dataesr/scientific-paragraphs-categorization huggingface dataset:
Great Britain, however, retained the stayat-home order at this time (except for England, which was therefore not included in the study). Great Britain therefore provided a more suitable setting for Study 2 to closely replicate Study 1. Both studies were preregistered on Open Science Framework (OSF; https://osf. io/gme2u/ registrations) and the ethics committee of the university approved their conduct (IRB00011540-2020-38). All material and data are also available through the OSF link.
ACKNOWLEDGMENTS We acknowledge financial support from the  South African Research Chairs Initiative (NRF  grant  89967  to CH) and the  DSI-NRF Centre of Excellence for Invasion Biology  for the workshop in Stellenbosch in September 2018 that brought 16 researchers together to discuss the ideas that resulted in this paper. GL is supported by the  BiodivERsA-Belmont Forum  Project " Alien Scenarios " ( FWF  project no I  4011-B32 ). MM acknowledges support from  ARC   DP200101680. JMJ acknowledges support from the  Deutsche Forschungsgemeinschaft (DFG ; grant  JE 288/9-2 ) and the  AlienScenarios BiodivERsA-Belmont Forum project (BMBF  grant  01LC1807B ). JMC was supported by the  German Centre of Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig  (funded by the  German Research Foundation ;  FZT 118 ).
More specific criteria comprise: -Data availability: indicators have to be accessible, frequent, and easy to collect among the target cities; -Data quality: indicator data accurate, representative of the selected cities, and suitable for comparison among the target cities; -Indicator relevance according to the EU strategy and goals towards AVs and sustainable mobility (presented in Section 2. 2) -Indicators reflect the balance among the sustainability dimensions. iv) The discussion and validation of the indicators were done with a total of 9 experts and partners from AVENUE project. The process of discussion and consulting occurred via phone or e-mail. The consulted experts and partners were researchers from environmental sciences, social sciences, mobility systems, information science, experts on people with reduced mobility, automated shuttles manufacturers, and public transport operators.
Even more, the conceptualisation is based on a systematic process that adopts transparency and consistency, which results in the selection process being more credible and acceptable with policymakers  (Castillo et al. , 2010). As the next steps, the assessment process entails the choice of methods, data collection phase, and analysis. Main methods for data gathering comprehend the use of: -existing database (city, regional, research institutes, or more generic data, from the ministries for example) -empirical data from pilot projects deploying AVs and SAEV -qualitative methods, such as surveys and interviews -calculation method, e. g. , traffic modelling -spatial analysis methods, e. g. , using geographic information system (GIS) The use of existing surveys to measure social acceptance, or the application of local surveys, as in the case of the AVENUE project, can provide inputs about the population awareness of automated driving, openness to use it, and opinions about potential benefits and concerns towards the new mode of transport.
Acknowledgements The research leading to these results has received funding from the  European Union 's  Horizon 2020 research and innovation program  under grant agreement No. 769033 , for the  AVENUE  project. We would like to thank  Tobias Viere ,  Sylvie Mira-Bonnardel ,  Rémy Le Boennec ,  Gabriella Loveday  and all the project partners for the feedback and fruitful discussions that contributed to this paper.
Acknowledgements. This article draws on research undertaken for my forthcoming monograph: Conscription, US Intervention and the  Transformation of Ireland  ( 1914Ireland  ( -1918) (London, UK, 2022). I thank  Carl Griffin ,  Annie Tindley  and  Tom Williamson , the editors of  Rural History , for supporting the publication of this article, and the two anonymous reviewers for their detailed comments and suggestions, along with  Sarah Loom  for her time spent on editing the article.
To study how the economic impact of divorce varies by family size, we use long-running data from the cross-national equivalent file (CNEF). We concentrate on the US Panel Study of Income Dynamics (PSID), but we add a comparative perspective from data of the British Household Panel Study (BHPS), the German Socio-Economic Panel Study (GSOEP), and the Household, Income, and Labour Dynamics in Australia Survey (HILDA). The surveys included in the CNEF comprise high-quality harmonized data on household income. To study how the impact of divorce on household income varies by family size, we use between-within random-effects models allowing us to estimate within-person changes following divorce as well as betweenperson differences before divorce. We also include a control sample of individuals who stay married to adjust our analyses for overall age trends and family size effects.
