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The Molecule Cloud - compact visualization of large collections of molecules

Overview of attention for article published in Journal of Cheminformatics, July 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 (94th percentile)
  • Good Attention Score compared to outputs of the same age and source (71st percentile)

Mentioned by

blogs
2 blogs
twitter
15 X users
facebook
1 Facebook page
googleplus
1 Google+ user

Citations

dimensions_citation
43 Dimensions

Readers on

mendeley
102 Mendeley
citeulike
2 CiteULike
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Title
The Molecule Cloud - compact visualization of large collections of molecules
Published in
Journal of Cheminformatics, July 2012
DOI 10.1186/1758-2946-4-12
Pubmed ID
Authors

Peter Ertl, Bernhard Rohde

Abstract

Analysis and visualization of large collections of molecules is one of the most frequent challenges cheminformatics experts in pharmaceutical industry are facing. Various sophisticated methods are available to perform this task, including clustering, dimensionality reduction or scaffold frequency analysis. In any case, however, viewing and analyzing large tables with molecular structures is necessary. We present a new visualization technique, providing basic information about the composition of molecular data sets at a single glance.

X Demographics

X Demographics

The data shown below were collected from the profiles of 15 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 102 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 4 4%
United States 4 4%
Bulgaria 1 <1%
Italy 1 <1%
France 1 <1%
Czechia 1 <1%
Brazil 1 <1%
Mexico 1 <1%
United Kingdom 1 <1%
Other 0 0%
Unknown 87 85%

Demographic breakdown

Readers by professional status Count As %
Researcher 34 33%
Student > Ph. D. Student 20 20%
Student > Master 8 8%
Professor > Associate Professor 6 6%
Student > Bachelor 5 5%
Other 13 13%
Unknown 16 16%
Readers by discipline Count As %
Chemistry 36 35%
Computer Science 14 14%
Agricultural and Biological Sciences 9 9%
Biochemistry, Genetics and Molecular Biology 8 8%
Pharmacology, Toxicology and Pharmaceutical Science 6 6%
Other 12 12%
Unknown 17 17%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 24. 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 01 July 2022.
All research outputs
#1,609,467
of 25,713,737 outputs
Outputs from Journal of Cheminformatics
#102
of 981 outputs
Outputs of similar age
#8,944
of 178,291 outputs
Outputs of similar age from Journal of Cheminformatics
#2
of 7 outputs
Altmetric has tracked 25,713,737 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 981 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 10.0. 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 178,291 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 94% of its contemporaries.
We're also able to compare this research output to 7 others from the same source and published within six weeks on either side of this one. This one has scored higher than 5 of them.