dataesr/scientific-paragraphs-categorization huggingface dataset:
LC Solution V. 1. 24 SP1 system software was used to control the equipment and to obtain data and responses from the LC system. A reversed phase ODS-Hypersil Thermo Scientific C18 column (250 x 4. 6 mm i. d. , 5-µm particle size) (Bellefonte, United States) was used for chromatographic separation. The analyses were performed according to the procedure previously described by Quirós et al,  [19]  in a gradient elution mode with a 0. 8 mL/min flow, employing two mobile phases: A (water/acetonitrile/acetic acid, 67:32:1, v/v/v) and B (water/acetic acid, 99:1, v/v). The gradient profile was 0-4 min, 20-30% A and 80-70% B; 4-8 min, 30-40% A and 70-60% B; 8-12 min, 40-65% A and 60-35% B; 12-16 min, 65-80% A and 35-20% B; 16-20 min, 80-95% A and 20-5% B; 20-22 min, 95-97% A and 5-3% B; 22-24 min, 97-100% A and 3-0% B; and 24-35 min, 100% A, followed by re-equilibration of the column for 5 min.
Acknowledgements The authors wish to acknowledge the  UNIPAMPA  ( Universidade Federal do Pampa) ,  FAPERGS (Fundação de Amparo a Pesquisa do Estado do Rio Grande do Sul) ,  CAPES  ( Coordenação de Aperfeiçoamento de Pessoal de Nível Superior)  and  CNPq  ( Conselho Nacional de Desenvolvimento Científico e Tecnológico)  for their financial support. We also thank  Campos de Cima Winery (Itaqui, RS) , who kindly donated samples of Tannat red wine (2006 vintage).
INTRODUCTION Functional traits impact plant fitness via their effects on recruitment, growth, reproduction and survival  (Lavorel & Garnier 2002; Violle et al. 2007). Scaling from functional traits measured on individuals up to community-level distributions of trait values has provided insight into a variety of issues surrounding community assembly and ecosystem processes  (McGill et al. 2006). Traits have been particularly useful in species-rich forests where the rarity of many species has stymied efforts to characterize their niche requirements  (Baraloto, Paine, Poorter, et al. 2010; Fortunel et al. 2013). Standardized protocols for the measurement of functional traits and intensive efforts to compile trait data have fuelled explosive growth of functional trait databases  (Kattge et al.
Trait values for all individuals of each species in the plot are then set equal to the conspecifics of the sampled individuals  (Kraft, Valencia, & Ackerly 2008). This strategy requires a full botanical inventory prior to sampling. The DATABASE strategy samples one individual per species from the region and applies that individual's trait values to all conspecifics  (Paine et al. 2012; ter Steege et al. 2006). Note that our implementation of the DATABASE strategy may overestimate its accuracy, because the trait values we use come from a regional data base including only the plots under consideration, rather than from other regions, and because species-trait combinations for which no data were available were left as missing data, rather than being replaced with genus-or family level means  (Baraloto, Paine, Patiño, et al.
2010). Finally, the basis of comparison for all sampling strategies was the BRIDGE strategy, in which every individual in the plot is sampled  (Baraloto et al. 2012). We simulated each strategy 500 times in each of the nine plots in the BRIDGE network. Sampling strategies, except those for which sampling intensity does not vary (BRIDGE, DATABASE and ONE PER SP), were executed over a gradient of sampling intensity  (1, 2, 3, 5, 10, 20, 30, 40  and 50% of individuals sampled per plot). Sampling intensities greater than 50% are impractical as they require essentially as much work as does 100% (BRIDGE) sampling. In contrast to  Baraloto et al. (2010a) , the sampling of individuals was probabilistic. Thus, in RANDOM, all individuals in a plot had an equivalent probability of being sampled (equal weights for all individuals), whereas unequal weights were used in all other strategies.
