dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements The author thanks  Dr. Linde Egberts  for reviewing his work and supporting the publication of this chapter. Acknowledgements This chapter is based on my involvement as an advisory partner in the  INTER-REG Europe project Hericoast  ( 2016-2020 ) on behalf of Research institute CLUE+ of the  Vrije Universiteit Amsterdam. I thank the partners in the project, particularly  Laura Cuevas Ortiz  and  Kåre Kristiansen , for their valuable feedback on the chapter. I also thank professor  Hans Renes  for providing comments and discussion on the core ideas of this chapter. Index Inbat al-miyah al-khafiya (manual), 49-51 Indian Plate, 46 Industrialization and colonization role, of deltas, 419-423 Industrial Revolution, 248 Inner colonization (inner expansion), 137 Interconnected cultural frontiers, 335-338  Intergovernmental Panel on Climate Change (IPCC 2007 ), 423  International Architectural Exhibition  ( Internationale Bau Austellung ,  1989 ), 238 International Scientific Group on Water and Heritage, 9 International World Water System Heritage Program, 4  Intubazione , 33 Inundation, 272  Iranian Plateau , 43 Ir.
METHODS Whole-genome sequencing was performed on an Illumina MiSeq instrument (2 × 150 bp), and the sequences were processed using the Nullarbor bioinformatic pipeline software v1. 20 and RAST server. The sequences reported here are available at the National Center for Biotechnology Information's BioProject database under accession PRJNA446073. Quantification of biofilm formation was assessed with crystal violet staining in polystyrene 96-well plates. CF bronchial epithelial cell line CFBE41o-and primary nasal epithelial cell were infected with a multiplicity of infection of 100 using an inoculum taken from cultures of S. aureus grown in brainheart infusion (BHI) until the exponential growth phase. Infected cells were kept for 6 days in a medium that contained 50 μg/mL gentamicin to kill extracellular bacteria.
E-tests were used to determine minimal inhibitory concentrations (MICs) to gentamicin. Gentamicin has a high bactericidal effect on S. aureus USA300-LAC (MIC = 2 μg/mL) and a very poor penetration inside eukaryotic cells. In addition, the measured MICs to gentamicin of all tested clinical strains were similar to that of USA300 reference strain (≤2 μg/mL). Hence, an accumulation of gentamicin within epithelial cells should impact equally all tested strains and is unlikely to explain differences between isolates. For proteomics, proteins were digested and analyzed using liquid chromatography (LC) coupled with tandem mass spectrometry (nanoLC-MS/MS). For metabolomics, metabolite profiling of S. aureus isolates was performed using LC-MS. The MS proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE  [31]  partner repository with the dataset identifier PXD011281.
Statistical Analyses Data were analyzed using R or GraphPad Prism software. Results are presented either with 1 representative experiment for clarity or as means ± standard deviation. The number of biological and technical replicates is indicated per figure. For 2-sample comparisons, statistical significance was measured using unpaired 2-tail Student t test or Wilcoxon rank sum test as indicated in the figure legends. For comparisons between more than 2 groups, statistical significance was measured using 1-way analysis of variance with multiple comparisons (Dunnett's correction) performed, with each value compared to that of the reference strain. P values <. 05 were considered to be statistically significance.
Ethics Statement All experiments were performed in accordance with the guidelines and regulations described by the Declaration of Helsinki and the low Huriet-Serusclat on human research ethics, and informed consent was obtained for all participating patients. Serial isolates of S. aureus were obtained from airway secretions from 4 patients with chronic lung infection at the Necker-Enfants Malades University Hospital, Paris, France. Sputum sampling is part of routine standard care. The research procedure is validated by Ile de France 2 IRB (ID-RCB/Eudract: 2016 A00309-42).
