dataesr/scientific-paragraphs-categorization huggingface dataset:
Measurement of Linearity and Sensitivity The calibration curves were prepared using the ISTD (List on Supplementary Materials Table  S1 ) solubilized in 75 µL of MeOH:IPA:H 2 O (65:35:5; v/v/v). The ISTD were diluted to obtain 11 different concentrations, ranging from 244 pg/µL to 250 000 pg/µL (final concentration), and 1 µL was injected (n = 3). The repeatability of the areas and retention times was obtained using the relative standard deviation (RSD, %) of the peak areas and retention time: RSD = (standard deviation/mean) * 100. The ranges were processed using the TargetLynx application in the MassLynx software (waters).
Data Analysis/Normalization The UPC 2 -QTOF was controlled using the MassLynx software, version 4. 1. The data files (. raw) were converted into. abf using an analysis base file converter. The quantification and identification were performed using MS-DIAL, version 4. 85, with the import of the. abf data files. The data processing with MSDIAL was realized in an in-house database, including the molecule, mass to charge ratio, retention time, and selected adduct. Three methods were created to process the lipids. The first one was used for FAs, PGs, PIs, and PSs, the second for Ceramides, PCs, mono hexosyl ceramides, SMs, fatty acyl-carnitines, LPCs, PEs, lactosyl ceramides, and LPEs, and the last one for CEs, TGs, and DGs. All peak areas were extracted from MS-DIAL  [28]. The areas were normalized by the ISTD areas of each family and multiplied by the quantity of ISTD injected (see Supplementary Materials Table  S2 ) to obtain the relative quantity.
Conclusions Lipids are key metabolites in several physiological mechanisms, so large and rapid profiling is very important in obtaining them, especially in clinical applications. Due to the diversity and complexity of these molecules, unbiased lipidomic profiling is a real challenge. In this publication, we aimed to develop a rapid and robust technique using Supercritical Fluid Chromatography, coupled with the high-resolution mass spectrometry detector, QTOF. First, 17 different lipids class were separated on a polar DEA column, the conditions of source ionization and detection were optimized to obtain the best sensitivity, and an in-house database was built to interrogate the mass spectrometry profiles using MS-DIAL. The method was validated on the reference material, NIST plasma (SRM 1950),.
Glycans have a critical role in health and disease. As a direct result, there is keen interest to identify and increase glycan data in bioinformatics databases like ChEBI and PubChem, and in connecting them to biomedical resources at the EMBL-EBI and NCBI. GlyTouCan is a comprehensive archival database that contains over 118,000 glycans, obtained primarily through batch upload from glycan repositories, submissions from glycoprotein databases, and individual laboratories. In many instances, the glycan structures deposited in GlyTouCan may not be fully defined or have supporting experimental evidence or biological source content. Databases like ChEBI and PubChem were designed to accommodate complete atomistic structures with welldefined chemical linkages. As a result, they cannot easily accommodate the structural ambiguity inherent in many glycan database representations.
Consequently, there is a need to organize glycan data coherently to improve the connectivity across the major NCBI, EMBL-EBI, and glycoscience databases. This paper outlines a workflow developed in collaboration between GlyGen, ChEBI, and PubChem to improve the visibility and connectivity of glycan data across these resources. GlyGen hosts a subset of glycans (~29,000) from the GlyTouCan database and has submitted valuable glycan annotations to the PubChem database and registered or mapped over 10,500 (including ambiguously defined) glycans into the ChEBI database. The integrated glycans were prioritized based on links to PubChem and connectivity to glycoprotein data. The pipeline provides a blueprint for how glycan data can be harmonized between different resources. The current PubChem, ChEBI, and GlyTouCan mappings can be downloaded from GlyGen (https://data.
Even though it is expected that the glycan analysis provides topology information, it is difficult to transfer such data from literature to bioinformatics databases without a curator's effort to evaluate composition and other supplementary information to generate fully determined structures. As a result the knowledge transfer from literature to bioinformatics resources is lengthened significantly. Among the available glycan and glycoconjugate database resources available, GlyTouCan  (Tiemeyer, Aoki et al. 2017 ) (designed to serve a function analogous to what GenBank serves for nucleotide sequences) provides a repository of glycans which are fully-defined or with structural ambiguity at multiple levels which can described by the Glycan naming and subsumption ontology or GNOme ((http://obofoundry.
