iPPI-DB is heavily linked to multiple existing reference databases, and data themselves reflect a formal description of the knowledge represented in the source publications. These data are provided through the contribution interface, which relies on external web services. These services query multiple databases  (Agarwala et al. , 2017; Ashburner et al. , 2000; Carbon et al. , 2019; Finn et al. , 2016; Jupp et al. , 2015; Mir et al. , 2018; Shefchek et al. , 2020)  to retrieve various information during the contribution process, using HTTP/REST web services. These web services are either directly accessed from custom python code, or through the BioServices python package  (Cokelaer et al. , 2013).
Conclusion and perspectives In a context of increasing pressure to fast-track pharmaceutical innovation, it is essential to rely on powerful tools to apprehend the full extent of available data. This comes with the collection of properly annotated pharmacological information, cross-references with complementary databases and intuitive tools to navigate them. The pharmacological modulation of PPI targets offers endless applications and benefits for human health. To this end, we have completely redesigned iPPI-DB, our database of PPI modulators. First, we completely revised the layout and implementation of the query interface, in order to make it more intuitive, interactive and powerful. Second, in the big data era, it has become clear that both automated processes and community curation represent complementary solutions to guarantee the growth and sustainability of reference databases.
In the case of a specialized database such as iPPI-DB, community curation represents the strongest strategy when assisted by appropriate tooling and automation, to facilitate the extraction of highly intricate DARCLP relationships. In the near future, we plan to expand our links and connections with complementary initiatives and strengthen our network of partnerships. We aim to develop tailored projects around the iPPI-DB initiative by implicating communities of experts in specific fields such as antimicrobial resistance, or specific pathologies, and by focusing on specific families of PPI targets or disease-associated pathways. With regards to the mode of contribution, we plan to combine our current expert contribution interface with automated approaches such as text-mining, in order to further facilitate and accelerate the process, while preserving the high quality of expert-curated database.
Finally, we also will explore various mechanisms to disseminate the information stored in this database to other resources, through linking from other databases. Along this line, we are currently finalizing the implementation of external links from Europe PMC to iPPI-DB, in order for each compound of our database to be associated with corresponding article accessible from Europe PMC. By providing a community-driven interface to add new data and robust querying tools, the new iPPI-DB version represents a unique portal for chemists and biologists willing to initiate drug discovery projects against PPI targets.
Acknowledgements This work used the computational and storage services (TARS cluster, VM Hosting) provided by the  IT department at Institut Pasteur, Paris. The authors wish to acknowledge in particular the help and technical advice of  Eric Deveaud ,  Emmanuel Guichard ,  Thomas Me  ´nard and  Youssef Ghorbal  ( IT Department, Institut Pasteur ). They also want to acknowledge the technical help of  Tru Huynh  ( Structural Bioinformatics Unit, Institut Pasteur ). They thank  Jon Ison ,  Benjamin Bardiaux  and  Pascal Campagne  for their proofreading of the paper. Marvin JS  ( 20. 5. 0 ,  2020 , http://www. chemaxon. com) is used for drawing and displaying chemical structures in both Query mode and Contribution mode of iPPI-DB. Pipeline Pilot  (server 19. 1) is used to prepare the DrugBank database from a SDF file prior to chemical similarity search 202 (2020).
Dong et al. [36]  proposed an algorithm to construct the k-NN graphs efficiently by an iterative procedure. Other efficient graph search algorithms are the navigable small world graph  [37]  and the hierarchical navigable small world (HNSW) graph  [38]. In these methods, a small world graph is an approximation of a Delaunay graph. The HNSW graph has multiple layers. The bottom layer includes all points. With increasing layer number, the number of points decreases. These graphsearch algorithms are compared with the diversified proximity graph, which is based on an existing k-NN graph, in  [39]. In  [40] , a navigating spreading-out graph is proposed that enables billion-scale data set searches for online sales applications. The same authors introduce the satellite system graph (SSG) in  [41].
Performance studies using an exact 12-d tree. Initially, we compare the DD-solver's computation times for increasingly fine data sets of 10 3 , 10 4 , 10 5 , and 10 6 points with 20 random samples each. The studies were performed using a AMD Ryzen TM 9 3900X 12-core processor. The c++ implementation makes use of the eigen3-library and the build-in Sparse-LU solver provided with this library. The nearest-neighbor problem is solved using a 12-d tree based on an own implementation according to  [15, 16]. Times are measured for the assembly and LU decomposition of K, the right-hand side assembly of E and S, solving the two equation systems (5a) and (5b), and finding the nearest-neighbors in the data sets. The results of this study are depicted in Fig. 3a. On the one hand, it can be observed that the total computational time increases heavily with the number of data points.
Acknowledgments MO gratefully acknowledges the support of the  Deutsche Forschungsgemeinschaft (DFG)  through the  Sonderforschungsbereich 1060  "The mathematics of emergent effects". SR and RE gratefully acknowledge the financial support of the  Deutsche Forschungsgemeinschaft (DFG)  through the project  RE 1057/40-2  "Model order reduction in space and parameter dimension -towards damagebased modeling of polymorphic uncertainty in the context of robustness and reliability" within the  priority program SPP 1886  "Polymorphic uncertainty modelling for the numerical design of structures". Finally, all authors acknowledge the financial support of the  DFG  and  French Agence Nationale de la Recherche (ANR)  through the project " Direct Data-Driven Computational Mechanics for Anelastic Material Behaviours " (project numbers:  ANR-19-CE46-0012-01 ,  RE 1057/47-1 ) within the  French-German Collaboration for Joint Projects in Natural, Life and Engineering (NLE) Sciences.
