dataesr/scientific-paragraphs-categorization huggingface dataset:
Data analysis Raw data. Analyses were carried out using three-dimensional GM data that facilitate detailed assessment of the anterior facial shape variations and relationships to facial block orientation  [31, 39]. The symmetrical configuration of landmarks coordinates was subjected to GM in order to depict relationships between facial shape and position  [40, 41]. This widely-used technique allows the quantification and description of the morphological variations within a set of specimens  [15, 22, [29] [30] [31] [32] 35, [42] [43] [44]. Additionally, GM analyses allow size and shape to be assessed independently  [45]. Angles comparison. In order to appraise for the differences between the midsagittal landmarks used in our analysis and the PM plane, the difference between the values of PM-NHA and StSp-NHA was measured.
NHA is defined as the segment between (1) the midsagittal projection of the supero-inferior midpoint between the lower and upper orbital rims and (2) the superoinferior midpoint between the superior orbital fissures and the inferior rims of the optic canals  [8, 20]. We performed an ANOVA to test for significant differences between PM-NHA and StSp-NHA values in each taxon. Using ANOVA, we also test if the values of each angle (PM-NHA, StSp-NHA and StFc-NHA) are significantly different across Homo, Pan and Gorilla. Overall interspecific variation. Morphologika v2. 5 software was used to perform a Procrustes superimposition  [40, 41]  and a principal component analysis (PCA)  [45, 46]  including all the taxa and using landmarks of set 1 and set 2, pooled together. This PCA allowed assessing the overall interspecific variation in the sample and the distribution of individuals in the shape space  [47].
Allometry and variance dependence of integration. Allometry is a factor that might influence patterns of morphological integration  [48]. Previous studies highlighted the covariation of facial size with the rest of the cranium, particularly the cranial base  [29, 49]. Furthermore, static allometry  [50]  can be expected between male and female specimens  [51, 52]. For this reason, we tested the influence of size on each set of landmarks for each taxon in our study. We used multivariate regressions of Procrustes coordinates on the logarithm of centroid size (log CS)  [53]  for each taxon, using MorphoJ v1. 02 software to test for potential influence by allometry  [54]. Centroid size is defined as the square root of the sum of squared distances of a set of landmarks from their centroid  [41].
Craniofacial integration. Intraspecific covariance between facial block orientation and anterior facial shape was assessed by performing Partial least squares (PLS) analyses for each taxon. This method has been shown to be suitable for the study of covariation between two sets of variables (blocks)  [12, 13, 15, 21, 29, 41, 45, 58, 59 ]. In our study two blocks were defined, which correspond to the two sets of Procrustes coordinates: block 1 (set 1) represents facial block orientation and block 2 (set 2) represents anterior facial shape (Fig. 1 ). The aim of the PLS is to maximize the covariance patterns between two blocks of variables rather than the intra-block variance. PLS describes data in terms of a score for each specimen along a single axis, similar to a principal component that is generated in a PCA.
The primary difference is that, unlike principal components, which produces principal axes, PLS produces pairs of axes. The PLS was performed between the two blocks using MorphoJ v1. 02 software  [54]. Since allometry can inflate measures of integration, the PLS analyses were recomputed using the residuals of the multivariate regression of shape variables on the logarithm of centroid size as variables. It allows the effect of size to be removed from the analyses  [48]. We used the RV coefficient to measure the correlation resulting from the PLS  [60]. The calculation of this coefficient is equivalent to the calculation of the correlation coefficient of a regression between two variables. The RV coefficient is a measure of the global integration between blocks. It ranges from zero to one with a zero value indicating that the two blocks are independent, and a value of one indicating that they diverge from one another only by a combination of rotation, translation and/or scaling  [48, 61].
