dataesr/scientific-paragraphs-categorization huggingface dataset:
As other authors (e. g. Ehsani and Quiel, 2009; Dr agut¸and Eisank, 2011; Prasannakumar et al. , 2011; Sleszy nski, 2012) , we used the DEM SRTM (Shuttle Radar Topography Mission) for the morphometric analysis of the study area. This topographic data is the result of collaboration between the National Geospatial Intelligence Agency (NGA) and NASA. The objective of this project is to produce a digital topographic data covering the surface of the Earth using radar interferometry, with the relatively short wavelength (5. 6 cm) of the SRTM instrument. During the mission by the Space Shuttle Endeavour, 12. 3 Tbyte data were collected, corresponding to 99 97% of the total Earth surface. The altitude of the shuttle flight was 233 km and the inclination of shooting is 57  (Rabus et al. , 2003). The SRTM data, used in this paper, have a resolution equal to 3 arc-sec (about 90 m resolution) and was projected in WGS84  (Rabus et al.
, 2003). The vertical accuracy of this elevation data varies from 5 m to 10 m and the horizontal accuracy varies between 7 m and 13 m  (Gorokhovich and Voustianiouk, 2006; Rodríguez et al. , 2006). On the other hand, other indices were obtained from the morphometric analysis of the hydrographic network. The shape of the drainage network is sensitive to changes in the morphology of the terrain. Thus, morphometric analyses of drainage networks have a great significance for structural and tectonic studies Sif el Laham, 7-J. Asker, 8-J. Morra, 9-J. Sidi Bou Helal]; 10-J. Ben Kreir, 11J. Chemsi, 12-J. Berda, 13-J. Bou Jera, 14-J. Sehib; [Gafsa chain: 15-J. Bou Douaou, 16-J. Bou Hedma, 17-J. El Biadh, 18-J. Orbata, 19-J. El Onk, 20-J. Ben Younes, 21-J. Bou Ramli]; [Metlaoui chain: 22-J. Biadatt, 23-J.
To quantify this parameter, a variety of methods has been proposed in the literature  (Hobson, 1972; Beasom et al. , 1983; Riley et al. , 1999; Sappington et al. , 2005). In this study, we used the method of "surface area" exposed by  Jenness (2004)  and integrated into the extension "DEM Surface Tools © , Version 2. 1. 292". This tool was developed in Visual Basic and run on the platform of ArcGIS desktop. This method highlights the terrain roughness by calculating the ratio (Se/Sp) between the effective surface (Se) and the planimetric surface (Sp). Therefore, for flat land (Se ¼ Sp), this ratio will be equal to 1. However, a slope of the high terrain induces a larger effective area (Se >> Sp), so the ratio will be high. The topographic data used in these analyzes are those of SRTM. The calculation of the vertical roughness of Gafsa region landform showed that the highest values is located in the Gafsa chain and specifically on the southern flank of J.
Acknowledgements The research leading to these results has received funding from the  EU   7th Framework Programme  ( FP7/2007-2013 ) under Grant Agreement No. 257740  ( NoE TREND ). We are thankful to all the TREND researchers for their intense work that is only partially reported here. A special thank to  Marco Ajmone Marsan , the coordinator of TREND that also contributed to the writing of this paper. Finally, this research is dedicated to the memory of  Fabio Neri , the founder of TREND, who tragically and unexpectedy passed away on April 16th, 2011.
Transcriptome analysis based on a de novo assembly of next generation RNA sequences is now performed routinely in many laboratories. The generated results, including contig sequences, quantification figures, functional annotations and variation discovery outputs are usually bulky and quite diverse. This article presents a user oriented storage and visualisation environment permitting to explore the data in a top-down manner, going from general graphical views to all possible details. The software package is based on biomart, easy to install and populate with local data.
