dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgement. IASI is a joint mission of  EUMETSAT  and  CNES. The authors acknowledge the AERIS data infrastructure for providing access to the IASI data in this study and the  ULB-LATMOS  for the development of the retrieval algorithms. The MODIS MCD64 and MOD14 products were retrieved from the  NASA EOSDIS Land Processes Distributed Active Archive Center (LP DAAC) ,  USGS Earth Resources Observation and Science (EROS) Center ,  Sioux Falls ,  South Dakota  (https://lpdaac. usgs. gov/products/mcd64a1v006/, last access: 24 June 2020, and https: //lpdaac. usgs. gov/products/mod14v006/,last access:  24 June 2020 ). The MODIS aerosol product MOD04 version 6. 0 was retrieved from the online archive, courtesy of  NASA's EOSDIS Level 1 and Atmosphere Archive and Distribution System (LAADS) Distributed Active Archive Center (DAAC) of the NASA Goddard Space Flight Center (GSFC)  (https://ladsweb.
modaps. eosdis. nasa. gov/archive/allData/61/MOD04_L2, last access:  24 June 2020 ). The CALIPSO Lidar Level 2 vertical feature mask (v4-20) was obtained from  NASA   Langley Research Center's Atmospheric Sciences Data Center. The global simulations for the construction of the initial and boundary conditions for CHIMERE were provided by the  MACC-II  project, which is funded through the  European Union   Framework 7 program. It is based on the MACC-II reanalysis for atmospheric composition; full access to and more information about the data can be obtained through the  MACC-II  website https://atmosphere. copernicus. eu/,last access:  24 June 2020 ). Financial support. This research has been supported by the  MIS-TRALS program  (ADEME,  CEA, INSU, and Meteo-France) , as part of the  ChArMEx  project, for the development of APIFLAME, and by the  Centre National d'Etudes Spatiales  ( CNES, France ) for the analysis of IASI observations.
Code availability. The APIFLAME v2. 0 biomass burning emissions model and associated documentation are available for download at https://doi. org/10. 14768/20190913001. 1  (Turquety et al. , 2019). The global burned area derived from the MODIS satellite observations for the period 2014-2017 are available at https://doi. org/10. 14768/20190913002. 1 (Turquety,  2019a), and the files corresponding to the case study in southern Europe during the summer of 2016 are available at https://doi. org/10. 14768/20190913003. 1 (Turquety, 2019b). API-FLAME is a model under constant development, and the latest version, as well as its documentation and a test case, is available at http://www. lmd. polytechnique. fr/chimere/CW-fires. php (last access: 24 June 2020). Supplement. The supplement related to this article is available online at: https://doi.
RNA-sequencing (RNA-seq) data analysis of the different stages of root nodules formation in peanut Arachis hypogaea investigate the genetic features. Genes related to the root nodules formations in this plant are extensively studied [1] [2] [3] [4] [5], but less information is present for their relations with long noncoding RNAs (lncRNAs). Bioinformatics techniques are utilised here to identify the novel lncRNAs present in the publically available RNA-seq data reported  [6]  for the different stages of root nodules formation in this plant. Highly correlated, significant, and Differentially Expressed (DE) gene-lncRNA pairs are also detected to understand the epigenetic control of lncRNA. These pairs are further differentiated between cis and trans antisense lncRNAs and lincRNAs based on their functions and positions from the genes.
Rhizobial invasion and root nodule organogenesis is the host controlled in the majority of the leguminous plans  [25]. Rhizobia enter through infection thread and root nodule primordium in these plants. As an exception, rhizobia directly invade root cortical cell via epidermal cracks in dalbergoid legumes like Arachis hypogaea  [6]. Wild diploids parents of this plant are Arachis duranensis and Arachis ipaensis and their genomes are used for mapping purpose. Transcriptome data are downloaded for the different stages of symbiosis in A. hypogaea like root invasion stage (1DPI), root nodule primordia formation (4DPI), the spread of infections in the root nodule (8DPI), immature root nodules with rod-shaped rhizobia (12DPI), and mature root nodules with spherical symbiosomes (NOD). Differential gene expression for the progress of symbiosis in A.
