dataesr/scientific-paragraphs-categorization huggingface dataset:
Methods Sampling, chronology development and radiocarbon dating. Subfossil pine trunks (Pinus sylvestris L. , n trees = 18: 16 in-situ, 2 uprooted) were collected adjacent to the Barbiers riverbed in southeastern France (44°21′11″N, 5°49′50″E, Figs  S2, S7 ) from an alluvial deposit spread across three tributaries on the southern foothills of Saint-Genis Mountain  18, 35. Cross-sectional disks were cut and their surfaces were polished using various sandpapers (80 to 1200 grit sizes) to assist with tree-ring identification. Tree-ring width was measured on numerous tracks per disk using a LINTAB measuring table combined with TSAP-Win software. Samples were initially radiocarbon dated at low-resolution  18, 35  , within 1σ 14 C error range, and tree-ring width series of all trees were cross-dated visually and statistically (TSAP-Win) to build two initial floating chronologies (BarbA n trees = 6, BarbB n trees = 3, Fig.
This analysis permitted the inclusion of Barb-17 and Barb-5 (both initially BarbB) into the BarbA chronology, where initial tree placement results 18 required secondary confirmation, as there was more than one statistically plausible cross-dated position. A sequence age of 12 836 to 12 594 cal BP was found for the Barb12-Barb17 sequence, thus allowing the connection of the two tree-ring width chronologies into one  21  and placing the entire floating dendroisotope chronology (this study) at 12 906 to 12 594 cal BP (Fig. S3 ). Chronological synchronization between the three annually resolved data sets from Greenland (NGRIP  6  ), western Germany (Meerfelder Maar  37  ) and southern France (Barbiers, this study) was completed using (1) volcanic tie points (Vedde Ash  38  ) and (2) radiocarbon wiggle matching  21.
+. f 147rH 0 03T 0 11TRX 0 62 (2) δ = -. + -. -. C ( 0 The resultant δ 18 *O SW was subtracted from δ 18 O cell to extract the proportion of δ 18 O cell changes due to changes in (leaf level) vapour pressure (∆δ  18  O cell-SW ) over sourcewater; utilized in a dual-isotope modelling approach  21  to infer periods of high and low humidity, by comparing decadal-scale trajectories (+/-) of ∆δ  18  O cell-SW and δ 13 C (Fig. S4 ). Inter-annual δ 18 O cell variability (Fig. 2d , ∆‰) was calculated by subtracting current year δ 18 O cell (‰) from previous year δ  18  O cell (‰) to calculate ‰ difference per year of each individual tree. These values were then converted to absolute differences and subsequently averaged to produce a mean curve (∆‰). Change point analysis was completed using an R package ('changepoint')  53  to find the position of multiple change points within the modelled sourcewater time series (δ 18 *O SW ) according to mean and variability; with four and one change points found, respectively (Table  S2 ).
Acknowledgements This study was funded by the joint  German Research Foundation  (DFG, no. HE3089/9-1  and  KR726/10-1 ) and  Swiss National Foundation  (SNF, no. 200021L_157187/1 ) collaborative project (DACH), ' Improving Late Glacial European tree-ring chronologies for accurate climate archive dating' and it is a contribution to the climate initiative REKLIM Topic 8 ' Abrupt climate change derived from proxy data ' of the  Helmholtz Association. UB received additional funding from the  Czech Republic Grant Agency  project no. 17-22102  S.
The work described in this paper concerns enriching the database with meta-data from analysing email exchanges during collaborations and understanding better the complementarity of using email with a conferencing tool. The study focuses on collaborations within the Orange Company. However, we believe that the results will be applicable to other companies or other institutions since the need to enhance collaborations is a common goal and our method of tracing Internet messages can be applied to all situations where email is used. Section 3 presents this proposal and section 4 describes the adopted methodology. The results concerning conversation threads in collaborative business messaging are presented and discussed in Section 5.