org/ontology/gno, http://gnome. glyomics. org) (See Figure  1 ). Depositions made in GlyTouCan may also include monosaccharide compositions without any structural context or structural representations without complete description of the linkages between their monosaccharide building blocks. In addition to providing accessions for individual glycans (which are adopted as primary identifiers by glycoprotein-centric databases such as GlyGen  (York, Mazumder et al. 2020) , GlyConnect  (Alocci, Mariethoz et al. 2019)  and UniCarbKB  (Campbell, Peterson et al. 2014 )), GlyTouCan also provides cross-references to contributing databases and includes other annotations such as sequences, literature evidence, and Symbol Nomenclature for Glycans (SNFG) images. Similarly, chemical databases like ChEBI  (Degtyarenko, de Matos et al.
2008)  and PubChem  (Kim, Chen et al. 2021)  provide unique identifiers for chemical entities (glycans) and host text-string representation (such as SMILES, InChI, LINUCS, etc. ), 2-D structure, and literature evidence for compounds. These databases also host annotation and cross-references to reaction databases Rhea  (Lombardot, Morgat et al. 2019 )) and pathway database Reactome  (Jassal, Matthews et al. 2020 ) that show the individual compounds that take part in any given reaction (i. e. the reactants and products). Additionally, ChEBI also provides ontology (http://www. obofoundry. org/ontology/chebi. html) for chemical structures which is used by a number of research groups (including Gene Ontology  (Ashburner, Ball et al. 2000 , Gene Ontology 2021 ) and model species group). Additionally, GlyGen  (York, Mazumder et al.
2020 ) is a glycoinformatics resource with extensive glycoprotein and glycan data integrated from various databases. GlyGen presents a dedicated entry page for proteins/glycoproteins (e. g. https://www. glygen. org/protein/P14210-1) and glycans (e. g. https://www. glygen. org/glycan/G17689DH). To facilitate interoperability, GlyGen uses accessions from UniProtKB(UniProt 2019) and GlyTouCan as primary accessions to identify proteins and glycans respectively. Glycan pages present cross-references to other glycoprotein databases (like GlyConnect, UniCarbKB, etc. ) and other glycan or chemical databases (such as PubChem, MatrixDB  (Chautard, Ballut et al. 2009) , Glycosciences. DB  (Bohm, Bohne-Lang et al. 2019), etc. ) , unique digital representation (such as GlycoCT  (Herget, Ranzinger et al.
2008) , WURCS  (Matsubara, Aoki-Kinoshita et al. 2017) , and IUPAC), harmonized symbolic nomenclature representations (SNFG  (Varki, Kannagi et al. 2015 )), and additional annotations which are of importance to glycan-focused researchers, including glycan classifications (N-linked, O-linked, complex core 1, etc), relevant supporting publications, as well as annotations related to disease, expression, mutation, etc. It is important to enhance the connectivity across GlyTouCan, ChEBI and PubChem databases by promoting interoperability of glycan data available at GlyGen, provided by its project partners. This will allow GlyGen to connect glycans to glycoproteins, reaction and pathway annotations and ontologies provided by GNOme and ChEBI. This will allow users to navigate the same glycan and its associated annotations across multiple resources through one portal.
Data flow of glycans across different bioinformatics databases: GlyTouCan and ChEBI databases routinely submit entities to the PubChem Substance database as MDL SDF files. These contain the chemical structures (as embedded molfiles), names, synonyms and other information for glycans. PubChem assigns a unique Substance Identifier (SID) to each submitted glycan entity that are standardized to the PubChem Compound database (identified by CID) such that each SID is mapped to a single CID. Multiple SIDs can map to the same CID (see Figure  2 ). PubChem only accepts glycans where the arrangement of the monosaccharides, glycosidic bonds and linkages or anomeric positions are known. However, ambiguity regarding the first linkage (between the glycan and conjugate) and stereochemistry of any particular atom is tolerated within PubChem.
As a result, not all GlyTouCan accessions are included in the PubChem database. Additionally, before the start of the collaboration only a small portion of the available glycans in PubChem were mapped to the ChEBI database resulting in low connectivity across GlyTouCan, PubChem and ChEBI databases (See Figure  3 ). This dataflow is utilized by GlyGen to submit glycan annotations to PubChem, connect GlyTouCan accessions to ChEBI IDs, and increase the PubChem CID to ChEBI ID mapping.
