dataesr/scientific-paragraphs-categorization huggingface dataset:
| Procedures, design and participants This study employed data from the PsyCorona Survey, a cross-national longitudinal study aimed to investigate people's responses to the COVID-19 pandemic, both at the individual and country levels (cfr. PsyCorona Project: https://psycorona. org). The study was approved by the institutional review board at New York University Abu Dhabi (protocol HRPP-2020-42) and the Ethics Committee of Psychology at Groningen University (protocol PSY-1920-S-0390). Each participant gave informed consent before beginning the survey. Data were collected online through Qualtrics' panel management service between 19 March 2020 and 13 March 2021. The PsyCorona Survey, available in 30 languages, was distributed in 115 countries through a combination of convenience sampling, snowball sampling and paid procedures, recruiting over 60,000 participants.
After completing this initial survey, respondents who provided contact information were invited to participate in subsequent follow-up surveys, for a total of 20 follow-up assessments. For the purposes of the present study, we used data collected at the time of the baseline and at the seventh follow-up assessment  (May 16th, 2020). Precisely, we used the measures of societal discontent and intention to help collected at the baseline and the measure of intention to help collected at the seventh follow-up, which was assessed with only two items (out of eight) already measured at the baseline. Additionally, we decided to include only countries that had more than 150 participants, in order to obtain an average degree of reliability for a multilevel analysis  (Kline, 2016). Thus, the final sample of the cross-sectional study included 61,734 participants (61.
1% female) from 42 countries, aged from 18 to over 75 years old. In the final sample, 23. 5% of participants had a higher education, 30. 4% had a bachelor's degree, 16. 1% had a master's degree and 5. 1% had a PhD degree (see Tables  1  and 2  T A B L E 1 Descriptive statistics of participants included in the analyses: country, N per country, gender and age. N. B. Total percentages may not reach 100% due to missing data that were not included in the table for descriptive statistics of the sample). It is to note that representative samples in terms of age and gender were collected in 20out of 42countries and specifically in Argentina, Australia, Brazil, Canada, China, France, Germany, Italy, Japan, the Netherlands, Philippines, Republic of Serbia, Romania, Russia, South Africa, South Korea, Spain, Turkey, the United Kingdom and the United States of America.
| RESULTS We conducted the analyses using SPSS Statistic version 25. 0. Before testing our hypotheses, we investigated differences between countries in societal discontent and intention to help. We conducted two analyses of variances (ANOVA) using Tuckey's-b post-hoc test. Results showed a significant difference between countries in societal discontent F (41, 61,461) = 153. 965, p <. 001, MSE = 84. 440, partial η 2 = 0. 09, revealing that participants in Hong Kong S. A. R. and Chile were the least satisfied, while those in China and Kosovo were the most satisfied with society. Furthermore, results showed a significant difference between countries in intention to help, F (41, 61,217) = 166. 249, p <. 001, MSE = 213. 176, partial η 2 = 0. 10, revealing that participants in Russia, Japan and Ukraine were the least motivated to help others whereas those in Philippines, Bangladesh and Pakistan were the most motivated.
| Longitudinal effects To investigate the cross-level hypothesis over time, we used the measure of COVID-related intention to help at the seventh follow-up, which was composed by two items, already measured at the baseline ('I am willing to help others who suffer from coronavirus', 'I am willing to protect vulnerable groups from coronavirus even at my own expense'; Cronbach's alpha = 0. 82). We tested the longitudinal model using data from 12 countries, specifically from Canada, France, Germany, Greece, Italy, the Netherlands, Republic of Serbia, Romania, Spain, Ukraine, the United Kingdom and the United States of America. Using the same analysis strategy of cross-sectional effects, we examined the predictive effects of societal discontent at the individual level, societal discontent at the country level and their interaction, at the baseline, on the intention to help others, measured at the seventh follow-up.
As with every research, this study has limitations. In the present research, we measured the intention to help others rather than the actual behaviour. Thus, future studies should address this issue by considering real helping behaviour. Moreover, our data come from a cross-sectional study, which is susceptible to common method/sources biases. However, common method/sources bias can inflate the entity of relationships between variables (i. e. , 'main effects'), but simulation studies suggest that it may lead to an underestimation of interaction effects  (Evans, 1985; McClelland & Judd, 1993). This evidence reduces concerns about the possibility that the results for the interactive effect of societal discontent at the individual level and societal discontent at the country level may be explained by common method variance.
