dataesr/scientific-paragraphs-categorization huggingface dataset:
Dataset 1 Dataset 1 included Dutch-speaking university students (N ¼ 182) who participated in the study in exchange for course credits and/or a financial reward of five euros. The sample consisted of mainly Dutch students (96%) with a mean age of 20. 6 years. Nearly half of the respondents were male (42. 9%). The majority were bachelor's degree students (82. 4%) with a major in economics or psychology. Four of the respondents had entrepreneurial experience.
Datasets 2 and 3 Respondents of datasets 2 and 3 were recruited using a Dutch company that facilitates coaching services, such as assessment tools and training programs, for coaching businesses and organizations about entrepreneurship. Dataset 2 consisted of 3022 respondents. Most respondents were salaried workers (38. 3%), followed by entrepreneurs (29. 1%), unemployed individuals (19. 1%), and students (13. 6%). Unemployed individuals were not included in the main analysis, thus leading to a final sample of 2446 respondents. To examine the link between narcissism and entrepreneurial intention as well as entrepreneurial choice, we split dataset 2 into two subsets. The first subset consisted of 410 students (53% male; 48% bachelor's degree or higher) and was used for study 2. The second subset consisted of 2036 salaried workers and entrepreneurs (49% male; 58.
4% bachelor's degree or higher) and was used for study 4. Notes. Total sample size ¼ 4798. Datasets 2 and 3 include start-ups and entrepreneurs with various levels of experience. SME is a small-to medium-sized enterprise. Dataset 3 dataset consisted of 1449 respondents. Most respondents were salaried workers (37. 8%), followed by entrepreneurs (32. 9%), students (15. 7%), and unemployed individuals (13. 6%). Our final sample contained 1252 respondents after unemployed individuals were removed. As we did with dataset 2, we created two subsets of dataset 3. The first subset consisted of 228 students (59. 6% male; 40. 3% bachelor's degree or higher) and was used for study 3. The second subset consisted of 1024 salaried workers and entrepreneurs (49. 7% male; 65. 5% bachelor's degree or higher) and was used for study 5.
Datasets 4 and 5 Datasets 4 and 5 were collected by an organization that aims to promote the mental and physical health of self-employed workers. Dataset 4 consisted of 257 owners of small and medium-sized enterprises (SMEs) located in France. The majority of the respondents were male (72. 0%), married (55. 1%), and the founder of the business (68. 8%). The respondents of this sample had an average age of 47. 2 years. The average number of employees was 4. 72. Most of the businesses (95. 1%) had a turnover of less than two million euros. Dataset 5 consisted of 340 SME owners located in Japan. The majority of the respondents were male (89. 5%), married (86. 2%), and founder of the business (61. 8%). The average age of the SME owners was 63. 6 years. The average size of the business was 13. 63 employees, and the majority of the companies (78.
Dataset 6 Dataset 6 was collected in 2014 as part of a larger research project on entrepreneurship in the Netherlands. The original dataset consisted of 321 CEOs and 188 top management team members of SMEs in the Netherlands. Businesses in this dataset have an average size of 37. 06 employees, and the majority belongs to the manufacturing and professional services industry. For this study, we focused only on the data collected from CEOs. The CEOs in this dataset were mainly male (96%) with an average age of 51. 1 years. The CEOs surveyed had worked in their current firms for an average of 16. 2 years, and approximately 40% of the CEOs were founders or co-founders of their firms.
Results The relationship between the degree of narcissism and the six aspects of entrepreneurship was investigated using IBM SPSS (version 24). An overview of the findings across all eleven studies can be found in Fig. 1  and Table 2 (see Tables  S19  and S20  for more details). The measures of narcissism and the entrepreneurial aspects examined in each study are outlined in Table  2. The descriptive statistics, correlations, internal consistency scores of the measures in our datasets, and the resulting regression coefficients, can be found in the SI Appendix. To minimize data loss, we handled missing data using pairwise deletion. We discriminate between bivariate correlations and what we refer to as our main results. Unlike bivariate correlations, our main results are obtained by performing specific regression analyses in which controls are included.
