dataesr/scientific-paragraphs-categorization huggingface dataset:
Data Data from PSP  (Fox et al. 2016) , primarily from its 4th solar encounter, but also from all of the first four orbits, were used for this study. Magnetic field, B, and electron density, n e , were obtained from the MAG and RFS/LFR instruments of the FIELDS suite  (Bale et al. 2016) ; the electron density measurement is described in  Moncuquet et al. (2020). Ion (proton) velocity, v i , and temperature, T i were obtained primarily from from the SPAN-I  (Livi et al. 2020 ), but also the SPC  (Case et al. 2020) , instruments of the SWEAP suite  (Kasper et al. 2016). The SPAN-I data consist of bi-Maxwellian fits to the proton core population, described in  Woodham et al. (2020) , with the same selection criteria used for excluding bad fits from the dataset, and this data is used for v i and T i unless stated otherwise.
Acknowledgements. CHKC was supported by  STFC   Ernest Rutherford Fellowship   ST/N003748/2  and  STFC  Consolidated Grant  ST/T00018X/1. LDW was supported by  STFC  Consolidated Grant  ST/S000364/1. JCP was partially supported by  NASA  grants  NNX16AH92G ,  80NSSC19K0275  and  NSF  grant  AGS-1752827. SB was supported by  NASA  grants  NNX16AH92G ,  80NSSC19K0275  and  80NSSC19K1390. KGK was supported by  NASA ECIP  grant  80NSSC19K0912. SDB was supported in part by the  Leverhulme Trust   Visiting Professorship programme. We thank the members of the FIELDS/SWEAP teams and PSP community for helpful discussions. PSP data is available at the SPDF (https://spdf. gsfc. nasa. gov).
STM/AFM measurements All experiments were performed in commercial ultrahigh vacuum (UHV) low-temperature microscopes with combined STM/AFM capabilities (Specs-JT Kolibri: PtIr tip, f0 = 1 MHz, Q = 120 k, K = 540 kN/m and Createc-qPlus: PtIr tip, f0 = 30 kHz, Q = 50k, K = 1. 8 kN/m). To manipulate the chains, the metallic tip was approached to the chain end at Vbias = 5 mV until a characteristic, abrupt change in the current channel was observed. To achieve sub-molecular resolution, the tip apex was functionalized with a CO molecule lifted from the Au(111) substrate  11. All STS data were acquired in constant height mode (open feedback loop) using the lock-in technique (Nanonis internal) with a bias modulation amplitude of 5 mV and frequency 932 Hz. Prior to STS data acquisition, the tips were calibrated with reference to the Au(111) Shockley surface state.
PIMD calculations All the simulations were performed with 3 quinone molecules in local orbital DFT with local basis set Fireball code  35  ; the surface was simulated using the interface forcefield  38. DFT Fireball calculations used the BLYP exchange-correlation functional  27, 36  with D3 corrections  37. Classical MD was performed using the QM/MM method Fireball/Amber 34 , while PIMD was performed using the i-PI software  32  with QM/MM interactions calculated by Fireball and LAMMPS  33. We have used 512 PIMD replicas at 20 K and 1024 replicas at 10 K. To see the convergence with the number of replicas, see the supplementary material. For the PIMD QM/MM simulation, an initial minimization of 10000 steps was performed followed by a classical QM/MM of 20 000 steps with a time step of 0. 5 fs.
Comparison of FHI-AIMS and Fireball DFT methods Path Integral Molecular Dynamics (PIMD) has been performed with the local orbital Fireball DFT package. To validate the accuracy of Fireball BLYP-D3 we compared it with the potential energy landscape of a single proton transfer in a periodic system calculated with the FHI-AIMS package using B3LYP-TS. Figure  S11  compares the calculated energy barriers, which are similar. This justifies our use of the Fireball package to increase the conformational sample in our PIMD simulations.
