dataesr/scientific-paragraphs-categorization huggingface dataset:
Policy aims LIFEPATH is devoted to develop policy implications of the scientific findings. Early work in this project will develop conceptual models of the intermediate mechanisms and pathways through which SES gradients influence organic parameters. These models will be empirically examined using data from the study countries to produce evidence of the importance of different pathways in explaining SES inequalities in chronic disease occurrence. The LIFEPATH project takes the innovative step of combining these results with the existing scientific and policy literatures, with the aim of developing a health impact assessment framework (HIA) which will allow the research team to simulate the impact of different policy choices on the overall occurrence of disease and its distribution across different SES groups.
SES variables and harmonisation The LIFEPATH consortium brings together data from early life and adult European cohorts with intense phenotyping and repeat biological samples, other large cohorts with biological samples and a large registry dataset with over a million individuals and very rich information on work trajectories and health (D  'Errico et al, 2017). To merge and analyse together data from the different LIFEPATH cohorts, information on occupational class, education, father's occupational class and income has been harmonised. The harmonisation was performed in the initial phases of the project for each of the early life cohorts and adult cohorts participating in the study (Table  1 ). Detailed codebooks have been created and given that SES indicators relevant for adults differ from those important to characterise children SES, the cohorts have been harmonised using different methods.
Data was used from 7,573 members of the 1958 British birth cohort follow-up to age 44. AL was constructed using 14 biomarkers representing four physiological systems. We assessed the contribution of financial/materialist, psychological/psychosocial, educational, and health behaviours/BMI pathways over the life course, in mediating the associations between ME, PO and AL. ME and PO were mediated by three pathways: educational, material/financial, and health behaviours for both men and women. A better understanding of embodiment processes leading to disease development may contribute to developing adapted public policies aiming to reduce health inequalities  (Barboza Solís et al. , 2016). In the same cohort we also showed that AL was associated with subsequent health status.
Pregnant or breastfeeding women were not included. The general participation rate was 27. 1%. At baseline, we collected data on cardiovascular and metabolic risk factors as well as on the prevalence of kidney and cardiovascular diseases. During follow-up, we are collecting data on new kidney and cardiovascular events. The primary endpoints are fatal and non-fatal strokes, ischaemic heart disease, heart failure and chronic kidney disease. The 1,128 participants (537 men and 591 women) belong to 272 nuclear families. strength.
COLAUS Study N=6,733 Vollenweider, P. , Waeber, G. , Firmann, M. et al. BMC The CoLaus (COhorte LAUSannoise) is an ongoing prospective study assessing the clinical, biological and genetic determinants of cardiovascular disease in the city of  Lausanne, Switzerland (Firmann et al. , 2008)     The E3N study (Étude Epidémiologique auprès de femmes de la Mutuelle Générale de l'Education Nationale) is a prospective cohort of 98,995 women aged 40-65 years at recruitment in 1990 and it constitutes the French component of EPIC. The study was established to produce a large mass of data relevant for the identification of environmental and molecular causes of cancer and other chronic diseases, and to contribute to the development of effective public health strategies. Data on residence, education, early life events, exposures, lifestyle factors and life events were collected at baseline and follow-up questionnaires were sent every 2-3 years with more than 80% response.
Mortality data were obtained by means of regular record linkage with various French mortality databases. The CONSTANCES cohort was established in late 2012  (Zins et al. , 2015). It was designed as a randomly selected representative sample of French adults aged 18-69 years at inception; 200,000 subjects will be included over a fiveyear period. At enrolment the participants fill questionnaires collecting data on health, lifestyle, individual, familial, social and occupational factors, and life events and benefits from a comprehensive health examination. The follow-up includes a yearly self-.
The initial survey was conducted between 2003 and 2006 and Deaths, CVD events, diabetes, hypertension733 participants aged between 35 and 75 years; the first follow-up survey was conducted 5. 5 years afterwards and included 5,064 participants. In each survey, data on socio-economic status, lifestyle, mental status and cardiovascular risk factors is collected by questionnaire or clinical examination. established in 2007/2008. The study takes place over seven years and follows the progress of two groups of children: 1)containing 11,134 participants aged 9 months at baseline with three study waves (9 months, 3 years and 5 years) and 2) containing 8,568 participants aged 9 years at baseline (9 years, 13 years). The dataset gathered contains multiple SES measures of parents at each wave and economic strain and anthropometric measures at each wave of child and parents.
