dataesr/scientific-paragraphs-categorization huggingface dataset:
Participants The analysis was carried out on participants drawn from the IMAGEN sample (see for further details: Schumann et al  44. For IMAGEN, a general population sample of Caucasian adolescents were recruited from eight sites across France, Ireland, England and Germany. Data was collected at age 14, 16 and 19 years. After a conservative quality control of MRI acquisitions and DAWBA questionnaires, participants with complete data were used in the subsequent data analysis. No statistical analyses were used to pre-define sample size. However, the sample size used was simlar to that reported in previous studies  10, 12.
The strength and difficulties questionnaire (SDQ) was also used in the present investigation as this questionnaire contributes to the assignment of diagnostic status in the DAWBA  45. Questions in the SDQ are categorized into broad internalising and externalizing domains. The data of four of the questions asked had a standard deviation of zero amongst the participants asked, and were therefore not used in subsequent analyses. The full set of psychiatric questions asked in the present investigation can be found in Supplementary Table  1 , the questionnaire items that were omitted from the analysis are marked here. At the time of the analysis conducted here, DAWBA/SDQ data had been collected for 1510 participants. Of these, data was incomplete for 239 participants, and was not used.
VBM Pre-processing At the time this investigation was conducted, T 1 data had been acquired for 1400 participants. All scans were visually inspected and manually reoriented. 285 scans were discarded from the analysis for either movement artifacts, strong field inhomogeneities, abnormal field of view, abnormally reduced cerebellum and for brace artefacts. The resulting 1,115 scans were used to build the study specific template. Baseline and Follow up two scans were preprocessed using both the 2008 version of the Voxel Based Morphometry toolbox (VBM8) running in SPM8 (v. 5236). Given the young adults recruited in IMAGEN, we first used VBM8 in order to avoid using adult tissue probability maps (TPM) to initiate the segmentation process. The VBM8 toolbox segmentation relies on an adaptive Maximum a Posterior technique and TPMs used in VBM8 are for registration purposes only.
Diffeomorphic registration (Dartel) was then used to register the 1,115 images, and to generate the study-specific population average template  47. We then resliced the data to 1. 5x1. 5x1. 5mm voxel size. Smoothing was carried out using an isotropic 8 mm full width at half maximum Gaussian smoothing kernel. We created a mask for the sample by taking the mean across all VBM maps included in the sample. We thresholded the mask at >0. 4. We used a stringent mask to avoid overfitting the data  48. We then extracted all voxel values within this mask, resulting in 241,544 grey matter voxels.
TBSS Pre-processing At the time this study was conducted, DTI data had been acquired for 1412 participants. Of these, 71 were not usable due to: signal dropouts or too much rotation. Diffusion imaging data was pre-processed using software from the FSL toolbox (www. fmrib. ox. ac. uk/fsl)  50. We preprocessed the remaining 1341 scans using tract based spatial statistics (TBSS)  19. Pre-processing was carried out in the following manner: An affine registration was applied to the first B 0 image for head motion and eddy current correction. Brain extraction was carried out using BET. Diffusion tensor fitting was then used to obtain fractional anisotropy (FA) maps for each participant. All participants' FA data was aligned into a common space using the non-linear registration tool FNIRT, using a b-spline representation of the registration warp field.
Resting State fMRI Preprocessing At the time of this investigation, we had collected rsfMRI scans for 1067 participants. Of these scans, 157 were not used, either because over 5% of scans in that participant exhibited artifacts of some kind, or if over 5% of volumes showed a fractional displacement of over 0. 5mm. Preprocessing of resting-state data was performed with routines from FMRIB's Software Library (FSL v5. 0. 9)  50 and Advanced Normalization Tools (ANTs v1. 9. 2)  51. 1) Motion correction was carried out, applying a rigid body registration of each volume to the middle volume (FSL MCFLIRT). 2) Non-brain tissue was removed (FSL BET). 3) Spatial smoothing was applied using a 5mm FWHM Gaussian kernel.
