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Mendeley readers
Attention Score in Context
| Title |
KID - an algorithm for fast and efficient text mining used to automatically generate a database containing kinetic information of enzymes
|
|---|---|
| Published in |
BMC Bioinformatics, July 2010
|
| DOI | 10.1186/1471-2105-11-375 |
| Pubmed ID | |
| Authors |
Stephanie Heinen, Bernhard Thielen, Dietmar Schomburg |
| Abstract |
The amount of available biological information is rapidly increasing and the focus of biological research has moved from single components to networks and even larger projects aiming at the analysis, modelling and simulation of biological networks as well as large scale comparison of cellular properties. It is therefore essential that biological knowledge is easily accessible. However, most information is contained in the written literature in an unstructured way, so that methods for the systematic extraction of knowledge directly from the primary literature have to be deployed. |
Mendeley readers
The data shown below were compiled from readership statistics for 53 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 4 | 8% |
| Germany | 2 | 4% |
| Mexico | 2 | 4% |
| Denmark | 1 | 2% |
| Spain | 1 | 2% |
| Unknown | 43 | 81% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 13 | 25% |
| Researcher | 10 | 19% |
| Student > Ph. D. Student | 7 | 13% |
| Student > Postgraduate | 4 | 8% |
| Professor | 3 | 6% |
| Other | 12 | 23% |
| Unknown | 4 | 8% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 19 | 36% |
| Computer Science | 17 | 32% |
| Medicine and Dentistry | 4 | 8% |
| Business, Management and Accounting | 1 | 2% |
| Nursing and Health Professions | 1 | 2% |
| Other | 4 | 8% |
| Unknown | 7 | 13% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. 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 12 November 2010.
All research outputs
#12,850,437
of 22,656,971 outputs
Outputs from BMC Bioinformatics
#3,775
of 7,236 outputs
Outputs of similar age
#72,369
of 94,657 outputs
Outputs of similar age from BMC Bioinformatics
#38
of 59 outputs
Altmetric has tracked 22,656,971 research outputs across all sources so far. This one is in the 42nd percentile – i.e., 42% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,236 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 45th percentile – i.e., 45% 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 94,657 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 23rd percentile – i.e., 23% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 59 others from the same source and published within six weeks on either side of this one. This one is in the 30th percentile – i.e., 30% of its contemporaries scored the same or lower than it.