dataesr/scientific-paragraphs-categorization huggingface dataset:
Inclusion in the study was performed in two steps. First, volunteer students who wished to participate in the study were registered on the website (www. i-share. fr). Second, students completed and validated the baseline questionnaire, collecting information on sociodemographic characteristics, personal and family medical history, student lifestyle and mental health issues. Each participant received an online follow-up questionnaire on each anniversary of the date of the completion of the baseline questionnaire. Our study used the data from the first two follow-ups available on July 9, 2018. For this study, we used the data from participants who had completed at least one of the two follow-up questionnaires. This study was approved by the National Commission for Information Technology and Liberties (CNIL) [DR-2013-019].
Fourth, we estimated a fully adjusted model considering ADHD symptoms, perceived stress and all the co-variables identified as potential confounders. We then compared the strength of the association between ADHD and suicidal ideation from the models. When all conditions for mediation were met: association between ADHD and perceived stress; association between perceived stress and suicidal ideation (step 1) and difference between the strength of ADHD effect on suicidal ideation (step 1 and 3)), we then performed a Structural Equation Modelling (SEM) to test the mediation pathway (using Lavaan package in R). The total and direct effects of ADHD on suicidal ideation were estimated. Then, the indirect effect of ADHD on suicidal ideation through perceived stress was estimated by the product of the effect of ADHD on stress and the effect of stress on suicidal ideation.
We estimated the proportion of the total effect explained by perceived stress (proportion mediated=indirect effect/total effect). Since gender is related to ADHD (males are overrepresented)  (Green and Rabiner, 2012; Weyandt and DuPaul, 2006; Weyandt and Dupaul, 2008)  and suicidal ideation (females are overrepresented)  (Mortier et al. , 2017b) , the contribution of ADHD to suicidal ideation via perceived stress could vary according to the gender. We then have conducted separate exploratory analyses for females and males. The R statistical software (R version 3. 6. 2) was used to conduct the analyses.
Results The flow chart is shown in Figure  1. A total of 4,374 students (among the 14,912 students recruited at baseline) had completed at least one of the two follow-up questionnaires, which made them eligible for the present study. Among them, 4,333 had no missing data on suicidal ideation at follow-up and were then included in the main analyses. Table  S1  (supplemental material) shows the comparison between participants (N= 4,333) versus nonparticipants (N=10,579) at follow-up. Participants were more often females (p<0. 001), smoked less tobacco (p<0. 001), consumed less cannabis (p<0. 001) and alcohol (p<0. 001), lived more often alone (p<0. 001), reported a higher overall life satisfaction (p<0. 001), had slightly lower scores of ADHD symptoms (p<0. 001) and perceived stress (p<0. 001).
Intracerebral electrode implantation and SEEG recording Intracerebral electrodes (Dixi Medical, Besançon, France) were stereotactically implanted in the patient's brain for purely clinical pur-poses, i. e. , in order to delineate the seizure onset zone  ( Talairach and Bancaud, 1973 ). The sites of electrode implantation were determined based on non-invasive data collected during an earlier phase of the investigation. Each 0. 8 mm diameter intracerebral electrode contains 8-15 independent recording contacts of 2 mm in length separated by 1. 5 mm from edge to edge ( Fig. 1 B , for details about the electrode implantation procedure, see  Salado et al. , 2018 ). The exact anatomical location of each recording contact was determined by coregistration of post-operative non-stereotactic CT-scan with a pre-operative T1weighted MRI.
Frequency domain processing We analyzed segments of SEEG corresponding to stimulation sequences (69-second segments, -2 s to + 67 s). The 69 s data segments were cropped to contain an integer number of 1. 2 Hz cycles beginning 2 s after the onset of the sequence until approximately 60 s, before stimulus fade-out (69 identity change cycles ≈ 58 s). Sequences were then averaged in the time-domain, separately for each condition and each participant. No further pre-processing was applied to the recordings. Subsequently, a Fast Fourier Transform (FFT) was applied to these averaged segments and amplitude spectra (see examples in Fig. 2 A ) were extracted for all contacts.
