dataesr/scientific-paragraphs-categorization huggingface dataset:
However, there are considerable differences between the number of puppies identified by ICAD and those registered by SCC. In 2016, ICAD identified 749,720 new dogs, with puppies comprising over 90%, compared to 227,993 puppies registered by the SCC. This discrepancy of nearly half a million puppies is explained by the fact that puppies recorded by ICAD are not purebred and may or may not have been born in France. Although the demand for new dogs or puppies in France is not known, database numbers indicate that the production of puppies is an important pillar of the dog breeding industry. The origin of puppies is an important consideration because puppy source has been suspected to be associated with behavioral issues. A summary of published data on behavioral problems shows that dogs from puppy mills and pet stores tend to have an increased incidence of behavioral disorders such as fear and aggression towards other dogs and humans  [2].
Statistical Analysis Descriptive statistics were used to characterize relevant parameters. Categorical variables were expressed as frequency counts and percentages. Cross analyses were performed to further describe the categorization of the activity, breeding population size, number of litters per year, the accommodations provided inside houses and/or kennels, and the methods used, both to monitor and reduce stress at whelping. All categorical variables were analyzed using the Chi square test or the Fisher exact test. All tests were performed with type one error (α) set at 5% and the SAS software package (version 9. 3)  [17]  was used for all statistical analyses.
A small proportion of our sample completed our survey after being contacted through the CERCA-EnvA mailing list (11. 9%) or through the participant list of a roadshow co-organized in 2017 by Ceva and Royal Canin (5. 7%). For all survey-based studies where participants are self-selected, controlling bias is impossible. Most likely, for responders to an online survey that is not mandatory and offers no reward, participation is limited to individuals that are most interested in the subject matter surveyed. Our findings, therefore, may not reflect the overall practices of dog breeders across France, but it is representative of a considered part of the sector. The features of our sampling and results were similar to a study of the Australian Kennel Club  [15]. For example, data from the Australian responders aligned with our results in that over half of the breeders (58.
Participants Participants were 24 healthy, full-term 4-month-old infants (11 females, mean age = 4 months 3 days, range = 3 months, 14 days -4 months, 22 days). Data from an additional 17 infants were excluded from the final sample because they failed to complete testing due to fussiness or lack of interest (n = 10), looking time in at least one test trial more than 3 standard deviations (SD) from the overall group mean (n = 4), experimental error (n = 1), or parental interference (n = 2).
Apparatus Each infant was tested while sitting in an infant seat approximately 60 cm from the monitor where the stimuli were presented (23" screen size, 1920 X 1080 pixel resolution, refresh rate of 60 Hz) in a dimly lit room. A video camera was positioned just above the stimulus presentation monitor and directed to the infant's face. The live image of the infant's face was displayed on a television monitor to allow the online coding of the infant's looking times through the E-Prime 1. 0 program by the experimenter, who was blind to the habituation condition to which the infant was assigned and the order of test trials. The image of the infant's face was also recorded via a Mini-DV digital recorder, and data from all test trials were subsequently coded offline by a second coder. Intercoder agreement (Pearson correlation) between the two observers who coded the data live or from digital recording, as computed on total fixation times on each of the six test trials, was r =.
07 s, SEM = 2. 79 vs. familiar, M = 9. 36, SEM = 2. 6, p =. 70; decreasing habituation condition: novel, M = 9. 45 s, SEM = 2. 79 vs. familiar, M = 6. 63 s, SEM = 2. 6, p =. 12; all other comparisons, ps ≥. 56 ). For the Test trial type x Test trial order interaction, post hoc tests showed that infants looked significantly longer to the novel than to the familiar order when the first test trial they received included the novel order (novel: M = 10. 77 s, SEM = 2. 79 vs. familiar: M = 7. 23 s, SEM = 2. 6; p =. 04), but not when the first test trial included the familiar order (novel: M = 6. 74 s, SEM = 2. 78 vs. familiar: M = 8. 76 s, SEM = 2. 6; p =. 4). All other effects or interactions were not significant (all Fs < 4. 3, ps >. 05). Finally, in order to further explore the Test trial type x Habituation order condition interaction, we analyzed data for individual infants for the two habituation conditions separately using non-parametric tests.