Proximities Balancing We also analysed the Sub-Proximities. Applying a threshold to the Gold Standard values identified pairs of messages that have the highest subjective proximities. However the data dispersion analysis in Table  3  indicates that we cannot directly compare the Gold Standard with the calculated proximities since the difference between the two sets of data, in particular regarding average values, is too great. Hence we apply a compensatory coefficient to the Gold Standard so that its average equals that of the Calculated Proximities (Table  6 ). We should apply the compensatory coefficient to the computed proximities rather than to the Gold Standard (which is the reference). However, some of the CPs would have a value greater than 1, which is meaningless. After applying this compensatory coefficient, the average absolute deviation between the Gold Standard and calculated proximities changes from 0.
186 to 0. 124, giving a more reasonable comparison between with fewer differences between the dispersions of the datasets. Figure  2  shows the calculated proximities of the collaborative conversation threads. We focus on the Gold Standard proximities with the highest values between the three conversation threads (A, B and C). An interesting calculated proximity is the one between messages A7 and B1, which belong to different ECTs and share an attachment name identical to the subject of the conversation A. The calculated proximity is 0. 719 and the Gold Standard value is 0. 636 which is close (less than 0. 1). From the sub-proximities in table 7 the semantic and temporal proximities have a value of 1 and 0. 977 respectively, whereas the interlocutors proximity is only 0. 179. This means that a collaborative thread has been identified by the calculation of proximities (and verified by the Gold standard) and that this collaboration is due to the messages being semantically and temporally close, and not due to interlocutors' closeness.
Introduction: To evaluate the expected impact of the Algeria national immunization program (NIP) and potential impact for a Tunisia NIP, this study assessed the public health and economic value of vaccination, through a cost-effectiveness analysis, for a PCV13 or PCV10 NIP, compared with no vaccination. Methods: A decision-analytic model was programmed in Microsoft Excel TM and adapted to evaluate the clinical and economic outcomes of PCV vaccination. Assuming a steady state, the model estimated invasive pneumococcal disease (IPD; bacteremia and meningitis), all-cause pneumonia (inpatient and outpatient), and allcause otitis media cases as well as the associated costs from a payer perspective. The base case scenario assumed direct effects for both PCVs and indirect effects (against IPD) for PCV13 only.
The PMS component is the heart of the application;, it loads the unclassified profiles from the ontology and invokes an external inference engine called Pellet  [23]. The Semantic Query-Enhanced Web Rule Language (SQWRL) is the language used to read profiles from the ontology; it is a query language designed specifically to read information  from ontologies  [24]. An example to load student transcripts from the ontology is shown below: Student(?s)^hasTranscript(?t) ^consistsOfEnrolment(?t,?e) ^is-An-EnrolmentForCompetency(?e,?c) ^hasGrade(?e,?g) → sqwrl:select(?s,?t,?c,?g) This query will return the list of students along with their full transcript which contains the gained competencies and the grades they obtained for each competency. SQWRL is accessed through the SQWRL API (Application Programming Interface) which is a Java-based API and is the interface between the PMS component and the ontology.
V. THE ONTOLOGY DESIGN Several existing methodologies for constructing ontologies are available. We have followed the ''Ontology Development 101'' developed by Natalya Noy and Deborah McGuinness  [27]. The language chosen to write the ontology is the OWL 2 Web Ontology Language  [28] ,  [29] , and the tool used to build the model is Protégé (Version 4. 3)  [30]. To develop the ontology, we considered the following steps:.
VIII. ONTOLOGY EVALUATION To assess the proposed ontology, the occupation of Information Security Analyst has been used to evaluate the Computer Science and Engineering (Qatar University) curricula. The data used to feed the job occupation and its competencies is derived from the data published on O * Net. The profile describing the job in O * Net is quite extensive, so we have opted to take only a subset of the highly important competencies based on the importance ranking of the O * Net. Therefore, any competency with 50% or more importance has been selected. The data used to feed the courses and study plans were derived from the Computer Science and Engineering curriculum. Two study plans were defined: one study plan containing courses on Computer Networks and Computer Security and we called it sp1 while the other study plan sp2 is missing these courses.
Stratified sampling is therefore an advantage rather than an impediment. Elongated blanks are here defined as any blank with a length:width ratio of at least 2. Kmeans cluster analysis  (Pargeter & Pargeter 2016; Pargeter & Redondo 2016)  was used to distinguish large elongated blanks (or blades) from small elongated blanks (bladelets) in the assemblages. Inter-assemblage differences were quantified using non-parametric statistical   (Dunn 1964; Carlson 2017: 182). All statistical tests have been performed with R, R Studio and the graphs have been generated using the ggplot2 package (R Core Team 2016; RStudio Team 2015;  Wickham 2009). A list and description of the variables recorded as well as the full database are presented in Supplementary Information (SI) 1 and 2. Table  3  shows the sample studied.