Acknowledgements O. S. thanks the  Centre National de la Recherche Scientifique (CNRS)  and the  Ministère de la Recherche et des Nouvelles Technologies. This work was supported by the  Czech Science Foundation  (Reg. No. 18-09914 S, 20-13692X ), We acknowledge  CzechNanoLab Research Infrastructure  supported by  MEYS CR  ( LM2018110 ). P. J. acknowledges support from  Praemium Academie  of the CAS. O. M. was supported by the  Primus16/SCI/27/247019  grant from  Charles University. Computational resources were provided by the  CESNET LM2015042  and the  CERIT Scientific Cloud LM2015085 , provided under the programme " Projects of Large Research, Development, and Innovations Infrastructures".
Acknowledgements. This work was made possible by  Institut Pascal at Université Paris-Saclay  with the support of the P2I research departments and the  P2IO Laboratory of Excellence (program  " Investissements d'avenir "  ANR-11-IDEX-0003-01   Paris-Saclay  and  ANR-10-LABX-0038 ). This project has received support from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sk - lodowska-Curie  grant agreement No  860881-HIDDeN  and the  CNRS PICS MicroDark. The work of K. A. O. was supported in part by  DOE  grant  DE-SC0011842  at the  University of Minnesota.
Aims. Methyl isocyanate (CH 3 NCO) and glycolonitrile (HOCH 2 CN) are isomers and prebiotic molecules that are involved in the formation of peptide structures and the nucleobase adenine, respectively. These two species are investigated to study the interstellar chemistry of cyanides (CN) and isocyanates (NCO) and to gain insight into the reservoir of interstellar prebiotic molecules. Methods. ALMA observations of the intermediate-mass Class 0 protostar Serpens SMM1-a and ALMA-PILS data of the low-mass Class 0 protostar IRAS 16293B are used. Spectra are analysed with the CASSIS line analysis software package in order to identify and characterise molecules. Results. CH 3 NCO, HOCH 2 CN, and various other molecules are detected towards SMM1-a. HOCH 2 CN is identified in the PILS data towards IRAS 16293B in a spectrum extracted at a half-beam offset position from the peak continuum.
CH 3 NCO and HOCH 2 CN are equally abundant in SMM1-a at [X]/[CH 3 OH] of 5. 3×10 -4 and 6. 2×10 -4 , respectively. A comparison between SMM1-a and IRAS 16293B shows that HOCH 2 CN and HNCO are more abundant in the former source, but CH 3 NCO abundances do not differ significantly. Data from other sources are used to show that the [CH 3 NCO]/[HNCO] ratio is similar in all these sources within ∼10%. Conclusions. The new detections of CH 3 NCO and HOCH 2 CN are additional evidence for a large interstellar reservoir of prebiotic molecules that can contribute to the formation of biomolecules on terrestrial planets. The equal abundances of these molecules in SMM1-a indicate that their formation is driven by kinetic processes instead of thermodynamic equilibrium, which would drive the chemistry to one product.
Observations and spectra of Serpens SMM1 SMM1 was observed on 27-March-2019 during ALMA cycle 6, as part of project #2018. 1. 00836. S (PI: N. F. W. Ligterink). The region was observed using a total of 42 antennae with baselines spanning 15 -332 meters in configuration C43-5. The on-source integration time was 50 minutes, towards the phase centre α J2000 = 18:29:49. 80 δ J2000 = +01:15:20. 6. Spectra were recorded in select frequency windows between 217. 59 and 235. 93 GHz, at resolutions of 488. 21 kHz (0. 33 km s - 2  ) and 1952. 84 kHz (1. 25 km s -2 ) for the continuum window, see Table  1. The data were calibrated and imaged with version 5. 4. 0-70 of the Common Astronomy Software Applications (CASA). Bandpass and flux calibration was conducted on J2000-1748, while phase calibration was performed on J1851+0035.
The flux uncertainty was ≤20%. To reach the desired sensitivity, the measurement sets were cleaned using the Hogbom algorithm  (Högbom 1974)  and Briggs weighting with a robust parameter of 0. 5. This resulted in an angular resolution of 1. 32×1. 04 and an rms noise of 2. 6 mJy beam -1 km s -1 in the final spectral data cubes. The primary beam of the observations was 26. Due to the line-richness of the source, the following procedure was followed to properly subtract the continuum from the line observations. We imaged all spectral windows without the continuum removed and used the corrected sigma-clipping method of the STATCONT package 1  (Sánchez-Monge et al. 2018)  to extract a continuum-subtracted line cube. STATCONT can only subtract zeroth-order polynomials, while in this dataset non-zeroth-order baselines are visible.