A serious game can be defined as any software with a purpose other than gaming  [29]. A welldesigned serious game combines psychological factors, design, and technology to engage learners in voluntary training  [24]. The serious game can be positioned at different stages of the patient's care. During the operation, in orthopaedics for example, to help students better understand the management of bleeding in surgery, a simulation game has been developed with features such as networking or timed challenges  [26]. Another plastic surgery software called Z-DOC allows students to practice Z-plasty, also with a scoring system  [27]. These games can occur at different times during the surgical formation, like PeriopSim 1 (Conquer experience, Surrey, Canada) which is a neurosurgical application which teaches the young beginners the name of the different surgical instruments with a system of score and time defined to create a competition's feeling between students and thus to motivate them to learn  [30].
Journal of Applied Research in Memory and Cognition, 8(1),  108-117. https://doi. org/10. 1016/j. jarmac. 2018. 09. 005Funding This research was funded by the  Institute for Advanced Study in Toulouse (IAST)  Multidisciplinary Prize 2019. Bago was supported by  ANR  grant  ANR-17-EURE-0010  ( Investissements d'Avenir program ), and  ANR Labex IAST. Rand acknowledges support from the  William and Flora Hewlett Foundation , the  Omidyar Network , the  John Templeton Foundation  grant  61061.
Data collection Semi-structured interviews were conducted with 441 plum tree owners from April to July 2018 (Table  1 ). Interviews were conducted in French and based on an opportunistic sampling with owners who would then point to possible respondents (snowballing approach). We collected information on the status of D. edulis tree owners (age and gender, not used in this study) to keep a good representation of the different age categories and gender. We included in the analysis their ethnicity and their location on the gradient (urban, peri-urban or rural dwellers). Prior to the interview, tree owners were informed of the research intentions and of their right to participate or decline. At the end of the interview, tree owners were given a form stating that the interview had been conducted in accordance with the principles of free and informed consent, which they could sign if they agreed.
Funding This project has been supported by  Agropolis Fondation  under the reference ID  1605-042  through the  «Investissements d'avenir» program  ( Labex Agro :  ANR-10-LABX-0001-01 ), under the frame of I- SITE MUSE  ( ANR-16-IDEX-0006 ). We acknowledge the  Agence Universitaire de la Francophonie (AUF)  for.
Conclusions The present study goal was to build on the work of  Devers et al. (2020a)  to provide a long-term daily reanalysis of precipitation and temperature at high resolution over France. Two reanalyses were produced based on DA using the SCOPE Climate downscaled reconstruction  (Caillouet et al. , 2019)  as background. FYRE Daily (resp. FYRE Yearly) used daily (resp. annual) observations in the DA process. These two intermediate reanalyses were then hybridized to derived the final FYRE Climate reanalysis corresponding to the study objective. Section 4. 1 showed that both FYRE Daily and FYRE Climate have strong similarities with the current reference Safran reanalysis over the period 1950-2000, and clearly improve on the SCOPE Climate background. Devers et al. (2020a)  even found that FYRE Daily performs better than Safran with respect to independent data on a set of experiments over the 2009-2012 period.
, 2015)  were computed with FreeNA  (Chapuis & Estoup, 2007). As F ST is dependent on the within-population variability  (Charlesworth 1998; Jost 2008; Gerlach et al. 2010) , we also calculated the pairwise D EST of  Jost (2008)  with the SMOGD software  (Crawford, 2010). Second, we performed Bayesian clustering analyses within each geographical area with Structure version 2. 3  (Pritchard, Stephens, & Donnelly, 2000). These analyses were carried out only for samples within the USA and China given that the other areas (i. e. , Japan and Portugal) had been analyzed previously  (Mallez et al. , 2015). We used the admixture model with correlated allele frequencies. The number of clusters, K, varied from 1 to the total number of site samples per area, i. e. , 18 for the USA and 4 for China. Twenty independent runs per K were carried out with 10 6 MCMC iterations each, following a burn-in period of 2x10 5 iterations.
