dataesr/scientific-paragraphs-categorization huggingface dataset:
e. , the most frequent hirers) unlikely wanting to associate with gambling due to recent controversies (e. g. illegal skins betting  [58] , lootboxes and gambling  [149] , etc. ). There was only one role in the dataset (i. e. , 'Esports Trader') and it was related to analysis of professional-level esports play in order to determine appropriate odds for other betting companies. These findings broadly accord with industry reports, such as the one published by Hitmarker  [57]. Though the schema for classification is different, and it is unclear whether the inclusion criteria differ, the results highlight the high demand for software engineering and marketing skillsets, followed by design, operations, sales, and business development, with a long scree of other disciplinary areas. Some roles such as events, community management, and product management, remain a relatively small proportion of the overall number of positions advertised by these services; despite their importance to the sector.
Starting with the higher education courses included in a previous review  [63] , the working group identified 65 higher education courses catering to the esports industry, gathering publicly available information and assembling a dataset  4. In the interest of scope, courses that focused almost entirely on games development were excluded from this sample. This is because the group identified a considerable number of such courses (> 500) with most having only a tangential link to esports or did not explicitly mention esports. Of the 65 esports courses, 29% were arts-related, 37% were science-related, and 34% were awards in business administration. There were 15 at postgraduate level, and 50 at undergraduate level. Nearly all of the qualifications at postgraduate level were either a generalist conversion course for people interested in directing their career towards esports or an MBA course with a focus on the esports industry.
Communication, organisation, and collaboration are naturally needed for effective team working, especially in interdisciplinary contexts which make up the bulk of a lot of esports practice where individuals may have skills that cross several disciplines or need to work with many others in different disciplines. When looking at the different contexts that exist within esports, broadcasters, community managers, data analysts, events managers, coaches and marketing personnel will all need to be able to work with other people and departments to collectively deliver events, broadcasts, a good standard of performance from players, and to foster positive engagement with audiences. Research, reflection, and general analytical skills would be at the cornerstone of any university curriculum that encourages its students to be able to find, synthesise, and share information as well as critically reflect on the world around them as well as their own continuing development.
Disciplinary Interface. There would also need to be some form of disciplinary interface. This is due to the multidisciplinary nature of esports and the complex way in which 'spokes' could branch from the 'hub' and interact with each other. The careers, skills and subjects that esports captures are quite diverse and do not always neatly overlap. For example, the skills to become an esports presenter are different to those required of an esports community manager, an esports showrunner, or an esports data analyst. However, all of these roles will contend with communication and collaboration skills, and face operational challenges together. Similarly, they will all benefit from generic esports learning outcomes such as an appreciation of the context of the esports ecosystem. Part of the difficulty in proposing recommendations for an esports curriculum is that the word esports as an umbrella term is not always clear-cut in practice, or more accurately may not yet be clear-cut.
Spokes Branching out from the center of the model are the 'spokes'. These are disciplinary specialisms emerging from the esports sector. This is based on the acknowledgement that a single holistic curriculum will not adequately serve every single specialist career identified within esports so far. A more likely goal will be to recommend that any given curriculum be tailored to the specific specialist focus of the educators. These learning outcomes can then be organised around various modules that best fit the course design. Key areas of disciplinary specialism include: Of the specific esports competencies proposed here, none of them are absolutely unique to the field of esports. They are shared by a range of specialist vocations. For example: data analysis relates to data science; content creation and streaming relate to media production; marketing, branding, and community engagement relate to public relations generally; leagues and tournaments relate to events management; performance, health and well-being promotion relate to sports science and psychology; and so on.
Until 2017, women for whom this screening showed an increased risk of Down syndrome were offered a diagnostic examinationamniocentesis or chorionic villus samplingto determine fetal karyotype. Only that year did HAS include the study of fetal DNA in its strategy for detecting Down syndrome among women at increased risk  [2]. Since 1997, the National Health Insurance Fund had provided full coverage of screening for Down syndrome by the first-trimester ultrasound and the serum marker assays for all women with health insurance. Despite hopes that the disparities in prenatal diagnosis of Down syndrome observed before 1997 would disappear with its full financial coverage, they have persisted, as Khoshnood et al. showed in an analysis of data from the Paris Registry of Congenital Malformations covering the period from 1983 to 2003  [4].
