dataesr/scientific-paragraphs-categorization huggingface dataset:
Experimental Environment and Data Set The hardware platform is an Intel (R) Core (TM), i7-8550U CPU s 1. 80 HZ, windows 10, 8G memory. The experimental computing environment is Python 3. 5, TensorFlow 1. 7. 0 and Keras framework. Taxi trajectory data came from Wuchang District of Wuhan, Hubei Province, from 13 February 2014 to 19 February 2014, 6,705,086 taxi trajectory data were excluded in total by abnormal data cleaning, covering working days, holidays and special holidays (Valentine's Day), which are typically representative and stable  [38]. To reflect real-life situations better, we divided the test set and training set into two parts according to date. We divided the total 70-day trajectory data by date. The training set contains 60-day trajectory data, and the test set contains 10-day trajectory data.
Data Analysis Data were reported as means (standard deviations) for continuous variables or number (percentages) for categorical variables. All statistical analyses were performed using the commercially available statistical software, SPSS Statistics version 23 (IBM, Chicago, IL, USA) and Microsoft Excel ® 2010 (Microsoft Corporation, Redmond, WA, USA). Using the Shapiro-Wilks W-test, normality of the data distribution was not confirmed. To examine mental wellbeing, PA, sedentary behavior, and sleep differences induced by the lockdown, comparisons among pre-, and during lockdown were carried out using Wilcoxon signedrank tests. Cross-table Chi-square (X 2 ) analysis was used to assess the changes compared with pre-lockdown, and the results are presented as numbers and proportions (n, %). Effect size (ES) for non-parametric tests was calculated using Rosenthal  [60]  formula: ES = Z/ √ n.
Data Set Selection and Sample Description By the 28th of June 2020, 548 responses from older adults (aged > 55 years according to Petry  [61] , Coolidge et al. [62] ; Laguna et al. [63] ) were collected. Based on the age groups classification of Reynolds et al. [64] , from the 548 participants, 76% were considered as young-old (56-65 years old), 19. 1% were middle-old (66-75 years old), and 4. 9% were classified as old-old and oldest-old adults (>75 years old). Removal of responses including data entry errors (n = 25) resulted in a selection of 523 participants. A screening of participants' health status for eligibility against inclusion and exclusion criteria led to the exclusion of six participants with cognitive decline/impairment. The present study focuses on the final selected data set (i.
Regarding the measurement of the economic channel, the LFS allows for the detailed computation across occupations of unemployment rates associated with Arabic/non-Arabic names. Finally, the LFS data collection is based on a large representative set of more than 10,000 sampling units spread all over the country, each unit consisting of a residential block of 20 adjacent households, all of which are surveyed. This feature enables us to define a set of relevant peers at the very local level where many social interactions have been shown to occur in France (see  Goux and Maurin, 2007; Maurin and Moschion, 2009). We use this set of peers for two purposes: First, to measure the horizontal transmission of naming choices from nearby neighbors. Second, we compute the Local Information on Penalty (LIP hereafter) as the average unemployment differential between Arabic and non-Arabic name holders across these neighbors' occupations.
As discussed below, our identification strategy will be based on residential allocation of households within the public housing sector. Thus, we report on both the total sample and on the sample of public housing residential blocks. The time span of the rolling panel is too short (6 quarters) to exploit time variation in the socio-economic composition within residential blocks. Thus, we keep one observation per member of the household, which generally corresponds to the first wave of interviews. Table  1  reports the main descriptive statistics of the full database when we use this selection criterion. Our total sample is made up of 10,541 blocks, with 1,535 blocks containing one public housing unit. The average block size is 18. 31 adjacent households, and each household consists of around 3.
We provide two exercises to that effect below. Observables in public/private housing. Table  6  contains a number of characteristics of public vs private housing tenants among our population of interest. The list of variables covers a range of labor market outcomes and skill-levels. The first point of note is contained in the last row of the table : A large fraction of households with Muslim/Arabic cultural background in France lives in public housing. In our dataset, over 50% of babies born from those households live in public housing units (992 versus 972). This implies that, independently of potential differences with the private housing sample, the sample we use for estimation and counterfactual exercises contains most of the relevant observations. Regarding observable characteristics, Table  6  shows some expected differences in socioeconomic variables: Households living in public housing units have higher unemployment propensities for both parents, and the mothers' labor force participation is lower.
G. 4. 2 Tests on the refusal rate of public housing offers The previous tests point out the absence of self-selection along ethnic lines among the movers, but self-selection could occur prior to the move. In this case the sample of movers that we observe in the database would be biased. We address this issue by looking at households that have refused a public housing dwelling offer. We show that even if households declined at least one offer, possibly due to the ethnic diversity or the socio-economic composition of the neighborhood, they were still unable to choose the ethnic and socioeconomic composition of the housing block in which they ended up living. In this section, we report the results for the refusal rate of public housing offers depending on the neighborhood's ethnic composition (measured with a standard fractionalization index; see  Alesina et al.
