dataesr/scientific-paragraphs-categorization huggingface dataset:
Sample size The objective was to include 500 questionnaires. This sample size was considered to be large enough to have representative data and accurate estimations. We assumed that for a 4-week inclusion period, approximately 20 to 30 questionnaires would be collected per GP. Thus, we needed the participation of at least 25 GPs to reach 500 questionnaires. Of the 6201 general practitioners in the Auvergne-Rhône-Alpes region, 150 were selected after randomisation. In case of refusal of a general practitioner drawn at random, this one would be replaced by a doctor of the same gender (the next one in the list) in order to keep a gender balance.
Statistical analysis The study sample is described by numbers and percentages for categorical data and by means ± standard deviations and medians and interquartile ranges for continuous data. Daily screen time was graphically analysed, and normality was assessed using Shapiro-Wilk's test. Daily screen time (weekdays and weekend days) was analysed using the Mann-Whitney test for two group comparisons and the Kruskal-Wallis test for 3 (or more) groups. Relationships between daily screen time and continuous data were analysed using Spearman's correlation coefficient. In order to perform a multivariate Poisson regression of daily screen time, we first transformed screen time into event of exposure, considering 1 h of screen exposure as one event. Thus, children with no screen exposure were considered having 0 event, children with exposure between 1 minute and 60 minutes were considered having 1 event, children with exposure between 61 minutes and 120 minutes were considered having 2 events and so on.
Setting and study population This study is based on data from 1785 participants of the French TEMPO cohort. TEMPO is a longitudinal followup study that aims to better understand which factors are associated with mental health and substance use, including cigarette smoking patterns. The cohort was set up in 2009 among young adults  (22-35 years)  who had previously taken part in a study on children's psychological problems and access to mental health care between 1991 and 1999  [29]. TEMPO participants were followed via self-completed questionnaires in 2009, 2011, 2015, and 2018  [29]. Between March 2020 and April 2021, TEMPO cohort participants completed nine questionnaires aiming to better understand their experience of the COVID-19 pandemic. The first eight questionnaires were sent exclusively by email, and the ninth questionnaire was sent by either email or post.
Measures Participants' self-reported smoking tobacco status was measured in 2009, 2011, 2015, 2018, 2020 and 2021 (daily, occasional, former smoker or non-smoker) and the average number of cigarettes smoked per day was recorded. The measure of tobacco use for 2020 included participants' smoking status the first time they answered a COVID-19 questionnaire between March and July 2020 (during the first 8 waves of data collection), and the 2021 measure included data collected in wave 9 (collected from December 2020 to May 2021). All participants who reported smoking on average ≥ 1 cigarette per day were considered to be regular smokers  [30]. Interpersonal change in cigarette smoking between 2018 (the last available measure before pandemic) and 2021 was measured using participants' smoking status and if smoking, the number of cigarettes smoked in 2018 and in the latest available measurement of smoking status from the TEMPO COVID-19 questionnaire (from here the 2021 measurement) (for the majority from wave 9 (n = 535), wave 1-7 (n = 147) and wave 8 (n = 34)).
Study limitations and strengths The interpretation of our results needs to be considered in light of several possible limitations. First, the size and composition of the study sample, which was primarily female, highly educated, and with a higher household income than the general French population  [34] , limit the generalizability of our results. However, the use of IPW to account for non-response is likely to have decreased the risk of selection bias. While the sample gives the study enough power to compare two education groups, the prevalence of smoking among persons belonging to the low education group is likely to be higher in the general population, as our cohort is not representative due to the participant selection  [29]. Second, as no data was collected between 2018 and the onset of the COVID-19 pandemic, any changes during this period were not accounted for.
