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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Bacterial cell identification in differential interference contrast microscopy images
|
|---|---|
| Published in |
BMC Bioinformatics, April 2013
|
| DOI | 10.1186/1471-2105-14-134 |
| Pubmed ID | |
| Authors |
Boguslaw Obara, Mark AJ Roberts, Judith P Armitage, Vicente Grau |
| Abstract |
Microscopy image segmentation lays the foundation for shape analysis, motion tracking, and classification of biological objects. Despite its importance, automated segmentation remains challenging for several widely used non-fluorescence, interference-based microscopy imaging modalities. For example in differential interference contrast microscopy which plays an important role in modern bacterial cell biology. Therefore, new revolutions in the field require the development of tools, technologies and work-flows to extract and exploit information from interference-based imaging data so as to achieve new fundamental biological insights and understanding. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 75 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 2 | 3% |
| Portugal | 1 | 1% |
| India | 1 | 1% |
| Germany | 1 | 1% |
| Belgium | 1 | 1% |
| United States | 1 | 1% |
| Unknown | 68 | 91% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 20 | 27% |
| Researcher | 11 | 15% |
| Student > Master | 11 | 15% |
| Student > Bachelor | 10 | 13% |
| Student > Doctoral Student | 3 | 4% |
| Other | 11 | 15% |
| Unknown | 9 | 12% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 15 | 20% |
| Agricultural and Biological Sciences | 15 | 20% |
| Biochemistry, Genetics and Molecular Biology | 8 | 11% |
| Computer Science | 6 | 8% |
| Physics and Astronomy | 5 | 7% |
| Other | 13 | 17% |
| Unknown | 13 | 17% |
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 23 April 2013.
All research outputs
#18,337,420
of 22,708,120 outputs
Outputs from BMC Bioinformatics
#6,291
of 7,256 outputs
Outputs of similar age
#147,138
of 195,118 outputs
Outputs of similar age from BMC Bioinformatics
#116
of 124 outputs
Altmetric has tracked 22,708,120 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,256 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 5th percentile – i.e., 5% 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 195,118 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 12th percentile – i.e., 12% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 124 others from the same source and published within six weeks on either side of this one. This one is in the 2nd percentile – i.e., 2% of its contemporaries scored the same or lower than it.