dataesr/scientific-paragraphs-categorization huggingface dataset:
Data analysis. Rank-ordered predictors. To rank-order the 50 strongest SWB predictors, we used machine learning, which is an application of artificial intelligence whose algorithms build models through iterative analyses of sample data to generate statistical predictions that they are not specifically programmed to test. First, we used the "VSURF" program within the statistical package R  65  , whose iterative regression modeling was used to identify the independent, multivariate contribution of each variable to the overall explained variance (R 2 ) for the model predicting SWB. After this selection process was completed, a machine-learning regression model was built using the Random Forest algorithm  66. The Random Forest algorithm (which was also used to perform the missing-data imputations) uses a random subset of predictors to test the strength of each predictor in a model through a process called recursive partitioning.
As such, this measure reflects the extent to which each factor reduces the difference between the predicted and actual SWB values, with higher values indicating stronger, more accurate predictors of SWB, which were then used to rank-order the top 50 SWB predictors. In machine learning, the original dataset is split into at least two sets: one to train the model (usually 70-80% of the sample), and the other to estimate its predictive performance (usually 20%-30% of the sample). In the current study, the training set constituted 70% of the sample, while the testing set comprised 30%. The final model predicted a majority of the variance in SWB (R 2 = 58. 71%), with very low residual error (RMSE = 0. 41). However, because these MSE analyses do not include main effects or inferential results, additional analyses were conducted to determine the effects sizes and significance levels for each of the top 50 SWB predictors.
Fewer than half of the health behaviors were among the top 50 SWB predictors, and only two of thesemoderate and vigorous exercise-were statistically significant. Given previous research showing, for example, a significant negative impact of cigarette use on QoL and SWB  53, 54  , these results may reflect the multivariate nature of the current analyses, and its controlling for a large number of other factors (and their multicollinearity). It also suggests that at least some of these health behaviors may influence SWB indirectly through health outcomes. For example, alcohol consumption was significantly related to chronic illness, activity limitations, and perceived health, all of which were significantly correlated with SWB. These potential mechanisms can be further clarified by testing mediational models with these and similar other datasets.
Data Analysis Statistical analyses were conducted using two different kinds of analysis of variance (ANOVA). The first analysis was based on the design "5 age levels × 2 groups" with separate ANOVAs for each index (i. e. , DA, EP, and ASI). The second analysis was based on the design "5 age levels × 2 groups × 2 ears", with separate ANOVAs for each condition (directed or forced condition vs free recall condition). Significance level was set at 0. 05. All analyses were run using Jamovi software (https://www. jamovi. org/download. html, accessed on 14 May 2022).
The ASD diagnosis was conducted following the Diagnostic and Statistical Manual of Mental Disorders-5 (DSM-5, (W. M. Association (2013); A. P. Association (  2013 )) criteria, and confirmed by the administration of gold standard instruments. The sample was formed based on the database of clinical data of the Lab. The socioeconomic status of the families (SES), assessed through the four-factor index of social status, indicated a middle status for the participants  (Hollingshead, 1975. This study included a total of N = 17 therapists. In N = 14 cases, there was a therapist change at some time point during intervention, due to logistic or clinical reasons. Nonetheless, all therapists followed the same protocols and objectives, and received the same training and supervision. Clinical reasons for therapist change include the need for the child to generalize the acquired skills.
e. , the actual interplay where both the social partners are engaged in a reciprocal social play activity or routine). The code allows the automatic extraction of quantitative behavioral descriptors of the interaction in terms of frequencies, proportions, latencies, durations, and succes rates. More details about the ICS are included in Supplementary Materials. The observer was masked to children's clinical profiles and coded the different videos in random order. The coding window was set to 20 min, extracted from the middle of the session to avoid the initial -and potentially more stressful-moments of interaction and the last ones, where the child may be more tired or face more challenges. The duration of the intervention sessions usually ranged between 60 and 90 min. The videos were annotated using BORIS (Behavioral Observation Research Interactive Software), an open source tool for behavioral observational research  (Friard & Gamba, 2016).
