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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Exploring semantic deep learning for building reliable and reusable one health knowledge from PubMed systematic reviews and veterinary clinical notes
|
|---|---|
| Published in |
Journal of Biomedical Semantics, November 2019
|
| DOI | 10.1186/s13326-019-0212-6 |
| Pubmed ID | |
| Authors |
Mercedes Arguello-Casteleiro, Robert Stevens, Julio Des-Diz, Chris Wroe, Maria Jesus Fernandez-Prieto, Nava Maroto, Diego Maseda-Fernandez, George Demetriou, Simon Peters, Peter-John M. Noble, Phil H. Jones, Jo Dukes-McEwan, Alan D. Radford, John Keane, Goran Nenadic |
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 % |
|---|---|---|
| Spain | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 50 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 50 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 8 | 16% |
| Student > Master | 7 | 14% |
| Student > Doctoral Student | 5 | 10% |
| Lecturer | 3 | 6% |
| Researcher | 3 | 6% |
| Other | 7 | 14% |
| Unknown | 17 | 34% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 12 | 24% |
| Computer Science | 11 | 22% |
| Business, Management and Accounting | 2 | 4% |
| Agricultural and Biological Sciences | 2 | 4% |
| Pharmacology, Toxicology and Pharmaceutical Science | 1 | 2% |
| Other | 4 | 8% |
| Unknown | 18 | 36% |
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 13 January 2020.
All research outputs
#20,588,763
of 23,173,635 outputs
Outputs from Journal of Biomedical Semantics
#337
of 366 outputs
Outputs of similar age
#305,637
of 359,983 outputs
Outputs of similar age from Journal of Biomedical Semantics
#7
of 8 outputs
Altmetric has tracked 23,173,635 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 366 research outputs from this source. They receive a mean Attention Score of 4.6. This one is in the 1st percentile – i.e., 1% 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 359,983 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
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.