dataesr/scientific-paragraphs-categorization huggingface dataset:
For example, only certain age groups are eligible to go to day care or school, only employed people are bound to go to work, etc. Once these restrictions are implemented, people will move in order to match the proportions of journeys per motive at each hour. If the person is bound to move, a number of choices are made in the following order: (i) transportation mode; (ii) destination commune; (iii) travel distance; (iv) travel time; (v) activity duration (see also Fig. 7 ). For journeys to work and back, the INSEE provides a detailed dataset with the principal modes of transport. This information is part of the exposure factors assigned in the previous module (Sect. 4. 1). The only stochastic choice here is for the two-wheel case, which has a 40 % and 60 % share between bicycles and motorcycles, respectively  (EGT, 2010).
For the rest of the journeys, we match the proportions of the transportation modes per motive and hour from the EGT (2010). In some cases, the destination commune is known (the person goes to work, to study, or back home). For other cases, we only know whether the destination commune lies in the same department as the residence or in a different department. In this case, we first define the destination department based on data on the interdepartmental flows. Then we combine two pieces of information to assign the destination commune: -We use the data on the average distance travelled per means of transportation. We assume a straight line connecting the centroids of the communes. Several possible destination communes are selected based on the distance criterion. -We use the information on the destination commune for the journeys in EGT (  2010 ) to assign a degree of attractiveness to the communes of the Île-de-France region for each motive.
Acknowledgement. The authors acknowledge AIRPARIF for developing and providing the bottom-up anthropogenic emissions inventory used in this study. We also acknowledge RATP for maintaining the SQUALES network and publishing the data, as well as STIF, OMNIL, and DRIEA for rendering public the EGT2010 dataset. Finally, we acknowledge  Raphael Lachieze Rey  for his valuable help in the statistical modelling. Financial support. This work has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement no. 727816 , as well as from  ANSES ,  ADEME ,  BelSPO ,  UBA , and the  Swedish EPA  under the  ERA-ENVHEALTH network  grant agreement no. 219337. This work was performed using HPC resources from  GENCI TGCC  under grant no. A0050110274. Review statement.
Code and data availability. The source code of the EXPLUME v1. 0 model as well as all necessary input data for the Île-de-France region (open source; see the acknowledgements) are available under https://doi. org/10. 5281/zenodo. 3352713 (Valari and Markakis, 2019). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/gmd-13-1075-2020-supplement.
Statistical Analysis Data are presented as absolute and frequencies, percentages, mean ± standard deviation. The data with non-parametric distribution was compared between groups (Boys and girls) by the test U of Mann-Whitney. The data with parametric distribution was compared between groups (boys and girls) by the non-paired Student's t tests. The significance was set at p <. 05. The software Statview software version 5. 0 (SAS Institute, Inc. , USA) was used for statistical analyses.
99, 95% CI 0. 86-10. 40)  (Baldi et al. , 2011). A more recent case-control study also found an increased but not significant risk of adult brain tumors (OR 1. 22; CI95% 0. 88-1. 69) among subjects living very near power lines (less than 50 m)  (Elliott et al. , 2013). At last, a meta-analysis on ELF-MF and childhood brain tumors based on data from ten studies found a non-significant association (OR 1. 14; 95% CI: 0. 61, 2. 13) for children exposed to more than 0. 4 μT in the home at diagnosis, the home at birth or the longest home  (Kheifets et al. , 2010). But there were many differences between the studies in the exposure assessment: long-term measurements, spot measurements or calculated fields. Furthermore, most of them did not consider histological subtypes of brain tumors. Exposure to ELF-MF emitted by power lines can be assessed by two approaches: direct and indirect methods.