Statistical analysis The raw annotations produced by the BORIS coding interface were first preprocessed by a pipeline of Python scripts to automatically compute the interaction feature set, aggregated at the session-level. All statistical analysis and model development were performed using the R environment for statistical computing. For statistical inference, data were tested for normality, i. e. , Shapiro-Wilk normality test and homogeneity of variances, i. e. , Levene's test. When appropriate, paired Student's t-tests were employed to test for longitudinal changes in outcome or process measures. Otherwise, paired Wilcoxon-Mann-Whitney signed-rank with continuity correction tests were used. Effect sizes and correlations were computed using Cohen's d and Pearson's product-moment correlation coefficient (r).
We computed interaction descriptors at the four time points, as well as the variations between them. Variations were calculated using the formula: Var T1-T0 = Desc T1 -Desc T0 Desc T0 (2) An analytic pipeline for model selection and comparison was employed based on different evaluation metrics. Models were tested for assumptions in terms of homoscedasticity, i. e. , studentized Breusch-Pagan test, normality of residuals, Cook's distance, and autocorrelation, i. e. , Durbin-Watson test. Data were standardized by a robust normalization based on the 10th and the 90th percentile difference to account for possible outliers. Multicollinearity was addressed by computing the Variance Inflation Factor (VIF)  Akinwande et al. (2015). To take into account the small sample size, the maximum number of predictors for candidate models was set to N = 3 (Austin & Steyerberg, 2015.
Credit author statement Giulio Bertamini, Arianna Bentenuto, Cesare Furlanello, and Paola Venuti contributed to the study conception and design. Material preparation and data collection were performed by Giulio Bertamini and Silvia Perzolli. Eleonora Paolizzi annotated the videos, as expert and trained observer. Statistical methodology was designed by Giulio Bertamini and Cesare Furlanello. Statistical analysis was performed by Giulio Bertamini. The first draft of the manuscript was written by Giulio Bertamini Silvia Perzolli. All authors commented on previous versions of the manuscript. All authors contributed to review and edit the manuscript. All authors read and approved the final manuscript. Paola Venuti and Cesare Furlanello supervised the study, for the clinical and statistical modeling parts, respectively.
Outcome Our outcome variable was the burden of OUD 2019. Subsequent analyses involved the burden of low back pain and that of other adult mental and substance use disorders for the same year. These were extracted from estimates of the Global Burden of Disease (GBD) study 2019 as the 2019 agestandardized Disability-Adjusted Life Years rates per 100000 inhabitants (hereby 2019 DALYs)  [23]. Mental and substance use disorders included in the study were: alcohol use disorders, anxiety disorders, bipolar disorder, cannabis use disorders, depressive disorders, eating disorders, schizophrenia. Using a 5 stars classi cation system, the Global Burden of Disease (GBD) provides a grading of data quality for each country, which depends on data availability, completeness, detail of mortality data and percentage of deaths coded to ill-de ned codes or highly aggregated causes  [23].
Globalization level Low  [41, 64]   The Socio-Demographic Index (SDI) for the year 2018, extracted from the GBD database  [26]. The SDI is a composite indicator of development status, and is measured as the geometric mean of indices of total fertility rate, mean education for those aged 15 and older, and lag distributed income per capita. Its range is 0-1, with values closer to 1 indicating a higher level of development; The Unemployment rate for the year 2018 (unemployed citizens as a percentage of the labor force), extracted from the World Bank database  [27] ; A measure of income inequality for the year 2018, as measured by the p90p100 index, extracted from the World Inequality Database [28]. This measure represents the ratio of individuals whose income belongs to the top 10% of the population, divided by the entire population.
