dataesr/scientific-paragraphs-categorization huggingface dataset:
A set of 43 compounds was discarded, containing duplicate or organometallic or inorganic compounds. Decoding an InChI is achieved through the dedicated software library linked to RDKit and may result into unexpected results. For example, aliphatic amides were reconstructed from their InChI as their iminol tautomer, which is correct because all tautomers of a given molecule share the same InChI. Transforming aliphatic iminols into alphatic amides was undertaken using a chemical transformation rule coded as a reaction "SMILES arbitrary target specification" also known as reaction SMARTS or SMIRKS  [35]. A set of such rules was applied to fix unlikely tautomeric forms. This step would have benefited from the application of a recent molecule standardization software related to RDKit  [36]. RDKit does not handle the axial chirality of substituted allenes or spirans, possibly resulting in incorrect structures upon InChI decoding.
Structures of compounds for which the InChI to structure conversion and back-conversion to InChI (the so-called "round-trip") fails to be consistent were tentatively obtained by means of the ChemDraw software driven by a python for win32 script. An identifier resolution using the Chemical Identifier Resolver (CIR) from the US National Health Institute (NIH) is attempted in case of persisting failure  [37]. After final checking of round-trip consistency, the nmrshiftdb2-predicted 13 C-NMR chemical shift lists  [38]  were appended to compound data, resulting in a SDF file containing 211,280 records. Even though the initial CSV files assigned a chemical name to some of the compounds in UNPD, an alternative naming procedure was carried out. The PubChem website offers a file that relates InChI and PubChem Compound Identifier (CID) and another one that relates CID and synonym lists.
Two independent classification systems are available in PNMRNP, one (CL1) is the result of a locally developed attempt that is not comprehensive but that may meet some needs while the other one (CL2) relies on the well-established ClassyFire software  [41]. Chemical classes in CL1 are defined according to the presence of specific substructures (subgraphs or molecular graphs) and are identified using SMILES that are interpreted as SMARTS  [42]. Chemical classification is organized in PNMRNP with four levels, so that menthol is reported to be a secondary metabolite, a terpene, a monoterpene, and a menthane compound. More precisely substructures are identified as deriving from primary metabolites (identifier: 01) such as amino-acids, sugars, or lipids and are otherwise classified as being specifically related to secondary metabolites (identifier: 02).
Terpenes (02-02) include monoterpenes (02-02-01) that share the menthane skeleton (02-02-01-001). Sugar containing compounds (01-01) were identified through a set of 1296 SMILES chains covering open chain and cyclic sugars with possible features such as deoxy-and aminosubstitution (63 classes of sugars, overall). Hexopyranoses (01-01-14), with their five asymmetric carbons, thus featuring alpha-and beta-anomeric forms, are identified by a set of 32 SMILES chains to which a generic one without chirality indicator is added. The rather ubiquitous αand β-D-glucopyranose molecular sub-units are identified as 01-01-14-005 and -006, respectively. The idea of searching for sugars in NPs was put into practice recently in the framework of the COCONUT NP database development and the possible in silico deglycosylation  [43].
The CL1 data items in PNMRNP include the lists of atoms concerned by each detected substructure. A part of the classification was inspired by "Pharmacognosy", a book by J. Bruneton  [44] , and another part from the skeleton library included in the resource files of the LSD software, a library itself borrowed from the SISTEMAT knowledge base  [45]. The catalog of SMILES that resulted from the CL1 effort toward a chemical classification of NPs is available as a supplementary information file in Excel format. Chemical taxonomy in the second classification system (CL2) in PNMRNP results from replies to queries sent to the web interface of ClassyFire. This system deals with chemistry as a whole, distinguishing between organic and inorganic compounds at the first level, named "Kingdom" by reference to the classification of living beings.
The overall hierarchy of chemical classes covers up to eleven levels. The recently reported classification tool named NPClassifier specifically targets NPs  [46]. Classification CL2 was introduced with version 2 of PNMRNP  [47]. The link between biological and chemical classifications is highlighted by considering that a molecule can be recognized by ClassyFire as a Strychnos alkaloid (i. e. , from a plant of the Strychnos genus) on a sole structural basis, without any reference to its source, possibly natural or synthetic. The natural origin of so-called "organic" compounds has become difficult to ascertain without resorting to proprietary databases, so that a NP-likeness score, a calculated molecular property, is invoked in order to evaluate to which extent a natural product is natural  [48].