In addition, the effect of face inversion was computed for each contact as the amplitude difference between upright and inverted conditions. Contacts located in the same individually defined anatomical region (see section "Contact localization in the individual anatomy " below) were grouped, separately for the left and the right hemisphere. In each anatomical region, we statistically compared group-level FID amplitude differences between hemispheres using general linear mixed effect models implemented in the lme4 package  ( Bates et al. , 2015 )  in R v4. 0. 0. Statistical models were fitted using REML and p -values were obtained using Satterthwaite's approximation for degrees of freedom. Models included a random intercept per participant to account for both inter-participants variability and non-independence of contacts within a participant.
Discussion We report an original neural cartography of unfamiliar face individuation across the human VOTC, using data collected in a large sample of individuals implanted with intracerebral electrodes. To our knowledge, only a handful of studies reported human intracranial recordings during unfamiliar FID. Specifically,  Puce et al. (1999)  showed amplitude decreases of low-frequency late ECoG (Electrocorticography) responses in the ATL to repeated presentations of faces, while  Engell and McCarthy (2014)  described high-frequency activity decreases over the middle FG at an earlier latency with a subset of the same data. Using MVPA,  Ghuman et al. (2014)  found significant decoding of individual face images and features in 4 patients with recordings over the middle FG, and  Davidesco et al.
, 2018 Jonas et al. , , 2014 ; ; Meadows, 1974 ; Pitcher et al. , 2007 ). Such right hemispheric dominance is clearly supported by the present direct recordings of intracerebral neurophysiological responses, in line with effects observed on the scalp in the same paradigm  ( Liu-Shuang et al. , 2014 ; Rossion et al. , 2020 ). The lack of strong right hemispheric lateralization of this effect in fMRI may be due to the slow temporal dynamics of the fMRI responses, which favors a spread of responses to the other hemisphere. Importantly, despite their wide cortical distribution, high-level unfamiliar FID responses are not found everywhere in the occipito-temporal cortex. Rather, they are anatomically confined to the right IOG, the right latFG and extending to the bilateral ATL. Whether this cortical strip appears continuous due to the averaging methods across individual brains or reflects a genuine functional organization cannot be determined with the present dataset.
, 2020 ). The FID response is also strong when head orientation varies randomly in depth (i. e. full-front faces, profile, 3 4 profile faces, etc. ) but decreases linearly over the right occipito-temporal cortex with increasing viewpoint variation  ( Rossion et al. , 2020 ) , indicating that this response is viewpoint-dependent to some extent. Exploring the neural basis of this viewpoint-dependency with the present intracerebral recording approach would be highly valuable, in particular to evaluate whether there is a progressive increase of invariance to changes in viewing conditions in FID responses from posterior (IOG) to more anterior (latFG or antFG) regions in the human brain (see  Ramirez, 2018 ). An important issue to consider is that the data reported here was collected on a specific clinical population, namely patients with longterm drug-resistant epilepsy.
| Preprocessing All EEG analyses were carried out using Letswave 5. c (https://www. letsw ave. org) and Matlab 2014 (The Mathworks) and followed procedures validated in several studies using letter strings or face and object stimuli (see e. g. Retter & Rossion, 2016). After band-pass filtering between 0. 1 and 100 Hz, EEG data were segmented to include 2 s before and after each sequence, resulting in 44 s segments. Data files were then downsampled to 256 Hz to reduce file size. Artifact-ridden electrodes were replaced using linear interpolation with neighboring electrodes. All electrodes were re-referenced to the common average. EEG recordings were then segmented again from stimulation onset until 39. 996 s, corresponding exactly to 48 complete 1. 2 Hz cycles within stimulation. This is the largest amount of complete cycles of 833 ms at the frequency of deviant stimuli (1.
