dataesr/scientific-paragraphs-categorization huggingface dataset:
MRI data quality assessment Preprocessed MRI data were assessed for quality using the mriqc package  92  (the code was adapted from https://github. com/chrisfilo/mriqc and can be found at https://github. com/NeuroanatomyAndConnectivity/pipelines/tree/master/src/lsd_lemon, release v2. 0), implemented in Python. mriqc creates a report for each individual scan based on assessment of movement parameters, coregistration, and temporal signal-to-noise (tSNR) calculations. For comparison, all individual-level scores are displayed with respect to the group-level distribution. We visually inspected the quality assessment reports for each subject to ensure adequate coregistration and fieldmap correction. As motion during the resting-state fMRI scan poses a substantial source of noise  93  , we characterized motion for each run as the mean and maximum framewise displacement (Fig.
2 ). Overall, the summary of motion parameters demonstrates that the data are largely of sufficient quality, with 89. 2% of runs showing less than one voxel (2. 3 mm) maximum framewise displacement, and a mean framewise displacement of 0. 18 mm (SD = 0. 08 mm). Fieldmap correction provides an approach to correct for distortions due to susceptibility artifacts. While unable to recover signal loss, the correction of such nonlinear distortions improves coregistration between scan types, and group-level alignment  94. As an example, we present a single dataset, pre-and post-fieldmap correction, in Fig. 3. As expected, fieldmap correction primarily shifted voxels within ventral regions. Temporal signal-to-noise (tSNR), which is calculated on the voxel-level as the mean signal divided by the standard deviation, offers a general overview of the local differences across the brain.
Usage Notes The MRI dataset can be accessed at https://openneuro. org, http://fcon_1000. projects. nitrc. org, or https:// www. gwdg. de/ and the behavioral data is available at http://www. nitrc. org (http://nitrc. org/projects/ mpilmbb/). The following data are publicly available: 1) MRI data (structural and functional), 2) general demographic of the studied population, 3) summary scores and/or indexes of the questionnaires and tasks, and 4) raw scores of the measures that do not possess summary scores and have not been classified as sensitive. All MRI datasets are made available in NIFTI format, and all anatomical scans have been defaced. The dataset, protocols, and software used in the acquisition and processing of the data are documented, curated, and available for download. For access to the behavioral data, users must first agree to the terms of data usage, which prohibit any usage that aims to identify the individuals based on these phenotypic data.
1. OpenfMRI. org(now renamed to OpenNeuro) platform also hosts the raw data (Data Citation 3)The OpenfMRI repository provides API access available via https://openfmri. org/dataset/api/. In addition, similar to all other datasets in OpenfMRI, our dataset is available via Amazon Web Services S3 object data store under the s3://openneuro/ds000221/ 2. International Neuroimaging Data-sharing Initiative, INDI (Data Citation 4) 3. Gesellschaft für wissenschaftliche Datenverarbeitung mbH Göttingen (GWDG): https://www. gwdg. de/ Raw and preprocessed data at this location is accessible through web browser (https://ftp. gwdg. de/pub/ misc/MPI-Leipzig_Mind-Brain-Body/) and a fast FTP connection (ftp://ftp. gwdg. de/pub/misc/MPI-Leipzig_Mind-Brain-Body/). In the case the location of the data changes in the future, the location of the dataset can be resolved with PID 21.
Code availability All code that was implemented for data acquisition and processing is available online (https:// neuroanatomyandconnectivity. github. io/opendata/). Data handling and computation of summary measures were implemented in Python. The pipeline used for MRI preprocessing is also available (https://github. com/NeuroanatomyAndConnectivity/pipelines/tree/v2. 0/src/lsd_lemon, releasev2. 0). The tasks that the participants received were implemented using the Python package PsychoPy2 Experiment Builder v1. 81. 03  87, 88  , OpenSesame 0. 27. 4 89 , and Presentation® software (Version 16. 5, www. nature. com/sdata/ Data Citations 1. Buckner, R. L. , Roffman, J. L. & Smoller, J. W. Harvard Dataverse https://doi. org/10. 7910/DVN/25833 (2014). 2. Mendes, N. et al. Harvard Dataverse https://doi.
