dataesr/scientific-paragraphs-categorization huggingface dataset:
The 10-20 system is based on the relationship between the electrode location and the sub-regions of the cerebral cortex. Each point on the system indicates a possible position for the electrode, with a letter to identify the lobes and a number to identify the hemisphere. The 10-20 system ensures accurate electrode placement, enabling researchers to obtain reliable EEG signals. Spontaneous EEG signals were continuously registered using the Emotiv TM Epoc software with a down sampling rate of 128 Hz after filtering. Emotiv Epoc + headsets were connected to the Emotiv Pro software, which first checks the contact quality of the electrodes. During data acquisition with this system, the contact quality is given as 0 = no contact, 1 = low contact quality, 2 = medium contact quality, 3 = good contact quality, and 4 = excellent contact quality.
All electrodes were kept on quality level 4 during the experiment. Four scalp location (Figure  4 ), frontal (F3, F4, AF3, AF4, F7, F8), fronto-central (FC5, FC6), temporal (T7, T8) and parietaloccipital (O1, O2, P7, P8) were chosen to gather the signals from the 14 electrodes  (Lord and Opacka-Juffry, 2016; Wind et al. , 2020). Once the raw signals were recorded in Emotiv Pro, we exported them to MATLAB (EEGLAB) for processing the signals.
Measurement and analysis Prior to the test sessions, the Polar H10 electrodes were moistened with room-temperature water prior to being placed on the xiphoid process of the sternum with the chest strap fitted around the participant's chest (just below the chest muscles)  (Ammar et al. , 2021). The Android app "Polar Sensor Logger"  (Happonen, 2019)  was used to record the RR-intervals for all participants simultaneously. Two minutes of resting in an upright sitting position were recorded. The recorded data was later imported and analyzed in Kubios HRV Standard 3. 5. 0 analysis software  (Tarvainen et al. , 2014). Using Kubios, artifact removal and detrending were performed as necessary preprocessing steps. Therefore, RR intervals that were larger or smaller than a set threshold compared to the local average were corrected by replacing the identified artifacts with interpolated values using cubic spline interpolation.
Data analysis The neurophysiological data are subjected to a preliminary evaluation [artifact correction, independent component analysis (ICA)] with the Matlab-based EEG software EEG Lab and a subsequent in-depth evaluation using spectral analysis. Mean and standard deviation (SD) values were calculated for each variable. The Shapiro-Wilk W-test was used to check if variables did not statistically deviate from their normal distribution. Data are then processed using inferential statistical methods (e. g. , multivariate analysis of variance). To estimate the meaningfulness of significant differences, effect sizes were calculated as partial etasquared (ηp2) for the main effects and the interaction between them and as Cohen's d (d) for the paired comparison. Values of 0. 01, 0. 06, and 0. 13 for partial eta-squared and 0.
Participants characteristics Fifty participants were screened, and 24 were deemed eligible to participate in the Aeries intervention (16 females and 8 males, age: 25. 2 ± 3. 1 years old, weight: 68. 6 ± 5. 8 kg, height: 1. 7 ± 0. 11 m). Twelve were allocated to the control group and twelve to the dynamic group. One participant in the dynamic group dropped out during the training intervention due to a medical problem (SARS-COV2). Twenty-three healthy adults completed the study. The dynamic group used the Aeris Palaver standing table with active floor mats and Muvman chairs, and the control group used the conventional conference table with static seating. In the present results section, data from these 23 participants was included in the final analysis. Figure  5  shows the flowchart of the subject's screening and participation.
