ChEBI dataset in Turtle, N-Triples, JSON-LD, and RDF/XML. https://www.ebi.ac.uk/chebi/

ChEMBL is medicinal chemistry database by the team of dr. J. Overington at the EBI: http://www.ebi.ac.uk/chembl/ It is detailed in this paper (doi:10.1093/nar/gkr777): http://nar.oxfordjournals.org/content/early/2011/09/22/nar.gkr777.short This project develops, releases, and hosts a RDF version of ChEMBL, independent from the ChEMBL team who make their own RDF version. The main SPARQL end point is available from Uppsala University at: http://rdf.farmbio.uu.se/chembl/sparql

In this paper, we have investigated the chemical adsorption behavior of O2 on five types of organic sulfur (thiol, sulfoxide, thioether, sulfone, and thiophene) in polycyclic aromatic hydrocarbon (PAH) sheets using density functional theory (DFT) calculations. Here, the adsorption energy of O2-organic sulfur exceeds that of O2-PAH. Sulfone tends to be more favorable for oxidation reactions than other organic sulfur compounds and PAH by energy gap and deformation charge density analyses. A large charge transfer occurs between O2 and organic sulfur compounds by charge analysis. A radical distribution function (RDF) analysis shows that O2/CO2/N2 is preferentially adsorbed on nitrogen/sulfur/oxygen-containing functional groups in coal. To inhibit the reaction of sulfur-containing coal with oxygen, the physical adsorption of pure gas (CO2/O2/N2) and binary mixed gases (CO2 + O2/N2 + O2/CO2 + N2) is conducted at different temperatures and geological depths using molecular dynamics (MD) and grand canonical Monte Carlo (GCMC) simulations. The adsorption capacities of five types of organic sulfur with respect to the pure gases decrease with increasing temperature and increase with increasing depth. For O2/CO2, CO2/N2, and O2/N2 binary gas systems, the order with respect to adsorption amount is CO2 > O2 > N2. The factor of adsorption capacities is also evaluated, and the results show that pore volume plays a key role in adsorption behavior.

The MNXref reconciliation of metabolites and biochemical reactions namespace

This RDF data contains the core information of chemical substances such as NIkkaji Number, English/Japanese label, Type, Chemical structure diagram, and same object which were extracted extracted from the Basic Information RDF. We recommend using this RDF together with the 'InChI and InChIKey' 'Canonical SMILES' or 'MOL/SDF' RDF.

The Molecular Entities in Linked Data (MEiLD) dataset comprises data of distinct atoms, molecules, ions, ion pairs, radicals, radical ions, and others that can be identifiable as separately distinguishable chemical entities. The dataset is provided in a JSON-LD format and was generated by the SDFEater, a tool that allows parsing atoms, bonds, and other molecule data. MEiLD contains 349,960 of 'small' chemical entities. Our dataset is based on the SDF files and is enriched with additional ontologies and line notation data. As a basis, the Molecular Entities in Linked Data dataset uses the Resource Description Framework (RDF) data model. Saving the data in such a model allows preserving the semantic relations, like hierarchical and associative, between them. To describe chemical molecules, vocabularies such as Chemical Vocabulary for Molecular Entities (CVME) and Simple Knowledge Organization System (SKOS) are used. The dataset can be beneficial, among others, for people concerned with research and development tools for cheminformatics and bioinformatics. In this paper, we describe various methods of access to our dataset. In addition to the MEiLD dataset, we publish the Shapes Constraint Language (SHACL) schema of our dataset and the CVME ontology. The data is available in Mendeley Data.

The files contain knots and coefficients of third order (quadratic) B-spline representation approximating the structure factor S(q) appearing in the equation for power spectral density of particle or cavity monolayer. The structure factor depends on the radial distribution function (RDF) of objects forming the monolayer. In our computations, we used a B-spline representation of the RDF computed for a hard disk system of surface coverage 0.85. The representation, averaged over 26 replicas of RDF, was calculated as described at http://dx.doi.org/10.17632/3csw4wmjnr.1. With the RDF representation, we numerically computed the structure factor using the procedure DBFQAD of SLATEC library. This way we got 1E5 values of the structure factor at equidistant wavenumbers in the interval from 1E-3 to 1E2. Finally, we fit a B-spline representation to the discrete function S(q). For that, we used the B-spline fitting procedure splrep of the package SciPy.interpolate included in the Python-based open-source library SciPy. We used a forth order (cubic) B-spline with the default knot vector generated by the procedure splrep, i.e., with the knot separation distance equal about 1E-3. To calculate the structure factor with the B-spline you can use the procedures splev or BSpline of the module SciPy.interpolate of SciPy library v. 1.7.1. The knot vector in the attached files begins and ends with three improper knots, in accordance with the requirements of the procedures. For details, see the paper: P. Weroński & K. Pałka, "Roughness spectroscopy of particle monolayer: Implications for spectral analysis of the monolayer image", Measurement 196 (2022) 111263.

