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Exploring semantic deep learning for building reliable and reusable one health knowledge from PubMed systematic reviews and veterinary clinical notes

Overview of attention for article published in Journal of Biomedical Semantics, November 2019
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Mentioned by

twitter
1 tweeter

Citations

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1 Dimensions

Readers on

mendeley
32 Mendeley
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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

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 32 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 32 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 7 22%
Student > Ph. D. Student 6 19%
Student > Doctoral Student 3 9%
Researcher 2 6%
Unspecified 1 3%
Other 5 16%
Unknown 8 25%
Readers by discipline Count As %
Medicine and Dentistry 12 38%
Computer Science 5 16%
Agricultural and Biological Sciences 2 6%
Business, Management and Accounting 1 3%
Unspecified 1 3%
Other 2 6%
Unknown 9 28%

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
#14,643,500
of 16,600,302 outputs
Outputs from Journal of Biomedical Semantics
#334
of 351 outputs
Outputs of similar age
#274,457
of 327,999 outputs
Outputs of similar age from Journal of Biomedical Semantics
#16
of 18 outputs
Altmetric has tracked 16,600,302 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 351 research outputs from this source. They receive a mean Attention Score of 4.5. 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 327,999 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 18 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.