dataesr/scientific-paragraphs-categorization huggingface dataset:
The usefulness of such a tool should be tested. For example, if species or taxonomic names are not apparent in the titles, abstracts and keywords of journal articles, they may be overlooked. Alternatively, papers of very peripheral relevance may be fed to the species database and overwhelm the editors and readers. In the latter case additional filters could be placed to constrain the RSS feed. The PESI partners intend to explore these options further on when the GBIF tool becomes available, a collaboration highlighted at the EDIT second publishers workshop in Bratislava (Suppl. material 26). The highest priority journals for species databases to be linked are those describing new species, and rationalising species nomenclatures (e. g. identifying synonyms or reclassifying species). One of the leading taxonomic journals in this field is Zootaxa, which publishes almost five times more new species than the next ranked journals (http:// www.
This would manage editorial processes (i. e. , paper submission, assignment of referees, editorial decisions), and publication (e. g. , when online, when open access) and archiving. Importantly, each paper, past and future, would be indexed by species names and classification, and geographic area studied, so as to directly link papers with species in the PESI databases. This portal would be called Biotaxa and is now operational and serving 30 journals, including those that describe 30% of all new species each year. We envisage that moving the editorial process of taxonomic publishing online by providing a permanent lowcost portal for publication and by archiving an intimately linked with the expert-edited species databases will revolutionise the taxonomic publications process (see also 'Outlooks' section).
PESI & INSPIRE The INSPIRE directive establishes an infrastructure for spatial information in Europe to support community environmental policies and activities, which may have an impact on the environment, operated by the Member States of the European Union. As part of the INSPIRE Data Specification for the spatial data theme Species Distribution (Commission Regulation (EU) No 1253/2013), PESI is selected as a formal taxonomic standard for Europe, meaning that it is the first prioritised taxonomic reference classification to be used for data connected to species names.
Network governance Considering the overall decline of taxonomy as a scientific discipline, the maintenance of a basic (taxonomic) expertise capacity will be essential to satisfy the knowledge needs for a wide range of biodiversity-related information services in the near future, including the pan-European checklists. In PESI we moved forward from the efforts of EDIT on establishing a collaborative workforce, adding the integrated PESI expert networks into a single EDIT Expert Database and by defining more sophisticated social networking standards. To support the process of expert network integration, the SMEBD licensing model was applied. Further, the role of taxonomic institutes on the sustainable hosting of the taxonomic databases was studied  (Costello et al. 2014)  as well as the need for a federal facility, offering secretarial support on the expert network functioning.
A network of European leading taxonomic institutions forms the Consortium of European Taxonomic Facilities (CETAF), holding the majority of the worlds' biodiversity collections and their data. CETAF's mission contains the enhancement of Europe's knowledgebase capacity on taxonomy for a wide range of scientific and popular users (Price 2014). CETAF incorporates a legacy of past EC projects as working commissions and interest groups. CETAF has recognised the importance of data curation  (CETAF 2004 , CETAF 2008)  and is interested to consider the set up of a special body to take care of the integrated management of the PESI taxonomic workforce, including the continuing hosting of the EDIT Expert Database, as a European Clearing House on taxonomic expertise.
PESI already supports the publishing of data papers accompanying the WoRMS, Fauna Europaea and Euro+Med updating process (see 'Results'). More emphasis will be put on this work within the EU BON project, to trigger expert participation, to automate the dissemination and back-linking (tagging) of taxon names (within data papers) to external resources  (Penev et al. 2010 , Catapano 2010) , and to support the evaluation of gaps in taxonomic information and knowledge (see 'e-Publishing' subsection, below).
Geographic extensions Indexing biodiversity is a global challenge. PESI contributes to worldwide efforts on preparing global catalogues (like CoL) and supports relevant name services (like GBIF-ECAT) on increasing the resolving power for integrating biodiversity data. More particularly, PESI has extended the pan-European checklists geographic scope by involving Focal Points from outer European Union territories, with the ultimate intention of covering the whole Palearctic (see 'Outlooks' section). The Palearctic is the largest biogeographic area of the world, containing a very rich and unique flora and fauna within a vastly diverse environment of unique habitats, especially in biogeographic transition and refuge zones regions, like the Caucasus. Integrating the available taxonomic expertise, data and resources into shared research infrastructures is crucial for globalising biodiversity assessments.
