dataesr/scientific-paragraphs-categorization huggingface dataset:
The  NIST ASD  development is supported in part by  NASA. GDZ and HEM work on CHIANTI has been supported by the  UK STFC  and SOLID (First European SOLar Irradiance Data Exploitation), a collaborative  SPACE  Project under the  Seventh Framework Programme  ( FP7/2007-2013 ) of the  European Commission  under Grant Agreement N  313188. The Spectr-W 3 project activities are currently supported in part by the  Russian Foundation for Basic Research ,  RFBR  grant Nr. 14-07-00863. LSR and IEG wish to thank the support from  NASA Planetary Atmospheres  Grant  NNX13AI59G. ExoMol is supported by  ERC  Advanced Investigator Project  267219. ZB and KB acknowledge support from the  United States National Science Foundation  under grants  PHY-1403245  and  PHY-1520970 , and by the XSEDE supercomputer allocation  PHY-090031.
AD and SM thank  P Rousseau  and  B A Huber  for fruitful discussions,  Quentin Marie  for the preparing of the RADAM-ION database website structure and  Université de Caen Normandie  for hosting the website. BA is supported by the  Dept. of Science and Technology (DST), Govt. of India. Part of this research was supported by the  Austrian Science Fund (FWF) :  P26635. Support from  Nano-IBCT COST Action MP1002 (Nanoscale Insights into Ion Beam Cancer Therapy)  is also acknowledged. YLB and VGT acknowledge support from LIA  SAMIA  and from  Tomsk State University D Mendeleev funding program. Belgrade node activities and the corresponding research are supported by projects  III44002 ,  176002  and  171020  of the  Ministry of Education, Science and Technological Development of Republic of Serbia.
iao. ru) sites providing supplementary functionalities of relational database under control of MySQL database tools including graphical inter- face for theoretical spectra simulation and comparisons with experimental records. 3. 12. MeCaSDa, ECaSDa, TFMeCaSDa, SHeCaSDa The MeCaSDa, ECaSDa, TFMeCaSDa and SHeCaSDa databases (Ba et al 2013) provide calculated spectroscopic data for the CH 4 , C 2 H 4 , CF 4 and SF 6 molecules, respectively, devoted to the high resolution spectroscopy of the ozone molecule, related properties and data sources. SMPO contains infor-mation on original spectroscopic data recovered from com- 3. 13. Carbon dioxide spectroscopic databank (CDSD)-296 and CDSD-1000 prehensive analyses and modelling of experimental spectra as well as associated software for data representation written in PHP Java Script, C++ and FORTRAN.
Applications for including isotopologues. These databases not only provide spectroscopic line lists (line positions, intensities and line broadening coefficients) for infrared absorption and Raman scattering spectra, but also a full description of molecular eigenstates with all quantum numbers. The data result from the most recent fits of experimental data, see for example (Nikitin et al 2013, Amyay et al 2015) for CH 4 , (Ballandras et al 2013) for C 2 H 4 , (Faye et al 2014) for SF 6 and (Boudon et al 2011, 2013) for CF 4. All analysis calculations are performed using the XTDS software package (Wenger et al 2008). These databases are complementary to other databases, such as HITRAN (Rothman et al 2013), which mostly rely on experimental data. The calculated data may be less accurate in some spectral regions, but the calculated data sets are more complete since all information is calculated in a given spectral region.
(Brunetto and Roush 2008) andal 2013, Sabri et al 2015). Through the VAMDC portal, transmission and optical depth data for water-ice experiments are available for the temperature range from 12. 5 to 150 K(Baratta et al 2003, Leto and Baratta 2003). The public database also includes optical constants of solid state CO, CO 2(Baratta and Palumbo 1998, Palumbo  et al 2006), and CH 4(Brunetto and Roush 2008)deposited at various temperatures. LASP is hosted at http://vamdclasp. oact. inaf. it/GUI/index and additional information can be found at http://web. ct. astro. it/weblab/. 3. 23. Grenoble astrophysics and planetology solid spectroscopy and thermodynamics (GhoSST).