Patients and samples A measles outbreak occurred from April to June 2017 in the south-east of Gabon. A surveillance network of infectious diseases was set up according to the guidelines of the Gabonese Ministry of Health. Suspected measles cases were patients who visited health centers in the two main cites (Franceville and Moanda) of the Haut-Ogooue's province in Gabon, with clinical symptoms such as fever (≥ 38 °C), maculopapular rash, cough, coryza and conjunctivitis. Clinical specimens of urine, blood, throat and nasal swabs were collected and sent to the Centre International de Recherches Médicales de Franceville (CIRMF). Demographical data such as the patient's name, age, sex and neighborhood of residence were recorded. Furthermore, clinical data (fever, cough, coryza, rash, conjunctivitis, diarrhea, vomiting), the date of the onset of the rash and the vaccination status were also reported.
The generated amplicons were sequenced by the Sanger method with a 3500 Genetic Analyzer (Applied Biosystems). Phylogenetic analyses were performed using a multiple sequence alignment of 10 MeV sequences obtained from patients of our study with a selection of reference strains divided into 6 genotypes among the 24 available in the GenBank database. The sequences were compared with measles strain sequences on the BLAST (Basic Local Alignment Search Tool) and MeaNS (Measles Nucleotide Surveillance) databases for obtaining the genotype of the strains. Sequences were aligned using ClustalX (version 1. 81). Phylogenetic relationships were reconstructed using the best-fitting ML model based on the Akaike information criterion, GTR (general time reversible) + ModelTest. The phylogenetic tree was built using the maximum-likelihood method with the PhyML algorithm  [13] [14] [15] [16] [17] [18]  and drawn using FigTree v.
Phylogenetic analysis The phylogenetic analysis based on 456 nucleotides sequence of the nucleoprotein N gene was performed using 10 sequences of this measles outbreak and sequences of  the GenBank database. According to the WHO nomenclature used to describe the genotypes of measles virus strains  [2] , our sequences were named as follows: MVs (extracted from clinical material), city and country where the case occurred, number of the week and years the clinical symptoms appeared, followed by the isolate number (Table  3 , Fig. 2 ). We obtained 36 sequences from patients infected with the measles virus among which 22 were similar to the sequence from isolate 3, 2 were similar to the sequence from isolate 23, 1 was similar to the sequence from isolate 17 and another was similar to the sequence from isolate 71 (Fig.
2 ). All the 8 sequences obtained from the specimens from Moanda's health center, were similar to the sequence from isolate 3 from Franceville. The 10 obtained sequences were compared with WHO reference sequences of measles on database of MeaNS which are recommended for genotype identification. Genotyping based on the C-terminal 450 nucleotides of the N gene of measles virus revealed that the 10 strains displayed 99 and 98% identity at the nucleotide level with the strains MVi/Ibadan. NGA/0. 97/ and MVi/ New_York. USA/0. 94/, respectively, belonging to the genotype B3. Morever, the obtained strains displayed 99% identity with a strain isolated in France in 2016 (Accession no. KX838946) and clustered with genotype B3.
The enriched library was sequenced in the Illumina MiSeq PE300 platform (Macrogen Inc. ). Reads were processed in Geneious 10. 2. 2 (Biomatters Ltd). Primer design was carried out in Primer3 software  (Koressaar & Remm, 2007; Untergrasser et al. , 2012)  implemented in Geneious 10. 2. 2. A total of 108 primer pairs, each targeting a different locus, were identified and organized into 31 multiplexes using Multiplex Manager  (Holleley & Geerts, 2009). The computer-designed multiplexes were validated and checked for polymorphism using DNA samples from an additional set of seven individuals. The polymerase chain reactions (PCRs) were carried out following  Schuelke (2000). As oligonucleotide tails, we used the universal sequences M13 (GGA AAC AGC TAT GAC CAT), CAG (CAG TCG GGC GTC ATC), and T3 (AATTAA CCC TCA CTA AAGGG) labeled with the HEX dye, the FAM dye, and the TAMRA dye, respectively.
We finally applied this genotyping protocol to the 120 viperine snake samples to assess markers' quality. To avoid any bias in further analyses, we first identified populations showing HWE across the maximum number of markers. We used the test_HW function from the R-distribution of the Genepop software  (Rousset, 2008)  to assess HWE for each locus and each population, and only retained loci showing HWE. Considering each populations independently, we then estimated for each locus the number of alleles (n a ), observed and expected heterozygosity (H O and H E, respectively) using FSTAT 2. 9. 3. 2  (Goudet, 1995). We also computed null allele frequency along with 95% confidence intervals using the null. all function from the R-package PopGenReport  (Adamack & Gruber, 2014). Loci showing a lower bound exceeding a null allele frequency of 5% where discarded.
