dataesr/scientific-paragraphs-categorization huggingface dataset:
Recent advances in web technologies now allow users to have web applications as powerful as stand-alone software. Such applications can be accessed from any operating system while avoiding cumbersome installation and parameterizing steps. Moreover, centralizing data on a server overcomes the need for renewed copies and updates of data among the various devices researchers generally use. The next sections describe CompPhy's architecture and functionalities. Then, an example of a typical use of the platform shows how this tool can help distant scientists to collectively analyze trees.
Implementation CompPhy is based on a classic web architecture, see Figure  1. The main part is composed of PHP modules coupled to a MySQL database. The database stores information on projects and data related to trees, such as their Newick format, annotations and commands to draw them. The ScripTree interpreter for phylogenetic graphics is in charge of generating tree pictures  [7]. It manages tree edition and annotation. ScripTree takes text files as input to produce a tree picture: the tree in Newick format, a script for the graphical rendering and annotations in CSV format. However, users need no knowledge of ScripTree to operate CompPhy: they upload trees in Newick format and CompPhy maintains the script and annotation files Figure  1  The architecture of the CompPhy platform. Projects and their trees are stored in a relational database; ScripTree  [7]  is used as the picture generating module.
Perl is used for tree modifications requiring computation, e. g. , restricting trees to common taxa, and as a wrapper for external tree computations modules, e. g. , computing a consensus with PAUP*  [23]. Users interact with CompPhy from their browser, whose content is connected to the CompPhy core essentially through the jQuery javascript framework. according to the users' requests on its interface. Nonetheless, a dedicated zone for the MANUAL TUNING OF THE PICTURES allows users with knowledge of ScripTree to tweak these files for a more specific or elaborated tree rendering. Tree pictures are generated by ScripTree in SVG format, which allows users to interact with pictures, e. g. , by pointing at taxa to be swapped, and it is now accepted by all recent browsers. A set of Perl scripts also participates in the core of CompPhy: they modify the Newick format encoding trees when needed, e.
g. , to restrict them to a subset of taxa. These scripts also interface external programs such as PAUP*, PHYLIP, PhySIC_IST and Spruce, in which case format transformation is often needed. Moreover, as CompPhy is developed according to established programming frameworks, it is relatively easy to add new computational modules in the future. The communication between the server-side of CompPhy and client browsers heavily relies on the AJAX framework, which basically makes CompPhy behave similarly to a stand-alone application in many respects, often avoiding the need to reload the whole web page in response to users' interactions. By adding different plugins to jQuery, CompPhy also handles the display, manipulation and refresh of SVG pictures on the client side, and this generally makes CompPhy more user friendly by displaying tooltips, modal boxes, etc.
Data management Data in CompPhy is organized around the project concept, that basically pools a set of analyzed trees and associated documents. Each project has an administrator who can invite other people to become members of the project. By default, projects are created with a private status, so only the project members can access the data after being identified by CompPhy. This policy guarantees data privacy while still allowing data sharing. In contrast, a public project can be accessed by any guest to which the URL is sent. Without opening an account on CompPhy, a guest can see the project trees and examine them on the workbenches. However, no guest can make changes to the trees or data of the project. A TODO LIST reminds project members of the next tasks to be performed in the dataset analysis.
Distance, consensus and supertree computation A commonly used distance to measure the disagreement between two trees is that proposed by  [19]. It counts the number of clades (or bipartitions) present in one tree but not in the other. CompPhy proposes to indicate this value for two trees on the workbenches by interfacing PHYLIP treedist program  [24]. This distance originally applies to two trees with identical taxon sets, so when provided with trees on different taxon sets, CompPhy first restricts the compared trees to their common taxa, as often done in the field. When comparing two trees with conflicting topologies, it is often useful to highlight in evidence the largest common structure they share. Users can thus drag two trees on the workbenches and ask CompPhy to compute their MAXIMUM AGREEMENT SUBTREE CONSENSUS.
The bond between steel and concrete is essential for the existence of reinforced concrete structures, as both materials act together to absorb structural strain. The bond phenomenon is considered to be complex regarding many factors that affect it. Several types of bond tests have been proposed over years. One is the modified proposed of pull-out test, which was elaborated by Lorrain and Barbosa [1] called APULOT test (Appropriete pull-out-test). Based on experimental results obtained by Vale Silva[2] either by conventional pull-out tests, or by modified pull-out test, APULOT, seeks to know the numeric behavior of bond steel-concrete through a numerical simulation using a calculation code ATENA which is based on the Finite Element Method (FEM). The numerical simulation provided better evaluate the stress distribution and cracking that occurs during the test, thereby becoming a valuable tool to support the experimental project that aims to validation, validation partially or not recommend the modified bond test steel-concrete -APULOT test -as quality control test of structural concrete.
