dataesr/scientific-paragraphs-categorization huggingface dataset:
3 months; sd = 2. 88) with no missing literacy skills data for either session. However, not all the environmental data are available for each child. To ensure that the 129 children for whom we had no socioeconomic data were comparable to the other 2,638 children in the analysis of the influence of environmental variables, we conducted means comparisons tests on cognitive variables at age 4, and literacy variables at age 6. The results show that the 129 children differed significantly only on listening comprehension (LC) at age 6 (see Appendix B). We examined the distribution of these 129 children and the 5 SES categories across the 5 profiles (see Appendix C) and found that these children are similarly distributed across the profiles. This suggests that the data for 129 children and the 2,638 children are comparable.
We calculated a global score with both the 1y-total and 3y-total scores. If HLP data were obtained only at 3y and not at 1y, we retained them; but if HLP data were provided only at 1y but not at 3y, we discarded them. Finally, in order to estimate the quality of HLP, we calculated an inverse score from the raw scores so that the low scores corresponded to poor HLP and the high scores to rich HLP (m = 6. 19; sd = 1. 4; range: 4-14. 3). Pairwise correlations indicate significant coefficients between the mother's and father's HLP scores at 1 year (r = 0. 26), between the 1y-mother scores Frontiers in Education 06 frontiersin. org and 3y-referent scores (0. 24) and between the 1y-father scores and 3y-referent scores (0. 14). Parents' educational level (PEL; 1y). The International Standard Classification of Education (ISCED; UNESCO, 2012) was used to classify the educational levels of the mother and father separately.
Review Ke et al. conducted a systematic review of research papers on Developmental Coordination Disorders (DCD) in past decades. They noted that since the unification of the DCD definition in 2013, the number of published related research papers has been steadily increasing, particularly in developing countries. These papers come from different disciplines, mainly including cognitive neuroscience, psychology, clinical medicine and physical education, focusing on different aspects of DCD research. Over the last 3 years, research in this field has increased interest in neurodevelopmental mechanisms, perinatal factors, proteomics and paid more attention to the combination of multidisciplinary and multimodal data. As a result, some large cohort study databases are increasingly incorporating such comprehensive data.
Participants The software G*Power  (Faul et al. , 2007)  was used to a priori calculate the least required sample size, based on procedures suggested by  Beck (2013). Values for α were set at 0. 05 and power at 0. 80. Based on a study with similar paradigm  (Graja et al. , 2021)  and discussions between the authors, effect sizes were estimated as 0. 39. In total, to reach the desired power, data from at least 10 participants were deemed to be sufficient to minimize the risk of incurring a type 2 statistical error. Eleven professional male football players (age: 19. 27 ± 0. 9 years. ; height: 1. 79 ± 0. 04 m; body mass: 70. 49 ± 3. 97 kg; BMI: 21. 81 ± 1. 59 kg/ m 2 ) from the Tunisian league2 team volunteered to participate in this study. After receiving a description of the protocol, risks, and benefits of the study, each volunteer provided written informed consent prior to participation.
Dietary intake analysis The participants were required to record their food and nutrients intakes in a diary over a span of 3 days for each week of physical testing, which was completed with an interview by an experienced dietician. Dietary records were analyzed by the same dietician using the Bilnut program (Nutrisoft, Cerelles, France) and the foodcomposition tables of the National Institute of Statistics of Tunis (1978) 1 using a computerized nutrition system, the NUTRISOFT-BILNUT (Version. 2. 01, Paris, France).
The profile of mood states The profile of mood states (POMS) questionnaire developed by  McNair (1971)  was used to collect data about the mood of 1 https://data. bnf. fr/fr/11864527/institut_national_de_la_statistique_ tunisie/?fbclid=IwAR14-99nieeV572ADx3ZXqxsK46vY4KL9hPnJZQ8cBV3HP JPFt6NgzJlw3U participants during each test session. It is a self-report questionnaire consisting of 65 items that aims to measure different emotional conditions (including five negative moods (tension-anxiety, depression, anger-hostility, vigor-activity, fatigue and confusionbewilderment), one positive mood (vigor) and interpersonal relationships) and a total mood disturbances score (TMD) for which all scales, except interpersonal relationships, were taken into account [TMD = (Tension + Depression + Anger + Fatigue + Confusion) -Vigor].
