dataesr/scientific-paragraphs-categorization huggingface dataset:
SESAM The SESAM database represents a recent initiative aiming to update the results displayed in MOLAT and offers new tools based on the VAMDC project. The present SESAM database is restricted to the VUV spectrum H 2 and its isotopes through its Lyman B 1 Σ u -X 1 Σ g , Werner C 1 Π u -X 1 Σ g , B' 1 Σ u -X 1 Σ g and D 1 Π u -X 1 Σ g electronic transitions as well as the CO spectrum, corresponding to the A 1 Π-X 1 Σ system (sometimes called the fourth positive system) and the close perturbing e 3 Π-X and a 3 Σ-X systems. The present status offers the possibility to search the transitions of H 2 , HD, D 2 and CO in specific wavelength ranges, as displayed in Table  1. The original data have been written in the XML format as defined by the XSAMS group  [31]  and allow one to fully recover the identifications of the quantum numbers involved in the transitions, the wavelengths (in Å), the transition wavenumbers σ in reciprocal centimeters and the oscillator strengths f.
That information allows for the computation of the emission transition probabilities (A in s -1 ) from the following formula: A u→l = 2πe 2 σ 2 mc 0 • g l g u • f lu , where u/l refer to the upper/lower states and g u , g l are the corresponding statistical weights. When σ is expressed in reciprocal centimeters, the emission probability (s -1 ) is obtained from A u→l = 0. 667 • σ 2 • g l g u • f lu. The complete source files are also downloadable in ascii format, allowing the users to process them with their favorite software. The number of queries of SESAM via the web has been rather limited up to now, about hundred per year, whereas those dedicated to MOLAT reached more than five hundred queries per year since 2016. The access via VAMDC was not available until recently. A number of improvements are foreseen for SESAM, resulting from the recent achievements obtained in theoretical studies of the various corrections (non-adiabatic, relativistic, radiative) introduced in the H 2 , HD and D 2 potential curves  [32]  as well as the amazing spectral resolution of electronic spectra obtained thanks to vacuum-ultraviolet Fourier transform spectrometers put in front of synchrotron radiation sources  [33, 34].
VAMDC Environment The VAMDC infrastructure is based on a set of standards that, together, provide the interoperability layers. The corner stone of the system is the data model, called XSAMS (XML schema for atomic, molecular and solid data  [37] ), which is now officially endorsed by the IAEA. It covers many fields of atomic and molecular physics and provides a common definition of the quantities to the data provider. This schema are used at different levels. First and foremost, being an XML schema, it defines the structure of an XML document. Instances of these schema are used as data exchange formats between the elements of the infrastructures. They are also used to define the names of queryable and searchable quantities in the query language vocabulary. VAMDC also defined an access protocol to query the VAMDC compatible databases.
It provides a common query language allowing a user to query many services at the same time. With each one returning the result in the same XSAMS format, the user can use the same tools to analyze them. The VAMDC-compatible databases are called VAMDC nodes. They must be compatible with the standards. In order to make their implementation simpler, data producers can use the VAMDC node-software  [38] , a python middleware that can be connected to the database, taking care of understanding the request and generating the XSAMS documents. All the VAMDC nodes are registered into a service registry. It stores the capabilities of each node (which are the queryable parameters and returned quantities). It can be searched by a user but also by software, which can discover existing services according to the quantities it returns for example.
Implementation on the Databases As implementing standards is a tedious process, VAMDC provides a middleware application, called the node software, taking care of it. This python application, based on the Django framework, is able to understand the user queries, translate them into SQL so that the underlying database can execute them and generate a XSAMS document from the results. The main condition to be able to use the node software is that the database relies on a relational database management system, such as MySQL, MariaDB or PostgreSQL. Since its origin, STARK-B relied on MySQL. Integrating it into VAMDC implied few modifications in its schema, mainly adding new mandatory information that was not available at the time (like the InchI/InchIKey description of chemical species  [40] ). From the SESAM point of view, work began with VAMDC in mind from the ground up.
