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Solving Software Challenges for Exascale

Overview of attention for book
Attention for Chapter 1: Solving Software Challenges for Exascale
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (79th percentile)
  • High Attention Score compared to outputs of the same age and source (93rd percentile)

Mentioned by

blogs
1 blog
twitter
1 tweeter

Citations

dimensions_citation
28 Dimensions

Readers on

mendeley
518 Mendeley
citeulike
1 CiteULike
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Chapter title
Solving Software Challenges for Exascale
Chapter number 1
Book title
Solving Software Challenges for Exascale
Published in
Lecture notes in computer science, February 2015
DOI 10.1007/978-3-319-15976-8_1
Book ISBNs
978-3-31-915975-1, 978-3-31-915976-8
Authors

Stefano Markidis, Erwin Laure, Szilárd Páll, Mark James Abraham, Carsten Kutzner, Berk Hess, Erik Lindahl, Páll, Szilárd, Abraham, Mark James, Kutzner, Carsten, Hess, Berk, Lindahl, Erik, Páll Szilárd

Editors

Markidis, Stefano, Laure, Erwin

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 518 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 <1%
Chile 1 <1%
Brazil 1 <1%
Germany 1 <1%
Czechia 1 <1%
Sweden 1 <1%
Canada 1 <1%
United Kingdom 1 <1%
Unknown 509 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 111 21%
Researcher 88 17%
Student > Master 72 14%
Student > Bachelor 68 13%
Student > Doctoral Student 42 8%
Other 71 14%
Unknown 66 13%
Readers by discipline Count As %
Chemistry 129 25%
Biochemistry, Genetics and Molecular Biology 95 18%
Agricultural and Biological Sciences 42 8%
Physics and Astronomy 30 6%
Computer Science 25 5%
Other 94 18%
Unknown 103 20%

Attention Score in Context

This research output has an Altmetric Attention Score of 8. 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 November 2020.
All research outputs
#3,375,576
of 19,763,264 outputs
Outputs from Lecture notes in computer science
#862
of 7,935 outputs
Outputs of similar age
#55,475
of 275,439 outputs
Outputs of similar age from Lecture notes in computer science
#14
of 199 outputs
Altmetric has tracked 19,763,264 research outputs across all sources so far. Compared to these this one has done well and is in the 82nd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,935 research outputs from this source. They receive a mean Attention Score of 4.8. This one has done well, scoring higher than 89% 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 275,439 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 79% of its contemporaries.
We're also able to compare this research output to 199 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.