Acknowledgments We wish to thank the following institutions and people for allowing us the access to their specimens or their data of virtual cranial data:  Mr. W. Wendelen  of the  Royal Museum for Central Africa (Tervuren, Belgium), Pr. C. P. E. Zollikofer  and  Dr. M. Ponce de Leo  ´n of the  Anthropologisches Institut und Museum (Zu ¨rich, Switzerland ) and the  Natural History Museum  ( London, United Kingdom ). We also thank the following people and facilities for the CT-scans data acquisition: the  Department of Radiology of UZ Leuven (Leuven, Belgium) , the  Kantonsspital Winterthur (Winterthur, Switzerland ) and the  Hammersmith Hospital (London, United Kingdom). We thank  Daniel E. Lieberman  and one anonymous reviewer for their valuable comments on earlier drafts of this manuscript.
This work was supported by  Minisere Francais de l'Enseignement Superieur et de la Recherche ,  Agence Nationale de la Recherche  ( ANR-09-BLAN-0238 ), and Ministere  Francais des Affaires Etrangeres et Europeennes. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
The data consist of information collected from pre-and post-questionnaires and from activity tracks (logs), as well as from analysis of the discourses that participants exchanged inside the peer forum. They help trace the evolution of the training activity of each participant in the VLE and also provide insights into the capacity of the VLE to support training practices based on reflection, participation and collaboration. The results outline the interest of such environment for adviser training and shed new light on the crucial notion of "community of practice" (CoP) for professional development.
Data collection and analysis Data were collected from the seven trainees enrolled in the session and the three trainers. They consist of: • declarative data from individual pre-questionnaires and final self-assessments, included in the training, and from post-questionnaires about the quality of the learning environment and the way it might help them achieve their professional goals; • tracking of activity within the VLE; as the VLE contents were developed with DRUPAL (3), we used the integrated PIWIK (4) web analysis software to provide logs and statistics about the participants' actions (e. g. downloads or posts to the forum), screen pages consulted and time spent; • interactions between participants through the different threads of the forum. The study reports on a preliminary assessment of the usefulness and the affordances of the VLE for teachers willing to develop and improve their advising skills.
Part of the time they spent on the platform may not have been linked to their actions as trainers but rather as researchers and developers. Once the website development is stabilised, it will become possible to assess trainers' activity more accurately. Finally, information gathered through the PIWIK software shows that participants spent from thirty minutes to twelve hours on the website, revealing different degrees of involvement in the training session. The triangulation of these data with the analysis of pre-and post-questionnaires and the analysis of forum use suggests several reasons for such differences. It appears that the trainees who spent most time on the website were also advisers in practice at their SAC at the period when the training session took place. Those who spent the least time on the website were on holiday and did not have ongoing encounters with learners at that time.
17 Frantext is an online database containing about 4000 texts in the areas of sciences, arts, literature, techniques, that spans five centuries (16 th -21 st centuries). about that, we will see (go-PRES-P1 to see) in the remainder of the story that those two magistrates got the main glory from that war. " In on va voir, the subject can referentially be equivalent to vous ( vous allez voir) as much as to nous ( nous allons voir). 18 In both cases, the focus is on the narrator-addressee, the one revealing to the other what will be shown. However, the turn of phrase is sometimes expressed in such a way as to make its metanarrative import explicit: ( "The Hermondures, not being satisfied with the victory they won against the Cattes, also put to the sword all who escaped from the battle, sparing neither men nor horses.
Results The complete dataset of blood isotopic results is reported in the Appendix (Table  S1  and S2, ESI †), and a synthetic overview containing statistical results is given in Table  4. The range of values for duplicates is lower than the two standard-deviations (Appendix, Table  S1 , ESI †). We checked that isotopic fractionation is mass-dependent for all the samples, i. e. No correlation is found between the isotopic diet proxies (d  13  C and d 15 N) and the metal isotope compositions (Table  3 ), while d 13 C and d 15 N seem to be weakly correlated (R 2 = 0. 23, p = 0. 002). A significant difference for d 66 Zn exists between premenopausal and postmenopausal women (t test, p o 0. 05). For Cu, despite the fact that the d 65 Cu values of postmenopausal women cover the entire range of the observed isotopic variations, premenopausal and postmenopausal women are significantly different (t test, p o 0.
