dataesr/scientific-paragraphs-categorization huggingface dataset:
Acquisition of the EEG Signal For EEG data recording we used a 128 channel device (Geodesic HidroCel GSN 128 EGI, Electrical Geodesic Inc. ) built in a soft cap. The signal was amplified by an optic isolated system of high impedance (200 M ) with a converter A/D of 24 bits, and a nominal gain of 20x. The acquisition was performed at a sampling frequency of 500 Hz, and the signal was analogically filtered (Butterworth first order band-pass filter of 0. 1-200 Hz; Geodesic EEG System 300, Electrical Geodesic Inc. ). The impedance of the electrodes was maintained below 60 k for all the recordings. The electrode positioned on the vertex (Cz) was used as a reference during the acquisition.
Pre-processing of the EEG Signal Firstly, the recorded signal was re-referenced from Cz to the average reference using NetStation software (Electrical Geodesic Inc. ). Following this procedure, the data was analyzed offline using EEGLab in MatLab's environment (Math Works, Version 2011a). The raw data was segmented in events of 4,000 ms, discarding the first 1,000 ms of the fixation cross and the last 1,000 ms of the PLD of each event. This segment duration was chosen considering a window of interest of the first 1,000 ms after the stimuli onset (see also  Hirai et al. , 2003; Jokisch et al. , 2005; Krakowski et al. , 2011; Saunier et al. , 2013). A proportional segment (1,000 ms) of fixation cross period was employed to calculate the baseline. Next, a baseline correction was applied using the last 200 ms of the fixation cross signal period.
ACKNOWLEDGMENTS Firstly, we would like to thank to  José Magalhães de Oliveira  and  Mario Fiorani Jr  for their support, and to the referees for the revision of this work. This work was funded by  Conselho Nacional de Desenvolvimento Científico e Tecnológico CNPq  (grant numbers  306817/2014-4  and  480108/2012-9 ),  Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro FAPERJ  (grants  E-26/110. 526/2012 ; E-26/010. 002902/2014 )and  Financiadora de Estudos e Projetos FINEP (PROINFRA HOSPITALAR  grant  18. 569-8 ). This work is also part of the  Fundação de Amparo a Pesquisa do Estado de São Paulo FAPESP's Research, Innovation and Dissemination Center for Neuromathematics-NeuroMat  ( FAPESP grant   2013/07699-0 ). This work was supported by  CAPES-COFECUB  (Projects no.
Statistical analysis All analyses were conducted using STATISTICA 7. 1 software. Preliminary checks via visual inspection of histograms and c o r t e x 8 7 ( 2 0 1 7 ) 1 7 4 e1 8 5 the ShapiroeWilk tests indicated that the data were not normally distributed in the majority of cases (p <. 05). Therefore, we performed group analyses on the epidemiologic data, standardised neuropsychological tests, the experimental measures of VOC, and a correlational analysis based on our prediction regarding the relation between VOC and proprioceptive deficits, using non-parametric tests and corrections for multiple comparisons, as appropriate (detailed below).
Lesion analysis methods The location and extent of brain lesions was mapped in each patient, based on a CT-scan (or a 1. 5T MRI-scan for 5 patients) obtained within the first week of admission. The scans of three hemiplegic patients (in the HP group) were unavailable and these patients were therefore excluded from further imaging analyses. The native structural scan of each patient was not normalised, but reoriented and aligned to match the stereotaxic space of the T1-weighted MRI scan template from the MNI (Montreal Neurological Institute), provided within the MRIcron software (http://www. mccauslandcenter. sc. edu/mricro/mricron/; Rorden & Brett, 2000). The quality of normalisation when working with a sample such as ours of elderly stroke patients presents a range of problems (e. g. , larger ventricles) and can greatly disrupt the normalisation process leading to inaccurate spatial normalisation  (Brett, Leff, Rorden, & Ashburner, 2001; Rorden & Karnath, 2004 ).
