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MoleculaRnetworks: An integrated graph theoretic and data mining tool to explore solvent organization in molecular simulation

Overview of attention for article published in Journal of Computational Chemistry, January 2012
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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 (92nd percentile)
  • High Attention Score compared to outputs of the same age and source (92nd percentile)

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1 blog
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61 Mendeley
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3 CiteULike
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Article details
Title
MoleculaRnetworks: An integrated graph theoretic and data mining tool to explore solvent organization in molecular simulation
Published in
Journal of Computational Chemistry, January 2012
DOI 10.1002/jcc.22917
Pubmed ID
Authors
Abstract

This work discusses scripts for processing molecular simulations data written using the software package R: A Language and Environment for Statistical Computing. These scripts, named moleculaRnetworks, are intended for the geometric and solvent network analysis of aqueous solutes and can be extended to other H-bonded solvents. New algorithms, several of which are based on graph theory, that interrogate the solvent environment about a solute are presented and described. This includes a novel method for identifying the geometric shape adopted by the solvent in the immediate vicinity of the solute and an exploratory approach for describing H-bonding, both based on the PageRank algorithm of Google search fame. The moleculaRnetworks codes include a preprocessor, which distills simulation trajectories into physicochemical data arrays, and an interactive analysis script that enables statistical, trend, and correlation analysis, and other data mining. The goal of these scripts is to increase access to the wealth of structural and dynamical information that can be obtained from molecular simulations.

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X Demographics

X Demographics

The data shown below were collected from the profiles of 10 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 61 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 %
United States 6 10%
Japan 3 5%
Sweden 1 2%
Russia 1 2%
Italy 1 2%
Germany 1 2%
Czechia 1 2%
Australia 1 2%
Unknown 46 75%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Student > Ph. D. Student 20 33%
Researcher 11 18%
Professor > Associate Professor 5 8%
Student > Bachelor 4 7%
Professor 4 7%
Other 10 16%
Unknown 7 11%
Readers by discipline
Readers by discipline Count As %
Chemistry 25 41%
Agricultural and Biological Sciences 8 13%
Computer Science 8 13%
Materials Science 3 5%
Mathematics 2 3%
Other 8 13%
Unknown 7 11%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 14. 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 21 October 2013.
All research outputs
#2,961,729
of 29,338,119 outputs
Outputs from Journal of Computational Chemistry
#95
of 2,411 outputs
Outputs of similar age
#20,273
of 268,088 outputs
Outputs of similar age from Journal of Computational Chemistry
#1
of 14 outputs
Altmetric has tracked 29,338,119 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,411 research outputs from this source. They receive a mean Attention Score of 3.9. This one has done particularly well, scoring higher than 96% 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 268,088 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 92% of its contemporaries.
We're also able to compare this research output to 14 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 92% of its contemporaries.