dataesr/scientific-paragraphs-categorization huggingface dataset:
Participants and Procedure A community-based sample of young adolescents (N=2000) was recruited for the IMAGEN study (for details on the IMAGEN project, see  Schumann et al. (2010)   [78] ). In brief, participants and their parents were recruited via high-schools in 8 European study sites. The geographical areas were selected to have minimal ethnic diversity to maximize homogeneity as a prerequisite for future genome-wide association analyses. Private and state-funded schools were targeted in order to obtain a diverse sample of socio-economic status, emotional and cognitive development. The IMAGEN study included, in the first phase, a home assessment using an online computer platform (described below) and 1 or 2 study-centre visit. After data quality control, complete and reliable data sets of personality, psychiatric symptoms and substance use behaviors were available for 1938 participants with an average age of 14.
Thresholds used to assess goodness of fit are traditionally CFI>0. 90, RMSEA<=0. 05 and SRMR<0. 05. Models were compared using Akaike's information criterion (AIC) and the Bayesian information criterion (BIC), both useful in comparing two non-nested models. The best balance of fit and parsimony is illustrated by smaller values in both of these indices. Path loadings were considered significant for a value above 0. 3. The best fitting model was then controlled for gender, language, and site. The language variable was orthogonally transformed into two variables, which were both entered into models, as was Gender (with female=0 and male=1), while site was entered as a cluster variable. Personality correlates were then added to the best fitting structural model. The gray matter image pre-processing followed the optimized VBM protocol implemented in Statistical Parametric Mapping (SPM)  [90].
Pre-processing incorporated image registration and classification into a single generative model  [91]. Segmented gray matter data were scaled by the inverse-Jacobian of the local transformations (i. e. , modulated) in order to preserve volume. The spatially normalized (standard MNI space) and modulated gray matter partitions were smoothed using a 8 mm full-width at half maximum Gaussian kernel. Only voxels with an absolute value greater than 0. 05 were included in the statistical analysis. The factor scores were extracted (using SPSS 19 software) in order to look at neural correlates using VBM analysis. Total gray matter volume, age, sex, handedness, pubertal development status and site were entered as nuisance covariates. Externalizing behaviors factor scores were included as nuisance variables when investigating compulsivity factor scores, and vice versa.
Acknowledgements Jean-Baptiste Poline  contributed to the development of the neuroimaging data for archives, processing and standardization, as well as to the biostatistics infrastructure. The other members of the  IMAGEN consortium  (http:// www. imagen-europe. com)are the following : King's  College, Institute of Psychiatry, London, UK :  L Reed ,  S Williams ,  A Lourdusamy ,  S Costafreda ,  A Cattrell ,  C Nymberg ,  L Topper ,  L Smith ,  S Havatzias ,  K Stueber ,  C Mallik ,  T-K Clarke ,  D Stacey ,  C Peng Wong ,  H Werts ,  S Williams ,  C Andrew ,  S Desrivieres ,  S Zewdie  (Coordination office). Department of Psychiatry and Psychotherapy ,  Campus Charité Mitte ,  Charité Universitätsmedizin Berlin, Berlin, Germany :  A Heinz ,  I Häke ,  N Ivanov ,  A Klär ,  J Reuter ,  C Palafox ,  C Hohmann ,  C Schilling ,  K Lüdemann ,  A Romanowski ,  A Ströhle ,  E Wolff ,  M Rapp.
The IMAGEN study receives research funding from the  European Community 's  Sixth Framework Programme  ( LSHM-CT-2007-037286 ). Further support was provided by the  FP7  projects ADAMS (genomic variations underlying common neuropsychiatric diseases and disease-related cognitive traits in different human populations; 242257), the  Innovative Medicine Initiative Project EU-AIMS  ( 115300-2 ), the  UK National Institute for Health Research Biomedical Research Centre Mental Health , the  Medical Research Council   Programme Grant  ' Developmental pathways into adolescent substance abuse ' (93558), the  German Bundesministerium für Bildung und Forschung (NGFN Plus ; FKZ :  01GS08152 ), as well as the  Fondation de Recherche Santé-Québec  ( Chercheur-Boursier Pc 20393 ). The poster «A phenotypic structure for compulsive-like behaviors in adolescents» was presented in three occasions :  Canadian College of Neuropharmacology Conference (Montreal, Canada ; May May 20-23rd 2011),  University of Montreal Psychiatry Teva  and  Shire Pharmaceuticals , and has received recent research grants from  Lilly ,  GSK  and  Lundbeck.