Introduction The massive sequencing cost decrease has attracted a large community of new users, some of them studying organisms for which the reference genome sequence is still not available. When trying to understand mechanisms taking place at the gene level they usually would start with a de novo transcriptome assembly approach. Software packages such as Trinity  [1]  or Oases  [2]  are mature enough to produce reliable contigs from short reads. The analyses performed on the assembled contigs generate a large amount of heterogeneous results including variations, functional annotation and expression measurements. The processing steps of these pipelines are usually shared inside the community but the parameters, the tools and the reference databases used are specific. The results are often provided throught a WEB server including a BLAST query form and download links.
Cbrowse  [3]  is a WEB environment presenting this kind of results throught graphical views and query forms. The functional annotation part is not implemented yet and the query possibilities are very limited. The Galaxy  [4]  engine provides users with an interface to create and track workflow executions. It already embeds RNA-Seq analysis and assembly components. However, none of them offers user-friendly query and vizualisation features designed for RNA-Seq de novo annotation. In its last version (0. 8), biomart  [5]  is developed as an easily extensible query infrastructures which can be specialized in the presentation of focused data types. On top of the database and beside the proposed query forms it is possible to add new pages as plug-ins in order to present data in a user-friendly way.
Implementation and installation RNAbrowse is based on a newly defined biomart schema specialized in RNA-Seq de novo assembly. It includes two marts, storing contig and variant data, and some extra tables for metadata. The WEB-based user interface is implemented as a biomart plugin using the latest jquery and highcharts features. To perform all third parts requests, the software takes advantage of the biomart API and implements several biomart processors to prepare the data before presenting it. The command line administration tool is based on a workflow execution environment called jflow. It is developed in Python. The installation requires a standard unix server and a MySQL database. The downloadable installation archive, provided on the project web-site, has only to be uncompressed before setting up a first instance.
WEB interface The web interface was designed to display the data starting with general views before zooming into detailed ones. It is organised in four levels. The first, called the instance level, corresponds to the home page of the web-site presenting all the available projects. A project gathers the data of an assembly. The second level is entered after having chosen a project. The introduction page contains a picture of the assembled species, a description text and a table including all the analysis processing steps with software names, parameters and versions in order to ease materials and methods writing. The third level is accessed through the menu bar items at the top of the project page and presents general informations about contigs and variants. It also includes the download page.
Administration tools and extensions The administration interface is a command line tool with which the environment can be set up. It includes database creation, data formatting and loading and web server managing. The loading process requiers standard file formats, provided by most commonly used tools. During the database upload phase, datafile format compliance is checked. It is possible to set up a minimum environment with just three files: a contig Fasta file, the corresponding annotation file and an alignment file. The reads quantifications can be provided as an input file or calculated during the upload process. The number of input files being potentially quite important for large projects, a configuration file template is provided. It can be used as a loading script parameter. Depending on the provided inputs, RNAbrowse processing steps can be quite time consuming.
Thus, in a production environment, the tool can be set up to use different schedulers such as condor, sge, moab, workqueue or mpi-queuein in order to parallelize the loading. If the loading process fails, a recovery command is available to rerun it. The website provided to the biologists can be secured using the biomart Open-ID or jetty realm features. Users with some programming knowledge can add new graphics by loading the corresponding data in the analysis table and writing a javascript module.
Discussion RNAbrowse is a simple and efficient solution to give access to RNA-Seq de novo results on the Internet. It includes many features that help biologists to analyse and extract biologically meaningful information from their data. The installation and user manuals as well as a general documentation are available on the project website: http://bioinfo. genotoul. fr/RNAbrowse. The software has been designed to be easily extended by developpers having some biomart insights.
This paper focuses on the important problem of semanticaware search in textual (structured, semi-structured, NoSQL) databases. This problem has emerged as a required extension of the standard containment keyword based query to meet user needs in textual databases and IR applications. We provide here a new approach, called SemIndex, that extends the standard inverted index by constructing a tight coupling inverted index graph that combines two main resources: a general purpose semantic network, and a standard inverted index on a collection of textual data. We also provide an extended query model and related processing algorithms with the help of SemIndex. To investigate its effectiveness, we set up experiments to test the performance of SemIndex. Preliminary results have demonstrated the effectiveness, scalability and optimality of our approach.
