dataesr/scientific-paragraphs-categorization huggingface dataset:
Mapping of hotspots Hotspots were detected using the LDhot program  (Auton et al. 2014). For computational efficiency, LDhot was run on the 100-kb alignments (data set 2). A background recombination map was first estimated for each alignment using the interval program of LDhat with a u value of 0. 005  (Auton and McVean 2007). The resulting maps were highly correlated with the maps based on 1-Mb alignments, and showed little effect of the discretization scheme. The background recombination map was used as input to LDhot with default parameter values and 1000 simulations. Significant hotspots were filtered for further analysis. First, only the hotspots with a value of r between 5 and 100 across the hotspot coordinates were selected, because higher values are most likely artifacts and the performance of LDhot is low for weak hotspots  (Auton et al.
Data availability Sequence data has been deposited under the National Center for Biotechnology Information (NCBI) Illumina reads for Z. ardabiliae are available from NCBI under the Biosample accession numbers SAMN05818736-SAMN05818752. Illumina reads for Z. tritici are available from NCBI under the BioProject accession number PRJNA312067. All scripts and data sets necessary to reproduce the analyses and figures in this manuscript may be accessed on FigShare under https:// doi. org/10. 6084/m9. figshare. 3806244. v1.
SNP density and filtering based on confidence intervals: LDhat and LDhelmet have been developed for recombination analyses in animals  (Auton and McVean 2007; Auton et al. 2012; Chan et al. 2012) , and their performance on data from haploid eukaryotes with high recombination rates has not been tested. Therefore, we next assessed the robustness of the composite likelihood approach using simulations with distinct sample sizes and SNP densities. We report that the interval program infers recombination rate with the highest reliability for intermediate diversity levels (u = 0. 0005 or 0. 005). Furthermore, while larger sample sizes decrease the variance in the estimate, we show that LDhat reliably infers recombination when as few as 10 haploid genomes are used (Figure  2 ). We observe that r generally tends to be underestimated, and its estimation variance is larger for small sample sizes.
Second, many of the SNPs discarded for having a large confidence interval in the estimation of recombination rates by LDhat are singletons. To further assess the potential impact of putative sequencing errors, we ran LDhat on the Z. tritici data set after discarding all singletons and filtering for confidence interval as described above. The resulting recombination map appeared to be highly correlated to the map including all singletons (Figure  S1  and Table  2 ), and the correlation of the filtered map with the crossover map was significant, but weaker than when including them (Kendall's t = 0. 3083, P , 2. 2e 216 ) (Table  2 ). We therefore conclude that putative sequencing errors have no significant impact on our inferred recombination maps. Effect of population structure: Previous studies reported that Z.
Because the resulting correlation with the crossover map was slightly lower than when using the complete data set, we use the complete data set for the further analyses. As little is known about the population structure of Z. ardabiliae, we conducted additional simulations to assess the putative impact of structure on the inference of recombination rate. We used a five-islands structure model, with sample sizes equal to 2, 3, 4, 5, and 6 in each deme, with a total sample size of 20; which is comparable to the 17 genomes of the Z. ardabiliae data set analyzed here. Migration rates between demes were symmetrical and all equal, and we tested several rates. We find that while r is systematically overestimated in the presence of population structure, it is remarkably proportional to the true value, in particular after filtering on the confidence intervals (Figure  S2 ).
2015). Antagonistic coevolution of these interacting proteins is often reflected in accelerated evolution and signatures of positives selection  (Stukenbrock and McDonald 2009). To assess the role of recombination on effector evolution, we first predicted effector proteins computationally in the secretomes of both species using the EffectorP software  (Sperschneider et al. 2016). This approach identified 868 putative effector proteins in Z. tritici and 1122 and Z. ardabiliae. By comparing the recombination rates in different genomic regions encoding effector and noneffector genes, we show a significantly lower recombination rate in exons and introns of effector proteins in Z. ardabiliae (Wilcoxon rank test, P = 1. 305e 24 for exons and 2. 534e 25 for introns, P-values corrected for multiple testing) (Figure  5B ).
