dataesr/scientific-paragraphs-categorization huggingface dataset:
Data sources We conducted a systematic literature search of MEDLINE, Embase, and PsycInfo databases for studies published from January 1st, 1960 to December 31st, 2020. The Medical Subject Heading (MeSH) terms "suicide", "attempted suicide" and "completed suicide" were combined with the MeSH terms "intelligence tests", "academic performance", and "educational measurement", and with the Title/Abstract (TIAB) terms "Suicide" and "Intellectual Quotient", "Intelligence Quotient", "Intelligence", "Intellectual ability", "Academic performance", "Academic outcome", "School performance", "School grades", "Mathematics", "Reading" "Wechsler Intelligence Scale for Children", "WISC", "Adapted Test for Everyday Attention for Children", "Raven* Standard Progressive Matrices", "Wide Range Achievement Test", WRAT3, and "OTIS intelligence test".
Acknowledgements MO holds a  Chercheur-Boursier Junior 1 award  from the  Fonds de Recherche du Québec en Santé  and is funded by the  American Foundation for Suicide Prevention Young Investigator Award. MCG holds a Canada Research Chair (tier 2) in Youth Mental Health and Suicide Prevention from the  Canadian Institutes of Health Research (CIHR). GT holds a Canada Research Chair (tier 1) in Major Depressive Disorder and Suicide from the  CIHR. MO, GT, and MCGi are supported by the  Fonds de recherche du Québec -Santé (FRQS)  through the  Quebec Network on Suicide ,  Mood Disorders  and  Related Disorders.
Exteroceptive measures The European Test of Olfactory Capabilities (65) was used to assess olfactory sensitivity. Participants were asked to select a bottle (from 16 sets of four bottles) that contains a speci c odor (i. e. , discrimination), and state the nature of this odor (i. e. , identi cation). Participants were also asked to evaluate the hedonic value of the detected odor. HRV HRV was measured using heartbeat interval data recorded over a 10-minute period using an electrocardiogram (EKG), with the BIOPAC system (MP35 Systems; CE; 1 kHz sampling rate) and three electrodes. At baseline, subjects were asked to sit with their knees bent and their palms pointing upwards at rest. The recording took place in the afternoon, after lunch on the rst day of the workshop. Measures of the response to the stress situation were recorded before, and at the end of the simulation exercises.
The HRV analysis followed guidelines (66-67). Circadian variation was accounted for using the PyHRV python library (68). The following data were also recorded: weight, height, BMI, IMG, smoking habits, most recent alcohol intake (> 24h), most recent caffeine (coffee/ tea) consumption (> 1h), most recent meal (> 2h), most recent physical activity (> 12h), and sleep quality on the day of the simulation exercise, and the previous day. Raw EKG data were ltered between 3 and 45 Hz using a nite impulse response bandpass lter. The lter order was set to 300 (0. 3 times the sampling rate). R-peaks were automatically detected using the BioSPPy python library (69). Once the signal had been ltered, a Hamilton segmentation was performed, followed by an R-peak correction with a tolerance set to 0. 05. The validity of the R-wave detection was manually examined to ensure correct detection.
If an EKG sequence was too noisy when viewing the superposition of all QRS complexes, a time interval was manually removed to improve data quality. RR intervals were detected automatically, with the HRV analysis module using linear interpolation, and corrected manually for artifacts and ectopic beats. In the case of outliers, the RR intervals considered as correct were manually modi ed. Time domain analysis: time-domain HRV measurements included: mean RR (the mean interbeat interval); SDNN (the standard deviation of the normal-to-normal RR interval), which is composed of both sympathetic and parasympathetic activity; RMSSD (root mean square of successive differences between adjacent RR intervals), which re ects parasympathetic activity; and pNN50 (the percentage of adjacent NN intervals that differ from each other by more than 50 ms).
