dataesr/scientific-paragraphs-categorization huggingface dataset:
Social-housing (Camus et al. , 2013b) For each group, data were collected in a randomized order at two time points (morning and afternoon) on 6 consecutive days. More sessions were needed, as such housing allowed more complex behaviors. The observer was sitting 1 m away from the front of the outdoor cages. In both conditions, we used a scan-sampling method, appropriate for time budgeting  (Altmann, 1974) , in which behavioral parameters were assessed every 2 min during 30-min sessions resulting in 90 scans per male, or every 6 min during 2-h sessions resulting in 240 scans per female. We focused on behavioral profiles rather than single items and used two repertoires: one reporting the interaction with the environment (see Table  S1 ) and one describing the position within the environment (see Table  S2 ) according to published protocols  (Camus et al.
FACTOR ANALYSES Following our previously described procedures  (Camus et al. , 2013a,b) , data from single-and socially-housed individuals were separately submitted to multiple correspondence analysis (MCA; SPAD © 7. 4, Coheris) that uses chi-square criterion to assess differences and similarities between frequencies of qualitative variables  (Montaudouin and Le Pape, 2004). We used grouped behaviors, grouped body postures, body orientations, and locations as active variables. A hierarchical clustering analysis was then performed on the individuals' coordinates to describe interindividual similarities  (Henry et al. , 2005). For each resulting cluster of individuals, the mean occurrence percentage of each behavioral item was calculated and reported on radar graphs. The "behavioral diversity" was assessed as the mean number of distinct behaviors observed during the scans.
STATISTICAL ANALYSES The statistical analyses were conducted using Statistica©8. 0 (StatSoft, Inc. ). For each population (rhesus, cynomolgus, single-or socially-housed) data collected were not normally distributed. Therefore, non-parametric Mann-Whitney U-tests were used to compare behavioral variables between two populations. A correction for small group size was applied when the group contained less than 10 individuals. Spearman rank correlations were performed to assess correlations between age, weight and the collected variables within groups of individuals. As numerous tests were performed, a Bonferroni adjustment was applied to keep the type I error constant. The accepted P level becomes the α probability divided by the number of hypothesis tests: 0. 00040 (124 hypotheses) and 0. 00028 (177 hypotheses) in single-and social-housing conditions, respectively.
Non-depressive cynomolgus displayed more "on bars" posture, while rhesus were more often slumped. As a result of the posture, cynomolgus were more often located in the upper part of the cage whereas rhesus mostly stayed at the bottom of the cage. In addition, the percentage of time spent at the back of the cage was higher in rhesus compared to cynomolgus macaques. Rhesus spent more time facing the wall relative to cynomolgus which faced the exterior more often. Finally, non-depressive rhesus directed their gazes at the still environment more than cynomolgus monkeys which looked at the observer significantly more. Since weight and/or age differed between the species in the single-housed monkeys, we tested whether these variables could impact the occurrences of other collected parameters. No significant Spearman rank correlations were reported in the depressivelike or non-depressive populations (data not shown).
DISCUSSION In this study, depressive-like profiles among both single-and socially-housed rhesus monkeys were identified, via home cage observations and subsequent multiple component and hierarchical analyses. Inter-species differences were detected regarding time budgets in both housing conditions by comparing current rhesus monkey datasets with our previously acquired cynomolgus datasets  (Camus et al. , 2013a,b). We highlighted speciesspecificity among the non-depressive populations while rhesus and cynomolgus depressive-like individuals did not differ much.
FINANCIAL SUPPORT This work was supported by Biothèque Primate-Centre National de la Recherche Scientifique Life Sciences Department (Erwan Bezard) and an unrestricted grant from Motac Neuroscience Ltd. , UK (Erwan Bezard). The funders had no role in study design, data collection and analysis, decision Collected detailed items (adapted from  Camus et al. , 2013a,b)  were grouped for multiple component analysis (MCA). Table  S2|  Locations, distances to nearest peer, body postures, and orientations items displayed by rhesus and cynomolgus monkeys. Collected detailed items (adapted from  Camus et al. , 2013a,b)  were grouped for multiple component analysis (MCA).
