dataesr/scientific-paragraphs-categorization huggingface dataset:
b Two adult females were carrying an infant, and in order to avoid any disturbance, they were not equipped with a telemetric harness  (Lemasson and Hausberger, 2011). Cumulatively, we collected data from twelve De Brazza's monkey, six Campbell's monkey, and seven red-capped mangabey adult females. Subjects were provided with fruit, vegetables, and commercial monkey chow daily. Water was available ad-libitum. Animal care and research protocols used in this work complied with the current laws of France and the UK and were conducted under license from the Direction Départementale des Services Vétérinaires (DDSV n • 04672).
DATA COLLECTION Observations were performed outdoors (to ensure high quality recordings suitable for acoustic analysis) between 09:00 and 18:00, outside of feeding times. Subjects were observed in a random order using the focal sampling method, sessions being distributed throughout the day for every subject. In De Brazza's monkeys and red-capped mangabeys, calls of focal (and occasionally of nonfocal) individuals were recorded using a directional microphone (Sony ECM-672 for the females housed in Paimpont, Sennheiser MKH-70 for the females housed in Howletts and Port Lympne) connected to a digital stereo recorder (Sony DAT TCD-D100 for the De Brazza's monkeys housed in Paimpont, Marantz PMD-660 for the other groups). In guenons, females frequently produce soft sounds with their mouths closed, which made it difficult to identify the caller within a multi-female group, even in captivity.
Call types analysis The vocal repertoires of the three species based on acoustic, contextual, and phylogenetic analyses have been described earlier (guenons  : Gautier, 1988; De Brazza's monkeys: Bouchet et al. , 2012b; Campbell's monkeys: Lemasson and Hausberger, 2011; red-capped mangabeys: Bouchet et al. , 2010). We focused on three call types that are contextually equivalent across the three species but differ in their social function: a contact call (produced mostly during affiliative interactions), a threat call (uttered by aggressors during agonistic interactions) and an alarm call (elicited by external disturbances) (Figure  1 ). Sonograms were drawn by a Fast-Fourier Transform (1024-pt FFT length containing 256 samples and zero padding, incremental step: 32 pt) using ANA acoustic software, a customized AMIGA micro-computer program  (Richard, 1991)  recently adapted to be run on PC implemented on LINUX.
Calls recorded during both focal and non-focal observations were examined here, but signals with excessive background noise were excluded from the analyses. In order to best control for data set balance, we randomly selected, when possible, ten exemplars per call type and per individual (sample sizes are given in Table  2 ). We performed measurements at both the sound unit and the call levels (time resolution: 2. 49 ms, frequency resolution: 50 Hz). A unit was defined as the basic element of a call, represented as a continuous tracing along the temporal axis of the sonogram (also referred to as "note" or "syllable" in the monkeys and birds literature, see  Bouchet et al. , 2010). For calls composed of more than one unit type ("CH", "WaHoo", "Un+(Uh)"), we performed measurements on the "principal unit" type only (i.
For variables giving a significant F (P ≤ 0. 05), we then computed the stereotypy index Hs  (Searby et al. , 2004; Pollard and Blumstein, 2011). The information content of one parameter, Hs, is derived from the F-value found in the One-Way ANOVA, but, unlike F, Hs does not vary with sample size. Information capacity measurements are therefore more reliable when comparing samples. For a given parameter, Hs is expressed as: Hs = log 2 F × n(k -1) k(n -k) where F = result of ANOVA, n = number of calls, and k = number of individuals contributing to the dataset. Hs (in bits/signal) represents the number of binary decisions necessary to discriminate between N objects. The higher the value of Hs, the greater potential the parameter has for encoding individual identity. Parameters with an Hs < 1 can be considered as "lowinformative"  (Searby et al.
We performed Kruskall-Wallis tests on the frequencies of utterance computed for the categories "single," "repeated," and "combined" patterns separately, followed by post-hoc multiple comparisons  (Siegel and Castellan, 1988). Furthermore, within each species, we tested whether one category of pattern ("single," "repeated," or "combined") was more frequent than another by performing Friedman tests followed by post-hoc multiple comparisons  (Siegel and Castellan, 1988). Statistical analyses were performed using SPSS 13. 0, Minitab 12. 2 and Microsoft Excel. We set significance at α = 0. 05.