This can be extended to the durability of the structures. According to  Tassios [6]  the efficiency of the steel-concrete connection can be quantified by the bond stress versus sliding ratio, which expresses the strain that arises in the concrete steel interface related to the relative displacement between the steel bar and the concrete that surrounds it. A little sliding suggests local damage or the interface accommodations. When these two actions occurred together, i. e. , when the slide reaches the maximum value, indicate the destruction of the bond, which can be associated with a certain state of deformation and cracking. Some limits values of sliding and procedures of structural design were presented in Bond steel-concrete: simulation analysis of the pull-out tests and APULOT using the program ATENA the standards, which are usually associated with an unacceptable state of deformation and cracking  (Caetano [8] ).
The L determination, ei, the value of the ultimate bond strength, it is obtained by the correlation curve adopted by Lorrain and Barbosa [1] (Figure  3 ). In determining the anchorage length using Equation 1 adopts the materials resistance values used as the age provided of the tests. Vale Silva [2] used the value of L = 10φ in his experimental tests as the average of concrete compressive strength (f cm ) of 27,8 MPa at 28 days and 6φ as the average of concrete compressive strength (f cm ) 49,3 MPa at 28 days. These data are used for the numerical analysis of this test.
Calculation Code: ATENA The calculation code ATHENA (Advanced Tool for Engineering Nonlinear Analysis) has been used in structural engineering to simulate the behavior of concrete and reinforced concrete structures, as well as of concrete cracking. It uses the finite element method for the nonlinear analysis of structures, simulating the real behavior of structures either through linear or nonlinear analyses. The total action active was defined by the time integral of forces increments, with application of the Newton-Rhapson or Arc-Length Methods. The materials were characterized through their properties and physical parameters and into groups according to their constituent features. Among the properties of steel, are its stress-strain ratio in the elastic-linear or multilinear mode, with or without a defined flow level; the nonlinear model of loading and unloading cycles, and the criterion of von Mises flow.
Pull-out test An analysis was performed with the experimental data obtained by Valley Silva [2] to the concrete compressive strength of 27,8 In order to reduce the number of elements, and consequently, the processing time, we used the "axial symmetry" analysis of the AT-ENA software considering the axisymmetric model, with the axial symmetry axis in the center of the concrete prism. Figure  6  shows  Figure  7  reveals that the numerical results curves approach the range of experimental results. Figure  8  shows the distribution of stresses, cracking and the deformed structure for the 8. 0 mm, 10. 0 mm and 12. 5 mm bars. There is also an increase in the stress value and the cracking amount too, as well as the bar diameter increases from 8,0 mm to 12,5 mm, and the increase in the anchorage length.
In this paper, we introduce R. TeMiS, a free software text mining solution aiming at exploring new dimensions in text mining with a particular focus on media framing analysis. R. TeMiS is especially designed to provide help in a) the automation of corpus construction and management procedures based on the use of large media content databases and b) the extension of the range of statistical tools available to social scientists exploring texts through R coding (one-and two-way tables, timelines, hierarchical clustering, content analysis, geographical mapping…). A case study on the media framing of Julian Assange from January 2010 to December 2011 is conducted.
Due to the digitalization of newspaper archives on the web, the availability of articles on platforms such as Lexis-Nexis or Factiva, the slow but growing textualization of audio-visual media, scholars working in media studies have particularly benefited from this new abundance of research materials and promising shift in methodologies. 3  Conventional content analysis -a method consisting of sampling newspaper archives and assigning abstract categories to passages of these articles in order to measure the recurrence of « themes » or « issues » within that content -has experienced a decline in this field of research. 4 It is increasingly challenged by new corpus-driven text mining techniques based on an exhaustive analysis of digitalized corpora extracted from databases, using keywords searches to select the material and inductive methods based on semantic (instead of thematic) fields identification within the corpus (frequency, concordances, collocation analysis).
In this article, we present R. TeMiS, a new R package that intends to fill this gap by providing users with a graphical interface (Bouchet-Valat and Bastin, 2013). 7 Even though R. TeMiS is a general purpose text mining tool, we will focus in this article on its use in media studies. The argumentation will thus be illustrated with a study on the media framing of Julian Assange from January 2010 to December 2011. This study is based on the analysis of a corpus of 667 news dispatches published in English by the three top international news agencies: Agence France Presse (AFP), Reuters and Associated Press (AP). 8 We do not aim here at a comprehensive analysis of this corpus but only to provide the reader with a good overview of R. TeMiS's possibilities using a real case study of media framing issues.