Vernacular names and Europeana Vernacular names are the most important search terms for non-professional users to retrieve biodiversity information. By means of the network of Focal Points (see 'Results' section), PESI collected substantial additional information on European species, including non-scientific names. As part of the OpenUp! project, Opening Up the Natural History Heritage for Europeana  ( Berendsohn and Güntsch 2012) , PESI participated in the development of an information infrastructure, harvesting vernacular names from various resources, to deliver suitable search terms and controlled vocabularies to advance the Europeana portal functioning, thus PESI serving as a meta-data repository for local biodiversity information, serving other initiatives.
Some of the GNUB services are indicated in the ZooBank interface shown in Fig. 22. GNUB/GNA provides a single shared platform for all cross-links, such that anytime a record is indexed in GNA, it is automatically cross-linked to all other data systems that are indexed in GNA. Unlike most existing biodiversity data initiatives, the components of GNA (particularly GNUB) are not intended to provide novel information; rather, GNUB is an index of core facts that are shared across all of biology. Nothing in GNUB is original or novel content; it merely represents a structured way of organizing information to facilitate broader data integration among other databases that do contain original information. Thus, the GNUB index does not compete with other data resources; but rather serves as a core infrastructure for cross-linking (and thereby empowering) other biological data sources.
Acknowledgements PESI was funded by the  European Commission  under the  Seventh Framework Program  (project reference:  RI-223806 ), contributing to the  FP7-INFRASTRUCTURES 'Capacities' sub-program  under the  Coordination and Support Actions (CSA) funding scheme  (call:  FP7-INFRASTRUCTURES-2007-2 ). Continuing support for the coordination and management of PESI was given by the  University of Amsterdam  (via the former Zoological Museum Amsterdam), and key partner institutes, including  Botanischer Garten und Botanisches Museum Berlin-Dahlem (BGBM) ,  Royal Botanical Gardnes, Kew (RBG)  and  V laams Instituut voor de Zee (VLIZ). The pan-European checklists initial set ups were established as part of the respective EC- FP4 ERMS  ( MAS3-CT97-0146 ), EC- FP5 Euro+Med  ( EVR1- CT-2000-40004 ) and EC- FP5 Fauna Europaea  ( EVR1-1999-20001 ) projects.
A first pilot on integrating the pan-European checklist was carried out during the EC- FP5 EuroCAT  ( EVR1- CT-2002-20011 ) project. Preparatory work on PESI was done during the  EC-FP6 EDIT (GCE 018340 ) and  MarBEF  ( SUSTDEV 505446 ) Networks of Excellence (NoEs). Support on e-Publishing and optimising platform interoperability was given by the  EC-FP7   ViBRANT  ( RI-261532 ) project and support on preparing taxonomic workflows and on installing web services was provided by the  EC-FP7 BioVel (INFRA 283359 ) and  EC-FP7 iMarine (CONNECT 283644 ) projects. The further development of PESI (as ' EU-Nomen ') is partially covered by the  EC-FP7  EU  BON  project ( ENV 30845 ) and partially supported by national LifeWatch nodes, including  Li feWatch Flanders and LifeWatch Greece , spin-offs of the  EC-FP7 LifeWatch ( INFRA 211372 ) project.
of European standards relevant to, for instance, national and European regulations and environmental monitoring, (2) collecting and transferring local expertise and applied tools, (3) lobbying and public policy assistance at national and European level, and (4) supporting closer collaboration of scientific contributor and user communities across Europe. Focal points contribute country-specific information about species, relevant databases, local literature, experts, professional societies and major users such as government organisations. These focal points (either individuals or organisations) complement the taxonomic network through: (1) liaising with national governmental bodies on the implementation.
