INCF endorsed standards & best practices

INCF endorses community standards and best practices for neuroscience that comply with the FAIR principles

INCF provides a process for endorsement of FAIR community standards or best practices (SBPs), where existing ones or the development of new ones can be proposed. Community members can propose existing or suggest to develop new SBPs for endorsement by INCF. The SBP endorsement process includes an open community review, followed by a decision of the SBP subcommittee.

Listed below are SBPs that have been endorsed by INCF:

Brain Imaging Data Structures (BIDS) Neuroimaging
NeuroML Computational neuroscience
PyNN Computational neuroscience




Brain Imaging Data Structure (BIDS)INCF endorsed

BIDS is simple and intuitive way to organize and describe your neuroimaging and behavioral data.

Submitted for endorsement: August 3, 2018
Endorsed: November 2, 2018.
Submission and public review comments are available here.


NeuroMLINCF endorsed

NeuroML is a modelling language built on the LEMS (Low Entropy Model Specification) language, which allows machine readable definitions of the cell, channel and synapse models which form the core of the language. This increases transparency of model structure and dynamics and facilitates automatic mapping of the models to multiple simulation formats. Software libraries for reading, writing and running simulations using the languages are under active development in Java and Python.

Submitted for endorsement: January 18, 2019
Endorsed: March 20, 2019
Submission and public review comments are available here.


PyNNINCF endorsed

PyNN (pronounced 'pine') is a Python package for simulator-independent specification of 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. PyNN provides a library of standard neuron, synapse and synaptic plasticity models, which have been verified to work the same on the different supported simulators. PyNN also provides a set of commonly-used connectivity algorithms (e.g. all-to-all, random, distance-dependent, small-world) but makes it easy to provide your own connectivity in a simulator-independent way, either using the Connection Set Algebra or by writing your own Python code.

Submitted for endorsement: January 18, 2019
Endorsed: March 20, 2019
Submission and public review comments are available here.