dataesr/scientific-paragraphs-categorization huggingface dataset:
The experiment involved two parts: voluntary force-pulse production and reacting to unexpected perturbations. Prior to each trial, the subjects placed the fingers on the centers of the sensors and relaxed; sensor reading was set to zero. As a result, the sensors measured only active pressing forces. Then, a verbal command was given to the subject and data acquisition started. The subject was given 2 s to place the cursor over the initial target. During the force-pulse trials, the subjects were asked to produce a force pulse from the initial target in less than 1 s by an instructed finger (Figure  7A ). Each finger performed three pulse trials in random order. In perturbation trials, one of the sensors unexpectedly moved up by 1 cm over 0. 5 s. This led to an increase in total force (Figure  7B ), while changes in the moment of force depended on what finger was perturbed.
Experiments with visual feedback perturbations Four 6-axis force/moment sensors (Nano-17, ATI Industrial Automation, USA) mounted on the table were used to measure normal forces produced by the tips of the index (I), middle (M), ring (R), and little (L) fingers. To increase friction between the digits and the sensors, 320-grit sandpaper was placed on the contact surfaces of the sensors. The centers of the sensors were evenly spaced at 30 mm. The output signals from the sensors where digitized with the 16-bit resolution (PCI-6225, National Instrument) at 100 Hz. A LabVIEW program was used to provide visual feedback and store the data on the computer. Offline processing and analysis was done in Matlab. At the beginning, the four-dimensional space of the finger forces was mapped onto the twodimensional space of the screen according to a very natural rule: the vertical coordinate showed changes in the net resultant finger force (F), while the horizontal displacement showed the net moment of the finger forces (M).
Texts were assigned to conditions in a counterbalanced manner. During the playback condition, participants listened to their own voice recorded (see 2. 3. for recording data acquisition details) earlier during the read condition. Obviously, playback condition was performed in all subjects after the read condition. This playback condition was used (i) to assess the impact of possible sensory prediction about upcoming speech (as subjects had some hints about speech content and production from the prior read condition) on speech brain tracking and on tracking directionality, and (ii) to control for potential differences in speech rhythm between listen and read. For both listen and playback conditions, sounds were played with VLC running on a MacBook pro and delivered at 60 dB (measured at ear-level in every participant) through a front-facing flat-panel loudspeaker (Panphonics Oy, Espoo, Finland) placed ~2 m from the participants.
, 2001; Vigario et al. , 2000). Independent components displaying a correlation exceeding 0. 15 with any EOG or ECG signals were subtracted from MEG data. The mean ± SD of rejected components was 7. 2 ± 1. 4 (read), 5. 1 ± 1. 8 (listen), 4. 9 ± 2. 0 (rest), and 5 ± 2. 0 (playback). Finally, when the maximum MEG amplitude exceeded 5 pT (magnetometers) or 1 pT/cm (gradiometers), data within one second before and after the excessive amplitude were marked as artifact-contaminated to avoid analysis of MEG data compromised by any other artifact source that would not have been removed by the temporal signal space separation or independent component analysis. Speech temporal envelopes were obtained from all sound recordings as the rectified sound signals low-pass filtered at 50 Hz. Speech temporal envelopes were further resampled at 1000 Hz time-locked to MEG signals.
Coherence was previously used to assess the coupling between voice and brain signals at the frequencies corresponding to phrasal/sentential (<1 Hz) and syllable (4-8 Hz) rates  (Bourguignon et al. , 2013; Luo and Poeppel, 2007; Molinaro and Lizarazu, 2017; Peelle et al. , 2013; Poeppel, 2003; Vander Ghinst et al. , 2016). Coherence was first estimated at the sensor level. Data from gradiometer pairs were combined in the direction of maximum coherence as done in  Bourguignon et al. (2015). Coherence at phrasal/sentential level was taken at the frequency bin corresponding to 0. 5 Hz, and coherence at syllable level was taken as the mean across coherence at frequency bins comprised in 4-8 Hz. Coherence was also evaluated at the source level using a beamformer approach since this method has a high sensitivity to activity coming from locations of interest while attenuating external interferences such as reading-induced head movement, eye movements, or muscle artifacts  (Hillebrand et al.
