dataesr/scientific-paragraphs-categorization huggingface dataset:
| Statistical analysis Categorical and continuous variables were compared using the chi-squared and Wilcoxon rank sum tests respectively. The association of DOT with the composite outcome was assessed using unadjusted and adjusted logistic regression in order to estimate the odds of unfavourable TB outcomes compared to SAT. Continuous predictor variables were modelled using restricted cubic splines with three knots. Covariates with missing data (excluding missing data for the combined primary outcome) were imputed once using predictive mean matching  [17]. Subjects with missing outcome data were assumed to have been alive and event free until the end of follow-up. All p-values were two-sided and considered statistically significant if <0. 05. R version 3. 4. 2 (www. r-project. org) was used for analysis and analysis scripts are available online (http://biostat.
Data imputation, based on the median value over the whole sample, was also applied to replace any missing data and outliers. Receptivity of cognitive training was assessed from the relative change after training (i. e. , relative change = (post -pre) / pre) for both Stroop scores (i. e. , Colour-Word Stroop task and Emotional Stroop task). For each task, the relative change after training was computed from Stroop scores already screened and cleaned for aberrant values. Then, data imputation, based on the median value over the whole sample, was applied to replace any missing data and outliers. This procedure was used to obtain as much data as possible and thus to increase the statistical power of our statistical analyses.
MRI acquisition Functional MRI data were acquired at the CIREN facility using a 3 T MRI scanner (GE, DISCOVERY MR750 3 T MR System, United Sates). We acquired anatomical MRI data using a high-resolution 3-D T1weighted gradient sequence (field of view = 256 mm, slice thickness = 1 mm, matrix size = 256 x 256 mm, resolution = 1 mm 3 , flip angle = 11°, repetition time=8. 2 ms, echo time=3. 2 ms, inversion time = 400 ms) and an rs-fMRI using a T2*-weighted echo-planar imaging (EPI) sequence (field of view = 192 mm, slice thickness = 3 mm, 42 slices, matrix size = 64 x 64 mm, resolution = 3 × 3 x 3 mm 3 , flip angle = 90°, repetition time=2000 ms, echo time=23 ms). During the rs-fMRI sequence, the participants were asked to lay still with their eyes closed, but not to fall asleep, instead to try to think of nothing at all, and in the case of a sticky thought, to think of a starry sky.
MRI analysis 2. 4. 1. Preprocessing of rs-fMRI data MRI preprocessing and statistical analyses were performed using SPM12 (Wellcome Department of Imaging Neuroscience, London, UK) running on MATLAB 2014b (Mathworks Inc. , Natick, USA) with dedicated toolboxes (the advanced version of Data Processing Assistant for Resting-State fMRI, DPARSF; Yan and Zang, 2010, http://www. restfmri. net) and ArtRepair (http://cibsr. stanford. edu/tools/humanbrain-project/artrepair-software. html; Mazaïka et al. , 2007). These software were used to pre-process the fMRI data and compute the ReHo and fALFF images. An in-house MATLAB function was used to estimate the median value of the ReHo and fALFF signals within individual Regions of Interest (ROIs) derived from the AAL2 atlas  (Rolls et al. , 2015; Tzourio-Mazoyer et al.
Statistical analysis To confirm that ReHo and fALFF signals were associated with IC efficiency  (Hu et al. , 2014; Liu, Chen et al. , 2015; Tian et al. , 2012) , we conducted univariate linear models with Stroop interference scores as the co-variable and brain hemispheres (i. e. , "right" vs. "left") as the within-factor. Both the Colour-Word Stroop task (Cool IC) and the Emotional Stroop task (Hot IC) were analysed. Data from participants in both the AC and IC training groups were pooled into a common sample as was done in the pre-test session; participants had not yet followed any training. Univariate linear models were used to test whether the relative changes in Stroop scores after training in both the Cool and the Hot Stroop tasks differed between the training groups (i. e. , CA vs. CI).