| Overall responses and electrodes of interest In both grades, significant responses (Z score >2. 58) were found in all conditions (PF-W, PF-PW, PW-W and PSY-SY) at exactly 6 Hz and several harmonics at middle occipital (MO) electrodes. As determined by grand-averaged data (see Methods), the highest number of consecutive significant harmonics were five in grade 1 (from 6 to 30 Hz) and seven in grade 2 (from 6 to 42 Hz) (Figure  2 ). In order to select an identical number of harmonics across grades, the sum of baseline subtracted amplitudes was computed on the highest number of harmonics (i. e. seven in grade 2). Electrodes were then ranked according to their largest amplitude values. This ranking procedure revealed that in all conditions and in both grades, the largest response was recorded at three MO electrodes: O1 (3.
| Overall responses and electrodes of interest Discrimination responses were significant (Z scores >2. 58) in both grades for letter strings (words, pseudowords) and symbol strings within their respective visual control stimuli (PF-W, PF-PW and PSY-SY conditions), at exactly 1. 2 Hz and several harmonics at several electrodes. On the contrary, for words within pseudowords (PW-W condition), there was no significant response (Figure  3 ). Significant harmonics were determined on the grand-averaged data for all participants, and per condition separately. A maximum of five consecutive harmonics (from 1. 2 to 7. 2 Hz, excluding the base rate at 6 Hz) were found to be significant in grade 2, and four in grade 1 (see Methods). We have thus computed the sum of five harmonics for each condition, in order to have the same number of summed harmonics in the compared conditions (Figure  3 ).
with deviant letter strings (words or pseudowords, N = 20 each) inserted in base pseudofont strings (PF, N = 20). The third condition assessed lexical access, with deviant words (W, N = 20) inserted in base pseudowords (PW, N = 20). The last condition, which aimed at controlling for visual familiarity and for non-specific/general age-related changes, assessed discrimination of familiar keyboard symbol strings (SY, N = 20) inserted in base pseudosymbol strings (PSY, N = 20). Words were selected from the Manulex database (Lété, Sprenger-Charolles, & Colé, 2004) and were constituted of four (N = 10) or five letters (N = 10). Pseudowords were pronounceable letter strings respecting the phonological rules in French. They.
Given the far less widespread prevalence of studies investigating footedness in general, it is unsurprising that the items of the WFQ have not been investigated with such rigor as is the case for the EHI. Up until now, factorial validity of the WFQ has only been compared between the mobility and the stability subscales of the questionnaire  (Kapreli et al. , 2015). Since the study found high correlations between the two subscales, it was concluded that the WFQ subscales measure a single footedness factor. While the two subscales seem to measure similar constructs of footedness, a specific investigation of individual items on the mobility subscale has not supported a one-dimensional measure of footedness. Tran and Voracek (2016)  used psychometric measures in a well-powered sample and could show that a two-dimensional rather than a onedimensional model provides a much better fit to the data for items that are also used in the WFQ.
EEG Recording, Preprocessing, and Analysis EEG signals were recorded with a mobile recording system (LiveAmp 32, Brain Products GmbH, Gilching, Germany). The LiveAmp 32 comprises 32 Ag-AgCl electrodes arranged according to the international 10-20 system (C3/C4, FP1/FP2, F3/F4, F7/F8, FC1/FC2, FC5/FC6, FT9/FT10, T7/T8, CP1/CP2, CP5/CP6, TP9/TP10, P3/P4, P7/P8, and O1/O2). The FCz electrode served as primary reference during recording. Signals were amplified using a wireless amplifier (analog-to-digital conversion: 24-bit) and recorded using the Brain Vision analyzer software at a sampling rate of 1000 Hz. Impedances were kept below 10 kHz during the recording session to ensure good signal quality. The EEG system furthermore featured three acceleration sensors in the X, Y, and Z direction located at the backside of the skull that recorded movements of the head.