LncRNAs identification and validation: RNA-seq data are downloaded from https : //www. ncbi. nlm. nih. gov/sra?term = SRP 107173  [6]. These data contain transcriptomic expressions of six root nodule developmental stages after the infection with Bradyrhizobium Semia 6144. Three replicates per stage are converted into single ended "*. fastq" files through "fastqdump". These files are passed through Trimmomatic v0. 36  [27]  for the removal of adapters and low-quality nucleotide bases. The trimmed reads are mapped with genomes of two diploid parents of A. hypogaea which are A. duranensis and A. ipaensis by HISAT2  [28]. Resulted BAM files are assembled by StringTie v1. 3. 2  [29]. Assembled gene transfer format (GTF) files are merged with "stringtie -merge" for each genome. Mapped transcripts are extracted by "gffread -w" with the inputs of respective merged GTF files and genome files.
CD-HIT-EST  [30]  removes the redundancies of extracted transcripts, and the non-redundant transcripts are used as input for the possible lncRNAs identifications. A "Python"  [31]  based pipeline is developed to filter the expected lncRNAs from the total mapped transcripts. First, it removed transcripts less than 200bp long and stored in a FASTA file. The second step is the removal of transcripts with ORFs more than 120 amino acids in length. In the third step, filtered transcripts are checked for homology with the known protein. Internal BLASTX  [32]  is used to align transcripts with universal protein (UniProt) consortium  [33]  database which is downloaded from (http : //www. uniprot. org/downloads), dated 7 th March 2018. Transcripts align with proteins with e-value less than 0. 001 are expelled from the transcripts database.
This alignment check reduced the false positive scoring cases formed due to the translation of pseudo-genes. Filtered transcripts are uploaded on the online version of Coding Potential Calculator 2 (CPC2)  [34]  for the validation. This calculator uses Support Vector Machine (SVM)  [35]  over the four intrinsic parameters such as Fickett TESTCODE score  [36] , ORF length, ORF integrity, and Isoelectric point (pI). Another "Python" script is developed which takes the input of the results of CPC2 and removes "coding" and "partial coding" transcripts from the long noncoding transcripts database. Presences of transfer RNAs (tRNA) in the long noncoding transcripts database are checked by ARAGORN (http : //mbio -serv2. mbioekol. lu. se/ARAGORN/index. html) [37]. Fast and sensitive SortMeRNA  [38]  is used to remove ribosomal RNA (rRNA) from the long noncoding transcripts database.
These filtered long noncoding transcripts database are consider as potential lncRNAs. correlated. This correlation is performed through Pearson correlation test  [16] , and significant and highly correlated gene-lncRNA pairs are identified. These pairs are classified into DE gene-lincRNAs pairs, cis-antisense gene-lncRNA pairs, and trans-antisense gene-lncRNA pairs based on lncRNAs locations from the gene  [12] [42]. KOids related to these pairs are identified and utilised for the biological pathway analysis.
Background: Formal definitions allow selecting terms (e. g. , identifying all terms related to "Infectious disease" using the query "has causative agent organism") and terminological reasoning (e. g. , "hepatitis B" is a "hepatitis" and is an "infectious disease"). However, the standard international terminology Medical Dictionary for Regulatory Activities (MedDRA) used for coding adverse drug reactions in pharmacovigilance databases does not beneficiate from such formal definitions. Our objective was to evaluate the potential of reuse of ontological and non-ontological resources for generating such definitions for MedDRA.
INTRODUCTION Formal representation of semantics as provided by computational ontologies and associated semantic Web techniques have been extensively used in medical data integration systems in the last decade  (Sheth et al. , 2005) , and they now tend to be acknowledged as a powerful means to improve the quality of the processing chain of medical data, process automatic extraction of information and knowledge from large databases or ensure semantic interoperability between disparate data processing systems  (Park and Hardiker, 2009; Schriml et al. , 2012; Schulz and Jansen, 2013). In the medical domain, classic terminologies are gradually giving way to clinical terminologies, in which terms are defined using knowledge representation languages  (Rossi Mori et al. , 1998). An example is SNOMED Clinical Terms (SNOMED CT), a general clinical terminology whose objective is to represent all possible terms required for coding the patient record and other applications for representation of biomedical information  (Khorrami et al.