CSEARCH The web interface of CSEARCH was also considered for NP structure dereplication besides of KS and PNMRNP. The CSEARCH web server accepts requests made of a list of 13 C-NMR chemical shifts, at best with each value associated to a multiplicity indication (number of attached hydrogen atoms, as deduced from DEPT or multiplicity-edited 2D HSQC spectra) and returns within a few minutes a list of structures sorted in the decreasing order of likelihood, proposed from a database containing several tens of millions of compounds and their predicted chemical shift values  [49]. This database mostly contains structures of synthesized molecules and has no built-in concept of NP, resulting in hard to exploit results if the query is not accurate enough but may also give the solution of the submitted problem ranked in the first places, if not in first place.
Databases and Dereplication To sum up briefly, KnapsachSearch may be considered as a part of a "two-pillars and half" approach to dereplication, while a "true three pillars" would have been achieved if spectroscopic data were of experimental origin instead of being predicted. PNMRNP can be qualified as "two-pillars" with its predicted spectroscopic data (a half-pillar) and biological taxonomy replaced by chemical taxonomy (a second half-pillar). The "one-pillar and half" NMRPREDICT/CSEARCH approach, dealing with structures and predicted 13 C-NMR spectroscopy only, should be considered before any other one, if pertinent. A tentatively exhaustive (and even more than that) source of NP data, COCONUT, collects structures from various sources to propose a publicly available document-oriented database of about 400,000 compounds, some of them being clearly not so natural.
The 1 H, 13 C, 1 H-1 H COSY, 1 H-1 H ROESY, 1 H- 13 C multiplicity-edited HSQC, and 1 H- 13 C HMBC NMR spectra of most of fractions from extract 3 were recorded. A 1 H-15 N HMBC spectrum of extract 3 was also recorded, also offering a rapid and rough estimate of extract complexity by inspection of the projection of this 2D spectrum on the 15 N chemical shift axis (Figure  3 ). Database creation was undertaken prior to and during the course of U. peruviana compound identification. The search by means of KS for the compounds reported in KNAp-SAcK and related to 67 genera from the Amaryllidaceae family resulted in 249 structures, among which 209 contained at least one nitrogen atom and were thus considered as possible alkaloids. These structures were imported by ACD/Labs "C+H NMR Predictors and Database" software as a new database and semi-automatically supplemented with ACD/Labs-predicted 1 H-and 13 C-NMR data by means of the protocol reported in Figure  S4  to produce database DB1.
The same set of 209 records, each including nmrshiftdb2predicted 13 C-NMR data, was imported by the same ACD/Labs software after appropriate reformatting of the writing of chemical shift values to yield database DB2. Six small databases containing 2 to 15 records where derived from DB2 by selecting the molecules according to the molecular formula obtained by LC-HRMS analysis of extract 3, after having verified that no compound in DB2 contains two nitrogen atoms. Database DB3 was created by the same process as DB2 but starting from the 211,280 records of PNMRNP. The latter has also been filtered to retain compounds that include one of the eight substructures that are commonly found in Amaryllidaceae alkaloids  [55]  (Figure  S5 ) to give DB3 , with 635 structures. A collection of 693 compounds was created from COCONUTv1 and named DB4, retaining the compounds that contain one of the eight Amaryllidaceae substructures after an initial step that selected 109,638 compounds with more than 12 carbon atoms and with one or two nitrogen atoms.
Fraction A4, Major Compound This compound is also the major compound in fractions A3 and A5. The CSEARCH algorithm succeeded to retrieve tazettine 1 (structure in Figure  4 ) as a likely compound from the list of the 18 13 C-NMR chemical shifts and multiplicities from fraction A4. The molecular formula was constrained to include only C, H, N, and O atoms with a molecular mass comprised between 250 u and 400 u. Only a single chemical shift value was considered slightly unsatisfactory with δ C 29. 6 predicted by CSEARCH at position 4 and δ C 25. 9 observed (full atom numbering is reported in Figure  S5 ). The analysis of the NMR spectra led to the identification of an aromatic ring substituted by a methylenedioxo bridge, a N-Me group, an ether O-Me group, and a hemiacetal group. The list of the C 18 H 21 NO 5 Amaryllidaceae alkaloids in the KNApSAcK database contained two compounds among 12 that shared these NMR-derived structural features.
