dataesr/scientific-paragraphs-categorization huggingface dataset:
Evaluation tools The model is evaluated at the sites using the two components of NEE: the gross primary productivity (GPP) and the ecosystem respiration rate (R eco ), both estimated via the fluxpartitioning method described in  Reichstein et al. (2005). This method extrapolates night-time measurements of NEE, representing night-time R eco , into daytime R eco using a short-term-calibrated temperature response function. GPP is then derived as the difference between R eco and NEE. We acknowledge that GPP and R eco are not fully independent data (with respect to the assimilated NEE) and are essentially model-derived estimates somewhat conditional on our underlying assumptions, which will be kept in mind during the analysis. Additionally, measurements of the normalized difference vegetation index (NDVI) made by the MODIS (Moderate Resolution Imaging Spectroradiometer) instrument are used to evaluate the simulated phenology at the global scale.
From 2000 to 2010, the calculated reflectances (from measured irradiances) have been corrected for atmospheric absorption, scattering  (Vermote et al. , 2002)  and directional effects  (Vermote et al. , 2009)  in order to obtain a daily NDVI product with a 5 km spatial resolution. Observations contaminated with snow cover were removed from the analysis using MODIS' quality filter, and we discarded NDVI observations below 0. 2 in order to minimize the impact of baresoil reflectance. Spatial averaging is used to match the ERA-Interim resolution (0. 72 • × 0. 72 • ) used for the global-scale simulations. Because it is directly derived from surface reflectances, we preferred NDVI to other satellite products such as FAPAR (Fraction of Absorbed Photosynthetically Active Radiation) or LAI, the latter requiring intermediate processing steps usually involving radiative transfer models, and thus possibly adding uncertainty to the retrieved data  (Garrigues et al.
72 • × 0. 72 • ) to a 2. 5 • × 3. 75 • resolution (latitude, longitude). The LMDZ (Laboratoire de Météorologie Dynamique zoom) atmospheric transport model  (Hourdin et al. , 2006 ) is used at this resolution to convert these terrestrial fluxes into monthly atmospheric CO 2 concentrations, during the period 1989-2009. In order to complete the carbon balance at the planetary scale, we also transport the global oceanic and fossil net carbon fluxes respectively taken from a climatology  (Takahashi et al. , 2009)  and from the EDGAR database (Emissions Database for Global Atmospheric Research; http://edgar. jrc. ec. europa. eu). The contribution of biomass burning is neglected, because regrowth of burnt vegetation is not accounted for in this version of OR-CHIDEE, and so are the evasion of CO 2 from aquatic bodies and emissions from harvested wood and agricultural products.
g. Fig. 1  in  Gurney et al. , 2003). The simulated interannual variability of the C CO 2 is evaluated using the model-data RMSD of monthly anomaly from the detrended smooth seasonal signal calculated above: C CO 2 ,anom = C CO 2 ,month -C CO 2 ,month allyears , (6) where C CO 2 ,month allyears is the all-time average, for each month of the year.
9 % for the RMSD between modelled and measured monthly anomaly (not shown). Forty-five locations, out of the 53 used in this study, display an improvement with RMSD decreases of up to 27 %, while at the remaining 8 locations the degradation of the simulated interannual variability remains small with RMSD increases always smaller than 1. 5 % (not shown). These results suggest that despite the relative shortness (1-3 years) of most of the FluxNet data sets selected to optimize the ORCHIDEE model, the diversity of the covered weather situations gives a modest, yet consistent source of information to better reproduce interannual variations of carbon fluxes at the global scale.
g. Kaminski et al. , 2013) , would be beneficial. Using more site-specific years of flux data will also allow for a systematic in situ evaluation of the multisite parameters across time periods, regions and climate regimes by separating training sites from evaluation sites. Such a procedure was not applied in this study due to the small number of sites for some PFTs, but remains essential for testing a LSM used for climate projections. More generally, we suggest that the assimilation of FluxNet data should be considered as a baseline for the development of multidata assimilation systems where more complementary data streams are combined. In particular, daytime and night-time NEE could replace the daily values used here, and adding measurements of leaf area index, soil respiration fluxes (e.
Together with a simultaneous optimization of several PFTs, building standard spatialized parameter covariance tables from databases of plant traits and soil characteristics (e. g. Kattge et al. , 2011)  and "preliminary" posterior multisite parameter error covariance matrices (e. g. supplementary material of  Kuppel et al. , 2012)  might soon become necessary to consistently apply model-data fusion to more sophisticated mechanistic ecosystem models.
