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X Demographics
Mendeley readers
Attention Score in Context
| Chapter title |
Epidemiological Surveillance and Typing Methods to Track Antibiotic Resistant Strains Using High Throughput Sequencing
|
|---|---|
| Chapter number | 20 |
| Book title |
Antibiotics
|
| Published in |
Methods in molecular biology, January 2017
|
| DOI | 10.1007/978-1-4939-6634-9_20 |
| Pubmed ID | |
| Book ISBNs |
978-1-4939-6632-5, 978-1-4939-6634-9
|
| Authors |
Miguel Paulo Machado, Bruno Ribeiro-Gonçalves, Mickael Silva, Mário Ramirez, João André Carriço, Machado, Miguel Paulo, Ribeiro-Gonçalves, Bruno, Silva, Mickael, Ramirez, Mário, Carriço, João André |
| Editors |
Peter Sass |
| Abstract |
High-Throughput Sequencing (HTS) technologies transformed the microbial typing and molecular epidemiology field by providing the cost-effective ability for researchers to probe draft genomes, not only for epidemiological markers but also for antibiotic resistance and virulence determinants. In this chapter, we provide protocols for the analysis of HTS data for the determination of multilocus sequence typing (MLST) information and for determining presence or absence of antibiotic resistance genes. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 50% |
| Unknown | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Practitioners (doctors, other healthcare professionals) | 1 | 50% |
| Members of the public | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 9 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 9 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 2 | 22% |
| Other | 1 | 11% |
| Librarian | 1 | 11% |
| Professor | 1 | 11% |
| Student > Bachelor | 1 | 11% |
| Other | 0 | 0% |
| Unknown | 3 | 33% |
| Readers by discipline | Count | As % |
|---|---|---|
| Immunology and Microbiology | 3 | 33% |
| Economics, Econometrics and Finance | 1 | 11% |
| Social Sciences | 1 | 11% |
| Medicine and Dentistry | 1 | 11% |
| Unknown | 3 | 33% |
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 22 March 2017.
All research outputs
#20,356,726
of 25,837,817 outputs
Outputs from Methods in molecular biology
#8,511
of 14,362 outputs
Outputs of similar age
#308,886
of 425,363 outputs
Outputs of similar age from Methods in molecular biology
#659
of 1,088 outputs
Altmetric has tracked 25,837,817 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 14,362 research outputs from this source. They receive a mean Attention Score of 3.5. This one is in the 36th percentile – i.e., 36% 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 425,363 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 23rd percentile – i.e., 23% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 1,088 others from the same source and published within six weeks on either side of this one. This one is in the 35th percentile – i.e., 35% of its contemporaries scored the same or lower than it.