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Integrating the Allen Brain Institute Cell Types Database into Automated Neuroscience Workflow

Overview of attention for article published in Neuroinformatics, August 2017
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Title
Integrating the Allen Brain Institute Cell Types Database into Automated Neuroscience Workflow
Published in
Neuroinformatics, August 2017
DOI 10.1007/s12021-017-9337-x
Pubmed ID
Authors

David B. Stockton, Fidel Santamaria

Abstract

We developed software tools to download, extract features, and organize the Cell Types Database from the Allen Brain Institute (ABI) in order to integrate its whole cell patch clamp characterization data into the automated modeling/data analysis cycle. To expand the potential user base we employed both Python and MATLAB. The basic set of tools downloads selected raw data and extracts cell, sweep, and spike features, using ABI's feature extraction code. To facilitate data manipulation we added a tool to build a local specialized database of raw data plus extracted features. Finally, to maximize automation, we extended our NeuroManager workflow automation suite to include these tools plus a separate investigation database. The extended suite allows the user to integrate ABI experimental and modeling data into an automated workflow deployed on heterogeneous computer infrastructures, from local servers, to high performance computing environments, to the cloud. Since our approach is focused on workflow procedures our tools can be modified to interact with the increasing number of neuroscience databases being developed to cover all scales and properties of the nervous system.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 23 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 23 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 26%
Other 4 17%
Researcher 4 17%
Professor > Associate Professor 3 13%
Student > Doctoral Student 1 4%
Other 3 13%
Unknown 2 9%
Readers by discipline Count As %
Neuroscience 9 39%
Computer Science 4 17%
Engineering 3 13%
Immunology and Microbiology 1 4%
Biochemistry, Genetics and Molecular Biology 1 4%
Other 2 9%
Unknown 3 13%