dataesr/scientific-paragraphs-categorization huggingface dataset:
Method We collected data from GfK panelists (N = 2010), who evaluated a single brand randomly chosen from a larger set of 20 brands that were familiar to participants. To increase external validity, brands were chosen from five different product categories (four brands per category, see Appendix A) that varied according to their utilitarian and symbolic value, type of goods (e. g. , products and services, durable vs. nondurable), and type of consumption (public vs. private). In each category, the brands were selected with a twofold objective: (1) to ensure variance in terms of their associated values (i. e. , broad coverage of the 1 Brands were chosen so as to cover the four higher-order dimensions in Schwartz's framework. That is, brands were intentionally included to highly embody at least one of the four dimensions, and we included brands likely to embody each of the four dimensions.
The study is registered in Clinical Trials (ClinicalTrials. gov) under number NCT03271320. All participants had to be aged between 18 and 65 years. To be eligible for the study, patients had to have SSc meeting the ACR-EULAR 2013 criteria, regardless of severity. Patients with any of the following connective tissue diseases were not included: rheumatoid arthritis  (Arnett and al. , 1988) , Sjögren syndrome  (Vitali and al. , 1996) , systemic lupus erythematosus  (Hochberg, 1997) , Sharp syndrome (Alarcón-Segovia, 1994). Controls with any connectivite tissue disease were not included. For all participants, any history of serious psychiatric pathology leading to hospitalizations in a psychiatric ward was considered a non-inclusion criterion, as was any reported acquired (e. g. burn) or congenital aesthetic malformation.
For all patients and controls included in the study, the following data were collected: demographic information (age, sex, number of children, tobacco use, level of education), current treatments, family history and lifestyle (marital status, occupation). For patients, an overall assessment of SSc was performed, which included the following: year of onset of Raynaud's phenomenon; year of the rst manifestation of SSc other than Raynaud's phenomenon; treatment for SSc; skin involvement: modi ed Rodnan skin score (mRss), calcinosis, telangiectasia, skin ulcers; Medsger score; European Scleroderma Activity (EUSTAR) score; cardiopulmonary and digestive involvement.
Associations and comparisons made are summarized in Fig. 1a. Statistical testing was done at the two-tailed α level of 0. 05. Data were analyzed using SAS software, Version 9. 4 (SAS Institute, Cary, NC). We estimated the common standard deviation to be 3 at the AES between cases and controls. Under these conditions, with a power of 90% and an alpha risk of 5%, we recruited 88 patients and 88 controls.
When the participant gave his response, freezing stopped and the simulation resumed; the aircraft performed the avoidance and realigned on its initial trajectory. A feedback on the accuracy of the avoidance maneuver was then displayed. which were positioned according to the extended 10-20 system  (Jasper, 1958). The reference and ground electrodes used for EEG data acquisition were positioned on the forehead (respectively AFz and Fpz electrodes). Blinks and eye movements were also monitored using four pure silver electro-oculography electrodes: two positioned above and below the left eye on the median axis for vertical activities and two at the eyes' outer canthi for horizontal activities. The ground electrode for the EOG was placed on the right earlobe. In addition, participants were instructed to limit blinking and eye-movements.
Signal impedance was kept below 10 kΩ for all electrodes. The signal was amplified using an ActiCHamp™ system (Brain Products, Inc. ), digitized at a 24-bit rate and sampled at 1000 Hz, with a 0. 05 μV resolution. All EEG data analyses were performed using EEGLAB v2019. 1  (Delorme & Makeig, 2004)  and Fieldtrip  (Oostenveld et al. , 2011 ) MATLAB (  R2019b ) toolboxes (The MathWorks, Inc. ). The raw EEG data were re-referenced offline to the linked mastoids. The signal was segmented into 30s-epochs that started with trial onset. The signal was then down-sampled to 500 Hz and band-pass filtered between 0. 5 and 40 Hz (8th order band-pass Butterworth filter from ERPLAB; Lopez-Calderon & Luck, 2014). Artifacts related to ocular movements (saccades and blinks) were visually identified and manually rejected after decomposing the data with an Independent Component Analysis (ICA) with the extended infomax algorithm included in EEGLAB.