Data collection The study was presented to participants on the rst day of the workshop. They signed the clinical consent form, inclusion and exclusion criteria were checked, and baseline (psychological, physiological, and sensory) measures were recorded in the afternoon, without PPE. The three simulations took place on the last day of the workshop, in the morning, with participants wearing CBRN PPE, and a respiratory ltering device. The following pre-training measures were recorded: emotions (SPANE), anxiety (STAI-Y), and HRV (a 10minute EKG recording in a seated position). At the end of each of the three simulation exercises a debrie ng was held, and post-simulation measures were recorded after this debrie ng with a series of questionnaires that assessed state anxiety as perceived stress, and mental load.
Data analysis Data were analyzed using Python (Python Software Foundation, Wilmington, v3. 8) and JASP (JASP, Amsterdam, v 0. 16. 2). RMSSD post-simulation data were standardized prior to clustering. With respect to the stress response, the homoscedasticity of variance and the normality of the distribution were con rmed, justifying the use of parametric ANOVA and post-hoc tests. When these criteria were not met, non-parametric tests were used. With respect to the HRV response, we ran a clustering analysis based on the post-training median RMSSD (i. e. , a stress-adapted subject versus a non-stress-adapted subject). The median was used to divide subjects into two groups: the 'High Parasympathetic' group (n = 14), referred to in the following as the HP group, and the 'Low Parasympathetic' group (n = 14), referred to as the LP group.
Discussion Our study evaluated the impact of a CBRN simulation on participants' psychological, cognitive, physiological, and exteroceptive capacities. The aims were to determine: (i) how these capacities deteriorate; (ii) which psychophysiological pro les are most at risk; and (iii) how the risk can be reduced, and what countermeasures can be used. Our results con rm that medical trainings generate a state of anxiety among participants the preceding day. State anxiety (measured using the STAI-Y scale), decreased signi cantly between pre-training and the rst simulation, before stabilizing during the following simulations. This con rms that the simulations generate anticipatory anxiety and is a and a good stress model to study. Our subjective, psychological data are con rmed by the HRV, as mean RR decreased signi cantly between baseline and pre-training, and between baseline and post-training (a decrease was also observed between pre-training and post-training, but it was not signi cant).
Such data could be valuable in evaluating the operational consequences, depending on the operator's pro le. Concerning the choice of data, it would have been interesting to evaluate the psychological impact of the simulation on the fear of a negative evaluation. The latter is a sub-dimension of anxiety that contributes to the stress response in a social evaluation context and can create an attentional bias  (74). We could also have administered a questionnaire to assess the participant's personality, as this would have provided additional data on dispositional characteristics. Concerning the HP / LP clusters, the choice to separate the two groups based on the post-simulation RMSSD measure is questionable. Our goal was to observe the psycho-physiological pro le of individuals, based on a situational physiological factor.
Methods: Data collection started on May 4, 2020, and ended on June 10, 2020, which corresponds to the first wave of the COVID-19 pandemic in Europe. At the time the study took place, French-speaking countries in Europe (France, Switzerland, Belgium, and Luxembourg) all implemented lockdown or semilockdown measures. The survey consisted of a questionnaire collecting demographic information (sex, age, education level, and country of residence) and information about socioeconomic circumstances during the first lockdown (eg, economic situation, housing, and employment status) and was followed by several instruments assessing various psychological and health-related constructs. Inclusion criteria for the study were being at least 18 years of age and having a good understanding of French. Self-report data were collected from 725 participants aged 18-77 (mean 33.
Procedure Participants for this study were recruited using an online survey (created with Qualtrics), which was disseminated via social media (ie, Twitter, LinkedIn, Facebook, and Instagram). The study was also disseminated via the research networks of the authors and the scientific societies they are affiliated with. Data collection started on May 4, 2020, and ended on June 10, 2020, which corresponds to the first wave of the COVID-19 pandemic in Europe. At the time this study took place, French-speaking countries in Europe (France, Switzerland, Belgium, and Luxembourg) all implemented lockdown or semilockdown measures. The survey consisted of a questionnaire collecting demographic information (sex, age, education level, and country of residence) and information about socioeconomic circumstances during the first lockdown (eg, economic situation, housing, and employment status) and was followed by several instruments assessing various psychological and health-related constructs.
