dataesr/scientific-paragraphs-categorization huggingface dataset:
The flux calibration procedure is explained in Sect. 4. In Sect. 5 we combine the HBA flux density measurements with previously published values and analyse the broadband pulsar spectra. A summary is given in Sect. 6. The results of the census measurements (flux densities, DMs and total intensity pulse profiles) will be soon made available through the European Pulsar Network (EPN) Database for Pulsar Profiles 2 , as well as via a dedicated LOFAR web-page  3. and |Gb| > 3 • , respectively). For the full list of selection criteria see Table  1. Right: Distribution of all known pulsars (grey dots) and the LOFAR census pulsars (red circles) on the period-period derivative, P -Ṗ, diagram. Pulsars with an unknown Ṗ are shown at Ṗ= 10 -21 s s -1 in the diagram.
Observations Observations were conducted in February-May 2014 using the HBAs of the LOFAR core stations in the frequency range of 110-188 MHz (project code LC1_003). Complex-voltage data from the stations were coherently summed. The total observing band was split into 400 sub-bands, 195 kHz each. Each sub-band was additionally split into 32-256 channels and full-Stokes samples were recorded in PSRFITS format  (Hotan et al. 2004) , with time resolution of 163. 84-1310. 72µs, depending on the number of channels in one sub-band. Larger number of channels was chosen for pulsars with higher DMs in order to mitigate the intrachannel dispersive smearing. For a more detailed description of LOFAR and its pulsar observing modes, we refer a reader to  van Haarlem et al. (2013)  and  Stappers et al. (2011).
Each pulsar was observed during one session for either 1000 spin periods, or at least 20 min. The PSRFITS data were subsequently stored in the LOFAR Long-Term Archive 5. Observations were pre-processed with the standard LOFAR pulsar pipeline  (Stappers et al. 2011) , which uses the PSRCHIVE software package  (Hotan et al. 2004; van Straten et al. 2010). The data were dedispersed and folded with ephemerides either from the pulsar catalogue or the timing observations with the Lovell or Green Bank telescopes. For the Crab pulsar, we used the Jodrell Bank Crab monthly ephemeris 6   (Lyne et al. 2015). For folding the data, we chose the number of phase bins to be equal to the power of two that matched the original time resolution most closely (but not exceeding 1024). Sometimes, in order to increase the signal-to-noise (S/N) ratio, the number of bins was reduced by a factor of two, four or eight.
RFI excision The 77 hours of census observations sampled radio signals from various on-sky directions during both day and night. This makes these data suitable for exploring RFI (radio frequency interference: any kind of unwanted signals of non-astrophysical origin) environment on the site of the LOFAR core stations. A selective analysis of small random subsets of data, performed with the rfifind program from the PRESTO 7  software package  (Ransom 2001) , showed that the majority of RFI was shorter than one minute in duration and/or narrower in frequency than a 195-kHz sub-band (see also  Offringa et al. 2013). The real-time excision of RFI, however, was not possible on the full time-and frequency-resolution data due to limited computing power, and thus was performed on folded archives with 1min sub-integrations and 195-kHz sub-bands.
Detection and ephemerides update For most of our pulsars the epoch of observation lay outside the validity range of the available ephemerides. Thus, we expected the observed pulsar period, P, and DM to be somewhat different 8 https://github. com/plazar/coast_guard from the values predicted by the ephemerides. We performed initial adjustment of P and DM with the PSRCHIVE program pdmp, which maximises integrated S/N of the frequency-and time-integrated average profile over the set of trial values of P and DM. The output plots from pdmp (namely, maps of integrated S/N values versus trial parameters together with timeintegrated spectra and frequency-integrated waterfall plots) were visually inspected for a pulsar-like signal. Out of 194 census pulsars, 158 were detected in such a manner, all with integrated S/N greater than 8.