The VLSM approach  (Bates et al. , 2003)  identifies voxels significantly associated with a cognitive deficit in a group of patients, and involves running a t-test for continuous data, comparing patients' scores on a neuropsychological test in those patients with versus without a lesion at every voxel. This technique as implemented in the software package NPM (non-parametric mapping; http://www. mccauslandcenter. sc. edu/mricro/npm/; Rorden, Karnath, & Bonilha, 2007) was used to identify anatomical regions associated with (1) the baseline sense of body ownership using a binomial classification (0 ¼ DSO, 1 ¼ normal) based on the Cutting questionnaire  (Cutting, 1978 ) N ¼ 28); (2) Proprioceptive deficits, using the scale out of 9, in the three groups (N ¼ 28); (3) Visual Capture of Ownership, using the 11-point scale, in the three groups (N ¼ 28); (4) Failures in the Visual Capture of Ownership using the reversed 11-point scale in the three groups (N ¼ 28).
Acknowledgements We thank the patients and their relatives for their kindness and willingness to take part in the study, and to the staff and PACS office at participating hospital sites for their kind assistance with this study. We are also very grateful to  Cristina Papadaki  for help with patient recruitment and testing. This work was done when  Dr Besharati  was based at the  Academic Unit of Neuropsychiatry, King's College London, located at St Thomas's Hospital. No conflicts of interest were reported.
Funding This work was funded by an  European Research Council (ERC)   Starting Investigator Award  for the project ' The Bodily Self ' N  313755  to A. F. , a  Neuropsychoanalysis Foundation Fellowship  to P. M. J. , and a Commonwealth Scholarship, an  Oppenheimer Memorial Trust Fellowship , and a  Neuropsychology International Fellowship Award  from the  British Psychological Society  in conjunction with the  British Neuropsychological Society  to S. B.
Feedback In this section, we analyze the data collected in the experiment as described in the precedent section, questions were either open or rated on a 7-point Likert scale. Firstly, what came out of the discussions with the participants after the experiments is a sense of satisfaction for the product they tested. For each of the participants, they showed interest for VR world as a teaching tool; all of them were satisfied by the experiment and 64% of them rated their satisfaction at 7 (the maximum value) which is very encouraging for the interest aroused by MecaTeam3D (fig. 5 ). Furthermore 47% of the teachers think that MecaTeam3D could help them to train their students. To study this "Mirror Effect", we examined what skills the teacher could identify in MecaTeam3D to ensure they were in adequacy with the skills that we targeted.
Material The stimuli consisted of 53 French target words and 53 French target pseudowords. The mean number of letters was 7 (SD = 1. 41) and the mean number of syllables was 1. 75 (SD = 0. 52). The mean frequency of the words was 98 occurrences per million (SD = 150) according to the Manulex database  (Lété, Sprenger-Charolles, & Colé, 2004). All primes were English pseudoword fragments selected from the beginning of English words. For example, the unrelated prime gloa is the beginning of gloat (/gləʊt/). Only 14 primes out of 159 (53 x 3 prime conditions) in total were derived from English words with a low frequency of 1. 04 (SD = 2. 18; Children's Printed Word Database,  Masterson, Dixon, & Stuart, 2002). Three different primes were assigned to each of the targets: phonological, orthographic-control and unrelated.
Procedure Children were assessed individually at their school in a quiet room. They were tested in a single session lasting about twenty minutes. They were seated in front of a DELL computer using E-prime software. The lexical decision task involved 12 practice trials followed by a series of 106 (53 words, 53 pseudowords) experimental trials. Each trial began with the display of a fixation cross (800 ms) then a hash mark mask (800 ms), followed by a briefly presented fragment prime. The duration of the prime was 60 ms, which is typical in masked priming  (Forster, Mohan, & Hector, 2003) , and is an optimal duration to obtain phonological priming effects, as has been shown among skilled adult readers  (Ferrand & Grainger, 1993; Ziegler et al. , 2000). The prime was immediately followed by the target.
Results Latencies and accuracy for the target words were analyzed. Two items were excluded because their error rates were larger than 30% (see Appendix A). Outliers (i. e. , data beyond 3 SD, by participants) were not included in the analyses (0. 96% of the data). The cleaned data ranged from 326 to 2870 ms. The overall error rate was 12. 81% for the third graders and 7. 49% for the fifth graders (see   (Ratcliff, 1993). An additional benefit of this transformation is that it allows the testing of absolute (as opposed to proportional) differences between children from the two different grades. For instance, as can be seen in the Table, the Grade 3 children were slower than the Grade 5 children. As a result, potentially significant interactions between the between-participants variable (Grade) and the different priming effects could merely be artefacts of differences in overall speed between the Grade 3 and 5 children.