PILS observations and spectra of IRAS 16293B In this work, the column density of HOCH 2 CN is determined independently from the detection presented by  (Zeng et al. 2019 ) by analysing data from the Protostellar Interferometric Line Survey (PILS). Other species relevant to this work are also searched for in the PILS data set. The observational details of the PILS survey have been presented in various other publications (e. g. ,  Jørgensen et al. 2016 ) and here only the most relevant information is presented. In short, the PILS survey makes use of ALMA band 7 observations, covering a frequency range from 329 to 363 GHz at a spatial resolution of 0. 5. To investigate the chemical inventory of IRAS 16293B, spectra are extracted at several positions. These positions are on the peak continuum, a halfbeam offset from the peak continuum, and a full-beam offset from the peak continuum.
Analysis method The spectra were analyzed with the CASSIS 2 line analysis software. Spectral line lists were obtained from the JPL database for molecular spectroscopy  (Pickett et al. 1998) , the Cologne Database for Molecular Spectroscopy  (CDMS, Müller et al. 2001 , 2005) , and from literature. An overview of the spectroscopic line lists used in this work and the laboratory works they are based on is given in Appendix A. Given a spectroscopic line list as input, CASSIS can produce synthetic spectra of a molecule based on parameters such as column density (N T ), excitation temperature (T ex ), peak gas velocity (V LSR ), line width at half maximum (∆V), and source size (θ source ). These parameters were given as free parameters to a Monte-Carlo Markov Chain (MCMC) algorithm and χ 2 minimisation routine.
Molecule Lines best-fit parameters and thus column density and excitation temperature of a molecule. For the analysis, optically thin lines (τ 1. 0) were used. The τ-value was approximated from the by-eye synthetic fit (see below) of the observed rotational lines with the CASSIS software. The molecules were assumed to be in local thermodynamic equilibrium (LTE). Errors on physical parameters take the uncertainty of the fit and the flux uncertainty as input and are calculated from the spread in χ 2 values around the minimum to a 3σ confidence level. In this work, spectral lines of a molecule were identified in the observed spectra and a by-eye synthetic fit of the lines was made. For the by-eye fit, line width and peak velocity are determined from prominent spectral lines of molecules such as HNCO and CH 3 OH and used as a first approximation for other molecules.
IRAS 16293B Because the chemical inventory of IRAS 16293B is well characterised with results from the PILS survey, this data set is used to search for HOCH 2 CN and related species to make an unbiased chemical comparison with SMM1-a. While HOCH 2 CN was identified towards IRAS 16293B by  Zeng et al. (2019) , this detection cannot be confirmed in the PILS spectrum at the fullbeam offset position (the position commonly used for the molecular analysis of IRAS 16293B with PILS data, see Fig. 3 ). Figure  6  shows the HOCH 2 CN a-type transitions (J 0,J -J 0,J ) at this position covered by the PILS spectral range with a synthetic glycolonitrile spectrum at N T = 1. 0×10 15 cm -2 and T ex = 150 and 300 K. A synthetic spectrum of the previously identified molecules towards IRAS 16293B at the full-beam offset   4 ) Identified lines of HOCH 2 CN towards SMM1.
5), which covers the entire emitting area. Because the chemical inventory at the full-beam offset position of the PILS data is best characterised, the tentative detection of HOCH 2 CN towards this position, with a column density of ∼1. 0×10 15 cm -2 ), is used for further analysis in this paper. However, the identification of HOCH 2 CN towards the half-beam offset position in combination with the moment 0 map support the detection of HOCH 2 CN towards IRAS 16923B by  Zeng et al. (2019). The related species CH 2 CNH, CH(O)CN, and NH 2 CH 2 CN are also searched for in the PILS dataset, but clear and unblended lines are not identified. For these species upper limit column densities are determined at the full-beam offset position at excitation temperatures of 100 and 300 K. The upper limit column densities and abundances of all species are presented in Table  3.
