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Mendeley readers
Attention Score in Context
| Title |
Imaging ATUM ultrathin section libraries with WaferMapper: a multi-scale approach to EM reconstruction of neural circuits
|
|---|---|
| Published in |
Frontiers in Neural Circuits, June 2014
|
| DOI | 10.3389/fncir.2014.00068 |
| Pubmed ID | |
| Authors |
Kenneth J. Hayworth, Josh L. Morgan, Richard Schalek, Daniel R. Berger, David G. C. Hildebrand, Jeff W. Lichtman |
| Abstract |
The automated tape-collecting ultramicrotome (ATUM) makes it possible to collect large numbers of ultrathin sections quickly-the equivalent of a petabyte of high resolution images each day. However, even high throughput image acquisition strategies generate images far more slowly (at present ~1 terabyte per day). We therefore developed WaferMapper, a software package that takes a multi-resolution approach to mapping and imaging select regions within a library of ultrathin sections. This automated method selects and directs imaging of corresponding regions within each section of an ultrathin section library (UTSL) that may contain many thousands of sections. Using WaferMapper, it is possible to map thousands of tissue sections at low resolution and target multiple points of interest for high resolution imaging based on anatomical landmarks. The program can also be used to expand previously imaged regions, acquire data under different imaging conditions, or re-image after additional tissue treatments. |
Mendeley readers
The data shown below were compiled from readership statistics for 182 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 1% |
| Japan | 1 | <1% |
| Brazil | 1 | <1% |
| Unknown | 178 | 98% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 38 | 21% |
| Student > Ph. D. Student | 29 | 16% |
| Student > Bachelor | 16 | 9% |
| Professor | 15 | 8% |
| Student > Master | 14 | 8% |
| Other | 34 | 19% |
| Unknown | 36 | 20% |
| Readers by discipline | Count | As % |
|---|---|---|
| Neuroscience | 42 | 23% |
| Agricultural and Biological Sciences | 40 | 22% |
| Engineering | 15 | 8% |
| Biochemistry, Genetics and Molecular Biology | 11 | 6% |
| Medicine and Dentistry | 8 | 4% |
| Other | 26 | 14% |
| Unknown | 40 | 22% |
Attention Score in Context
This research output has an Altmetric Attention Score of 20. 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 10 May 2024.
All research outputs
#1,862,241
of 25,882,826 outputs
Outputs from Frontiers in Neural Circuits
#62
of 1,304 outputs
Outputs of similar age
#17,943
of 243,671 outputs
Outputs of similar age from Frontiers in Neural Circuits
#2
of 20 outputs
Altmetric has tracked 25,882,826 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,304 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.8. This one has done particularly well, scoring higher than 95% 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 243,671 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 92% of its contemporaries.
We're also able to compare this research output to 20 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 90% of its contemporaries.