dataesr/scientific-paragraphs-categorization huggingface dataset:
acknowledgements This work was funded by the  German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig  (funded by the  German Research Foundation ;  DFG FZT 118 ). We thank  Jitendra Gaikwad  for his precious help with the technical aspects of the database storage and management through the iDiv Biodiversity Portal (https:// idata. idiv. de/,https://doi. org/10. 25829/idiv. 286-21-2695 )and the iDiv IT Support for their help in the CESTES website development (https://icestes. github. io/). The datasets provided by  A. Jeliazkov  were collected with financial support from the  Fédération d'Ile-de-France pour la Recherche en Environnement  ( FIRE FR-3020 ). The dataset provided by  R. Carvalho  and  F. L. Tejerina-Garro  had financial support from the  Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq No.
Code availability In addition to the Excel storage of the datasets, the CESTES database has also been stored as an. RData object to facilitate its further use for analyses in R  109. It comes with R code scripts that allow further checking, processing, transforming and exploring the database content (for more details, see the Usage Notes section). We provide all this information in a folder called "rCESTES. zip" within the "CESTES" folder at the following links: • Figshare repository (fixed version): https://doi. org/10. 6084/m9. figshare. c. 4459637. • iDiv Biodiversity Portal (evolutive version): https://doi. org/10. 25829/idiv. 286-21-2695.
The particle size distributions of the batch-annealed states for both alloys are shown in Fig. 2 (c, g ), where a relatively broad size distribution can be observed (also indicated in Fig. 2  (a-b, e-f)). Cementite chemistry after batch annealing was characterised via transmission electron microscopy (TEM), equipped with energy-dispersive X-ray spectroscopy (EDS). More than 20 particles from several fields of view on direct extraction carbon replica samples  [39]  were analysed to determine the cementite compositions for each condition. Point analyses were performed on a FEI Tecnai G2 T20 TEM (operated at 200 kV) with a Bruker QUANTAX 30 mm2 silicon drift detector. EDS spectra quantification was performed using the Cliff-Lorimer ratio approach  [40]  as embodied in Bruker ESPRIT 2 microanalysis software.
The measured cementite composition is described by the U-fraction that measures a species substitutional site occupancy. The Mn U-fraction distributions in cementite are shown in Fig. 2 (d, h ) for the Fe-0. 2C-5Mn and Fe-0. 4C-2Mn alloys. The average cementite Mn U-fractions and the associated standard deviations for each alloy after batch annealing are listed in Table  1  , and compared with the equilibrium calculations (TCFE9 database, Thermo-Calc) at 600 °C. It is expected that these tempering treatments lead to significant Mn partitioning to cementite  [41] [42] [43]. The mean cementite Mn compositions ( Table  1  and Fig. 2 (d, h )) are close to the equilibrium values at 600 °C. The measured Si and Al contents in cementite are low enough to be within measurement uncertainties and are not reported here.
In situ experimental set-up High intensity in situ neutron powder diffraction was used to monitor austenite formation during intercritical annealing, and this was conducted at the high intensity powder diffractometer, Wombat  [44]  , at Australian Nuclear Science and Technology Organisation (ANSTO). 4 pieces of each sample (1. 5 mm × 5 mm × 50 mm) were stacked together in a 9mm-diameter vanadium can under transmission geometry in the neutron diffractometer. The Table  1. Compositions of investigated alloys in this study. The measured average cementite particle radius with standard deviations (from SEM image analysis) of batch-annealed state are listed for each alloy. The measured cementite Mn U-fractions (mean values and standard deviations) by TEM-EDS after batch annealing, in comparison with values from equilibrium calculations (TCFE9 database, Thermo-Calc), are listed for each alloy.
Quantitative phase analysis The diffraction data were analysed by Rietveld-based quantitative phase analysis methods, as embodied in the TOPAS software package (version 5, Bruker AXS GmbH). Neutron wavelength, zero error and instrument functions were derived by simultaneously refining the scans of NIST LaB 6 , Al 2 O 3 and Si standards. The sample displacements (parallel and perpendicular to the incident beam) were corrected using the model by Scarlett et al. [47]. For each set of in situ data collection across the intercritical thermal cycle, the sample displacements are assumed to be constant. They are initially refined by simultaneously fitting 6 patterns at different stages of transformation and later fixed in the sequential batch processing. To model the preferred orientation effect from cold rolling, the generalised spherical harmonics model  [48]  was used.
