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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...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2014
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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. |
format | Online Article Text |
id | pubmed-4005939 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
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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