Study Data were collected within the "PRAUSE" survey conducted in Poitou-Charentes, France from 2011 to 2013  (Canivet et al. , 2015). A total of 466 retired volunteers aged 55 years and older (mean age = 75. 72; SD = 9. 84) were included in the survey. They have been contacted through a sampling frame of 3716 address records from the census surveys conducted by the French Institute of Statistics and Economical Studies (Institut national de la statistique et des études économiques -INSEE). This mastersample for regional extensions was very representative of the senior population of Poitou-Charentes (France). The inclusion criteria of the study were: (1) to be 55 years old or more, (2) to live in Poitou-Charentes (France), (3) to be retired, in a situation of long-term unemployment, long-term sick leave or never engaged in the paid labor force.
Controlled Inhibition Inhibitory performance was assessed using a computerized reaction time task. The task was constructed and administered using E-Prime 2. 0 software (Psychology Software Tools, Pittsburgh, PA, United States). The stimuli were arrows that appeared on the right or left of the computer screen. Participants responded as accurately and as rapidly as possible using a button press with a millisecond precision (Serial Response Box TM , Psychology software Tools, Pittsburgh, PA, United States) according to the direction indicated by the head of the arrow. If the arrow pointed to the right, the participant had to press the right key of the Serial Response Box with the right forefinger and if the arrow pointed to the left, he/she had to press the left key with the left forefinger (see Figure  2 ).
Genotyping DNA was extracted from buccal cells using a QIAamp DNA Blood Mini Kit (QIAGEN GmbH -QIAGEN Strasse 1. 40724 Hilden, Germany) according to the manufacturer's protocol. SNPs were genotyped using polymerase chain reaction and restriction fragment length polymorphism (PCR-RFLP) analysis. PCR amplification of the BDNF polymorphism Val66Met (rs6265) was performed using the forward primer 5 -GCCTACCCAGGTGTGCGG-3 and the reverse The allelic frequency was estimated using Hardy Weinberg equilibrium and the khi 2 test. The distribution of genotypes in the sample did not differ from the Hardy-Weinberg equilibrium (p = 0. 65). Genetic data were analyzed using a dominance model that combined Met carriers into a single group (Met/Met + Val/Met) because the Val/Met and Met/Met genotypes were previously associated with decreased cognitive performance compared to Val-homozygous genotypes  (Miyajima et al.
Physical Activity The Historical Leisure Activity Questionnaire (HLAQ) The level of current PA was evaluated during the second session using the HLAQ  (Kriska et al. , 1988). This validated questionnaire was used to assess the history of PA weighted by their relative intensity. Participants were asked to report the frequency, type, intensity, and hours of PA performed during the present year. We used the Compendium of Physical Activities Tracking Guide 2011  (Ainsworth et al. , 2011)  to obtain a specific metabolic equivalent (MET) for each PA. We calculated the average energy expenditure (Mets-h/week) for each participant using the HLAQ data and the compendium. We classified the participants according to their METs-h/week following World Health Organization (WHO) recommendations: participants with greater than or equal to 7.
The NASA/JSC Physical Activity Scale All participants were asked to rate their current regular weekly physical practices during the first session to identify their PA level on a score from 0 to 7 using the NASA/JSC Physical Activity Scale  (Jackson et al. , 1990). We used these data to confirm the participant's PA level in cases they were near the 7. 5 METsh/week (±0. 50). Seventy-eight participants were evaluated using this verification method. They were considered active if their NASA/JSC PA score was strictly higher than 3. Participants who scored level 4 or above practiced regularly intensive PA at least 3 h per week (three participants). Participants were classified as inactive if their NASA/JSC PA score was strictly lower than 3 (one participant).
FUNDING The PRAUSE survey was supported by the  French National Research Agency  ( ANR-12-MALZ-005-01 ), the  Regional Council of Poitou-Charentes  (Convention n •  10/RPC-R-050 ), the  European Funds for Regional Development (FEDER -Dossier n • 34150) , the  Poitou-Charentes Regional Department for Youth, Sports and Social Cohesion  (Contract n • 782997), the  Poitou-Charentes Health Regional Agency, and Calyxis.
