dataesr/scientific-paragraphs-categorization huggingface dataset:
(b) Summarizing disparity using disparity indices Most disparity datasets are multidimensional and, consequently, a large component of any disparity analysis involves considering how to extract a meaningful (i. e. interpretable) summary of disparity (figure  2 ). This summary is usually achieved with a disparity measure or index  [30]. As with any summary of multidimensional data, disparity indices will reflect only some aspects of the morphological variation, never its whole complexity  [46]. It is, therefore, often beneficial to use more than one index to summarize different aspects of variation, guided by the aim of the study. When considering only one dimension, disparity indices can be used to compare the spread of distributions (e. g. the range, quantiles or variance) or the differences in the central tendencies (i.
(c) Testing for differences in disparity No matter which disparity indices have been calculated, the research question must be framed in an appropriate statistical context. The multidimensional statistical toolkit for ecology and evolution has greatly expanded in recent years  [53, 54] , but some of these advances have yet to be implemented in disparity analyses. Instead, hypothesis testing has been mostly confined to a small set of well-established methods. One commonly used test is the non-parametric permutation analysis of variance (PERMANOVA)  [55] , which tests whether two groups share the same centroid and dispersion based on a distance matrix between observations. The past Illustration of the relationships between the different morphospaces and visualization of the same dataset (the 'penguin' dataset of  [44, 45] ).
However, bootstrapped data are pseudoreplicates and thus non-independent and can increase the false positive rate (Type I error)  [58]. This, again, highlights the importance of tailoring the statistical test to the data and question at hand. Finally, it is important to understand the limitations of the dataset for performing statistical analysis. Mainly, disparity analyses should be restrained to groups within the same morphospace and are more difficult between different morphospaces. This can be the case when comparing elements with different numbers of landmarks or different landmark configurations that will result in different morphospaces; comparing disparity indices between these is not trivial.
(d) Disparity and phylogeny As with all comparative datasets, the data used in disparity analyses are not independent because close relatives will tend to have more similar morphologies than more distant relatives  [59]. Thus, for disparity analyses that consider groups with phylogenetic relationships (which is common), the nonindependence between observations should be taken into account. It has been noted, however, that some popular phylogenetic correction methods (like phylogenetic PCA) can be inappropriate, especially when using only the first d-axes of the ordination, and can lead to incorrect interpretations of the data (such as wrongly supporting 'early burst' type patterns;  [60] ). Furthermore, any use of phylogenies in disparity analyses must also carefully consider the underlying model of trait evolution.
Many of the problems in morphological disparity analysis arise from 'blind' application of established methodological pipelines without consideration of the biological question being addressed. We advocate that researchers should assemble their analytical protocol based on an experimental approach that explores the impact of competing methods, such as choice of indices, ordination method and ancestral state estimation method on disparity analysis results. Thankfully, this is becoming easier through the availability of diverse, welldocumented R packages for multidimensional analysis  [42, [65] [66] [67] [68]. Many of the methods employed in disparity analysis are used more widely in other fields, including genomics and ecology, which also encompass analyses of multidimensional datasets  [69] [70] [71] [72].
Figure2. Illustration of the relationships between the different morphospaces and visualization of the same dataset (the 'penguin' dataset of [44, 45] ). Morphospaces: different mathematical representations of a morphospace. A trait matrix can be an ordinated matrix (e. g. in [20] ) or transformed into a distance matrix (e. g. in [31] , not represented here). Here, we consider all these matrices as being morphospaces, i. e. objects containing all the combinations of traits and observations (albeit transformed differently). Visualization: different ways to represent the morphospace in 2D. Visualizations can use either trait plots (directly from the trait matrix); or ordination axis plots (directly from the ordinated matrix). Note that in 2D representations, it is good practice to plot both axes on the same scale to avoid visually distorting the importance of one axis.