Methods Data for this analysis come from the National Perinatal Surveys conducted in 2010 and 2016. These crosssectional surveys collected data from all births at a gestational age of at least 22 weeks or with a birth weight of at least 500 grams during a single week, in all maternity units in metropolitan France and the overseas territories  [9]. The data came both from the medical records, mainly on delivery, and the child's condition at birth, and a postpartum face-to-face questionnaire-based interview with parturients before their discharge. This interview focused on their prenatal care and their socioeconomic characteristics  [9]. Women who did not speak French, were younger than age 18, or those who experienced a stillbirth or termination of pregnancy were excluded from the study. In 2010, 13 894 women with 14 142 babies were included, and in 2016, the sample comprised 15 187 women with 15 418 babies.
The two survey samples were first compared to verify the absence of major heterogeneity and to analyze the trends in Down syndrome screening uptake. The two samples were then merged and analyzed together to improve the statistical power of the analysis. Merging two survey databases to reach an appropriate size for some groups to allow an adequately powered analysis was possible because 1) the national screening strategies were the same over the two study periods, 2) we were able to test and demonstrate the absence of interaction between survey year and maternal place of birth, and 3) we verified that, although there were differences between the two surveys, these were not major. We selected women who had lived in metropolitan France for at least one year when they gave birth and whose place of birth was known: 13 507 women in 2010 and 11 137 women in 2016.
Statistical analyses Pearson's Chi-square tests were used to compare the percentages of women who underwent Down syndrome screening and the opportunity to make an informed choice between 2010 and 2016, according to the mother's country of birth. Differences were considered significant when p was less than 0. 05. The analysis was performed after merging the 2010 and 2016 databases. To study the association between the mothers' country of birth and proportions of the different prenatal screening tests for Down syndrome, as well as the opportunity to make an informed choice about it, we began by testing the interaction between survey year and maternal birthplace. It did not appear significant. We then used multivariate multinomial logistic regression models adjusted for the survey year, the individual factors (maternal age, parity, educational level, and prenatal care), and for the maternity ward characteristics (level of perinatal care: I, IIA, IIB, and III).
Model description The model description followed the ODD (Overview, Design concepts, Details) protocol for describing individual-and agent-based models  (Grimm et al. 2006 , Grimm et al. 2010). The model was implemented in NetLogo (v. 5. 2. 0), a free software platform for agent-based simulations as an integrated modeling environment, and is available on GitHub (https://github. com/LucileMarescot/RestrictingOffspringBetweenGroupContact_IBM). We used R v. 3. 5. 3. (R Core Team 2019) and the packages ggplot2  (Wickham 2016) , tidyverse  (Wickham et al. 2019a) , dplyr  (Wickham et al. 2019b) , patchwork  (Pedersen 2019) , showtext  (Qiu 2020) , here  (Müller 2017)  and ggtext (Wilke 2020) to produce the figures.
To avoid naïve comparisons of fade-out probabilities owing to the limited number of receptions of our simulations that could generate substantial bias in the calculated probabilities, we filtered the results by running a Fisher's exact test for each paired scenario. We tested if differences in epidemic fade-out probabilities were significant and plotted the distributions of differences between epidemic fade-out probabilities only for the paired scenarios with a p-value < 0. 05. Finally, we ran a regression tree with the R package rpart (Therneau and Atkinson 2018), using the ANOVA method, to partition the difference in epidemic fade-out probabilities between the ORC and the baseline scenario. We used this approach to detect under which specific conditions (i. e. trait values) ORC showed the greatest effectprovided it did so  (Breiman et al.
Data collection and analysis Steering Committee members in each country took responsibility for collecting the data and completing the survey. Data were collected through online searches of publicly accessible sources; these included government departments of health and justice, prison administrations and prison advocacy organisations. The sources used for each question were recorded (e. g. website addresses), together with the date on which the information was accessed. The responses from each country were collated for each question in order to facilitate analysis; each question was then analysed in turn. Numerical data were subjected to simple descriptive statistical analysis (the small number of participating countries limited the scope and depth of statistical analysis). For free text data, thematic comparisons were made between different countries as far as possible.
Prisons and prison populations The number of prisons in each country (excluding immigration detention and removal centres) ranged from 15 in Scotland to 188 in France, but data were not collected about the number of inmates in each prison, the population of each country, or any geographical factors that might impact on the number of prisons required. Although all countries reported having separate institutions for young offenders, there was no evidence of prisons specifically for older people. Prison population rates range from 88 per 100,000 of the population in Belgium to 205 in Czech Republic  [17]. All countries segregate prisons by gender apart from France, where most prisons are mixed with male and female units. Males make up the vast majority of prison populations; across all eight countries females accounted for only 5.
ACKNOWLEDGEMENTS The authors would like to thank the  EAPC  for its continued support of this work in hosting meetings at EAPC Congresses and publicising the work of the Task Force on its website. We also thank the members of the  Task Force Steering Committee  for their involvement in and support of this project. We are particularly grateful to  Gail Allan ,  Juliana Bindasova ,  Olga Bindasova  and  Steven Vanderstichelen  for their help with data collection in Scotland, Czech Republic, Slovakia and Belgium respectively.