Nevertheless, cigarettes (either pre-rolled or roll-yourown tobacco) are the most frequently used tobacco product in France, as in other countries  [36] , so the underestimation of use is likely to be minor. A broader assessment of the types of tobacco products that can be used (e. g. cigars, pipes, e-cigarettes), would have probably yielded larger estimates of socioeconomic inequalities in this area, as people with lower educational attainment are more likely to use non-cigarette tobacco products  [25, 37, 38]. Despite these limitations, our study has several stengths which need to be highlighted. First, we used longitudinal data from the French TEMPO cohort, a community sample of young and middle-aged participants with information from 2009 to 2021, thereby limiting the recall bias observed in cross-sectional studies.
Statistical analyses were performed using SPSS version 24 and Amos software. Exploratory factor analysis (N = 568) and con rmatory factor analysis (N = 534) were performed to test the factorial structure of the DBQ-L. Linear regression analyses were performed to examine the association between DBQ-L, sociodemographic and relevant tra c variables. A total of 1102 drivers aged between 18 and 82 years participated in this study with an average age of 34. 64 (SD = 12. 33). Of the total, 68. 4% of participants were male having an average driving experience of 3. 46 years (SD = 12. 33). The DBQ-L total scale revealed high internal consistency (Cronbach's α = 0. 892). Exploratory and con rmatory factor analysis supported the original four-factor structure re ecting aggressive violations, ordinary violations, errors, and lapses dimensions.
Construct and factorial validity: The two tests of Kaiser-Meyer-Olkin (KMO) and Bartlett were performed to evaluate the adequacy of data for factor analysis. To examine the appropriateness of the factor structure and to increase the validity, we split our data into 2 random samples, one for the exploratory factor analysis (EFA) and one for the con rmatory factor analysis (CFA). A sample of 580 drivers was used for conducting a factor analysis using the principal axis factoring (PAF) method with varimax rotation on retained items from the DBQ through SPSS to identify DBQ-L dimensions. Based on Eigenvalues >1 and scree plot, it was decided on the number of factors. A minimum factor loading of 0. 40 per item is required to be included in the factor. A parallel analysis (PA) was performed to determine the number of components or factors to retain from factor analysis.
Con rmatory Factor Analyses (CFA) and psychometric analyses The 45-item version of DBQ-L was subjected to con rmatory analysis using SPSS Amos to examine the good t of the model to the Lebanese drivers and to con rm its factorial structure. Con rmatory factor analyses (CFA) were used to compare items organization of 2 models: (a) exploratory factor model, and (b) Lebanese adapted model. To identify the best tting model, the following goodness-of-t statistics  [35]  were calculated using IBM AMOS 24. 0. The structural models were considered as a good t to the data when Chi-squared value (χ 2 )/ degree of freedom(df) χ2/df < 5[36], normative t index (NFI)>0. 9, goodness of t (GFI)>0. 9, the comparative t index (CFI)>0. 9 and the root mean square residual (RMSEA)<0. 08 [37]. In case of a poor t, modi cation indexes were observed to identify additional parameters that improved the goodness of t of the models.
Questionnaire development An anonymous Arabic, self-administrated questionnaire was developed in the Arabic language. It included a brief introduction of the study objectives and instructions on how to ll out the questionnaire as well. It comprised mainly closed-ended questions and consisted of three main sections: (a) sociodemographic characteristics; (b) tra c-related variables; and (c) the DBQ scale. The rst section collected socio-demographic data of the participants, including gender, age, marital status, pro le, education level, and residency. The second section covered the topic of tra c variables where drivers were asked about their driving experience and their annual mileage. The third section of the questionnaire included the over mentioned adapted Arabic version of DBQ to objectively assess Lebanese aberrant drivers' behaviors.
Data collection procedure: Since no updated o cial list of drivers is available, potential respondents were recruited from public places such as shopping malls and parking stations via a face-to-face approach. Four well-trained data collectors were responsible for the dissemination of questionnaires. These investigators were students in the tra c major at the Lebanese Higher Technical School collectors. The entire procedure included the administration of the preliminary questions to assess rst the eligibility of the participant based on the preset inclusion criteria. Before participation, study objectives and general instructions were delivered orally by data collectors to the eligible participants. Written informed consent was obtained from eligible respondents who were willing to complete a questionnaire.