, 2005). To do so, individual MRIs were first segmented using Freesurfer software (Martinos Center for Biomedical Imaging, Massachusetts, USA;  Reuter et al. , 2012). Then, the MEG forward model was computed for three orthogonal tangential current dipoles placed on a homogeneous 5-mm grid source space that covered the entire brain (MNE suite; Martinos Center for Biomedical Imaging, Massachusetts, USA;  Gramfort et al. , 2014)  and further reduced to its two first principal components. Finally, coherence maps were produced within the computed source space at 0. 5 Hz and 4-8 Hz using Dynamic Imaging of Coherent Sources (DICS)  (Gross et al. , 2001) , and further interpolated onto a 1-mm grid. Both planar gradiometers and magnetometers were used for inverse modeling after dividing each sensor signal by its noise variance.
Directionality assessment The directionality of the coupling between the voice signals and the activity within brain areas displaying a significant local maximum of coherence (see 2. 8. ), was assessed with renormalized partial directed coherence (rPDC)  (Schelter et al. , 2009 (Schelter et al. , , 2006)). To this aim, the time-course of brain electrical activity within these brain areas was estimated with the beamformer described in 2. 5. , in the direction maximizing the coherence with speech temporal envelope. Source and voice signals were low-pass filtered at 10 Hz and downsampled at 20 Hz. Then, for each source separately, a vector autoregressive (VAR) model of order 40 was fitted to the source and the voice data using the ARfit package  (Schneider and Neumaier, 2001). The rPDC was then estimated based on the Fourier transform of the VAR model coefficients.
Bourguignon has been supported by the program Attract of Innoviris (grant 2015-BB2B-10), by the Spanish Ministry of Economy and Competitiveness (grant PSI2016-77175-P), and by the Marie Skłodowska-Curie Action of the European Commission (grant 743562). Nicola Molinaro has been supported by the Spanish Ministry of Economy and Competitiveness (grant PSI2015-65694-P), the Agencia Estatal de Investigación (AEI), the Fondo Europeo de Desarrollo Regional (FEDER) and by the Basque government (grant PI_2016_1_0014). Mikel Lizarazu has been supported by the Agence Nationale pour la Recherche (grants ANR-10-LABX-0087 IEC and ANR-10-IDEX-0001-02 PSL). Xavier De Tiège is Post-doctorate Clinical Master Specialist at the Fonds de la Recherche Scientifique (F. R. S. -FNRS, Brussels, Belgium). This research was supported by the Basque Government through the BERC 2018-2021 program and by the Spanish State Research Agency through BCBL Severo Ochoa excellence accreditation SEV-2015-0490.
Animals Animal care and experimentation complied with both French and European Community regulations (Official Journal of the European Community L 358, December 18, 1986), and M. C. was authorized by the French government to use animal models (authorization no. 76-107). Male C57Bl/6 mice (Elevage Janvier, Le Genest St Isle, France) were acclimatized in individual cages at 23 °C during 7 days. During this period and all the experiment, the 12-h light-dark cycle was inverted (dark phase: 9:30 AM -9:30 PM). At day (D) 1 of the experiment, mice were randomized into 4 groups depending on the body weight : ABA (n=8), Feeding-time restriction (FTR; n=8), ad libitum fed controls with monitored activity (CTPA; n=8) and without (CT; n=8) as previously described  [35]. ABA and CTPA mice were placed in individual cages with an activity wheel with RunningWheel software (Intellibio, France).
Analysis of differential protein expression Images of stained 2D gels were scanned by an ImageScanner II (GE Healthcare) calibrated with a grey scale marker (Kodak, Rochester, NY) and digitalized with Labscan 6. 0 software (GE Healthcare). Analysis of differential protein expression including spot detection, quantification, matching, and comparative analysis was performed using Progenesis samespot software (GE Healthcare). Each protein sample was subjected to 2D-PAGE at least 3 times to minimize run-to-run variation, and each set of 3 gels was compared using Progenesis to confirm the non-appearance of statistically differential spots within the set of gels. The expression level was determined by the relative volume of each spot in the gel and expressed as %volume, calculated as spot volume/Σvolumes of all spots resolved in the gel.