« The classical role of cultural scholarship as a testing ground of critical social theory, Gerbner concluded, is to be strengthened, broadened, and deepened -not abolished -in the analysis of mass media content through the newer, more systematic and refined methodologies » (Gerbner, 1958). 7 R. TeMiS has been developed as a specific menu of the R Commander, a well-known and very robust GUI for R (Fox, 2005). For that reason, the package name in R conforms to the Rcmdr plugins syntax («RcmdrPlugin. temis») but can be shortened as R. TeMiS (for R Text Mining Solution). R. TeMiS also refers to Artemis, the sister of Apollo, a goddess who was highly venerated in ancient Greece. She was often depicted as a huntress wandering in forests, uncultivated areas and other wild lands. Instructions to download and launch R.
In the second section, we introduce the importation, coding and management of media text corpora in R. TeMiS. In the third section, we illustrate the package's variables visualization features. The fourth section is devoted to elementary statistics with R. TeMiS. The fifth section addresses hierarchical clustering and correspondence analysis. The sixth section provides an illustration of the advantages of R for extending the range of statistical procedures that are currently available (with a focus on geographical mapping of term frequencies). Advantages of a free software text mining approach to media studies Opting for free software or open-source statistical solutions has not only to do with broad preferences for costless software solutions. It has also to do with workflow choices that have an effect on the results produced.
This results in a risk of introducing bugs that can go unnoticed since the code cannot be checked and the same analysis cannot always be performed using different programs to compare results. The third key feature of free software text mining is reusability. Integration with a free general-purpose statistical framework such as R means that all the methods developed for this environment are made available to text mining. By default, R. TeMiS makes use of many free software packages 12 , which have in some cases been modified by their authors to better suit their needs. Even more features are offered to advanced users by giving them access via manual code edition to any method they want. This is congruent with the spirit of free software, and contrary to proprietary software solutions that do not allow users to modify software code to fit specific scientific needs (outside the usual parameters toolboxes).
R. TeMiS thus gives the user full control over the code producing the analyses. Following the general principle adopted by the R Commander, the commands generated by the user's actions in dialog boxes are printed in a script box and can be edited and run as plain code. This allows users to check what is being done by the software and optionally extend standard analyses with custom R commands most fitted to their needs. Importing, coding and managing large-n media corpora with R. TeMiS An important feature of the R. TeMiS approach to media studies is to facilitate the constitution of media corpora. Handling and coding corpora is a key moment in media research. Yet, too often, it is considered only as a preliminary step, and not part of the analysis. Text mining solutions typically require the researcher to produce specific types of documents (or files) with a specific formatting that can only be obtained manually.
g. , deletion of documents, errors in automatic replacement formulas, etc. ) or losing track of the changes performed (because users modify their source corpus, changes cannot be undone unless a very precise record has been kept). Last but not least, such corpora produce locked-in effects due to dependency on the software used. 13 e integrated approach that has been adopted in R. TeMiS consists of providing the user with corpus importation filters that allow the use of many kind of corpora without customized intervention by the researcher. These filters simplify the characterization of the contents with relevant metadata (date, author, source, etc. ) by automatically retrieving variables when they are available (as is often the case in structured media contents databases) or by allowing the researcher to document his corpus without modifying it.
Whenever necessary the R. TeMiS package provides tools for traceable corpus changes from within the software (i. e. without manually modifying the corpus). To do so, R. TeMiS supports different kinds of structured source files corresponding to various kinds of research materials: 12This is notably the case for stemming and, for corpora importation, but also for graphics, which are usually very poor in proprietary solutions but of very high quality in R. 13 For example, when variables are added manually within the corpus, using a syntax that is specific to the software (thus making it difficult to perform different kind of analysis without recoding the corpus and then facing the waste of time and high probability of errors issues). • series of plain text files (. txt) contained in a directory (typically the result of audiovisual media materials transcriptions or a sociological interviews campaign); • spreadsheet-like files (.
While importing the corpus, R. TeMiS automatically retrieves variables from the Factiva. html files and asks the user which should be retained. Only two are kept in the following: the Origin (AFP, AP or Reuters) and the Date (in a YY-MM-DD format). 14 Many other corpus importation filters can be developed pertaining to every user's need. R. TeMiS automatically retrieves variables in the case of spreadsheet files, structured Factiva files and Twitter searches. For plain text files, the user only has to fill variables information for each document or provide the R Commander with a spreadsheet-like file with one row per document (identified by its order) and one column per variable. 15 Corpus heterogeneity can stem from complex factors: in some cases keyword searches can produce artifacts when the same term refers to very different things; media contents can also be indexed strangely in databases, such as grouping all « News in Brief » kinds of articles together, regardless of subject.
