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X Demographics
Mendeley readers
Attention Score in Context
| Chapter title |
Yersinia pestis in the Age of Big Data.
|
|---|---|
| Chapter number | 9 |
| Book title |
Yersinia pestis: Retrospective and Perspective
|
| Published in |
Advances in experimental medicine and biology, January 2016
|
| DOI | 10.1007/978-94-024-0890-4_9 |
| Pubmed ID | |
| Book ISBNs |
978-9-40-240888-1, 978-9-40-240890-4
|
| Authors |
Ruifu Yang, Vladimir L. Motin |
| Editors |
Ruifu Yang, Andrey Anisimov |
| Abstract |
As omics-driven technologies developed rapidly, genomics, transcriptomics, proteomics, metabolomics and other omics-based data have been accumulated in unprecedented speed. Omics-driven big data in biology have changed our way of research. "Big science" has promoted our understanding of biology in a holistic overview that is impossibly achieved by traditional hypothesis-driven research. In this chapter, we gave an overview of omics-driven research on Y. pestis, provided a way of thinking on Yersinia pestis research in the age of big data, and made some suggestions to integrate omics-based data for systems understanding of Y. pestis. |
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 % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 14 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 14 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 4 | 29% |
| Student > Ph. D. Student | 2 | 14% |
| Lecturer > Senior Lecturer | 1 | 7% |
| Lecturer | 1 | 7% |
| Professor | 1 | 7% |
| Other | 3 | 21% |
| Unknown | 2 | 14% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 3 | 21% |
| Computer Science | 2 | 14% |
| Unspecified | 1 | 7% |
| Chemical Engineering | 1 | 7% |
| Agricultural and Biological Sciences | 1 | 7% |
| Other | 3 | 21% |
| Unknown | 3 | 21% |
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 05 October 2017.
All research outputs
#18,475,157
of 22,893,031 outputs
Outputs from Advances in experimental medicine and biology
#3,314
of 4,952 outputs
Outputs of similar age
#284,601
of 393,734 outputs
Outputs of similar age from Advances in experimental medicine and biology
#284
of 443 outputs
Altmetric has tracked 22,893,031 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,952 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.1. This one is in the 19th percentile – i.e., 19% 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 393,734 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 15th percentile – i.e., 15% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 443 others from the same source and published within six weeks on either side of this one. This one is in the 24th percentile – i.e., 24% of its contemporaries scored the same or lower than it.