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PyXNAT: XNAT in Python

Overview of attention for article published in Frontiers in Neuroinformatics, January 2012
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Title
PyXNAT: XNAT in Python
Published in
Frontiers in Neuroinformatics, January 2012
DOI 10.3389/fninf.2012.00012
Pubmed ID
Authors

Yannick Schwartz, Alexis Barbot, Benjamin Thyreau, Vincent Frouin, Gaël Varoquaux, Aditya Siram, Daniel S. Marcus, Jean-Baptiste Poline

Abstract

As neuroimaging databases grow in size and complexity, the time researchers spend investigating and managing the data increases to the expense of data analysis. As a result, investigators rely more and more heavily on scripting using high-level languages to automate data management and processing tasks. For this, a structured and programmatic access to the data store is necessary. Web services are a first step toward this goal. They however lack in functionality and ease of use because they provide only low-level interfaces to databases. We introduce here PyXNAT, a Python module that interacts with The Extensible Neuroimaging Archive Toolkit (XNAT) through native Python calls across multiple operating systems. The choice of Python enables PyXNAT to expose the XNAT Web Services and unify their features with a higher level and more expressive language. PyXNAT provides XNAT users direct access to all the scientific packages in Python. Finally PyXNAT aims to be efficient and easy to use, both as a back-end library to build XNAT clients and as an alternative front-end from the command line.

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 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 75 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 2 3%
United States 2 3%
France 2 3%
Latvia 1 1%
Brazil 1 1%
Germany 1 1%
Canada 1 1%
Netherlands 1 1%
Japan 1 1%
Other 1 1%
Unknown 62 83%

Demographic breakdown

Readers by professional status Count As %
Researcher 20 27%
Student > Ph. D. Student 13 17%
Student > Master 10 13%
Other 7 9%
Student > Bachelor 6 8%
Other 13 17%
Unknown 6 8%
Readers by discipline Count As %
Computer Science 15 20%
Engineering 13 17%
Medicine and Dentistry 9 12%
Agricultural and Biological Sciences 7 9%
Neuroscience 6 8%
Other 15 20%
Unknown 10 13%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 30 January 2013.
All research outputs
#14,161,257
of 22,694,633 outputs
Outputs from Frontiers in Neuroinformatics
#482
of 743 outputs
Outputs of similar age
#153,491
of 244,145 outputs
Outputs of similar age from Frontiers in Neuroinformatics
#16
of 24 outputs
Altmetric has tracked 22,694,633 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% 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 8.4. This one is in the 31st percentile – i.e., 31% 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 244,145 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 35th percentile – i.e., 35% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 24 others from the same source and published within six weeks on either side of this one. This one is in the 25th percentile – i.e., 25% of its contemporaries scored the same or lower than it.