Data collection Data were collected through standardized questionnaires delivered as face-to-face structured interviews. When cases were deceased or in a severe clinical condition, a relative was invited to complete a simplified questionnaire. Their matched controls completed the simplified questionnaire themselves. The questionnaire covered sociodemographic characteristics, medical history, lifestyle, detailed occupational and environmental data and lifetime residential history including all addresses from birth to the date of diagnosis, where the individual had been living for one year or more. For each address, street number, type and name, zip code, city, arrival and departure year were collected.
Residential exposure data Addresses collected in the residential history of cases and controls and located in mainland France and Corsica (93% of collected addresses) were geocoded according to four steps (Fig. 1 ). The first step linked an address directly during input with a geographical database of all addresses in France (PointAdresse Database from the French National Geographical Institute V2. 1) (67% of addresses) (Institut de l'InformationGéographiqueetforestière). If the address was not in PointAdress, we used an online geocoder, based on data from Google Maps (2% of addresses) (On line Geocoder. Availab). In the third and fourth steps, we used data from another geographical database containing public and administrative buildings, the BDTopo database V2. 1, also from the French National Geographic Institute (Institut de l'InformationGéographiqueetforestière.
Base de données BD TOPO), consisting in locating addresses which could not be geocoded by the previous methods: in the third step, at the municipality's town hall building center (30%) and, if that was not possible, in the fourth step, at the municipality's church center (1%). If more than one church existed in the municipality, we chose the church located in the most densely populated area. Addresses located abroad or in French Overseas Departments or Territories were not geocoded, for feasibility reasons (7% of collected addresses). Geo-positioning was carried out for all addresses, at least at the municipality level. Because no past or present data on ELF-MF measurements in the environment were available for the areas of the study, we decided to use residential proximity to highvoltage power lines as a proxy to estimate lifelong ELF-MF exposure, based on a database from the single French electricity distribution company ("Réseau de Transport d'Electricité", RTE) (http://www.
rtefrance. com, 2016), with the help of a GIS-based system. The RTE geographical database contains information on all power lines located in mainland France, overhead or underground, in 2013 (Fig. 2 ). Power lines were categorized according to their voltage: < 45 kV; 45 kV; 63 kV; 90 kV; 150 kV; 225 kV; 400 kV.
Assessment of ELF-MF exposure We chose to estimate ELF-MF exposure by a proxy such as residential proximity to power lines, from 1965 to 2006. We did not make a distinction between overhead and underground lines during the construction of the exposure indexes and we assumed that the French electricity network did not change between 1965 and 2013, the date of the RTE database. If the address of the residence had not been collected or could not be geocoded, we considered that exposure to ELF-MF was null at this given address. We created two different exposure scores using QGIS V2. 14. 3 geographical information system software  (QGIS Development Team, 2009. Open Source Geospatial Foundation). One metric was based on exposure corridors around the lines, representing a threshold for exposure to ELF-MF of approximatively 0.
, 2004) , associated with measurements  (Kheifets et al. , 2015)  or associated with data from the literature  (Habermann et al. , 2010) ; ii) by using data from the literature and from technical reports  (Blaasaas and Tynes, 2002; Wartenberg et al. , 1993). According to the literature and for a threshold of 0. 3 μT, the correspondences between voltage and distance were as follows: i) 63 kV: 60 m; ii) 90 kV: 80 m; iii) 150 kV: 100 m; iv) 225 kV: 150 m; and v) 400 kV: 200 m. Then, we calculated the number of power lines close enough for a residence to be in the exposure corridor. If the residence was not in a corridor, we considered that exposure to ELF-MF was null. Finally, to take account of cumulated exposure in every residence inhabited by a subject during his life, we created an exposure corridor score by calculating cumulative duration living in 1, 2 and 3 or more exposure corridors for each subject during his lifetime.
For the score based on distance, we calculated the distance between the residence and: i) the nearest power line, ii) the nearest high voltage line (≤200 kV) and iii) the nearest very high voltage line (> 200 kV). The threshold of 50 m was selected according to data from the literature  (Marcilio et al. , 2011; Elliott et al. , 2013; Sermage-Faure et al. , 2013). If the residence was at more than 50 m from a line, we considered that exposure to ELF-MF was null. Then, we created the distance exposure score by calculating cumulative duration of exposure (< 50 m or not) during lifetime. For both exposure metrics, the cumulative duration was defined as the sum of the durations living in a place of residence considered as exposed.
