dataesr/scientific-paragraphs-categorization huggingface dataset:
1, -l 32) and more lenient (BWA parameters: -n 0. 1, -l 16) mapping parameters. Subsequently, the region around each SNP was evaluated within a 50-bp window, and was accepted as true when fulfilling the following criteria: (i) the ratio of coverage between the lenient and stringent mapping was not higher than 1. 00, (ii) there were no heterozygous positions within this window when considering a high stringency mapping and (iii) no missing regions/bases were observed within close proximity to the identified SNP (see Supplementary Data 2). Note that the above criteria in SNPEvaluation have been determined and evaluated in UDG-treated metagenomic data  28  and, therefore, would need to be re-adapted for non-UDG-treated data that are heavily affected by aDNA deamination. Heterozygosity estimates.
Heterozygous variant calls were investigated given the disparity of branch lengths observed in certain newly reconstructed and previously published genomes (see Supplementary Figs. 14  and 16 ). Our approach takes into account the "haploid" nature of prokaryotic genomes, suggesting that "heterozygous" SNPs could either arise as a result of mixed infections or from erroneous mapping of DNA reads that belong to closely related bacterial contaminants. We performed SNP calling with the UnifiedGenotyper in GATK  75  , using the "EMI-T_ALL_SITES" option that generated a call for all positions in the reference genome. We then used MultiVCFAnalyzer v0. 85 (ref. 33 ) to compile a SNP table of variant positions with allele frequencies 10-90% across our dataset, hence accounting for all ambiguous heterozygous positions.
Variant positions across this set of genomes were used for the analysis, allowing for up to 3% missing data (550 SNPs). We used TempEst v1. 5 (http://tree. bio. ed. ac. uk/software/tempest/) for calculating the root-to-tip regression in relation to specimen or sampling ages. The calculated correlation coefficient (R) and R 2 values were 0. 57 and 0. 33, respectively, which permitted the proceeding with molecular dating analysis. The Bayesian framework BEASTv1. 8 (ref. 85 ) was used to assess the substitution rate variation across the Y. pestis Branch 1 using the 2. MED KIM10 as outgroup. Our BEAUti setup took into consideration all archaeological, radiocarbon and sampling dates of both ancient and modern genomes (Supplementary Data 6) that were used as calibration points for the Bayesian phylogeny.
Divergence dates for each node in the tree were estimated as years before the present, where the year 2005 was set as the present since it represents the most recently isolated modern Y. pestis strain on Branch 1 (1. ORI MG05). Monophyletic clades were defined based on the ML phylogeny (Supplementary Fig. 12 ). The GTR  79  substitution model (4 gamma rate categories) and a lognormal relaxed clock (clock rate tested and strict clock rejected in MEGA7 77 ) were used to set up two separate analyses using the coalescent constant size  86  and coalescent Bayesian skyline 87 demographic models. For each analysis, three independent chains of 50,000,000 states each were carried out and then combined using LogCombiner to ensure run convergence, with 10% burn-in. In addition, we estimated marginal likelihoods to determine the best demographic model for our dataset using path sampling and stepping stone sampling (PS/SS) implemented in BEAST v1.
8 (ref. 85 ). For this, each of the described runs was carried out for an additional 50,000,000 states (500,000 states divided into 100 steps) using an alpha parameter of 0. 3, which determined the coalescent Bayesian skyline model as better fit for the current dataset. The results produced by the run considering this demographic model were then viewed in Tracer v1. 6 (http://tree. bio. ed. ac. uk/software/tracer/) to ensure all relevant effective sample sizes (ESS) were >200. We used TreeAnnotator  85  , to produce a maximum clade credibility (MCC) phylogeny using the best-fit model with 10% burn-in, which resulted in the processing of 13,503 trees. The MCC tree was viewed and modified in FigTree v1. 4 (http://tree. bio. ed. ac. uk/software/figtree/) where branch lengths were represented as a function of age and mean rates were used to colour individual branches.