Acknowledgments This work was conducted while the first author was in France as an invited professor. Thanks to a  Sorbonne University grant  ( SU-15-I-PACHA07 ), following a general funding by the  French National Agency for Research (ANR)  and the  IDEX Sorbonne-Universit és  (Program  ANR-11-IDEX-0004-02 ). The concepts discussed in this article were developed in an informal outdoor seminar. We thank  Clemence and Madeline  for their practical help during this seminar.
Here we present preprocessed MRI data of 265 participants from the Consortium for Neuropsychiatric Phenomics (CNP) dataset. The preprocessed dataset includes minimally preprocessed data in the native, MNI and surface spaces accompanied with potential confound regressors, tissue probability masks, brain masks and transformations. In addition the preprocessed dataset includes unthresholded group level and single subject statistical maps from all tasks included in the original dataset. We hope that availability of this dataset will greatly accelerate research.
Amendments from Version 1 -We have extended the Introduction to give a clearer view on the purpose and possibilities this dataset gives. -We have changed the heading 'Dataset validation' to 'Selected results'. -We have expanded the description of preprocessing and introduction to the FMRIPREP package. -We have clarified that not all subjects performed all of the tasks. -We added a data processing overview figure (new Figure  1 ). -We have spelled out the BIDS acronym and added a reference to a paper with more information. -We have added information about NIFTI and GIFTI file formats. -We have applied the suggested changes to the second paragraph of the Introduction -We have added information about the subjects -We have clarified that the FS analysis was done in parallel with the alignment.
Introduction Recently, the Consortium for Neuropsychiatric Phenomics published a dataset  1  with neuroimaging as well as phenotypic information for 272 participants. The subject population consists of healthy controls (130 subjects), as well as participants with diagnoses of adult ADHD (43 subjects), bipolar disorder (49 subjects) and schizophrenia (50 subjects). The goal of the study is to examine brain function and anatomy for these common neuropsychiatric syndromes. The study focuses on memory and response inhibition, with a large battery of questionnaires, neurocognitive tasks, a neuropsychological assessment and multiple neuroimaging modalities. Details on the complete assessment for each subject can be found in the data descriptor  1. It is undoubtedly a rich resource for the academic community that can help shed light on the relationship between brain and behavior, especially with respect to neuropsychiatric disorders.
However, before any brain-behaviour relationships can be answered, computationally expensive processing steps need to be performed  2. In addition to requiring a substantial amount of computing resources, a certain level of expertise in MRI data processing and fMRI task modelling is required before the data can be used to test scientific hypotheses. To facilitate answering scientific questions using the CNP dataset, we have performed standard preprocessing as well as statistical modeling on the data, and are making the results of these analyses openly available. The preprocessing was designed to facilitate a wide range of analyses, and includes outputs in native (aligned with participants T1 weighted scan), MNI (volumetric) and fsaverage5 (surface) spaces. The data have not been denoised, but potential confound regressors have been calculated for each run, giving researchers the freedom to choose their own denoising schemes.
Preprocessing The input dataset was acquired from OpenfMRI. org  6 -accession number ds000030, revision 1. 0. 3. Even though the original dataset included data from 272 participants, seven were missing T1 weighted scans (see Table  1 ) and thus only data from 265 participants were preprocessed. Results included in this manuscript come from preprocessing performed using FMRIPREP version 0. 4. 4 (http://fmriprep. readthedocs. io). This recently developed tool is a robust preprocessing pipeline based on the Nipype workflow engine  7. FMRIPREP aims at combining different implementations of various MR signal processing algorithms (from established software packages such as FSL, AFNI, or ANTs) to deliver a robust spatial normalization and nuisance estimation workflow. The tool was run with the following command line arguments: --participant_label {sid} -w $LOCAL_SCRATCH --output-space T1w fsaverage5 template -nthreads 8 --mem_mb 20000 Where {sid} was the participant label and $LOCAL_SCRATCH was temporary folder for storing intermediate results.