, 2018). SNOMED CT presents several advantages compared with classic terminologies, especially the ability to apply techniques of semantic reasoning in order to build new groups of terms, whereas classic terminologies are limited to default groupings (generally made manually by experts) that are already specified as part of the terminology  (Bousquet et al. , 2005). Medical Dictionary for Regulatory Activities (MedDRA) is a classic terminology used by regulatory authorities and pharmaceutical companies for coding adverse drug reactions (ADR) in pharmacovigilance databases  (Brown et al. , 1999). MedDRA terms are not formally defined and search is therefore limited to existing categories  (Bousquet et al. , 2005). It is frequently difficult to identify the exact MedDRA category that represents a given medical condition under investigation in a sufficiently specific and exhaustive way, for example, during a pharmacovigilance database search  (Brown, 2003).
We present in this article Abbreviations: ADR, Adverse drug reactions; AERS, Adverse Events Reporting System; Ci4SeR, Curation Interface for Semantic Resources; HLGT, Higher Level Group Term; HLT, High Level Term; ICD-10, International classification of diseases, 10th edition; LALR, Lexically assign logically refine; LLT, Lowest Level Term; LOINC, Logical Observation Identifiers Names & Codes; MedDRA, Medical dictionary for drug regulatory activities; NEC, Not elsewhere classified; NOS, Not otherwise specified; OAE, Ontology of Adverse Events; OWL, Web Ontology Language; PT, Preferred Term; SMQ, Standardized MedDRA Queries; SNOMED CT, SNOMED Clinical Terms; SOC, System Organ Class; UMLS, Unified Medical Language System; WHO ART, World Health Organization-Adverse Reaction Terminology. several complementary methods that may benefit different levels of automation and could also be reused in order to semantically enrich other terminologies.
Similar to  Maedche and Staab (2001) , we consider that the target application may serve as a measure for validating the implemented ontology and believe that such criteria is more important than criteria relying only on the evaluation of the ontology without taking into account the context where it is used. This pilot study confirmed the validity of our approach and justifies that we continue the implementation of our mappings between MedDRA and SNOMED CT. In order to address a safety issue, it is necessary to identify case reports in pharmacovigilance databases relative to this issue. A safety issue may concern the causality assessment of drug D in the occurrence of medical condition C. Figure  3  shows the example of three use cases where one is evaluating the causal role of suspected drug D in three medical conditions: a) upper gastrointestinal hemorrhage, b) medical conditions with symptom of erythema, and c) fungal infectious disorders.
One particularity of NeON matching well with our specific approach is that it also takes into account reusing and reengineering of non-ontological resources, which is not the case of other methodologies such as METHONTOLOGY  (Fernández-López et al. , 1997)  and On-To-Knowledge  (Sure et al. , 2004). These non-ontological resources may consist of structured data such as terminologies  (Jimeno-Yepes et al. , 2009)  or databases, unstructured data (e. g. , articles), or semi-structured data (e. g. , XML, JSON)  (Qawasmeh et al. , 2018). In addition to these nine scenarios, NeON also integrates support activities such as knowledge acquisition, documentation, and evaluation that should be carried out during the whole ontology development cycle.
Problems With Mapping Other Layers Than the PT Level We have tried to map other layers (SOC, HLT, and HLGT). For instance, the cardiac disorders SOC concept has for formal definition hasFindingSite some "Heart Structure. " While some SOC, HLGT, and HLT were accurately defined, essentially thanks to mappings in UMLS, it was decided not to present the results in this article. We used formal definitions associated with the three MedDRA higher levels only in the Ci4SeR tool  (Souvignet et al. , 2014) , where the curator could use them if she considered them as relevant. We decided not to map the LLT because PT is the preferred level for case report analysis and search in pharmacovigilance databases. We explained in previous work why the MedDRA hierarchy cannot be converted into a subsumption tree because this sometimes causes semantic inconsistencies  (Bousquet et al.