Krishnakumar and his collaborators have initiated a programme aimed at the creation of the IAMDB. The aim was two fold: (1) create 'IAMDB' as a repository for the A+M data in India; 3. 28. Indian atomic and molecular database (IAMDB) The Indian atomic and molecular research community has been actively involved in atomic and molecular research for decades, especially in the areas of collision physics, spectroscopy, astrochemistry and chemical kinetics. There is a wide spectrum of activity in both theory and experiment covering collisions of electrons, ions and photons and atomic and molecular spectroscopy (.
This article describes a generic programmatic method for mapping chemical compound libraries on organism-specific metabolic networks from various databases (KEGG, BioCyc) and flat file formats (SBML and Matlab files). We show how this pipeline was successfully applied to decipher the coverage of chemical libraries set up by two metabolomics facilities MetaboHub (French National infrastructure for metabolomics and fluxomics) and Glasgow Polyomics (GP) on the metabolic networks available in the MetExplore web server. The present generic protocol is designed to formalize and reduce the volume of information transfer between the library and the network database. Matching of metabolites between libraries and metabolic networks is based on InChIs or InChIKeys and therefore requires that these identifiers are specified in both libraries and networks.
INTRODUCTION Metabolomics is the real-time outcome of the organism metabolism. To provide physiological interpretations and new hypotheses based on metabolomics datasets obtained on biofluids, tissue, or cellular extracts; it is of outmost importance to put the identified metabolites in a biological context. However, the analytical methods used in metabolomics do not allow coverage of the whole range of small molecules, introducing possible bias in the interpretation of wholeorganism metabolism. Identifying which part of the metabolism can be detected in a metabolomics experiment could lead to more robust metabolomics studies. The chemical diversity of small molecules is vast as evidenced by the massive size of current databases such as PubChem  (60,870,896 compounds, October 2015; Kim et al.
, 2016 ), eMolecules (4,840,559 compounds referenced in ChemSpider, October 2015; Pence and Williams, 2010) , or  MolPort (5,292,051 compounds referenced in ChemSpider, October 2015). Nevertheless, most of these compounds are drugs or synthetic compounds and are thus not necessarily related to the endogenous metabolism (in which metabolites are created or consumed by cellular processes). Since, metabolomics aims at deciphering metabolic modulations induced by environmental or genetic factors on this intracellular metabolism  (Nicholson et al. , 1999; Fiehn et al. , 2000) , researchers generally focus on endogenous metabolites and only monitor a small portion of these databases  (Ramautar et al. , 2013). This explains the success of biology-oriented chemical databases such as the Human Metabolome Database  (HMDB, 41,993 compounds, October 2015; Wishart et al.
, 2013). These databases are largely used for metabolite annotation purpose, for instance to assign putative names to masses obtained using high resolution mass spectrometry. However, annotation can lead to ambiguities and requires a final identification step (to reach level 1 as described in  Sumner et al. , 2007)  to provide high quality metabolite lists. The last processing step in annotation is achieved by comparing experimental spectra to those obtained using standard compounds  (Creek et al. , 2014). In order to increase the number of possibly-identified compounds, metabolomics facilities are building libraries of these standard molecular fingerprints. These libraries are currently gathering hundreds of standards and corresponding spectra (proton Nuclear Magnetic Resonance [NMR) or Gas/Liquid Mass Spectrometry (LC/GC MS)].
In this article, we will consider two libraries as a proof of concept: the MetaboHub (French National infrastructure for metabolomics and fluxomics) PeakForest database and the one assembled by Glasgow Polyomics facility (GP). Biological variability implies that metabolism (and related metabolome) differs from one organism to the other. Consequently, the number of metabolites referenced in the chemical library which can be detected in a given organism will highly depend on the organism. This discrepancy in coverage of organism metabolomes has to be taken into account by metabolomics facilities since, they will have to deal with samples coming from a large range of organisms. The proposed computational solution aims at identifying how much of a specific organism metabolome is covered by a library and which parts of the metabolism can be monitored.
