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X Demographics
Mendeley readers
Attention Score in Context
| Chapter title |
Using CellProfiler to Analyze and Quantify Vascular Morphology.
|
|---|---|
| Chapter number | 13 |
| Book title |
Xylem
|
| Published in |
Methods in molecular biology, January 2017
|
| DOI | 10.1007/978-1-4939-6722-3_13 |
| Pubmed ID | |
| Book ISBNs |
978-1-4939-6720-9, 978-1-4939-6722-3
|
| Authors |
Liam Campbell, Manoj Kumar, Simon Turner |
| Editors |
Miguel de Lucas, J. Peter Etchhells |
| Abstract |
Computational programs can be used in place of time-consuming, error-prone manual data collection. CellProfiler is a free, open source program that allows researchers to automate image analysis and collect large amounts of phenotypic data relatively easily. Here, we describe how to adapt CellProfiler to analyze cross sections of xylem tissue and use it to gather a variety of information on traits such as cell size, shape, and number. We provide step-by-step instructions to create a typical CellProfiler analysis pipeline, alongside explanations of important modules, options and parameters available to the user. |
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 % |
|---|---|---|
| Switzerland | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 17 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 17 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 5 | 29% |
| Student > Master | 3 | 18% |
| Researcher | 2 | 12% |
| Student > Bachelor | 1 | 6% |
| Unspecified | 1 | 6% |
| Other | 2 | 12% |
| Unknown | 3 | 18% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 5 | 29% |
| Agricultural and Biological Sciences | 4 | 24% |
| Neuroscience | 3 | 18% |
| Unspecified | 1 | 6% |
| Medicine and Dentistry | 1 | 6% |
| Other | 0 | 0% |
| Unknown | 3 | 18% |
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 16 January 2017.
All research outputs
#18,518,987
of 22,940,083 outputs
Outputs from Methods in molecular biology
#7,929
of 13,127 outputs
Outputs of similar age
#311,012
of 420,863 outputs
Outputs of similar age from Methods in molecular biology
#692
of 1,074 outputs
Altmetric has tracked 22,940,083 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 13,127 research outputs from this source. They receive a mean Attention Score of 3.4. This one is in the 24th percentile – i.e., 24% 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 420,863 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 15th percentile – i.e., 15% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 1,074 others from the same source and published within six weeks on either side of this one. This one is in the 21st percentile – i.e., 21% of its contemporaries scored the same or lower than it.