Its range is 0-1, with values closer to 1 indicating a higher level of inequality; An index of urbanization for the year 2018, extracted from the GBD database (from 0, low degree of urbanity to 1, high degree of urbanity) [26]; An index of child sexual abuse for the year 2018. From the GBD database, we extracted the agestandardized summary of exposure value (SEV) for children sexual abuse, which measures a population exposure to children sexual abuse and takes into account the contribution of that risk to disease burden  [26]. SEV is reported on a scale from 0-100%; SEV takes the value zero when there is no excess risk for a population and the value 100 when the population is at the highest level of risk; An index of data quality as measured by the GBD  [23]. We decided to include data quality as a covariate in our statistical model because the quality of available data was not equally distributed across Globalization categories.
We used the default hyperparameters of the SuperLearner R package for the MARS (inc. maximum degree of interaction = 2) and RF algorithms (inc. number of trees = 500; number of variables to possibly split at in each node = 2; minimum node size = 5 for the outcome regression and 1 for the exposure mechanism). Cross-Validated R 2 and multi-class Area Under the Receiving Operating Curves were our main indicators of performance. As an example, we report the performances of ML algorithms for the outcome regression and the exposure mechanism when analyzing the association between globalization and the burden of OUD 2019 in Supplementary Table  1  (trimming probabilities of exposure to the 99. 9th percentile and not removing outliers).
Sensitivity analyses Finally, to check the robustness of our results, we ran two other sets of analysis where: -we used two other levels of truncation for each country's probability of exposure (truncating probabilities to the 99th and 97. 5th percentiles instead of the 99. 9th percentiles). -we removed outlier observations, de ned as those with an outcome value inferior to the 1st percentile or superior to the 99th percentile (Supplementary Table  2 ). The STROBE for cross-sectional analysis guided the writing of this manuscript  [36]. For this country-level analysis that relied on publicly available data, no ethics approval was required. All analyses were performed using R version 4. 1 and package lmtp  [37].
Limitations First, our sample included countries and not individuals, hence we have no information as to which societal strata may speci cally be impacted by globalization. In the US for instance, a prominent feature of the current opioid epidemic is that it is heavily concentrated among low-income white communities  [51] , which our research cannot con rm. Second, with our data we have no indication as to how globalization (and in particular cultural de jure globalization) may be speci cally linked to OUD, e. g. via increased prescriptions, higher demands, or heightened feelings of despair. Besides, other aspects of economic and social globalization showed signi cant effects on OUD in our sensitivity analyses. Further studies should con rm whether they are linked to increased opioid prescriptions, despair, or other risk factors of OUD.
Data processing Data processing was performed using MATLAB (version r2020b; Mathworks, Natick, MA). Position data from the tablet were low-pass filtered with a dual-pass, no-lag Butterworth filter (cutoff frequency: 10 Hz; order: 2). These data were used to determine MT per block which corresponded to the mean time (in ms per element) between the validation of the first and 12 th elements in the sequence, corresponding to 11 pointing movements. As the path taken to validate the first element in a sequence was not always the same (i. e. , at the start of a block, the previous target was the calibration target, whereas for the rest of the block it was the last element of the previous sequence), MT was only calculated for the 11 remaining elements. MT per element was averaged across the block (i.
e. , for each of the 11 elements across the 10 repeated sequences). Outlier values (median ± 2. 5 SD) were removed from the analysis  (Leys et al. , 2013). These represented 2. 2% of the MT per block data. Data analyses were performed with the software JASP (version 0. 14. 1; JASP, 2020) and consisted of running repeated-measures ANOVAs on MT per block with within factors (i. e. , Blocks) and/or between factors (i. e. , Groups). The level of significance was set at 0. 05 for all statistical analyses, and the effect size was reported for all significant effects (eta-squared, η 2 ;  Cohen, 1988). When necessary, we applied the Greenhouse and Geisser correction and reported the estimate of sphericity. Post hoc tests (Bonferroni) were also performed when necessary. When required, Kruskal-Wallis test was used when the Shapiro-Wilk test rejected the data normality hypothesis.