Additional analyses: curvilinear relationship So far, we have examined the relationship between narcissism and entrepreneurship using a linear model. However, it is possible that the relationships are non-linear  (Goncalo et al. , 2010; Grijalva et al. , 2015). Across all 20 relationships that we examined, we found only one significant curvilinear relationship between narcissism and entrepreneurial intention in dataset 2 (p <. 05). Specifically, entrepreneurial intention increased up to the narcissism score of approximately. 70 (which accounted for approximately 89% of the cases) and decreased beyond this level.
Discussion The primary objective of the present study was to provide an empirical account of the alleged link between narcissism and entrepreneurship. Our findings, which are based on six datasets and organized according to eleven studies, suggest that narcissism manifests differentially across the entrepreneurial process. Of the entrepreneurial aspects examined in this study, entrepreneurial intention had the most consistent link with narcissism. One reason that narcissists are attracted to entrepreneurship may be the high person-job fit. In particular, the needs for attention, admiration, power, and autonomy of narcissists fit well with the heroic public image  (Mathieu and St-Jean, 2013; Wooldridge, 2009)  and entrepreneurs' relatively challenging and risky work environment  (Stewart and Roth, 2001).
For entrepreneurial choice, our findings are inconclusive. This may be explained by the different measures of narcissism adopted in the two relevant datasets. Since the Dirty Dozen sub-measure of narcissism mainly reflects the dimensions of grandiosity and entitlement but not the dimension of leadership/authority, it may not be a useful measure for comparing entrepreneurs and salaried workers. Furthermore, it is plausible that narcissists are attracted not only to entrepreneurship but also other career choices (e. g. , artists, politicians, journalists) that fuel their need for attention and a sense of entitlement. However, we found that the likelihood of becoming an entrepreneur versus non-founder CEO is positively associated with narcissism as captured by the Dirty Dozen sub-measure of narcissism.
Hence, we are not able to establish causal relationships between narcissism and entrepreneurship. Although narcissism is generally regarded as a stable trait that is unlikely to change over time  (del Rosario and White, 2005; John and Robins, 1994) , longitudinal research is recommended to confirm the causal link between narcissism and entrepreneurship. Second, narcissistic individuals who tend to have an inflated self-view and a high self-enhancement motive may respond in a socially desirable manner. Therefore, we attempted to limit the social desirability bias by stressing respondents' confidentiality and the importance of truthful answers to our questionnaires. Consistent with previous research  (Foster and Trimm, 2008; Sedikides et al. , 2004) , we found no significant association between narcissism and socially desirable responses in dataset 4.
Moreover, in our study, narcissism is not associated with business performance. In short, we find no clear evidence of social desirability bias. Third, because only self-reported data were used, our findings may be affected by common method bias  (Podsakoff et al. , 2003). Our results from the Harman's single factor test suggested that the first factor explains no more than 30% of the variance across all examined datasets, which is well below the 50% threshold suggested by  Podsakoff and Organ (1986). Nonetheless, we encourage future research to examine the link between narcissism and entrepreneurship using data obtained from different sources (e. g. , colleagues, family members) and different methodologies (e. g. , text analysis, behavioral measures). The present research focuses on the link between narcissism and entrepreneurship.
Acknowledgement The present research was supported by  LabEx Entreprendre of the Universit e de Montpellier  ( Montpellier Management , MOMA) which is funded by the  French government (LabEx Entreprendre ,  ANR-10-Labex-11-01 ) as well as by the public research centre Montpellier Research in Management ( EA 4557 ,  Universit e de Montpellier). The present research was also supported by a grant of  Erasmus University Rotterdam called Research Excellence Initiative  ( REI 2015 ). We thank  Niels Rietveld  for his valuable advice and comments.
Materials Stimulation presentation and data collection were achieved using experimental software Presentation version 17. 2 (Neurobehavioral System Inc, Albany, CA) which provide a precision under 1 millisecond for motor response measures  [62]. The laptop was connected to an external display (40cm, refreshing at 60Hz) and to an adapted keyboard. On this keyboard, the keys were removed except those corresponding to the four letters D, F, G and H which were marked with yellow pallets. Preparatory attention and divided attention were tested with the standardized alert phasic test and divided attention test of the Test of Attentional Performance (TAP, Version 2. 3 Zimmermann and Fimm, 2002).