Statistical analysis The collected data was entered and analyzed using the statistical software SPSS (Statistical Package for Social Sciences), version 24. 0. Since missing data constituted < 10% of the total database, then it was not substituted. Before analysis, the distribution of each DBQ item was checked for normality. Descriptive analyses were done using counts and percentages for categorical variables and mean and standard deviation for continuous measures. A bivariate analysis was conducted using the ANOVA test to compare the means of DBQ subscales and the categorical variables. Pearson correlation was used for linear correlation between continuous variables. All variables that showed a p < 0. 2 in the bivariate analysis were included in the model as independent variables. Four stepwise linear regressions were performed taking the DBQ subscales as the dependent variable and the sociodemographic and tra c variables as dependent measures.
Con rmatory factor analysis The default model corresponding to the result of the exploratory analysis was not acceptable based on the resulted t indicators (Table  5 ). Based on modi cation indices, a high correlation between errors (e2 and e1, e3 and e4, e1 and e5) was found. Hence, several model speci cations were performed based to adjust the model to achieve a better t. The latter included drawing eight covariates between the following errors e8-e15, e1-e9, e16-e18, e18-e30, e27-e29, e21-e22, e16-e17 and e17-e18 respectively. The model used in con rmatory factor analyses is presented schematically in the annexed gure FA1. This resulted in a better tting than the default model. The model t measures of the data analysis were as follow χ2=3929. 5, χ2/df = 4. 221 <5; NFI = 0. 912>0. 9, CFI = 0.
Declarations Ethics approval and consent to participate All methods were performed in accordance with the ethical standards as laid down in the Declaration of Helsinki and its later amendments. As research involving the use of survey procedures are exempted from ethical approval in Lebanon and the risks of this research are no more than minimal, this study was exempted from ethical approval by the Ministry of Public Health. The study design assured adequate protection of study participants, and neither included clinical data about patients nor con gured itself as a clinical trial. Written informed consent was obtained for each participant. They were reassured that their participation is voluntary and that they were free to withdraw at any time. In addition, all information was gathered anonymously and handled con dentially.
Assortative matching Here, we investigate whether the information demand and supply of senders and decision makers lead to assortative matching between types with similar information preferences, which might help explain the formation of echo chambers. To evaluate assortative matching, we use the two dimensional type classification explained above, based on Part 1 choices. In particular, we look whether decision makers who avoid or seek information in Part 1 are more likely to match with a sender with similar preferences. Figure  4  shows the frequency of choice of senders who avoided disclosing information when deciding in Part 1, by decision maker's type. Frequencies are calculated using data of groups where decision makers have the opportunity to choose either type of sender, i. e. , groups where there was at least one Avoider and one Seeker among the senders.
, and thank  Björn Bartling  for helpful discussions. They thank  Q. Thevenet  for programming, and  J. for research assistance. This work has benefited from the financial support of the  French National Research Agency  ( FELIS ,  ANR-14-CE280010-01 ), from  IDEXLYON from Université de Lyon  (project  INDEPTH ) within the  Programme Investissements d'Avenir  ( ANR-16-IDEX-0005 ) and from the  LABEX CORTEX  ( ANR-11-LABX-0042 ) of  Université de Lyon , within the program  Investissements d'Avenir  ( ANR-11-IDEX-007 ) operated by the  French National Research Agency. It has also been supported by the  Van Gogh PHC program. Joel van der Weele  thanks financial support from the  Dutch Science Association (NWO)  via a personal  VIDI grant  ( 452-17-004 ). The research was further supported by a  Dutch Science Association  grant ( NWO VI.
Remerciements Les auteurs traduisent leurs sincères remerciements à la  société Taurus Gold pour l'accès accordé pour la campagne d 'échantillonnage, les notables des village  Aboulé et Kouakro pour leur disponibilité ainsi que les membres du Laboratoire des Sciences et Techniques de l'Eau et de l'Environnement de l'UFR-STRM de l'Université de Cocody, Abidjan ainsi que ceux du Laboratoire du Génie de L'Environnement Industriel de l'IMT Mines Alès.
