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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Ultrastructurally smooth thick partitioning and volume stitching for large-scale connectomics
|
|---|---|
| Published in |
Nature Methods, February 2015
|
| DOI | 10.1038/nmeth.3292 |
| Pubmed ID | |
| Authors |
Kenneth J Hayworth, C Shan Xu, Zhiyuan Lu, Graham W Knott, Richard D Fetter, Juan Carlos Tapia, Jeff W Lichtman, Harald F Hess |
| Abstract |
Focused-ion-beam scanning electron microscopy (FIB-SEM) has become an essential tool for studying neural tissue at resolutions below 10 nm × 10 nm × 10 nm, producing data sets optimized for automatic connectome tracing. We present a technical advance, ultrathick sectioning, which reliably subdivides embedded tissue samples into chunks (20 μm thick) optimally sized and mounted for efficient, parallel FIB-SEM imaging. These chunks are imaged separately and then 'volume stitched' back together, producing a final three-dimensional data set suitable for connectome tracing. |
X Demographics
The data shown below were collected from the profiles of 7 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 | 4 | 57% |
| Unknown | 3 | 43% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 4 | 57% |
| Scientists | 3 | 43% |
Mendeley readers
The data shown below were compiled from readership statistics for 149 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 1% |
| Ireland | 1 | <1% |
| United Kingdom | 1 | <1% |
| Czechia | 1 | <1% |
| Spain | 1 | <1% |
| Canada | 1 | <1% |
| Unknown | 142 | 95% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 32 | 21% |
| Student > Ph. D. Student | 29 | 19% |
| Student > Master | 12 | 8% |
| Student > Bachelor | 12 | 8% |
| Professor | 9 | 6% |
| Other | 26 | 17% |
| Unknown | 29 | 19% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 34 | 23% |
| Neuroscience | 24 | 16% |
| Biochemistry, Genetics and Molecular Biology | 12 | 8% |
| Medicine and Dentistry | 9 | 6% |
| Engineering | 9 | 6% |
| Other | 27 | 18% |
| Unknown | 34 | 23% |
Attention Score in Context
This research output has an Altmetric Attention Score of 32. 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 11 September 2023.
All research outputs
#1,169,483
of 24,417,958 outputs
Outputs from Nature Methods
#1,492
of 5,180 outputs
Outputs of similar age
#14,834
of 261,758 outputs
Outputs of similar age from Nature Methods
#32
of 96 outputs
Altmetric has tracked 24,417,958 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 5,180 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 35.8. This one has gotten more attention than average, scoring higher than 71% 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 261,758 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 94% of its contemporaries.
We're also able to compare this research output to 96 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 67% of its contemporaries.