↓ Skip to main content

Visualization of very large high-dimensional data sets as minimum spanning trees

Overview of attention for article published in Journal of Cheminformatics, February 2020
Altmetric Badge

About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#24 of 842)
  • High Attention Score compared to outputs of the same age (95th percentile)
  • High Attention Score compared to outputs of the same age and source (99th percentile)

Mentioned by

news
1 news outlet
blogs
1 blog
twitter
57 tweeters

Citations

dimensions_citation
105 Dimensions

Readers on

mendeley
177 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Visualization of very large high-dimensional data sets as minimum spanning trees
Published in
Journal of Cheminformatics, February 2020
DOI 10.1186/s13321-020-0416-x
Pubmed ID
Authors

Probst, Daniel, Reymond, Jean-Louis

Twitter Demographics

The data shown below were collected from the profiles of 57 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 177 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 177 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 44 25%
Researcher 26 15%
Student > Master 25 14%
Other 8 5%
Student > Doctoral Student 7 4%
Other 20 11%
Unknown 47 27%
Readers by discipline Count As %
Chemistry 35 20%
Computer Science 24 14%
Biochemistry, Genetics and Molecular Biology 16 9%
Pharmacology, Toxicology and Pharmaceutical Science 9 5%
Engineering 8 5%
Other 31 18%
Unknown 54 31%

Attention Score in Context

This research output has an Altmetric Attention Score of 46. 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 05 November 2022.
All research outputs
#777,181
of 23,049,027 outputs
Outputs from Journal of Cheminformatics
#24
of 842 outputs
Outputs of similar age
#21,369
of 455,513 outputs
Outputs of similar age from Journal of Cheminformatics
#1
of 18 outputs
Altmetric has tracked 23,049,027 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 96th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 842 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.9. This one has done particularly well, scoring higher than 97% 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 455,513 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 95% of its contemporaries.
We're also able to compare this research output to 18 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 99% of its contemporaries.