Finally, the skyline plot was produced and viewed using Tracer v1. 6 (http://tree. bio. ed. ac. uk/software/tracer/) after resampling states at a lower frequency (every 100,000) using LogCombiner  85. Gene presence/absence and deletion analysis. In order to investigate the virulence-associated gene profiles of the newly reconstructed second pandemic genomes, the highest quality (coverage) genome from every site (LAI009, NAB003, TRP002, MAN008, STA001, NMS002, LBG002, STN014, BRA001, BED030) was used for comparison with each other and with previously published representatives of ancient (London BD 8124/8291/11972, OSL-1 Ber45, London 6330, Bolgar 2370, Barcelona 3031, Ellwangen 549_O, OBS137, RISE509, RT5, Altenerding 2148) and modern (1. ORI-CO92, 0. PE2-PESTF, 0. PE4-Microtus 91001) Y. pestis isolates as well as a Y.
pseudotuberculosis strain (IP32953). All listed genomes were re-mapped against the CO92 chromosomal reference genome (NC_003143. 1) without the use of a mapping quality filter of (-q 0). The coverage across 80 chromosomal and 43 plasmid virulence-associated and evolutionary determinant genes that were previously defined  36  was calculated using bedtools  88. The results are plotted in the form of a heatmap using the ggplot2 (ref. 89 ) package in R version 3. 4. 1 (ref. 82 ) and can be viewed in Fig. 4. In addition, we used BWA-MEM  80  to explore the precise coordinates of observed gene or region losses in all affected genomes using default parameters. For the visualisation of an identified deletion across BED and OBS isolates, we computed the average coverage across 3,000-bp windows in representative Y.
Acknowledgements We thank  Aditya K. Lankapalli  and  Stephen Clayton  for computational analysis support. We thank  Guido Brandt ,  Antje Wissgott ,  Cäcilia Freund  and  Marta Burri  for laboratory support. We are grateful to  Monica Green  for critical comments on the manuscript. We thank  Hans Sell  and  Michelle O'Reilly  for graphics support. We thank  Rafail' M. Fattahov  for facilitating excavations of the Laishevo III archaeological site,  Ayrat Sitdikov  for providing access to the Laishevo III skeletal assemblage and  Elizaveta V. Volkova  for assisting with sampling of skeletal material. In addition, we would like to thank  Joke Somers  for the anthropological analysis and sampling of the Stans individuals. We thank  Bettina Jungklaus  for providing the samples from Brandenburg an der Havel,  Bernd Trautmann  for morphological analyses,  Jochen Haberstroh  and  Mathias Hensch  for providing archaeological information, and the staff of the SAPM for support during sample collection.
The Cambridge work is supported by the  Wellcome Trust  (Award no. 2000368/Z/ 15/Z ) and  St. John 's College, Cambridge (J. E. R. , T. K. , C. C. , C. L. S. ); the  European Union  through the  European Regional Development Fund  (Project No. 2014-2020. 4. 01. 16-0030 ) (C. L. S. ); and the  Estonian Research Council  personal research grant ( PRG243 ) (C. L. S). M. A. S. , M. K. , K. I. B. and J. K. were supported by the  Max Planck Society  and the  ERC  starting grant APGREID (to J. K. ). R. T. , A. H. and K. I. B. were supported by the  Max Planck Society.
In addition, litter-normalized Δ 13 C and Δ 15 N values of Protura were compared with published data on mycorrhizal and saprotrophic fungal sporocarps from coniferous forests  [56]  and other soil invertebrates from deciduous forests  [46]. The dataset from Trudell et al. [56]  included 105 ECM and 44 saprotrophic species. The dataset from Klarner et al. [46]  included 125 species of soil invertebrates from dominating taxonomic groups including Oribatida, Mesostigmata, Araneae, Collembola, Lumbricidae, Diplopoda, Chilopoda and others.
Acknowledgements We are grateful to  Christian Ammer  for help with sampling Protura at the Reyershausen field site. Further, we thank  Bernd Kopka  and  Thomas Klein  ( LARI, University of Göttingen ) for technical advice and help in using the plant growth chamber, and the  Centre for Stable Isotope Research and Analysis (KOSI, University of Göttingen ) for stable isotope analysis. We thank the managers of the three exploratories,  Swen Renner ,  Sonja Gockel ,  Kerstin Wiesner  and  Martin Gorke  and all former managers for their work in maintaining the plot and project infrastructure;  Simone Pfeiffer  and  Christiane Fischer  for giving support through the central office,  Jens Nieschulze  for managing the central data base, and for managing the central data base, and  Markus Fischer ,  Eduard Linsenmair ,  Dominik Hessenmöller ,  Daniel Prati ,  Ingo Schöning ,  François Buscot ,  Ernst-Detlef Schulze ,  Wolfgang W.
