dataesr/scientific-paragraphs-categorization huggingface dataset:
We recommend that practices such as study replication and data sharing be more commonly used in this research area. lack of open-science practices  (Tackett et al. , 2019) , including a lack of preregistration, replication, and openly available data and materials, there should be serious concerns regarding the reproducibility of findings in this research area. We sought to address this using the many-labs model developed by  Klein et al. (2014). We collected behavioral task data and assessed participants for hallucinatory experiences across 11 data-collection sites, as well as recruiting online. The aim was to recruit participants across the continuum of hallucinatory experiences and collect a large enough sample to provide the ability to detect small effect sizes. Because of methodological variability in the previous literature, we created a single centralized test battery used by all participating research groups.
Participants Participants were recruited via two methods: (a) lab data collection (i. e. , participants attended a datacollection site and took part in the study under laboratory conditions) and (b) online data collection (i. e. , participants were recruited online and completed the tasks on their own computer). Previous meta-analyses of comparable general-population studies have shown large effect sizes in this research area. For example,  Brookwell et al. (2013)  reported a g of 0. 8 (95% confidence interval, or CI = [0. 54, 1. 06]). Converting the lower CI in this estimate to r would give an effect size of. 26. Our main aim was to collect as large a sample as possible at as many data-collection sites as possible, so decisions regarding sample size were not based purely on power analyses; we preregistered a minimum sample size of 420 for lab-based data collection (based on the anticipated number of data-collection sites) and 800 for online data collection (based on available funding).
Task platform All tasks and questionnaires were programmed in Java-Script using the jsPsych toolbox (Version 6. 1; de  Leeuw, 2015)  and run from an Internet browser (code accessible at https://osf. io/eqy76/). For the purpose of this study, all measures were translated and back-translated from English into French, Czech, and Norwegian for use at datacollection sites in countries where these were the primary language, and verbal stimuli suited to each language were used for the source-memory and dichotic-listening tasks. For online data collection, participants were required to complete a task designed to ensure that they were wearing headphones (developed by  Woods et al. , 2011)  before gaining access to the main task platform (see Section S1 in the Supplemental Material). Additional attention checks are described below.
Auditory signal detection (lab data collection only) The auditory signal detection task required participants to respond as to whether they believed that a speech clip had been embedded in noise. The task was identical to that in previous studies (e. g. ,  Barkus et al. , 2011). The signal-to-noise ratio (SNR; i. e. , the ratio of the volume of the voice clip to the noise) was determined individually at each site using a short calibration task with participants who did not participate in the main study (n = 10 per site). This task was administered only with participants in the lab because calibration would not have been possible with online participants. In the main task, the participant was presented with seventy-two 3. 5-s bursts of pink noise. In the middle of 36 of these trials, a 1. 5-s speech clip was presented at one of four SNRs (speech present); in the other 36 trials, no speech clip was presented (speech absent).
Data analysis Exclusions based on preregistered criteria (e. g. , poor task performance) are outlined in Section S3 in the Supplemental Material. First, we examined associations between demographics and CAPS score and measures of alcohol, cannabis, cigarette usage, and nonverbal reasoning. These analyses were not preregistered and included for descriptive purposes. To assess associations between task performance and CAPS score, as detailed in Hypotheses 1 through 5, we calculated simple correlations (Spearman's r [r S ] for nonnormally distributed variables) with associated 95% CIs. For the preregistered analyses, when CIs included 0 (indicating a potential null effect), equivalence testing was conducted with upper and lower bounds of r S (0. 1 and -0. 1). Equivalence testing was done using the TOSTER package (Version 0.
Hypothesis 1: hallucinations and source memory We included 1,375 participants' data for the sourcememory task. Overall accuracy was well above chance (M = 64. 97%, 95% CI  = [64. 33, 65. 60] ). In terms of source judgments, participants were more likely to misattribute a heard item as imagined (hear-to-imagine error; M = 6. 26, 95% CI = [6. 08, 6. 45] ) than to misattribute an imagined item as heard (imagine-to-hear error; M = 4. 04, 95% CI = [3. 88, 4. 19]), t(1381) = 18. 29, p <. 001, d = 0. 49. The number of imagine-to-hear errors (i. e. , external misattributions) was used as the primary variable to assess source-monitoring performance (Hypothesis 1). There was no correlation between number of imagineto-hear errors and CAPS score, r S (1376) =. 02, 95% CI = [-. 03,. 07], p =. 461. Equivalence testing indicated that the effect was statistically indistinguishable from 0, given equivalence bounds of -0.