Acknowledgements This work was supported by grants from the  Bullukian Foundation  and the  Biomerieux Research Foundation. The  French archaeological Mission in Oriental Siberia (Ministere des Affaires Etrangeres et Europeennes, France) , the  North-Eastern Federal University (Yakutsk ,  Sakha Republic ), the program HUMAD from  IPEV (Institut Polaire Français Paul Emile Victor ) also funded the program. Administrative and research works of this project were permitted through the program of the  France Russia Associated International Laboratory  (LIA COSIE number  1029 ), associating the  North-Eastern Federal University (Yakutsk, Sakha Republic) , the  State medical University of Krasnoyask , the  Russia Foundation for the Fundamental Research (Moscow, Russia) , the  University of Paul Sabatier Toulouse III (France) , the  University of Strasbourg I (France)  and the  National Centre of Scientific Research (Paris, France ).
Study design and sampling methods We conducted a retrospective cross-sectional study based on computed tomography (CT) scans of a contemporaneous cohort of children of various origins who lived in the Toulouse, France, area. The sample was drawn from a database of CT scans taken between 2001 and 2010 in the Neuroradiology Unit of the Clinique Pasteur, Toulouse, and Toulouse University Hospital. CT scans of the children had been taken because of trauma, impacted or residual teeth, inflammation in the maxillary sinuses or neonatal distress. The whole database is securely hosted in the Anthropology Laboratory of Molecular Imaging and Synthesis (UMR5288, CNRS). A French institutional review board provided ethical approval of the study  [27]. The CT data were anonymous and numbered. Each number was entered into an Excel file with, exclusively, the date of birth, the date of the CT scan and the child's sex.
To be included in the present analysis, participants had to be younger than 18 years of age, have all their mandibular permanent teeth (erupted or not, except the wisdom teeth), have a Demirjian stage  [22]  of at least 6 for mandibular permanent incisors and at least 7 for the first mandibular permanent molars (to allow for space analysis using Nance's principles  [28] ) and have complete radiographic acquisition to permit assessment of dental age, facial skeletal age and dental crowding. From the initial database, sampling was performed in two phases. Participants were first randomly selected from the initial database, then inclusion criteria were applied to incrementally produce the working database until the sample size calculated a priori was attained (see Statistical considerations).
Data Facial skeletal age was estimated using a method previously developed to assess the skeletal age of children, based on the centroid size of the face and derived from the three-dimensional coordinates of anatomical landmarks  [27]. The anatomical locations of bilateral landmarks (mental foramen, anterior opening of the infraorbital canal, anterior opening of the supraorbital canal, supraorbital fissure at the level of the optic foramen and round foramina) were selected using AMIRA 3D analysis software (version 5. 2. 2; FEI Visualization Sciences Group, Mérignac, France), and centroid sizes were calculated using morphologika software  [29]. The centroid size is a measure of size uncorrelated with all pure shape changes  [30]. The facial skeletal age was calculated using least squares linear regression (with distinction between boys and girls and between children younger or older than 10 years)  [27].
Regarding synchronism, a child was considered as revealing asynchronous dentofacial development if he or she had (1) delayed facial skeletal growth and advanced dental maturation (DS/AD) or (  2 ) advanced facial skeletal growth and delayed dental maturation (AS/DD). Thus, we distinguished two types of asynchronous dentofacial development: the DS/AD type and the AS/DD type. By definition, we considered all other children as having synchronous dentofacial development. Space analysis required a comparison between the amount of space actually available for the alignment of the teeth and the amount of space necessary to align them properly  [32]. Space conditions were calculated following Nance's principles  [28]  using OsiriX imaging software (version 4. 0; http://www. osirix-viewer. com/). Individual mesiodistal crown lengths of all mandibular permanent teeth, exclusive of molars, were measured.
