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Data sharing in neuroimaging research

Overview of attention for article published in Frontiers in Neuroinformatics, January 2012
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • One of the highest-scoring outputs from this source (#10 of 848)
  • High Attention Score compared to outputs of the same age (98th percentile)
  • High Attention Score compared to outputs of the same age and source (96th percentile)

Mentioned by

news
4 news outlets
blogs
6 blogs
twitter
20 X users
facebook
3 Facebook pages
googleplus
5 Google+ users

Citations

dimensions_citation
225 Dimensions

Readers on

mendeley
354 Mendeley
citeulike
3 CiteULike
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Title
Data sharing in neuroimaging research
Published in
Frontiers in Neuroinformatics, January 2012
DOI 10.3389/fninf.2012.00009
Pubmed ID
Authors

Jean-Baptiste Poline, Janis L. Breeze, Satrajit Ghosh, Krzysztof Gorgolewski, Yaroslav O. Halchenko, Michael Hanke, Christian Haselgrove, Karl G. Helmer, David B. Keator, Daniel S. Marcus, Russell A. Poldrack, Yannick Schwartz, John Ashburner, David N. Kennedy

Abstract

Significant resources around the world have been invested in neuroimaging studies of brain function and disease. Easier access to this large body of work should have profound impact on research in cognitive neuroscience and psychiatry, leading to advances in the diagnosis and treatment of psychiatric and neurological disease. A trend toward increased sharing of neuroimaging data has emerged in recent years. Nevertheless, a number of barriers continue to impede momentum. Many researchers and institutions remain uncertain about how to share data or lack the tools and expertise to participate in data sharing. The use of electronic data capture (EDC) methods for neuroimaging greatly simplifies the task of data collection and has the potential to help standardize many aspects of data sharing. We review here the motivations for sharing neuroimaging data, the current data sharing landscape, and the sociological or technical barriers that still need to be addressed. The INCF Task Force on Neuroimaging Datasharing, in conjunction with several collaborative groups around the world, has started work on several tools to ease and eventually automate the practice of data sharing. It is hoped that such tools will allow researchers to easily share raw, processed, and derived neuroimaging data, with appropriate metadata and provenance records, and will improve the reproducibility of neuroimaging studies. By providing seamless integration of data sharing and analysis tools within a commodity research environment, the Task Force seeks to identify and minimize barriers to data sharing in the field of neuroimaging.

X Demographics

X Demographics

The data shown below were collected from the profiles of 20 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 354 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 14 4%
United Kingdom 8 2%
Germany 6 2%
Netherlands 3 <1%
France 2 <1%
Malaysia 1 <1%
Singapore 1 <1%
Turkey 1 <1%
Japan 1 <1%
Other 1 <1%
Unknown 316 89%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 76 21%
Researcher 75 21%
Student > Master 39 11%
Professor > Associate Professor 27 8%
Student > Bachelor 27 8%
Other 77 22%
Unknown 33 9%
Readers by discipline Count As %
Psychology 61 17%
Neuroscience 52 15%
Computer Science 39 11%
Agricultural and Biological Sciences 37 10%
Engineering 35 10%
Other 77 22%
Unknown 53 15%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 81. 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 01 September 2020.
All research outputs
#534,891
of 25,759,158 outputs
Outputs from Frontiers in Neuroinformatics
#10
of 848 outputs
Outputs of similar age
#2,772
of 251,832 outputs
Outputs of similar age from Frontiers in Neuroinformatics
#1
of 25 outputs
Altmetric has tracked 25,759,158 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 97th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 848 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.7. This one has done particularly well, scoring higher than 98% 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 251,832 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 98% of its contemporaries.
We're also able to compare this research output to 25 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 96% of its contemporaries.