Considerable research has demonstrated that teachers' self-efficacy plays a major role in implementing instructional practices. Only few studies, however, have examined the interplay between how teachers' self-efficacy and the challenges that lie outside their influence are related to their implementation of cognitive-activation strategies (CASs), especially in science classrooms. Using the Trends in Mathematics and Science Study 2015 data from science teachers in Grades 4, 5, 8, and 9, we explored the extent to which teachers' self-efficacy in science teaching and the perceived time constraints explained variations in the enactment of general and inquiry-based CAS. Findings from the overall sample showed that highly self-efficacious teachers reported more frequent implementation of both general and inquiry-based CAS, whereas those who perceived strong time constraints reported a less frequent use of inquiry-based CAS.
The Present Study Taking advantage of a large, high-quality dataset from TIMSS 2015, we investigate the interplay between teacher self-efficacy in science teaching and the perceived teaching challenges related to time constraints as variables that may explain variation in the implementation of CAS. First, due to the complexity of CAS, distinguishing between the generic and specific aspects of CAS and providing empirical evidence on the relevance of this distinction were critical steps in the present study to develop a more comprehensive understanding of CAS. Attending to the generic as well as subject-specific CAS is crucial to better understand the complex process of teachers' instructional decision-making that results in more effective science teaching  (Charalambous and Kyriakides, 2017; Mikeska et al.
Sample and Procedure The data were derived from the TIMSS study, an international large-scale survey that compares trends in mathematics and science performance in participating countries every fourth year  (Martin et al. , 2016a). TIMSS uses a two-stage stratified cluster design in choosing participants within a country -first, schools are sampled and then intact classrooms of students are selected randomly within the participating schools (see  Martin et al. , 2016b  for further details). Additionally, TIMSS collects data from teachers, school leaders, students, and parents, focusing on contextual variables related to student learning. The current study utilized science teacher data from the Norwegian TIMSS conducted in 2015. In this cycle, Norway changed the target population of students from Grades 4 and 8 to Grades 5 and 9 to improve the comparability to other Nordic countries  (Bergem et al.
, 2016a; Kavli, 2018). Specifically, whereas Norwegian children start school at the age of 6 years, Swedish, Danish, and Finish children start school at the age of 7 years. As a consequence, TIMSS 2015 included not only the samples of fourth and eighth graders (i. e. , benchmark samples), but also the samples of fifth and ninth graders. Using the Norwegian TIMSS 2015 data allowed not only for sampling across grade levels in primary and secondary schools but also for testing the robustness of the findings across grade levels. The sample consisted of N = 804 science teachers (62. 9% female; 74. 9% under the age of 50 years; teaching experience: M = 13. 1, SD = 10. 1 years). Detailed teacher characteristics are shown in Table  1. Note that teachers implement an integrated science curriculum in primary and lower secondary schools.
Data Analysis The teacher data were imported from TIMSS international database 2  , prepared using the IDB Analyzer Version 4. 0, and further analyzed with the statistical software Mplus 7. 4  (Muthén and Muthén, 1998-2018). The rate of missing data ranged from 9. 7% to 15. 8% at the level of item responses, and the full information maximum-likelihood estimation was used to handle the missingness  (Enders, 2010). To correct standard errors in the presence of missing data and possible deviations from normality, the robust maximum-likelihood estimator was used. All model comparisons involving chi-square statistics are therefore corrected according to  Satorra and Bentler (2010)  procedure. Furthermore, we used the TYPE = COMPLEX option to take into account the nesting of the teacher data in schools  (Muthén and Muthén, 1998-2018).
Limitations and Future Directions As this study presented a secondary analysis of TIMSS data, several limitations need to be considered: First, although we applied robust methods for analyzing the relations among latent instead of manifest variables and validated the findings across four grade levels, we cannot draw inferences about cause-and-effect relationships given the cross-sectional nature of the data. By taking a longitudinal perspective, future studies could establish whether these associations are causal and further investigate mediating variables that might affect the relationships demonstrated in this study. Second, the data were based on teachers' self-reports rather than student reports or classroom observations. Hence, our conclusions are established from the teacher perspective on the constructs under investigation.
