dataesr/scientific-paragraphs-categorization huggingface dataset:
En partant du postulat qu'il existe deux niveaux, ces sciences ont résolu trop rapidement les questions qu'elles auraient du laisser ouvertes à l'étude. Qu'est-ce qu'un élément ? Qu'est ce qu'un ensemble? Y a-t-il vraiment une différence entre les deux? Qu'entend-on par entité collective durable? Dans cet article, nous étudierons comment les traces numériques laissées par les acteurs dans les bases de données nouvellement disponibles pourraient modifier la nature même de ces questions sur l'origine de l'organisation sociale. Notre objectif est de tester une théorie sociale alternative développée par Gabriel  Tarde (1843 Tarde ( -1904) )  à l'aube de la sociologie et qui n'a jamais eu la chance de se développer en raison du manque d'outils empiriques adéquats  (Tarde 1903; Clark 2011 Clark [1969]]   ; Milet 1970; Candea 2010).
Souvenons-nous que notre analyse est strictement limitée au processus de recherches dans les bases de données numériques et que nous ne tenons pas compte de la manière dont ces éléments sont collectés dans la « vraie vie ». Nous admettons qu'identifier des monades ne sera pas toujours faisable. Pour la plupart des entités, le profilage sera impossible pour un certain nombre de raisons: a) nos techniques d'observation sont trop rudimentaires pour suivre chaque entité individuellement -c'est souvent le cas avec les fourmis d'une fourmilière, les cellules d'un organe, des acteurs humais dans un sondage de grande échelle; b) les entités sont vraiment interchangeables puisqu'il n'y a aucun moyen, même avec les Réseaux 10 outils de suivi les plus sophistiqués, de les différencier entre elles -ce sera le cas d'atomes dans un gaz  (Jensen 2001) ; c) même s'il était possible de les différencier, la plupart de ces informations devraient être effacées ou gardées secrètes pour des raisons d'éthique -c'est généralement le cas des appels téléphoniques, réseaux sociaux, fichiers médicaux, etc.
; d) bien qu'ils se revendiquent transparents et égalitaires, la plupart des bases de données actuels sont pleines d'inégalités de statuts et la plupart dépendent de définitions plutôt grossières du monde social. Nous affirmons simplement que chaque fois qu'il est possible de recourir aux profils, le principe monadologique se révélera fructueux. La raison pour laquelle nous insistons tant sur cet aspect est qu'il suit un autre des raisonnements de Tarde qui stipule que l'approche A-1 n'a pas du tout à être limitée aux acteurs humains. Chaque fois qu'un chercheur a réussi, par le biais d'une stratégie de recherche habile, à isoler des profils particuliers d'agentsbabouins  (Strum and Fedigan 2000) , bactéries  (Stewart et al. 2004 ), publications scientifiques  (Chavalarias and Cointet 2006) , réseaux sociaux  (White 2008) , corporations  (Stark and Vedres 2006) , pour citer quelques exemples qui ont fourni des résultats remarquables -la portée de l'approche A-2 s'est considérablement affaiblie.
Ainsi, les premiers primatologues considéraient les babouins comme vivant « à l'intérieur » d'une structure sociale très rigide dominée exclusivement par les mâles, jusqu'à ce que des techniques plus avancées d'individualisation permettent de cartographier la contribution de tous les individus superposés, révélant les qualités sociales remarquables des femelles babouins  (Strum 1995). C'est la raison pour laquelle nous sommes convaincus que la procédure de navigation A-1 apportera une alternative utile dans la collecte et l'organisation de bases de données.
Au lieu d'essayer de simuler et prédire l'ordre social, nous préférons suivre les traces laissées par le mouvement de acteurs afin de produire une base de données suffisamment riche  (Grauwin 2011). En d'autres termes, l'exploration de données n'est pas le résultat d'une pratique scientifique similaire à la simulation : au lieu de se demander comment les structures globales émergent des interactions locales, nous nous proposons d'illustrer un outil de Réseaux 15 navigation qui guide l'attention de l'observateur depuis des chevauchements confus vers les quelques éléments qui voyagent d'une monade à l'autre, un peu à la manière des normes et des standards dans les systèmes techniques (Gleenie and Thrift 2009). Avant de se plaindre que tout ceci est trop déroutant, il convient de se rappeler combien il était déroutant, au début, de devoir définir une structure générale (par exemple « le paradigme de l'auto-organisation »), pour ensuite montrer que la plupart des cas particuliers ne « rentrent pas » dans cette structure générale.