For each Structure analysis, the Clumpak server  (Kopelman, Mayzel, Jakobsson, Rosenberg, & Mayrose, 2015) , which combines the Clumpp  (Jakobsson & Rosenberg, 2007)  and Distruct softwares  (Rosenberg, 2004) , was used to postprocess the Structure outputs, i. e. , (i) to determine the highest level of genetic structure using the ΔK of  Evanno (2005)  and (ii) to identify the most frequent clustering pattern for each value of K (across the 20 independent runs) and to display the corresponding bar plots. - - - GuangXi 32 China -GuangXi ? 1 - - - Laoshan 39 China -Shandong -Laoshan 1982 1 - - - ShanDong 35 China -Shandong 1982 1 - - - Mad23PC ( §) 12 Madeira Island -Porto da Cruz 2008 1 - - - Mad24C ( §) 7 Madeira Island -Calheta 2008 1 - - - 128S ( §) 17 Portugal -Setubal -Grândola 1999 1. 07 0.
In the "Code" column, " §" indicates that the corresponding site sample was previously used by  Mallez et al. (2015). Third, the Structure software was also run with all site samples from all areas as previously described with K varying from 1 to 10 (which appeared to be high enough to capture the genetic pattern existing between the native and the invaded areas, see Results). For this Structure analysis, the Clumpak server  (Kopelman et al. , 2015)  was also used to postprocess the Structure outputs as presented above. Examination of Structure results allowed us to propose putative historical relationships existing between the different site samples. Finally, a neighbor-joining tree based on Cavalli-Sforza & Edwards' distances  (Cavalli-Sforza & Edwards, 1967) , corrected for null alleles with FreeNA  (Chapuis & Estoup, 2007)  was plotted and the mean individual assignment likelihoods (denoted L i→s ,  Paetkau, Slade, Burden, & Estoup, 2004 ) of each invasive site sample i to each putative source sample s were computed with GeneClass2 version 2.
ABC analyses All ABC model-choice analyses were performed with DIYABC Random Forest v1. 0  (Collin et al. 2021 ). This program implements in the context of ABC a supervised machine-learning algorithm called the Random Forest  (Breiman, 2001). Briefly, this non-parametric classification method uses hundreds of bootstrapped decision trees (creating the so-called forest) to perform classification using a set of predictor variables, here the summary statistics. To define the scenarios to compare, we took into account the historical knowledge (i. e. , dates of first observation), results from this study and previous analyses  (Mallez et al. , 2015). Particular site samples representing genetic units inferred from Structure analyses were used for the ABC analyses. As proposed by  Lombaert et al.
(2014) , we performed a step-by-step ABC analysis: we first tried to clarify the Asian invasion history (i. e. , the oldest invasive area) and then integrated Europe (i. e. , the most recent invasive area) into the scenarios. Thus, we first focused on establishing the number of independent introduction events in Japan (step 1) and in China (step 2), separately. Then, we studied the relationships between Japan and China (steps 3 and 4). Finally, we integrated the Portuguese site sample into the inferred Asian invasion scenario to determine its origin among all possible sources (steps 5 and 6). See Results for details, and Figure  S2  for a graphical representation of a few competing scenarios. All configuration files are available  (Mallez et al. 2021 ) at https://doi. org/10. 5281/zenodo. 4681180.
Within ABC analysis, the number of simulated datasets for each scenario was not uniform, because we took into account the dates of first observations and their prior range. For example, a Chinese population could be the source of a Japanese population in one scenario, but the opposite was more likely a priori. For each analysis, we simulated a mean of 20,000 datasets per competing scenario. To reduce the datasets, we used all available summary statistics (SuSts): the mean number of alleles, the mean expected heterozygosity and the mean allelic size variance per population and pairs of populations, the ratio of the number of alleles on the allelic size range  (Garza & Williamson, 2001) , the mean individual assignment likelihoods of population i to population j  (Pascual et al. , 2007) , the pairwise F ST  (Weir & Cockerham, 1984) , the shared allele  (Chakraborty & Jin, 1993)  and dµ²  (Goldstein, Linares, Cavalli-sforza, & Feldman, 1995)  distances between pairs of populations, the maximum likelihood estimate of admixture proportion for each trio of populations  (Choisy et al.