ACKNOWLEDGMENTS We thank  Neil Spooner  (Animal Director) as well as  Mark Kingston Jones  (Education officer) for permission to conduct research on  De Brazza 's monkeys in Howletts and  Port Lympne Wild Animal Parks. We are grateful to  Jean-Pierre Richard  (Rennes),  Lucien Macé ,  Philippe Bec ,  Anne-Sophie Pellier ,  Jean-Yves Durand ,  Xavier Compare (Paimpont) ,  Matt Ford ,  Della Hubbard ,  Adam May  (Howletts),  Simon Jeffery , and  Matt Crittenden  ( Port Lympne ) for technical assistance. We thank  Andrew MacIntosh  for his valuable comments and English corrections. We also thank  Associate Editor Simon Reader  as well as the two reviewers for the careful editing and for their helpful comments which helped us to improve the manuscript. This research received the financial support of the  French Ministry of Education and Research, ANR "ORILANG", PICS CNRS, IUF ,  Grant-in-Aid for JSPS fellows , and also benefited from a mobility grant awarded by  Rennes Métropole.
Near infrared absorbance spectra were collected for all samples on a monochromator (NIRS 6500, Foss NIRSystems, Laurel, MD, USA) every 2 nm in the 1102 to 2498 nm range. A representative subset of 20% of the samples (n = 89) were selected among the data set based on the Mahalanobis distances between samples. The selection procedure was performed using the SELECT procedure in WINISI software (WINISI III, Infrasoft International State College, PA, USA). These reference samples were analysed for FN concentration using the Dumas elementary analysis and for ADF concentration using the Dosi Fibre method  [38]. Concentrations were expressed in percentage of faeces dry mass. A multilinear regression model was established for each constituent between the spectral absorbency data and the chemical values of the reference samples, according to a partial least squares regression procedure.
Removing these individuals (i. e. , with a non steady VBC scores) from the dataset did not change the relationship between individual variation in body condition and explanatory variables tested in the following analyses, so we only reported results from the full dataset. Body condition of individuals was scored independently of their migration strategy classification that was not known at the time of observation. Only a portion of the impalas with a body condition score could be assigned to a seasonal range (2002: n = 92; 2003: n = 63) or a migration pattern (2002: n = 80; 2003: n = 53). In our first analysis we found a significant difference in body condition between years (LRT: χ 2 = 22. 78, p < 0. 001) and between the dry season ranges (LRT: χ 2 = 14. 81, p < 0. 001) (Figure  6 ). A significantly higher proportion of impalas were scored as poor in the first year than in the second year (difference of 2.
Acknowledgements This survey was realised within the framework of the  Biodiversity  Project, Zimbabwe, funded by the  FFEM (Fonds Français pour l'Environnement Mondial). We are grateful to  C. Muzeza  -manager of the  Chivaraidze Game Ranch  and his collaborators for permission and assistance to work at the ranch. We would like to thank  S. Le Doze  and  B. Butete  for assistance in field activities,  D. Bastianelli  and  L. Bonnal  for chemical and SPIR analysis,  T. Dodman  for the English revision and  M. Gély  for assistance in map production. We thank  Professor J. M. Fryxell  and  Dr. M. Hewison , as well as three other anonymous reviewers, for their insightful suggestions for improvement. This work benefited from comments of  Professor E. J. Milner-Gulland ,  Dr. J. Clobert ,  Dr. H.
DATA ANALYSIS Group differences on reading accuracy and speed were assessed by means of independent parametric t-tests (or non-parametric U-tests when the conditions for carrying out parametric analysis were not assumed) with group (control, dyslexic) as the betweensubjects factor. Individual reading performance was compared to age-matched norms from which individual and group average zscores were computed  (Bosse and Valdois, 2009). Regarding the cognitive skills of the dyslexic children, individual z-scores were computed according to the age-matched corresponding norms for the two phonological tasks  (Sprenger-Charolles et al. , 2005)  and for the VA Span tasks  (Bosse and Valdois, 2009). For the visual and the auditory search task separately, RTs for trials where the target was present and correctly detected, and d' scores were analyzed by means of mixed ANOVAs with group (control, dyslexic) as the between-subjects factor, and condition (O target/Q target; FM target/Steady target) as well as stimulus set size (4, 10, 16) as within subject factors.
