dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements Thanks are expressed to EC through the  European Research Council (ERC)  for funding the project  PHOSFUN  " Phosphorene functionalization: a new platform for advanced multifunctional materials " (Grant Agreement No. 670173 ) through an  ERC Advanced Grant. This study was supported by the  Deep Carbon Observatory (DCO)  initiative under the project  Physics and Chemistry of Carbon at Extreme Conditions , by the project " GreenPhos -alta pressione ", by the  Italian Ministero dell'Istruzione, dell'Università e della Ricerca (MIUR) , by  Ente Cassa di Risparmio di Firenze  under the project  Firenze Hydrolab2. 0and by  Fondazione Cassa di Risparmio di Firenze  under the project  HP-PHOTOCHEM. The authors acknowledge the  European Synchrotron Radiation Facility (ESRF)  for provision of synchrotron radiation facilities and thank  G.
Data availability The X-ray crystallographic coordinates for the structure of the (PH 3 ) 2 vdW compound reported in this study have been deposited at the Cambridge Crystallographic Data Centre (CCDC), under deposition number 2034375. These data can be obtained free of charge from The Cambridge Crystallographic Data Centre via www. ccdc. cam. ac. uk/ datarequest/cif. All other data that support the findings of this study are available from the corresponding author upon reasonable request.
The 1200 grooves per millimeter grating enabled a spectral resolution close to 1. 5 cm -1. For each point in the Raman maps, 5 spectra of 4 s of acquisition were averaged. A separation distance of 1 µm was used (both in X and Y directions) between each data points in the map. To spare time, only experimental data between 800 and 1800 cm -1 were collected, which corresponded to the spectral range available with the 1200 gr mm -1 grating at 785 nm without changing the grating position and increasing the acquisition time. Since most of the spectral changes were expected in this region (according to calculated DFT spectra), it was the best strategy to maintain reasonable acquisition time for Raman maps and benefit from spectral analysis. Theoretical Raman spectra were calculated using Gaussian 09 and GaussView  [44].
The quantum mechanical modelling method from geometry optimization and vibrational frequencies calculation was the density functional theory (DFT) using the B3LYP hybrid functional and the 6-311G++(d,p) basis set. A water solvation sphere was used (with a polarizable continuum model). The frequency of the calculated Raman modes were corrected using the equations given in reference  [45]. These calculated spectra were used as input spectral components to the Renishaw Wire 5. 2 software to perform component analysis maps based on non-negative least squares (NNLS) regression. To do so, the experimental Raman data were background corrected and cosmic peaks were removed (using the Renishaw Wire 5. 2 software). Experimental Raman spectrum intensity was normalized from 0 to 1, as well as the intensity of calculated spectra from DFT computations.
Acknowledgements The authors thank the  French PIA project  " Lorraine Université d'Excellence" (Reference  No. ANR-15-IDEX-04-LUE ). T. N. is grateful to the  Higher Education Commission of Pakistan  for funding his Ph. D. We also thank  Ms Claire Genois  for the ion chromatography determination of ion concentrations and  Dr Neus Vila  for her help on the 4-isopropylaniline distillation.
2015). The bond length of H 2 was taken as the vibrationally averaged value in the rovibrational ground state r H-H = 0. 767 Å  (Jankowski & Szalewicz 1998) , while for the linear HCO + molecule, the CO and CH distances were set to the experimental equilibrium values r CO = 1. 105 Å and r CH = 1. 097 Å  (Woods 1988). The ab initio calculations were performed at the explicitly correlated coupled-cluster level, including single, double, and perturbative triple excitations [CCSD(T)-F12a] using the MOLPRO package Figure 2. Ab initio energies computed at the CCSD(T)-F12a/aug-cc-pVTZ, CCSD(T)/aug-cc-pVQZ, and CCSD(T)/aug-cc-pV5Z level of theory and those extrapolated to the CBS limit, at θ 1 = 90 • and θ 2 = 0 •. (Werner et al. 2012 ). In such method, the direct inclusion of F12 terms in the triples is not available, and an improvement of the energy of the triples was obtained by scaling the triples' energy contribution  (Knizia, Adler & Werner 2009)  as implemented in MOLPRO  (Werner et al.
