dataesr/scientific-paragraphs-categorization huggingface dataset:
Movement sensor data The data from the optical sensor were analyzed using MATLAB (version 2018b, statistical toolbox, Mathworks, Massachusetts, USA). The optical sensor was not available for the first 10 participants (still under development due to unexpected delay), which is why we only included data from the optical sensor from 19 participants. The number of taps from each trial was extracted for participants 10-29. The frequency of taps was calculated by dividing the number of taps by each condition's total time. The number of taps as well as the frequency of taps was then averaged across participants for each condition. The statistical analysis focused on testing for main effects of synchrony, active or passive movements and congruent or incongruent rubber hand orientation in terms of the frequency of taps in line with the fMRI design.
fMRI data preprocessing, modeling and statistical inference The fMRI data from all participants were analyzed using Statistical Parametric Mapping 12 (SPM12; Wellcome Trust Center for Neuroimaging, University College London, UK). Before the functional imaging data underwent the preprocessing steps, all functional and anatomical images were rotated back to the standard position, which they deviated from due to the forward head tilt inside the scanner coil. After that, the preprocessing steps included motion correction, slice timing correction, coregistration, and normalization (to the Montreal Neurological Institute (MNI) standard brain). The functional images were resampled to a resolution of 2x2x2 mm, and spatial smoothing was applied using a 6-mm FWHM Gaussian kernel. The statistical analysis was performed by fitting a general linear model (GLM) to the data for each participant.
In brief, the localizer study included 27 participants looking at and controlling the index finger movement of a robotic hand wearing a plastic glove identical in shape and size to the rubber hand used in our current experiment. This robotic "rubber hand" was placed in view of the participant on a supporting table in a very similar arrangement to the one used in the current study. When the participant moved his or her index finger, the rubber hand moved its index finger in the same way and synchronously, triggering the moving rubber hand illusion (verified with illusion questionnaire ratings that were affirmative in most participants; data not shown). In the localizer study, we contrasted this illusion condition (corresponding to the A M S T C O condition in the present study) to a resting baseline condition where the participants were just looking at the rubber hand without performing or observing any movement.
Peaks from this localizer contrast were then used to define the coordinates in MNI space for the spheres (10 mm in radius) in the small volume corrections (Extended Data Table 12-2 for list of all peaks used from this localizer study to define the volumes of interest). For the intraparietal cortex -a region often associated with the RHI and illusory hand ownership in the previous fMRI literature -we added peaks from  (Ehrsson et al. , 2004)  since no activations were detected in the localizer contrast in this region. Similarly, for the left insular cortex and right angular gyrus in the temporoparietal junction (TPJ) region -two areas often associated with different aspects of agency in the previous literature -we used coordinates from classic neuroimaging agency studies  (Farrer et al. , 2003; Farrer & Frith, 2002)  to define peaks for small volume correction in these regions.
As mentioned, all main conclusions in the manuscript are based on activations that are significant (in one case, almost significant) after correction for multiple comparisons, i. e. , p<0. 05 after FWE correction. The visualization of the results is performed by overlaying the peaks on a 3D rendering of a standard MNI brain using Surf Ice (https://www. nitrc. org/projects/surfice/) as well as on sections from the average anatomical image for all participants. The anatomical localizations of the activations were based on macroanatomical landmarks (sulci and gyri) using the terminology from the Duvernoy and Parratte brain atlas  (Duvernoy, 1999). For peaks in the cerebellum, we used the SUIT toolbox for anatomical localization based on a probabilistic atlas of the cerebellum  (Diedrichsen et al.
, 2009 (Diedrichsen et al. , , 2011)). All coordinates for the activation peaks are given in MNI space. Contrast estimates for each significant peak were extracted using MATLAB (version 2018b) and presented in bar charts together with the corresponding standard errors (SEs) for purely descriptive purposes. In line with the SPM approach, we make no further statistical analyses on these bar chart plots, but all conclusions and statistical inferences are based on significant (two-way and three-way) interaction contrasts in line with our factorial design.
