| Title |
Advancing translational research with the Semantic Web
|
|---|---|
| Published in |
BMC Bioinformatics, May 2007
|
| DOI | 10.1186/1471-2105-8-s3-s2 |
| Pubmed ID | |
| Authors |
Alan Ruttenberg, Tim Clark, William Bug, Matthias Samwald, Olivier Bodenreider, Helen Chen, Donald Doherty, Kerstin Forsberg, Yong Gao, Vipul Kashyap, June Kinoshita, Joanne Luciano, M Scott Marshall, Chimezie Ogbuji, Jonathan Rees, Susie Stephens, Gwendolyn T Wong, Elizabeth Wu, Davide Zaccagnini, Tonya Hongsermeier, Eric Neumann, Ivan Herman, Kei-Hoi Cheung |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Science communicators (journalists, bloggers, editors) | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 415 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 46 | 11% |
| United Kingdom | 10 | 2% |
| Netherlands | 6 | 1% |
| Spain | 5 | 1% |
| Germany | 5 | 1% |
| Mexico | 4 | <1% |
| Brazil | 4 | <1% |
| Sweden | 3 | <1% |
| Japan | 3 | <1% |
| Other | 24 | 6% |
| Unknown | 305 | 73% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 103 | 25% |
| Student > Ph. D. Student | 79 | 19% |
| Student > Master | 40 | 10% |
| Other | 36 | 9% |
| Professor > Associate Professor | 33 | 8% |
| Other | 90 | 22% |
| Unknown | 34 | 8% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 126 | 30% |
| Agricultural and Biological Sciences | 90 | 22% |
| Medicine and Dentistry | 57 | 14% |
| Social Sciences | 22 | 5% |
| Biochemistry, Genetics and Molecular Biology | 11 | 3% |
| Other | 61 | 15% |
| Unknown | 48 | 12% |
Attention Score in Context
This research output has an Altmetric Attention Score of 22. 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 31 March 2017.
All research outputs
#1,512,299
of 23,289,753 outputs
Outputs from BMC Bioinformatics
#283
of 7,375 outputs
Outputs of similar age
#2,846
of 72,745 outputs
Outputs of similar age from BMC Bioinformatics
#3
of 48 outputs
Altmetric has tracked 23,289,753 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,375 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has done particularly well, scoring higher than 96% 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 72,745 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 96% of its contemporaries.
We're also able to compare this research output to 48 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 93% of its contemporaries.