The signals were preprocessed offline using the Brain Vision Analyzer (Brain Products GmbH, Gilching, Germany). Raw data were filtered from 0. 1 Hz (high pass filter) to 30 Hz (low pass filter) at a slope of 24 dB/octave. After filtering, all signals were manually inspected to exclude sections containing technical artifacts and to identify channels with poor recording quality. To remove systematic artifacts such as vertical and horizontal eye movements or pulse-related signals, we applied an infomax independent component analysis (ICA) to the remaining data. The FCz channel and channels of poor signal quality were then recalculated via topographic interpolation. To analyze the data, tasks were first epoched as a whole (60 s duration) and baseline-corrected using a 500 ms window prior to trial onset as the baseline.
Statistical Analysis Statistical analyses were performed using SPSS (version 21, Chicago, IL, United States). For the alpha band analysis of the EHI session, asymmetric electrode sites were identified across all task conditions by applying a repeated-measures ANCOVA with the within-subjects factors side of task performance (left or right) and electrode pair as well as the between-subjects factor handedness. We used the X, Y, and Z acceleration sensor signals during the task segments as covariates to identify whether head movements during these behaviors had a significant influence on task-related variables in the recordings. Here, we used the raw signal of the accelerometers as covariate as we wanted to identify how actual rather than processed movement signals influence the physiological signal.
Since these results are conform to a wide body of literature on motor asymmetries, we are confident in their validity. Nonetheless, it has to be noted that for example a large-scale imaging study on cortical asymmetry could for example not replicate effects found in previous studies with smaller sample sizes  (Guadalupe et al. , 2014). Therefore, the results of the present study need to be replicated and extended in larger datasets to ensure the of our findings and to clarify whether the nonsignificant difference between items was a result of a lack in statistical power. Another limitation of the study can be found in the a priori exclusion of mixed-handers as this group might have been informative regarding the specificity of our findings for left-and right-handers. We chose to exclude this group from the current study as we found very few mixed-handers according to the cut-offs used in this study during the prescreening process.
However, there was a considerable fraction of mixed-footers in the dataset that allowed for a comparison between left-, mixed-, and right-footers. We found that mixedfooters resemble right-footers in their neural activity patterns, a results that might have been due to the majority of mixedfooters being lateralized to the right rather than to the left (n = 7 for LQ < 0, n = 12 for LQ > 0). Interestingly, the mixed-footers seemed to be even stronger lateralized compared to right-handers. However, this result might have been due to variance in the signal and the reduced sample sizes per group. Finally, it should be noted that we identified the electrode pair of interest by using the behavioral assessment of handedness. This induces a certain circularity into the analysis as we try to distinguish between left-and righthanders on the physiological level, but used the EHI score as a tool to find the strongest distinction between them.
Institute of Innovative Research, Tokyo Institute of Technology, Japan Network analysis represents a valuable and flexible framework to understand the structure of individual interactions at the population level in animal societies. The versatility of network representations is moreover suited to different types of datasets describing these interactions. However, depending on the data collection method, different pictures of the social bonds between individuals could a priori emerge. Understanding how the data collection method influences the description of the social structure of a group is thus essential to assess the reliability of social studies based on different types of data. This is however rarely feasible, especially for animal groups, where data collection is often challenging. Here, we address this issue by comparing datasets of interactions between primates collected through two different methods: behavioral observations and wearable proximity sensors.
Moreover, observations are almost always biased to some extent because, for example, the visibility of animals is not uniform and some interactions are more easily defined and recognized than others (see e. g. [22] ). Recently, a number of technological developments have started to be adapted and implemented to gather high-resolution behavioral data on non-human animals, leading to the adoption of the term reality mining, widely used in computational social sciences for the study of human social behavior and relations  [23] , to the case of non-human animal societies  [24]. Machine-sensed data concerning the behaviour of animals can indeed now be collected and, most importantly, analysed. We refer to  [25]  for an overview of existing and emerging technologies used to collect data on movements, behaviour and interactions within animal groups.