Since the penetration of the convection in the inner regions, and therefore the abundances of C and N brought up to in the stellar surface, depends on the stellar mass and the mass is related to the age, then the [C/N] ratio can be used to estimate stellar ages (e. g. ,  Salaris et al. 2015; Lagarde et al. 2017). The aim of the present paper is to calibrate an empirical relation between the [C/N] ratio and stellar age using open clusters. Starting from the ages determined with isochrone fitting for the open clusters observed by Gaia-ESO and by APOGEE, we calibrate a relationship between cluster age and the [C/N] ratio in their evolved stars, accurately selecting them among post-FDU stars and studying the occurrence of non-canonical mixing. In Sec. 2, we present our datasets, describing our pre-selection of the Gaia-ESO and APOGEE catalogues and the sample of open clusters observed in both surveys.
2000; Blanton et al. 2017). The APOGEE dr14 spectra were reduced with the pipeline ASPCAP (APOGEE Stellar Parameter and Chemical Abundances Pipeline, García  Pérez et al. 2016). The dr14 release contains data for approximately 263,000 stars. As for the GES sample, we select stars based on the quality of their measurements, according to the following criteria: T eff > 0, eT eff < 500, log g > -0. 5, elog g < 0. 5 and SNR > 20. To identify the open clusters in the APOGEE catalogue, we start our search by defining a master list of known open clusters. We used the  Kharchenko et al. (2013)  catalogue, where coordinates and angular radii of clusters are listed. For most of the cluster in our list, a cluster membership has been recently published  (Donor et al. 2018). For those clusters, we thus select the member stars of  Donor et al.
The age scale of open clusters Star clusters offer the unique opportunity with respect to field stars since they have well-determined ages through isochrone fitting thanks to the many members observed across the cluster sequence. Thus they are powerful tools to calibrate relations between stellar ages and chemical properties of stars. A large sample of clusters is observed in the GES and APOGEE surveys, covering sizable ranges in ages, distances and metallicities. The latest available GES data release, idr5, contains 38 Galactic open clusters, and in 15 of them red giant stars have been observed, while there are 23 clusters in APOGEE with red giant stars (see Tables  1  and 2 ). In the present work, we adopt cluster ages from the recent literature. Since a reliable and homogeneous age determination of star clusters is important for our project, we check for consistency the literature ages against a common set of isochrones.
The Age-[C/N] relationship using open clusters In Table  4 , we present the results of our analysis: the mean [C/N] abundance ratio of the member giant stars (with selection criteria is the membership probability. described in the previous sections) and the number of used stars for each cluster in both the GES and APOGEE samples. The uncertainties in Table  4  are the formal errors on the weighted mean and, within brackets, the standard deviation σ, and they take into account of uncertainties on C and N abundances. Using the solar values from  Grevesse et al. (2007) , we compute the average [C/N] ratio in each cluster in the GES sample, while for APOGEE 5  clusters the solar-scaled abundance ratios are already given in their database. We notice that a recent paper  (Souto et al. 2019 ) re-analyzed 83 APOGEE dr14 spectra of stars in M67.
Application to field stars As mentioned by  Masseron & Gilmore (2015) , passing from [C/N] measurements to assign masses and ages is tempting, but at the same time it is risky because of several unknown or underestimated effects, such as, for instance, the exact evolutionary stage of each observed stars, the metallicity dependence of C and N abundances after the FDU, and the effect of the extra-mixing at different ages and metallicities (cf. Lagarde et al. 2017). Keeping in mind these limits, we combine the sample of giant stars in the GES and APOGEE databases with C and N abundances aiming at identifying age trends along the disc Article number, page 12 of 28 populations. Among giant stars, we select stars which have passed the FDU. To identify the maximum log g at which the FDU can appear, we consider our grid of isochrones (Pisa isochrones, Dell'Omodarme et al.
Summary and conclusions The databases of stellar spectra from high-resolution spectroscopic surveys, as GES and APOGEE, are providing extremely wide and accurate data-sets of stellar parameters and abundances for large samples of stars in the different Galactic components. Some abundance ratios, such as, for instance, [C/N], [Y/Mg], [Y/Al], [Ba/Fe] have been recognised to be powerful chemical clocks, i. e. , strictly related to the stellar ages. In particular, the [C/N] abundance ratio is a good age indicator for stars in the Article number, page 14 of 28   We use our relationship to derive the ages of field stars in a combined GES and APOGEE sample. In the plane [α/Fe] vs. [Fe/H], we see a clear dichotomy between the [C/N] abundances in the thin and thick disc as already observed by  Masseron & Gilmore (2015) , and consequently stellar ages.
