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Mendeley readers
Attention Score in Context
| Title |
Overview of the BioCreative III Workshop
|
|---|---|
| Published in |
BMC Bioinformatics, October 2011
|
| DOI | 10.1186/1471-2105-12-s8-s1 |
| Pubmed ID | |
| Authors |
Cecilia N Arighi, Zhiyong Lu, Martin Krallinger, Kevin B Cohen, W John Wilbur, Alfonso Valencia, Lynette Hirschman, Cathy H Wu |
| Abstract |
The overall goal of the BioCreative Workshops is to promote the development of text mining and text processing tools which are useful to the communities of researchers and database curators in the biological sciences. To this end BioCreative I was held in 2004, BioCreative II in 2007, and BioCreative II.5 in 2009. Each of these workshops involved humanly annotated test data for several basic tasks in text mining applied to the biomedical literature. Participants in the workshops were invited to compete in the tasks by constructing software systems to perform the tasks automatically and were given scores based on their performance. The results of these workshops have benefited the community in several ways. They have 1) provided evidence for the most effective methods currently available to solve specific problems; 2) revealed the current state of the art for performance on those problems; 3) and provided gold standard data and results on that data by which future advances can be gauged. This special issue contains overview papers for the three tasks of BioCreative III. |
Mendeley readers
The data shown below were compiled from readership statistics for 81 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 3 | 4% |
| Uruguay | 1 | 1% |
| Colombia | 1 | 1% |
| Spain | 1 | 1% |
| United Kingdom | 1 | 1% |
| Unknown | 74 | 91% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 16 | 20% |
| Researcher | 12 | 15% |
| Student > Master | 11 | 14% |
| Professor > Associate Professor | 7 | 9% |
| Student > Bachelor | 6 | 7% |
| Other | 20 | 25% |
| Unknown | 9 | 11% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 38 | 47% |
| Agricultural and Biological Sciences | 16 | 20% |
| Engineering | 8 | 10% |
| Biochemistry, Genetics and Molecular Biology | 2 | 2% |
| Medicine and Dentistry | 2 | 2% |
| Other | 1 | 1% |
| Unknown | 14 | 17% |
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 20 September 2011.
All research outputs
#12,851,465
of 22,659,164 outputs
Outputs from BMC Bioinformatics
#3,776
of 7,240 outputs
Outputs of similar age
#80,635
of 132,710 outputs
Outputs of similar age from BMC Bioinformatics
#47
of 83 outputs
Altmetric has tracked 22,659,164 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 7,240 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 132,710 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 38th percentile – i.e., 38% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 83 others from the same source and published within six weeks on either side of this one. This one is in the 40th percentile – i.e., 40% of its contemporaries scored the same or lower than it.