Participants In order to assess sample size, we capitalized on an existing dataset that was initially collected online via the Mechanical Turk recruitment platform to test the interaction effect of childhood environment and disgust sensitivity on susceptibility to social influence. All phases of the study (the informed consent, the pilot task and questionnaires) were coded using Qualtrics (Qualtrics, Provo, UT, q2016) and presented in a Web browser. The pilot experiment compared the influence exerted by social information on face approachability ratings in a condition where participants were primed with disgusting images and in another condition where they were primed with neutral images  [42]. For our current purposes, we restricted our analysis to the neutral condition (a condition that is fairly similar to the one we aim to use in the target study).
A target sample size of 140 US participants was chosen a priori and 143 participants completed the study. A series of quality checks was performed to ensure that each participant (i) had declared being 18 years old or more, (ii) had entered the correct verification code (generated by Qualtrics at the end of the procedure), (iii) did the task only once (by comparing the participants' IP addresses and the GPS coordinates provided by Qualtrics) and (iv) had enough valid data in each experimental condition. A total of 125 participants (59 females, 66 males) fulfilled these criteria (mean age ¼ 34 + 9). All participants reported being naive to the purpose of the experiment, gave their written informed consent and received payment for their participation in accordance with the standards of Mechanical Turk.
Stimuli and procedure The stimuli consisted of 24 emotionally neutral faces generated using FaceGen Modeller 3. 5 (Singular Inversions, 2007) according to the methods developed by Oosterhof & Todorov  [43]. The experimental post-test trials (24 faces × 6 feedback types)  procedure was adapted from  [36]. It lasted 16 min (+7 min) on average and was divided into three phases. In a first phase, participants watched a series of 24 neutral faces presented for 500 ms, a duration that has been shown to minimize participants' ability to encode the identity of faces but that provides enough time to consistently estimate social traits such as trustworthiness  [43] [44] [45]. After the presentation of each face, a numerical scale appeared to assess approachability. The scale remained on the screen until participants selected a value between 1 and 8 (figure  1 ).
Statistical and computational analyses of behavioural data All analyses were performed using Matlab version R2014b and R. Participants' performances were analysed in a series of t-tests and linear mixed models with the lme function of the nlme R package  [53]. All models used a maximum-likelihood fitting method and had random intercepts. Bayes factors (BF 10 ) with default Jeffreys -Zellner-Siow (JZS) priors were further calculated using the lmBF function to compare the predictive power of models  [54]. Values superior to 1 indicate greater evidence for the alternative model, while values inferior to zero indicate greater evidence for the reference model. Comparing models using Bayes factors allowed us to determine whether childhood harshness, unpredictability and adversity have an effect on social alignment scores and, if they do, which environmental variable has the greatest effect.
The mean rating in post-test trials m thus corresponds to a linear combination between the initial rating x ini and the group rating x group following: m ¼ x ini Á (1 À d) þ x group Á d: The probability of choosing the discrete rating x in post-test trials can be computed using the following equation: p(x) ¼ F x þ 1 2 , m, s À F x À 1 2 , m, s , where F(. ) is the cumulative normal density function. We obtained maximum-likelihood estimates of the two parameters d and s separately for each participant using gradient descent of the negative model likelihood using the 'interior-point' algorithm of the fmincon routine implemented in Matlab. We derived model predictions in terms of social alignment scores for all measures that were made directly from participants' behaviour, as a means to test the adequacy of the model.
Effect of childhood environment on social alignment scores The pilot study revealed a positive influence of childhood unpredictability and adversity on the social alignment score. We did not replicate this result in the pre-registered study. A general overview of the first-order Bayes factor did show that type 1 models with environmental variables as main effects were better at predicting the data than type 2 models with environmental variables as interaction terms. However, none of these type 1 models outperformed the fit of the baseline model  ). A positive social alignment score ( y-axis) indicates that participants adjusted their approachability ratings in line with the group, a negative social alignment score indicates that they adjusted their approachability ratings away from the group, and a near-0 score indicates that participants' rating changes were not biased in any direction.