The HMBC correlations of the 1 H-NMR signal of the OH group lead to retain only the planar structure proposed for compound A4. None of the five C 18 H 21 NO 4 Amaryllidaceae compound structures present in the KNApSAcK database satisfied the NMR-derived constraints. The proposed planar structure is the one of tazettine and criwelline, which are epimers at position 3  [56]. CSEARCH ranked the 6-OMe criwelline in second position. Tazettine was retained as the structure of the major compound in fraction A4 after the analysis of the ROESY spectrum and the measurement and 1 H-1 H coupling constants. Its molecular formula relates it to peak 2 in the chromatograms in Figure  2.
Fraction A7, Major Compound The CSEARCH algorithm failed to retrieve a likely structure from the list of the 19 chemical shifts drawn from the 13 C-NMR spectrum of fraction A7. The molecular formula was constrained to include only C, H, N, and O atoms with a molecular mass comprised between 300 u and 400 u. Only two C 19 H 23 NO 5 molecular structures of compounds from Amaryllidaceae were found in the KNApSAcK (DB2) database, among which only one contained three methoxy groups bound to an aromatic ring. This structural constraint was derived from the presence of three methyl signals in the 1 H-NMR spectrum that correlate in the HMBC spectrum with signals of aromatic carbons. This planar structure was confirmed by all available NMR data. None of the two C 19 H 25 NO 5 Amaryllidaceae compound structures present in the KNApSAcK database satisfied the NMR-derived constraints.
Fraction A11, Major Compound The 13 C-NMR spectrum of fraction A11 shows 16 peaks from a major compound whose positions were used as search keys in the CSEARCH data base. The molecular formula was constrained to include only C, H, N, and O atoms with a molecular mass comprised between 250 u and 400 u. The most likely proposed structure was the one of crinine 4, C 16 H 17 NO 3 (structure in Figure  4 ). Only a single chemical shift value was considered slightly unsatisfactory (δ C 40. 0 predicted by CSEARCH, δ C 44. 2 experimental, at position 11). The KNApSAcK database of Amaryllidaceae compounds contains four compounds for this molecular formula, and only three that contain four aromatic or olefinic methine groups: crinine, vittatine, and epivittatine which only differ by the absolute configuration of asymmetric centers.
The presence of trispheridine in fraction A2 and its NMR spectra assignment was confirmed by further studies. Searching in DB3 or in DB4 for trispheridine cannot be successful because its structure does not fit with any of those used in the definition of what an Amaryllidaceae alkaloid should be, even though this compound is present in the PNMRNP and COCONUTv1 database. The ClassyFire algorithm itself does not consider trisphaeridine as an alkaloid but NPClassifier identifies it as an Amarylidaceae (sic) alkaloid. Its NP-likeness is -0. 08, a value that would make it slightly closer to a non-NP (lowest value is -5) than to an NP (highest value is +5). Exploring the philosophical implications of this observation is left as an exercise to the reader.
Database Searches The structural identification of compounds A4, A7, A9 and A11 reported hereabove was carried out using lists of 13 C-NMR chemical shifts that were unambiguously drawn from spectra due to high sample purity (Table  S1 ). After this study, a question arose about the possible results of an identification process solely relying on these lists, without any other NMR information source, only taking into account the possible molecular formula derived from LC-MS data acquired on crude extract 3. The chemical shift lists were used as search keys in DB1 (209 structures from KNApSAcK), DB2 (209 structures from KNAp-SAcK), DB3 (211,280 structures from full PNMRNP), DB3 (635 structures from PNMRNP filtered for Amaryllidaceae-type alkaloids), and DB4 (693 structures from COCONUTv1 filtered for Amaryllidaceae-type alkaloids) with predicted chemical shifts by ACD/Labs software in DB1 and predicted by nmrshiftdb2 in all other DBs.
