dataesr/scientific-paragraphs-categorization huggingface dataset:
| DATA Following the 2003 heat wave in France, Santé publique France set up a new syndromic surveillance system, which included the Oscour ® network ("Organisation de la Surveillance Coordonnée des Urgences," Coordinated Health Surveillance of Emergency Departments), based on hospital EDs. The creation of this network was motivated by the need to provide high-quality information to public health authorities to help with evidence-based decision-making and to have a real-time assessment of the situation in secondary health care. 14 This surveillance network has already been described elsewhere. 6, 13  We included influenza-coded cases, which gathered visits with ICD-10 codes J09, J10 and J11 as clinical diagnosis. We used, as a measure of risk, the weekly proportion of influenza-coded cases among all coded visits, which comprises visits with all diagnostic types: diseases, accidents, injuries, etc.
| Implementation The model was implemented using the integrated nested Laplace approximation (INLA) method introduced by Rue et al,  18  which provides fast and reliable calculations of posterior marginal distributions, avoiding time-consuming MCMC simulations. First, the model was fitted to the ED data and the parameters of the spatial model were estimated. Then, the posterior mean and standard deviation for the response variable were predicted at each pixel of a regular grid covering France. We used a grid of 2 km resolution (572 × 530 pixels) to obtain a smooth posterior surface. Every week was fitted independently. All stages were coded using the R-INLA package (www. r-inla. org). 9] [20]  We provide a summary of key features and the R code for the model in Section 3 in Appendix S1. Two complementary maps were generated: in the first map, we used the posterior mean of the proportion of influenza-coded cases as a point estimate for each pixel, while in the second map, we represented the coefficient of variation (relative standard deviation) to highlight areas with more or less uncertainty.
| RE SULTS Over the study period (7 November 2016-2 April 2017) in metropolitan France, 5 589 477 visits were coded in the Oscour ® database, representing 74. 1% of all visits (coded and noncoded). Among the coded visits, 0. 82% (N = 45 565) were classified as influenza cases. The influenza epidemic lasted 10 weeks, from week 49 of 2016 (December 5-11) to week 6 of 2017 (February 6-12) (Figure  1 ). 21  present detailed results for 3 weeks during the ascending phase of the epidemic (December 5-25, 2016), when having good estimations in real time is the most important for decision-makers (Figure  1 ). Among the 645 EDs which provided data through the Oscour ® surveillance network during these 3 weeks, 509 (78. 9%) coded at least one influenza case. The mean number of coded visits per ED per week was 462 (interquartile range [IQR] 212-649).
The data are automatically extracted from the computerized medical files that are filled by clinicians as part of their routine activities, and transmitted every day, avoiding the reporting delays that can impair other surveillance systems, without requiring additional efforts from clinicians provided that they have a business software for emergency medicine. This gives a very detailed picture of the daily situation in secondary health care all over the country, this information being of great importance for healthcare managers. Although this surveillance might have suffered from limited spatial representativeness in some regions during its first years of existence,  29  the network now includes 92% of all visits and gives a satisfactory coverage of the metropolitan territory in all regions.
Recent advances in high-throughput technologies have resulted in a tremendous increase in the amount of omics data produced in plant science. This increase, in conjunction with the heterogeneity and variability of the data, presents a major challenge to adopt an integrative research approach. We are facing an urgent need to effectively integrate and assimilate complementary datasets to understand the biological system as a whole. The Semantic Web offers technologies for the integration of heterogeneous data and their transformation into explicit knowledge thanks to ontologies. We have developed the Agronomic Linked Data (AgroLD-www. agrold. org), a knowledge-based system relying on Semantic Web technologies and exploiting standard domain ontologies, to integrate data about plant species of high interest for the plant science community e.
g. , rice, wheat, arabidopsis. We present some integration results of the project, which initially focused on genomics, proteomics and phenomics. AgroLD is now an RDF (Resource Description Format) knowledge base of 100M triples created by annotating and integrating more than 50 datasets coming from 10 data sources-such as Gramene. org and TropGeneDB-with 10 ontologies-such as the Gene Ontology and Plant Trait Ontology. Our evaluation results show users appreciate the multiple query modes which support different use cases. AgroLD's objective is to offer a domain specific knowledge platform to solve complex biological and agronomical questions related to the implication of genes/proteins in, for instances, plant disease resistance or high yield traits. We expect the resolution of these questions to facilitate the formulation of new scientific hypotheses to be validated with a knowledge-oriented approach.