It is useful to have an indication of variation both within and between batches and we would recommend in case of colloidal nanoparticles in suspension to average the results of three different batches and at least 10 individual measurements per batch under the same conditions (when long integration times of ca. 10 sare needed) and ideally more than 100 individual measurements (for short integration times around 1s). In the case of assemblies or other solid substrates, depending on the complexity of the fabrication and uniformity of the materials,m easurements of tens of points on asurface can give an approximate indication of the uniformity of as ubstrate;t oo btain statistically significant results SERS data should be averaged from 3-5 samples with afew 100 sof individual measurements per sample recorded under the same conditions (typically short integration times < 1s)a nd distributed over the reported area.
Database survey A alkylated analogue has been reported, viz. 10-allyl-2,3-dihydro-1H-pyrrolo[2,1-c][1,4]benzodiazepine-5,11(10H,11aH)dione  (Benzeid et al. , 2009a) , as well as three similar structures, 2-hydroxy-10-propargylpyrrolo[2,1-c]  [1, 4] benzodiazepine-5,11-dione monohydrate  (Ourahou et al. 2010), rac-9,10dimethoxy-3-methyl-6-phenyl-7,7adihydrobenzo[b] benzo-  [4, 5] isothiazolo  [2,3-d][1,4] diazepine 12,12-dioxide  (Bassin et al. , 2011) and (S)-2,3,5,10,11,11a-hexahydro-1H-pyrrolo-[2,1-c][1,4] benzodiazepine-3,11-dione  (Cheng et al. 2007 ).
Acknowledgements This work was supported by  ANR-DFG  joint project  FOLDHYD  ( ME 1378/15-1  and  ANR-14-CE35-0016 ), the  Seventh Framework Programme  of the  European Union  through  Marie Curie Actions  ( PIIF-GA-2010-275209 , post-doctoral fellowship to M. L. S. ) and by the  Fondation Simone et Cino Del Duca de l'Institut de France  (postdoctoral fellowship to M. L. S. ). The authors thank  Brice Kauffmann  ( IECB-UMS 3033 ) for his help during data collection and resolution of the crystal structures. This work has benefited from the facilities and expertise of the  Biophysical and Structural Chemistry platform at IECB ,  CNRS UMS3033 ,  INSERM US001 ,  Bordeaux University, France.
Whole genome sequencing. The genomic DNA of five selected isolates (Table  S1 ) was extracted from an overnight culture using the NucleoSpin Microbial DNA (Macherey Nagel, Germany). The whole genome was sequenced using NovaSeq technology (Illumina). After Trimmomatic cleaning, de novo assembly was performed using Shovill (version 0. 9. 0). Virulence factors and resistance genes were sought from the assembled genomes using VirFinder, ResFinder (CGE, http://www. genomicepidemiology. org/), and CARD (https://card. mcmaster. ca/) databases. SNPs were determined using CSI Phylogeny 1. 4 (online tool available on the CGE website) set at its default parameters. The whole genome shotgun project was deposited in DDBJ/EMBL/GenBank under accession number PRJNA607033.
A policy of frequent bacteriological checks of the surgical wound was instigated in the veterinary hospital at the onset of the second epidemic cluster. Monitoring swabs were carried out in particular for difficult surgical operations. This policy enabled antibiotic treatment to be re-evaluated when needed, and any S. marcescens epidemic to be detected through its recurrent isolation. The surveillance of HAI in veterinary hospitals is the key to providing information for prevention and action. Key elements of early HAI identification include (1) a surveillance program tailored to the risks and needs of the veterinary practice and (2) routine use of diagnostic cultures and susceptibility data to establish practice-specific baseline levels of pathogen prevalence and antimicrobial resistance so as to detect changes  (Stull and Weese, 2015).
Electrochemistry The electrochemical properties of [Ru(DIP) 2 (mal)](PF 6 ) were investigated using cyclic voltammetry (CV) and rotating disc electrode (RDE) voltammetry. The RDE voltammogram (Figure  S2 ) displays three well-defined wave features, in addition to that of decamethylferrocene used as internal reference and located at +0. 090 V vs SCE. These electrochemical features are characterized by the same current intensity, which attests that the related redox processes involve the same number of electron transitions (in this case one-electron transition). CV experiment (Figure  S2 ) showed the complete reversibility of the redox processes. The redox potentials were assigned by comparison with the data reported in our recent paper on [Ru(DIP) 2 (sq)](PF 6 ) (Table  S1 ). [17] The process taking place at more positive potentials (+0.
