dataesr/scientific-paragraphs-categorization huggingface dataset:
All devices are connected through the isolated USB and are controlled by software custom-written in MATLAB 2015a (The Mathworks, Natick, MA), specifically using the Data Acquisition Toolbox for control of the 6216 NiDAQ. A graphical user interface based front-end (Fig. 3 ) allows control of all functions, automatically ensuring the correct sequence of power supply and   (A) The subject is seated in the chair slightly lowered below the normal MEG scan position. This is for their comfort during set-up of the electrodes and characterisation of the units found. (B) The amplifier head-stage is shown taped to the forearm with the electrodes visible. The upper electrode (red terminal) is the median nerve electrode with the reference (current return) underneath placed subcutaneously. The black wire is attached to the unseen 'ground' electrode.
The stimulator box is not shown in these photographs but is positioned on the floor close to the subject's feet. The operator is locating the afferent by stroking or touching the skin with a monofilament whilst listening to the audio and observing the traces, as shown in Fig. 5. The subject is wearing their individual head-cast. The wires into the head cast are actively driven location markers for head registration. For fMRI the cables going to the amplifier box are routed such that they cannot touch the subject and create an RF antenna loop which couples to the body. opto-switch switching. Acquisition data is sampled at 40 kHz, filtered within a selected band (usually 300-5000 Hz), recorded (if required), buffered to a rolling chart display, and streamed to the host computer's audio device.
Software-generated pulse sequences are produced at 50 kHz sampling rate, from a data file which defines the overall protocol. There is complete flexibility in the waveforms generated and this opens the possibility of applying any pattern of INMS (e. g. one that mimics the natural firing of mechanoreceptive afferents). The remote-control box allows the operator to control the important functions of the software, such as the Amplify/Stimulate switching function, controlling applied current, and initiating bursts of pulses for determining the current threshold for a perceived sensation. To assist the operator, a visual display of the screen of the INMS control computer is projected into the scan room (a standard feature of most MEG and fMRI laboratories), as well as an alphanumeric display on the remote control box giving information including the electrical current level and impedance.
The latter display is achieved using the SPI port of the Arduino board. Software is written for the Arduino to interpret a string of text sent to an attached COM port from MATLAB, and to put it on the screen. Two 15 mm tungsten electrodes (one insulated and one uninsulated) (FHC, Bowdoin, ME, USA) were inserted through and glued to the cap of a polythene bottle (200 ml, 50 mm diameter). A silver plated 'ground' electrode wire was also attached to the cap. The bottle was filled with saline (0. 5% by weight NaCl) such that the electrodes were covered. Leads were attached in order to plug into the amplifier head-stage. This bottle phantom was used for amplifier noise and stimulator tests prior to experiments taking place. In addition the 'ground' electrode was connected to copper tape contacts on the outside of the bottle.
Unitary data were band pass filtered (0. 3-4. 5 kHz) for visualisation online and recorded, using custom software written in MATLAB. Nerve data were only registered when the subject was positioned outside the bore of the 7T scanner or just below the sensor helmet in the MEG. Offline, the data were processed with a band pass smoothing filter in MATLAB (0. 16-2. 5 kHz) to remove high frequency artefacts from MEG recordings, and exported to Spike 2 software (CED, Cambridge, UK) for identification of spikes based on a combination of amplitude and spike shape. The INMS system was then switched to 'Stimulate' mode and positive current pulse trains of 60 Hz for 1 s were manually delivered by the operator, incrementing the current until a sensation was reported by the participant, or until 8 A. If the perceived location of the electrically elicited sensation exactly matched the location where mechanical stimulation of the skin generated a response  (Torebjörk et al.
5 (a randomly selected delay period) second rest period, repeated for 80 cycles in blocks of 10 cycles. MEG data were recorded at 1200 Hz using a 275 channel MEG system (CTF, Coquitlam, BC), with the reference array set to a synthetic 3rd order configuration. After completing an fMRI stimulation cycle, or one block of MEG stimulation, the sensation was checked by asking the participant if the sensation had changed in intensity or quality. If the sensation had faded during stimulation due to minute dislodgements of the electrode, the stimulation current was adjusted to give a comparable intensity of stimulation, with the same quality  (Torebjörk et al. , 1987). fMRI image data were analysed using a General Linear Model in mrTools (http://www. cns. nyu. edu/heegerlab). Statistical maps were formed by thresholding (Z > 3.