Statistical analyses Statistical analysis was performed using STATISTICA Software (StatSoft, France) and figures were created using GraphPad Prism 8 (GraphPad Software, San Diego, CA, United States). The Shapiro-Wilk test of normality revealed that the data were normally distributed; therefore, parametric tests were performed. PSQI and data of dietary intake (the average of the3 days) were analyzed using a one-way ANOVA [5 (testing periods)]. Data of (Oral temperature, body mass, BMI, POMS, Wingate test, HR and RPE) were analyzed using a two-way ANOVA [5 (Ramadan) × 3 (time-of-day)] with repeated measures. A three-way ANOVA for repeated measures was performed to determine the effects of time of day, Ramadan and exercise on the cognitive function (SRT, CRT, MRT and selective attention). To assess the ANOVA practical significance of data, effect sizes were calculated as partial eta-squared ( p 2 ).
Study limitations and implications for future research The current study presents some limitations that need to be considered. The lack of non-fasting controls is typical of most studies of Ramadan, and reflects ethical issues in Muslim majority countries such as Tunisia. In such environments, it is unfortunately not possible to recruit non-fasting study participants. Therefore, in such cases, RF measurements are frequently compared to the pre-Ramadan baseline as reference values, as usually reported in the literature. Furthermore, sleep measurements were recorded only subjectively using the PSQI. Highlighting potential circadian disruptions through actigraphy or polysomnography is a more powerful tool to monitor sleep behavior. This may be an important aspect for future investigation. Finally, the lack of data on blood glucose measurements can be considered as another limitation of the present investigation.
72  Thus, Hilbert agrees with Frege in the rejection of Gauss. Empirical data cannot falsify geometrical axioms. Even if we measure triangle sums smaller than π , we are not forced to accept hyperbolic geometry. However, unlike Frege, Hilbert admits that empirical data influence our choice of an axiom system for geometry. 73 He, therefore, rejects the Fregean idea of a hierarchical order of geometry and physics. This shows that Hilbert and Frege have different convictions regarding not just geometry but axiomatic theories in general. As seen in Sect. 3. 2, Frege claims that geometrical axioms cannot be refuted by physical experiments because they express truths about the knowledge source (Anschauung) on which physics also rests. For Hilbert, Anschauung is not a knowledge source for the axioms but an object of axiomatic analysis, which we have to presuppose to gain a "picture" of reality.
, USA We used the SPM8 software (http://www. fil. ion. ucl. ac. uk/spm/) for statistical analyses of functional data with a standard pipeline. We first corrected for head movements between scans by an affine registration and realignment to the mean of all images. The anatomical image was spatially normalized on the T1 template. The functional images were also normalized to the EPI template, which were thereby transformed into standard stereotaxic space and resampled with a 3 × 3 × 3 mm voxel size. The normalized images were spatially smoothed using an 8 mm full-width at half-maximum (FWHM) Gaussian kernel. We used the general linear model (GLM) framework implemented in SPM to analyze our data. We modeled the convolved standard hemodynamic response function with a delta (or "stick") function at the onset of the preparatory phase (appearance of white triangle -PREP regressor), at the onset of the joystick movement (start of movement -MOV regressor), and at the onset of the response screen ("yes" vs.
All second-level results are reported at a significance-level of p < 0. 05 using cluster-extent family-wise error (FWE) correction with a voxel-height threshold of p < 0. 001. In Figure  3 , activations are displayed at a cluster-size threshold of 30 voxels, using MRIcroGL (http://www. cabiatl. com/mricrogl/). Data and analysis scripts from this study will be made freely available upon acceptance.