These effects were also robust to the inclusion of the random factor 'study' in the model (using maximumlikelihood estimator) (Childhood Unpredictability: b ¼ 0. 03 + 0. 01, z ¼ 2. 36, p ¼ 0. 02, CI lower ¼ 0. 004, CI upper ¼ 0. 046; Childhood Adversity: b ¼ 0. 03 + 0. 01, z ¼ 2. 75, p ¼ 0. 006, CI lower ¼ 0. 009, CI upper ¼ 0. 054). Tests for heterogeneity also showed that the true effects in the two studies were not significantly different (Childhood Unpredictability: Q ¼ 2. 12, df ¼ 1, p. 0. 05; Childhood Adversity: Q ¼ 0. 33, df ¼ 1, p. 0. 05). The respective contribution of the pre-registered study in the estimated averaged effects of childhood unpredictability was 71%. However, it was only 7% for childhood adversity. Note finally that even though the results of the pilot study and pre-registered study taken independently showed that childhood unpredictability did not significantly impact the noise parameter s, such an effect was detected once the data of the two studies were pooled together (b ¼ 0.
Because noise is unequally distributed between the different ranks of the childhood unpredictability axis (see electronic supplementary material, figures S2 and S10, in which residuals are plotted against environment variables' score ranks), there is a greater chance that the regression slopes will be biased by outliers. Note that a similar problem applies to the distribution of harshness scores. Unlike the distribution of unpredictability scores, however, the distribution of harshness scores is negatively skewed and/or bimodal. In sum, even if our hypothesis had been valid, the presence of slope outliers might have made it particularly difficult to test. To overcome this problem, we first detected in both the pilot and the pre-registered datasets those of the participants who were outliers in the type 1 models regressing social alignment scores on harshness and unpredictability scores ( pilot study: 'childhood' scores; pre-registered study: 'childhood' and 'current' scores).
For each regression ( pilot study: n ¼ 2; pre-registered study: n ¼ 4), data points with a Cook's distance greater than a standard cut-off of 4/N participants were identified  [57]  (see electronic supplementary material, figure  S15 ). Note that the removed data points can be located at different coordinates of the data space regardless of their positive or negative impact on the regression slopes. In total, 14 participants of the pre-registered dataset (which represents 5% of the original sample) were identified as slope outliers in at least one regression and were thus removed from the pre-registered dataset. By comparison, only two participants of the pilot study were identified as slope outliers (which represents 1. 5% of the original sample), hence confirming that results of the pre-registered study are more corrupted by noise than results of the pilot study.
Re-analysing the pre-registered dataset without slope outliers After removing slope outliers, we found that models including environmental variables as independent fixed-effects predicted the observed data better than alternative type 2 models (first-order Bayes factor, figure  6a ). The second-order Bayes factor further revealed that the model including childhood unpredictability as a main effect had a better fit than the baseline model (figure  6b ). Childhood unpredictability was positively associated with social alignment scores in disagreement trials (b ¼ 0. 08 + 0. 03, t(246) ¼ 2. 60, p ¼ 0. 01) (see electronic supplementary material, figure  S16 ), though the evidence was weak (Childhood unpredictability versus Baseline: BF 10 s ¼ 1. 58 + 5. 36%). In addition, the type 1 model that included childhood unpredictability had a greater explanatory power than type 1 models that included current harshness (BF 10 s ¼ 5.
We reasoned that because the reliance on social information is a strategy that conveys immediate pay-offs (individuals exploit solutions that have already been successfully tried out by conspecifics), it should be affected by variations in people's ecology. We therefore tested the impact of two fundamental dimensions of early-life environmental risksharshness and unpredictability-(as well as their combination) on people's susceptibility to social influence within two independent samples recruited online. We initially used an analytic plan based on linear mixed models whose predictive value was further compared using Bayes factors. Results obtained in the pilot dataset (N ¼ 121) showed a positive main effect of childhood unpredictability on participants' susceptibility to social influence: the more unpredictable the participant's childhood environment was, the more they aligned their social judgements about unknown faces with the modal judgements provided by other people.