Household exposure to ELF-MF was based on a specific questionnaire on the use of electrical appliances, on field measurements and on the literature. The electrical appliances studied in the CERENAT Study were: electric shaver, hair dryer, heating blanket,  radio alarm, induction hob and hooded dryer. The data collected were the frequency of use (number of times per month) and the period of use in years. Field measurements (intensity in tesla and distance in meters from the source) were performed on these electrical appliances, when present in the homes of 14 staff members from the research team, as part of a pilot study. We used the literature to approximate the duration of use in minutes and when we had missing data from the other sources. Finally, we calculated a score by multiplying the duration of use, frequency of use, distance from the appliance, period of use and level of magnetic field emitted in μT.
For gliomas and meningioma, neuropathological assessment represented 96% of diagnoses and clinical and radiological assessment 4%. The median time between the index date and interview was 6 months (IQR: 4, 10) for cases and 21 months (IQR: 16, 29) for controls, and was similar for gliomas and meningiomas. Data was collected from relatives for 23% of gliomas and 6% of meningiomas. The average age at diagnosis was 55 years for gliomas and 60 years for meningiomas, and women represented respectively 42% and 76% of the subjects (Table  1 ). The level of education was higher in controls than in cases (p < 10 -3 ) as was the indirect occupational exposure to pesticides (p < 10 -3 ). On the contrary, the alcohol consumption, the household exposure to pesticides and the cellphone use were higher in cases than in controls.
The corridors were defined according to data from publications having used similar exposure scores  (Blaasaas and Tynes, 2002; Wartenberg et al. , 1993; Kliukiene et al. , 2004; Habermann et al. , 2010; Kheifets et al. , 2015). This exposure score allowed simultaneous exposure to several power lines to be taken into account. The exposure score based on distance took account only of the nearest line, but when power lines are close or cross, ELF-MF may partially cancel or add up, depending on several parameters such as the position of the lines, phase and intensity. Furthermore, even if the score based on distance did not take precise account of the voltage, exposure to high voltage (< 200 kV) and very high voltage (≥200 kV) lines was estimated separately. The CERENAT multicentric study was conducted on the general population and covered various socioeconomic statuses and environmental exposures.
Cases were included from a clinical network supported by population-based cancer registries, thus ensuring the reliability of diagnosis. Controls were selected randomly from the electoral rolls, which include 90% of people above 18 years and are representative of the French general population in terms of age and gender  (Pan Ké Shon, 2004). The delay between index date and interview was longer for controls, but we censured information on ELF-MF exposure one year before the index date. A large amount of data was collected through face-to-face interviews, such as adjustment variables and complete residential history. The availability of individual adjustment factors in a study on residential or geographical exposure to power lines is a strength, particularly when compared with ecological studies.
Ecological studies usually use aggregated data on potential confounders and could therefore lead to a lack of precision in the estimation of the associations. The availability of complete and detailed residential history for all participants is another strength of our study. We could estimate exposure for all residences occupied by a participant in mainland France during their lifetime. The chosen exposure metrics were based on one geographical data source, built for another purpose than epidemiology. Data from the electricity distribution company (RTE) was initially created to manage the transport and distribution of electricity and was not planned to be used to assess environmental exposure. However, the use of this source, with its advantages and limits, could show how to define the necessary parameters for a more appropriate exposure source.