The remaining glycans from the GlyGen set which represent predominately ambiguously defined structures will be manually registered in batches in the ChEBI database routinely. The integrated glycans are annotated with structure data (for glycans with a PubChem CID cross-reference), formula, charge, mass values, InChI, InChIKey and SMILES. Additionally, new ChEBI names (while retaining the original name as a synonym), more precise ontology terms (such as "amino tetrasaccharide" or acetamides") are added to generate 3-star curated status entries. The corresponding GlyGen, GlyTouCan cross-references are added under "Manual Xref" whereas PubChem IDs are listed under "Automatic X-ref" sections of the individual ChEBI entry pages respectively (e. g. https://www. ebi. ac. uk/chebi/searchId. do?chebiId=70967).
The mappings are available to download from GlyGen (https://data. glygen. org/GLY_000296) or ChEBI (ftp://ftp. ebi. ac. uk/pub/databases/chebi/Flat_file_tab_delimited/database_accession. tsv) or can be viewed through individual GlyGen pages (e. g. https://glygen. org/glycan/G78059CC#Cross-References).
Submitting Glycan annotations to PubChem: GlyGen submitted important glycan annotations such as glycan motif (e. g. Lewis X, Type 2 LN), composition (Hex5 HexNAc4 dHex1), per-methylated mass (2221. 13523366), classification (e. g. N-glycan (complex)) to PubChem Compound database using the GlyTouCan accession to PubChem CID mapping (e. g. https://pubchem. ncbi. nlm. nih. gov/compound/25098607#section=Biologic-Description) Additionally, GlyGen also submitted glycosylation annotation such as the glycosylation site, glycosylation type (e. g. N-linked), GlyTouCan accession, literature evidence and a glycosylation summary which list all glycosylation sites from GlyGen with or without glycan information. For example, for PubChem protein entry for the Epidermal growth factor receptor (UniProt: P00533), the summary describes total of 18 sites out of which 15 sites have 101 N glycans, 1 site has 1 O-glycan and 2 sites do not have any reported glycan information.
Increasing ChEBI cross-reference in PubChem Initially, only a small portion of glycans available in the PubChem database were mapped to ChEBI IDs. Due to this collaboration effort as the glycan data coverage increased in ChEBI more glycans were submitted to the PubChem database through ChEBI's routine submissions. As a result, the number of PubChem compounds with a ChEBI cross-reference increased over time (see Figure  5 ). However, this process does not include submission of ambiguously-defined glycans into PubChem.
Registration of high-value ambiguously defined glycans: As a proof of concept twenty-five high-value ambiguously defined glycans were registered into ChEBI manually (see Table  1 ). These glycans are selected based on the rules described in method section 4. 2 (Manual registration of glycans into ChEBI). The ChEBI entries are registered without images (due to high number of possible linkages) and are assigned ChEBI names with a prefix of its source database i. e. "GlyTouCan" (e. g. GlyTouCan G06110VR) in this case (e. g. https://www. ebi. ac. uk/chebi/searchId. do?chebiId=156559). Other related details (such as definition, ontology and WURCS) are manually added to convert such entries from registered 2-star status to 3-star status.
Novelty GlyTouCan, PubChem and ChEBI are major bioinformatics resources that are routinely used by many different research groups. However, the ChEBI and PubChem databases have significantly less glycan data compared to GlyTouCan. This novel pipeline allows integration of valuable glycan and glycan-related annotations hosted by GlyGen into PubChem and ChEBI databases while maintaining the links to GlyTouCan database. Additionally, ChEBI's routine submissions to PubChem allows the newly integrated glycans to be further integrated into the PubChem database. As a result, this pipeline is not only able to identify and increase the glycan data coverage in ChEBI and PubChem databases but is also able to synchronize and map these entries ensuring connectivity across these major resources. Additionally, GlyGen allows users to access such crossreferences under one portal while connecting the glycans to glycoprotein data.
Submitting glycan annotations to UniProt database Currently, the glycosylation data available in the UniProt database describes the protein, glycosylation site, associated literature (for reported glycosylation) and in some cases additional description (such as high mannose, etc) about the glycan or glycosylation. However it lacks the information on the specific glycan that may be reported on a particular site on the protein. This information will be provided by GlyGen (sourced from multiple resources) where specific glycans will be added to the protein and site data already present in the UniProt database. The glycans along with a 2-D image will be identified by a ChEBI ID and will be cross-referenced to GlyGen protein pages. The data will be accessed through the ProtVista viewer in UniProt (see Figure  7 ).