Introduction and background Agronomy is a multi-disciplinary scientific discipline that includes research areas such as plant molecular biology, physiology and agro-ecology. Agronomic research aims to improve crop production and study the environmental impact on crops. Accordingly, researchers need to understand the implications and interactions of the various biological processes, by linking data at different scales (e. g. , genomics, proteomics and phenomics). We are currently witnessing rapid advances in high throughput and information technologies that continue to drive a flood of data and analysis techniques within the domains mentioned above. However, much of these data or information are dispersed across different domain or model specific databases, varied formats and representations e.
g. , TAIR, GrainGenes and Gramene. Therefore, using these databases more effectively and adopting an integrative approach remains a major challenge. Among the numerous research directions that the field of bioinformatics has taken, knowledge management has become a major area of research, focused on logically interlinking information and the representation of domain knowledge  [1]. To this end, ontologies have become a cornerstone in the representation of biological and more recently agronomical knowledge  [2]. Ontologies provide the necessary scaffold to represent and formalize biological concepts and their relationships. Currently, numerous applications exploit the advantages offered by biological ontologies such as: the Gene Ontology  [3] -widely used to annotate genes and their products-Plant Ontology  [4] , Crop Ontology  [5] , Environment Ontology  [6] , to name a few.
Ontologies have opened the space to various types of semantic applications  [7, 8]  to data integration  [9] , and to decision support  [10]. Semantic interoperability has been identified as a key issue for agronomy, and the use of ontologies declared a way to address it  [11]. Furthermore, efficient knowledge management requires the adoption of effective data integration methodologies. This involves efficient semantic integration of the disparate data sources, making information machine-readable and interoperable. Accordingly, Semantic Web standards and technologies enforced by the W3C, and embracing Tim Berners-Lee's vision  [12] , offers a solution to facilitate integration and interoperability of highly diverse and distributed data resources. The Semantic Web technologies stack includes among others the following W3C Recommendations: the Resource Description Framework (RDF)  [13]  as a backbone language to describe resources with triples, RDF Schema (RDFS)  [14]  to build lightweight data schemas, Web Ontology Language (OWL)  [15]  to build semantically rich ontologies and the SPARQL Query Language (SPARQL)  [16]  to query RDF data.
There are other approaches to meet the current data integration challenges, e. g. , data warehouses. For instance, Intermine  [19]  has developed a sophisticated application to accommodate the dynamic nature of biological data and simplify data integration. However, with integrative biology gaining popularity, it is necessary to preserve and share the semantics between the various datasets and make information machine interoperable, enabling large scale analyses of information available over the Web. The Semantic Web approach provides an added value, playing a complementary role to the traditional methods of data integration. In the recent years, the biomedical community has strongly embraced the Semantic Web vision as demonstrated by a number of initiatives to provide ontologies  [20, 21]  and use them for producing semantically rich data such as in Bio2RDF  [22] , OpenPHACTS  [23] , Linked Life Data  [24] , KUPKB  [25] , and the EBI RDF Platform  [26].
aginfra. eu) are initiatives that work on improving data standards and promoting data interoperability in agriculture. Moreover, the community has recently also started to adopt AgroPortal  [11]  as an vocabulary and ontology repository for agronomy-and related domains such as nutrition, plant sciences and biodiversity-that support browsing, searching and visualizing domain relevant ontologies, ontology alignments and creation of semantic annotations. While plant-centric ontologies are now being used to annotate data by various databases developers  [2, 5, 28] , unlike in the biomedical domain, the adoption of Semantic Web in agronomy is yet to be completely exploited. Given that agronomic studies involve multiple domains, publicly available knowledge bases such as EBI RDF, Linked Life Data and Bio2RDF serves only limited agronomical information.