We did not differentiate underground and overhead lines in our analysis, as this information was not often defined in the literature. One publication considered that ELF-MF emitted by underground lines were much lower than ELF-MF emitted by overhead lines, which could lead to an overestimation of exposure  (Pedersen et al. , 2014). However, 86% of the power lines in the RTE database are overhead, so the impact on the exposure estimation should be limited. Other methods were used in the literature to estimate ELF-MF exposure emitted by power lines, and particularly mathematical modeling of magnetic flux density. These calculations needed numerous power line characteristics such as the power, annual charge, location of electric conductors on the line and phase  (Verkasalo et al. , 1993; Kheifets et al.
, 2015; Olsen et al. , 1993). These characteristics were not available in the RTE database. Another limitation of the exposure scores is the inability to take account of factors that could modify the electrical power, such as seasons (more electrical use in winter)  (Valjus et al. , 1995). This could lead to errors in the estimation of exposure. We defined one exposure time period from 1965 to the time of diagnosis of the case, based on the available version of RTE data and considered that the electricity network did not change during this period. Therefore, the exposure scores designed here possibly overestimated ELF-MF exposure because it took account only of the electricity network in 2013 yet the French electricity network has increased greatly since its beginnings and more particularly since the 1960s.
In order to assess exposure misclassification from the lack of historical data on power lines, sensitivity analysis were conducted, using the method described by  Lash al. (2014)  and it did not change the associations. On the other hand, we considered that the latency of exposure of 40 years was sufficient to show an association between ELF-MF exposure and the risk of brain tumor, although no accurate number for this latency is defined in the literature. The exposure metrics were based on self-reported residential history   covering the time period from birth to the time of study, which could lead to a misclassification of exposure, particularly for older addresses. But self-reported residential history has been considered reliable in literature  (Künzli et al. , 1996) , and having all addresses from birth to the time of diagnosis to assess ELF-MF exposure, and not just the last residence, is an actual strength in this study.
Objective: To report the 5-year hemodynamic and clinical outcomes in intermediate risk patients implanted with the Perceval sutureless aortic valve. Methods: During 2010-2013, 658 patients were enrolled at 25 European institutions in the prospective not-randomized CAVALIER Trial, aimed to demonstrate early and mid-term safety and effectiveness of the Perceval valve. All serious adverse events were adjudicated by an independent Clinical Events Committee and hemodynamic data were assessed by an independent Echo Core-lab at discharge, 1 year and then annually. Five-year follow-up was 100% complete; mean follow-up was 3. 8 years, late cumulative follow-up was 2352. 2 patient-years (pt-yrs). Results: Patient mean age was 78. 3±5. 6 years old and 40% (n=263) were octogenarian, mean STS-PROM was 7.
This paper presents the dataset of a survey on workplace health and safety training, employees' risk perceptions, behavioral safety compliance, and perceived job insecurity in Vietnam during COVID-19 pandemic. The data were collected through an online questionnaire completed by Vietnamese full-time employees between April and June 2020. Using Email, LinkedIn, and Facebook, the online questionnaire was sent to respondents who filled it out voluntarily. A twowave survey was conducted in order to lessen the common method bias. Totally, we received complete matched data for 732 full-time employees. All data were processed through SPSS 22. 0, AMOS 23. 0 and Smart PLS 3. 0. Besides descriptive statistics, the results of the explanatory factor analysis and the confirmation factor analysis were included in this paper, which may serve as a good reference for future studies.
Subject Business, Management and Accounting Specific subject area Human resource management, Workplace health and safety management, Risk perceptions, Employee perception and behavior. Type of data The data were collected through an online questionnaire completed by Vietnamese full-time employees between April and June 2020. Using E-mail, LinkedIn, and Facebook, the online questionnaire was sent to respondents who filled it out voluntarily. A two-wave survey was conducted in order to lessen the common method bias. Totally, we received complete matched data for 732 full-time employees. Data source location Region: Asia Country: Vietnam Data accessibility Mendeley depository Direct URL: http://dx. doi. org/10. 17632/8b3hrcykcr. 1.