2016; Stovall et al. 2015; Zakharenko et al. 2013). Pilia et al. (2016)  observed 100 pulsars with the LOFAR HBA antennas approximately two years before the census observations presented here. At this time of data acquisition, just over half of the current HBA band and fewer core stations were available in tied-array mode, resulting in lower (by a factor of a few) S/N in the average profiles and larger errors in DM determination. The other two works report DMs measured at frequencies below 100 MHz. Zakharenko et al. (2013)  observed nearby (DM < 30 pc cm -3 ) pulsars in the frequency range of 16. 5-33 MHz using the UTR-2 telescope. Observations were taken during three sessions in 2010-2011. The authors do not specify the exact epoch of DM measurements, so the inferred uncertainty of DMepoch is included in errors on |∆DM/∆t| in Fig.
In this work we present the DMs, flux densities and fluxcalibrated profiles for 158 detected pulsars. The average profiles, DM and flux density measurements will be made publicly available via the EPN Database of Pulsar Profiles and on a dedicated LOFAR web-page  25. The LBA  extension of the census has also been observed and its results will be presented in subsequent works. Owing to the large fractional bandwidth, we were able to measure DMs with typically ten times better precision than in the pulsar catalogue (with the median error of census DMs equal to 1. 5 × 10 -3 pc cm -3 ). For our DM measurements we assumed the absence of profile evolution across the HBA band. The value of DM could change by an amount that is an order of magnitude larger than the measurement error under different profile evolution assumptions.
Acknowledgements. This work makes extensive use of matplotlib 26 (Hunter 2007), seaborn 27 Python plotting libraries, and  NASA  Astrophysics Data System. We acknowledge the use of the  European Pulsar Network (EPN) database at the Max-Planck-Institut für Radioastronomie , and its successor, the new EPN Database of Pulsar Profiles, developed by  Michael Keith  and maintained by the  University of Manchester. AVB thanks  Olaf Maron  for kindly provided flux density measurements,  Dávid Cseh  for LOFAR calibration discussions,  Tim Pennucci  for his contribution to the Bayesian analysis of flux calibration uncertainty,  Menno Norden  for an RFI discussion, and  Tobia Carozzi  for development of the mscorpol package. The research leading to these results has received funding from the  European Research Council  under the European Union's  Seventh Framework Programme  ( FP7/2007-2013)  /  ERC  grant agreement nr.
The terminological component of naRyQ The terminological component of naRyQ contains the set of terms describing the studied domain and are used to annotate experimental data. Sub concepts of the generic concepts Relation Concept, Symbolic Concept and Quantity, as well as instances of the generic concept Unit Concept, are all denoted by at least one term of the terminological component. Each of those subconcepts or instances are, in a given language, denoted by a preferred label and optionally by a set of alternative labels, which correspond to synonyms or abbreviations. Labels are associated with a concept or an instance thanks to SKOS (Simple Knowledge Organization Scheme) labeling properties recommended by W3C (see http: //www. w3. org/TR/skos-reference/ for more details). Example 2: In Figure  3 , English terms Ethylene vinyl alcohol and EVOH denote the symbolic concept Ethylene Vinyl Alcohol.
Two examples of CCs are presented below. • CCs 1 Each anonymous class defined by a value restriction, owl:allValuesFrom or owl:someValueFrom links a pair property-class or property-data type. The property and the class used in the definition of the anonymous class must be defined in the OTR. • CCs 2 A concept (or relation) that subsumes another concept (or relation) should be defined in the OTR. We also defined two new structural constraints linked to terms, the terminological part of naRyQ represented with SKOS concepts being specific to our OTR according to the literature on ontology evolution (see  Tissaoui et al. (2011) ). • CCs 3 Each instance of skos:concept must have at least a preferred label. • CCs 4 Labels denoting a concept or a given instance should contain exactly one preferred label for each language.
The annotation subsystem of ONDINE The annotation subsystem of ONDINE from Figure  1  is implemented in the @Web software available at http:// www6. inra. fr/cati-icat-atweb/Web-platform. The @Web annotation module implements a complete workflow to extract experimental data tables from scientific documents and semantically annotate them with n-ary relation concepts from naRyQ. The five steps are presented in Figure  5. In the first step, relevant documents for the studied application domain described in naRyQ are retrieved from the Web and manually selected by an expert of the domain. Then, in the second step, each document is associated with a reliability assessment score (see  Destercke et al. (2013)  for more details). The third step deals with data tables extraction from the selected documents.