Un tel fichier est ce que les spécialistes des sciences sociales appellent une « institution », une « organisation », ou, plus simplement, un « groupe ». Ce nouveau point doit être abordé avec de grandes précautions car, dans l'approche A-2, il a été confondu avec celui de la structure considéré comme une entité d'un niveau supérieur, apparue mystérieusement suite à des interactions au niveau inférieur. Emergeant à un autre niveau, les structures sont dites indépendantes des interactions qui les ont créées et pourtant capables de leur envoyer des ordres, de définir des fonctions, d'attribuer des rôles aux « éléments » à la manière d'un méta-répartiteur. C'est cette confusion qui a créé l'idée d'une « personne morale » dont les humains ne seraient que de simples « membres » provisoires. Plus d'un discours émouvant a été prononcé par des responsables sur le contraste entre, par exemple, la « structure durable » de l'université et le rapide renouvellement de ses occupants éphémères et passagers -une approche A-2 par excellence… Dans l'approche A-1, les institutions ne ressemblent en rien aux structures, elles sont juste une certaine trajectoire à travers les données, trajectoire qui débute à un point d'entrée différent de la base de données : au lieu de demander quelles institutions apparaissent dans le profil d'un individu donné, nous demandons quels individus apparaissent dans le profil d'une institution.
C'est la même matrice mais pas la même navigation : les « totalités» ne sont rien de plus que d'autres moyens de traiter les profils entrecroisés. C'est ce type de navigation auquel Tarde a donné le nom ambigu d'« imitation » et ce type de dissémination qu'il a appelé « rayons imitatifs »  (Tarde 1903; Sperber 1996). Si nous avons raison, l'« imitation » pour lui n'est pas avant tout un phénomène psychologique, mais la prise de Réseaux 18 conscience que les monades partagent des caractéristiques modifiées par chaque partage, et dont le résultat est une liste composée du « même » élément répété différemment (Deleuze 1968). Il n'y a donc pas de distinction notable, réelle, ontologique entre les concepts d'individus, de groupes ou d'institutions. La seule différence dans ce que nous appelons institutions est la monade qui revient le plus souvent dans la base de données -et sa détection est empirique dépendant entièrement de la qualité de la base de données.
Conclusion Dans cet article, nous avons saisi l'occasion offerte par la soudaine prolifération de bases de données numériques pour revisiter l'ancienne théorie sociale proposée par Gabriel Tarde avant que soient disponibles un grand nombre d'outils statistiques et avant le retranchement de bien des théories sociales dans l'approche A-2. C'est parce que ces bases de données répandent l'expérience de définir un acteur susceptible par le réseau de ses attributs qu'il existe une chance d'échapper à la distinction individu/structure. Les monades dissolvent le dilemme, et redéfinissent la notion de totalité en la resituant comme étant l'héritage réciproque des entités qui s'entrecroisent Réseaux 20 Nous sommes bien conscients que ces bases de données sont pleines de défauts, qu'elles incarnent elles-mêmes une définition plutôt grossière de la société, qu'elles sont marquées par de fortes asymétries de pouvoir, et surtout, qu'elles ne caractérisent qu'un instant éphémère dans la traçabilité des liens sociaux.
EXPERIMENT PLANNING This paragraph gives an example of how scheduling techniques (coming from production management) are used for experiment planning. It has been applied for the parallel synthesis of zeolites. Its objective is the discovery of new zeolite phases, while reducing to a minimum the factors "time" and "amount of reactive". These synthesis are difficult to manage because of the time required which might be between 3 to 21 days for each sample. We then decided to carry out parallel experiments. This paragraph describes the computer science tool developed to tackle this problem. To identify XRD signal, a in-house software (ZeoStat similar to PolySnap) and a commercial software FPMatchÓ (provided by Socabim) was used. FPMatchÓ uses weighted least squares method. It was tested against PolySnapÓ and proved, in our cases, more efficient.