08) after false discovery rate correction and projected onto a flattened representation of the contralateral central sulcus to compare the spatial localisation with previously acquired finger somatotopy in primary somatosensory cortex (for more details, see  (Sanchez Panchuelo et al. , 2016) ). MEG data were visually inspected for artefacts, such as SQUID resets, and magnetomyographic or magnetooculargraphic contamination, where trials containing excessive artefacts were removed. To allow for the reconstruction of sensor data at the source level, three head position indicators (placed on the nasion, and left and right pre-auricular regions of the participant's face) were periodically energised to locate the participant's head within the dome. The coils' positions relative to the brain were determined during the manufacture of the head casts.
Data were frequency filtered into the beta band (13-30 Hz) and reconstructed in source space using a beamformer  (Robinson and Vrba, 1999; Van Veen et al. , 1997) , lead fields were computed using a dipole approximation  (Sarvas, 1987)  in conjunction with a multiple local sphere head model  (Huang et al. , 1999). Dipole orientation was determined using a non-linear search for optimum signal to noise ratio (SNR). To locate the source, a pseudo-T-statistical image was generated across a 2 mm isotopic grid spanning the right hemisphere's pre-and post-central gyri (active window: 0-1 s after stimulus onset; control window: 8-9 s after stimulus onset). After determining the source location by finding the maximum absolute pseudo-T score, 1-150 Hz filtered sensor data were source reconstructed at the target location and a time-frequency spectrogram (TFS) generated.
Stimuli The stimuli were created with Adobe Photoshop CS6. Sixteen photographs of the profile view of student volunteers (eight females) were taken, which all gave informed consent for publication of identifying images. The picture of the person was cropped and placed in the middle of a room, with the left, back, and right walls visible. The vanishing point of the room was localized at the level of the person's head. Half of the stimuli depicted the person looking at the left wall and the other half of the stimuli depicted the person looking at the right wall (Figure  1A ). Twenty objects with high familiarity and low visual complexity ratings were selected from the Moreno-Martı ´nez and Montotro (2012) database and were displayed on either the left or right side wall. Two different sets of stimuli were made.
EEG acquisition EEG data were recorded using a 128 scalps channel at a sampling rate of 512 Hz with an ActiveTwoBiosemi electrode system (Biosemi, Amsterdam, Netherlands). Two additional electrodes, CMS (Common Mode Sense) and DRL (Driven Right Leg), were used as reference and ground. Each individual's electrode impedances were adjusted below 650 mV prior to recording by injecting the electrode with a conductive gel (Signa). Eye-movements were recorded by four additional electrooculograms around the subjects' eyes.
EEG preprocessing Off-line analyses were performed with Letswave 5 (http://nocions. webnode. com/letswave) and Matlab 2012 (Math-Works, Natick, MA), similarly to previous FPVS studies that used this type of paradigm (e. g. Liu-Shuang et al. , 2014). Individual EEG data were first bandpass filtered between 0. 1 and 120 Hz (zero phase shift Butterworth filter, order 4). Then, we reduced the sampling rate down to 256 Hz to save space and processing time. Noisy channels containing deflections larger than 200 mV were interpolated by the nearest neighboring electrodes. In addition, one subject had only two ocular electrodes instead of four due to a technical problem during the setting up. EEG data were subsequently segmented for each trial, 2 s prior to the start of the trial plus 2 s after the end (-2 s to 96 s).
Piezoelectric EAPs are a good alternative in the development of flexible devices that present large displacements at a lower actuation voltage  [9]. In this paper, the development of a vibrotactile haptic button is presented. The EAP material and the technology used to build demonstrators are detailed. In particular, material properties investigations using picosecond ultrasonics (PU) are presented. Analytical calculations and finite element method (FEM) models were used to design the vibrotactile buttons and the optimization of the actuator design are reported. A first model allows the calibration of the material database by a static study of a cantilever. After the technological realization, the vibrotactile button was characterized dynamically. A second FEM model has been developed, corresponding to the full study of a circular membrane, and the geometry integrated in the dashboard.
