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Automated generation of massive image knowledge collections using Microsoft Live Labs Pivot to promote neuroimaging and translational research

Overview of attention for article published in Journal of Clinical Bioinformatics, January 2011
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1 tweeter

Citations

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2 Dimensions

Readers on

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18 Mendeley
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Title
Automated generation of massive image knowledge collections using Microsoft Live Labs Pivot to promote neuroimaging and translational research
Published in
Journal of Clinical Bioinformatics, January 2011
DOI 10.1186/2043-9113-1-18
Pubmed ID
Authors

Teeradache Viangteeravat, Matthew N Anyanwu, Venkateswara Ra Nagisetty, Emin Kuscu

Abstract

Massive datasets comprising high-resolution images, generated in neuro-imaging studies and in clinical imaging research, are increasingly challenging our ability to analyze, share, and filter such images in clinical and basic translational research. Pivot collection exploratory analysis provides each user the ability to fully interact with the massive amounts of visual data to fully facilitate sufficient sorting, flexibility and speed to fluidly access, explore or analyze the massive image data sets of high-resolution images and their associated meta information, such as neuro-imaging databases from the Allen Brain Atlas. It is used in clustering, filtering, data sharing and classifying of the visual data into various deep zoom levels and meta information categories to detect the underlying hidden pattern within the data set that has been used.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 18 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 11%
Finland 1 6%
Norway 1 6%
Unknown 14 78%

Demographic breakdown

Readers by professional status Count As %
Unspecified 4 22%
Student > Master 3 17%
Student > Ph. D. Student 3 17%
Researcher 3 17%
Other 2 11%
Other 2 11%
Unknown 1 6%
Readers by discipline Count As %
Unspecified 4 22%
Agricultural and Biological Sciences 3 17%
Computer Science 2 11%
Biochemistry, Genetics and Molecular Biology 2 11%
Medicine and Dentistry 2 11%
Other 3 17%
Unknown 2 11%

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 09 January 2012.
All research outputs
#11,173,782
of 12,558,412 outputs
Outputs from Journal of Clinical Bioinformatics
#58
of 60 outputs
Outputs of similar age
#184,724
of 216,833 outputs
Outputs of similar age from Journal of Clinical Bioinformatics
#16
of 16 outputs
Altmetric has tracked 12,558,412 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 60 research outputs from this source. They receive a mean Attention Score of 2.5. This one is in the 1st percentile – i.e., 1% 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 216,833 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 16 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.