EEG data acquisition and analysis During the multiple nap protocol, sleep was measured using 7 electroencephalographic (EEG) channels (Fz, C3, Cz, C4, Pz, Oz, O2), as well as two bipolar electrooculograms, and two bipolar submental electromyograms. Signals were recorded using V-Amp digital sleep recorders (Brain Products GmbH, Germany) with sintered MRI-compatible Ag/AgCl ring electrodes with a 15 kΩ resistor (EasyCap GmbH, Germany), and N7000 amplifiers (EMBLA, Natus Medical Incorporated, Planegg, Germany) with classical Ag/AgCl ring electrodes in Basel and Liège, respectively. The sampling rate was set at 500 Hz and signals were filtered online by applying a notch filter  (50 Hz). Out of the 550 sleep episodes (10 for each participant: sessions S1-S10), 4 recordings were not available (3 in the young and 1 in the older group) due to technical issues.
8. 1, r1980  [46] ) (http://dbm. neuro. uni-jena. de/cat/) that provides a fully automated method to estimate cortical thickness and the central surface of hemispheres based on the projection-based thickness (PBT) method  [47]. All images underwent automated segmentation of gray matter, white matter, and cerebrospinal fluid, affine registration to the Montreal neurological institute (MNI) template space, and subsequently, a nonlinear deformation. In CAT12, a fully automated method allows cortical thickness to be measured and the central surface to be reconstructed in a single step. The program uses tissue segmentation to estimate the white matter distance and projects the local maxima (which is equal to the cortical thickness) to other gray matter voxels using a neighbor relationship provided by the white matter distance.
This projection-based thickness method also includes partial volume correction, sulcal blurring, and sulcal asymmetries without sulcus reconstruction. The generated images were smoothed using a Gaussian kernel with a full-width-half-maximum (FWHM) of 20 mm for cortical thickness and 25 mm for gyrification index, respectively. The gyrification index was measured as a ratio of the external brain surface with the outer surface excluding the sulci. Gyrification index calculation was based on absolute mean curvature  [48]. Square root-transformed sulcus depth was based on the Euclidean distance between the central surface and its convex hull. Transformation with a square root function was used to render the data more normally distributed. Mean values of cortical thickness and gyrification index were extracted for 70 cortical areas (35 per hemisphere) defined by the Desikan Killiany atlas  [49].
Statistical analysis Statistical analyses were performed using the python package Pingouin (v0. 5. 1  [50] ) and statsmodel (v0. 13. 2  [51] ) implemented in python v3. 7. Mixed model of variances were first used to compare sleep parameters across naps (within-subjects factor) and between groups (between-subjects factor), including subject as a random factor. Separate models were performed for each sleep parameter (N1%, N2%, N3%, non-REM%, REM%, W%, SE, and SL). False Discovery Rate (FDR)-corrected p-values accounting for the number of computed models are reported in Supplementary material (8 models; see Table  S1 ). If the interaction term between age group and nap session reached significance, post hoc pairwise t-tests were performed to compare sleep parameters between groups for each nap opportunity.
To test the association between sleep parameters and gray matter indices (cortical thickness and gyrification), analyses of variance were performed over 70 cortical regions by using the Region of Interest Analysis module implemented in CAT12. Sleep parameters of interest (SE Night-Day and REM% Night-Day ) were entered as continuous predictors. Group (young/older) and scanning environment (Basel vs Liège) were added as categorical factors, and sex, mean SE, and REM% over all naps were added as covariates in the statistical model. Separate models were computed for cortical thickness and gyrification, respectively. F-statistics were first computed to test for any linear effects of the respective sleep parameters of interest (SE Night-Day or REM% Night-Day ). FDR correction was applied to account for the multiple cortical regions tested.