We extrapolate our relation to estimate the ages for thin disc stars and giving lower limit ages for the thick disc stars. As expected, we find that stars belonging to the thin disc are on average younger than the stars in the thick disc. Typical age of the thin disc stars decreases going into the inner part of the disc at low z and towards the outskirts. The former indicates that the metal rich stars in the inner disc represent the later phases of the Galactic evolution, while the latter is expected from an inside-out formation of the disc. These immediate applications of the relationships between ages and [C/N] probe the power of chemical clocks to improve our knowledge of stellar ages. The next step will be to estimate in a homogeneous way the age of the open clusters in the final data release of GES survey to put them on a common stellar age scale.
Acknowledgements. We are grateful to the referee for his/her comments and suggestions that improved our discussion. We thanks the APOGEE team, in particular  Katia Cunha ,  Sten Hasselquist  and  Gail Zasowski  for their suggestions and help to use and select the APOGEE results. Based on data products from observations made with  ESO Telescopes at the La Silla Paranal Observatory  under programme ID 188. B-3002. These data products have been processed by the  Cambridge Astronomy Survey Unit (CASU) at the Institute of Astronomy, University of Cambridge , and by the FLAMES/UVES reduction team at  INAF/Osservatorio Astrofisico di Arcetri. These data have been obtained from the  GES Survey Data Archive , prepared and hosted by the  Wide Field Astronomy Unit, Institute for Astronomy, University of Edinburgh , which is funded by the  UK Science and Technology Facilities Council (STFC).
This work was partly supported by the  European Union   FP7 programme  through  ERC  grant number  320360  and by the  Leverhulme Trust  through grant  RPG-2012-541. We acknowledge the support from  INAF and Ministero dell' Istruzione, dell' Università' e della Ricerca (MIUR)  in the form of the grant " Premiale VLT 2012 ". The results presented here benefit from discussions held during the GES workshops and conferences supported by the  ESF  ( European Science Foundation ) through the  GREAT Research Network Programme. T. B. was supported by the project grant ' The New Milky Way ' from the  Knut and Alice Wallenberg Foundation. M. acknowledges support provided by the  Spanish Ministry of Economy and Competitiveness (MINECO) , under grant  AYA-2017-88254-P. L. S. acknowledges financial support from the  Australian Research Council  ( Discovery  Project  170100521 ).
2  and Supplementary Fig. 3a ). We analysed the psaL distribution of cyanobacteria within a known genomic dataset and 14 additional heterocyst-forming strains. In all HCR, the psaL gene is located downstream of psaF and psaJ (psaF-psaJ-psaL) (Fig. 2  and Supplementary Fig. 3b ), in agreement with an earlier report  22. This PsaL encoded by psaF-psaJ-psaL, along with the prevailing tetrameric and dimeric PSI, clearly delineates the clade of cyanobacteria, namely HCR, represented by the green lines and the inset in Fig. 2. In cyanobacteria that fall outside of the HCR clade, psaL is frequently flanked by psaI (psaL-psaI) or, in a few cases, it is found as psaF-psaJ-psaL or isolated from other psa genes altogether (Fig. 2  and Supplementary Fig. 3a ). Besides the 9 strains with reported PSI trimers, BN-PAGE analyses of the PSI oligomers identified an additional 16 strains with trimeric PSI (shown in blue text, Fig.
1c  and Supplementary Fig. 2a ). Previous work on TS-821 suggested that the loop in PsaL between the second and third transmembrane helices might have key roles in PSI oligomerization and PsaL evolution  19. The structure of this loop was not resolved in the trimeric PSI crystal structure from T. elongatus (Protein Data Bank ID 1JB0), but subsequent improved plant PSI monomer structures showed the interaction between this loop sequence and PsaH  27, 28. TS-821 PsaL has an unusual multiproline motif in this loop sequence; we therefore examined the conservation of this loop sequence between PsaL second and third transmembrane helices in all HCR in our study in relation to their PSI oligomeric states (Supplementary Fig. 5 ). Using a logo plot to analyse these regions of all 38 HCR PsaLs, we identified a conserved proline-rich motif, frequently occurring as NPPXP followed by PNPP (Supplementary Fig.