Single-file solution for storing neuroscience data and metadata.
-
1
Ludwig-Maximilians Universität München, Department Biologie II, Germany
-
2
Universtät Tübingen, Institut für Neurobiologie, Germany
Formats and data models used in neuroscience are often domain-specific and typically designed to store a certain kind of data, e.g. time series or image data. Furthermore, file formats are often designed for efficiency with respect to recording software and hardware and, in the worst case, the stored data is only accessible via proprietary software. To support community-based tool development and data sharing, a common standard for data storage would be desirable. It is one of the goals of the INCF to specify a recommendation for such a common file format for electrophysiological data that is also able to store various metadata.
Here we present an approach to define a common file format based on a generic data model that can represent and describe multidimensional data. It is able to store time series, spike trains, images, image stacks and also more complex kinds of data. Further, tagging with events or annotating with arbitrary metadata, for example about stimulus conditions or hardware settings, is supported. Due to its flexible design the data model is compatible to other tools and formats and able to represent data from NEO (www.neuralensemble.org/neo) or Neuroshare (www.neuroshare.org) files.
One guiding principle of the model is that it guarantees just enough information, including units, sampling rates, array names, to create a plot of the contained data. While the data model is, at this minimum definition, not domain specific, its parts can be typed to represent domain specific entities. This ensures that software working on the data model can always read the data even without knowledge about the domain it is used in. At the same time this offers high degree of flexibility. The same applies to the metadata, which is organized according to the odML model (Grewe et al. 2011). This approach restricts the format but not the content while providing the means to use standardized terminologies. Linking between data and metadata is an integral feature of the approach.
In the HDF5 format, the data model is represented in a rather flat hierarchy. A file consists of the two main groups for data and metadata, respectively. Thus, data and metadata are stored in the same file while links can be established between both parts. Though it is of course possible to read these files with the standard HDF5 libraries, specific APIs provide a more convenient way to access the data on a higher abstraction level. Therefore, we started the development of a reference implementation in C++ that can be used to include the format in existing tools and environments and may serve as a guideline for implementations in other languages. For more information see www.g-node.org/pandora.
Acknowledgements
This work was conducted within the Electrophysiology Task Force of the INCF Program on Standards for Datasharing. Supported by the German INCF Node (BMBF grant 01GQ0801).
References
J. Grewe, T. Wachtler and J. Benda; Frontiers Neuroinformatics, 2011
Keywords:
data model for electrophysiology,
data annotation,
HDF5 storage,
integration of data and metadata,
neuroscience methods
Conference:
Neuroinformatics 2013, Stockholm, Sweden, 27 Aug - 29 Aug, 2013.
Presentation Type:
Poster
Topic:
General neuroinformatics
Citation:
Stoewer
A,
Benda
J,
Garbers
C,
Kellner
CJ,
Sobolev
A,
Wachtler
T and
Grewe
J
(2013). Single-file solution for storing neuroscience data and metadata..
Front. Neuroinform.
Conference Abstract:
Neuroinformatics 2013.
doi: 10.3389/conf.fninf.2013.09.00077
Copyright:
The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers.
They are made available through the Frontiers publishing platform as a service to conference organizers and presenters.
The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated.
Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed.
For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions.
Received:
08 Apr 2013;
Published Online:
11 Jul 2013.
*
Correspondence:
Mr. Adrian Stoewer, Ludwig-Maximilians Universität München, Department Biologie II, München, Germany, adrian@stoewer.me
Dr. Jan Grewe, Ludwig-Maximilians Universität München, Department Biologie II, München, Germany, jan.grewe@uni-tuebingen.de