ACKNOWLEDGMENTS We thank the  Institute of Beijing Xierxin Biology Resource  and the  Fangcheng Gang Spring Biological Technology Development Corporation (China)  for hosting the observers ( Sandrine M. J. Camus ,  Céline Rochais ), animal care and general technical assistance. We thank  Dr. Daniel Ko  ( Motac neuroscience LTD ) for proofreading the manuscript. We thank  Labex Brain  for infrastructural support. Sandrine M. J. Camus  was supported through an unrestricted research grant from  Motac neuroscience LTD.
For chords composed of more than 4 notes, FC did not make any false identifications but reported only a subsample of the notes composing the chord. FC's idiosyncratic note-calendar mapping was tested receptively using 14 1000 ms notes ranging from B3 flat to D5 sharp, produced with Final ® software, and a computer screen presentation of days of the week and months of the year. In each trial, a day (or a month) was visually presented, together with a note presented aurally. The association could be either congruent or incongruent per FC's system of mapping. Days and months were visually presented (in 24-point Arial font) in the center of the screen and remained presented until a response was given. Trials were interleaved with a blank screen for 2000 ms. There were 168 trials in the notes-days experiment.
FC was asked if the sound presented corresponded to the correct day or month and responded by pressing with his right hand a green button for accurate (congruent) correspondences and a red button for the inaccurate (incongruent) correspondences. The experiment was run with PsychoPy software  (Peirce, 2007). FC was able to correctly identify 100% of the congruent and incongruent day-note associations (see Table  2 ). His RT for congruent trials (after removing 3SD outliers, which represented 4. 76% of the trials) was 2. 369 s (SD = 0. 446). His mean RT for incongruent trials (after removing 3SD outliers which represented 1. 19 % of the trials) was 2. 309 s (SD = 0. 475). There was no difference between congruent and incongruent conditions on mean RT (p > 0. 4). In the month-note experiment, FC made 6 mistakes and was able to identify correctly 96.
Test results Forty numbers were randomly chosen between 1200 and 14,000. The time correspondence of these numbers was then calculated (e. g. , 3520 = 58 min and 42 s). For incongruent trials, the time correspondence was modified by ±10 min. (e. g. , 3520 = 52 min and 10 s). There were 80 trials: in half (40 trials), numbers were presented with the correct time correspondence whereas in the other half, numbers were presented with an erroneous time correspondence. For each trial, one number and one time (in 24-point Arial font) were presented in the center of a computer screen until FC gave his response by pressing with his right hand the corresponding button "c" for the correct association and "n" for the incorrect association. The experiment was run with PsychoPy software  (Peirce, 2007). FC correctly identified congruent and incongruent associations in 100% of the trials (see Table  2 ).
The experiment was run with PsychoPy software  (Peirce, 2007). In each trial, a number was visually presented with the written name of the congruent or an incongruent category. Numbers and categories were visually presented in the center of the screen and remained presented until a response was given. Number and categories were written in black in 20-point Arial font on a white background, with a 2000 ms blank screen ISI. FC was asked to press a green button for the correct or congruent category and a red button for incongruent categories with his right hand. FC was 97. 9% accurate according to his own classification (see Table  2 ). His four errors were in the incongruent condition (95. 8% of correct identification). RTs were also analyzed. Errors and RTs over 3 SD were excluded from analyses (13.
Verbal stimuli consisted of 42 taxonomically unrelated concrete nouns selected from the French "Lexique" database  (New et al. , 2004 , http://www. lexique. org), randomly divided into six lists (7 words per list, 21 words for each encoding condition), equated for word length and occurrence frequency. The encoding phase consisted of three blocks of "music encoding" and three blocks of "silence encoding," intermixed with 30-s rest periods. In each block, seven words were displayed successively with either a music or a silence background. The audio stimulation started 15 s before the first word was displayed, continued during the sequential display of words, and ended 15 s after the last word. Words in each block were presented at a rate of 4 s per word (28 s for the sequential presentation of seven words).