HeLa MCTSs growth inhibition. MCTSs were grown and treated as described above. MCTSs sizes were observed under a light microscope and pictures were taken with an iPhone 6s thanks to a phone microscope adaptor. Before imaging, the plate was shaken, and half of the media was exchanged to remove dead cells. Images were recorded before treatment (day 0) and at day 3, 6, 9 and 13 after treatment. Pictures were first processed using GIMP a crossplatform image editor with a batch automation plug-in. The MCTSs sizes were then calculated with SpheroidSizer, a MATLAB-based and open-source software application to measure the size of tumour spheroids automatically and accurately. Data analysis was done using GraphPad Prism software. Annexin V / PI assay.
ICP-MS studies. Daily, prior to the analytical sequence, the instrument was first tuned to produce maximum sensitivity and stability while also maintaining low uranium oxide formation (UO/U ≤ 5%). The data were treated as follow: intensities were converted into concentrations using uFREASI (user-FRiendly Elemental dAta proceSsIng ). [89] This software, made for HR-ICP-MS users community, is free and available on http://www. ipgp. fr/~tharaud/uFREASI.
Acknowledgements This work was financially supported by an  ERC   Consolidator Grant PhotoMedMet  to  G. G. ( GA 681679 ) and has received support under the program Investissements d'Avenir launched by the  French Government  and implemented by the  ANR  with the reference  ANR-10-IDEX-0001-02 PSL  (G. G. ). This work was financed by the  Swiss National Science Foundation  (Professorships  N° PP00P2_133568  and  PP00P2_157545  to G. G. ), the  University of Zurich  (G. G), the  Novartis Jubilee Foundation  (G. G. and R. R. ), the  Forschungskredit of the University of Zurich  (R. R. ), the  University of Trieste  (E. A. , FRA 2018), and the  UBS Promedica Stiftung  (G. G. and R. R. ). Ile de France Region  is gratefully acknowledged for financial support of 500  MHz NMR spectrometer of Chimie-ParisTech  in the framework of the  SESAME  equipment project.
Samples Coal samples were provided by the Penn State data bank (Table  1 ). Their maturity is rated by their mean maximum vitrinite reflectance (Ro, %), and their elemental ratio H/C and O/C are also provided. Type II kerogens were provided by Institut Français du Pétrole (Rueil-Malmaison France; courtesy of Mrs M. Vandenbroucke). They were recovered from different Toarcian wells and outcrops: MENIL/VAIR, outcrop (K99296); DICKEL4, 1416 m (K20505) and REHDEN9, 1306 m (K20507). The H/C and O/C elemental ratios were also provided, as well as the Ro reflectance of allochthonous vitrinite for K20507 and K20505. Allochtonous vitrinite is not a fully reliable tracer, because its diagenetic history before incorporation into the type II kerogen is unknown. For K20505 with respect to K20507, the vitrinite reflectance of 0.
However, even for mature coals and kerogens, a limited photochemical or thermal alteration cannot be ruled out. The raw UV-Raman spectra of terrestrial an extraterrestrial kerogens are available in the GhoSST/SSHADE spectral database  (Quirico, Eric; Montagnac, Gilles (2006a,b) ). The Raman spectra were corrected with a linear baseline between 830 and 1925 cm -1. The Raman G-and D-carbon bands were fitted with the so-called Lorentzian-Breit-Wigner-Fano (LBWF) spectral model following  Ferrari and Robertson (2000)  (Fig. 2 ): where ωG and ωD are the resonant frequencies (in cm -1 ) of the G and D bands, IG and ID their peak intensity, FWHMD and FWHMG their full width at half maximum, and Q the coupling coefficient. The fit finally provided, for each sample, FWHMD, FWHMG, ωG and ωD, as well as the ratios of peak intensities (ID/IG) and integrated intensities (ADDG=AD/[AD+AG]).
Note that the peak position of the G band does not correspond to the ωG, and is equal to ωG+FWHMG/2Q. For the sake of comparison with former studies  (Quirico et al. (2014)  and  Kuga et al. (2015) ) that used a slightly different (with or without constant) LBWF fit, the Raman spectra from  Quirico et al. (2014)  have been reanalyzed and the Raman parameters of  Kuga et al. (2015)  have been rescaled by applying a correction factor. These earlier studies used similar experimental conditions as those used here -300 µW on the sample, 40x objective and 600 g/mm grating -, making the comparison possible. The electronic data collected in this study can be freely downloaded from the SSHADE/GHOST spectroscopic database (https://www. sshade. eu/), at https://doi. org/10. 26302/SSHADE/EXPERIMENT_LB_20200414_001.