Those data tables are then presented to the domain expert for validation allowing him/her to select those that are relevant for the studied domain. In the fourth step, taking into account the content of the data table, the domain expert selects from the n-ary relation concepts defined in the studied domain conceptual component of naRyQ those relevant to semantically annotate the data table. In the fifth and last step of @Web annotation module, the annotated data tables are stored in a RDF triple store and could therefore be queried in the ONDINE querying subsystem (see Figure  1 ).
Unfortunatly, the domain expert cannot find in TRANSMAT the relevant n-ary relation concept having for experimental result the concept CO2 Permeability to annotate the data table of Figure  6  with. A new n-ary relation concept has therefore to be added to TRANSMAT using DynarOnto. The two next subsections will detail this update step and the data table annotation's step of @Web annotation module with the new version of TRANSMAT. Figure  6  A data table from a scientific document in matter transfer domain.
Conclusion We have presented in this paper an ontology evolution activity for a data integration system called ONDINE which proposes a complete workflow to annotate and query experimental data extracted from scientific documents. The core element of the ONDINE system is an Ontological and Terminological Ressource (OTR), called naRyQ, designed for experimental data representation. The goal of our ontology evolution activity was to better manage the evolution of the OTR naRyQ when a new domain was considered. The annotation of a data table with @Web annotation module reengineering existing ressources using the Scenario 6 from the NeON methodology (see  Suárez-Figueroa et al. (2012a) ). During those building processes, ontology engineers identified the need to assist the domain experts in their evolution activity in order to avoid errors and to reduce time.
Our ontology evolution activity was implemented as a Protégé plug-in, called DynarOnto, that assists Comment citer ce document : Ibanescu, L. , Buche, P. , Dervaux, S. , Touhami, R. , Dibie, J. (2016). Ontology evolution for an experimental data integration system. International Journal of Metadata, Semantics and Ontologies, 11 (4), 231-242. , DOI : 10. 1504/IJMSO. 2016. 10004259 domain experts. It was tested and evaluated for the evolution of an OTR applied to the domain of matter transfer. The results of the evaluation indicate a gain in time, nevertheless the real significant benefit was in the assistance given during the evolution process allowing the domain experts to make less errors. Further work will be to fully implement and to integrate our evolution activity in the @Web software using DynarOnto implementation choices.
Reconstructing genome history is complex but necessary to reveal quantitative principles governing genome evolution. Such reconstruction requires recapitulating into a single evolutionary framework the evolution of genome architecture and gene repertoire. Here, we reconstructed the genome history of the genus Lachancea that appeared to cover a continuous evolutionary range from closely related to more diverged yeast species. Our approach integrated the generation of a highquality genome data set; the development of AnChro, a new algorithm for reconstructing ancestral genome architecture; and a comprehensive analysis of gene repertoire evolution. We found that the ancestral genome of the genus Lachancea contained eight chromosomes and about 5173 protein-coding genes. Moreover, we characterized 24 horizontal gene transfers and 159 putative gene creation events that punctuated species diversification.
L. fantastica, L. waltii, and L. thermotolerans were isolated from plant-associated habitats, while L. meyersii was isolated from seawater. Consistently with ecological distribution, the homolog in L. meyersii is highly diverged and only partially similar to the members in the three other species. Overall, our study suggests that the contribution of HGTs on the evolution of the gene repertoire in yeast is probably underestimated. We also characterized 596 taxonomically restricted gene (TRG) families that are specific to the Lachancea clade without any detectable homolog in the nonredundant sequence database or conserved domain in the PFAM database (see Methods). Sixty-six TRG families (encompassing 316 CDS and four pseudogenes) comprise members in at least two different Lachancea species and/or several paralogs within a given Lachancea species (Supplemental Table  S5 ).
AnChro, a new computational tool to reconstruct ancestral genome architecture We developed a new computational method of ancestral genome reconstruction named AnChro. This tool is part of an integrated suite of software named CHROnicle (freely available at www. lcqb. upmc. fr/CHROnicle/). Briefly, in the first step of the reconstruction, SynChro identifies conserved synteny blocks between pairwise combinations of genomes  (Drillon et al. 2014 ). In the second step, ReChro constructs cycles of linked breakpoints between adjacent synteny blocks, and in the last step, AnChro infers the ancestral gene order by comparison with external reference genomes (see Supplemental Information). There are nine ancestral genomes in total in the Lachancea phylogenetic tree (named L. A1 to L. A9) (Figs. 2, 3 ).