All data from nurse visit (6,104). cancer cases, 800 controls. B-yet) Shiue et al, 2014 information about the 21 st century UK life, looking cancers (verified), CVD, vitamins 1,000 samples, 1,800 specifically at its participants' social and economic diabetes, asthma, osteoporosis, samples, Fatty acids 1,700 samples circumstances, attitudes, behaviours and health. depression, migraine Information is also collected on all new household members. The data contains information related to SES adult and early life. Gazel (France) Mortality, cancers, CVD events. Blood samples (serum, plasma, N= 20,625 Diabetes, asthma, depression, buffy-coat) for about 5,000 Goldberg, M. , sleep disturbances, migraine, subjects Zins, M. , quality of life, cognitive and Westerlund, H. et al, physical functioning. Systematic Lancet 2009 individual linkage to health insurance, hospital discharge, retirement and mortality NCDS (British 1958 The National Child Development Study (NCDS) is a birth Self-reported health outcomes national database.
Biomarkers from biomedical survey Birth Cohort Study) Constances cohort established in 1958, which follows the lives of over at each sweep (CVD, Cancer at age 44-46y (lipids, IGE, HbA1c, N= 18,555 Goodman, A. , Hypponen et al, 2005 N (Zins, M. , Goldberg, M. 17,000 people born in England, Scotland and Wales in a single week in 1958. The participants to the study are followed up at ages 7, 11, 16, 23, 33, 42, 46 and 50. A etc). All-cause mortality from register to 2008. Biomedical data from measurements (BP; IGF, CRP, Fibrinogen, Von Willebrand). Subset: metabolomics data (N=8,000). and Berkman, L. , et al, biomedical survey (for 9,377 cohort participants) was lung function; BMI) BMC Public Health. conducted when participants were aged 44-46 years 2010 (N=18,555). The datasets contains prospective SES adult life and early life.
Of those invited (N=4,320), 3,596 (83%) participated in the first cross-The Work History Panel is based on a sample of individuallevel data from the Social Security Administration archives in Italy, covering almost 8% of all Italian workers employed in the private sector in 1985-2010. Unlike all other cohorts in LIFEPATH, it does not have biological samples, but it provides very rich information on income, pensions, unemployment benefits, disability indemnities, workplace and job contracts, linked to hospital and mortality followup. Cohort administered questionnaire, a health examination every 5 Study design databases. Outcomes and clinical tests Already available/funded years and an annual linkage to social and health national biomarkers Work History Panel databases. Mortality and hospitalisation by Not available.
Acknowledgments We would like to warmly thank all the doctors who participated in this study;  Antoine Abhamon , our research assistant; the  Unions Régionales des Professionnels de Santé (URPS) Alsace and Pays de la Loire , who were responsible for the organizational aspects of the project; and  Drs Jean-Baptiste Caillard ,  Thomas Herault , and  Fabien Rougerie  for their support and assistance throughout the study. This project was supported by institutional funding from the  Faculty of Medicine, University of Geneva , and by a funding from the  URPS Alsace and Pays de la Loire  and from the  Collège Regional des Généralistes Enseignants Alsace.
Overview For this experiment, we recorded three foreign exchange students (Taiwanese, Brazilian, and Syrian) reading an identical passage in French containing intentional grammar errors. The students were all female, studying French at the University of Grenoble, a city in the French Alps. A total of 343 French university students, divided into three groups, listened to one of these clips per group. They wrote down errors they heard and assigned a global score for proficiency, pronunciation, and academic potential. The data were analyzed in two ways. The first compared differences in judgments across the three groups of participants, e. g. , judgments of those who heard the Taiwanese speaker were compared to those who judged the Brazilian, and Syrian speaker. Since these judgments were influenced by real differences in the heard speech patterns, we referred to this as the analysis of the objective differences on judgments effect (Objective difference analysis).