Information sources AgroLD was conceived to accommodate molecular and phenotypic information available on various plant species (see  Fig 1). The conceptual framework for the knowledge in AgroLD is based on well-established ontologies: GO, SO, PO, Plant Trait Ontology (TO) and Plant Environment Ontology (EO). Among these PO, TO and EO are currently developed by the Planteome project  [29]  (http://planteome. org). Furthermore, considering the scope of the effort, we decided to build AgroLD in phases. The current phase (phase I) covers information on genes, proteins, ontology associations, homology predictions, metabolic pathways, plant traits, and germplasm, relevant to the selected species. At this stage, we have incorporated the corresponding information from various databases, such as Gramene  [30] , UniprotKB  [31] , Gene Ontology Annotation  [32] , TropGeneDB  [33] , OryGenesDB  [34] , Oryza Tag Line  [35] , Green-PhylDB  [36]  and SNiPlay  [37].
Architecture AgroLD relies on the RDF and SPARQL technologies for information modelling and retrieval. We use OpenLink Virtuoso (version 7. 2) to store and access the RDF graphs. The data from the selected databases were parsed and converted into RDF using a semi-automated pipeline. The pipeline consists of several parsers to handle data in a variety of formats, such as the Gene Ontology Annotation File (GAF)  [38] , Generic File Format (GFF3)  [39] , HapMap  [40]  and Variant Call Format (VCF)  [41]. The number of tuples gives an idea of the number of elements we have annotated from the data sources (e. g. , 1160K Gene Ontology annotations). For this phase, each dataset was downloaded from curated sources and was annotated with ontology terms URIs by reusing the ontology fields when provided by the original source.
Additionally, we used the AgroPortal web service API to retrieve the URI corresponding to the taxon available for some data standards such as GFF. At the end of phase 1, early 2018, the AgroLD knowledge base contains around 100 million RDF triples created by converting more than 50 datasets from 10 data sources. Additionally, when available, we used some semantic annotation already present in the datasets such as, for instances, genes or traits annotated respectively with GO or TO identifiers. In that case, we produced additional properties with the corresponding ontologies thus adding 22% additional triples validated manually (see details in Table  1 ). The OWL versions of the candidate ontologies were directly loaded into the knowledge base but their triples are not counted in the total.
org/ensembl. plant/. New URIs were minted when not provided by the sources or the by Identifiers. org such as Trop-Gene and OryGenesDB; in such cases the URIs take the form http://www. southgreen. fr/agrold/ [resource_namespace]/[identifier]. Furthermore, properties linking the entities took the form: http://www. southgreen. fr/agrold/vocabulary/[property]. An outline of how the RDF graphs are linked is shown in Fig 3. About entity linking, we used the "key-based approach" which is the most common one. It combines the unique identifier/accession number of the entity shared with the community, with the URI basis pattern of the resource. Moreover, we also respected the "common URI approach" which recommends to use the same URI pattern when the same accession number is used in different datasets.
Therefore, defining the same URI for identical entities (represented by identifiers) in different datasets makes it possible to aggregate additional information for this entity. Additionally, we used cross-reference links (represented by identifiers from external datasets) by transforming them into URIs and linked the resource with the predicate "has_dbxref'. This greatly increases the number of outbound links, making AgroLD more integrated with other Linked Open Data. In the future, we will implement a "similarity-based approach" to identify correspondences between entities which have different URIs. To map the various data types and properties, we developed a lightweight schema (cf. https://github. com/SouthGreenPlatform/AgroLD) that glues classes and properties identified in AgroLD and the corresponding external ontologies.
