Data management routines for reproducible research using the G-Node Python Client library

Structured, efficient, and secure storage of experimental data and associated meta-information constitutes one of the most pressing technical challenges in modern neuroscience, and does so particularly in electrophysiology. The German INCF Node aims to provide open-source solutions for this domain t...

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Vydané v:Frontiers in neuroinformatics Ročník 8; s. 15
Hlavní autori: Sobolev, Andrey, Stoewer, Adrian, Pereira, Michael, Kellner, Christian J., Garbers, Christian, Rautenberg, Philipp L., Wachtler, Thomas
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Switzerland Frontiers Research Foundation 05.03.2014
Frontiers Media S.A
Predmet:
Collaboration
Conventions
Data analysis
data management
Data models
Design
Electrophysiology
Information sharing
Libraries
Management
Metadata
Nervous system
neuroinformatics
Neurophysiology
Neuroscience
python
Software
Web service
python
data management
neuroinformatics
electrophysiology
odml
neo
experimental workflow
web service
ISSN:1662-5196, 1662-5196
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Abstract Structured, efficient, and secure storage of experimental data and associated meta-information constitutes one of the most pressing technical challenges in modern neuroscience, and does so particularly in electrophysiology. The German INCF Node aims to provide open-source solutions for this domain that support the scientific data management and analysis workflow, and thus facilitate future data access and reproducible research. G-Node provides a data management system, accessible through an application interface, that is based on a combination of standardized data representation and flexible data annotation to account for the variety of experimental paradigms in electrophysiology. The G-Node Python Library exposes these services to the Python environment, enabling researchers to organize and access their experimental data using their familiar tools while gaining the advantages that a centralized storage entails. The library provides powerful query features, including data slicing and selection by metadata, as well as fine-grained permission control for collaboration and data sharing. Here we demonstrate key actions in working with experimental neuroscience data, such as building a metadata structure, organizing recorded data in datasets, annotating data, or selecting data regions of interest, that can be automated to large degree using the library. Compliant with existing de-facto standards, the G-Node Python Library is compatible with many Python tools in the field of neurophysiology and thus enables seamless integration of data organization into the scientific data workflow.
AbstractList Structured, efficient, and secure storage of experimental data and associated meta-information constitutes one of the most pressing technical challenges in modern neuroscience, and does so particularly in electrophysiology. The German INCF Node aims to provide open-source solutions for this domain that support the scientific data management and analysis workflow, and thus facilitate future data access and reproducible research. G-Node provides a data management system, accessible through an application interface, that is based on a combination of standardized data representation and flexible data annotation to account for the variety of experimental paradigms in electrophysiology. The G-Node Python Library exposes these services to the Python environment, enabling researchers to organize and access their experimental data using their familiar tools while gaining the advantages that a centralized storage entails. The library provides powerful query features, including data slicing and selection by metadata, as well as fine-grained permission control for collaboration and data sharing. Here we demonstrate key actions in working with experimental neuroscience data, such as building a metadata structure, organizing recorded data in datasets, annotating data, or selecting data regions of interest, that can be automated to large degree using the library. Compliant with existing de-facto standards, the G-Node Python Library is compatible with many Python tools in the field of neurophysiology and thus enables seamless integration of data organization into the scientific data workflow.
Structured, efficient, and secure storage of experimental data and associated meta-information constitutes one of the most pressing technical challenges in modern neuroscience, and does so particularly in electrophysiology. The German INCF Node aims to provide open-source solutions for this domain that support the scientific data management and analysis workflow, and thus facilitate future data access and reproducible research. G-Node provides a data management system, accessible through an application interface, that is based on a combination of standardized data representation and flexible data annotation to account for the variety of experimental paradigms in electrophysiology. The G-Node Python Library exposes these services to the Python environment, enabling researchers to organize and access their experimental data using their familiar tools while gaining the advantages that a centralized storage entails. The library provides powerful query features, including data slicing and selection by metadata, as well as fine-grained permission control for collaboration and data sharing. Here we demonstrate key actions in working with experimental neuroscience data, such as building a metadata structure, organizing recorded data in datasets, annotating data, or selecting data regions of interest, that can be automated to large degree using the library. Compliant with existing de-facto standards, the G-Node Python Library is compatible with many Python tools in the field of neurophysiology and thus enables seamless integration of data organization into the scientific data workflow.Structured, efficient, and secure storage of experimental data and associated meta-information constitutes one of the most pressing technical challenges in modern neuroscience, and does so particularly in electrophysiology. The German INCF Node aims to provide open-source solutions for this domain that support the scientific data management and analysis workflow, and thus facilitate future data access and reproducible research. G-Node provides a data management system, accessible through an application interface, that is based on a combination of standardized data representation and flexible data annotation to account for the variety of experimental paradigms in electrophysiology. The G-Node Python Library exposes these services to the Python environment, enabling researchers to organize and access their experimental data using their familiar tools while gaining the advantages that a centralized storage entails. The library provides powerful query features, including data slicing and selection by metadata, as well as fine-grained permission control for collaboration and data sharing. Here we demonstrate key actions in working with experimental neuroscience data, such as building a metadata structure, organizing recorded data in datasets, annotating data, or selecting data regions of interest, that can be automated to large degree using the library. Compliant with existing de-facto standards, the G-Node Python Library is compatible with many Python tools in the field of neurophysiology and thus enables seamless integration of data organization into the scientific data workflow.
Author Kellner, Christian J.
Garbers, Christian
Sobolev, Andrey
Rautenberg, Philipp L.
Pereira, Michael
Wachtler, Thomas
Stoewer, Adrian
AuthorAffiliation Department of Biology II, Ludwig-Maximilians-Universität München Planegg-Martinsried, Germany
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ContentType Journal Article
Copyright 2014. This work is licensed under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Copyright © 2014 Sobolev, Stoewer, Pereira, Kellner, Garbers, Rautenberg and Wachtler. 2014
Copyright_xml – notice: 2014. This work is licensed under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
– notice: Copyright © 2014 Sobolev, Stoewer, Pereira, Kellner, Garbers, Rautenberg and Wachtler. 2014
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Keywords python
data management
neuroinformatics
electrophysiology
odml
neo
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Reviewed by: Michael Hanke, Otto-von-Guericke-University, Germany; Thomas Deneux, Centre national de la recherche scientifique, France
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Edited by: Bertrand Thirion, Institut National de Recherche en Informatique et Automatique, France
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24273510 - Front Neuroinform. 2013 Nov 11;7:26
19521545 - Front Neuroinform. 2009 May 27;3:14
24600386 - Front Neuroinform. 2014 Feb 20;8:10
17562009 - BMC Neurosci. 2007 Jun 11;8:38
References_xml – reference: 24600386 - Front Neuroinform. 2014 Feb 20;8:10
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SubjectTerms Collaboration
Conventions
Data analysis
data management
Data models
Design
Electrophysiology
Information sharing
Libraries
Management
Metadata
Nervous system
neuroinformatics
Neurophysiology
Neuroscience
python
Software
Web service
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