Acknowledgements. This work has been supported by the  CARBONES  project, within the  EU's 7th Framework Program for Research and Development. E. Vangorsel ,  J. Beringer ,  H. R. da Rocha ,  J. Grace ,  B. Kruijt ,  M. Goulden ,  A. Black ,  L. Flanagan ,  B. Amiro ,  J. H. McCaughey ,  H. Margolis ,  A. Barr ,  A. Arain ,  T. Kato ,  T. Foken ,  A. Knohl ,  A. Don ,  C. Rebmann ,  M. -J. Sanz ,  M. T. Sebastiá ,  T. Vesala ,  T. Laurila ,  E. Dufrêne ,  A. Granier ,  D. Loustau ,  N. Zoltan ,  T. Hirano ,  G. Kiely ,  R. Valentini ,  D. Gianelle ,  A. Raschi ,  H. Kondo ,  A. J. Dolman ,  T. Johansson ,  M. Nilsson ,  A. Lindroth ,  A. Grelle ,  J. Moncrieff ,  M. Wilkinson ,  M. Torn ,  T. Meyers ,  A. D. Richardson ,  J. Randers ,  J. W. Munger ,  D. Hollinger ,  R. Matamala ,  W. C. Oechel ,  J.
Code availability The source code of the data assimilation system is available at https://pypi. python. org/pypi/ORCHISM. Regarding the ORCHIDEE vegetation model, the source files of the Tag version 1. 9. 5. 2 used for this study can be obtained upon request (see http://labex. ipsl. fr/orchidee/index. php/contact), while the associated documentation can be found at https:// forge. ipsl. jussieu. fr/orchidee/wiki/Documentation. Note that the tangent linear version of the ORCHIDEE model has been generated using commercial software (TAF; see Sect. 2. 2). For this reason, only the "forward" version of the OR-CHIDEE model is available for sharing, to which only the finite-differences method is employed for parameter optimization. Finally, the source code of the LMDZ atmospheric transport model can be found at http://web.
Participants and procedure The university of Strasbourg employs 4650 permanent staff: 2680 teaching/research staff, 1970 administrative/technical staff. All employees were invited to take part anonymously in the present study. Data collection consisted of filling out an online self-administered questionnaire. A total of 973 employees from the three original institutions (40% males and 60% females) completed and returned the survey. There were no differences in participation rates among the three institutions. The age boundaries span the full adult working life: from 22 to 68 (mean age = 42). All the types of university activities were equally represented: about one third held a teaching position and the remaining two thirds held a technician and/or an administrative position(s). This sample was randomly divided into two subsamples of equal size.
Statistical analysis All the analyses were performed using the SPSS version 15. 0  (SPSS Inc, 2006). A total of 111 participants with more than 10% missing data (viz. more than three missing items for JCQ) were excluded from the analyses. Participants with less than 10% missing data were kept by substituting the mean value for missing items (overall, 129 missing data were replaced). For the Confirmatory Factor Analyses (CFA), latent factors were defined by their respective items, correlations between latent factors were permitted, and Modification Indices were used to guide model refinements. The Maximum Likelihood method with the Chi 2 statistic (Chi 2 ) was used to test the fit of the models. Because the Chi 2 is known to be affected by sample size  (Kline, 1998) , other fit indices notably recommended by  Hu and Bentler (1999)  were also used: the normed Chi 2 (x 2 /df), the goodness of fit index (GFI), the comparative fit index (CFI), the root mean square error of approximation (RMSEA) and the Standardized Root Mean Square Residual (SRMR).
Travail informatique Intéressons-nous donc à l'évolution de cette erreur en fonction de h : faites le graphe (Excel et Matlab) de cette évolution (vous pourrez utiliser ce que vous avez fait la semaine dernière pour (1+h)-1 h pour des valeurs de h de plus en plus petites : 1, puis 1 a , puis 1 a 2 , puis 1 a 3 , puis. ). Utilisez des coordonnées logarithmiques (instruction Matlab loglog au lieu de plot).