Sentence splitting involves the segmentation of a sentence into two or more shorter sentences. It is a key component of sentence simplification, has been shown to help human comprehension and is a useful preprocessing step for NLP tasks such as summarisation and relation extraction. While several methods and datasets have been proposed for developing sentence splitting models, little attention has been paid to how sentence splitting interacts with discourse structure. In this work, we focus on cases where the input text contains a discourse connective, which we refer to as discourse-based sentence splitting. We create synthetic and organic datasets for discourse-based splitting and explore different ways of combining these datasets using different model architectures. We show that pipeline models which use discourse structure to mediate sentence splitting outperform end-to-end models in learning the various ways of expressing a discourse relation but generate text that is less grammatical; that large scale synthetic data provides a better basis for learning than smaller scale organic data; and that training on discourse-focused, rather than on general sentence splitting data provides a better basis for discourse splitting.
Related Work Together with deletion, reordering and substitution, sentence splitting is one of the main operations used in text simplification. Early work on simplification used a rule based approach to splitting  (Siddharthan, 2006; Siddharthan and Mandya, 2014). For instance,  (Siddharthan, 2006)  defines 26 handcrafted rules for simplifying apposition and/or relative clauses in dependency structures and 85 rules to handle subordination and coordination. Further work focused on learning statistical simplification models from parallel datasets of complex-simplified sentences derived from English Wikipedia and Simple English Wikipedia. (Zhu et al. , 2010)  introduces a syntax-based machine translation model where splitting probabilities are learned from syntactic structure. (Woodsend and Lapata, 2011)  induced a grammar from the parallel Wikipedia corpus annotated with syntactic trees and use an integer linear programming model for selecting the most appropriate simplification from the space of possible rewrites generated by the grammar.
They report learning 438 rules for sentence splitting. Probabilistic models have also been proposed. (Narayan and Gardent, 2014)  determine splitting points using a dedicated probabilistic module trained on the Parallel Wikipedia corpus annotated with semantic structures while  (Narayan and Gardent, 2016)  extends this approach to an unsupervised setting where splitting points are determined based on the maximum likelihood of sequences of thematic role sets present in the simplified version of English Wikipedia. More recent work has directly adressed the sentence splitting task. (Narayan et al. , 2017)  introduce a dataset for training sentence splitting models called WebSplit and report results for various neural models trained on this data, comparing a vanilla sequence-to-sequence model with a multisource and a semantically informed model.
(Aharoni and Goldberg, 2018)  present an alternative train/dev/test partition for WebSplit which better supports generalisation and show that adding a copy mechanism helps improve results. One limitation of the WebSplit corpus is that it uses a small vocabulary. To remedy this shortcoming,  (Botha et al. , 2018)   While these efforts are focused on syntax-or semantic-based sentence splitting, our work targets discourse-based sentence splitting. Closest to our work,  (Niklaus et al. , 2019b,a,c ) defines a set of 35 hand-crafted transformation rules to recursively decompose sentences into a hi-erarchical structure relating core sentences linked via rhetorical relations. They do not generate a wellformed text and the proposed rule-based approach will fail to easily generalise to other languages.
Creating C/S Pairs Organic, Parallel Data. We create this data by extracting discourse-split instances from two existing datasets, WikiSplit and MUSS. WikiSplit  (Botha et al. , 2018 ) is a sentence splitting dataset containing 1M single sentences alongside a two sentence variant which preserves their original meaning. This data is extracted from Wikipedia edit history, and therefore contains organic instances of C to S transformations. The multilingual unsupervised sentence simplification dataset (MUSS)  (Martin et al. , 2020)  contains 2. 7M pairs of text sequences mined from Common Crawl web data which were estimated to be paraphrases of each other using L2 distance on LASER embeddings. Filtering out only those pairs that represent a splitting operation yields a subset of 157K examples.
Like WikiSplit, this dataset is organically human-authored. To create a discourse splitting dataset, we then extract from these two datasets all instances such that either the input contains a discourse connective or the output contains a discourse adverbial. We consider the discourse relations specified in the Penn Discourse Treebank (PDTB) and select a subset of these which we determined to be commonly represented via an adverbial connective between two sentences. We then compile a set of intra-sentential connective analogues for each. Table  2  shows the set of discourse connectives and adverbials used together with their corresponding discourse relations. 3 They cover 7 out of the 15 second order relations occurring in the PDTB. Synthetic Data. The Common Crawl News corpus (CC-News)  (Nagel, 2016 ) is a large collection of news articles that have been scraped from the internet.
Creating (C,T,S) Triplets We use discourse trees (i) to derive (C, T, S) triplets from the parallel data and (ii) to create matching C texts for the S texts in D-CC-News-S. Creating Discourse Trees. For a given S, we employ the following rule-based method to derive a linearized tree of the form shown in Table  1 , T. The adverbial is removed from the sentence pair and mapped to the corresponding PDTB discourse relation while the two sentences are used as the tree's arguments and are rearranged into the linearized tree for the relation instantiated by the adverbial. The ordering of the arguments is determined according to a defined schema for each relation, as stipulated in the PDTB manual. Table 1, T shows an example of this for the Temporal. Asynchronous relation, where the arguments are ordered chronologically.
Training and Test Data Table  3  summarises the data used for training and development. For evaluation, we extracted a set of 352 (C, T, S) triples from the organic datasets (184 triples from WikiSplit and 168 from MUSS), making sure to maintain an approximately even distribution over the supported connectives. To ensure a high level of quality, we then manually corrected the contents of T , C, and S, where necessary i. e. , when C and S connectives did not match or when the wrong parts of the text have been flagged as relation arguments in T.