Procedure The participant was seated in standardized sitting posture in a quiet room without visual or auditory distractors. The viewing distance was approximately 80cm and the keyboard was at 50cm from the screen. The experimenter space was separated from the participant space by a curtain. Before the experiment, useful data about the participants (date of birth, gender, handedness assessed with the Edinburgh Handedness Inventory,  [63] ), were collected. Test of Attentional Performance (TAP). Attentional performance was assessed to explore the link between visuo-motor learning and attentional skills. This measure was also done to make sure that groups did not differ in terms of attentional functions. Each of the two neuropsychological tests was composed of two parts: a phase to familiarize the participant with the instructions and a test phase in which the results were recorded.
• In the Non-Congruent Audio-Visual (NonCongrAV) condition, an auditory stimulation was presented 200ms after each visual cue. If the participants pressed a key before this delay, the auditory stimulation was not presented. • In the Regular Rhythmic Auditory Stimulations (RegAud) condition, auditory stimulations were presented every 500ms independently of visual stimulations and participants' responses. • In the Irregular Rhythmic Auditory Stimulations (IrregAud) condition, auditory stimulations were presented irregularly and independently of visual stimulations and participants' responses. There was the same number of auditory stimulations in this soundtrack than in the RegAud condition but intervals were pseudo-randomly generated in a range of 0. 022s to 2. 891s and a mean of 0. 494s. This pattern was programmed with the free software Audacity.
Data analyses For all analyses, incorrect responses were not included in the RT analyses. Pre-tests. The laterality quotient was assessed with the Edinburgh Handedness Inventory  [63]. For the two attentional tests, mean reaction times and errors were computed by the TAP software. For the B0, the averaged reaction times (RTmean), variability of reaction times (RTsd) and errors were computed and compared between Conditions. SRTT. Average reaction times (RTmean), variability of reaction times (RTsd) and errors for all participants in each Condition were computed across trials of each of the block. Difference in performance between Blocks 1 to 5 was considered as a measure of general learning (difference B1-B5 = RTmeanB1-B5, RTsdB1-B5, ErrorB1-B5) whereas performance in Blocks 5 to 6 (fixed to pseudorandomized order of visual stimulations) was considered as a measure of specific learning of the sequence (difference B6-B5 = RTmeanB6-B5, RTsdB6-B5, ErrorB6-B5).
Indeed, the sequence of position was played out through the visual modality whereas we implemented a temporal structure through auditory stimuli. One hypothesis is that the temporal regularity of these stimuli could have prepared the attentional system to deal with specific stimuli arriving in the same temporal pattern [e. g. ,  [95, 96]. Indeed, these effects have already been shown using other tasks of implicit learning of pitch structures  [97, 98] , working memory  [99] , and statistical learning of artificial language  [100]. Moreover, this tempo seems to be well suited given that the optimum tempo for motor synchronization is between 400 and 800ms  [101]. Overall, the regularity of the RegAud may have facilitated the learning of the visual stimulations sequence and the increase in the number of errors at the random block (B6) suggests that participants continue to inappropriately play the sequence out  [18].
Design and study population Data were derived from a large population-based prospective cohort study: the Akershus Birth Cohort (ABC). Thus, the results of this study stem from secondary analyses. The ABC targeted all women scheduled to give birth at Akershus University Hospital (Norway), which serves around 350,000 inhabitants. Recruitment took place between November 2008 and April 2010, during the 17-week pregnancy routine examination. Women were eligible to participate if they could complete the questionnaires in Norwegian. Participants completed questionnaires during pregnancy, at 17 weeks and 32 weeks of gestation, and at eight weeks and two years postpartum. Medical information relating to the birth was registered in the hospital birth record. Participants who gave birth between May 2009 and September 2010 completed an additional questionnaire on pain at 48 hours postpartum.
