dataesr/scientific-paragraphs-categorization huggingface dataset:
• Do you have access to the original search strings, sources, and can you read these files (proper software available)? • Was the original search protocol adequate and appropriate or does it need revising? • Do you know when the initial search took place and which time boundaries were set up at that time? If not, can you contact the authors to get those details? • If relevant, do you have similar details regarding searches in grey literature? • Do you have access to the same sources of documents (e. g. database platforms), including institutional websites, subscriptions? • Will the same languages be used?.
Durations at the time of writing, in the Web of Science Core Collection some articles may be accessible from 1900 although by no means all, in Scopus they may date from 1960). food science and nutrition) versus multidisciplinary sources (Scopus, Web of Science); • Geographical regions (e. g. Latin America, HAPI- Hispanic American Periodicals Index, or Europe CORDIS). It may be necessary to search region-spe- cific bibliographic sources if the evidence-synthesis question has a regional focus [2]; • Document types: scientific papers, conference or proceedings, chapters, books, theses. Many univer- sity libraries hold digital copies of their theses, such as the EThOS British Library thesis database. Confer- ence papers may be a source of unpublished results relevant for the synthesis, and may be found through the BIOSIS Citation index or the Conference Pro- ceedings Citation Index (Thomson Reuters 2016, in [13]).
9 million ha to 6. 3 million ha between 1997 and 2007 in Indonesia  (Deininger, 2011 , see also  Li, 2011). 4  For example in  Mexico in 1992 , in Ethiopia in 1995 and 2002 , in Mozambique in 1997, etc. 5  For example through the Land Matrix Database project  (Anseeuw et al. , 2012). 6 In the 1970s, this debate also created much stimulation among the French-speaking scientific community, as shown for example by the works of C. Servolin (1972)  on French agriculture or those of the French school of economic anthropology  (Meillassoux, 1975)  in the case of developing countries. France, when the development of new farm types seemed to question the exclusivity of the family farm model  (Cochet, 2008; Hervieu and Purseigle, 2009; Purseigle and Chouquer, 2012, 2013; Cochet 2017)  Despite this recent explosion, few works have tackled directly the issue concerning the real impact of the capital/labour separation on the actual production process and value added distribution.
Study design and data sources A retrospective cohort was constituted, enrolling all HIV-infected adults (!18 years) released from the Remire-Montjoly CF between January 2007 and December 2013, after incarcerations of more than 30 days. In case of multiple incarcerations during this period, the last one was considered as the index incarceration. The data collection was performed at the end of 2014. Thus, all included patients had at least an observational period of a year, up to a maximum follow-up of seven years. The main source of data was the electronic medical records (EMR) NADIS, used for HIV follow-up at the regional French Guianese level. Patients were asked to sign a consent form before their data were included in NADIS. All registered infectiologists in French Guiana had access to the regional EMR for the post-release follow-up.
Additional data were collected from paper medical files used for routine follow-up by the three general practitioners in charge of the detainee's medical care, and the registries of the three regional hospitals. The mortality registries of the main towns of French Guiana (covering 80% of the French Guianese population) were also consulted. These sources were used to collect the data regarding: • Socio-demographic characteristics (including place of birth, and type of housing prior to incarceration) • Past incarcerations, date of entry and release of the index incarceration • Medical and psychiatric background, substance abuse, HIV history, comorbidity • HIV-clinical stage, opportunistic infections, immunological and virological results at the beginning and the end of the index incarceration • Post-release events, including linkage to specialized care, hospitalisations, outpatient care in the emergency departments, occurrences of opportunistic infections (IO) or any CDC-stage C events, further incarcerations • Date of death and other pejorative events when applicable.
g. :15. 4%  [6. 6-31. 8] for inmates born in sub-saharian Africa)  [6]. This also seems the case in French Guiana, as the prevalence among inmates born in Guyana is estimated around 10%. Despite the good virological response while incarcerated for those on ART, a high proportion of former inmates died in the few years following release. It has been described elsewhere that the medical benefit of incarceration was lost after release from the correctional facility, with a viral rebound and loss of CD4  [7, 9, 11, 12]. To our knowledge, the incidence of AIDSspecific mortality has been rarely reported among HIV-positive former inmates. Indeed, after searching the pubmed database, using keywords "(mortality OR death) AND (HIV Ã OR Univariate analyses were also performed for: HIV-diagnosed in detention, previous incarceration, ARV treatment on release (ongoing, newly initiated, none), substance abuse, known psychiatric disorders, having kids, duration of the index incarceration.