ProtVista is a protein browser providing a compact representation and access to the functional amino acid residue annotation in UniProt such as domains, sites, posttranslational modifications and variants. Protvista is available through the UniProt web site and the source code is publicly available through as a web component (https://github. com/ebi-webcomponents/protvista-uniprot). UniProt users will also be able to query for glycans connecting them to knowledge on proteins e. g. , proteins that bind them and enzymes that synthesize or degrade them.
Connecting to other important resources Mapping of GlyTouCan accession to ChEBI ID allows further extension to other major databases such as Rhea (enzyme-centric) or Reactome (pathway-centric) or UniCarbKB, GlyConnect, (or other glycoprotein-centric) resources which use ChEBI or GlyTouCan accessions as their primary identifiers (See Figure  8 ). This will allow `direct mapping. The mapped cross-references to Rhea and Reactome databases can be viewed through GlyGen glycan pages (currently on beta: https://beta. glygen. org/glycan/G96881BQ#Cross-References). Additionally, as GNOme also provides glycan-glycan relationships (using GlyTouCan accession as the primary identifier), the ambiguously defined glycans can be potentially linked to possible fullydetermined, structurally-related glycans within the ChEBI database.
Future plans In order to sustain the growing GlyGen glycan collection, one of the major goals is to automate ~90% of the submission pipeline and reduce the time required to manually register ambiguously defined glycans. We also plan to document this pipeline so that resources like GlyTouCan, GlyConnect, and others can utilize this pipeline to integrate their glycans into the ChEBI and the PubChem databases. Additional plans include the generation of tutorials so that users can directly register or map ambiguously-defined glycans into the ChEBI database. This can be achieved by using the PubMed ID or other publication or database identifier as a ChEBI name for the glycan entries in order to comply with the ChEBI registration protocols. Just as the usefulness of genomic and proteomic resources grew as their coverage of genes and proteins expanded, so too will the usefulness of glycan and glycosylation databases increase as the number of wellmapped glycan structures approaches comprehensive coverage.
org/ontology/gno, http://gnome. glyomics. org). (See Figure  9 ). GlyGen data processing and integration framework is utilized to quality control and standardized this data which is supplied to GlyGen portal  (Kahsay, Vora et al. 2020). As an attempt to harmonize glycans between GlyTouCan and ChEBI, the GlyGen glycan set (v. 1. 5. 13) comprising 29,290 glycans was identified as the primary list (see Figure  10 ). An integration pipeline was developed to map and register all GlyGen glycans into the ChEBI database. (see Figure  11 ).
Integration of glycans in ChEBI Using PubChem as an anchor database. PubChem Compound was used as an anchor database to identify the existing mapping between the GlyTouCan and ChEBI databases. Out of the GlyGen glycan list of 29,290 GlyTouCan accessions 10,496 were already cross-referenced to PubChem compound identifiers (CIDs). These mostly included glycans which are fully-determined with or without known first linkage (between the glycan and conjugate). This set also includes glycans which consists of a single monosaccharide as well glycans with one or more undefined atomic stereocenter. This mapping was utilized to retrieve the corresponding PubChem CID to ChEBI ID mapping. As a result, over 1,700 glycans were mapped between the GlyTouCan and ChEBI databases (See Figure  12 ). However, this method provided only about 5% coverage for the GlyGen glycan set.
As for the remaining PubChem CIDs where a ChEBI ID mapping was not directly available from the PubChem database, the integration pipeline was modified so that the structure data for individual entries (downloaded from PubChem) could be uploaded into applications like KNIME  (Fillbrunn, Dietz et al. 2017)  and ClassyFire  (Djoumbou Feunang, Eisner et al. 2016)  to generate a suitable SD file for ChEBI. In this process, the downloaded PubChem structure data for individual entries were uploaded to the KNIME application where the data was first cleaned (e. g. remove explicit hydrogens while maintaining the stereochemical integrity of the structure) and the additional information (such as associated names, synonyms and WURCS sequence) was extracted. The data was further uploaded to the ClassyFire application (at 12 requests per minute rate) in order to generate ontology terms.
