dataesr/scientific-paragraphs-categorization huggingface dataset:
■ ACKNOWLEDGMENTS This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie  grant agreement Nos 675007 and 702574. We also thank the  EPSRC  (doctoral studentship to B. S. ), FCT Portugal (FCT Investigator to G. J. L. B. , IF/00624/ 2015; FCT Stimulus to B. L. O. ,  CEECIND/02335/2017 ;  LISBOA-01-0145-FEDER-022170  and  FCT-PTDC/MEC-ONC/31627/2017  to R. F. ),  CNPq  (PhD scholarship to F. S. S. S. , 140485/2017-1; research grant to G. F. C. ,  311963/ 2017-0 ), the  Champalimaud Foundation and Congento (R. F. ) ,  H2020  ( EU_FT-ICR_MS, GA 731077 , C. C. ) and  CAPES  (Print call  88887. 310560-00  to J. B. D. and G. J. L. B. ) for funding. We also thank  Prof. Dario Neri 's laboratory for the generous gift of F16 antibody,  Dr Ester Jimeńez-Moreno  for providing ubiquitin, the  Champalimaud Fish  and  Rodents Facility  for their excellent animal care, the  Portuguese Mass Spectrometry Network  ( LISBOA-01-0145-FEDER-022125 ),  SeTIC/UFSC , special staff members ( Bruno Amattos ,  Guilherme Arthur Geronimo  and  Stephanie Andreon ) for supercomputing resources, infrastructure and assistance, and  Dr Vikki Cantrill  for her help with the editing of this manuscript.
The colorimetric method for the determination of Cr(VI) is based on its complexation with diphenylcarbazide in an acidic medium for 10 min, then, the produced red-violet complex can be measured by spectroscopy at 540 nm  [21]. The use of the colorimetric technique is limited to non-turbid and non-colored samples. Most of the existing analytical techniques present their lack of flexibility and do not allow simultaneous analysis of the compounds. Hence the interest of developing electrochemical techniques that present a good alternative thanks to their low cost, simplicity, sensitivity and selectivity. The interest in sensing traces of chromium compounds, in different real samples, has been substantially increased in recent decades. Referring to the data furnished by the SCOPUS database, an important increase in the number of scientific research studies has been devoted to chromium sensing in the last decade.
Acknowledgments: The authors want to acknowledge the financial support of  PHC Maghreb  (N •  39382RE ) and  CNRST  ( Centre National pour la Recherche Scientifique et Technique-Morocco ). This work was supported partially by the Project " NanoSWS ,  ERANETMED 2-72-328 " and by " PPR2 MESFCRS-CNRST 2017 Morocco 1141/16 ".
SFC-PICLAB hybrid 10-20 For the SFC-PICLAB hybrid 10-20 apparatus, the pump head used for pumping the CO2 was cooled to -8°C by a Minichiller cryostat (Huber, Offenburg, Germany). The injection valve was supplied with 30, 50, or 250 µL sample loops. The system also comprised a Smartline 2600 diode array detector (DAD) (Knauer, Berlin, Germany). The system was controlled and data were acquired and processed with SFC PicLab Analytic Online v. 3. 1. 2 and Offline v. 3. 2. 0 software, respectively.
SFC PICLAB-PREP200 The SFC PICLAB-PREP200 system was equipped with a K1800 pump (Knauer, Berlin, Germany) to pump the modifier and a diaphragm pump (Lewa, Neuville-sur-oise, France) with pump heads cooled to -4°C to pump CO2. A third pump, model 515 (Waters, Guyancourt, France), was used for ACD injection modes. The injection valve was supplied with either 2, 5 or 10 mL sample loops. The system comprised a Smartline 2500 UV detector (Knauer, Berlin, Germany). The system was controlled and data were acquired and processed with SFC PicLab200 plus Online and Offline v. 3. 2. 0 software, respectively.
Growing Interest from the Academic and Industrial Communities While haloaluminate ILs have been investigated since the 1950s, the first imidazolium-based IL was not studied until 1982  [28, 29]. However, only with the development of "air and moisture stable" ILs were they seriously considered as viable alternatives to conventional organic solvents  [30]. This was because haloaluminate-based ILs were very sensitive to moisture  [31]. Welton reported that the 1990s marked the birth of a new field of IL research, where other academics were slowly becoming more aware of their existence  [2]. Since 1990, there have been > 87,000 publications in the field, however, data from 2014 onwards suggest a plateau in the year-on-year increase that has come to be expected (Figure  3 ). The number of publications and patents each year from 1990 to 2019, found in the SciFinder TM database containing the concept "ionic liquid".
