dataesr/scientific-paragraphs-categorization huggingface dataset:
Analyses Data, excluding practice trials, were analyzed with the open-source software R (The R Core Team, 2018). The following packages greatly eased our work: afex  (Singmann et al. , 2019) ; dplyr  (Wickham, François, Henry, & Müller, 2019) ; emmeans  (Lenth, 2019) ; ggplot2  (Wickham, 2016) ; lme4  (Bates, Mächler, Bolker, & Walker, 2014). For exploratory analyses, we recovered the parameters of a drift diffusion model  (Ratcliff, 1978)  using the fast-dm-30. 2 software (Andreas  Voss & Voss, 2007). We decided to analyze accuracy only if: the group mean would score below 90% (and below 95% in congruent trials); if fewer than half of the subjects would present more than 90% success rate (95% in congruent trials). This was to avoid, in the FT task, ceiling effects. The group mean was eventually 84.
Likewise, all participants could attend all the three experimental sessions, and could receive GVS without inconveniences (e. g. excessively high impedance, excessive discomfort). No replacement was thus necessary. Data were analyzed through mixed-effects multiple regression models  (Baayen et al. , 2008)  using the lme4 package for R  (Bates, Maechler, Bolker, & Walker, 2014). Models had a logistic link-function, appropriate for binary variables, when assessing accuracy. Prior to fixed-effect testing, the most appropriate and parsimonious  (Bates, Kliegl, Vasishth, & Baayen, 2015)  matrix of random effects was chosen via an objective pipeline exposed at length elsewhere  (Blini, Desoche, et al. , 2018b; Blini, Tilikete, et al. , 2018a; Bonato et al. , 2019 ; also see the Supplementary Materials).
Acknowledgments EB was supported by: the  European Union 's  Horizon 2020 research and innovation programme (Marie Curie Actions)  under grant agreement  MSCA-IF-2016-746154 ; a grant from  MIUR (Departments of Excellence   DM 11/05/2017 n. 262 ) to the  Department of General Psychology, University of Padova. AF is supported by the  James S. McDonnell Scholar award. FHB received funding from the  French National Research Agency (ANR) ( ANR-14-CE13-0005-1 ). The study was performed within the framework of the LABEX.
Data preprocessing and statistical analysis Functional images were realigned, treated with the FSL ''Topup'' toolbox in order to correct EPI distortions due to B0 field inhomogeneity  (Andersson et al. , 2003) , normalized to the standard MNI brain space and spatially smoothed with an isotropic Gaussian filter (6 mm full width at halfmaximum, FWHM). First-level analysis was implemented in SPM12 software (https://www. fil. ion. ucl. ac. uk/spm/ software/spm12/). Data were high-pass filtered and modeled by regressors obtained by convoluting the experimental conditions and the button presses with the canonical SPM hemodynamic response function (a combination of 2 gamma functions, with a rise peaking around 6 s followed by a longer undershoot). Individual contrast images were computed in order to identify (1) color-sensitive regions (colored minus grey-scale images), (2) the Visual Word Form Area (VWFA, words minus numbers), and (3) the object-sensitive Lateral Occipital Cortex (LOC, Tools minus an average of Faces and Places).
Data preprocessing Data preprocessing and statistical analysis was performed with the CONN v. 17 functional connectivity toolbox  (Whitfield-Gabrieli and Nieto-Castanon, 2012). We used standard preprocessing steps including: slice-time correction, realignment, segmentation of structural data, normalization into standard stereotactic MNI space and spatial smoothing using a Gaussian kernel of 6 mm FWHM. To account for the fMRI signal attenuation, after slice-time correction, realignment, motion-correction and normalization, functional scans were subjected to intensity-based masking  (Peer et al. , 2016 ; see also  Siuda-Krzywicka, Witzel, et al. , 2019). We then used the Artifact Detection Tool (ART; https://www. nitrc. org/projects/artifact_detect/) to identify scans exceeding 3 SD in mean global intensity, and scan-to-scan motion that exceeded 0.
Acknowledgements: This work was supported by  ICM ,  INSERM ,  CNRS , the program " Investissements d'avenir " (grant number  ANR-10-IAIHU-06 ). K. S. K. was supported by the  Ecole des Neurosciences Paris Ile de France. C. W. was supported by the grant ' Cardinal Mechanisms of Perception ' No. SFB TRR 135  from the  Deutsche Forschungsgemeinschaft.
