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Hyppo-X: A Scalable Exploratory Framework for Analyzing Complex Phenomics Data

Overview of attention for article published in IEEE/ACM Transactions on Computational Biology and Bioinformatics, August 2021
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1 X user

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24 Mendeley
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Article details
Title
Hyppo-X: A Scalable Exploratory Framework for Analyzing Complex Phenomics Data
Published in
IEEE/ACM Transactions on Computational Biology and Bioinformatics, August 2021
DOI 10.1109/tcbb.2019.2947500
Pubmed ID
Authors

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Timeline Attention over time Attention Score history
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Activity
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X Demographics

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.
Mendeley demographics

Mendeley demographics

The data shown below were compiled from readership statistics for 24 Mendeley readers of this research output. Click here to see the associated Mendeley record.
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Geographical breakdown

Geographical breakdown
Country Count As %
Unknown 24 100%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Student > Ph. D. Student 5 21%
Researcher 3 13%
Lecturer 1 4%
Student > Master 1 4%
Professor > Associate Professor 1 4%
Other 0 0%
Unknown 13 54%
Readers by discipline
Readers by discipline Count As %
Agricultural and Biological Sciences 2 8%
Computer Science 2 8%
Arts and Humanities 1 4%
Mathematics 1 4%
Business, Management and Accounting 1 4%
Other 5 21%
Unknown 12 50%
Attention Score in Context

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 14 February 2022.
All research outputs
#22,771,990
of 25,387,668 outputs
Outputs from IEEE/ACM Transactions on Computational Biology and Bioinformatics
#822
of 1,081 outputs
Outputs of similar age
#376,854
of 438,069 outputs
Outputs of similar age from IEEE/ACM Transactions on Computational Biology and Bioinformatics
#16
of 21 outputs
Altmetric has tracked 25,387,668 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 1,081 research outputs from this source. They receive a mean Attention Score of 2.4. 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 438,069 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 21 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.