Although traditionally seen as antinomic to work, play has always existed in work organizations. Recently, as organizations increasingly and openly embrace play, research indicates the positive effects of play, such as on employees' well-being, attitude to work, and creativity. However, the difficulty in conceptualizing the different types of play in organizations and the absence of measurement tools have hindered large-scale study of play. In the present paper, we develop two measurement scales for two types of organizational play-diversionary and serious play. We use two datasets of French small businesses to develop and test the scales. We pre-test our initial set of items in a first dataset (N = 78). We perform correlation, reliability, and exploratory factor analyses on a second dataset (N = 278) using the items adjusted after the pre-test.
e. , involving work related activity), which may or may not be voluntary and enjoyable in nature. We develop our scales using two datasets of French small business owners. Our motivation to focus on small business owners 2 rather than employees and managers of large firms is driven by several reasons. First, entrepreneurship as a process embodies initiative, spontaneity and imagination, for which it shares stark similarity with play  (Hjorth et al. 2018; Hjorth 2005). However, surprisingly, while the popular press alludes that creating a company demands playful imagination, and entrepreneurs are people who 'work hard and play hard', the entrepreneurship literature has not yet taken an active interest to investigate the role of play in the entrepreneurial process. Second, unlike employees/managers working in large organizations, entrepreneurs have substantial freedom to create and shape their firm's environment  (Kauannui et al.
Study design, respondents and methodology In June 2022, we sent out our survey to 200 French SME owners through Amarok France. 3  The survey consisted of our initial 15 items each for serious play and diversionary play. In total we collected survey data from 123 SME owners, 80 participants reported complete responses on the serious play items, while 78 finished the diversionary play items Our final data sample of 78 was used as a pilot to pre-test the items and make adjustments where needed. We checked whether any of our items were problematic, i. e. , showing ceiling or floor effects (skewed distributions), or, extremely low inter-item and corrected item-total correlation (< 0. 30 are less desirable)  (Boateng et al. 2018; Cristobal et al. 2007 ). Based on these outcomes some items were deleted or revised, resulting in a second version of two sets of scale items, that we used in Study 2.
Study design, respondents and methodology Between September and December 2022, we collected data from a larger sample of French SME owners through five organizations in the domain of prevention and occupational health service, in order to make reliable judgements  (Boateng et al. 2018). These organizations included CMIST, PRESTAL, Masanté Pro, EnSanté, and Prévention et Santé. The total number of surveys sent out was 46,017. In total 575 people participated in the survey, of which 278 respondents fully completed the survey. The low response rate for voluntary surveys is common for this kind of organizations which are involved in regulatory or administrative tasks. For our analyses, we used these 278 complete responses. Response to the survey was on purely voluntary basis and did not entail any reward for the respondents.
Moreover, while there is no consensus on what constitutes as serious skewness, one selection criteria is that ceiling or floor effects-as measured by the percentage of participants that select the lowest or highest value respectively-are present when values are above 15%  (McHorney and Tarlov 1995)  or above 20%  (Andresen et al. 1998 ). In our study, we followed the more lenient cut-off (i. e. , above 20%) to make sure content validity remains high. In the second step, we estimated Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy and Bartlett's test of sphericity to assess the suitability of the data for factor analysis. We conducted exploratory factor analysis (EFA), using maximum likelihood extraction and eigenvalues in excess of one. To decide on the factor structure, we also conducted parallel analysis  (Horn 1965)  based on O'  Connor's (2000)  syntax, estimated with Monte Carlo simulation and 100 iterations.
Moreover, we included characteristics pertaining to the organization like organizational creativity, organizational structure measured through centralization, formalization and connectedness, age of the organization, number of employees, the industry/sector in which the company operates, relative performance and financial situation of the company, and number of days employees work from home. We came across two surprising findings which deserve further investigation. First the relationship between diversionary play (r = -0. 184, p = 0. 002) and serious play (r = -0. 091, p = 0. 129) on the one hand, and financial situation of the company on the other, is negative (although both types of play are positively linked with the relative performance of the company). We speculate that this relationship could be due to the fact that our data was collected just after the COVID-19 pandemic, which led to financial problems for many small businesses, both in the short term as well as in mid-long term  (Belitski et al.
