dataesr/scientific-paragraphs-categorization huggingface dataset:
These will be taken into use in the standard operation of the existing radars during the coming months, as part of the development undertaken in Work Package 11 of the Preparatory Phase (Software Development) with new computers being deployed to the Tromsø site to handle the required data processing and storage. While there are certain new coding applications which cannot be tested on the existing EISCAT system because of the limitations of the current hardware, there is another class of experiments which can only be run on the EIS-CAT_3D system because they are designed specifically for phased arrays. These experiments use the principle of "antenna coding", in which the shape of the array is used to add an extra degree of freedom to the transmitted modulation. An example, which has been the subject of considerable recent study in the optics and geophysics communities, is the use of twisted beams (see Fig.
Acknowledgements The authors wish to recognise the contributions of the European Union's  Seventh Framework programme , which supported the meetings of the working groups and funded the time of the convenors to construct the science case document. We would also like to thank the staff of the  EISCAT Scientific Association  for their assistance and support. In particular, we express our appreciation to  Stephan Buchert  and his colleagues at  IRF Uppsala , for hosting the annual series of EISCAT_3D User Meetings, which provided the venue for several of our working group discussions and were a rich source of ideas and discussions. In developing the science case, we have sought to reach out to researchers from both within and outside the current member countries of EISCAT. As well as participants from  Norway, Sweden, Finland, Japan , and the  UK , we are pleased to acknowledge the contributions of our colleagues from  Belgium, France, Germany, Italy , and  Poland.
Accepted Article This article is protected by copyright. All rights reserved. obtained in these studies have been hindered by the lack of reference sequences of taxonomically well-identified organisms. Although a number of curated reference databases are publicly available for ribosomal RNA genes of eukaryotes and prokaryotes, such as the Protist Ribosomal Reference Database (PR2;  Guillou et al. 2013) , SILVA  (Pruesse et al. 2007 ), Ribosomal Database Project (Cole et al. 2005 ), and Greengenes (DeSantis et al. 2006) , no reference database exists for the plastidial 16S rRNA gene of photosynthetic eukaryotes. Here, we describe an extensive reference database of the plastidial 16S rRNA gene including sequences from all major lineages of photosynthetic eukaryotes, comprising terrestrial, freshwater and marine organisms.
This database, named PhytoREF, has been built through the compilation of all of the publicly available plastidial 16S rDNA sequences (amplicons and sequences extracted from plastidial genomes), as well as novel Sanger amplicons that we obtained from a wide taxonomic spectrum of cultured microalgal strains. PhytoREF is not only a new resource to explore, evaluate and monitor the diversity of photosynthetic eukaryotes in aquatic and terrestrial ecosystems, but is also useful to taxonomically identify new plastidial 16S rDNA sequences and design primers and probes to target specific lineages of photosynthetic eukaryotes. PhytoREF will pave the way for a range of applications in bio-monitoring photosynthetic eukaryotes in various habitats (e. g. water, sediments and ice), paleoecological studies of primary producers in past environments, and dietary studies in unicellular and multicellular herbivores.
Accepted Article This article is protected by copyright. All rights reserved. plastid, chloroplast, 16S, small subunit), and BLAST searches with different query sequences of distinct photosynthetic eukaryotic lineages. Additional sequences were retrieved from the PR2 database (May 2014)  (Guillou et al. 2013 ; http://ssu-rrna. org/). When available in GenBank (release 201), the source literature for each sequence was searched to compile and/or verify their specific features (e. g. taxon names, culture strains), resulting in a bibliographic database of 565 source publications. 16S rDNA sequences were also extracted from all plastidial genomes available at http://www. ncbi. nlm. nih. gov/genomes/GenomesGroup. cgi?taxid=2759&opt=plastid. All plastidial 16S rDNA sequences that originated from an identified organism (e.
g. culture strain or isolated organism) were defined as reference sequences in the PhytoREF database. Environmental Sanger 16S rDNA sequences obtained in clone libraries were also retrieved from INSDC and included in PhytoREF. Those sequences that lacked taxonomic identification were assigned at the class level based on sequence similarity scores with the references sequences of PhytoREF. Finally, all of the 16S rDNA sequences of Cyanobacteria were extracted from SILVA (release 115,  Quast et al. 2013 ) in order to root the phylogenetic trees and unambiguously annotate and classify eukaryotic sequences. The cyanobacterial sequences are available as separate files at http://phytoref. org.