The estimation of the ET 0 requires the records of air temperature within the landslide area and relative humidity, solar radiation and wind speed within or close to the landslide area. In the case of a landslide-located weather station recording only the temperature, the first step (detailed in Sect. 2. 2) consists of a regional calibration of ET 0 and R S reduced-set equations (equations detailed in Appendix A). The calibrated methods then allow estimating evapotranspiration based only on temperature records. In the case of a landslide weather station recording the full set of parameters, the first step can be skipped and the FAO-56 PM method can then be used to estimate ET 0. The second step (detailed in Sect. 2. 3) consists in estimating the recharge-area parameters (surface runoff, SAWC and K c ) using a GIS (geographic information systems) composite method requiring field observations and spatial data sets (digital elevation models (DEMs), aerial photographs and geological maps).
, 1998) , require records of R S and temperature (Eqs. A7-A12 in Appendix A). As R S can be estimated with a calibrated R S temperature-based method, ET 0 can thus be obtained with temperature records only. ET 0 is calculated using data collected at each reference weather station (independent ET 0 estimates). These calculations follow the FAO-56 PM method outlined in the FAO-56 document  (Allen et al. , 1998). These independent ET 0 estimates are then used as pseudo-standards for the purpose of calibrating the regional-scale ET 0 methods. A linear regression is performed for each of the evapotranspiration methods and for each reference weather station (Eq. 2). The slope a and the intercept b of the best-fit regression line are used as local calibration coefficients. ET 0FAO-56 PM = aET 0method + b, (2) where ET 0FAO-56PM is the ET 0 estimated with the standard method and ET 0method is the ET 0 obtained by any of the five methods tested in this study.
Three weather stations located at less than 60 km from the studied site are used as reference weather stations: Grenoble-Saint-Geoirs, Saint-Jean-Saint-Nicolas and Saint-Michel-Maur (Table 1, Fig. 3 ). The Saint-Michel-Maur weather station does not measure R S , which is estimated with the Angström formula (Eq. A5 in  Appendix A) using sunshine duration data recorded at the station. The Angström formula empirical default coefficients are tuned with the two other weather stations (a S = 0. 232 and b S = 0. 574). Q 1 median Q 3 P R 1 R 2 P R 1 R 2 P R 1 R 2 P R 1 R 2 1101 1. The delimitation of the recharge area of the two-layer hydrosystem (Fig. 3 ) of the Séchilienne landslide is based on the geological and hydrochemical studies of Vengeon (1998),  Guglielmi et al. (2002)  and  Mudry and Etievant (2007).
The Séchilienne landslide is permanently monitored by a dense network of displacement stations managed by the CEREMA Lyon  (Duranthon et al. , 2003). In this study, one infrared station (1101) and three extensometer stations (A16, A13 and G5) are used. Stations 1101, A13 and A16 are representative of the most active zone (median displacements of 2. 5, 1. 75 and 2. 98 mm day -1 , respectively), while G5 is located on a much less active zone (median displacement of 0. 05 mm day -1 ; Fig. 3 , Table  2 ). The sensitivity analysis is performed on the A16 extensometer on the period from 1 May 1994 to 1 January 2012, period during which both A16 extensometer and recharge data sets are available. The performance test of the LRIW workflow against precipitation and R PMNE is performed on the four displacements station in the period from 1 January 2001 to 1 January 2012, period during which the four stations and recharge data sets are available.
The workflow is developed in order to be sufficiently simple to guide any non-hydrogeology specialist who intends to estimate the recharge signal in the case of rainfall-landslide displacement studies. Within this scope, a software is planned to be developed in the near future in order to provide a user-friendly tool for recharge estimation. In addition, the LRIW workflow also enables the reconstruction of retrospective time series for sites recently equipped with weather stations designed to measure a full set of parameters. A further step will have to account for the spatial and temporal variabilities of precipitation and recharge area properties, thus providing a better estimation of the recharge. In addition, taking recharge into account can assist in determining a warning rainfall threshold for the deep-seated slope movements.
B2 Results The recording period used for temperature calibration is from 6 July 2006 to 23 July 2012 (2193 records). This is a common data interval for the two weather stations used (La Mure and Luitel). The estimation of the local air temperature gradient shows a very good performance with R 2 equal to 0. 895 (LBCI = 0. 839) and 0. 916 (LBCI = 0. 862), and RMSE equal to 2. 12 and 2. 48, respectively, for minimum and maximum daily temperature calibrations. Equations (B2) and (B3) are used to estimate temperatures at Mont Sec with temperatures measured at La Mure. Rather than taking the elevation of the Mont Sec weather station (1147 m), the average elevation of recharge area (1200 m) is used, resulting in a difference of elevation with La Mure of 319 m. The estimated local air temperature gradient is 0.