Different Neuroimaging Processing Strategies A wide range of different preprocessing strategies can be applied in the analysis of neuroimaging data. Approaches to analysing DTI and T1 can be categorised into two broad types: voxelwise, and atlas based approaches  18, 55. We chose to analyse this data at the voxelwise level, as this allows for the highest level of spatial specificity. Although it is also technically possible to analyse rs-fcMRI data across the whole brain at the voxelwise level, this approach results in an enormous number of features: When mapping connectivity at the voxelwise level, in a dataset made up of N voxels, we are left with (N*(N-1))/2 connections between those voxels. In the current investigation, N = 57,053, leading to N*(N-1)/2 = 1. 63 billion inter-regional connections.
Multiple Sparse Canonical Correlation Analysis Regression The formulation of sparse canonical correlation analysis described in the text above is designed to find relations between two views of a dataset. However, we have collected data from several different neuroimaging modalities, and would like to utilize information from each of them. A somewhat naive approach to finding relations between psychiatric symptoms and multiple neuroimaging measures would be to include all available neuroimaging modalities in one view of the canonical relation, with psychiatric symptoms in the other view. However, this approach is likely to be problematic as different modalities are associated with very different numbers of features. For example, the functional connectivity data used in the present investigation has only 0.
6% of the number of features that the VBM data has. As such, if these modalities were entered into the same model, the VBM data would overwhelm the functional connectivity data. We developed an approach designed to maximise the cross-correlation between psychiatric symptoms, and multiple neuroimaging modalities simultaneously, we then combined these modalities in a linear regression model. Formulations of canonical correlation analysis that are able to find relations between more than two sets of data are termed multiple or generalised canonical correlation procedures. A widely used optimisation criteria for multiple canonical correlation analysis is to maximise the sum of correlations between each of the different views of a dataset  60. Witten et al have formulated a sparse version of multiple canonical correlation analysis  58  ; this formulation is designed to maximise the sum of correlations between all views of the data.
msCCA-regression was carried out using in-house codes written in MATLAB. This algorithm requires an initialization value. In the present study, initial weights were randomly generated. Weight values associated with psychiatric symptoms were always constrained to be positive to ensure interpretability. This study is designed to be exploratory in nature. Nevertheless, given the very large quantity of data we sought to integrate, it is likely that some simple priors will help to improve the stability of our results, so long as those priors are well supported. There is a great deal of evidence suggesting that psychopathology is associated with decreases in both grey matter, and fractional anisotropy, across psychiatric disorders  61, 62. For this reason, we constrained the canonical weights on VBM volume and FA to be negative.
Stability Selection Although msCCA-regression (and sCCA) have advantages over classical CCA in terms of interpretability, it can suffer from instabilities due to their utilization of an L 1 penalty to introduce sparsity  21. This is particularly true when p >> n, and when there is a high degree of collinearity in the data. Stability selection is a widely applicable feature selection procedure that can address this problem  21. This procedure has the added benefit that it makes the results less sensitive to the choice of L 1 penalty. The conceptual underpinning of stability selection is very simple: if a model is repeatedly resampled, features exhibiting a 'real' effect will be selected more often than noise. Using stability selection, data is repeatedly split into random sub-samples of size n t /2 (where n t is the total number of participants in the training dataset).
A related advantage is that it is possible to carry out further characterization of the test set results, due to the fact that we are able to estimate effect size in an unbiased way. In detail, the analysis design was carried out as follows: 1) Psychiatric symptom data, and data from the VBM, TBSS and rs-fcMRI neuroimaging modalities was extracted and transformed into x p i matrices, where is the number of participants included in the training dataset, and p i is the number of features included in each of the views of the data. 2) The full dataset was randomly split into training and testing sets. The training set was made up of 70% of the data whilst the testing set was made up of the remaining 30%. A c c e p t e d M a n u s c r i p t 3) The training data was then randomly split into a hundred further resamples.
This is done for each of the 10,000 permutations of the test labelling, producing a distribution of the maximal statistic. Correlations between symptom and neuroimaging measures in the experimental labelling are then significant at the FWE-corrected level α if they are above the 100*(1α) percentile of this distribution. This process is illustrated in Supplementary Figure  1. A c c e p t e d M a n u s c r i p t significant localizable effects in the training dataset, we then used these results to inform analyses carried out on the testing dataset. In this sense, the training dataset was used as a 'discovery dataset'. In the case of the TBSS and VBM data, we sought to localize associations between symptoms and the brain to the cluster-wise level. In the case of the rs-fcMRI data, we sought to localize changes to individual inter-regional connections.