Study sites Data were collected at two eld sites and ve captive facilities (zoos). We observed wild orang-utans at the long-term research sites of Suaq Balimbing (03°02'N; 97°25'E, Gunung Leuser National Park, South Aceh, Indonesia) and Tuanan (02°15'S; 114°44'E, Mawas Reserve, Central Kalimantan, Indonesia), on a population of wild Sumatran (Pongo abelii) and Bornean orang-utans (Pongo pygmaeus wurmbii), respectively. Both study sites consist mainly of peat swamp forest and show high orang-utan densities, with 7 individuals per km 2 at Suaq and 4 at Tuanan  41, 42. Captive Bornean orang-utans were observed at the zoos of Cologne and Munster, and at Apenheul (Apeldoorn), while Sumatran orang-utans were observed at the zoo of Zurich and at Hellabrunn (Munich) (see EEP studbook for details on captive groups  43.
Data collection Focal observations were conducted between November 2017 and October 2018 (Suaq Balimbing: November 2017 -October 2018; Tuanan: January 2018 -July 2018, European zoos: January 2018 -June 2018). At the two eld sites, they consisted of full (nest-to-nest) or partial follows (e. g. nest-to-lost or found-to-nest) of mother-infant units, whereas in zoos 6-hour focal follows were conducted. Behavioural data were collected following an established protocol for orang-utan data collection (https://www. aim. uzh. ch/de/orangutannetwork/sfm. html), using focal scan sampling. All observers were trained to use this protocol and inter-observer reliability tests were conducted after each training phase. MF collected data at both study sites ensuring the use of the same criteria during training see also  44.
Two different behavioural sampling methods were combined: First, intra-speci c communicative interactions of all social interactions of the focal as signaller and as receiver with all partners, but also among other conspeci cs present were recorded using a digital High-De nition camera (Panasonic HC-VXF 999 or Canon Legria HF M41) with an external directional microphone (Sennheiser MKE600 or ME66/K6). In captive settings with glass barriers, we also used a Zoom H1 Audio recorder that was placed in background areas of the enclosure whenever possible, to enable the recording of auditory communicative acts. Second, using instantaneous scan sampling at ten-minute intervals, we recorded complementary data on the activity of the focal individual, the distance and identity of all association partners, in case of social interactions the interaction partner as well as several other parameters.
Coding procedure A total of 2655 high-quality recordings of orang-utan interactions (wild: 1643, captive: 1012), which could each include multiple communicative acts, were coded using the program BORIS version 7. 0. 4. 45. We designed a coding scheme to enable the analysis of articulators and sensory modalities involved in presumably communicative acts directed at conspeci cs (i. e. close-range social behaviours that apparently served to elicit a behavioural change in the recipient and were mechanically ineffective; see also Table  1 ) and thus included "potential gestures"  46. Cases where acts were directed at observers or achieved their presumed goal sensu  46  (apparent aim of the signaller based on the individuals involved and the immediate social context) via physical force (e. g. nursing solicitations, infant collections) were thus excluded from the dataset.
While the speci c communicative acts comprising repertoires are out of the scope of this paper and part of different analyses  47  , we used this dataset to determine the dominant outcome of a communicative act in a given research setting and orang-utan species (see ESM_3_outcomes). During an initial training period, and afterwards in regular intervals, coding performance of all observers was evaluated with alternating datasets using the Cohen's Kappa coe cient to ensure inter-coder reliability  48. Trained coders (MF, NB, CF, CW, LM, MJ) proceeded with video coding only if at least a 'good' level (κ = 0. 75) of agreement was found for type of communicative act, articulator, sensory modality, presumed goal, and outcome. For further details on the distribution of coded interactions across species, settings and interaction dyads see Table  S3.