Value of the Data • This dataset advances the knowledge regarding the impact of workplace health and safety training on employees' perceived risk of COVID-19, their behavioral safety compliance at the workplace, and perceived job insecurity. • The present data is particularly useful for organizational behavior and human resource management researchers and organization managers to understand employees' perceptions and behavior during the pandemic. • The data can be reused for an empirical study that intends to examine workplace health and safety practices and employees' attitudes and behaviors during the COVID-19 pandemic in Vietnam, compared with other countries. • The dataset is a reference source for studies on workplace health and safety management as well as human resource management during a health crisis.
Respondents' profiles are shown in Table  1. The second section consisted of items related to workplace health and safety training (5 items, Cronbach's alpha = 0. 911), employees' risk perceptions of COVID-19 (6 items, Cronbach's alpha = 0. 72), employees' behavioral safety compliance (5 items, Cronbach's alpha = 0. 925), and their perceived job insecurity during COVID-19 pandemic (5 items, Cronbach's alpha = 0. 88) (see Table  2  ). We provided the results of exploratory factor analysis (EFA) with SPSS software, demonstrating that the 21 items were saliently loaded onto four dimensions, namely, workplace health and safety training, risk perceptions of COVID-19, behavioral safety compliance, job insecurity. The analysis was grounded on relevant ratios such as Kaiser-Meyer-Olkin statistic (KMO) equal to or higher than 0.
D. students) from three universities. The experts read the constructs' items adapted and provided us with suggestions to guarantee face validity. As a result, all the items were agreed by 75% or more of the experts and some wordings were adjusted. Moreover, to ensure the readability, the Vietnamese version was tested on five Vietnamese full-time employees and refined based on their feedback. The survey instrument consisted of 31 questions, including 23 statements of specific impact of COVID-19 which require participants to rate on a 5-point Likert scale, particularly 1 = Totally disagree; 2 = Somewhat disagree; 3 = Neither agree nor disagree; 4 = Somewhat agree; 5 = Totally agree. The data were collected through an online questionnaire completed by Vietnamese full-time employees between April and June 2020.
Using E-mail, LinkedIn, and Facebook, the online questionnaire was sent to respondents who filled it out voluntarily. We preferred this data collection method to reduce the risks of infection for participants and researchers. A two-wave survey was conducted in order to lessen the common method bias  [16]. We set a cover letter at the beginning of the questionnaire indicating the survey objective and the procedure of this survey, assuring respondents about the confidentiality of their data. At wave 1, respondents reported their socio-demographic information, E-mails, and workplace health and safety training, employees' risk perceptions of COVID-19. At this stage, we collected 917 respondents. Data on dependent variables (employees' behavioral safety compliance and perceived job insecurity) were collected at wave 2 after 10 days.
A short time frame between phase 1 and phase 2 was chosen to reduce the drop rate and memory bias  [17]. The two-wave data were matched through an identification code assigned to each respondent. The use of the codes allowed us to exclude participants' email addresses, ensuring the confidential nature of the survey. Totally, we received complete matched data for 732 full-time employees. All data were processed through SPSS 22. 0, AMOS 23. 0 and Smart PLS 3. 0.
FACS analysis Cell suspensions isolated from wounded tissue harvested at day 5 or 7 after wounding or in vitro cultured macrophages were used for FACS analysis, according to established methods with minor modifications  (Adamson et al. , 2016; Nassar et al. , 2012). Briefly, 1 Â 10 6 cells were suspended in 1% fetal bovine serum/phosphate buffered saline and incubated with a combination of fluorescence-conjugated antibodies in the dark for 1 hour at 4 C. FACS data were acquired using a BD LSR II cytometer (BD Pharmingen, San Diego, CA) and analyzed using FlowJo software (TreeStar Inc, Ashland, OR). Various cell populations were calculated as the percentage of total living cells. Specific marker expression is given as the geometric mean of conjugated fluorescence intensity. Wound macrophages were sorted using an Influx Flow cytometer (BD Pharmingen).