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Unsupervised vector-based classification of single-molecule charge transport data

Overview of attention for article published in Nature Communications, October 2016
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2 tweeters
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1 Google+ user

Citations

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58 Dimensions

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81 Mendeley
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Title
Unsupervised vector-based classification of single-molecule charge transport data
Published in
Nature Communications, October 2016
DOI 10.1038/ncomms12922
Pubmed ID
Authors

Mario Lemmer, Michael S. Inkpen, Katja Kornysheva, Nicholas J. Long, Tim Albrecht

Abstract

The stochastic nature of single-molecule charge transport measurements requires collection of large data sets to capture the full complexity of a molecular system. Data analysis is then guided by certain expectations, for example, a plateau feature in the tunnelling current distance trace, and the molecular conductance extracted from suitable histogram analysis. However, differences in molecular conformation or electrode contact geometry, the number of molecules in the junction or dynamic effects may lead to very different molecular signatures. Since their manifestation is a priori unknown, an unsupervised classification algorithm, making no prior assumptions regarding the data is clearly desirable. Here we present such an approach based on multivariate pattern analysis and apply it to simulated and experimental single-molecule charge transport data. We demonstrate how different event shapes are clearly separated using this algorithm and how statistics about different event classes can be extracted, when conventional methods of analysis fail.

Twitter Demographics

The data shown below were collected from the profiles of 2 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 81 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Switzerland 2 2%
Italy 1 1%
United Kingdom 1 1%
Unknown 77 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 25 31%
Student > Master 13 16%
Researcher 11 14%
Student > Doctoral Student 6 7%
Professor 5 6%
Other 12 15%
Unknown 9 11%
Readers by discipline Count As %
Chemistry 31 38%
Physics and Astronomy 16 20%
Engineering 8 10%
Computer Science 4 5%
Materials Science 4 5%
Other 8 10%
Unknown 10 12%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 October 2016.
All research outputs
#13,245,771
of 22,890,496 outputs
Outputs from Nature Communications
#38,632
of 47,156 outputs
Outputs of similar age
#165,293
of 321,456 outputs
Outputs of similar age from Nature Communications
#736
of 903 outputs
Altmetric has tracked 22,890,496 research outputs across all sources so far. This one is in the 41st percentile – i.e., 41% of other outputs scored the same or lower than it.
So far Altmetric has tracked 47,156 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 55.8. This one is in the 17th percentile – i.e., 17% 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 321,456 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 47th percentile – i.e., 47% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 903 others from the same source and published within six weeks on either side of this one. This one is in the 18th percentile – i.e., 18% of its contemporaries scored the same or lower than it.