These results mirror findings from other eukaryotic species (e. g. ,  Winckler et al. 2005; Singhal et al. 2015)  and suggest that distinct mechanisms determine the recombination landscape at fine and broad scales in these two species. Frequency and intensity of recombination hotspots is higher in Z. tritici The fine-scale LDhat recombination maps clearly reveal the presence of distinct peaks of recombination in both Z. tritici and Z. ardabiliae (Figure  3 ). We used the program LDhot to call positions of statistically significant recombination hotspots  (Auton et al. 2014)  and applied highly stringent selection criteria (see Materials and Methods) to obtain positions of the most significant hotspots for which the within-hotspot rate was at least 10 times higher than the flanking regions (Figure  8A ).
In addition, measurements of multiplicities and kinematic properties of jets in tt events are presented. The measurements are based on proton-proton (pp) collision data at a center-of-mass energy of  13  TeV corresponding to an integrated luminosity of 35. 8 fb -1  [2]. The data were recorded by the CMS experiment at the CERN LHC in 2016. Only tt decays into the +jets ( = e, µ) final state are considered, where, after the decay of each top quark into a bottom quark and a W boson, one of the W bosons decays hadronically and the other one leptonically. Hence, the experimental signature consists of two jets coming from the hadronization of b quarks (b jets), two jets from a hadronically decaying W boson, a transverse momentum imbalance associated with the neutrino from the leptonically decaying W boson, and a single isolated muon or electron.
This measurement continues a series of differential tt production cross section measurements in pp collisions at the LHC. Previous measurements of differential cross sections at √ s = 7 TeV  [3, 4]  and 8 TeV  [5] [6] [7] [8] [9] [10] [11]  have been performed in various tt decay channels. First measurements at  13  TeV are available  [12] [13] [14]. Previous studies of multiplicities and kinematic properties of jets in tt events can be found in Refs. [15] [16] [17]. With about 15 times more data and an improved understanding of systematic uncertainties, we provide an update and extension to the previous CMS analysis in the +jets channel at 13 TeV  [18]. We measure differential cross sections defined in two ways: at the particle level and the parton level. For the particle-level measurement a proxy of the top quark is defined based on experimentally accessible quantities, such as properties of jets, which are made up of quasistable particles with a mean lifetime greater than 30 ps.
Pred. Data Figure  4 : Distribution of the negative log-likelihood for the selected best permutation in the (left) parton-and the (right) particle-level measurements in data and simulations. Events generated with POWHEG+PYTHIA8 are used to describe the tt production. The contribution of multijet, DY, and W boson plus jets background events is extracted from the data (cf. Section 7). Combined experimental (cf. Section 9) and statistical uncertainties (hatched area) are shown for the total predicted yields. The data points are shown with statistical uncertainties. The ratios of data to the sum of the predicted yields are provided at the bottom of each panel. In Fig. 5 , the p T of the jets from the tt system, as identified by the reconstruction algorithm, and of the additional jets are presented and compared to the simulation.
Pred. Data Figure  5 : Comparisons between data and simulation at the particle level of the reconstructed distributions of the p T of jets as identified by the tt reconstruction algorithm. The simulation of POWHEG+PYTHIA8 is used to describe the tt production. The contribution of multijet, DY, and W boson plus jets background events is extracted from the data (cf. Section 7). Combined experimental (cf. Section 9) and statistical uncertainties (hatched area) are shown for the total predicted yields. The data points are shown with statistical uncertainties. The ratios of data to the predicted yields are given at the bottom of each panel.
Pred. Data Figure  6 : Comparisons of the reconstructed p T (upper) and |y| (lower) in data and simulations for the t (left) at the parton level and the t h (right) at the particle level. The simulation of POWHEG+PYTHIA8 is used to describe the tt production. The contribution of multijet, DY, and W boson plus jets background events is extracted from the data (cf. Section 7). Combined experimental (cf. Section 9) and statistical uncertainties (hatched area) are shown for the total predicted yields. The data points are shown with statistical uncertainties. The ratios of data to the predicted yields are given at the bottom of each panel. and M(tt) (lower) for the (left) parton-and the (right) particle-level measurements in data and simulation. The simulation of POWHEG+PYTHIA8 is used to describe the tt production.