01-80 Hz including a 50 Hz notch filter and was stored for off-line averaging. Electrical activity was recorded using two BrainAmp 32-channel amplifiers and the Recorder 1. 1 software. The EEG was analyzed using the Analyzer 2. 1 software (all by BrainProducts GmbH, Munich, Germany). Computerized artifacts rejection was performed prior to signal averaging in order to discard epochs in which deviations in eye position, blinks, or amplifier blocking occurred. Eye movements were the most frequent cause for rejection. Trials rejected for violating these artifact criteria were 10. 7% for the which-object condition and 11. 4% for the which-subject condition (t < 1). A total of 30 trials for each condition (object and subject) were averaged. ERPs locked to the onset of the critical word and extending until the sentence end (3000 ms) were averaged separately according to the conditions.
Ethical electronic health records An electronic health record (EHR) is a digital database that not only contains a patient's personal data but also archives longitudinal data about diseases, diagnostic procedures, treatments, and responses to therapy. Consequently, the most obvious advantage of EHRs is that they provide easy accessibility to medical data for medical professionals in case a patient moves and thus changes doctors  [64]. The introduction of EHRs can improve the safety, quality, and efficiency of medical systems over the long term because the improved flow of information means that redundant examinations can be avoided  [65]. A public funding source is needed to create an ethical EHR, ensure consistent standards, develop less error-prone interfaces, and avoid conflicts of interest so that the focus is always on patient well-being and evidence-based medicine  [66].
Enabling better healthcare research and saving resources Open Science, reasonable data protection, and a standard EHR will enable better healthcare research by providing large datasets and thus facilitating the evaluation of treatment strategies and the early diagnosis and prevention of diseases. Specifically tailored therapeutic approaches can be developed to offer patients the best treatment, which ultimately translates into the effective implementation of precision medicine  [70]. Currently, only 9% of patients with unipolar depression being treated in primary care receive adequate treatment. If patients with unipolar depression were diagnosed more reliably and treated more effectively, healthcare costs would decrease. The ratio of means costs is 2. 28-fold higher in adults with depression than in non-depressed adults  [71].
Attachment Multiple Model Interview Attachment representations of specific relationships were assessed with a semi-structured interview, the Attachment Multiple Model Interview (AMMI)  [29]. Attachment representations for each relationship (mother, father, partner) were coded on four attachment dimensions: security, deactivation, hyperactivation, and disorganization of the attachment system. Scores of security, deactivation, and hyperactivation vary from 0 to 8, whereas scores of disorganization vary from 0 to 16. The validation study demonstrated that the AMMI significantly discriminates internal working models specific to different relationships  [29]. Its validity has also been established with longitudinal data gathered from age 4 to 23, suggesting that AMMI scores at age 23 reflect corresponding cumulated lifetime scores for security, deactivation, and hyperactivation  [29].
Data Analysis To determine whether SES (BSMSS) had to be controlled for in the main analyses, correlation analyses were conducted to examine the links between SES and attachment dimensions, mother-infant bonding, and depression. To test whether severity of the depression and infant-mother bonding disturbances increased with insecurity within each attachment relationship, we examined correlations between attachment scores for each relationship (mother, father, partner) and, respectively, depression scores and bonding disturbance scores. Finally, Partial Least Squares-Path Modeling analyses (PLS-PM) were conducted to test the mediation model, according to which depression severity mediates the link between attachment and mother-infant bonding.
Mediation Model between Attachment to Each Parent and Mother-Infant Bonding via Attachment to the Partner and in Turn via Depression Severity Partial Least Squares Path Modeling (PLS-PM  [49] ) was used to test our conceptual mediation model. The analyses were conducted in R version 4. 1. 2 (The R Foundation for Statistical Computing, Vienna, Austria)  [50]  using the PLS-PM package  [51]. PLS-PM is a variance-based structural equation modeling technique that does not rely on normality assumptions and can be used with small sample sizes  [52]. In the PLS-PM approach, two models were tested: (1) the outer (measurement) model describes the relationships with the manifest variables (MVs) and their latent variables (LVs), and (2) the inner (structural) model describes the relationships between the latent variables.