In semantic-aware queries, some knowledge 6 needs to be taken into consideration while processing. Let's assume having a movie database, as shown in Table  1. Each movie, identified with an id, is described with some (semi-structured) text, including movie title , year and plot. For queries "sound of music", "Maria nun" and "sound Maria", the query result is movie O 3. However, if the user wants to search for a movie but cannot recall the exact movie title, it is natural to assume that (s)he may modify the query terms to some semantically similar terms, for example, "voice of music". Also, it is common that the terms provided by users are not exactly the same, but are semantically relevant to terms that the plot providers use. Clearly, the standard inverted index which only supports exact matching cannot deal with these cases.
Various approaches combining different types of data and se- Code R (1977): This CBS adventure series managed to combine elements of "Adam-12", "Emergency" and "Baywatch" at the same time. O 3 Sound of Music, The (1965): Maria had longed to be a nun since she was a young girl, yet when she became old enough discovered that it wasn't at all what she thought. • • • • • • mantic knowledge have been propose to enhance query processing (cf. Related Works). In this paper, we present a new approach integrating knowledge into a semantic-aware inverted index called SemIndex to support semantic-aware querying. Major differences between our work and existing methods include: -Pre-processing the index: Existing works use semantic knowledge to pre-process queries, such as query rewriting/relaxation and query suggestion  [2, 5] , or to post-process the query results, such as semantic result clustering  [16, 17, 25].
The goal of our work is, with the help of semantic knowledge, to find more semantically relevant results than what a traditional inverted index could provide, while doing it more efficiently than existing disambiguation techniques. In order to build SemIndex , we create connections between two data resources,a textual data collection and a semantic knowledge base, and map them into a single data structure. An extended query model with different levels of semantic awareness is defined, so that both semantic-aware queries and standard containment queries are processed within the same framework. Figure  1  depicts the overall framework of our approach and its main components. The Indexer manages SemIndex , while the Query Processor accepts semantic-aware queries from the user and processes the queries with SemIndex.
Representation and Definitions Textual Data Collection: In our study, a textual data collection could be a set of documents, XML fragments, or tuples in a relational or NoSQL database. Definition 1 A Textual Data Collection ∆ is represented as a table defined over a set of attributes A = {A 1 ,. , A p } where each A i is associated with a set of values (such as strings, numbers, BLOB, etc. ) called the domain of A i and denoted by dom(A i ). A semantic knowledge base (KB) can be associated to one or several attribute domains dom(A i ). Thus, given a table ∆ defined over A, objects t in ∆ are denoted as a 1 ,. , a p , where a i ∈ dom(A i ). Each a i from t is denoted as t. a i. Semantic Knowledge-Base: We adopt graph structures for modeling semantic knowledge. Thus, entities are represented as vertices, and semantic relationship between entities are modeled as directed edges  7.
2-Coupling Resources: When coupling both indexes, we get one SemIndex graph called GSemIndex. In GSemIndex , only searchable terms have (mainly string) values (which will be defined in Step 3 in the construction procedure). Weights are assigned to all edges and data nodes in the graph. To summarize, GSemIndex contains: -V i is a set of index nodes of GA ∪ Gkb -V d is the set of data nodes of GA -L is a set of labels in the Gkb and a special label contained, indicating the containment relationship from a searchable term in V i to an entity in V d -E is a set of ordered pairs of nodes in V i (index edges) and in V d (data edges) -S v is the function of string values defined only on searchable terms, which is a subset of V i -S e assigns Gkb relationship labels to index edges and contained label to index/data edges -W is the weighting function defined on all nodes in V i V d and edges in E.
-Step 2: Given a semantic knowledge graph G kb representing the semantic knowledge base KB given as input, it builds an inverted index for string values of each knowledge base entity, and construct the graph Gkb. -Step 3: it combines the two SemIndex graphs. Data nodes in the result graph GSemIndex are the set V d in GA (denoted as V A d ), while all other nodes are index nodes. This step denotes the searchable terms of Gkb as V kb i (which are vertices with one or more outgoing edges labeled contained ) and then merges the two sets of searchable terms V kb i and V A i (representing the index nodes of GA ) as follows: if string values of two vertices are equal, remove one of them and merge all the connected edges. We use V + i to denote the conjunctive set V kb i V A i , which is the set of all searchable terms in SemIndex.