The contribution of multijet, DY, and W boson plus jets background events is extracted from the data (cf. Section 7). Combined experimental (cf. Section 9) and statistical uncertainties (hatched area) are shown for the total predicted yields. The data points are shown with statistical uncertainties. The ratios of data to the predicted yields are given at the bottom of each panel. remaining fraction consists of multijet, DY, and W boson events. The background distributions are obtained after applying exactly the same tt reconstruction algorithm as in the signal region. The two b jet candidates still have the highest value of the b identification discriminant to maintain a similar number of allowed permutations of jets in the control and signal regions. To estimate the shape dependency on the selection of the control region, we vary the selection threshold of the b identification discriminant.
CMSSimulation Figure  8 : Migration studies of the (upper) parton-and (lower) particle-level measurements of p T (t h ), extracted from the POWHEG+PYTHIA8 simulation. Left: purity, stability, and bin efficiency. Right: bin migrations between detector and parton (particle) level. The p T range of the bins can be taken from the left panels. Each column is normalized such that the sum of its entries corresponds to the percentage of reconstructed events in this bin at the parton (particle) level. To control the level of regularization, the iterative D'Agostini method takes the number of iterations as an input parameter. The initial distributions for the D'Agostini unfolding are taken from the POWHEG+PYTHIA8 simulation. The number of iterations is chosen such that the compatibility between a model and the unfolded data at either the parton or particle level is the same as the compatibility between the folded model and the data at detector level.
11-12 and 13-14 for the parton and particle level, respectively. At parton level, the kinematic properties of t h and t are identical, and we measure the differential cross section as a function of the top quark p T using the higher-and lower-p T values in the tt pair, as shown in Fig. 15. The measured p T spectra of t h and t are consistently softer than predicted by all the simulations using the PYTHIA8 PS generator at both the parton and particle levels. Also the NNLO QCD+NLO EW calculation predicts a slightly harder p T spectrum than observed in the data. The POWHEG+HERWIG++ simulation describes the data well at the parton level. However, at the particle level, the p T (t h ) distribution is noticeably softer than in the data. In Figs. 16  and 17, the cross sections as a function of kinematic variables of the tt system are compared to the same theoretical predictions.
[30] [31] [32] [33] [34] [35]. These distributions are helpful to estimate the tt background contribution in searches for physics beyond the standard model that are looking for signatures with high jet multiplicities. The observation that the p T (t) distribution is softer in data than in the simulations is mainly true for events with zero or one additional jet. We also measure the properties of the individual jets in tt events. The absolute and normalized differential cross sections as a function of the p T of jets in the tt system and of the four leading additional jets are shown in Figs. 36  and 37, respectively. The trend of a softer p T spectrum of the top quark is also visible for all jets of the tt system. From these p T distributions we calculate the jet multiplicities with minimum p T thresholds of 30, 50, 75, and 100 GeV shown in Fig.
In the predictions, such collinear radiation is mainly described by the PS model. Since the parton-level prediction is not affected by the simulation of the final-state PS, this overestimation of radiation may explain the discrepancies between the parton-and particle-level predictions of POWHEG+HERWIG++ in the p T (t h ) and M(tt) distributions. Table  5  presents the results of the χ 2 tests comparing the absolute measurements involving multiplicities and kinematic properties of jets to the simulations. The corresponding results for the normalized measurements are shown in Table  6. Most of the kinematic distributions and multiplicities of the additional jets are reasonably well modeled by POWHEG+PYTHIA8. Inconsistencies with the data are observed for p T and η of jets, and p T (tt) for different jet multiplicities.