The ABC obtained ethical approval from the Regional Committees for Medical and Health Research Ethics (approval number S-08013a), and all participants provided written informed consent. Of the eligible women, 80% (n = 3,752) agreed to participate and returned the first questionnaire. Detailed information on participants and drop out in the ABC can be found in  Garthus-Niegel et al. (2018). In this study, we used data from the 17-weeks of gestation questionnaires, the eight weeks postpartum questionnaires, and the hospital birth record. Out of the 3,752 women who returned the 17-week questionnaire, we excluded those for which the hospital birth record was not available (n = 189). We also excluded women who received pethidine, which is an opioid (n = 2), or general anaesthetics (n = 73) during childbirth, because these drugs might independently affect PTSD symptom development.
Pain relief administered during childbirth Data regarding morphine or N 2 O administration during childbirth was extracted from the hospital birth record. Each drug was treated as a dichotomous variable, depending on whether it was used (1) or not (0). Both morphine and N 2 O, if needed, were given during labour. Morphine was administered intramuscularly. N 2 O was given through an inhalation mask. Participants were instructed to breathe in the mask as needed, and received 30 to 50% N 2 O.
Given that ageing may increase the risk of obstetrical complication  (Schummers et al. , 2018)  and thus affect drug administration, a post-hoc one-way between-subjects ANOVA was carried out to evaluate if age differed across the drug-administration groups. Group comparisons with a Bonferroni correction were used to assess drug group differences in age. Similarly, an independent sample t-test was conducted to compare age depending on birth medical severity. IBM SPSS 24 was used for all analyses in relation with sample description (Tables  1  and 2 ).
e. , modelling stress intensity) (see Supplementary material, section II, "Zero-inflated Tweedie compound Poisson model" for details on the reasoning behind this model choice). Note that two sets of regression coefficients result from this analysis, one for each part of the model. All analyses were performed using the 'cplm' R package  (Zhang, 2013). A model comparison approach, where models of varying complexity are considered in turn, was adopted. In our modelling strategy, for statistical but also interpretability reasons, no interaction effect was included without adding the corresponding main effects or lower order interactions. The four following structures have been fitted on both zeroinflated and Tweedie components: i) a full model including all variables and their interactions up to second order (third order interactions were not included to avoid zero counts cells); ii) a reduced model (reduced model 1) including all effects and first order interactions; iii) a reduced model (reduced model 2) including only main effects; iv) a restricted model where only significant predictors from the full model were included.
fMRI data collection, preprocessing and analyses We acquired functional MRI images with a 3T whole-body scanner (Trio TIM, Siemens, Germany) with a 12channel head-coil. Functional images were acquired with a susceptibility weighted EPI sequence with the following parameters: TR/TE = 2100/30 ms, flip angle = 80 degrees, PAT factor = 2, 64 × 64 voxel, 3. 2 × 3. 2 mm, 36 slices, 3. 2 mm slice thickness, 20% slice gap. We acquired structural images using a T1-weighted 3D sequence using the following parameters: MPRAGE, TR/TI/TE = 1900/900/2. 32 ms, flip angle = 9°, voxel dimensions: 0. 9 mm isotropic, 256 × 256 × 192 voxels. We presented task stimuli on a back-projection screen inside the scanner bore using an LCD projector (CP-SX1350, Hitachi, Japan). We recorded responses via buttons placed on the joystick used for the visuomotor reaching task (HH-JOY-4, Current Designs Inc.
, USA We used the SPM8 software (http://www. fil. ion. ucl. ac. uk/spm/) for statistical analyses of functional data with a standard pipeline. We first corrected for head movements between scans by an affine registration  (Friston et al. , 1995)  and realignment to the mean of all images. The anatomical image was spatially normalized on the EPI template. The functional images were also normalized to the EPI template, which were thereby transformed into standard stereotaxic space and resampled with a 3 × 3 × 3 mm voxel size. The normalized images were spatially smoothed using an 8 mm full-width at half-maximum (FWHM) Gaussian kernel. We used the general linear model (GLM) framework implemented in SPM to analyze our data. We modeled the convolved standard hemodynamic response function with a delta (or "stick") function at the onset of the preparatory phase (appearance of white triangle -PREP regressor), at the onset of the joystick movement (start of movement -MOV regressor), and at the onset of the response screen (yes vs.