Voilà qui constitue une majoration de l'erreur commise en prenant g h (x) à la place de f ′ (x). Remarquez que ce majorant est en O(h) (c'est à dire en gros "quelques fois h"), et dépend de M 2. Examinez maintenant l'impact d'une erreur commise sur la valeur évaluée de f (x) et de f (x + h). Soit M un majorant de cette erreur. Remarquez au passage que cette erreur peut être liée à une erreur de mesure (si les valeurs de f sont consécutives à une mesure), ou bien dues à l'"erreur d'arrondi", ou "erreur numérique" (c'est en particulier le cas pour 2 √ x et les expérimentations proposées dans ce TP). Remarquez de plus que quelle que soit son origine, cette erreur ne saurait être inférieure à M 0 ε où ε est la précision machine (10 -17 sous Excel, ou Matlab en précision "longue", 10 -7 sous Matlab en précision courte) et M 0 un majorant de |f (x)| et |f (x + h)|.
LES MÉTHODES D'ANALYSE Les traitements statistiques ont été effectués grâce au logiciel statistique SPSS, outil le plus fréquemment utilisé en sciences de gestion. Les principaux tests mobilisés, qui ne seront pas détaillés ici, sont des analyses de variance, des tests de différence de moyenne, des tests de fiabilité, des régressions simples ou encore des régressions logistiques binaires.
Lineage IV PPRV, which was believed initially to be confined to India and the Middle East, now has a wider geographic presence and appears to be evolving rapidly. Many aspects of PPRV evolution, such as ancestral virus location, divergence and time of origin, and historical and geographic patterns of spread, are poorly understood  (10). A better understanding of the evolution of PPRV would enable prediction of how these viruses will lead to further outbreaks and epidemics and provide data for control strategies. Advanced sequencing technologies have enabled molecular epidemiologic studies of viruses in which whole gene and complete genome data are used to enhance and clarify the evolutionary dynamics of viral infectious disease (11). We analyzed genome data for all 4 lineages of PPRV. This analysis will enable a more precise evolutionary and phylogenetic assessment of the relationships between lineages by reducing the associated estimation errors and increased higher confidence in estimates.
Sequence Datasets In addition to the 7 complete genomes sequences of PPRV generated in this study, another 7 complete genome sequences were obtained from GenBank (Table  1 ). However, of these 14 full genome sequences, Nigeria 1975/1 and Sungri 1996 represent vaccine strains generated after extensive serial passage of virus. Therefore, the evolutionary rate and time to most recent common ancestor (TMRCA) were compared with and without inclusion of vaccine strains. The complete genome sequences of 2 clinical isolates each from RPV (GenBank accession nos. AB547189 and X98291) and MV (accession nos. AF266288 and JF791787) and 12 PPRV isolates, excluding vaccine strains, (Table  1 ) were used for estimation of evolutionary rate and TMRCA. Furthermore, the coding and noncoding sequences of individual structural genes of PPRV (excluding vaccine strains) were used in this study.
Partial N gene sequences of PPRV (nucleotide positions 1253-1507) that have a detailed history of collection date and place were obtained from GenBank (available up to August 2013). These partial sequences were aligned by using the ClustalW algorithm in BioEdit software v7. 2. 0. (  21 ) and edited to remove unreliable sequences/regions. Furthermore, the identical sequences originating from the same geographic location, host, and year were excluded to avoid redundancy in subsequent analysis. The final dataset (partial N gene) contained 159 sequences sampled over a period of 45 years.
Bayesian Time-Scaled Phylogenetic Analysis Molecular evolutionary rate and divergence times were co-estimated. A Bayesian maximum clade credibility (MCC) phylogenetic tree was constructed by using Bayesian Markov chain Monte Carlo (MCMC) analysis and Bayesian evolutionary analysis sampling trees (BEAST) software package v1. 8. 0  (23) , and BEAST runs were performed by using the CIPRES Science Gateway  (24). For each sequence dataset, the best-fit nucleotide substitution model was determined on the basis of Akaike information criterion scores using JModel Test software v2. 1. 4 (25). An input file for BEAST analysis was obtained by using Bayesian evolutionary analysis utility software v1. 8. 0, in which sequences were tip dated according to the year of collection. Four molecular clock models (strict, uncorrelated lognormal distribution, uncorrelated exponential distribution [UCED], and random) were tested alongside different demographic models (nonparametric Bayesian skyline plot and the parametric constant and exponential growth), and the best models were selected by means of a Bayes factor (BF) test (  26 ) using marginal likelihoods values (2lnBF>2) obtained from Tracer v1.