VBM and TBSS clusters were defined using an 18-connectivity scheme. This means that voxels must be connected by a face or an edge to be considered a part of the same cluster. This analysis was carried out in the manner described below: 1) We calculated the grey matter volume and FA in spatially distinct clusters identified in the sCCA analysis applied to VBM and TBSS respectively. We extracted connectivity values with non-zero canonical weights. This was done in both the training and testing datasets. 2) We calculated Pearson's correlation coefficient between the mean of each spatially distinct cluster/connection, and the sum of symptom score values. This was done separately in the training and testing datasets. deflation procedure as an alternative  11, 70  ; this is the procedure we use in the present investigation.
Clinical Analyses Using mSCCA-regression, we found a set of neuroimaging features that correlate with a set of questions assessing psychiatric health. At the group level, participants who score more highly on the vector derived from neuroimaging data will suffer a larger number of psychiatric symptoms (as measured by the DAWBA). It might therefore be expected that participants with a clinical diagnosis of a psychiatric disorder would score more highly on this neuroimaging vector than healthy controls. To discover whether this was the case, we subjected clinical data to exactly the same pre-processing as the IMAGEN data; we then looked for changes in grey matter volume in the regions identified in the initial analysis. A (one-sided) twosample t-test was used to determine whether patients and controls differed significantly on this one-dimensional measure.
Rows associated with neuroimaging data in the training set were permuted and correlations between clusters/connections, and symptom clusters were recalculated. The maximal value was recorded. Training data was permuted 10,000 times; the maximum correlation value across all clusters/connections was recorded for each permutation. Clusters/connections exhibiting a significant effect in the training dataset were then determined by comparingA c c e p t e d M a n u s c r i p t.
Acknowledgments This work received support from the following sources: the  European Union -funded  FP6  Integrated Project IMAGEN (Reinforcement-related behaviour in normal brain function and psychopathology) ( LSHM-CT-2007-037286 ), the  Horizon 2020  funded  ERC Advanced Grant 'STRATIFY' (Brain network  based stratification of reinforcement-related disorders) (695313), ERANID (Understanding the  Interplay between Cultural, Biological and Subjective Factors in Drug Use Pathways)  ( PR-ST-0416-10004 ),  BRIDGET  ( JPND: BRain Imaging, cognition Dementia and next generation GEnomics ) ( MR/N027558/1 ),  Human Brain Project  ( HBP SGA 2, 785907 ), the  FP7  project  MATRICS  ( 603016 ), the  Medical Research Council Grant 'c-VEDA' (Consortium on Vulnerability to Externalizing Disorders and Addictions)  ( MR/N000390/1 ), the  National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London , the  Bundesministeriumfür Bildung und Forschung (BMBF  grants  01GS08152 ;  01EV0711 ;  Forschungsnetz AERIAL 01EE1406A ,  01EE1406B  ,  01ZX1314G ,  01GS08147 ), the  Deutsche Forschungsgemeinschaft (DFG  grants  SM 80/7-2 ,  SFB 940/2 ), the  Medical Research Foundation and Medical Research Council  (grants  MR/R00465X/1  and  MR/S020306/1 ), the  National Institutes of Health (NIH)  funded ENIGMA (grants  5U54EB020403-05  and  1R56AG058854-01 ).
Further support was provided by grants from:  ANR  (project  AF12-NEUR0008-01 -WM2NA , and  ANR-12-SAMA-0004 ), the  Fondation de France , the  Fondation pour la Recherche Médicale , the  Mission Interministérielle de Lutte-contre-les-Drogueset-les-Conduites-Addictives (MILDECA) , the  Assistance-Publique-Hôpitaux-de-Paris  and  INSERM  (interface grant),  Paris Sud University  IDEX 2012; the  National Institutes of Health, Science Foundation Ireland  ( 16/ERCD/3797 ),  U. S. A. (Axon, Testosterone and Mental Health during Adolescence ;  RO1 MH085772-01A1 ), and by  NIH Consortium  grant  U54 EB020403 , supported by a A c c e p t e d M a n u s c r i p t.