Statistical analyses For the dataset of around 7578 communicative acts resulting from the coding procedure, we used Generalized Linear Mixed Models  49  with a binomial error structure and logit link function. We investigated sources of variation in four sets of response variables, referring to (a) employment of sensory modalities (visual, tactile, auditory, seismic, multi-sensory), (b) employment of articulators (manual, bodily, facial, vocal, gaze, multi-articulatory), (c) effectiveness (i. e. whether or not the signaller received an apparently satisfactory response; sensu  11, 46  ), and (d) dominant outcome match (i. e. whether or not the presumed goal sensu 46 of a communicative act matched the major interaction outcome associated with it; see Table  1  for de nitions, and ESM_3_outcomes for all apparently satisfactory outcomes and the dominant outcome for every type of communicative act for every setting and species  47  ).
In models (a) and (b), we included orang-utan species ( sexually active, est. >15 yrs; 50 ) as our key test predictors. Because we assumed that the effect of research setting might depend on genetic predisposition (i. e. species), we included the interaction between these two variables into all models. To adjust for multiple comparisons, we tested interaction effects using pairwise contrasts with the function lsmeans (with argument "adjust" set to "sidak") of the package lsmeans. In the models (c) and (d), we included composition of multi-sensory (2 levels: visual/tactile only, visual/tactile plus other modality) or multi-articulator (manual/bodily/gaze only, manual/bodily/gaze plus other articulator) acts as only key test predictor. Communicative acts involving vocal, seismic, facial or vocal components were not common enough to allow inferential analyses (see Table  2 ).
In models (c) and (d), species, setting, kin relationship and age difference (see above) were included as control predictors rather than key test predictors as in models (a) and (b). To control for repeated measurements, the identity of the dyad, the subject and the partner were treated as random effects. We further included communicative act type (e. g. touch, raise limb etc. ,  47  ), group identity, coder identity and video le number (accounting for the fact that communicative acts of the same interaction are non-independent) as random effects. To keep type 1 error rates at the nominal level of 5%, we also included the relevant random slopes components within subject, recipient and dyad ID  52. All models were implemented in R (v3. 4. 1, 53 ) using the function glmer of the package lme4  54.
Acknowledgements We thank  Caroline Schuppli  (Suaq),  Erin Vogel  and  Suci Utami Atmoko (Tuanan) ,  Kerstin Bartesch  ( Tierpark Hellabrunn ),  Claudia Rudolf von Rohr  ( Zoo Zürich ),  Alexander Sliwa  ( Kölner Zoo ),  Simone Sheka  ( Allwetterzoo Münster ) and  Thomas Bionda  ( Apenheul Primate Park ) as well as all research staff and zoo keepers for a fruitful collaboration during this study. We gratefully acknowledge  Clemens Becker  for providing the EEP studbook, the  Indonesian State Ministry for Research and Technology (RISTEK) , the  Indonesian Institute of Science (LIPI) , the  Directorate General of Natural Resources and Ecosystem Conservation -Ministry of Environment & Forestry of Indonesia (KSDAE-KLHK) , the Ministry of Internal affairs, the  Nature Conservation Agency of Central Kalimantan (BKSDA) , the local governments in Central Kalimantan, the  Kapuas Protection Forest Management Unit (KPHL) , the  Bornean Orang-utan Survival Foundation (BOSF)  and MAWAS in  Palangkaraya.
Stroop Task The raw data were processed with a customized MATLAB routine (MathWorks Inc. , Natick, MA, United States). The number of errors was calculated for each trial condition (congruent, incongruent, neutral). Response time of each correct trial condition was then calculated and averaged for each participant. Response times values under or below two times standard deviation (±2 SD) of each participant mean were considered as outliers and deleted. On the basis of this criterion, over the 55 trials performed, two trials in the group of young participants and three in the older adults were discarded. The percentage of errors in congruent and neutral conditions was about 2% and about 10% in the incongruent condition.
Bimanual Coordination Data Analysis The raw data were processed with a customized MATLAB routine (MathWorks Inc. , Natick, MA, United States). They were filtered with a Butterworth filter (cut-off frequency 10 Hz, order 2). Then, amplitude centering procedure was used to remove frequency artifacts of the non-sinusoidal signals, when existing. According to  Lamb and Stöckl (2014, Eq. 4) , the normalization was based on the function: g y (ti) = 2 (y (ti) -min(y (t)) max yt -min y (t) -1 FIGURE 1 | Experimental setup. This function transforms the original values y(t) in such a way that the minimum value of g(y(t)) equals -1 and the maximum value of g(y(t)) equals 1. To calculate the continuous RP (CRP), a new signal generated representing the difference in phase angles of the two original signals  (Lamb and Stöckl, 2014) , we first calculated the phase of each hand on the basis of normalized phase portraits.