The cantilever displacement amplitude determination has been done using a chromatic confocal sensor STIL Initial, which allows the measurement of displacements without physical contact. For the considered cantilever, the maximum differential displacement is 4. 9 µm at 100 V. Material properties were adjusted in order to correlate the analytical calculation with the measured results. In particular, an EAP piezoelectric coefficient of -3 pC/N has been extracted. The Table  1  summarizes the main properties of the copolymer PVDF-TrFE with the d 31 adjusted from this study. The adjusted material database was introduced into a FEM model. COMSOL Multiphysics software was used for the FEM approach. It was used to simulate the maximum displacement of the cantilever in a static study, meaning that a DC voltage is applied on the electrodes and the displacement of the cantilever is observed.
This step is often not considered enough, while the model estimation greatly depends on a careful quality control  (Ducharme, Albaugh et al. 2016 ). Therefore, one of the most important challenges in neuroscience is to provide a consensual and unified vision of brain maturation and aging. In this study, we have addressed the previously mentioned limiting factors. First, thanks to the new paradigm of BigData sharing in neuroimaging  (Poldrack and Gorgolewski 2014) , we have been able to use a very high number of samples (N=3296) covering the largest lifespan period never studied (from few months to advanced age). Moreover, all the considered MRI scans obtained from several freely available databases were processed using the same advanced MRI processing pipeline  (Manjon and Coupe 2016). Thanks to the last advances in image processing, images from different age ranges can be analyzed with the same tool.
Datasets In this study, we used 3D T1-weight MRI obtained from nine freely available databases covering the entire lifespan. All the considered subjects are normal controls. The summary of used databases is detailed in Table  1  while details are provided latter in this section. The used images have been acquired on 1. 5T and 3T over 103 sites. After quality control, 2944 MRI were kept from the 3296 considered subjects. The gender proportion of these selected subjects is 47% of female. The covered age starts from 9 months to 94 years, with an average age of 39. 65 year and a standard deviation of 26. 62. Figure  1  shows the age distribution of the used subjects after quality. At least three different datasets are used for all the considered periods except for extreme ages (i. e. ,  [0] [1] [2] [3] [4]  year and [90-94] year) where only 2 datasets are available.
Moreover, more than 50 subjects by 5-years interval are used at the exception of the last  [90] [91] [92] [93] [94]  interval. In the following, more details about the different datasets used in this study are presented. • C-MIND (N=266, after QC N=236): The images from the C-MIND dataset (https://research. cchmc. org/c-mind/) used in this study consist of 266 control subjects. All the images were acquired at the same site on a 3T scanner. The MRI are 3D T1-weighted MPRAGE high-resolution anatomical scan of the entire brain with spatial resolution of 1 mm 3 acquired using a 32 channel SENSE headcoil.
Image processing All the images were processed with volBrain online software pipeline (http://volbrain. upv. es). The volBrain system is a fully open access academic platform that we have developed during the last 5 years. This web-based platform offers to freely share our computational resources and our last image processing methods to all researchers over the world. Therefore, we used volBrain in this study since we perfectly know the software, the generated reports facilitate a fast first stage QC and we recently demonstrated its robustness and accuracy  (Manjon and Coupe 2016). Recently, volBrain pipeline was compared with two well-known tools used on MR brain analysis (FSL and Freesurfer) showing significant improvements in terms of both accuracy and reproducibility for intra and inter-scanner scan rescan acquisition  (Manjon and Coupe 2016).
Moreover, we knew that the system was able to process large dataset since volBrain provides automatic brain volumetry in less than 15 minutes (including the generation of a pdf volumetry report summarizing the volumetric results). Since its deployment (2 years ago) volBrain has processed online more than 37. 000 brains for more than 1200 users. The volBrain pipeline consists of a set of steps aimed to improve the quality of the MR images to analyze and to locate them in a common geometric and intensity space prior to perform segmentation at several anatomical levels  (Manjon and Coupe 2016). In more details, volBrain pipeline includes the following preprocessing steps: 1) denoising using spatially adaptive non-local means  (Manjon, Coupe et al. 2010) , 2) rough inhomogeneity correction using N4 method  (Tustison, Avants et al.
