Neo: an object model for handling electrophysiology data in multiple formats
Neuroscientists use many different software tools to acquire, analyse and visualise electrophysiological signals. <br/>However, incompatible data models and file formats make it difficult to exchange data between these tools. <br/>This reduces scientific productivity, renders potentially...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2014-02-01
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| Series: | Frontiers in Neuroinformatics |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fninf.2014.00010/full |
| _version_ | 1818595370681237504 |
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| author | Samuel eGarcia Domenico eGuarino Florent eJaillet Todd R Jennings Robert ePröpper Philipp L Rautenberg Chris eRodgers Andrey eSobolev Thomas eWachtler Pierre eYger Andrew P Davison |
| author_facet | Samuel eGarcia Domenico eGuarino Florent eJaillet Todd R Jennings Robert ePröpper Philipp L Rautenberg Chris eRodgers Andrey eSobolev Thomas eWachtler Pierre eYger Andrew P Davison |
| author_sort | Samuel eGarcia |
| collection | DOAJ |
| description | Neuroscientists use many different software tools to acquire, analyse and visualise electrophysiological signals. <br/>However, incompatible data models and file formats make it difficult to exchange data between these tools. <br/>This reduces scientific productivity, renders potentially useful analysis methods inaccessible and impedes collaboration between labs.<br/>A common representation of the core data would improve interoperability and facilitate data-sharing.<br/>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. <br/>As a concrete instantiation of this object model we have developed an open source implementation in the Python programming language.<br/>In addition to representing electrophysiology data in memory for the purposes of analysis and visualisation, 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.<br/>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.<br/>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.<br/>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 visualisation.<br/>Software for neurophysiology data analysis and visualisation 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. <br/>We intend that Neo should become the standard basis for Python tools in neurophysiology. |
| first_indexed | 2024-12-16T11:14:56Z |
| format | Article |
| id | doaj.art-5898ec4bb9b146f8b361ab8b139b675e |
| institution | Directory Open Access Journal |
| issn | 1662-5196 |
| language | English |
| last_indexed | 2024-12-16T11:14:56Z |
| publishDate | 2014-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroinformatics |
| spelling | doaj.art-5898ec4bb9b146f8b361ab8b139b675e2022-12-21T22:33:38ZengFrontiers Media S.A.Frontiers in Neuroinformatics1662-51962014-02-01810.3389/fninf.2014.0001073927Neo: an object model for handling electrophysiology data in multiple formatsSamuel eGarcia0Domenico eGuarino1Florent eJaillet2Todd R Jennings3Robert ePröpper4Philipp L Rautenberg5Chris eRodgers6Andrey eSobolev7Thomas eWachtler8Pierre eYger9Andrew P Davison10CNRS UMR5292, INSERM U1028, Université Claude Bernard Lyon 1CNRSAix Marseille Université, CNRSLudwig-Maximilians-Universität MünchenTU BerlinLudwig-Maximilians-Universität MünchenUniversity of California, BerkeleyLudwig-Maximilians-Universität MünchenLudwig-Maximilians-Universität MünchenCNRSCNRSNeuroscientists use many different software tools to acquire, analyse and visualise electrophysiological signals. <br/>However, incompatible data models and file formats make it difficult to exchange data between these tools. <br/>This reduces scientific productivity, renders potentially useful analysis methods inaccessible and impedes collaboration between labs.<br/>A common representation of the core data would improve interoperability and facilitate data-sharing.<br/>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. <br/>As a concrete instantiation of this object model we have developed an open source implementation in the Python programming language.<br/>In addition to representing electrophysiology data in memory for the purposes of analysis and visualisation, 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.<br/>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.<br/>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.<br/>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 visualisation.<br/>Software for neurophysiology data analysis and visualisation 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. <br/>We intend that Neo should become the standard basis for Python tools in neurophysiology.http://journal.frontiersin.org/Journal/10.3389/fninf.2014.00010/fullElectrophysiologySoftwareinteroperabilitypythonFile formats |
| spellingShingle | Samuel eGarcia Domenico eGuarino Florent eJaillet Todd R Jennings Robert ePröpper Philipp L Rautenberg Chris eRodgers Andrey eSobolev Thomas eWachtler Pierre eYger Andrew P Davison Neo: an object model for handling electrophysiology data in multiple formats Frontiers in Neuroinformatics Electrophysiology Software interoperability python File 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 | Electrophysiology Software interoperability python File formats |
| url | http://journal.frontiersin.org/Journal/10.3389/fninf.2014.00010/full |
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