They were then behaviorally tested for item and source-memory  (Glisky et al. , 1995). The retrieval test included the previously presented 42 words, together with 42 new words (lures) matched for word length and occurrence frequency. In a yes/no recognition task, participants were asked to say whether they had already seen each word before (yes/no button on the keyboard; item-memory task). If so, they were asked to indicate in which context they had seen it (music/silence/I do not know; source-memory task). In this way, we tested subjects' capacity to remember specific episodes. The presentation of task instructions and stimuli, as well as the recording of behavioral responses, were controlled by E-Prime software (Psychology Software Tools, Inc. ) running on a laptop with a 15 monitor.
fNIRS MEASUREMENTS An eight-channel fNIRS system (Oxymon Mk III, Artinis Medical Systems B. V. , The Netherlands) was used to measure the concentration changes of O 2 Hb and HHb (expressed in micromolar) using an age-dependent constant differential path-length factor given by 4. 99 + 0. 0067 × (age 0. 814)  (Duncan et al. , 1996). Data were acquired at a sampling frequency of 10 Hz. The eight fNIRS optodes (four emitters and four detectors) were placed symmetrically over the dorsal part of the PFC (Brodmann areas 46 and 9, EEG electrodes AF7/8, F5/6, F3/4 and AF3/4 of the international 10/10 system)  (Okamoto et al. , 2004; Jurcak et al. , 2007) , and the distance between each emitter and detector was fixed at 3. 5 cm (Figure  3B ). To optimize signal-to-noise ratio during the fNIRS recording, the eight optodes were masked from ambient light by a black plastic cap that was kept in contact with the scalp with elastic straps, and all cables were suspended from the ceiling to minimize movement artifacts  (Cui et al.
Since SPSS software reports a partial Eta-squared value, which have been demonstrated to overestimate effect sizes (see  Levine and Hullett, 2002) , we calculated Eta-squared by dividing Type III Sum of squares of each condition and interaction by the corrected total (i. e. , the sum of all Type III Sum of squares and error values computed in the statistics). The significance level was set at p < 0. 05.
Written informed consent was obtained from each participant in the study. Three participants gave responses more than twice the standard deviation from the mean of the group, and their data were removed from the analyses. Apparatus and stimuli. The stimuli were displayed on a 17-inch color flat monitor (1280 3 1024). All stimuli were handled and displayed using the Psychophysics Toolbox for Matlab  (Brainard 1997 45 ; Pelli 1997 46 ). Each stimulus was a light-gray disk (fifteen pixels in diameter, about 0. 38u of visual angle) moving on a black background. A push-button was situated in front of the screen at a comfortable distance (about 30 cm from the participant) to allow participants to give their responses. The stimulus moved vertically in a straight line, as described below, within a presentation window of 1000 3 740 pixels centered on the screen.
Acoustic model Using the CATT-Acoustics software 2  , 3D architectural acoustic models were created for the two environments shown in Fig. 1. The geometrical model of Environment 1 is shown in Fig. 2. This allowed us to digitally simulate the acoustical conditions of the two environments, and to play back virtual sounds as if these were reproduced inside the environment, at a given sound source and listener position. Room Impulse Response (RIR) measurements were performed at two positions for each environment (one position in the middle and one at the far end, near the staircase). The simulation's material definitions were adjusted so as to obtain the same room acoustical parameters, the reverberation time calculated in octave bands, between the simulated and measured RIR in order to minimize the differences between the real and the virtual environments.