MATERIALS AND METHODS The study reported in this paper, including the entire method, analysis and criteria for exclusion of participants were preregistered on the OSF (Open Science Framework) database before running the experiment. The preregistration can be accessed with the following link: https://osf. io/wmnvg/?view_ only=89ee189843b34f01bc81a14a86396141. The materials, collected data, and data analysis are freely available to readers through the same link.
20-month-olds were tested in the lab; 3-to-4-yearolds were tested in a public preschool in Paris. This study was carried out in accordance with the recommendations of our local ethics committee (i. e. , Comité d'éthique de la recherche en santé -CERES, Paris), with written informed consent from all parents of our participants. All parents gave written informed consent in accordance with the Declaration of Helsinki. Our protocol was also approved by the Comité d'éthique de la recherche en santé -CERES, Paris. An additional eighteen 20-month-olds and six 3-to-4-yearolds participated but were not included in the final analysis due to fussiness during the experiment (6 toddlers, 1 preschooler), technical problems (3 preschoolers), exposure to other languages than French at home (1 toddler, 2 preschoolers), crying during the experiment (2 toddlers), or because of missing eyetracking data representing more than 50% of unusable test trials (9 toddlers).
FUNDING This research was supported by a postdoctoral fellowship from the  Fyssen Foundation  to AdC and to MB. It was also supported by grants from the  National Institute of Child Health and Human Development  (Grant  R01HD37507  to JT and Grant  HD083310  to SW),  Fondation de France , and the  ANR  ( ANR-13-APPR-0012   LangLearn ,  ANR-17-CE28-0007-01   LangAge , and  ANR-17-EURE-0017  FrontCog).
Design Between October 2013 and January 2014, students were recruited as follows: for the Rouen group during their medical check-up. The study sample consisted of replies from 731/923 students in the Rouen group yielding a 79% response rate. The study design has been approved by the "Commission Nationale de l'Informatique et des Libertés" (The French Electronic Data Protection Authority) and Rouen University Hospital's Institutional Review Board without mandatory informed consent.
Data collection Rouen University students were invited to take part in a cross sectional study during a health forum on campus, by email, or during their mandatory medical survey at the university medical department. The students filled a confidential self-administered online form on the TS1C website or a paper questionnaire. The questionnaire takes 20 minutes to complete. All questions in the online questionnaire were compulsory. The online questionnaire was created with compulsory answers, meaning the student could not continue with the questionnaire if a previous question remained unanswered.
Ethics approval and consent to participateThe study design has been approved by the "Commission Nationale de l'Informatique et des Libertés" (The French Electronic Data Protection Authority) and Rouen University Hospital's Institutional Review Board without mandatory informed consent (Clinical Trials: ID: NCT03218670). -Consent to publish The authors consent to publish -Availability of data and materials Data base eating disorder and irritable bowel syndrom university students [Data set]. Zenodo. http://doi. org/10. 5281/zenodo. 1051103 -Competing interests.
- Ethics approval and consent to participateThe study design has been approved by the "Commission Nationale de l'Informatique et des Libertés" (The French Electronic Data Protection Authority) and Rouen University Hospital's Institutional Review Board without mandatory informed consent (Clinical Trials: ID: NCT03218670). -Consent to publish The authors consent to publish -Availability of data and materials Data base eating disorder and irritable bowel syndrom university students [Data set]. Zenodo. http://doi. org/10. 5281/zenodo. 1051103 -Competing interests.
