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Mendeley readers
Attention Score in Context
| Title |
An improved ontological representation of dendritic cells as a paradigm for all cell types
|
|---|---|
| Published in |
BMC Bioinformatics, February 2009
|
| DOI | 10.1186/1471-2105-10-70 |
| Pubmed ID | |
| Authors |
Anna Maria Masci, Cecilia N Arighi, Alexander D Diehl, Anne E Lieberman, Chris Mungall, Richard H Scheuermann, Barry Smith, Lindsay G Cowell |
| Abstract |
Recent increases in the volume and diversity of life science data and information and an increasing emphasis on data sharing and interoperability have resulted in the creation of a large number of biological ontologies, including the Cell Ontology (CL), designed to provide a standardized representation of cell types for data annotation. Ontologies have been shown to have significant benefits for computational analyses of large data sets and for automated reasoning applications, leading to organized attempts to improve the structure and formal rigor of ontologies to better support computation. Currently, the CL employs multiple is_a relations, defining cell types in terms of histological, functional, and lineage properties, and the majority of definitions are written with sufficient generality to hold across multiple species. This approach limits the CL's utility for computation and for cross-species data integration. |
Mendeley readers
The data shown below were compiled from readership statistics for 46 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 3 | 7% |
| Spain | 1 | 2% |
| United Kingdom | 1 | 2% |
| Unknown | 41 | 89% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 18 | 39% |
| Student > Ph. D. Student | 7 | 15% |
| Other | 4 | 9% |
| Student > Doctoral Student | 2 | 4% |
| Professor | 2 | 4% |
| Other | 7 | 15% |
| Unknown | 6 | 13% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 17 | 37% |
| Medicine and Dentistry | 7 | 15% |
| Computer Science | 4 | 9% |
| Immunology and Microbiology | 4 | 9% |
| Biochemistry, Genetics and Molecular Biology | 3 | 7% |
| Other | 4 | 9% |
| Unknown | 7 | 15% |
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 10 September 2023.
All research outputs
#7,453,126
of 22,785,242 outputs
Outputs from BMC Bioinformatics
#3,023
of 7,279 outputs
Outputs of similar age
#33,000
of 93,957 outputs
Outputs of similar age from BMC Bioinformatics
#15
of 52 outputs
Altmetric has tracked 22,785,242 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,279 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 50% 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 93,957 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 19th percentile – i.e., 19% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 52 others from the same source and published within six weeks on either side of this one. This one is in the 34th percentile – i.e., 34% of its contemporaries scored the same or lower than it.