Hypothesis 2: hallucinations and dichotic listening We included 1,262 participants' data for the dichoticlistening task. Across the whole sample, a right-ear advantage was observed; participants also successfully oriented their attention in the forced-left and forced-right conditions, as in previous research (e. g. ,  Bless et al. , 2015;  see Section S5 in the Supplemental Material). There was no correlation between CAPS score and performance in the nonforced condition of the dichoticlistening task, as assessed by the number of right-ear responses (Hypothesis 2), r S (1263) =. 006, 95% CI = [-0. 05, 0. 06], p =. 842, and equivalence testing indicated that the effect was statistically indistinguishable from 0 (p <. 001). Similarly, there was no association between CAPS score and the number of correct left-ear responses, r S (1263) =.
Hypothesis 4: hallucinations and auditory signal detection Auditory signal detection data were collected only from participants who took part in the lab-based version of the study (n = 594). The mean hit rate was comparable with that in previous studies using this task (M = 74. 39%, 95% CI = [73. 18, 75. 59]), as was the false-alarm rate (M = 23. 29%, 95% CI = [21. 44, 25. 15]). There was a positive association between CAPS score and false-alarm rate (Hypothesis 5), r S (581) =. 14, 95% CI = [. 06,. 22], p <. 001. Additional analysis also showed a positive association between CAPS score and hit rate, r S (581) =. 18, 95% CI = [. 10,. 26], p <. 001, and a negative association between CAPS score and β, r S (581) = -. 17, 95% CI = [-. 25, -. 09], p <. 001, indicating that increased CAPS score was associated with a reduced threshold for accepting the presence of a stimulus.
Constructing a model to predict hallucinatory experiences from cognitive task performance We constructed three linear mixed models (using the lme4 package in R;  Bates et al. , 2015) , each with CAPS score as the dependent variable and data-collection site as a random effect (intercept). This analysis was conducted on data collected only in the lab so signal detection data could be included. Predictor variables were centered and standardized. Assumptions regarding multicollinearity, homoscedasticity, homogeneity of variance, and normality of random effects were met. However, inspection of quantile-quantile (QQ) plots suggested nonnormality of residuals; therefore, CAPS score was log transformed, and the models were recomputed. For these models, QQ plots suggested normality of residuals; therefore, the models with a log-transformed CAPS score were used.
Funding This research was supported by  Wellcome Trust  Grant  WT108720  awarded to  C. Fernyhough. Funding for the online component of this study was in part supported by the  Australian National Health and Medical Research Council (NHMRC)  via a project grant ( GNT1060664 ) awarded to  S. Rossell. S. Rossell  was supported by an  NHMRC   Senior Research Fellowship  ( GNT1154651 ), and  W. L. Toh  was supported by an  NHMRC   New Investigator project grant  ( GNT1161609 ). E. Kozáková  and  Y. Zaytseva  were supported by the  Internal Grant Agency of the Ministry of Health of the Czech Republic  (Grant No. AZV 17-32957A ) and by the project " Sustainability for the National Institute of Mental Health " under Grant No. LO1611. K. Hugdahl  and  J. Bless  were supported by grants from the  European Research Council  (Advanced Grant No.
Background: Improving feed efficiency (FE) is an important goal due to its economic and environmental significance for farm animal production. The FE phenotype is complex and based on the measurements of the individual feed consumption and average daily gain during a test period, which is costly and time-consuming. The identification of reliable predictors of FE is a strategy to reduce phenotyping efforts. Results: Gene expression data of the whole blood from three independent experiments were combined and analyzed by machine learning algorithms to propose molecular biomarkers of FE traits in growing pigs. These datasets included Large White pigs from two lines divergently selected for residual feed intake (RFI), a measure of net FE, and in which individual feed conversion ratio (FCR) and blood microarray data were available.
