| Title |
NetiNeti: discovery of scientific names from text using machine learning methods
|
|---|---|
| Published in |
BMC Bioinformatics, August 2012
|
| DOI | 10.1186/1471-2105-13-211 |
| Pubmed ID | |
| Authors |
Lakshmi Manohar Akella, Catherine N Norton, Holly Miller |
| Abstract |
A scientific name for an organism can be associated with almost all biological data. Name identification is an important step in many text mining tasks aiming to extract useful information from biological, biomedical and biodiversity text sources. A scientific name acts as an important metadata element to link biological information. |
X Demographics
The data shown below were collected from the profiles of 5 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 | 20% |
| United States | 1 | 20% |
| Japan | 1 | 20% |
| Canada | 1 | 20% |
| Unknown | 1 | 20% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 4 | 80% |
| Scientists | 1 | 20% |
Mendeley readers
The data shown below were compiled from readership statistics for 86 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 4 | 5% |
| Brazil | 2 | 2% |
| Germany | 2 | 2% |
| Japan | 2 | 2% |
| Norway | 1 | 1% |
| Spain | 1 | 1% |
| United Kingdom | 1 | 1% |
| Venezuela, Bolivarian Republic of | 1 | 1% |
| Mexico | 1 | 1% |
| Other | 0 | 0% |
| Unknown | 71 | 83% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 33 | 38% |
| Student > Master | 14 | 16% |
| Student > Bachelor | 5 | 6% |
| Professor > Associate Professor | 5 | 6% |
| Other | 5 | 6% |
| Other | 13 | 15% |
| Unknown | 11 | 13% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 32 | 37% |
| Computer Science | 23 | 27% |
| Medicine and Dentistry | 6 | 7% |
| Linguistics | 2 | 2% |
| Business, Management and Accounting | 2 | 2% |
| Other | 11 | 13% |
| Unknown | 10 | 12% |
Attention Score in Context
This research output has an Altmetric Attention Score of 16. 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 19 January 2016.
All research outputs
#2,062,132
of 23,577,654 outputs
Outputs from BMC Bioinformatics
#519
of 7,400 outputs
Outputs of similar age
#13,363
of 170,326 outputs
Outputs of similar age from BMC Bioinformatics
#8
of 102 outputs
Altmetric has tracked 23,577,654 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,400 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 92% 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 170,326 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 92% of its contemporaries.
We're also able to compare this research output to 102 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 92% of its contemporaries.