) or the NucleoSpin® Extract II kit (Macherey-Nagel, Hoerdt, France), and sequenced in both forward and reverse directions using the ABI-PRISM Big Dye Terminator Cycle Sequencing Kit (Applied Biosystems). Raw Sanger sequences were edited and assembled with ChromasPro v1. 7. 5 (Gene Codes), and primer sequences were trimmed off. The new plastidial 16S rDNA sequences were deposited in GenBank under the accession numbers LN735194 to LN735532 (Table  S1 ), and can also be retrieved on the PhytoREF web interface at http://phytoref. org.
Construction of the PhytoREF database The core content of the database is composed of the reference plastidial 16S rDNA sequences from public databases with unambiguous taxonomic assignation, and the novel sequences obtained from duly identified cultures. Each reference sequence, including taxonomic affiliation, was validated and filtered following different steps: (i) sequences shorter than 400 bp from cultures, and shorter than 800 bp from public sequences (including environmental sequences) were removed; (ii) sequences with more than 10 consecutive non-ACGT characters were also.
Accepted Article This article is protected by copyright. All rights reserved. discarded; (iii) sequence alignments were performed for different well-defined taxonomic groups (e. g. at the class level) using MAFFT v6. 953b with default options  (Katoh et al. 2002) , and visualized to verify the presence of introns or putative chimeric sequences; (iv) poorly aligned or difficult-to-align nucleotide positions were removed for subsequent phylogenetic analyses using the program trimAl v1. 4 program (with a -gt value of 0. 8, and -st value of 0. 001; Capella-Gutierrez 2009); (v) phylogenetic trees were constructed separately for each taxonomic group (i. e. generally at the class level) using FastTree v. 2. 1. 1, a fast and accurate approximate maximum-likelihood method using the GTR model  (Price et al.
2010) , in order to identify mislabeled sequences and other possible conflicts, and to build up the taxonomic framework (see below). Additional publicly available plastidial 16S rDNA sequences with uncertain taxonomic status were subsequently added to this validated core dataset. These sequences were assigned to a given phylum using a similarity threshold based on global pair-wise alignments (using a Needleman-Wunsch algorithm) against the reference sequences. Sequences of each phylum were then aligned based on conserved 2D structures and sequences of the archaeal 16S, bacterial 16S and eukaryotic 18S small subunit ribosomal RNA using the SSU-align program and Infernal software package, which generate large-scale alignments of up to millions of sequences  (Nawrocki et al. 2009 ). 2D-based alignments allowed us to verify whether the new sequences corresponded to the 16S rRNA gene or other ribosomal genes.
The taxonomic framework of PhytoREF For every new validated sequence, we established a standardized and ranked taxonomy with 10 levels: 1-Domain; 2-Super-group; 3-Phylum; 4-Class; 5-Subclass; 6-Order; 7-Sub-order; 8-Family; 9-Genus; and 10-Species. For the "Super-group", "Phylum" and "Class" levels, the taxonomic framework of PhytoREF was derived from the PR2 database (http://ssu-rrna. org/; Guillou et al. 2013) , which mainly follows a comprehensive recent classification framework of eukaryotes  (Adl et al. 2012). The "Family" and "Order" levels of terrestrial, marine and freshwater micro-and macroalgae were based on the taxonomic classification system of the AlgaeBase database  (Guiry and Guiry 2014; Guiry et al. 2014 ; http://www. algaebase. org/). For the taxa that were not present in AlgaeBase and PR2 (mostly embryophytes), the taxonomic classification of NCBI (May 2014) was followed (taxdump.
nodes and taxdump. names files at http://www. nlm. nih. gov/research/umls/sourcereleasedocs/current/NCBI/metarepresentation. html ). Overall, the standardized taxonomic framework established in PhytoREF was designed to assist in the analysis of large datasets of environmental plastidial 16S rDNA amplicons generated by high-throughput environmental metabarcoding. For some 16S rDNA sequences, it was not possible to define an accurate and/or complete taxonomic identity because the taxonomic description of the corresponding organism is not fully resolved, e. g. only at the "Family" or "Genus" level. In these cases, the sequence was labeled as described for the PR2 database. For instance, the taxonomic path of a sequence identified up to the "Family" level would be: Family, Family_X (for the "Genus" level), and Family_XX (for the.