It should be noted that while this observation may mean that while there has been a stabilisation in the number of publications over the last six years (Figure  3 ), the number of research topics is still increasing, bringing the use of ILs to more applications. Furthermore, although simulation and modelling have long been used to predict IL properties and optimise the combination of ions, QSAR (quantitative structure-activity relationship) and QSPR (quantitative structure-property relationship)-based models now seem to be widely used due to the net improvement of the artificial intelligence  [202] [203] [204]. Due to the many possible combinations (>×10 6 ) of cations and anions, questions are raised regarding how to identify the optimum ion pairing for a specific application. However, thanks to the intensive free-to-access database, ILThermo, generated and updated by the National Institute of Standards and Technology (NIST)  [22, 23] , several research groups have successfully developed predictive tools to evaluate or predict some properties of pure ILs (density, viscosity, thermal and electrical conductivity, speed of sound, heat capacity, etc.
)  [205, 206]  as well as in mixtures with other fluids (phase equilibria, thermodynamic and transport properties of IL-based mixtures, etc. )  [207] [208] [209] [210] [211] [212]  through using group contribution models  [213] [214] [215] [216] [217] [218] [219] [220] [221] [222] [223] [224] , QSPR  [225] , empirical or semi-empirical equations  [226] [227] [228] , ab-initio  [229, 230] , molecular dynamic simulations  [231] [232] [233] , as well as equations of state  [234] [235] [236]. Prior to being able to truly estimate their properties in silico, each collected dataset must first be critically evaluated to provide a unique recommended dataset for a given property of an individual IL as a function of temperature and pressure. The critical evaluation of the data is generally performed by using statistical and non-statistical analysis of collected datasets [237]; however, during this analysis the uncertainty in a given property caused by the purity (i.
With these conditions of analysis, recommended datasets can then be used to implement fitting parameters of established equations (like the DIPPR 105 which describes the density of a fluid as a function of temperature) into chemical engineering software such as Aspen Plus, to properly describe ILs prior to being used in any chemical-engineering process and cost evaluations. In addition to the development of models for evaluating the physical properties of ILs, there is also a need for accurate datasets to further allow the construction of life cycle analysis (LCA), life cycle costing (LCC), techno-economic assessments (TEA) models to aid the scale-up process and allow researchers to focus on economically sustainable processes  [242]. It should be realised that the chosen ions can be limited by commercial availability and cost, and so, a compromise must be found.
The database in Table of the Supplementary Information 1 (SI1) lists the species used for deconvolution with their respective FPs obtained from the NIST database. The 54 species used in our deconvolution were chosen based on our knowledge of the expected chemistry in a mixture of N 2 and CH 4 at 89. 2 nm, with possible traces of water (see Methods). On this initial database, the relative intensities of the fragments of each molecule were varied by ±30% randomly to create n = 1 million new databases. Each of these databases was then tested on its ability to deconvolve the mass spectrum over the range from m/z 25 to m/z 60. The part of the spectrum below m/z 25 is not considered as a relevant criterion since most molecules fragment to these low masses. Of these one million deconvolutions, only the best 5% corresponding to the minimum residuals are retained as statistical solutions (see Fig.
S1 in SI2 ). This set of statistical solutions is used to obtain the relative abundances of the species in the initial database by multiplying the relative intensity of the peaks by its electronic impact ionization cross section at 70 eV. This deconvolution set gives solutions for 30 species in the initial database. However, these species are not systematically found in each deconvolution and only those species whose mixing ratio is determined in a sufficiently large number of deconvolutions (>5% of the solutions) are kept here. The final list with 23 compounds present in the mass spectrum in Fig. 1  along with their recovered mixing ratios and standard deviation (1σ) are listed in Table  1. The probability distribution function of each of the compounds in Table  1  is given in Fig. S2  of the SI2.
n-species. Several nitrogenous species were also found, containing one to two nitrogen atoms. The simplest is hydrogen cyanide (HCN), whose formation comes from the reaction between N( 4 S) with small radicals coming from the photolysis of CH 4 : CH 2 and CH 3 (see Reactions 13)  51, 59. Another way out of the reaction between N( 4 S) and CH 3 is the formation of the radical H 2 CN which by reaction with hydrogen atoms is also a minor source of HCN (see Reactions 14)  51, 59. As for the nitrogen atoms N( 2 D), they allow methanimine (CH 2 NH) to form very quickly by reaction with CH 4 , which is not found in the deconvolution analysis because its fragmentation spectrum is not known and is therefore not present in the database. However CH 2 NH is undeniably formed in all photochemical environments with N 2 and CH 4 and is a major contributor to the growth of nitrogen chemistry in the reactor.