Acknowledgements. This research was funded by the  SLAMS program  ( Séchilienne Land  movement:  Multidisciplinary Studies ) of the  National Research Agency (ANR). The meteorological data were provided by  Météo France ,  LTHE, ONF  and  CEREMA Lyon. Aerial photographs and the digital elevation model were provided by IGN. Displacement data were supplied by  CEREMA Lyon. The authors acknowledge the support of  Jean-Pierre Duranthon  and  Marie-Aurélie Chanut  from the  CEREMA Lyon  and  Jean-Paul Laurent  from the  LTHE. Appreciation is also given to  Eric Lucot  for his kind advice on soil log interpretation, to  Patrick Giraudoux  for his support implementing bootstrap tests and to  Pete Milmo  for English and technical proof reading. Lastly, the authors thank the editor and the three anonymous referees for their insightful comments.
Most of the time, for phylogeny, MEGA  [42]  or MAFFT  [43]  are recommended for tree reconstruction. We can use different algorithms: neighbour joining  [36]  or maximum parsimony. The search for SNPs in the core genome can be used to estimate the age of species of interest  [44]. However, for this kind of analysis it is necessary to possess genomes of very close species to be able to produce a phylogenetic tree and study in detail the mutational events that led to the separation into two different species. This kind of work has been carried out on Y. pestis, in which a comparative analysis was conducted with Yersinia pseudotuberculosis and Yersinia enterocolitica  [45]. Resistome and mobilome. To study the resistome, there are databases such as the ARDB (Antibiotic Resistance Genes DataBase)  [46] , which can be used to look specifically for genes of resistance present in isolates of interest.
This database was used, for instance, for Mycobacterium tuberculosis  [47]. Finally, it is also important to study the mobilome  [48]. This represents the set of all the mobile elements (and hence selfish genes) contained in the studied genomes. Generally, we look for the clustered regularly interspaced short palindromic repeats (CRISPRs) with CRISPRs finder  [49] , phages with PHAST  [50]  or RAST  [51]  and insertion sequences with IS finder  [52]. Dedicated software. The increase in the number of pangenomic studies led to the development of automated tools, which are more or less specialized. The first one, PANOCT  [53]  (pangenome orthologue clustering tool), is a tool completely dedicated to orthologue searches. There is no online version, but the source code is available at http://panoct.
Introduction In the last decade, data on molecular interactions has increased at a tremendous pace. For instance, the STRING database  [1] , which contains protein-protein interaction (PPI) data, grew from 261,033 proteins in 89 organisms in 2003 to 9,643,763 proteins in 2031 organisms in 2015, more than doubling the number of proteins in the database every two and a half years. The same trends can be observed for other types of biological networks, including metabolic, gene-regulatory, signal transduction and metagenomic networks, where the latter can incorporate the excretion and uptake of organic compounds through, for example, a microbial community  [2, 3]. In addition to the plethora of experimentally derived network data for many species, the structure and behavior of molecular networks have also become intensively studied over the last few years  [4] , leading to the observation of many conserved features at the network level.
0. In an extensive computational study on protein interaction networks for six different species, we compare Natalie 2. 0 to the two established methods GRAAL and IsoRank as well as to the recent L-GRAAL method, which has been shown to perform very well in recent studies  [5, 16]. We evaluate the number of conserved edges in terms of edge correctness (EC), induced and symmetric substructure scores (ICS and S3), as well as functional coherence of the modules in terms of gene ontology (GO) annotation. We find that Natalie 2. 0 outperforms the alternative methods with respect to several quality measures and running time. Our software tool Natalie 2. 0 as well as all data sets used in this study are publicly available at [17]. Natalie 2. 0 can also be run via the NatalieQ  [18]  web interface at  [19].
