This is the trajectory file meant to work with the rdf2d-example example folder of d-SEAMS. Check Github to navigate the source code in the browser. The filename needs to be preserved to run without any changes.

NBDC NikkajiRDF is RDF data of Japan Chemical Substance Dictionary (Nikkaji), which is one of the largest chemical substance databases in Japan.

The MNXref reconciliation of metabolites and biochemical reactions namespace

Present chemical data storage methodologies place many restrictions on the use of the stored data. The absence of sufficient high-quality metadata prevents intelligent computer access to the data without human intervention. This creates barriers to the automation of data mining in activities such as quantitative structure−activity relationship modelling. The application of Semantic Web technologies to chemical data is shown to reduce these limitations. The use of unique identifiers and relationships (represented as uniform resource identifiers, URIs, and resource description framework, RDF) held in a triplestore provides for greater detail and flexibility in the sharing and storage of molecular structures and properties.

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

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"

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.

.alpha.-Hexachlorocyclohexane at IL_EPA_WQX-RDF-1

This is the datasets used for Linked Open Data instant level quality analysis for cultural heritage (2020). 7Z and ZIP versions are available for both Excel 2006 and R 4.0.3. The compressed files include, Excel spreadsheets (.xlsx, .csv), VBA scripts (.bas), and R scripts (.r). Please read the full documentation in Linked_Open_Data_Instance_Level_Analysis_Procedure.pdf.

Nano-electronic Simulation Software (NESS)

This data set contains:

1. Trajectories of glass and liquid Ge2Sb2Te5 models generated with the GAP. There are 5 models labeled 001-005 of 315 atoms and one large model of 7,200 atoms labeled 7K.

2. Structural analysis data including: radial distribution functions, rings, voids, tetrahedral angular order parameters.

3. The inter-atomic potential used to carry out the simulations.

4. The crystallization trajectory of one of the GAP models is provided separately due to the size.

This data archive accompanies the master thesis Advancing Technologies for Unravelling Skin Lipidomes by Qiyu Liu.

This dataset primarily focuses on the fatty acid (FA) profiles of sebum, a key lipid mixture involved in skin barrier function. Using advanced LC-MS techniques, including derivatization with 4-I-AMPP+ and analysis by LC-UVPD-MS/OzID-MS, we comprehensively characterized 692 FAs (C8–C40) with varying unsaturation levels. The dataset highlights novel structural features, including branched-chain and very-long-chain FAs, providing new insights into sebum composition and its potential links to skin and neurological disorders. This resource supports further research into lipid metabolism and biomarker discovery.

DBGI tropical pilot mass spectrometry dataset

ttl files generated through the ENPK workflow

This data set accompanies the manuscript Combining Charge-Switch Derivatisation with Ozone-Induced Dissociation for Facile Fatty Acid Analysis by Berwyck L.J. Poad, David L. Marshall,Eva Harazim, Rajesh Gupta,Venkateswarra R. Narreddula, Reuben S. E. Young, Todd W. Mitchell, Eva Duchoslav, J. Larry Campbell, James A. Broadbent, Josef Cvačka and Stephen J. Blanksby (In Press).

Abstract: The specific positions of carbon-carbon double bond(s) within an unsaturated fatty acid exert a significant effect on the physical and chemical properties of the lipid that ultimately inform its biological function(s). Contemporary liquid-chromatography mass spectrometry (MS) strategies based on electrospray ionisation coupled to tandem MS can easily detect fatty acyl lipids but generally cannot reveal those specific site(s) of unsaturation. Herein, we describe a novel and versatile workflow whereby fatty acids are first converted to fixed charge N-(4-aminomethylphenyl) pyridinium (AMPP) derivatives and subsequently subjected to ozone-induced dissociation (OzID) on a modified triple quadrupole mass spectrometer. The AMPP modification enhances the detection of fatty acids introduced by direct infusion. Fragmentation of the derivatised fatty acids also provides diagnostic fragment ions upon collision-induced dissociation that can be targeted in precursor ion scans to subsequently trigger OzID analyses in an automated data-dependent workflow. It is these OzID analyses that provide unambiguous assignment of carbon-carbon double bond locations in the AMPP-derivatized fatty acids. The performance of this analysis pipeline is assessed in profiling the patterns of unsaturation in fatty acids within the complex biological secretion vernix caseosa. This analysis uncovers significant isomeric diversity within the fatty acid pool of this sample, including a number of hitherto unreported double bond-positional isomers that hint at the activity of potentially new metabolic pathways.

Data file types consist of SCIEX Analyst (.wiff) files, and Python (.py) files.

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

Ammonia-nitrogen at IL_EPA_WQX-RDF-3

This is an RDFied version of the dataset published in Choi, JS., Ha, M.K., Trinh, T.X. et al. Towards a generalized toxicity prediction model for oxide nanomaterials using integrated data from different sources. Sci Rep 8, 6110 (2018)

The original dataset publication DOI: https://doi.org/10.1038/s41598-018-24483-z

The Original publication authors: Jang-Sik Choi, My Kieu Ha, Tung Xuan Trinh, Tae Hyun Yoon & Hyung-Gi Byun

This RDF data contains canonical SMILES of chemical substances registered in Nikkaji by using CHEMINF, SIO and other ontologies. We recommend using this RDF together with the Core RDF.

Pendimethalin at IL_EPA-RDF-1