Acknowledgments This research project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie  grant agreement No  655459 , from the  Fyssen Foundation  (grants to AL) and from the  French National Research Agency  project ' Big Dry '  ID   ANR14-CE31-0023-03. We would like to thank the department of Ancient Egypt and Sudan of the  British Museum  for permission to study the lithic assemblages of  E71K18C  and  E71K20. Many thanks to  Erella Hovers  for her support and valuable comments throughout this research. We thank  S. Kadowaki , three anonymous.
Data Analysis All analyses were performed using R-project software (R Core Team, 2015) and RStudio software  (RStudio Team, 2015). In the following analyses, ANOVAs were Greenhouse-Geisser corrected  (Greenhouse and Geisser, 1959)  when the assumption of sphericity was violated; uncorrected degrees of freedom and epsilon values (εGG) are reported. In the post hoc analyses all p-values have been corrected with Holm's method  (Holm, 1979). For the OM effect, effect sizes are reported following the recommendation of  Lakens (2013). Additional analyses of children's performance (absolute error) and of the operational bias (ratio) are reported in the Appendix A.
Included countries and sampling This study uses data from seven European countries: the United Kingdom, France, Belgium, Switzerland, Estonia, Romania, and Spain. Prior to this study, five country case studies have been conducted that provide detailed analysis of inter-ethnic partnership in the United  Kingdom, Spain, Estonia, Switzerland, and Romania (González-Ferrer et al. 2016; Hărăguș 2016; Kulu and Hannemann 2018; Potârcă and Bernardi 2018; Puur et al. 2018) , which inspired the idea for this comparative paper. In order to make this paper even more representative of the European context, further research partners from France and Belgium were recruited to participate in this study. Data The information focuses on the ethnic Hungarian population in the region of Transylvania and contains retrospective partnership information, including the ethnic background of each partner.
In the case of Switzerland, data from the Family and Generations Survey (originally Enquête sur les familles et les générations (EFG) 2013) is used. This survey was conducted by the Federal Statistical Office as part of the new census of the Swiss population. Among other factors, the EFG survey collected information on the individual's current marriage and retrospective information on marital union history. Estonian data was retrieved from two retrospective studies: The Estonian Generations and Gender  Survey (2004 Survey ( /2005) )  and the Estonian  Family and Fertility Survey (1994). For Belgium, this study uses data from the 2001 Census, which contains retrospective information on the first marriage of women, including the ethnicity of the partner. For Spain, this study exploits data from the 2007 National Immigrant Survey (NIS), which was conducted by the Spanish National Institute of Statistics.
Given the comparative nature of this study and the harmonisation process between the different data sources, only the most important demographic and socioeconomic confounders could be included. This study analyses time until first marriage using age as the baseline, categorised into five-year age groups  (15-19; 20-24; 25-29; 30-34; 35 and older). Individuals become at risk of marriage at their 15 th birthday. Legal age for marriage varies across the seven countries analysed here, as well as among the various countries of origin. For immigrants who arrived in the country of destination at later ages, risk time also includes time spent in the country of origin. Therefore, the start age is set early deliberately in order to capture young age marriages in some migrant groups. Rare cases where an individual stated that s/he married before the age of 15 were removed from the sample.
(4) Finally, Equation 5 presents a log-linear model for the hazard of first marriage while including additional covariates: ln = + , ( 5 ) where reflects the log baseline hazard function of first marriage in terms of age, is a covariate matrix, and β represents a vector of the parameters measuring their effects. Both types of marriage (endogamous and exogamous) are treated as competing risks, w ith a n i ndiv idu al b e ing c e ns o re d f o r o ne ty pe of marriage i n t he c as e t ha t t he alternative event occurs. Those two competing risks lead to the following model: ln = + ln = +. ( 6 ) Data from each country was aggregated using the different combinations of age, birth cohort, education, and ethnic group, and subsequently pooled into one common database. Marriage risk was analysed using a Poisson regression model (  6 ) distinguishing between the competing outcomes of endogamy (A) and exogamy (B), while considering the impact of the sociodemographic confounders.