What did the researchers do and find? We analysed data collected in the UK during the COVID-19 pandemic that assessed intentions to engage in collective actions challenging pandemic-related restrictive policies implemented by the government. We relied on an existing model of collective action, the "IDEAS" model, and analysed how well the model could predict respondents' intentions. The IDEAS model stands for Identity, Deprivation, Efficacy, Action, and Subjective Wellbeing. It proposes that the starting point for people to engage in collective action is when they feel their group (but not necessarily themselves personally) is deprived because of the actions of the government. This sense of deprivation should trigger feelings of discrimination and of anger, which then lead to beliefs that social change is necessary.
Method Participants and Procedure The present data are part of a large-scale research project aiming to track social cohesion in the UK during COVID-19  (Abrams, Broadwood, et al. , 2021). The survey assessed the views of a representative panel from respondents in the UK regarding the current political situation, social cohesion, political views, and views on COVID-19. 1 Participants were drawn from the general population of the regions of Scotland and Wales as well as the county of Kent in England. These areas were chosen because of their disparities in terms of demographics, political preferences, and history-so that considering them together would provide a comprehensive overview of citizens' perceptions in the UK. An external research partner (Qualtrics Panels) distributed the online survey, recruiting and remunerating the participants directly.
Data were collected between 7 and 27 October 2020, when the UK authorities were discussing the need for a second national lockdown, triggering important anti-lockdown protests  (Gayle, 2020). For context, a brief timeline of the evolution of government measures is provided in Electronic Supplementary Material (ESM1). A total of 1,536 respondents (50. 8% male, M age = 44. 12, SD = 16. 54; all demographics, for the total sample and for each regional subsample, are reported in ESM2) completed the online questionnaire. We determined sample size prior to data collection based on feasibility and funding capacities. In addition, the design of the study, hypotheses, sample size, and rules for participants exclusions were preregistered (see Supplementary Materials). 2 The preregistration also includes the items developed for and used in the study although, as we detail below, final analyses discarded three of the initially planned items.
3  Alongside other measures that are part of separate projects, participants completed measures for cognitive and affective collective relative deprivation, perceived discrimination, intergroup emotions, national identification, collective efficacy, social change beliefs, and collective action intentions, as well as personal relative deprivation and subjective well-being. Participants also reported their political orientation (1 = Left, 7 = Right, M = 3. 50, SD = 1. 45). Zero-order correlations between all constructs are reported in Table  1. All data as well as code for the analyses are publicly available on the OSF page dedicated to the project (see Supplementary Materials).
Background: Following a traumatic brain injury (TBI), people frequently have difficulty understanding nonliteral language, including irony and indirect requests. Despite the handicap that these disorders can represent in daily life, they are rarely treated clinically, and remediation studies are scarce. Aims: The present study thus aimed to evaluate the effectiveness of an explicit metapragmatic therapy targeting nonliteral language comprehension and taking into account cognitive processes likely to underlie this comprehension (i. e. , contextual processing, theory of mind, and executive functions). Methods & Procedure: This study was registered on the ClinicalTrials. gov system (ID: NCT04708561) before participant recruitment. Two adults with severe TBI took part in the study. An ABA design with multiple baselines was used to assess the evolution of treated and non-treated written literal and nonliteral story comprehension, as well as ecological tasks and tasks assessing the cognitive processes of interest (theory of mind and executive functions).
Context and ethical approvals For P1, the baselines and generalisation tasks assessments (phases A 1 and A 2 ) were conducted in a hospital and the treatment sessions (phase B) at the participant's home. For P2, all phases were conducted at the participant's home. The study was approved by the Cantonal Human Research Ethics Commission (CER-Vaud; Project ID: 2020-00301) and registered on the ClinicalTrials. gov system (ID: NCT04708561). All participants meeting the inclusion criteria were informed about the study by their neuropsychologist or speech and language pathologist. They were given two weeks of reflection. To help them make an informed decision about participating in the study, clear explanations about the study were provided, and an information sheet was given to them. If they agreed, a first meeting with the investigator was organised.