Accepted Article This article is protected by copyright. All rights reserved. Despite sampling of every species, and approximately one-third of the individuals in a plot, the ONE PER SP strategy performed no better than sampling an equivalent number of randomly selected individuals. Notably, the DATABASE strategy estimated means and variances approximately as well as field-based sampling strategies that were executed at 10% sampling intensity. There was considerable variation among traits in the probability of correctly estimating their mean and variance, as certain traits were recalcitrant to intensified sampling. Accurate estimates of mean leaf toughness and δ 15 N were achieved only rarely, regardless of the sampling intensity or strategy employed. We partitioned the variance in the accuracy of estimating means and variances of functional trait values to determine the relative importance of sampling strategy, sampling intensity and trait identity (Fig.
Abundance-based trait sampling strategies therefore yield poor results in diverse ecosystems not because common species have atypical traits, but rather because even the common species in such species-rich ecosystems are represented by few individuals. They should be more accurate in ecosystems in which more individuals are concentrated among fewer species. We reiterate the caution sounded by  Baraloto et al. (2010a)  regarding the use of database-derived trait values to estimate community-level trait distributions. They are tempting because the cost associated with extracting trait data from a database is minuscule, compared to that incurred through even low-intensity field work  (Baraloto et al. 2010a). Moreover, global trait databases such as TRY  (Kattge et al. 2011 ) are essential for broad-scale comparative studies.
At the relatively small scale of this study, however, the DATABASE strategy rarely yielded accurate estimations of means or variances. Sampling as few as 10% of individuals was typically sufficient to yield more accurate estimates that those obtained through DATABASE lookups. The ONE PER SP strategy  (Kraft et al. 2008 ) yielded good performance in almost all cases (Fig. 1 ). In diverse ecosystems, however, it could require sampling of half of all individuals  (Pitman et al. 2001). We conclude that, despite the commitment of time and money required, there is no substitute for extensive field sampling to accurately characterize the distribution of trait values in species-rich forests.
ACKNOWLEDGEMENTS The authors declare no conflicts of interest. We thank  Angela Moles  and an anonymous reviewer for their insightful comments on an earlier version of the manuscript. SD was supported by The  Leverhulme Trust ,  United Kingdom , and by  CONICET and FONCyT, Argentina. The  Agence National de la Recherche, France , supported this research thorough a grant from its Biodiversité section, as well as an " Investissement d'Avenir " grant (CEBA,.
The International Polar Year (IPY) 2007-2008 was a synchronized effort to simultaneously collect data from polar regions. Being the fourth in a series of IPYs, the demand for interdisciplinarity and new data products was high. However, despite all the research done on land, people, ocean, ice and atmosphere and the large amount of data collected, no central archive or portal was created for IPY data. In order to improve the availability and visibility of IPY data, a concerted effort between PANGAEA -Data Publisher for Earth and Environmental Science, the International Council for Science (ICSU) World Data System (WDS), and the International Council for Scientific and Technical Information (ICSTI) was undertaken to extract data resulting from IPY publications for long-term preservation. Overall, 1380 IPY-related references were collected.
Of these, only 450 contained accessible data. All data were extracted, quality checked, annotated with metadata and uploaded to PANGAEA. The 450 articles dealt with a multitude of IPY topics -plankton biomass, water chemistry, ice thickness, whale sightings, Inuit health, alien species introductions by travellers or tundra biomass change, to mention just a few. Both the Arctic and the Antarctic were investigated in the articles, and all realms (land, people, ocean, ice and atmosphere) and a wide range of countries were covered. The data compilation can now be found with the identifier doi:10. 1594/PANGAEA. 150150, and individual parts can be searched using the PANGAEA search engine (www. pangaea. de) and adding "+project:ipy". With this effort, we hope to improve the visibility, accessibility and long-term storage of IPY data for future research and new data products.
org). The US National Snow and Ice Data Center (http://nsidc. org/data) and the Advanced Cooperative Arctic Data and Information Service (www. aoncadis. org) as well as the Database of the Norwegian Polar Institute (http://metadata. data. npolar. no/datasets) and the DAMOCLES database of the Norwegian Meteorological Institute (http://damocles. met. no) offer data on Arctic IPY research. Norway also initiated the Norwegian permafrost database,  NORPERM, during IPY 2007 -2008 (Juliussen et al. , 2010). The New Zealand National Institute of Water and Atmospheric Research operates a Coastal and Marine Data Portal (www. os2020. org. nz) for selected projects, Australia stores data in the Australian Antarctic Data Centre (www1. data. antarctica. gov. au), and Canadian polar data can be obtained via the Polar Data Catalogue (www.