Using Another Medical Dictionary for Regulatory Activities-to-SNOMED Clinical Terms Mapping Resources To complete the mappings selected from UMLS, we also made use of Nadkarni and Darer's propositions of mappings  (Nadkarni and Darer, 2010). Using one year of data (recorded between July 1, 2008 and April 30, 2009) from the US Food and Drug Administration Adverse Events Reporting System (AERS) pharmacovigilance database, the authors identified 3,705 MedDRA PT that collectively accounted for 95% of case reports. The 3,705 selected MedDRA terms correspond to high-frequency terms in the US Food and Drug Administration database and potentially have a great added value. After eliminating terms already mapped to SNOMED CT concepts in UMLS, they attempted to map manually the remaining terms (786 in total) with software assistance.
Selected Rule Output : result of the decomposition "Ear and labyrinth disorders" (A AND B). q "ear disorders" "labyrinth disorders" "Manic and bipolar mood disorders and disturbances" ((A AND B). q). (C AND D) "manic mood disorders" "bipolar mood disorders" "manic mood disturbances" "bipolar mood disturbances" "Blood and lymphatic system disorders" (A AND B). q "blood system disorders" "lymphatic system disorders" "neutrophil count decreased" for the neutropenia condition), as such results are frequently used to describe ADRs in pharmacovigilance databases. However, it turned out that very few MedDRA concepts located in the investigations branch could be identified through the procedures described in the previous sections, in particular the mapping from UMLS. A large majority of MedDRA concepts in SOC investigations thus remained undefined in OntoADR.
This is what we have done in our implementation of OntoADR  (Bousquet et al. , 2014)  in the form of an OWL-DL file and in the form of a database  (Souvignet et al. , 2016b). We have no knowledge of other works in which the formalization of complex terms involving AND/OR relations has been performed in an automated way. We have not proposed formal definitions of LLT because this level is reserved for the coding of case reports, in order to improve the accuracy of coding, but it is not useful for grouping data for analysis (which is performed at the PT level). Although the analysis of pharmacovigilance databases is performed preferentially at the PT level, it could be important to also define the upper levels: SOC, HLGT, and HLT. This formalization would bring several advantages: i) preferred terms may inherit properties from their parents that allows to give them a formal definition in case the synonymous SNOMED CT concept has no definition, or there is no SNOMED CT concept mapped to this PT in UMLS; ii) This would allow to calculate by the means of terminological reasoning high level MedDRA categories in which PTs should be included and therefore restore multiple inheritance that does not exist in MedDRA.
CONCLUSION The possibility of selecting terms using formal definitions and terminological reasoning are major advantages of clinical terminologies with formal semantics such as SNOMED CT, which present several advantages compared with classic terminologies. MedDRA, as a standard international terminology for the coding of ADRs in pharmacovigilance databases, could beneficiate from these knowledge engineering techniques, but MedDRA terms have to be defined using formal languages first. As defining manually MedDRA terms takes much time, it is important to reuse as much as possible ontological and non-ontological resources available to expedite the generation of formal definitions. The collection of methods we present can collectively support a semiautomatic semantic enrichment of MedDRA. Perspectives are to implement more efficient techniques to find more logical relations between SNOMED CT and MedDRA in an automated way.
FUNDING This work was funded by the grant N° ANR-16-CE23-0011-01 from the ANR, the French Agence nationale de la Recherche through the PEGASE (Pharmacovigilance enrichie par des Groupements Améliorant la detection des Signaux Emergents). Some results described in this article were obtained as part of the PROTECT consortium (Pharmacoepidemiological Research on Outcomes of Therapeutics by a European Consortium, www. imi-protect. eu) which is a public-private partnership coordinated by the European Medicines Agency. The PROTECT project has received support from the Innovative Medicine Initiative Joint Undertaking (www. imi. europa. eu) under grant agreement no. 115004, resources of which are composed of financial contribution from the European Union's Seventh Framework Program (FP7/2007-2013) and EFPIA companies' in kind contribution.