0. 9  12. Functional data was skullstripped using combination of BET (from FSL) and 3dAutoMask (from AFNI) tools and was coregistered to the corresponding T1 weighted volume using boundary based registration with 9 degrees of freedom -implemented in FreeSurfer v6. 0. 0  13. Motion correcting transformations, transformation to T1 weighted space and MNI template warp were applied in a single step using antsApplyTransformations v2. 1. 0 with Lanczos interpolation. Three tissue classes were extracted from T1 weighted images using FSL FAST v5. 0. 9 14. Voxels from cerebrospinal fluid and white matter were used to create a mask in turn used to extract physiological noise regressors using the principal component analysis-based method known as aCompCor  15. The mask was eroded and limited to subcortical regions to limit overlap with grey matter, six principal components were estimated.
Framewise displacement and dvars  16  was calculated for each functional run using Nipype implementation. In addition to those regressors global signal and mean white matter signal was also calculated. The whole dataset was preprocessed on the Stanford Sherlock supercomputer in total three times. After each iteration the decision to modify the preprocessing was purely based on the visual evaluation of the preprocessed data and not based on results of model fitting. First iteration (using FMRIPREP 0.
Volume-based task analysis For a full description of the paradigms for each task, please refer to 1. We analysed the task data using FSL  17  and AFNI  18  , implemented using Nipype  7. Spatial smoothing was applied using AFNI's 3dBlurInMask with a Gaussian kernel with FWHM=5mm. Activity was estimated using a general linear model (GLM) with FEAT  17. Predictors were convolved with a double-gamma canonical haemodynamic response function  19. Temporal derivatives were added to all task regressors to compensate for variability in the haemodynamic response function. Furthermore, the following regressors were added to avoid confounding due to motion: standardised dvars, absolute dvars, the voxelwise standard deviation of dvars, framewise displacement, and the six motion parameters (translation in 3 directions, rotation in 3 directions).
There is a total of 16 regressors. Not all subjects performed all tasks. Furthermore for subjects who are missing at least one regressor used in the contrasts, the task data are discarded. This is the case for example when no correct answers are registered for a certain condition in the SCAP task. For the SCAP task, we discarded 16 subjects; 14 subjects were removed for TASKSWITCH, 2 subjects for STOPSIGNAL, 2 subjects for BART, and 12 for PAMRET. Thus the total number of subjects modelled in the BART task is 259, while 244 subjects were modelled for the SCAP task. 254 subjects were included the TASKSWITCH task analysis, 197 subjects in the PAMRET task and 255 subjects in the STOPSIGNAL task. All modelled contrasts are listed in the Supplementary material. As is shown, all contrasts are estimated and tested for both a positive and a negative effect.
Selected results To validate the quality of volumetric spatial normalization we have looked at the overlap of the EPI derived brain masks in the MNI space (across all participants and runs -total of 1,969 maskssee Figure  2 ) and visualized alignment of a single line of voxels across all runs (see Figure  3 ). The within subject coregistration and between subject normalization worked well for the vast majority of participants, creating a very good overlap. All of the issues observed while processing the dataset are listed in Table  1. A selection of the tested contrasts in the task analyses is shown in Figures  4 to 8. Figures were generated using nilearn  22. • *T1w_target-MNI152NLin2009cAsym_warp. h5 Composite (warp and affine) transform to transform participant's T1 weighted image into the MNI space (HDF5 format).
Derivatives related to EPI files are in the func subfolder: • *bold_space-<space>_brainmask. nii. gz Brain mask for EPI files. • *bold_space-<space>_preproc. nii. gz Motioncorrected (using MCFLIRT for estimation and ANTs for interpolation) EPI file First of all, I would like to congratulate the authors for making the dataset available, which should allow interested scientists to explore the data and enrich the research they conduct with more information that can eventually lead to helpful new discoveries. It is remarkable that more than one processing stream was used (FSL and AFNI for functional, and volumetric and surface-based for structural), and further, three different inference approaches were considered, all of which are a great bonus in terms of comparisons among methods. I have very few concerns about the current version of the manuscript (v1, dated 28/July/2017): Page 2, 1st column, 2nd paragraph of the Introduction: "giving researchers the freedom to fit many different models that incorporate different denoising schemes": as stated, it may suggest that it would be adequate to simply run multiple models, without attention to excess error due to multiple testing.