The SPARQL language is a powerful tool to mine and extract meaningful information from the knowledge base. In the first example of the supplementary S3 File, we compare two queries to answer the question: "Identify wheat proteins that are involved in root development. ". While the first one (S3_Q1) using a simple search-which is a direct translation of SQL-with the corresponding id ("GO_0048364", "GO_2000280") shows 73 entries, the second one (S3_Q2) using a property path query (i. e. , query the descending class hierarchy for a given trait ontology term) shows 137 entries, thus more than 80% of additional results. In that case, the use of property path algorithm shows the efficiency in retrieving a comprehensive answer. But the SPARQL language performs also very well with complex queries such as: "Retrieve individuals which have positive SNP variant effect identified for proteins associated with a QTL" available in S3_Q3.
This type of query involves several datasets and uses graph traversal property of SPARQL to perform the query. Because SPARQL is hard to handle for non-technical users, the SPARQL Query Editor includes a list of modularized example queries, customizable according to the users' needs. For the comparison, we consider a sample question: 'Retrieving genes that participate in Calvin cycle'; (Q6 in the online list of modularized queries). As illustrated in Fig  4 , the user can run the query to retrieve the list of genes participating in the given pathway (Fig  4A ). Additional information on a gene of interest can be retrieved by clicking on the URI. For example, clicking on AT1GI870 (http://identifiers. org/ensembl. plant/AT1G18270) redirects the users to the gene information provided by Gramene/Ensembl Plants resource  (Fig 4B).
The query can be saved and the results can be downloaded in a variety of formats such as JSON, TSV, and RDF/XML. Additionally, user defined queries could also be uploaded. The Explore Relationships tool is based on RelFinder visualization module. This tool aids in visualizing relationships between entities and searching entities by keyword when their URIs are ignored. However, the original version of RelFinder was developed (in ActionScript) and configured for DBpedia. We proposed a configuration and modification of the system suitable for AgroLD. The configuration mainly concerns the SPARQL access point, the properties to be considered for the search of entities and for the description of the resources. Furthermore, we have added some biological examples to guide users. In Fig  5 , the tool is used to search for genes involved in Calvin cycle by entering the name of the entities.
e. , plain text not formally expressed) and new research methods to identify biological entities and reconstruct their relations further allowing the discovery of relevant links between related resources are required. • The explosion of data in agronomy forces database providers to augment the frequency of their releases. The survey shows a growing interest of using up to date information from the original sources. This have to be taken into account for the updating process in AgroLD. • The user interfaces show some limitations to manage responses with large number of results, e. g. , to filter and rank them with precision score. These limitations identified in the current version of AgroLD will be improved in the following versions. We will focus on the following areas: • User Interface: we plan to explore features offered by Elastic search tool (https://www.
elastic. co), to enabling Quick Search retrieving more textual information and hiding the technical details. Further, we will improve the performance and expand the API suite to cover other entities represented in AgroLD (e. g. , genomic annotation and homology information). • Content: integrate information on gene expression such as IC4R  [51] , Gene Expression Atlas  [52] , on gene regulatory networks such as RiceNetDB  [53]  and explore linking text-mined annotations from publications. Support molecular interaction networks per species and also allow knowledge transfer between species. • Knowledge discovery: explore methods to aid generating hypotheses by retrieving implicit knowledge, e. g. , inference rules, automatic data linking, entity recognition, text mining, automatic semantic annotations.
• Data provenance: develop a provenance and annotation model. Set up a validation process to allow users validating computed facts such as semantic annotations automatically produced and attached to a biological entity. • Updates: To keep AgroLD updated with the latest available data, by processing regular data updates and potentially re-building the entire repository from scratch every 12 months. Processing regular data update is a hard issue has the original databases do not always provide an automatic way to obtain the differential data between releases. From experience, we know that regularly rebuilding the entire knowledge base is for us a good alternative to avoid dealing with data diffs. Additionally, we plan to fully automate the current ETL workflow.