The study was conducted on two cohorts of high school students as part of the large collaborative research project (ProFAN) launched in 2017 by the French Ministry of Education, and involving a large sample of vocational high schools with students from three different disciplines (business, health services, and electricity). The analyses focused on data from the initial baseline of the first and second cohorts, which have been acquired respectively in fall 2018 and 2019 from only the sample of business students. 1.
We conducted model building using a backward model selection before final parameter estimation and inference (see online supplementary analyses of our OSF project at https://osf. io/cz9h2/), and we used the AIC for selecting the "best" model. The candidate model with the lowest AIC value being considered the model that best fitted the data. The mGLMMs were fitted using the "fitmv" function of the spaMM package in R  (Rousset and Ferdy, 2014). Model parameters were estimated by restricted maximum likelihood estimation (REML) based on the Laplace approximation. The comparison using AIC of all our tested mGLMMs led us to consider the model with Task, Gender Diversity and Creative Cognition as predictor variables as the most parsimonious model for the given data. Since the log of the expected count is modeled as a function of the predictor variables, the formula for the predicted (conditional) mean value of each creative performance measure is: Creative Performance = exp (β0 + β1 * T + β2 * CC + β3 * GD1 + β4 * GD2 + β5 * GD3 + β6 * GD4) with T is the task (gendered vs neutral), CC is the Creative Cognition, and GD is Gender Diversity (GD 1: all-men groups vs all-women groups; GD 2: "solo" groups with only one-woman vs all-women groups; GD 3: groups of two men and two women vs all-women groups; GD 4: "solo" groups with only one-man vs all-women groups).
Procedure Noting the significant role that OSNs play in French teenagers' lives, we gathered data from France to test our model. Adolescent populations are vulnerable in research contexts  (Levine et al. , 2004) , so precise standards exist for the ethical conduct that researchers must follow when they work with teenagers  (Crane & Broome, 2017). In particular, there is an inherent power differential, created by age differences and the varying social standings between participants and the researcher  (Crane & Boom, 2017). Therefore, we administered paper-and-pencil surveys in classrooms during regular class hours, in the presence of a teacher (to maintain classroom order) and one of the authors. The teachers were physically present in the classroom to ensure the protection of the teens' interests.
" We also did not gather any identity information (e. g. , name, student ID). During the data collection, the researcher remained neutral and adopted an egalitarian friendship pose toward the teenage informants, following an approach recommended by previous researchers  (Tisdall, Davis, & Gallagher, 2009). Two private schools and two public schools in the city of Lille, in Northern France, were selected to participate the study. In cooperation with each school, participants were recruited from 15 approved classes. Participants completed a questionnaire as a part of an omnibus survey on consumer-related topics. Each session was approximately one hour in length. Prior to its administration, parents received letters inviting their children to participate. Study participation required signed parental consent and individual participants' assent.
g. , naming distant [less close] acquaintances, giving names at random)  (Gentina et al. , 2014). We calculated degree centrality using Ucinet 6. 0. software  (Borgatti, Everett, & Freeman, 2002). To measure offline friendship quality, we asked the teens to write a list of 10 peer names (their closest friends) at school and to answer follow-up questions about their good friends, using  Bukowski, Hoza, and Boivin's (1994)  friendship quality scale (1 = not true; 5 = really true). We used  Chaplin et al. 's (2018)  5-item ToM scale to assess teenagers' ToM. To measure the frequency of OSN activities, we used three items to measure the frequency of teens' engagement on OSN activities (1 = never; 5 = always) for each of the four platforms. To measure levels of OSN concerns, we used two items with 5-point scales (1 = strongly disagree; 5 = strongly agree)  (Social Media, Social Life, 2012).
561). Fourth, our research contributes methodologically, by proposing better ways to measure offline friendship quantity. Previous research  (Gentina et al. , 2014; Lee et al. , 2010)  has mainly used self-reported measures to assess offline friendship quantity; these measures may suffer from social desirability bias and/or impression management attempts. Our research overcomes these issues by measuring offline friendship quantity using social network data derived from peer-reported friendship. Consumer behavior researchers can identify adolescent social positions within peer groups by analyzing friendship network data. This social network approach also can complement other social position identification methods, such as observation or social integration questionnaires, that directly ask teenagers with whom they interact in their peer networks  (Kratzer & Lettl, 2009).