Objectives The number of data handled by an HTE methodology is relatively large (several tens of millions per year). It has therefore been necessary to create databases allowing long-term storage and easy processing of the data. Commercial tools specific to HTE (pipeline pilot from Accelrys, etc. ) were difficult to use since the aim was to integrate HTE tools as a standard pilot plant in the global Information System. Similarly, the university platforms resulting from global project: CombiCat (catalyst design and optimisation by fast combinatorial analysis,  [33] ), TOPCOMBI (towards optimised chemical processes and new materials discovery by combinatorial science  [34]  are too specific to be used (see  [35] [36] [37] [38]  for global review)). We then developed databases based either on proprietary development (Java J2EE developments  [39, 40] ) according to standard MVC  [41] [42] [43]  or off-the-shelf tools (Teexma  [44] ).
Description of the Global IFP Energies Nouvelles Information System We develop a global information system (either for HTE and conventional pilot plants) based on several databases (Fig. 8 )  [45] : -a real-time database (Historian) to manage the sensors (around 15 000 sensors are managed); -a Laboratory Information Management System (LIMS) database (via software package SQL*LIMS) to manage gas, liquid and solid samples analyses; -a catalyst database (CataSepa) produced via a specific development based on standard J2EE to manage catalysts information (around 100 000 catalysts are stored with complete characterization); -a process database (Proceed) to manage Process data coming from pilot plant (HTE or conventional) (around 200 pilot plants are managed, which corresponds to about 1 000 000 balances).
Importance of Queries Chemical repositories are complex. They involve hundreds of tables in numerous database instances. Access to the relevant information may be the result of business rules depending on data statuses (pending, validated, suspicious, etc. ) or data source (e. g. analytical method for a content). Lastly, since research engineers constantly explore new avenues, the models are never frozen and must evolve dynamically. They are all modelled as meta-models or ontologies (models which describe themselves) in order to store new types of information by simple configuration. This extreme storage flexibility, however, makes the data extremely difficult to access. The standard query models via data warehouses and data marts  [9, 46, 47]  that are widely found in the finance sector cannot be implemented with scalable self-described models.
Consequently, traditional intelligence business tools (requesters, OnLine Analytical Processing (OLAP) cubes) cannot be used since they only apply to a fixed data model. The intelligence must therefore be built into the modules and data access functions provided in our framework. Each data silo has its own high-level functions. They provide easy access to a single datum (catalyst DRT, sulphur content of a cut, test start date, etc. ). They can be used with no knowledge of the underlying data model or specific business rules (data status, data priority). They hide the complexity of the repositories, thereby allowing flexible access to data. Consequently, there is no need for a dedicated datamart with creation of reports by computer specialists for each new requirement. The user has then a set of dedicated functions providing access to centralised data (unitary, search, report) which, combined with the database insertion functions from Excel, can be used to analyse data from a single tool: Excel.
Data Analysis Simple data can therefore be easily analysed in Excel. For multi-dimensional data, however, the use of a spreadsheet is sometimes limiting. One can use dedicated software (Matlab, Scilab, SAS, Sptofire, etc. ). However in order to optimize productivity, we have decided to develop our own/proprietary dedicated software program (called Visu3D) to visualise data, explore data and build quickly correlation or models. This tool uses visual analytics concepts. Visual analytics is a new domain. It differs from ordinary visualization: the active role is played by the computer in the presentation of information to the viewer. For the first time, we have a marriage of analytic statistical algorithms and visual presentation in an interactive environment. Before visual analytics, exploring high dimensional data with widgets like rotation controls, slice-and-dice tools, filter sliders, lensing tools and real-time brushes was a haphazard enterprise.
Excel Application to Configure and Monitor an HTE Pilot Plant This paragraph describes an Excel application to configure and monitor an HTE pilot plant: a Synthesizer Large Trolley (SLT) developed by Chemspeed Technologies. This robot can be used to synthesise 16 catalysts in parallel (Fig. 14 ). It therefore offers considerable time savings: 16 catalysts are synthesised in 1 day (as compared with 8 days using standard methods on the bench). The use of this robotic platform consists in performing a series of defined tasks, in a specific order: stirring, pH measurement, addition of products (solids or liquids) in defined quantities, etc. Inputting the setpoint values and reading the saved values proved time-consuming and extremely repetitive. Storing information in databases can be also long and repetitive.