Acknowledgments Zakaryah Abdulkarim  was funded by a PhD student grant from the  Karolinska Institutet  ( Clinical Scientist Training Programme ). Henrik Ehrsson  and project costs were supported by funding from the  Swedish Research Council , The  Göran Gustafsson Foundation  and the  European Research Council  under the European Union's  horizon 2020 research and innovation programme. Arvid Guterstam  was supported by the  Wenner-Gren Foundations  and the  Swedish Brain Foundation. We thank  Martti Mercurio  for help with the experimental setups and the staff at the  Karolinska University Hospital Solna's MR-center  for support with the fMRI. We thank  Konstantina Kilteni  for valuable input during the analysis of the fMRI data.
Concerning the population, we included people during childbirth and early postpartum phase and attendees of the birthing context (e. g. health care professionals, doulas, partners). We excluded studies on people using reproductive health services other than those revolving around childbirth (e. g. fertility treatment, abortion). Primary quantitative studies of different types were included (e. g. cross-sectional, cohort-studies), whereas qualitative and mixed-methods studies and secondary research, reviews, editorials, commentaries or conceptual articles were excluded. The scope was limited to studies published between 2010-2020 since the thematic framework proposed by Bowser and Hill in 2010  [8]  led to a significant increase in research. After having included and read all eligible studies, we extracted all relevant information into a pre-structured data extraction table.
Results Our searches yielded 8689 hits in April 2019 and 937 hits in March 2020 (update conducted in PubMed only). After removing duplicates, we screened 9153 records based on the title and abstract. This led to the exclusion of 9046 records (Fig. 1 ). We read in full text 107 publications, of which 82 studies met the predefined inclusion criteria. All relevant information was extracted in a data extraction table (Additional file 4).
Experimental procedure The laboratory experiment involved exposing the participants to four different sequences of repeated whole-body temperature step-change transients including two warm exposures ("1 Warm" and "2 Warm") and two cool exposures ("1 Cool" and "2 Cool") as shown in Fig. 1. The temperature step-change transients were realised by making the participants move between two identical rooms kept at constant thermal conditions. After each transient, the occupants remained for 30 min in the room to reach steady-state thermal conditions. Both environmental and subjective (thermal sensation, thermal preference, and thermal comfort) data were collected during each exposure. The study was conducted during one week of July 2021 (for the warm conditions) and one week of October 2021 (for the cool conditions) during the French summer and autumn respectively.
Measurements Air temperature , globe temperature , relative humidity , and air velocity were measured with laboratory-grade equipment according to ISO 17772  [17]. The characteristics of the equipment used are reported in Table  2. The air temperature was recorded at 0. 1, 0. 6, and 1. 1 m height  [18]  close to the participants (at a maximum distance of 1 m). Carbon dioxide and illuminance were also recorded. The mean radiant temperature was computed using the function psychrometrics. t_mrt from the pythermalcomfort Python package  [19]. Fanger's index was also computed using the same package. Furthermore, a fully equipped weather station measuring the outdoor conditions was located on the façade of a building nearby. The sampling time-step for all monitoring equipment was set to 60 s. The participants filled in a questionnaire describing their whole-body thermal perception at 10 min intervals starting from either 09:30 (morning tests) or 14:30 (afternoon tests).
The second question was the Thermal Comfort Vote (TCV) on a 6-point scale, including "Very Comfortable" (+3), "Comfortable" (+2), "Slightly Comfortable" (+1), "Slightly Uncomfortable" (-1), "Uncomfortable" (-2), and "Very Uncomfortable" (-3). The third question was the Thermal Preference Vote (TPV) on a 7-point scale, including "Much Cooler" (-3), "Cooler" (-2), "Slightly Cooler" (-1), "No Change" (0), "Slightly Warmer" (+1), "Warmer" (+2), and "Much warmer" (+3). To facilitate comparisons with previous works and datasets  [20] , the French translation of the three questions is based on the French version of EN ISO 10551  [21]  except for the thermal comfort question which was slightly modified to include the "Very Comfortable" and "Very Uncomfortable" votes. We judged that these more extreme votes were relevant for the dynamic conditions studied that could induce thermal alliesthesia  [7].
