You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output.
Click here to find out more.
X Demographics
Mendeley readers
Attention Score in Context
| Title |
Mining locus tags in PubMed Central to improve microbial gene annotation
|
|---|---|
| Published in |
BMC Bioinformatics, February 2014
|
| DOI | 10.1186/1471-2105-15-43 |
| Pubmed ID | |
| Authors |
Chris J Stubben, Jean F Challacombe |
| Abstract |
The scientific literature contains millions of microbial gene identifiers within the full text and tables, but these annotations rarely get incorporated into public sequence databases. We propose to utilize the Open Access (OA) subset of PubMed Central (PMC) as a gene annotation database and have developed an R package called pmcXML to automatically mine and extract locus tags from full text, tables and supplements. |
X Demographics
The data shown below were collected from the profiles of 14 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 4 | 29% |
| United States | 2 | 14% |
| Japan | 1 | 7% |
| France | 1 | 7% |
| Canada | 1 | 7% |
| Norway | 1 | 7% |
| Unknown | 4 | 29% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 7 | 50% |
| Scientists | 6 | 43% |
| Science communicators (journalists, bloggers, editors) | 1 | 7% |
Mendeley readers
The data shown below were compiled from readership statistics for 32 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 3 | 9% |
| Sweden | 1 | 3% |
| Unknown | 28 | 88% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 17 | 53% |
| Other | 3 | 9% |
| Student > Bachelor | 2 | 6% |
| Student > Ph. D. Student | 2 | 6% |
| Student > Doctoral Student | 1 | 3% |
| Other | 4 | 13% |
| Unknown | 3 | 9% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 16 | 50% |
| Computer Science | 6 | 19% |
| Biochemistry, Genetics and Molecular Biology | 2 | 6% |
| Immunology and Microbiology | 1 | 3% |
| Decision Sciences | 1 | 3% |
| Other | 1 | 3% |
| Unknown | 5 | 16% |
Attention Score in Context
This research output has an Altmetric Attention Score of 15. 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 26 February 2014.
All research outputs
#2,114,311
of 22,743,667 outputs
Outputs from BMC Bioinformatics
#567
of 7,267 outputs
Outputs of similar age
#26,446
of 307,217 outputs
Outputs of similar age from BMC Bioinformatics
#13
of 99 outputs
Altmetric has tracked 22,743,667 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 90th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,267 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 307,217 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 91% of its contemporaries.
We're also able to compare this research output to 99 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 86% of its contemporaries.