polardata. ca). Fortunately, there has been some success in facilitating access to some of these archives by working toward a common Arctic data portal; see http://nsidc. org/ acadis/search/. For a good illustration of the inherent problems and challenges connected to creating common data portals, see  Mokrane and Parsons (2014). As can be seen, IPY data are available from all these distributed sources (and the above-mentioned ones are just some examples). Nevertheless, in the world of data centres and data providers, there are various things a scientist searching for data has to contend with. The problems most often encountered are the following: (1) project databases are not maintained after the project ends and data links thus get 404 errors; (2) the actual data are nowhere to be found, i.
org) had the intention of centralizing and improving this situation  (Parsons et al. , 2011). However, the website is not supported any more 1. This example nicely illustrates the importance of stable and permanent links and reliable long-term maintenance of databases. Generally speaking, IPY data are often fragmented, sometimes poorly managed, hidden or hard to find. A large part of the IPY knowledge is recorded in publications, but the related data mostly are contained in pdf tables and thus not machine-readable and unavailable for further processing.
Implementation Our motivation to extract IPY data from publications had the following aims: -make data machine-readable and thus usable for the public; -allow the integration into existing data and thus the compilation of individual new data products with, e. g. , a data warehouse (http://wiki. pangaea. de/wiki/Data_ warehouse 2 ), which serves the IPY demand of interdisciplinarity to create new knowledge; In order to address these issues, a concerted effort between PANGAEA -Data Publisher for Earth and Environmental Science, the International Council for Science (ICSU) World Data System (WDS), and the International Council for Scientific and Technical Information (ICSTI) was undertaken to extract data resulting from IPY publications for long-term preservation. The data rescue started in 2013 and ended at the beginning of 2015.
It was organized into iterative tasks: researching and identifying legacy data from the scientific literature, extracting (i. e. capturing or digitizing) the numeric values, generating ISO 19115 (ISO, 2014) standard metadata, performing quality assurance and control processes on captured tables, and publishing data and its metadata with appropriate citations through PANGAEA (http://www. pangaea. de). - The process of researching and identifying legacy data in IPY publications began with the compilation of a list of 1380 references by ICSTI, using keywords relevant to IPY projects as well as author and project names retrieved from IPY-DIS data files. Bibliographic searches using Web of Sci- ence and Pascal databases were conducted with broad search criteria in order not to miss relevant articles.
This bibliography served as a basis for the PANGAEA editor to filter out journal articles containing extractable data -either from the articles themselves (in the form of tables) or from the supplement supplied with the publication. Extraction and digitization of the data were performed such that numerical data or data tables were transformed into an ASCII format. Preparation of data included a technical quality control and editorial review (checking for typos, the correctness of geocoding and units, the precision of values, etc. ) and annotation with metadata. Data and metadata were imported into the relational database of the PANGAEA system.
Results of literature extraction In total 450 of the 1380 articles collected by ICSTI fulfilled the criteria needed for PANGAEA. These 450 articles contained 1270 extractable data sets (i. e. data tables), which were assembled into 450 so-called parents, meaning that, if an article contained several data sets, the data sets ("children") were combined into a general parent with slightly reduced (general) metadata and with links to the single data sets containing all metadata and data. The parents have a clearly defined citation showing their status as a supplement to the paper in question. All 1270 data sets are now available to the public by open access (doi:10. 1594/PANGAEA. 150150); they are identified with a citable and persistent DOI (which eliminates 404 errors) and are directly linked to the article and authors from which they originate.