Participants The final sample included 24 4-month-olds (8 females, mean age = 4 months 5 days, range = 3 months 15 days -4 months 14 days). Data from an additional 10 infants were discarded due to fussiness or lack of interest resulting in failure to complete all testing trials (n = 2), or looking time in at least one test trial more than 3SD from the overall group mean (n = 8).
Participants The final sample included 24 4-month-olds (14 females, mean age = 4 months 7 days, range = 3 months 28 days -4 months 20 days). Data from an additional 18 infants were discarded due to fussiness resulting in failure to complete all testing trials (n = 12), looking time in at least one test trial more than 3SD from the overall group mean (n = 5), or parental interference (n = 1).
Acknowledgments We are indebted to the families that participated in these studies. Carlo Toneatto made the program to run the experiments. We thank  Lionel Cohen ,  Alice Debost ,  Sarah Hausberger  and  Lucile Jarielle  for their help in the recruiting of participants and coding of the data. We would like to thank the reviewers, whose comments contributed to improve the paper. This research was supported by a  Fyssen Foundation   Research Grant  and an  ANR  ( Agence National de la Recherche Scientifique ) grant ( ANR-15-CE28-0003-01 NUMSPA ) to MDdH.
Experimental design The overall experiment (main text Fig  1 ) comprised 2 sessions that took place approximately a week apart from one another. In Session 1, participants performed an artificial language incidental rule-learning task twice (Part 1, behavioral only for both the intervention and control groups; Part 2, behavioral for the control group and during fMRI scanning for the intervention one), followed by an offline recognition test (see S1 Text and S4 Table for further details on the offline recognition test). The artificial language (L1) and its nonadjacent dependencies were the same for both Part 1 and 2 of Session 1. For the intervention group, the fMRI data were used to identify relevant activity within the lPL during late stages of rule learning in order to select appropriate coordinates for rTMS stimulation.
Rule-learning task Three different artificial languages containing 28 bisyllabic (consonant-vowel-consonantvowel) nonsense words each were created. One language (L1) was used in Session 1 and the remaining 2 languages (L2 and L3) in Session 2 (main text,  Fig 1). The order of languages was counterbalanced between sessions and participants (see S5 Table for the full list of stimuli used). Twenty-eight words were created for each language stream and synthesized using Mbrola speech synthesizer software  [67]  by concatenating diphones from the Spanish male database (https://packages. debian. org/stretch/mbrola-es2) at 16 KHz. Words (385 ms) were combined using Adobe Audition software to form 3-word phrases with 100-ms gaps between word. Phrase stimuli were presented via Presentation software (Neurobehavioral Systems) through appropriate headphones and at a volume level adjusted for the participant.
The probability of target occurrence in both the rule and the no-rule blocks was therefore 50%. Note that in the set of no-rule trials, the C element (the participant's target) occurred also in the last position, but, in contrast to the rule block, this could not be predicted on the basis of previous elements. Participants were presented with the randomized 96 rule and 96 no-rule phrases in 4 alternated rule and no-rule blocks, with the order of blocks counterbalanced between participants. In the fMRI version of the task, data were acquired in 2 runs, including a block of rule and no rule each (counterbalanced). A short break was given between runs in the fMRI. A single offline recognition test was issued after the fourth block (see S1 Text). In order to obtain a measure of incidental rule learning, participants performed a cover word-monitoring task.
Linear mixed model analysis In order to assess online rule learning in the rule-learning task within each experimental session, we used a linear mixed model approach to fit learning slopes that reflect RT gains over trials for the rule and no-rule conditions. The use of mixed models to compare the slope between conditions allows the use of RT data for all trials and subjects, which results in a more sensitive measure of the online learning process than a single mean value per subject  [46, 68]. Analyses were performed using the lme4  [69]  and lmerTest  [70]  packages as implemented in the R statistical language (R Core Team, 2012). Our basic model included RT (rt) and trial as continuous variables, no rule/rule (NR_R) and target/no target (TNT) as 2-level factors, and subject as a factor with as many levels as participants.