Moreover, combining measures of childhood harshness and childhood unpredictability into a single index of early-life adversity led to an even stronger effect than the two dimensions taken in isolation. Finally, a canonical computational model of choice showed that these effects were more likely accounted for by an increased motivation to match other people's choices, rather than by a corruption of internal representations by noise. The relationship between the environmental variables and social alignment scores went in the same direction in the pre-registered analyses run on the replication sample (N ¼ 262), but did not reach statistical significance. A meta-analysis conducted on both datasets revealed that childhood unpredictability and childhood adversity were significantly related to participants' susceptibility to follow other people's opinion, while the average effect of childhood harshness was close to significance.
We therefore went beyond the pre-registered analyses and tried to identify the reasons why these effects were not directly detected in the replication dataset. The characteristics of the populations tested in both the pilot study and the pre-registered study presented important shortcomings that we had not anticipated. Both datasets were notably characterized by extremely skewed distributions of childhood unpredictability raw scores, which led to an unequal residual variance in social alignment scores across the different score ranks of the childhood unpredictability axis. The fact that noise is unequally distributed between the different ranks of the childhood unpredictability axis (see electronic supplementary material, figure  S2  and S10 , in which residuals are plotted against environment variables' score ranks) increases the chance that the regression slopes are biased by outlier data points.
Similar problems were also observed with harshness scores. In sum, even if our main hypotheses had proven valid, the properties of the pre-registered dataset itself may have prevented us from testing them properly. To check this possibility, we first looked at both datasets to identify outlier participants on the type 1 models regressing social alignment scores on environmental harshness and unpredictability. We found less than 1. 5% outlier participants in the pilot study, but more than 5% outlier participants in the preregistered study, which confirmed that the pre-registered dataset was more corrupted by noise. To counteract these limitations, we removed slope outliers following a standardized procedure of noise minimization  [57]  and ran the same analyses on this new set of participants (N final ¼ 262-14 ¼ 248).
These unregistered analyses revealed an effect of childhood unpredictability on participants' performance, hence confirming the result obtained in the pilot dataset. Second, results showed that the effect of childhood unpredictability on social alignment scores was greater than the effect of unpredictability experienced by the participants at the time of testing. In the next sections, we review our initial hypotheses in the light of the results obtained from the pre-registered and the unregistered analyses. 4. 1. Main hypothesis #1: childhood unpredictability, harshness and/or adversity are positively associated with social alignment scores in disagreement trials, but not in control trials Results of the pre-registered and the unregistered analyses detailed in this article converge on the fact that, overall, childhood environmental variables are not associated with mean rating change in control trials.
On the other hand, information provided by the meta-analysis and by the analyses excluding slope outliers in the replication sample showed that childhood unpredictability and childhood adversity were positively associated with social alignment. Our main hypothesis #1 is therefore partially verified. 4. 2. Main hypothesis #2: the effect of childhood environment on social alignment scores obtained in disagreement trials is caused by an increased valuation of social feedbacks, rather than by a corruption of participants' internal representations by noise First, results of the analyses of the two datasets showed that childhood environment variables were unrelated to the noise parameter fitted by our computational model of choice. It is, however, important to note that an effect of childhood unpredictability on the noise parameter was detected once the data of the two studies were pooled together by the meta-analysis.