Stimuli Eight professional actors (four males) were hired and instructed to begin at neutral and to increase their expression of anger in seven to nine 3 s increments according to the experimenters signal in front of a camera until deemed satisfactory. Performances were filmed with two cameras: one was facing the actor; the second at a 45uangle relative to the first creating the impression that the expression was aimed toward the observer (oriented-to-self condition) or toward another (oriented-to-other condition). Videos were edited using Windows Movie Maker and several 2 sec (25 frames per second) fragments were selected to obtain two extracts for each condition from neutral to extreme anger with two different viewpoints. Clips of actors seen from the side were flipped to obtain equal numbers of left and right videos and faces were blurred using the Adobe After-effect software, to preclude extraction of any emotional cues conveyed by them and restricting information to the body.
Each video was followed by an inter-stimulus interval of 1000 ms. Additionally, 15 oddball stimuli (upside-down video-clips; see below) and 38 null events (black screen of 2 sec) were included pseudo-randomly within the stimulus sequence. The order of the stimuli was fully randomized. Subjects were instructed to press a button each time the upsidedown video-clip appeared to ensure they paid attention to all the stimuli throughout the session. The participants performed at 100% of accuracy (at mean 648622 ms) in this oddball task. Data acquisition and analysis. Using the acquisition system ADInstruments (ML870/Powerlab 8/30), EMG activity was continuously recorded using Sensormedics 4 mm shielded Ag/AgCl miniature electrodes (Biopac Systems, Inc). Fixation cross and stimuli onset were automatically signaled on the channels of the LabChart Pro software by a PCMCIA Parallel Card (Quatech SPP-100).
e. , to calculate the change from baseline). EMG activity was thus defined as the change from the baseline occurring between 0 and 1000 ms after stimuli onset  [10, 23]. Finally, mean levels of corrugator and zygomaticus activity were computed separately for each experimental condition. Physiological data were first submitted, separately for each muscle, to repeated measures ANOVA using Target of Attention (Self or Other), Levels of Emotion  (1, 2, 3, 4)  and Time Windows  (10)  as within-subject factors. Second, when the Time Windows factor interacted with another factor of interest, we performed post-hoc t-tests to determine the time windows for which the effect occurred and submitted the mean activity of these windows to a new ANOVA using Target of Attention (Self or Other) and Levels of Emotion (1, 2, 3, 4) as within-subject factors.
Data processing The pleasantness scores were summed, leading to a total score per sample which reflected the overall pleasantness level evaluated by the eight subjects. These total ordinal scores are indicated in Table  1. All analyses focused on 20 mm (i. e. between 40 and 60 mm of each material) of the active steady-state fingertip slip phase. During this phase, force data were numerically low-pass filtered (butterworth 4 th order filter) at 800 Hz and the fingertip position signal was differentiated with respect to time to estimate the exploration velocity. The software package MatlabH (version 7. 10) was used to process force as well as fingertip position data. We firstly computed the mean velocity, v, the mean tangential force component, f T , as well as the mean normal force component, f N , per sample exploration and per participant.
To account for these observations, the raw friction force data of each participant and each exploration was resampled with respect to space, using the corresponding fingertip positions. The change of variable makes the signal insensitive to small variation of velocity across the exploration that would otherwise have affected the temporal frequency content. Following the analysis detailed in  [5] , as many as 40 000 equally spaced force samples over a 20 mm extent could be produced, which according to the Nyquist sampling theorem could account for 1 mm surface details. A Fast Fourier Transform analysis was then performed on the re-sampled signal to obtain a spatial spectrum. The rapid force fluctuations for each surface could then be analyzed in terms of spatial frequencies. The analysis focused on spatial frequencies situated in the range 0.