Outdoor recreation provides vital interactions between humans and ecological systems with a range of mental and physical benefits for people. Despite the increased number of studies using crowdsourced online data to assess how people interact with the landscape during recreational activities, the focus remains largely on mapping the spatial distribution of visitors or analyzing the content of shared images and little work has been done to quantify the perceptions and emotions people assign to the landscape. In this study, we used crowdsourced textual data from an outdoor activity-sharing platform (Wikiloc), and applied Natural Language Processing (NLP) methods and correlation analysis to capture hikers' perceptions associated with landscape features and physical outdoor activities. Our results indicate eight clusters based on the semantic similarity between words ranging from four clusters describing landscape features ("ecosystems, animals & plants", "geodiversity", "climate & weather", and "built cultural heritage"), to one cluster describing the range of physical outdoor activities and three clusters indicating hikers' perceptions and emotions ("aesthetics", "joy & restoration" and "physical effort sensation").
The association analysis revealed that the cluster "ecosystems, animals & plants" is likely to stimulate all three identified perceptions, suggesting that these natural features are important for hikers during their outdoor experience. Moreover, hikers strongly associate the cluster "outdoor physical activities" with both "joy & restoration" and "physical effort sensation" perceptions, highlighting the health and well-being benefits of physical activities in natural landscapes. Our study shows the potential of Wikiloc as a valuable data source to assess human-nature interactions and how textual data can provide significant advances in understanding peoples' preferences and perceptions while recreating. These findings can help inform outdoor recreation planners in the study region by focusing on the elements of the landscape that peoples perceive to be important (i.
Materials and methods Our study aims to understand hikers' preferences and perceptions during an outdoor experience using crowdsourced textual data from Wikiloc. To achieve our goal, we used the Auvergne region in France as a case study. Auvergne is a mountainous region with volcanic massifs ranging from 159 to 1874 m above sea level. The 26,000 km 2 region is covered by a mix of cropland, forests and grassland livestock farming systems. Auvergne is mostly rural, with urban areas only representing 3% of its area (Corine Land Cover, 2018: https://land. copernicus. eu /pan-european/corine-land-cover/clc2018). It also contains four regional nature parks, a UNESCO heritage site and diverse archaeological and historical sites (Supplementary Materials, Fig. S1 ). These various biophysical and cultural characteristics make Auvergne an attractive region for hiking.
We adopted a methodology based on a data-driven approach  (Sebei et al. , 2018)  consisting of three main steps: data collection, data processing and data analysis (Fig. 1 ). The collection of Wikiloc data in Auvergne was done in a Python environment (script available at this link: https://github. com/achaiallah-hub/Wiki4CES). Data processing, analysis and charts were done in R, version 4. 1. 0 (R Core Team, 2021).
Data collection Wikiloc offers over 14 million hiking trails and has increasing popularity with the number of users increasing from 3. 5 million in 2019 to over 11. 3 million in 2023 (https://www. wikiloc. com). The Wikiloc platform allows users to record and share outdoor trails, providing associated textual descriptions, photographs, and their spatial location (latitude and longitude) on the trail. The type of outdoor activity is also recorded (hiking, cycling, or running), as well as the date the trail was made. Wikiloc has a broad user base and potential as a data source for diverse CES  (Callau et al. , 2019; Chai-allah et al. , 2023; Norman and Pickering, 2019). While the trails and photographs from this platform have been used in CES studies  (Callau et al. , 2019; Chai-allah et al.
Data processing To identify the landscape features, physical outdoor activities, and the perceptions mentioned by hikers during their outdoor experience, we extracted the most frequent single words (unigrams) from Wikiloc posts. The extraction of unigrams consisted of three steps: text translation, data pre-processing, and word frequency counting. First, since the text description included five different languages (French, English, Dutch, Italian, and Spanish), we translated all text descriptions into English using DeepL (https://www. deepl. com). Second, data preprocessing consists of using natural language processing (NLP) methods to perform automated text mining in R, primarily using the packages tidytext  (Silge and Robinson, 2017)  and tm  (Feinerer and Hornik, 2018)  for tokenization, lemmatization and English stop words removal based on a predefined list of common English words (e.