However, in our reactor the statistical analysis found only one of the two isomers although both FPs are present in the database. This absence comes from the fact that CH 2 CCH 2 tends to be easily isomerized to CH 3 C 2 H. The abundance of CH 3 CCH 2 H is therefore self-sustaining while CH 2 CCH 2 tends to disappear but both isomers are likely to be formed in the reactor. The fact that our statistical analysis finds only one of the two isomers, shows us its sensitivity to be able to discriminate the presence of molecule with the same raw formula once the respective FPs are known. On the opposite, butane (C 4 H 10 ) is formed via a different formation pathway than in Titan's atmosphere. In Titan's atmosphere, C 4 H 10 is formed from propylene (C 3 H 6 ) which itself is derived from C 2 H 6  15.
Notably the reaction involving HCN and ammonia (NH 3 ) has been suggested as a route of formation to cyanamide. However, the presence of cyanamide in this work is unlikely because cyanamide is in the solid phase at room temperature. Therefore, if cyanamide had formed during the experiment, it would likely have deposited on the chamber wall as a solid and would not condense in the cold trap. Deconvolutions of the mass spectrum by removing cyanamide from the database show that only the relative abundances of ketene and diazomethane are affected. The results show an increase in the abundance of ketene at 1. 1 ppm (1σ = 130%) and diazomethane at 3. 5 ppm (1σ = 91%). It is therefore interesting to see that the results of the algorithm are robust and that the elimination of cyanamide reinforces the presence of a diazotized compound, as diazomethane.
Then, the optical depth, τ, is defined as the product of these three parameters. As an indication, the distance after the indium membrane at which the absorption of the radiation reaches a maximum (τ = 1 and a transmission of 37%) in the reactor is around 8 cm (while our reactor is more than 20 cm long), considering that we have a non-homogeneous medium with two different gases present and that each contributes to the extinction. The contribution of each of the gases must then be summed: σ λ ∑       × ( ) X i i X i. At 89. 2 nm, the absorption cross section of N 2 is 4. 17 × 10 -19 cm 2 and that of CH 4 is 5. 20 × 10 -17 cm 2 (Southwest Research Institute database: https://phidrates. space. swri. edu) and the total gas density is 2. 43 × 10 16 cm -3 linked to the working pressure (1 mbar) via the perfect gas law.
These fragmentation patterns are obtained from databases such as the National Institute of Standards and Technology (NIST) (http://webbook. nist. gov/), but are highly dependent of the geometry of the ionization source of the instrument for the measurement. This means that typically, the fragments intensity for a given species measured in a lab can vary by up to 50% compared to databases, rendering decomposition doubtful. The method used here allow for compensating this issue by using a Monte-Carlo sampling of the fragmentation patterns, and performing the mass spectra deconvolution not once but several thousands of times with as many different fragmentation pattern. This allows for the retrieval of the probability densities. The retrieved composition is then corrected to account for the ionization cross section of each compounds to ultimately retrieve the mixing ratio of each individual compound detected in the mass spectrum.
The THz comb is radiated from a fiber-coupled, strip-line-shaped LT-InGaAs/InAlAs photoconductive antenna PCA1 (TERA 15-TX-FC, Menlo Systems, bias voltage = 20 V, and optical power of 20 mW) excited by λ1-comb pump light and then passes through a low-pressure gas cell (length = 38 cm and diameter =17 mm). The THz comb is then detected by another fiber-coupled, dipole-shaped LT-InGaAs/InAlAs photoconductive antenna PCA2 (TERA 15-RX-FC, Menlo Systems, optical power = 20 mW) that is pumped by the λ2-comb light. The electrical output of PCA2 is amplified by a current preamplifier (AMP, bandwidth =3. 8 MHz, and gain = 1×10 6 V/A). The temporal waveform of the output is acquired with a data acquisition board. -9-A portion of the separated λ1-comb and λ2-comb light is fed into a sumfrequency-generation cross-correlator (SFG-X), whose setup is constructed based on a noncollinear configuration with a piece of a β-BaB2O4 (BBO) crystal.