To help in making sense of the ever-increasing number of data sources available on the Web, in this article we tackle the problem of enabling automatic discovery and querying of data sources at Web scale. To pursue this goal, we suggest to (1) provision rich descriptions of data sources and query services thereof, (2) leverage the power of Web search engines to discover data sources, and (3) rely on simple, well-adopted standards that come with extensive tooling. We apply these principles to the concrete case of SPARQL micro-services that aim at querying Web APIs using SPARQL. The proposed solution leverages SPARQL Service Description, SHACL, DCAT, VoID, Schema. org and Hydra to express a rich functional description that allows a software agent to decide whether a microservice can help in carrying out a certain task.
datasets based on metadata, such as the manifold CKAN-based portals  1  like Datahub 2  and the data portals of European states and institutions. Some portals specialize in specific dataset formats or interface technologies. For instance, ProgrammableWeb. com  3 egisters Web APIs, a loosely defined category of lightweight Web services also referred to as REST-like or Lo-REST  [25]  services, while LODAtlas  [26]  and SPARQLES  [31]  focus on RDF datasets and SPARQL endpoints respectively. Even though some of these portals have gained significant popularity due to the large number of datasets that they index, they suffer relentless flaws. Firstly, they are centralized registries with a somehow restricted scope. Consequently, potential data consumers may have to query several portals one by one, accommodating the various query interfaces, to discover suitable datasets.
Secondly, in many cases, datasets are manually registered and annotated by dataset producers, thereby raising concerns about outdated metadata or deprecated services. Thirdly, metadata-based search results have a limited relevance. Typically, searching datasets by keywords and data formats is a first step in the discovery process, but a potential consumer needs deeper insight in the data themselves and the technical interfaces available to query the dataset. In this respect, WSDL-based semantic Web services (e. g. OWL-S  [4]  or SAWSDL  [8] ) tackled this question with a thorough description of the exchanged messages, yet often failing to describe the actual dataset being queried. Besides, they were better suited to the controlled environment of companies  [20]  than the open environment of the Web.
By contrast, the VoID vocabulary  [1]  can help describe RDF datasets with regards to vocabularies, classes and properties used, links to other datasets, etc. But it does not address the description of what properties a resource may typically have nor how the resources relate to each other, which are key criteria in the discovery and selection of datasets. To spur and enable automatic discovery and consumption of datasets at Web-scale, we believe that a few principles should drive future research and developments. (1) Metadata-based search is not enough. As we pointed out above, metadata-based search using e. g. keywords, data formats, vocabularies or even classes and properties used in an RDF dataset, is just a first step in the discovery process. For example, assume a biologist wants to develop a software agent capable of browsing Linked Data and gathering photos related to biological species.
The agent may submit several queries to repositories such as LODAtlas looking for datasets whose textual description contains keywords "photo" and "biodiversity", or those whose VoID description (if any) mentions classes representing photographies and biological taxa. Within the matching datasets, however, nothing guarantees that photographic resources do actually depict biological species; photographies may well be scans of academic papers related to the species. Hence, the agent has no choice but to query the dataset in order to get insight into it and find out if it matches the search. This simple example illustrates the lack of in-depth semantic description of datasets, that would consist of the resources (what are the actual properties of photographic and taxonomic resources) along with their mutual relationships.
(2)  The discovery of datasets at Web-scale should leverage the power of Web search engines. Major search engines such as Google, Yahoo and Bing crawl and index an unprecedented breadth of information every day. They already harvest the content of specialized open data portals, in particular by taking advantage of the growing use of the Schema. org vocabulary  [12]. Google has recently opened a beta service specifically dedicated to dataset search  4. Therefore, despite concerns raised by the Web centralization effect of search engines, it is worth studying how we can take advantage of their services to enable the discovery and querying of datasets at Web scale. (3) The description of datasets and their query services should rely on well adopted (de-facto) standards. Enabling the automatic discovery and querying of datasets at Web-scale means that, at some point, a consensus should be reached with respect to technologies and practices.