Accepted Article This article is protected by copyright. All rights reserved. "Species" level). Moreover, some key groups of microalgae have only been classified into informal clades and sub-clades based on published phylogenetic analyses without morphologybased taxonomy (e. g. prasinophyte clades VII, IX; Apicomplexa-related lineages I -V). For the PhytoREF database, information about the molecular clade was verified through specific phylogenetic analyses with the 16S rRNA gene and indicated at different taxonomic ranks. For instance, prasinophytes belonging to clade VII and sub-clade A1 are annotated: clade 7 ("Order" level), clade_7A ("Family" level), clade_7A1 ("Genus" level) and clade_7A1+sp ("Species" level). A confidence level for taxonomic assignation (named Refseq) was given to each PhytoREF sequence, indicating the level at which a given sequence is unambiguously assigned (RefSeq=1: Eukaryota; RefSeq=2: super-group; RefSeq=3: Phylum; RefSeq=4: Class; RefSeq=5:Order; RefSeq=6:Family; RefSeq=7:Genus).
Overview of PhytoREF database The PhytoREF database (release 1) currently contains 6,490 partial and complete plastidial 16S rDNA sequences (of which 6,051 sequences are > 800 bp long). In total, 411 novel sequences from marine microalgal strains were produced in this study and 6,079 sequences retrieved from public databases (5,200 amplicons from organisms and environmental samples, and 879 sequences extracted from plastidial genomes, Fig. 1 ). 2D alignments combined with BLAST analyses allowed us to determine that 52 sequences (mostly from streptophytes) considered as.
Accepted Article This article is protected by copyright. All rights reserved. 16S rDNA in GenBank were actually nuclear 18S rDNA, and were therefore excluded from PhytoREF. In addition to sequences from identified plastid-bearing organisms, PhytoREF contains 1,867 environmental Sanger sequences from clone libraries, which have been assigned to known eukaryotic lineages based on sequence similarity (combining a Needleman-Wunsch algorithm and phylogenetic analyses). Every PhytoREF sequence was quality-checked, phylogenetically-analyzed and classified following our standardized taxonomy. In addition to the taxonomic path, all sequences were associated to a suite of descriptors, such as the organism, molecular origin (amplicon or extracted from genomes), GenBank accession number, cultured strain, and original publication.
Taxonomic composition of PhytoREF All of the known major lineages of photosynthetic eukaryotes from terrestrial, freshwater and marine environments are represented in PhytoREF. At the super-group level, the composition of the database is as follows: Archaeplastida (3,834 sequences), Stramenopila (1,704 sequences), Alveolata (144 sequences), Hacrobia (501 sequences), Excavata (288 sequences), and Rhizaria (20 sequences) (Figs. 2  and 3 ). Although our effort while building PhytoREF (in particular in producing novel plastidial reference sequences) mainly focused on marine microalgal taxa, reference sequences from streptophytes (i. e. from mosses and ferns to gymnosperms and angiosperms), and marine and freshwater macroalgae (e. g. , Rhodophyceae, Phaeophyceae, Ulvophyceae) were also included in the final reference database of all known photosynthetic.
Accepted Article This article is protected by copyright. All rights reserved. eukaryotes. Thus, PhytoREF can be used in metabarcoding surveys to study communities of microalgae in different marine and freshwater habitats (e. g. seawater, estuaries, brackish waters, lakes), as well as to detect the presence of macroalgae and streptophytes in aquatic systems as reproductive stages (gametes, pollen) or in the digestive tracts of herbivores. Land plants (streptophytes) are numerically the dominant group in PhytoREF with 2,973 sequences, representing 373 families and 796 genera. The macroalgae from the classes Rhodophyceae (61 genera), Phaeophyceae (7 genera), and Ulvophyceae (18 genera) are represented by 161, 46, and 82 sequences, respectively (Fig. 3 ). Diatoms (Bacillariophyta), green algae (Chlorophyta) and Haptophyta (coccolithophores and their relatives) are numerically the most important microalgae in the database with 1094, 653 and 369 sequences, respectively, covering a wide taxonomic diversity with 109, 79, and 34 described genera and 268, 113, and 67 described species, respectively (Fig.