First, they were considered according to the accuracy of the system response (Error vs. Correct) and task difficulty (Easy vs. Difficult). Then they were considered according to the accuracy of the response of the system (Error vs. Correct) and the moment of the experiment (Beginning vs. End). For this second analysis, the two first blocks (beginning) and two last blocks (end) of every experimental session were averaged for each participant, independently of task difficulty. For both analyses, trials wrongly classified by the participant (false alarms, omission, etc. ) were not considered. EEG data were analyzed both in the time domain, through the usual event-related potentials (ERPs), and in the time-frequency domain, through the event-related spectral perturbations (ERSPs). Finally, the variations in the EEG activity across trials were assessed with ERPimages and inter-trial coherence (ITC).
ERP analysis Mean ERPs were averaged in the time domain for the four conditions for the first (accuracy x task difficulty) and second (accuracy x moment of experiment) analysis. Significant differences in ERPs between the various conditions for each analysis were assessed using a cluster-based permutation test with the Fieldtrip toolbox of MATLAB. This test is based on the cluster mass test  (Maris & Oostenveld, 2007; Oostenveld et al. , 2011)  and identifies spatio-temporal clusters presenting a significant difference in ERP data between the conditions in a given time period. This method is now commonly used in EEG experiments  (Sassenhagen & Draschkow, 2019; Somon et al. , 2019b)  and has for main benefit to require no a priori on the statistical distribution of the data. Likewise, there is no need to predefine the localization or the time-window for the ERP analyses.
ERSP analysis EEG data were also analyzed in the time-frequency domain through Event-Related Spectral Perturbations (ERSP) analyses. This type of analysis makes it possible to uncover evoked as well as induced oscillations by studying their power variation across time for each frequency band. For each electrode, ERSPs were obtained through a complex Morlet wavelet transformation applied for every trial. For an optimal frequency resolution, wavelet cycles were fixed at 3 cycles at the lowest frequency (3 Hz) and increased linearly until they reached a maximum of 8 cycles for higher frequencies (40 Hz). Time-frequency data across trials were then averaged per condition for each participant and baseline corrected through dB-normalization. The literature review as well as visual inspection of the time-frequency maps revealed activity in the lower alpha (α: 8-10 Hz) and theta (θ: 4-8 Hz) frequency bands, either related to performance monitoring processes or attentional resources recruitment.
Analysis according to accuracy and task difficulty Permutation analysis on time-frequency data at electrode FCz across all frequencies showed no effect of accuracy or task difficulty on spectral power time-locked to system response. Nevertheless, visual inspection of the averaged time-frequency maps across trials per condition showed a predominant performance monitoring related low frequency activity (theta and lower alpha) following system response observation, irrespective of its accuracy. F I G U R E 3 Boxplots of participants' accuracy (%) for the system supervision task according to (a) the difficulty level (Easy -green, left -vs. Difficult -orange, right) and (b) the moment of the experiment (Beginning -red, left -vs. End -blue, right). Boxes show the median (midline) and the (first and third) quartiles of the dataset while the whiskers extend to show the rest of the distribution.
e. , a very common situation in flight or during air traffic control. We recorded the EEG activity of 18 participants interacting with a conflict avoidance simulator (the Pilot-System Interaction Lab -  Gouraud et al. , 2018; Le Goff et al. , 2018). Two levels of task difficulty were considered (easy and difficult conditions) related to perceptual difficulty. Their influence on supervision-related brain activity and the impact of the simulator response accuracy were measured. Then, we assessed how the moment of the experimental session modulates the supervisionrelated brain activity according to the accuracy of system responses. Thanks to a robust statistical analysis with no a priori on the data  (Maris & Oostenveld, 2007) , we were able to observe an ERP differentiating between the cerebral activity related to correct and erroneous system response detection over a wide range of brain regions.
Every dot is the pooled data of an animal (N = 12). Right lower panels: Penis puff responses (blue) led to significantly higher amplitudes (mean 0. 96 ± 0. 27 mV) in LFP than leg puff responses (yellow; mean amplitude 0. 31 ± 0. 09 mV). Student's t-test p < 0. 001. B1: Light-induced LFPs were first mapped in Gal-ChR2 mice. Afterwards the mapping pipette was left at the location where the strongest light triggered LFPs were encountered. Subsequent sensory stimulation of penis and leg were conducted while monitoring LFP and EMG activity in parallel. B2: Example traces for optogenetically induced LFP activity in Gal-ChR2 animals (purple) while BSM activity (black) was monitored in parallel. Note the light-locked responses of the BSM with parallel timed LFP activity (putative Gal+ population activity around the central canal in L2/3).