Phase A1: Pre-treatment The pre-treatment phase included two assessments (Pre-1 and Pre-2), covering baselines, secondary outcome measures, control task, neuropsychological and pragmatic examination (see Figure  1 ). The baselines consisted of the written intention attribution task described above. The 50 stories were administered in two sessions spaced one week apart (pre-1 and pre-2). To control for a possible order or fatigue effect, the order of the stories was randomised between the two assessment sessions. Responses were recorded, transcribed, and then rated as 0, 1, or 2 according to the instructions given by each story author. Twenty-five percent of the data set was scored at the end of the therapy by a person who was blind to the completion time (pre-, post-, or maintenance). The interrater reliability (Cohen's Kappa) was good, P1: K =.
Phase A2: Post-treatment The post-treatment phase included two assessments (Post and Follow up) to assess any immediate gains from therapy and their maintenance. The treated and untreated lists (baselines), the generalisation tasks, and the control tasks were thus repeated two days after the last session and then administered again one month later. It should be noted that data from the participant P1 questionnaires could not be collected at the follow-up assessment. A satisfaction interview was also administered in the follow-up session by a different person from the therapist.
Since these statistics can only be used in the case of stable performance in the pre-treatment phase, Wilcoxon tests were performed to compare performances at the two pre-treatment assessments. The statistically non-significant difference attested to stable performance, P1: Z =. 655, p >. 05; P2: Z =. 447, p >. 05. To explore the generalisation of treatment effects to untreated items (objective 3), the analyses described above (WEST-ROC, WEST-Trend, WEST-COL) were replicated on the untreated list. The stability of performance in the pre-treatment phase for the untreated list, P1: Z =. 739, p >. 05; P2: Z =. 816, p >. 05, necessary for the WEST-COL statistics, was also demonstrated. A qualitative analysis was also performed on the data from each intention of the written intention attribution task to analyse, more specifically, the progression of irony and indirect request comprehension.
Prepandemic sample The prepandemic (control) sample (Fig. 1 ) was drawn from previous studies  (Congard et al. , 2019; Pavani et al. , 2017 Pavani et al. , , 2020)) , and comprised individuals who had been questioned about their affect and emotion regulation strategies in natural everyday situations. It should be noted that the composition of this sample varied between affect and strategy, as not all data came from visual analogue scales. For each of the prepandemic versus lockdown comparisons, we constructed a prepandemic sample that was matched with the lockdown sample for sex and age. For affect, the prepandemic sample consisted of 42 women and 25 men with a mean age of 33. 1 years (range = 19-67, SD = 12. 81). This sample was similar to the lockdown sample in terms of both gender ratio, χ 2 (1) = 0.
Lockdown sample Participants in the lockdown sample were recruited during the first week of each the three French lockdowns. For the first measurement period (20-30 March 2020), 3013 participants registered on Qualtrics (platform used for completing the questionnaire), but 646 questionnaires were excluded during data cleaning (too many missing responses, incorrect responses to two control questions, weird employment status, excessively large house). The remaining 2367 participants who responded to the first questionnaire created a specific code to allow them to be identified at subsequent measurement timepoints. During the second measurement period (23-30 November 2020), 753 participants responded (after data cleaning), including 261 from the first assessment. Finally, of the 474 respondents for the third measurement period (from 19 to 26 April 2021), 168 responded to all three questionnaires.
Multivariate growth models for affect: predictive role of regulation strategies We wanted to examine changes in affect as well as in the use of emotion regulation strategies over time (i. e. , time-varying covariates). More specifically, we investigated whether regulation strategy use was predictive of changes in affect over time, and explored the invariance of the regression parameters over time (i. e. , invariance of relations between affect and emotion regulation strategies across lockdowns). We expected individuals to improve their choice of emotion regulation strategies across the lockdowns. Longitudinal data were modelled with the lavaan package  (Rosseel, 2012) , with first-order autoregressive models. The aim of this analysis was to predict affect scores at time T according to regulation strategies at time T, taking account of the initial affect level (i.