Participants Judges for this study were graduate and undergraduate students at the University of Grenoble in France. The age of the judges (N =343) had a mean (M) value of 20. 2 years, with a standard deviation (SD) of 3. 18. Data from judges were eliminated if they selfidentified on a questionnaire that they either a) did not speak French as a first language, b) were not born in France, or c) fluently spoke the first language of the speaker they were listening to. The first two criteria were chosen to ensure that all judges were proficient in French language and grew up surrounded by French culture. The third criterion was chosen to eliminate the possibility that a judge who spoke the same language as the speaker they listened to might demonstrate some form of bias toward the speaker. After these eliminations, the final pool of valid judge data contained 313 samples.
We call this protocol 'Guess origin'. After completing their evaluation, the judges recorded biographical information including their date of birth, country of birth, age, first language(s), and additional languages spoken. After completing about half of the data collection, a survey of responses revealed that almost no listeners identified the origins of the Syrian speaker as "Arabic" 4. In order to ensure that we were able to complete the second analysis, measuring the effects of an "Arabic" stereotype, we changed the protocol for the remainder of the data collection. In our second protocol, which we call 'Explicitly informed', rather than giving participants a photo and asking them for the geographical origins of the speaker, the researcher told judges the first language of the speaker (Arabic, Mandarin, or Portuguese) before the clip began.
However, recent studies suggest that growth effects of related variety may be specific to knowledge-intensive industries only  (Content and Frenken, 2016). In consequence, we focus here solely on innovation production and on patent intensive sectors. Moreover, we compute variety indexes using the technological classification provided in patent documents. In particular, we exploit technology information using the International Patent Classification (IPC) codes contained in patent applications to the European Patent Office (EPO) to build the diversity indexes, establishing a more direct link between regional diversification and its underlying technological nature. Second, and more importantly, our study is one of the few investigating cross-regional linkages and related variety, for which trade data has mostly been used to depict linkages across regions  (Boschma and Iammarino, 2009; Tavassoli and Carbonara, 2014).
This indicator is built in a similar fashion to  Boschma and Iammarino (2009) : %&'(%)*%++ = ,-(. / (1) '(. (1) (10) where ,-(. / (1) is the entropy measure obtained with data for extra-regional backward citations in four-digit technologies (subclasses) other than j, but within the same 35field technology, and '(. (  1 ) is the relative size of the four-digit patent technology j in the total regional patenting. The idea is that for each four-digit patent technology in a region (e. g. , technology C07G), we measure the entropy of the citations to patents from the other four-digit subclasses (e. g. , C07K, C12M, C12N, C12P, C12Q, C12R, and C12S) pertaining to the same 35-field sector (e. g. , the biotechnology field), excluding the focal four-digit subclass itself (i. e. , subclass C07G). In order to complement the analysis, and again in line with  Boschma and Iammarino (2009) , we also use an index to determine the similarity between the external knowledge entering a region and its existing knowledge base (SIMILARITY).
Data We use a sample of 255 NUTS2 European regions of 25 countries -EU-27 (except Cyprus and Malta, as well as Denmark and Greece, for which we have very little information at the NUTS2 level) plus Norway and Switzerland, to estimate a regional KPF from 1999 to 2007. Our dependent variable, innovation output, is measured by patent applications, a variable widely used in the literature to proxy innovation outcomes. As widely documented, this proxy presents serious caveats since not all inventions are patented, nor do they all have the same economic impact, as they are not all commercially exploitable  (Griliches, 1991). In spite of these shortcomings, patent data have been considered useful for proxying inventiveness as they present minimal standards of novelty, originality and potential profits, and as such are a good proxy for economically profitable ideas  (Bottazzi and Peri, 2003).