All syllables were normalized for homogeneous output volume using Adobe Audition (Adobe Systems Software Ireland). Variants at 440 Hz were used to build sequences of 6 alternating stimuli consisting of either the syllables "/ba" and "/pi" or the syllables "/co" and "/te. " A higher (554 Hz) or lower pitch (440 Hz) syllable was then attached 800 ms following the end of each sequence and represented the target syllable upon which the pitch discrimination was to be judged. Sequences could be either informative, with the first syllable matching the target syllable in identity, or noninformative, that is, the identity of the target syllable could not be anticipated (50% probability) from the first syllable. The time interval between the syllables of a given sequence was also manipulated to be either constant (fixed at 400 ms; rhythmic condition) or pseudorandom (ranging between 100-700 ms; mean 400 ms; nonrhythmic condition).
fMRI preprocessing and regression analysis Data were preprocessed using Statistical Parameter Mapping software (SPM8, Wellcome Trust Centre for Neuroimaging, University College, London, UK, https://www. fil. ion. ucl. ac. uk/spm/ ). The 2 functional runs were first realigned, and a mean image of all the EPIs was created. This mean image was then coregistered to the T1 image, which was then segmented into gray and white matter by means of the Unified Segmentation algorithm  [71]. The resulting normalization parameters were applied to normalize the whole functional set to the MNI space. Finally, functional EPI volumes were resampled into 2 × 2 × 2 mm voxels and spatially smoothed with an 8-mm FWHM kernel. An event-related design matrix was specified using the canonical hemodynamic response function.
rTMS was applied within an average of 3 mm from that peak. The control group did not receive rTMS but followed the same behavioral protocol. Because no rTMS was applied, data from L2 and L3 sessions were merged for this group. When applied onto the lPL, the handle of the coil was angled at 45˚away from the midline. In contrast, the coil was placed in a vertical position (with the coil handle pointing backwards) for stimulation at POz. Stimulation sites were identified on subject's scalp using the SofTaxic navigator system (EMS, Bologna, Italy) according to the T1 image and a marker of the individual target location, using the coordinates of the functional localizer (as described above). Mean stimulation MNI coordinates located at the lPL were x = -47. 85 ± 6. 91, y = -39. 6 ± 8. 62, and z = 48.
This work was supported by the  European Research Council  grant  ERC-StG-313841  ( TuningLang ) and the  BFU2017-87109-P Grant  from the  Spanish Ministerio de Ciencia e Innovacion (RdD-B)  which is part of  Agencia Estatal de Investigacion (AEI)  (Co-funded by the  European Regional Development Fund. ERDF , a way to build Europe), the  European Research Council  ( Proof of Concept, ERC, 727595 ) (SS-F), the  Juan de la Cierva Post-Doctorate Fellowship  ( JCI-2012-12335 ,  Ministerio de Economia y Competividad)  (MR) and  CERCA Programme / Generalitat de Catalunya  for institutional support. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
We chose 14 CCTP samples to create a corpus accessible via Sketch Engine  (Kilgarriff et al. , 2014). In this corpus, we identified linguistic features corresponding to the professional communication skills targeted (see Table  1 ). The observed errors mainly correspond to these features. During the course, the participants completed worksheets containing activities partly inspired by their own errors, and they answered two online questionnaires. The data obtained inform about the learners' progress and remaining needs. We conclude from this study that the specialised CCTP corpus offers enough data to support students who have to write a pedagogical version of a CCTP. However, more training time is needed to better explain to them the technical features of Sketch Engine. They also need to learn how to notice linguistic features and report their findings.
We exploited this powerful technique to investigate the combined impact of a congenital auditory deprivation and the use of a sign language since birth on different face processing functions. All experiments implemented in the present study were previously validated in hearing individuals ( Liu-Shuang et al. , 2014 ;  Rossion et al. , 2015 ; Dzhelyova et al. , 2017 ). Pictures were presented at a fixed rate of 6 Hz (six images/s) with a target stimulus being presented every five stimuli (i. e. 6/5 = 1. 2 Hz). The target was defined as a face among other objects in experiment 1; a face with an emotional expression among neutral faces in experiment 2 and the image of a face having a different identity among images of faces having the same identity in experiment 3. In experiment 2 and experiment 3, images were selected from the same dataset, but stimulus categories were orthogonally organised: while in the Emotional Facial Expression Discrimination experiment the identity of the faces was randomized across facial expressions, in the Individual Face Discrimination experiment emotional facial expressions were randomized across face identities.