Note that this is not guaranteed to happen. Therefore, we abort the scheme after exceeding a time limit or a pre-specified number of iterations. In addition, we terminate if α has dropped below machine precision. Algorithm 1 gives the pseudo code of this procedure. Algorithm 1: SUBGRADIENTOPT(λ, M, N ) 1 α ← 1; n ← N ; m ← M 2 [LB * , UB * ] ← [Z lb (λ), Z LD (λ)] 3 while g(λ) = 0 do 4 λ ← λ -α(ZLD(λ)-Z lb (λ)) g(λ t ) 2 g(λ t ) 5 if [LB * , UB * ] \ [Z lb (λ), Z LD (λ)] = ∅ then n ← n -1 ; 6 else 7 LB * ← max[LB * , Z lb (λ)] 8 UB * ← min[UB * , Z LD (λ)] 9 m ← m -1 10 if n = 0 then α ← α/2; n ← N ; 11 if m = 0 then α ← 2α; m ← M ; 12 return [LB * , UB * ].
Experimental Evaluation From the STRING database v8. 3  [1] , we obtained PPI networks for the following six species: C. elegans (cel), S. cerevisiae (sce), D. melanogaster (dme), R. norvegicus (rno), M. musculus (mmu) and H. sapiens (hsa). We only considered interactions that were experimentally verified. Table  1  shows the sizes of the networks. We performed, using the BLOSUM62 matrix, an all-against-all global sequence alignment on the protein sequences of all 6  2 = 15 pairs of networks. We used affine gap penalties with a gap-open penalty of 10 and a gap-extension penalty of 2. The first experiment in Section 4. 1 compares the performance of IsoRank, GRAAL, L-GRAAL and Natalie 2. 0 in terms of a variety of topological measures. In Section 4. 2, we evaluate the biological relevance of the alignments produced by the four methods.
Acknowledgments We thank  SARA Computing  and  Networking Services  for their support in using the Lisa Compute Cluster. In addition, we are grateful to  Bernd Brandt  for helping out with various bioinformatics issues and also to  Samira Jaeger  for providing code and advice on the GO similarity experiments. We thank  Noël Malod-Dognin  for helping us with running L-GRAAL and for general discussions. We also thank the anonymous referees for carefully reading our work and their comments.
Müller presents the CoreGram project, which aims at providing HPSG grammars for various typologically distinct languages. In this approach, a multilingual grammar is used to represent a common core shared by these languages. This article gives theoretical linguistic motivations behind multilingual grammars, along with theoretical grammar development concepts, and information about the concrete implementation of the corresponding HPSG grammars. Perrier and Guillaume present FriGram, a broad-coverage French IG, which relies on a modular architecture and can be interfaced with various lexicons. This article also addresses grammar design and maintenance issues by presenting grammar-consistency principles which are implemented within FriGram. The authors also report on the current status of the grammar (coverage, comparison with other resource grammars for French, evaluation).
acknowledgements As guest editors of this special issue, we would like to thank the members of the editorial board and editorial team of the Journal of Language Modelling for their work regarding the reviewing, copy-editing, and typesetting of the articles submitted to this issue. We are also grateful to the members of the guest editorial board. This issue would not have been possible without their valuable contribution to the reviewing process. Finally, we would like to express our gratitude to Adam Przepiórkowski and Agnieszka Mykowiecka for their support throughout the editorial process. Timothy Baldwin, John Beavers, Emily M. Bender, Dan Flickinger, Ara Kim, and Stephan Oepen (2005) , Beauty and the Beast: What running a broad-coverage precision grammar over the BNC taught us about the grammar  [ 14 ].
Despite all the effort put in developing such methods, there remains a number of issues that need to be addressed: (i) to predict the characteristic hairpin structure of a pre-miRNA, the vast majority of the existing software rely on a folding algorithm of cubic time complexity which is suitable when the input is small enough, but can become impracticable when the size of the input increases; (ii) for longer pre-miRNAs (such as in plants), such folding methods moreover produce hairpin structures different from the ones provided in MIRBASE   [6]  which uses sRNAseq data to do so; (iii) together with folding, most methods then rely on further information that must be learned from previously validated miR-NAs of closely related genomes (at a minimum within the same clade, plant or animal) for the final prediction of new miRNAs in order either to set the parameters of the model or to restrict the search to a limited space.