At the same time, the higher luminosity of SMM1 results in a larger area where hot core conditions are present, thus compensating for the lower spatial resolution. Furthermore, the SMM1-a spectrum is extracted towards the continuum peak, but for IRAS 16293B a full-beam offset position from the continuum peak is used. Therefore, when comparing molecular ratios between the two sources, they may not only be affected by different physical conditions but also due to the way the sources were observed. Observational parameters of SMM1-a, IRAS 16293B, and other sources used for comparisons are listed in Table  D. 1. HOCH 2 CN is thus far only detected in two sources, SMM1a and IRAS 16293B  (Zeng et al. 2019) , and therefore a chemical comparison is limited to these two objects. To use molecular ratios that are unbiased by observational parameters, only data of the chemical inventory of IRAS 16293B obtained with PILS survey data at the full-beam offset position is used for the source comparison.
Conclusions This publication presents the simultaneous detection of the C 2 H 3 NO isomers methyl isocyanate (CH 3 NCO) and glycolonitrile (HOCH 2 CN). Both species are identified towards the intermediate-mass Class 0 protostar Serpens SMM1-a. This is only the second interstellar detection of glycolonitrile, while for methyl isocyanate it is the first detection towards an intermediate-mass protostar. Additionally, CH 3 OH, HNCO, CH 3 OCHO, CH 3 CH 2 OH, a-(CH 2 OH) 2 , D 2 CO, and NH 2 CN are detected. CH 2 CNH, CH(O)CN, and NH 2 CH 2 CN, molecules that are related to HOCH 2 CN, are searched for but not identified. Data from the PILS survey towards IRAS 16293B are analysed in search for HOCH 2 CN and this molecule is identified in a spec-trum extracted at a half-beam offset position from the continuum peak of IRAS 16239B.
The comparison of molecular ratios between SMM1-a and IRAS 16293B show that HOCH 2 CN and HNCO are significantly more abundant in the former source. The molecular ratios of HOCH 2 CN hint that the formation of this molecule does not heavily depend on solid-state radical-radical addition reactions, such as HOCH 2 + CN and HO + CH 2 CN. Formation via the thermal Strecker-like reaction [X + CN -] + H 2 CO in ice mantles cannot be confirmed nor ruled out based on the current data but may be a prominent formation pathway. To investigate the possibility that CH 3 NCO formation is related to HNCO, the ratios of these molecules in SMM1-a and other sources are analysed. These ratios are found to be uniform throughout all sources at [CH 3 NCO]/[HNCO] = ∼10 %. This indicates that there is a chemical link between both species, but also that its ratios is already set at an early stage of starformation.
Acknowledgements. We thank  E. G. Bøgelund ,  S. F. Wampfler ,  M. N. Drozdovskaya ,  B. Kulterer , and  B. A. McGuire  for helpful discussions on the observations, spectroscopy, and the chemistry of the C 2 H 3 NO isomers. The authors acknowledge assistance from Allegro, the  European ALMA Regional Center node in the Netherlands. We thank the anonymous referee for their thorough review of this manuscript and helpful comments. This paper makes use of the following ALMA data: ADS/JAO. ALMA# 2018. 1. 00836. S and ADS/JAO. ALMA# 2013. 1. 00278. 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.
NFWL is supported by the  Swiss National Science Foundation (SNSF) Ambizione  grant  193453. JKJ is supported by the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  through  ERC Consolidator Grant  " S4F " (grant agreement No  646908 ). AC acknowledges financial support from the  Agence Nationale de la Recherche  (grant  ANR-19-ERC7-0001-01 ).