Data Theory Figure  42 : Differential cross section at the particle level as a function of jet ∆R j t. The upper two rows show the ∆R j t distributions for the jets in the tt system, the lower two rows the distribution for additional jets. The data are shown as points with light (dark) bands indicating the statistical (statistical and systematic) uncertainties. The cross sections are compared to the predictions of POWHEG combined with PYTHIA8 (P8) or HERWIG++ (H++) and the multiparton simulations MG5 aMC@NLO (MG5)+PYTHIA8 FxFx and SHERPA. The ratios of the predictions to the measured cross sections are shown at the bottom of each panel.
Data Theory Figure  43 : Normalized differential cross section at the particle level as a function of jet ∆R j t. The upper two rows show the ∆R j t distributions for the jets in the tt system, the lower two rows the distribution for additional jets. The data are shown as points with light (dark) bands indicating the statistical (statistical and systematic) uncertainties. The cross sections are compared to the predictions of POWHEG combined with PYTHIA8 (P8) or HERWIG++ (H++) and the multiparton simulations MG5 aMC@NLO (MG5)+PYTHIA8 FxFx and SHERPA. The ratios of the predictions to the measured cross sections are shown at the bottom of each panel.
Data Theory Figure  44 : Differential cross section at the particle level as a function of ∆R t. The upper two rows show the ∆R t distributions for the jets in the tt system, the lower two rows the distribution for additional jets. The data are shown as points with light (dark) bands indicating the statistical (statistical and systematic) uncertainties. The cross sections are compared to the predictions of POWHEG combined with PYTHIA8 (P8) or HERWIG++ (H++) and the multiparton simulations MG5 aMC@NLO (MG5)+PYTHIA8 FxFx and SHERPA. The ratios of the predictions to the measured cross sections are shown at the bottom of each panel.
Data Theory Figure  45 : Normalized differential cross section at the particle level as a function of ∆R t. The upper two rows show the ∆R t distributions for the jets in the tt system, the lower two rows the distribution for additional jets. The data are shown as points with light (dark) bands indicating the statistical (statistical and systematic) uncertainties. The cross sections are compared to the predictions of POWHEG combined with PYTHIA8 (P8) or HERWIG++ (H++) and the multiparton simulations MG5 aMC@NLO (MG5)+PYTHIA8 FxFx and SHERPA. The ratios of the predictions to the measured cross sections are shown at the bottom of each panel.
Summary Measurements of the absolute and normalized differential and double-differential cross sections for tt production in proton-proton collisions at √ s = 13 TeV have been presented. The data correspond to an integrated luminosity of 35. 8 fb -1 recorded by the CMS experiment. The tt production cross section is measured in the +jets channels at the parton and particle levels as a function of the transverse momentum p T and absolute rapidity |y| of the top quarks and p T , |y|, and invariant mass of the tt system. In addition, at the particle level detailed studies of multiplicities and kinematic properties of the jets in tt events are performed. The dominant sources of uncertainty are the jet energy scale uncertainties on the experimental side and parton shower modeling on the theoretical side.
Figure36: Differential cross section at the particle level as a function of jet p T. The upper two rows show the p T distributions for the jets in the tt system, the lower two rows the distribution for additional jets. The data are shown as points with light (dark) bands indicating the statistical (statistical and systematic) uncertainties. The cross sections are compared to the predictions of POWHEG combined with PYTHIA8 (P8) or HERWIG++ (H++) and the multiparton simulations MG5 aMC@NLO (MG5)+PYTHIA8 FxFx and SHERPA. The ratios of the predictions to the measured cross sections are shown at the bottom of each panel.
Figure37: Normalized differential cross section at the particle level as a function of jet p T. The upper two rows show the p T distributions for the jets in the tt system, the lower two rows the distribution for additional jets. The data are shown as points with light (dark) bands indicating the statistical (statistical and systematic) uncertainties. The cross sections are compared to the predictions of POWHEG combined with PYTHIA8 (P8) or HERWIG++ (H++) and the multiparton simulations MG5 aMC@NLO (MG5)+PYTHIA8 FxFx and SHERPA. The ratios of the predictions to the measured cross sections are shown at the bottom of each panel.