Acknowledgements The research leading to these results has received funding from the  European Union 's  Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement no. 320116  for the research project  FamiliesAndSocieties. The authors also acknowledge support from the  Estonian Research Council  (grant  PRG71 ). This publication further benefitted from the support of the  Swiss National Centre of Competence in Research LIVES -Overcoming vulnerability: Life course perspectives (NCCR LIVES ), which is financed by the  Swiss National Science Foundation  (grant number:  51NF40-160590 ). The authors are grateful to the  Swiss National Science Foundation  for its financial assistance.
Study Population The ANRS-IPERGAY study was a double-blind randomized combined prevention trial conducted in France and Canada, which consisted in providing sexual activity-based PrEP to MSM to prevent HIV transmission. Briefly, the main inclusion criteria were as follows: HIV-negative males or transgender women having sex with men, aged 18 years or older, and at high risk of HIV acquisition (defined as unprotected anal sex with at least 2 different partners over the previous 6 months). The following PrEP dosage scheme was prescribed: 2 pills between 2 and 24 hours preceding a sexual encounter, followed by 1 pill 24 hours and another 48 hours after the first drug intake. Participants completed questionnaires at annual and biennial visits, which collected, respectively, data on participation and active attendance in community-based activities on prevention during the previous 12 months, together with psychosocial data, and data on sociodemographic and socioeconomic characteristics (age, educational level, and employment status).
Participants also completed an online questionnaire every 2 months (follow-up), which collected data on sociodemographic characteristics, alcohol and recreational drug use, sexual behaviors, and PrEP adherence during their most recent sexual encounter. Molina et al  (2015) provide a comprehensive description of the ANRS-IPERGAY trial's methodology and results. In November 2014, all participants still being followed up (n = 336) were invited to voluntarily enroll in the OLE study of the ANRS-IPERGAY trial, which immediately followed the discontinuation of the placebo-controlled randomized phase. As part of the OLE study, participants would have access to PrEP until its full approval by the French National Agency for Medicines and Health Products Safety (ANSM), which was set to occur before June 30, 2016 (in reality, full approval came in January 2016).
Participants in the screening period (n = 33) who had not yet been randomly assigned were also eligible, as long as they met all the inclusion criteria for the double-blind trial. All participants in the OLE study (n = 361) provided oral informed consent, 353 (98%) of them also providing written informed consent. 24 Only the latter were retained for analysis. A total of 3046 questionnaires were available for analysis, accounting for 3450 visits. In July 2015, participants were asked to answer an additional section in the 2monthly follow-up questionnaire, which addressed the use of an extended list of psychoactive substances during their most recent sexual encounter. This substudy included only retained participants in the OLE study with available data regarding their use of these psychoactive substances, during their most recent sexual encounter (n = 331, accounting for 1657 questionnaires).
Other Explanatory Variables Data at the most recent annual and biennial visits (see above) were assessed by evaluating the consumption of psychotropic drugs (anxiolytics and antidepressants) during the previous 12 months, lifetime experience of depression, and participants' scores for the brief sensation seeking scale (BSSS-4). 25 ata collected on sexual behaviors during participants' most recent sexual encounter included: type of partner (main partner, known or unknown casual partner, sex party); type of sexual practice (oral sex only, oral sex and/or insertive anal sex, oral sex and/or insertive anal sex and/or receptive anal sex); hardcore sexual practices (fisting, sadomasochistic practices, or other hard practices); high-risk HIV exposure (condomless anal sex or not); and HIV-risk perception (perceived level of risk using a 10-point visual scale).
26 Indeed, one of the main advantages of this model is that it accounts for the unknown correlation between outcomes when the data set has a longitudinal structure. 27 Potential correlates of correct PrEP use were tested for in the univariable analyses, including chemsex practice, sociodemographic, economic, and psychosocial characteristics at the most recent follow-up visit, and sexual behaviors at the most recent sexual encounter. Variables with a P-value #0. 25 in the univariable analyses were considered eligible for the multivariable analysis, except for partner type, which although highly associated with correct PrEP use, was too closely correlated with practicing chemsex to be of use. To test whether missing data on PrEP adherence at the most recent sexual encounter might have biased the estimations of the regression analyses, we used a 2-step Heckman model  28  adapted for longitudinal studies.