Accepted Article This article is protected by copyright. All rights reserved. cultures of photosynthetic dinoflagellates. Consequently, one shortcoming of PhytoREF is the limited number of dinoflagellate sequences (34 sequences representing 15 genera), a caveat to consider when interpreting metabarcoding datasets using PhytoREF. It is important to note that several plastidial 16S rDNA sequences from GenBank were re-assigned in the database because they were mislabeled as "dinoflagellate" when in fact they correspond to plastids of photosynthetic eukaryotes "stolen" by dinoflagellate hosts (kleptoplastids). For instance, the dinoflagellate Dinophysis can sequester plastids of different microalgal prey, such as cryptophytes, raphidophytes and chlorophytes  (Kim et al. 2012). This issue was also found in all other organisms present in PhytoREF that can either establish kleptoplastidy (e.
PhytoREF: a new tool to explore the ecology of photosynthetic eukaryotes To date, PhytoREF is the only tool in molecular ecology specifically designed to explore the total diversity of photosynthetic eukaryotes from complex marine and terrestrial ecosystems using metabarcoding or metagenomics approaches. Although the taxonomic resolution of the plastidial 16S rDNA barcode is not as high as that of established barcodes like the mitochondrial cytochrome c oxydase I gene for animals  (Herbert et al. 2003)  and the large subunit of ribulose.
Description of the PhytoREF web interface The PhytoREF web interface provides easy and rapid access to all reference plastidial 16S rDNA sequences, and allows users to explore the database with interactive graphs (e. g. Krona pie charts) and perform different search options. Sequences can be retrieved in Fasta format either by taxonomic rank (e. g phylum, genus, species) using a taxonomy browser, or through specific identifiers such as the GenBank accession number or the culture strain code (e. g. AY702161 or RCC393). Information associated with each sequence can be also downloaded as a tab-separated file, and different formats of the database are proposed to be used by the QUIIME, Mothur or TreeDyn programs. In addition, for each sequence, publication metadata such as title, authors and abstract are available on the web site.
Conclusion and perspectives PhytoREF is the first resource allowing exploration of the total diversity of photosynthetic eukaryotes in any given ecosystem. It can be used for a range of purposes, such as: (i) annotation and classification of new plastidial 16S rDNA sequences; (ii) taxonomic assignation of environmental sequences from massive metabarcoding and metagenomic datasets; and (iii) design of primers and probes to target any group of photosynthetic eukaryotes. All of the main eukaryotic lineages that have a functional or relict plastid are represented in PhytoREF, including free-living, mutualistic or parasitic organisms from aquatic and terrestrial habitats. Some organisms are under-represented in the current PhytoREF version, such as dinoflagellates and freshwater microalgae, which may lead to coarse taxonomic assignations.
Figure2: Distribution and number of PhytoREF plastidial 16S rDNA sequences in the tree of eukaryotic life. The schematic phylogenetic tree is based on up-to-date phylogenomics and morphological evidence (Burki & Keeling 2014). Each plastid-containing eukaryotic lineage is highlighted in green, and the number of plastidial 16S rDNA sequences available in the PhytoREF database is indicated in small grey circles.
Acknowledgments This work was supported by the  OCEANOMICS  project, funded by the  French Government  and managed by the  Agence Nationale de la Recherche , under the grant agreement " Investissements d'Avenir "  ANR-11-BTBR-0008. Financial support for this work was also provided by the  European Union  programs MicroB3 (UE-contract-287589) and  MaCuMBA  ( FP7-KBBE-2012-6-311975 ) and the  French "Investissements d'Avenir" program EMBRC-France. This work was granted access to the  HPC  and visualization resources of " Centre de Calcul Interactif" hosted by "Université Nice Sophia Antipolis ". We are grateful to  Ales Horák ,  Jan Janouškovec ,  Chris Jackson ,  Akira Kuwata ,  Camille Poirier  and  Daphné Grulois  for providing 16S rDNA sequences and microalgal strains.