A 9-min linear gradient (3%-80% acetonitrile in 0. 1% formic acid) at a flow rate of 400 nL/min was used and the eluent was analyzed with an Ion Trap mass spectrometer. For protein identification, MS/MS peak lists were extracted and compared with the protein databases by using the MASCOT Daemon version 2. 2. 2 (Matrix Science) search engine. The searches were performed with the following specific parameters: enzyme specificity, trypsin; one missed cleavage permitted; no fixed modifications; variable modifications, methionine oxidation, cysteine carbamidomethylation, serine, tyrosine and threonine phosphorylation; monoisotopic; peptide charge, 2+ and 3+; mass tolerance for precursor ions, 1. 5 Da; mass tolerance for fragment ions, 0. 6 Da; ESI-TRAP as instrument; taxonomy, Bacteria; National Center for Biotechnology Information (NCBI) database [NCBInr 20120531 (18280215 sequences, 6265275233 residues)] (Bethesda, MD).
Protein hits were automatically validated if they satisfied one of the following criteria: identification with at least two top ranking peptides (bold and red) each with a MASCOT score of more than 54 (p < 0. 01), or at least two top ranking peptides (bold and red) each with a MASCOT score of more than 47 (p < 0. 05). To evaluate false-positive rates, all the initial database searches were performed using the "decoy" option of MASCOT. Results were considered relevant if the false-positive rate never exceeded 1%.
Statistical analysis Data were analyzed and graphs were plotted using the GraphPad Prism 5. 02 (GraphPad Software Inc. , San Diego, CA, USA). Normality was evaluated by the Kolmogorov-Smirnov test. Group differences were analyzed by the analysis of variance (ANOVA) or the nonparametric Kruskal-Wallis (K-W) test with the Tukey's or Dunn's post-tests, according to the normality results. Where appropriate, individual groups were compared using the Student's t-test or the Mann-Whitney (M-W) test according to the normality results. Data are shown as means ± standard error of means (s. e. m), and for all test, p < 0. 05 was considered statistically significant.
EEG Activity EEG activity was recorded continuously at 1,000 Hz with a Geodesic 64-channel EEG sensor net (Electrical Geodesics Inc. , Eugene, OR, USA). Data pre-processing was performed with BrainVision Analyzer 2 (Brain Products, Germany). The raw recordings were referenced to the averaged activity of the 64 electrodes before being synchronized with respect to the time of the auditory cue (i. e. , the imperative signal in the movement conditions). Then, ocular artifacts (e. g. , blinks, saccades) were subtracted from the EEG recordings by removing the corresponding component as revealed by the independent component analyses (ICA). In both conditions, we averaged the data for each participant and electrode. The EEG activities associated with the planning of the arm movement were therefore analyzed without considering the amplitude of the rotations that preceded the arm movements.
We estimated the neural sources of the late SEPs using the dynamical Statistical Parametric Mapping (dSPM,  Dale et al. , 2000)  implemented in the Brainstorm software  (Tadel et al. , 2011 , freely available at: http://neuroimage. usc. edu/brainstorm). We used the data from all processed sensors and averaged for each participant, condition, and electrode. The forward model was computed using a three-shell sphere boundary element model (BEM) on the anatomical MRI brain MNI Colin27 template (15,000 vertices), a predominant volume conductor model  (Mosher et al. , 1999; Huang et al. , 2016). The baseline used to compute the co-variance matrices was set between -1. 5 s and -1 s prior to the auditory cue, i. e. , before the rotations as the longest recorded rotation duration was 1,490 ms. Based on classical topographical maps of the cortex, we manually defined several region of interests (ROIs) in the frontal, parietal and occipital lobes.
Procedure If a translation was needed for conducting the study, the co-author received a standardized package for the translation of the BenCor-R, the 4DHS, and the data collection. They then followed a standardized translation/back-translation procedure (for details, see  Heintz et al. 2018). The online samples were collected via different platforms (mostly SurveyMonkey). All samples are convenience samples, recruited via mailing lists, personal contacts, social media, university campuses, libraries, and lectures. In most samples, the BenCor-R was presented first, followed by the SWLS and the 4DHS. In some samples, this order was reversed or additional measures were employed that are not relevant for the present study. Participants provided either online or written informed consent in accordance with the Declaration of Helsinki.