Funding. A. G. was funded by  European Research Council Starting  grant no. 637171  ADaPTiVE; A. L. J. was funded by an  Irish Research Council Laureate  Award  IRCLA/2017/186 ; E. S. was funded by a  University of Adelaide  Research Fellowship; E. E. S. was funded by a  Leverhulme Trust Research Project  Grant (grant no. DGR01020 ); P. C. J. D. was funded by  NERC  (grant nos NE /P013678/1 ;  NE/ N002067/1 ) and  BBSRC  (grant no  BB/N000919/1 ); S. L. B. was funded by  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (grant agreement no. 756226 ,  ERC Starting Grant :  PalM ) and a  Leverhulme Trust Research Project  Grant (grant no. RPG-2017-167 ); T. G. was funded by  ARC   DP170103227  and  FT180100634  awarded to V. W. ; G. H.
As there were two datasets in this experiment (unlimited colour choice and best exemplar for each concept), we use the frequency data from the unlimited colour choice and best exemplar for each concept to explore consistency for tile-concept, lightness-concept, and category-concept associations. Illustrative visualisations for these data can be seen in Figure  6 , which shows contour plots for the eight 'Universal' concepts identified in Experiment 1 (see Supplementary Information Figure  1  for a full set of contour plots of the Experiment 2 data for all words and language groups). The contour plots show both clear universal tendencies in terms of the colours selected for specific concepts by participants in Experiment 2 as well as clear differences. For example, the contours for the concept 'excited' were very similar across languages in terms of the frequency with which different tiles were chosen and therefore the colour categories associated with this concept, but it is clear that the best exemplar differed considerably across language group with English participants selecting a much lighter hue than Chinese monolingual participants.
Lightness-concept association For the lightness-concept association analysis, the tiles were collated into three lightness ranges by dividing the WCS stimulus grid (Figure  1 ) into three sections. The first three rows (A, B, and C) were classified as 'Light' (Munsell values 8, 9, 9. 5), the middle four rows (D, E, F, and G) were classified as 'Medium' (Munsell values 4, 5, 6, 7) and the remaining three rows (H, I, and J) were classified as 'Dark' (Munsell values 1. 5, 2, 3). In this analysis, we were interested in which particular concepts were associated with a range of colour tiles that are consistently categorised as one of the three lightness range in both unlimited choice and best exemplar dataset. In other words, which concepts are specified to light, medium, and dark across both tasks? We chose to focus on concepts with less than 50% consensus to a specific tile in order to ensure that the majority of the associations were not just with one tile (i.
Colour category-concept association We used the colour naming data from two separate studies to label each of the 330 tiles in the WCS stimulus grid with one of the 11 basic colour terms (BCT). As for Experiment 1,  Berlin and Kay's (1969, 1991)  data were used to determine the name of each tile associated with a BCT (see Figure  2 ). Non-BCT tiles were defined as those tiles not belonging to any of the 11 BCT categories in Berlin and Kay's English colour naming data. Although most of the tiles selected by English, bilingual Chinese, and monolingual Chinese participants fell within one of Berlin and Kay's BCTs (73%, 75%, and 67. 5% respectively), some BCT tiles were selected only marginally more often than non-BCT tiles. This includes the English concept 'unclean' (51% of tiles selected were BCT tiles), and monolingual Chinese concepts, 'sweet and sour' (52%), 'bright' (51%), and fresh' (50%).
Next, we extended the colour categories by using  Lindsey and Brown's (2014)  colour naming data to identify the colour category of all those tiles in the WCS that were unnamed in Berlin and Kay's data. Thus, all tiles in the WCS were categorised into one of the 11 basic colour categories. This classification system was used to explore the frequency of colour-concept associations in relation to these broader colour categories. In this analysis, we were interested in (1) whether the choice of colour category (colour category  concept associations) was similar for the unlimited choice dataset and the best exemplar dataset; and (2) whether the most consistent concept-colour category association (Experiment 1) is similar to the most consistent colour category-concept association (Experiment 2).