Its results may not be generalized to any other territory. Since 2013, the French ART recommendation has changed toward universal treatment, independently of CD4 counts. It would certainly be of interest to compare the same mortality indicators, as well as retention in care, for patients released before and after 2013. One main limitation of this study was the lack of statistical power, due to the small size of our sample. Indeed, the trends of our results highlight the possible correlation between housing status and mortality. We aim to investigate the post-carceral social and medical services use in the future, to gain some insights on what has happened since release. The quality of some data may be questioned, due to the retrospective nature of the study. Data collected from daily clinical reports were not standardized and may not always be reliable, notably for non medical information.
Chroma (C*) and hue (h) could be calculated according to the following equations (the value of h is in degrees): C* = (a*)² + (b*)² (1) h = arctan b*a* (2) In addition to these parameters, spectra were recorded for the total reflectance in the spectral range of 380-740 nm at intervals of 10 nm. Seven to ten measurements were carried out for each sample, and the results averaged at each interval (Table  1 ). The color data L*, a*, and b* were recorded and the two-dimensional color space, formed by a* and b*, discussed below. The importance of uniformity in sample preparation, and the angle of the measurements are taken relative to the polishing lines, was first pointed out by  Yonehara et al. (2004). Yonehara et al. (2004)  studied the relationship between Ra (surface roughness), Gs (glosss), and the color of different alloys.
The axe contains 2. 9 wt. % arsenic, 8. 9 wt. % antimony and 4. 9 wt. % nickel (Photograph: M. Kuijpers, © Institut für Ur-und Frühgeschichte der Universität Wien). Table  1  -Alloy composition and color data (SR values) of the metal samples. The composition of the alloys is given in wt. %. Alloys with * are as-cast and may contain up to 0. 5 total wt. % of Si, Cl, Ca, P, S, Fe, and K. All other alloys were made with electrolytic copper and were cold-rolled. The composition of the alloys with * were studied by WDXRF, and the chemical composition of all other alloys by EDXRF. The CIELAB coordinates are shown in Figures  4 5 6 7 8 and are the average of 7-10 spectra.
Alloy composition and color data (SR values) of the metal samples. The composition of the alloys is given in wt. %. Alloys with * are as-cast and may contain up to 0. 5 total wt. % of Si, Cl, Ca, P, S, Fe, and K. All other alloys were made with electrolytic copper and were cold-rolled. The composition of the alloys with * were studied by WDXRF, and the chemical composition of all other alloys by EDXRF. The CIELAB coordinates are shown in Figures5-9and are the average of 7-10 spectra.
Acknowledgements The authors would like to acknowledge the financial support provided by the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Skłodowska-Curie Actions  (grant agreement no. 656244 ); the  Innovational Research Incentives Scheme (VICI) 'Economies of Destruction' of the Netherlands Organization for scientific Research ; and the  Deutsche Forschungsgemeinschaft  for funding the research group FOR 550. The photometric analyses were carried out at the  IRAMAT, University of Bordeaux.
IV. Replication Analysis Among the reviewed studies, all those that estimate an RD design specify a quartic polynomial of the assignment variable, but only  Grenet (2013)  and  Clark and Royer (2013)  test it against alternative specifications, such as a local linear regression. Based on the suggestion in  Lee and Lemieux (2010) , in this section we test the sensitivity of the results reported in  Harmon and Walker (1995) ,  Oreopoulos (2006)  and  Devereux and Hart (2010)  to two important factors: namely, their sensitivity across alternative specifications and across comparable datasets. For this replication analysis we use both the GHS and the FES data. From the GHS, we use four separate subsets of data: namely, the  GHS 1979 GHS -1998   Harmon and Walker (1995). 16 Results for the pooled sample are available from the authors upon request.