Test 3 -A Real Network In this last test, we present, with only an illustrative purpose, a more realistic scenario, which shows the potential of the model proposed. We consider the road network of the Royal Docks area around the London City Airport, including the Borough of Newham and the east part of the Canary Wharf business area. The network, due to the intensity of the displacements, and the presence of various bottlenecks created by the Thames river meander, is particularly exposed to traffic congestion. The road data has been provided by OpenStreetMap, licensed under the Open Data Commons Open Database License (Fig. 9 (a)). In this scenario, we compare the network performances in presence of a different level of information of the drivers. For better clarity, we focus on three particular spots (cf.
The resolution of the Eikonal equation is comparable to the complexity of a Breadth-first search (often called BFS) algorithm applied on the city network. The Riemann problem complexity is localized on each internal junction and it just requires small matrix multiplication of the size of the incoming and outcoming junction's roads. Finally, the resolution of the density PDEs is performed with an explicit logic, thus no linear system resolution is required. We remark that the implementation performance is not in the focus of the paper, thus no particular optimization for a better quality of the code is explored. However, despite the naive MATLAB implementation we perform, we obtain very good results in the executions times. Indeed, we obtain the static solution in the smallsize problems in the order of the minute, and for the London network in the order of the hour.
COVID-19 has a dramatic impact on the use of public transport (PT). Most European cities report a decline in PT use during 2020 and 2021. Nevertheless, not all cities report similar decline patterns or comparable resilience paths. We investigate the resilience patterns of PT use during 2020 and 2021 in 15 European cities from 11 different countries using clustering and regression analysis of data originating from Google Mobility Reports, the Oxford Policy Stringency Tracker, and COVID-19 reports. Results highlight the variety of resilience patterns of PT use in these 15 cities. These patterns vary in time and space. PT use in some cities recovered faster and more significantly than in others. Findings also suggest that changes in retail and recreational routines had the highest impact on the resilience of PT use in most cities.
Data and methods To test the above assumptions, we use data on the change in daily routines during 2020 and 2021: workplace presence, time spent at home, number of visits to retail and recreational amenities, stringency level of COVID-19 measures, and the daily number of COVID-19 cases. These variables are, to some degree, proxies for the core factors depicted in pink in Fig. 2. The other factors could not be tested for lack of appropriate data.
Case studies The resilience of PT ridership is investigated in 15 of the most populated EU cities: Vienna (Austria), Prague (Czech Republic), Copenhagen (Denmark), Paris (France), Budapest (Hungary), Rome (Italy), Amsterdam and Rotterdam (the Netherlands), Warsaw (Poland), Bucharest (Romania), Madrid, Barcelona, Seville and Valencia (Spain) and Stockholm (Sweden). We refer to these entities as cities, despite their different administrative organizations. The selection of these cities is based on three criteria: (i) availability of data at the city level, (ii) diversification of countries and contexts, and (iii) selection of most populated cities where PT was likely to be used before COVID-19.
Description of clusters For the 2020 results (Fig. 3a ) we find: 50% of PT use data falls within the medium impact cluster (MIC). This cluster groups weeks and cities where the average drop in PT use is medium ( -40%). 22% of PT use data falls within the HIC cluster. This cluster groups weeks and cities where the average drop in PT use is the highest (around -65%). The LIC cluster groups 20% of observations. This cluster is char- acterized by a low decline in PT use during 2020 and a decreasing trend from Monday ( -15%) to Sunday ( -35%). Finally, 6% of PT data, grouped in the NIC cluster, shows no impact of COVID-19 on PT use during 2020. Patterns of the impact of COVID-19 on PT use can be investigated in space and time. During 2020, the NIC cluster is exclusively present during the period from 17/02 to 08/03 that preceded the official declaration of COVID-19 as a global pandemic.
