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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Dendritic tree extraction from noisy maximum intensity projection images in C. elegans
|
|---|---|
| Published in |
BioMedical Engineering OnLine, June 2014
|
| DOI | 10.1186/1475-925x-13-74 |
| Pubmed ID | |
| Authors |
Ayala Greenblum, Raphael Sznitman, Pascal Fua, Paulo E Arratia, Meital Oren, Benjamin Podbilewicz, Josué Sznitman |
| Abstract |
Maximum Intensity Projections (MIP) of neuronal dendritic trees obtained from confocal microscopy are frequently used to study the relationship between tree morphology and mechanosensory function in the model organism C. elegans. Extracting dendritic trees from noisy images remains however a strenuous process that has traditionally relied on manual approaches. Here, we focus on automated and reliable 2D segmentations of dendritic trees following a statistical learning framework. |
X Demographics
The data shown below were collected from the profiles of 3 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 Kingdom | 2 | 67% |
| Unknown | 1 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 67% |
| Science communicators (journalists, bloggers, editors) | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 22 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 22 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 6 | 27% |
| Researcher | 4 | 18% |
| Professor | 4 | 18% |
| Librarian | 2 | 9% |
| Student > Master | 2 | 9% |
| Other | 2 | 9% |
| Unknown | 2 | 9% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 6 | 27% |
| Engineering | 5 | 23% |
| Biochemistry, Genetics and Molecular Biology | 3 | 14% |
| Chemical Engineering | 2 | 9% |
| Neuroscience | 2 | 9% |
| Other | 1 | 5% |
| Unknown | 3 | 14% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. 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 27 February 2015.
All research outputs
#15,325,572
of 22,793,427 outputs
Outputs from BioMedical Engineering OnLine
#424
of 824 outputs
Outputs of similar age
#133,591
of 228,766 outputs
Outputs of similar age from BioMedical Engineering OnLine
#6
of 14 outputs
Altmetric has tracked 22,793,427 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 824 research outputs from this source. They receive a mean Attention Score of 4.6. This one is in the 36th percentile – i.e., 36% 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 228,766 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 32nd percentile – i.e., 32% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 14 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 50% of its contemporaries.