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Mendeley readers
Attention Score in Context
| Title |
Integrating findings of traditional medicine with modern pharmaceutical research: the potential role of linked open data
|
|---|---|
| Published in |
Chinese Medicine, December 2010
|
| DOI | 10.1186/1749-8546-5-43 |
| Pubmed ID | |
| Authors |
Matthias Samwald, Michel Dumontier, Jun Zhao, Joanne S Luciano, Michael Scott Marshall, Kei Cheung |
| Abstract |
One of the biggest obstacles to progress in modern pharmaceutical research is the difficulty of integrating all available research findings into effective therapies for humans. Studies of traditionally used pharmacologically active plants and other substances in traditional medicines may be valuable sources of previously unknown compounds with therapeutic actions. However, the integration of findings from traditional medicines can be fraught with difficulties and misunderstandings. This article proposes an approach to use linked open data and Semantic Web technologies to address the heterogeneous data integration problem. The approach is based on our initial experiences with implementing an integrated web of data for a selected use-case, i.e., the identification of plant species used in Chinese medicine that indicate potential antidepressant activities. |
Mendeley readers
The data shown below were compiled from readership statistics for 57 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 3 | 5% |
| Malaysia | 1 | 2% |
| Ghana | 1 | 2% |
| Norway | 1 | 2% |
| United Kingdom | 1 | 2% |
| South Africa | 1 | 2% |
| Unknown | 49 | 86% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 13 | 23% |
| Student > Ph. D. Student | 10 | 18% |
| Researcher | 7 | 12% |
| Other | 6 | 11% |
| Student > Bachelor | 5 | 9% |
| Other | 12 | 21% |
| Unknown | 4 | 7% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 15 | 26% |
| Agricultural and Biological Sciences | 12 | 21% |
| Medicine and Dentistry | 10 | 18% |
| Social Sciences | 4 | 7% |
| Psychology | 2 | 4% |
| Other | 4 | 7% |
| Unknown | 10 | 18% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. 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 18 July 2011.
All research outputs
#22,759,452
of 25,374,647 outputs
Outputs from Chinese Medicine
#517
of 660 outputs
Outputs of similar age
#174,868
of 184,296 outputs
Outputs of similar age from Chinese Medicine
#7
of 11 outputs
Altmetric has tracked 25,374,647 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 660 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.0. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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 184,296 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 11 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.