Procedure and participants A self-administered questionnaire was used for collecting data from 400 local panelists recruited through a private Internet survey company based in Shanghai. 1 Prior Institutional Review Board (IRB) approval was obtained from both of the authors' institutions. The participants completed the questionnaire without monetary compensation. Answers were automatically and anonymously recorded, as guaranteed to the participants. The 400 participants were recruited with an equal distribution of 80 individuals within each of the five following categories: currently being solely or mainly a rider of either a bike (human-propelled; 50% male riders), a bicyclestyle e-bike (55% males), a scooter-style e-bike (62. 5% males), a non-electric engine scooter (LPG scooter, gasoline scooter; 50% males), or a motorcycle (72.
Data analysis The data analyses were performed using SPSS and AMOS software. First, the A-TRIBE inventory responses were analyzed to determine the A-TRIBE's construct and internal validity. Principal axis factoring (PAF) was employed along with split-half test analysis and computation of internal consistency (Cronbach's alpha value, 1951) for item reduction to the final 12-item structure. A Bartlett sphericity test and a Kaiser-Meyer-Olkin test of sampling adequacy ensured data factorability. Subsequently, confirmatory factor analysis (CFA) was conducted using the unweighted least squares (ULS) estimation method (Browne, 1982, cited in  [22, 31]. Next, we assessed the relationship between the A-TRIBE inventory items' sum-scores and external variables (criteria) as listed in the "Measurements" section using the eta (η) correlation ratio  [44].
To further establish the A-TRIBE's predictive validity, forthcoming research studies would examine the relationships between the items of risky riding behaviors and other variables, such as traffic violations, incidents or accidents. Another research direction will seek to identify among cognitive and psychosocial determinants of risky riding behaviors (such as those related to personality, conative, social or cultural dimensions) some levers for modifying riding in favor of greater safety. The effectiveness of future safety programs could be increased by investigating such determinants of risky riding that may for some be shared between several types of two-wheeler users or specific to one type of user. The data collected here with this instrument are situational. However, this inventory might be easily transferred with some slight adaptations for application in the European context.
Analysis The data presented below are based on sociodemographic and open questions. Quantitative data were treated with the Software GraphPad Prism 8. These data concerned sociodemographic questions (gender, age), followed by questions about their current practice in AAI (their registration status, the populations and the animal species they work with) and questions about their professional background (their training in AAI and their education institution, their training in the medico-social field and their training in animal behavior). Descriptive characteristics of the cohort were calculated and presented as means averages for continuous variables and percentages for categorical variables. The qualitative method was selected for four open questions: • How do you define your practice of AAI? • What motivated you to work in AAI? • For you, what is the role of the animal in AAI? • For you, what is the role of the human actor (handler) in AAI? We used a phenomenological method because it "describes the meaning for several individuals of their lived experiences of a concept or a phenomenon; describing what all participants have in common as they experience a phenomenon"  [46].
Demographic characteristics of our sample Our sample was composed of 111 handlers in AAI. They were mostly women (94. 59%; N = 105) with a mean age of 41 years (min 20 years; max 68 years). Handlers worked with a broad range of populations; based on the two first pathologies cited we analyzed data on 166 answers. Beneficiaries were mostly elderly with dementia (30. 12%; N = 50), followed by people with mental and/or motor disability (22. 29%; N = 37), followed by Pervasive Developmental Disorders (13. 85%; N = 23) and people with various mental health problems (13. 25%; N = 22). Also, 47. 75% (N = 53) of the handlers in our sample only worked with dogs, the rest worked with two species on average (range 1-7), mainly small pets such as guinea pigs and rabbits (45. 04%; N = 50). Handlers' professional backgrounds were different.
We collected our database in the following way. During a period of approximately six years, we analysed the controversies pertaining to vaccines in Frenchspeaking countries via both qualitative and quantitative methods drawing on media contents, Google keywords searches and qualitative interviews (ANONYMOUS). This allowed the identification of 69 vaccine-critical websites, including those of all the prominent actors of these controversies. Using the Web crawler Hyphe (41), we analysed the external links of these websites, to see which sites were cited by this group. We obtained a list of 4485 websites. Following the standard methodology for the study of vaccine criticism on the internet, we excluded, non-French language websites, commercial websites, academic pages, libraries as well as media or media-related pages  (15).
Addressing the commentary sections Commentary sections allow anyone to publish material on another individual or groups' website, and in particular, to share hyperlinks. Yet, it does not mean in any way that the site's owner(s) agrees to these contents. In the case of a controversial topic such as vaccines, finding comments actually opposing the content of the page is very likely, which can bias our analyses. Unfortunately, web crawlers are unable to distinguish the "commentary" sections from the rest of the page, as they can be coded in numerous and sometimes unpredictable ways. We therefore had to systematically read the HTML code of the 157 websites allowing comments on their pages. We identified the tags demarcating the beginning and ending of each website's "commentary" section, then systematically removed the comments and the associated links from our database.
