Title |
Automatic classification and segmentation of single-molecule fluorescence time traces with deep learning
|
---|---|
Published in |
Nature Communications, November 2020
|
DOI | 10.1038/s41467-020-19673-1 |
Pubmed ID | |
Authors |
Jieming Li, Leyou Zhang, Alexander Johnson-Buck, Nils G. Walter |
X Demographics
The data shown below were collected from the profiles of 19 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 8 | 42% |
Switzerland | 1 | 5% |
India | 1 | 5% |
France | 1 | 5% |
Canada | 1 | 5% |
Unknown | 7 | 37% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 9 | 47% |
Scientists | 6 | 32% |
Science communicators (journalists, bloggers, editors) | 3 | 16% |
Practitioners (doctors, other healthcare professionals) | 1 | 5% |
Mendeley readers
The data shown below were compiled from readership statistics for 57 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 57 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 9 | 16% |
Researcher | 6 | 11% |
Other | 5 | 9% |
Professor | 4 | 7% |
Student > Postgraduate | 3 | 5% |
Other | 5 | 9% |
Unknown | 25 | 44% |
Readers by discipline | Count | As % |
---|---|---|
Biochemistry, Genetics and Molecular Biology | 14 | 25% |
Chemistry | 6 | 11% |
Agricultural and Biological Sciences | 4 | 7% |
Engineering | 2 | 4% |
Neuroscience | 2 | 4% |
Other | 2 | 4% |
Unknown | 27 | 47% |
Attention Score in Context
This research output has an Altmetric Attention Score of 10. 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 07 December 2020.
All research outputs
#3,281,035
of 23,306,612 outputs
Outputs from Nature Communications
#29,808
of 48,184 outputs
Outputs of similar age
#91,555
of 505,997 outputs
Outputs of similar age from Nature Communications
#933
of 1,402 outputs
Altmetric has tracked 23,306,612 research outputs across all sources so far. Compared to these this one has done well and is in the 85th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 48,184 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 56.2. This one is in the 38th percentile – i.e., 38% 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 505,997 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 81% of its contemporaries.
We're also able to compare this research output to 1,402 others from the same source and published within six weeks on either side of this one. This one is in the 33rd percentile – i.e., 33% of its contemporaries scored the same or lower than it.