The data can be searched for with the PAN-GAEA search engine using standard search terms and adding "+project:ipy". In PANGAEA, there are no metadata without data, and no data without metadata. All data taken at a specific point in space and time are geo-and time-coded. When downloading several files of similar data, the tables can be directly compared. PANGAEA is laid out as a permanent facility, holding mandates of the ICSU World Data System as well as the World Meteorological Organization (WMO). Therefore, PANGAEA is guaranteeing the long-term availability and accessibility of the archived data and metadata in secure and machine-readable formats. For an example of an IPY data set in PANGAEA, see Fig. 1  (Toyota et al. , 2011b). In total, 1400 different parameter-unit combinations were used to describe the data.
Most parameters belonged to the field of marine taxa (abundances, biomass etc. ) and chemistry (water chemistry, organic pollutants etc. ); see Table  1  for PANGAEA parameter groups. General parameters included unspecific descriptive ones such as length, height, distance, type, area, number of XY, percentage, and so on. Often, a comment had to be added to these parameters (e. g. doi:10. 1594/PANGAEA. 836655) to describe out-of-the-way (i. e. exotic) data. We extracted articles on plankton biomass, water chemistry and ice thickness, but also the ones dealing with whale sightings, Inuit health, alien species introductions Please note, that the data sets mentioned here can consist of core documentations, single radiosonde flights and other lower-level data types. by travellers or tundra biomass change.
Other IPY-related data in PANGAEA As a voluntary contribution of PANGAEA to IPY 2007-2008, various old books on IPY 1882-1883 research were digitized, and extractable data were archived in PANGAEA  (Krause et al. , 2010). This additional effort resulted in 94 data sets for IPY 1 (http://www. pangaea. de/search?q= ipy-1), which can now be compared to and/or combined with recent IPY data. PANGAEA also contains 705  IPY-2007 IPY- -2008 -related singular data sets not connected to publications. Due to the fact that these data sets derive from continuous, polar research observations and measurements (mostly from Germany), they are not explicitly labelled as IPY data. An overview of these data sets which complement the IPY data discussed above can be found in Table  2. Last but not least, PANGAEA was one of the data sources used  (Van de Putte et al.
, 2014)  for a major outcome of the  IPY 2007 IPY -2008: :  The Biogeographic Atlas of the Southern Ocean (http://atlas. biodiversity. aq). The Biogeographic Atlas of the Southern Ocean was published as a compilation of all benthos data available so far from the Southern Ocean floor. Due to the fact that many scientists from the international community archived their data in PANGAEA, the repository was able to make a substantial contribution to this census as an IPY legacy.
Availability and distribution of IPY-related data in PANGAEA All data sets related to the 450 publications are accessible via the DOI doi:10. 1594/PANGAEA. 150150. Apart from the standard search via the PANGAEA search engine, IPY data in PANGAEA are distributed and can be found in various ways: the content of PANGAEA is integrated into the Data Citation Index of Thomson Reuters. It is furthermore distributed via web services through portals, search engines and library catalogues (http://wiki. pangaea. de/wiki/Portal). To give an example, the IPY data set of  Toyota et al. (2011b)  can be found easily via Google with various three to four letter search terms (try, e. g. , sipex snow toyota, tateyama ice sipex, or searching for the complete title of the article). IPY data sets can also be found via the DataCite Metadata search (http://data.
datacite. org/10. 1594/PANGAEA. 839311) and via the catalogue of the German National Library of Science and Technology (TIB). WorldCat -the world's largest network of library content and services -incorporates the content of all repositories, catalogues and databases which can be utilized following the OAI-PMH (Open Archives Initiative -Protocol for Metadata Harvesting) standard and thus also includes the PANGAEA content with its IPY collection. Last but not least, Elsevier journals display a data reference and a map as soon as article-related data are archived in PANGAEA (see, e. g. , doi:10. 1016/j. dsr2. 2010. 12. 002; Toyota et al. , 2011a). Efforts to make the PANGAEA content available via the GCMD and in particular via the specific IPY and Arctic/Antarctic portals are in progress.
