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X Demographics
Mendeley readers
Attention Score in Context
| Title |
The semantics of Chemical Markup Language (CML): dictionaries and conventions
|
|---|---|
| Published in |
Journal of Cheminformatics, October 2011
|
| DOI | 10.1186/1758-2946-3-43 |
| Pubmed ID | |
| Authors |
Peter Murray-Rust, Joe A Townsend, Sam E Adams, Weerapong Phadungsukanan, Jens Thomas |
| Abstract |
The semantic architecture of CML consists of conventions, dictionaries and units. The conventions conform to a top-level specification and each convention can constrain compliant documents through machine-processing (validation). Dictionaries conform to a dictionary specification which also imposes machine validation on the dictionaries. Each dictionary can also be used to validate data in a CML document, and provide human-readable descriptions. An additional set of conventions and dictionaries are used to support scientific units. All conventions, dictionaries and dictionary elements are identifiable and addressable through unique URIs. |
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 % |
|---|---|---|
| Germany | 1 | 33% |
| Unknown | 2 | 67% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 2 | 67% |
| Members of the public | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 87 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 2% |
| Switzerland | 1 | 1% |
| Germany | 1 | 1% |
| Iran, Islamic Republic of | 1 | 1% |
| United Kingdom | 1 | 1% |
| Unknown | 81 | 93% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 18 | 21% |
| Researcher | 13 | 15% |
| Other | 11 | 13% |
| Student > Master | 9 | 10% |
| Student > Bachelor | 6 | 7% |
| Other | 19 | 22% |
| Unknown | 11 | 13% |
| Readers by discipline | Count | As % |
|---|---|---|
| Chemistry | 34 | 39% |
| Engineering | 6 | 7% |
| Physics and Astronomy | 6 | 7% |
| Agricultural and Biological Sciences | 6 | 7% |
| Computer Science | 5 | 6% |
| Other | 14 | 16% |
| Unknown | 16 | 18% |
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 16 October 2019.
All research outputs
#3,632,237
of 25,079,131 outputs
Outputs from Journal of Cheminformatics
#336
of 942 outputs
Outputs of similar age
#18,915
of 141,523 outputs
Outputs of similar age from Journal of Cheminformatics
#15
of 21 outputs
Altmetric has tracked 25,079,131 research outputs across all sources so far. Compared to these this one has done well and is in the 85th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 942 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.2. This one has gotten more attention than average, scoring higher than 64% 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 141,523 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 86% of its contemporaries.
We're also able to compare this research output to 21 others from the same source and published within six weeks on either side of this one. This one is in the 33rd percentile – i.e., 33% of its contemporaries scored the same or lower than it.