dataesr/scientific-paragraphs-categorization huggingface dataset:
Source reconstruction Source estimation was performed using Brainstorm software  ( Tadel et al. , 2011 ) , which is based on a distributed dipoles model fitting approach. Sources were extracted by applying a dynamic statistical parametric mapping (dSPM;  Dale et al. , 2000 ) , which has been shown to provide a better localization of deep sources than standard minimum norm procedures  ( Lin et al. , 2006 ). The dSPM adopts minimum-norm inverse maps to estimate the locations of scalp electrical activities. Before source estimation, the EEG data were re-referenced to the common average reference  ( Michel et al. , 2004 ). Moreover, the activity was normalized by an estimate at the individual level of the noise standard deviation at each electrode location  ( Hansen et al. , 2010 ). To this end, for each participant and location, noise covariance matrices and individual noise standard deviations were calculated using single trials baseline intervals data ( -100 to 0 ms).
The boundary element method (BEM) provided in OpenMEEG was adopted as a head model (default parameters in Brainstorm were selected). The BEM provides anatomical information by three realistic layers  ( Gramfort et al. , 2010 ; Stenroos et al. , 2014 ). Source estimation was performed by selecting the option of constrained dipole orientations  ( Tadel et al. , 2011 ). Single-trial data were averaged for each individual, then the estimate of active sources was calculated at the individual level. Based on the existing literature  ( Finney et al. , 2001 ; Sandmann et al. , 2012 ; Cardin et al. , 2013 ; Bottari et al. , 2014 ; Stropahl et al. , 2015 )  an Auditory region of interest (ROI) was defined a priori in each hemisphere before any statistical comparison was run (see Supplementary Figure  S2 ).
Behavioural data For each participant and experiment, we calculated the mean accuracy (percentage of correct responses) and the mean RT for the detection of the colour change of the fixation cross. As control analyses, two separate ANOVAs (IBM SPSS Statistics V21. 0. 0) were run using as dependent measures of accuracy and RT for each experiment (FO, EM, ID). For both EM and ID experiments, the ANOVAs included Condition (upright and inverted) as within-participants factors and Group (CD and HC) as between-participants factor.
Acknowledgements This work was supported by the  European Research Council  (  ERC-2009-AdG 249425 -CriticalBrainChanges  and the  German Research Foundation  (  DFG Ro 2625/10-1  ) to  Brigitte Roeder ). G. D. was supported by a postdoc fellowship of the  Alexander von Humboldt Foundation. D. B. was funded by  PRIN 2017  research grant (Prot. 20177894ZH). The authors declare no competing financial interests. The datasets and code used in the current study are available from the corresponding author on reasonable request.
Here we argue that fitness benefits might shape the tendency for members of a given pride to remain cohesive or to split into smaller subgroups, which is a much more flexible strategy than adding or removing members from the pride. Social bonds are therefore likely to form the basis of how species such as lions respond socially to ecological processes. We expect to observe a trade-off between maintaining fewer but stronger bonds when conditions are good and maintaining more but weaker bonds when conditions are poor and more challenging. In this study, we combine data on the fine-scale patterns of association among individuals across multiple prides of African lions with data on the prey herds in each pride's territory within Hwange National Park, Zimbabwe. We consider a herd of prey to represent a resource patch available to lions  (Carr & Macdonald 1986) , and the abundance, dispersion, and richness of these patches (see Table  S1 ) as important attributes that can influence the opportunities for social interactions  (Tanner & Jackson 2012).
Lion pride observations In this study, we used data from seven GPS-collared lions (two adult females and five adult males) from four different prides for the study period 2013 -2015. Lion prides were located with the help of GPS radio-collars and at times opportunistically. The prides were observed at least five times per month to record the size and composition of their group at that time. When a group was observed, we recorded the pride name, identity of individuals present, as well as their age, sex and reproductive state (whether they had cubs). We also recorded their activities during observations and these included resting, hunting, feeding and walking. All lion individuals are recognizable by whisker patterns that are unique to each individual and natural markings such as scars, muzzle spots and tooth irregularities  (Pennycuick & Rudnai 1970).