The temperature band represents the uncertainty of ± 10 °C at the set point temperatures. The dependence on the measured initial Mn U-fraction ( Table  1  ) includes the average measurement as well as the width of standard deviation of TEM-EDS measurements. The equilibrium austenite formation starting temperature (Ae 1 ) and finishing temperature (Ae 3 ) are highlighted. These thermodynamic calculations are based on TCFE9 database, Thermo-Calc. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article. ) lations is the Mn U-fraction in cementite (37% for A from TEM-EDS measurement vs. 25% for B as an arbitrarily smaller number under PLE growth mode). The simulation was conducted in spherical geometry in DICTRA  [ 57 , 58 ]  with the θ 1 -γα configuration and a cell size of 10 0 0 nm.
The mobility enhancement of 2332 for Mn within cementite is used based on the extrapolation of the fitted expression in  [41]  , and a comparison between the Arrhenius expressions of this extrapolated cementite Mn diffusivity and that in MOBFE4 database is shown in Fig. 12. The used extrapolated Mn diffusivity in cementite (660-920 °C) lies between the Mn diffusivities in BCC and FCC. From the simulation results ( Fig. 11 a ), higher initial cementite Mn U-fraction (A) leads to lower saturation level of austenite formation kinetics, which can be nearly 20% lower than equilibrium calculation. Similar simulation findings were reported by Lai et al. [27]. They explained this transformation plateau as due to the relative position of the operative tie-line compared to the full equilibrium one defined by the bulk composition.
Acknowledgements The authors gratefully acknowledge the support of  ArcelorMittal  and the  Australian Research Council  through the  Linkage Grant Scheme  (  LP150100756  ). YXW and LYW gratefully acknowledges the award of the  Australian Government Research Training Program. The authors gratefully acknowledge the support of the  ANSTO  for providing the powder diffraction facilities used in this work and financial support ( Proposal 5329 & 4507 ), and the help from  Mr. Stanley Lee  ( ANSTO ),  Dr. Qi Zhang  and  Dr. Shengchao Yang  during the data collection. YXW gratefully acknowledges  Prof. Sybrand van der Zwaag  ( TU delft ) for the stimulating discussions during his visit to Monash,  Dr. Xiang Gao  for collecting the TEM EDS data,  Ms. Xiaohan Weng  for collecting part of the SEM data and  Mr.
Numerical solver foamStar is an in-house code co-developed by Bureau Veritas and Ecole Centrale de Nantes in the OpenFOAM framework for solving wave-structure interactions problems. It is based on the standard multi-phase solver interDymFoam in the OpenFOAM package  [23, 25]. Two special modules are introduced for wavestructure interactions. The wave generation module is taken from waves2Foam and the mechanical solver is devised to solve the dynamics of rigid and flexible floating bodies in waves. Some examples of 3D wave generation of realistic sea spectrum can be found in  [26].
Time integration scheme A time integration scheme in OpenFOAM can be selected among Implicit Euler (Euler), Crank-Nicolson (CN), and second-order backward differentiation formula (BDF2). The supplied multiphase solver of OpenFOAM, interDymFoam, uses a special module which is called Multidimensional Universal Limiter with Explicit Solution (MULES) to solve the α-transport equation. MULES employs a method based on predictor and corrector steps to ensure α boundedness. As an example, when the Euler scheme is selected, the predictor step is as follows: V n+1 P α * P -V n P α n P ∆t + N f F b α,f = 0 (6) where the superscripts * and n denote the predictor and time iteration, respectively. The time step between two successive time iterations is ∆t. The equation flux is F b α,f where the superscript b denotes that the flux is bounded.
4) In the equation (A. 3), F CN α,f -F b α * ,f represents the flux contribution from higherorder scheme. The flux limiter λ f ∈ [0, 1] is computed from α * in the predictor step, or in the previous corrector step if the iterative correction is considered. The corrector step can be solved iteratively by updating α * = (α * * ) prev and the bounded flux F b α * ,f = (F b α * ,f ) prev + (λ f ) prev (F CN α,f -F b α * ,f ) prev where the subscript (•) prev represents the previous corrector step. After the corrector step, α n+1 is taken from the α * *. The computed α n+1 from the MULES algorithm can be different with α n+1 from the classical Crank-Nicolson scheme given in (A. 1) due to the existence of flux limiter λ f which introduced to maximize the α-transportation while keeping the boundness.