In this method, the differences between the age estimate and the chronological age are plotted against the averages of age estimates and chronological ages. We used the Bland-Altman plot to investigate any possible relationship of the discrepancies between age estimations and chronological age (that is, proportional bias). Proportional bias occurs when age estimation and chronological age do not agree equally throughout the range of measurements. Horizontal lines were drawn at the mean difference and at the limits of agreement (mean difference ±1. 96). The software packages used were Scilab software (version 5. 3. 3; Scilab Enterprises, Versailles, France) for estimating dental age using the Bayesian approach and R version 2. 15. 1 software (The R Project for Statistical Computing; http://www.
This finding could not be attributed to the welldocumented systematic differences between boys and girls regarding the timing of dental or skeletal maturation, because age estimations were performed separately on each sex subset within the database, in accordance with the methods we used. Several explanations could be suggested. First, our study was designed primarily to assess the link between dentofacial asynchronism and dental crowding. This unexpected finding could be explained by a study design inadequate to test the hypothesis of a difference in AS/DD asynchronism between girls and boys. Second, we cannot reject a negative potentiation between delayed facial skeletal growth and advanced dental maturation among girls or a positive potentiation among boys. The reasons for these putative synergistic effects remain unclear, but they could be related to sexual dimorphism.
Again, despite a significant correlation between dental maturation and bone age found in many studies of various populations  [54] [55] [56] , such correlations are generally considered to be moderate at best  [57]. This could explain why dental development indicators are not reliable predictors of an individual's stage of skeletal development. The present study has several limitations. We considered the chronological age as the reference for estimating both dental age and facial skeletal age. The concepts of "advanced" and "delayed" maturation are relative to the mean values of the population from which the estimation methods have been developed. The population analyzed in our study comprised a random sample of the database used to generate the method for estimating facial skeletal age. Both methods used in this analysis are freely available  [25, 27]  and need to be studied in other populations.
R. Quam  kindly revised the English. Thanks to the reviewers for their comments that have improved this work. Thanks to  Bordeaux 1 University  and the project Transitions convention 20051403003AB de la  Région Aquitaine  for financial support (MDG, BM). AGO has a  Marie Curie-IEF research fellowship. AGO and JLA are supported by the  Spanish Ministerio de Economía y Competitividad  (project  CGL2012-38434-C03-01 ).
The development of automated high-intensity macromolecular crystallography (MX) beamlines at synchrotron facilities has resulted in a remarkable increase in sample throughput. Developments in X-ray detector technology now mean that complete X-ray diffraction datasets can be collected in less than one minute. Such high-speed collection, and the volumes of data that it produces, often make it difficult for even the most experienced users to cope with the deluge. However, the careful reduction of data during experimental sessions is often necessary for the success of a particular project or as an aid in decision making for subsequent experiments. Automated data reduction pipelines provide a fast and reliable alternative to user-initiated processing at the beamline. In order to provide such a pipeline for the MX user community of the European Synchrotron Radiation Facility (ESRF), a system for the rapid automatic processing of MX diffraction data from single and multiple positions on a single or multiple crystals has been developed.
Introduction The combination of highly intense focused X-ray beams, automatic sample changers, automated beam delivery, online data analysis and fast readout detectors at synchrotron macromolecular crystallography (MX) beamlines now allows for the collection of hundreds of datasets during each assigned experimental session  (Arzt et al. , 2005; Beteva et al. , 2006; Bourenkov & Popov, 2010; Bowler et al. , 2010; Cherezov et al. , 2009; Cipriani et al. , 2006; de Sanctis et al. , 2012; Flot et al. , 2010; Gabadinho et al. , 2010; Incardona et al. , 2009; Jacquamet et al. , 2009; Leslie et al. , 2002; McCarthy et al. , 2009; McPhillips et al. , 2002; Nurizzo et al. , 2006; Soltis et al. , 2008). In many cases, complete diffraction datasets can be collected in under one minute  (Hu ¨lsen et al.