The main objective of the study is to analyse the role of childhood experience (along with other covariates) in determining the frequency of forest visits. The study is conducted using survey data from nine European countries including Austria, Czech Republic, Denmark, France, Germany, Poland, Slovakia, Switzerland, and United Kingdom. It aims to address three research questions: 1) How does childhood forest experience (i. e. frequency of visits and/or type of residential settlement during childhood) influence the frequency of forest visits at adulthood? 2) What is the role of other determinants of forest visitation frequency? and 3) To what extent does adulthood frequency of forest visitation varies among the nine European countries? It is hypothesised that childhood forest experience plays a significant role in determining the level of forest visitation frequency at adulthood.
Survey and sampling strategy The source of data for the present study is a web-based questionnaire survey conducted in nine European countries. The survey has been undertaken from January to February 2017 by professional survey companies operating in the respective countries, using a self-interviewing survey mode. The main purpose of the survey was to analyse preferences for different attributes of forest sites in these countries. A central part of this questionnaire is built around questions regarding current forest recreation practices and childhood experiences. The surveying companies in the respective countries selected adult respondents from their online pool of respondents. The companies followed a 'quota-based' sample selection procedure that ensured representativeness in terms of gender, age, and education.
Acknowledgements First, the authors acknowledge the contribution of anonymous reviewers providing critical comments and helpful suggestions that make the message of the paper stronger. The first author acknowledges the funding from the  Education, Audio-visual, and Culture Executive Agency (EACEA)  of the  European Commission  through  Erasmus Mundus Action 1 Doctoral Fellowship  as part of the  Erasmus Mundus Joint Doctoral Program ''Forests and Nature for Society" (FONASO). The authors affiliated with Centre for Macro-ecology, Evolution and Climate acknowledge the funding from the  Danish National Research Foundation  (grant number  DNRF96 ). This research has also been supported by the  Czech Science Foundation  grant No. 19-26812X  (M. S. ) as well as by the  Gregor Louisoder Environmental Foundation (Munich, Germany)  (M.
0000388FWA00005831) and by the National Commission on Informatics and Liberty (CNIL no. 908450 and no. 909216). Electronic informed consent was obtained from all participants. The NutriNet-Santestudy is registered in ClinicalTrials. gov (NCT03335644). All questionnaires can be accessed at https://info. etude-nutrinet-sante. fr/en/node/11. The data of the study are protected under the protection of health data regulation set by the CNIL; however, they are available upon request for review by the steering committee of the NutriNet-Santestudy. Analyses were hypothesis-oriented based on the relationship between dietary scores and weight gain or prospective occurrence of overweight or obesity. A single non-prespecified analysis was performed to investigate an unexpected result. All methods have been described in line with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement (see S1 STROBE Checklist).
Dietary data Participants in the NutriNet-Santecohort provide, at baseline then twice a year, 3 non-consecutive 24-hour dietary records assigned over a 2-week period. The 3 recording days are randomly assigned to 2 weekdays and 1 weekend day to account for intra-individual variability in intake. All food and drink consumption throughout the entire day (midnight to midnight) was recorded by participants via a dedicated online platform providing a food browser (grouped by category) and a search engine that allows searching for any food by name. Participants declared consumed amounts as absolute units when known (in grams or milliliters), using common household measures, or using generic food portion size from previously validated pictures  [24]. Consumptions were weighted according to weekday versus weekend day, and daily energy and nutrient intakes were computed using validated and constantly updated composition tables including more than 3,000 food items  [25].
Outcome data Height and weight were self-reported at enrollment and at least yearly thereafter using a webbased anthropometric questionnaire  [31]. Participants were asked to assess these data during a medical or occupational health examination by a physician, or by self-measurement using standardized procedures (on flat surface, lightly dressed, and without shoes). Although selfreported, these data have been validated against clinical measures, with intraclass correlation coefficients of 0. 94 and 0. 99 for height and weight, respectively, and a correct BMI classification in 93% of cases  [32]. BMI (kg/m 2 ) was computed by dividing weight by height squared. Overweight was defined as having a BMI � 25 kg/m 2 , and obesity as having a BMI � 30 kg/ m 2 , following the World Health Organization reference values  [9].