The high-resolution N H 2 map was also compared to two nearinfrared extinction maps. The first extinction map was derived from 2MASS data following  Bontemps et al. (2010)  and Schneider et al. (  2011 ), with a FWHM spatial resolution of ∼120. The other extinction map was also made from 2MASS point-source data, using the NICEST near-infrared color excess method (Lombardi 2009, and Lombardi et al. in prep 1. ). For both comparisons, the Herschel column density map was first smoothed to the resolution of the extinction maps and converted to visual extinction units assuming N H 2 (cm -2 ) = 0. 94 × 10 21 A V (mag)  (Bohlin et al. 1978). We then compared the data points of the converted Herschel maps to those of the A V maps on the same grid. The resulting A V,HGBS /A V,Bontemps and A V,HGBS /A V,Lombardi ratio maps are shown in Fig.
E. 1 over the field covered by Fig. 2. The median values of both ratios over the whole area are ∼0. 5. In the extinction range A V = 3-7 mag, the ratios are also similar, ∼0. 6 and ∼0. 5, respectively. Both extinction maps are in good agreement with each other, while the HGBS column density map indicates lower values by a factor of < 2. This may be the result of a combination of various factors, such as different techniques applied to the Herschel and 2MASS datasets, and less reliable dust opacity assumptions in low extinction regions, which are more exposed to the stronger radiation field in Orion B than in other HGBS regions (cf. Roy et al. 2014).
Filamentary structure of the Orion B cloud complex Following  André et al. (2014)  and  Könyves et al. (2015) , we define an interstellar filament as any elongated column-density structure in the cold ISM which is significantly denser than its surroundings. We adopt a minimum aspect ratio of ∼ 3 and a minimum column density excess of ∼ 10% with respect to the local background, that is, ∆N fil H 2 /N bg H 2 > 0. 1, when averaged along the length of the structure. In order to trace filaments in the Herschel maps and in the column density map, we used the multi-scale algorithm getfilaments (Men'shchikov 2013). Briefly, by analyzing highly-filtered spatial decompositions of the images across a wide range of scales, getfilaments separates elongated clusters of connected pixels (i. e. , filaments) from compact sources and non-filamentary background at each spatial scale, and then constructs cumulative mask images of the filamentary structures present in the original data by accumulating the filtered signal up to an upper transverse angular scale of interest.
Multiwavelength core extraction with getsources Compact sources were extracted simultaneously from the PACS and SPIRE images with the getsources algorithm (Men'shchikov et al. 2012) 2. A complete summary of the source extraction method can be found in Sect. 4. 4 of  Könyves et al. (2015) , and full technical details are provided in  Men'shchikov et al. (2012). Briefly, the extraction process consists of a detection and a measurement stage. At the detection stage, getsources analyzes fine spatial decompositions of all the observed wavebands over a wide range of scales. This decomposition filters out irrelevant spatial scales and improves source detectability, especially in crowded regions and for extended sources. The multiwavelength design combines data over all wavebands and produces a wavelength-independent detection catalog.
At the measurement stage, properties of detected sources are measured in the original observed images at each wavelength. For the production of HGBS first-generation catalogs of starless and protostellar cores, two sets of dedicated getsources extractions are performed, optimized for the detection of dense cores and YSOs/protostars, respectively. In the first, "core" set of extractions, all of the Herschel data tracing column density are combined at the detection stage, to improve the detectability of dense cores. The detection image is thus combined from the 160 µm, 250 µm, 350 µm, and 500 µm maps, together with the high-resolution column density image (see Sect. 3. 1) used as an additional "wavelength". The 160 µm component of the detection image is "temperature-corrected" to reduce the effects of strong, anisotropic temperature gradients present in parts of the observed fields (see  Könyves et al.
A power-law fit to the cumulative mass fraction for A V 3 gives M(> N H 2 ) ∝ N -1. 1±0. 2 H 2. second set of extractions, the only Herschel data used at the detection stage are the PACS 70 µm data. This allows us to exploit the result that the level of point-like 70 µm emission traces the internal luminosity (and therefore the presence) of a protostar very well (e. g. ,  Dunham et al. 2008). At the measurement stage of both sets of extractions, source properties are measured at the detected positions of either dense cores or YSOs/protostars, using the observed, backgroundsubtracted, and deblended images at all five Herschel wavelengths, plus the high-resolution column density map. The only difference from the procedure described in  Könyves et al. (2015)  is that, following improvements in the preparation of maps for source extraction, a new method of background flattening, called getimages (Men'shchikov 2017), was used before performing the protostellar set of getsources extractions.