ACKNOWLEDGMENTS This research was supported by the  CNRS (Center National de la Recherche Scientifique) , the  French Ministery of "Enseignement Supérieur et de la Recherche ," and the  European commission  (Call ID FP7-PEOPLE-2010-IEF-Panel SOC-Proposal N • 274352-Acronym: BIRD attributed to  Marie Lallier ). We thank  Marie-Ange N'Guyen ,  Emilie Longeras ,  Catherine Billard ,  Camille Chabernaud  and  Luke Armstrong  for their valuable help in this research and thank all the children and their family for their participation.
TESTING THE LINK BETWEEN VISUAL PROPRIOCEPTION AND WARINESS OF HEIGHTS Two studies were recently conducted by  Dahl et al. (2013)  to test the relation between visual proprioception and wariness of heights proposed by  Bertenthal and Campos (1990). The first study examined whether newly crawling infants who were highly responsive to peripheral optic flow would be more likely to avoid heights. Wariness of heights was assessed on a visual cliff and postural compensation to peripheral optic flow was assessed by moving the side walls in a moving room. Under the infant's seat in the moving room were force sensors that recorded postural sway in the fore and aft directions. Cross correlating the postural sway data with the displacement of the side walls provided an index of the strength of the coupling between vision and posture.
ACKNOWLEDGMENTS This research was supported by grants from the  Japanese Society for the Promotion of Science  ( KAKENHI-22330191 ), the  John D. and Catherine T. MacArthur Foundation , the  National Institutes of Health  ( HD-07181 ,  HD-07323 ,  HD-25066 ,  HD-36795  and  HD-39925 ), and the  National Science Foundation  ( SBR-9116151 ,  BCS-0002001 , and  BSC-0958241 ), by a fellowship from the  Center for Advanced Study in the Behavioral Sciences , and by a " Research Infrastructure in Minority Institutions " award from the  National Center on Minority Health and Health Disparities ,  P20 MD00262.
Each of the 10 identical ALDM test systems used in the research consisted of an experimental chamber (0. 7 9 0. 7 9 0. 8 m) freely accessible from the living enclosure. Upon entering the test chamber, a baboon faces an opaque panel within which a (7 9 7 cm) viewing port and two (8 9 5 cm) hand ports are positioned to see and touch a 19-inch LCD touch screen monitor (model ET1739L from Elotouch; Berwyn, IL) at eye level 25 cm behind the view port. Each ADLM system had a network-controlled PC computer (Dell precision 67, 3. 2-GHz) and comprised a ''homemade'' dispenser delivering dry-wheat reinforcers (www. ebly. co. uk) inside the test chamber. The test program, written in E-prime (V2 professional, Psychology Software Tools, Pittsburgh), automatically identified an animal via the microchip when it placed its forearm within an arm port and determined its last stopping point in trial presentations, in order to assign the independent variables to be experienced during the trial.
Each 12 items block was separated by a rest period of 2 min. Response latencies and accuracy were recorded with the E-Prime experimental software (Version 1. 2, Pittsburgh, PA, USA) and constituted dependent variables of interest. One point was given if both correct images were picked, as suggested by Peters  [51]. Experimental procedure. Both WDay and MDay groups (n = 10) were subjected to MR training at 8:00 am and were retested on the same day at 8:00 pm, while WNight and MNight groups were first trained in the evening at 8:00 pm and then retested the next morning at 8:00 am (Fig. 1 ). The experiment was divided into four phases: Familiarization session. During this session, instructions were given to participants who had to find the two correct answers that matched the target item, as fast and accurately as possible.
Data Analysis We first checked the Gaussian distribution of the data as well as the homoscedasticity. The sphericity assumption was further tested using the Mauchly's sphericity test. Data showed that the sphericity was not violated, and thus that parametric statistical tests could be safely used. For each test session (pre-training, posttraining and re-test), we analyzed two dependent variables, namely response accuracy (total number of correct answers) and response latencies (mean time lasting from the apparition of the MR item to the second key pressed). To compare the level of performance between women and men at the beginning (first block) and at the end of training (eighth block), we performed two one-way Analyses of Variance (ANOVAs) with SESSION (pre-and post-training) as within-subject factor and GENDER (Men vs.