8 ) 8 We then permuted the training data, and repeated steps 3-7. This was done for 10,000 different permutations of the training data labelling. In each case, we recorded the correlation between the neuroimaging model, and the canonical correlate of psychiatric symptoms. In this way, we built up a permutation distribution to assess the significance of the relation between symptom and neuroimaging data in the experimental labelling, within the training dataset.
3 ) 3 Rows associated with neuroimaging data in the training set were permuted and correlations between clusters/connections, and symptom clusters were recalculated. The maximal value was recorded. Training data was permuted 10,000 times; the maximum correlation value across all clusters/connections was recorded for each permutation. Clusters/connections exhibiting a significant effect in the training dataset were then determined by comparingA c c e p t e d M a n u s c r i p t.
Data Collection We have conducted a narrative literature review of articles published up to April 4, 2019, of the validation and monitoring frameworks of BITs. The main databases that we searched were MEDLINE, PsycINFO, Web of Science, Science Direct, and the Journal of Medical Internet Research (JMIR) database. They have been chosen as reference databases in biomedicine and psychology and comprise the largest general scientific database  [24]. The search keywords were as follows: ("model" OR "framework" OR "process" OR "evaluation process" OR "validation process") AND ("behavior" OR "behavior change" OR "behavioral intervention") AND ("digital health" OR "ehealth" OR "mhealth" OR "connected health" OR "medical app" OR "smartphone" OR "iphone" OR "email" OR "text message" OR "SMS" OR "mobile app" OR "smartphone app" OR "connected health" OR "wearable").
Universities were the main institutions (34/46, 74%), with 16 out of 46 (35%) from the United States. A total of 29 out of 46 frameworks (63%) were supported by a research grant: out of 46 frameworks, 29 (63%) were funded by a public grant, 12 (26%) were funded by the NIH, and 6 (13%) were funded by the English public health system. Out of 46 frameworks, 5 (11%) were funded by universities, 3 (7%) were funded by the private sector, and 3 (7%) were funded by a public-private partnership.
In addition to the linear sequence, 17 out of 46 frameworks (37%) had an iterative structure  [12, 17, 31, 32, 42, 44, 52, [54] [55] [56] 61, 62, 66, 69, 70, 72, 73] , allowing a refining step at each phase in the case of suboptimal results. The iterative structure is one of the assets used by the Multiphase Optimization STrategy (MOST) and the Obesity-Related Behavioral Intervention Trials (ORBIT) model frameworks to optimize interventions  [42, 62]. A total of 15 out of 46 frameworks (33%) were also associated with an evolutive process, creating a cyclic organization to improve the product progressively  [30, 34, 35, [37] [38] [39] 43, [47] [48] [49] 51, 58, 60, 67, 68]. Finally, 11 out of 46 frameworks (24%) also integrated a parallel process  [29, 36, 40, 41, 46, 57, 59, 64, 65, 71, 74] ; 1 of those was the Agile software management framework, which aims for a high degree of adaptability to satisfy the clients  [40].
The first is that of permanent technological innovation. It is embodied in the Agile software management framework. Flexibility, based on user demand, dictates each evolution. Health is a market like any other. If this approach was to win in this area, for example, to make substantial savings in clinical research, no counterpart could be asked for, concerning health benefits. Big data analyses will not guarantee a relevant comparability between BITs  [96]. The second problem at the opposite extreme is that marketing authorization of the BIT must be conditional on the completion of a well-designed randomized controlled trial. This process has been a part of the success of drugs in the last century. These trials are long, costly, and debatable in terms of methodology for BITs, if only for the choice of control group  [97].
