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Multi-level meta-workflows: new concept for regularly occurring tasks in quantum chemistry

Overview of attention for article published in Journal of Cheminformatics, October 2016
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4 tweeters

Citations

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3 Dimensions

Readers on

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21 Mendeley
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2 CiteULike
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Title
Multi-level meta-workflows: new concept for regularly occurring tasks in quantum chemistry
Published in
Journal of Cheminformatics, October 2016
DOI 10.1186/s13321-016-0169-8
Pubmed ID
Authors

Junaid Arshad, Alexander Hoffmann, Sandra Gesing, Richard Grunzke, Jens Krüger, Tamas Kiss, Sonja Herres-Pawlis, Gabor Terstyanszky

Abstract

In Quantum Chemistry, many tasks are reoccurring frequently, e.g. geometry optimizations, benchmarking series etc. Here, workflows can help to reduce the time of manual job definition and output extraction. These workflows are executed on computing infrastructures and may require large computing and data resources. Scientific workflows hide these infrastructures and the resources needed to run them. It requires significant efforts and specific expertise to design, implement and test these workflows. Many of these workflows are complex and monolithic entities that can be used for particular scientific experiments. Hence, their modification is not straightforward and it makes almost impossible to share them. To address these issues we propose developing atomic workflows and embedding them in meta-workflows. Atomic workflows deliver a well-defined research domain specific function. Publishing workflows in repositories enables workflow sharing inside and/or among scientific communities. We formally specify atomic and meta-workflows in order to define data structures to be used in repositories for uploading and sharing them. Additionally, we present a formal description focused at orchestration of atomic workflows into meta-workflows. We investigated the operations that represent basic functionalities in Quantum Chemistry, developed the relevant atomic workflows and combined them into meta-workflows. Having these workflows we defined the structure of the Quantum Chemistry workflow library and uploaded these workflows in the SHIWA Workflow Repository.Graphical AbstractMeta-workflows and embedded workflows in the template representation.

Twitter Demographics

The data shown below were collected from the profiles of 4 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 21 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 2 10%
Brazil 1 5%
Unknown 18 86%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 4 19%
Student > Master 3 14%
Professor 3 14%
Student > Doctoral Student 2 10%
Student > Bachelor 2 10%
Other 3 14%
Unknown 4 19%
Readers by discipline Count As %
Chemistry 6 29%
Computer Science 5 24%
Engineering 3 14%
Agricultural and Biological Sciences 2 10%
Medicine and Dentistry 1 5%
Other 0 0%
Unknown 4 19%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 October 2016.
All research outputs
#6,497,714
of 11,350,788 outputs
Outputs from Journal of Cheminformatics
#331
of 444 outputs
Outputs of similar age
#122,520
of 257,366 outputs
Outputs of similar age from Journal of Cheminformatics
#21
of 26 outputs
Altmetric has tracked 11,350,788 research outputs across all sources so far. This one is in the 40th percentile – i.e., 40% of other outputs scored the same or lower than it.
So far Altmetric has tracked 444 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.7. This one is in the 21st percentile – i.e., 21% 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 257,366 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 49th percentile – i.e., 49% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 26 others from the same source and published within six weeks on either side of this one. This one is in the 19th percentile – i.e., 19% of its contemporaries scored the same or lower than it.