We looked at two aspects of lion group (1) pride (all the individuals within a group) and (  2 ) subgroup (individuals of a pride present at each observation). We recorded all individuals present together as being connected  (Whitehead & Dufault 1999; Farine 2015). The GPS collars recorded locations every two hours day and night, and we regularly downloaded this positional data to estimate lion seasonal home range. Only individuals that had collar data with fixes covering the whole season were included in calculating seasonal home range.
Social networks construction We used lion pride observational data to construct a social network for each pride in each dry season in each year, with observations ranging from 16 to 66 observations per season (Table  S3 ). Thus, each social network represented the patterns of associations within a pride over a four-month period. The social networks contained each of the individuals in one pride as nodes and pairwise association indices as edge weights. Because we did not have complete data on observations of all groups simultaneously, we had to convert the number of associations into an association rate (the propensity for individuals to be seen together). To calculate these edge weights, we used the Simple Ratio Index as an estimate of the proportion of time two individuals spent together  (Cairns & Schwager 1987; Hoppitt & Farine 2017 ).
Social network analysis We first used the multiple regression quadratic assignment procedure (MRQAP) to test if the tendency of lions to associate with individuals of the same sex, same age or same reproductive state had an effect on lion social structure. MRQAP tests the matrix equivalent of a linear regression and are widely used for hypothesis testing in networks  (Farine 2017). We conducted a separate MRQAP for each pride in each season with association as the dependent matrices and age based homophily, sex based homophily and reproductive state based homophily as the independent variables. The homophily matrices were constructed by assigning similar pairs a value of 1, while dissimilar pairs received a value of 0. We conducted these tests using the MRQAP function with a custom null model option in 'asnipe' package in the statistical software R  (Farine 2013 ).
Hypothesis testing We first preliminarily tested for the correlation between the four measures of prey availability, and the correlations were generally moderate to low (-0. 59 ≤ r ≤ 0. 62), still each measure was analysed separately. All analysis were done in the statistical software R (Bates, Mächler, Bolker & Walker 2015) using linear mixed effects regression analysis and the identity link in the lme4 package. We assessed the relationship between the different network metrics (modularity, mean weighted degree and network density for each pride and subgroup network) and (i) the four measures of prey availability, (ii) a measure of interspecific competition (ratio of hyaena abundance to lion pride size), and (iii) a measure of vegetation cover (percentage of closed vegetation). The network metric was the response variable while the above ecological variables that can potentially influence lion social structure were the fixed effects; with lion pride ID included as the random effect.
Null models Social data are typically non-independent  (Croft, Madden, Franks & James 2011) , in order for one individual to have a connection, it requires another to do so as well. We used null models to estimate the statistical significance in the relationships between the ecological variables and the connections among individuals within prides and within subgroups. Null models allow us to account for confounding non-social factors that affect the observations of co-occurrences among individuals, such as sampling effort, so that only the signal of social factors that shapes the social network are evaluated when estimating significance  (Farine & Whitehead 2015; Farine 2017). We first did a pre-network permutation test in the 'asnipe' package  (Farine 2013)  to compare the mean weighted degree, network density and modularity of the study pride networks to that of a random pride networks.
Pre-network permutations work by sequentially swapping observations of pairs of individuals between groups for each iteration of the randomization to increasingly randomize the observed data  (Bejder, Fletcher & Brager 1998). After each swap, the associations among all individuals are recalculated and the above three network measures recalculated. We conducted 10,000 such swaps, thus generating a null distribution from 10,000 randomised networks. We then re-ran the same null model procedure but restricted swaps to only occur within the subgroups that were identified within each network (i. e. within each pride). We calculated the mean weighted degree and network density for each subgroup networks and compared to that of random subgroup networks from the previous null model. Conducting this analysis allowed us to determine whether individuals are trading-off within-subgroup social investment versus pride-level social investments because in the first null model the associations were re-distributed across subgroups, whereas in the second null model the associations remained within subgroups.