The semTools package  (Jorgensen et al. 2018)  in R was used for the measurement invariance analyses. Finally, correlations were computed between the two BenCor-R subscales and the SWLS and the 4DHS in each sample. Pearson correlations with bootstrapped 95% confidence intervals (CIs with 100 iterations) were computed using the Psych package (Revelle 2019) in R. To investigate the unique correlations of benevolent and corrective humor beyond social fun/entertaining and mockery, respectively, partial correlations were computed with 95% empirical CIs.
Study Objectives: New challenges in sleep science require to describe fine grain phenomena or to deal with large datasets. Beside the human resource challenge of scoring huge datasets, the interand intra-expert variability may also reduce the sensitivity of such studies. Searching for a way to disentangle the variability induced by the scoring method from the actual variability in the data, visual and automatic sleep scorings of healthy individuals were examined. Methods: A first dataset (DS1, 4 recordings) scored by 6 experts plus an autoscoring algorithm was used to characterize inter-scoring variability. A second dataset (DS2, 88 recordings) scored a few weeks later was used to investigate intra-expert variability. Percentage agreements and Conger's kappa were derived from epoch-by-epoch comparisons on pairwise, consensus and majority scorings.
Results: On DS1 the number of epochs of agreement decreased when the number of expert increased, in both majority and consensus scoring, where agreement ranged from 86% (pairwise) to 69% (all experts). Adding autoscoring to visual scorings changed the kappa value from 0. 81 to 0. 79. Agreement between expert consensus and autoscoring was 93%. On DS2 intra-expert variability was evidenced by the kappa systematic decrease between autoscoring and each single expert between datasets (0. 75 to 0. 70). Conclusions: Visual scoring induces inter-and intra-expert variability, which is difficult to address especially in big data studies. When proven to be reliable and if perfectly reproducible, autoscoring methods can cope with intra-scorer variability making them a sensible option when dealing with large datasets.
1 In the daily practice, this countermeasure does not cope with possible time-on-task effects often observed in monotonous tasks,  20  especially when confronted with large datasets. Furthermore, periodic reappraisal of scoring rules  [16] [17] [18] 21  adds to the difficulty of applying consistently these rules, adding another source of variability. 19 In conclusion, visual scoring introduces exogenous, potentially confounding, sources of variability. Mitigating solutions can be implemented only when dealing with a "reasonable" amount of data. In order to score datasets made of thousands of sleep recordings, an error-minimizing, stable and reproducible method must be considered. Although such automatic method would not sizeably reduce inter-scorer variability, which is largely due to epochs on which scoring rules application is ambiguous  12, 22  , it would resolve intra-scorer variability.
Participants and Experimental design Data were extracted from a study aiming at characterizing sleep-wake related phenotypes. The study was approved by the Ethics Committee of the Faculty of Medicine of the University of Liège and participants gave their written informed consent. All volunteers were free of medication or psychoactive drugs, non-smokers and moderate caffeine and alcohol consumers. Semi-structured interviews established the absence of medical, traumatic, psychiatric or sleep disorders. Exclusion criteria were a poor sleep quality as assessed by the Pittsburgh Sleep Quality Index (score >5),  24  excessive daytime sleepiness (score >10 on Epworth Sleepiness Scale),  25  night shifts during the preceding year, travels through more than one time zone during the last three months, depression (score >19 on the Beck Depression Inventory II)  26  or a body mass index >27 kg/m².
The experimental design consisted of a 3-week actigraphic field recording followed by a 6-day laboratory protocol. A first habituation night aimed at ruling out participants with sleep disorders. Then four full-night polysomnographic (PSG) recordings were acquired under successive sleep conditions: 8h baseline night (BAS), extended sleep opportunity (EXT, i. e. , 12h sleep opportunity at night, and 4h nap in the afternoon), 8h night preceding 40h of sleep deprivation (BEF) and a recovery night (REC). Sleep schedule was individually adjusted to habitual sleep time, as assessed during the pre-laboratory field study. Sleep EEG data were recorded using a V-Amp 16 amplifier (Brain Products GmbH, Gilching Germany). EEG data were digitized at a sampling rate of 500 Hz with a bandpass filter from DC to Nyquist frequency and magnitude resolution of 0.
049 µV/bit. During BAS, EXT, BEF and REC nights, recordings included 10 EEG channels (F3, Fz, F4, C3, Cz, C4, Pz, O1, O2, A1, reference: right mastoid), two EOG (horizontal and vertical EOG), two chin EMG and two ECG. For the current retrospective study, the recording database includes data collected in 24 healthy, young, male participants (aged 21. 6 ± 2. 5 years, in the range 18-26), randomly selected from a larger sample of male volunteers.