2004 ) and the axes obtained from a linear discriminant analysis on summary statistics  (Estoup et al. 2012). We grew a classification forest of 1,000 trees based on all simulated datasets. The random forest computation applied to the observed dataset provides a classification vote representing the number of times a model is selected among the 1000 trees. The scenario with the highest classification vote was selected as the most likely scenario. We then estimated its posterior probability by way of a second random forest procedure of 1,000 trees as described by  Pudlo et al. (2016). To evaluate the robustness of our inferences, we performed the ABC analyses with two different sets of prior distributions (hereafter referred to as "main" and "alternative" prior sets; Table  2 ). The generation time of the PWN in natura is not well-known due to a lack of direct observations of the PWN inside the tree.
Finally, we used DIYABC 2. 1. 0 (Cornuet et al. 2014)  to check the adequacy between the observed dataset and the selected scenario, i. e. , the capacity of this scenario to generate datasets similar to the observed one (model checking analysis,  Cornuet et al. 2010). To do so, we simulated a new reference table consisting of 100,000 datasets of the selected scenario. Then, 1,000 new datasets were simulated by drawing the parameter values from the posterior distributions of the selected scenario. Each observed SuSt was confronted to the distribution formed by the SuSts obtained from the new simulated datasets, which gave a rejection probability for each observed SuSt. A large number of probabilities being obtained, we adjusted the significance threshold with the false discovery rate method  (Benjamini & Hochberg, 1995).
However, in addition to using other key statistics, the ABC method allows us to consider complex evolutionary scenarios, involving for example intense bottlenecks or unsampled populations. Indeed, previous studies using simulated datasets have shown that F ST and mean assignment likelihoods, used alone, can be misleading in some situations  (Guillemaud et al. , 2010; Lombaert et al. , 2010; 2018). On the contrary, the inference of multiple independent introductions from the native area in Asia (Japan1, Japan2 and China2 ) is not consistent with the Bayesian clustering of individuals  (Pritchard et al. , 2000)  and distance genetic trees  (Saitou & Nei, 1987). To our knowledge, such discrepancies have not been observed yet, although a simulation study showed that classic clustering methods such as Structure analyses  (Pritchard et al.
Indeed, the use of simulated datasets enabled  Lombaert et al. (2018)  to show that multiple introductions from a native population with small effective size, and thus low diversity, could often lead to single introduction patterns with Structure. Conversely, using a model-based stochastic method, such as ABC, a genetic structure with invading samples closer to each other than to the native area may lead to the choice of independent introduction scenario because it is probable to get such structure with this scenario when the diversity is low. Overall, the ABC method, explicitly based on models, seems appropriate for the PWN which is characterized by a markedly low intra-population diversity and a very strong inter-population genetic structure. However, one should remain cautious when interpreting our results.
(2018)  who did not use ABC analyses. More precisely, analyzing datasets, simulated under chosen scenarios and known levels of genetic diversity, may allow to assess the behavior of these analyses in relation to the level of genetic diversity. These simulations would thus be useful to (i) determine the real impact of the genetic diversity on the outcomes, (ii) assess the expected benefits of increasing the number of genetic markers and (iii) verify that the interpretations made from these methods in the biological invasion framework are appropriate.
Acknowledgements We would like to thank  Pedro Barbosa ,  Douglas LeDoux ,  Julia Thompson ,  Margarida Espada ,  Paulo Vieira ,  Jonathan D. Eisenback ,  Mark Harrell ,  Manuel Mota ,  Takuya Aikawa ,  Mitsuteru Akiba ,  Hajime Kosaka ,  Lilin Zhao  and  Jianghua Sun  for technical support, precious help in sampling and fruitful discussions. This work was funded by the  EU   REPHRAME  project ( KBBE. 2010. 1. 4-09 ). A previous version of this article has been peer-reviewed and recommended by  Peer Community In Zoology  (https://doi. org/10. 24072/pci. zool. 100006).