All DBs were formatted for being read by the ACD/Labs DB software so that the same search tool can be used for compound identification. The poor prediction of a single chemical shift in the targetted compound may result in a global failure of the search, to which it can be remedied either by decreasing the number of experimental chemical shifts to be taken into account or by increasing the allowed chemical shift deviation. Table  S2  shows the influence of these parameters on the number and nature of solutions, it illustrates the difficulty of identifying pure compounds without ambiguity solely on the basis of lists of 13 C-NMR chemical shift values and molecular formula.
NMR NMR analyses were performed in DMSO-d 6 at 298 K on an Avance AVIII-600 spectrometer (Bruker, Karlsruhe, Germany) equipped with a cryoprobe optimized for 1 H detection and fitted with cooled 1 H, 13 C and 2 H coils and preamplifiers. TopSpin 3. 2 (Bruker, Karlsruhe, Germany) was used for data acquisition using standard microprograms. Data processing relied on TopSpin 4. 0. The central resonance of DMSO-d 6 (septet) was set at δ C 39. 8 for 13 C-NMR spectrum referencing. The central resonance of residual DMSO-d 5 (quintet) was set at δ H 2. 5 for 1 H-NMR spectrum referencing.
Conclusions The rapid identification of natural products, either pure or in mixtures, depends on the availability of databases that connect together molecular structures, taxonomic information, and spectroscopic data, which constitute the three pillars of dereplication. We propose to the scientific community two easily findable NMR-based tools, the PNMRNP database that derives from earlier works on MS 2 spectra prediction, and KnapsackSearch, a database generator that provides focused libraries of compounds whose content is oriented by biological taxonomy. These tools were involved in the study of an iberoamerican plant, Urceolina peruviana, in a way that relies strongly on 13 C-NMR spectroscopy but also on other 1D and 2D NMR techniques as well as on preparative fractionation methods particularly suitable for alkaloid purification and on liquid chromatography coupled to high-resolution MS.
The identification of five known compounds by these means is reported. The fully unambiguous characterization of a compound within a mixture may be reached only after purification and an exhaustive analytical study. However, the rapid and context adapted structure analysis is feasible by means of an approach that relies on computer databases and that adequately contributes to the study of complex natural substances. cessing parameters may be freely consulted. These archives also contain the database files or ways to access them and files that were intermediately created for the composition of this article.
* This is a full and extended version of the conference paper [19]. † P. Gawrychowski  was partially supported by the  Bekker  programme of the  Polish National Agency for Academic Exchange  ( PPN/BEK/2020/1/00444 ) and the grant  ANR-20-CE48-0001  from the  French National Research Agency (ANR). T. Starikovskaya was partially supported by the grant  ANR-20-CE48-0001  from the  French National Research Agency (ANR). 1.
, 2008). ISTerre also ensured national distribution of data from temporary seismic deployments using the national instrument pool, but most research laboratories also ran their own, smaller instrument pools. • Institut de physique du globe de Paris (IPGP) distributed data from the GEOSCOPE network  (IPGP and EOST, 1982) , via the GEOSCOPE data center, with a historical strong role of GEOSCOPE in the promotion of open data distribution at a worldwide level  (Roult et al. , 1999). IPGP also operates three seismic networks in the Antilles  (IPGP, 2008a,d,b)  and one in Reunion Island  (IPGP, 2008c) , and those datasets were at the time planned for integration in the IPGP data distribution. • Géoazur and École et Observatoire des Sciences de la Terre (EOST): The metropolitan French broadband and short-period data were loosely integrated in a national cooperation related to long-term Earth observations, but each regional network was operated independently, with no joint instrumentation policy, and distributed mainly by these two institutions.
, 2013). An important and explicit rule of Résif-SI is that Résif-DC does not in any way modify the data or metadata provided by A-nodes. The data and metadata completion and validation process at A-nodes is iterative. Because A-nodes are responsible for retaining all raw data collected by any means (including data in proprietary formats) for, at least, two years, all or part of the processing phases can be easily reversed, if necessary. The integration protocol specifies: • the formats allowed as input: miniSEED  (Ahern et al. , 2012)  or PH5  (Hess et al. , 2018)  for data, StationXML 1. 1 (FDSN, 2019) for metadata; • the transfer modalities -based on rsync  (Tridgell and Mackerras, 1996)  push-and acknowledgment -based on rsync get or a webservice; • the checks carried out before the metadata and the data are integrated into the database and the data archiving; • the integration modalities: a product submission is a transaction; • the structure of the integration report to the partner.