Such analysis is preferably performed in several steps. In a first step, the Kaiser-Meyer-Olkin criterion was used to measure the sampling adequacy. Its value varies between 0 and 1 and should be higher than 0. 5. Furthermore, the Bartlett's test of sphericity was used to assess whether the initial dataset was suitable for PCA, i. e. the test of the null hypothesis that the original correlation matrix is an identity matrix, since a PCA can only be conducted if there is correlation between the analyzed variables. Then, the Kaiser criterion was used to define the number of factors to be extracted. This method is based on the principle of retaining only the factors associated with eigenvalues greater than one (i. e. the total variance of each factor). Thereafter, the oblique rotation, namely the direct oblimin rotation, was performed to maximize the loading of each variable on one of the extracted factors, while minimizing the loading on all other factors.
, 2011 , for lexical tasks tapping phonotactic knowledge). In summary, a wealth of behavioral research has shown that both phonotactic and phonemic knowledge emerge toward the end of the first year. Moreover, contrasts between native sound categories come to involve a left-dominant brain network around this age as well. Finally, adult neuroimaging work suggests that there is an overlap between the network processing phonemic and phonotactic dimensions, although the crucial data on this is missing in infancy. The present study thus set out to complete this picture. We used a change detection paradigm similar to those previously used in NIRS research to study the brain network involved in processing two types of contrasts: one relying on phonotactic knowledge and the other on phonemic sound categories.
ACKNOWLEDGMENTS This work was supported in part by  Grant-in-Aid for Scientific Research (KAKENHI) (B)  (Project No. 24118508 ) and  Grant-in-Aid for Scientific Research on Innovative Areas  (Project No. 24300105 ) to  Yasuyo Minagawa-Kawai , a grant from the  Agence Nationale pour la Recherche  ( ANR-09-BLAN-0327  SOCODEV), and a grant from the  Foundation de France  to  Emmanuel Dupoux , a fellowship from the  Ecole de Neurosciences de Paris  to  Emmanuel Dupoux  and  Alejandrina Cristia , and a grant from the  Fyssen Foundation  to Alejandrina Cristia. We thank  Yutaka Sato  for his help with the previous NIRS data, and  Sayaka Ishii , and  Noriko Morisawa  for helping with NIRS experiment. We gratefully acknowledge  Sylvie Margules  for carrying out the infant recruitment and  Anne Bachmann  for designing and constructing the probe pad.
ACKNOWLEDGMENTS We would like to thank  Lewis Bott ,  Benjamin Spector , and audiences at MIT in October 2012 for relevant comments on earlier versions of this paper. We also would like to thank the two anonymous referees for their detailed review and thoughtful comments which have helped clarify the present article. We are grateful to  Isabelle Brunet ,  Anne-Caroline Fiévet  and  Amanda Swenson  for their practical help. The research leading to these results has received funding from the  European Research Council  under the European Union's  Seventh Framework Programme  (FP/2007-2013)/ ERC  Grant Agreement n. 313610 and was supported by  ANR-10-IDEX-0001-02  and  ANR-10-LABX-0087.
Acknowledgements The authors would like to acknowledge  Mike Coleman  for help with computer programming;  Steve Nevard ,  Bronwen Evans Hugh Rabagliati  for help with recording the stimuli; the assistance of  Anastasia Archonti ,  Hillit Dayan  and  Angela Pozzuto  in testing participants; and the help of Hugo Peyre with confirmatory factor analysis. The authors are most grateful to all the children who participated, as well as their schools and their parents. Author contributions: H. K. J. v. d. L. , F. R. and C. R. M. designed the overall study and its implementation throughout. S. R. helped design and provided materials for the auditory tasks. C. R. M. collected the data. F. R. and C. R. M. analysed the data. F. R. , C. R. M. and H. K. J. v. d. L. wrote the paper.
Data analysis There was no significant gender difference on MI and actual times or on any rating provided by the two experts, and thus there was no need to subdivide the group by gender. One-way repeated measures analyses of variance (ANOVA) with two conditions (motionless imagery vs. corresponding actual times, and dynamic imagery vs. corresponding actual times, respectively) were performed to compare chronometric data. Repeated measures ANOVAs were also computed to compare the number of successful jumps ("hit/miss"), the overall technique score, and expert ratings in each experimental condition. Data were then split into hits and misses and an additional 2 (Dynamic vs. Motionless imagery) x 2 (Hit vs. Miss) repeated measure ANOVA was performed to compare actual and MI times. Effect sizes, including partial eta-squared (η 2 ) for ANOVA results are also provided.