For each simulated dataset, 1000 independent profiles of dependent genetic markers data, whose both marginal and joint distributions are estimated using a publicly available GWAS dataset  (Wellcome Trust Case Control Consortium, 2007) , are generated using the R package GenOrd  (Barbiero and Ferrari, 2015). Then, consistently with model (1), for each simulated profile of genetic markers data, the response is simulated according to a logistic linear regression model, where the only nonzero coordinates of the vector β of regression coefficients in scenario 1 are for the 5th and 10th genetic marker, whereas in scenario 2, a few other coefficients around the 5th and 10th genetic marker are also set to nonzero values. For each simulated dataset, the vector Z of pointwise test statistics as defined by Eq.
Impact of the pattern of association signal on detection performance Many comparative studies  (Derkach et al. , 2014; Barnett et al. , 2017)  report that the relative power of aggregation tests highly depends on the pattern of correlation across the pointwise test statistics. The following illustrative study aims to shed light on the fact that, even for a same correlation structure, the compared performance of signal detection methods is also markedly affected by the pattern of the pointwise association parameter µ introduced in expression (5). The simulation settings presented in Section 2. 1 introduce two patterns for µ displayed in Fig. 1 , corresponding each to a different scenario for the regression parameter β. For 1000 simulated datasets in these two scenarios, the p-value of the aggregation methods introduced in Table  1  is estimated using a Monte-Carlo estimation of the null distribution based on 1000 random permutations of the observations of Y.
2 on 1000 simulated datasets. The empirical type I error rate is then estimated by the proportions of p-values smaller than the nominal level α over the 1000 simulations, for different values of α. Table  S1  in the supplementary material reproduces the results for each combination of a parametric dependence model (equicorrelation and autocorrelation), a level of dependence across explanatory variables (ρ), a number p of explanatory variables and a nominal level α for the test. It confirms that in these situations the empirical type-I error rates are close to the level α. For the simulations conducted under data-driven dependence patterns, three blocks of SNPs corresponding to genes PDZRN4 (68 SNPs), DTD1 (49 SNPs) and KCNN3 (37 SNPs) are extracted from the WTCCC dataset  (Wellcome Trust Case Control Consortium, 2007)  and used to estimate correlations across genetic markers.
, 0. 9, 1} is considered to define intermediate situations in terms of association signal strength. For each gene and two scenarios of association signals, 1000 datasets are simulated and the p-value for the test of β = 0 is calculated by all the global testing methods introduced above. The corresponding proportions of p-values smaller than 0. 05 over the 1000 simulations are displayed in Fig. 4. As previously mentioned, the first striking result is the strong inconsistency of the ranking of global testing methods for a given gene, or equivalently for a given dependence pattern. Interestingly, the MGF-R test is the only method with an acceptable power in all scenarios, close to the most powerful method in scenarios (a), (c), (e) and even the best method in scenarios (b), (d) and (f). In all scenarios, either the L 2 -norm test or the Hotelling's t-square test has high power thus confirming that the class T 2 introduced in Section 3 covers an interesting scope of test statistics.
1, 0. 2,. , 0. 9, 1}. For each number p of explanatory variables, dependence pattern and vector of regression coefficients β, 1000 datasets are simulated and the p-value for the test of β = 0 is calculated by all the global testing methods introduced above. The proportions of simulations for which the p-value is smaller than 0. 05 are displayed in Fig. 5  along the signal strength. First, it can be deduced that, due to the strong regularity of the correlation models, the ranking of global testing methods is not markedly different between the two dependence patterns, dominated by the two higher criticism methods and the max-based method minP. The sum-of-squared-based testing methods L 2 -norm and MGF-R seem to be slightly less powerful, SKAT and the Hotelling's t-square test being markedly underpowered.
Application to real datasets Using benchmark genetic datasets with corresponding binary phenotype, the MGF-R test is now used to test the association between the response variable and biologically validated genes. Since the MGF-R procedure involves the estimation of pairwise correlations between explanatory variables, as the other global testing methods handling dependence across pointwise test statistics, it can be expected that it would suffer from instability in small sample designs. This explains why the first illustrative genetic association studies conducted on a very small number of dogs has been chosen. For each dataset, the MGF-R procedure is compared to the alternative methods introduced in Section 2. 2. It is demonstrated that the MGF-R test can successfully detect genes known to be associated to the phenotype, whereas other tests cannot.