The order of the experiments was the same for every participant: FO, ID and EM. However, we report methods and results of each experiment with the following order FO, EM, ID as it better represents the hierarchy within the face processing system. The stimuli were delivered on a Dell computer monitor with 1680 × 1050 resolution and a refresh rate of 60 Hz, by adopting a sinusoidal contrast modulation (0-100%) at 6 Hz using the Sinstim Toolbox created in Matlab 2009 (MathWorks Inc. , Natick, MA). Thus, each stimulation cycle lasted 167 ms. The full luminance value of each pixel of an image was reached around 83 ms after the onset of a stimulus, that is, at half cycle. During EEG recording, participants were comfortably sitting at 80 cm viewing distance from the computer screen. Prior to testing, participants were instructed to adopt a relaxed sitting position, to refrain from moving and to continuously fixate on a small black cross (16 pixels resolution, 5 mm in size, 0.
Procedure Stimulus presentation was controlled using Matlab 2009. Fast Periodic Visual Stimulation (FPVS, Rossion, 2014b ) was applied, with sinusoidal contrast modulation at the base frequency of 6 Hz. A face image appeared after four images of other objects, that is, at a frequency of 1. 2 Hz. To compose a sequence, the stimulus selection for both stimulus categories was random. Frequencies of interest included the base frequency (6 Hz), the oddball frequency (1. 2 Hz), and harmonics for the oddball frequency  (e. g. , 2. 4 Hz, 3. 6 Hz, etc. ). Stimulation sequences were shown to test the ability to discriminate between the face-stimuli and other objects from other categories. Since multiple object identities were used, categorization required generalizing across multiple face exemplars and objects.
Stimuli Due to time constraints, we limited the investigation to the discrimination of three emotional facial expressions (namely, fear, disgust and happiness) with respect to a neutral facial expression. Fourteen full coloured front face pictures from the Karolinska Directed Emotional Faces dataset were selected  ( Lundqvist et al. , 1998 )  comprising 7 males and 7 female identities with 4 facial expressions: 3 emotional expressions corresponding to fear, disgust, and happiness plus 1 neutral. Therefore, 56 stimuli were used in total. Participants were unfamiliar with the pictures prior to testing. Each face picture had an equalized mean pixel luminance during stimulation. The original background was replaced with a grey colour.
EEG acquisition The EEG was recorded with 74 Ag/AgCl passive electrodes (Easycap) referenced to the right earlobe and acquired using the BrainVision software (Brain Products, GmbH). The sampling rate was 500 Hz (hardware bandpass filter with a passband of 0. 032-200 Hz). Electrode positions included standard international 10-20 system locations and additional intermediate positions (see supplementary Fig. S1 ).
Data analysis in the frequency-domain The analysis in the frequency domain was performed in Letswave 6 (Matlab-based toolbox; http://www. nocions. org/letswave). The same pre-processing procedures (tuned for the Fast Periodic Visual Stimulation) were applied to the datasets of all three experiments, according to most recent studies with such paradigms (e. g. ,  Yan et al. , 2019 ; Retter et al. , 2020  ; see also the review of  Rossion et al. , 2020 ). First, a Butterworth bandpass filter was applied to the EEG data (fourth-order, 0. 1-120 Hz cut-off), which was down-sampled from 500 to 250 Hz, and segmented from -2 s to 66 s in order to include 2 s of recording before the first stimulus onset and 2 s after the end of each sequence. For each participant, recording sequences of each experiment were concatenated (separately for each experiment).
All channels were finally re-referenced to a common average reference. At the single participant level, each epoch was further segmented to an integer number of 1. 2 Hz cycles, starting at the onset of the sequence and lasting 60 s (corresponding to 72 cycles, 15,000 time points in total, in FO experiment) or 50 s (corresponding to 60 cycles, 12,500 time points in total, in EM and ID experiments). This procedure was applied to avoid spectral leakage to neighbouring frequencies. The number of bins for a frequency of interest was computed through a Matlab custom made script integercycle , multiplying the value of the sampling rate by the length of the segment. To increase SNR, epochs corresponding to stimulation sequences of the same condition were averaged in the time domain, separately for each participant.