446 (0. 0171) 58 1. 49×10 -4 10 0 10 10 -9 0 9 10 219 805. 163 (0. 0965) 58 46524 35 4 31 2 -35 3 32 2 218 943. 289 (0. 0068) 560 CDMS 34 6 28 2 -34 5 29 2 221 612. 904 (0. 0080) 548 31 5 27 1 -31 4 27 0 231 789. 850 (0. 0120) 506 2. 24×10 -5 Carbon monosulfide 45502 CDMS 1. 00×10 14 125 8. 84×10 -5 a-H 2 CCHOH CH 3 CN, ν 8 =1 Molecule D 2 CO 12 CH 3 OH CH 18 3 OH NH 2 CN HN 12 CO HN 13 CO CH 3 CH 2 OH C 33 S Vinylalcohol 44507 CDMS 1. 20×10 17 125 7. 38×10 -5 s-H 2 CCHOH Vinylalcohol 44506 CDMS 1. 20×10 17 125 1. 51×10 -6 Table (B. 1) Continued. Molecule Database entry Transition Frequency E up A ij J, K a , K c , (F) (MHz) (K) s -1 22 5 18 2 -22 4 19 2 231 790. 056 (0. 0035) 245 8. 46×10 -5 13 5 8 2 -13 4 9 2 233 571. 051 (0. 0032) 108 7. 54×10 -5 14 5 10 2 -14 4 11 2 233 601. 554 (0. 0032) 120 7.
71×10 -5 12 5 8 2 -12 4 9 2 234 255. 240 (0. 0032) 97 7. 39×10 -5 11 5 6 2 -11 4 7 2 234 406. 433 (0. 0033) 87 7. 17×10 -5 10 5 5 2 -10 4 6 2 234 666. 142 (0. 0035) 78 6. 89×10 -5 10 5 6 2 -10 4 7 2 234 714. 782 (0. 0035) 78 Table (C. 1) Molecules and parameters used for the IRAS 16293B synthetic spectrum Table (C. 1) Continued. 6. 90×10 -5 Table (B. 1) Continued. Molecule Database entry Transition Frequency E up A ij J, K a , K c , (F) (MHz) (K) s -1 24 12 12 0 -23 12 11 0 221 609. 140 (0. 0010) 332 2. 50×10 -4 24 12 13 0 -23 12 12 0 221 609. 140 (0. 0010) 332 2. 50×10 -4 25 2 23 1 -24 2 22 1 234 584. 932 (0. 0300) 157 4. 36×10 -4 25 2 23 0 -24 2 22 0 235 112. 110 (0. 0300) 152 3. 94×10 -4 CH 3 OCHO 60003 18 10 9 3 -17 10 8 3 219 822. 126 (0. 1000) 355 1. 11×10 -4 JPL 18 10 8 3 -17 10 7 3 219 822.
573 (0. 0023) 139 2. 91×10 -4 22 6 16 1 -21 6 15 0 235 304. 050 (0. 0026) 143 2. 90×10 -4 14 5 9 1 -13 4 10 1 235 326. 413 (0. 0021) 64 2. 39×10 -5 21 4 18 0 -20 3 17 0 235 327. 161 (0. 0027) 122 6. 30×10 -5 23 2 21 1 -22 2 20 0 235 600. 178 (0. 0022) 139 3. 28×10 -4 24 4 21 0 -23 4 20 1 235 620. 372 (0. 0027) 155 2. 88×10 -4 26 1 26 0 -25 1 25 1 235 834. 239 (0. 0040) 159 26 0 26 0 -25 0 25 1 235 834. 327 (0. 0040) 159 SiO Silicon monoxide 44505 CDMS 7. 00×10 13 300 3. 22×10 -4 c-C 2 H 4 O Ethylene oxide 44504 CDMS 4. 10×10 15 125 3. 22×10 -4 Molecule Name Tag Database N T Molecule Name Tag Database N T T ex T ex (cm -2 ) (cm -2 ) (K) (K) CCH Ethynyl radical 25501 CDMS NH 2 CHO Formamide 45512 CDMS 1. 00×10 16 3. 00×10 13 120 HCN Hydrogen cyanide 27501 CDMS CH 3 CDO Acetaldehyde 45524 CDMS 7.
Acknowledgements. We are grateful to the anonymous referee for careful comments that helped to clarify the paper. TC would like to acknowledge financial support from the  China Scholarship Council  (CSC). JFD acknowledges funding from from the  European Research Council (ERC)  under the  H2020 research & innovation programme  (grant agreement #  740651  NewWorlds). This research made use of the SIMBAD database operated at  CDS, Strasbourg, France , and the  NASA 's Astrophysics Data System Abstract Service.