This post-selection procedure allowed us to remove dubious sources from the final catalog of cores in online Table  A. 1. To eliminate extragalactic contaminants from our scientific discussion, we cross-matched all selected sources with the NASA Extragalactic Database 3 (NED). We found matches within 6 for four classified external galaxies, which were removed from our scientific analysis. As Orion B lies toward the outer Galaxy and below the Galactic plane, we can expect several extragalactic contaminants in the low column density portions of the field, beyond the four sources listed in the NED database. Using Herschel's SPIRE data from the HerMES extragalactic survey  (Oliver et al. 2012) ,  Oliver et al. (2010)  determined that the surface number density of extragalactic sources at the level S 250µm = 100 mJy is about 12.
8±3. 5 deg -2. In the ∼19 deg 2 area observed with Herschel in Orion B, our catalog includes three candidate cores with 250 µm integrated flux densities below 100 mJy and 26 candidate cores with 250 µm integrated flux densities below 200 mJy, mostly in low background emission spots. Rougly 1/3 of these candidate cores with low flux densities at 250 µm have Unclassified Extragalactic Candidate matches within 6. These 26 tentative cores are listed in our observed core catalog (online Table  A. 1), but excluded from the scientific analysis. Likewise, we checked likely associations between the selected Herschel cores and objects in the SIMBAD database or the Spitzer database of Orion by  Megeath et al. (2012). Any match is reported in the online catalog of Table  A. 1. In particular, 30 associations with a Spitzer source were found using a 6 matching radius, where we used the same selection of YSOs from  Megeath et al.
g. , Fig. 2 ) were also observed by IRAC as part of the Spitzer Orion survey  (Megeath et al. 2012 ) and thus provide a common field where Herschel and Spitzer source counts can be directly compared. The various numbers of dense cores and YSOs identified by the two surveys in this common field are as follows: -Herschel starless cores: 1032; above the mass completeness limit of 0. 4 M : 338 -Herschel candidate prestellar cores: 538; above 0. 4 M : 333 -Herschel robust prestellar cores: 361; above 0. 4 M : 312 -Spitzer Class II YSOs: 312; corrected for incompleteness: 427 -Spitzer entire sample of Class 0/I-flat spectrum-Class II YSOs: 428; corrected for incompleteness: 586 For our lifetime estimates, we used the numbers of Spitzer sources corrected for incompleteness with the help of Chandra data and other techniques as described in detail in  Megeath et al.
Summary and conclusions Using SPIRE and PACS parallel-mode data from the Herschel Gould Belt survey, we obtained an extensive census of dense cores in the Orion B star-forming region. We analyzed both the physical properties of the cores and the connection with interstellar filaments. Our main results and conclusions can be summarized as follows: 1. As in other HGBS clouds, the high-resolution (∼ 18 or ∼ 0. 03 pc) Herschel column density map of Orion B shows a highly filamentary distribution of matter and the column density PDF features a prominent power-law tail above A V ∼ 3 mag. 2. Based on multi-scale, multi-wavelength core extraction with the getsources algorithm we identified 1768 starless dense cores, 804 candidate prestellar cores, 490 robust prestellar cores, and 76 protostellar cores in the ∼19 deg 2 field that was imaged with both SPIRE and PACS at five wavelengths between 70 µm and 500 µm.
Acknowledgements. SPIRE has been developed by a consortium of institutes led by  Cardiff Univ. (UK)  and including:  Univ. Lethbridge (Canada) ;  NAOC (China) ;  CEA ,  LAM (France) ;  IFSI ,  Univ. Padua (Italy) ;  IAC (Spain) ;  Stockholm Observatory (Sweden) ;  Imperial College London ,  RAL ,  UCL-MSSL ,  UKATC ,  Univ. Sussex (UK) ; and  Caltech ,  JPL ,  NHSC ,  Univ. Colorado (USA). This development has been supported by national funding agencies:  CSA (Canada) ;  NAOC (China) ;  CEA ,  CNES ,  CNRS (France) ;  ASI (Italy) ;  MCINN (Spain) ;  SNSB (Sweden) ;  STFC ,  UKSA (UK) ; and  NASA (USA). PACS has been developed by a consortium of institutes led by  MPE (Germany)  and including  UVIE (Austria) ;  KUL, CSL, IMEC (Belgium) ;  CEA, OAMP (France) ;  MPIA (Germany) ;  IFSI, OAP/AOT ,  OAA/CAISMI, LENS, SISSA (Italy) ;  IAC (Spain).