Participants Sixty participants were recruited at the campus of the University of Lille. The sample size for repeated measures ANOVA with within-between interaction was determinated (using GPower software) to detect medium sample size (0. 25) with an alpha of 0. 05 and power of 0. 95. Two participants were excluded due to technical problems during one of the experimental tasks. Final sample consisted of 58 participants (34 females, M age = 24. 12, SD = 3. 85), resulting in 20 participants in abstract RNT condition, 19 in concrete RNT, and 19 in the control condition. There were no significant differences between experimental groups in age (F(2,55) = 3. 10, p < 0. 1, η p 2 = 0. 10) and in gender (χ 2 (2, n = 58) < 1).
The neutral and negative cue words were selected on the basis of their affective valence and matched on familiarity and word length (the differences on frequency and length were non-significant between neutral and negative words) according to French Language Corpus (Interactive Language Toolbox; KU Leuven, 2014). RNT-related cue words were individually selected by each participant (see section "Goal Cueing Task"). The words were randomly selected from the set of each valence list. The task was preceded by 12 training trials, followed by 80 experimental trials with an equal number of valid and invalid trials. Attentional disengagement was assessed by the attentional disengagement index proposed by  Koster et al. (2005). The attentional disengagement index was calculated for negative stimuli (correct response time (CRT) to invalid trials with negative cue minus CRT to invalid trials with neutral cue).
Procedure Participants were recruited on the campus by the experimenter. During the testing phase, participants were alone in the experiment room. They were instructed to solicit experimenter's help, if needed. All the tasks (including questionnaires) were programmed in Inquisit version 4 software and displayed on a 13. 3 inches screen. All participants signed an informed consent form prior to their participation. First, participants were asked to provide demographic information: age, sex, and educational level. Then, they filled a pre-induction PANAS (PANAS 1) to assess their emotional state. The PANAS was also administrated after each of the tasks presented below (PANAS 2 -after goal cueing task; PANAS 3after abstract vs. concrete RNT or control induction; PANAS 4 -after exogenous cueing task.
Modelling relationships between entities in real-world systems with a simple graph is a standard approach. However, reality is better embraced as several interdependent subsystems (or layers). Recently the concept of a multilayer network model has emerged from the field of complex systems. This model can be applied to a wide range of real-world datasets. Examples of multilayer networks can be found in the domains of life sciences, sociology, digital humanities and more. Within the domain of graph visualization there are many systems which visualize datasets having many characteristics of multilayer graphs. This report provides a state of the art and a structured analysis of contemporary multilayer network visualization, not only for researchers in visualization, but also for those who aim to visualize multilayer networks in the domain of complex systems, as well as those developing systems across application domains.
In this case, layers may be characterised by entity type (either person or company). Other definitions of layers are also possible as illustrated in Section 2. Examples of multilayer networks can be found in the domains of biology (the so-called "omics" layers), epidemiology  [94, 107, 125] , sociology (in a broad sense, including fields such as criminology, for instance)  [17, 19, 28, 32, 40, 44, 46, 80] , digital humanities  [37, 89, 114] , civil infrastructure  [22, 31, 35]  and more. Multilayer networks have been explicitly recognised as promising for biological analysis  [49]. We give more details in Section 2. 4. In the area of network visualization many systems visualize datasets having many characteristics of multilayer networks, albeit under a different title. Multi-label, multi-edge, multi-relational, multiplex  [22, 102] , heterogeneous  [37, 108] , and multimodal  [46, 55] , multiple edge set networks  [28] , interdependent networks  [43] , interconnected networks  [107]  and networks of networks  [68]  are amongst the many names given to various types of data that are encapsulated by the Multilayer Networks definition of Kivelä et al.
While multiple aspects are a possibility for multilayer network data sets, it is not a requirement. A multilayer data set may be defined by a single aspect, which categorises multiple layers. See Table  1  for a sample list of aspects and layers extracted from the literature surveyed as part of this report. Kivelä et al. [73]  provide further examples in their extensive list of multiplex datasets and their associated layers. Incidentally, Wehmuth et al. [128]  propose an alternative definition they call MultiAspect graphs where they formally define what can be considered as an aspect. Unsurprisingly, they also form a network where nodes are defined using Cartesian products collecting multiple values into a single entity. The authors describe MultiAspect graphs as forming a generalisation of Kivelä et al.