Merging the three datasets allowed considering FCR values (Mean = 2. 85; Min = 1. 92; Max = 5. 00) for a total of n = 148 pigs, with a large range of body weight (15 to 115 kg) and different test period duration (2 to 9 weeks). Random forest (RF) and gradient tree boosting (GTB) were applied on the whole blood transcripts (26,687 annotated molecular probes) to identify the most important variables for binary classification on RFI groups and a quantitative prediction of FCR, respectively. The dataset was split into learning (n = 74) and validation sets (n = 74). With iterative steps for variable selection, about three hundred's (328 to 391) molecular probes participating in various biological pathways, were identified as important predictors of RFI or FCR. With the GTB algorithm, simpler models were proposed combining 34 expressed unique genes to classify pigs into RFI groups (100% of success), and 25 expressed unique genes to predict FCR values (R 2 = 0.
However, among genes identified as differentially expressed between steers with low or high BW gain and feed intake, only few of them were similarly found across different cohorts  [18]. This highlights the importance of incorporating different datasets to cover various experimental conditions and to avoid the limits of each design (number of samples/ number of treatments) for biomarker discovery. This study aimed to identify reliable sets of expressed genes in the whole blood to predict the RFI group or individual FCR value. For that, ML algorithms were applied on a merged transcriptomic dataset from three independent experiments where meta-data for RFI and FCR were also available in growing pigs.
Animals and FE traits Three independent experiments  [19] [20] [21]  were merged to reanalyze gene expression levels in whole blood from a total of 148 females and barrows. These experiments all included purebred French Large White pigs of two lines divergently selected for RFI during 7th to 9th generations, and were based on different dietary treatments. The distribution of FCR values for the 148 pigs considered in the merged dataset was illustrated in Fig. 1 , according to the RFI group and their experiment of origin. The FCR averaged 2. 85 kg feed/kg BW, and covered a large range of values (Min = 1. 92; Max = 5. 00). It was generally lower for pigs of the low RFI line than for pigs of the high RFI line, but there was an interpenetration between the two lines within each experiment and between experiments.
Model performance in RFI classification Merging the transcriptomic data of the three independent experiments resulted in a new dataset of 26,687 annotated expressed probes across the 148 blood samples. The random forest (RF) and gradient tree boosting (GTB) procedures were applied to this merged dataset to find the most important transcripts that allow the classification of pigs (low RFI/high RFI). These algorithms were considered to produce an excellent fit of predicted to observed values even when the specific nature of the relationships between the predictor variables and the dependent variable was very complex  [22]. In the two procedures, a randomly selected bootstrap sample set was used as a learning dataset (n = 74 pigs), whereas the remaining samples (n = 74 pigs) were used in a test dataset for validation.
Learning and validation datasets including transcriptomic data and meta-data (RFI group, FCR) are freely available at https://doi:1 0. 15454/J4XOPD. From the RF procedure, a total of 778 probes (out of the 26,687 annotated probes) were first selected to provide an accurate classification of pigs into low and high RFI groups during the training step. In the validation step (Supp. Table  S1 ), the RF model further selected 328 probes (out of the 778 probes) as very important variables (VIP) for RFI classification. The accuracy of the Fig. 1  Distribution of feed conversion ratio across the dataset. Pigs of low and high residual feed intake (RFI) lines were considered in three different experiments unraveling different periods for blood sampling. Feed conversion ratio (FCR) was measured for each pig during specific test periods.
The first dataset included 21 pigs, the second dataset included 48 pigs and the third dataset included 79 pigs. In the merged dataset, 148 pigs were thus analyzed. Feeding conditions, test periods and age and body weight of pigs when blood sampling was performed, are detailed in Material and Methods model was estimated by the proportion (%) of good classification, and the optimal model was selected according to the receiver operating characteristic curve (ROC) as a diagnostic ability of the binary classifier system. Iterative steps allowed to obtain the best model (96% of success on average) with a subset of 50 molecular probes (out of the 328 VIP). It provided a good prediction for 94. 74% of the high RFI pigs and 97. 22% of the low RFI pigs, respectively (Table  1 ), so that the prediction accuracy was similar for the two RFI lines (chi2 = 0.