Visualizing relationships among variables within the corpus: the time and source structure behind the coverage of the Assange case Media analysis often requires the manipulation of contextual variables describing the documents before launching textual data exploration within documents. For that purpose, R. TeMiS provides users with meta-data visualization tools such as one-and two-way tables using meta-data variables (with optional plotting of the results). Plotting the number of dispatches produced by each news agency is interesting for the Assange case (Fig. 3 ). This plot clearly shows that AFP devoted far more coverage than Reuters and AP to Julian Assange in 2010-2011. Whether this is due to editorial choices putting Assange on the media agenda more often or artifactual factors linked to different dispatches publication policies is of course impossible to say without further qualitative analysis of the corpus.
But performing such analysis with big media corpora that has been automatically retrieved from databases or web archives induces some methodological peculiarities due to various sources of heterogeneity and redundancy within the corpus. R. TeMiS provides many computing and visualizing parameters that have been designed to help the user create the best possible representation of his corpus (not taking into ac-21 Other measures can be computed by R. TeMiS, both by document and by levels of a variable: vocabulary summary (number of terms, diversity and complexity) ; co-occurring terms (terms that tend to appear in the same documents as a chosen term); dissimilarity table (Chi-squared distance between row profiles of the document-term matrix collapsed according to the levels of a variable). count the unlimited possibilities offered by a direct access to the R code).
In what follows CA will be used to illustrate an inductive way to identify frames within the Assange corpus. 22 first issue in large-n Factiva corpora is the artifacts created by the presence of irrelevant terms in parts of the corpus. In the Assange corpus it appears that the recurrence of the term « ID » in Reuters documents (a reference to other dispatches identification number within the database) produces artifactual results that are very obvious in the first CA produced if one does not intervene on the corpus. Previously excluding the term is of course the best thing to do. When dealing with large corpora, it can also be very useful to limit the number of terms taken into account and thus dramatically reduce the computing requirements of CA and HC in terms of both memory and time. R.
67), « say » (0. 61). « Pentagon » also appears there, which is congruent with AP's national origin. Fig. 9  -CA on DTM aggregated by Origin (terms most contributive to axis 2) Mapping Julian Assange? Due to the fact that R. TeMiS edits plain R code at every step of the lexical analysis, it enables using this code to extend the range of statistical procedures applied to the document-text matrix beyond classical text mining. Every kind of statistics or visualization method can be applied to the DTM. The Assange corpus offers a good example of this feature. The framing of Assange by the three news agencies entails a geographical dimension: media frames, in this case, are also about drawing a world map to portray Assange. The rape case for instance mostly connects Assange to Sweden and Great Britain.
These tables have strong limitations. The first involves the various terms used to refer to the same country (country names, city names and adjectives). The second involves the fact that comparisons are hindered by the production of three different tables instead of one. A little piece of R code, written directly in the Rcmdr script window, can help by constructing groups of terms and calculating frequencies for these groups. In Table  2  for instance, terms have been grouped using two-digit country names as level. 31 29 Thinking that way when studying international news agencies is also interesting because news agencies are often suspected of being biased according to their national origin. 30 Every geographical term with more than 20 occurrences in its stemmed form in the whole corpus has been retained.
But it relatively under-reported for Sweden, Great Britain, the US and Germany. AP over-reported the U. S. as could be expected but underreported Great Britain (on the contrary, Reuters over-reported both the US and the UK). A little R coding can help to geographically plot these data. R provides packages, like the maps package (Becker et al. ,  2013), that can easily draw maps and plot points using standard geographical coordinates. Transforming Table  2  into a data frame and adding two new variables (a latitude and a longitude for each country) is all we require. It can be done manually but the risk of making errors would then be significant. Instead, finding a data frame with country centroïds coordinates and importing it in R to merge it with the frequencies data frame is a better option.
x="row. names", by. y="ISO3136") # Drawing of the three maps library(maps) map(database="world", col="grey") title("Agence France Presse") points(dat$LONG, dat$LAT, pch=21, cex=10*sqrt(dat$AFP), bg="#00000040") map(database="world", col="grey") title("Associated Press") Fig. 10  -Mapping J. Assange in the three agencies points(dat$LONG, dat$LAT, pch=21, cex=10*sqrt(dat$AP), bg="#00000040") map(database="world", col="grey") title("Reuters") points(dat$LONG, dat$LAT, pch=21, cex= 10*sqrt(dat$Reuters), bg="#00000040") world agencies. 34 This of course is an interesting result if one looks at broad differences in the way that news is shaped at the global level for events with a clearly episodic nature. But it is also very interesting to notice that using sophisticated tools with fine tuning options can help to identify differences in the dispatches of the three agencies.