We retrieve patent data at the regional level from the OECD REGPAT database -July 2013 edition  (Maraut et al. , 2008). When patents have been produced by inventors resident in different NUTS2, they have been fractionally assigned to the different regions, according to the number of inventors out of all inventors listed in a patent living there -fractional counting. We slightly modify our dependent variable in order to account not only for the quantity of patents produced, but also for their quality -as explained in previous sections. As largely argued in the related literature, the number of forward citations received presumably conveys information about the relevance of patents, thus providing a way of assessing the enormous heterogeneity in the value of patents  (Hall et al. , 2005). This extreme is confirmed by several studies that have found strong correlations between the number of forward citations received and the economic value of patents  (Trajtenberg, 1990; Harhoff et al.
, 1999; Lanjouw and Schankerman, 2004). We therefore use citations as an imperfect, but widely used, proxy for patent quality and weight the number of patents by the number of citations the patent has received in subsequent patent documents. 7 As for the explanatory variables, R&D expenditures data (both private and public expenditures in regions) were collected from Eurostat and some National Statistical Offices. Data to measure ShareInd and High-tech Empl were collected also from Eurostat. As for the level of human capital of regions, which likely determines the regions' capacity to transform technological inputs into outputs, we use the variable HRST, which, according to Eurostat, include all tertiary educated workers employed in science and technology occupations (over all workers in the region).
8 As mentioned above, variety indexes are constructed using the information of IPC codes listed in patent documents (again from the OECD REGPAT database -July 2013 edition). Again, based on the available data, there are 635 four-digit patent classes, 35 technological fields and 5 broad fields. Knowledge flows are proxied through patent citations as explained in section 3. 3. We use unit-record data retrieved from EPO patents to construct the patent citation variables (OECD Citations database, July 2013 edition; see  Webb et al. , 2005). All the patent data used to build the focal explanatory variables are retrieved for moving time windows of five years. Table  1  provides summary statistics of the variables used in the present analysis whereas the correlation matrix of explanatory variables is given in Table  A.
Robustness analysis Several robustness analyses are presented in the online appendix. In table A. 4 we test the theoretical statements discussed earlier through the use of a more general dependent variable on regional economic performance, such as the annual growth rate of GDP per capita. Despite the fact that GDP growth does not reflect a direct measure of innovation, its use avoids potential criticisms derived from the use of patent data to build both the dependent and independent variables, as we did in previous sections. Data on regional GDP per capita is retrieved from Eurostat, and the dependent variable is computed as the log of the ratio between per capita GDP at time t 1 and per capita GDP at t 0. Moreover, regressions include the log of per capita GDP at t 0 as an additional control, as done in much of the growth literature.
Acknowledgements The authors would like to share their appreciation for the helpful comments received from  Ron Boschma ,  Tom Broekel ,  Jackie Krafft ,  Francesco Quatraro  and  Pier Saviotti , as well as the participants of the  Summer School "Knowledge Dynamics, Industrial Evolution , Economic Development"  KID2015 in Nice and in the 3 rd Geography of Innovation Conference in Toulouse. The authors also acknowledge financial support from the project " Redes de colaboración tecnológica e innovación. Determinantes y efectos sobre la competitividad de las empresas españolas " funded by the  Fundación BBVA Ayudas a Proyectos de Investigación 2014. Ayudas Fundación BBVA. Ernest Miguelez  acknowledges financial support from the  Regional Council of Aquitaine (Chaire d'Accueil en Economie de l'Innovation, to Francesco Lissoni).
Determination of dental age In the case of the equids, we looked at the eruption of deciduous teeth and their replacement by permanent teeth  [14]. We used the nomenclature for the anatomical descriptions and for assessing the orientation of the jugal teeth established by Levine  [14]. The dental ages of permanent superior and inferior premolars and molars with completely worn occlusal surfaces were calculated based on their crown heights. To estimate age, the parameters of a third-order polynomial regression that relates crown height to age were used  [13]. The relevant equation is AGE ¼ P 3 k¼0 a k Á ðcrown heightÞ k ; were a k is the regression coefficient  [13, 14, 96]. The data used in the calculation of the polynomial regression are those provided by Fernandez and Legendre  [13]. In keeping with the recommendations of Fernandez et al.