Study Samples We used data from the US Panel Study of Income Dynamics  (PSID 1970 (PSID -2015) )  included in the Cross-National Equivalent File (CNEF) to analyze how women's economic wellbeing changed following divorce. PSID is a nationally representative household panel, which sampled approximately 18,000 individuals within 5,000 households in 1968 and continued to collect economic, sociological and demographic information annually until 1997. Since then information has been collected on a biennial basis. From the other CNEF countries, we added data from Germany  (GSOEP 1984 (GSOEP -2014)) , the UK  (BHPS 1991 -2008 ), and Australia (HILDA 2001 -2015). We generated a divorce and a control sample from the PSID. Our divorce sample was restricted to women who were observed to transition from a marital union to divorce between ages 18 and 50.
" Among all the data provided by Ancine, three figures caught our attention: from 2004 to 2008, the cinema audience for Brazilian films went from 16,410,957 to 9,143,052. As for foreign films, they were screened in front of 80,322,112 people in 2008. (Source: Ancine, 2009). This also means alternative sources need to be put in place throughout the cinema and audiovisual value chain. Even though Canal Brasil broadcasts a majority of Brazilian works in terms of airtime, its audience remains low. Likewise for TV Cultura, despite the promotional efforts made during the four editions of Doc. TV. And above all, the revenue increases are too low. In addition, recent political changes in the country make the prospect of a Brazil open to all directions and to the multiplicity of social and intimate adventures even more distant.
Possible Causes For A Cooler North Atlantic During The Early Last Interglacial We hypothesize that the cooler North Atlantic temperatures are caused by a glacial/interglacial transition leading into the LIG rather than a ngerprint of a quasi-equilibrated AMOC weakening induced by freshwater forcing. To test our hypothesis, we use a temporally aligned synthesis of North Atlantic SSTs together with new reconstructions of the isotopic composition of seawater (δ 18 O sw ) and planktic foraminiferal calcite (δ 18 O c ) (Figure  1 , see Methods and Supplementary Material for further details). Importantly, the temperature reconstructions are not based on oxygen isotopes, which allows us to separate temperature anomalies from oxygen isotope anomalies. The dataset shows distinct cooling of -3. 4˚C ± 1.
Data Acquisition Participants from the Age well-randomised controlled trial only were scanned at the Cyceron Centre (Caen, France) on the same MRI (Philips Achievia 3. 0T scanner). A high-resolution T1weighted anatomical image using a three-dimensional fast-field echo sequence (Sagittal 3D-FFE, TR/TE = 7. 1/3. 3 ms, FOV = 256 × 256 mm 2 , 180 slices, 1 × 1 × 1 mm 3 ) and a three-dimensional fluid-attenuated inversion recovery (FLAIR; sagittal; repetition time, 4,800 ms; echo time, 272 ms; inversion time, 1,650 ms; field of view, 250 × 250 mm2, 180 slices, voxel size: 0. 98 × 0. 98 × 1 mm 3 ) were acquired. Data Pre-processing T1-weighted images were segmented using the Statistical Parametric Mapping (SPM12) software's multiple channels segmentation procedure (http://www. fil. ion. ucl. ac.
uk/spm/ software/spm12) with the corresponding FLAIR images. Grey matter (GM) segments were spatially normalised to the Montreal Neurological Institute (MNI) template and modulated, correcting for the effects of non-linear warping but not affine transformation in order to take into account brain size variation. The anterior cingulate cortex GM volume values were obtained by applying the corresponding binary mask from the Harvard-Oxford cortical/subcortical atlases  (Desikan et al. , 2006; Makris et al. , 2006)  on the pre-processed GM maps. The anterior and posterior hippocampus were automatically segmented from T1-weighted images using the Automatic Segmentation of Hippocampal Subfield (ASHS) software  (Xie et al. , 2019)  with the ASHS-T1 pipeline (https://sites. google. com/ view/ashs-dox/home).