, 2014  for a discussion on this topic). We propose in this article to use InChIs and InChIKeys as shared identifiers  (Heller and McNaught, 2006; Heller et al. , 2013). This article describes a novel protocol designed to perform chemical library mapping on genome-scale metabolic networks. This protocol makes it possible for a chemical library to send a list of identifiers to a network database and then receive statistics on the coverage of this list on metabolic networks. We propose an overall architecture to establish a remote dialogue between chemical library and network repository. We use two chemical libraries (PeakForest, GP library) and a metabolic network repository (MetExplore) as data sources, and highlight how to deal with some specific issues such as the identifiers used to perform the mapping.
Overall Architecture for Remote Access We used the concept of "Software As A Service" (SaaS) introduced by  Dai et al. (2012)  and defined as online-software services and remote access facilities which make bioinformatics tools available through the web. With this approach, existing applications, resources and/or algorithms are wrapped in a system which can run massive jobs online with a high frequency cycle. Such SaaS architecture is thus very well suited for metabolome mapping on metabolic networks. This solution has the advantage of being more flexible and versatile than other bioinformatics approaches such as developing a standalone functional package or binary software application. Another solution would be to offer web forms on top of the server containing metabolic networks to query the database with a list of metabolites and get a mapping back, but this solution would lack flexibility, and in particular it would not allow complex queries (for more detailed description, please refer to Section "The SaaS code of conduct" in Supplemental Data Sheet 1).
Mapping requires accessing both the network database and the chemical library which are often stored on different systems and in different locations (see Figure  1 ). A way to connect these two data sources, shown on the left of Figure  1 , consists in copying the resource (e. g. , a copy of the network database) in FIGURE 1 | Overall concept of the protocol. The aim of this study is to advance the networking of resources (Resource Network) by proposing a way to interconnect two independent resources: a GEM's database and a chemical library (Relationships in the network). This is achieved through a framework of web services that will mutually enrich each side Graphical User Interface (GUI). the in-house system (the chemical library). Requests are then performed locally and return the same results for the copied release.
Principle of Web Services The automatic mapping of a chemical library is established through a programmatic interaction between the library and the network database (see Figure  2  for an overview). To allow data exchange between both components, we use web services (i. e. , "a software system designed to support interoperable machineto-machine interaction over a network, " W3C, World Wide Web Consortium, definition), and structured files. One advantage of web services is that they allow exchanges between two servers working on different configurations (e. g. , two operating systems). As result of W3C specifications and standards, web services are built on a "language transparency" policy: each side (client and server) can use different technologies and programming languages and accesses (consumes) other services written in any kind of language.
Common Descriptors for Metabolites in Libraries and Network Databases Most metabolic networks are created for mathematical simulation purposes and are not necessarily built with the aim of importing "omics" data. This implies that metabolic networks often contain specific identifiers for metabolites. As an example, D-glucose and water are present in most networks but may have a specific and different identifier in each network (see Table  1 ). Metabolomics community is putting some efforts in order to reference metabolites using controlled vocabularies and specific identifiers  (Salek et al. , 2015). Among them, most commonly used ones are ChEBI  (Hastings et al. , 2013) , KEGG  (Kanehisa et al. , 2014) , and PubChem  (Kim et al. , 2016)  identifiers. Nevertheless, these identifiers do not provide any structural information on compounds and when dealing with compounds which are not referenced in any database, one needs nevertheless a way to identify these compounds.
To overcome this issue, identifiers describing chemical structure, and thus independent from any database, are increasingly used in metabolomics. The IUPAC Organic Nomenclature provides this information but this naming convention generates long and complex names. For example, the IUPAC name of the D-Glucose is: (3R,4S,5S,6R)-6-(hydroxymethyl)oxane-2,3,4,5-tetrol. To ensure consistency and include structural information in our computational method, we chose the InChI (IUPAC International Chemical Identifier) and the InChIKey which are two other structural identifiers receiving a lot of interest in the field  (Heller and McNaught, 2006; Heller et al. , 2013; Galgonek and Vondrášek, 2014). InChI identifiers provide a formal and non-ambiguous identification of compounds (see Figure  3  for examples).