In addition, in MSM chemsexers, PrEP could act as a bridge for psychosocial follow-up, and constitutes a key element in a combined prevention strategy among high-risk MSM. 43 ome study limitations have to be acknowledged. First, the data used came from the IPERGAY trial, which only selected MSM who reported HIV high-risk behaviors. Second, because those included were motivated to participate in the trial, they are not representative of the general population of MSM. However, the population studied here may constitute the most suitable MSM subgroup for receiving PrEP to prevent HIV transmission. Third, our findings are based on self-reported data, which are known to be subject to desirability bias. However, in terms of drug use, it has been shown that self-reported questionnaires are reliable.
44 Moreover, regarding self-reported adherence, we used an objective measurement based on PrEP pill count, which we found to be associated with practicing chemsex, although it cannot be considered an exact measure of adherence. Finally, because of the high proportion of missing data, we had to use a Heckman model to test for potential selection bias. As the IMR was not significant, it means that our results are not biased by missing data on self-reported correct PrEP use. To conclude, our findings showed that chemsexers are more likely to report high-risk sexual practices but also have a higher perception of HIV risk. Furthermore, they are also more likely to use PrEP correctly when practicing chemsex. Not only is PrEP a suitable tool to reduce HIV-risk transmission among chemsexers, it also provides a unique opportunity to concomitantly offer other prevention interventions in this key at-risk population.
ACKNOWLEDGMENTS The authors thank the participants of this study for their time and dedication to this research for the benefit of their community; our community advisory board- S. Karon ,  D. Villard ,  J. M. Astor ,  D. Ganaye ,  T. Craig ,  B. Brive ,  R. Orioli ,  M. Vanhedde ,  H. Baudoin , and  H. Fisher- for their continuous support during the study; members of COQ-SIDA in Canada and REZO in Canada (including  D. Thompson ) who supported this work;  Drs. Stephen Becker ,  Papa Salif Sow ,  Josy Presley , and  Mary Aikenhead at the Bill and Melinda Gates Foundation ;  Dr. James Rooney ,  Andrew Cheng ,  Howard Jaffe ,  Cécile Rabian , and  Pascal Petour at Gilead Sciences  for their assistance in study-drug provision; members of ANRS:  V. Doré ,  I. Porteret ,  L. Marchand ,  S. Lemestre ,  A.
Supported by  ANRS , the  Canadian HIV Trials Network , the  Fonds de Dotation Pierre Bergé pour la Prévention , and the  Bill and Melinda Gates Foundation. Gilead Sciences  donated the  TDF-FTC  and placebo used in the study, and partly funded for the pharmacokinetics analysis. Presented at the 9th International AIDS Conference in Paris; July 23-26; 2017. J. C. has received consulting fees from  Gilead ,  AbbVie , and  BMS. C. T. reports receiving support from  Gilead Sciences and Pfizer. G. P. has received consulting fees from  BMS ,  Boehringer Ingelheim ,  Tibotec ,  Nephrotek ,  Gilead Sciences, Roche ,  MSD ,  Abbott , and  ViiV Healthcare , and research grants from  BMS  and  Gilead Sciences. J. -M. M. reports receiving support as an adviser for  Gilead Sciences, Merck, Janssen, Bristol-Myers Squib (BMS) , and  ViiV Healthcare , and research grants from  Gilead Sciences and Merck.
In this paper we combine household surveys, national accounts, income tax data and wealth data in order to estimate income concentration in the Middle East for the period 1990-2016. According to our benchmark series, the Middle East appears to be the most unequal region in the world, with a top decile income share as large as 64%, as compared to 37% in Western Europe, 47% in the USA and 55% in Brazil. This is due both to enormous inequality between countries (particularly between oil-rich and population-rich countries) and to large inequality within countries (which we probably under-estimate, given the limited access to proper fiscal data). We stress the importance of increasing transparency on income and wealth in the Middle East, as well as the need to develop mechanisms of regional redistribution and investment.