-Lexical Query: When = 2, besides from the previous case, lexical connections, i. e. , links between terms, may be involved in the result. -Synonym-based Query: When = 3, synsets are also involved in the query processing. Note that there is no direct edge between textual object and synset node. -Extended Semantic Query: When 4, the data graph of SemIndex can be explored in all possible ways. When grows larger, the data graph is explored further to reach even more results.
Experimental Setup We ran our experiments on a PC with Intel 2GHz Dual CPU and 2GB memory. SemIndex was physically implemented in a MySQL 5. 1 database with the query processor written in Java. We downloaded 90,091 movie plots from the IMDB database 9  , and used WordNet 3. 0 as our semantic knowledge base. We build SemIndex on plot contents and movie titles, which means that each textual object is a movie title combined with its plot (cf. Table  2 ).
k, NE and NO are the number of query terms, visited entity nodes and object nodes during query processing. Recall that CPU cost of our algorithm is O((NE + NO) × k 2 ), which can be examined from the first query group. Also note that the total number of textual objects in our plot dataset is 90K, 30 Fig. 6. Processing Time (Q2-4) From Table  4 , we see that most queries are processed within 2 seconds, which is positively encouraging, except for Q2-4, since it is of an extended semantic type, which retrieves 16830 results in about a half minute. In fact, while the total number of textual objects in our plot dataset is around 90K, Q2-4 visits about 80K of object nodes which explains the significant increase in time. Also, we realize that the initial response times of Q1-5 and Q1-6 are quite different.
To our knowledge, one existing method in  [1]  has investigated this approach, introducing a joint index over ontologies and text. The authors consider two input lists: containing text postings (for words or occurrences), and lists containing data from ontological relations (for concept relations). The authors tailor their method toward incremental query construction with context-sensitive suggestions. They introduce the notion of context lists instead of usual inverted lists, where a prefix contains one index item per occurrence of a word starting with that prefix, adding an entry item for each occurrence of an ontological concept in the same context as one of these words, producing an integrated 4-tuples index structure (prefix, terms) ↔ (term, context, concepts). The method in  [1]  is arguably the most related to our study, with one major difference: the authors in  [1]  target semantic fulltext search and indexing with special emphasis on IR-style incremental query construction, whereas we target semantic search in textual databases: building a hybrid semantic inverted index to process DB-style queries in a textual DB.
Conclusions and Future Work In this paper, we introduce a new semantic indexing approach called SemIndex , creating a hybrid structure using a tight coupling between two resources: a general purpose semantic network, and a standard inverted index defined on a collection of textual data, represented as (multi)graphs. We also provide an extended query model and related processing algorithms, using SemIndex to allow semantic-aware querying. Preliminary experimental results are promising, demonstrating the scalability of the approach in querying a large textual data collection (IMBD) coined with a full-fledge semantic knowledge base (Word-Net). We are currently completing an extensive experimental study to evaluate SemIndex 's properties in terms of: i) genericity: to support different types of textual (structured, semi-structured, NoSQL) databases, ranking schema, and knowledge-bases, ii) effectiveness: evaluating the interestingness of semanticaware answers from the user's perspective, and iii) efficiency: to reduce index's building and updating costs as well as query processing cost.
Programme structure and milestones The structural details of the programme comprising the three workshops are highlighted in Table  2. Workshop 1 concentrates on development of study protocols, with due attention to ethics; Workshop 2 focuses on electronic quality-assured data capture and analysis using open and free access software (EpiData software, EpiData Association, Odense, Denmark -freely available at http://www. epidata. dk), with the data workshop tailored to meet the data capture requirements of individual study protocols; Workshop 3 focuses on writing up the paper, providing guidance on how to submit the paper to a peer-reviewed journal and how to handle peer review, and finally on promoting and assessing the effects of the research on policy and practice. The training format includes lectures, break-out mentor groups and plenary sessions where participants present their work (protocols and papers in progress) to the whole group and receive feedback from their peers and facilitators.