Model performance in FCR prediction When addressing continuous variables such as FCR, regression trees rather than classification trees must be built. The transcriptomic dataset was similarly split into training (n = 74) and validation (n = 74) datasets. About 1393 probes (out of the 26,687 annotated probes) were selected during the training procedure. The performance of the models was then evaluated by using the validation set, and models with the best R 2 and the lowest Root Mean Squared Error (RMSE) were retained (Table  3 ). The accuracy of prediction by the GTB algorithm (R 2 0. 80; RMSE ~0. 23) exceeded that obtained by the RF procedure (R 2 ~0. 65 and RMSE ~0. 29). Due to this large difference in model performance between the two algorithms in regression, also mentioned by others  [23] , only the results of the GTB model for FCR prediction were described in this study.
85, respectively, and the error made by the model was evaluated at 7% on the average. The samples (n = 5) having the highest residual (> 0. 15) all corresponded to pigs of the high RFI line but from different experiments (1 pig from experiment 1, 1 pig from experiment 2, and 3 pigs from experiment 3, Supp. Fig. S1 ), suggesting no bias due to the independent datasets. Without these few samples (5 out of 74), the prediction accuracy was improved (R 2 = 0. 94).
The 728 molecular probes selected to predict FCR values corresponded to 477 unique annotated genes that were clustered in 10 biological pathways (Table  5 ). Significant pathways were related to immune and defense response (regulation of A gradient tree boosting (GTB) algorithm was applied on transcriptomic dataset (26,687 annotated molecular probes) from the whole blood of 148 growing pigs. Data were split into training (n = 74) and validation test (n = 74) subsets to evaluate model performance in classifying pigs into low or high residual feed intake (RFI) groups. The unique genes corresponding to the most relevant annotated probes able to attribute RFI class for each pig were listed. The score attributed to each probe gave hierarchy of importance in the predictive model leukocyte activation, regulation of cytokine production, regulation of acute inflammatory response and positive regulation of immune response), glycoprotein metabolic process, regulations of protein transport and of peptidase activity, and protein amino acid autophosphorylation.
Similarly, HTRA1 was Very important genes for prediction of feed efficiency traits (RFI: residual feed intake; FCR: feed conversion ratio) were clustered into functional groups using DAVI D tool. The enrichment score (E > 1) for each cluster and P-value of the enrichment for the corresponding Gene Ontology (GO) terms are provided. Iterative steps for model reduction have been further applied on these transcripts of genes to obtain smaller sets of predictors listed as differentially expressed in both muscle and liver between low and high RFI pigs of two different populations  [24] ; this serine protease mediates multiple biological processes by antagonizing IGF-binding proteins and proteins of the TGF-beta family  [43]. In line, the expression level of IGF2 in muscle was identified as a reliable predictor for RFI breeding values  [26]  and increasing muscle growth through the IGF-1/2 signaling pathway was proposed as a potential strategy for the improvement of FE in Yorkshire pigs from 30 to 90 kg BW  [44].
General design This study reused phenotypic data obtained in pigs from the three independent experiments that were previously published  [19] [20] [21] , to avoid the needs of new sampling in living animals while obtaining a high number of animals allowing robust predictions. The application of ML procedures on the merged dataset (n = 148 pigs) avoided the overfitting often observed when simple classification or regression procedures are used for a limited number of animals and a high number of dependent variables, and the leave-one-out method was an additional way to resampling the datasets. Thus, this study fits with the 3R (Replacement, Reduction and Refinement) principles.