Traditional statistics (Mann-Whitney U test, Spearman rank correlations, Kruskal-Wallis test, Wilcoxon signed-rank test, and multiple linear regression) were computed using R version 4. 0. 3 (R Foundation for Statistical Computing), and machine learning analyses (elastic net regression) were computed using the Scikit-learn version 0. 24 Python module  [64]. As most study variables did not follow a normal distribution, preliminary analyses were conducted to support the use of a linear supervised machine learning-based elastic net regression. We thus computed 1 traditional multiple linear regression and 2 generalized linear models (negative binomial and quasi-Poisson regressions). These 3 models all presented a significant P value (<. 001) and showed similar results. Additional preliminary analyses are available from the OSF  [36].
The network is shown in Fig. 11 , on which we use the community detection algorithm of the software Gephi  [37] , based on modularity maximization, to obtain the three following groups: • group I (blue): node activity interduration, edge weight, size of connected components, ETN vector and (2,1)-ETN weight; • group II (orange): node, edge and newborn edge activity duration, degree assortativity and (3,1)-ETN weight; • group III (green): edge activity interduration, events activity duration and clustering coefficient.
The class of models we have considered could also be extended, e. g. by adding group memberships, or by considering various types of Hebbian processes: delayed or anti-Hebbian process, or allowing negative interactions and possibly negative social bonds  [34, 35]. The empirical temporal networks we use represent face-to-face interaction data. Individuals are represented as nodes, and an edge is drawn between two nodes each time the associated individuals are interacting with each other. In practice, interactions are detected by wearable sensors that exchange low-power radio signals  [1, 2]. This typically allows to detect face-to-face close proximity (∼ 1 meter) with a temporal resolution of about 20 seconds. The data sets we consider here are publicly available thanks to two independent collaborations.
B. Statistical properties Empirical temporal networks show broad distributions for many observables  [6] , which indicates strong fluctuations as well as the absence of any characteristic scale. These distributions are very similar for temporal networks obtained in various social contexts, which indicates that their statistical properties may be emergent properties that do not depend strongly on the microscopic dynamics of human relationships. Instead they may depend on a small number of features of these relationships, which would make it possible to obtain realistic temporal networks without a deep understanding of human behaviour. We compare the statistical properties of the empirical data sets with respect to the observables we have considered in this paper. For the point observables studied, conferences show different properties from the two schools and the workplace.
Conferences have a high clustering coefficient (∼ 0. 7) and a negative degree assortativity (∼ -0. 1). The three other data sets have a lower clustering coefficient (0. 3-0. 5) and a positive degree assortativity (0. 02-0. 07). On the other hand, distribution and vector observables show similar properties across data sets (as illustrated in Supplemental Figure  1 ) despite their differences in number of nodes and edge activity (the mean number of interactions per time step). In particular the duration of an interaction follows the same statistics in the conferences data sets, where the edge activity is high, as in the workplace data set, where the edge activity is low. Thus it seems that the duration of a face-to-face interaction is not influenced by how rare those interactions are. As the number of observables is high, it is useful to introduce a global similarity matrix relative to each type of observable.
Background Meta-analytic findings documented a substantial impact of self-esteem on a broad range of psychological and behavioral indicators, thus highlighting its high clinical relevance. Proving a simple and costeffective measure of global self-esteem to the Arabic-speaking community, who mostly live in low-and middleincome countries, and where research may be challenging, would be highly valuable. In this context, we sought to investigate the psychometric characteristics of an Arabic translation of the Single-Item Self-Esteem Scale (A-SISE) in terms of factor structure, reliability, and construct validity. Methods A total of 451 participants were enrolled between October and December 2022. An anonymous selfadministered Google Forms link was shared on WhatsApp. To examine the factor structure of the A-SISE, we used the FACTOR software.