Spinalization was performed in between the thoracic segments 5 and 6. For electrical stimulations, a 1MΩ tungsten electrode (World Precision Instruments) was lowered at each microstimulation site (ranging from ~550 μm to ~950 μm of depth in the dorsal ventral axis, in each spinal cord segment), and currents ranging from 50 μA to 140 μA were injected using a stimulus isolator (Model no. A365RC, World Precision Instruments) while possible movements were documented. Various stimulation protocols were tested (single, 5Hz, 50Hz). Eventual activity in the BSM and TA was monitored via EMG recordings using a custom-made amplifier (x 1000) and filtered at 2 kHz. Data acquisition and analysis were performed using spike2 software (CED Cambridge). Tungsten electrodes were moved along the rostral caudal axis of the spinal cord and electrolytic lesions were placed at the location where the biggest EMG responses were encountered.
Recordings were amplified (Dagan BVC-700A, Dagan, Minneapolis, MN), low-pass filtered at 10 kHz and sampled at 50 kHz by a data-acquisition interface (Power 1401, CED, Cambridge, England) and controlled and analyzed by the spike2 software (CED, Cambridge, England). In vitro patching of identified BSM MNs in new born male Gal-ChR2 animals New-born male Gal-ChR2 mice aged postnatal day (P2) to P6 were used in accordance with the guidelines of the French Agriculture and Forestry Ministry for handling animals. The protocol was approved by the local ethics committee of the University of Bordeaux (approval number 2016012716035720). To record specifically from identified MNs innervating the BSM, a crystal of cholera toxin β-subunit conjugated to AlexaFluor 594 (Thermo Fisher Scientific, C34777) was inserted into the BSM with an insect pin 20-24 h before slice preparation procedure in cryo-anesthetized mouse pups.
Electrical and/or mechanical stimulation protocols were then repeated in the anesthetized preparation (N = 7). For local field potential (LFP) recordings of optogenetically tagged Gal+ or BSM MNs, a glass electrode (resistance ranging from 4 to 6 MΩ) made of borosilicate glass tubes (Hilgenberg) was lowered at the position of the Gal+ cells or the BSM-MNs and optogenetic stimulations were performed until encountering the maximal light triggered LFP response. Subsequently the glass pipette was left in the place at which the highest light triggered LFP response of BSM-MNs or Gal+ cells was encountered. Finally the penis and leg were mechanically stimulated with a locally applied airpuff (5Hz; 5 pulses, 100ms) while monitoring LFP responses and EMG responses in BSM and TA muscles. Analysis was performed in spike2 and data plotted in Excel and Matlab.
Quantification and Statistical Analysis Histological analysis After immunohistochemical labeling of cFos (as described above), the spinal cord sections were imaged using a Slide Scanner (Zeiss AxioScan. Z1, Zeiss Microscopy). The obtained images were processed using the Zen Software (Zen 2. 6, Zeiss Microscopy), and tiff images of each channel (488 for the cFos, cy3 for the Gal-cre TdTomato cells and cy5 for the Nissl staining) were exported. These tiff files were, together with the corresponding image of the spinal segment from the mouse spinal cord atlas  (Watson et al. , 2009) , opened in Photoshop (Adobe Photoshop, Adobe). Once the overlap between the spinal atlas outlines and the immunohistochemical tiff image was adjusted, the cells present in laminae X (around the central canal and the location of the Gal+ cells) of the spinal cord were manually counted by placing dots on each cell.
Behavioral Analysis The DTR behavioral experiments were carefully recorded using two point gray cameras (Teledyne FLIR), at 60 frames per second. The cameras acquired a top and front video of the cage and were controlled using a bonsai script (Bonsai Visual Reactive Programming). Afterwards, the videos were analyzed using Python Video Annotator (developed at the Champalimaud Foundation). A wide range of behaviors was annotated, namely: sniffing of the anogenital area of the female; mount attempts (when the male was not able to perform intromissions) mounts with probing (shallow pelvic movements when the male is trying to intromit but still has not inserted the penis); mounts with intra-vaginal thrustings; intra-vaginal thurstings (when the male successful inserted the penis in the female's vagina); and finally ejaculation (time between the beginning of shivering and the moment the male dismounted the female).