Statistical analysis Due to the longitudinal nature of the collected timeseries data, a Mixed-effects Linear Model (MLM) is employed for all the statistical analysis by treating the participants as a random factor. The adopted randomeffects structure is a random intercept for each group. Maximum likelihood is the chosen estimation method for the parameters in the MLM model. A "top-down" modelling strategy is used, starting with the maximum model followed by a stepwise backward elimination procedure with only significant predictors kept in the model at the end of the procedure. The open-source Python package scipy. stats is used for all the statistical analyses. Differences at p≤0. 05 are considered statistically significant.
Data collection Data were collected using a face-to-face structured interview before 1:00 a. m. by recruited volunteers trained in addiction care or harm reduction strategies. The following data were collected: sociodemographic characteristics, substance use (alcohol, tobacco consumption and other psychoactive substances as an open question) and harm reduction (knowledge about harm reduction and access to prevention or care facilities). In this work, we analysed the music type of the included festivals and the following sociodemographic data: age, gender, region of residence, employment status, education level, household type and the existence of a long-term disease or long-term drug treatment. Education level was categorized as follows: the first stage of primary education or secondary education (collège) corresponding to the International Standard Classification of Education (ISCED) 0 to 2, high school/baccalaureate diploma (ISCED 3-4) and higher education (ISCED 5 to 8).
Strengths and limitations The main strength of this study is the study population of festival attendees included in a variety of music festivals, "hidden persons who use substances" who are difficult to reach in the research field. However, we should note that the size of the study population is quite moderate, impacting the generalizability of our results. The standardized data collection process by trained interviewers (volunteers trained in addictology or harm reduction strategies) ensures that the data are of good quality. Moreover, we analysed drug use in the previous year, allowing the study of all types of consumption, including nonparty consumption, and preventing seasonal variations. However, we have to emphasize some limitations. We chose a population of festival attendees because they are known to be particularly at risk for drug use.
In addition, drugs were analysed as they were reported by the participants, but persons who use drugs cannot be certain of the exact content of the drugs used. For example, mephedrone was reported in our work but its sale is very restricted in France since 2010  [36]. These limitations are well known in drug surveys, which is why biological studies are complementary tools for monitoring drug use  [7, 33]. To finish, it should be noted that our data pre-dates the COVID-19 pandemic, and we know that the health crisis has an impact on substance use  [30].
Methods This study presents a secondary analysis of the Age-Well trial (ClinicalTrials. gov: NCT02977819), which randomised 137 healthy older adults (age range: 65 to 84 years) to an 18-month meditation training, an active comparator (English language training), or a passive control. Well-being was measured at baseline, mid-intervention, and 18-month post-randomisation using the Psychological Well-being Scale (PWBS), the World Health Organisation's Quality of Life (QoL) Assessment psychological subscale, and composite scores reflecting the meditation-based well-being dimensions of awareness, connection, insight, and a global score comprising the average of these meditation-based dimensions.
Data sharing policies described in the Medit-Ageing charter are in compliance with our ethics approval and guidelines from our funding body. Data contain potentially identifying or sensitive patient information. To request data, please contact the data access committee via the official project website (https://silversantestudy. eu/2020/09/25/ data-sharing). Funding: The Age-Well Randomised Controlled Trials is part of the Medit-Ageing project funded through the European Union in Horizon 2020 program related to the call PHC22 "Promoting mental well-being in the ageing population" and under grant agreement No667696. FC was supported by Fonds National de la Recherche Scientifique (FRS-FNRS, Belgium). The funders had no role in the study design, data acquisition, data analysis, data interpretation or manuscript writing.