Conclusion Data in the agronomic domain are highly heterogeneous and dispersed. For agronomic researchers to make informed decisions in their daily work it is critical to integrate information at different scales. Current traditional information systems are not able to exploit such data (i. e. , genes, proteins, metabolic pathways, plant traits, and phenotypes), in efficient way. To this end, the application of Semantic Web, initiated in the biomedical domain, provides a good example to follow by capitalizing on previous experiences and addressing weaknesses. To further build on this line of research in agronomy, we have developed AgroLD. We have demonstrated the advantages of AgroLD in data integration over multiple data sources using plant domain ontologies and Semantic Web technologies.
To date, AgroLD contains 100M of triples created by transforming more than 50 datasets coming from 10 data and annotating with 10 ontologies. The impact of AgroLD is expected to grow with an increase in coverage (with respect to the species and the data sources) and user inputs. For instance, when user feedback and implementation of inference rules are put within a context that supports searching and recommendations, then we have the beginnings of a platform that can support automated hypotheses generation. AgroLD is one of the first RDF linked open data knowledge-based system in the agronomic domain. It demonstrates a first step toward adopting the Semantic Web technologies to facilitate research by integrating numerous heterogeneous data and transforming them into explicitly knowledge thanks to ontologies.
Acknowledgments Authors thank the technical staffs of the  South Green Bioinformatics platform  for their support. Authors thank the providers of databases listed in Fig 1, who kindly gave access to their publicly datasets. Authors thank the expert biologists and bioinformaticians who contributed to the testing sessions and helped us to improve the content of the system and the user interface. Authors specially thank  Dr. Patrick Valduriez  and  Dr. Eric Rivals  for their supports and advises in this project.
This research was supported by the  Computational Biology Institute of Montpellier  ( ANR-11-BINF-0002  -http://www. agencenationale-recherche. fr/ProjetIA-11-BINF-0002project:http://www. ibc-montpellier. fr), the  Institut Francais de Bioinformatique  ( ANR-11-INBS-0013http ://www. agence-nationale-recherche. fr/ProjetIA-11-INBS-0013-project: http://www. francebioinformatique. fr), the  Labex Agro  ( ANR-10-LABX-001-01  -http://www. agence-nationalerecherche. fr/ProjetIA-10-LABX-0001-project: http://www. agropolis-fondation. fr/) all bypass of the  French ANR Investissements d'Avenir program  (http://www. agence-nationale-recherche. fr/investissements-d-avenir). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
These two additional approaches (enclosed by dashed lines) are performed by the pipeline but are not primarily used to infer the presence of a provirus genome, these events should be identified as discordant read mates mapping elsewhere in the reference genome as they may frequently map to the internal region of non-allelic proviral copies. The pipeline extracts reads mapped to the solo LTR and mates of discordant reads to conduct homology-based searches using the discordant read mates as queries against the consensus sequence of the internal region of the respective provirus as defined in the Repbase database  [59]  (see also Methods). Presence of at least four reads with significant homology to the internal sequence indicates the presence of a potential allele containing a provirus. In addition to the principal approach described above, the pipeline employs two alternate methods to detect the presence of a provirus at a locus (Fig.
Known and new dimorphic HERVs predicted through the findprovirus pipeline The findprovirus pipeline was used to identity dimorphic candidates for HERV-K(HML2), (hereafter simply noted as HERV-K), HERV-H, and HERV-W families in a dataset consisting of whole genome sequence data for 279 individuals from the SGDP  [58]. Solo LTRs annotated in the hg38 reference genome for HERV-K (LTR5_Hs) (n = 553), HERV-H (LTR7) (n = 689) and HERV-W (LTR17) (n = 476) were used as initial queries (see Methods). The pipeline reports the following results: (i) number of discordant reads mapping to the region; (ii) number of informative discordant reads (i. e. their mates have a significant hit with the respective HERV coding sequence); (iii) percentage of reference solo LTR allele aligned to de novo assembled contigs from the reads; (iv) ratio of average read depth of the element to the average read depth at all solo LTRs of that individual; (v) average mappability of regions where informative discordant reads are mapped; and (vi) prediction on the presence or absence of the provirus allele.