Acknowledgments We thank the  Zimbabwe Research Council  and the  Director of Zimbabwe Parks and Wildlife Management Authority (ZPWMA)  for permission to undertake this research and the assistance of ZPWMA staff during field data collection specifically  Lovelater Sebele  and  Trust Dube. We also thank the  Hwange Lion Project  field staff for assistance with data collection particularly  Lowani Mpofu. MMM is grateful to  Jon Cummings , C. V  Starr Foundation  and the  Cecil Fund  for supporting studentship costs. Research was supported by  Darwin Initiative for Biodiversity Grants, Mitsubishi Fund for Europe and Africa ,  R. G Frankenberg ,  Boesak and Kruger ,  Rufford Maurice Laing , SATIB Trust,  Eppley ,  Panthera ,  Robertson  and  Recanati-Kaplan Foundations  and Riv and Joan Winant.
Apparatus The experimental session was run using Matlab on a Macintosh Mac Pro workstation with an RME Fireface 800 soundcard. Stimuli were created with an in-house Matlab program and were presented diotically through headphones (HD 280 PRO by Sennheiser electronic GmbH & Co. KG, Germany). The experimental setup was calibrated using a Brüel & Kjaer 2238 Mediator sound-level meter, coupled with the mounting plate provided for circumaural headphones; a 1-kHz pure tone at a level of 70 dB SPL was used for the calibration. Each tone of a sequence was normalised in loudness accordance with EBU R 128 to be played at 70 phons. Each participant was tested in a double-walled IAC sound-insulated booth. 2. 4 Procedure Since previous studies showed that the type of attention allowed by the taskattention-divided or attention-directed -had no effect on perceptual results and, neither for non-musicians nor for musicians  (Ouimet et al.
Data recording. Participants' motor performances were recorded using the Qualisys motion analysis system, through three Oqus infrared cameras (sampling rate 200 Hz). During each movement, the three cameras tracked the 3D coordinates in space (x, y, z) of five passive markers placed on the participant's right hand, more specifically on the index tip, the index base, the thumb tip and the scaphoid and pisiform bones of the wrist. No markers were placed on the hand of the confederate. A sixth marker was placed on the top of the wooden object in order to analyze its position in relation to the targets and check for precision. The cameras were calibrated at the beginning of each experimental session. A calibration was considered satisfactory when the system reached a standard deviation accuracy below 0.
Data processing and statistical analysis. Motor performances recorded by the Qualisys system were processed by an in-house script adapted from the RTMocap toolbox for Matlab  58. In line with the existing literature  34, 35, 38  , we analyzed only the trajectory of the wrist marker placed at the level of the scaphoid, which expresses arm movements without including wrist rotation. Each action was composed of two phases: the grasping phase and the placing phase. Action onset was considered as the first moment when the wrist marker reached 20 mm. s -1  35. Action end corresponded to the moment when the wrist marker reached 20 mm. s -1 following peak velocity  35. In the event that this threshold was not reached, the local minima following peak velocity was considered as the action end. For the purpose of the current study, only the parameters recorded during the preparatory action were considered.
5 standard deviations from the mean. Among the initial 2140 movements, 116 were removed from the data set, resulting in a loss of 5. 42% of the data. Statistical analyses and plots were performed with R version 3. 5. 1 (R Core Team 2018) and R Studio version 1. 1. 456. Each parameter of interest was analyzed as a function of the Motor goal (Small target, Large target) and Social goal (Personal action, Social action) using a mixed effects model approach. Mixed effects models are used to study the effect of experimental factors (called fixed-effects parameters) on the variable of interest, while taking into account the possible influence of other sources (referred as random effects parameters, e. g. , inter-individual differences in sensitivity to the variables). Mixed effects models are particularly relevant for repeated measures experimental plans, as they can handle missing data and provide parameter estimates with acceptable type-I and type-II errors  59, 60.