, 2006). Although the increase in throughput is a boon to productivity, data collection under such circumstances introduces potential pitfalls. The first and most benign effect is a vast increase in the amount of work and book-keeping necessary if all datasets are to be processed and analysed. In such situations it can be difficult even to identify the best dataset [for example, highest overall resolution, best overall hI/(I )i] from a particular project or experimental session. However, this scenario can be remedied by a systematic approach, at the expense of time and manpower. More insidious is the potential for the inefficient use or waste of valuable beamtime, particularly as the careful reduction of data at the beamline is often essential in order to make correct decisions in the planning of further experiments  (Dauter, 1999).
However, until a dataset has been processed, analysed and in some cases used in phasing protocols, its usefulness is unknown. It is thus highly desirable to know, in real time, the utility of a dataset (e. g. whether datasets are incomplete, of poor quality or simply superfluous). The automatic reduction of data provides feedback to the user regarding data quality and, particularly if it is provided rapidly  (Holton & Alber, 2004; Vonrhein et al. , 2011; Winter, 2010) , it allows the user either to plan further optimization of the experiment in hand or to decide to move on to the next project. Implementing an automatic data processing system for an MX beamline cannot, unfortunately, be accomplished simply by installing a generic pre-existing package. A significant portion of the work in implementing such a system is in modifying the existing beamline control environment (e.
Architecture An overview of the Grenoble automatic data processing system (GrenADeS) system we have developed is shown in Fig. 1. Several programming languages and protocols have been employed to integrate the disparate technologies involved. The ESRF MX beamline control graphical user interface (GUI) MxCuBE  (Gabadinho et al. , 2010)  and the autoprocessing server are written in Python. Perl is used as a 'glue code' to pipeline XDS  (Kabsch, 2010) , XSCALE  (Kabsch, 2010) , SCALEPACK2MTZ  (Winn et al. , 2011) , SCALA  (Evans, 2011) , POINTLESS  (Evans, 2011) , TRUNCATE  (Winn et al. , 2011)  and SHELXC/D/E  (Sheldrick, 2010). The technology behind the ISPyB database  (Delageniere et al. , 2011)  comprises a relational database, Java web services and a web-based front end.
MxCuBE and the autoprocessing server MxCuBE is the generic beamline control GUI used on all the Joint Structural Biology Group (JSBG) MX beamlines at the ESRF  (Gabadinho et al. , 2010). The process of automatic data reduction begins when the user initiates a data collection in MxCuBE and has enabled the 'Process and Analyze Data' check box (activated by default). This triggers a signal that is transmitted via XMLRPC (extensible markup language remote procedure call) to a dedicated autoprocessing server installed on the beamline control computer (Fig. 1 ). The XMLRPC server is configured via an XML file, which controls the downstream programs that will be executed and determines how data processing should be executed. Three types of operation are currently supported: 'before', 'after' and 'image'.
'Before' and 'after' specify whether processing of the whole dataset should be started as soon as data collection has commenced or only after a full dataset acquisition has been completed. In 'image' mode, processing is carried out on each image individually and provides statistics (i. e. number of spots etc. ) that can be used in diffractionbased automatic centring protocols  (Song et al. , 2007)  and mesh scanning procedures  (Bowler et al. , 2010; Aishima et al. , 2010). An arbitrary number of programs can be started via the autoprocessing server. The system runs in two modes: 'fast processing mode' and 'full processing mode', which have as their goals immediate user feedback at the expense of accuracy and thoroughly processed data that can be used for more demanding downstream applications, such as phasing (Fig.
Fast processing mode Fast processing relies on the built-in capability of XDS to determine the Bravais lattice during the 'CORRECT' step  (Kabsch, 2010)  and results are reported only for the XDS-chosen Bravais lattice. To increase speed, both process and thread-level parallelization on a dedicated 286 core cluster are employed to minimize spot picking and integration times. The spot range used for indexing the dataset is determined as follows: MxCuBE provides a template XDS. INP file to the processing software with an initial spot range (typically the first 20 images collected) specified. In most cases, this is sufficient for accurate indexing, but in order to improve the success rate, the processing software adds a second ten-image spot range. Ideally these images should be 90 away from the starting angle of the data collection.