Contrast images from one-sample t-tests corresponding to each event (PREP, MOV, RESP) and their parametric modulators (CONF, ERR, YN, ANG), were fed into a second-level random-effect analysis. All second-level results are reported at a significance-level of p < 0. 05 using cluster-extent family-wise error (FWE) correction with a voxel-height threshold of p < 0. 001. In Figure  3 , activations are displayed at a cluster-size threshold of 30 voxels, using MRIcroGL (http://www. cabiatl. com/mricrogl/). Data and analysis scripts from this study will be made freely available upon acceptance.
Acknowledgements and funding: MP is supported by two Postdoc. Mobility fellowships from the  Swiss National Science Foundation  ( P2ELP3_187974 ;  P400PM_199251 ). IB is supported by a  PRD fund  from the  University Hospital of Geneva  ( PRD 22-2020-1 ) as well as a  Scientific chief resident fellowship  from the  University of Geneva , Switzerland. This study was conducted on the imaging platform at the  Brain and Behavior Lab (BBL) , at the  University of Geneva, Switzerland  and benefited from support of the BBL technical staff. Special thanks to  Christophe Mermoud  for programming of the experimental protocol. We thank  Nathan Faivre  for constructive comments on the manuscript. by IB and YC. MP, RS, YC and IB performed the data analysis. MP,.
After each situation, the participants were asked to score their alertness, and how mentally demanding were the minutes prior to the takeover. Time to eyes on (i. e. , before first gaze on the road), time to hands on (i. e. , before first contact with the wheel), and time before intervention were measured. Furthermore, takeover quality was recorded. Data analysis showed a significant effect of the task difficulty on alertness and mental load ratings: the control group rated both values lower than the other groups. No difference in alertness was found between easy and difficult tasks; however, difficult task yielded higher mental load. The tablet position had a significant influence on the reaction times, which were longer for the handheld group. Differences were also found in terms of takeover quality, with the handheld group having larger lane deviation and shorter time to lane crossing.
More specifically, the game was either offered via an auditory mode (i. e. , the experimenter read questions aloud, and participants were asked to answer aloud), or via a visual mode (i. e. , questions were presented on a tablet, and participants had to tap the answers). In this study, the NDRTs were presented as distractions for the drivers. It was expected that the multiplication of stimuli which came from the same cognitive modality, especially in visual cases will lead to poorer performance, in line with Wickens' multiple resources theory  (Wicken, 2002). Classical driving data were recorded from the simulator as the takeover time, some lateral and longitudinal variables (i. e. , minimum acceleration, standard definition of lateral position (SDLP), and steering wheel angle), and time to collision.
e. , two, five, and eight seconds. Each participant experienced one of these time conditions. During the automated drive, participants successively watched two videos on a tablet. They had no specific instructions about its manipulation during transition of control. To measure participant engagement in the task, unexpected items were placed on road sides (e. g. , a giant gorilla). In a post-simulation questionnaire, participants were asked to recall what they saw. Their performance to identify the items was used to evaluate task engagement: a participant who missed all the items was considered as highly engaged in the task. Driving behavioural data were recorded, including standard deviation of road offset (i. e. , distance from the centreline of the road), standard deviation of steering wheel position, and  deviation from a lane in curve.
Statistical analysis Quantitative variables are described as mean ± SD and categorical variables as number (percentage). We compared GHQ-12 and perceived stress scores between areas with high and low epidemic intensity, using the z test or Welch's F and Tukey statistics, as appropriate. To identify factors associated with perceived stress intensity, linear mixed effects modeling using the restricted maximum likelihood method was used via the lmer function in the R package lme4  [29, 30]. We estimated the effect of the epidemic intensity (high-vs. low-intensity zone) on the perceived stress of all professionals (as assessed by the composite scale comprising Khalid's 13 items and 27 PS-ICU items), controlling for gender, having a family member at risk of developing COVID-19 (due to the presence of a chronic comorbidity or advanced age putting them at risk of a severe form of COVID-19), being a professional who usually works in ICU (versus professionals who usually work in other units, but were requisitioned or volunteered to work in the ICU during the crisis), and number of years' experience (i.