Activists' visibility in the media To assess our actors' visibility in the media, we drew on Europresse, a reference when it comes to studying not only the French press, but more globally the French-speaking press ( (  42 )). This database archives 1562 French speaking sources, including many print or online media from France, Belgium, Canada and Switzerland. We systematically looked for mentions of their names during a period ranging from January 1st, 1990 to April 1st, 2017 (after the end of our data collection) in French-Speaking print media. addThe year 1990 was a turning point in the history of vaccine critical movements in France. Following internal dissensions and a decade of decline, the main vaccine-critical organization (the National League for Vaccination Freedom), split in two, leading to a new era for vaccine-critical mobilisations  (43).
It operates through the Alipay payment application (used by 900 million Chinese), assigning each citizen a color code -green, yellow or red -based on their health status. When a user grants the software access to personal data, an element of the program called ʻreportInfoAndLocationToPoliceʼ sends the personʼs location, the name of the city and an identification code number to a server (see Aumercier 2020). The software does not clearly tell users its connection to the police, but according to Chinaʼs state-run Xinhua news agency and an official police social media account, law enforcement authorities have been a key partner in developing the system. Other countries in the world -Unites States, Australia, Canada, New Zeland, United Kingdom, South Korea, Singapore, Israel etc. -have already followed the Chinese example and set up digital monitoring of citizens via smartphone applications to fight against COVID-19 (see Human Rights Watch 2021).
In practice, researchers may delete extreme data (i. e. , winsorizing) or delete missing values. This method is very effective at reducing the variability of data in order to render non-significant results significant. This practice is often called cherry-picking, and is used to find the strongest possible support for hypotheses or empirical findings from the dataset  (Bloomfield et al. , 2018; Harvey, 2017). Regrettably, this practice has been documented by previous studies to be one of the most common QRPs among scholars, as in the findings of  John et al. (2012)  and  Agnoli et al. (2017). Other studies show how false-positive findings can occur in data collection and analysis  (Simmons et al. , 2011). Dropping or adding hypotheses and changing the relationships in the model are the third and fourth likely actions.
Sample and Data Collection In Study 1, we chose business scholars affiliated with the top 250 universities in Indonesia as our sample. We opted for business scholars rather than students because scholars have more experience with the process of research and publication. This position is aligned with previous studies conducted by  Bedeian et al. (2010) ,  John et al. (2012) ,  Braun and Roussos (2012) ,  Banks et al. (2016)  and  Motyl et al. (2017). For details on the survey and the sampling frame, see Appendix A in the supplementary material, available online. After exceeding the specified cut-off date, we received 472 responses, giving a total response percentage of 39. 20%. This response rate is generally consistent with -or even slightly higher than -previous relevant studies  (Banks et al.
05, very few of the Indonesian scholars surveyed admitted ever falsely claiming that results were unaffected by demographic variables (QRP 9) or stopping data collection after achieving the desired results concerning a specific finding (QRP4). Similar results were also found in studies in the US, Italy and Brazil. We then inspected the QRP items individually to find out which actions are most commonly used by business scholars in our context. We found that the self-admission rates for QRP1 (Failing to report all dependent measures that are relevant to a finding), QRP2 (Collecting more data after seeing whether results were significant in order to render nonsignificant results significant), QRP3 (Failing to report all conditions that are relevant to a finding), QRP6 (Selectively reporting results regarding a specific finding that 'worked') and QRP10 (Falsifying data) show that these are the most common QRPs among business scholars in Indonesia.
We used a t-test to compare the two response waves. Our results found no significant differences (p > 0. 05) between the two waves using either Levene's test or equality of means testing for the variables tested (see Table  2 ). We, therefore, conclude that non-response bias is not a threat to our data analysis in this study. In addition, we assessed common method bias, which often occurs when using the self-reporting method. We used Harman's single factor test to detect this bias. We found that five significant factors were extracted, with the largest percentage of variance due to one factor being 36. 83% (less than 50%). Therefore, we conclude that common method bias does not occur in our measurements. Once again, while we did not find any evidence of these biases in our results, we do acknowledge that these biases may still exist.
Data Analysis We selected the partial least squares-structural equation modeling (PLS-SEM) method, using the SmartPLS 3 software  (Ringle et al. , 2015) , to test the proposed model of QRPs. PLS-SEM is a useful method when the model under consideration is still in the early stages of development. Given that the model we are proposing is still primitive, with a relative scarcity in terms of existing literature and prior knowledge, PLS-SEM is seen as a suitable approach  (Lohmöller, 1989; Noonan & Wold, 1986). We calculated the minimum sample requirements for our model under this system and found that our data exceed the minimum requirement of 146 cases (where the minimum absolute significant path coefficient = 1. 97, significant level = 0. 05 and required power level = 0. 80). In short, we herein follow the best practice reporting guidelines that have been documented regarding this method  (Benitez et al.