Acknowledgments. We thank the young adults who took part in the FOCUS survey as well as the current and past researchers and staff involved with this survey. Author contributions. RK and MJR are the principal investigators of the FOCUS study from which these data were derived (https://www. focus-study. me). PJC and NB were responsible for data collection and survey administration. PJC conducted the statistical analysis. PJC and RK drafted the manuscript, and FD, DG, JVL, and MJR critically revised the manuscript. All authors read and approved the final manuscript. Funding statement. This study (The  FOCUS Study ) is supported by the  Canadian Institutes of Health Research (CIHR , Funding Reference Numbers:  VR5 172673  and  AWD-017639 ) and by the  French National Research Agency  ( ANR-21-COVR-011 ).
, 2005). Thus, empathic responses in these regions may reflect a process that represents bodily and affective states originating in both the self and others, with the aim of guiding behavioral responses  (Singer and Lamm, 2009). Since the present study aimed at highlighting modulations of empathic reactions as a function of a plexiglass barrier between an observer and an observed face, two methodological/analytical choices were made. First, all of the presented results emerged from a comparison between the brain activity (ERPs and source activations) elicited when the plexiglass was present vs when it was absent, for the two stimulation conditions (i. e. gentle touch vs pain), separately. Second, to manage the multiple comparison problem and the risk of type-I error-which are particularly relevant to large, spatio-temporal datasets such as those produced by the electroencephalographic (EEG) research-the state-of-the-art cluster-based permutation approach was used, considering two classical and dissociable temporal windows associated with the well-known and above-mentioned aspects of empathy: experience sharing and mentalizing  (Maris and Oostenveld, 2007).
Participants Data were collected from 30 volunteer healthy students (7 male) from the University of Padova. Data from five participants were excluded from the analyses due to excessive electrophysiological artifacts. All participants reported normal or corrected-tonormal vision, normal audition and no history of neurological disorder. The final sample included 25 participants (4 male; M age = 20. 4 years, s. d. = 1. 93, 1 left-handed), in line with a reference study  (Schiano Lomoriello et al. , 2018). A power analysis using data simulation for cluster-based permutation tests  (Wang and Zhang, 2021)  revealed that a sample size of 24 participants is sufficient to obtain the power of at least 80% when detecting differences in ERP data between two conditions (i. e. barrier vs no-barrier) in a within-subject design.
Stimuli The stimuli were 12 digital photographs of white faces with neutral facial expressions from the Eberhardt Lab Face (ELF) database (Mind, Culture, & Society Laboratory, Stanford University). Each face was digitally manipulated to obtain static images for two conditions: one in which faces were receiving a painful stimulation (insertion of a syringe) on the left or right cheek and one in which faces were being gently touched with a Q-tip on the left or right cheek. All faces were presented in an upright orientation in the dimensions 2. 5 ∘ × 3. 3 ∘ (width × height). Stimuli were presented on a 17-inch cathode ray tube monitor controlled by a computer running E-prime software.
EEG data preprocessing EEG data were collected and recorded by means of 64 active electrodes, which were distributed on participants' scalp according to the extended 10/20 system, with an elastic actiCAP positioned with reference to the left ear lobe. The EEG was re-referenced offline to the average of the left and right earlobes. The horizontal EOG (i. e. HEOG) was recorded bipolarly from two external electrodes positioned laterally to the left and right external canthi. The vertical EOG (i. e. VEOG) was recorded from Fp1 and one external electrode placed below the left eye. The electrode impedance was kept <10 KΩ, due to the highly viscous electrogel and the properties of active electrodes. Offline EEG processing and analyses were conducted using the BrainVision Analyzer software (Brain Products).