Coal and kerogens maturity In the range Ro=0. 3-1%, several fitted Raman band parameters show a large evolution below 0. 5% and constant values above (Fig. 7 ). In Raman spectra acquired with a 514 nm excitation, a linear correlation between FWHMG and ωG is reported for immature coals  (Quirico et al. , 2003). Looking back in detail this old dataset, we observe that the data points cluster into three groups in a FWHMG vs. ωG diagram: RO < 0. 4%, 0. 4 <RO (%) <1 and RO > 1% (Fig. 13 ). It suggests the existence of a small but significant change of the polyaromatic structure of coals at Ro ~ 0. 4 %, and points to a significant evolution above 1 Ro%. Thus in the maturity range Ro < 1%, coals clearly experience a chemical evolution (modifications of H/C and O/C, Table  1 ), but also some structural modifications.
ACKNOWLEDGEMENTS We thank three anonymous reviewers for their comments and suggestions that improved the manuscript. This work has been funded by the  Centre National d'Etudes Spatiales (CNES-France). We thank the  Museum National d'Histoire Naturelle (Paris, France)  for providing the meteorite samples, the  Institut Français du Pétrole  for the type II kerogens and the  Coal Sample Bank of the Penn State University  for the coal samples. The Raman facility in ENS-Lyon is supported by the  Institut National des Sciences de l'Univers (INSU).
Data were collected in the [15-110°] 2θ scattering angle range for a total acquisition time of 3 h with a 0. 499° step. The NIST standard reference material LaB6 (NIST SRM 660b) was used to take account the instrumental broadening. The XRPD pattern of LaB6 was recorded with the same data collection conditions (step and counting time) used for the analysis of MgF2 samples. XRPD patterns were refined by using Le Bail method  [33]  implemented in the Fullprof program  [34]. The X-ray line broadening due to the nanometric size of the sample contribution was calculated by using Thompson-Cox-Hastings pseudo-Voigt function  [35]  that includes size and strain-broadening terms for both Lorentzian and gaussian components. The two parameters (Y and F) of the Lorentzian component of this function were refined to calculate the apparent crystallite size <L> with Fullprof using Langford's method  [36].
Mean diameters of the nanoparticles (DTEM) were deduced by statistical evaluation of about 100 particles using ImageJ software (https://imagej. nih. gov/ij/) [37]. For all measurements, the standard deviation was around 25%. The specific surface areas were measured at 77 K using a TriStar II 3020 (Micrometrics, Merignac, France). The MgF2 samples were degassed under vacuum at 100 °C for 12 h prior to measurement. The specific surface areas were calculated using the Brunauer-Emmett-Teller (BET) method. For accurate determination of specific surface areas as low as 30 m 2 •g -1 , a minimum of ≈ 350 mg of powder had to be used, as the lowest surface measured using a TriStar II 3020 is around 10 m 2 in the cell. After synthesis and calcination under air and O2 of MgF2 powders, Fourier transform infrared spectroscopy (FT-IR) spectra were collected in air at room temperature with a Bruker ALPHA FT-IR spectrometer (Bruker, Wissembourg, France) equipped with the Platinum QuickSnap ATR sampling module.
A method to reduce repeatability error in multivariate data for Analysis of variance-Simultaneous Component Analysis (REP-ASCA) has been developed. This method proposes to adapt the acquisition protocol by adding a set containing repeated measures for describing repeatability error. Then, an orthogonal projection is performed in the row-space to reduce the repeatability error of the original dataset. Finally, ASCA is performed on the orthogonalized dataset. This method was evaluated on NIR spectral data of coffee beans. This study shows that the repeatability error due to physical variations between measurements can alter results of the analysis of variance. These effects are predominant in factors analysis and can be seen on spectra as constant or non-constant baselines. By reducing repeatability error.
This is especially true when we want to study the influence of independent variables, i. e. , the so-called factors. The use of design of experiments (DoE) allows defining the experiments to perform in an optimal way, in order to test the influence of the identified factors on the observed responses  [3, 4]. The statistical analysis called the ANalysis Of Variance (ANOVA) of a dataset from an experimental design, was historically formalized in 1925 by Fischer with the first edition of  [5]. The aim of this analysis is to split the total variance of the measured responses into several sources of variances  [6]  However, the repeatability error may alter the results of ANOVA, even when the factors have a relevant influence on the response(s) of a studied system. Indeed, it may become more complicated to differentiate which part of the variance is explained by the factors and which part is due to the repeatability error.