Behavioral data Practice-dependent learning. First, we aimed to determine whether women and men were comparable in terms of performance during the pre-training. In this session, women and men respectively performed 5. 3060. 49 and 7. 1560. 51 correct sequences (one point for two correct answers per item, n = 20). Corresponding mean (6SD) sequence durations were 15. 4461. 09 s and 13. 8460. 88 s, respectively (Table  S1 ). A oneway ANOVA on the number of correct answers yielded a significant effect of GENDER in favor of men (F 1,38 = 7. 11, P,0. 01, g p 2 = 0. 15, Fig. 2 ), while there was no difference when comparing mean response latencies (F 1,38 = 1. 37, P = 0. 24, g p 2 = 0. 03). The number of correct items during post-training was 9. 45  (60. 44)  in the men group and 8. 30  (60. 78)  in the women group.
Crucially, regions strongly associated with ToM processing, namely the dorsal mPFC and left TPJ, were also recruited during the same experiment, irrespective of the presence of emotional prosody. The application of functional connectivity analyses to a second fMRI dataset further revealed that the large set of brain regions involved in emotional classification could be subdivided into two main functional networks  [5] : one that gathered perisylvian language areas, and one that overlapped ToM regions  [2]. Given this involvement of a distinct coherent network of putatively ToMrelated regions (''Medial network'', including the pCC, left TPJ, dmPFC and vmPFC), it seemed even more likely that the emotional sentence processing entailed a form of ToM process. The fact that the Medial network included the vmPFC was particularly interesting.
fMRI Protocol The TOM, PLAUTOM and PLAUEMO data were acquired in a second fMRI session with the same participant, several months (10. 5 on average) after a first session during which the EMO and GRAM data were acquired. Each participant performed two different runs of each task while in the magnet, for a total of 10 runs over both sessions. For each task, each run included 24 sentences, and was organized following a slow eventrelated design (with a long interval between each stimulus so as to allow the BOLD response to go back to baseline). The different sentence categories occurred randomly, but in the same order for all participants. After the end of the sentence, the participants had to respond manually within 3 s. In order to keep the participants focused on the experiment, after each sentence classification trial, the participants performed a ''beep detection task''.
Image Acquisition The data were acquired on the Philips Intera Achieva 3T scanner at the GIP Cyceron (Caen, France). The anatomical scans consisted of a T1-W sagittal 3D scan (18062566256 voxels, with a 1-mm isotropic resolution, FA = 10u, TE = 4. 6 ms, TR = 20 ms, TI = 800 ms, SENSE factor = 2 in both AP and LR directions), and of a coarser T2*-W scan, with a T2-FFE sequence (1286128670 voxels, 2 mm isotropic resolution, TE = 30 ms, TR = 3500 ms, FA = 90u, SENSE factor = 2), for cross-modal registration with the EPI-BOLD time-series. For the functional MRI, the sequence parameters were: 31 axial slices with a 64664 matrix, 3. 75 mm isotropic voxel resolution, interleaved acquisition, TE = 35 ms, TR = 2 s, FA = 80u, no parallel imaging.
Image Processing Image analysis was performed using the SPM5 software. The T1-weighted scans of the participants were normalized to a sitespecific template (T-80TVS) matching the MNI space, using the SPM5 ''segment'' procedure with otherwise default parameters. So as to correct for subject motion during the fMRI runs, within each run, the EPI-BOLD scans were realigned using a rigid-body registration. The EPI-BOLD scans then were registered rigidly to the structural T2-weighted image, which was itself registered to the T1-weighted scan. The combination of all registration matrices allowed warping the EPI-BOLD functional scans to the standard space. Once in the standard space, a 6-mm FWHM Gaussian filter was applied.
Behavioral Data Analysis We compared the accuracy (mean number of correct responses per run, CR), the response times (RT) of correct answers, between the TOM and PLAUTOM tasks using Wilcoxon rank tests. Likewise, we compared the RTs and CRs of the PLAUEMO task with those of the previously described EMO and GRAM tasks  [5]. We also evaluated the effects of sex, age and education level on RTs for each of the 5 tasks, using linear models. On the basis of the participants' answers to the debriefing questionnaires, we computed descriptive statistics regarding the various task-solving strategies used by the participants to complete the TOM and PLAU tasks.
