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Mendeley readers
Attention Score in Context
| Title |
Semantic representation of monogenean haptoral Bar image annotation
|
|---|---|
| Published in |
BMC Bioinformatics, February 2013
|
| DOI | 10.1186/1471-2105-14-48 |
| Pubmed ID | |
| Authors |
Arpah Abu, Lim Lee Hong Susan, Amandeep Singh Sidhu, Sarinder Kaur Dhillon |
| Abstract |
Digitised monogenean images are usually stored in file system directories in an unstructured manner. In this paper we propose a semantic representation of these images in the form of a Monogenean Haptoral Bar Image (MHBI) ontology, which are annotated with taxonomic classification, diagnostic hard part and image properties. The data we used are basically of the monogenean species found in fish, thus we built a simple Fish ontology to demonstrate how the host (fish) ontology can be linked to the MHBI ontology. This will enable linking of information from the monogenean ontology to the host species found in the fish ontology without changing the underlying schema for either of the ontologies. |
X Demographics
The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Malaysia | 1 | 33% |
| Norway | 1 | 33% |
| Australia | 1 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 67% |
| Scientists | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 18 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| South Africa | 1 | 6% |
| Unknown | 17 | 94% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 5 | 28% |
| Student > Master | 3 | 17% |
| Professor > Associate Professor | 2 | 11% |
| Student > Doctoral Student | 2 | 11% |
| Librarian | 1 | 6% |
| Other | 2 | 11% |
| Unknown | 3 | 17% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 8 | 44% |
| Computer Science | 5 | 28% |
| Nursing and Health Professions | 1 | 6% |
| Social Sciences | 1 | 6% |
| Unknown | 3 | 17% |
Attention Score in Context
This research output has an Altmetric Attention Score of 5. 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 April 2019.
All research outputs
#6,013,847
of 22,696,971 outputs
Outputs from BMC Bioinformatics
#2,248
of 7,254 outputs
Outputs of similar age
#66,512
of 287,465 outputs
Outputs of similar age from BMC Bioinformatics
#45
of 141 outputs
Altmetric has tracked 22,696,971 research outputs across all sources so far. This one has received more attention than most of these and is in the 73rd percentile.
So far Altmetric has tracked 7,254 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 gotten more attention than average, scoring higher than 68% 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 287,465 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 76% of its contemporaries.
We're also able to compare this research output to 141 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 67% of its contemporaries.