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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Improving integrative searching of systems chemical biology data using semantic annotation
|
|---|---|
| Published in |
Journal of Cheminformatics, March 2012
|
| DOI | 10.1186/1758-2946-4-6 |
| Pubmed ID | |
| Authors |
Bin Chen, Ying Ding, David J Wild |
| Abstract |
Systems chemical biology and chemogenomics are considered critical, integrative disciplines in modern biomedical research, but require data mining of large, integrated, heterogeneous datasets from chemistry and biology. We previously developed an RDF-based resource called Chem2Bio2RDF that enabled querying of such data using the SPARQL query language. Whilst this work has proved useful in its own right as one of the first major resources in these disciplines, its utility could be greatly improved by the application of an ontology for annotation of the nodes and edges in the RDF graph, enabling a much richer range of semantic queries to be issued. |
X Demographics
The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 67% |
| Germany | 1 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 67% |
| Scientists | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 65 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Germany | 2 | 3% |
| Brazil | 1 | 2% |
| Canada | 1 | 2% |
| Spain | 1 | 2% |
| United States | 1 | 2% |
| Unknown | 59 | 91% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 21 | 32% |
| Student > Ph. D. Student | 13 | 20% |
| Student > Master | 7 | 11% |
| Professor > Associate Professor | 5 | 8% |
| Student > Bachelor | 4 | 6% |
| Other | 9 | 14% |
| Unknown | 6 | 9% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 15 | 23% |
| Computer Science | 15 | 23% |
| Chemistry | 12 | 18% |
| Biochemistry, Genetics and Molecular Biology | 5 | 8% |
| Pharmacology, Toxicology and Pharmaceutical Science | 2 | 3% |
| Other | 8 | 12% |
| Unknown | 8 | 12% |
Attention Score in Context
This research output has an Altmetric Attention Score of 10. 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 25 June 2012.
All research outputs
#3,316,809
of 24,143,470 outputs
Outputs from Journal of Cheminformatics
#325
of 891 outputs
Outputs of similar age
#20,363
of 159,405 outputs
Outputs of similar age from Journal of Cheminformatics
#6
of 7 outputs
Altmetric has tracked 24,143,470 research outputs across all sources so far. Compared to these this one has done well and is in the 86th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 891 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.7. This one has gotten more attention than average, scoring higher than 63% 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 159,405 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 87% of its contemporaries.
We're also able to compare this research output to 7 others from the same source and published within six weeks on either side of this one.