| Title |
EnvMine: A text-mining system for the automatic extraction of contextual information
|
|---|---|
| Published in |
BMC Bioinformatics, June 2010
|
| DOI | 10.1186/1471-2105-11-294 |
| Pubmed ID | |
| Authors |
Javier Tamames, Victor de Lorenzo |
| Abstract |
For ecological studies, it is crucial to count on adequate descriptions of the environments and samples being studied. Such a description must be done in terms of their physicochemical characteristics, allowing a direct comparison between different environments that would be difficult to do otherwise. Also the characterization must include the precise geographical location, to make possible the study of geographical distributions and biogeographical patterns. Currently, there is no schema for annotating these environmental features, and these data have to be extracted from textual sources (published articles). So far, this had to be performed by manual inspection of the corresponding documents. To facilitate this task, we have developed EnvMine, a set of text-mining tools devoted to retrieve contextual information (physicochemical variables and geographical locations) from textual sources of any kind. |
Mendeley readers
The data shown below were compiled from readership statistics for 88 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 4 | 5% |
| United Kingdom | 4 | 5% |
| Germany | 3 | 3% |
| Brazil | 3 | 3% |
| Canada | 2 | 2% |
| Mexico | 2 | 2% |
| Spain | 2 | 2% |
| Russia | 1 | 1% |
| Japan | 1 | 1% |
| Other | 1 | 1% |
| Unknown | 65 | 74% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 19 | 22% |
| Student > Ph. D. Student | 18 | 20% |
| Student > Master | 10 | 11% |
| Student > Bachelor | 7 | 8% |
| Lecturer | 5 | 6% |
| Other | 18 | 20% |
| Unknown | 11 | 13% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 34 | 39% |
| Computer Science | 21 | 24% |
| Environmental Science | 5 | 6% |
| Medicine and Dentistry | 5 | 6% |
| Biochemistry, Genetics and Molecular Biology | 4 | 5% |
| Other | 9 | 10% |
| Unknown | 10 | 11% |
Attention Score in Context
This research output has an Altmetric Attention Score of 11. 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 27 March 2018.
All research outputs
#2,880,487
of 22,660,862 outputs
Outputs from BMC Bioinformatics
#1,009
of 7,241 outputs
Outputs of similar age
#11,201
of 95,879 outputs
Outputs of similar age from BMC Bioinformatics
#6
of 71 outputs
Altmetric has tracked 22,660,862 research outputs across all sources so far. Compared to these this one has done well and is in the 87th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,241 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 well, scoring higher than 86% 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 95,879 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 88% of its contemporaries.
We're also able to compare this research output to 71 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 91% of its contemporaries.