2010) , 3) affine registration to MNI152 space using ANTS software  (Avants, Tustison et al. 2011) , 4) SPM based fine inhomogeneity correction  (Ashburner and Friston 2005)  and 5) histogram based intensity standardization. After the preprocessing, the intracranial cavity is segmented using NICE method  (Manjon, Eskildsen et al. 2014) , tissue classification is performed using TMS method  (Manjón, Tohka et al. 2010 ) and finally subcortical structures are estimated using an extended version of the non-local label fusion method  (Coupe, Manjon et al. 2011). All the segmentation methods of volBrain use a library of 50 experts manually labelled cases (covering almost the whole lifespan) needed to perform the labeling process at different levels. More details can be found in  (Manjon and Coupe 2016).
Statistical Analysis The statistical analysis was performed with Matlabã software. In order to determine the best general models for each structure, several models were tested from the simplest to the most complex on all the dataset (i. e. , female and male at the same time). A model is kept as a potential candidate only when F-statistic based on ANOVA for model vs. constant model is significant (p<0. 05) and when all its coefficients are significant using t-statistic (p<0. 05). At the end of the selection procedure, we used the Bayesian Information Criterion (BIC) to select the best model among models being significant compared to constant model and having all coefficients significant. BIC provides a measure of the trade-off between bias and variance and thus select the model explaining most the data with minimum parameters.
Afterwards, this general model type is applied on female and male separately to estimate gender specific models. At the end, to study trajectory difference in terms of volume and shape between both female and male,  "  +  (. interactions are tested over the selected general model. All the reported parameters (t-statistic, F-statistic, BIC and R 2 ) were internally estimated by Matlabã using default parameters. The following models were considered as potential candidates: 1. Linear model In the literature, structure trajectories have been mainly modeled using low order polynomial function (see  (Walhovd, Westlye et al. 2011 ) for review). However, to follow structure trajectories across the entire lifespan, we propose to consider hybrid models able to track rapid growth during childhood and to capture complex volume decrease from adulthood to elderly.
Limitations In our opinion, one of the strengths of our study is to use multiple datasets to be able to cover the entire lifespan. However, this point can be also viewed as a weakness since the use of multiple datasets may introduce bias. Indeed, pooling databases having different age ranges could lead to find artificial differences. It has to be noted that we limited this aspect by using at least 2 different overlapping databases for each 5y intervals. Moreover, the preprocessing pipeline of volBrain has been designed to limit the impact of acquisition protocol by proposing advanced denoising filter and tissue-based intensity normalization. Therefore, after preprocessing, images are better homogenized in terms of signal-to-noise ratio and tissue contrast limiting the impact of using different acquisition protocols and scanners.
In addition, during our QC all images with motion and ghosting artifacts were removed as well as the image having high anisotropic voxel resolution. Finally, several studies showed that age-related volume differences are consistent between datasets when using the same analysis tool  (Fjell, Westlye et al. 2009 , Walhovd, Westlye et al. 2011 , Mills, Goddings et al. 2016). Moreover, recent papers based on a large scale study over adulthood  (Potvin, Mouiha et al. 2016 , Potvin, Dieumegarde et al. 2017)  showed that the impact of MRI scanner manufacturer and magnetic strength is negligible compared to impact of age of the structure trajectories. After our quality control step, no images of subjects younger than 9 months remained. Therefore, the newborn period is not well covered by our samples and thus results obtained before 9 months of life may be inaccurate.
2013 , Mills, Goddings et al. 2016). Finally, for adulthood, our results on normalized subcortical structures volume are highly consistent with results presented in the longitudinal study published by  Pfefferbaum et al. (2013). Nevertheless, we think that in a further work, a mixed cross-sectional / longitudinal study  (Giedd, Blumenthal et al. 1999 ) could be done since some of the used datasets contain longitudinal data.