Neuropsychological Data Participants from the Age well-randomised controlled trial only underwent the following neuropsychological tests: the Dementia Rating Scale-2 (DRS-2,  Jurica et al. , 2001) , the Wechsler Adult Intelligence Scale Revised (WAIS-IV,  Wechsler, 1981) , the California Verbal Learning Test (CVLT-II,  Delis et al. , 2000) , the Wechsler Memory Scale (WMS-III,  Powel, 1988)  and the Category fluency developed by a reflection group on executive functions evaluation (GREFEX). The Pre-clinical Alzheimer's Cognitive Composite 5 (PACC5) is a global cognitive composite sensitive to detecting and tracking pre-clinical AD-related decline  (Papp et al. , 2017). To calculate the PACC5 in Age-Well, we first standardised the scores of each of its constituents: the Dementia Rating Scale-2 (total score), WAIS-IV Coding (raw score), CVLT-II (delayed recall score), WMS-III Logical Memory (delayed recall score), and Category Fluency (number of correct animals recalled in 2 min).
ACKNOWLEDGMENTS The authors thank all the Medit-Ageing Research Group members, IMAP members and the  Cyceron direction department, and MRI-PET  staff members for their help with recruitment and data acquisition or administrative support. We acknowledge the  Euclid team , the sponsor ( Institut National de la santé et de la Recherche Médicale ) and the participants of the study for their contribution. We are grateful to  Natalia Vilor-Tejedor  for her insightful comments and  Claudine Berr  for providing us a French translation of the  Lifetime of Experiences Questionnaire.
FUNDING This study was supported by the  European Union 's  Horizon 2020 Research and Innovation Program  (Grant agreement no. 667696 ),  Fondation Plan Alzheimer  (Alzheimer Plan  2008-2012 ),  Programme Hospitalier de Recherche Clinique  ( PHRCN 2011-A01493-38  and  PHRCN 2012 12-006-0347 ),  Agence Nationale de la Recherche  ( LONGVIE 2007 ),  Institut National de la Santé et de la Recherche Médicale (INSERM) ,  Région Normandie ,  Association France Alzheimer ,  Fondation Alzheimer ,  Fondation Recherche Alzheimer, Fondation d'Entreprise MMA des Entrepreneurs du Futur , and  Fondation Entrepreneurs MMA. EA-U holds a  Ramón y Cajal fellowship  ( RYC2018-026053-I ). The funders and sponsor had no role in the design and conduct of the study, collection, management, analysis, and interpretation of the data, preparation, review, or approval of the manuscript, and decision to submit the manuscript for publication.
DATA AVAILABILITY STATEMENT The datasets presented in this article are not readily available because IMAP data are made available upon request to the sponsor (Caen University Hospital) and the principal investigator (chetelat@cyceron. fr), and MEDIT-Ageing/Silver Sante Study data (SCD-well and Age-well trials) are made available upon request following a formal data sharing agreement and approval by the consortium and executive committee (https://silversantestudy. eu/). Requests to access the datasets should be directed to chetelat@cyceron. fr or https:// silversantestudy. eu/.
Data Register-based mortality data were collected and harmonized for all-cause mortality and lung cancer mortality by sex, age (5-year age group from 30 to 34 to 75-79 years, except Norway for which only 10-year age groups were available for the ages 30-49), and educational level over the period 1971-2012 in 11 European countries (Table  1 ). For those of age 30-39 and 40-49 in Norway, we estimated the mortality number in 5-year age groups using the Human Mortality Database (www. mortality. org). Most data comprise national populations, except for Italy, where we obtained data from Turin only; for Spain, from Barcelona only; and for the UK, from England and Wales only. Details on data sources can be found in Supplementary file 1. Educational level was measured as the highest level achieved and coded into three groups based on the International Standard Classification of Education (ISCE D-97): Up to lower secondary education (ISCED 0, 1 and 2; "low"), completed upper secondary education (ISCED 3 and 4; "mid") and tertiary education (ISCED 5 and 6; "high").