For reasons of space, we show the results of this last exercise, as well as the results when using our new larger GHS dataset in Table  C. 1 in Appendix C. The 2SLS estimates in the upper panel of Table  1  appear sensitive to the trend specification. Using higher-order polynomial controls results in both higher point estimates and greater statistical significance in the estimated effects. One plausible interpretation of this might be that, by estimating higher-order polynomials, greater weight is being placed on later cohorts of individuals. This would be in line with  Gelman and Imbens' (2014)  suggestion that use of higher-order polynomials in RD analysis places greater weight on observations away from the discontinuity, and it may also result in a greater likelihood of finding statistical significance in the estimated effects.
This applies to all datasets considered -including our own 20. In light of these results, and in an attempt to understand which model is preferable, we retrieved the Akaike Information Criterion (AIC) for each of these models, and the result of this exercise is shown in     Oreopoulos (2006)  and H&W is  Harmon and Walker (1995). *** Significant at the 1 percent level. ** Significant at the 5 percent level. * Significant at the 10 percent level. Table  3  shows our Local Average RD Wald estimates. Heteroskedasticity-robust standard errors were clustered by year-of-birth, and 1,000 bootstrap replications were conducted for inference. Starting from an optimal bandwidth (in the sense of  Imbens and Kalyanaraman, 2012) , results are also shown for observations within half the optimal bandwidth and observations within twice the optimal bandwidth.
The running variable is still the year of birth, and the optimal bandwidth was at least greater than two year-of-birth cohorts in almost all cases  22. Local regression estimates appear partially consistent with the results in Table  1. In fact, we retrieve a 6% RoRtE estimate, both when we use our larger dataset and when we use Devereux and Hart's (2010) data. Use of the Oreopoulos  ' (2006)  dataset results in a 20% RoRtE estimate, while use of GHS survey years similar to  Harmon and Walker's (1995)  dataset results in a different RoRtE estimate for the compliers to the 1947 ROSLA (although in this case the small size of the optimal bandwidth may explain the lack of significance in the result). Local Average RD Wald estimates do not appear robust to modifications of the chosen bandwidth; however, caution is needed in the interpretation of the estimates derived using observations within half the optimal bandwidth in Table  3.
In addition to replicating  Harmon and Walker (1995) , we also move progressively towards the specification in Devereux and Hart (2010), to test the stability of their results not only across sampling datasets, but also across different specifications. In the analysis of the FES data, we follow  Harmon and Walker (1995)  and we use hourly earnings (rather than weekly earnings) to measure the returns to schooling. Using a quadratic of age and focusing exclusively on the male sample, an accurate replication of their estimate is reported in Table  4 , row 1, columns (1) and (4). However, this result critically depends on the age controls used, and on the inclusion of controls for the birth cohort. In fact, when the specification of  Devereux and Hart (2010)  is applied to the FES 1978-86 survey data, the results change significantly; once again, the 2SLS estimates appear sensitive to the chosen functional form.
In our specifications, we also include varying sets of additional controls   at the individual level to test for the sensitivity of our estimates to equation specification. Since information on month of birth was not available from the FES, here we only use data from the 1983-1998 GHS surveys. Our sample comprises all individuals born in Britain from 1921-1965, and aged 28-64 at the time of the survey. Also, we still set to missing cases for which hourly wage observations are less than £1 or more than £150 (in December 2001 pounds), we exclude cases where weekly hours are greater than 84, less than 1, or missing, and we estimate robust standard errors, allowing for clustering by year-or by month-year of birth (according to which polynomial is estimated). This results in a sample of 117,144 individuals; 76,935 of these reported complete information on the month-year of birth, as well as on the month-year of the interview.