Word2vec is a widely known word embedding technique (e. g. ,  Mikolov et al. , 2013; Naili et al. , 2017) , and has been a successful tool for investigating CES from social media data (e. g. ,  Gugulica and Burghardt, 2023). Word2vec can be carried out in two ways: by creating and training your own word2vec space based on two different architectures, namely, Skip-gram, which predicts the target word based on a nearby word, and Continuous Bag-of-Words (CBOW), which learns to predict a target word according to its context  (Mikolov et al. , 2013) , or by using pre-trained models (e. g. ,  Baroni et al. , 2014). Training a new custom word2vec model requires a very large dataset, increases the computational effort, and could be biased by the geographic origin of the data. Therefore, we performed the semantic analysis using the pre-trained baroni word2vec model provided at http://www.
lingexp. uni-tuebin gen. de/z2/LSAspaces  (Günther et al. , 2015). This model has been demonstrated to generate high-quality performance with dense word vectors and to produce the best results regarding semantic similarity tasks  (Baroni et al. , 2014). This space was created using the CBOW algorithm and contains vectors for 300,000 different words, covering a broad variety of different topics. It was trained from a 2. 8-billion-word corpus, a concatenation of the ukWaC corpus (web pages material from. uk domain;  Baroni et al. , 2009) , Wikipedia, and the British National Corpus  (BNC Consortium, 2007). The cosine similarities of the extracted high-frequency words from our corpus were then carried out using the baroni word2vec space and the LSAfun package in R  (Günther et al. , 2015). The package was originally written for LSA, but can be used with all vector space models and its combination with pre-trained spaces was reported to produce adequate results  (Günther et al.
, 2015). Cosine similarity is measured as the cosine of the angle between vectors, with values that range from 0 to 1, with 1 indicating total similarity and 0 representing total dissimilarity. Finally, the similarity matrix was transformed into a distance matrix by calculating the cosine dissimilarity (1 -cosine similarity) in order to be used in hierarchical clustering. The hierarchical clustering was made using Ward's distance method and the fastcluster R package  (Müllner, 2013). The optimal number of clusters was determined by the Gab statistic method  (Tibshirani et al. , 2001) , indicating k = 8 as an optimal number of clusters (Supplementary Materials, Fig. S2 ). The results of word clustering were manually verified to ensure the accuracy of the clusters and the list of words per cluster was used as a basis for labeling the clusters following the existing literature on CES and human-nature interaction  (Kaiser et al.
Clusters association To examine the perceptions that hikers associate with landscape features and physical outdoor activities, the Phi coefficient was carried out through the widyr package in R  (Silge and Robinson, 2017). The Phi coefficient is a measure of binary correlation between two categorical variables, which indicates how often they appear together relative to how often they appear separately. Here, it was used to indicate the number of documents (the number of trails) where, for example, clusters A and B appear, or neither do, compared to the number of documents where one appears without the other, considering eq. 1: ϕ = n 11 *n 00 -n 10 *n 01 ̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅ ̅ (n 1. *n 0. *n. 0 *n. 1 ) √ (1) Where n 11 is the number of documents with both clusters A and B, n 00 is the number of documents with neither clusters A and B, n 10 is the number of documents with cluster A but without cluster B, n 01 is the number of documents with cluster B but without cluster A, n 1.
Data collection and preprocessing A total of 978 hiking trails were associated with textual data, corresponding to 86. 7% of the collected data, with an average text description length of 14 words (SD = 28. 6). Half of the trails were recorded in the summer months from June to August (481 trails), and 34% of the trails fell on weekends (Supplementary Materials, Fig. S3 ). The maximum number of trails uploaded by a single user was 30, while 207 users uploaded only one trail over the four-year study period. Moreover, 183 unique words occurred at least three times in total and in a positive context, with a cumulative frequency of 3553. The words "view", "trail" and "walk" were the most frequent unigrams and no negative words such as "sad" or "boring" were found in the final dataset (Supplementary Materials, Fig.