Pigs and blood samples The three independent datasets referred to purebred French Large White pigs produced in a divergent selection experiment for RFI. The selection program was described in full details elsewhere  [49] , including the equation to calculate RFI from a regression between observed feed intake and that expected based on requirements for maintenance (based on the metabolic BW) and performance (average daily gain, backfat thickness). From birth to weaning, all pigs were reared in the selection farm of INRAE (UE Genesi, Le Magneraud & R o u i l l é , F r a n c e ; h t t p s : / / d o i. o r g / 1 0. 1 5 4 5 4 / 1. 5572415481185847E12). All pigs were weaned at 28 days (d), and were first fed ad libitum with standard starter and weaner diets. During subsequent test periods in dedicated buildings, pigs have undergone different feeding conditions depending on the experiments as described below.
As indicated in the referenced publications  [19] [20] [21] , the three experiments were conducted in accordance with the French legislation on animal experimentation, and the protocols were approved by regional ethical committees evaluating the research question, design, plan analysis, animal care and monitoring, and ways to minimize pain and consider limit points (especially regarding jugular blood sampling). At the end of each experiment, pigs were slaughtered using approved procedures, including electronarcosis followed by jugular exsanguination. The first dataset  [19]  included 21 castrated males from the 7th generation of selection (n = 10 low RFI pigs and n = 11 high RFI pigs) housed at thermo-neutrality (24 °C) The third dataset  [21]  included 79 castrated males and females from the 9th generation of selection (n = 37 low RFI pigs and n = 42 high RFI pigs).
Pigs were reared at the experimental INRAE pig facility at Le Magneraud, France (UE Genesi; https://doi. org/10. 15454/1. 5572415481185847E12). Blood was sampled at 40 d of age from the jugular vein. At 70 d of age, pigs were transferred to group-housing facilities equipped with single-place electronic feeders. The first half of the pigs were fed standard diets, whereas the second half was fed a high-fiber diet during the growing-finishing phases  [21]. The FCR was then calculated from 90 d to 161 d of age. In the three datasets, the reference to low or high RFI line was indicated for each pig, and the FCR value was individually attributed. Other factors (sex, season, generation, diet) were not taken into account.
In the three transcriptomic datasets, raw spot intensities have been submitted to quality filtration based on four criteria: background intensity value, diameter, saturation and uniformity of the spot, and intensities of filtered spots were log2 transformed and mediancentered to correct for microarray effect. For the current study, the three microarray datasets were then merged into a single new dataset. There was no exclusion of any animals in this merged dataset. To obtain consolidated expression values across the three independent datasets, the molecular data have been normalized by mean centering, i. e. subtracting the mean value across all probes from all raw values for each pig sample in the merged dataset. The merged dataset also included meta-data such as the experiment of origin (1, 2, and 3), RFI group (n = 71 pigs of low RFI line, n = 77 pigs of high RFI line) and FCR value (n = 148 pigs).
Supervised methods to identify important variables for the prediction of FE traits The merged dataset was used to search the most important molecular predictors for the RFI group and FCR value, by using ML methods. The experimental unit was the pig. Among the panel of ML methods for dimensionality reduction, classification and regression used in livestock breeding  [14] , the RF and GTB procedures were chosen in the current study and were compared for performance in classification (RFI group) and regression (FCR value) procedures. These two ML methods use decision trees, but RF uses a large number of trees combined by averaging or "majority rules" at the end of the process  [50] , whereas GTB starts the combining process of decision trees at the beginning  [27, 51, 52]. Other differences include how trees are built: RF builds each tree independently, while GTB builds one tree at a time but in an additive model proceeding in a forward stage-wise sequential error-correcting process to combine results along the way and converge to an accurate model  [29].
Sequential steps for learning, validation, and finally, selection of the best models were performed according to standards described by Fernandez-Lozano and colleagues  [53]. Models were generated from RF and GTB algorithms with Salford Predictive Modeler 8. 0 (SPM 8. 0®). The RF models were generated with about 1500 trees for classification of RFI and regression for FCR. For that, a randomly selected bootstrap sample set was created by using 50% of the original dataset for learning (n = 74 pigs). Consequently, each bootstrap sample called "outof-bag" data (OOB) excluded 50% of the data that were further used for validation (n = 74 pigs), and the leaveone-out method assessed the performance by resampling the training set. The test dataset allowed cross-validation ensuring that the training of the model was not biased.