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SPICES: a particle-based molecular structure line notation and support library for mesoscopic simulation

Overview of attention for article published in Journal of Cheminformatics, August 2018
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (63rd percentile)

Mentioned by

twitter
8 tweeters

Citations

dimensions_citation
1 Dimensions

Readers on

mendeley
8 Mendeley
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Title
SPICES: a particle-based molecular structure line notation and support library for mesoscopic simulation
Published in
Journal of Cheminformatics, August 2018
DOI 10.1186/s13321-018-0294-7
Pubmed ID
Authors

Karina van den Broek, Mirco Daniel, Matthias Epple, Hubert Kuhn, Jonas Schaub, Achim Zielesny

Abstract

Simplified Particle Input ConnEction Specification (SPICES) is a particle-based molecular structure representation derived from straightforward simplifications of the atom-based SMILES line notation. It aims at supporting tedious and error-prone molecular structure definitions for particle-based mesoscopic simulation techniques like Dissipative Particle Dynamics by allowing for an interplay of different molecular encoding levels that range from topological line notations and corresponding particle-graph visualizations to 3D structures with support of their spatial mapping into a simulation box. An open Java library for SPICES structure handling and mesoscopic simulation support in combination with an open Java Graphical User Interface viewer application for visual topological inspection of SPICES definitions are provided.

Twitter Demographics

The data shown below were collected from the profiles of 8 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 8 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 8 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 3 38%
Student > Ph. D. Student 2 25%
Researcher 2 25%
Student > Bachelor 1 13%
Readers by discipline Count As %
Chemistry 3 38%
Agricultural and Biological Sciences 3 38%
Computer Science 1 13%
Unknown 1 13%

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 2018.
All research outputs
#3,798,925
of 13,652,000 outputs
Outputs from Journal of Cheminformatics
#321
of 550 outputs
Outputs of similar age
#95,826
of 268,134 outputs
Outputs of similar age from Journal of Cheminformatics
#1
of 1 outputs
Altmetric has tracked 13,652,000 research outputs across all sources so far. This one has received more attention than most of these and is in the 72nd percentile.
So far Altmetric has tracked 550 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.3. This one is in the 40th percentile – i.e., 40% 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 268,134 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 63% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them