Moreover, for visualization purposes, the SNR was calculated for each participant and condition as follows: the amplitude at the frequency of interest was divided by the average amplitude of 20 surrounding bins (we did not consider the same bins which were excluded in the baseline-correction procedure, see above). This procedure was implemented to correct for the overall noise level and to better visualise the data  ( Dzhelyova et al. , 2017 ). For each frequency of interest, we then identified the set of consecutively significant baseline-corrected harmonics in the FFT grand averaged data across participants, conditions, and groups. Harmonics of the 1. 2 Hz oddball frequency, which corresponded to the base frequency or its harmonics, the 5th (6 Hz), 10th (12 Hz), and 15th (18 Hz) were not considered, because the responses to stimuli presented at both base and oddball frequencies are confounded at these frequencies.
, 2017 ). Electrode selection included four pairs (P7-P8, PO7-PO8, P9-P10, PO9-PO10) covering the posterior lateral portions of left and right hemispheres. At the single participant level, data were averaged across selected electrodes separately for each hemisphere. The resulting amplitude values were used for statistical comparisons. For each of the three experiments, we separately ran a mixed-design analyses of variance (ANOVAs; IBM SPSS Statistics V21. 0. 0) with group (CD, HC) as between-participant factor and the following within participant factors: For FO experiment: Hemisphere (left, right); for EM experiment: Condition (upright, inverted), Hemisphere (left, right); for ID experiment: Condition (upright, inverted), Hemisphere (left, right). For the FO experiment, the analyses at posterior electrodes aimed at specifically testing for the two-way Hemisphere * Group interaction effect (Condition as a factor was not present in this experiment) and, we limited the analysis to this pre-specified effect (as opposed to investigating all main effects and interactions as it is done in exploratory analyses; see  Cramer et al.
, 2015 )  were applied. Data were resampled to 250 Hz. In order to remove non-stereotypical artefacts (such as sudden increases of muscle activity) from the data, continuous datasets were segmented into consecutive epochs with a length of 1 s. Segments displaying a joint probability of activity  ( Delorme et al. , 2007 )  larger than three standard deviations (SD) were removed before performing the ICA. To remove typical artefacts such as eye movements and eye blinks, an independent component analysis based on the extended Infomax  ( Bell and Sejnowski, 1995 ; Jung et al. , 2000a Jung et al. , , 2000b ) )  was performed. In order to reduce computational time, the number of decomposed components was reduced from 74 to 50. The ICA weights were then associated to the raw EEG (continuous and unfiltered data).
ICA components representing artefacts were identified with the semi-automatic algorithm CORRMAP  ( Viola et al. , 2009 ). Following artefact-related component removal the data were seg-mented accordingly to stimulus presentation. Consecutive epochs with a length of 0. 7 s ( -0. 2 to 0. 5 s) were generated. Epochs were time-locked to the 0% contrast time-point in the sinusoidal contrast modulation. In the ID experiment, epochs were calculated for the presentation of all face stimuli, but only for the upright condition. In order to match the SNR-level between the brain responses to different stimulus categories, in the FO experiment, only object images presented immediately before face stimuli were selected. Analogous to the FO experiment, only the face stimuli having the same identity, presented immediately before the face having a new identity were considered.
Epochs having a joint probability of activity greater than four standard deviations  ( Stropahl et al. , 2018 )  were automatically rejected (mean epochs removed per condition and group were: FO: CD = 9. 2%, SD = 3. 3; HC = 8. 1%, SD = 2. 1; ID: CD = 8. 9%, SD = 1. 5; HC = 8. 6%, SD = 1. 8). Finally, the data were baseline corrected using the -100 to 0 ms pre-stimulus period, and digitally filtered (low-pass filter with a 40-Hz upper cut-off, order 100). To visualize the time-course of the EEG signals, consecutive epochs with a length of 5. 6 s (0 to 5. 6 s) were extracted from the data after artefact-related component removal. The first 15 cycles of stimulation from the start of each sequence (3 times AAAAB sequences) were discarded from analyses, to only include EEG signal in which the periodic response had already been built.