In the UK (England and Wales), only two groups could be distinguished, namely the "low" (ISCED 0, 1, 2, and 3) and "high" (ISCED 5 and 6). Birth cohorts were reconstructed using age and period (birth cohort = periodage) in the mortality dataset by country, sex, and level of education. Because data were collected in 5-year age groups and 1-to 5-year periods, the birth cohorts reconstructed were in 6-to 10-year ranges with overlaps. For example, those of age 30 to 34 who died during 1980-1984 belong to birth cohort 1946-1954, and those of age 35 to 39 who died during the same period belong to birth cohort 1941-1949. In each country-sex-education level-age-period group, we excluded cells in which the number of deaths due to lung Table  1  Characteristics of the mortality data and population data used in the analyses in each country: Types of data set (longitudinal data or cross-sectional data: unlinked or repeated); geographic coverage of the dataset (National or Urben); the period covered in the dataset; the number of birth cohorts constructed based on the dataset; the oldest birth cohort constructed; the youngest birth cohort constructed; person-years at risk followed up; number of deaths due to lung cancer; and the percentage of lung cancer deaths with low education in each gender.
The PGW method The PGW method, introduced by Preston et al. [19] , is based on a regression analysis which uses the lung cancer death rate to estimate smoking-attributable mortality from all causes, and was originally performed on data of people aged 50 and higher in 21 high-income countries from 1950 to 2007. For the age group 30-49, we used an extended PGW model developed by Martikainen et al. [20]. We applied this method to each country-sexeducation level-cohort group in our data. First, we compared lung cancer death rates (M) in our study to lung cancer death rates among non-smokers (λ) in the Second Prospective Cancer Prevention Study (CPS-II) to get the proportion of lung cancer deaths attributable to smoking (A L ¼ M -λ M ). Second, we calculated the proportion of other deaths attributable to smoking ( A O ¼ 1 -e -β 0 ðM -λÞ ).
Our study has several other limitations. First, it is a simple descriptive analysis, without a formal age-periodcohort analysis, which does not allow us to quantify the extent of the cohort-related changes, as compared to period-and age-related changes in mortality. Although our results are highly suggestive of cohort effects, it will be interesting to see this confirmed in a formal ageperiod-cohort analysis  [28, 29]. Second, despite the large size of our dataset, the scope of our analyses was sometimes restricted by small number problems. Due to the slower evolution of higher educational attainment among women, especially in Mediterranean countries, we could not always closely follow the evolution of inequalities in smokingattributable mortality among women. Moreover, to calculate age-standardized smoking-attributable mortality and inequalities for different birth cohorts, we stratified mortality by sex, level of education, age, and period, causing small numbers of deaths in some cells.
The CCMF method does not fully adjust for these differences in age structure. Finally, the coverages of data were different between countries. Italy and Spain did not offer national-level datasets, but mortality patterns have been shown to differ little between these (large) urban regions compared with the national average  [31, 32]. Moreover, there were only two levels of education in the data from the UK, resulting in some mid-educated (ISCED 3) populations categorized as low-educated. This might have caused underestimation of the inequalities of the UK. The strengths of our study include using a unique set of data on mortality by education, covering a wide range of time periods and countries. Most mortality data were based on individual mortality follow up, avoiding numerator-denominator bias  [33].
Before implementation, interventions should be equity-checked and evaluated  [43]. Population-level interventions such as price increase to tobacco and sale restriction to minors have the potential to reduce inequalities  [44]. pension system," which has received financial support from Netspar (Network for Studies on Pensions, Aging and Retirement). Data were partly collected as part of the DEMETRIQ project, which also received support from the European Commission (grant number FP7-CP-FP grant no. 278511). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Pekka Martikainen was funded by the Academy of Finland and MINDMAP, a European Commission HORIZON 2020 research and innovation action grant 667661. Mall Leinsalu was supported by the Estonian Research Council (grant PRG722).
Acknowledgements The permission of the  Office for National Statistics  to use the Longitudinal Study is gratefully acknowledged, as is the help provided by the staff of the  Centre for Longitudinal Study Information & User Support (CeLSIUS). CeLSIUS is supported by the  ESRC Census of Population Programme  ( Award Ref :  ES/ K000365/1 ). The authors alone are responsible for the interpretation of the data. This work contains statistical data from ONS which is Crown Copyright. The use of the ONS statistical data in this work does not imply the endorsement of the ONS in relation to the interpretation or analysis of the statistical data. This work uses research datasets that may not exactly reproduce National Statistics aggregates. The mortality data for Switzerland were obtained from the  Swiss National Cohort , which is based on mortality and census data provided by the Federal Statistical Office and was supported by the  Swiss National Science Foundation.