Conclusions Our effort can be described as successful post-IPY data management. However, notwithstanding the overall positive effect of this work, it also shows the limitations of data management which is not synchronized with science activities in A. Driemel et al. : IPY data legacy a timely way. Large parts of existing data from IPY projects are still stored in publications only -the IPY publication database is still growing and now contains almost 4000 entries for  IPY 2007 -2008 (Goodwin et al. , 2012, http ://www. nisc. com/ipy/), but no funds remain to extract the relevant data. Another problem encountered by the PANGAEA editor performing the digitization was that often only graphs and figures were included in the article, with no numerical data that could be extracted. Even more data are probably still stored on personal computers and will never be available to the public.
tion, 2015). 2 One example: searching for "Chaetoceros socialis" +project:ipy results in two hits in PANGAEA. Removing "+project:ipy" results in 368 hits (including the two hits from IPY). Now one can click on "data warehouse" (top right) and choose latitude, longitude and Chaetoceros socialis, and one can download all geocoded abundance data on this species stored in PANGAEA. Earth Syst. Sci. Data, 7, 239-244, 2015   www. earth-syst-sci-data. net/7/239/2015/.
Simulation and definition of signal events Monte Carlo (MC) simulated data samples for the tt signal are generated by the MADGRAPH (v. 5. 1. 3. 30) event generator  [23]  with matrix elements (ME) at leading order, allowing up to three additional partons including b quarks. The generated events are interfaced with PYTHIA (v. 6. 426)  [24]  to provide the showering of the partons, and to perform the matching of the soft radiation with the contributions from the ME. The τ lepton decays are handled with TAUOLA (v. 2. 75)  [25]. The POWHEG (v. 1. 0) generator  [26] [27] [28]  at NLO, interfaced with PYTHIA, is used for cross-checks and systematic studies. A Z/γ * +jets background sample is simulated in MAD-GRAPH. The ttH process is modelled using PYTHIA. The electroweak production of single top quarks (pp → tW and pp → tW) is simulated in POWHEG with an approximate next-to-nextto-leading-order (NNLO) cross section calculation  [29].
The scale factor for c jets is not measured, owing to the limited amount of data, and is set to unity. Light-flavour scale factors are determined from a control sample enriched in events with a Z boson and exactly two jets. Heavy-flavour scale factors are derived from a tt enriched sample with exactly two jets, excluding Z → events. The background contributions arising from Z/γ * +jets events is estimated in data using the number of events having a dilepton invariant mass of 76 < M < 106 GeV/c 2 , scaled by the ratio of events that fail and pass this selection in the Drell-Yan simulation  [39, 40]. The multijet and diboson background contributions are negligible after the full event selection.
The b-tagging discriminator can be affected by the jet energy scale (JES) variations. The systematic uncertainty in the jet energy scale  [44]  is obtained by varying the jet energy scale factor by one standard deviation for each quark flavour. The uncertainty in the jet energy resolution (JER) is assessed by smearing the simulated jet energy resolution by 10% on average, taking into account the η dependence  [44]. The uncertainty arising from constraining the ratio of the ttbj to ttbb contributions in the fit to match the MC prediction is evaluated by comparing the result with and without the constraint. The number of pileup interactions in data is estimated from the measured bunch-to-bunch instantaneous luminosity and the total inelastic cross section. The systematic uncertainty in the number of pileup events is estimated by conservatively varying this cross section by 5% to cover all the uncertainties in the modelling of the pileup physics.
Summary A measurement of the cross section ratio σ ttbb /σ ttjj has been presented by the CMS experiment, using a data sample of pp collisions at a centre-of-mass energy of 8 TeV, corresponding to an integrated luminosity of 19. 6 fb -1. The individual cross sections σ ttjj and σ ttbb have also been determined. The cross section ratio was measured in a visible phase space region using the dilepton decay mode of tt events and corrected to the particle level, corresponding to the detector acceptance. The measured cross section ratio in the visible phase space is σ ttbb /σ ttjj = 0. 022 ± 0. 003 (stat) ± 0. 005 (syst) with a minimum p T for the particle-level jets of 20 GeV/c. The cross section ratio has also been measured in the full phase space with minimum parton-jet p T thresholds of p T > 20 and >40 GeV/c in order to compare with a NLO QCD calculation of the cross section ratio.