It is noteworthy that Rome has only one week within the LIC cluster, whereas other cities have two. This is due to the fact that the first major COVID-19 outbreak in Europe (EU) was first officially reported in Italy, hence the earliest impact of COVID-19 on PT use in Italy, and Rome in particular. The HIC cluster is predominant from 16/03 to 17/05 and during the last two weeks of December 2020. During these periods, most EU countries faced and feared major outbreaks of COVID-19 and took very strict measures to control the spread of the virus, including lockdowns. Noteworthy is the predominance of this cluster in Amsterdam where more than 25 weeks of 2020 belong to this cluster, whereas, in other cities, less than 10 weeks of data belong to the HIC cluster (Fig. 4a ). Contrary to Amsterdam, Stockholm has the lowest share of the HIC cluster, meaning that PT use in Stockholm was less impacted by COVID-19 during 2020 than in Amsterdam.
Introduction The COVID-19 pandemic was an unprecedented test of the military's role during emergencies. Nearly every country in the world was affected by the pandemic between March and July of 2020, and a majority of countries responded in this initial phase by deploying the military. These deployments, however, varied significantly in scope and character. In Israel, military units manufactured ventilators and personal protective equipment; in Italy, soldiers patrolled the streets of major cities to enforce restrictions on movement; and in Morocco, the military announced lockdowns from loudspeakers on tanks. 1  In this paper, we have two goals. First, we document the variation in early pandemic-related deployments with an original dataset covering every country with a standing military during the initial pandemic response.
Our dataset captures seven distinct categories of pandemic-related deployments, listed here from most to least prevalent: logistic operations, enforcement operations, international involvement, border protection, information provision, intelligence operations, and domestic protection. Second, we demonstrate that capacity and governance explain a significant amount of the variation in pandemic-related deployments, shaping the way that countries choose to respond to the pandemic militarily. Overall, three quarters of countries with a standing military responded with a deployment at the outset of the pandemic. Logistics operations and enforcement of lockdowns, mandates, or restrictions on movement were the most common kinds of deployments. Countries with particularly high numbers of cases were more likely to deploy the military, but the mission of these deployments was shaped by governance.
Data We collected original data on pandemic-related military deployments taking place from the beginning of the pandemic through August 2020 in order to focus on states' initial securitization responses. On a country-by-country basis, we conducted targeted searches for news articles on Google using the following search string: military AND (COVID OR Corona) AND [country name]. 6 We documented military actions reported in mainstream news media, government sources, or non-governmental organization sources (for example, the World Health Organization). In cases where information was sparse, we supplemented the initial search with additional searches of the Factiva news database. 7 Media reports and government press statements constituted the majority of our raw material, which consisted of some 1500 separate reports.
Finally, because even OECD democracies were responding with lockdowns and other restrictive policies, autocracies had no reason to fear that the policy itself would result in international censure. 12 6 We coded all countries included in the Correlates of War system membership datasets. The means of implementing these policies, however, might warrant censure, for example when excessive violence was used to enforce freedom of movement restrictions. Measuring the level of violence used by security forces in enforcing COVID measures or the level of popular resistance to COVID policies might face the informational challenges (and autocratic bias) well-known to researchers on contentious politics  (Dietrich and Eck, 2020) , but the same problems do not obtain for our topic. In order to meet the inclusion criteria for our dataset, a deployment must be explicitly motivated by the COVID-19 pandemic.
Independent variables The independent variables in our analysis measure aspects of capacity and governance. Capacity: We conceptualize capacity as two-sided: a state's capacity to manage the pandemic increases with the resources available to it to control the virus, and decreases with the severity of the pandemic. We measure pandemic severity using data from the World Health Organization (2020b) on the cumulative number of COVID cases in the country through July 31, 2020. 17 COVID cases are logged due to their skewed distribution. We measure a state's healthcare capacity as the number of hospital beds per capita at the outset of the pandemic (World Health Organization, 2020a). We additionally include gross domestic product (GDP) per capita in current USA dollars as a general indicator of a country's financial resources  (The World Bank, 2015).