Consequently, the database structure is quite similar to the XSAMS structure. The Figure  1  presents the lowest level of this structure. At the center of the database is the transition. It has a relation with the upper and the lower level. Each one of them is linked to a chemical species (identified by its InchIKey). towards the visible region of the spectrum thanks to the relation λ obs = λ vacuum × (1 + z) where z is 207 the redshift. A tool allowing to compute the spectra at any redshift will be made available as well. The other parts of the databases have nothing in common as they deal with very different data. Once it has been properly configured, the node software execute the queries against the content of those tables. In Appendices A and B, tables listing the queryable and returned quantities for both nodes are available.
is based on a set of standards that, together, provide the 211 interoperability layers. The corner stone of the system is the data model, called XSAMS (XML Schema 212 for Atomic, Molecular and Solid Data [35]  ), now officially endorsed by the IAEA. It covers many 213 fields of atomic and molecular physics and provide a common definition of the quantities to the data 214 provider. This schema is used at different levels. First and foremost, being an XML Schema it defines 215 the structure of an XML document. Instances of these schema are used as a data exchange format 216 between the elements of the infrastructures. It is also used to define the names of queryables and 217 searchables quantities in the query language vocabulary. VAMDC also defined an access protocol to 218 query the VAMDC compatible databases.
It provides a common query language allowing a user to 219 query many services at the same time. Each one returning the result in the same XSAMS format, the 220 user can use the same tools to analyze them. The VAMDC compatible databases are called VAMDC 221 nodes. They must be compatible with the standards. In order to make their implementation simpler, 222 data producers can use the VAMDC node-software [36], a python middleware that can be connected 223 to the database, taking care of understanding the request and generating the XSAMS documents. All 224 the VAMDC nodes are registered into a service registry. It stores the capabilities of each node (what 225 are the queryable parameters and returned quantities). It can be searched by a user but also by a 226 software, that can discover existing services according to the quantities it returns for example.
Acknowledgments: This work has also been supported by the  Paris Observatory (LERMA and Paris Astronomical Data Centre (PADC)) , the  CNRS  and the  Programme National de Physique Stellaire (INSU-CNRS ). The cooperation agreements between Tunisia (DGRS) and  France (CNRS)  (project code  09/R 13. 03 , number  22637 ) are also acknowledged. The support of  IAEA  is gratefully acknowledged. The support of  Ministry of Education, Science and Technological Development of the Republic of Serbia  through projects  176002  and  III44022  is also acknowledged.
SPHERE data Six new high contrast, IRDIS  (Dohlen et al. 2008; Vigan et al. 2010)  and IFS  (Claudi et al. 2008; Zurlo et al. 2014 ) coronagraphic SPHERE  (Beuzit et al. 2019 ) data were obtained between October 2018 and February 2020. The first three datasets were taken as part of the SPHERE SHINE GTO  (Desidera et al, in prep. ) , and the last three as part of a classical program (PI Lagrange; 0104. C-0418(D), 0104. C-0418(E), 0104. C-0418(F)). The October 2018 data immediately followed the first observations (in Sept. 2018) of β Pictoris b after its closest approach to the star described in  Lagrange et al. (2019a). All but one of the new datasets presented here were obtained in the H2 (central wavelength = 1. 59 µm; width = 0. 055 µm) and H3 (central wavelength = 1. 67 µm; width = 0. 056 µm) IRDIS narrow bands, as well as in YJ (0.
GRAVITY data Six new pieces of high accuracy astrometric data were obtained with the GRAVITY instrument (GRAVITY Collaboration 2017) in the K band. The first astrometric measurement is an updated value of the astrometry published by GRAVITY Collaboration (2020). Most of the other observations were obtained in the context of the ExoGRAVITY large program (PI: S. Lacour; 1104. C-0651), which is an interferometric survey of the dynamics and chemical composition of directly imaged exoplanets. An extra data point was taken as backup for the NGC1068 observations (PI: O. Pfuhl, 0104. B-0649). The log of the observations are presented in Table  1. Three of the observations were performed in medium resolution (R = 500), two in high resolution (R = 4000); and during one night (7 Jan 2020), the observations were taken with both modes.
The observation sequence was the same as in GRAVITY Collaboration (2019): the fringe tracker  (Lacour et al. 2019 ) uses the central star to correct the atmospheric piston, but also to serve as a reference for the astrometry. The science fiber feeding the spectrometer points, alternatively, between the position of the star and the position of the planet. While the star can be seen on the acquisition camera, the position of the planet was obtained from dedicated software based on GPI data, which is available to the community 2 , or by our estimations when using SPHERE data as input for the orbit estimation. The reduction starts by using the official GRAVITY pipeline  (Lapeyrere 2014)  to reduce the images to uncalibrated visibilities. The complex visibilities are then all phased with respect to the fringe tracker phase.
