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.
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.
The Data Acquisition, Quality and Curation for Observational Research Designs (DAQCORD) Guidelines are the first comprehensive set of data quality indicators for large, clinical observational studies. They were developed around the needs of neuroscience projects, but we believe they are relevant and generalisable, in whole or in part, to other fields of health research, and also to smaller observational studies and preclinical research.