5 software (http:// beast. bio. ed. ac. uk/tracer). For each analysis, 2 independent MCMC chains were run to get a final output of 10,000 trees (ESS >200 for all the parameters estimated) and were assessed for their proper mixing, convergence, and consistency by Tracer v1. 5 with 10% burn in. The 2 individual runs were combined by using LogCombiner v1. 8. 0 in the BEAST software package. The nucleotide substitution rate (substitutions/site/year) and the TMRCA (year) values were obtained from Tracer v1. 5. The posterior tree distributions were summarized by using TreeAnnotator (http://beast. bio. ed. ac. uk/treeannotator) and exclusion of the first 10% of the trees as burn in. Phylogenetic MCC tree with median node heights were visualized in FigTree software v1. 4. 0 (http://www. molecularevolution.
Phylogeographic Reconstruction Bayesian phylogeographic analysis was performed by using complete PPRV genome sequence and partial N gene sequence datasets, and isolates were annotated according to their location (longitude and latitude). Partial N gene data were chosen instead of F gene data because of increased divergence reported for the N gene (2). For complete genome datasets, sequences from 14 viruses were considered, including 2 vaccine strains (Nigeria 1975/1 and Sungri 1996) to represent all PPRV-endemic areas. Phylogeographic diffusion along the posterior sets of trees and relationships between these locations were identified by using the Bayesian stochastic search variable selection procedure in BEAST v1. 8. 0  (27). Discrete phylogeographic analysis was performed by using the continuous time Markov chain with the flexible Bayesian skyride tree prior.
Evolutionary Rate Estimates Complete genome sequences of 12 PPRV and partial N gene dataset (n = 159) were analyzed by using the coalescent-based Bayesian MCMC approach. The general time-reversible nucleotide substitution model with a gamma distribution for rate variation was selected on the basis of Akaike information criterion scores. Bayes factor test with marginal likelihood comparisons showed that the relaxed UCED clock model best fitted the PPRV complete genome and partial N gene datasets (Table  4 ). The 2lnBF value was >78 between UCED and strict clocks and 2-6 between UCED/uncorrelated lognormal distribution and UCED/ random clocks, which provided strong evidence for the UCED clock model. There was no difference between different demographic models compared within the UCED clock model (2lnBF <2).
However, the exponential demographic model was chosen because it provided a narrow margin of 95% highest posterior density (HPD) estimates. Accordingly, the UCED and exponential growth model have been directly used for the individual PPRV gene dataset and the PPRV/RPV/MV complete genome dataset to estimate the TMRCA and substitution rate per site per year. When we used the UCED and exponential growth models, we found that the mean evolutionary substitution rate of the PPRV complete genome was estimated to be 9. 09 × 10 -4 (95% HPD 2. 13 × 10 -4 -1. 64 ×10 -3 ). When 2 complete genome sequences of vaccine strains were added into this analysis, the same models (general time-reversible nucleotide substitution model with a gamma distribution, UCED, and the exponential growth demographic models) were best fitted, and the mean substitution rate/site/year was reduced to 7.
86 × 10 -4 (95% HPD 2. 17 × 10 -4 -1. 4 ×10 -3 ). Furthermore, the evolutionary nucleotide substitution rate for combined PPRV/RPV/MV complete genomes was 1. 89 × 10 -3 (95% HPD 5. 55 × 10 -4 -3. 31 × 10 -3 ). Analysis of individual genes of the PPRV coding region dataset, coding and noncoding region datasets, and partial N gene dataset are shown in Table  4.
Population Demography of PPRV The demographic history of PPRV was investigated by using the partial N gene sequence dataset according to the BSP method implemented in BEAST. The BSP with an assumed piecewise-constant model has facilitated estimation of effective population size through time. The BSP showed that the population did not show much genetic diversity (effective number of infections) until the mid-1990s when diversity started to increase. Toward the first decade of the 21st century, the population size appeared to reach a peak and then showed a small decrease until the most recent sampling in 2012 (Figure  4 ). The HPD interval size for the plot is narrow, which indicates strong support for this population trend.
However, greater substitution rates were observed in the GC rich regions of the F and M genes. Similarly, the substitution rate was greater, as predicted because of the variability seen at the nucleotide level, in the highly variable region of the N gene sequence (255 nt) for PPRV. The TMRCA estimation was not possible for lineage I and II viruses (vaccine strain was omitted) because only 1 complete genome sequence was available for each lineage. Therefore, more complete genome sequences are required to study evolutionary and phylogenetic relationships for these lineages. Biased estimates in substitution rate and TMRCA were observed by using datasets that included tissue culture-passaged, attenuated vaccine strain complete genome sequences, in which slower evolutionary substitution rates and earlier TMRCA were predicted.