For the δC µ 9 case we find in the recent B + mode a best fit value which is larger by a factor two as compared to the one in the B 0 mode. However, regarding NP significance the hierarchy among the various one-parameter fits is the same in both modes. It is again the coefficient C µ 9 which is favoured by both sets of data. This consistency is also manifest when we fit for the chiral Wilson coefficients, given in Table  2. Thus, at the level of the one-operator fits the two sets guide us to the same NP patterns. In Table  3 , we directly compare the NP significance that we obtain for C µ 9 with the NP significance LHCb quotes in their analysis of the B 0 mode. The drastic difference, 5. 4σ versus 3. 3σ, reflects the fact that LHCb uses the Flavio package  [7]  while in the present analysis SuperIso  [8]  is used.
Therefore, we consider the NP significance of a multi-dimensional fit using the full set of 20 Wilson coefficients. The results are given in Table  5. In Ref. [3]  we found that the update of the B 0 dataset changed the global fit significantly (by 0. 8σ) compared to the previous global fits (see Table  8  in Ref. [5] ). Surprisingly, this is also true when including the B + data. Adding the new data on the B + mode to the global fit we find an additional increase of the NP significance by 0. 5σ in spite of the comparably larger experimental error. However, as shown above, the new data on the B + mode leads also to a best fit value for the favoured NP Wilson coefficient δC µ 9 which is different from the best fit value of the same coefficient in the global fit by a factor two. In summary, the new data add another consistent piece to the New Physics interpretation of the various tensions in the b → s data.
Acknowledgements: The work of T. H. was supported by the  Cluster of Excellence "Precision Physics, Fundamental Interactions, and Structure of Matter" (PRISMA +   EXC 2118/1 ) funded by the  German Research Foundation (DFG)  within the  German Excellence Strategy  (Project  ID  39083149), as well as BMBF Verbundprojekt  05H2018 -Belle II. T. H. thanks the  CERN theory group  for its hospitality during his regular visits to CERN.
Acknowledgments We would like to thank  Emil Bjerrum-Bohr ,  Poul Damgaard ,  Paolo di Vecchia ,  Ludovic Planté  for discussions and comments. The research of P. Vanhove has received funding from the  ANR  grant " Amplitudes "  ANR-17-CE31-0001-01 , and is partially supported by  Laboratory of Mirror Symmetry NRU HSE, RF Government  grant, ag. N •  14. 641. 31. 0001. P. V. is grateful to the I. H. E. S. for allowing to use their computer resources.
Principles for interactive GIS (geographic information system) tools and 3D/4D models of deposits and mineralized belts are developed by projects such as FOREGS, ProMINE, and Minerals4EU  [65, 86]  as a one-off. However, the periodical update of Europe's CRM list and other developments including land-use issues and the mining status in Europe calls for continues updating of validated, comparable, and timely updated information and the related maps provided through a publicly accessible and coordinated database. GeoERA Raw Materials takes advantage of the optimized network established by the GSOs, Europe's long tradition in mining and quarrying, and new exploration methods, models, and data to unlock domestic resource potential. It comprises four projects ranging from dimension stone (EuroLithos) to seabed (MINDeSEA) and land-based (FRAME) minerals supported by a data management project (Mintell4EU).
Based on the respective national databases on sites and commodities, GeoERA Raw Materials compiles and further unifies the geoscientific knowledge of Europe. Currently, 30 data providers from 29 European countries add to the established harvesting routine that collects, validates, and stores data in a central database on Europe's resource potential, and more are about to join in 2021. The common and harmonized Minerals Inventory of known mineral resources, mining sites, and their status are visualized and publicly accessible. The identification and mapping of principal metallogenic areas that define models for different types of mineralization use modern and newly developed methods and technologies. Critical mineral potential mapping and quantitative mineral assessments on land and on the European seabed include the revisiting of residuals in EU historical mining sites.