Method All data and materials can be found at https://osf. io/xge8q/. Data sources. We used two data sources: MTurk, which we used to obtain a convenience sample, NORC's Amerispeak panel, which we used to obtain a larger nationally representative sample. Although we recruited our MTurk sample prior to the representative sample, we focus our primary inferences on the representative sample for two reasons: (1) our primary analyses for the nationally representative sample were preregistered (https://osf. io/u6byt); and (2) the representativeness of this sample allows our analyses to speak to the likely characteristics of the general US adult population. Table  1  displays some basic information about our samples. We provide a detailed analysis of the demographics of the nationally representative sample in the results section.
io/b56xe/). We eliminated anyone gave an answer to the yes/no questions indicating they were not a member of the intended sample. A total of 480 people in the alt-right dataset identified as alt-right in both identification probes, and 390 in the non-alt-right dataset said they were not alt-right at the second identification probe. We did some additional screening of both datasets using the free response data to ensure each group of people were who they said they were (details available at https://osf. io/b56xe/), resulting in the elimination of 36 people from the alt-right dataset and 8 people from the non-alt-right dataset. This left 447 people from the alt-right dataset and 382 from the non-alt-right dataset for analysis. Definition of "alt-right support". Our definition of "alt-right" for the MTurk sample is anyone who answered affirmatively to the two yes/no questions of alt-right identification.
nationally representative sample (see below), the results were similar enough to give us confidence in the scales we had constructed. Table  2  provides an overview of the measures we included in our surveys, as well as their reliabilities. Many measures are shared across the samples. However, to save time and resources, we compressed many of the measures from the MTurk survey in the survey we fielded to our nationally representative sample. These changes are described in the table and in the sections below. Below we also provide an overview of measures unique to the nationally representative sample. Some measures were unique to the MTurk sample but not discussed in detail here; descriptions of these measures and their data can be found can be found at https://osf. io/d2zxs/ Benevolent sexism 3.
Results Data analytic plan. Our primary interest centered on our preregistered comparisons between the alt-right sample and both the non-alt-right Trump voters and non-alt-right people who did not vote for Trump. We made these comparisons using two dummy-coded contrasts with the alt-right as the reference group. We also conducted more exploratory comparisons between the Trump voters and the non-Trump voters to help contextualize the results, and to evaluate the extent to which non-alt-right supporting Trump voters appeared more similar to altright adherents vs. non-Trump voters. We placed primary focus on the "yes/no" definition of altright support in the nationally representative dataset; we examine the alternative definitions and the MTurk sample as part of our robustness analyses, reported in full below.
Complex survey design. Our use of a complex survey design in our nationally representative sample demanded special procedures to ensure that we could properly make inferences about the US population of alt-right adherents. To this end, in any analyses using the nationally representative dataset, we used weights proportionate to the inverse of the probability of selection from the NORC national sampling frame/address-based sample. These survey weights were further adjusted for non-response and raked to current US population totals  (Little, 1993). Due to the fact that we oversampled Trump voters relative to non-Trump voters, we also stratified by voter status in all analyses  (Little, 1993). The survey weights ensure less bias in population estimates at the cost of increased variance, and therefore a lower effective sample size.
Context. Consistent modeling of protoplanetary disks requires the simultaneous solution of both continuum and line radiative transfer, heating/cooling balance between dust and gas and, of course, chemistry. Such models depend on panchromatic observations that can provide a complete description of the physical and chemical properties and energy balance of protoplanetary systems. Along these lines we present a homogeneous, panchromatic collection of data on a sample of 85 T Tauri and Herbig Ae objects for which data cover a range from X-rays to centimeter wavelengths. Datasets consist of photometric measurements, spectra, along with results from the data analysis such as line fluxes from atomic and molecular transitions. Additional properties resulting from modeling of the sources such as disc mass and shape parameters, dust size and PAH properties are also provided for completeness.
