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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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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (60th percentile)

Mentioned by

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2 tweeters
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1 Google+ user

Citations

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Readers on

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46 Mendeley
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 46 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Switzerland 2 4%
Italy 1 2%
United Kingdom 1 2%
Unknown 42 91%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 18 39%
Researcher 7 15%
Student > Master 6 13%
Student > Doctoral Student 4 9%
Professor 4 9%
Other 7 15%
Readers by discipline Count As %
Chemistry 15 33%
Physics and Astronomy 14 30%
Engineering 6 13%
Computer Science 4 9%
Agricultural and Biological Sciences 3 7%
Other 4 9%

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
#5,784,843
of 11,263,363 outputs
Outputs from Nature Communications
#12,299
of 16,803 outputs
Outputs of similar age
#100,283
of 259,596 outputs
Outputs of similar age from Nature Communications
#671
of 860 outputs
Altmetric has tracked 11,263,363 research outputs across all sources so far. This one is in the 48th percentile – i.e., 48% of other outputs scored the same or lower than it.
So far Altmetric has tracked 16,803 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 46.7. This one is in the 25th percentile – i.e., 25% 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 259,596 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 60% of its contemporaries.
We're also able to compare this research output to 860 others from the same source and published within six weeks on either side of this one. This one is in the 21st percentile – i.e., 21% of its contemporaries scored the same or lower than it.