A Matlab program is implemented using CPLEX 12 to solve problem P 1 , its linear programming (LP) relaxation, and each LP relaxation P1 within each iteration of the VNS algorithm. The numerical experiments have been carried out on a Pentium IV, 1 GHz with 2 GoBytes of RAM under windows XP. In Table  1 , column 1 shows the number of nodes considered in each instance. Commonly, the maximum number of nodes in WBANs can be up to 256 nodes  (Fang and Dutkiewicz, 2009). Columns 2 and 3 show the number of stages and number of nodes per stage for each of the instances. Columns 4 and 5 provide the optimal solution of P 1 and the cpu time in seconds CPLEX needs to get that solution. Columns 6 and 7 provide the optimal solutions for the LP relaxation of P 1 and the CPLEX cpu time in seconds as well. Similarly, columns 8 and 9 show the best solution found with the VNS approach and its cpu time in seconds.
Thus it is not clear how any of the upflowing AR-associated plasma could reach the source surface at 2. 5 R  and contribute to the slow solar wind. However a detailed examination of solar-wind in-situ data obtained by the Advanced Composition Explorer (ACE) spacecraft at the L 1 point shows that increases in O 7+ /O 6+ , C 6+ /C 5+ and Fe/O -a FIP-bias proxyare present before the heliospheric current sheet crossing. These increases, along with an accompanying reduction in proton velocity and an increase in density are characteristic of both AR and slow-solar wind plasma. Finally we describe a two-step reconnection process by which some of the upflowing plasma from the AR could reach the heliosphere.
These authors found evidence from the Advanced Composition Explorer (ACE) in-situ data for the presence of active-region material in the slow solar wind that was associated with the passage of the two active regions across the disc in January 2008. Presence of (additional) ejected plasmoid material in the heliospheric plasma sheet (HPS) was suggested by  Foullon et al. (2011). Composition in particular is key to understanding the origins of the solar wind  (Geiss, Gloeckler, and von Steiger, 1995). The fast wind [V ≥ 600 km s -1 ] is believed to originate in coronal holes and exhibits a composition characteristic of the photosphere with charge states that indicate temperatures in the 0. 8 MK to 1. 0 MK range  (von Steiger et el. , 2000). The origins of the slow wind [V ≈ 400 km s -1 ] remain controversial.
Measurement of the upflow plasma parameters, with reference in particular to the FIP-bias, is then described and the results displayed. The global magnetic field configuration is presented which shows the relationship of AR 10978 to the heliospheric current sheet (HCS). A detailed global PFSS model was then constructed to allow a search for open field paths by which the upflow plasma could reach the heliosphere. In-situ plasma parameters and magnetic field data obtained by the ACE spacecraft at L 1 were investigated and the data back-mapped to the Sun. The discussion in Section 3 relates the arrival of active-region plasma at ACE to the presence of a high altitude magnetic null located North of AR 10978. Conclusions are given in Section 4.
Properties of the Upflowing Plasma from AR 10978 Knowledge of plasma parameter values [n e , T e , and composition] is important in tracking the location and flow of plasma in the solar atmosphere. The above parameters were measured by  Brooks and Warren (2011)  for both of the upflow regions associated with AR 10978 during the period 9 to15 December 2007. The emission-line intensity data used in the study were obtained and processed as described in their article using the Hinode/EIS instrument. Density [n e ] in the upflow regions was measured using the diagnostic intensity ratio [I Fe XIII (202. 044 Å) /I Fe XIII (203. 826 Å) ] and is shown in Figure  2b  for the 9 to 15 December interval. The ratio of emission-line intensities from low-FIP and high-FIP ions can be used to identify coronal plasma and in particular to allow estimates of FIP bias or f FIP = A SA /A ph : the ratio of solar atmosphere to photospheric element abundance.