This development has been supported by the funding agencies  BMVIT (Austria) ,  ESA-PRODEX (Belgium) ,  CEA/CNES (France) ,  DLR (Germany) ,  ASI (Italy) , and  CICT/MCT (Spain). V. K. would like to thank  Richard Allison , and  Richard Parker  for their help and discussions on mass segregation with the MST method. This work has received support from the  European Research Council  under the European Union's  Seventh Framework Programme  ( ERC Advanced Grant  Agreements no. 291294 -ORISTARS -and no. 267934 -MISTIC ) and from the  French National Research Agency  (Grant no. ANR-11-BS56-0010 -STARFICH ). D. A. acknowledges support by  FCT/MCTES  through national funds (PIDDAC) by the grant  UID/FIS/04434/2019. N. S. and S. B. acknowledge support by the  French ANR  and the  German DFG  through the project  GEN-ESIS  ( ANR-16-CE92-0035-01/DFG1591/2-1 ).
G. J. W. gratefully acknowledges support from The  Leverhulme Trust. We also acknowledge support from the  French national programs of CNRS/INSU  on stellar and ISM physics (PNPS and PCMI). This research has made use of the SIMBAD database, operated at  CDS, Strasbourg (France) , and of the NASA/IPAC Extragalactic Database (NED), operated by the  Jet Propulsion Laboratory, California Institute of Technology , under contract with the  National Aeronautics and Space Administration.
Plasmids are genetic elements that enable rapid adaptation and evolution by transferring genes conferring selective advantages to their hosts. Conjugative plasmids are predominantly responsible for the global dissemination of antimicrobial resistance, representing an important threat to global health. As the number of plasmid sequences grows exponentially, it becomes critical to depict the global diversity and decipher the distribution of circulating plasmids in the bacterial community. To this end, we created COMPASS, a novel and comprehensive database compiling 12,084 complete plasmids with associated metadata from 1571 distinct species isolated worldwide over more than 100 years. The curation of the database allowed us to identify identical plasmids across different bacteria revealing mainly intraspecies dissemination and rare cases of horizontal transmission.
, 2017). Even though several bioinformatics tools can be applied to reconstruct plasmid sequences from short reads, a contiguous assembly is still difficult to obtain  (Zhou and Xu, 2010; Lanza et al. , 2014; Antipov et al. , 2016; Rozov et al. , 2017). This pitfall can be overcome by using long read sequencing technology such as Oxford Nanopore or PacBio Technologies  (Hoffmann et al. , 2017; Margos et al. , 2017; Li et al. , 2018). With the advances of sequencing technologies and bioinformatics tools, the number of plasmid sequences available in public databases is growing rapidly. However, comparing newly sequenced plasmids with existing resources is cumbersome because of partial and misannotated sequences as well as a paucity in bioinformatic tools to explore the large amount of available data.
The creation of a curated and comprehensive complete plasmid database is critical to integrate and elicit understanding of the available data. The first attempt was made by  Orlek et al. (2017a) , but the database was restricted to complete plasmids belonging to the Enterobacteriaceae family  (Orlek et al. , 2017a). More recently, a database containing all the complete plasmids from the NCBI's RefSeq database was integrated in the visualization and exploration tool pATLAS to facilitate the identification of plasmid from assemblies  (Jesus et al. , 2019). A similar database called PLSDB was also implemented in a user friendly and interactive web server, which offers the possibility to upload new sequences and query against the database  (Galata et al. , 2019). Both PLSDB and pATLAS databases allow the browsing and filtering of plasmids with their associated metadata and annotations.
There are also bioinformatics tools that use plasmid databases as a reference for plasmid detection and identification. For example, the dataset that was created by  Robertson and Nash (2018)  and implemented in the MOB-suite software to reconstruct draft genome assemblies using known plasmid sequences. In the present paper, we unveil the construction and the curation of a Comprehensive and Complete Plasmid Database we named "COMPASS. " This new database constituted of 12 084 plasmid sequences expands previously described resources and provides a full description of its content in terms of relevant properties (size, taxonomy, geographical origin, and year of isolation). With the objective of getting a global overview of the diversity of circulating plasmids, we characterized the database by in silico typing based on replication and mobilization systems and predicted the mobility from the analysis of dissemination markers.