Statistical Analysis Statistical analysis was performed with homemade code in Matlab and Python (scipy and statsmodels). All error ranges represent standard error of the mean. For two-sample comparisons of a single variable, Student's t test was used, unless in cases when the underlying distributions were non-Gaussian (Shapiro-Wilk test, p<0. 05), where a two-tailed Mann-Whitney-U Test was performed. When multiple variables were compared, a Kruskal Wallis test or a Wilcoxon-Signed-Rank test were used since the data did not follow a Gaussian distribution. Probabilities of the null hypothesis p<0. 05 were judged to be statistically significant. Elements of violin plots: center line, median; box limits, upper (75) and lower (25) quartiles; and whiskers, 1. 5x interquartile range. To estimate the inter-thrust interval (ITI) versus mount progression (Figure  7T ), the thrust number was divided by the total number of thrusts in the mount, what we call mount progression.
Acknowledgments We thank  B. Roska  for reagents (PRV) and much valuable advice, and  A. Sathyamurthy  for technical assistance (immunohistochemistry protocol for cFos in the spinal cord). We thank the  Lima lab  for their critical input during the course of this project and in particular  Nicolas Gutierrez  for the support with the Matlab code. We also thank the community at the  Champalimaud Research , in particular the  Carey Lab  for reagents and technical assistance, the  ABBE Facility , the  Scientific Hardware, Histopathology and Rodent Champalimaud Research Platforms  for technical assistances, and  Zachary Mainen  for reviewing the manuscript. This work was developed with the support from the  Champalimaud Foundation , the  Institut de Neurosciences Cognitives et Intégratives d'Aquitaine (INCIA) , a  Human Frontier Science Program Postdoctoral Fellowship  ( LT000353/2018-L4 ) (C.
L. ), a  H2020 Marie Skłodowska-Curie Actions Individual Fellowship (799973 ) (C. L. ), a  Fundação para a Ciência e a Tecnologia (FCT)  PhD Fellowship ( PD/BD141576/2018 ) (A. R. M. ), the research infrastructure Congento, co-financed by the  Lisboa Regional Operational Programme  ( Lisboa2020 ), under the  PORTUGAL 2020 Partnership Agreement , through the  European Regional Development Fund (ERDF) ,  FCT  under the project  LISBOA-01-0145-FEDER-022170 , an InterEmerging Actions 2020 (301137) (S. Q. L. ) and an  European Research Council (ERC)   Consolidator Grant  ( 772827 ) (S. Q. L. ).
WHO Anthro software version 3. 2. 2 (Geneva, Switzerland) was used to calculate BMI for age and sex, expressed in z-scores. The sample size was estimated using GPower ® (Franz Faul, Universitat Kiel, Germany), version 3. 1. 9. 2. F tests were used for the multiple linear regression models. Gross Motor Quotient of The Gross Motor Development Test -second edition (TGMD-2) was considered a dependent variable, and the EFs (inhibition, working memory, and cognitive flexibility) as independent variables, using the correlation values between these variables for preschoolers obtained by Cook et al. [54]. As such, it was possible to calculate the squared multiple correlations (R 2 ). The calculated R 2 value was 0. 35, from which we obtained an effect size f 2 equal to 0. 56. Thus, the sample size was estimated at 24 volunteers for each linear regression model, considering a power of 0.
Statistical analysis Statistical analysis was performed using SPSS 22. 0 (Inc. , USA). The Shapiro-Wilk test was performed to verify data normality and the Levene test to check for homoscedasticity. Regarding outliers, no variables were detected. Pearson's chi-squared test was applied to compare the frequency of children in categorical variables (sex, socioeconomic status, and maternal education) between eutrophic and overweight/obese groups. The Mann-Whitney test was used to examine differences between groups. This is because the variables presented non-normal distribution and/or were heteroscedastic. Thus, the means were used only for the classification of gross motor performance according to the TGMD-2  [76]. For the effect size of the comparisons between the groups, φ and φ c (for Pearson's Chi-squared analyses with categorical or nominal variables, respectively), and r (Mann-Whitney)  [96]  were calculated.
