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Mendeley readers
Attention Score in Context
Title |
Jdpd: an open java simulation kernel for molecular fragment dissipative particle dynamics
|
---|---|
Published in |
Journal of Cheminformatics, May 2018
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DOI | 10.1186/s13321-018-0278-7 |
Pubmed ID | |
Authors |
Karina van den Broek, Hubert Kuhn, Achim Zielesny |
Abstract |
Jdpd is an open Java simulation kernel for Molecular Fragment Dissipative Particle Dynamics with parallelizable force calculation, efficient caching options and fast property calculations. It is characterized by an interface and factory-pattern driven design for simple code changes and may help to avoid problems of polyglot programming. Detailed input/output communication, parallelization and process control as well as internal logging capabilities for debugging purposes are supported. The new kernel may be utilized in different simulation environments ranging from flexible scripting solutions up to fully integrated "all-in-one" simulation systems. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
Country | Count | As % |
---|---|---|
Australia | 1 | 50% |
Unknown | 1 | 50% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 1 | 50% |
Science communicators (journalists, bloggers, editors) | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 6 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 6 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 2 | 33% |
Student > Master | 2 | 33% |
Unknown | 2 | 33% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 2 | 33% |
Chemistry | 1 | 17% |
Engineering | 1 | 17% |
Unknown | 2 | 33% |
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 28 May 2018.
All research outputs
#16,388,648
of 24,143,470 outputs
Outputs from Journal of Cheminformatics
#808
of 891 outputs
Outputs of similar age
#213,898
of 334,389 outputs
Outputs of similar age from Journal of Cheminformatics
#11
of 11 outputs
Altmetric has tracked 24,143,470 research outputs across all sources so far. This one is in the 21st percentile – i.e., 21% of other outputs scored the same or lower than it.
So far Altmetric has tracked 891 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.7. This one is in the 4th percentile – i.e., 4% 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 334,389 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 27th percentile – i.e., 27% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 11 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.