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Data dictionary services in XNAT and the Human Connectome Project

Overview of attention for article published in Frontiers in Neuroinformatics, July 2014
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (55th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (53rd percentile)

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Title
Data dictionary services in XNAT and the Human Connectome Project
Published in
Frontiers in Neuroinformatics, July 2014
DOI 10.3389/fninf.2014.00065
Pubmed ID
Authors

Rick Herrick, Michael McKay, Timothy Olsen, William Horton, Mark Florida, Charles J. Moore, Daniel S. Marcus

Abstract

The XNAT informatics platform is an open source data management tool used by biomedical imaging researchers around the world. An important feature of XNAT is its highly extensible architecture: users of XNAT can add new data types to the system to capture the imaging and phenotypic data generated in their studies. Until recently, XNAT has had limited capacity to broadcast the meaning of these data extensions to users, other XNAT installations, and other software. We have implemented a data dictionary service for XNAT, which is currently being used on ConnectomeDB, the Human Connectome Project (HCP) public data sharing website. The data dictionary service provides a framework to define key relationships between data elements and structures across the XNAT installation. This includes not just core data representing medical imaging data or subject or patient evaluations, but also taxonomical structures, security relationships, subject groups, and research protocols. The data dictionary allows users to define metadata for data structures and their properties, such as value types (e.g., textual, integers, floats) and valid value templates, ranges, or field lists. The service provides compatibility and integration with other research data management services by enabling easy migration of XNAT data to standards-based formats such as the Resource Description Framework (RDF), JavaScript Object Notation (JSON), and Extensible Markup Language (XML). It also facilitates the conversion of XNAT's native data schema into standard neuroimaging vocabularies and structures.

X Demographics

X Demographics

The data shown below were collected from the profiles of 3 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 52 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 1 2%
Netherlands 1 2%
United States 1 2%
Unknown 49 94%

Demographic breakdown

Readers by professional status Count As %
Student > Master 9 17%
Student > Ph. D. Student 8 15%
Researcher 7 13%
Professor > Associate Professor 4 8%
Librarian 4 8%
Other 9 17%
Unknown 11 21%
Readers by discipline Count As %
Computer Science 17 33%
Social Sciences 7 13%
Engineering 5 10%
Psychology 3 6%
Medicine and Dentistry 2 4%
Other 7 13%
Unknown 11 21%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 17 July 2014.
All research outputs
#7,444,605
of 22,757,541 outputs
Outputs from Frontiers in Neuroinformatics
#363
of 743 outputs
Outputs of similar age
#73,557
of 227,670 outputs
Outputs of similar age from Frontiers in Neuroinformatics
#7
of 15 outputs
Altmetric has tracked 22,757,541 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 743 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.3. This one is in the 49th percentile – i.e., 49% 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 227,670 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 55% 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.