Tracing propagation behaviour One can also be interested in observing the state of the social network when the number of activated nodes reaches a given value. Because the different views in the software are linked together, any operation in one view impacts the other views. A node/edge selection (highlighted in blue in Figure  5 ) in the derivation tree implies the same in the scatterplot and vice versa. A similar kind of operation in one of the graph states will echo through all the states containing the same elements, thus highlighting the selected nodes/edges everywhere they are. Such behaviour is available thanks to the inner datastructure of PORGY, leaving the nodes unmodified as long as they are not subject to a rule application. Consequently, selecting an element of interest in one of the intermediate graphs allows us to follow and determine in which state this element is altered.
As shown in Figure  1 , the architecture of the recommendation system that we propose is composed of two modules operating three models: a trace collection module, a pedagogical content model, a learner model, a collaboration model and a recommendation module. This system is used to collect traces of users and store them in a database of traces. A trace is a time sequence of observed containing all user actions to perform a given task. The first module of our system treats the collecting primary traces in native format. The second module classifies the primary traces coming from the first module as high-level traces along the trace model  (Li, 2013). Depending on the content of traces model and pedagogical content model, algorithms for learning indicators calculations of the learner are applied in the recommendation module.
Collect of traces The traces collection is to observe the student in a learning situation and memorize his activities traces to infer the learner model. This collection mode is interesting because it captures the learner interaction the learner without distracting him from his main task. In E-Memorae2. 0, these actions are stored in the database traces and classified according to the actions model of the E-MEMORAe 2. 0 platform. According to this model, we have three types of traces: Private traces belonging to the private space, traces of collaboration that belong to the space of collaboration and individual traces that are private traces and traces of collaboration. For each type of traces, we have three types of activities that can be conducted by the learner: learning resources consultations (documents), resources creation (conversations, meetings, questions, answers, notes and wikis) and resources additions (documents and annotations).
Conclusion The traces are very important elements in collaborative environments. Their analysis aims to understand and follow the learning of a learner or group of learners and qualify the use, usability and acceptability of collaborative environment to make it more adaptive. In this paper, we proposed an architecture for a recommendation system for the learner. This architecture is based on an original model of the learner tak-ing into account the definition of data (learning indicators). A knowledge base containing this information was constructed. Interaction data recorded were used to construct indicators of learners' state, group state and the progression of the training session. The absence of such indicators in current learning and teaching environments has allowed us to justify our work.
The smart world is expected to be an attractive perspective IEEE Access Special Section Editorial involving ubiquitous sensing, computing, and communication to achieve comprehensive interconnections of physical perception, cyber interaction, social correlation, and cognitive thinking. In the paper by Ning et al. (From Internet to smart world), the authors introduce the evolution of the smart world. Physical-based coordination, social-inspired interactivity, brain-abstracted cooperativity, and cyber-enabled homogeneity are, respectively, discussed as the characteristics of the smart world. With the further adoption of the Internet of Things and sensor technology, all kinds of intelligent transportation system (ITS) applications based on a wide range of traffic sensor data have had rapid development.
When willing to prepare and to build operational Product Life cycle Management interoperability within a Dynamic Manufacturing Network (DMN) in a mature digital business ecosystem such as Aeronautic, Space and Defense, the approaches proposed by the Enterprise Application Interoperability are insufficient when willing to address the existing interoperability brakes Some of these brakes have been addressed in project such as IMAGINE and SIP@SystemX, allowing to experiment innovative way of using standards based enterprise modeling and also to identify some additional gaps for applying model base enterprise modeling to PLM interoperability within a DMN. After defining the business and the scientific contexts, the paper describes this new approach which consists in federating the usage of several PLM, Business, Information and ICT standard through the usage of an enterprise modeling standardized language, ArchiMate, and associated modeling tool Archi created using ArchiMate as an EMF DSL.