3 M Jup would be needed between β Pic b and the inner location of the outer planetesimal belt to explain the cavity. There can, therefore, be two additional planets with masses between 0. 3 and 2. 5 M Jup between 10 and 50 au that could be explored further observationally using more SPHERE data and combining them as in this present work, or more sensitive instruments on the JWST or on the ELT. We note that gas observations in β Pic  (Matrà et al. 2017; Cataldi et al. 2018)  show that the atomic gas does not extend in the inner regions, as expected from previous models  (Kral et al. 2016 (Kral et al. , 2017)) , and it is mainly colocated with the outer planetesimal belt, which may be a sign that viscous spreading is halted by a planet inward of 50 au, as demonstrated in  Kral et al. (2020).
They certainly represent interesting targets for forthcoming JWST and ELT observations. Finally, given the properties found in this paper, β Pictoris b and c can explain the relative void of material in the inner 10 au disk. allowed us to use all the 7000 RV points and to implement an accurate handling of the Gaia data. Indeed the Gaia DR2 data (Gaia Collaboration 2018) only provides the five astrometric parameters solution obtained assuming a single star model. To simulate how these five parameters are influenced by the configuration of the system, we simulated the Gaia observations and it's five parameter solution. For each epoch of observation of Gaia A18, page 14 of 17 A. M. Lagrange et al. :  Unveiling the β Pictoris system (obtained using the DR2 nominal scanning law provided within the Gaia auxiliary data 9  ), we simulated the reflex motion of the primary due to the presence of the two companions.
Knowing, through the scanning law, the scan angle and the parallax factor of each observatoin, we simulated fake intermediate astrometric data (IAD) in the HIPPARCOS way and the five parameter solution was simply derived through a linear least squares solution (e. g. ,  van Leeuwen & Evans 1998). Generating such a solution for each MCMC trial can be cumbersome, but it is quick for the AD method. To be able to combine the HIPPARCOS IAD of van Leeuwen (2007) with Gaia DR2 data, we needed to first put both systems in the same reference frame, in terms of proper motions  (Lindegren et al. 2018; Brandt 2018; Kervella et al. 2019 ) and of the parallax zero point  (Arenou et al. 2018). For simplicity in the handling of the HIPPARCOS IAD, we converted the Gaia DR2 data to the HIPPARCOS reference system.
From those tests, we conclude that the RVs are fully driving the solution concerning the mass of the components. Adding the HIPPARCOS-Gaia constrain does not change the results significantly. The HIPPARCOS-Gaia data are consistent with the solutions presented here at the 2σ level. 2. 68 ± 0. 02 3. 0 ± 0. 02 0. 27 ± 0. 06 89. 01 ± 0. 01 -148. 24 ± 0. 02 -71. 9 ± 13. 8 3. 01 ± 0. 6 7. 8 ± 0. 4 1. 78 ± 0. 03 19. 4 ± 0. 02 -0. 016 ± 0. 004 Notes. Here, we indicate the mean ± stdev when the distribution is Gaussian, or, otherwise, the position of the peak (noted "(p)"). Tp, expressed in yr, corresponds to the time of periastron passage since JDB = 2 454 000. A18, page 17 of 17.
Acknowledgements. We acknowledge support from the  French CNRS , from the  Agence Nationale de la Recherche  ( ANR-14-CE33-0018 ), and the  OSUG LABEX. A. V. and G. O. acknowledge funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No. 757561 ). A. A. and P. G. were supported by  Fundação para a Ciência e a Tecnologia , with grants reference  UIDB/00099/2020  and  SFRH/BSAB/142940/2018. R. G. L. acknowledges support by  Science Foundation Ireland  under Gant. No. 18/SIRG/5597. Based on observations collected at the  European Southern Observatory  under  ESO  programmes  0104. C-0418 (D),  0104. C-0418 (E),  0104. C-0418 (F),  1104. C-0651, 098. C-0739 (A),  0104. C-0418 (A),  0104. C-0418 (C). A18, page 15 of  17 A&A 642 ,  A18  ( 2020 ).