These input tasks have therefore been automated by the development of an Excel interface. For each new series of syntheses, the interface is used to (Fig. 15):  -automatically save all the set point values in the pilot plant control software; -retrieve the synthesis data stored in the pilot plant control software and display them for the user in an Excel sheet; -save all these data in the dedicated database (CataSepa, cf. Sect. This was carried out by a link between Excel and the pilot plant through a dedicated Application Programming Interface (API) provided by ChemSpeed (Fig. 16 ). The storage of data (set points and results) in our data base dedicated to catalysts (CataSepa) is carried out using proprietary tools developed in our global framework (cf. Sect. 3. 2). This type of interface can be considered for all pilot plants.
Similar interfaces have been developed for other robots of different brand (Tecan, Zinsser, etc. ). This interfacing limits retranscription errors and considerably reduces the overall times for transcription from one software program to another. For example, about 150 parameters must be transferred from an Excel file for a global preparation of 16 catalysts on a Chemspeed platform. Manual input would require about 2 hours. Input errors would be inevitable. With the current interface, it takes less than one minute. The risk of input error is reduced. For other transcriptions with different tools, the time of 5 to 6 hours necessary for manual input is reduced to a maximum of 1 hour with automated input.
Integration of an HTE Pilot Plant in a Global Environment with Security Constraints When integrating a HTE pilot plant in a research centre, the "safety" aspects cannot be neglected. The operating mode must be globalized by using standard supervisors. This paragraph describes the adaptation of an Avantium Flowrence TM unit in the complete fleet of IFPEN pilot plants. With this kind of pilot plant, the alarms generated are stored in each specific database. Then, they cannot be analysed by the global supervisor. A link of these specific data bases to our global hypervisor (FIX) has then been -analysis of the alarm table to transfer the alarms to the hypervisor; -purge of acknowledged alarms. The processing principle is described by the previous flowchart (Fig. 17 ).
Monitoring HTE Pilot Plant This paragraph shows an example of a catalyst test monitoring. A special interface between the pilot plant (Flowrence from Avantium) and Excel has been developed using methods similar to the one presented in Section 4. 1. All measures can be first visualized in Excel and then in Visu3D (cf. Sect. 3. 4). The following diagram describes the interfacing between the various software programs (Fig. 18 ). At the end of each test run, a validation phase is carried out to define useful data and delete aberrant data (outlier). This will simplify multi-test and/or multi-reactor operation. For example, a standard display is the deviation between the process value of the sensors and the setpoints requested (Fig. 19 ). In this case, the test pressure is 59 bar with a maximum tolerated deviation of ±0.
20  at top right). In the proposed example, we can conclude that, in this pressure range, there is no effect, since at iso-conversion of A, for example between 56% and 60% for reactors 1, 5, 9 and 13, the pressure varies from 58. 8 bar to 59. 5 bar; -analysis of B/A selectivity. In the example proposed (see Fig. 20  at bottom right), we can conclude that the higher the A-conversion, the more selective the catalysts. The test data can be subsequently analysed in combination with catalytic data, for example the area, porous volume, metal contents, etc. (see Sect. 3. 3), and a 2D or 3D graph plotted. Moreover, as all test runs are stored in our database (Proceed), and comparison between runs from HTE and conventional pilot plant is very easy. Figure  21   Users can easily access their data from Excel and can therefore retain their standard data processing tool; -the "safety" aspects.
Once the entity has been introduced (and therefore activated/accessible in discourse), it may be promoted to a topic status. A connection can be made between textual structure (Quaestio model), topic continuity and accessibility  (Chafe, 1976). In Ariel's theory, the topic element is more salient -and therefore more accessible -than non-topic elements. Object pronouns (which belong to the focus component in our database) 4  are nevertheless highly accessible markers as well. We therefore wish to test the following hypotheses: 1) Child L1 learners overuse HAM markers even if the referent has not been properly introduced within discourse (they rely heavily on the extralinguistic context and.
This makes this task appropriate for a study of reference maintenance strategies. The experimental setting was slightly diff erent for children and adults. With children, two interviewers were used. One gave instructions and the other was a naïve interlocutor who was not in the room when the fi lm was displayed, and to whom the child told what had happened in the cartoon. With adults, only one interviewer was used. The instruction given by the interviewer was: "Watch the cartoon and then tell me what happened, as I haven't seen the movie". We collected a database of oral productions by four-, seven-and ten-year-old children, adult French learners of English and adult English learners of French. We are well aware of the limitations implied by such a small sample and are planning a new data collection to supplement our database.
