| Title |
PubChemRDF: towards the semantic annotation of PubChem compound and substance databases
|
|---|---|
| Published in |
Journal of Cheminformatics, July 2015
|
| DOI | 10.1186/s13321-015-0084-4 |
| Pubmed ID | |
| Authors |
Gang Fu, Colin Batchelor, Michel Dumontier, Janna Hastings, Egon Willighagen, Evan Bolton |
| Abstract |
PubChem is an open repository for chemical structures, biological activities and biomedical annotations. Semantic Web technologies are emerging as an increasingly important approach to distribute and integrate scientific data. Exposing PubChem data to Semantic Web services may help enable automated data integration and management, as well as facilitate interoperable web applications. This work, one of a series covering the PubChemRDF project, describes an approach to translate PubChem Substance and Compound information into Resource Description Framework (RDF) format. Basic examples are provided to demonstrate its use. The aim of this effort is to provide two new primary benefits to researchers in a cost-effective manner. Firstly, we aim to remove the inherent limitations of using the web-based resource PubChem by allowing a researcher to use readily available semantic technologies (namely, RDF triple stores and their corresponding SPARQL query engines) to query and analyze PubChem data on local computing resources. Secondly, this work intends to help improve data sharing, analysis, and integration of PubChem data to resources external to NCBI and across scientific domains, by means of the association of PubChem data to existing ontological frameworks, including CHEMical INFormation ontology, Semanticscience Integrated Ontology, and others. With the goal of semantically describing information available in the PubChem archive, pre-existing ontological frameworks were used, rather than creating new ones. Semantic relationships between compounds and substances, chemical descriptors associated with compounds and substances, interrelationships between chemicals, as well as provenance and attribute metadata of substances are described. Graphical abstract:Schematic representation of the semantic links for PubChem compounds and substances. |
X Demographics
The data shown below were collected from the profiles of 18 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Netherlands | 4 | 22% |
| United States | 3 | 17% |
| Ecuador | 1 | 6% |
| Germany | 1 | 6% |
| Switzerland | 1 | 6% |
| Spain | 1 | 6% |
| Japan | 1 | 6% |
| Sweden | 1 | 6% |
| Unknown | 5 | 28% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 10 | 56% |
| Scientists | 8 | 44% |
Mendeley readers
The data shown below were compiled from readership statistics for 111 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Brazil | 2 | 2% |
| Japan | 2 | 2% |
| Germany | 1 | <1% |
| Canada | 1 | <1% |
| Netherlands | 1 | <1% |
| Spain | 1 | <1% |
| Singapore | 1 | <1% |
| Unknown | 102 | 92% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 30 | 27% |
| Student > Ph. D. Student | 22 | 20% |
| Student > Bachelor | 15 | 14% |
| Student > Master | 10 | 9% |
| Professor > Associate Professor | 7 | 6% |
| Other | 10 | 9% |
| Unknown | 17 | 15% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 25 | 23% |
| Agricultural and Biological Sciences | 18 | 16% |
| Chemistry | 16 | 14% |
| Biochemistry, Genetics and Molecular Biology | 8 | 7% |
| Pharmacology, Toxicology and Pharmaceutical Science | 8 | 7% |
| Other | 18 | 16% |
| Unknown | 18 | 16% |
Attention Score in Context
This research output has an Altmetric Attention Score of 25. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 29 January 2023.
All research outputs
#1,440,481
of 24,578,676 outputs
Outputs from Journal of Cheminformatics
#94
of 913 outputs
Outputs of similar age
#18,114
of 267,697 outputs
Outputs of similar age from Journal of Cheminformatics
#4
of 22 outputs
Altmetric has tracked 24,578,676 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 913 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.4. This one has done well, scoring higher than 89% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 267,697 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 93% of its contemporaries.
We're also able to compare this research output to 22 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 86% of its contemporaries.