5 ) that communicate with each other via an Advanced Message Queuing Protocol system  (ISO/IEC 19464, 2014). Four databases are populated at this stage: (1) inventories of networks, stations, channels, and responses; (2) waveform inventories  (PostgreSQL databases, PostgreSQL Global Development Group, 1996) ; (3) metrics and data quality information (MongoDB -EIDA WFCatalog database,  MongoDB, Inc. , 2009; Trani et al. , 2017) ; (4) elements for monitoring the integration operations (PostgreSQL). Consistency of information within and between databases (1), (2), and (3) is guaranteed by construction. Data are archived in a SeisComP Data Structure (SDS) structure  (GFZ and gempa GmbH, 2008, if miniSEED data)  or as HDF5 record  (Folk et al. , 2011, if PH5 data). Résif-DC also computes on the fly, with each integration, some useful side products.
Metadata enhancement Résif-SI has progressively improved the quality and consistency of metadata by establishing a controlled vocabulary and recommendations for standardizing the content of text fields in metadata. Initially, the metadata of A-nodes were managed in local databases and formatted using the opensource portable data collection centers (PDCC) tool by the Incorporated Research Institutions for Seismology (IRIS,  Casey, 2016)  or in-house tools. Each A-node has its specificity regarding metadata. In particular, SisMob and RAP have to deal with a large diversity of instruments and very complex metadata. With this context, in-house tools to manage the metadata were mandatory. Indeed, all the constraints on the metadata for Résif-SI networks could not be included into any standard software (e.
g. ,  SeisComp, GFZ and GEMPA GmbH, 2008)  at the time. Before 2019, metadata were edited and submitted in SEED dataless format  (Ahern et al. , 2012) , and Résif-DC ingested the metadata and delivered it in the normalized StationXML format  (FDSN, 2019). Today, all A-nodes produce StationXML metadata and most of Résif-SI are moving toward a common toolbox and database for metadata edition. The Metadata editing made easy: YASMINE section gives additional information on this project, which has high priority in Résif-SI, because StationXML gives visibility and full acknowledgment of all dataset contributors-from station to data distribution (see the Data fairness section). This normalized information is automatically exported to the Résif-SI web portal and to other services. Moreover, StationXML allows referencing to persistent network identifiers to facilitate citation and includes new fields necessary, for example, for OBS data (see the Marine data section).
At this juncture, we have secured a collaboration with the Computing Centre of the National Institute of Nuclear Physics and Particle Physics (CC-IN2P3, IN2P3/CNRS, 1976) located in Lyon, to serve as this remote site. Although, the recovery plan is not fully formalized, we guarantee that the data hosted at Résif-DC will survive the mentioned risks, as long as it has been stored for, at least, six months. An additional copy for recent data are ensured by A-nodes, which are committed to keeping a secure copy of their datasets for a duration of two years. Altering or destroying the data hosted by Résif-DC would require an operator or an attacker to get access to high privileges on three independent infrastructures, namely: • Write access to the Résif-DC infrastructure would allow to delete live data in the archive.
Their implementation is based on IRIS WebServiceShell (IRIS, 2016), which we interfaced (via Python) with our databases and archives. All the other webservices have been implemented in Python and ObsPy  (Beyreuther et al. , 2010; Megies et al. , 2011; Krischer et al. , 2015)  within the Flask framework  (Ronacher, 2010) , and their codes are fully available on public code repositories (see Data and Resources). The basic Résif-DC data services are: • A SeedLink server  (GFZ and gempa GmbH, 2008) , accessible at rtserve. resif. fr (standard port 18000), for real-time access to miniSEED data. This service, based on IRIS ringserver  (Trabant, 2011) , provides a single access point to all the real-time data streams of the A-nodes for which metadata is publicly available; • The standard fdsn-dataselect webservice (FDSN, 2013) to access validated or near real-time data -public or restricted-for all of the miniSEED archive.