Acknowledgments We thank the  Universidade Federal dos Vales do Jequitinhonha e Mucuri  for institutional support. We thank the  CNPq  ( 303539/2021-6 ),  CAPES , and  FAPEMIG  ( APQ-01898-18 ) for support and scholarships. The authors are grateful to the municipal education secretary and the directors of the public schools of Diamantina (MG).
Questionnaire design A self-administered structured questionnaire previously designed and used in a preliminary KAP survey on COVID-19 in Egypt and Nigeria  [18]  was utilized in this survey. Consent to use this survey tool was sought and approval granted. This questionnaire was primarily designed in English language (with the option of auto-translation to other languages by Google Translate). These translations have already been approved in Arabic and French by language experts who analyzed the linguistic contents during the translation-back translation exercise. To assess the content validity, clarity, ease of response, scope, and face validity of the questions for suitability in a multi-regional setting, three independent reviewers were selected to validate the questionnaire. Furthermore, to check for technical glitches, the aptness of the survey tool, and typographical errors, a pre-test survey was performed on 20 volunteers drawn from the surveyed regions.
Data analysis The Microsoft Excel 2019 was utilized to summarize the data collected and subjected to further statistical analysis using the Open-Source Epidemiologic Statistics for Public Health (Open-Epi), v. 3. 01 (updated 2013/04/06), and the Statistical Package for the Social Sciences (SPSS) software, v. 22. The demographic information of participants (independent variables) was summarized using univariate analysis (frequency and proportions). To subject the responses on knowledge, attitudes, and perceptions to inferential statistics, first a numeric scoring model was employed  [19]. The knowledge, attitudes, and perceptions levels of respondents were treated as the outcome (dependent) variables. For ease of analysis, these dependent variables were further classified as satisfactory or unsatisfactory (binary variables) centered on computed cut-off points.
Data collection The survey was conducted continuously from August to October 2021. Camera traps were placed on the two islands for data collection. Neighbor cameras were set up systematically with equidistant 500 meters on the large island and 250 meters on the small island. Thus, the camera trapping device covered both environments. From a survey within a radius of 100 meters around the systematized trapping points, the camera traps were installed considering the frequent presence of animal signs (footprints, food remains, droppings) but also the presence of streams and fruit trees  (Kouakou et al. , 2021). Camera traps were placed firmly against tree trunks at a height of about 60 cm from the ground to optimize the chances of capturing large and small mammals. The geographical coordinates of the trees on which the cameras were fixed were recorded using a Global Positioning System (GPS).
Diversity Specific richness (S) is defined as the total number of species recorded in a given environment  (Magurran, 2004). The evaluation of the diversity of the mammalian fauna was made through the calculation of the Shannon-Wiener index (H') with "Past 3"software. It makes it possible to compare the richness in mammalian biodiversity of the two islands. Through Past 3 software, the T-test was used to compare the level of biodiversity richness of the two islands. The Piélou equitability index was calculated using the formula below. It makes it possible to measure the distribution of individuals within species, independently of specific richness  (Tuomisto, 2010). Its value varies from 0 (dominance of one of the species) to 1 (uniform distribution of individuals within the species). N the total number of individuals of all species in the sample.
Acknowledgements The authors of this article would like to convey their deep gratitude and recognition to the Heads of Institutions and Structures that have made it possible to carry out this study. The  « Office Ivoirien des Parcs et Réserves (OIPR)  » funded research work on the  Azagny National Park. The  « Centre Suisse de Recherches Scientifiques en Côte d'Ivoire »  provided camera traps for data collection. Thanks, are also due to the anonymous referees who contributed to improving this manuscript.
Data acquisition When participants (n = 24, 17 women, µ = 26. 76 years, σ = 7. 96) arrived at the MRI centre, one experimenter presented them the cover story ("you will participate to a neuromarketing experiment by discussing in real time about the meaning of images prepared for two upcoming advertising campaigns"). The experimenter then introduced them to the agents they would be discussing with. The human partner was a confederate of the experimenter gender-matched with each participant, and the artificial agent was a back-projected conversational robotic head  (Al Moubayed et al. , 2012)  with appearance, voice and accessories reminiscent of the natural agent (Figure  1 ). During functional magnetic resonance imaging (fMRI) acquisition, participants discussed online with these two agents alternatively while lying supine in the scanner and having their brain activity recorded.