Recordings were made for the real sound sources present in the corridors with the microphone placed as close as possible to the noise sources in order to minimize acoustical contributions of the room in the recordings. For sources that were artificially simulated in the real environments by playing audio files over loudspeakers, the same audio files were retained. Two audio samples were selected for the finger snap and footstep noise, allowing for source variation. A total of 3564 and 2511 signals were convolved for Environments 1 and 2 respectively. The convolved 9-channel 2 nd -order Ambisonic signals corresponding to the seven static sources were summed for each receiver position. Each of the resulting mixes was then read in a customised software platform, created using the MaxMSP programming environment, and played back synchronously in a loop (the length of the signals was approximately 2 minutes).
2 nd -order Ambisonic to binaural conversion The resulting 2 nd -order Ambisonic 9-channel audio stream, comprising the sum of the static sources, finger snapping, and footstep noise, was rendered binaurally over headphones employing the approach of virtual speakers (see  McKeag & McGrath (1996) ;  Noisternig et al. (2003) ). The conversion from Ambisonic to stereo binaural signals was realized through the development and implementation of an additional MaxMSP software module, employing a head orientation tracking device (XSens MTi ). The 9-channel synthesized/mixed 2 nd -order Ambisonic signal was processed in real-time, performing rotations in the Ambisonic domain as a function of the participant's head orientation. The rotated signal was then decoded on a virtual loudspeaker system with the sources placed on the vertices of a dodecahedron, at 1 m distance around the center.
These 12 decoded signals were then rendered as individual binaural sources via twelve instances of a binaural spatialization algorithm, converting each of the 12 monophonic signals to stereophonic binaural signals (see schematic architecture in Fig. 3 ). The 12 binauralized virtual loudspeaker signals were then summed and rendered to the participant, providing the complete virtual auditory environment. The binaural spatialization algorithm used was based on the convolution of the signal to be spatialized with a Head Related Impulse Response (HRIR) extracted from the IRCAM Listen HRTF database 3. More information about this approach can be found in  McKeag & McGrath (1996). The high performance spatialization engine provides binaural 3D audio rendering over headphones  (Katz et al. (2010) ).
Development summary In order to summarize and clarify/simplify the development exercise described in the previous sections, the following list can be considered: • In order to simulate digitally the acoustical conditions of the two environments, and to play back virtual sounds as if these were reproduced inside the environment, two 3D architectural acoustic models were created using the CATT-Acoustics software. • Due to the complexity of the rendered environments, in order to allow for the listener to move within the virtual environment and interactively listen to the 3D soundscape, a high performance hybrid method was developed. Navigation was limited to one dimension, and the movement was implemented through panning between pre-computed positions within the environment. Figure  4 : Blind individual performing virtual navigation using the joystick and wearing headphones equipped with orientation sensor.
Inferential statistics were performed to identify whether the differences including confidence ellipses real (red --) and virtual (blue --) navigation conditions. Confidence ellipse for Environment 1 excluding S7, who identified less than 20% elements, is shown (red -• -). Results from the preliminary study with 3D recordings condition ambisonic and binaural for common participants are also included for comparison. between groups were statistically significant. As the data sets were small and not normally distributed, exact Mann-Whitney tests were used. For Environment 1, correlation values for the real navigation did not differ significantly from the virtual (MW U = 5. 50, p = 0. 175). The effect size (r) was -0. 465. For Environment 2, correlation values for the real navigation did differ significantly from the virtual (MW U =< 0.
Acknowledgments The authors would like to thank all the participants who took part in the study, and in particular  Mr. Bernard Lebahr  for accepting to be photographed for Fig. 4. This research was supported in part by a grant from the  European Union  to  Michel Denis  (STREP "Wayfinding", Contract # 12959 ). Experiments were approved by the  Ethics Committee of the National Center for Scientific Research (Comité Opérationnel pour l'Ethique en Sciences de la Vie ). The authors would like to thank  Peter Norrie  ( DMU ) for assisting with some statistical analysis. Final thanks are offered to  Dr. Nakaya  for providing the bi-dimensional regression analysis spreadsheet.