Devereux and Hart (2010)  also focus on the 1947 reform; they follow Oreopoulos (2006)  by estimating monetary returns to compulsory schooling including a quartic of year of birth and using similar estimation samples. Like Oreopoulos (2006) , they include individuals who were born between 1921 and 1951 and are aged between 28 and 64. They complement the GHS with the New Earnings Survey Panel Dataset (NESPD), which offers a large sample of high-quality administrative earnings data. Devereux and Hart (2010)  do not find any significant returns to compulsory schooling for the pooled sample, whereas they find IV estimates for men that are much closer to the conventional OLS estimates. Dickson and Smith (2011)  use the Quarterly Labour Force Survey (LFS) data to exploit separately the 1963 reform and the 1972 reform to investigate whether an additional year of schooling is beneficial per se or whether qualifications are key.
Grenet (2013)1963 reform to examine the impact of qualifications on future labour market outcomes. They use data from the 1993-2003 LFS, from the Youth Cohort Study and a dataset that combines information from the New Earnings Study and the Joint Unemployment and Vacancies Operating System Cohort. They focus on pupils born after the introduction of the 1963 reform, and they document the importance of academic qualifications on future labour market outcomes, especially for women. Buscha and Dickson (2012) use the UK Household Longitudinal Study (UKHLS) and they focus on the effects of the 1972 reform on the earnings for individuals in their early 50s. They estimate the returns to secondary education on (log)hourly pay to be at 18. 4% for the pooled sample, 22. 6% for men, and 16. 9% for women.
THE GERMAN SOCIOECONOMIC PANEL The German Socioeconomic Panel (SOEP) is a representative longitudinal household survey that started in 1984. We use data from the 29 subsequent annual waves. Respondents are followed over multiple waves, but the panel is unbalanced as many respondents enter the sample after 1984 or leave the sample before 2012. The sample is restricted to 222,726 person-wave observations for which we observe occupation in the previous year, educational attainment, and health in the previous and in Figure  1  shows that we observe 31,216 individuals for at least one period and 10,577 individuals for at least nine periods, a more restricted sample that we will use in a robustness check.
Sample selection and estimation Our sample selects individuals who were working in the previous year, since only for them we observe their occupation. Obviously, our results should therefore be interpreted as the treatment effect on the working population-and not on children, students, the unemployed, the disabled, and retirees-but that seems to be the policy-relevant effect. Note that even for individuals who quit their job in the current period, we can still estimate the effect of occupation in the previous period on their current health. We use HSAT (on a 0-10 integer scale) as a proxy for health as dependent variable in a linear dynamic panel data model. Ferrer-i Carbonell and  Frijters (2004)  and  Frijters, Haisken-DeNew, and Shields (2005)  show that for the variable that measures satisfaction with life on a 10-point scale, assuming ordinality or cardinality makes little difference, such that a linear specification is acceptable.
Health measures. SAH-measured on a 5-point scale-was only included in the SOEP questionnaire in 1992 and from 1994 onwards. Column 2 of Table  5  substitutes SAH for HSAT and the coefficient estimates reveal similar signs and sizes to our main findings in column 6 of Table  3 , supporting our conclusion that physical strain and low job control are harmful to health at older ages. Time-varying health changes. We now turn to Table  6  for a second set of robustness checks, now investigating the sensitivity of our results toward changes in the empirical specification. Although the lagged dependent variable captures unobserved time-varying shocks that decay exponentially, we cannot rule out that other time-varying shocks influence both occupational switches as well as changes in health. The SOEP data includes information on "the number of hospital days in the past year" and "sickness absenteeism days in the past year.
For example, occupations with harmful ergonomic workplace conditions may simultaneously be characterized by low control possibilities at work, which may exert an independent effect on health. Consequently, separating out these effects is crucial for better-targeted efforts at reducing sickness absenteeism and disability by adjusting specific labor conditions. In this paper, we make three contributions. First, by proposing a dynamic theoretical model as the foundation of our empirical specification, we highlight the various sources of selection into occupation, and we make explicit under which conditions the coefficient of occupational stressors can be interpreted as causal. Second, we estimate the empirical equation deriving from the theory using a detailed German longitudinal dataset over many time periods (29 years).