Here, we address this issue by collecting, describing, analyzing and comparing two data sets based on dyadic interactions between individuals belonging to a group of Guinea Baboons (Papio papio). The data span a time-window of almost one month between June and July 2019 and were collected through two different methods: (i) behavioral observations by trained human observers and (ii) an infrastructure based on wearable sensors (see http://www. sociopatterns. org/). For these two data sets, we first test the agreement between observations and the sensors data at the level of single events: we systematically check whether an observed interaction was also registered by the sensors. Overall, only a limited fraction of observed interaction events were registered, with strong fluctuations depending on the day of observation and type of behavior.
However, and despite this poor agreement at the level of single events, we show that the timeaggregated networks, and hence the pictures of the group social structure, are remarkably similar. Finally, we analyse the amount of time that it takes using each data collection method to obtain a robust social network, by comparing the social networks obtained using different time aggregation windows. Strikingly, the social network obtained with just one day of sensor data is very similar to the one based on the aggregation over one whole month of data. Comparatively, the network obtained from observations fluctuates more between short and long aggregation windows because of stronger sampling effects. This shows the potential of wearable sensors infrastructures to detect changes in a social group organization on short timescales and to also monitor its long term evolution.
5 m) of individuals A and B  [14]. Moreover, the radio frequency emitted by the RFID tags was absorbed by body water, so that the radio packets tended to propagate mostly towards the front of the individual wearing the device. The packets exchange rate depended thus on the mutual orientation of the individuals and the infrastructure detected mainly face-to-face contacts. The detected spatial proximity relations were relayed from RFID tags to radio receivers (RFID readers), which were installed around the enclosure and connected to a local area network (LAN). A central server received the data, timestamping and storing each event. Data were finally aggregated with a temporal resolution of 20 seconds (for more details see  [14] ): we thus defined two individuals to be in contact during a 20s time window if their sensors exchanged at least one packet during that interval, and the contact event was considered over when the sensors did not exchange packets over a 20s interval.
In the following, we will refer to the observed behaviors corresponding to interactions as 'interactions' or 'observed interactions', and to the contacts collected by the sensors as 'contacts' or 'contact events'. For the first ten days of data collection (June 13 th -23 rd ) only two readers were installed around the enclosure, whereas in the successive days a third reader was added to ensure a better coverage. The data collection went on even after the observation period was over and is on-going at the time of writing of this paper. We consider here mainly the data collected between June 13 th and July 10 th , 2019, i. e. , during the period of the observations, and also use the data collected afterwards and until August 27 th to assess stability over longer time scales.
Data analysis Comparison of the two data sets at the level of single events. We compute the fraction of observed interactions that were also detected by the wearable sensors infrastructure as follows. Each observed interaction event involves two individuals i and j and is assigned a time t (for point events) or an interval [t start , t stop ] (for state events), with t start and t stop the moments of beginning and end of the interaction, respectively. Note that the observed interactions are often directed, with an actor individual and a recipient individual. However, since the proximity events registered by the wearable sensors are not directed, we consider undirected versions of the behavioral data (i. e. , the direction of the interaction is not taken into account). An observed interaction event is then considered as tracked if at least one RFID packet was exchanged between the sensors of individuals i and j within the time window [t -∆t, t + ∆t] or [t start -∆t, t end + ∆t], for point and state events respectively, where ∆t is a tolerance interval.
Single interaction and contact events Behavioral data The total number of behaviors recorded for the entire group of 19 individuals is 5, 377. From this full data set of observations we keep just the behaviors involving the 13 individuals that were carrying collars with wearable sensors. Among the 995 observed interactions regarding this juvenile/adult subgroup, 944 (∼ 95%) are affiliative social behaviors (Grooming, Resting, Presenting, Grunting-Lipsmacking, Touching, Mounting, Embracing, Playing with) which are the most relevant to this study. Moreover, grooming and (social) resting represent more than 98% of the state events and ∼ 65% of the total. Figure  2  represents the distributions of durations for the observed interactions (i. e. , with associated duration) both for the subgroup of collared individuals and for the whole group, which includes very young juveniles and babies.