For this end, we extended and refined the earlier work by Bonelli et al. [40]  to examine in more detail spatial population structure in the paper wasp, P. biglumis, by adding another mitochondrial gene and analyzing all samples also in nuclear markers. Moreover, we included similar data on the parasite P. atrimandibularis. As topography of our study area is dominated by high mountains and local adaptation has been found in both species  [24, 31, 34, 41] , we predict that both host and parasite populations are spatially structured, with limited gene flow at both local and regional scales. Only part of the host populations are parasitized by P. atrimandibularis and only part of the nests in infested host populations are parasitized and, moreover, reproduction is dominated by a single female in both host and parasite nests  [24, 42, 43].
Part of the samples was previously used by Bonelli et al. [40]. We analyzed P. biglumis and P. atrimandibularis mtDNA sequences separately to assess phylogenetic relationships among samples. We first aligned sequences using the CLUSTALW algorithm  [68]  implemented in the software GENEIOUS ® Pro 5. 6. 6 and corrected them manually. Concatenated sequences were analyzed using three methods, neighbor-joining (NJ), maximumlikelihood (ML), and Bayesian inference (BI). The NJ method was applied using PHYLO_WIN  [69]  and the ML method using PHYML  [70] , while BI was carried out using 3. 1. 2 [71] , running it for 5,000,000 generations. The NJ analysis was conducted using the Kimura 2-parameter distance option. MrAIC was used to find an appropriate sequence evolution model for the data (P. biglumis: GTRI-HKYI for BI and GTRI for ML; P.
atrimandibularis: GTRG for BI and GTRI for ML)  [72]. No a priori assumptions about tree topologies were made, and analyses were performed using uniform priors. As outgroups, we used COI (GenBank accession number: HQ947784) and 16S (GenBank: GU596651) sequences of P dominula for both species and own samples of P. gallicus for P. biglumis. We reconstructed a minimum spanning network separately for the two species to show evolutionary relationships between haplotypes by using TCS v. 1. 2. 1. [73].
Molecular Analyses Wasps were analyzed using six DNA microsatellite loci previously developed for P. dominula  [52] , see also  [49]. We also generated amplified fragment length polymorphism (AFLP) fragments for all sampled wasps using standard protocols modified from Vos et al. [53]  and following Whitlock et al. [54]  for data normalization, filtering, locus selection, and phenotype calling. Finally, we sequenced portions of the mitochondrial COI and 16S genes for 130 P. biglumis and 24 P. atrimandibularis wasps. All details of laboratory procedures are given as a Supplement File.
Population Genetic Analyses In each local population, we tested random mating (deviation from Hardy-Weinberg equilibrium) in each locus and linkage disequilibrium in each locus pair, and described the amount of genetic variation as allele count, allelic richness (A R ,  [55] ) and unbiased gene diversity (H E ,  [56] ). For the AFLP and mitochondrial sequence data, we calculated unbiased haploid gene diversity (h AFLP , h mt ) and for the latter also nucleotide diversity (π). We tested the difference in diversity measures in the host and parasite populations, as well as in parasitized and non-parasitized host populations with Mann-Whitney tests in IBM SPSS v. 25  [57]. Due to the uneven sample sizes in the two study species, we compared the cumulative haplotype count using rarefaction  [58].
Singleton haplotypes are in bold. Third, we made a model-based Bayesian clustering analysis to assess the number of genetic clusters in the data in all classes of markers, by using software Baps 6. 0  [66, 67]. In a mixture analysis, Baps aims to cluster samples to groups so that the allele or haplotype frequency differences among the groups are maximized. We first ran the analyses multiple times defining each time an increasing maximum number of clusters (K) that ranged up to the number of sampling sites. Then we chose a smaller range of values around the largest probability of K in the preliminary runs, and reran the analysis in that range five times for each maximum K. As an additional prior, we used spatial information with the nuclear data and population membership with the mitochondrial data.
Phylogenetic Analyses We analyzed P. biglumis and P. atrimandibularis mtDNA sequences separately to assess phylogenetic relationships among samples. We first aligned sequences using the ClustalW algorithm  [68]  implemented in the software Geneious ® Pro 5. 6. 6 and corrected them manually. Concatenated sequences were analyzed using three methods, neighbor-joining (NJ), maximum-likelihood (ML), and Bayesian inference (BI). The NJ method was applied using Phylo_win  [69]  and the ML method using PhyML  [70] , while BI was carried out using MrBayes 3. 1. 2 [71] , running it for 5,000,000 generations. The NJ analysis was conducted using the Kimura 2-parameter distance option. MrAIC was used to find an appropriate sequence evolution model for the data (P. biglumis: GTRI-HKYI for BI and GTRI for ML; P.