This is mainly due to the fact that these networks are built using genome annotation and are mostly used to interpret gene related data. To overcome this limitation we developed in MetExplore an automatic method of adding metabolite identifiers to networks. Provided that the metabolic network mentions common names for metabolites, we use the Chemical Translation Service (CTS,  Wohlgemuth et al. , 2010)  and UniChem  (Chambers et al. , 2013)  to find identifiers from commonly used databases in metabolomics, such as KEGG, PubChem, and ChEBI. We then re-iterate over the retrieved identifiers to cross reference and check those identifiers. The human model Recon2v02  (Thiele et al. , 2013)  originally had 51% of its metabolites with either an InChI or a SMILES identifier, after the enrichment process, this went up to 77%.
Dialogue Protocol between Chemical Library and Network Database The proposed protocol relies on the dialogue between web services located on two servers (the library and the network database). Figure  5  shows the overall process of communication. First the chemical library calls the network web server to inform that it is going to perform a mapping and provides its connection information (the address of its web service) (b). Once this information is received by the network server, it calls back the chemical library to get the metabolome to be mapped [Figure  5 (2)]. The library sends back the content of its database [Figure  5 (3)]. In order to automatise the process, the library web service returns a JSON array with all compound identifiers (InChIs or InChiKeys) from the chemical library.
The URL of this method is sent as a parameter to the MetExplore (3) Shows that some layer identifiers can be present multiple times inside a single InChI string. Here the "/t" layer is present a second time as a sub-layer of the isotopic layer. This is used to show the asymmetric center created by the specification of the deuterium isotope. For each InChI, there is the corresponding InChIKey. We can see that the InChIKey's first block is always the same, this is because the molecular skeleton of the compound is the same in the three examples. mapping web service. Finally, the network server replies with the resulting mapping and its corresponding identifier [Figure  5(4)]. Each time the mapping web service is called, it retrieves a list of publicly available networks from the database (256 public networks, 108 having been enriched with a sufficient number of InChIs for mapping).
RESULTS In this section, we present the pipeline implementation to map the content of two chemical libraries, MetaboHub PeakForest, and Glasgow Polyomics database. Files retrieved by the MetExplore web service were used as data sources to build summary tables of library coverage on a selection of model organisms or on an exhaustive list or organisms. Both libraries link back to MetExplore allowing analyzing their content in the context of metabolic networks. The MetaboHUB PeakForest database, through its metabolic profiles storage and annotation services, hosts more than 1900 metabolites (October 2015). The content of PeakForest has been put together by a network of four French metabolomic facilities: The Bordeaux Metabolome Platform (BMP) which is specialized in metabolomics/lipidomics targeted or untargeted profiling methods for plant samples and new plant compounds identification, the Clermont-Ferrand Metabolism Exploration Platform (PFEM) with its expertise on studying the effects of nutrition on main the physiological functions in human and animal models, the Paris Metabolome IDF, which brings its knowledge and experience in mass spectrometry based analysis of human biofluids and cell extracts for biomarker discovery, and the Toulouse MetaToul platform, which provides expertise in identification and analysis of metabolic pathways and metabolic networks, measurement of metabolic fluxes, chemometrics, metabolic phenotyping, and biomarker identification.
This database encompasses substantial annotation and identification work carried out on hundreds of metabolomic studies with several models and phenotypes and confirmed by chemical standard analysis. Reference metabolites and their fingerprints found in PeakForest cover several model species from a large taxonomic spectrum among which bacteria (Escherichia coli), plants (Arabidopsis thaliana), mammalian (Homo sapiens, Mus musculus). PeakForest provides web service methods allowing remote access to its chemical library. In addition to this possibility of targeted queries (compound per compound), a web service method was developed in order to send the whole chemical library content to MetExplore mapping service. This additional feature did not require an extensive coding (e. g. , 60 lines in Java, 60 lines in Perl-The web services documentation URL with examples is provided in the Supplemental Data Sheet 4) and was facilitated by the fact that an effort was performed by the four facilities to annotate all compounds using InChIs.