Overlap between ToM and the Emotional Component of Sentence Comprehension In accordance with our starting hypothesis, the results confirmed that a same coherent network contributes to both ToM and emotional components of sentence comprehension. The distributed network shared by ToM and emotional components of sentence processing displays similarities with the default mode network, especially the dmPFC subsystem and core components  [11]. This network has been associated with several more or less overlapping cognitive functions, such as story comprehension  [12] , self-projection during mind wandering, ToM, prospection and episodic memory  [13, 14] , or semantic processing  [15] [16] [17]. Within Overlap between emotional and ToM sentence processing (in blue). Three-plane views and surface renderings of the significant activations during language comprehension (conjunction of PLAUTOM and TOM, in warm colors) and ToM sentence comprehension) as compared with a plausibility judgment task on sentences of comparable complexity (TOM -PLAUTOM, in green).
Both regions may thus participate of the interface between the ToM and the language-related networks, which seems necessary for accurate verbal communication, for instance in the case of irony or indirect requests  [27] [28] [29]. The dmPFC is more strongly connected with the IFG during the processing of ironic as opposed to literal texts, a contrast that also evidences the involvement of ToM regions in the pragmatic aspects of sentence comprehension  [29]. The engagement of the dmPFC region during sentence comprehension is further suggested by the fact that, in the whole brain analyses, a significant cluster was found just next to this left dmPFC ROI in the conjunction between TOM and PLAUTOM. This result is consistent with previous observations of activity in the dmPFC region during language tasks involving series of sentences, in the absence of ToM and in relation with text-coherence building or reasoning  [30, 31].
chinensis in literature  [40, 41]. In its introduced range, where P. chinensis identification is not problematic, localities were extracted from literature, museum records and personal collecting (BG). For models requiring absence data, 10,000 pseudo-absence (background) points were generated randomly from all around the world to provide background data. This is a classic procedure because confirmed absence data is difficult to obtain for most species and requires great sampling efforts  [42]. True absence data might improve the model accuracy because some pseudo-absence points may be drawn from regions where the species is actually present, but has not been recorded. However, it is not possible to base our projections on true absences due to lacking large-scale absence data of the species.
Climatic Predictors Climatic predictor data was sourced from the Worldclim dataset. The 19 Worldclim variables represent annual trends (e. g. mean annual temperature, annual precipitation) and extreme limiting environmental factors (e. g. temperature of the coldest and warmest months, precipitation of the wettest or driest quarter) and are known to influence species distributions  [45, 46]. All Worldclim variables are 30-year averages of monthly temperature and rainfall values from 1960-1990  [43] , which is characteristic of the climate that the species experienced when the occurrence point was collected or the species established in this locality. We modelled the species niche based on 4 of the 19 bioclimatic variables that were not collinear (pair-wise r Pearson ,0. 75). The selected variables were (in the order of their relative contribution to the Maxent model): Precipitation of the driest month, isothermality, precipitation of the warmest quarter and maximum temperature of the warmest month.
Therefore the projections at different time horizons can be compared. In order to consider a range of possible future climates, we used downscaled climate data from three Global Circulation Models (GCMs), provided by different climate centres, each based on different geophysical assumptions: the CCCMA-GCM2 model; the CSIRO-MK2 model; and the HCCPR-HADCM3 model  [47]. We also used two extreme Special Report on CO 2 Emission Scenarios (SRES): the optimistic B2a and pessimistic A2a scenarios. In total, we used six future climatic scenarios (3 GCM62 SRES). Data for the future climatic projections were climate data averaged across a decade, centred on the focal year (e. g. 2020)  [47]. Worldclim data is the standard source of climate data for species distribution models. However it is poor at interpolating climate in topographically complex regions such as mountains or coastal regions  [48].
STATISTICAL PROCEDURES Statistical analysis was performed with the program SigmaStat 3. 1® (Sysstat Software Inc. ). All variables have been initially analyzed with the Kolmogorov-Smirnov test to know whether the data varies significantly from the pattern expected if the data was drawn from a population with a normal distribution. Furthermore, the Levene test was performed to probe the homogeneity of variances across groups. Variables that failed the Kolmogorov-Smirnov or the Levene test were analyzed with nonparametric statistics using the Kruskal-Wallis one-way analysis of variance on ranks and Mann-Whitney rank sum tests for pair-wise multiple comparisons. Variables that passed the normality test were analyzed by means of One-Way ANOVA's and Holm-Sidak and Student t-tests for pair-wise multiple comparisons.