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Seed: a user-friendly tool for exploring and visualizing microbial community data

Overview of attention for article published in Bioinformatics, October 2014
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (93rd percentile)
  • High Attention Score compared to outputs of the same age and source (93rd percentile)

Mentioned by

blogs
1 blog
twitter
34 X users

Readers on

mendeley
80 Mendeley
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2 CiteULike
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Article details
Title
Seed: a user-friendly tool for exploring and visualizing microbial community data
Published in
Bioinformatics, October 2014
DOI 10.1093/bioinformatics/btu693
Pubmed ID
Authors

Daniel Beck, Christopher Dennis, James A Foster

Abstract

In this article we present Simple Exploration of Ecological Data (Seed), a data exploration tool for microbial communities. Seed is written in R using the Shiny library. This provides access to powerful R-based functions and libraries through a simple user interface. Seed allows users to explore ecological datasets using principal coordinate analyses, scatter plots, bar plots, hierarchal clustering and heatmaps. Availability and implementation: Seed is open source and available at https://github.com/danlbek/Seed.

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X Demographics

X Demographics

The data shown below were collected from the profiles of 34 X users 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 80 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 %
Germany 3 4%
United States 2 3%
Brazil 2 3%
Sweden 1 1%
Norway 1 1%
United Kingdom 1 1%
France 1 1%
China 1 1%
Canada 1 1%
Other 1 1%
Unknown 66 83%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Researcher 30 38%
Student > Ph. D. Student 16 20%
Student > Master 8 10%
Student > Bachelor 7 9%
Professor > Associate Professor 6 8%
Other 9 11%
Unknown 4 5%
Readers by discipline
Readers by discipline Count As %
Agricultural and Biological Sciences 39 49%
Computer Science 13 16%
Biochemistry, Genetics and Molecular Biology 8 10%
Environmental Science 6 8%
Engineering 3 4%
Other 5 6%
Unknown 6 8%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 25. 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 20 March 2015.
All research outputs
#1,888,320
of 33,079,649 outputs
Outputs from Bioinformatics
#743
of 14,225 outputs
Outputs of similar age
#17,850
of 296,457 outputs
Outputs of similar age from Bioinformatics
#13
of 201 outputs
Altmetric has tracked 33,079,649 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 14,225 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.1. This one has done particularly well, scoring higher than 94% of its peers.
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 296,457 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 93% of its contemporaries.
We're also able to compare this research output to 201 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 93% of its contemporaries.