Date of censoring was June 30 th. Author gender was determined as per the previous described methodology  [5]. Of the 377 PubMed references found, 326 (86. 5%) presented complete datasets for first and last authors. We found that 36. 5% (119 of 326) of first and 19. 0% (62 of 326) last authors were female (Fig. 1 ). We did not have demographic data regarding age and parenting, so no correlations could be established. However, we can draw the preliminary conclusion that the gender gap in authorship of COVID-19-related papers in nuclear medicine did not widen. Future gender studies covering the entire duration of the COVID-19 pandemic will ultimately show whether it will have had an impact on female scientific output in general, including grant applications and the initiation/finalization of research projects during the pandemic.
Introduction How do we make sense of income inequality and residential segregation in cities as different as Houston, Hong Kong, and Johannesburg? Finding common ground between cities in disparate national context has the potential to illuminate overlooked factors that influence segregation and suggest new directions for study. For example, Melbourne in Australia and Boston in the United States have much in common: near identical population size, large immigrant populations in primarily white regions, sprawling suburbs, and similar levels of income inequality before redistribution. Yet, our data show that Melbourne is less segregated than not only Boston, but also nearly all American cities. Is Melbourne less segregated only because it is in Australia or are there characteristics unique to Melbourne and other Australian cities that set them apart? This chapter introduces the most comprehensive international database on segregation by income to date as a tool that can help elucidate such questions.
2018). Figure  2. 1 shows the location of cities and countries included in the sample. In nine of these countries, we have spatially small-scale data on income (or some close equivalent) for all large urban areas. In the other five, there is either only one large urban area in the country or we had access to data for a single city. We are still in the early stages of developing the international database, which limits the scope of the analysis to income. However, the results show the potential of these data and of expanding the database. For each city, we calculate the dissimilarity index to summarize the metropolitan level of segregation in an intuitive and easily comparable measure. We measure residential segregation between the bottom and top income quintiles and between the bottom and middle-income quintiles.
However, even this step proves challenging. The norm is to use commuting patterns to estimate the extent of the regional market (OECD 2012). Basically, a functional urban area is the sum of all urban clusters where a substantial share (15%) of residents commute to the largest cities in the region. The lack of such data in many countries has led researchers to look for alternatives to achieve the consistency that is essential to robust results  (Bosker et al. 2018). For this study, we use the OECD harmonized database of Functional Urban Areas (FUA). The OECD database covers Australia, Canada, Denmark, France, Ireland, Mexico, the Netherlands, the United Kingdom, and the United States. For South Africa, New Zealand, and Brazil, which are not in the database, we use an alternative definition (based on administrative definition) closest to the scale of the region.
We limit the sample to FUA and regions with a population over 500,000 people to ensure each city in the sample has sufficient data coverage in every country. 2 This gives us a sample of 194 urban regions in a total of 14 countries. In five countries, however, the data include only one city either due to data availability (Japan) or because the country has only one large FUA (Denmark, Hong Kong, Ireland, New Zealand). For each of these countries, a further obstacle is the differences in data type, spatial scale, and data collection methodology. Some differences are easily, although not perfectly, remedied. For example, France collects income data as decile threshold values. Each tract is assigned Euro denominated values that correspond to each 10% of the population of the tract. For example, if the 10% of the population with the lowest incomes have income below e8500, that is the value reported in the data.
We use a relatively crude method to pick the quintile thresholds. We estimate the income distribution for the entire region and pick the income categories closest to the quintile threshold. 3 For example, the first/bottom quintile of the income distribution of New York is $23,200. It falls within the income category bounded by $20,000 and $25,000; therefore, we use all households with income below $25,000 in every tract as the bottom quintile; if the quintile value were to fall closer to the lower bound, we would use all households in and below the $20,000 category. The results are an approximation of income quintile, but one that, on average, is close enough to separate the population into relevant categories. The last cause of concern is the method of data collection. Census offices tend to collect a comprehensive set of data on the full population and then collect a more extensive survey on a subset of the population.
Like the rest of the book, we used the Dissimilarity Index (DI) as our measure of segregation. We used the dissim function in the "seg" package in R to calculate the index for every city in the sample  (Hong et al. 2019). We ran the operation for the bottom and middle quintile, and the bottom and top quintiles. The dissimilarity index has many shortcomings (e. g. ,  Napierala and Denton 2017; Reardon et al. 2006 ), but remains useful as an intuitive indicator of a city's spatial structure. In interpreting the index, however, it is important to keep in mind that there is no such thing as no segregation, nor is the absence of segregation desirable  (Ellickson 2006). There is a level of segregation that would always be present purely by virtue of the distribution of the housing stock and the impossibility of restricting people's residential choice  (Sander and Kucheva 2016).