The SIMBAD astronomical database hosted by the CDS provides basic data, cross-identifications, bibliography and measurements for astronomical objects outside the solar system. The CDS receives the bibliographic meta-data of the articles published in the main astronomical journals directly from the publishers. How we receive the data and their format vary from one publisher to the next. These data are first extracted and stored in files with a standardised format. Then, to avoid errors or misprints, we perform different tests on these data: -Author names are compared to a reference list maintained at CDS, and the keywords are compared with the AAS list -Astronomical objects are verified by checking their name in the SIMBAD database -A completion test checks that all of articles of a journal volume are present The next step identifies whether an astronomical object appears inside a title, a keyword or an abstract, and if so, we add a link to the object in SIMBAD.
Once all of the verifications and corrections have been made we add the meta-data into SIMBAD. We also add other information such as the number of different astronomical objects studied in the paper, the presence tables and their links to VizieR, any new acronyms, as well as some comments. New developments are in progress to automatically extract the data from the tables in the articles (that have not been processed by, or provided to VizieR). In addition, each night automatic checks are executed to list the new data and to test the coherence of these data in SIMBAD.
Introduction: The context The objective of the Centre de Données astronomiques de Strasbourg  [1]  is to collect, homogenize, distribute and preserve astronomical information published in astronomical journals for the usage of the whole community. The articles published in the astronomical literature represent the thread of our activity. Before looking for data inside the paper, we have to create the bibliographical entries for the SIMBAD and VizieR databases. By special agreements with the main journals, the CDS receives meta-data of articles in electronic form. How we receive the data and their format vary from one publisher to the next, and also vary over time. The first operation is the extraction of the required information, and the second step is to store e-mail: thomas. delacour@astro.
Checks and SIMBAD update To ensure the quality of the data and to avoid errors that can occur during the data extraction, we carry out a multitude of checks. For each issue or volume we process, we check the completeness of it to be sure that no articles are missing, and when we have an erratum we add this information to the related article. We also maintain a correlation list between the bibcodes and the DOIs. All author names are compared to the list of all authors already existing in our databases. If an author is not found, it must be verified to check if it is a new one or a misprint. In the same way we check the keywords with the list a keywords from the AAS, and when a keyword does not match any in the list the program will display a message. When an individual object is given in a keyword we first validate the identifier and then add a link to SIMBAD.
Once all the data have been checked and corrected we add them into SIMBAD. It is important to point out that no corrections are made to the published data. We only correct errors made during our transcriptions. When there is an error on an object, we add a macro latex that will allow us to link the misnamed object directly to the right object in SIMBAD. In the other cases, we add a note to the data in SIMBAD. The journal editors are contacted, if needed, to signal an error. At that stage, the documentalists in charge of updating SIMBAD begin to link the articles to the SIMBAD astronomical objects  [4].
A Readme file containing information about the article is generated along with a summary of different tables and reference articles. After creation of all files documentalists can verify the information and data by comparing them to the original tables and article. Once verifications are done the data are put in Vizier and can be consulted on the website along with tables. In further updates of SIMBAD, we add other information to the bibliographical data such as the number of different astronomical objects studied in the paper, the presence of one or more tables and the links to VizieR, the creation of a new acronym, and also some comments.
Daily and weekly tests Information processing at CDS is a chain  [6]. To monitor the progress of the work among all the team members and assure the quality of the data, we have developed automatic test processes and we send the results to the concerned documentalists. Several tests are launched every day and/or week automatically. EPJ Web of Conferences 186,  12004 (2018)  https://doi. org/10. 1051/epjconf/201818612004 LISA VIII Concerning the data we receive from the publishers, every night a program compares the present bibliographical content with the one from the day before. It checks for new references entered in SIMBAD, the deleted references, the number of references, and new authors names. It checks also the objects names in the keywords and the object names tagged by the authors in some journals (A&A).