Neuroscience information exchange format - NIX -

data model for storing fully annotated datasets

Nix_logo

C++ library for storing scientific data in the NIX data model.

The NIX data model allows to store fully annotated scientific datasets, i.e. the data together with its metadata within the same container. Our aim is to achieve standardization by providing a common/generic data structure for a multitude of data types. See the wiki or the introduction for more information.

The current implementations store the actual data using the HDF5 file format as a storage backend.

NIX emerged from the activities of the Electrophysiology Task Force of the INCF Datasharing Program (2010-2015). It is a registered research resource with the RRID:SCR_016196.


The NIX ecosystem

APIs

Language bindings

We provide bindings for:

Viewer

Used by


Getting started

Installation

Platform specific installation instructions can be found:

Introduction

This introduction guides through the NIX data model and shows how to use it with the nix c++ library.

Tutorial and demos for using the python library (nixpy) can be found here:


Getting support

If you experience problems using NIX feel free to join our IRC channel #gnode at FreeNode or write an email to dev@g-node.org. If you find a bug please report it using the project issue tracker.

Contributing

Any kind of contribution is welcome! This includes reporting bugs and issues. If you want to contribute to code or documentation please refer to the contributing guide.

License

This project is open source published under the BSD-3 license see license file for details.


Contact

The project is maintained by the German Neuroinformatics Node, G-Node. G-Node at GitHub, email.

Citing

If you use NIX, it would be much appreciated if you would cite it in publications with its identifier RRID:SCR_016196 and/or the reference:

Stoewer A, Kellner CJ, Benda J, Wachtler T and Grewe J (2014). File format and library for neuroscience data and metadata. Front. Neuroinform. Conference Abstract: Neuroinformatics 2014. doi: 10.3389/conf.fninf.2014.18.00027

Referenced By

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