
@article {3577,
	title = {NSDF: Neuroscience Simulation Data Format.},
	journal = {Neuroinformatics},
	volume = {14},
	year = {2016},
	month = {2016 Apr},
	pages = {147-67},
	abstract = {<p>Data interchange is emerging as an essential aspect of modern neuroscience. In the areas of computational neuroscience and systems biology there are multiple model definition formats, which have contributed strongly to the development of an ecosystem of simulation and analysis tools. Here we report the development of the Neuroscience Simulation Data Format (NSDF) which extends this ecosystem to the data generated in simulations. NSDF is designed to store simulator output across scales: from multiscale chemical and electrical signaling models, to detailed single-neuron and network models, to abstract neural nets. It is self-documenting, efficient, modular, and scalable, both in terms of novel data types and in terms of data volume. NSDF is simulator-independent, and can be used by a range of standalone analysis and visualization tools. It may also be used to store variety of experimental data. NSDF is based on the widely used HDF5 (Hierarchical Data Format 5) specification and is open, platform-independent, and portable.</p>
},
	issn = {1559-0089},
	doi = {10.1007/s12021-015-9282-5},
	author = {Ray, Subhasis and Chintaluri, Chaitanya and Bhalla, Upinder S and W{\'o}jcik, Daniel K}
}