First, in the guidelines provided to authors, editors should require authors to make their research data and materials publicly available  (Sijtsma, 2016; Wigboldus & Dotsch, 2016). Thus, anyone will be able to replicate the results of the study findings. We argue that this method may already have been applied by most journals. Second, conducting a multi-stage review  (Kepes et al. , 2014) , often referred to as a pre-registered report, is recommended  (Bosco et al. , 2016; Chambers, 2014; Nosek & Lakens, 2014). In this way, the peer-review process will be divided into two stages. First, scholars submit their research proposal, including research objectives, hypotheses, research models and study design before conducting their scientific investigation. Next, assessments will be carried out separately before and after the data are collected and analyzed in order to produce empirical findings.
Considering that pupils were nested within different classrooms and that 33 teachers conducted the assessments, we investigated the critical assumption regarding independence of observations before conducting the statistical analyses. For this purpose, we calculated an intraclass correlation coefficient (ICC) with a fully unconditional model (i. e. , considering the assessing teacher's effect random) using the mixed model procedure in SPSS. An ICC of 9. 69% was thus obtained, which was not trivial (i. e. , greater than 10% of the total variance in the mathematic outcome)  29. We therefore assumed that it was reasonable to consider that there was no violation of the independence assumption, and consequently that it was not required to account for the hierarchical structure of our data using multilevel methods.
Methods The study (including experimental protocol and data collection) was conducted in accordance with ethical principles for research involving human subjects (World Medical Association Declaration of Helsinki) and was approved by the DSDEN-73 committee and its Academic Director (DASEN) of French National Education (MEN). The study carried out in France was also conducted in accordance with the Swiss recommendations of the ethic committee of the Faculty of Psychology and Educational Sciences (FPSE) from the University of Geneva. A written informed consent of each child's parent was obtained from all participants. Participants. In total, 706 children (age range: 35 to 71 months, M = 54. 35, SD = 10. 41; 354 girls) were retained for the study. They were recruited in 33 classes from 27 rural (< 1000 inhabitants; n = 271), village (1000-5000 inhabitants; n = 260), and urban (5001 to + 20′000 inhabitants; n = 175) public preschools located over a very large area of Savoie Department (France, 73).
These were single-, double-and triple-grade classes, taught by experienced teachers, with between 8 and 29 pupils per class (see Supplementary Table  S1  for details). Children were all French-speaking and came from families with low to high SES. Data from children reported to have special educational needs because of learning difficulties or any other disability (e. g. , deafness, Down's syndrome, etc. ) (n = 10), with scores on the different tasks less or greater than 3 standard deviations from the participants' mean scores (n = 7), as well as data that encountered procedural errors in their collection (n = 11) were excluded from the study. Children were tested in schools by their teachers who had been previously trained and continuously monitored via a digital platform. The assessments of academic-mathematic performance and emotion knowledge were conducted under normal classroom conditions (with a preliminary pilot experiment to test the emotion task's protocol we designed in order to verify its feasibility and validity).
Data analysis. Given that teachers conducted the assessments during their classes (under regular classroom conditions) and that some tasks were based on their observations (especially the social behaviour and locomotor activity tasks and the emotion knowledge's labelling sub-task), the data they collected were blind-coded by two different experimenters before being statistically analysed. Statistical analyses were computed using both TIBCO Statistica 13. 2 and IMB SPSS Statistics 26. 0 computer software. A p value ≤ 0. 05 was considered significant. Relations between the different measures. We performed correlation analyses (partial Pearson's r) among emotion knowledge total score, social behaviour, locomotor activity, academic-mathematic performance scores (in percentages) and participants' age in months (integrated as a continuous independent variable in the correlation matrix) while controlling for the gender, living location, and assessing teacher independent variables.
Before proceeding with this analysis, we verified that our data met the assumptions associated with multiple linear regression (linear relation multivariate normality, little or no multicollinearity, no auto-correlation, homoscedasticity of error variance). Mediation analyses. The mediation analyses were conducted following the same statistical logic adopted by various authors in recent previous studies  12, 13, 26, 62. We used the PROCESS macro (Version 3. 5. 3) for SPSS, Model 4  63  to test both of our simple mediation models, which assumed that (a) social behaviour mediated the effect of emotion knowledge on mathematics, and (b) emotion knowledge mediated the effect of locomotor activity on academic-mathematic performance scores. Percentile bootstrap confidence intervals of 95% for the mediation model were obtained using 5′000 bootstrap samples.