In the present study, we specifically fitted each parameter dataset with a generalized linear mixed effects model, using the glmer function of the lme4 1. 1-21 package  61. For each of the kinematic parameters mentioned above, we applied a model incorporating Motor goal (Small target, Large target) and Social goal (Personal action, Social action) as fixed effects, including both main effects and the interaction effect. As random effects, our model included a by-subject random intercept and by-subject random slopes for the effect of Motor and Social goals. A by-confederate random intercept was not added in the final random structure of the model, as its specification did not statistically improve the model. Therefore, such a random effects structure was chosen on the basis of a compromise between the most complete random effect structure  60  and the optimal random effect structure supported by the data  62, 63  , in order to avoid model over-parametrization.
In addition to the fixed and random effects, glmer requires users to specify the type of error distribution. For this purpose, we analyzed the distribution followed by the response variable and residuals (separately for each kinematic parameter), by computing kurtosis and skewness and visually analyzing the distribution through histograms and Q-Q plots. Specifically, we fitted a Gaussian distribution to the peak wrist velocity and the percentage of deceleration time datasets for both grasping and placing phases, and to the movement time dataset for the grasping phase only. We fitted a log-normal distribution to the peak wrist elevation datasets for both grasping and placing phases, and to the movement time dataset for the placing phase only (see Table  2 ). The model parameters (relating to fixed effects and random effects) were estimated by Laplace approximation and statistically tested with Wald's χ2.
Acknowledgements This work was funded by the  French National Research Agency  ( ANR-11-EQPX-0023 ) and also supported by  European  funds through the program  FEDER SCV-IrDIVE. MFG was financed by the  Region Hauts-de-France  and the  University of Lille. We are grateful to  Anya Attou  for her contribution to the data collection,  Laurent Ott  for his support in the computer programming of the study and  Celia Moreira  (mathematical researcher at  FCUP-CMUP, University of Porto ) for her helpful suggestions for statistical analysis.
Two weeks later, they participated in an experiment that took place during their regular tutorial sessions. Participation was voluntary. Only students who completed both the online questionnaire and the experiment were eligible for the money prizes. To ensure anonymity, we matched survey and experimental data using a unique identifier created by 1 3 Strategic Decision-Making in a Global Context: The… the participants from several information items, such as 'first two letters of mother's first name. ' To ensure that the Dutch language treatment is indeed a native-language treatment, we excluded nine individuals, who indicated that Dutch is not their native language. The final sample consists of 238 individuals. Using a student sample is consistent with standard practices employed in studies published in top tier strategy and international business outlets (e.
Control Variables While randomization of language treatments, on average, provides a safeguard against systematic biases in observed manipulated variables' effects due to individual-level heterogeneity, including related control variables increases the power of the analyses by controlling for otherwise random variation, helping to filter out confounding effects on variables not experimentally manipulated. As demographic control variables, we include age (in years) and gender (1 indicates female; 0 indicates male). Additionally, conscientiousness can, and in our data does, correlate with the five remaining personality dimensions of the HEXACO personality inventory. To reduce the likelihood of the effect of conscientiousness being confounded by these other personality traits, we included the remaining five dimensions as control variables (each based on ten Likert-type items with responses ranging from 1 to 5, as for conscientiousness): honesty-humility (α = 0.
Nonverbal empathy In clinical practice, empathic skills are currently measured with paper-based scales, either self-reported by the physician (such as the Jefferson Scale of Physician Empathy;  (Hojat et al. , 2002) ) or evaluated through observational scales (e. g. , the Global Consultation Rating Scale;  (Burt et al. , 2014)  or the Empathic Communication Coding System  (Bylund and Makoul, 2005) ). However, these measures are very dependent on the coder and do not capture variation in empathy over time. Therefore, we decided to assess objective and automatic emotion recognition by using emotion recognition software. During the interview, participants were video-recorded and their emotions were analyzed by Affectiva software 3. This system is based on face tracking to automatically detect emotions of individuals, following the Facial Action Coding System (FACS)  (Friesen and Ekman, 1978).
