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Integrated platform and API for electrophysiological data

Recent advancements in technology and methodology have led to growing amounts of increasingly complex neuroscience data recorded from various species, modalities, and levels of study. The rapid data growth has made efficient data access and flexible, machine-readable data annotation a crucial requis...

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Detalles Bibliográficos
Autores principales: Sobolev, Andrey, Stoewer, Adrian, Leonhardt, Aljoscha, Rautenberg, Philipp L., Kellner, Christian J., Garbers, Christian, Wachtler, Thomas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4005939/
https://www.ncbi.nlm.nih.gov/pubmed/24795616
http://dx.doi.org/10.3389/fninf.2014.00032
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author Sobolev, Andrey
Stoewer, Adrian
Leonhardt, Aljoscha
Rautenberg, Philipp L.
Kellner, Christian J.
Garbers, Christian
Wachtler, Thomas
author_facet Sobolev, Andrey
Stoewer, Adrian
Leonhardt, Aljoscha
Rautenberg, Philipp L.
Kellner, Christian J.
Garbers, Christian
Wachtler, Thomas
author_sort Sobolev, Andrey
collection PubMed
description Recent advancements in technology and methodology have led to growing amounts of increasingly complex neuroscience data recorded from various species, modalities, and levels of study. The rapid data growth has made efficient data access and flexible, machine-readable data annotation a crucial requisite for neuroscientists. Clear and consistent annotation and organization of data is not only an important ingredient for reproducibility of results and re-use of data, but also essential for collaborative research and data sharing. In particular, efficient data management and interoperability requires a unified approach that integrates data and metadata and provides a common way of accessing this information. In this paper we describe GNData, a data management platform for neurophysiological data. GNData provides a storage system based on a data representation that is suitable to organize data and metadata from any electrophysiological experiment, with a functionality exposed via a common application programming interface (API). Data representation and API structure are compatible with existing approaches for data and metadata representation in neurophysiology. The API implementation is based on the Representational State Transfer (REST) pattern, which enables data access integration in software applications and facilitates the development of tools that communicate with the service. Client libraries that interact with the API provide direct data access from computing environments like Matlab or Python, enabling integration of data management into the scientist's experimental or analysis routines.
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spelling pubmed-40059392014-05-02 Integrated platform and API for electrophysiological data Sobolev, Andrey Stoewer, Adrian Leonhardt, Aljoscha Rautenberg, Philipp L. Kellner, Christian J. Garbers, Christian Wachtler, Thomas Front Neuroinform Neuroscience Recent advancements in technology and methodology have led to growing amounts of increasingly complex neuroscience data recorded from various species, modalities, and levels of study. The rapid data growth has made efficient data access and flexible, machine-readable data annotation a crucial requisite for neuroscientists. Clear and consistent annotation and organization of data is not only an important ingredient for reproducibility of results and re-use of data, but also essential for collaborative research and data sharing. In particular, efficient data management and interoperability requires a unified approach that integrates data and metadata and provides a common way of accessing this information. In this paper we describe GNData, a data management platform for neurophysiological data. GNData provides a storage system based on a data representation that is suitable to organize data and metadata from any electrophysiological experiment, with a functionality exposed via a common application programming interface (API). Data representation and API structure are compatible with existing approaches for data and metadata representation in neurophysiology. The API implementation is based on the Representational State Transfer (REST) pattern, which enables data access integration in software applications and facilitates the development of tools that communicate with the service. Client libraries that interact with the API provide direct data access from computing environments like Matlab or Python, enabling integration of data management into the scientist's experimental or analysis routines. Frontiers Media S.A. 2014-04-23 /pmc/articles/PMC4005939/ /pubmed/24795616 http://dx.doi.org/10.3389/fninf.2014.00032 Text en Copyright © 2014 Sobolev, Stoewer, Leonhardt, Rautenberg, Kellner, Garbers and Wachtler. http://creativecommons.org/licenses/by/3.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Sobolev, Andrey
Stoewer, Adrian
Leonhardt, Aljoscha
Rautenberg, Philipp L.
Kellner, Christian J.
Garbers, Christian
Wachtler, Thomas
Integrated platform and API for electrophysiological data
title Integrated platform and API for electrophysiological data
title_full Integrated platform and API for electrophysiological data
title_fullStr Integrated platform and API for electrophysiological data
title_full_unstemmed Integrated platform and API for electrophysiological data
title_short Integrated platform and API for electrophysiological data
title_sort integrated platform and api for electrophysiological data
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4005939/
https://www.ncbi.nlm.nih.gov/pubmed/24795616
http://dx.doi.org/10.3389/fninf.2014.00032
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