The generated ontology terms were then converted to the best matching ChEBI ontology terms and an SD file for ChEBI upload was generated. A default classification value (such as carbohydrates and carbohydrates-derivatives) was added in cases where ClassyFire was unable to generate an ontology term for a record. The files generated by the KNIME application were submitted to the ChEBI loader tool (in batches of 250 records) and the GlyTouCan accession (via PubChem CID) was either mapped to an existing ChEBI ID (if the structure was already present in ChEBI) or registered as a new entry with 2-star status. This method allowed integration of any GlyTouCan accession (within GlyGen) with a PubChem CID into the ChEBI database (see Figure  13 ).
Manual registration of glycans into ChEBI The PubChem Compound database facilitated mapping or registration of roughly 30% of the GlyGen glycans (GlyTouCan accessions) into the ChEBI database. The remaining set of 18,794 glycans lacked a PubChem CID, likely as they did not fit the 1b) Shows a type of glycan (G69616RY) where there is no information about the linkage or anomeric configuration, but the arrangement of monosaccharides and glycosidic bonds is known. Such glycans belong to the "Topology" (http://purl. obolibrary. org/obo/GNO_00000015) instance in GNOme. 1c) Shows a type of glycan (G83385WA) where only the information about type and number of monosaccharides is known in addition to complete or partial knowledge of the stereochemistry of the involved monosaccharides. Such glycans belong to the "composition" (http://purl.
ACKNOWLEDGEMENTS Kiyoko Aoki-Kinoshita ,  Terence Murphy  and  Andrew Leach  ABBREVIATIONS EMBL-EBI:  EMBL-European Bioinformatics Institute SIB: Swiss Institute of Bioinformatics ChEBI: Chemical Entities of Biological Interest CFG: Consortium for Functional Glycomics GNOme: Glycan Naming Ontology CID: PubChem Compound Identifier SID: PubChem Substance Identifier WURCS: Web3 Unique Representation of Carbohydrate Structures SMILES: Simplified Molecular-Input Line-Entry System InChl: International Chemical Identifier IUPAC: International Union of Pure and Applied Chemistry  HCV: Hepatitis C virus X-ref: Cross-reference.
In order to enhance the intensity and resolution of the diffraction maxima, a Bragg-Brentano focusing geometry was used. X-ray patterns were recorded at an angular range (2θ) of 20-100°and at a scanning rate 0. 5°/min. The Rietveld refinement of obtained powder XRD pattern was carried out using the FULLPROF program software. Refined parameters include: overall scale factor, background (BGP), lattice parameters, atomic positions and orientation. DFT-Calculated XRD patterns were acquired using the VESTA software  46. TEM. TEM studies were carried out on a JEOL JEM-2100F electron microscope and a FEI Tecnai G20, both operating at 200 kV. For this analysis, approximately 2 mg of each sample was dispersed in 20 ml of high purity cyclohexane (Merck, 99. 9%) via sonication (~15 s); then a drop of each suspension was deposited on carbon-coated grids (400 mesh) covered with thin amorphous carbon film (lacey carbon).
To avoid contamination, specimens were inserted in the TEM immediately following preparation. Bright field images were collected at several magnifications in order to observe structure and size homogeneity. Calculated HRTEM images were obtained with the help of the MacTempasX software, Total Resolution LLC. The simulated electron diffraction pattern images were obtained with the SingleCrystal software (CrystalMaker Software Ltd. ). DFT calculations. DFT calculations were performed using the QUANTUM ESPRESSO Simulation Package code  47. The Perdew-Burke-Ernzerhof exchangecorrelation functional with the generalized gradient approximation  48  was used. The Projector-Augmented Wave approach (PAW)  49  was used together with plane-wave basis sets. Since Ni 2 P in both bulk and nanocrystalline forms is metallic there is no need for using hybrid functionals such as HSE06  50  , or GGA + U 51 to describe Ni d-bands.
NMR Knight shift calculations were performed on bulk Ni 2 P, and the 4 different Ni 2 P slabs by using the full-potential linearized augmented planewave method, as implemented in the Wien2k DFT software package  36. The k-mesh convergence was checked up to 100,000 points for the bulk materials and up to 5000 points for the slabs. Other computational parameters, such atomic sphere radii as well as potentials and wave functions inside the atomic spheres, were as set by Wien2k defaults. The plane-wave basis set size was determined by setting RK max = 8, whereas for presented results we have used the LDA approximation  54. All structures, including the bulk material were structurally relaxed. In case of the bulk material the initial lattice parameters were taken from the Rietveld analysis of the XRD pattern.