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X Demographics
Mendeley readers
Attention Score in Context
| Title |
No-boundary thinking in bioinformatics research
|
|---|---|
| Published in |
BioData Mining, November 2013
|
| DOI | 10.1186/1756-0381-6-19 |
| Pubmed ID | |
| Authors |
Xiuzhen Huang, Barry Bruce, Alison Buchan, Clare Bates Congdon, Carole L Cramer, Steven F Jennings, Hongmei Jiang, Zenglu Li, Gail McClure, Rick McMullen, Jason H Moore, Bindu Nanduri, Joan Peckham, Andy Perkins, Shawn W Polson, Bhanu Rekepalli, Saeed Salem, Jennifer Specker, Donald Wunsch, Donghai Xiong, Shuzhong Zhang, Zhongming Zhao |
| Abstract |
Currently there are definitions from many agencies and research societies defining "bioinformatics" as deriving knowledge from computational analysis of large volumes of biological and biomedical data. Should this be the bioinformatics research focus? We will discuss this issue in this review article. We would like to promote the idea of supporting human-infrastructure (HI) with no-boundary thinking (NT) in bioinformatics (HINT). |
X Demographics
The data shown below were collected from the profiles of 19 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 States | 6 | 32% |
| France | 2 | 11% |
| Brazil | 1 | 5% |
| Canada | 1 | 5% |
| Sweden | 1 | 5% |
| Bosnia and Herzegovina | 1 | 5% |
| Comoros | 1 | 5% |
| Unknown | 6 | 32% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 10 | 53% |
| Scientists | 9 | 47% |
Mendeley readers
The data shown below were compiled from readership statistics for 63 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 5 | 8% |
| United Kingdom | 1 | 2% |
| Indonesia | 1 | 2% |
| Unknown | 56 | 89% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 13 | 21% |
| Researcher | 13 | 21% |
| Other | 6 | 10% |
| Professor > Associate Professor | 6 | 10% |
| Professor | 5 | 8% |
| Other | 17 | 27% |
| Unknown | 3 | 5% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 15 | 24% |
| Biochemistry, Genetics and Molecular Biology | 9 | 14% |
| Computer Science | 9 | 14% |
| Medicine and Dentistry | 7 | 11% |
| Engineering | 4 | 6% |
| Other | 13 | 21% |
| Unknown | 6 | 10% |
Attention Score in Context
This research output has an Altmetric Attention Score of 12. 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 2021.
All research outputs
#3,146,687
of 25,619,480 outputs
Outputs from BioData Mining
#60
of 325 outputs
Outputs of similar age
#28,423
of 229,293 outputs
Outputs of similar age from BioData Mining
#4
of 8 outputs
Altmetric has tracked 25,619,480 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 325 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.4. This one has done well, scoring higher than 81% 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 229,293 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 87% of its contemporaries.
We're also able to compare this research output to 8 others from the same source and published within six weeks on either side of this one. This one has scored higher than 4 of them.