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X Demographics
Mendeley readers
Attention Score in Context
| Title |
BOSS: context-enhanced search for biomedical objects
|
|---|---|
| Published in |
BMC Medical Informatics and Decision Making, April 2012
|
| DOI | 10.1186/1472-6947-12-s1-s7 |
| Pubmed ID | |
| Authors |
Jaehoon Choi, Donghyeon Kim, Seongsoon Kim, Sunwon Lee, Kyubum Lee, Jaewoo Kang |
| Abstract |
There exist many academic search solutions and most of them can be put on either ends of spectrum: general-purpose search and domain-specific "deep" search systems. The general-purpose search systems, such as PubMed, offer flexible query interface, but churn out a list of matching documents that users have to go through the results in order to find the answers to their queries. On the other hand, the "deep" search systems, such as PPI Finder and iHOP, return the precompiled results in a structured way. Their results, however, are often found only within some predefined contexts. In order to alleviate these problems, we introduce a new search engine, BOSS, Biomedical Object Search System. |
X Demographics
The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| India | 2 | 50% |
| United Kingdom | 1 | 25% |
| Unknown | 1 | 25% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 50% |
| Practitioners (doctors, other healthcare professionals) | 2 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 10 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 10 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Bachelor | 2 | 20% |
| Student > Master | 2 | 20% |
| Student > Ph. D. Student | 1 | 10% |
| Professor | 1 | 10% |
| Researcher | 1 | 10% |
| Other | 1 | 10% |
| Unknown | 2 | 20% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 3 | 30% |
| Computer Science | 2 | 20% |
| Agricultural and Biological Sciences | 1 | 10% |
| Medicine and Dentistry | 1 | 10% |
| Biochemistry, Genetics and Molecular Biology | 1 | 10% |
| Other | 0 | 0% |
| Unknown | 2 | 20% |
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 22 May 2012.
All research outputs
#13,013,978
of 22,665,794 outputs
Outputs from BMC Medical Informatics and Decision Making
#914
of 1,978 outputs
Outputs of similar age
#88,618
of 162,571 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#21
of 40 outputs
Altmetric has tracked 22,665,794 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 1,978 research outputs from this source. They receive a mean Attention Score of 4.9. This one has gotten more attention than average, scoring higher than 52% 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 162,571 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 45th percentile – i.e., 45% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 40 others from the same source and published within six weeks on either side of this one. This one is in the 45th percentile – i.e., 45% of its contemporaries scored the same or lower than it.