Acknowledgements We gratefully acknowledge financial support from the  French State , managed by the  French National Research Ageny  in the frame of the Investments for the future  Program IdEx Bordeaux  ( ANR-10-IDEX-03-02 ,  HL-MRI  Project),  Cluster of excellence CPU  and TRAIL ( HR-DTI ANR-10-LABX-57 ). We also want to think the financial support from the  CNRS  thanks to the multidisciplinary project " Défi imag 'In and the dedicated volBrain support. This research was also supported by  Spanish   TIN2013-43457-R grant  from the  Ministerio de Economia y competitividad Moreover , this work is based on multiple samples. We wish to thank all investigators of these projects who collected these datasets and made them freely accessible. The C-MIND data used in the preparation of this article were obtained from the  C-MIND Data Repository  (accessed in Feb 2015) created by the  C-MIND study of Normal Brain Development.
This is a multisite, longitudinal study of typically developing children from ages newborn through young adulthood conducted by  Cincinnati Children's Hospital Medical Center  and  UCLA  and supported by the  National Institute of Child Health and Human Development  (Contract #s  HHSN275200900018C ). A listing of the participating sites and a complete listing of the study investigators can be found at https://research. cchmc. org/c-mind. The NDAR data used in the preparation of this manuscript were obtained from the  NIH -supported National Database for Autism Research (NDAR). NDAR is a collaborative informatics system created by the  National Institutes of Health  to provide a national resource to support and accelerate research in autism. The NDAR dataset includes data from the  NIH Pediatric MRI   Data Repository  created by the  NIH MRI Study of Normal Brain Development.
facilitated by the  Foundation for the National Institutes of Health  (www. fnih. org). The grantee organization is the  Northern California Institute for Research and Education , and the study was coordinated by the  Alzheimer's Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the  Laboratory for NeuroImaging at the University of California, Los Angeles. This research was also supported by  NIH  grants  P30AG010129 ,  K01 AG030514  and the  Dana Foundation. The OASIS data used in the preparation of this manuscript were obtained from the  OASIS  project funded by grants  P50 AG05681 ,  P01 AG03991 ,  R01 AG021910 ,  P50 MH071616 ,  U24 RR021382 ,  R01 MH56584. See http://www. oasis-brains. org/ for more details. The AIBL data used in the preparation of this manuscript were obtained from the  AIBL  study of ageing funded by the  Common-wealth Scientific Industrial Research Organization (CSIRO ; a publicly funded government research organization),  Science Industry Endowment Fund, National Health and Medical Research Council of Australia  (project grant  1011689 ),  Alzheimer's Association, Alzheimer's Drug Discovery Foundation , and an anonymous foundation.
• NDAR (N=612, after QC N=382): The Database for Autism Research (NDAR) is a national database funded by NIH (https://ndar. nih. gov). This database included 13 different cohorts acquired on 1. 5T MRI and 3T scanners. In our study we used 415 images of control subjects from the NIHPD (http://www. bic. mni. mcgill. ca/nihpd/info/data_access. html) dataset and 197 images of control subjects from the Lab Study 19 of National Database for Autism Research. For the NIHPD, T1-weighted images were acquired at six different sites with 1. 5 Tesla systems by General Electric (GE) and Siemens Medical Systems. The MRI are 3D T1-weighted spoiled gradient recalled (SPGR) echo sequence with following parameters: TR = 22-25 ms, TE = 10-11 ms, flip angle = 30•, FoV = 256 mm IS × 256 mm AP, matrix size = 256 × 256: 1 × 1 × 1 mm 3 voxels, 160-180 slices of sagittal orientation.
The participants chosen from the Lab Study 19 of National Database for Autism Research (NDAR) were scanned using a 3T Siemens Tim Trio scanner at each site. The MRI are 3D MPRAGE sequence (voxel dimensions : 1. 0 × 1. 0 × 1. 0 mm 3 ; image dimensions: 160 × 224 × 256, TE = 3. 16 ms, TR = 2400 ms). • ABIDE.
(N=528, after QC N=492): The images used in this study consist of 528 control subjects acquired at 20 different sites on 3T scanner. The MRI are T1-weight MPRAGE image and the details of acquisition, informed consent, and site-specific protocols are available on the website. • ICBM from the Autism Brain Imaging Data Exchange (ABIDE) dataset (http://fcon_1000. projects. nitrc. org/indi/abide/).