More than 40% have interstitial lung disease, many are frail, and use of immunosuppressant drugs is common  [7, 8]. The Scleroderma Patient-centered Intervention Network (SPIN) Cohort routinely collects mental health outcomes at 3-to 6-month intervals  [8] [9] [10]. The SPIN COVID-19 Cohort was initiated to collect data during COVID-19 and allows comparison of mental health symptoms prior to and during COVID-19 for participants enrolled in both cohorts. Our objective was to compare anxiety (PROMIS Anxiety 4a v1. 0 scale  [11, 12] ) and depression (Patient Health Questionnaire-8 [PHQ-8] [13]) symptoms before and after onset of COVID-19 among people with SSc, including (1) continuous score changes; (2) proportion with change scores of at least one minimal clinically important difference (MCID); (3) proportion initially under a cutoff threshold who changed by at least 1 MCID and reached the threshold; and (4) factors associated with changes, including country, comparing results from Canada, France, the United Kingdom, and the United States.
Patientreported data during COVID-19 included immunosuppressant drug use, COVID-19 positive test status, financial resource adequacy (Consumer Financial Protection Bureau Financial Well-Being Scale  [16] ), and anxiety and depression symptoms. Details are available in the study protocol. Anxiety Symptoms. The PROMIS Anxiety 4a v1. 0 scale  [11, 12]  includes 4 items asking participants, in the past 7 days, how often: (1) "I felt fearful"; (2) "I found it hard to focus on anything other than my anxiety"; (3) "My worries overwhelmed me"; and (4) "I felt uneasy". Items are scored 1-5 with response options "never" to "always". Higher scores represent more anxiety. Possible raw scores range from 4 to 20. Raw scores are converted into T-scores standardized from the general US population (mean = 50, standard deviation = 10).
Model variables were selected a priori and entered simultaneously. For continuous variables, we assessed linearity via restricted cubic splines. Missing data were dealt with using multiple imputation via chained equations with 20 imputations. Variables entered in models included pre-COVID-19 symptoms of anxiety or depression (continuous), male sex (reference female), age (continuous), non-White race or ethnicity (reference White), education years (continuous), living alone (reference living with others), country (reference France), working part-or full-time (reference not working), time since SSc diagnosis (continuous), diffuse subtype (reference limited or sine scleroderma), interstitial lung disease presence, interference from breathing problems (continuous), overweight or obese (reference normal body mass index or less), overlap syndrome presence, PROMIS Physical Function 4a v1.
Strengths and limitations This is the first study to compare mental health outcomes among people vulnerable during COVID-19 due to a pre-existing medical condition. The SPIN Cohort is a well-characterized, ongoing cohort, and    people with SSc are representative of other patient groups who are vulnerable during COVID-19. There are also limitations to consider. First, the SPIN Cohort is a convenience sample, although participant characteristics are similar to other large SSc cohorts  [8]. Second, participants complete questionnaires online, which may reduce generalizability. Third, it was not possible to capture and include local variables, such as the degree participants' communities were affected or whether public health interventions were consistently followed in those communities. Nonetheless, data were collected at a time when social isolations were generally at their most conservative.
| Data analyses Internal consistency of the instrument was assessed by Cronbach alpha (where alpha values 0. 7 ¼ acceptable, 0. 8 ¼ good, 0. 9 ¼ excellent; DeVellis, 2016). Inter-rater reliability compared assessments by the patient themselves with those of a cohabitating adult family member. Test-retest reliability was assessed between scores from the first study related assessment with those of the retest. Both reliability measures were estimated using intra-class correlation coefficients (ICC) and presented with corresponding 95% confidence intervals and p-values (F test), following interpretation guidelines by Ciccetti (i. e. , <0. 40 bad; 0. 40-0. 59 fair; 0. 60-0. 74 good; 0. 75-1. 00 excellent;  Cicchetti, 1994). Differences in distribution of initial and retest or second-rater scores were compared with paired t-tests or Wilcoxon signed-rank tests, as appropriate, and p-values presented.