French speakers use relative clauses more than English speakers (FrL1: 3. 4%, EngL1: 0. 3%); this is probably due to the numerous presentative constructions. Zero anaphora is seldom used by French speakers ("chien": 5. 5%, "garçon": 3. 2%) but  equently by English speakers ("dog": 22. 7%, "boy": 20%), who resort to coordinate clauses with zero anaphora to mark topic continuity and enhance discourse cohesion  (Givón, 10. In Tables  4 to 8 , percentages represent the number of occurrences of a category out of the total number of references to "chien"/"dog" and "garçon"/"boy" (all reference markers included) in the database. For example, there are 607 references to "dog" in the EngL1 corpus, 13. 5% of which are defi nite NPs ("the N") in topic. 1. 6% of references to "boy" are made with the relative pronoun "who".
4% among EngL1) is indeed remarkably high. Francophones mostly described a little boy (only 2/10 mentioned a little girl), which compelled them to regular reintroductions to avoid referential ambiguity. Finally, we found dislocations in French ("le chien il") to be in equent in our adult database (around 2%), contrary to what happens in the child data. Dislocations in French are limited to very informal contexts and are therefore not common in a narrative discourse aimed at an adult interviewer, even if the stimulus is a cartoon for children.
Discussion To sum up, our initial hypotheses are confi rmed: the marking of referential accessibility refl ects a problem of under-specifi cation in children, while adult learners opt for over-specifi cation, to prevent referential ambiguity, due to their awareness of the discourse issues involved. Our results highlight the impact of cognitive maturity and of crosslinguistic infl uences in the acquisition of L1 and L2 French and English, although the small size of our database (6 to 10 speakers in all groups) limits the scope of our conclusions. As regards the impact of cognitive maturity, we noticed that younger children are under-specifi c in their choice of referring expressions: they favour HAM, i. e. , pronouns, even when the referent is not easily retrievable in context. We also observed that children's use of referring expressions evolves  om under-specifi city to more adult-like forms  om age 4 to 10; as expected, the use of HAM forms develops between 4 and 10 years old; but ten-year-olds (especially the French children) still use ambiguous HAM forms, contrary to adults.
Lastly, we make use of the possibility of testing the significance of interaction between different factors in order to evaluate which syntactic factors have a different behavior according to the medium used (spoken vs. written). To build our database, we first extracted the attributive As that appeared in both positions in the syntactically annotated newspaper corpus French Treebank (FTB,  Abeillé and Clément 2004) , leaving aside As with post-adjectival dependents. We then extracted the same As from the spoken corpus C-ORAL-ROM (CORAL,  Cresti and Moneglia 2005). Besides the variable capturing the medium used (SF vs. WF), these data were annotated for 11 variables concerning the syntactic environment of each A in context: (1) the A is coordinated, (2) the A is modified by an adverbial element; the NP contains (3) an other A in postposition, (4) an other A in anteposition, (5) a relative clause, (6) a PP; the determiner of the NP is (7) demonstrative,  (8)  possessive, (9) definite; a measure of collocation for (10) the ordered sequence A+N and (11) the ordered sequence N+A (collocations estimated with χ 2 ,  Manning and Schütze, 1999).
We also differentiated two lemmas in context for 5 As: ancien 'ancient/former', pur 'pure', seul 'alone/ single', simple 'simple/modest', propre 'own/clean'. The database contains 6612 occurrences of attributive As (4986 in FTB, 1626 in CORAL) representing 170 lemmas, with 68. 9% of anteposition (67. 1% in FTB, 74. 3% in CORAL). There is variation according to the lemmas: for instance, the A unique 'unique' is anteposed in 20% of the cases, whereas sérieux 'serious' appears in this position in 51. 4% and petit 'small' in 98. 6%. Moreover, there is less alternation in spoken data than in written ones: the 170 lemmas appear in both positions in FTB, while only 56 (72,8% of the 130 lemmas attested in CORAL) of them are really alternating in CORAL. This seems to reveal that in spoken French, the As tend to have a more fixed behavior than in the written variant.