Data processing In the oscillatory movements and walking tasks, raw data consisted of 3D coordinates in the 3D working space. Using a purposely built program for 3D motion capture analysis in Matlab (Mathworks Inc, MA, USA), the signal was first filtered using a low-pass Butterworth filter with a cut-off frequency of 5 Hz. The waveforms were then converted to the frequency domain using a fast Fourier transform (FFT). The dominant peak in the resulting spectrum of amplitudes was identified using a peak finding function in Matlab. Exploitable data thus consisted of the average frequency (Hz) of the 12 trials for each movement and each participant. It is noteworthy that not all markers' data were used because some markers were not relevant to analyze a limb's resonant frequency. For all the lower limb movements and the walking task (Figures  1B, C ) the ankle marker was used to compute the oscillation frequency.
Material Musical material consisted of six different songs purposely generated with the software Garage Band (Apple Inc. , CA, USA) using MIDI loops. MIDI loops were chosen because their tempo could be changed without provoking too much distortion. The exact composition of each song is given in the Appendix section 2. Every song was composed according to a 4/4 metric and started at a reference tempo (set in the software) of 60 BPM. The tempo increased incrementally by 6 BPM steps every 22 s. A particular tempo was maintained for 20 s and the transitions between two tempi lasted 2 s. In the end, participants heard 11 levels of tempo from 60 to 120 BPM. The total length of each song was 240 s (20 s * 11 levels + 2 s * 10 transitions). Each song was different in genre and instruments. A 1. 5 s fade-in/fade-out at the beginning and ending of each song was added in order to avoid startling the participants.
Data processing Raw data consisted of 3D (X, Y, Z) coordinates in space of all markers. Another Matlab program was purposely created to process the 3D Motion Capture data. Raw data was filtered using a one-dimensional order-7 median filter 3. In order to compute the movement "tempo 4  ," the signal was first de-trended (i. e. , centered around a 0 mean) to account for the eventual displacement of the participant inside the working area. We then extracted the onsets (in seconds) of the coordinates of all peaks along the movement axis on the same side (i. e. , a full cycle, back, and forth) using a peak finding function in Matlab. The tempo was then calculated by dividing 60 by the average inter-onset interval (IOI) for a whole 20 s segment. Transitions between tempi were not taken into account.
In total, the recording was divided into 13 segments. The first segment, referred to as "Baseline" corresponded to the first minute of silence during which participants performed the movement without any music. The 11 next segments were the segments when music was present (e. g. , segment 2 corresponds to 60 BPM music while segment 12 corresponds to 120 BPM music). The thirteenth segment, referred to as "Silence" corresponds to the last minute of silence at the end of the experiment. In this second study, no motion capture data was collected for the walking task. The walking tempo of participants was measured by video analysis. Participants walked for a little more than 1 min and the walking pace was calculated by measuring the number of steps taken in 60 s. This period of 60 s started after a few steps were taken.
DISCUSSION We investigated how the perception of rhythmic music influenced self-paced movements. Our focus was to unveil the basic mechanisms of entrainment to music through the interaction between motor resonance and motor constraints. In other words, we tried to uncover how motor resonance with music might influence self-paced oscillatory movements using different limbs. The data we collected are intricate. In order to understand what happened during the experiment, based on four different notable moments during each recording session, we computed three indices: Music Starting (Baseline/Second Segment comparison), Music Stopping (Twelfth Segment/Silence comparison) and Baseline-Silence Difference (Baseline/Silence comparison). These three indices gave us information on how different movements were influenced by the appearance and disappearance of a musical pulse.
MATERIALS AND METHODS Participants Data analyzed in this study were obtained from 1583 14-year-old adolescents, participants of the IMAGEN project, for which magnetic resonance images passing quality control procedures were available. Recruitment procedures have been described previously,  15  and written informed consent was obtained from all participants and their legal guardians. Individuals completed an extensive battery of neuropsychological, clinical, personality and drug use assessments online and at the testing centers. Participants were excluded if they had contraindications for magnetic resonance imaging (for example, metal implants and claustrophobia). Some individuals were only included in part of the analyses, depending on availability of the genotype, imaging and cognitive data for each participant.