Aims. The purpose of this data collection is to provide a solid base that can enable consistent modeling of the properties of protoplanetary disks. To this end, we performed an unbiased collection of publicly available data that were combined to homogeneous datasets adopting consistent criteria. Targets were selected based on both their properties but also on the availability of data. Methods. Data from more than 50 different telescopes and facilities were retrieved and combined in homogeneous datasets directly from public data archives or after being extracted from more than 100 published articles. X-ray data for a subset of 56 sources represent an exception as they were reduced from scratch and are presented here for the first time. Results. Compiled datasets along with a subset of continuum and emission-line models are stored in a dedicated database and distributed through a publicly accessible online system.
Introduction Knowledge is advanced with the systematic analysis and interpretation of data. This statement is especially valid in fields such as contemporary astrophysics, amongst others, where observational data play a fundamental role in describing objects and phenomena on different cosmic scales. Data alone is however not sufficient; it is the accurate description of data, the evaluation of the data quality (collectively coined as metadata), and the inte-gration of data into large datasets that can provide a solid basis for understanding the mechanisms involved in diverse physical phenomena. Such datasets can then be analyzed consistently and systematically through meta-analysis to confirm existing and reveal new trends and global patterns. The study of star and planet formation, in particular, is a field that requires extensive wavelength coverage for an appropriate characterization of sources.
In this paper we present a coherent, panchromatic observational datasets for 85 protoplanetary disks and their host stars, and derive the physical parameters and properties for a subset of 24 discs. The present collection was created as one of the two main pillars (the other being consistent thermochemi-cal modeling) of the "DiscAnalysis" (DIANA) 1 project, aiming to perform a homogeneous and consistent modeling of their gas and dust properties with the use of sophisticated codes such as ProDiMo  (Woitke et al. 2009; Kamp et al. 2010; Thi et al. 2011; Woitke et al. 2016; Kamp et al. 2017) , MCFOST  (Pinte et al. 2006 (Pinte et al. , 2009) )  and MCMax  (Min et al. 2009 ). In the context of the DiscAnalysis project, data assemblies for each individual source along with modeling results for both continuum and line emission are now publicly distributed through the "DiscAnalysis Object Database" (DIOD) 2.
The Data The majority of the sample sources consists of Class II and III, T Tauri and Herbig Ae systems. Selected targets cover an age spread between ∼ 1 and 10 million years and spectral types ranging from B9 to M3. Sources were selected based on availability and overlap of good quality data across the electromagnetic spectrum. We avoided known multiple objects where disc properties are known to be modified by the gravitational interaction of the companion and that at different wavelengths and angular resolutions may appear as single objects. We also avoided highly variable objects and in most cases edge-on disc geometry, as in such configurations the stellar properties are not well constrained and often remain unknown. In terms of sample demographics, the sample consists of 13 Herbig Ae, 7 transition disks, 58 T Tauri systems along with 7 embedded (Class I) sources or systems in an edge-on configuration (Table  1 ).
Most of the data presented here were retrieved from public archives but were also collected from more than 100 published articles. In a few cases, unpublished datasets were collected through private communications. An exception to the above is the X-ray data that were reduced for the purposes of this project and are presented in this paper for the first time. Datasets consist of photometric data points along with spectra, where available. Together, they provide a complete description of the spectral energy distribution (SED). Such data were assembled from more than 150 individual filters and spectral chunks observed with ∼50 different telescopes/facilities. Information on the gas content of disks is provided in the form of measured fluxes per transition for different atoms and molecules, and when available, as complete spectral line profiles.
A basic data quality check was performed using the following scheme: for data assembled from large surveys we propagated the original data quality flags; however, in cases that more datasets exist at the same or adjacent wavelengths, flags were modified to reflect inconsistencies and systematic (e. g. calibration) errors. In all cases links to the relevant papers are maintained so that the end-user can efficiently trace back the original data resources. An example showing different qualities of assembled data are given in the SED plots in Fig. 1 , while the complete collection of SEDs for all sources is provided as online material in Fig. 2. In the following sections we provide a detailed account of the major facilities/resources used to assemble our data sample. An overview of the assembled photometric/spectroscopic datasets per wavelength regime along with information on the number of line fluxes and high resolution imaging information for each individual source is provided in Table  1.