Data treatment There were no missing responses in the dataset. To examine the factor structure of the A-SISE, we used an exploratory factor analysis, using a principal component analysis using the FACTOR software  [71]. We verified all requirements related to item-communality  [72] , average item correlations, and item-total correlations  [73]. The Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy (which should ideally be ≥ 0. 80) and Bartlett's test of sphericity (which should be significant) ensured the adequacy of our sample  [74]. The procedure followed for determining the number of dimensions was the Parallel Analysis (PA)  [75] , using the Pearson correlation matrix. Weighted Root Mean Square Residual (WRMR) were also calculated to assess the model fit (values < 1 have been recommended to represent good fit  [76].
Material and design The experiment was developed and presented using Opensesame  (Mathôt et al. , 2012)  on a 15" laptop. The experimental material consisted of 240 target items to recall (120 words and 120 pseudowords). Words were selected from the Lexique3 database  (New et al. , 2001)  and were high frequency (M = 202. 33, SD = 104. 86, in occurrence per million) singular common nouns, four to eight letters and one to two syllables long. Pseudowords were generated from the word list using the Wuggy software  (Keuleers & Brysbaert, 2010) , applying the following options: match letter and subsyllabic segments length and match transition frequencies. One pseudoword was selected for each corresponding word, avoiding real words phonological equivalents, and trying to respect French orthographical coherence.
To increase data size for delayed recall and avoid a floor effect, the experiment was constituted of 3 blocks of 16 trials. At the end of each block, participants were required to complete an unrelated distraction task of multiplication problems (e. g. , 4 × 5 = 20?) for 1 min. After this distracting task, participants performed a delayed recall in which they were invited to recall the 80 memoranda of the block in any order and without time limit. This delayed recall stopped on participant's input, showing a break screen before continuing to the next block. After the last delayed recall, participants completed a form on maintenance strategies used, entering a use percentage for each strategy proposed. A fillable line was also added, allowing them to add a specific strategy that they used but was not listed in the form.
Strategies By design, data collected through the strategies survey was unsuited for statistical analyses as it consisted mainly of between one and three values for each participant and only zeros for every other strategy. Nonetheless, descriptive analysis could provide some insight in possible differences when the task was completed with words or pseudowords (Figure  6 ). First, we observed that verbal rehearsal was overwhelmingly more used that any other strategy (words: M = 56. 32; pseudowords: M = 60. 15). There seemed to be no notable difference between the two participants groups, except for a slight increase in use of stories (words: M = 19. 95; pseudowords: M = 9. 82) and visual scenes (words: M = 9. 07; pseudowords: M = 3. 32) when maintaining words. Reported use of attentional refreshing was low (words: M = 1.
Main analyses Bayesian analyses of variance were conducted at immediate and delayed recall on mean correct recall percentage, using cognitive load, number of presentations and item type as predictive factors and subjects as a random variable. Each combination of main effects and interactions was tested against the null model (including only a random effect of subjects). BF inclusion or BF exclusion was calculated on matching models for each effect. As in Experiment 1, default priors provided by the BayesFactor package were used. Because of the design used, the number of presentations variable had three levels at immediate recall (1 vs. 2 vs. 3) but only two levels at delayed recall (1 vs. 3). As in Experiment 1, survivability of relevant interactions to a monotonic transformation of the measurement scale was tested using the same method.
FUNDING INFORMATION This work was performed within the framework of the  LABEX CORTEX  ( ANR-11-LABX-0042 ) of  Université de Lyon , within the program " Investissements d'Avenir"  ( ANR-11-IDEX-0007 ) operated by the  French National Research Agency (ANR). This research was supported in part by a grant from the  Agence Nationale de la Recherche  ( ANR-18-CE28-0012 ) awarded to  Gaën Plancher , Project REFLECTOR and by the  Institut Universitaire de France.
Introduction: Mild Neurocognitive Disorders (mNCD) are characterized by a subtle decline in cognitive performance. Discourse analysis is a research method that investigates the verbal production of patients and has been used to screen for subtle cognitive impairment. Studies using discourse analysis are, however, rarely replicated. One reason for this is likely to be the lack of consistency across studies regarding the selection and de nition of relevant linguistic features. The goal of this review is to identify reliable linguistic markers for the detection of subtle cognitive impairment in connected speech, and generate hypotheses associating these markers with underlying cognitive functions in a given discourse context. Methods and analysis: Following the PRISMA guidelines, we will conduct a systematic review including ve databases: PubMed, ScienceDirect, Embase, Web of Science, Google Scholar.