Similar observations were reported for PPRV/RPV/MV N gene sequence analyses, in which a slower and biased nucleotide substitution rate was observed when vaccine strain sequences  (33)  were included in the analysis and faster substitution rates and later TMRCA predictions were suggested when vaccine strain sequence data were excluded  (34). Spatial and temporal dynamics of RNA viruses are often reflected by their phylogenetic structure  (37). Potential divergence events for different PPRV lineages were inferred by using rooted, time-measured phylogenetic trees with higher confidence from the PPRV complete genome sequence dataset. The inferred phylogeny supports the initial divergence of lineage III isolates, followed by lineage I isolates; lineage II and IV isolates were predicted to have diverged from each other at a later time.
In conclusion, these findings suggest that the origin of PPRV was in western Africa, which then spread to eastern Africa, the Middle East, and Asia. However, although these predictions are suggestive of a potential origin for PPRV, caution must be exercised in their interpretation because estimates of geographic origin rely on available datasets, and these datasets need enhancing to provide greater confidence for phylogenetic assessment. As more sequence data become available for PPRV and the other morbilliviruses, ancestral origins of each virus and intraspecies differentiation might become more clear.
Acknowledgment We thank  Vincent Michaud  for his critical reading of and comments on the manuscript. This study was supported by grants  EU-BBSRC Anihwa BB/L013657/1 ,  BBSRC-DFID CIDLID BB/H009485/1 , and  DBT-BBSRC FADH BB/L004801/1. Mr Muniraju  is a final year doctoral student at The  Pirbright Institute, Pirbright, UK. His primary research interests are epidemiologic studies of PPRV and developing marker vaccines for peste des petits ruminants by using reverse genetics techniques.
Le but de ce papier est de proposer une comparaison systématique des systèmes d'innovation des 4 grands pays asiatiques leaders en matière de développement industriel. Cette comparaison est faite sur la base d'une approche originale. On se propose en effet de décrire les traits principaux de leur capacité d'invention à travers quelques caractéristiques de leurs inventeurs les plus productifs (prolifiques par la suite) : leur productivité, leur mobilité, la valeur de leur invention, leur degré de spécialisation technologique. Cette approche est fondée sur la pertinence de travaux récents décrivant les « lois » évolutionnistes de la productivité des inventeurs et de leur mobilité (ceci est décrit dans la section 3). La mobilisation d'une base de données de brevets américains permet d'estimer des relations pour chacune des grandes caractéristiques des inventeurs prolifiques et de mesurer le poids des divers déterminants (section 4).
Elle nous dit également autre chose que ces dernières qui ne « descendent » pas jusqu'à ce niveau micro. et Trajtenberg (2001a). Une première exploitation d'envergure utilisant le potentiel de cette base de données est fournie par l'étude  de Hall, Jaffe et Trajtenberg (2001b). Le livre  de Jaffe et Trajtenberg (2002)  aborde d'autres thématiques toujours sur la même base de données de brevets. La thèse de Volodine (2012) fait utilement le point sur l'évolution de la base de données du NBER et signale des améliorations possibles (non prises en compte ici). Elle est maintenant assez largement utilisés dans les travaux académiques. Pour comparer les performances technologiques des pays on ne peut avoir recours aux données de dépôts de brevets dans les systèmes nationaux de brevets. En effet les entreprises du pays ont un avantage dans leur système national (plus grande facilité de dépôt).
MATÉRIELS ET MÉTHODE La recherche des types taxonomiques a été menée à partir des bases de données et dans les herbiers (par nous-mêmes et par les personnels gérant les collections concernées) des 26 institutions suivantes connues pour posséder des récoltes de L'Herminier et/ou des spécimens identifiés (ou cités) par Fée, mais également des récoltes présentes dans les herbiers de Mougeot et de Bory de Saint-Vincent citées dans les protologues de Fée  (Proctor 1977 ; Stafleu & Cowan 1981 ; Hertel & Schreiber 1988 ; Proctor 1989 ; Christenhusz 2009 ; Cuccuini 2009  ; iers 2012) : A, AUT, B, BM, BR, E, F, FI, GH, IJ, K, L, MO, MHLCLFE, MPU, NTM, NY, OXF, P, RB, S, STU, UC, US, WU, Z (incl. ZSS, ZT) ; ces acronymes d'institutions sont ceux admis à l'Index Herbariorum du New York Botanical Garden  (Holmgren et al.