The same is true for MWC 480 (mid panel), the Akari data points however show some deviation when compared to the Spitzer/IRS spectra. For a weaker source like LkCa 15 (right panel), the scatter is significant due to certain, not well pointed observations, and therefore the SED is not well defined. SED plots for all sources are given as online data in Fig. 2. Lower row presents the actual modeled data for the three sources, after being hand-selected for consistency.
X-rays While X-rays do not provide direct information about the disk, they can represent an important part of the total stellar radiation field which is directly affects the physical and chemical structure of the disc. We mined the XMM-Newton 3  (Jansen et al. 2001)  and Chandra 4  (Weisskopf et al. 2000)  mission-archives for X-ray observations of our target-list and obtained data for 56 sources (Table  2 ). X-ray data was extracted by using the SAS software (version 12. 0. 1) for the XMM-Newton data and the CIAO software (version 4. 6. 1) for the Chandra data. The CALDB calibration data used for the spectral extraction of the Chandra data were taken from version 4. 6. 2. , while the XMM-Newton calibration data is put on a rolling release and thus has no version number. In order to get the source spectra, we selected a circular extraction region around the center of the emission, while the background area contained a large source-free area on the same CCD.
The extraction tools (EVSELECT for XMM and SPECEX-TRACT for Chandra) delivered the source and background spectra as well as the redistribution matrix and the ancillary response files. The spectra were modeled by using the package XSPEC (Arnaud 1996), assuming a plasma model (VAPEC -an emission spectrum for collisionally ionized diffuse plasma, based on the ATOMDB code [v. 2. 0. 2]) combined with an absorption column model (WABS) based on the cross-sections from  Morrison & Mc-Cammon (1983). The element abundance values in the VAPEC models were set to typical values for pre-main sequence stars, 3 http://xmm. esac. esa. int/xsa/ 4 http://cxc. harvard. edu/cda/ as chosen by the XEST project (see also  Table 3, Güdel et al. 2007) , unless otherwise noted in Table  3. Either a one component (1T), a two component (2T) or a three component (3T) emission model is fitted to the data.
Ultraviolet Ultraviolet data were collected from different resources. Spectra were obtained from the archives of the International Ultraviolet Explorer (IUE)  5  , the Far Ultraviolet Spectroscopic Explorer (FUSE)  6  and the Hubble Space Telescope (HST)  7. Hubble data originate from three instruments, namely the Space Telescope A&A proofs: manuscript no. _AA_2018_32860 All multi-instrument data is integrated over a number of wavelength bins and then combined with weights as 1/σ 2 , where sigma is the given instrument error after integration. An iterative procedure is carried out where the number of retrieved spectral points is lowered step-by-step, until statistically relevant data is The idea for this procedure is from  Valenti et al. (2000 Valenti et al. ( , 2003)) , but we have modified it to include multi-instrument data, and we have added the idea to lower number of bins until statistically significant data is obtained.
III. Improved distances, ages, and kinematics  (Holmberg et al. 2009 ). -Catalogue of stars measured in the Geneva Observatory photometric system  (Rufener 1988 ). (Høg et al. 2000). -The HIPPARCOS and TYCHO catalogues. (ESA 1997). -SDSS g,r,i,z filters calculated from HIPPARCHOS and TY-CHO data  (Ofek 2008 ). -Catalogue of photoelectric photometry in the Vilnius system  (Straizys et al. 1989 ). -Hipparchos catalogue photometric filters  (Perryman et al. 1997)  The offset positions for different sets of observations along with proper motion vectors were visually inspected and subsequently selected/deselected by hand. In order to maintain homogeneity in our datasets, fluxes and corresponding errors were converted from original units to Jy. Data from different catalogues were cross-correlated and checked against and flags were applied according to their quality.