The PeakForest browser supports natural language searches allowing users to retrieve data with biological terms (e. g. , species, tissues, or biofluids) to find reference compounds or fingerprints. However, it does not provide a complete view on how much an organism metabolome can be covered by the content of the library. The interaction with a resource such as MetExplore allows PeakForest's users to evaluate the relevance and the coverage of database information when they annotate a particular biological matrix. PeakForest provides a summary table (Figure  6 ) compiling the mapping of the chemical library content against different MetExplore's genome scale reconstructions of metabolic networks. A selection of nine model organisms was made based on the principal MetaboHub fields of application.
It provides the percentage of coverage for nine model organisms. Each organism name in the table is hyperlinked to the corresponding mapping in MetExplore. If the user clicks on the name it will automatically launch MetExplore with the corresponding mapping of the chemical library. This mapping is automatically updated once a week to take into account potential changes in the library or in the networks. The table is also automatically updated every week by using the web service pipeline. GP compounds library contains a list of 240 metabolites that are routinely run as standard compounds for metabolomic analyses. As GP contributes to a wide range of research areas, it is meaningful to provide information to its user on the coverage of a maximum number of organisms available within MetExplore database.
For this reason no filter on organism is applied and the mapping is performed on all enriched organisms available in MetExplore database. The library coverage table is then built and made available to GP users. This table currently contains almost 60 different metabolic networks. Currently, the mapping has to be launched manually in order to update the coverage table; however PiMP constantly provides an open access to the list of InChIs corresponding to Glasgow Polyomics standard compound library and automatically parses the result sent back by MetExplore to generate the new table. This task is achieved using Django, a python web framework with which PiMP is developed. The table is made interactive to the user using javascript and allows search and filtering. The name is also clickable, allowing the visualization in a new window of the web browser of the selected mapping in MetExplore.
DISCUSSION Reactions forming metabolic networks are gathered based on genomes. Since, functional genome annotation is not completed (Blaby-Haas and de Crécy-Lagard, 2011) reactions may be missing in the network, consequently their substrates and products may not be referenced in the network. This explains why some metabolites in a library are expected to be found but not mapped on the network. Moreover, the level of curation of metabolic networks is very variable. For some organisms (e. g. , Human,  Thiele et al. , 2013 or parasite Trypanosoma brucei Shameer et al. , 2015)  large group of experts have been put together to work on adding missing reactions and remove falsely predicted ones. Other networks have been automatically created from genome data (5455 networks in tier 3 section of BioCyc).
In that case the mapping is not possible. However, genome scale network modeling community is putting some efforts to improve these parts of the metabolism  (Smallbone, 2013). The application of the proposed protocol on a large range of organisms requires, for most networks, the addition of InChIs to the metabolites in chemical databases. Although genome-scale networks increasingly contain this information, some efforts are still needed to systematically provide better identifiers for metabolites. For this solution to be widely implemented, the metabolic networks of network repositories need to be enriched in terms of metabolite identifiers. Mapping metabolomes on genome-scale networks can be rendered more difficult by the compartmentalization of metabolites (when the modeled organism contains cellular compartments).
Given, that some metabolites will be present multiple times in a single network due to this phenomenon of compartmentalization, they will artificially increase the mapping coverage. Bias can be reduced by creating an uncompartmentalized version for each network, but this requires an unambiguous method to identify all instances of all metabolites across compartments. At present, this represents a considerable challenge due to missing identifiers for some parts of the metabolism. On the library side, analysts are increasingly keeping "unknown" metabolites with the idea of building a complete database for annotation when better identification algorithms and standards become available. For these compounds, scientists can only provide partial information (mono-isotopic or average mass, chemical raw formula) but no structural identification (in consequence: no InChI).