Data extraction and inclusion Data from four categories will be manually extracted: • Population: Number and age for both case and control populations. • Tasks: Type involving connected speech. • Tests: Neuropsychological tests used, cognitive domains tested. • Methods: Linguistic features, statistical tests, outcomes. In the case of missing information we will contact the authors to request the data. Unavailable data will be identi ed as such in our summary tables. All extracted data will be recorded in an excel database and summarised in narrative and/or table formats.
Data synthesis This section will provide information on the reliability of linguistic features as markers for subtle cognitive decline. We will incorporate the data in a narrative synthesis organised according to the study population, connected speech tasks, analysed linguistic features, and methods used to compare cases and controls. We will develop descriptive themes to describe the data, look for similarities and differences across studies, and when possible, merge similar data into higher-level categories and themes. For example, we will report the description of linguistic features and the measures for each article. We will then group and label those elements that share similar characteristics. We aim to provide easy-to-use guidelines to harmonise linguistic data across studies. The reliability of a marker will be evaluated according to 1) the clarity with which it is described in the paper 2) Its ability to discriminate between individuals with mNCD and control subjects.
Driving simulator A high-fidelity driving simulator was built for the purpose of the CMI Project at IRT SystemX (Palaiseau, France) where this study took place (see Figure  1 ). It was composed of a full-car cab with seven visual channels, providing a high-fidelity graphic resolution and realistic driving environment. Three visual channels were located in front of the vehicle providing a 180° field of view. Three visual channels were display screens showing the view from the rearview and side mirrors. The remaining visual channel was a virtual instrument's cluster displaying the instrument cluster. The SCANeR software version 1. 9 (SCANeR Software, 2020) was used to simulate the driving environment. The simulated vehicle had an automatic gearbox and two modes of automated driving could be activated (i.
Eye-tracking glasses Our choice of eye-tracking technique took into consideration the areas fixated by drivers during the event of the scenarios. This measure allowed evaluating where the participants looked for information depending on the situation. The SMI Eye-Tracking Glasses, a pair of glasses equipped with infrared sensors to monitor eye movements (saccades, fixations, and blinks), and a frontal camera to record the field of vision were used. The eye-tracking data were recorded at a sampling frequency of 60 Hz. The glasses were connected to a mobile phone (Samsung Galaxy Note 4) that allowed us to power the glasses, calibrate the gaze measures, display the visual behavior in real time, and store the video and audio recordings. Eye-tracking data were extracted using the BeGaze version 3.
7 software. We also used this software to map the fixations. This mapping consisted in associating each recorded fixation with an Area Of Interest (AOI) and was carried out by a third-party project partner. The AOIs were the instrument's cluster, the exterior environment, and the interior environment. The BeGaze software then calculated the fixation count and duration for each AOI. The proximity to the limits of the ACC system is represented by a vertical halo (e. g. , fog area). The proximity to the limits of the LCA is represented by a horizontal halo (e. g. , bent road). Adapted/Reproduced with permission from IRT SystemX. 10. 3389/fpsyg. 2023. 1107847 Frontiers in Psychology 07 frontiersin. org.
Acknowledgments The authors would like to thank  Pierre-Vincent Paubel  for his contribution to data processing and eye-tracking data collection. We thank the  Renault Group  for its participation in the  CMI  project, especially  Jinghao Wang ,  Yanna Carli ,  Clément Tichadou , and  Benjamin Vailleau. We also thank the members of the  CMI  project: at  IRT SystemX Virgil Rousseau ,  Clarisse Lawson-Guidigbe ,  Kahina Amokrane Ferka , at  Valeo Nicolas Brice ,  Jean-Marc Tissot , and  Alexandre Joannides , at  Ircam Nicolas Midariis  and  Andrea Cera.