Competing interests: GC, FC, OMK, AL, and NLM have received research support from the EU's Horizon 2020 research and innovation programme (grant agreement number 667696). GC has received research support from Inserm, Fondation d'entreprise MMA des Entrepreneurs du Futur, Fondation Alzheimer, Programme Hospitalier de Recherche Clinique, Region Normandie, Association France Alzheimer et maladies apparentees and Fondation Vaincre Alzheimer (all to Inserm), GC and AL have received research support and personal fees from Fondation d'entreprise MMA des Entrepreneurs du Futur. All other authors have declared that no competing interests exist. The data underlying this report are made available on request following approval by the executive committee and a formal data sharing agreement (https://silversantestudy.
eu/2020/09/ 25/data-sharing). The Material can be mobilized, under the conditions and modalities defined in the Medit-Ageing Charter by any research team belonging to an Academic institution, for carrying out a scientific research project relating to the scientific theme of mental health and well-being in older people. The Material may also be mobilized by non-academic third parties, under conditions, in particular financial, which will be established by separate agreement between Inserm and by the said third party. Data sharing policies described in the Medit-Ageing charter are in compliance with our ethics approval and guidelines from our funding body. Data contain potentially identifying psychological well-being as a trainable skill that can be cultivated by specific practices  [22]. The cultivation of inner flourishing and the alleviation of suffering have been central tenets of the Greco-Roman philosophical schools as well as Buddhist meditative traditions for millennia but only recently have researchers begun to explicitly synthesise these contemplative perspectives with contemporary scientific models of well-being  [22] [23] [24].
The intervention was delivered in Caen (France). Written informed consent was obtained from all participants after the procedures had been explained to them and prior to participation. Age-Well received ethics approval from the Comitede Protection des Personnes CPP Nord-Ouest III in Caen (trial registration number: EudraCT: 2016-002441-36; IDRCB: 2016-A01767-44; ClinicalTrials. gov Identifier: NCT02977819). The Age-Well trial has been performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments.
Participants A total of 137 participants were randomised. Two participants were excluded from the trial after randomisation: one participant presented with amyotrophic lateral sclerosis and one participant had previously experienced a head trauma with loss of consciousness for more than one hour. Another participant died before the end of the trial. The present study thus included data from 134 cognitively unimpaired, older adults (age range: 65 to 78 years [meditation]; 65 to 84 years [English training]; 65 to 75 years [no-intervention]), who had no major neurological or psychiatric disorder, no present or past regular or intensive practice of meditation, were native French speakers, were retired for at least one year, and had completed at least seven years of formal education (Table  1 ).
The estimates and pvalues presented here slightly differ from the primary outcome paper because the models here used data from all three time points (V1, V2, V3), whereas the primary outcome paper used data from only two time points (V1, V3). Here, we report additional data (i) comparing the trajectories of these composite scores between the mindfulness module and loving-kindness and compassion module (i. e. , presenting data from mid-intervention at 9 months), (ii) comparing the meditation training to no-intervention, and (iii) assessing potential moderating effects on the composite scores (see below for a presentation of the potential moderator variables). These additional analyses are exploratory and, therefore, not controlled for multiple comparisons. The three composite scores comprise scales or subscales from six self-report measures (see Table  2 ), which are described in S1 Table in S1 File.
Our sample comprised mostly well-educated, healthy participants that were recruited from a single geographic location, whose cultural, economic, and social characteristics may not be representative of other regions, limiting the generalisability of our results to populations of older adults with more demographically diverse compositions. Furthermore, we used previously published composite scores of meditation-based psychological capacities to reflect the well-being dimensions of Dahl et al. 's training-based framework for human flourishing  [22]. These self-report scales were designed to capture trait-level individual differences and may be less sensitive to measuring process-level aspects of meditation-based well-being dimensions. Furthermore, the study's reliance on selfreported data may have introduced a degree of bias, as participants' responses could have been influenced by their perceptions, memory, or understanding of the questions.