To examine whether the participants' choice of colour category associated with a concept was similar or different across the unlimited choice and best exemplar datasets we conducted a likelihood ratio chi-square test (LRT) for each word on a two-way contingency table of colour category by dataset. The LRT assumes that colour responses to each word follow a multinomial distribution. We used a LRT, rather than a chi-square test of independence, as there were cells with zeros (i. e. for a given word there were some colours that were never associated with that word). There was no evidence of independence of the colour category choices for the best exemplar and unlimited choice datasets for any concept for any language group, thus, suggesting that there is consistency in the colour categoryconcept association across both datasets.
As both the unlimited choice dataset and the best exemplar dataset were not independent, we decided to use data from the best exemplar task to illustrate any universal, common, and cultural specific themes emerging from the categoryconcept association. Emerging themes in category-concept associations. To explore participants' patterns of responding on the 11 colour categories, as for Experiment 1, we ran a hierarchical cluster analysis based on proportional counts for the best exemplar dataset using correlation based distance. This started with each concept in its own cluster and then, using participants' best exemplar for each concept response, at each stage relaxed the criterion for separation so that the next most similar concept to the existing cluster was linked until all of the concepts were joined in a complete classification tree.
Conclusions We have created a large dataset of conceptual associations in English and Chinese bilingual and monolingual speaking populations through first identifying concepts that were strongly associated with a colour category and then reversing the process to identify colours that were associated with those concepts. The integration of both methods using the WCS stimulus grid meant that we were able to identify concepts that have the strongest association with a specific colour category, exemplar and lightness range in order to accurately predict the effects of colour on cognition and behaviour. It is clear that colour associations vary with culture and this should be borne in mind when trying to generalise findings of the current paper to other groups of people. Our findings suggest that using the right colours and lightness in a culturally nuanced way in contexts such as product marketing, education, web development, information visualisation (e.
Factor validity, reliability and invariance of the SPPCS A confirmatory factor analysis (CFA) with diagonally weighted least squares estimation 1 was performed to test the psychometric properties of the French version of the SPPCS in the whole sample and in females and males separately. This analysis was performed using lavaan software in R 3. 5. 2 (Rosseel, 2012). Considering all the data, the percentage of missing values was 1. 5%. In order to accommodate this, the listwise deletion was used. The initial estimation of the 13-factor model yielded a good fit to the data in the general population (e. g. ,  Hooper, Coughlan, & Mullen, 2008)   : χ2 (1299) = 2243. 26, CFI =. 98, RMSEA =. 04[. 038-. 043], TLI =. 98, and WRMR = 1. 19. The scale had good internal consistency in each dimension (see Table  1 ).
05 (Byrne & van de Vijver, 2010)  and, (b) the ∆CFI had a decrease higher than. 010 (Cheung & Rensvold, 2002)  with the ΔRMSEA had an increase higher than. 015 (Chen, 2007). French and Finch (2006)  have recommended to use chi-square difference test criterion in multiple-group confirmatory factor analysis with a large number of factors. Thus, this criterion was privileged. This analysis was performed using semTools and lavaan software in R 3. 5. 2 (Jorgensen et al. , 2019; Rosseel, 2012). Each model was tested with weighted least square mean and variance adjusted estimation. Results are presented in Table  5. Configural invariance model showed acceptable criteria and could be used as the baseline model. Metric invariance model demonstrated acceptable criteria. Scalar invariance model had good fit and was not rejected, as the strict invariance model.
To establish this list of dimensions representing the basis of the performance habits framework, general principles for mapping reviews were followed  (Miake-Lye et al. , 2016). Mapping reviews are particularly useful when there is a large diversity of research as a first step to a systematic review and to identify gaps within an area  (Cooper, 2016; Perryman, 2016). The database search 2  was directed to identify metaanalyses and reviews concerning factors influencing human performance, with the aim to derive higher-order dimensions of strategies improving performance that can be turned into habits. Eight main dimensions emerged from the visual map of the mapping review. First, we find the classical three dimensions identified by neuroscience to underpin performance  (Briguglio et al. , 2020) : eat (i.