Discussion Our work combined a significant methodological contribution and a comprehensive comparative genomic analysis on a highquality genome data set that we generated to achieve a detailed reconstruction of genome history in the model Lachancea yeast genus. We discovered relationships between genome size, gene content, chromosomal rearrangements, and rates of protein divergence that suggest the existence of several evolutionary principles so far uncharacterized. Our methodological contribution consists in the development of AnChro for the reconstruction of ancestral genome architecture. AnChro proposes a new conceptual framework based on two original principles. First, our algorithm uses synteny blocks resulting from pairwise comparisons between extant genomes. This preserves the information of synteny conservation shared between closely related genomes even if more distantly related species are present in the clade.
Annotation of Lachancea genomes The genomes of L. kluyveri and L. thermotolerans were used as references for gene structure annotation in the seven newly sequenced genomes and for the reannotation of L. waltii. We first completed the two reference genome annotations by detecting genes in inter-genic regions through BLASTX against the UniProt fungi database. Gene models were annotated for the seven newly sequenced genomes with an annotation transfer pipeline that we developed with the AMADEA Biopack platform (Isoft, http://www. isoft. fr/ bio/biopack_en. htm) (Supplemental Methods). Manual curation of gene models consisted of resolving gene models with missing start or stop codons, with not properly defined introns or with frameshifts. Additional CDSs were identified in intergenic regions of the 10 species by BLASTX search against the nr database and manual curation.
Moreover, ORFs longer than 150 amino acids without any homologs in the nr database were predicted with Orffinder (NCBI). tRNA genes were predicted with tRNAscan-SE (v. 1. 3. 1) (Lowe and Eddy 1997)  with default searching parameters of tRNAscan and EufindtRNA; covariance model: tRNA2-euk. cm. The snRNA genes were searched by BLASTN using L. kluyveri and L. thermotolerans known snRNA sequences as query. We identified complete and partial transposable elements as well as solo-LTRs using BLAST against known transposable elements of Ty1/Copia, Ty3/Gypsy, and class-II superfamilies. Elements of the Rover and Roamer families are described elsewhere  (Sarilar et al. 2014). The position of centromeres in the seven newly sequenced Lachancea genomes and in L. waltii was inferred from synteny conservation with already annotated centromeres in L.
thermotolerans, L. kluyveri, and Zygosaccharomyces rouxii  (Souciet et al. 2009 ). CDEI, CDEII, and CDEIII motifs were identified with the MEME program  (Bailey and Elkan 1994) , using the oops mode on both strands (Supplemental Methods). The functional annotation of protein-coding genes has been established on the basis of homology with S. cerevisiae genes (SGD S288C ORF translations, release February 3, 2011, available at http:// downloads. yeastgenome. org/sequence/S288C_reference/ orf_protein/) or the NCBI Reference Sequence (RefSeq) database (release 58 of March 11, 2013, available at http://www. ncbi. nlm. nih. gov/refseq/) for putative genes without homologs in S. cerevisiae (Supplemental Methods).
Gene families An all-against-all BLASTP (version 2. 2. 28+) comparison was performed between amino acid translations of all CDS from the 10 Lachancea species and S. cerevisiae, with default options and Smith-Waterman alignment  (Altschul et al. 1997). Hits with an E-value lower than 10 -3 were clustered with TribeMCL with an inflation value I = 6. 5 (Supplemental Methods;  Enright et al. 2003). The detailed composition of all gene families and singletons is provided in Supplemental Table  S12 ; their repartition among the 10 species, in Supplemental Table  S2. Systematic search for homologs to the Lachancea protein families was performed in the nr database with PSI-BLAST using a position-specific scoring matrix (PSSM) built from the family multiple alignments (only one iteration is performed).