Data analysis First, the unconditioned TS intensity required to elicit the MEP target , at rest and during MI was quantified and expressed in %MSO in both experiments. CC-BY-NC-ND 4. 0 International license a certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under The copyright holder for this preprint (which was not this version posted June 5, 2020. ; https://doi. org/10. 1101/846931 doi: bioRxiv preprint 8 Then, to probe the influence of the different CS on TS intensity, the amount of SICI (expressed in INH%) was quantified for each condition using the following equation  (Fisher et al. , 2002) : Additional analyses were performed to control for potential methodological biases. The RMS values were compared across conditions in both experiments, using the same analyses described above.
Procedure about the construction of the photograph dataset One-hundred and sixteen static visual stimuli were ordered to a professional photographer (Adelie Granon) thanks to the grant mentioned in the acknowledgments meaning that all pictures pertained to the Principal Investigator of the study (S. Granon). The photograph search for rural or urban landscapes of our daily living environment. For the "Clean" condition, no waste was included in the pictures whereas for the "Polluted" condition the landscapes incorporated visible waste from the point of view of the photographer (from a path or close to a road). Hereafter, all the pictures were included in the following steps of the experiment. They defined two conditions: "Clean" and "Polluted" (for an example, see  Fig 1). Stimuli were randomly presented through the E-Prime 2 Software (Psychology Software Tools, Inc.
Data recording and analyses Pictures selection. First, we conducted a global analysis on the pleasure and approach desire that were evoked by each condition. The goal was to check if scenes of the "Clean" condition evoked a higher pleasure but also a higher approach desire than the one of the "Polluted" condition. To do so, we did a repeated-measures one-way ANOVA on the data. When sphericity was not accepted, a Greenhouse-Geisser (GG) correction was applied. Then, we conducted a per-picture analysis in order to extract the most relevant pictures for each condition. For the "Clean" condition, we set a threshold at 6 in order to extract scenes that will have a mean evoked pleasure and approach desire statistically higher than 6 and not only neutral (around 5). For the "Polluted" condition, we set a threshold at 4 in order to extract scenes that will have a mean evoked pleasure and a mean evoked approach desire statistically lower than 4 and not only neutral.
For each scene, we conducted a t-test for paired measures. The statistical significance was set for p-value < 0. 05. Population and pictures characteristics effects. We performed analyses in search for picture characteristics (urban/rural context, presence of other individuals) and population characteristics (gender, categories of age) that could impact ratings. For each case, we started by a two-factor ANOVA with repeated measures on the data upon the environmental features (urban vs rural), the presence of individuals and the gender. For age's categories, we conducted a three-way ANOVA with repeated measures. When an interaction was found between the condition ("Clean" vs "Polluted") and another criterion, we checked the impact of this criterion in each condition using a repeated-measure one-way ANOVA.
In each case, blue dots represent pictures that were not statistically relevant for the pleasure rating. https://doi. org/10. 1371/journal. pone. 0234210. g005 Fig 6. Mean evoked pleasure according to the environmental context (Urban VS Rural) in the "Clean" and "Polluted" conditions (mean ± SEM). In each condition, we have 29 pictures in an urban context and 29 pictures in a rural context. We found an interaction effect between condition and context (p = 6. 63 x 10 -23 ). Results showed an effect of environmental context in the "Clean" condition (p = 4. 62 x 10 -23 ) but also in the "Polluted" condition (p = 5. 32 x 10 -08 ). Significant differences are shown as ��� p < 0. 001. https://doi. org/10. 1371/journal. pone. 0234210. g006 we selected 30 scenes for the "Clean" condition and 23 for the "Polluted" condition from a larger pictures database.