The search was completed on January 13, 2020, with the Web of Science, all databases included, for the past 10 years (period 2010-2020). The following keywords were entered: "meta-analysis" or "review" in the "Title" field and "performance" in the "All Fields" field. All document types (full papers, proceedings papers, meeting abstract, etc. ) in English language were considered. All Web of Science categories were included, and.
34,654 unique results were retrieved. The results were exported to the reference software Zotero. From the titles and abstracts screening, SL and DK extracted higher-order themes matching the intended scope of the framework (i. e. , strategies to improve performance that can be turned into habits) and represented them on a visual map. The visual map was then refined with TH, FJ, and EM until consensus was reached. Frontiers in Psychology | www. frontiersin. org.
METHODS This was a cross-sectional study that analyzed, separately, two waves of data from participants enrolled in the Scleroderma Patient-centered Intervention Network (SPIN) COVID-19 Cohort. 15, 16 We used baseline data (Wave 1) for initial validation and item selection, and data from Wave 2 (two weeks later) for verification. The SPIN COVID-19 Cohort study was approved by the Research Ethics Committee of the CIUSSS du Centre-Ouest-de-l'Île-de-Montréal.
" The survey was anonymous with only information on country collected. A total of 121 people provided between 1 and 10 fears. Respondents were from Canada (N = 77), the United States (N = 8), New Zealand (N = 4), Australia (N = 2), France (N = 2), the United Kingdom (N = 2), Colombia (N = 1), the Netherlands (N = 1), the Philippines (N = 1), and Turkey (N = 1); country was not provided by 22 respondents. Mean number of item suggestions was 4. 0 (standard deviation = 2. 3); median was 4 (25th percentile = 2; 75th percentile = 5). Original survey data can be found at https://osf. io/ka43f/. 4 employed content analysis to categorize fears into common themes to support item development. 20 One investigator (BDT) initially read all responses and generated a set of initial item themes. Then, that investigator and 12 members of the research team together reviewed fears listed by 10 respondents, classified them into initial item themes, and discussed a coding approach.
To assess a Pearson's correlation with 95% confidence and a precision of 0. 10, a sample size of ≥ 403 is required for a correlation of 0. 30, and ≥ 275 for a correlation of 0. 50. 46 Based on sample size requirements for CFA and correlation analyses, the available number of patients 563 from the Wave 2 dataset was more than sufficient. EFA and CFA were conducted using Mplus 7, and all other statistical analyses were conducted using SPSS (Version 25).
Confirmatory Factor Analysis in the Wave 2 Dataset CFA was performed on the remaining 10 items to confirm the single-factor structure of the fear questionnaire using Wave 2 data. In the initial CFA, in which measurement errors between all items were specified as uncorrelated, model fit for the hypothesized single-factor model was suboptimal (χ 2 (35) = 541. 6, p < 0. 001, TLI = 0. 94, CFI = 0. 95, RMSEA = 0. 16). Inspection of the modification indices indicated that model fit would be improved if the error terms of Items 1 and 14, Items 9 and 10, and Items 12 and 13 were freed to covary. Items 1 ("I will become infected when I have to leave the house to get supplies or when supplies are brought to me") and 14 ("I will be infected with the virus") both measure fear of being infected with COVID-19.
Financial support The study was supported with funding from the  Canadian Institutes of Health Research (CIHR) ; the  McGill Interdisciplinary Initiative in Infection and Immunity Emergency COVID-19 Research Fund ;  Scleroderma Canada , made possible by an educational grant for patient support programming from  Boehringer Ingelheim ;  Scleroderma Society of Ontario ;  Scleroderma Manitoba ;  Scleroderma Atlantic ;  Scleroderma Australia ; Scleroderma New South Wales;  Scleroderma Victoria ;  Scleroderma Queensland ;  Scleroderma SASK ;  Scleroderma Association of BC. Drs. Wu  and Levis were supported by a postdoctoral fellowship training award from the  Fonds de recherche du Québec -Santé  (FRQ-S);  Dr. Henry  was supported by a  Mitacs postdoctoral  fellowship award; and  Mr. Harb  and  Ms.