Wikiloc as text-based social media The current study suggests Wikiloc as a rich source of textual data to study human-nature interactions. The platform has the potential to solve the issue of data availability rising with the decreasing popularity of Flickr, the principal data source for social media-based studies, restrictive access to data for research purposes (like in Instagram and Twitter), or even ceased operating (e. g. , Panoramio)  (Ghermandi et al. , 2023). Moreover, Wikiloc textual data presents multiple advantages over most text-based social media sites by being geolocated and without a character limit (e. g. Reddit posts are not geolocated and Twitter has a 280character limit)  (Fox et al. , 2021b). This has allowed us to assess a large spectrum of hikers' preferences and perceptions.
Limitations and future research Although this study demonstrates that hikers' preferences and perceptions can be achieved by combining textual data from Wikiloc and NLP methods, several caveats need to be considered. First, demographic and sample biases with the socio-demographic information of social media users are never entirely clear, difficult to obtain and can change following the popularity of the platforms, which keeps the question of data representativeness challenging. In a recent study,  Venter et al. (2023)  found that despite the increased proportion of total recreationists that Strava-an outdoor activity-sharing platform similar to Wikiloc-represents over time, it is biased toward higher-income populations, males, and middle-aged people between 35 and 54 years. Such an analysis must be extended to Wikiloc and considered in future studies.
While this automatic translation might have enabled us to unify the dataset and have more compact clusters considering the multiple languages found in the original dataset, it may have masked subtle linguistic delicacies and metaphorical expressions in different languages. Future studies would do well to include the linguistic distinctions between social media users so that we can explore how different language communities perceive landscape features during recreational activities. Yet we need to emphasize that word-sense ambiguity issues caused certain misclassification but have occurred very sporadically and were easily noticeable in the manual checking. However, these NLP algorithms are expected to improve in the future, primarily by including textual data from social media  (Gugulica and Burghardt, 2023)  or applying the emerging transformer-based models, for example, the Bidirectional Encoder Representations from Transformers (BERT) that have been shown as a promising tool for text classification  (Devlin et al.
, 2018; Hunter et al. , 2023). Fourth, while social media data has the advantage of being spontaneously produced, people are more likely to share what they like and think is worth sharing, resulting in more sharing of positive experiences over negative ones  (Hausmann et al. , 2020; Wan et al. , 2021) , and hence creating an overall positivity bias and incomplete image of humannature interactions. Future research should combine crowdsourced data analysis and online surveys with users of the studied platforms to get a more exhaustive and complete understanding of human-nature interactions. Finally, we used only one outdoor activity-sharing platform, Wikiloc, and focused on one recreational activity, hiking. This potential bias can be minimized by including other platforms and exploring how other user groups such as runners or bikers interact and perceive landscape features.
Conclusion Our work complements previous research with three major contributions. First, we analyzed hiking data from Wikiloc, a crowdsourced sports platform, which provides access to a more homogenous segment of the population because users share similar interests in outdoor activities. It offers the opportunity to assess how one or multiple user groups of recreationists interact with nature which is hardly possible in other common platforms used in CES assessment such as Flickr. Second, we explored textual data that allows access to users' feelings and perceptions, which were scarcely studied in the literature as the focus was on image content. This process has the potential to enhance the current understanding of what landscape features mean for people by identifying those that people find to be important and therefore to be considered in landscape management.
Acknowledgements This work was financially supported by the  French government 's national research agency (ANR) and its " Investissements d '. NFs contribution to the manuscript was supported by the  Eric and Wendy Schmidt AI in Science Postdoctoral Fellowship , a  Schmidt Futures program. FGs contribution was supported by  DFG Emmy-Noether grant  "What's in a name?" (project nr. 459717703 ). We thank  Anne de la Foye  for her help with  R  and  Valentin H. Klaus  for commenting on an earlier version of this manuscript.