Participants did not experience more Flow over sessions; instead, their trialwise subjective anticipation of performance (estimated from a power-law model over trials) was strongly related to Flow. Murch et al. report on three experiments which aim to identify objective markers of flow while gambling. The authors collected self-reported gambling flow and physiological data as measured by the cardiac pre-ejection period (PEP). The authors did not find evidence of changes in PEP when interacting with the electronic gambling machines but only interactions between subjective and objective measures during the first block of each experiment. In a study examining the effect of positive feedback on perceived self-efficacy, flow and performance, Peifer et al. hypothesize that self-efficacy acts as a mediator, whereby positive feedback on one task imparts a positive effect upon performance and flow in subsequent tasks.
Is large-scale population screening coming to psychiatry? Large-scale population screening has long been proposed in oncology, though it still remains controversial. 1  Psychiatry has mostly been insulated from the screening fever, perhaps because of the scarcity of reliable biomarkers of psychiatric illness  2  or the fact that questionnaire-based screening is simultaneously time-consuming and not empirically supported (eg, for depression). 3 Can machine learning approaches be a game changer? Using a 1:1 case-control design on one of the largest real-world databases of electronic health records (EHRs), covering approximately 15% of the US population in both primary and secondary care, Lars Lau Raket and colleagues  4  developed a risk prediction model to detect individuals at risk for developing a first episode of psychosis.
The Dynamic ElecTronic hEalth reCord deTection (DETECT) model was able to identify individuals with first episode of psychosis who later developed schizophrenia with a prognostic accuracy of 0•774 and an area under the receiver operating characteristics (AUROC) of 0•856 in the validation dataset, and with a prognostic accuracy of 0•724 and AUROC of 0•799 in the external validation subset. Although replication in a population-based sample is explicitly advocated, DETECT's main appeal rests with its possible usefulness for large-scale screening of EHRs, by potentially identifying individuals not actively seeking help at secondary care units, such as clinical high risk for psychosis (CHR-P) specialised clinics. Important questions arise about both the opportunity and ethics of large-scale population screening.
Statistical analysis We created two summary scores: the first four subtests (Orientation, Sequences, Time Units and Telling the time on a clock) were summed to create Summary Score 1 (Conventional time summary score); the remaining three subtests (Life-Span, Birthdays and Interview duration) were added to form Summary Score 2 (Estimative time summary score). Statistical analyses focused on the internal consistency of these scores, and their correlations after adjusting for age. Norms by grade (mean, SD, range, quartiles) are presented for all subtests, summary scores and the total score. Data from a clinical sample (children treated for a cerebellar tumor) was compared with these norms.
However, in contrast to other species, we have also tried to adapt ourselves to disturbed environmental conditions like night work, or to other sleep disruptions like social communication (by internet, smartphones or new technologies) or work-related transportation needs. All of these environmental and societal factors have drastically changed in recent decades and are of great interest to sociologists, economists, urbanists, and ecologists. Specifically, these professionals are more frequently studying the evolution of these factors independently of the human behavior itself, making large open databases available to sleep researchers. In order to facilitate using these large data sets, we would like to examine how we may assess SWR at the population level, in terms of its environmental and societal interactions.
Selected fields (Figure 1) Based on the proposed pertinence criteria, the team of scientists selected NOISE and LIGHT POLLUTION as pertinent fields for this overview. They excluded TEMPERATURE, due to confusion on the sources of heat discussed in the open data sets. From the second analysis, the team selected TRANSPORTATION and INTERNET-SMARTPHONES for the overview. ELECTROMAGNETIC FIELDS was also selected and associated with INTERNET-SMARTPHONE as a prospective field. For NIGHT WORK/SHIFT WORK, there were not enough open data sets that followed the criteria of data availability and access, its reuse and redistribution, or its universal participation.
-In New York City, a network of 55 sensor nodes was deployed across the city over two years, collecting sound pressure level (SPL) and audio data in order to investigate and ultimately aid in the mitigation of urban noise. This network cumulatively represents over 75 years of calibrated, highresolution SPL measurements and 35 years of audio data. In addition, high-frequency telemetry data were also collected and analyzed over an 18-month period across 31 of the sensors, providing an indication of sensor health. These data were used to develop a prototype model for pre-failure detection, which can identify sensors in a pre-failure state 69. 1% of the time. With this approach, it is possible to precisely detect the 24-hour noise level around these 55 sensor points (ie, Figure  2 : 24hour Noise at 725 Broadway on July 30, 2019).