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Neo: an object model for handling electrophysiology data in multiple formats
Neuroscientists use many different software tools to acquire, analyze and visualize electrophysiological signals. However, incompatible data models and file formats make it difficult to exchange data between these tools. This reduces scientific productivity, renders potentially useful analysis metho...
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/PMC3930095/ https://www.ncbi.nlm.nih.gov/pubmed/24600386 http://dx.doi.org/10.3389/fninf.2014.00010 |
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author | Garcia, Samuel Guarino, Domenico Jaillet, Florent Jennings, Todd Pröpper, Robert Rautenberg, Philipp L. Rodgers, Chris C. Sobolev, Andrey Wachtler, Thomas Yger, Pierre Davison, Andrew P. |
author_facet | Garcia, Samuel Guarino, Domenico Jaillet, Florent Jennings, Todd Pröpper, Robert Rautenberg, Philipp L. Rodgers, Chris C. Sobolev, Andrey Wachtler, Thomas Yger, Pierre Davison, Andrew P. |
author_sort | Garcia, Samuel |
collection | PubMed |
description | Neuroscientists use many different software tools to acquire, analyze and visualize electrophysiological signals. However, incompatible data models and file formats make it difficult to exchange data between these tools. This reduces scientific productivity, renders potentially useful analysis methods inaccessible and impedes collaboration between labs. A common representation of the core data would improve interoperability and facilitate data-sharing. To that end, we propose here a language-independent object model, named “Neo,” suitable for representing data acquired from electroencephalographic, intracellular, or extracellular recordings, or generated from simulations. As a concrete instantiation of this object model we have developed an open source implementation in the Python programming language. In addition to representing electrophysiology data in memory for the purposes of analysis and visualization, the Python implementation provides a set of input/output (IO) modules for reading/writing the data from/to a variety of commonly used file formats. Support is included for formats produced by most of the major manufacturers of electrophysiology recording equipment and also for more generic formats such as MATLAB. Data representation and data analysis are conceptually separate: it is easier to write robust analysis code if it is focused on analysis and relies on an underlying package to handle data representation. For that reason, and also to be as lightweight as possible, the Neo object model and the associated Python package are deliberately limited to representation of data, with no functions for data analysis or visualization. Software for neurophysiology data analysis and visualization built on top of Neo automatically gains the benefits of interoperability, easier data sharing and automatic format conversion; there is already a burgeoning ecosystem of such tools. We intend that Neo should become the standard basis for Python tools in neurophysiology. |
format | Online Article Text |
id | pubmed-3930095 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-39300952014-03-05 Neo: an object model for handling electrophysiology data in multiple formats Garcia, Samuel Guarino, Domenico Jaillet, Florent Jennings, Todd Pröpper, Robert Rautenberg, Philipp L. Rodgers, Chris C. Sobolev, Andrey Wachtler, Thomas Yger, Pierre Davison, Andrew P. Front Neuroinform Neuroscience Neuroscientists use many different software tools to acquire, analyze and visualize electrophysiological signals. However, incompatible data models and file formats make it difficult to exchange data between these tools. This reduces scientific productivity, renders potentially useful analysis methods inaccessible and impedes collaboration between labs. A common representation of the core data would improve interoperability and facilitate data-sharing. To that end, we propose here a language-independent object model, named “Neo,” suitable for representing data acquired from electroencephalographic, intracellular, or extracellular recordings, or generated from simulations. As a concrete instantiation of this object model we have developed an open source implementation in the Python programming language. In addition to representing electrophysiology data in memory for the purposes of analysis and visualization, the Python implementation provides a set of input/output (IO) modules for reading/writing the data from/to a variety of commonly used file formats. Support is included for formats produced by most of the major manufacturers of electrophysiology recording equipment and also for more generic formats such as MATLAB. Data representation and data analysis are conceptually separate: it is easier to write robust analysis code if it is focused on analysis and relies on an underlying package to handle data representation. For that reason, and also to be as lightweight as possible, the Neo object model and the associated Python package are deliberately limited to representation of data, with no functions for data analysis or visualization. Software for neurophysiology data analysis and visualization built on top of Neo automatically gains the benefits of interoperability, easier data sharing and automatic format conversion; there is already a burgeoning ecosystem of such tools. We intend that Neo should become the standard basis for Python tools in neurophysiology. Frontiers Media S.A. 2014-02-20 /pmc/articles/PMC3930095/ /pubmed/24600386 http://dx.doi.org/10.3389/fninf.2014.00010 Text en Copyright © 2014 Garcia, Guarino, Jaillet, Jennings, Pröpper, Rautenberg, Rodgers, Sobolev, Wachtler, Yger and Davison. 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 Garcia, Samuel Guarino, Domenico Jaillet, Florent Jennings, Todd Pröpper, Robert Rautenberg, Philipp L. Rodgers, Chris C. Sobolev, Andrey Wachtler, Thomas Yger, Pierre Davison, Andrew P. Neo: an object model for handling electrophysiology data in multiple formats |
title | Neo: an object model for handling electrophysiology data in multiple formats |
title_full | Neo: an object model for handling electrophysiology data in multiple formats |
title_fullStr | Neo: an object model for handling electrophysiology data in multiple formats |
title_full_unstemmed | Neo: an object model for handling electrophysiology data in multiple formats |
title_short | Neo: an object model for handling electrophysiology data in multiple formats |
title_sort | neo: an object model for handling electrophysiology data in multiple formats |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3930095/ https://www.ncbi.nlm.nih.gov/pubmed/24600386 http://dx.doi.org/10.3389/fninf.2014.00010 |
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