2014a). The sequencing data were de-multiplexed using deML  (Renaud et al. 2014b ) and the sequences aligned against the human reference genome 19 using BWA's aln algorithm  (Li and Durbin 2009). After duplicate removal using PicardTools v2. 1. 1 (https://github. com/broadinstitute/picard), we performed an iterative alignment for each library individually to obtain mtDNA consensus sequences. In the first step, we extracted all sequencing reads of a library that aligned either to the mitochondrial genome or to a list of nuclear copies of mtDNA (NUMTs)  (Li et al. 2012). We subsequently aligned these reads to the revised Cambridge Reference Sequence (rCRS;  Andrews et al. 1999 ) using BowTie2's very-sensitive algorithm  (Langmead and Salzberg 2012)  and called a consensus sequence. In the second step, the reads were re-aligned to the library's respective consensus sequence generated in the first step, using the same BowTie2 settings.
After the second alignment step, we called a final consensus sequence that was used throughout the rest of the analysis. Final sequences in fasta format were aligned to the Revised Cambridge Reference Sequence (rCRS  (Andrews et al. 1999 )) with the multiple sequence alignment software Mafft  (Katoh and Standley 2013) , and manually inspected for alignment errors with Bioedit ver. 7. 2. 5  (Hall 1999). The two poly-C regions  (np 303-315 and 16,183-16,194)  were excluded from the subsequent analysis; although one position (16,189) diagnostic for haplogroup B2 is therefore not considered in the haplogroup-calling analysis, the additional substitutions defining this haplogroup that occur elsewhere in the mitochondrial genome enable unambiguous assignment of sequences to lineage B2.
We excluded Barasano (n=4), Kubeo (n=5), Cofan (n=6), Cabiyari (n=1), Guambiano (n=1), and Nasa (n=1) from all the analyses except the haplotype networks, since this analysis represents the evolutionary relationships among individual sequences. We furthermore excluded nine individuals with maternal ancestry outside of NWA (labeled 'Mestizo' in Table  1 ) from all analyses. After merging and filtering as described above, 412 sequences from 24 groups were kept in the population-based analyses. Based on information from D-PLACE  (Kirby et al. 2016 ) and HG database (https://huntergatherer. la. utexas. edu/home, accessed on 06. 06. 2017), we divided the populations into agriculturalists (AG) and hunter-gatherers (HGP). In the latter category we included the Nukak, who currently still practice a foraging way of life, as well as the Puinave, Sikuani, and Guayabero, who have Multidimensional Scaling analysis (MDS) was performed on the matrix of pairwise ΦST values to visualize the distances between populations.
2016; Yunusbayev et al. 2012). For the matrix of river distances a value of zero was given to populations living along the same river or on rivers that are closely connected, and a value of one was given to populations living on different rivers. The sharing of haplotypes between populations was estimated with in-house R scripts as the proportion of pairs of identical sequences shared between populations. Additionally, networks of haplotypes were constructed with the software Network ver. 4. 6. 1. 3 and visualized with Network Publisher ver. 2. 0. 0. 1 (http://www. fluxus-engineering. com). Finally, Bayesian skyline plots (BSP) were constructed with BEAST ver. 1. 8. 2  (Drummond et al. 2012). For this analysis, the best substitution model was estimated with jModeltest 2. 1. 7 (Darriba et al.
RESULTS We generated 439 complete mitochondrial sequences to an average coverage per sample of 1030x, which were deposited in GenBank with accession numbers: XXXXXXXX-XXXXXXXX and XXXXXXXX-XXXXXXXX. All sequences belonged to one of the main Native American haplogroups, namely A2, B2, C1 and D1. Haplogroups A2 and C1 were the most frequent lineages in the NWA populations (excluding the so-called 'Mestizos'), with more than half of all sequences belonging to A2 (90 haplotypes in 138 sequences) and C1 (95 haplotypes in 181 sequences) together; in contrast, B2 (49 haplotypes in 73 sequences) and D1 (32 haplotypes in 38 sequences) were less frequent. Table  2  provides a breakdown of the haplogroup frequencies for the ethnolinguistic groups included in the population analyses. [Table  2 here]  A Correspondence Analysis (CA) (Figure  2 ) shows the clustering of populations based on the frequency of sub-haplogroups.