ACKNOWLEDGEMENTS The authors acknowledge funding from the  Netspar  research grant "Occupational choice and health: which jobs offer better health prospects for a long career?";Ravesteijn receives funding from the  Commonwealth Fund  and from the  SPRINTT  project of the  Innovative Medicines Initiative Joint Undertaking  under grant agreement number  115621 , resources of which are composed of financial contribution from the  European Union 's  Seventh Framework Program  ( FP7/2007-2013 ) and  EFPIA  companies' "in kind contribution. "van Doorslaer and van Kippersluis acknowledge funding from the  National Institute on Aging of the National Institutes of Health  ( R01AG037398 ), and van Kippersluis further acknowledges funding from  NWO  ( Veni grant   016. 145. 082 ). We wish to thank anonymous reviewers, conference and seminar attendees in Sydney, Frankfurt, and  Los Angeles  for their valuable comments, with special thanks to  Owen O'Donnell.
Acknowledgements PLdL was supported by a  ParaFrap post-doctoral fellowship. MAP is an  Arthritis Research   UK Career Development Fellow. SB and GL acknowledge support from  ANR-11-LABX-0024  and the  CNRS  ( UMR8104 ); SB acknowledges grant  ANR-13-JSV3-0003  and GL acknowledges INSERM ( U1016 ) support. r e f e r e n c e s.
The goal of this paper is to experiment with a mapping methodology. We do so, first, mapping the scientific supply via the research landscape of obesity as defined by topic modelling of publications titles and abstracts and, second, mapping social demand according to political discourse in the European parliament. The results of a semantic mapping exercise are highly contingent on three factors: (i) choice of data source or database; (ii) the method used for delineating the corpus of the problem; (iii) the algorithms that define the cognitive categories of the corpus-which from a research portfolio perspective are the research options. Let us stress that all these choices may have a large effect on the results. This means that the maps presented are specific and partial representations of obesity research, and that other, complementary and/or contrasting representations are possible and equally legitimate.
Here, we use Web of Science and PubMed as data source of scientific publications. Notice that science supply could be drawn from more comprehensive databases, particularly regarding developing countries, such as CABI (specialised in agriculture and global health), which might lead to serious differences, as shown by  Rafols, Ciarli and Chavarro (2015). Also science supply could be represented as well by many other type of data grants' abstracts rather than publications which might lead to a different configuration, as  Talley et al. (2011)  did for the US National Institutes of Health. Topic delineation is controversial for issues such as obesity. The question is: what should be considered as research that matters to obesity? Only the publications that directly relate to obesity? Or also the publications that touch upon the issue more indirectly such as diet, physical exercise or public transportation? The answer is possibly a grey scale.
Science supply data In this subsection we present the data used to represent supply (i. e. research activities) and in the next subsection data for capturing on perspective on the diversity of the demand (i. e. societal needs). In order to define the relevant corpus for the research activity on obesity, we follow a two step method. First, we retrieve all publications MeSH term matching the search obes* in MEDLINE/PubMed during the 2000-2013 period. This search was performed on October 16, 2014 and it returned 87,315 records. Then, we launched medlineR, a routine based on the R language that allows the user to match data from Medline/PubMed with records indexed in the ISI Web of Science (WoS) database  (Rotolo and Leydesdorff, 2015). The routine identified 71,055 WoS records (WoS core collections), with 'article' or 'review' as document types.
We fit the LDA model with the Mallet package  (McCallum, 2002)  to various corpora and we analyse and display the results with R scripts and interactive visualisation provided by the LDAvis program  (Sievert and Shirley, 2014). On a LDAvis figure, topics are close if they have similar term distribution. Topic dissimilarity is calculated with Jensen-Shannon divergence between the distributions, represented in a two dimensional space with the first two dimensions from a principal coordinate analysis. For the largest science corpus (i. e. corpus C), after removing standard stop words and terms appearing in less than 100 documents, we get a list of 12,428 terms. We then fit the document:term matrix to the 20 topic LDA model. Resulting topics have weights varying from 8. 2% of the corpus for topic S9 (denoted 9 on the figure) to 2.