The observed manifolds consist of a number of closely spaced rotational transitions, assigned by K, as tabulated in the Jet Propulsion Laboratory (JPL) spectral database  [44]. Multipeak fitting analysis was performed, in which a Lorentzian lineshape was used for each rotational line and a global linewidth parameter was applied to all the lines. The line positions and intensities were fixed parameters, while the acetonitrile partial pressure and linewidth were left as free parameters. An example is presented in the red and blue plots of Fig. the assumption that all K lines have the same linewidth. As shown by the red line in Fig. 7 (a) -(f), the database fitting spectra match the experimental data very well. These analyses suggest that our THz-DCS scheme with a reduced system complexity has the capability to realize low-pressure gas spectroscopy with a MHz-order spectral resolution.
DISSUSSION We first discuss the potential of the mode-resolved adaptive sampling THz-DCS scheme for low-pressure, Doppler-limited gas spectroscopy. We confirmed clear differences in the absorption linewidth as the pressure was reduced. By using multipeak curve fitting analysis based on the JPL spectral database, the results indicated that the observed pressure broadening coefficient lies between the values for self-broadening (912 kHz/Pa) and broadening by nitrogen (91. 2 kHz/Pa)  [45, 46]. The Doppler-limited linewidth of CH3CN gas was 382 kHz (FWHM) at 0. 2 THz and 1. 34 MHz (FWHM) at 0. 7 THz  [47]. Consistent with these values, mode-resolved adaptive sampling THz-DCS can interrogate low-pressure gases with absorption features approaching the Doppler limit. We next discuss the possibility of employing the proposed method for THz spectroscopy with improved precision.
Star-formation Rate and Stellar Mass Estimates. Star formation rates and stellar mass estimates are derived from a joint analysis of photometry and ground-based spectroscopy, modeling the rest-frame ultraviolet to near-infrared spectral energy distribution 8,10. These papers adopt the Calzetti  33  dust attenuation curve and parameterized star formation histories when tting the stellar continuum with stellar population synthesis models. Both exponentially decaying 8 and similar star formation histories that allow linear growth before the exponential decay 10 yield consistent stellar mass and star-formation rate estimates and are generally well-suited to describe quiescent galaxies  34. The procedures to t the data to stellar population models marginalize over the redshift, velocity dispersion, age, metallicity, dust attenuation, and the emission line parameters, including an analysis of systematic uncertainties introduced by the model assumptions.
5GHz total usable bandwidth. The data were reduced using the standard ALMA pipeline and imaged with natural weighting to maximize sensitivity. The observations were designed to avoid spatially resolving the target sources to the extent possible, and reach spatial resolutions ~1. 0-1. 5 arcseconds. We also created lowerresolution images of each source with a uv taper and found no evidence for extended emission in any source. Flux densities for the two detected sources were measured from the peak pixel values in the images. For the remaining undetected sources, we place upper limits on the 1. 3mm emission using the image root-mean-square values. For the four undetected REQUIEM-ALMA galaxies, each non-detection map is divided by the magni cation and the individual maps' demagni ed root-mean-square de nes the weight when averaging to generate a weighted stack.
Acknowledgements: This paper makes use of the following ALMA data: ADS/JAO. ALMA # 2018. 1. 00276. S, ADS/JAO. ALMA # 2019. 1. 00227. S. ALMA is a partnership of  ESO  (representing its member states),  NSF (USA)  and  NINS (Japan) , together with  NRC (Canada) ,  MOST  and  ASIAA (Taiwan) , and  KASI (Republic of Korea) , in cooperation with the  Republic of Chile. The Joint ALMA Observatory is operated by  ESO ,  AUI/NRAO  and  NAOJ. The  National Radio Astronomy Observatory  is a facility of the National Science Foundation operated under cooperative agreement by  Associated Universities, Inc. This work uses observations with the  NASA/ESA Hubble Space Telescope , obtained at the  Space Telescope Science Institute , which is operated by the  Association of Universities for Research in Astronomy, Inc.