After data normalization, filtering, locus selection, and phenotype calling we ended up with 77 and 76 polymorphic AFLP markers in P. biglumis and P. atrimandibularis, respectively. P. biglumis data included 127 unique profiles, of which only 26 (20%) were shared by multiple individuals and seven (6%) were found within the same geographic region. In P. atrimandibularis, we found 25 different profiles, of which four (16%) were shared by multiple individuals and two of these were found within the same geographic region. Our host populations had similar levels of genetic variation in both nuclear markers (Table  1 ). On average, parasite populations had larger gene diversity in the DNA microsatellite loci compared to the host populations (Mann-Whitney test, p = 0. 032), but the levels of genetic variation were similar in the allele count and richness, as well as in all measures of genetic variation in AFLP markers (Table  1 ; Mann-Whitney tests, all p's > 0.
05 Pg C year -1 for 1959-2015, in contrast to a much larger E legacy of 0. 79 ± 0. 31 Pg C year -1 over post-1700 agricultural lands. We therefore conclude that pre-1700 permanent agricultural lands have a negligible contribution to the magnitude, and IAV of E legacy and E LUC (Supplementary Fig. 10 ). E fire includes emissions from deforestation and fuel wood harvest. As forcing data for these processes were shared between ORCHIDEE and HN2017, it is reasonable that both E fire estimates showed similar temporal patterns (Fig. 4 ). In reality, the dynamics of deforestation in the tropics are driven by both complex social and economic factors, as well as suitable climate conditions that allow effective removal of aboveground forest biomass  11, 27. The HN2017 result exhibited more IAV than ORCHIDEE in E fire after 1995, because it included peatland fire emissions from an independent fire emissions database  19  , whereas this was not included in ORCHIDEE.
Transitions between natural grassland and agricultural land were handled in a similar approach, except that all biomass carbon stocks were transferred to the litter pool (i. e. , no fire-induced immediate carbon emissions), and higher priority was given to young cohorts in both conversions of grassland to agricultural land and agricultural abandonment into grassland. Due to a lack of savanna vegetation type in ORCHIDEE, land-use transitions involving savanna were integrated into those of forest and grassland. Because transitions between any pair of land-use types were explicitly represented in ORCHIDEE, the spatial-scale nature of LUC activities was independent of the spatial resolution of the model simulation, but depended on the input LUC forcing datasets (refer to Supplementary Note 6 for detailed discussions).
Historical forest area changes and wood harvest biomass were driven by exactly the same data used in HN2017 for different geographical regions of the world (see Supplementary Figs. 1  and 2 ; more details are provided in Supplementary Note 1). Gridded annual agricultural area changes were derived from the LUH2 dataset  42. When agricultural area changes could not be matched by changes in forest imposed by HN2017, they were implemented as transitions with natural grassland. To ensure the comparability with the HN2017 bookkeeping model, we implemented the same LUC parameterizations in ORCHIDEE. Please refer to Supplementary Table  1  for details in association with various E LUC flux components. Shifting cultivation is a local-scale subsistence agricultural practice that involves conversion of forest or natural grassland into agricultural land, maintaining such agricultural land for a certain period, and then setting it as fallow and repeating the whole cycle.
Despite its existence as an early form of human land use and being active in certain regions of the tropics today, the areas subjected to shifting cultivation were of great uncertainty  43. Several DGVMs reported additional carbon emissions by further accounting for shifting cultivation, but its exact contribution to the global land carbon balance remains elusive  44. For these reasons, shifting cultivation was not included in the HN2017 study, or the ORCHIDEE baseline simulation for consistency purpose. Nevertheless, for the purpose of uncertainty analysis of our baseline results, we performed an additional sensitivity simulation accounting for shifting cultivation starting as early as 1500 (the SC-sensitivity run). Historical areas subjected to shifting cultivation between forest (or grassland) and agricultural lands were extracted from the LUH2 data 42 (see Supplementary Note 2 for details of shifting cultivation implementation in ORCHIDEE), while all other drivers were the same as the baseline simulation.