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X Demographics
Mendeley readers
Attention Score in Context
| Title |
The Efficiency of the Human CD8+ T Cell Response: How Should We Quantify It, What Determines It, and Does It Matter?
|
|---|---|
| Published in |
PLoS Computational Biology, February 2012
|
| DOI | 10.1371/journal.pcbi.1002381 |
| Pubmed ID | |
| Authors |
Marjet Elemans, Nafisa-Katrin Seich al Basatena, Becca Asquith |
| Abstract |
Multidisciplinary techniques, in particular the combination of theoretical and experimental immunology, can address questions about human immunity that cannot be answered by other means. From the turnover of virus-infected cells in vivo, to rates of thymic production and HLA class I epitope prediction, theoretical techniques provide a unique insight to supplement experimental approaches. Here we present our opinion, with examples, of some of the ways in which mathematics has contributed in our field of interest: the efficiency of the human CD8+ T cell response to persistent viruses. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Science communicators (journalists, bloggers, editors) | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 72 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 3 | 4% |
| Germany | 1 | 1% |
| Sweden | 1 | 1% |
| Netherlands | 1 | 1% |
| India | 1 | 1% |
| United States | 1 | 1% |
| Unknown | 64 | 89% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 21 | 29% |
| Student > Ph. D. Student | 17 | 24% |
| Student > Bachelor | 7 | 10% |
| Student > Master | 5 | 7% |
| Student > Doctoral Student | 3 | 4% |
| Other | 10 | 14% |
| Unknown | 9 | 13% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 21 | 29% |
| Medicine and Dentistry | 11 | 15% |
| Immunology and Microbiology | 10 | 14% |
| Biochemistry, Genetics and Molecular Biology | 9 | 13% |
| Computer Science | 3 | 4% |
| Other | 7 | 10% |
| Unknown | 11 | 15% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. 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 24 February 2012.
All research outputs
#19,975,266
of 25,411,814 outputs
Outputs from PLoS Computational Biology
#7,963
of 8,976 outputs
Outputs of similar age
#129,058
of 169,055 outputs
Outputs of similar age from PLoS Computational Biology
#87
of 113 outputs
Altmetric has tracked 25,411,814 research outputs across all sources so far. This one is in the 18th percentile – i.e., 18% of other outputs scored the same or lower than it.
So far Altmetric has tracked 8,976 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. This one is in the 8th percentile – i.e., 8% 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 169,055 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 21st percentile – i.e., 21% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 113 others from the same source and published within six weeks on either side of this one. This one is in the 18th percentile – i.e., 18% of its contemporaries scored the same or lower than it.