This method does not focus on the repeatability error. In this study, a method named Reduction of the error of repeatability-Analysis of variance-Simultaneous Component Analysis (REP-ASCA) is proposed to reduce repeatability error by adding additionnal dataset. REP-ASCA is evaluated on NIR spectral data collected on coffee beans. The objectives of this paper are: (1) to suggest a method (REP-ASCA) to reduce the repeatability error (2) to apply REP-ASCA to real data from NIR spectroscopy (3) to study the influence of the number of measurement in the additionnal set on the reduction of the repeatability error.
Experimental data The REP-ASCA was tested on data resulting from near infrared spectroscopy of coffee beans  [18]. The aim of the experiment was to study species and roasting times influences on spectra. Spectra were recorded over a spectral range of 1000 nm to 2500 nm with a resolution of 1. 2 nm and transformed in pseudo-absorbance (-log 1/R). Initially, ten geographical origins were compared representing a total of 800 spectra. For this study, only one origin (80 spectra) was selected. Two species of coffee (Robusta and Arabica) were roasted during four different periods (0mn, 25mn, 50mn, 75mn) at a constant temperature of 180 • C. Seven successive spectral measurements were collected on each sam-ple. This DoE yielded a matrix X of 56 spectra (2 species x 7 spectral measurement x 4 duration times).
This is because each spectrum was measured on a different area of the same sample. In each area, the reflectance intensity varies according to the geometric configuration of the measured grains in relation to the sensor. These aspects are also observed over the whole databases X and X s. The baselines also vary in slopes (see Fig. 1b ). This is due to a variation of particle size and surface roughness of the product  [30]. It can therefore be assumed that the internal structure of coffee beans varies within the same sample. A more detailed analysis of 1b shows three distinct spectral regions for baseline variations: • Between 1000 nm and 1400 nm, a negative slope is observed, which varies for each repetition. • Between 1400 nm and 2000 nm, baselines are increasing, but appear to have identical slopes to ones observed at other spectral ranges.
The impact of the number of observations on the structure of the subspace of W is studied using the RV coefficient between the matrices J n and J n-1 with n ranging from 4 to 24 spectra. Bootstrap is performed to check that the results are not due to the random selection of observations available. The graph 5 shows the evolution of the mean and standard deviation of this coefficient according to n. Mean values are higher than 0. 90 for all the For n < 8 , RV coefficients have low values meaning that the repeatability error is not correctly described. Additional observations must then be added. The increase of RV coefficient values with n is expected. Indeed, adding an observation has less impact in a dataset containing many observations. The plateau located from n = 10 shows that the structure of subspace W is stabilizing.
Here, to study the interaction term and the roasting time term, it is necessary to have at least 20 spectra in X s. As a general, the number of observations must be sufficient to reduce the repeatability error. The more components to be removed, the higher the number of spectra required to be included in X s. In the previous study  [18] , the Standard Normal Variate transform (SNV)  [35]  was used to reduce additive effects. This pretreatment is common in spectroscopy. It corrects the constant baselines over the whole dataset but SNV cannot correct non-constant (sloping or curving) baseline effects  [36, 9]. The advantage of the proposed method is to reduce the repeatability error with more accuracy. Based on the analysis of repeated measures, the components of the repeatability error contain non-constant (sloping or curving) baselines.
The choice of the number of components to be removed is done by studying the impact of projections orthogonally to these components on the various factors of the analysis of variance. Moreover, this choice can be improved by the inspection of the loadings of the main components of the repeatability error. While removing the error due to repeatability, the results obtained with REP-ASCA allow to highlight the spectral regions due to the factors of interest. REP-ASCA was tested on a dataset containing a repeatability error mainly caused by samples with highly variable physical specificities and associated measurement errors (particle size, angle of view, etc). It would be interesting to apply REP-ASCA to another dataset containing other kinds of.
LC/ESI-MS Analysis. LC/ESI-MS analyses were conducted using an Agilent 1290 Infinity system coupled to an Agilent 6530 Q-TOF tandem mass spectrometer equipped with an Agilent jet stream (AJS) electrospray (ESI) ion source. MassHunter Workstation software B4. 00 was used for instrument control, data analysis, and processing. 10 μL of the sample was injected after filtration on a 0. 2μm cellulosic filter, of 15 mm inner diameter, provided by Agilent Technologies. Separation was performed using a Zorbax Eclipse Plus C 18 column (150 mm × 2. 1 mm, particle size 3. 5 μm) equipped with a guard column Zorbax Eclipse Plus C 18 (50 mm × 2. 1 mm, particle size 5. 0 μm) supplied by Agilent Technologies. e column was kept at 35 °C in the column oven. e mobile phase was composed of water acidified with 0.