Sample selection Participants were drawn from the NutriNet-Santecohort (N = 138,014). Data used in the present paper were based on participants included between 7 March 2009 and 12 December 2014 and followed up until 30 September 201 at the most. A detailed flowchart is presented in Fig 1. Exclusions led to a working sample of 54,089 participants, and analyses on overweight and obesity were performed on 32,954 and 44,026 participants without overweight and obesity at baseline, respectively. To assess potential selection bias, the working sample was compared to the whole NutriNet-Santecohort regarding sociodemographics. We also conducted a sensitivity analysis on a subpopulation ("sensitivity subpopulation") excluding any participant who had a cancer diagnosis during follow-up (N = 1,981), bariatric surgery (N = 90) or an eating disorder (N = 2,153), or extreme BMI (as per the first percentile, N = 790).
Fixed effects of dietary scores (in quintiles and continuous), time, and their interaction were entered in the model, with a random effect for participant and time. BMI and energy intake without alcohol were considered as their natural logarithm to improve model fit, and models' residuals were graphically tested. Time was calculated as the difference between the date of an anthropometric questionnaire and the baseline date. Spatial power law SP(POW)(time) from SAS MIXED procedure was used because the correlation changed as a function of time, since the time interval between each questionnaire was not constant. Different variance-covariance matrix structures were tested, and the best one (unstructured) was selected based on AIC. Models were first adjusted for sex, age, energy intake without alcohol, and number of completed 24-hour dietary records, with second-and third-order interactions between score, time, and sex (model "m0"), and then further adjusted for height, month of inclusion (in order to take into account potential seasonal effects), physical activity (in metabolic equivalents  [36] ), occupation (8 categories as per the French National Institute of Statistics and Economic Studies classification  [37] ), smoking status (non-smoker, former smoker, or smoker), educational level (primary, secondary, or university), monthly income (�1,800 /CU, 1,800-2,700 /CU, or >2,700 /CU), and cohabiting status (model "m1").
The data were thus left-truncated and right-censored. Linear trend across dietary score quintiles was tested by assigning the median value in each quintile, which was then entered as a continuous variable in the model. Models were adjusted as in the multilevel models, except that age is already handled in the timescale, and that interaction with sex was not significant and was therefore removed. Each continuous-adjustment covariable was corrected for log-linearity using restricted cubic splines with 3 nodes  [40]  using the rms package for R  [41]. The proportional hazard assumption was tested graphically using Schoenfeld residuals and the Grambsch-Therneau test  [42]. All analyses were performed in men and women altogether as no interaction with sex was detected. As a sensitivity analysis, we replicated these Cox models within the sensitivity subpopulation (excluding any case of cancer, bariatric surgery or eating disorder, or extreme BMI), with the same methodology.
Acknowledgments The authors warmly thank all the volunteers of the NutriNet-Santecohort. We also thank  Younes Esseddik , Frederic Coffinieres,  Thi Hong Van Duong ,  Paul Flanzy ,  Regis Gatibelza ,  Jagatjit Mohinder , and  Maithyly Sivapalan  (computer scientists);  Cedric Agaesse (dietitian) ;  Julien Allègre ,  Nathalie Arnault ,  Laurent Bourhis ,  Veronique Gourlet, PhD , and  Fabien Szabo de Edelenyi, PhD  (data-managers/biostatisticians);  Roland Andrianasolo, MD , and  Fatoumata Diallo, MD  (physicians); and  Nathalie Druesne-Pecollo, PhD  (operational coordinator), for their technical contribution to the  NutriNet-Santestudy.