Study design and procedures Our study uses longitudinal data collected in Kinshasa, the Democratic Republic of the Congo, as part of the GEAS, implemented by the Kinshasa School of Public Health. The study includes an intervention arm to evaluate the Growing Up Great (GUG) project. In this article, we draw on data from the first two waves of data collection (T0 and T1) for both the intervention and control groups. Data collection took place in two of the largest urban poor communes of Kinshasa, Masina, and Kimbanseke, with a combined population of more than 1. 8 million residents, more than 90% of whom live in poverty. In Kinshasa, 16% of early adolescents children are out of school, and the literacy rate for 15-to 24-year-olds is 73% for girls compared with 91% for boys. One in five girls are married, and 13% give birth by 18 years of age  [27].
A total of 2,842 adolescents were enrolled in the study at T0, and 2,533 of them were identified and reinterviewed 1 year later (T1). The lost to follow-up (LFU) rates were highest among out-of-school adolescents in the intervention group (18%) and adolescents who lived with no parents (17%). No other significant patterns of LFU were observed, especially with respect to SDS scores. In addition to LFU, we excluded 14 participants with poor data quality, 45 who had missing data on the SDS at either time point, and participants who had missing values on key sociodevelopmental factors (n ¼ 229) and on adjusted covariates (n ¼ 82). Our sample consisted of 2,163 adolescents (1,030 boys and 1,133 girls).
Data collection Data collection at T0 occurred between June and November 2017 and at T1 between October and December 2018. Both surveys collected information on adolescents' social context and their health and their perceptions of gender norms. Information on the adolescent's family and socioeconomic circumstances was collected at T0. A more detailed description of the GEAS survey instruments is available at https://www. geastudy. org. Interviews were conducted in Lingala or French based on adolescent preference.
Procedure Following local research approval, participants received a document explaining the study. The research team met each volunteer couple to explain the research purpose, procedure, and confidentiality protocol in detail. Written consent was obtained prior to participation. Subsequently, interviews were scheduled and conducted at participants' homes or at the hospital. In both cases, interviews were conducted in a quiet and comfortable setting. Couples were interviewed individually the same day. Individual interviews were undertaken as they can capture both patients' and partners' individual subjective experience. 9, 36 Individual interviews enable each participant to speak freely and give their own perspective of the experience lived. 37 Recruitment ceased when we reached data saturation (i.
Data analysis All interviews were digitally audio-recorded and transcribed verbatim. We gave a fictitious name to each participant and selected the same initial for both members of a couple. Interview duration ranged from 22 to 100 minutes (average of 47 minutes). A thematic analysis following an inductive approach was used to identify patterned meaning across the dataset. This method and exploratory approach allowed us to make valid inferences from verbal content to describe a specific and little-known phenomenon. 38, 39 Following this method, the information was organized by major themes to descriptively summarize all the views collected. The analysis was conducted independently by two researchers (LL & KL) following Braun and Clarke's method:  40  (1) reading and re-reading the interviews to ensure familiarity with the material; (2) generating initial codes by coding interesting features line by line; (3) searching themes by gathering data from individual subjects; (4) reviewing themes; (5) defining, naming and writing a detailed analysis of each theme.
Differences in findings were discussed with AU until consensus was reached. Afterward, the results were discussed with the scientific team and the data were coded with QDA Miner 4 Software (Provalis Research). The analysis resulted in an initial agreement of 81% (Krippendorff 's alpha score). The first author (LL) conducted the interviews and transcribed them verbatim. The data were analyzed by LL and KL (health psychologists and researchers) under the supervision of AU, a health psychology researcher trained in thematic analysis who specializes in qualitative research. Additionally, LL and KL participated in two training sessions in thematic analysis. KL is a researcher specialized in qualitative research and trained in thematic analysis, interpretative phenomenological analysis, and lexical analysis.