Data Analysis We defined the response time as the time at which the participant's stylus crossed the midline of the target. The spatial error was measured with respect to deviation from the center of the target. The 100 mm lateral stimulus space was divided into 5 bins of 20 mm each. For each participant, response times that exceeded thrice the standard deviation of response times within these bins were rejected (,1. 5% of trials on average). Hardware delays were compensated online to calculate feedback (described in  [20] ). A hit was registered if the participant's stylus was within the target region within 150 ms of its presentation (the displaytime for the target). Responses, over the course of the experiment, were grouped in 10 bins of 50 trials each. We performed robust regressions with a logistic weighting function (tuning factor = 1.
Sampling In 2008, around 58,000 GPs (31. 6% of them women) were in private practice in France  [29]. This survey is the third in a series nested in the national panel of French GPs, designed to collect data regularly about their activity and practices. Its methodology has been presented elsewhere  [30] [31] [32]. Briefly, 5170 GPs were randomly selected from the Ministry of Health's exhaustive database of health professionals in France. Sampling was stratified for location of the general practice (urban, suburban, or rural), gender, age (,49, 49-56,. 56), and annual workload, defined by number of office consultations and house calls (,2849, 2849-5494,. 5494) in 2008. Information on each GP was obtained from the National Health Insurance Fund (NHIF). To limit a selection bias that might result from particular opinions/attitudes, the specific topics to be studied were not mentioned to GPs before they were asked to participate in the panel.
Two items assessed GPs' experience with suicide among their patients: had any patient attempted suicide and had any completed suicide in the past 5 years (yes, no). Finally, data on their patients' characteristics were extracted from the NHIF databases, and dichotomized according to the median in the sample. We used proportions of patients on their patient list (all patients seen at least once during 2011) with each of the following characteristics: aged 70 years or more; covered by CMUC (Complementary Universal Health Insurance, a program that exempts individuals who have annual incomes below 9000 J from any out-of-pocket costs  [36] ), an indicator of a very low socioeconomic status or deprived clientele; and with a chronic disease (ALD status: attributed by the NHIF to persons with specific and expensive chronic diseases that make them eligible for 100% reimbursement for treatment).
Acknowledgments We thank all GPs who participated in the survey as well as the members of the supervisory committee of the  French National Panel of Ambulatory General Practices  (DREES; ORS and  URPS-ML of Bourgogne, Pays de la Loire , and  Provence-Alpes-Cote d'Azur). The authors also thank  Jo Ann Cahn  for supervising the English of this manuscript.
To obtain fluctuating noise with comparable energetic masking characteristics as those for the babbles, we derived 42 fluctuating noise samples directly from the 42 samples of the 4-talker babbles that were previously mentioned, according to the following protocol: Using MATLABß (R2010a, The MathWorks, Inc. , Natick, Massachusetts, USA), we first computed the energy root mean square (rms) of the original sample and extracted its temporal envelope by applying a 60 Hz low-pass filter. Then, a fast Fourier transform (FFT) was used to extract the power spectrum and phase distribution of the original signals. The original phase distribution was randomized, and the original modulation was reintroduced by multiplying the randomphased signal by its original envelope. Finally, an inverse FFT was used to generate new signals that were normalized to the rms power of the original sample.
Target words. Target words, all of which were in French, were recorded by a female native French speaker and differed from those used in the babbles. Eighty-four French disyllabic words were selected with a middle range of frequency of occurrence (ranging from 0. 29 to 175. 65 per million; mean = 17. 16, SD = 30. 43), according to the Lexique2 database  [21]. This range was selected to avoid extremely high-and lowfrequency items that the participants may typically overuse or use too seldom. Stimuli and word lists. Target words were inserted 2. 5 s from the start of a 4-s-long background sample at a SNR of 25 dB. Thus, all participants had the same duration of exposure to the background sound before a target word was presented. Among the 84 stimuli, 6 were used as practice items, representing the 6 conditions (2 background types (babbles vs.