A search for homologs to Lachancea singletons was performed in the nr database with BLASTP  (Altschul et al. 1997). Hits with an E-value lower than 10 -3 with at least 25% sequence identity and coverage of the longest sequence of at least 50% were considered as significant. Similarity search against the PFAM database (version 27. 0) was performed with hmmsearch from the HMMER3 package  (Mistry et al. 2013) , and hits with an E-value lower than 10 -5 were considered significant. Search for conserved protein domains was also performed with rpsblast from the BLAST 2. 2. 29+ distribution, against the CDD database, version 01/10/2014.
Gene content evolution Gene acquisitions and losses were inferred with the BadiRate program  (Librado et al. 2012). For nonvertically inherited gene families (HGTs and TRGs), we used the birth, death, and innovation (BDI) model with free rates (FRs) estimated by the Wagner parsimony method (CWP). For vertically inherited families, we used the birth and death model with FR and CWP, assuming that all families derived from ancestral genes present in the common ancestor of all Lachancea species. CAI values were calculated for all TRGs using CAIJava,  (Carbone et al. 2003). Rates of synonymous substitutions (d S ) and rates of nonsynonymous substitutions (d N ) were estimated with the yn00 program from the PAML package  (Yang 1997). For the 127 L. kluyveri-specific TRGs, the CDS were considered physically absent from a given L.
Ancestral genome reconstruction Ancestral genome reconstruction was performed with the CHROnicle suite of programs freely available at www. lcqb. upmc. fr/CHROnicle/ that comprises SynChro, ReChro, and AnChro. All the details about the ancestral gene order reconstruction steps, the identification of chromosomal rearrangements in the different branches of the tree, and the validation of the reconstructions are in the Supplemental information file. AnChro source code can also be found in the Supplemental Material.
Acknowledgments This work was supported by the  Agence Nationale de la Recherche  ( GB-3G ,  ANR-10-BLAN-1606 ). We thank  Guillaume Achaz ,  Gilles Charvin ,  Frédéric Devaux ,  Cécile Fairhead ,  Romain Koszul ,  Gianni Liti ,  Marie-Claude Marsolier Kergoat , and  Conrad Nieduszynski  for fruitful discussions.
Data access Accession numbers and/or website sources for all yeast species used in this work are listed in Supplemental Table  S3. Genome sequences and (re)annotations of the 10 Lachancea species are available on the GRYC website: http://gryc. inra. fr. The sequencing reads and the seven new genome assemblies from this study have been submitted to the European Nucleotide Archive (ENA; http://www. ebi. ac. uk/ena) under the following accession numbers: PRJEB12910, PRJEB12928, PRJEB12929, PRJEB12930, PRJEB12931, PRJEB12932, and PRJEB12933.
Modeling tools and operators help the user / developer to identify the processing field on the top of the sequence and to send into the computing module only the data related to the requested result. The remaining data is not relevant and it will slow down the processing. The biggest challenge nowadays is to get high quality processing results with a reduced computing time and costs. To do so, we must review the processing sequence, by adding several modeling tools. The existing processing models do not take in consideration this aspect and focus on getting high calculation performances which will increase the computing time and costs. In this paper we provide a study of the main modeling tools for BigData and a new model based on pre-processing.
Introduction More than 2. 5 Exabytes of data are created everyday on Internet, based on the automatically generated user information. Social networks, mobile devices, emails, blogs, videos, banking transactions and other consumer interaction, are now driving the successful marketing campaigns, by establishing a new digital channel between the brands and their audiences. Powerful tools are needed to store and explore this daily expending BigData, in order to submit an easy and reliable processing of user information. Expected quick and high quality results are as much important as priceless investments for marketers and industrials. Traditional processing engines face their limits in this challenge, as the information keeps growing in volume and variety, thing that can be handled only by nonrelational data modeling techniques.
The challenge of BigData is to query data easily  [24]. Creating data models on physical data help to manage raw data. Data Modeling provides a visual way in order to manage data resources and create data architecture. This will help user / developer creating more applications to optimize data reuse and reduce computing costs. The modeling tools to discuss in this paper help to better handle the processing of BigData. For starting, we make a quick review on common data sources, data engines and nonrelational databases. In section 2, we will discuss the use of the main modeling tools available for BigData. We will point-out by then the forces and weaknesses of every technique. In section 3, we suggest new approach of using modeling toolbox for BigData. This approach is implemented in our BigData Workbench software that we are also going to discuss.