The SPARC data structure is a consistent file structure and naming convention, based on the Brain Imaging Data Structure (BIDS) to ensure that the diverse types of data in SPARC is organized in a similar manner.
The purpose of this best practice is to elaborate the principles of open and reproducible research for neuroimaging using Magnetic Resonance Imaging (MRI). It covers: 1. experimental design reporting, 2. image acquisition reporting, 3. preprocessing reporting, 4. statistical modeling, 5. results reporting, 6. data sharing, and 7. reproducibility. For each of seven areas of a study, a tabular listing of over 100 items to help plan, execute, report, and share research in the most transparent fashion is provided.
NeuroML is a simulator-independent, XML-based standardized model description language for computational neuroscience that provides a common data format for defining and exchanging descriptions of neuronal cell and network models. NeuroML focuses on models which are based on the biophysical and anatomical properties of real neurons, i.e. which include details of the detailed neuronal morphologies, the membrane conductances which underlie action potential generation and which are based on known anatomical connectivity.
PyNN is a simulator-independent language for building neuronal network models. The PyNN API aims to support modelling at a high-level of abstraction (populations of neurons, layers, columns and the connections between them) while still allowing access to the details of individual neurons and synapses when required.