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Visualization and correction of automated segmentation, tracking and lineaging from 5-D stem cell image sequences

Overview of attention for article published in BMC Bioinformatics, January 2014
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#13 of 4,188)
  • High Attention Score compared to outputs of the same age (97th percentile)
  • High Attention Score compared to outputs of the same age and source (99th percentile)

Mentioned by

news
7 news outlets
blogs
2 blogs
twitter
7 tweeters
facebook
1 Facebook page
googleplus
1 Google+ user

Citations

dimensions_citation
28 Dimensions

Readers on

mendeley
48 Mendeley
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Title
Visualization and correction of automated segmentation, tracking and lineaging from 5-D stem cell image sequences
Published in
BMC Bioinformatics, January 2014
DOI 10.1186/1471-2105-15-328
Pubmed ID
Authors

Eric Wait, Mark Winter, Chris Bjornsson, Erzsebet Kokovay, Yue Wang, Susan Goderie, Sally Temple, Andrew R Cohen

Abstract

Neural stem cells are motile and proliferative cells that undergo mitosis, dividing to produce daughter cells and ultimately generating differentiated neurons and glia. Understanding the mechanisms controlling neural stem cell proliferation and differentiation will play a key role in the emerging fields of regenerative medicine and cancer therapeutics. Stem cell studies in vitro from 2-D image data are well established. Visualizing and analyzing large three dimensional images of intact tissue is a challenging task. It becomes more difficult as the dimensionality of the image data increases to include time and additional fluorescence channels. There is a pressing need for 5-D image analysis and visualization tools to study cellular dynamics in the intact niche and to quantify the role that environmental factors play in determining cell fate.

Twitter Demographics

The data shown below were collected from the profiles of 7 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 48 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Switzerland 1 2%
Australia 1 2%
Japan 1 2%
Netherlands 1 2%
Unknown 44 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 14 29%
Researcher 11 23%
Student > Master 7 15%
Unspecified 4 8%
Other 3 6%
Other 9 19%
Readers by discipline Count As %
Agricultural and Biological Sciences 13 27%
Engineering 7 15%
Computer Science 7 15%
Biochemistry, Genetics and Molecular Biology 6 13%
Unspecified 6 13%
Other 9 19%

Attention Score in Context

This research output has an Altmetric Attention Score of 71. 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 19 December 2014.
All research outputs
#168,552
of 11,178,588 outputs
Outputs from BMC Bioinformatics
#13
of 4,188 outputs
Outputs of similar age
#4,131
of 203,561 outputs
Outputs of similar age from BMC Bioinformatics
#1
of 100 outputs
Altmetric has tracked 11,178,588 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 98th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,188 research outputs from this source. They receive a mean Attention Score of 5.0. This one has done particularly well, scoring higher than 99% 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 203,561 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 97% of its contemporaries.
We're also able to compare this research output to 100 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 99% of its contemporaries.