The pre-registered analyses of the replication dataset failed to reveal such a hierarchy. However, the meta-analysis showed that the effect of childhood unpredictability averaged across both studies was significant, while the average effect of childhood harshness was not. This was further confirmed by the analyses of the replication dataset after excluding slope outliers, which revealed a significant effect of childhood unpredictability on the one hand, and an absence of effect of childhood harshness on social alignment scores on the other hand. Altogether, these results suggest that the exploratory hypothesis #1 can be considered valid. 4. 4. Exploratory hypothesis #2: childhood adversity-the combination of childhood harshness and unpredictability-has a greater impact on susceptibility to social influence than its two dimensions taken in isolation In the pilot study, childhood adversity had a greater impact on social alignment scores than childhood harshness and unpredictability taken independently.
However, the same analyses performed on the replication dataset did not show such an effect. Neither the meta-analysis nor the replication analyses excluding slope outliers found this effect. Therefore, our exploratory hypothesis #2 is not verified. 4. 5. Exploratory hypothesis #3: the effect of childhood environment on susceptibility to social influence is stronger than the effect of current environment The analyses of the replication dataset after excluding slope outliers suggest that our hypothesis #3 is partly verified. Indeed, results showed that the model including childhood unpredictability as main predictor had a better fit than equivalent models with current harshness, unpredictability and adversity. After having reviewed our working hypotheses in light of the results obtained from the pre-registered and the unregistered analyses, we now devote the next sections to potential limitations and ambiguities raised by our experimental design and results.
In the present task, positive and negative social judgements do not carry the same risks: positive judgements, while increasing potential benefits from cooperative interactions, increase the risk of being harmed or cheated. By contrast, negative judgements, because they lead individuals to avoid the interaction, do not carry the same risk of exploitation. Consequently, participants who experienced higher levels of unpredictability should accept negative judgements more easily. This is not what we observed in our data. This is particularly noteworthy because the error-management decision bias described above is indeed present in the results of the pre-registered dataset: the valence of the disagreement with other people has a main effect on social alignment scores, with participants being more prone to change their ratings in line with the group when the group provided a more negative rating on first sight.
Figure3. Analyses of the pilot dataset. Bayesian analyses of models with and without environmental variables as predictors of social alignment score in disagreement trials (a -c), of mean rating change in agreement trials (d ), and of the parameters delta (e) and sigma ( f ) fitted by our computational model. The baseline model does not include any environmental variable; alternative models include each environmental variable as a main effect (type 1) or as a term interacting with disagreement valence and disagreement strength (type 2) (a -c,e,f ) or control conditions (d ). First-order Bayes factors compare the baseline model and alternative type 1 and 2 models with a null model including the intercept only. Second-order Bayes factors compare alternative type 1 and 2 models with the baseline model.
This study provides taxonomic and distributional data of bryozoan species from the Ross Sea area, mainly around Terra Nova Bay, based on specimens curated at the Italian National Antarctic Museum (MNA, Section of Genoa). Bryozoan specimens were collected at 75 different sampling stations in the Ross Sea and in the Magellan Strait, in a bathymetric range of 18-711 meters, during 13 expeditions of the Italian National Antarctic Research Program (PNRA) conducted between 1988 and 2014. A total of 282 MNA vouchers corresponding to 311 specimens and 127 morphospecies have been identified and included in the present dataset. 62% of the species were already reported for the Terra Nova Bay area, where most of.
Introduction In the last 30 years, several Italian expeditions have been conducted in the Ross Sea leading to the publication of contributions on different taxonomic groups. Among the different phyla, Bryozoa was extensively studied until 2000 with papers reporting data on bryozoan species obtained during the first Italian Antarctic campaigns in Terra Nova  Bay (i. e. , 1988 Bay (i. e. , -1995) )   (Di Geronimo and Rosso 1990; Rosso 1990 Rosso , 1991 Rosso , 1992a Rosso , 1994;; Rosso and Sanfilippo 2000). From 2000 onwards, new Antarctic campaigns were annually conducted and the number of new samples acquired by the Italian National Antarctic Museum (MNA, section of Genoa) progressively increased. However, no new taxonomic characterization was carried out on this material until now. Few samples from the Magellan Strait, collected during the VI Italian National Antarctic Program (PNRA) expedition in 1991, are also included here.