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The preprocessed connectomes project repository of manually corrected skull-stripped T1-weighted anatomical MRI data

Overview of attention for article published in Giga Science, October 2016
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (88th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (53rd percentile)

Mentioned by

blogs
1 blog
twitter
14 tweeters
facebook
1 Facebook page
googleplus
1 Google+ user

Citations

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11 Dimensions

Readers on

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26 Mendeley
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Title
The preprocessed connectomes project repository of manually corrected skull-stripped T1-weighted anatomical MRI data
Published in
Giga Science, October 2016
DOI 10.1186/s13742-016-0150-5
Pubmed ID
Authors

Benjamin Puccio, James P. Pooley, John S. Pellman, Elise C. Taverna, R. Cameron Craddock

Abstract

Skull-stripping is the procedure of removing non-brain tissue from anatomical MRI data. This procedure can be useful for calculating brain volume and for improving the quality of other image processing steps. Developing new skull-stripping algorithms and evaluating their performance requires gold standard data from a variety of different scanners and acquisition methods. We complement existing repositories with manually corrected brain masks for 125 T1-weighted anatomical scans from the Nathan Kline Institute Enhanced Rockland Sample Neurofeedback Study. Skull-stripped images were obtained using a semi-automated procedure that involved skull-stripping the data using the brain extraction based on nonlocal segmentation technique (BEaST) software, and manually correcting the worst results. Corrected brain masks were added into the BEaST library and the procedure was repeated until acceptable brain masks were available for all images. In total, 85 of the skull-stripped images were hand-edited and 40 were deemed to not need editing. The results are brain masks for the 125 images along with a BEaST library for automatically skull-stripping other data. Skull-stripped anatomical images from the Neurofeedback sample are available for download from the Preprocessed Connectomes Project. The resulting brain masks can be used by researchers to improve preprocessing of the Neurofeedback data, as training and testing data for developing new skull-stripping algorithms, and for evaluating the impact on other aspects of MRI preprocessing. We have illustrated the utility of these data as a reference for comparing various automatic methods and evaluated the performance of the newly created library on independent data.

Twitter Demographics

The data shown below were collected from the profiles of 14 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 26 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 26 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 23%
Professor > Associate Professor 5 19%
Student > Doctoral Student 4 15%
Researcher 4 15%
Student > Postgraduate 3 12%
Other 4 15%
Readers by discipline Count As %
Neuroscience 13 50%
Agricultural and Biological Sciences 3 12%
Engineering 3 12%
Computer Science 2 8%
Psychology 1 4%
Other 3 12%
Unknown 1 4%

Attention Score in Context

This research output has an Altmetric Attention Score of 16. 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 13 December 2016.
All research outputs
#756,576
of 11,563,317 outputs
Outputs from Giga Science
#191
of 427 outputs
Outputs of similar age
#29,955
of 254,998 outputs
Outputs of similar age from Giga Science
#7
of 15 outputs
Altmetric has tracked 11,563,317 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 427 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 23.2. This one has gotten more attention than average, scoring higher than 55% 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 254,998 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 88% of its contemporaries.
We're also able to compare this research output to 15 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 53% of its contemporaries.