Visual Scoring Six expert visual scorers, five seniors and a junior, from the same sleep laboratory were trained together to homogenize the application of the AASM scoring visual rules 9 on 30s-epochs, using the same scoring software, FASST, (http://www. montefiore. ulg. ac. be/~phillips/FASST. html). 27 The training was considered achieved for a given expert when 80% of scoring agreement was reached with the expert leader, considered as the laboratory gold standard. Concerning the specific case when technical artefacts obscured the EEG, since the study framework was sleep research, such epochs were labeled as artefact. In this paper, a human scorer is designated by 'visual scorer', 'V', or 'expert' while 'A' refers to automated analysis Aseega. Depending on the context, 'scorer' and 'scoring' refer to automated or visual scorer.
Datasets The recording database consisted in two datasets, DS1 and DS2, referred to as scoring condition, which differed according to the temporal proximity to the training sessions. The scoring sessions of DS1 started 1. 5 month after DS1 within a 6 months period. DS1 and DS2 excluded the recordings used for the training sessions. The first dataset includes 4 recordings from 2 volunteers: 3 recordings from subject #0981 (BAS, EXT, and BEF) and 1 from subject #1004 (BAS). Each recording was scored independently by all 6 experts, thus providing 24 visual scorings, and by ASEEGA, providing 4 automatic scorings. DS1 was used to assess the inter-scorer agreements as follows: • by computing the inter-expert (V) agreement; • by computing the inter-scorer (V+A) agreement, pooling visual and automatic scorings; • by comparing automatic (A) scoring vs.
visual (V) scorings. The second dataset includes the 4 sleep nights (BAS, EXT, BEF and REC) from 22 other subjects, providing 88 recordings. Each recording of DS2 was scored twice: once visually by one of the 6 experts, providing 88 visual scorings, and once by ASEEGA, providing 88 automatic scorings. Note that when a subject was assigned to an expert, the 4 nights of this subject were scored by this expert. Based on the automatic-expert agreements, DS2 was used to assess the intra-expert agreement as follows: • by comparing DS1 vs. DS2 to assess the impact of the scoring condition; • by comparing BAS, EXT, BEF, REC to assess the impact of the sleep condition. The composition of the two datasets is summarized in Table  2. Table  2 : Cohort breakdown into 2 data sets DS1 & DS2, according to the number of times recordings were visually scored by the 6 visual scorers involved (V 1 …V 6 ).
Statistics Scorings were compared on an epoch-by-epoch basis. In order to mitigate the risk of overweighting the agreement calculated on short night and underweighting the agreement calculated on long nights, comparisons in each dataset were based on pooled night scorings rather than on averaged agreements over nights. 23 Hence, for each dataset separately, the hypnograms of a given scorer were concatenated into a single continuous sequence of sleep stages. After the computation of the contingency matrix, epochs labeled "artefact" by Aseega or by visual scoring were discarded from subsequent statistical analysis. Two metrics of epoch-by-epoch agreement between scorers, whether automatic or visual, were used. First, the percentage agreement, defined as the percentage of epochs that were assigned the same label, i.
e. sleep stage, by 2 or more scorers. Second, the Conger's kappa (κ), which is the generalization of the Cohen's kappa coefficient  30  to the comparison of more than two raters. In the case of 2 raters, Conger's kappa and Cohen's kappa are equivalent. 31 In order to have an homogeneous statistical criteria we computed Conger's kappa instead of Fleiss kappa, sometimes used in multi-scorer comparison  19  but which reduces to a different 2-rater agreement coefficient called Pi. 32, 33  Dataset 1 Subjects Visual scorers Auto D a t a s e t 1 Subject index # recordings per subject V 1 V 2 V 3 V 4 V 5 V 6 A 981 3 x x x x x x x 1004 1 x x x x x x x D a t a s e t 2 In a first step, we studied visual scoring in the light of the number of experts involved in the process. Do experts converge and how? How adding up experts does impact on inter-expert agreement? We studied the evolutions of both the visual percentage agreement and kappa as functions of the number of experts involved, N e.