58 [0. 32], t=8. 55, p<. 001) and the South Oaks Gambling Screen (SOGS)  47  (M[SD] gamblers: 8. 64 [4. 46], controls: 0. 21 [0. 54], t=8. 99, p<. 001). These scores were highly correlated in the gamblers (r=. 95) and were thus combined for correlational analyses into a single "addiction severity" score by averaging their respective z-scores. Due to an incidental finding during fMRI which may have impacted spatial normalization, one control participant was excluded from the imaging data analysis. This participant was however retained for all behavioral and modeling analyses. Participants were recruited via postings in local internet bulletin boards, and reported no history of neurological or psychiatric disorder except for depression. No participants were currently undergoing any psychiatric treatment.
Hierarchical Bayesian models Models were fit to all trials from all participants using a hierarchical Bayesian modeling approach with separate group-level Gaussian distributions for all parameters for controls and gamblers. Posterior distributions were estimated using Markov Chain Monte Carlo as implemented in the JAGS software package 50 (Version 4. 3) using the Wiener module for JAGS that implements the Wiener First Passage Time 51 distribution, in combination with Matlab (The MathWorks) and the matjags interface (https://github. com/msteyvers/matjags). For group-level means, we used uniform priors defined over numerically plausible parameter ranges (see Table  2 ). For group-level precisions, we used Gamma distributed priors with shape and scale parameters of. 001. For each model and group, we ran 2 chains with a burn-in period of 50.
Parameter recovery simulations We ran parameter recovery simulations to ensure that known parameters underlying the datagenerating process could be recovered using our modeling procedures. For this purpose, we simulated 10. 000 full data sets from the posterior distribution of the best-fitting models, randomly selected 10 of these simulated data sets, and re-fit the models to these simulated data. Parameter recovery was then assessed via two procedures. For subject-level parameters, we examined the correlation between generating and estimated parameters across all ten simulations. For group-level means and standard deviations, we examined whether the estimated 95% highest posterior density intervals contained the true generating parameter value.
FMRI data acquisition MRI data were collected on a Siemens Trio 3T system using a 32-channel head coil. Participants performed a single run of 60 trials in total (following a short break after completion of our previously reported task  30  ). Each volume consisted of 40 slices (2 x 2 x 2 mm in-plane resolution and 1-mm gap, repetition time = 2. 47s, echo time 26ms). We tilted volumes by 30° from the anterior and posterior commissures connection line to reduce signal drop out in the ventromedial prefrontal cortex and medial orbitofrontal cortex  53. Participants viewed the screen via a head-coil mounted mirror, and logged their responses via the index and middle finger of their dominant hand using an MRI compatible button box. Highresolution T1 weighted structural images were obtained following completion of the cognitive tasks.
FMRI preprocessing All preprocessing and statistical analyses of the imaging data was performed using SPM12 (Wellcome Department of Cognitive Neurology, London, United Kingdom). As in our previous study in this sample  30  , volumes were first realigned and unwarped to account for head movement and distortion during scanning. Second, slice time correction to the onset of the middle slice was performed to account for the shifted acquisition time of slices within a volume. Third, structural images were co-registered to the functional images. Finally, all images were smoothed (8mm FWHM) and normalized to MNI-space using the DARTEL tools included in SPM12 and the VBM8 template.
Introduction Dogs, either as companions or working animals, are an important part of global society. In France, dogs also play a significant role in public life. With 6. 95 million dogs residing in the country in 2018 and a high ratio of dogs to humans (17/100), France ranks fifth in the European Union in terms of number of dogs owned  [1]. To better track dog numbers, a law has been enforced since 1999 where all dogs must be identified by a microchip or tattoo. In 2013, a national database for identification of domestic carnivores (Identification des Carnivores Domestiques-ICAD) was implemented. This database manages all the identification documents of dogs and provides a system for tracing identification numbers. In addition, the French Kennel Club (Société Centrale Canine-SCC) is responsible for registering purebred puppies born in France.