This is the dataset used in the publication of "Resource Description Framework (RDF) Modeling of Named Entity Co-occurrences in Biomedical Literature and Its Integration with PubChemRDF"

PoLyInfo is a polymer database of the National Institute for Materials Science (NIMS) of Japan. In our previous work, to make the PoLyInfo data machine-readable and further machine-understandable, we built PoLyInfoRDF to store these data in the standard Resource Description Framework (RDF) format and then defined its schema in the Shape Expressions (ShEx) language. When designing the schema, it is important to modularize the schema such that the common components are reusable. This is the objective of this study and is essential for efficiently defining schemas of the descriptors and properties, which constitute the core of PoLyInfo, a large collection of experimentally measured polymer characteristics. As an example of modularization, descriptors of the source-based name and molecular formula both include a string value, hence their schemas may well share (‘inherit’) the schema for string values, which would be defined once and subsequently reused throughout the entire set of schemas. Actually we noticed a considerable amount of common portions among schemas of descriptors and properties, and clarified a ‘schema hierarchy’ to reflect the above ‘inheritance’ relationships, separately from the ontological ‘concept hierarchy’. We then investigated the extent to which the adapted strategy was able to successfully define the PoLyInfoRDF schema. Under this schema hierarchy, inheritance mechanisms in ShEx played a significant role in sharing common portions effectively in a well-organized manner. We expect future developments based on our approach to contribute to the standardization of scientific data representation in RDF by providing a library of reusable schemas.

The dataset used in the paper is the MUTAG RDF dataset, which contains a set of molecular structures and their corresponding properties.

Project that developed an open access discovery platform, called Open Pharmacological Space (OPS), via a semantic web approach, integrating pharmacological data from a variety of information resources and tools and services to question this integrated data to support pharmacological research. The project is based upon the assimilation of data already stored as triples, in the form subject-predicate-object. The software and data are available for download and local installation, under an open source and open access model. Tools and services are provided to query and visualize this data, and a sustainability plan will be in place, continuing the operation of the Open PHACTS Discovery Platform after the project funding ends. Throughout the project, a series of recommendations will be developed in conjunction with the community, building on open standards, to ensure wide applicability of the approaches used for integration of data.

The intrinsic relationships between nanoscale features and electronic properties of nanomaterials remain poorly investigated. In this work, electronic properties of 622 computationally optimized graphene structures were mapped to their structures using partial-least-squares regression and radial distributions function (RDF) scores. Quantitative structure–property relationship (QSPR) models were calibrated with 70% of a virtual data set of 622 passivated and nonpassivated graphenes, and we predicted the properties of the remaining 30% of the structures. The analysis of the optimum QSPR models revealed that the most relevant RDF scores appear at interatomic distances in the range of 2.0 to 10.0 Å for the energy of the Fermi level and the electron affinity, while the electronic band gap and the ionization potential correlate to RDF scores in a wider range from 3.0 to 30.0 Å. The predictions were more accurate for the energy of the Fermi level and the ionization potential, with more than 83% of explained data variance, while the electron affinity exhibits a value of ∼80% and the energy of the band gap a lower 70%. QSPR models have tremendous potential to rapidly identify hypothetical nanomaterials with desired electronic properties that could be experimentally prepared in the near future.