Acknowledgments The  Medit-Ageing Research Group  includes:  Claire Andre ,  Florence Allais ,  Julien Asselineau ,  Eider Arenaza-Urquijo ,  Sebastian Baez ,  Martine Batchelor ,  Axel Beaugonin ,  Maelle Botton ,  Pierre Champetier ,  Anne Chocat ,  Nina Coll ,  Sophie Dautricourt ,  Pascal Delamillieure ,  Vincent De La Sayette ,  Marion Delarue ,  Harriet Demnitz-King ,  Yacila I. Deza-Araujo ,  Titi Dolma ,  Stephanie Egret ,  Francesca Felisatti ,  Eglantine Ferrand-Devouges ,  Eric Frison ,  Francis Gheysen ,  Agathe Joret Philippe ,  Elizabeth Kuhn ,  Brigitte Landeau ,  Gwendoline Ledu ,  Valerie Lefranc ,  Maria Leon ,  Florence Mezenge ,  Inès Moulinet ,  Theresa Mueller ,  Valentin Ourry ,  Cassandre Palix ,  Leo Paly ,  Geraldine Poisnel ,  Anne Quillard ,  Geraldine Rauchs ,  Florence Requier ,  Eric Salmon ,  Lena Sannemann ,  Christine Schwimmer ,  Edelweiss Touron ,  Denis Vivien ,  Patrik Vuilleumier ,  Cedrick Wallet , and  Tim Whitfield.
Morphometry To control for a potential influence of head size on the learning and memory performances of hornets, we froze-killed the conditioned hornets and made morphological measurements with the ToupView software coupled to a Nikon SMZ 745T dissecting microscope (objective ×0. 67) with a Toup-Cam camera model U3CMOS. We measured head width as a proxy of head size  (Riveros and Gronenberg 2010). Note that 4 out of the 20 V. crabro were physically damaged and removed from these analyses.
Statistics We analyzed the data in R 4. 0. 4. From the raw data (available in Dataset S1 and S2), we extracted the first choice of each hornet at each trial (CS+: 1, CS-: 0) of differential and reversal conditioning. We then tested the influence of species (V. crabro or V. velutina) and trials 1-10 on first choice (CS+), using a generalized linear mixed model (GLMM; R package lme4,  Bates et al. 2015) , with binomial error structure and identity as a random factor, followed by an ANOVA (R package car, Fox and Weisberg 2019). For each conditioning protocol, we compared the number of learners in the two species using a chi-square test with a continuity correction (R function chisq. test). We analyzed the effect of head width and inter-trial interval on learning scores (0-10) using a linear mixed model (R package lme4) with the colony as a random factor for V.
We addressed these points in two investigations conducted in two different countries. A first investigation used a between-subjects design comparing a data set collected with the RTS in France with two data sets collected in Hong Kong with the cable-driven robot producing a linear or oval trajectory with constant contact-force and duration  (Schirmer et al. , 2021; Wijaya et al. , 2020). In this study, participants rated the pleasantness, humanness, intensity, and roughness of stroking. A second study, conducted in Hong Kong, used a withinsubjects design and compared the effect of four different trajectories on ratings of pleasantness and humanness only. Trajectories were delivered using the cable-driven robot and entailed a direct imitation of the RTS, a durationcontrolled adaptation of the RTS, a duration and force-controlled linear trajectory, and a duration and force-controlled oval trajectory.
Study 1 Methods This research was approved by the national ethical committee (committee Est-III for the protection of persons in France) in France and the survey and behavioral research ethics committee at The Chinese University of Hong Kong. All participants received information prior to the study and signed informed consent forms. The data were collected in the first half of 2021, thus during the COVID-19 pandemic. At the time, experimental procedures were permitted, but it is noteworthy that the participants from both countries were experiencing similar restrictions in social contact.
Participants We recruited 26 participants in France who were stroked using the RTS (Dancer Design, St. Helens, UK). These participants had a mean age of 25. 3 years (SD 5. 1) and comprised 13 women and 13 men. All participants described themselves as right-handed, apart from one participant who was predominantly left-handed. In addition, we recruited two groups of participants in Hong Kong that were stimulated using a cable-driven robot. One group of 34 participants with a mean age of 22 years (SD 3. 8) was stroked with a linear trajectory, whereas another group of 34 participants with a mean age of 21. 2 years (SD 2. 7) was stroked with an oval trajectory. Both Hong Kong groups had a balanced number of women and men and were right-handed. No participants were excluded from the data analysis.