Participants All participants gave their informed written consent and the study received agreement of the local ethic committee of Sainte Anne Hospital (CPP Ile de France 3 nu2687). Twenty healthy young (YA) (25-45 years old, mean = 29. 265. 5,10 women) and 17 healthy old (OA) adults (65-80 years old, mean = 68. 264. 4,10 women) all right-handed (according to the Edinburgh Handedness Inventory;  [38] ), and native French speakers participated to the study. Medical, demographic, and psychometric data were obtained prior to the scanning session. All participants were unmedicated, living at home and rigorously screened for uncontrolled hypertension and cerebrovascular risk factors. Exclusion criteria included presence of history of alcohol or substance abuse, head trauma, major disease affecting brain function, neuropsychiatric disorders (tested with the Mini-International Neuropsychiatric Interview,  [39] ), depression (tested with Geriatric Depression Scale,  [40] , cut-off score.
"). We computed for each participant a global ratio of specificity (EPI score) totaling up the sum of spatiotemporal, other contextual and phenomenological details divided by the sum depending on the number of EAM. fMRI Method MRI data acquisition. All data were acquired with a 3 T scanner (Discovery MR 750, General Electric Healthcare). The anatomical scan used an inversion recovery 3-D T1-weighted gradient-echo sequence images (TE = 4. 3 ms, TR = 11. 2 ms, TI = 400 ms, matrix = 3846384, slice thickness = 1. 2 mm). Functional images were acquired using a gradient echo echoplanar (EPI) sequence (TE = 30 ms, TR = 2000 ms, flip angle = 90u, matrix = 64664, slice thickness = 3 mm, 42 contiguous sections). The first four volumes of each functional run were discarded in order to allow longitudinal magnetization to approach equilibrium.
Pre-processing of fMRI data. All data were processed using SPM5 software (Statistical Parametric Mapping 5, Welcome Dept. Cognitive Neurology, UK; www. fil. ion. ucl. ac. uk/spm). Standard pre-processing procedures were applied to MRI data. EPI volumes were corrected f or slice timing, realigned to the first image, coregistered with the high-resolution T 1 -weighted image and normalized into a standard stereotaxic space. The normalization used the Montreal Neurological Institute (MNI) template and the rigid transformations computed during the segmentation of the high-resolution T 1 -weighted image. Finally, the normalized EPI volumes were smoothed using an isotropic Gaussian kernel filter of 5 mm full-width half-maximum. Statistical first level analysis of fMRI data. Only responses meeting criteria for the EAM and SAM conditions were used for the subsequent analyses.
Gathering of the data In the present study, the gathered descriptions were collected "in real time," during induction of the illusion, the interviewees describing their experience as it unfolded. Other descriptions were given after the end of the stroking period. Once the stroking period started, participants were asked to describe what they perceived or felt whenever they wanted to. With their agreement, participants' descriptions were recorded. In cases where participants spontaneously expressed a particular feeling, either through words, exclamations or gestures, they were questioned about what they had just said or expressed. In cases where they did not spontaneously express any particular feeling, questions such as "And now, what do you feel?" or "And now what's happening?" or "Do you feel anything toward the rubber hand?" were used to trigger participants' descriptions.
We then looked at the experiential categories that characterized each of the phases previously determined, identifying the synchronic structure of each phase for a given participant in this stroking condition. In this way we created an individual representation of the structure of the experience of each stroking condition for each participant. We repeated this procedure for each participant and then compared the individual structures, looking for invariants across different participants. Based on the invariants, we constructed the generic structure of the experience for each stroking condition. A more detailed description of the analysis of the data is provided in the Appendix and in  Valenzuela-Moguillansky (2012).
Image processing, segmentation and asymmetry analysis To evaluate the morphological differences between the dyslexic and typical participants, the structural T1 images were used to perform a voxel-wise analysis of the tissue asymmetry, directly derived from the voxel-based morphometry (VBM) approach  [67] , involving the creation of GM/WM asymmetry maps and performance of between-group comparisons using these maps. The analysis was performed using the VBM8 toolbox (Gaser, http://dbm. neuro. uni-jena. de/vbm), which employs the unified segmentation approach  [68]  implemented in SPM8 (Wellcome Department of Imaging Neuroscience, http://www. fil. ion. ucl. ac. uk/spm/software/spm8/). This method involves a multiple-step procedure that alternates tissue classification, bias correction and normalization.