A "Savanturiers de l'ingénierie" project We will illustrate our point with an example of a Savanturiers de l'ingénierie" project. Paragraphs written in italics are related to work products, inputs or outputs of an engineering step. Paragraphs written in normal font discuss steps activities. Statement of work. A class of 7-9 year old pupils study the language that bees use to transmit information on the foraging areas. When an exploratory bee has found a food source, it is able to convey different information to its fellow bees with a dance called the waggle dance. For the project, we are interested in two pieces of information: the orientation and the distance of the food source in respect to the hive. This information will allow other bees to go to the food source and forage there. The project aims to reproduce the bees' behavior using mbot robots and Scratch programming.
Hence the project was divided into three different sub-projects, each of them coded separately. Coding sub-problems was too hard for almost all the pupils. Mathematical notions such as angle and distance are seen at the end of the primary school curriculum and thus were missing. Compass programming in Scratch is difficult and compass calibration is tricky. In the best case, some pupils correctly reproduced the Scratch code provided to solve sub-problems but most of them failed. Savanturiers projects are not intended to be assessed as other school work, but presented and explained to other pupils, parents and teachers. Because it was the first year we had performed Savanturiers projects, we did not organize such a presentation.
The difficulty in learning design Computer Science in primary school is strongly influenced by constructionism, the theory of learning developed by Papert and Harel, who sees learning as a process where the learner builds his knowledge interacting with the object of study  [1] , hence the idea that we teach programming through the construction of programs, ie a consciously hands-on approach. It is difficult to convey complex algorithms to children, whereas we can get them to manipulate parts of complex programs. One goal of the Scratch online community, according to Brennan and Resnick, is to support young designers in reusing and remixing, by helping them to come up with ideas and code that will serve as a basis for creating things which are much more complex than they could have created on their own  [2].
Student support of the project. The solution was deployed in the classroom during several training sessions. In the first session, the two pairs of students were made aware of the level of attention and understanding of a class in a priority education area. Students trained pupils to take a single measurement, display a value and compare the value to a threshold. The students also interviewed the class teacher on her project and had a better idea about the project difficulties and what kind of artifacts could work for pupils. The second session was held the following week with a pair of students, where pupils coded a small Scratch program performing noise or gas measurements. They also worked on the concept of threshold and alert, but it was too difficult for them. The two last sessions took place at the end of the school year with three students.
It led students to understand what was required and students also trained teachers to program a Caesar cipher using Scratch and to exchange messages between mbot robots. During a session at the school, a pair of students trained the primary school pupils in the same techniques. The workshop at the college mobilized the project mentor and four volunteer Mathematics students and required 24 robots. Each primary school pupil was paired with a sixth grade pupil and each child had a robot that allowed him or her to send and receive the encrypted messages. Encryption and decryption were done using Scratch programs made by the pairs of computer science students.
Designs 2019, 3, 53 3 of 17 Ease allowance is also one of the important features discussed in a few publications. For example, a model of ease allowance for garments based on a 3D reverse procedure was introduced  [22]. The body morphology analysis made it possible for a garment to be virtually designed. They concluded that the results were useful to design a customized garment on a virtual body for the ready-to-wear sector. Also, the morphology data has been applied in garment design. For instance, a new set of variables was defined regarding stature, crotch and leg length of body measurements and the evaluated parameters for each joint in the body skeleton  [23]. The database was created based on the measurements to apply in shopping via the internet. The body was deformed according to the measurements and the garments were fitted on the deformed body for tight, loose and freely float clothes around the body.
They tried to virtually fit a pattern on a pregnant body considering that good fit requires a proportional balance between body and garment pattern. They suggested that the patterns changed with respect to the body changes during pregnancy although they have not increased consistently. The main drawback of their work was that the sleeveless sheath dress was not closely fitted to the body in the abdomen to the hip area. Furthermore, the lower body shape of late pregnant women in the Shanghai area of China was investigated  [31]. The data of different pregnant women have been collected and analyzed. The pregnant women with different body changes were categorized and it was concluded that this data would be practical for the pattern design of maternity dresses and industries involved. A comfortable garment had to be designed for the maternity period with the potential of shielding electromagnetic radiation as this was the ultimate goal of our research.