, under  NASA  contract  NAS 5-26555. KEW wishes to acknowledge funding from the  Alfred P. Sloan Foundation ,  HST-GO-14622 ,  HST-GO-15663 , and the unwavering support of her partner amid the global pandemic. CCW acknowledges support from the  National Science Foundation   Astronomy and Astrophysics Fellowship  grant  AST-1701546  and from  NIRCam Development  Contract  NAS50210  from  NASA Goddard Space Flight Center  to the  University of Arizona. S. T. acknowledges support from the  ERC   Consolidator Grant funding scheme  (project  ConTExt , grant No. 648179 ), FV from the  Carlsberg Foundation   Research Grant   CF18-0388 , and  GEM  from the  Villum Fonden  research grant  13160. The Cosmic Dawn Center is funded by the  Danish National Research Foundation  under grant No. 140. CP is supported by the  Canadian Space Agency  under a contract with  NRC  Herzberg Astronomy and Astrophysics.
28 The approach demonstrated effective correction of EOF-caused shifts in migration times, albeit in a small cohort of samples, thus being able to improve the reproducibility of the migration index below 1. 5%. The use of μ eff raises the potential to aid compound identification in biological samples, notably when using metabolite libraries of electrophoretic mobilities. Such chemical libraries prove especially useful in CE, where migration times may exhibit higher variability. In 2018, Drouin et al. published the first μ eff database for 458 endogenous metabolites,  29  as well as its use to identify compounds across different laboratories. However, the utility of this approach was examined only by a single individual responsible for preparing the BGE and analyzing the samples in two different laboratories.
More details about the experimental conditions used with each platform are given in Table  S2. Data Processing. Initially, 17 groups were involved in this Metabo-ring trial. One data set was triaged as peak widths about 5 min at baseline were obtained which is unusual for capillary electrophoretic separations of small molecules. Therefore, this data set was excluded from further data processing. The primary MS data were received and centralized as manufacturer raw data and processed using Skyline. 44 The data files from Bruker instruments were converted into mzML format using MSConvert  45  prior to their import into Skyline. For robust and transposable results from one laboratory to another, a two-marker conversion was used for μ eff determination using an in-house software. Paracetamol (μ eff = 0 mm 2 kV -1 min -2 ) and procaine (μ eff = 1559 mm 2 kV -1 min -1 ) were used as references for μ eff calculation.
7%, respectively, with a maximum observed for inosine in both cases. Therefore, these findings clearly indicate the presence of a significant contribution of the interlaboratory variability, leading to higher values of relative standard deviation (RSD) for reproducibility. To find the main source for the observed variation, we determined the bias of each compound as measured by each individual analytical platform. Figure  2  reveals a relatively high degree of variation, especially for the slower-migrating compounds (longer migration times). This is expected since for a constant measurement uncertainty, the relative error increases as the reference values get closer to 0, as it is the case in the μ eff scale (Table  S1 ). Interestingly, three data sets presented a lower μ eff for the latemigrating compounds (i.
1% for compounds with high and medium μ eff , RMTs trended with migration time. Indeed, as shown in Figure  3B , minimal dispersion of the RMT values was observed for the compounds with migration time similar to that of the IS, whereas metabolites with extreme RMT presented with a high interlaboratory variability. As the approach using the in-house metabolite library is based on the relative error to a reference value (i. e. , the average μ eff ), it is important to observe the bias of each parameter. As shown in Figure  3A , excluding inosine, 98% of the μ eff measurements are within a 5% bias limit, while 6 platforms presented RMT outside those limits (Figure  3B ), with the maximal bias up to 28%. These results confirm the theoretical normalization power of the μ eff conversion of MT in comparison to RMT, especially in the context of creating an interlaboratory database.
Further dilution of the samples (10-times) led to bias below 5% for all platform and compounds, except for inosine due to slight variation in BGE composition as discussed above. Concerning urine, results were highly similar to only one CE platform presenting a bias above 5% from samples diluted 5-fold, excluding inosine. However, as shown in Figure  4F , one platform presented higher deviation for spermine compared to more concentrated samples (Figure  D ). No explanation has been found so far to explain this deviation in the 10-fold diluted samples while no drift was observed in the 5-fold dilution of urine. Proposed: Metabolite Identification Guidelines Based on a Common Library. This work demonstrated the utility of an interlaboratory CE-MS database to enhance metabolomics. With the use of standardized BGE conditions (10% acetic acid here), conversion from migration times to μ eff was found to efficiently normalize separation between 13 different laboratories with interlaboratory variability below 3.