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.
Neo is an object model for handling electrophysiology data in multiple formats. It is suitable for representing data acquired from electroencephalographic, intracellular, or extracellular recordings, or generated from simulations. Neo has been implemented as a Python package for working with electrophysiology data, together with support for reading a wide range of neurophysiology file formats (including Spike2, NeuroExplorer, AlphaOmega, Axon, Blackrock, Plexon, Tdt, Igor Pro), and support for writing to a subset of these formats plus non-proprietary formats including Kwik and HDF5.
The neuromorphological file format balances structure with flexibility by storing each modeled object as a unique data element and providing mechanisms for grouping any number and type of data elements. File-level metadata is retained to provide detail on the origin of the sample, ensuring that the provenance of derivative data is tracked and that important source information is not separated from the corresponding data.
Neuroscience information exchange format (NIX) data model allows storing fully annotated scientific datasets, i.e. the data together with rich metadata and their relations in a consistent, comprehensive format. Its aim is to achieve standardization by providing a common data structure and APIs for a multitude of data types and use cases, focused on but not limited to neuroscience.
Neurodata Without Borders (NWB) is a data standard for neurophysiology, providing neuroscientists with a common standard to share, archive, use, and build analysis tools for neurophysiology data. NWB is designed to store a variety of neurophysiology data, including data from intracellular and extracellular electrophysiology experiments, data from optical physiology experiments, and tracking and stimulus data.
NeuroImaging Data Model (NIDM)-Results provides a representation of mass univariate neuroimaging analysis results, unified across analysis software packages. Implementation of NIDM-Results within FSL and SPM, two of the main neuroimaging software packages, provides an automated solution to share maps generated by neuroimaging studies along with their metadata.
The Brain Imaging Data Structure (BIDS) is a standard prescribing a formal way to name and organize MRI data and metadata in a file system that simplifies communication and collaboration between users and enables easier data validation and software development through using consistent paths and naming for data files.
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.
Open metadata markup language (odML) is a format for storing metadata in an organised human- and machine-readable way. It does not constrain the metadata content, while providing a common schema (with implementations in XML, JSON, YAML) to integrate metadata from various sources. In addition, odML facilitates and encourages standardization by providing terminologies for metadata.