.alpha.-Hexachlorocyclohexane at IL_EPA_WQX-RDF-1

Ammonia-nitrogen at IL_EPA_WQX-RDF-3

Beryllium at IL_EPA-RDF-4

This data publication provides raw data, processed data, dataframes, and representative scripts to support our recent work on NMR spectroscopy of aqueous alkali fluoride solutions. The repository contains both experimental NMR spectroscopy data and multiscale modeling data for 23Na and 19F in sodium fluoride (NaF) solutions, as well as 19F data for other Group I alkali metal fluorides (LiF, KF, RbF, and CsF). A comprehensive collection of raw and processed experimental data for NaF and TMAF NMR measurements is included in the file Experimental_NMR_data_NaF.zip (see the enclosed README.txt file for more information). This repository also contains all cluster geometries and a comprehensive set of dataframes for quantum chemical NMR shielding tensors, radial distribution functions, and ion-pair speciation calculations. Representative scripts in Python and Perl are provided for data analysis and processing. The data and scripts presented here support the findings reported in the associated publication, "NMR Spectroscopy and Multiscale Modeling Shed Light on Ion-Solvent Interactions and Ion Pairing in Aqueous NaF Solutions." A subsequent publication, currently in preparation, extends this work to other Group I alkali metal fluorides. Please see the README.md file for additional markdown-formatted details on the software used and additional notes for the data and scripts included in this publication. * Description of each zip file - csvs.zip: CSV files containing analysis data, including: * m-AMOEBA09_nmr.csv: raw quantum chemical NMR data for each cluster configuration reported for F and Na * m-AMOEBA09_nmr_FI.csv: NMR data for free ions * m-AMOEBA09_nmr_ionpair_distance.csv: quantum chemical NMR data averaged as a function of ion-pair distance. See Jupyter notebook * m-AMOEBA09_nmr_concentration.csv: temperature and concentration dependence determined from combining ion-pair distance data with ion-pair speciation data
* m-AMOEBA09_XF-rdf.csv: Radial distribution functions for XF (X = alkali metal) pairs * m-AMOEBA09_XF-speciation.csv: Speciation data for XF pairs - ipynbs.zip: Jupyter notebooks used for data analysis and visualization - params.zip: Parameter files for Tinker MD, including the modified AMOEBA09 forcefield (m-AMOEBA09) (params/xf_m-amoeba09.prm) and the minimally augmented def2-TZVP basis sets (ma-TZVP) were used for quantum chemical calculations (params/ma-TZVP.gbs). reference: J. Zheng, X. Xu, and D. G. Truhlar Theor. Chem. Acc. 128, 295-305 (2010)Source of basis sets downloaded in Gaussian format - scripts.zip: Representative scripts used for analysis, including: * mdanalysis: Scripts using MDAnalysis for trajectory analysis * perl: Perl scripts used for data processing, including NMR workflow scripts (lib/NMRWorkflow) - XF_MD_NMR.dgraphs MacOS datagraph file containing graphing of dataframes in csvs directory - xyzs.zip: Coordinate files for ion-pair water clusters extracted from MD trajectories, organized by simulation timestep (1fs or 2fs) and alkali metal fluoride (e.g., NaF, LiF, KF, RbF, CsF) * Disclaimer Trade names are provided only to specify the source of information and procedures adequately and do not imply endorsement by the National Institute of Standards and Technology. Similar products by other developers may be found to work as well or better.

Pendimethalin at IL_EPA-RDF-1

This data set accompanies the manuscript Differential-Mobility Spectrometry of 1-deoxysphingosine Isomers: New Insights into the Gas Phase Structures of Ionized Lipids by Berwyck L. J. Poad, Alan T. Maccarone, Haibo Yu, Todd W. Mitchell, Essa M. Saied, Christoph Arenz, Thorsten Hornemann, James N. Bull, Evan J. Bieske, Stephen J. Blanksby.

ABSTRACT: Separation and structural identification of lipids remains a major challenge for contemporary lipidomics. Regioisomeric lipids differing only in position(s) of unsaturation are not differentiated by conventional liquid chromatography-mass spectrometry approaches leading to the incomplete, or sometimes incorrect, assignation of molecular structure. Here we describe an investigation of the gas phase separations by differential mobility spectrometry (DMS) of a series of synthetic analogues of the recently described 1-deoxysphingosine. The dependence of the DMS behavior on the position of the carbon-carbon double bond within the ionized lipid is systematically explored and compared to trends from complementary investigations, including collision cross sections measured by drift tube ion mobility, reaction efficiency with ozone, and molecular dynamics simulations. Consistent trends across these modes of interrogation point to the importance of direct, through-space interactions between the charge site and the carbon-carbon double bond. Differences in the geometry and energetics of this intra-molecular interaction underpin DMS separations and influence reactivity trends between regioisomers. Importantly, the disruption and reformation of these intra-molecular solvation interactions during DMS are proposed to be the causative factor in the observed separations of ionized lipids which are shown to have otherwise identical collision cross sections. These findings provide key insights into the strengths and limitations of current ion-mobility technologies for lipid isomer separations and can thus guide a more systematic approach to improved analytical separations in lipidomics.

Depth at IL_EPA-RDF-2