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Attention Score in Context
Chapter title |
Utilization of Multidimensional Data in the Analysis of Ultra-High-Throughput High Content Phenotypic Screens
|
---|---|
Chapter number | 16 |
Book title |
High Content Screening
|
Published in |
Methods in molecular biology, January 2018
|
DOI | 10.1007/978-1-4939-7357-6_16 |
Pubmed ID | |
Book ISBNs |
978-1-4939-7355-2, 978-1-4939-7357-6
|
Authors |
Judith Wardwell-Swanson, Yanhua Hu |
Abstract |
High Content Screening (HCS) platforms can generate large amounts of multidimensional data. To take full advantage of all the rich contextual information provided by these screens, a combination of traditional as well as nontraditional hit identification and prioritization strategies is required. Here, we describe the workflow and analytics of multidimensional high content data to differentiate, group, and prioritize hits. |
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 % |
---|---|---|
United States | 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 11 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 11 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 3 | 27% |
Student > Ph. D. Student | 1 | 9% |
Unspecified | 1 | 9% |
Lecturer | 1 | 9% |
Student > Master | 1 | 9% |
Other | 0 | 0% |
Unknown | 4 | 36% |
Readers by discipline | Count | As % |
---|---|---|
Biochemistry, Genetics and Molecular Biology | 2 | 18% |
Pharmacology, Toxicology and Pharmaceutical Science | 1 | 9% |
Unspecified | 1 | 9% |
Business, Management and Accounting | 1 | 9% |
Computer Science | 1 | 9% |
Other | 0 | 0% |
Unknown | 5 | 45% |
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 09 November 2017.
All research outputs
#18,560,904
of 22,988,380 outputs
Outputs from Methods in molecular biology
#7,948
of 13,149 outputs
Outputs of similar age
#330,352
of 442,161 outputs
Outputs of similar age from Methods in molecular biology
#950
of 1,497 outputs
Altmetric has tracked 22,988,380 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 13,149 research outputs from this source. They receive a mean Attention Score of 3.4. This one is in the 24th percentile – i.e., 24% 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 442,161 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 14th percentile – i.e., 14% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 1,497 others from the same source and published within six weeks on either side of this one. This one is in the 20th percentile – i.e., 20% of its contemporaries scored the same or lower than it.